Human factors significantly affect the productivity of AI-related underwriting systems, with trust, cognitive load, and quality of decision being the leading factors. The implementation of AI has made the data collection process automatic, improved the decision-making accuracy, and opened up avenues for sophisticated risk assessment.
For two decades, the atomic unit of e-commerce has been the keyword. However, as AI evolves from a tool that helps humans find products to an agent that buys them on our behalf, the keyword is no longer sufficient. This article examines the watershed moment of multimodal search—the transition where digital catalogs stop being static lists of text and become dynamic ecosystems that can "see" and "listen." We break down the physics of meaning behind vector embeddings, the battle between "build vs. buy" search strategies, and the rise of 3D spatial indexing.
The automotive industry is undergoing a paradigm shift driven by the exponential growth of data from connected vehicles and evolving customer expectations. This transformation introduces both opportunities and challenges, compelling manufacturers to adopt advanced AI technologies. Retrieval-Augmented Generation (RAG) emerges as a pivotal enabler in this context, offering capabilities that span product development, manufacturing optimization, predictive maintenance, and hyper-personalized in-vehicle experiences.
Sustainability has become a critical aspect of railway manufacturing engineering projects due to environmental concerns, regulatory requirements, and the need for long-term economic viability of large-scale infrastructure systems. Beyond strict regulations, innovative technologies, and stakeholder coordination, Germany is one of the examples of the progressive implementation of sustainability principles in the railroad industry. The paper provides a concept synthesis of the sustainable practices in the railway engineering project based on the literature in manufacturing engineering, railway systems, project management and sustainability research.
One of the challenges that has posed the greatest difficulty in the development process of full-stack applications today is the high level of user intent-invoked backend services, particularly as the level of difficulty of the application has been raised. Integration protocols that are default APIs in Java systems are likely to force programmers to directly couple front-end processes to the facilities at the back end. In this way, Java systems are also more expensive to create and slow.
The operational challenges experienced by healthcare systems worldwide due to increased patient volumes, shortages of personnel, and complex care processes. The predictive analytics is a new way of combating these inefficiencies through the use of data-driven models and workflow decisions. The paper examines the usability and limitations of predictive analytics in healthcare workflow optimization. It gathers evidence on the available literature that includes all varieties of models, implementation strategies and operational outcomes of patient flow management, emergency department capacity planning, staff scheduling, and resource allocation.
The cloud computing has become a significant component of the majority of the current digital infrastructures. But with its extensive usage has come several complicated security challenges which are difficult to address using the traditional models of depending on perimeters. Among the effective ways of addressing these threats is the concept of Zero Trust Architecture (ZTA), which is premised upon the idea of never trust, always verify. Simultaneously, it is possible to find more complex and unknown cyber threats in the dynamic cloud environment with great potential of AI and ML technologies.
Cloud computing, which makes use of shared, Internet-based computing resources, has emerged as a dominant model in software development in recent years. It makes the software development process easy and fast by offering the backbone to the applications. This paper examines the history, architectural principles, and current backend practices that are informing the modern web development, focusing especially on the .NET ecosystem, the MVC principles of architectural design, and API-based backend design.
The Customer Service workspace channels like SMS and webchat are a tough nut to crack to automate within the CRM systems. Mainly because of the unstructured, informal and context dependent nature of human language. Classical chatbots work based on the traditional rule based and key word driven workflows. They often fail with the real-world conversational variability leading to poor customer experiences and agent workload increase. The latest improvements in Natural Language Processing (NLP), especially with the large language models (LMs) fundamentally shifted how CRM systems interpret and respond to customer messages.
Identity and Access Management (IAM) is an important factor in ensuring secure and effective access control in cloud computing environments. This work presents a sequential machine-learning-based model to optimize IAM policies using a high-dimensional Cloud Access Control Parameter Management dataset. The paradigm incorporates systematic preprocessing of data, feature engineering, label encoding, feature selection using Boruta, feature scaling, and hybrid data balancing, 80: 20 train -test split and 5-fold cross-validation.
MySQL is an open-source relational database management system that is most popular and recognized by its reliability, flexibility, performance, as well as high security properties. With growing data-driven application use in the healthcare sectors, among others, such as finance, e-commerce, and cloud computing, MySQL database management becomes more of a best practice in terms of system upkeep, information quality, and efficiency. This paper provides an in-depth overview of MySQL database administration methods and best practices including its architecture, storage, transaction management, security, performance optimization, backup and recovery mechanisms, and high-availability solutions.
An integral component of a sustainable urban transformation is the creation of sustainable infrastructure. Sustainable industrial infrastructure is essential for promoting long-term economic growth with the least amount of environmental damage and the greatest amount of social well-being. The conventional infrastructure development methods have been quite focused in most cases on productivity and cost effectiveness without considering the overall sustainability implications providing sustainability metrics and optimization in regards to industrial infrastructure systems.
With the growing use of digital therapeutics (DTx) in behavioural health, there are serious challenges associated with regulatory compliance, data privacy and software security, particularly given sensitive patient data and the current development of global standards including HIPAA, GDPR and the Digital Personal Data Protection (DPDP) Act. The current DevOps practices, which are maximally agile delivery and scalable, do not involve any inherent mechanisms of active security enforcement and ongoing regulatory compliance. The paper presents the concept of DevSecOps, which is the expansion of DevOps that incorporates the concept of security and compliance into the software development life cycle, in advancing the safety, transparency, and reliability of behavioural health DTx platforms.
Nuclear energy is an important zero-carbon energy source as global energy needs increase. Artificial Intelligence (AI) is being used to manage the nuclear industry and optimize operations while maximizing safety. But deploying opaque “black-box” algorithms in safety-critical environments presents profound challenges. In this manuscript where we will discuss how Explainable Artificial Intelligence (XAI) aims to reconcile the powerful predictive capability of these models with a need for transparency, accountability and human alignment. We review XAI applications in fault detection, predictive maintenance, severe accident prediction, small modular reactor optimization, and nuclear nonproliferation.
In the era of automation, society has invested significant effort in automating repetitive processes across various
sectors to reduce the manufacturing time of many products. However, we have not given similar attention to automating
software development, as it involves complex decision-making, contextual understanding, and requires human expertise
and coordination.
This study proposes a deep learning-based Capsule Network (CapsNet) model for five-class ECG rhythm
classification using one-dimensional heartbeat segments derived from the MIT-BIH Arrhythmia Database. The analysis
focused on five clinically significant rhythm classes: Normal sinus rhythm (N), supraventricular premature beat (S),
premature ventricular contraction (V), fusion beat (F), and unclassifiable beat (Q). In the preprocessing stage, raw
ECG recordings were segmented into fixed-length heartbeat samples of 300 points based on annotation positions.
An AI-based Key Performance Indicator (KPI) framework is developed for monitoring and controlling ventilation quality using embedded devices.The system integrates real-time data from MQ-02 and MQ-135 gas sensors, processed through machine learning algorithms to generate predictive outputs that guide ventilation control decisions. The trained models, optimized for embedded deployment, track essential KPIs such as air quality index, ventilation efficiency, and response time, enabling smart, adaptive ventilation management. By embedding these models into compact, low-cost IoT devices, the solution ensures scalability, low power consumption, and reliable operation in diverse environments.
With the increasing use of rechargeable batteries in electric vehicles, renewable energy storage, and portable electronics, efficient and safe battery operation has become critical. This project presents the design and implementation of a Battery Management System (BMS) with integrated thermal protection to ensure reliable performance and extended battery life. The proposed hardware system monitors key battery parameters such as voltage, current, state of charge (SoC), and temperature using dedicated sensors. A microcontroller-based control unit processes the data to balance individual cells, prevent overcharging, deep discharging, and short circuits. Thermal protection is achieved by integrating temperature sensors and heat management circuits, which trigger cooling mechanisms or disconnect the battery during overheating conditions.
Industrial fire accidents pose a significant threat to human safety, infrastructure, and economic stability, particularly in high-risk environments such as manufacturing plants, oil refineries, and power stations. This project presents an intelligent fire accident prediction system using machine learning techniques, specifically Artificial Neural Networks (ANN), to analyze critical environmental and operational parameters such as temperature, humidity, smoke levels, and flame detection. A structured dataset representing multiple scenarios—including Normal conditions, Cooking/Steam, Electrical Short, and Active Fire—is used to train and evaluate the model. The proposed system focuses not only on detecting active fire incidents but also on identifying early warning signs, such as electrical faults and abnormal thermal variations, enabling proactive prevention. By leveraging the nonlinear learning capability of ANN, the model effectively distinguishes between safe and hazardous conditions, significantly reducing false alarms while improving detection accuracy.
In recent years, road safety has become a significant concern due to the rise in traffic violations and accidents. One of the major causes of fatalities is the non- compliance with helmet usage among two-wheeler riders.
Artificial intelligence (AI) is becoming critical in improving behavioral interventions in health, sustainability, and digital well-being. This review proposes a theory-based framework combining the Theory of Planned Behavior (TPB) with recommendation-system architecture and principles of EAST behavioral design (Easy, Attractive, Social, Timely).
The advent of cloud computing has posed tremendous platform security concerns due to the distribution, dynamic as well as multi-tenant nature of modern infrastructure.
A Review of Human Factors in AI-Powered Underwriting Systems: Trust, Cognitive Load, and Decision Quality
Kirti Vedi
Download
Human factors significantly affect the productivity of AI-related underwriting systems, with trust, cognitive load, and quality of decision being the leading factors. The implementation of AI has made the data collection process automatic, improved the decision-making accuracy, and opened up avenues for sophisticated risk assessment.
Revolutionizing Online Shopping: The Power of Multimodal Search in E-CommerceNitin Patki
Download
For two decades, the atomic unit of e-commerce has been the keyword. However, as AI evolves from a tool that helps humans find products to an agent that buys them on our behalf, the keyword is no longer sufficient. This article examines the watershed moment of multimodal search—the transition where digital catalogs stop being static lists of text and become dynamic ecosystems that can "see" and "listen." We break down the physics of meaning behind vector embeddings, the battle between "build vs. buy" search strategies, and the rise of 3D spatial indexing.
Leveraging Retrieval-Augmented Generation (RAG) AI for Transforming Automotive Design, Manufacturing, and In-Vehicle
ExperiencesNaveen Kumar Bonagiri
Download
The automotive industry is undergoing a paradigm shift driven by the exponential growth of data from connected vehicles and evolving customer expectations. This transformation introduces both opportunities and challenges, compelling manufacturers to adopt advanced AI technologies. Retrieval-Augmented Generation (RAG) emerges as a pivotal enabler in this context, offering capabilities that span product development, manufacturing optimization, predictive maintenance, and hyper-personalized in-vehicle experiences.
Sustainability Integration in Railway Manufacturing Engineering Projects: A Conceptual ReviewAravindh Balan
Download
Sustainability has become a critical aspect of railway manufacturing engineering projects due to environmental concerns, regulatory requirements, and the need for long-term economic viability of large-scale infrastructure systems. Beyond strict regulations, innovative technologies, and stakeholder coordination, Germany is one of the examples of the progressive implementation of sustainability principles in the railroad industry. The paper provides a concept synthesis of the sustainable practices in the railway engineering project based on the literature in manufacturing engineering, railway systems, project management and sustainability research.
LLM-Enhanced Java APIs for Intent-Driven Backend Invocation in Full-Stack SystemsSohith Sri Ammineedu Yalamati
Download
One of the challenges that has posed the greatest difficulty in the development process of full-stack applications today is the high level of user intent-invoked backend services, particularly as the level of difficulty of the application has been raised. Integration protocols that are default APIs in Java systems are likely to force programmers to directly couple front-end processes to the facilities at the back end. In this way, Java systems are also more expensive to create and slow.
A Predictive Analytics Approach to Optimizing Workflow Efficiency in Healthcare SystemsSohan Manmeet Sethi
Download
The operational challenges experienced by healthcare systems worldwide due to increased patient volumes, shortages of personnel, and complex care processes. The predictive analytics is a new way of combating these inefficiencies through the use of data-driven models and workflow decisions. The paper examines the usability and limitations of predictive analytics in healthcare workflow optimization. It gathers evidence on the available literature that includes all varieties of models, implementation strategies and operational outcomes of patient flow management, emergency department capacity planning, staff scheduling, and resource allocation.
Advances AI-Enabled Identification of Threats within Zero-Trust Architectures for Secure Cloud Infrastructures: A
Comprehensive Survey Rajendra Prasad Sola
Download
The cloud computing has become a significant component of the majority of the current digital infrastructures. But with its extensive usage has come several complicated security challenges which are difficult to address using the traditional models of depending on perimeters. Among the effective ways of addressing these threats is the concept of Zero Trust Architecture (ZTA), which is premised upon the idea of never trust, always verify. Simultaneously, it is possible to find more complex and unknown cyber threats in the dynamic cloud environment with great potential of AI and ML technologies.
An Overview of MVC-Based and API-Centric Backend Architectures in .NET Ecosystems Rajeev Kallayil
Download
Cloud computing, which makes use of shared, Internet-based computing resources, has emerged as a dominant model in software development in recent years. It makes the software development process easy and fast by offering the backbone to the applications. This paper examines the history, architectural principles, and current backend practices that are informing the modern web development, focusing especially on the .NET ecosystem, the MVC principles of architectural design, and API-based backend design.
AI & NLP in CRM: How Large Language Models are Changing Customer Interactions in SMS & Webchat Krishna Chaithanya Vuppala
Download
The Customer Service workspace channels like SMS and webchat are a tough nut to crack to automate within the CRM systems. Mainly because of the unstructured, informal and context dependent nature of human language. Classical chatbots work based on the traditional rule based and key word driven workflows. They often fail with the real-world conversational variability leading to poor customer experiences and agent workload increase. The latest improvements in Natural Language Processing (NLP), especially with the large language models (LMs) fundamentally shifted how CRM systems interpret and respond to customer messages.
An Intelligent Machine Learning Framework for Optimizing Identity and Access Management (IAM) Policies in Cloud
InfrastructureJiwan Prakash Gupta
Download
Identity and Access Management (IAM) is an important factor in ensuring secure and effective access control in cloud computing environments. This work presents a sequential machine-learning-based model to optimize IAM policies using a high-dimensional Cloud Access Control Parameter Management dataset. The paradigm incorporates systematic preprocessing of data, feature engineering, label encoding, feature selection using Boruta, feature scaling, and hybrid data balancing, 80: 20 train -test split and 5-fold cross-validation.
A Survey of MySQL Database Administration Techniques and Best PracticesHari Babu Dama
Download
MySQL is an open-source relational database management system that is most popular and recognized by its reliability, flexibility, performance, as well as high security properties. With growing data-driven application use in the healthcare sectors, among others, such as finance, e-commerce, and cloud computing, MySQL database management becomes more of a best practice in terms of system upkeep, information quality, and efficiency. This paper provides an in-depth overview of MySQL database administration methods and best practices including its architecture, storage, transaction management, security, performance optimization, backup and recovery mechanisms, and high-availability solutions.
Sustainability Metrics and Optimization Techniques for Industrial Infrastructure Aravindh Balan
Download
An integral component of a sustainable urban transformation is the creation of sustainable infrastructure. Sustainable industrial infrastructure is essential for promoting long-term economic growth with the least amount of environmental damage and the greatest amount of social well-being. The conventional infrastructure development methods have been quite focused in most cases on productivity and cost effectiveness without considering the overall sustainability implications providing sustainability metrics and optimization in regards to industrial infrastructure systems.
The Role of DevSecOps in Enhancing Digital Therapeutics Platforms for Behavioural HealthSunjhla Handa
Download
With the growing use of digital therapeutics (DTx) in behavioural health, there are serious challenges associated with regulatory compliance, data privacy and software security, particularly given sensitive patient data and the current development of global standards including HIPAA, GDPR and the Digital Personal Data Protection (DPDP) Act. The current DevOps practices, which are maximally agile delivery and scalable, do not involve any inherent mechanisms of active security enforcement and ongoing regulatory compliance. The paper presents the concept of DevSecOps, which is the expansion of DevOps that incorporates the concept of security and compliance into the software development life cycle, in advancing the safety, transparency, and reliability of behavioural health DTx platforms.
AI in the Use of Nuclear Energy: Explainable Artificial Intelligence for Transparent, Safe, and Regulatory-Compliant Nuclear OperationsSusmit Sen, Kabita Paul, Sujit Murumkar
Download
Nuclear energy is an important zero-carbon energy source as global energy needs increase. Artificial Intelligence (AI) is being used to manage the nuclear industry and optimize operations while maximizing safety. But deploying opaque “black-box” algorithms in safety-critical environments presents profound challenges. In this manuscript where we will discuss how Explainable Artificial Intelligence (XAI) aims to reconcile the powerful predictive capability of these models with a need for transparency, accountability and human alignment. We review XAI applications in fault detection, predictive maintenance, severe accident prediction, small modular reactor optimization, and nuclear nonproliferation.
AI-First Software Development Lifecycle: An Agent-Driven Framework for Autonomous Planning, Coding, Testing,
and Deployment Ambar Nath Saha, Debashis Patra
Download
In the era of automation, society has invested significant effort in automating repetitive processes across various sectors to reduce the manufacturing time of many products. However, we have not given similar attention to automating software development, as it involves complex decision-making, contextual understanding, and requires human expertise and coordination.
A Deep Capsule Network for Five-Class ECG Rhythm Classification from 1-D Heartbeat Segments
Ali Osman SELVİ, Shamistan HUSEYNOV
Download
This study proposes a deep learning-based Capsule Network (CapsNet) model for five-class ECG rhythm classification using one-dimensional heartbeat segments derived from the MIT-BIH Arrhythmia Database. The analysis focused on five clinically significant rhythm classes: Normal sinus rhythm (N), supraventricular premature beat (S), premature ventricular contraction (V), fusion beat (F), and unclassifiable beat (Q). In the preprocessing stage, raw ECG recordings were segmented into fixed-length heartbeat samples of 300 points based on annotation positions.
An AI-Based Ventilation KPI Using Embedded IoT Devices
Mrs.Shiny Pradheepa, Madhumitha
Download
An AI-based Key Performance Indicator (KPI) framework is developed for monitoring and controlling ventilation quality using embedded devices.The system integrates real-time data from MQ-02 and MQ-135 gas sensors, processed through machine learning algorithms to generate predictive outputs that guide ventilation control decisions. The trained models, optimized for embedded deployment, track essential KPIs such as air quality index, ventilation efficiency, and response time, enabling smart, adaptive ventilation management. By embedding these models into compact, low-cost IoT devices, the solution ensures scalability, low power consumption, and reliable operation in diverse environments.
Battery Management System (Bms) With Thermal Protection
J. Sabari Ganesh, Dr. S M Rajkumar, S. Rakesh
Download
With the increasing use of rechargeable batteries in electric vehicles, renewable energy storage, and portable electronics, efficient and safe battery operation has become critical. This project presents the design and implementation of a Battery Management System (BMS) with integrated thermal protection to ensure reliable performance and extended battery life. The proposed hardware system monitors key battery parameters such as voltage, current, state of charge (SoC), and temperature using dedicated sensors. A microcontroller-based control unit processes the data to balance individual cells, prevent overcharging, deep discharging, and short circuits. Thermal protection is achieved by integrating temperature sensors and heat management circuits, which trigger cooling mechanisms or disconnect the battery during overheating conditions.
Industrial Fire Accident Predictions using machine language
Mr. S. Manoj Kumar, S. Thanumalaya Perumal, Mr. S. Raksesh
Download
Industrial fire accidents pose a significant threat to human safety, infrastructure, and economic stability, particularly in high-risk environments such as manufacturing plants, oil refineries, and power stations. This project presents an intelligent fire accident prediction system using machine learning techniques, specifically Artificial Neural Networks (ANN), to analyze critical environmental and operational parameters such as temperature, humidity, smoke levels, and flame detection. A structured dataset representing multiple scenarios—including Normal conditions, Cooking/Steam, Electrical Short, and Active Fire—is used to train and evaluate the model. The proposed system focuses not only on detecting active fire incidents but also on identifying early warning signs, such as electrical faults and abnormal thermal variations, enabling proactive prevention. By leveraging the nonlinear learning capability of ANN, the model effectively distinguishes between safe and hazardous conditions, significantly reducing false alarms while improving detection accuracy.
Image Based Helmet Detection Using Deep Neural Networks
Ms. Muthulakshmi P, Dr. Vinukumar K, Mr. Rakesh S
Download
In recent years, road safety has become a significant concern due to the rise in traffic violations and accidents. One of the major causes of fatalities is the non- compliance with helmet usage among two-wheeler riders.
AI-Driven Behavioural Interventions Integrating Cognitive Frameworks with Intelligent Systems
Rahamath Mohamed Razikh Ulla
Download
Artificial intelligence (AI) is becoming critical in improving behavioral interventions in health, sustainability, and digital well-being. This review proposes a theory-based framework combining the Theory of Planned Behavior (TPB) with recommendation-system architecture and principles of EAST behavioral design (Easy, Attractive, Social, Timely).
Autonomous Quarantine Networks: AI-Driven Incident Isolation in Cloud Infrastructure
Lathakannan Arumugam
Download
The advent of cloud computing has posed tremendous platform security concerns due to the distribution, dynamic as well as multi-tenant nature of modern infrastructure.