ELE Times

As technology rapidly evolves, deep learning a subset of machine learning that uses neural networks to model and understand complex patterns in data has emerged as a transformative force across industries, powering innovations from autonomous vehicles to intelligent automation;

A deep learning framework is a software library that simplifies building and training neural networks by providing pre-built components like layers, optimizers, and tools for automatic differentiation. A deep learning framework operates in four key stages: model definition involves specifying the neural network architecture using a programming interface; forward propagation processes input data through the network to generate predictions; loss calculation and backpropagation compute errors and adjust weights using automatic differentiation; and optimization trains the model using algorithms like SGD or Adam, followed by deployment to various platforms. These frameworks support both training and inference, enabling smooth transition from experimentation to production.

Major AI applications are powered by well-known deep learning frameworks: Google Translate’s neural machine translation is powered on TensorFlow, whereas PyTorch facilitates OpenAI’s GPT models and Meta’s research. Keras is utilized in the classification of instructional images; MXNet makes it possible for Amazon Alexa to recognize voice; Caffe aids medical image analysis; JAX handles physics simulations and protein modeling; and ONNX helps deploy models across platforms like PyTorch to TensorFlow for edge devices

Here are the top 10 deep learning frameworks-

1. TensorFlow

Developed by Google Brain, TensorFlow is put forward across many consumers as the most famous deep learning framework in 2025. It was first used by Google internally for some research and development projects back in 2015, but after seeing the immense potential of the framework, it was decided that an official public release would be made. It is a highly scalable and flexible framework supporting multiple programming languages and hardware platforms including CPUs, GPUs, and TPUs. TensorFlow sees many applications including image recognition, speech synthesis, and fraud detection. Its lightweight mobile offerings TensorFlow Lite and TensorFlow.js bring AI to phones and browsers.

2. PyTorch

PyTorch, created by Meta AI, is beloved by researchers for dynamic computation and intuitive design. Initiated in 2016 by a group of individuals from Facebook’s AI lab comprising Adam Paszke, Sam Gross, Soumith Chintala, and Gregory Chanan, the framework powers the latest NLP models such as ChatGPT and BERT. It is largely deployed in academic research, autonomous driving systems, and in real-time computer vision applications.

3. Keras

Keras is designed to be a high-level API for easily and quickly building and training neural networks. First made public in 2015 as part of the ONEIROS project (Short for Open-ended Neuro-Electronic Intelligent Robot Operating System).

Being a frontend for TensorFlow, it is great for beginners and rapid prototyping. Because of the simple handling of pre-trained models, Keras is commonly found in tasks involving sentiment analysis, recommendation engines, and medical image classification.

4. MXNet

MXNet excels for multi-GPUs setup, supported now by Apache Software Foundation and Amazon. It is used for real-time object detection in retail, multilingual NLP models, and voice assistants, including Alexa. The fact that it supports multiple languages means it’s an excellent option for global development teams.

5. Caffe

Created by the Berkeley Vision and Learning Center (BVLC), Caffe is a deep learning framework well renowned for its speed and modularity in image processing tasks. It found popular uses with an expressive architecture and efficiently implemented CNNs. Caffe is popular for real-time image classification, facial recognition systems, and visual search engines.

6. JAX

Created by Google Research, JAX is a pretty cool toolkit. It offers NumPy-like syntax combined with automatic differentiation and GPU/TPU adaptations. It can be used for scientific computations, custom ML algorithms, and large-scale training of neural networks.

7. Theano

Theano was one of the first frameworks for deep learning, developed by the Montreal Institute for Learning Algorithms (MILA). It discontinued maintenance after some time. Today, though mostly abandoned, Theano’s legacy still lives in the other popular frameworks like TensorFlow and PyTorch. Theano is still used actively for symbolic differentiation and efficient GPU numerical computation in some academic research.

8. MindSpore

MindSpore, developed by Huawei, is targeted for AI across cloud, edge, and devices. It finds applications in natural language processing, computer vision, and autonomous systems. Due to its favorable attention toward privacy protection and efficient deployment, MindSpore is catching up in sectors like healthcare and smart manufacturing.

9. Deeplearning4j (DL4J)

Developed by Skymind, DL4J is tailored for Java-based enterprise AI solutions. It’s applied in financial modeling, cybersecurity threat detection, and customer churn prediction. Its integration with Hadoop and Spark makes it ideal for big data analytics.

10. Chainer

Developed by Preferred Networks in Japan, Chainer is recognized for its flexibility in defining networks of all kinds. Reinforcement learning for gaming purposes, bioinformatic research, and robotics are ways in which this application develops capabilities. Its “define-by-run” architecture is supposed to allow dynamic learning systems. It is loved by the experimental kinda-AI set.

Conclusion:

The importance of selecting a framework may increase as deep learning continues to influence the technologies of the future. There are several different ecosystems, each with its own unique set of advantages. TensorFlow chose production-grade scalability, while the other chose a research-friendly strategy. The combination of cloud integration, open-source innovation, and hardware acceleration will guarantee that deep learning remains at the forefront of AI advancements across a range of industries well into 2025.

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The Ministry of Commerce and Industry has clarified that no additional tariffs have been imposed on Indian exports to the United States in the pharmaceutical and electronics sectors. As per a written reply in the Lok Sabha, this announcement would bring relief to exporters in these sensitive sectors facing concerns about possible duty hikes.

In the meantime, the Ministry said that other goods have been subjected to a reciprocal tariff of 25% from August 7 and that this applies to around 55% in value of India merchandise exports to the US. Furthermore, on August 27, the ad valorem duty of 25% on certain goods will come into being.

The government undertakes consultations with stakeholders, including exporters, MSMEs, and the industry, for the assessment of these measures. It was emphasised that top priority will continue to be given to protecting the interest of farmers, workers, entrepreneurs, and all sections of the industry.

On the trade diplomacy front, India and the US are continuing negotiations on a Bilateral Trade Agreement (BTA) aimed at enhancing market access, reducing tariff and non-tariff barriers, and improving supply chain integration. Talks began in March 2025, with five rounds completed so far the latest held in Washington from July 14 to 18. The US delegation is set to arrive in India by the end of August for the sixth round of Bilateral Trade Agreement negotiation.

The Department of Commerce is closely monitoring the situation to evaluate the potential repercussions of the tariff changes and is working on strategies to mitigate any adverse effects. Measures such as export promotion and market diversification are being explored to support affected industries.

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In a move to boost India’s electronics manufacturing ecosystem, the Union Cabinet has approved the setup of 4 semiconductor fabrication units at Odisha, Punjab, and Andhra Pradesh.

Having a combined investment of around ₹4,600 crore, these projects will generate direct employment for about 2,034 people and provide a great fillip towards making the country self-reliant in the semiconductor field. This initiative comes under the India Semiconductor Mission (ISM), which is one of the flagship programmes created primarily to lessen the import dependence and create strong manufacturing capabilities within the country.

According to the India Semiconductor Mission, the Union Cabinet approved the setup of four semiconductor manufacturing projects-

Odisha will host two plants in Bhubaneswar’s Info Valley. SicSem Pvt Ltd, in collaboration with UK’s Clas-SiC Wafer Fab Ltd, will set up India’s first commercial compound semiconductor fab to produce 60,000 wafers and 96 million packaged units annually, for use in defence, EVs, railways, chargers, and renewable energy. The second unit, by 3D Glass Solutions Inc., will establish an advanced packaging and embedded glass substrate facility with annual capacity of 69,600 glass panels, 50 million assembled units, and 13,200 3DHI modules for defence, AI, photonics, and high-performance computing.

In Andhra Pradesh, ASIP and South Korea’s APACT Co. Ltd will set up a 96 million unit semiconductor plant for mobile, automotive, and consumer electronics.

In Punjab, CDIL will expand its Mohali facility to produce 158.38 million high-power discrete devices annually, including MOSFETs, IGBTs, and Schottky Bypass Diodes for automotive electronics, EV charging, and industrial use.

According to the Union Minister Ashwini Vaishnaw, these units will cater to both domestic needs and strategic needs, including electronics, telecommunications, defense, and space technologies. Spread across three states, the approved units represent a major infrastructure-building step for semiconductors in India, with a heavy emphasis on job creation, technology innovation, and attracting even more private investment.

Conclusion:

With the approvals, India is decisively charting a course that places it among the global semiconductor firms. Through a mix of value creation through strategic investments, job creation, and technology capacities, the government wants to strategically position itself to meet domestic demand as well as become a deserving assembly hub on the world scale. Industry analysts consider that these projects might be the very foundation that leads toward a self-sustaining semiconductor ecosystem, capable of lessening import dependence and setting India on the pathway of being a key player in future-ready technologies.

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Deep learning is a subfield of machine learning that applies multilayered neural networks to simulate brain decision-making. The concept is essentially interchangeably with human learning systems which allow machines to learn from data, thus constituting many AI applications we use today-dotting, speech recognition, image analysis, and natural language processing areas.

Deep Learning History:

Since the 1940s, when Walter Pitts and Warren McCulloch introduced a mathematical model of neural networks inspired by the human brain, the very onset of deep learning can be said to have started. In the 1950s and 60s, with pioneers like Alan Turing and Alexey Ivakhnenko laying the groundwork for neural computation and early network architectures, it proceeded forward. Backpropagation emerged as a concept during the ’80s but became very popular with the availability of large computational prowess and data set in 2000. The dawn of newfound applications truly arose in 2012 when, for instance, AlexNet, a deep convolutional neural network, took image classification to another level by dramatically increasing accuracy. Since then, deep learning has become an ever indomitable force for innovation in computer vision, natural language processing, and autonomous systems.

Types of Deep Learning:

Deep learning can be grouped into various learning approaches, depending on the training of the model and the data being used-

• Supervised deep learning models are trained over labeled datasets, which have all input data paired with the corresponding output data. The model tries to learn to map the input data to the output data so that it can later generalize for unseen data through prediction. Among the popular examples of fulfillment of these tasks are image classification, sentiment analysis, and price or trend prediction.
• Unsupervised deep learning operates over unlabeled data, with the system expected to unearth underlying structures or patterns on its own. It is used in clustering similar data points, reducing the dimensionality of data, or detecting relationships among large-size datasets. Examples are customer segmentation, topic detection, and anomaly detection.
• Semi-supervised deep learning places a small set of labeled data against a large set of unlabeled data, striking a balance between accuracy and efficiency in medicine and fraud detection. Self-supervised deep learning lets models create their own learning labels, opening the two fields of NLP and vision to tasks requiring less manual annotation.
• Reinforcement deep learning is a training methodology for machine-learning models where the agent interacts with an environment, receiving rewards or penalties based on its actions. The aim is to maximize the obtained reward and its performance over time. This learning technique is used to train game-playing AIs such as AlphaGo, autonomous navigation, and robotic manipulation.

Deep learning utilizes the passage of data through an array of artificial neural networks, where each subsequent layer extracts successively more complex features. Such networks learn by adjusting the internal weights via backpropagation so as to minimize prediction errors, which ends up training the model to discern various patterns in the input and finally make recognition decisions with respect to the raw input in the form of images, text, or speech.

Deep Learning Applications:

• Image & Video Recognition: Used in facial recognition, driverless cars, and medical imaging.
• Natural Language Processing (NLP): Used to power chatbots, and virtual assistants like Siri and Alexa, and translate languages.
• Speech Recognition: Used for voice typing, smart assistants, and live transcription.
• Recommendation Systems: Personalizes Netflix, Amazon, and Spotify.
• Healthcare: For disease detection, drug discovery, and predictive diagnosis.
• Finance: Used for fraud detection, assessing risks, and running algorithmic trading operations.
• Autonomous Vehicles: Enable cars to detect objects, navigate roads, and make decisions related to driving.
• Entertainment & Media: Supports video editing, audio generation, and content tagging.
• Security & Surveillance: Supports anomaly detection and crowd monitoring.
• Education: Supports the creation of intelligent-tutoring systems and automated grading.

Key Advantages of Deep Learning:

• Automatic Feature Extraction: There is no need for manual data preprocessing. The programs glean important features from raw data on their own.
• High Accuracy: Works extremely well where organization is difficult, such as image recognition, speech, and language processing.
• Scalability: Can deal with huge datasets, much heterogeneous at that, which include unstructured data like text and images.
• Cross-Domain Flexibility: Offers applications in all sectors, including health care, finance, and autonomous systems.
• Continuous Improvement: Deep learning models get even better with the passage of time and more data-ought to be especially more on GPUs.
• Transfer Learning: These kinds of models can be used for other domains after a little setting up; this minimizes human effort and also time required in model engineering.

Deep Learning Examples:

Deep learning techniques are used in face recognition, autonomous cars, and medical imaging. Chatbots and virtual assistants work through natural language processing, speech-to-text, and voice control; recommendation engines power sites like Netflix and Amazon. In the medical field, it assists in identifying diseases and speeding up the drug-discovery process.

Conclusion:

Deep learning changes industries as it can cater to intricate data. The future seems even more bright because of advances like self-supervised learning, multimodal models, and edge computing, which will enable AI to be more efficient in terms of time, context-aware, and capable of learning with the lightest assistance of humans. Deep learning is now increasingly becoming associated with explanations and ethical concerns, as explainable AI and privacy-preserving techniques grow in emphasis. From tailor-made healthcare to the autonomous system and intelligent communication, deep learning will still do so much to transform our way of interfacing with technology and defining the next age of human handwork.

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As the world embraces the age of technology, semiconductors stand as the architects of the digital lives we live today. Semiconductors are the engines running silently behind everything from our smartphones and PCs to the AI assistants in our homes and even the noise-canceling headphones on our ears. Now, that same quiet power is stepping out of our pockets and onto our roads, initiating a second, parallel revolution in the automotive sector.

As we turn towards the automotive industry, we see a rise in the acceptance of electric and autonomous vehicles that has necessitated the use of around 1,000 to 3,500 individual chips or semiconductors in a single machine, transforming modern-day vehicles into moving computational giants. This isn’t just a trend; it’s a fundamental rewiring of the car. Asif Anwar, Executive Director of Automotive Market Analysis at TechInsights, validates this, stating that the “path to the SDV will be underpinned by the digitalization of the cockpit, vehicle connectivity, and ADAS capabilities,” with the vehicle’s core electronic architecture being the primary enabler. Features like the Advanced Driver Assistance System (ADAS) are no longer niche; they are central to the story of smart, connected vehicles on the roads. In markets like India, this is about delivering “premium, intelligent automotive experiences,” according to Savi Soin, President of Qualcomm India, who emphasizes that the country is moving beyond low-end models and embracing local innovation.

To understand this revolution—and the immense challenges engineers face—we must first dissect the new nervous system of the automobile: the array of specialized semiconductors that gives it intelligence.

The New Central Nervous System of Automotives

• The Brains: Central Compute System on Chips (SoC)

It is a single, centralized module comprising high-performance computing units that brings together various functions of a vehicle. These enable modern-day Software-Defined Vehicles (SDVs), where features are continuously enhanced through agile software updates throughout their lifecycle. This capability is what allows automakers to offer what Hisashi Takeuchi, MD & CEO of Maruti Suzuki India Ltd, describes as “affordable telematics and advanced infotainment systems,” by leveraging the power of a centralized SoC.

Some of the prominent SoCs include the Renesas R-Car Family, the Qualcomm Snapdragon Ride Flex SoC, and the STMicroelectronics Accordo and Stellar families. These powerful chips receive pre-processed data from all zonal gateways (regional data hubs) through sensors. Further, they run complex software (including the “Car OS” and AI algorithms) and make all critical decisions for functions like ADAS and infotainment, effectively controlling the car’s advanced operations; hence, it is called the Brain. The goal, according to executives like Vivek Bhan of Renesas, is to provide “end-to-end automotive-grade system solutions” that help carmakers “accelerate SDV development.”

• The Muscles: Power Semiconductors:

Power semiconductors are specialized devices designed to handle high voltage and large currents, enabling efficient control and conversion of electrical power. These are one of the most crucial components in the emerging segment of connected, electric, and autonomous vehicles. They are crucial components in electric motor drive systems, inverters, and on-board chargers for electric and hybrid vehicles.

Some of the prominent power semiconductors include IGBTs, MOSFETs (including silicon carbide (SiC) and gallium nitride (GaN) versions), and diodes. These are basically switches enabling the flow of power in the circuit.

These form the muscles of the automotives as they regulate and manage power to enable efficient and productive use of energy, hence impacting vehicle efficiency, range, and overall performance.

• The Senses: Sensors

Sensors are devices that detect and respond to changes in their environment by converting physical phenomena into measurable signals. These play a crucial role in monitoring and reporting different parameters, including engine performance, safety, and environmental conditions. These provide the critical data needed to make decisions in aspects like ADAS, ABS, and autonomous driving. 

Some commonly used sensors in automobiles include the fuel temperature sensor, parking sensors, vehicle speed sensor, tire pressure monitoring system, and airbag sensors, among others.

These sensors, like lidar, radar, and cameras, sense the environment ranging from the engine to the roads, enabling critical functions like ADAS and autonomous driving, hence the name Senses. These are one of the crucial elements in modern automotive, as their collection enables the SoC to make decisions.

• The Reflexes and Nerves: MCUs and Connectivity

Microcontrollers are small, integrated circuits that function as miniature computers, designed to control specific tasks within electronic devices. While SoCs are the “brains” for complex tasks, MCUs are still embedded everywhere, managing smaller, more specific tasks (e.g., controlling a single window, managing a specific light, basic engine control units, and individual airbag deployment units). 

Besides, the memory inside the automobiles enables them to store data from sensors and run applications while the vehicle’s communication with the cloud is enabled by dedicated communication chips or RF devices (5G, Wi-Fi, Bluetooth, and Ethernet transceivers). These are distinct from SoCs and sensors.

Apart from these, automobiles comprise analog ICs/PMICs for power regulation and signal conditioning.

Design Engineer’s Story: The Core Challenges

This increasing semiconductor composition naturally gives way to a plethora of challenges. As Vivek Bhan, Senior Vice President at Renesas, notes, the company’s latest platforms are designed specifically to “tackle the complex challenges the automotive industry faces today,” which range from hardware optimization to ensuring safety compliance. This sentiment highlights the core pain points of an engineer designing these systems.

Semiconductors are highly expensive and prone to degradation and performance issues as they enter the automotive sector. The computational giants produce a harsh environment, including high temperature, vibrations, and humidity, and come with an abundance of electric circuits. These factors together make the landscape extremely challenging for designing engineers. Some important challenges are listed below:

1. Rough Automotive Environment: The engine environment in an automobile is generally rough owing to temperature, vibrations, and humidity. This scenario poses a significant threat, as high temperatures can lead to increased thermal noise, reduced carrier mobility, and even damage to the semiconductor material itself. Therefore, the performance of semiconductors heavily depends on conducive environmental conditions. Design engineers must manage these complex environmental needs through select materials and specific packaging techniques.
2. Electromagnetic Interference: Semiconductors, being small in size, operating at high speed, and sensitive to voltage fluctuations, are highly prone to electromagnetic interference. This vulnerability can disrupt their operations and lead to the breakdown of the automotive system. This is extremely crucial for design engineers to resolve, as it could compromise the entire concept of connected vehicles.
3. Hardware-Software Integration: Modern vehicles are increasingly software-defined, requiring seamless integration of complex hardware and software systems. Engineers must ensure that hardware and software components work together flawlessly, particularly with over-the-air (OTA) software updates.
4. Supply-Chain-Related Risks: The automotive industry is heavily reliant on semiconductors, making it vulnerable to supply chain disruptions. Global shortages and geopolitical dependencies in chip fabrication can lead to production delays, increased costs, and even halted assembly lines.
5. Design Complexity: The increasing complexity of automotive chip designs, driven by features like AI, raises development costs and verification challenges. Engineers need to constantly update their skills through R&D to address these challenges. This is where concepts like “Shift-Left innovations,” mentioned by industry leaders, become critical, allowing for earlier testing and validation in the design cycle. To solve this, Electronic Design Automation (EDA) tools are used to test everything from thermal analysis to signal integrity in complex chiplet-based designs.
6. Safety and Compliance: Automotive systems, especially those related to safety-critical functions, require strict adherence to standards like ISO 26262 and ASIL-D. Engineers must ensure their systems meet these standards through rigorous testing and validation.

Conclusion

Ultimately, the story of modern-day vehicles is the story of human growth and triumphs. Behind every advanced safety system lies a design engineer navigating a formidable battleground. The challenges of taming heat, shielding circuits, and ensuring flawless hardware-software integration are the crucibles where modern automotive innovation is forged. While the vehicle on the road is a testament to the power of semiconductors, its success is a direct result of the designers who can solve these complex puzzles. The road ahead is clear: the most valuable component is not just the chip itself, but the human expertise required to master it. This is why tech leaders emphasize collaboration. As Savi Soin of Qualcomm notes, strategic partnerships with OEMs “empower the local ecosystem to lead the mobility revolution and leapfrog into a future defined by intelligent transportation,” concluding optimistically that “the road ahead is incredibly exciting and we’re just getting started.”

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The rapid growth of machine learning development companies in India is shaking up industries like healthcare, finance, and retail. With breakthrough innovations and cutting-edge machine learning innovations, these companies enter 2025 as leaders. From designing algorithms to addressing custom machine learning development needs, these companies are interjected into the future of India. This article highlights the top 10 machine learning companies shaping India’s technological landscape in 2025, focusing on their cutting-edge innovations and transformative impact across various sectors.

1. Tata Consultancy Services (TCS)

Tata Consultancy Services (TCS) is an important player in India’s machine learning landscape, weaving ML within enterprise solutions and internal operations. TCS with more than 270 AI and ML engagements applies machine learning in fields like finance, retail, and compliance to support better decisions and to automate processes. Areas such as deep learning, natural language processing, and predictive analytics fall within their scope. TCS offers frameworks and tools for enhancing the client experience, improving decision-making, and automating processes. TCS also has its platform, namely Decision Fabric combine ML with generative AI to deliver scalable, intelligent solutions.

2. Infosys

Infosys is India’s pride in cutting-edge machine learning innovation, transforming enterprises with an AI-first approach. Infosys Topaz, the company’s main product, combines cloud, generative AI, and machine learning technologies to improve service personalization and intelligent ecosystems while automating business decision-making processes. Infosys Applied AI provides scaled ML solutions across industries, from financial services to manufacturing, integrating analytics, cloud, and AI models into a single framework. In terms of applying machine learning to various industries such as banking, healthcare, and retail, Infosys helps its clients automate operations and forecast market trends.

3. Wipro

Wipro applies machine learning in its consulting, engineering, and cloud services to enable intelligent automation and predictive insights. Its implementations range from machine learning for natural language processing, intelligent search, and content moderation to computer vision for security and defect identification and predictive analytics for product failure prediction and maintenance optimization. The HOLMES AI platform by Wipro predominantly concentrates on NLP, robotic process automation (RPA), and advanced analytics.

4. HCL Technologies

HCL Technologies provides high-end machine learning solutions through AION, which helps in streamlining the ML lifecycle by way of low-code automation, and Graviton, which offers data-platform modernization for scalable model building and deployment. Use tools like Data Genie for synthetic data generation, while HCL MLOps and NLP services allow smooth deployment along with natural-language-based interfaces. Industries including manufacturing, healthcare, and finance are all undergoing change as a result of these advancements.

5. Accenture India

A global center of machine learning innovation in India, Accenture India works with thousands of experts applying AI solutions across industries. It sustains the AI Refinery platforms for ML scale-up across finance, healthcare, and retail. To solve healthcare, energy, and retail issues, Accenture India applies state-of-the-art machine learning technologies with profound domain knowledge of those service areas. The organization offers AI solutions that include natural language processing, computer vision, and data-driven analytics.

6. Tech Mahindra

Tech Mahindra’s complete breadth of ML services incorporates deep learning, data analytics, automation, and so on. Tech Mahindra India uses ML in digital transformation in telecom, manufacturing, and BFSI sectors. The ML services it provides are predictive maintenance, fraud detection, and intelligent customer support. It offers its services to manufacturing, logistics, and telecom sectors, helping them in their operations and decision-making.

7. Fractal Analytics

Fractal Analytics is one of India’s leading companies in decision intelligence and machine learning. Qure.ai and Cuddle.ai are platforms where ML is applied for diagnosis, analytics, and automation. Being a company that highly respects ethical AI and innovation, Fractal seeks real-time insights and predictive intelligence for enterprises.

8. Mu Sigma

Mu Sigma uses machine learning within its Man-Machine Ecosystem, creating a synergy between human decision scientists and its own analytical platforms. The ML stack at Mu Sigma caters to all aspects of enterprise analytics: starting from problem definition, using natural language querying, sentiment analysis to solution design with PreFabs and accelerators for rapid deployment of ML models. The company also offers services such as: predictive analytics, data visualization, and decision modeling using state-of-the-art ML algorithms to solve some of the most challenging problems faced by businesses.

9. Zensar Technologies

Zensar Technologies integrates ML with its AI-powered platforms to support decision-making, enhance customer experience, and increase operational excellence in sectors like BFSI, healthcare, and manufacturing. The rebranded R&D hub, Zensar AIRLabs, identified three AI pillars-experience, research, and decision-making-where it applies ML to predictive analytics, fraud detection, and digital supply chain optimization.

10. Mad Street Den

Mad Street Den is famous for the AI-powered platform, Vue.ai, providing intelligent automation across retail, finance, and healthcare. Blox-the horizontal AI stack of the company-uses computer vision and ML to enhance customer experience, increase efficiency in operations, and reduce the dependence of large data science teams. With a strong focus on scalable enterprise AI, Mad Street Den is turning global businesses AI-native through automation, predictive analytics, and decision intelligence-real-time.

Conclusion:

India, for instance, is witnessing a surge in machine learning ecosystem driven by innovation, scale, and sector-specific knowledge. Starting from tech giants like TCS and Infosys to quick disruptors like Mad Street Den and Fractal Analytics, these companies have redefined the way industries operate in automated decision-making, outcome predictions, and angle personal experiences. With further development into 2025, their contributions will not only help shape the digital economy of India but also set the country on the world map for AI and machine-learning aptitude.

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Without requiring explicit programming for every situation, machine learning is a potent method in computer science that teaches systems to identify patterns and gradually enhance their performance. These systems are not an assemblage of rigidly set rules-they take data, predict an outcome, and change their course of action depending upon what they have learned.

Machine learning stands out as a significant technology due to its flexibility.

Machine learning thus stands as one of the major technological developments. It enables a machine to learn from data and improve with experience, without explicitly being programmed. Patterns discovered by machine learning models from data are used for forecasting or decision-making. Machine learning help companies automate processes, make better decisions, and glean insights. Machine learning is transforming industries worldwide from personalized content recommendations to breakthroughs in medical diagnostics. Some of the top 10 machine learning applications and use cases shaping the world today.

1. Personalized Recommendations

A number of recommendation engines nowadays are created by online retailers and streaming sites that, depending on such data as location and past activity.

Machine learning lends to recommendation engines that suggest product, movie, or music according to past behavior of the user. Systems work on collaborative filtering, content-based filtering, etc.-methods of personalizing one’s experience.

Use Case:

Netflix recommends shows and movies based on what you have enlightened, whereas Amazon recommends items that are frequently purchased together.

2. Fraud Detection

Banks use ML in real time to detect and prevent frauds. They work by analyzing patterns and variations in normal transaction behavior so that banks and credit card companies could detect suspicious activities concerning money laundering or unusual spending conduct.

Use Case:

Mastercard, for instance, uses AI to detect possible frauds in real-time and, under some circumstances, even predict some before they occur to protect a customer from theft.

3. Predictive Maintenance

Machine learning is widely used in industries to forecast equipment failure before it actually happens. From an analysis of sensor data, such models forecast maintenance requirements for machines, thereby reducing downtime and saving costs.

Use Case:

Airlines keep track of engine performance to schedule repairs proactively.

4. Healthcare & Medical Diagnosis

ML allows doctors to diagnose diseases faster and more precisely. It analyzes medical imaging or patient records to detect conditions early, such as tumors or diabetes. Tools are increasingly in use to recommend personalized treatments. Machine learning anticipates interactions between various substances and thus helps to speed up the drug discovery process and cut down on research expenses.

Use Case:

AI imaging systems to spot tumors in X-rays or MRIs, predictive models to identify patients at risk of diabetes.

5. Autonomous Vehicles

Machine learning interprets sensor data, does object recognition, and cultivates decision-making scenarios for a closed-loop system for self-driving cars. Private entities such as Tesla and Waymo employ computer vision and reinforcement learning to drive autonomously and provide an autonomous ride service.

Use Case:

Tesla Autopilot applies deep learning for semi-autonomous driving including features such as lane keep assistance and adaptive cruise control.

6. Natural Language Processing (NLP)

NLP enables machinery to understand, interpret, or generate human language. It is employed in chatbots, voice assistants, sentiment analysis, and translating tools.

Use Case:

For instance, GPT-based models can write essays, summarize articles, or answer questions with human-like fluency. NLP bridges the gap between human communication and machine understanding.

7. Facial Recognition

The most important thing that machine learning can help facial recognition systems do is to identify individuals. Machine learning is a technique that enables images and videos to be identified and classified.

Use Case:

Used widely in smartphones for unlocking purposes, and airports for security checks as well as by law enforcement agencies, it is, however, very controversial in terms of ethics, privacy, and surveillance.

8. Sentiment Analysis

The other important application of machine learning is sentiment analysis conducted on social media data. Sentiment analysis in real-time determines the feelings or opinions of a writer or speaker.

Use Case:

The sentiment analyzer can quickly provide insight into the true meaning and sentiment of a published review, email, or other documents. This sentiment analysis tool can be used for decision-making applications and for websites that provide reviews.

9. Spam Filtering and Email Automation 

ML is used by email services for message categorization and spam detection. These are models that learn from user behavior and content of a message to distinguish genuine emails from junk. This saves time and keeps users safe from scams.

Use Case:

Email platforms like Gmail, Outlook, and Yahoo manage inboxes, automating responses and filtering out unwanted messages with high precision.

ML is used by social media companies to target advertisements, identify hazardous content, and curate content feeds. The content-feed is algorithmically curated with the consideration of user engagements, and the same engine judges the advertisement placements. This keeps the user hooked-but it also creates discourse on algorithmic bias and user mental health.

Use Case:

Machine learning is employed by social media platforms like Facebook, Instagram, and Twitter to provide the best user experience by curating personalized content, targeting advertisements, and restraining harmful posts.

Conclusion:

Machine learning has come to revamp industries in the way that it provokes smarter decisions, smarter experiences, and smarter predictions. From healthcare to finance to social media, machine learning inhabits the very core of how people live and work. And as implementation increases, so does the need for ethical and responsible use in making sure that these powerful benefits are distributed fairly.

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President Donald Trump announced plans to impose a 100% tariff on computer chips, which would certainly increase the price of gadgets, cars, home appliances, and other items considered necessary for the digital age.

Speaking from the Oval Office alongside Apple CEO Tim Cook, Trump said, “There will be a tariff of approximately 100% on chips and semiconductors. In order to increase the company’s domestic investment commitment and possibly avoid future iPhone levies, Trump also declared that Apple would invest an additional $100 billion in the US.

However, companies that manufacture semiconductors within the United States would be exempt from these tariffs.

Effect Across Industries

Chips for computers are the crucial element in question. For consumer gadgets, cars, home appliances, and industrial systems, that is the solution to the usage of current technology.

Shortages of chips in the middle of the COVID-19 pandemic caused inflation in prices and disruptions in the supply chain. Global chip sales have grown by 19.6% over the past year, indicating strong demand.

Trump’s approach deviates from the CHIPS and Science Act’s deliberate approach. The law was signed in 2022 by President Joe Biden with a promise of $50 billion worth of subsidies, tax breaks, and research funding for domestic chip production.

Conclusion:

While it is meant to strengthen U.S. manufacturing, many critics warn that this policy might backfire. Hence, as chips cost more, profits get thinner while companies hike consumer prices. No official word has so far come from these titans of electronics, Nvidia and Intel, concerning the announcement.

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By: STMicroelectronics

eDesignSuite Power Management Design Center is more than just a new console, but a new way to choose more responsible products, simulate higher loads, and shave weeks off the design phase of power circuits. The suite includes a power supply and LED lighting design tool, a digital workbench, and a power tree designer. Concretely, ST is now supporting 30 kW applications, and users can expect support for even greater power solutions as we update our web application. Put simply, engineers have access to new customization and simulation capabilities, which expand the scope of the Power Management Design Center and lower the barrier to entry for power application design.

Users will instantly be familiar with the new console of the Power Management Design Center because it remains extremely close to the version ST launched when we moved to HTML 5. After all, its intuitiveness is a key component for engineers working on complex power applications. What has primarily changed is that we are now offering the ability to highlight devices considered “responsible”, meaning they enhance energy efficiency. Behind the scenes, it required us to create new categories and labels, update our databases, and determine which devices would have the most significant impact on a design. For users, all it takes is a single click to know how to optimize their design meaningfully.

And this is just the beginning. We are already working on providing impact simulations to ensure designers can concretely see how choosing a responsible device affects their design. Too often, engineers recognize that a gain of just one percentage point in efficiency can make a significant difference. Unfortunately, communicating this fact can be as tricky. That’s why we are working to make eDesignSuite a scientific witness of what it means to design with responsible solutions, such as helping determine the amount of greenhouse emissions saved and other real-world environmental benefits. In a nutshell, we are taking the simulation engine that has made eDesignSuite so popular and using its rigorous models to make “responsible” a lot more tangible.

The Digital Workbench in eDesignSuite Power Management Design Center got support for new topologies that primarily focus on energy storage. Interestingly, some topologies exhibit bidirectional behavior, playing a crucial role in designing systems that not only supply power to battery chargers, such as those in electric vehicles, but also enable power to be fed back to the grid or homes, as seen in Vehicle-to-Grid (V2G) and Vehicle-to-Home (V2H) applications. These new topologies are especially promising when working with gallium nitride or silicon carbide. Their arrival in Digital Workbench means that engineers can now use our wizard to expedite their development and more accurately size the other components of their design, among other benefits.

ST will continue to innovate by introducing advanced topologies. Recently, we launched a 7 kW two-channel multilevel buck converter and a 10 kW two-channel multilevel boost converter, both featuring Maximum Power Point Tracking (MPPT), which aids in designing for solar applications. Indeed, as solar energy is a volatile source due to frequent fluctuations caused by the sun’s movement or clouds, it is imperative to track these changes to optimize power conversion. That requires an MCU like the STM32G4 and a topology capable of handling these constraints. By offering such new topologies in Digital Workbench, engineers can significantly reduce their time-to-market.

The fact that we are supporting new topologies and offering a new console highlights the unique demands engineers are facing. As markets require more efficient systems, simulations are ever more critical. That’s why eDesignSuite also got a new electro-thermal simulator, the so-called PCB Thermal Simulator. In a nutshell, it opens the door to temperature analysis based on the electrical performance of a PCB by simply analyzing Gerber files.

Specifically, by using a precise thermal model and iterative calculations, the ST PCB Thermal Simulator quickly and precisely estimates temperatures on both sides of the board. It can also simulate inner layers and allows manual or automatic placement of heat sources (custom or predefined devices), heat sinks, copper areas, and thermal vias. Simulation results are displayed as colored maps overlaid on both sides of the board. Users can inspect any point for temperature values and export results as CSV files. Hence, while the first applications of the electro-thermal simulator primarily focused on motor control, others are now starting to adopt the technology, and we are ensuring that more industries can benefit from it.

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Over the last decade, the electronics manufacturing sector in India has undergone a massive transformation to become a great production center. The sector has witnessed immense growth, both in terms of output and exports, due to some strategic government measures and the rise in foreign investment.

Creating quite a buzz in 2014 was a relatively modest electronic labor market in India, while now, by 2025, it has transformed into a full-blown ecosystem. The value of electronic products made has multiplied six times, and exports have increased eightfold, all pointing toward the enhanced global competitiveness of the sector.

Mobile Manufacturing takes center stage, the mobile segment has witnessed the explosive growth. While in 2014, there were only two manufacturing units, now India has 300 manufacturing facilities. Production has ascended from ₹18,000 crore to ₹5.45 lakh crore, and exports have increased from ₹1,500 crore to ₹2 lakh crore, that is 127% growth.

Policy Reforms:

The government-led PLI scheme is one of the aspects that contributed to this change. The scheme takes in investment upward of ₹13,000 crore and causes production of nearly ₹8.57 lakh crore, as well as creation of more than 1.35 lakh direct jobs. The scheme’s impact on international trade is demonstrated by the ₹4.65 lakh crore in export data.

Inflows of FDI and Semiconductor Push

The Semicon India programme being carried out under six projects is undergoing investments of more than Rs 1.55 trillion and is expected to create over 27,000 direct jobs and build a solid chip-making ecosystem.

Foreign direct investment into electronics manufacturing has now crossed $4 billion since FY21, and around 70% of this inflow is linked to PLI beneficiaries. This is the investing community`s vote of confidence.

With a budget of ₹22,919 crore, the Electronics Components Manufacturing Scheme (ECMS) was introduced with the goal of increasing domestic capabilities and decreasing reliance on imports.

It is purported that one direct job in electronics sector results in three indirect jobs, thus substantially contributing to the broader socio-economic impacts in the country.

Conclusion:

India’s electronics rush is not just numerically driven, but strategically poised for attaining self-reliance and global influence and technological leadership. With this momentum intact, the country is moving to become a major force in the global electronics supply chain.

The post India’s Electronics Industry Booms with 127% Export Growth in Mobiles appeared first on ELE Times.

Machine learning architecture means the designing and organizing of all of the components and processes that constitute an entire machine learning system. The ML Architecture lays down the framework to design machine learning systems, indicating how data is to be handled, models to be built and analyzed, and predictions to be made. Depending on the particular use case and the set of requirements, the architecture can vary.

A highly scalable and performant machine learning system can be realized through proper architecture.

Types of Machine Learning Architecture:

1. Supervised Learning Architecture Unsupervised Learning Architecture

By definition, Supervised Learning uses labeled data to train models: this means each input has a corresponding correct output value. The supervised learning architecture begins with gathering the datasets-full labeled, then undergoes Data Preprocessing to make sure the labels match up with the inputs correctly. Afterward, with the data ready, it proceeds to training it with the algorithm, like Linear Regression, Logistic Regression, SVM, or Random Forests. This method is very suitable when making predictions like: House Price Predictions, Email classifications as Spam or Not Spam, Medical diagnoses based on test results. Supervised learning’s primary benefit is its excellent accuracy when given clean, properly labelled data. It necessitates a lot of labelled data, though, and its preparation can be costly and time consuming.

2. Unsupervised Learning Architecture

In unsupervised learning, unlabeled data is used. Hence, the system tries to find the patterns or the clusters without much explicit guidance. Data acquisition for this architecture is more flexible because no labels are required. Preprocessing, however, serves a vital purpose in ensuring the data is consistent and meaningful. Algorithms used in unsupervised learning include K-Means Clustering, Hierarchical Clustering, and PCA. This approach is applicable in customer segmentation, anomaly detection, or market basket analysis. The biggest attraction for unsupervised systems is that they work on data that is generally easier to come by. But since results fundamentally depend on pattern discovery, interpretation of such results may require domain knowledge.

3. Reinforcement learning

Reinforcement learning is based on the principle of learning through the interaction with an environment. The architecture is designed to have a setting representing the environment, where a model will choose an action and get feedback on that action being rewarded or penalized. This feedback interchanges are insect within the structure that meanwhile allows model improvement from trial and error. Some popular algorithms are Q-Learning, Deep Q-Networks (DQN), and Policy Gradient methods.

Reinforcement learning finds its way into robots, game AI, and autonomous systems. Its strongest suit is adapting to a dynamic environment wherein the reward is a function of a series of other actions. Hence training can take ages and require a lot of computing power.

Machine Learning Architecture Diagram:

An overview of the many different parts required to create a machine learning application is given by a machine learning architecture diagram.

Simple Machine Learning Architecture Diagram:

Explanation of the Machine Learning Architecture Diagram:

The diagram outlines the step-by-step process of building and running a machine learning system.

1. Data Collection – It helps to treat data as raw material that comes to the project from some arbitrary source like a data base, sensor, API, or web scraping.
2. Data Preprocessing – Raw data are often incomplete or inconsistent. In this stage, the data are cleaned and formatted and then prepared so that the model understands them. This is a stage of utmost importance for accuracy.
3. Feature Extraction / Selection – Some data is never as useful as others. Herein, the most important variables (features) that determine predictions are picked and retained while other ones that are irrelevant are discarded.
4. Model Selection & Training – The type of problem being solved will determine the algorithm choice; the model is then fitted with the historical data to learn patterns and relationships.
5. Model Evaluation – The model is tested on new data to assess its accuracy, efficiency, and ability to make real predictions.
6. Deployment – Once the model works fine, it is incorporated into a live application or system for real-time prediction or decision-making.
7. Monitoring & Maintenance – The model goes through performance tracking as time passes by. It shall be updated or re-trained every time its accuracy is compromised due to real-world data change.

The feedback loop often sends the process to the earlier stages due to new data, ensuring the improvement of the model all the time.

Conclusion:

Machine Learning architecture is more than just a technical plan it is a backbone that decides how well an ML system learns, adapts to changes, and gives out results. A bad architecture disrupts data flow, creates delays in training, and compromises the reliability of predictions over time. Properly-designed ML architectures can be used by businesses and researchers to address problems accurately and at scale. As data grows and technologies evolve, these architectures shall continue to be the power that fuels innovations that re-shape industries by helping people make better decisions while changing everyday life.

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The new 9288-000 Series connectors enable quick, easy, and tool-free in-field termination; establish durable, reliable, high-integrity connections; and deliver excellent electrical and mechanical performance in lighting and industrial applications.

KYOCERA AVX, a leading global manufacturer of advanced electronic components engineered to accelerate technological innovation and build a better future, released the new 9288-000 Series hermaphroditic wire-to-wire (WTW) and wire-to-board (WTB) connectors for lighting and industrial applications.

These unique two-piece connectors facilitate WTW termination with two identical mating halves, which simplifies BOMs, and WTB termination with one those halves and an SMT version. Both halves of the new 9288-000 Series hermaphroditic connectors feature orange or white glass-filled PBT insulators equipped with plastic latches for good mechanical retention and the company’s field-proven poke-home contact technology, which enables quick, easy, and tool-free in-field wire termination. Simply strip and poke wires to insert and twist and pull to extract. Made of phosphor bronze with lead-free tin-over-nickel plating, these poke-home contacts also establish durable, reliable, high-integrity connections and deliver excellent electrical and mechanical performance.

The new 9288-000 Series hermaphroditic WTW and WTB connectors are currently available with 2–4 contacts on a 5mm pitch, compatible with 16–18AWG solid or stranded wire, and rated for 6A (18AWG) or 7A (16AWG), 600VACRMS or the DC equivalent, 20 mating cycles, three wire replacement cycles, and operating temperatures extending from -40°C to +125°C. They are also UL approved and RoHS compliant and shipped in tape and reel or bulk packaging.

“The new 9288-000 Series hermaphroditic wire-to-wire and wire-to-board connectors leverage our field-proven poke-home contact technology to enable quick, easy, and tool-free in-field termination and durable phosphor bronze contact materials to deliver excellent electrical and mechanical performance throughout the product lifecycle,” said Perrin Hardee, Product Marketing Manager, KYOCERA AVX. “They also feature a locking mechanism to further improve reliability, and WTW versions simplify BOMs since they’re comprised of two of the same part number.”

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The term ‘machine learning’ is used to describe the process of turning the machines smarter day by day in today’s technologically advanced environment. Machine learning serves as the foundation for the creation of voice assistants, tailored recommendations, and other intelligent applications.

The core of this intelligence is the machine learning algorithm, through which a computer learns from data and then makes decisions to some lower or higher extent without human intervention.

This article will explore what these algorithms are, the types, and their common daily life application, in addition to the top 10 machine learning algorithms.

Machine learning algorithms are sequences of instructions or models that allow computers to learn patterns from data and make decisions or prediction under conditions of uncertainty without explicit programming. Such an algorithm helps machines improve their performance in some task over time by processing data and observing trends.

In simple words, these enable computers to learn from data, just as humans learn from experience.

Types of Machine Learning Algorithms:

Machine learning algorithms fall into three main types-

1. Supervised learning

These are systems of algorithms that work on data feeding from a system or set of systems and help form a conclusion from the data. In supervised learning, algorithms learn from labeled data, which means the dataset contains both input variables and their corresponding output. The goal is to train the model to make predictions or decisions. Common supervised learning algorithms include:

• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forests
• Support Vector Machines
• Neural Networks

2. Unsupervised learning

In this type of algorithms, the machine learning system studies data for pattern identification. There is no answer key provided and human operator instructing the computer. Instead, the machine learns correlations and relationships by analysing the data available to it. In unsupervised learning, the machine learning algorithm applies its knowledge to large data sets. Common unsupervised learning techniques include:

• Clustering
• Association
• Principal Component Analysis (PCA)
• Autoencoders

3. Reinforcement learning

Reinforcement learning focuses on regimented learning. That is, a machine learning algorithm is given a set of actions, parameters, and an end value. Reinforcement learning is trial and error learning for the machine. It learns from past experiences and begins to modify its approach depending on circumstances.

• Q-learning
• Deep Q-Networks
• Policy Gradient Methods
• MCTS(Monte Carlo Tree Search)

Applications of Machine Learning Algorithm:

Many sectors utilize machine learning algorithms to improve decision-making and tackle complicated challenges.

• In transportation, machine learning enables self-driving cars and smart traffic systems
• In the healthcare sector, the algorithms promote disease diagnosis.
• In the finance industry, it power fraud detection, credit scoring and stock market forecasting.
• Cybersecurity relies on it for threat detection and facial recognition.
• Smart assistants, where NLP—drives voice recognition, language understanding, and contextual responses.

It also plays a vital role in agriculture, education, and smart city infrastructure, making it a cornerstone of modern innovation.

Machine Learning Algorithms Examples:

Machine learning algorithms are models that help computers learn from data and make predictions or decisions without being explicitly programmed. Examples include linear regression, decision trees, random forests, K-means clustering, and Q-learning, used across fields like healthcare, finance, and transportation.

Top 10 Machine Learning Algorithms:

1. Linear Regression

Linear regression is a supervised machine learning technique, used for predicting and forecasting continuous-valued sales or housing prices. It is a technique that has been borrowed from statistics and is applied when one wishes to establish a relationship between one input variable (X) and one output variable (Y) using a straight line.

2. Logistic Regression

Logistic regression is a supervised learning algorithm primarily used for binary classification problems. It allows to classify input data into two classes on the basis of probability estimate and set threshold. Hence, for the need to classify data into distinct classes, logistic regression stands useful in image recognition, spam email detection, or medical diagnosis.

3. Decision Tree

Decision trees are supervised algorithms developed to address problems related to classification and prediction. It also looks very similar to a flow-chart diagram: a root node positioned at the top, which poses the first question on the data; given the answer, the data flows down one of the branches to another internal node with another question leading further down the branches. This continues until the data reach an end node

4. Random Forest

Random forest is an algorithm which offers an ensemble of decision trees for classification and predictive modelling purposes. Unlike a single decision tree, random forest offers better predictive accuracy by combining predictions from many decision trees.

5. Support Vector Machine (SVM)

Support vector machine is a supervised learning algorithm that can be applied for both classification and the prediction of instances. The appeal of SVM lies in the fact that it can build reliable classifiers even when very small samples of data are available. It builds a decision boundary called a hyperplane; a hyperplane in two-dimensional space is simply a line separating two sets of labeled data.

6. K-Nearest Neighbors (KNN)

K-nearest neighbor (KNN) is a supervised learning model enhanced for classification and predictive modelling. K-nearest neighbour gives a clue about how the algorithm approaches classification: it will decide output classes based on how near they are to other data points on a graph.

7. Naive Bayes

Naive Bayes describes a family of supervised learning algorithms used in predictive modelling for the binary or multi-class classification problems. It assumes independence between the features and uses Bayes’ Theorem and conditional probabilities to give an estimate of the likelihood of classification given all the feature values.

8. K-Means Clustering

K-means is an unsupervised clustering technique for pattern recognition purposes. The objective of clustering algorithms is to partition a given data set into clusters such that the objects in one cluster are very similar to one another. Similar to the KNN (Supervised) algorithm, K-means clustering also utilizes the concept of proximity to find patterns in data.

9. Principal Component Analysis (PCA)

Principal Component Analysis (PCA) is a statistical technique used to summarize information contained in a large data set by projecting it onto a lower-dimensional subspace. Sometimes, it is also regarded as a dimensionality reduction technique that tries to retain the vital aspects of the data in terms of its information content.

10. Gradient Boosting (XGBoost/LightGBM)

The gradient boosting methods belong to an ensemble technique in which weak learners are iteratively added, with each one improving over the previous ones to form a strong predictive model. In the iterative process, each new learner is added to correct the errors made by the previous models, gradually improving the overall performance and resulting in a highly accurate final model

Conclusion:

Machine learning algorithms are used in a variety of intelligent systems: from spam filters and recommendation engines to fraud detection and even autonomous vehicles. Knowledge of the most popular algorithms, such linear regression, decision trees, and gradient boosting, explains how machines learn, adapt, and assist in smarter decision-making across industries. As data grows without bounds, the mastery of these algorithms becomes ever so vital in the effort toward innovation and problem solving in this digital age.

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Vishay Intertechnology, Inc. introduced two new IHDM Automotive Grade edge-wound, through-hole inductors in the 1107 case size with soft saturation current to 422 A. Featuring a powdered iron alloy core technology, the Vishay Inductors Division’s IHDM-1107BBEV-2A and IHDM-1107BBEV-3A provide stable inductance and saturation over a demanding operating temperature range from -40 °C to +180 °C with low power losses and excellent heat dissipation.

The edge-wound coil of the devices released provides low DCR down to 0.22 mΩ, which minimizes losses and improves rated current performance for increased efficiency. Compared to competing ferrite-based solutions, the IHDM-1107BBEV-2A and IHDM-1107BBEV-3A offer 30 % higher rated current and 30 % higher saturation current levels at +125 °C. The inductors’ soft saturation provides a predictable inductance decrease with increasing current, independent of temperature.

With a high isolation voltage rating up to 350 V, the AEC-Q200 qualified devices are ideal for high current, high temperature power applications, including DC/DC converters, inverters, on-board chargers (OBC), domain control units (DCU), and filters for motor and switching noise suppression in internal combustion (ICE), hybrid (HEV), and full-electric (EV) vehicles. The inductors are available with a selection of two core materials for optimized performance depending on the application.

Standard terminals for the IHDM-1107BBEV-2A and IHDM-1107BBEV-3A are stripped and tinned for through-hole mounting. Vishay can customize the devices’ performance — including inductance, DCR, rated current, and voltage rating — upon request. Customizable mounting options include bare copper, surface-mount, and press fit. To reduce the risk of whisker growth, the inductors feature a hot-dipped tin plating. The devices are RoHS-compliant, halogen-free, and Vishay Green.

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Under the claim “Accelerating transformation for a sustainable future,” TDK presents highlight solutions from September 17 to 19, 2025, at the Bangalore International Exhibition Centre (BIEC).

At hall 3, booth H3.D01, visitors can explore innovations in automotive solutions, EV charging, renewable energy, industrial automation, smart metering, and AI-powered sensing TDK’s technologies support the region’s shift toward cleaner mobility, intelligent infrastructure, and energy efficient living across automotive, industrial, and consumer sectors TDK Corporation will showcase its latest component and solution portfolio at electronica India 2025, held from September 17 to 19, 2025, at the Bangalore International Exhibition Centre (BIEC).

With the theme “Accelerating transformation for a sustainable future,” TDK presents technologies that reflect the region’s priorities in mobility electrification, industrial modernization, renewable energy, and digital infrastructure. The exhibit at hall 3, booth H3.D01 features live demonstrations and expert-led insights across key applications — from electric vehicles and smart factories to energy-efficient homes and immersive digital experiences.

TDK’s solution highlights at electronica India 2025:

Automotive solutions: Explore TDK’s comprehensive portfolio for electric two-wheelers and passenger vehicles, including components for battery management, motor control, onboard charging, and ADAS. Highlights include haptic feedback modules, Hall-effect sensors, and a live demo of the Volkswagen ID.3 traction motor featuring precision sensing technologies.

EV charging: Experience innovations in DC fast charging, including components for bi-directional DC-DC conversion, varistors, inductors, and transformers. A live 11 kW reference board demonstrates scalable, efficient charging for India’s growing e-mobility infrastructure.

Industrial automation: Discover intelligent sensing and connectivity solutions that boost uptime and efficiency. Live demos include SmartSonic Mascotto (MEMS time-of-flight), USSM (ultrasonic module), and VIBO (industrial accelerometer) – all designed to support predictive maintenance and smart infrastructure.

Energy & home: TDK presents high-voltage contactors, film capacitors, and protection devices for solar, wind, hydrogen, and storage systems. Explore TDK’s India-made portfolio of advanced passive components and power quality solutions, developed at the company’s state-of-the-art Nashik and Kalyani facilities. These technologies support a wide range of applications, including mobility, industrial systems, energy infrastructure, and home appliances.

Smart metering: TDK showcases ultrasonic sensor disks, NTC sensors, inductors, and RF solutions that enable accurate and connected metering for electricity, water, and gas, supporting smarter utility management.

ICT & Connectivity: Explore AR/VR retinal projection modules, energy harvesting systems, and acoustic innovations. Highlights include PiezoListen floating speakers, BLE-powered CeraCharge demos, and immersive sound and navigation technologies for smart devices and wearables.

Accessibility & Innovation: TDK presents the WeWALK Smart Cane, powered by ultrasonic time-of-flight sensors, accelerometers, gyroscopes, and MEMS microphones — enhancing mobility and independence for visually impaired users.

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Today’s world includes self-driving cars, voice assistants, recommendation engines, and even medical diagnoses thrive powered at their core by robust machine learning frameworks. Machine learning frameworks are the solution that really fuels all these intelligent systems. This article will delve into the definition and what it means to function as a machine learning framework, mention some popular examples, and review the top 10 ML frameworks.

A machine learning framework is a set of tools, libraries, and interfaces to assist developers and data scientists in building, training, testing, and deploying machine learning models.

It functions as a ready-made software toolkit, handling the intricate code and math so that users may concentrate on creating and testing algorithms.

Here is how most ML frameworks work:

1. Data Input: You feed your data into the framework (structured/unstructured).
2. Model Building: Pick or design an algorithm (e.g neural networks).
3. Training: The model is fed data so it learns by adjusting weights via optimization techniques.
4. Evaluation: Check the model’s accuracy against brand new data.
5. Deployment: Roll out the trained model to implementation environments (mobile applications, website etc.)

Examples of Machine Learning Frameworks:

• TensorFlow
• PyTorch
• Scikit-learn
• Keras
• XGBoost

Top 10 Machine Learning Frameworks:

1. TensorFlow

Google Brain created the open-source TensorFlow framework for artificial intelligence (AI) and machine learning (ML). It was created to make it easier to create, train and implement machine learning models especially deep learning models across several platforms by offering the necessary tools.

Applications supported by TensorFlow are diverse and include time series forecasting, reinforcement learning, computer vision and natural language processing.

2. PyTorch

Created by Facebook AI Research, PyTorch is an eminent, yet beginner-friendly academic research framework. PyTorch uses dynamic computation graphs that provide easy debugging and testing. Being very flexible, it is mostly preferred while conducting deep learning work with a number of breakthroughs and research papers taking PyTorch as their primary framework.

3. Scikit-learn

Scikit-learn is a Python library built upon NumPy and SciPy. It’s the best choice for classical machine learning algorithms like linear regression, decision trees, and clustering. It’s simple API with documented instructions for use makes it fit for handling small to medium-sized datasets when prototyping.

4. Keras

Being a high-level API, Keras is tightly integrated into TensorFlow. More modern deep learning techniques promoted and supported from the interface deliver ease in realizing ML problems. Keras covers all the stages that an ML engineer goes through in the realization of a solution: data processing, hyperparameter tuning, deployment, etc. Its intention was to enable fast experimentation.

5. XGBoost

XGBoost- Extreme Gradient Boosting-is an advanced machine-learning technique geared toward efficiency, speed, and utmost performance. It is a GBDT-based machine-learning library that is scalable and distributed. It is the best among the machine learning libraries for regression, classification, and ranking, offering parallel tree-boosting.

The understanding of the bases of machine learning and the methods on which XGBoost runs is important; these are supervised machine learning, decision trees, ensemble learning, and gradient boosting.

6. LightGBM

LightGBM is an open-source high-performance framework and is also created by Microsoft. It is the technique on gradient boosting used in ensemble learning framework.

LightGBM is a fast gradient boosting framework that uses tree-based learning algorithms. It was developed in the product environment while keeping the requirements of speed and scalability in mind. Training times are much shorter, and the computer resources are fewer. Memory requirements are also less, making it suitable for resource-starved systems.

LightGBM will also, in many cases, provide better predictive accuracy because of its novel histogram-based algorithm and optimized decision tree growth strategies. It allows for parallel learning, distributed training on multiple machines, and GPU acceleration-to scale to massive datasets while maintaining performance

7. Jax

JAX is an open-source machine learning framework based on the functional programming paradigm developed and maintained by Google. JAX stands for “Just Another XLA,” where XLA is short for Accelerated Linear Algebra. It is famous for numerical computation and automatic differentiation, which assist in the implementation of many machine learning algorithms. JAX, being a relatively new machine learning framework, is some way in providing features useful in realizing a machine learning model.

8. CNTK

Microsoft Cognitive Toolkit (CNTK) is an open-source deep learning framework developed by Microsoft to implement efficient training of deep neural networks. It is scalable in training models across multiple GPUs and across multiple servers, especially good for large datasets and complex architectures. Weighing its flexibility, CNTK supports almost all classes of neural networks and is useful in many kinds of machine-learning tasks such as feedforward, convolutional, and recurrent networks.

9. Apache Spark MLlib

Apache Spark MLlib is Apache Spark’s scalable machine learning library built to ease the development and deployment of machine learning apps for large datasets. It offers a rich set of tools and algorithms for various machine learning tasks. It is designed for simplicity, scalability and easy integration with other tools.

10. Hugging Face Transformers

Hugging Face Transformers is an open-source framework specializing in deep learning paradigms developed by Hugging Face. It provides APIs and interfaces for the download of state-of-the-art pre-trained models. Following their download, the user can then fine-tune the model to best serve his or her purpose. The models perform usual tasks in all modalities, including natural language processing, computer vision, audio, and multi-modal. Hugging Face Transformers represent Machine Learning toolkits for NLP, trained on specific tasks.

Conclusion:

Machine learning frameworks represent the very backbone of modern AI applications. Whether a beginner or a seasoned pro building very advanced AI solutions, the right framework will make all the difference.

From huge players such as TensorFlow and PyTorch down to niche players such as Hugging Face and LightGBM, each framework claims certain virtues that it is best suited for in different kinds of tasks and industries.

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Keysight BreakingPoint QuickTest simplifies application performance and security effectiveness assessments with predefined test configurations and self-stabilizing, goal-seeking algorithms

Keysight Technologies, Inc. announced that Fortinet chose the Keysight BreakingPoint QuickTest network application and security test tool to validate SSL deep packet inspection performance capabilities and security efficacy of its FortiGate 700G series next-generation firewall (NGFW). BreakingPoint QuickTest is Keysight’s turn-key performance and security validation solution with self-stabilizing, goal-seeking algorithms that quickly assess the performance and security efficacy of a variety of network infrastructures.

Enterprise networks and systems face a constant onslaught of cyber-attacks, including malware, vulnerabilities, and evasions. These attacks are taking a toll, as 67% of enterprises report suffering a breach in the past two years, while breach-related lawsuits have risen 500% in the last four years.

Fortinet developed the FortiGate 700G series NGFW to help protect enterprise edge and distributed enterprise networks from these ever-increasing cybersecurity threats, while continuing to process legitimate customer-driven traffic that is vital to their core business. The FortiGate 700G is powered by Fortinet’s proprietary Network Processor 7 (NP7), Security Processor 5 (SP5) ASIC, and FortiOS, Fortinet’s unified operating system. Requiring an application and security test solution that delivers real-world network traffic performance, relevant and reliable security assessment, repeatable results, and fast time-to-insight, Fortinet turned to Keysight’s BreakingPoint QuickTest network applications and security test tool.

Using BreakingPoint QuickTest, Fortinet validated the network performance and cybersecurity capabilities of the FortiGate 700G NGFW using:

• Simplified Test Setup and Execution: Pre-defined performance and security assessment suites, along with easy, click-to-configure network configuration, allow users to set up complex tests in minutes.
• Reduced Test Durations: Self-stabilizing, goal-seeking algorithms accelerate the test process and shorten the overall time-to-insight.
• Scalable HTTP and HTTPS Traffic Generation: Supports all RFC 9411 tests used by NetSecOPEN, an industry consortium that develops open standards for network security testing. This includes the 7.7 HTTPS throughput test, allowing Fortinet to quickly assess that the FortiGate 700G NGFW’s SSL Deep Inspection engine can support up to 14 Gbps of inspected HTTPS traffic.
• NetSecOPEN Security Efficacy Tests: BreakingPoint QuickTest supports the full suite of NetSecOPEN security efficacy tests, including malware, vulnerabilities, and evasions. This ensures the FortiGate 700G capabilities are validated with relevant, repeatable, and widely accepted industry standard test methodologies and content.
• Robust Reporting and Real-time Metrics: Live test feedback and clear, actionable reports showed that the FortiGate 700G successfully blocked 3,838 of the 3,930 malware samples, 1,708 of the 1,711 CVE threats, and stopped 100% of evasions, earning a grade “A” across all security tests.

Nirav Shah, Senior Vice President, Products and Solutions, Fortinet, said: “The FortiGate 700G series next-generation firewall combines cutting-edge artificial intelligence and machine learning with the port density and application throughput enterprises need, delivering comprehensive threat protection at any scale. Keysight’s intuitive BreakingPoint QuickTest application and security test tool made our validation process easy. It provided clear and definitive results that the FortiGate 700G series NGFW equips organizations with the performance and advanced network security capabilities required to stay ahead of current and emerging cyberthreats.”

Ram Periakaruppan, Vice President and General Manager, Keysight Network Test and Security Solutions, said: “The landscape of cyber threats is constantly evolving, so enterprises must be vigilant in adapting their network defenses, while also continuing to meet their business objectives. Keysight’s network application and security test solutions help alleviate the pressure these demands place on network equipment manufacturers by providing an easy-to-use package with pre-defined performance and security tests, innovative goal-seeking algorithms, and continuously updated benchmarking content, ensuring solutions meet rigorous industry requirements.”

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Anritsu Corporation announced that it has helped AMD accelerate the testing of electrical compliance for PCI Express (PCIe) specification for pre-production AMD EPYC CPUs. Achieving a maximum data rate of 64 GT/s using the high-performance Anritsu BERT Signal Quality Analyzer-R MP1900A, testing was done under challenging backchannel conditions with insertion loss exceeding the specified 27 dB in the CEM specification, along with stress conditions applied using Spread Spectrum Clocking (SSC).

“In collaboration with Anritsu, we have achieved a stable demonstration of electrical compliance up to 64 GT/s,” said Amit Goel, corporate vice president, Server Engineering, AMD. “This early validation furthers our commitment to delivering reliable, high-speed I/O for future platforms powered by our next-generation AMD EPYCTM CPUs.”

“AMD is a key technology partner in advancing PCIe technology,” said Takeshi Shima, Director, Senior Vice President, Test & Measurement Company President of Anritsu Corporation. “We will continue to respond to various test needs and expand functions for PCIe compliance testing, while also contributing to quality evaluation and design efficiency of PCIe devices through proposals to standards organizations.”

PCIe 6.0 technology is the next-generation standard that provides a bandwidth of 64 GT/s per lane and up to 256 GB/s in a x16 configuration as a high-speed interface between internal devices such as CPUs, GPUs, SSDs, and network cards. While maintaining compatibility with previous standards, it achieves highly reliable and efficient communications in fields such as AI (Artificial Intelligence), HPC (High Performance Computing), and high-speed storage, greatly contributing to improving the performance of next-generation data centers and analytical systems.

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With India undergoing rapid urbanisation and infrastructural growth, there is a demand for materials that are either strong, resilient, or sustainable. These very TMT bars constitute the crux of modern construction. The city-based constructions in the country represent a host of varieties, such as residential, flyovers, industrial plants, skyscrapers, bridges, and buildings. The TMT bars warrant strength with a little flexibility so that its enhancement work on both durability and safety.

The whole range of TMT bars is competing to attract the buyers who seek high-performance bars with most advanced features. Raw materials, the technological process used for their manufacture, strength, ductility, and resistance to corrosion are among the differences existing among bars.

Here follows an all-encompassing guide for the Top 10 TMT Bar Brands in India, showing the feature-based distinction, application, and technology advantage.

1. TATA Tiscon 550SD:

TATA Tiscon is the pioneer in the Indian TMT industry, being the first rebar brand in India. Supported by TATA Steel, it came out with the super ductile 550SD TMT bar using an advanced technology of Morgan, USA. TATA Tiscon 550SD bars, being GreenPro certified, hence are the environmentally friendly variety. With very high tensile strength and flexibility, they become an ideal candidate for earthquake-prone zones and heavy-duty infrastructure.

2. SAIL’s SeQR:

Outstanding ductility is attributed to SAIL’s SeQR TMT bars that are manufactured by Steel Authority of India Limited, along with fire resistance and UTS/YS ratio. These bars are heat resistant up to the temperature of 600°C, and special corrosion-resistant varieties (HCR) are available for coastal or damp environments.

The TMT bars by SAIL possess excellent energy absorption that is desired for resisting the shocks from seismic or other sudden structural stresses.

3. JSW Neosteel:

Produced from virgin iron ore, JSW Neosteel 500D/550D bars offer superior metallurgical quality. Because of their weldability and flexibility, they can resist seismic forces, making them sought after in regions prone to earthquakes.

Their low carbon content maintains structural integrity and allows easy fabrication, especially for large projects.

4. Jindal Panther:

Jindal Panther TMT bars have ductility and bonding strength imparted by German TEMPCORE technology. Uniform rib patterns allow a better grip for concrete, which serves the objectives for high-rise buildings.

The FE 500D grade is said to embody the right mix of strength and flexibility.

5. SRMB Steel:

The SRMB uses special X-pattern ribs to ensure better grip with cement and thereby minimize slippage and.mvare a better performance of the structure. These bars come with ISO and BIS certification, and therefore, they have good corrosion resistance and can be used for various residential and commercial applications.

6. Kamdhenu TMT:

These micro-alloyed steel bars from Kamdhenu are ISI-certified and supplied all over India. They are classified as 550D TMT bars and have properties like good elongation, flexibility, and fire resistance.

They are a cost-effective option for home construction, real estate, and semi-urban projects.

7. Vizag Steel:

Produced by the RINL- Rashtriya Ispat Nigam Limited, the Vizag Steel TMT bars find wide use in government and public infrastructure projects. Due to their uniform quality and corrosion-resistant properties, they are used for large-scale civil projects that include metros, flyovers, and industrial buildings.

8. Shyam Steel:

Shyam Steel FE 500D TMT bars have advantages of fire resistance, corrosion protection, and elongation values and are ISO-certified with German technology. Such bars are recommended for high-rise residential complexes as well as commercial buildings.

9. Electrosteel:

Electrosteel TMT bars, widely renowned for their bendability, low carbon content, and resistance to rust, give the buyers value for their money in terms of durability. Private contractors and small-scale builders looking for good value for money are going for these.

10. Essar TMT Bars:

Essar TMT bars, processed by Thermex technology, ensure uniformity, weldability, and good finish; hence, they find widespread use in commercial buildings, real estate projects, and infrastructure and thus guarantee long durability.

Comparison:

Choosing the right TMT bars is the cornerstone of structural integrity and durability into the future. Some factors are considered when choosing a TMT bar:

Grade of the Bar:

FE 415 is suitable for small residential buildings. FE 500 and 550D are used for high-rises, bridges, and commercial structures because of their higher tensile strength.

Corrosion Resistance:

Bars such as SAIL SeQR HCR or JSW Neosteel can be used for corrosion resistance in the coastal or humid environment.

Earthquake Resistance:

In seismic zones, bars with high ductility and UTS/YS ratio are required, such as Tata Tiscon 550SD or Jindal Panther.

Certifications & Quality Assurance:

Look for brands certified by BIS, ISO, or GreenPro, which assures compliance with Indian construction standards.

Conclusion:

India’s future infrastructure will rely on materials that combine strength and safety with sustainability. The right choice of TMT bar brand is therefore important for structural integrity. Whether it be for a small house or a mega commercial complex, the above-mentioned brands provide features suitable for a whole range of applications.

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MPU Integrates a Quad-Core CPU, an NPU, High-Speed Connectivity and Advanced Graphics to Power Next-Generation HMI Devices with Full HD Display

Renesas Electronics Corporation, a premier supplier of advanced semiconductor solutions announced the launch of its new 64-bit RZ/G3E microprocessor (MPU), a general-purpose device optimized for high-performance Human Machine Interface (HMI) applications. Combining a quad-core Arm Cortex-A55 running at up to 1.8GHz with a Neural Processing Unit (NPU), the RZ/G3E brings high-performance edge computing with AI inference for faster, more efficient local processing. With Full HD graphics support and high-speed connectivity, the MPU targets HMI systems for industrial and consumer segments including factory equipment, medical monitors, retail terminals and building automation.

High-Performance Edge Computing and HMI Capabilities

At the heart of the RZ/G3E is a quad-core Arm Cortex-A55, a Cortex-M33 core, and the Ethos-U55 NPU for AI tasks. This architecture efficiently runs AI applications such as image classification, object recognition, voice recognition and anomaly detection while minimizing CPU load. Designed for HMI applications, it delivers smooth Full HD (1920×1080) video at 60fps on two independent displays, with output interfaces including LVDS (dual-link), MIPI-DSI, and parallel RGB. A MIPI-CSI camera interface is also available for video input and sensing applications.

“The RZ/G3E builds on the proven performance of the RZ/G series with the addition of an NPU to support AI processing,” said Daryl Khoo, Vice President of Embedded Processing at Renesas. “By using the same Ethos-U55 NPU as our recently announced RA8P1 microcontroller we’re expanding our AI embedded processor portfolio and offering a scalable path forward for AI development. These advancements address the demands of next-generation HMI applications across vision, voice and real-time analytics with powerful AI capabilities.”

The RZ/G3E is equipped with a range of high-speed communication interfaces essential for edge devices. These include PCI Express 3.0 (2 lanes) for up to 8Gbps, USB 3.2 Gen2 for fast 10Gbps data transfer, and dual-channel Gigabit Ethernet for seamless connectivity with cloud services, storage, and 5G modules.

Low-Power Standby with Fast Linux Resume

Starting with the third-generation RZ/G3S, the RZ/G series includes advanced power management features to significantly reduce standby power. The RZ/G3E maintains sub-CPU operation and peripheral functions while achieving low power consumption around 50mW and around 1mW in deep standby mode. It supports DDR self-refresh mode to retain memory data, enabling quick wake-up from deep standby for running Linux applications.

Comprehensive Linux Software Support

Renesas continues to offer the Verified Linux Package (VLP) based on the reliable Civil Infrastructure Platform, with over 10 years of maintenance support. For users requiring the latest versions, Renesas provides Linux BSP Plus, including support for the latest LTS Linux kernel and Yocto. Ubuntu by Canonical and Debian open-source OS are also available for server or desktop Linux environments.

Key Features of RZ/G3E

• CPU: Quad-core Cortex-A55 (up to 1.8GHz), Cortex-M33
• NPU: Ethos-U55 (512 GOPS)
• HMI: Dual Full HD output, MIPI-DSI / Dual-link LVDS / Parallel RGB, 3D graphics, H.264/H.265 codec
• Memory Interface: 32-bit LPDDR4/LPDDR4X with ECC
• Connectivity for 5G Communication: PCIe 3.0 (2 lanes), USB 3.2 Gen2, USB 2.0 x2, Gigabit Ethernet x2, CAN-FD
• Operating Temperature: -40°C to 125°C
• Package Options: 15mm square 529-pin FCBGA, 21mm square 625-pin FCBGA
• Product Longevity: 15-year supply under Product Longevity Program (PLP)

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