VOLUME 12, ISSUE 12, DECEMBER 2024

Abstract: The rapid advancement of technology has given rise to neobanks, digital-first financial institutions that operate without traditional physical branches. These neobanks, also known as challenger banks, have disrupted the traditional banking landscape by offering customer-centric services through mobile and online platforms. This paper explores the factors contributing to the rise of neobanks, including technological advancements, changing consumer preferences, and regulatory support. The impact of neobanks on the financial sector is analyzed, with a focus on increased competition, financial inclusion, and the disruption of traditional business models. Additionally, the paper addresses the challenges and risks faced by neobanks, such as regulatory compliance and profitability concerns. The study concludes by discussing the future trajectory of neobanks and their potential to further transform the financial sector. The findings suggest that while neobanks have introduced significant innovations, their long-term success will depend on their ability to navigate regulatory landscapes and sustain profitability.

Keywords: Neobanks, digital banking, financial sector, fintech, competition, financial inclusion, regulatory challenges, profitability, banking innovation.

Abstract: The application of AI is revolutionizing healthcare by enabling customized treatment and medication recommendations. AI analyses diverse datasets, such as EHRs, genetic information, and real-time patient monitoring through wearable devices, to provide actionable insights based on individual biological and lifestyle characteristics. This transition from generalized protocols to personalized care significantly improves treatment efficacy and patient outcomes. This paper explores the methodologies and technologies of AI in personalized healthcare, highlighting ML and DL algorithms that process structured and unstructured data. NLP extracts critical insights from clinical documents, excelling in precision diagnostics and optimizing treatment pathways. Key applications include oncology, where AI tailors cancer treatments based on tumor genomics, and cardiology, with predictive models for cardiac risks. AI also aids in managing chronic and rare diseases through early intervention and tailored therapies. Challenges include ethical issues like data privacy and fairness, alongside regulatory and technical barriers. Addressing these challenges will foster a patient-centric healthcare ecosystem.

Keywords: AI, Personalized Medicine, Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), Electronic Health Records (EHRs), Pharmacogenomics, Wearable Devices, Precision Healthcare, Data Privacy, Oncology, Cardiology, Chronic Disease Management, AI Ethics, Blockchain.

Abstract: The article present systematic Study based on effect of radio frequency Radiation on animals. From past few decade.There has been a study carried out on what Are the effect of radio frequency on human. With the rapid development of electronic information in the past 30 years, technical achievements based on electromagnetism have been widely used in various fields pertaining to human production and life. Consequently, electromagnetic radiation (EMR) has become a substantial new pollution source in modern civilization. The biological effects of EMR have attracted considerable attention worldwide.

Abstract: The Campus Bite (CB) is a cutting-edge software solution designed to revolutionize food service operations in institutional canteens. The system seamlessly integrates various aspects of canteen management, including menu planning, inventory management, order processing, billing, and customer feedback. By automating these processes, CB minimizes manual errors, reduces wait times, and enhances the overall dining experience. Key features of CCH include Automated menu planning with nutritional analysis and recipe management. Real -time inventory management with alerts and notifications. Online ordering and payment system with mobile app integration. Customer feedback and rating system for continuous improvement. Comprehensive reporting and analytics for data-driven decision making. By implementing CB, institutions can: Improve operational efficiency and reduce cost. Enhance customer satisfaction and loyalty. Ensure food safety and quality control. Make data-driven decisions with real-time insights. CB is an ideal solution for schools, colleges, offices, hospitals, and other institutions seeking to modernize their food service management. By leveraging technology, CB optimizes canteen operations, ensuring a seamless and enjoyable dining experience for customers.

Keywords: Campus dining, Food delivery, Meal plans, Healthy eating, Student meal, Cafeteria services, Sustainability.

Abstract: We present here an FPGA-based system, capable of simultaneous measuring and control of visible and infrared lighting, temperature and humidity values of a greenhouse or industrial environment. The system uses DE10-Lite FPGA board with four sensors connected to it and in conjunction with the easy way of expanding both its software and hardware by adding more sensors, in order to cover larger areas, it is considered to be of low cost. All sensors and time values act as input. Sensors’ range values and time duration for each of them are initially set by the programmer. The program used in this work converts analog inputs to digital values and displays corresponding voltage measurements in seven-segment displays of the board. A series of processes is activated upon system is set to ON, in order to achieve checking and control of parameter values. Blue LEDs and corresponding control systems are activated if related sensors’ values become less than lower critical values set by programmer, while red LEDs and corresponding control systems are activated for sen- sors’ values overcoming upper critical values. Especially for visible light values, step motor for opening or closing cur- tains is also activated and FPGA’s board LEDs and buzzer connected to it are also ON, when upper critical value is exceeded. Yellow LEDs are activated for each parameter exceeding time set values. Finally an alarm level system turns on corresponding LEDs, depending on the number of parameter values that simultaneously exceed range values set by programmer.

Keywords: Sensors, Self-regulating system, FPGA, VHDL, Buzzer, LEDs

Abstract: The integration of AI-powered fraud detection systems within professional and contractors insurance claims marks a pivotal advancement in risk management methodologies. This system leverages machine learning algorithms and predictive analytics to scrutinize large datasets, identifying suspicious activity that may escape conventional review processes. By automating the analysis of claims data—including patterns of behavior, historical claims information, and demographic factors—these systems allow insurers to allocate resources more efficiently and strategically. Moreover, the application of natural language processing enables enhanced examination of unstructured data sources, such as emails and claims narratives, further enriching the analysis and revealing potential fraud indicators that typically remain unaddressed.
The essence of AI-driven fraud detection lies in its capability to evolve continually. The dynamic nature of fraudulent tactics necessitates a responsive approach; thus, these systems are designed to learn from every interaction, adapting to emerging patterns and techniques used by fraudsters. By employing neural networks, algorithms can fine-tune their predictions based on real-time data inputs, significantly increasing the likelihood of identifying fraudulent claims at earlier stages of the claims process. As a result, not only can insurers mitigate losses associated with fraudulent activities, but they can also improve customer relations by reducing the time taken to process legitimate claims. In aligning fraud detection methodologies with the principles of artificial intelligence, the insurance industry is not merely enhancing existing frameworks but is transforming its operational paradigms. This synergy between technology and traditional claims processing positions insurers to respond to an ever-evolving landscape of risk with agility and precision. Thus, AI-powered systems emerge not merely as tools for detection but as integral components of a proactive risk management strategy, empowering insurers to safeguard their financial sustainability while fostering a more secure environment for their clients.

Keywords: AI-powered, fraud detection, systems, professional insurance, contractors insurance, claims, machine learning, pattern recognition, anomaly detection, risk assessment, data analysis, automated verification, claims processing, fraud prevention, predictive analytics, deep learning, claims validation, insurance fraud, real-time monitoring, fraud patterns, detection algorithms, financial risk, insurance claims, fraud detection models, insurance industry, technology, automation, fraud mitigation, intelligent systems.

Abstract: Data analytics workflows expressed through Directed Acyclic Graphs run commonly today in serverless computing environments such as Google Cloud Platform because these settings provide better scalability and efficient cost benefits. Serverless workflows help scientists and business professionals deal with complex analytics requirements across astronomy, science, and social science, as well as bioinformatics and neuroscience. The data pipelines require specific programming models and runtime environments to handle their complex requirements. This paper investigates execution methods for data analytics DAGs through Google Cloud Platform services. This research demonstrates both the designed architecture and the achieved results of workflow implementations that will provide instrument pipelines as a service.

Keywords: Data analytics, serverless computing, Directed Acyclic Graphs, Google Cloud Platform, Cloud Functions, data pipelines, workflow management.

Abstract: Data-driven decision support enables the consolidation, organization, and analysis of amassed health data for more effective decision-making. It complements AI solutions by directly addressing core user needs prior to the availability of clinical AI. Decision-theoretic models provide a normative framework for qualitative and quantitative models. Methods for evidence synthesis underpin clinical decisions supported by data without requiring AI. Technologies that enable these capabilities comprise database systems that collect and store data, tools that facilitate querying and exploration, statistical approaches that extract patterns and structure, and predictive models that summarize risk. The ability to perform medical decision-making has always been constrained by the complexity and uncertainty of the clinical environment. Data-driven decision support refers to a set of processes, methods, and supporting technologies that consolidate, organize, and analyze the accumulated clinical data to make decision-making easier and more reliable. Data- driven decision support does not rely on Artificial Intelligence (AI) methods, although it can help pave the way for them. Recent advancements in Natural Language Processing and Convolutional Neural Networks have made AI tools seem right around the corner, but it is important to remember that these technological trends might not deliver AI solutions for all clinical tasks any time soon. Data-driven decision support can provide answers when AI methods are either not available or cannot be trusted, such as in the early phase of development for a given medical task.

Keywords: Data-Driven; Clinical Decision Support; Decision Theory; Evidence Synthesis; Statistical Methods; Health Informatics; Clinical Decision Support Systems (CDSS); Evidence-Based Medicine; Health Informatics; Medical Data Analytics; Rule-Based Expert Systems; Electronic Health Records (EHR); Statistical Decision-Making; Knowledge- Based Systems; Clinical Guidelines and Protocols; Data Quality and Standardization.

Abstract: Continuous auditing is defined as a review of internal controls, transactions, or balances on an ongoing basis, with corresponding evidence creation, through the integration of traditional and emerging technologies. Within the context of modern AI advancements, continuous auditing must increasingly deliver the following principles: 1. Ongoing monitoring of controls and related risk indicators; 2. Sampling or testing of controls on a risk-based, continuous, and non-disruptive basis; 3. Continuous assurance of specific assertions; 4. Exploratory analysis, much of it driven by anomaly detection and classification; and 5. Control testing and assurance that are comparatively transparent and incorporate audit trail verification. These principles are intended to transcend simple automation of historic data analysis and offer insight to those responsible for governance.

The delivery of contemporary continuous audit disciplines is driven by responsible use of AI-powered technologies, especially in the domains of natural language processing, machine learning, and advanced robotics. The evolving possibilities around continuous auditing using AI to meet these principles are becoming possible in response to advances in business process automation and AI-enabled transaction processing; stakeholder expectations around ongoing assurance; the emergence of third-party assurance-as-a-service providing the leveraging of AI capabilities for assurance; the governance, risk management, and compliance focus on ongoing risk assurance and control testing; and increasing regulatory pressure for continuous audit frameworks in specific domains such as banking, treasury, procurement, and revenue assurance based on qualitative and quantitative management information.

Keywords: AI-enabled continuous auditing, Corporate governance analytics, Real-time audit monitoring, Intelligent risk assessment, Automated internal controls, Machine learning in auditing, Continuous compliance assurance, Audit data analytics, Predictive risk modeling, Governance, risk, and compliance (GRC) automation, Anomaly and fraud detection systems, Algorithmic audit assurance, Explainable AI for audit decisions, Continuous financial oversight, Technology- driven audit governance.

Abstract: Generative AI systems, capable of producing coherent, contextually relevant text, images, and other media from prompt inputs, have become increasingly accessible. The potential for Generative AI to improve patient care and clinician efficiency has generated considerable interest in the healthcare sector, focusing on the enhancement of clinical documentation and patient engagement processes. The use of Generative AI in these domains is discussed with a focus on the underlying technology, implementation considerations for healthcare organizations, and case studies demonstrating the effectiveness of Generative AI in real-world deployments.

Automated generation of clinical notes based on free-text summaries, unstructured summaries of patient examinations and assessments, or conversational inputs is explored, along with the code-based structuring of free-text notes and the application of standardization templates to ensure compliance. The generation of patient education materials appropriate for health literacy levels and cultural backgrounds, the scheduling of appointments, and the triaging of patient queries using Generative AI are also covered. Ethical considerations—especially with respect to data governance and the potential for biased, adversarial, or inaccurate output—are flagged throughout, along with the importance of establishing and maintaining high- quality workflows for the use of Generative AI services.

Keywords: Generative Artificial Intelligence In Healthcare, Clinical Documentation Automation, Patient Engagement Systems, Automated Clinical Note Generation, Unstructured Medical Text Processing, Conversational AI In Healthcare, Clinical Workflow Efficiency, Health Literacy–Aware Content Generation, Patient Education Automation, Appointment Scheduling Systems, Intelligent Patient Triage, Healthcare AI Implementation, Clinical Standardization Templates, Data Governance In Healthcare AI, Ethical AI In Medicine, Bias And Risk Management, Adversarial AI Concerns, Compliance In Clinical Documentation, Real-World Healthcare AI Deployments, Human-Centered Clinical AI Systems.

Abstract: The influence of AI on healthcare data engineering across the data lifecycle is vast, enabled by key capabilities that affect processes from creation to sharing and archiving. Data quality and provenance are primary concerns, and healthcare organizations can achieve better, faster results when appropriate AI methods are applied. For hospitals and healthcare institutions with a network of branches, real-time sharing of data is critical, allowing clinical and operational decisions to be based on up-to-date information from across the entire organization. Machine learning acts as a principal driver of operations, from clinical decision support to analytics, diagnostics, resource optimization, workload assessment, and demand forecasting. Support from a well-defined data architecture does not eliminate the risk of unanticipated outcomes or the need for adequate protection of patient information, which is safeguarded by privacy-preserving techniques that include access control and auditability. Common standards such as Health Level 7 Fast Healthcare Interoperability Resources, Digital Imaging and Communications in Medicine, Representational State Transfer and gRPC, and the OpenAPI Specification and Postman ecosystem simplify integration, while consistent adherence enhances usability and reliability.

Keywords: AI-Driven Healthcare Data Engineering, Healthcare Data Lifecycle Management, Clinical Data Quality And Provenance, Real-Time Healthcare Data Sharing, Distributed Hospital Data Systems, Machine Learning In Healthcare Operations, Clinical Decision Support Systems, Healthcare Analytics And Diagnostics, Resource Optimization In Healthcare, Demand Forecasting In Hospitals, Healthcare Data Architecture, Patient Privacy Protection, Privacy- Preserving Data Techniques, Access Control And Auditability, Healthcare Interoperability Standards, HL7 FHIR Integration, DICOM Medical Imaging, API-Driven Healthcare Systems, Secure Healthcare Data Exchange, Scalable Health Data Platforms.

Abstract: The mission of intelligent transportation systems (ITS) is to enhance transportation safety, efficiency, mobility, and sustainability through the integration of data from multiple sources. Despite the celebrated success of artificial intelligence (AI) in development and commercialization, its growing capacities, accessibility, and affordability have not been capitalized on in ITS. This is due partly to the lack of comprehensive and clearly delineated means for infrastructure provision, timeliness, and system continuity—especially in maintenance, operations, and expansion—and partly to the underlying data fundamentals. AI engenders unprecedented opportunities for transportation solutions by providing its own data engineering. Examination of AI-Powered Data Engineering methods reveals the enabling means of AI-based data engineering for ITS.
Data engineering encompasses the core functions for the acquisition, preparation, and deployment of data suitable for analysis and modeling. Like the broader IT domain, AI-Powered Data Engineering for ITS operates on a foundation of data acquisition and ingestion; integration and interoperability; architecture; optimization; security; governance; agency; and deployment. The function for data acquisition and ingestion serves the dual purpose of ingesting system-based information—such as from traffic signals and detection cameras used for system administration—as well as supporting predictive models for demand and congestion forecasting conversation systems for route planning and incident mitigation.

Keywords: Intelligent Transportation Systems, AI-Powered ITS, Transportation Data Integration, AI-Based Data Engineering, Transportation Safety And Efficiency, Mobility And Sustainability, Multi-Source Transportation Data, ITS Infrastructure Provisioning, Real-Time Transportation Analytics, Data Acquisition And Ingestion, Transportation Data Interoperability, ITS Data Architectures, Predictive Traffic Modeling, Demand And Congestion Forecasting, Route Planning Optimization, Incident Detection And Mitigation, Transportation System Operations, ITS Data Governance And Security, AI-Driven Transportation Solutions, Scalable ITS Data Pipelines.

Abstract: At a time when Higher Education (HE) institutions are struggling to improve student engagement and learning outcomes while closing achievement gaps within their increasingly diverse student communities, personalized learning (PL) in the form of tailored learning pathways is gaining traction. However, the cost, resource, and time implications in the design of PL solutions, especially in Higher Education, have constrained broader PL uptake, initial attempts in so- called personalized artificial intelligence (AI) have neglected critical dimensions of the student modelling component, and adaptation to actual student needs has remained an aspirational goal rather than reality. By drawing on innovative concepts from learning analytics, education data mining, adaptive instructional design, and adaptive cloud-integrated AI research, an adaptive cloud-integrated AI system is proposed that empowers any educator to create a PL solution in their discipline yet adapts to evolving student needs and interests in real-time, requires no up-front configuration, and can be deployed across disparate subject areas and learning contexts.

The architecture encompasses a cloud-deployed data foundation that supports student modelling and profiling, a recommender engine, and an adaptive personalization engine. Elements and algorithms within the three system components are presented and showcased in a range of Higher Education disciplines, illustrating how the integration of existing student activity data with cloud-hosted Repository and Knowledge Graph data learning pathways can be designed to mirror unit, course and program-level Learning Outcomes antennas without the time, resourcing or ongoing expertise overhead of traditional solutions. Insights from Greater Sydney and Auckland case studies indicate that PL, in various forms, improves student engagement and perception of learning outcomes. Disparate student characteristics add support for a degree of equity and access inference. However, the personalization process must be customized and continually refined to maximize its catering for the full range of student diversity.

Keywords: Personalized Learning in Higher Education, Adaptive Learning Pathways, Cloud-Integrated Educational AI, Student Modelling and Profiling, Learning Analytics, Education Data Mining, Adaptive Instructional Design, AI-Driven Personalization Engines, Recommender Systems for Learning, Knowledge Graph–Based Learning Design, Repository- Integrated Learning Content, Real-Time Learning Adaptation, Learning Outcomes Alignment, Scalable Personalized Learning Architectures, Equity and Access in Education, Student Engagement Analytics, Adaptive Educational Systems, Cross-Disciplinary Learning Platforms, Cloud-Based Learning Infrastructure, Intelligent Higher Education Systems.

Abstract: Cloud computing environments host sensitive data and critical systems of organizations of any size and sector. Their security is vital and yet very hard to achieve since the technology is still at an early stage and continuously evolving. Organizations have only limited control over the environment they use, putting them at the mercy of malicious actors. AI can reinforce security in many ways and applied in a focused manner, it can help detect and respond to threats more effectively at a broader level. The integration of AI helps organizations to assess data protection risk, comply with data protection laws, and reduce the cost of compliance.
AI-driven threat detection and response combines AI model training, validation, and testing with containerization-based operation and monitoring to automatically contain and remediate threats in near-real time of automated workflow systems. Decision support systems rule-based or machine learning-enhanced for vulnerable domains and escalation procedures establish human-in-the-loop conditions that balance business requirements and risk appetite with operational overhead. The AI-enhanced approach complements, but will not entirely replace, traditional human-operated SOC playbooks. Decision support systems with a focus on managing uncertainty can aid human operators by suggesting which playbooks to execute next and what data to request from detection systems like threat intelligence feeds, sandboxing solution employable on-demand, or dedicated malware analysis clusters.

Keywords: Cloud Security Management, AI-Driven Threat Detection, Automated Threat Response, AI-Enhanced Security Operations Centers (SOC), Cloud Computing Risk Management, Data Protection and Compliance, AI-Based Security Analytics, Containerized Security Operations, Near-Real-Time Incident Remediation, Security Workflow Automation, Human-in-the-Loop Security Systems, Decision Support for Cybersecurity, Uncertainty Management in Security Operations, Rule-Based and Machine Learning Security Models, Threat Intelligence Integration, Automated Playbook Execution, Malware Analysis and Sandboxing, Secure Cloud Architectures, AI-Assisted Compliance Management, Resilient Cloud Security Systems.

Abstract: Population ageing in India is accelerating rapidly due to demographic transition characterized by declining fertility and increasing life expectancy. This systematic review and meta-analysis aims to synthesize evidence on age- related problems among the elderly population (≥60 years) in India, with a specific focus on rural–urban disparities. A comprehensive literature search was conducted across PubMed, Scopus, Web of Science, and Google Scholar for studies published between 2000 and 2022. A total of 52 studies met the inclusion criteria, encompassing a combined sample size of approximately 102,500 elderly individuals.

The pooled prevalence of chronic diseases was 64%, with hypertension (49%) and diabetes (34%) being the most common conditions. Mental health issues were substantial, with depression affecting 36% and anxiety disorders affecting 29% of the elderly. Economic dependency was reported in 68% of cases, significantly higher in rural areas (74%) compared to urban areas (59%). Social isolation was more prevalent in urban settings (32%) than rural areas (24%), reflecting the impact of nuclear family structures.

Rural–urban disparities were evident across multiple domains, including healthcare access, financial security, and social support systems. Rural elderly populations faced greater economic hardship and limited healthcare infrastructure, whereas urban elderly experienced higher levels of psychological distress and social isolation.

The findings highlight the multidimensional challenges associated with ageing in India and underscore the need for targeted policy interventions addressing rural–urban inequalities. Strengthening geriatric healthcare, expanding social security, and promoting community-based support systems are essential to ensure healthy ageing and improved quality of life for older adults in India.

Keywords: Ageing, Elderly, India, Rural-Urban Disparities, Chronic Diseases, Mental Health, Meta-analysis