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Decision Tree learning is a widely used method in machine learning and data analysis for making decisions and predictions. It employs a tree-like model of decisions, where each internal node represents a test on a feature, each branch corresponds to an outcome of the test, and each leaf node signifies a final decision or classification. The process begins at the root node, which encompasses the entire dataset, and progressively splits into branches based on feature values, ultimately leading to distinct outcomes. This hierarchical structure allows for intuitive visualization and interpretation of decision-making processes. Decision Trees are incredibly versatile and find applications across a wide range of fields. Highlighted below are the top 10 decision tree learning real-world applications and use cases.

1. Fraud Detection

Identifying and preventing fraudulent transactions is one of the primary use cases of Decision Trees, and they are especially beneficial in banking as well as e-commerce centers. For instance, Decision Trees can flag suspicious transactions such as sudden exorbitant spending or transactions from new locations, which helps enterprises to minimize financial risks and combat security threats.

2. Customer Segmentation

Decision Trees are particularly useful in marketing, where customers can be classified into groups based on age, income, and even purchase and browsing history. This form of segmentation is especially useful for marketing as it helps personalize communication and enhances engagement by ensuring the right message is delivered to the appropriate audience.

3. Medical Diagnosis

Decision trees in the healthcare sector are essential for assisting clinicians in making predictions about the likelihood of a disease for a patient. This is derived from the patient’s symptoms, tests, and previous medical records. The trees’ logic is clear, which gives the doctors a chance to follow each step of reasoning, and this makes the tools invaluable in clinical decision support systems.

4. Recommendation systems

Decision trees are used in recommendation systems, such as on Netflix and Amazon, to suggest items, movies, or services by analyzing user preferences, browsing history, and ratings. These models help personalize the user experience and increase engagement by suggesting items that align with individual tastes.

5. Predictive Maintenance

In the sectors of manufacturing and transportation, decision trees based on sensor data, usage patterns, and equipment operating conditions are used to forecast equipment failure. This provides timely maintenance and improves the chance to provide uninterrupted service.

6. Autonomous Driving Decision Systems

Decision trees are important to the development of autonomous vehicles because they incorporate decision making models in driving systems. With their complex environments, these vehicles have to make safe and efficient decisions while learning the rules of the road, functionality of other vehicles, and traffic control. The vehicles accelerate, brake, and even change lanes based on the output of decision trees.

7. Cybersecurity Threat Detection

The use of decision trees in threat detection provides a more in-depth look into network traffic, different login schemes and their failures, as well as different system behaviors. Their use aids in the prevention of attacks and protection of crucial information.

8. Filtering of Email Spam

In order to classify messages, email providers analyze the words used, the sender’s reputation, and the structure of the message. They classify the messages using decision trees as either spam or legitimate email. Making email spam free and increasing security for the users.

9. Space Agencies and Aerospace Companies

Space and aerospace companies use decision trees in monitoring spacecraft systems and in predicting component failure and assist in mission planning. They help ensure safety and reliability in high-stakes environments.

10. Navigation and GPS Functionality

Decision trees are used by mapping and navigation software to provide the best possible route possibilities while accounting for user preferences, roadwork, and traffic conditions. Decision trees also consider the user’s objectives, whether to minimize travel time, fuel consumption, or increase safety. 

Conclusion:

Decision trees learning have a wide array of uses in data driven decision making, and thus can be considered a very strong and useful methodology. Their unique and flexible structure, ease of understanding and use, and transparency make decision trees very useful from the healthcare sector and the finance sector all the way to public administration and environmental care sectors. Decision trees can be used and are very crucial in the healthcare sector to help make very important and life saving decisions, and businesses also stand to benefit through the use of decision trees in optimizing their strategies. The impact of decision trees is very important and will grow even further as technology advances.

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Prime Minister Narendra Modi and his Japanese counterpart Shigeru Ishiba visited the Tokyo Electron Factory (TEL Miyagi) in Sendai. This visit was significant because it marked a focus of India and Japan’s cooperation in advanced technologies, especially semiconductors. The two leaders also emphasised the importance of this industry by taking the bullet train from Tokyo to Sendai, which is more than 300 km.

During the visit, Modi engaged with TEL executives regarding their position in the global semiconductor ecosystem and future partnerships with India. He emphasized how India’s growing manufacturing ecosystem and Japan’s cutting-edge semiconductor machinery and technology work in tandem.

In his remarks at the India–Japan Economic Forum, Modi highlighted semiconductors, batteries, and robotics as focus areas for Make in India collaborations. Prime Minister Ishiba laid out three goals: building stronger people-to-people ties, fusing technology with green initiatives, and boosting cooperation in high-tech fields, especially semiconductors.

The visit to Sendai came as a follow-up of the bilateral agreements made under the India-Japan Industrial Competitiveness Partnership and the Economic Security Dialogue. Both these agreements cover fields like critical minerals, ICT, pharmaceuticals, and more. An understanding was made to speed up the projects in these fields alongside semiconductors.

Involvement from the private sector is increasing steadily. Japanese firms have entered into around 150 MOUs over the last two years in sectors such as aerospace, automotive, semiconductors, energy, and human resources, as per the Ministry of External Affairs of India. Modi also remarked that the Digital Partnership 2.0, AI collaboration, and work on rare earth minerals will continue to be the focus of partnership.

Modi and Ishiba reiterated their vision of developing strong and trusted supply chains and India and Japan’s roles as critical partners in the framework of global technology security by keeping semiconductors as the focus of this visit.

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Union Minister Ashwini Vaishnaw inaugurated India’s first tempered glass manufacturing facility in Noida, marking a major milestone in the country’s electronics manufacturing ecosystem.

Noida now owns the distinction of having inaugurated India’s first tempered glass manufacturing unit, a step ahead in the electronics manufacturing journey.

The plant built in collaboration US technology giant Corning is owned and operated by Optiemus infracom. The factory will manufacture tempered glass for smartphones and other electronic devices, which is used as a protective layer and is used extensively.

Optiemus has emerged as a key player in India’s electronics manufacturing ecosystem, known for its strategic partnerships and innovation, Minister Vaishnaw described Optiemus, “a new gem in India’s fast-growing electronics manufacturing ecosystem,” and further stated that production of covered glass with Corning’s collaboration is slated to begin before the end of this year.

Investment and Production Capacity:

The Noida facility has been built with an initial investment of ₹70 crore and is, and it is furnished with an annual capacity of 2.5 crore units. In addition to supporting domestic manufacturing, the plant is projected to generate more than 600 direct jobs in the area.

Optiemus has set forth expansion plans of a larger scale. In the second phase of growth, the company aims to significantly increase its capacity to 20 crore units per year for the domestic market as well as for exports.

Phase 2 Expansion: 

For the next phase, the company wishes to open another plant in Noida with an annual capacity of 10 crore units. In addition, a new plant in southern India with a capacity of 15 crore tempered glass units is planned. An additional ₹800 crore is earmarked for this expansion, with the southern plant receiving more than ₹450 crore.

In addition, the company plans to launch its own brand of tempered glass, RhinoTech, in September 2025. Emphasizing domestic manufacturing, a ‘Made in India’ tag will be attached to the product. RhinoTech will have consumer-friendly features. For instance, it will be covered by a one-year warranty with unlimited replacement, which is bound to add value to the product in the market.

While speaking at the event, Minister Vaishnaw focused on the achievements of India’s electronics sector. In the past 11 years, this sector’s production value has increased six times, reaching ₹11.5 lakh crore. Exports have also grown to more than ₹3 lakh crore, and the industry supports 25 million jobs both directly and indirectly across the country.

The inauguration of this factory marks India’s entry into the tempered glass manufacturing industry, which was previously reliant on imports. The impact of this development is the expected improvement of the supply chain for smartphones and other electronic devices, which is in line with the government initiative to make India a global hub for electronics production.

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Reinforcement learning (RL), a subfield of machine learning in which agents learn by interacting with their surroundings, is gaining significant popularity in India’s quickly developing AI ecosystem. RL is being used in a variety of areas, including financial modeling, smart energy grids, and autonomous systems. Indian businesses are using RL to innovate and create scalable solutions that are on par with international standards, rather than merely adopting it. The top 10 reinforcement learning companies in India will be explored in this article:

1. Tata Consultancy Services (TCS)

As the global IT leader, TCS focuses on integrating RL into supply chain optimization, autonomous systems, and intelligent automation. It is AI laboratories work on adaptive algorithms that learn from changing environments in logistics, manufacturing, and operations for better decision making. The company also uses its platform TCS iON to apply RL to the fields of education and skill development, employing gamified and tailored learning to increase motivation and achieve better educational results.

2. Infosys

As led by the Infosys Topaz platform, the AI-first initiative of the company shows faster advances in Reinforcement Learning (RL). The platform’s robotics, enterprise automation, and conversational AI are improved by RL and RLHF (Reinforcement Learning with Human Feedback). The completion and integration of these technologies enable the creation of adaptive, scalable, and self-learning enterprise solutions, such as automated fraud detection systems, predictive analytics, and enhanced customer care.

3. Wipro

Wipro is currently engaging with Reinforcement Learning (RL) to upgrade automation, simulation, and intelligent systems across multiple sectors. The company utilizes RL in industrial automation and flight simulation, employing adaptive learning models to improve control mechanisms and decision-making procedures. Wipro’s investigations also extend to scalable RL methodologies for manufacturing and financial services, which facilitate more intelligent resource allocation and operational forecasting.

4. HCL Technologies

HCL Technologies is continuously refining the applications of Reinforcement Learning (RL) across various focus areas, including cybersecurity, workforce analytics, and education. In workforce analytics, HCLTech uses RL for the customization of learning pathways and the prediction of talent development, enabling companies to match employee evolution with their strategic objectives. Their partnership with Pearson brings even greater value in the education sector, where RL-driven adaptive learning systems customize services to the learners and enhance the mastery of skills.

5. ValueCoders

ValueCoders is an Indian software company specializing in adaptive smart system software development for healthcare, finance, and education sectors. They use computer vision, reinforcement learning, and MLOps to ease decision automation, enhance personalization, and boost system performance over time for their clients.

6. Locus

Locus is a top-class supply chain and logistics company that focuses on streamlining and automating supply chain operations with the use of reinforcement learning (RL). With Locus, businesses can now enhance the planning of delivery routes, scheduling of deliveries, and even the allocation of resources. This allows companies to better control and reduce costs, increase the efficiency of their operations, and better respond to fluctuating demand and traffic conditions.

7. Mad Street Den

Mad Street Den is the only company to blend reinforcement learning and computer vision through its Vue.ai platform to enhance personalized retail experiences. Their adaptive systems are designed to optimize merchandising, styling, and customer engagement on behalf of global fashion and e-commerce brands.

8. Arya.ai

With a deep focus on reinforcement learning and deep neural networks, Arya.ai addresses autonomous decision systems. Their SaaS products with real-time adaptation enabled for finance, insurance, and robotics industries address fraud detection, claims automation, and smart underwriting.

9. Infilect

Infilect uses visual intelligence platforms to implement RL in retail. Their technologies optimize pricing, merchandising, and shelf availability using RL-driven analytics, which helps brands lower stockouts and increase in-store compliance.

10. Flutura Decision Sciences

The major industries of oil and gas, chemicals, and heavy machinery benefit from Flutura Decision Sciences’ artificial intelligence and reinforcement learning approaches to machine learning, which are used to develop their industrial internet of things platform, Cerebra. With Flutura, these industries can improve asset performance, anticipate failures, and minimize downtime. To offer complex system digital twins, Cerebra delivers diagnostics and prognostics, which are supported by physics models, heuristics, and machine learning.

Conclusion:

With smart healthcare, smart agriculture, and smart city systems, autonomous systems powered by reinforcement learning are ready to take off, marking the beginning of the AI revolution. With the development of edge AI and quantum computing, real-time decision-making will be dominated by RL. Due to the culture of innovation, availability of skilled resources, and the country’s bold vision, India has the potential to lead the world in adaptive intelligent systems in the upcoming years.

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High Security Integration, Low Power, and Small Package, Providing Cost-Effective RoT

Nuvoton Technology released the upgraded NuMicro M2354, tailored for applications such as server RoT, smart city, IoT, and smart metering.

NuMicro M2354 is an Arm TrustZone microcontroller based on the Armv8-M architecture and powered by the Arm Cortex-M23 CPU, designed to enhance IoT security. It is suitable for long-term confidentiality requirements and highly sensitive data protection scenarios.

The M2354 operates at frequencies up to 96 MHz, offers a wide operating voltage range of 1.7V to 3.6V, and a broad operating temperature range of -40°C to +105°C. The power consumption is 89.3 μA/MHz in LDO mode and 39.6 μA/MHz in DC-DC mode. The Standby Power-down mode consumes less than 2 µA, and the Deep Power-down mode without VBAT consumes less than 0.1 µA, effectively extending the device’s battery life and meeting the needs of long-term IoT operation.

For Secure FOTA, the M2354 has built-in dual-bank Flash Memory of up to 1024 KB and 256 KB of SRAM. In addition to supporting eXecute-Only-Memory (XOM) to prevent code theft, it also integrates a cryptographic hardware accelerator that supports FIPS PUB 197/180/180-2/180-4 and NIST SP 800-38A, as well as a hardware key store to protect against side-channel and fault injection attacks. In terms of secure boot mechanism, the upgraded M2354 supports the Root of Trust architecture based on DICE, implemented in Mask ROM, and supports ECDSA P-521. This feature automatically generates a unique device identity and establishes a chain of trust during boot, effectively verifying firmware version and preventing firmware rollback and tampering attacks. Furthermore, M2354 is compliant with PSA Level 3 and SESIP Level 3 security certifications, which meet the demands of the EU’s Cyber Resilience Act (CRA).

M2354 supports a wide range of peripherals, including CAN, USB 2.0 full-speed OTG, PWM, UART, SPI/I2S, Quad-SPI, I²C, and RTC.

M2354 also integrates several analog components, including analog comparators, ADC, and DAC.

The package options include LQFP-48, LQFP-64, and LQFP-128. The upgraded M2354 also offers a compact WLCSP49 package. With support of the SPDM (Security Protocol and Data Model) secure communication protocol, the upgraded M2354 is well-suited for Root of Trust applications in server motherboards and daughterboards.

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Reinforcement Learning (RL), unlike other machine learning (ML) paradigms, notably supervised learning, has an agent learning to act optimally within a given environment, one step at a time. At each step, it is given feedback in the form of a reward or a penalty. The goal is to learn a policy a strategy for selecting actions that maximize the total reward over a certain time horizon. There are no inputs or outputs to fit to (as in traditional supervised learning), so RL agents must balance exploring unknown actions to discover their worth and exploiting known good actions to maximize rewards.

Reinforcement Learning History:

Reinforcement learning began with behavioural psychology’s theory of behaviourism in the early 1900s. Behaviourism postulated learning as a trial and error process propelled by rewards and punishments. This concept was later adapted and formalised into computer science mathematical models that paved the way for the development of optimisation and machine learning algorithms. Reinforcement learning is akin to optimising methods where the desired function is not explicitly given but is instead hinted at through trial and error.

How does reinforcement learning work:

To enhance decision-making, reinforcement learning works by training an agent to interact with an environment. The agent gets to perform actions. After each action, the agent gets feedback in terms of rewards or penalties associated with the specific action.

Types of Reinforcement Learning:

1. Value-Based Reinforcement Learning

This method requires an agent to learn a value function that predicts the reward for performing an action in a particular state and Q-learning is the most well-known. An agent updates its Q-values in Q-learning according to the received reward and acts to maximize these Q-values.

2. Policy-Based Reinforcement Learning

Policy-based methods focus on learning the policy itself, which is the set of rules mapping states to actions, instead of estimating value functions. This is crucial in cases with complex or continuous action spaces. Methods like REINFORCE and Proximal Policy Optimization (PPO) are good examples of algorithms that follow this paradigm.

3. Model-Based Reinforcement Learning

This refers to methods which try to construct a model of the environment that can predict the following state and reward given the current state and action. Using this model, the agent can plan and make decisions ahead of time. While this method is efficient in terms of samples, its implementation can be complicated to do correctly.

4. Actor-Critic Methods 

These hybrid methods combine the strengths of value-based and policy-based approaches. The actor updates the policy based on feedback from the critic, which evaluates the action taken. This results in more stable and efficient learning, especially in complex environments.

Applications of Reinforcement Learning:

1. Self-Driving Cars

Self-driving cars use reinforcement learning to understand their surroundings. They identify the best routes, change lanes, avoid obstacles, and optimize their overall driving.

2. Automated Machines

Automated machines use reinforcement learning to master new skills like walking, picking up objects, and putting them together. As they deal with new items and different tasks, they improve how they do things in due course.

3. Medicine

Personalized treatment is now possible because of reinforcement, which allows crafting adaptive treatment plans for patients. It is also useful in optimizing clinical trials and in the management of chronic illness.

4. Investment

In portfolio management and trading, reinforcement learning technologies attempt to make investment choices by evaluating prevailing market patterns and modifying tactics geared towards greater returns.

5. Recommendation Systems

Reinforcement learning is used to improve the recommendation systems. As users interact with the content, the system learns users preferences and dynamically suggests content making the platform personalized and more engaging.

Reinforcement Learning Examples:

Reinforcement learning is integrated into numerous fields enabling the technology to thrive. In game playing, RL has enabled breakthroughs like AlphaGo which mastered complex games such as Go and chess through self-play. In autonomous driving, self-driving cars use RL to make decisions like lane changes and obstacle avoidance by learning from real and simulated environments. In robotics, RL helps machines learn tasks like walking, grasping, and assembling by adapting to physical feedback. In finance, RL algorithms optimize trading strategies and portfolio management by analyzing market data. Lastly, in recommendation systems, platforms like Netflix and Amazon use RL to suggest content or products based on user behavior, enhancing engagement and satisfaction.

Reinforcement Learning Advantages:

Reinforcement learning is adaptive and its methods are goal driven. As an example, it can be very effective in environments that are constantly changing and that require very little supervision. It is a type of learning that is guided by rewards or feedback, in which an agent learns to improve its behavior over time based on interaction with the environment.

Conclusion:

As the rest of intelligent systems, reinforcement learning is, for now, an incredible advancement and is bound to become even more so. The level of innovation that RL will bring about will be unimaginable given the availability of more processing power and much more sophisticated algorithms. Preemptive systems, self-learning autonomous agents, and machines that collaborate with humans are only the beginning. Personalized medicine, self-developing robots, and adaptive learning systems will all lean on RL technologies. These technologies will not just adapt to the world, but will actively ‘mold’ it, in essence, making the word ‘transformative’ obsolete in describing the level of change these technologies will bring.

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Infineon Technologies AG announced that its microcontrollers (MCUs) in the new PSOC Control C3 Performance Line family are compliant with Post-Quantum Cryptography (PQC) requirements for firmware protection outlined in the Commercial National Security Algorithm (CNSA) Suite 2.0. The MCUs also support PSA (Platform Security Architecture) Level 3 compliance. By complying with both standards, Infineon’s PSOC Control C3 Performance Line meets the security needs of a wide range of industrial applications and eases their transition to increased security in the PQC era.

“With the PSOC Control C3 family, we are setting a new standard for security in industrial microcontrollers, building on decades of proven experience in MCUs and secured electronic systems,” said Steve Tateosian, SVP and General Manager, IoT, Consumer and Industrial MCUs, Infineon Technologies. “Infineon is committed to meeting and evolving industry requirements for MCU embedded security that provides stringent protection against quantum-based attacks on critical systems.”

Changes in security architecture for the PQC era include the replacement of Elliptic Curve Cryptography (ECC) based asymmetric cryptography as well as increasing the size of Advanced Encryption Standard (AES) keys and Secure Hash Algorithm (SHA) hash sizes. The algorithms and implementation guidelines provided by CSNA 2.0 help to facilitate a smoother transition to Post-Quantum Cryptography.

About PSOC Control C3 family

The PSOC Control C3 family of MCUs provide real-time control for motor control and power conversion applications. New MCUs of the PSOC Control C3 Performance Line enable system performance at high switching frequencies and increase control loop bandwidth. That is achieved with proprietary autonomous hardware accelerators as well as high resolution and high performing analog peripheral support. The family supports systems designed with wide-bandgap switches while achieving best-in-class control loop frequencies, accuracy and efficiency for applications such as data centers, telecom, solar and electric vehicle (EV) charging systems.

Specific security features include support for Leighton-Micali Hash-Based Signatures (LMS), which is an efficient post-quantum cryptography FW verification algorithm integrated with SHA-2 hardware acceleration for peak performance. To maximize ease of use, Infineon’s Edge Protect Tools and ModusToolbox will support everything a customer needs to provision LMS keys as well as options for hybrid post-quantum cryptography where customers may use both LMS and ECC to sign firmware updates which can be verified by Infineon chips.

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