As the global impact of climate change intensifies, the need for precise carbon footprint analysis has become essential for both organizations and individuals committed to reducing greenhouse gas (GHG) emissions. A carbon footprint represents the total volume of GHG emissions associated with an entity's activities, quantified in carbon dioxide equivalents (CO₂e). This study delves into advanced methodologies for accurately assessing carbon footprints, emphasizing the three primary scopes of emissions: Scope 1 (direct emissions from owned or controlled sources), Scope 2 (indirect emissions from purchased electricity, steam, heating, and cooling), and Scope 3 (all other indirect emissions occurring throughout the value chain).
Risk assessment, price of insurance, and financial forecasting have been impacted by the growing integration of Artificial Intelligence (AI) in actuarial decision-making. The accuracy of the models is enhanced using AI, the complex calculations are automated, and real-time decision-making via AI models is possible. Nevertheless, these new advances highlight ethical issues related to bias, lack of transparency, accountability, along data privacy risk. Responsible use of AI requires a solid set of rigorous ethical principles to prevent it from reinforcing systemic biases and producing unjust consequences.
Machine learning (ML) has shown to be a game-changer in big data analytics, allowing businesses to glean valuable insights from massive and intricate datasets. Highlighting the methods, tools, and applications that drive innovation and decision-making across sectors, this review article investigates the complementary nature regarding artificial intelligence and large analytics. Exploring the three cornerstones of under supervision, under supervision reinforcement education, and machine learning, and how each tackles issue like choosing characteristics, prioritization of data, and system adaptability.
The need to secure mobile devices has never been higher than it is now, given the growing danger of Android ransomware assaults. In order to address this issue, this paper suggests a lightweight machine learning (ML) technique that employs Android malware detection using Decision Tree (DT) and Support Vector Machine (SVM) classifiers. In order to maximize model efficiency, the research was conducted using an Android ransomware dataset and approaches including feature selection, under-sampling, and categorical conversion. At 97.24%, 98.50%, and 98.40%, respectively, ransomware detection accuracy, precision, recall, and F1 scores outperform SVM and traditional machine learning models.
Onboarding and offboarding processes play a very significant role in employee integration, retention and organizational success in the telecom industry. Structuring onboarding makes the job more satisfying, productive and culturally aligned; structured offboarding guarantees this knowledge is transferred, maintains a good employer reputation and builds long term professional relationships. In this study, key onboarding frameworks, including Bauer’s Four Cs, are reviewed, and the part of emerging technologies such as augmented reality (AR) in training experience improvement has been explored.
Fraudulent articles have cropped up all over the web and spread like wildfire. They constitute falsified facts, phony scientific facts, discriminatory articles, satirical items and misleading articles aimed at demeaning other groups or individuals. It is imperative to contain such articles as they create chaos and lead to unwise decision making. In this project, machine learning and deep learning approaches are used to flag fake news items. Part of the dataset is manually scraped from the web and the other half is publicly available. Feature extraction techniques like Bag of Words, TF-IDF, N-grams, word embeddings like GloVe are explored.
Artificial Intelligence (AI) and smart technologies are increasingly integral to the digital financial ecosystem, transforming how financial services operate and innovate. This paper provides a comprehensive overview of how AI is reshaping finance, highlighting key applications in fraud analytics, banking operations, investment intelligence, financial process automation, decentralized finance (DeFi), and inclusive financial services. We review current literature and industry trends, noting that AI adoption in finance has accelerated dramatically – for instance, over 75% of large banks are projected to have fully integrated AI strategies by 2025allaboutai.com.
Privacy has been a fundamental concern for humanity since early civilization, and the rapid increase in information sharing has only intensified this issue. Social media has become a primary medium for communication and connection worldwide. Notably, a January 2018 report from Facebook stated that the platform had over 2.2 billion active monthly users [1], illustrating the vast number of people who share personal information online. This paper examines data privacy breaches and the ethical policies designed to protect data privacy. Additionally, it presents a cause-and-effect model analyzing significant data privacy breaches from the past.
For many years, attempts to forecast the behavior of the stock market have fascinated scholars and analysts. However, accuracy never repeats itself due to the interconnection and intricacy of many determinants. The determinants influence one another within a tangled net of impact, complicating prediction modeling. During this age of the digital internet, though, sheer quantities of data on sites have presented fresh avenues for study. This covers various opinions from leading specialists, reputable news organizations, and investing weblogs. Further, social network websites are areas where individuals freely make statements of their thoughts and feelings concerning trend developments in markets.
Intraoperative brain mapping plays a pivotal role in modern neurosurgery, ensuring that critical brain functions are preserved while minimizing the risk of permanent damage. Current techniques, such as electrocortical stimulation, functional MRI, intraoperative neurophysiological monitoring, and near-infrared spectroscopy, offer valuable insights into brain activity during surgery. However, each method has its limitations, ranging from invasiveness and limited spatial resolution to the inability to monitor deep brain structures or provide real-time feedback. As the need for more precise, efficient, and less invasive mapping solutions grows, the development of new technologies and approaches becomes imperative.
Business analytics depends heavily on causal inference within data-driven strategy because this technique helps organizations advance from generic correlations to strong cause-and-effect relationships. A/B testing has established itself as the principal method for conducting scalable causal analysis because digital experimentation continues to grow rapidly. The review explores the conceptual bases together with practical applications and experimental approaches, and present-day difficulties regarding business-oriented causal inference specifically within A/B testing scalability.
In this review, the effect of concurrent Voice over LTE (VoLTE) and Narrowband Internet of Things (NB-IoT) deployment on the LTE network performance is evaluated. These technologies complement each other, performing different roles within contemporary mobile networks. NB-IoT takes care of delay-tolerant massive machine-type communications, and VoLTE offers real-time voice communication. The coexistence of Wi-Fi and LTE in the shared LTE infrastructure creates radio resource management and latency control challenges, as well as quality of service.
The quick expansion of artificial intelligence (AI) has produced systems that go beyond basic automation and into sectors where human and artificial intelligence interaction is increasingly crucial. The evolution of "agentic artificial intelligence," in which AI systems have some degree of autonomy in interaction and decision-making, largely drives this transformation. Agentic artificial intelligence is the capacity of artificial intelligence to both pursue goals freely within pre-defined systems and keep the capability to adapt and learn from its environment.
Edge Artificial Intelligence (Edge AI) marks a revolutionary development in the field of autonomous systems. Real-time responsiveness is even more important as autonomous systems run in dynamic and unpredictable surroundings. By integrating intelligence directly into edge devices, Edge AI solves the latency and bandwidth constraints of conventional cloud-based AI models by enabling instantaneous responses free from depending on far-off servers.
Large Language Models (LLMs), such GPT-4 and its successors, which have let machines understand, generate, and respond to natural language with hitherto unheard-of fluency and contextual awareness, have greatly driven the fast development of Artificial Intelligence (AI). These models are now being included into agentic artificial intelligence systems, which are autonomous agents meant to run with minimum human intervention, therefore changing the way machines understand and act upon complicated directions.
Urbanisation and the notable increase in private car ownership have raised traffic signal timing optimisation to a vital area of attention for improving urban mobility, relieving road congestion, and thereby reducing environmental impacts. With special focus on the implementation and advantages of the Synchro software platform, this work presents a comprehensive and wide-ranging analysis of the theoretical underpinnings, methodological developments, and practical applications related with signal timing optimisation.
The high stakes related to patient safety, regulatory compliance, and the rising threat of counterfeit goods mean that the distribution of medical devices calls for an especially transparent and secure supply chain architecture. Offering decentralisation, immutability, and automated smart contracts—blockchain technology has become a potent weapon for enhancing security and traceability in healthcare logistics. Examining their structures, consensus processes, and connection with technologies like IoT, this paper summarises the present situation of blockchain applications in medical device distribution.
The change in monolithic to microservices architectures in recent years has transformed the construction and administration of scalable systems. This paper investigates how popular Java-based microservices framework Spring Boot might be integrated with Azure Service Bus, a cloud-native message broker. The research shows the benefits of asynchronous communication in distributed systems by analysing architectural patterns, design principles, and experimental performance evaluations: enhanced scalability, fault tolerance, and throughput among other things.
Artificial intelligence (AI) shall be the core factor that influences telehealth by improving access to care, personalization, and efficiency. This article explores AI-driven telemedicine innovations in real-time diagnostics, remote patient monitoring, and administrative automation while discussing issues of algorithmic bias and data privacy. We review the existing literature to answer the question of how AI models like Med-Gemini and tools such as AI scribes can enhance clinical decision-making while mitigating provider burnout. Case studies will provide evidence of such capabilities for bridging healthcare disparities in underserved regions; ethical considerations will highlight the importance of implementing such systems equitably.
The accelerated growth of online shopping has revolutionized global markets, transforming logistics networks and supply chain operations. This paradigm has also created intense environmental sustainability issues, especially with regard to excess carbon emission, wastage, and misuse of resources. In face of heightened regulation and rising awareness among consumers, there is now an onus on companies to adopt green practices within their supply chains. The present paper presents a comprehensive assessment of sustainability issues in e-commerce and investigates how emerging technologies, namely Artificial Intelligence (AI), Internet of Things (IoT), and blockchain, support green supply chain management.
The capstone project is based on the Kaggle competition developed by Zillow Inc., the online real estate database company. To provide better values to its customers, Zillow provides an estimate of the home sale price, called Zestimate. Zestimate is very popular, because it provides first time consumers, information about the house and housing market at no cost.“Zestimates” are estimated home values based on millions of statistical and machine learning models that analyze hundreds of data points on each property. By continually improving the median margin of error, from 14% at the onset to 5% today, Zillow has established itself as one of the largest and trusted online real estate database for the US market.
Bluetooth Low Energy (BLE) technology has emerged as a key enabler of smart indoor positioning systems and person detection frameworks, offering low-cost, low-power, and scalable deployments. This article reviews core BLE-based localization principles, including signal attenuation modeling, trilateration, and fingerprinting. We further examine their integration with real-world platforms such as ESP32 microcontrollers and Apple Watch wearables in the context of smart homes, hospitals, and secure buildings. This review also explores BLE’s role in security surveillance, multi-resident activity recognition, privacy-aware access control, and BLE mesh networks.
The integration of AI and machine learning tools into cloud systems marks a transformative step in automation and intelligence for cloud environments. This paper explores initial methodologies for embedding AI/ML models into cloud-based infrastructures to optimize resource management, enhance data processing, and automate routine operations. The proposed approach uses containerized ML models deployed alongside scalable cloud services, enabling adaptive automation and seamless integration. Experimental studies highlight significant gains in operational efficiency, predictive analytics, and cost optimization. These findings set a foundation for advancing AI-driven cloud systems.
The construction industry, which has been sluggish to adopt new technologies in the past, is about to undergo a major change thanks to the use of Artificial Intelligence (AI) and Machine Learning (ML). This study looks at how AI and ML can change the way construction works by making project planning, resource allocation, safety, cost control, and quality assurance better. It stresses the need for strong foundational systems, especially Enterprise Resource Planning (ERP) and standardized business processes, to make sure that data is consistent, high-quality, and easy to get to, all of which are important for AI/ML success.
Innovations in artificial intelligence together with the fast development of sensor technology have driven the creation of intelligent sensor fusion systems capable of efficiently interpreting and merging several data sources. Deep learning methods including convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and graph neural networks (GNNs) have radically changed how sensor data is analysed, therefore enabling more exact and context-aware decision-making. From raw data aggregation to high-level semantic awareness, these techniques enable fusion at several levels.
Aerospace, telecommunications, automotive, and consumer electronics are just a few of the sectors where fast, high-reliable electronic systems are in more demand. Printed circuit boards (PCBs), which form the essential infrastructure for signal transmissions, power delivery, and temperature management, underlie these systems. PCB design becomes more difficult and defect probability rises as components shrink and operational frequencies rise. PCB faults—from manufacturing variances, material deterioration, or environmental stresses—can cause extreme performance decline or complete system failure.
The fast expanding Internet of Things (IoT) has produced an exponential increase in the number of linked devices, all of which demand great performance within rigorous power, area, and cost constraints. Conventional VLSI design techniques—which give accuracy and predictable performance top priority—are increasingly unsustainable for IoT edge nodes with constrained resources. Usually having limited battery life, these devices are situated in environments where low latency, real-time data processing, and long operational lifetime are vitally essential.
Driven by continuous breakthroughs in nanotechnology and its atomic and molecular integration with electronics, nanoelectronics is one of the most changing and fast developing subject within modern science and engineering. The worldwide race for leadership in this field has become more fierce in recent years as nations understand the crucial part nanoelectronics research and development (R&D) performs in economic competitiveness, national security, artificial intelligence (AI), healthcare innovation, and advanced computing technologies. This research paper aims to present a comprehensive analysis of global key trends in nanoelectronics R&D, thereby stressing the main actors, technologies, institutions, laws, and problems defining the terrain in 2025.
Based on the basic ideas of quantum mechanics, quantum information science (QIS) offers a transforming edge in computing, communication, and sensing by handling jobs beyond the reach of conventional systems. The hunt for materials able to consistently and at scale support and control quantum states forms the core of this fast changing discipline. With their adjustable optical, magnetic, and electrical characteristics, nanomaterials present great potential in this regard. Crucially for the evolution of quantum bits (qubits), quantum gates, and readout systems, their nanoscale size provide exact control over quantum states, coherence times, and entanglement mechanisms.
In today’s digital-first world, organizations rely on real-time data ingestion to drive decisions, monitor operations, and deliver personalized user experiences. This review explores the growing role of Apache Kafka and Amazon Web Services (AWS) ingestion tools—including Kinesis, Lambda, Glue, and MSK—in building scalable, fault-tolerant, and low-latency data pipelines. Through comparative analysis of architectural designs, performance benchmarks, and cost models, the review identifies the strengths and limitations of each approach.
As the gaming industry becomes more global and decentralized, bringing third-party games onto private platforms like Xbox, PlayStation, and Steam is getting complex. Manual onboarding is often slow, error-prone, and difficult to scale. To tackle this, automated onboarding systems have been developed to streamline submission, improve consistency, and meet technical and regulatory standards.This review explores how modern onboarding frameworks are evolving, with focus on the use of AI, the compliance checks, the metadata automation, and the DevOps practices.
As organizations move toward cloud-first strategies, seamless integration between Oracle Cloud Applications and third-party systems has become a mission-critical requirement. This review explores the current landscape of Oracle Cloud integration, highlighting architectural approaches, middleware tools, real-world implementations, and experimental outcomes. Oracle Integration Cloud (OIC), Oracle API Gateway, and SOA Suite offer scalable frameworks to automate processes, ensure secure data flow, and maintain compliance across hybrid IT environments.
The rise of automation in industry now makes protecting IoT networks very important. A flexible and effective network intrusion detection system (NIDS) helps reduce the number of cyber threats. In such ever-changing environments, traditional Intrusion Detection Systems (IDSs) failing to identify new or changing threats is a common occurrence. This research explores how to improve network security via accurate intrusion detection using AI approaches, particularly Machine Learning (ML) and Deep Learning (DL).
Thanks to AI-based log analysis in JVM, predictive maintenance is making support proactive, thus catching potential system failures early and raising reliability while reducing time spent offline. This review analyses the best computer science methods for parsing Java logs, choosing important parts from the logs and detecting anomalies. Logs that are not already in a standard format are parsed and turned into useful templates for straightforward analysis. Employing sequence embeddings and graph representations makes it easier to predict system behavior from log events. LSTM, BiLSTM and Transformer anomaly detection and prediction models are tested and results show that Transformer provides the highest accuracy of the three, whereas BiLSTM and LSTM show better trade-offs.
The Oracle Financial Accounting Hub (FAH) and third-party financial systems integration are important to other organizations where they intend to streamline the financial operations, compliance, and accuracy in reporting. The paper will present some integration techniques used (that is, by reviewing traditional batch processing, API-based types of solutions, middleware frameworks, and event-driven architecture) with their advantages and disadvantages. The major problems of FAH integration are interoperability of the legacy systems, limits of real-time processing data, compliance with regulations (e.g., IFRS 17, SOX), and scalability.
Carbon Footprint Analysis at Battery Manufacturing plant with Strategic ManagementPratik Dahule
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As the global impact of climate change intensifies, the need for precise carbon footprint analysis has become essential for both organizations and individuals committed to reducing greenhouse gas (GHG) emissions. A carbon footprint represents the total volume of GHG emissions associated with an entity's activities, quantified in carbon dioxide equivalents (CO₂e). This study delves into advanced methodologies for accurately assessing carbon footprints, emphasizing the three primary scopes of emissions: Scope 1 (direct emissions from owned or controlled sources), Scope 2 (indirect emissions from purchased electricity, steam, heating, and cooling), and Scope 3 (all other indirect emissions occurring throughout the value chain).
Ethical Frameworks and Value Alignment for AI in Actuarial Decision-MakingNihar Malali, Sita rama Praveen Madugula
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Risk assessment, price of insurance, and financial forecasting have been impacted by the growing integration of Artificial Intelligence (AI) in actuarial decision-making. The accuracy of the models is enhanced using AI, the complex calculations are automated, and real-time decision-making via AI models is possible. Nevertheless, these new advances highlight ethical issues related to bias, lack of transparency, accountability, along data privacy risk. Responsible use of AI requires a solid set of rigorous ethical principles to prevent it from reinforcing systemic biases and producing unjust consequences.
The Role of Machine Learning in Big Data Analytics: Tools, Techniques, and ApplicationsNirav kumar Prajapati
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Machine learning (ML) has shown to be a game-changer in big data analytics, allowing businesses to glean valuable insights from massive and intricate datasets. Highlighting the methods, tools, and applications that drive innovation and decision-making across sectors, this review article investigates the complementary nature regarding artificial intelligence and large analytics. Exploring the three cornerstones of under supervision, under supervision reinforcement education, and machine learning, and how each tackles issue like choosing characteristics, prioritization of data, and system adaptability.
Securing Android Systems Using Scalable and Lightweight ML for Ransomware Classification and Identifications
Mani Gopalsamy
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The need to secure mobile devices has never been higher than it is now, given the growing danger of Android ransomware assaults. In order to address this issue, this paper suggests a lightweight machine learning (ML) technique that employs Android malware detection using Decision Tree (DT) and Support Vector Machine (SVM) classifiers. In order to maximize model efficiency, the research was conducted using an Android ransomware dataset and approaches including feature selection, under-sampling, and categorical conversion. At 97.24%, 98.50%, and 98.40%, respectively, ransomware detection accuracy, precision, recall, and F1 scores outperform SVM and traditional machine learning models.
Analyzing the Effectiveness of Onboarding and Offboarding Processes in the Telecom IndustryChekone
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Onboarding and offboarding processes play a very significant role in employee integration, retention and organizational success in the telecom industry. Structuring onboarding makes the job more satisfying, productive and culturally aligned; structured offboarding guarantees this knowledge is transferred, maintains a good employer reputation and builds long term professional relationships. In this study, key onboarding frameworks, including Bauer’s Four Cs, are reviewed, and the part of emerging technologies such as augmented reality (AR) in training experience improvement has been explored.
Fake News Detection: Benchmarking Machine Learning and Deep Learning ApproachesManan Buddhadev, Virtee Parekh
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Fraudulent articles have cropped up all over the web and spread like wildfire. They constitute falsified facts, phony scientific facts, discriminatory articles, satirical items and misleading articles aimed at demeaning other groups or individuals. It is imperative to contain such articles as they create chaos and lead to unwise decision making. In this project, machine learning and deep learning approaches are used to flag fake news items. Part of the dataset is manually scraped from the web and the other half is publicly available. Feature extraction techniques like Bag of Words, TF-IDF, N-grams, word embeddings like GloVe are explored.
Reimagining Finance with Artificial Intelligence: Smart Technologies Reshaping the Digital EconomyAkash Vijayrao Chaudhari
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Artificial Intelligence (AI) and smart technologies are increasingly integral to the digital financial ecosystem, transforming how financial services operate and innovate. This paper provides a comprehensive overview of how AI is reshaping finance, highlighting key applications in fraud analytics, banking operations, investment intelligence, financial process automation, decentralized finance (DeFi), and inclusive financial services. We review current literature and industry trends, noting that AI adoption in finance has accelerated dramatically – for instance, over 75% of large banks are projected to have fully integrated AI strategies by 2025allaboutai.com.
Ethics, Privacy, and Security: Analyzing Data Breaches and their ImpactManan Buddhadev
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Privacy has been a fundamental concern for humanity since early civilization, and the rapid increase in information sharing has only intensified this issue. Social media has become a primary medium for communication and connection worldwide. Notably, a January 2018 report from Facebook stated that the platform had over 2.2 billion active monthly users [1], illustrating the vast number of people who share personal information online. This paper examines data privacy breaches and the ethical policies designed to protect data privacy. Additionally, it presents a cause-and-effect model analyzing significant data privacy breaches from the past.
Mining Reddit for Market Moves: NLP-Driven Stock Prediction with ML and Deep LearningManan Buddhadev, Virtee Parekh
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For many years, attempts to forecast the behavior of the stock market have fascinated scholars and analysts. However, accuracy never repeats itself due to the interconnection and intricacy of many determinants. The determinants influence one another within a tangled net of impact, complicating prediction modeling. During this age of the digital internet, though, sheer quantities of data on sites have presented fresh avenues for study. This covers various opinions from leading specialists, reputable news organizations, and investing weblogs. Further, social network websites are areas where individuals freely make statements of their thoughts and feelings concerning trend developments in markets.
Creating Real-Time Intraoperative Brain Mapping Tools That Don’t Disrupt SurgerySimrith Pulicharla
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Intraoperative brain mapping plays a pivotal role in modern neurosurgery, ensuring that critical brain functions are preserved while minimizing the risk of permanent damage. Current techniques, such as electrocortical stimulation, functional MRI, intraoperative neurophysiological monitoring, and near-infrared spectroscopy, offer valuable insights into brain activity during surgery. However, each method has its limitations, ranging from invasiveness and limited spatial resolution to the inability to monitor deep brain structures or provide real-time feedback. As the need for more precise, efficient, and less invasive mapping solutions grows, the development of new technologies and approaches becomes imperative.
The Role of Causal Inference in Business Decision-Making and A/B Testing at ScaleRajesh Sura
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Business analytics depends heavily on causal inference within data-driven strategy because this technique helps organizations advance from generic correlations to strong cause-and-effect relationships. A/B testing has established itself as the principal method for conducting scalable causal analysis because digital experimentation continues to grow rapidly. The review explores the conceptual bases together with practical applications and experimental approaches, and present-day difficulties regarding business-oriented causal inference specifically within A/B testing scalability.
Impact Analysis of VoLTE and NB-IoT Implementation on Carrier Network PerformanceKranthi Kiran Kusuma
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In this review, the effect of concurrent Voice over LTE (VoLTE) and Narrowband Internet of Things (NB-IoT) deployment on the LTE network performance is evaluated. These technologies complement each other, performing different roles within contemporary mobile networks. NB-IoT takes care of delay-tolerant massive machine-type communications, and VoLTE offers real-time voice communication. The coexistence of Wi-Fi and LTE in the shared LTE infrastructure creates radio resource management and latency control challenges, as well as quality of service.
Agentic AI in Human-AI Collaboration FrameworksVishnu Lakkamraju
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The quick expansion of artificial intelligence (AI) has produced systems that go beyond basic automation and into sectors where human and artificial intelligence interaction is increasingly crucial. The evolution of "agentic artificial intelligence," in which AI systems have some degree of autonomy in interaction and decision-making, largely drives this transformation. Agentic artificial intelligence is the capacity of artificial intelligence to both pursue goals freely within pre-defined systems and keep the capability to adapt and learn from its environment.
Edge AI for Real-Time Decision Making in Autonomous SystemsVishnu Lakkamraju
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Edge Artificial Intelligence (Edge AI) marks a revolutionary development in the field of autonomous systems. Real-time responsiveness is even more important as autonomous systems run in dynamic and unpredictable surroundings. By integrating intelligence directly into edge devices, Edge AI solves the latency and bandwidth constraints of conventional cloud-based AI models by enabling instantaneous responses free from depending on far-off servers.
Goal Decomposition & Self-Planning in Agentic AI with LLM Backends Vishnu Lakkamraju
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Large Language Models (LLMs), such GPT-4 and its successors, which have let machines understand, generate, and respond to natural language with hitherto unheard-of fluency and contextual awareness, have greatly driven the fast development of Artificial Intelligence (AI). These models are now being included into agentic artificial intelligence systems, which are autonomous agents meant to run with minimum human intervention, therefore changing the way machines understand and act upon complicated directions.
Signal Timing Optimization In Urban And Suburban Networks Sathish Rao
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Urbanisation and the notable increase in private car ownership have raised traffic signal timing optimisation to a vital area of attention for improving urban mobility, relieving road congestion, and thereby reducing environmental impacts. With special focus on the implementation and advantages of the Synchro software platform, this work presents a comprehensive and wide-ranging analysis of the theoretical underpinnings, methodological developments, and practical applications related with signal timing optimisation.
Blockchain Technology for Enhanced Traceability and Security in Medical Device Distribution
Sandeep Shenoy Karanchery Sundaresan
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The high stakes related to patient safety, regulatory compliance, and the rising threat of counterfeit goods mean that the distribution of medical devices calls for an especially transparent and secure supply chain architecture. Offering decentralisation, immutability, and automated smart contracts—blockchain technology has become a potent weapon for enhancing security and traceability in healthcare logistics. Examining their structures, consensus processes, and connection with technologies like IoT, this paper summarises the present situation of blockchain applications in medical device distribution.
Scalable Microservices Architecture using SpringBoot and Azure Service BusSachin Sudhir Shinde
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The change in monolithic to microservices architectures in recent years has transformed the construction and administration of scalable systems. This paper investigates how popular Java-based microservices framework Spring Boot might be integrated with Azure Service Bus, a cloud-native message broker. The research shows the benefits of asynchronous communication in distributed systems by analysing architectural patterns, design principles, and experimental performance evaluations: enhanced scalability, fault tolerance, and throughput among other things.
Advancing Inclusive Telehealth Through Automation Using Artificial Intelligence: Opportunities, Challenges, and Future
DirectionsDatta Snehith Dupakuntla Naga, Ashim Gautam Upadhaya, Minal Vipul Patel
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Artificial intelligence (AI) shall be the core factor that influences telehealth by improving access to care, personalization, and efficiency. This article explores AI-driven telemedicine innovations in real-time diagnostics, remote patient monitoring, and administrative automation while discussing issues of algorithmic bias and data privacy. We review the existing literature to answer the question of how AI models like Med-Gemini and tools such as AI scribes can enhance clinical decision-making while mitigating provider burnout. Case studies will provide evidence of such capabilities for bridging healthcare disparities in underserved regions; ethical considerations will highlight the importance of implementing such systems equitably.
Sustainable E-Commerce: Leveraging AI, IoT, and Blockchain for Greener Supply Chains Susmitha Nair
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The accelerated growth of online shopping has revolutionized global markets, transforming logistics networks and supply chain operations. This paradigm has also created intense environmental sustainability issues, especially with regard to excess carbon emission, wastage, and misuse of resources. In face of heightened regulation and rising awareness among consumers, there is now an onus on companies to adopt green practices within their supply chains. The present paper presents a comprehensive assessment of sustainability issues in e-commerce and investigates how emerging technologies, namely Artificial Intelligence (AI), Internet of Things (IoT), and blockchain, support green supply chain management.
Improve Zillow’s Home Value Prediction Estimator (Zestimate)Arun Raveendran Nair
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The capstone project is based on the Kaggle competition developed by Zillow Inc., the online real estate database company. To provide better values to its customers, Zillow provides an estimate of the home sale price, called Zestimate. Zestimate is very popular, because it provides first time consumers, information about the house and housing market at no cost.“Zestimates” are estimated home values based on millions of statistical and machine learning models that analyze hundreds of data points on each property. By continually improving the median margin of error, from 14% at the onset to 5% today, Zillow has established itself as one of the largest and trusted online real estate database for the US market.
Bluetooth Low Energy (BLE)-Based Person Detection for Smart Surveillance and Indoor TrackingAbhay Mangalore
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Bluetooth Low Energy (BLE) technology has emerged as a key enabler of smart indoor positioning systems and person detection frameworks, offering low-cost, low-power, and scalable deployments. This article reviews core BLE-based localization principles, including signal attenuation modeling, trilateration, and fingerprinting. We further examine their integration with real-world platforms such as ESP32 microcontrollers and Apple Watch wearables in the context of smart homes, hospitals, and secure buildings. This review also explores BLE’s role in security surveillance, multi-resident activity recognition, privacy-aware access control, and BLE mesh networks.
Integrating AI and Machine Learning in Cloud Systems for Enhanced AutomationSrinivasa Subramanyam Katreddy
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The integration of AI and machine learning tools into cloud systems marks a transformative step in automation and intelligence for cloud environments. This paper explores initial methodologies for embedding AI/ML models into cloud-based infrastructures to optimize resource management, enhance data processing, and automate routine operations. The proposed approach uses containerized ML models deployed alongside scalable cloud services, enabling adaptive automation and seamless integration. Experimental studies highlight significant gains in operational efficiency, predictive analytics, and cost optimization. These findings set a foundation for advancing AI-driven cloud systems.
Optimizing Business Systems and Processes for AI/ML Integration in the Construction IndustryPrashant Gupta, Vinay Singh
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The construction industry, which has been sluggish to adopt new technologies in the past, is about to undergo a major change thanks to the use of Artificial Intelligence (AI) and Machine Learning (ML). This study looks at how AI and ML can change the way construction works by making project planning, resource allocation, safety, cost control, and quality assurance better. It stresses the need for strong foundational systems, especially Enterprise Resource Planning (ERP) and standardized business processes, to make sure that data is consistent, high-quality, and easy to get to, all of which are important for AI/ML success.
Intelligent Sensor Fusion Using Deep Learning for Next-Generation Electronic ApplicationsMadesto Agnus
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Innovations in artificial intelligence together with the fast development of sensor technology have driven the creation of intelligent sensor fusion systems capable of efficiently interpreting and merging several data sources. Deep learning methods including convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and graph neural networks (GNNs) have radically changed how sensor data is analysed, therefore enabling more exact and context-aware decision-making. From raw data aggregation to high-level semantic awareness, these techniques enable fusion at several levels.
AI-Enhanced PCB Fault Detection and Diagnostics in High-Speed ElectronicsMohandass
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Aerospace, telecommunications, automotive, and consumer electronics are just a few of the sectors where fast, high-reliable electronic systems are in more demand. Printed circuit boards (PCBs), which form the essential infrastructure for signal transmissions, power delivery, and temperature management, underlie these systems. PCB design becomes more difficult and defect probability rises as components shrink and operational frequencies rise. PCB faults—from manufacturing variances, material deterioration, or environmental stresses—can cause extreme performance decline or complete system failure.
Design of Low-Power VLSI Circuits Using Approximate Computing for IOT.Mohandass
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The fast expanding Internet of Things (IoT) has produced an exponential increase in the number of linked devices, all of which demand great performance within rigorous power, area, and cost constraints. Conventional VLSI design techniques—which give accuracy and predictable performance top priority—are increasingly unsustainable for IoT edge nodes with constrained resources. Usually having limited battery life, these devices are situated in environments where low latency, real-time data processing, and long operational lifetime are vitally essential.
Global Leadership in Nanoelectronics R&DDr. A. Punitha
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Driven by continuous breakthroughs in nanotechnology and its atomic and molecular integration with electronics, nanoelectronics is one of the most changing and fast developing subject within modern science and engineering. The worldwide race for leadership in this field has become more fierce in recent years as nations understand the crucial part nanoelectronics research and development (R&D) performs in economic competitiveness, national security, artificial intelligence (AI), healthcare innovation, and advanced computing technologies. This research paper aims to present a comprehensive analysis of global key trends in nanoelectronics R&D, thereby stressing the main actors, technologies, institutions, laws, and problems defining the terrain in 2025.
Nanomaterials in Quantum Information ScienceDr. N. Rajkumar
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Based on the basic ideas of quantum mechanics, quantum information science (QIS) offers a transforming edge in computing, communication, and sensing by handling jobs beyond the reach of conventional systems. The hunt for materials able to consistently and at scale support and control quantum states forms the core of this fast changing discipline. With their adjustable optical, magnetic, and electrical characteristics, nanomaterials present great potential in this regard. Crucially for the evolution of quantum bits (qubits), quantum gates, and readout systems, their nanoscale size provide exact control over quantum states, coherence times, and entanglement mechanisms.
Real-Time Data Ingestion with Kafka and AWS ToolsSarvesh Kumar Gupta
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In today’s digital-first world, organizations rely on real-time data ingestion to drive decisions, monitor operations, and deliver personalized user experiences. This review explores the growing role of Apache Kafka and Amazon Web Services (AWS) ingestion tools—including Kinesis, Lambda, Glue, and MSK—in building scalable, fault-tolerant, and low-latency data pipelines. Through comparative analysis of architectural designs, performance benchmarks, and cost models, the review identifies the strengths and limitations of each approach.
Automated Game Onboarding Frameworks for Third-Party Integration in Proprietary EcosystemsPrem Nishanth Kothandaraman
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As the gaming industry becomes more global and decentralized, bringing third-party games onto private platforms like Xbox, PlayStation, and Steam is getting complex. Manual onboarding is often slow, error-prone, and difficult to scale. To tackle this, automated onboarding systems have been developed to streamline submission, improve consistency, and meet technical and regulatory standards.This review explores how modern onboarding frameworks are evolving, with focus on the use of AI, the compliance checks, the metadata automation, and the DevOps practices.
Oracle Cloud Integration: Connecting Oracle Cloud Applications with Third-Party SystemsSravana kumar Yeruva
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As organizations move toward cloud-first strategies, seamless integration between Oracle Cloud Applications and third-party systems has become a mission-critical requirement. This review explores the current landscape of Oracle Cloud integration, highlighting architectural approaches, middleware tools, real-world implementations, and experimental outcomes. Oracle Integration Cloud (OIC), Oracle API Gateway, and SOA Suite offer scalable frameworks to automate processes, ensure secure data flow, and maintain compliance across hybrid IT environments.
Towards Robust Industrial IoT Security based on Artificial Intelligence Approach for Intrusion Detection Networks
Alpeshkumar Kathiriy
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The rise of automation in industry now makes protecting IoT networks very important. A flexible and effective network intrusion detection system (NIDS) helps reduce the number of cyber threats. In such ever-changing environments, traditional Intrusion Detection Systems (IDSs) failing to identify new or changing threats is a common occurrence. This research explores how to improve network security via accurate intrusion detection using AI approaches, particularly Machine Learning (ML) and Deep Learning (DL).
AI-Augmented Log Analysis for Predictive Maintenance in Distributed Java ApplicationsRajeev Kumar Sharma
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Thanks to AI-based log analysis in JVM, predictive maintenance is making support proactive, thus catching potential system failures early and raising reliability while reducing time spent offline. This review analyses the best computer science methods for parsing Java logs, choosing important parts from the logs and detecting anomalies. Logs that are not already in a standard format are parsed and turned into useful templates for straightforward analysis. Employing sequence embeddings and graph representations makes it easier to predict system behavior from log events. LSTM, BiLSTM and Transformer anomaly detection and prediction models are tested and results show that Transformer provides the highest accuracy of the three, whereas BiLSTM and LSTM show better trade-offs.
Integrating Oracle Financial Accounting Hub with Third-Party Systems Manjunath Rallabandi
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The Oracle Financial Accounting Hub (FAH) and third-party financial systems integration are important to other organizations where they intend to streamline the financial operations, compliance, and accuracy in reporting. The paper will present some integration techniques used (that is, by reviewing traditional batch processing, API-based types of solutions, middleware frameworks, and event-driven architecture) with their advantages and disadvantages. The major problems of FAH integration are interoperability of the legacy systems, limits of real-time processing data, compliance with regulations (e.g., IFRS 17, SOX), and scalability.