VOLUME 12, ISSUE 12, DECEMBER 2023

Abstract: As technology continues to evolve, there is a requirement to explore new ways of designing and developing middleware architectures to guarantee that they can take benefit of the latest advancements. Conventionally many types of heterogeneous devices are connected to RFID, Smart Sensors, NFC and numerous communication protocols due to its latest progresses. This paper has been prepared over the set of Heterogeneous IoT devices deployed on Node across the set of homes in an apartment. Using some set of heterogeneous devices the data captured is viewed through the console in the context of edge computing. Moving forward this set of data will be uploaded to azure cloud where it will be analysed and visualisation will be done using Power BI.

Keywords: Heterogeneous IoT Objects, Smart Sensors, Azure cloud, Power BI. Cite: Ashok Kumar N, Dr. D.Ramesh, "Home Automation System’s Data Collection using dissimilar IoT objects, Analysis and Visualisation through Power BI", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 12, pp. 23-30, 2023, Crossref https://doi.org/10.17148/IJARCCE.2023.121204.

Abstract: Road traffic injury has appeared as a severe problem today, claiming more than 1.25 million lives each year worldwide and draining 3% of the total global GDP. According to the National Highway Transportation Safety Administration, about half a million people got injured in 2014 due to distracted driving related car clashes. In this work, we have considered the brainwave, heart rate and blood pressure level of a distracted driver. Within the various source of distraction (e,g, multitasking, severe weather condition, external sound effects) we monitored the driving behavior through EEG signal. In particular, the alpha, beta, gamma, delta and theta brainwaves have a significant connection with the emotion, stress and other psychological responses. Our EEG data analysis can provide a pathway to detect the physiological condition of distracted drivers and avoid road accidents.

Keywords: Electroencephalogram, brainwave, driver monitoring, driver distraction, attention level, meditation level Cite: Yihang Chu, Hasanur Rahman Chowdhury, Abu Mitul, Nezam Uddin, Mohammad Jobayer Hossain, Dean M. Aslam, "Nature of Distracted Driving in Various Physiological Conditions ", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 12, pp. 1-9, 2023, Crossref https://doi.org/10.17148/IJARCCE.2023.121201.

Abstract: The world of e-sports is growing rapidly in Indonesia, starting from the regional, national, and even international levels. Players or game lovers, especially in the e-sport category, are very enthusiastic about various events, competitions organized by companies or individuals, and so on. However, players or lovers of e-sport games need a system for mutual communication, discussion, and finding information related to e-sport games ranging from events, competitions, patches, tutorials, problems, and others. The application of forums to the world of esports can be implemented using mobile applications, where this mobile e-sport forum is expected not only as a place of discussion but also as a social media place for pro players and e-sport gamers themselves that can be accessed anywhere and anytime. The Rational Unified Process (RUP) method is used to create applications. Using object-oriented development with UML for the approach. The system implementation process uses the Java programming language and the database uses Firebase for the testing system using API 27 Android 8.0.

Keywords: forum, e-sport, social media, Rational Unified Process (RUP), mobile. Cite: Ragil Aldyansyah, Tri Widodo, "Transforming E-Sport Communities: Mobile-Based Online Forum Development", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 12, pp. 10-17, 2023, Crossref https://doi.org/10.17148/IJARCCE.2023.121202.

Abstract: This article presents a review of current advances and future prospects in the field of fore- casting renewable energy generation using machine learning (ML techniques. With the increasing penetration of renewable energy sources (RES) into the electricity grid, accurate forecasting of their generation becomes crucial for efficient grid operation and energy man- agreement. Traditional forecasting methods have limitations, and thus ML. This paper reviews the different approaches and models that have been used for re- new able energy forecasting and discusses their strengths and limitations. It also highlights the challenges and future research directions in the field, such as dealing with uncertainty and variability in renewable energy generation, data availability, and model interpretability. Improved irradiance forecasting ensures precise solar power generation forecasts, resulting in smoother operation of the distribution grid. Empirical models are used to estimate irradiation using a wide range of data and specific national or regional parameters. In contrast, algorithms based on Artificial Intelligence (AI) are becoming increasingly popular and effective for estimating solar irradiance. Although there has been significant development in this area elsewhere, employing an AI model to investigate irradiance in Bangladesh is limited. This research forecasts solar radiation in Bangladesh using ensemble machine-learning models. The meteorological data collected from 32 stations contain maximum temperature, minimum temperature, total rain, humidity, sunshine, wind speed, cloud coverage, and irradiance. Finally, this paper emphasizes the importance of developing robust and accurate renewable energy forecasting models to enable the integration of RES into the electricity grid and facilitate the transition towards a sustainable energy future.

Keywords: Accurate predictions; Energy management; Machine Learning; Renew- able Energy Forecasting, solar irradiance; machine-learning; ensemble models; performance matrices; prediction error. Cite: Mr.Meghraj Chougule, Solwat Kimyanand Bharat, "Forecasting Renewable Energy Generation with Machine learning: Latest Advances and Future Possibility", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 12, pp.31-39, 2023, Crossref https://doi.org/10.17148/IJARCCE.2023.121205.

Abstract: This research paper explores the importance of Cybersecurity Awareness Training in organizations and examines the efficacy of utilizing the National Institute of Standards and Technology (NIST) Cybersecurity Framework as a comprehensive guide for developing and implementing such training programs. The paper provides an in-depth analysis of the background and current state of cybersecurity threats, reviews relevant literature on cybersecurity awareness, discusses the key components of the NIST Cybersecurity Framework, highlights its strengths and limitations, offers recommendations for optimizing training initiatives, and concludes with a reflection on the critical role of cybersecurity awareness in safeguarding organizational assets. Keywords: NIST Cybersecurity Framework, Cyber Threats, AI-Based Training Programs, Risk Management, Data Security, Compliance and Regulations, Incident Response and Recovery, Collaboration Cite: Pranav Nair, "Enhancing Cybersecurity Awareness Training through the NIST Framework", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 12, pp. 18-22, 2023, Crossref https://doi.org/10.17148/IJARCCE.2023.121203.

Abstract: Artificial intelligence (AI) has been applied to enhance operational efficiency, supply chain management, and consumer experience as well as mate size in the banking and e-commerce sectors, with the main objective being the development of standardized, trustworthy procedures for product quality control and the investigation of novel approaches to contact and assist clients at a minimal expense. Deep learning is a branch of artificial intelligence and machine learning are two of the most widely applied methodologies. These models are utilized by individuals, organizations, and governmental bodies to forecast and get insights from data. Machine learning models for the complexity and diversity of data are presently being developed in the food industry. The applications of artificial intelligence and machine learning to banking, corporate management, and e-commerce are covered in this article. Applications for forecasting, inventory management, fraud detection, sales growth, profit maximization, and portfolio management are the most often utilized ones.

Keywords: Artificial Intelligence, Machine Learning, Financial Industries, E-Commerce, Deep Learning. Cite: Korke P S, Melge P P, "A REVIEW OF THE USE OF ARTIFICIAL INTELLIGENCE IN FINANCE, E-COMMERCE, AND COMPANY MANAGEMENT", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 12, pp. 40-45, 2023, Crossref https://doi.org/10.17148/IJARCCE.2023.121206.

Abstract: The Paper presents the design of the Development of Voice Controlled Home Automation system. Home automation system helps in providing support for the elderly and disabled. Home automation system must comply with house standards and convenience of reducing the power consumption. The home automation system controls all lights and electrical appliances in a home or office using voice commands. This paper is about home automation system which would use a smartphone to enable any naive user to operate all the appliances. The system has three main components: an Arduino microcontroller for connecting the appliances, a Bluetooth module HC05 for signal transfer, and a smartphone running the Android application. The smartphones interacts with the Arduino via Bluetooth and decodes the user’s voice command. The main aim of the system development is low cost and scalable according to requirements.

Keywords: Arduino Uno, HC-05 Bluetooth Module, Home Automation, Smartphone, Voice Control Cite: Mr. Vishal Gupta, Aakash Tiwari, Deepak Verma, Ajay Raj Sharma, Krishan Kant Katyayan, "DEVELOPMENT OF VOICE CONTROLLED HOME AUTOMATION SYSTEM ", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 12, pp. 46-50, 2023, Crossref https://doi.org/10.17148/IJARCCE.2023.121207.

Abstract: With the marvelous increase in videotape and image databases, there's an inconceivable need for automatic understanding and examination of information by intelligent systems as it's getting to be plainly distant. Face plays an important part in social intercourse for conveying the identity and passions of a person. mortal beings do haven't a tremendous capability to identify different faces than machines. So, automatic face discovery system plays a significant part in face recognition, facial expression recognition, head- disguise estimation, and mortal – computer commerce etc. Face discovery is a computer technology that determines the position, and size of mortal face in a digital image. Face discovery has been a name among motifs in computer vision literature. This paper represents a comprehensive check of different ways explored for face discovery in digital images. Different challenges and operations of face discovery are also introduced in this paper.

Keywords: Face Detection, Face Recognition, knowledge-based, feature-based, template-based, appearance-based, image, resolution, noise, occlusion. Cite: Ms. Kanika Kundu, "FACE DETECTION: A SCIENCE OF DETECTING AND RECOGNIZING HUMAN FACE", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 12, pp. 51-55, 2023, Crossref https://doi.org/10.17148/IJARCCE.2023.121208.

Abstract: This academic research investigates integrating blockchain technology with cloud computing, focusing on exploring potential synergies and applications. It provides insights into the merging landscapes of blockchain and cloud services, addressing the benefits, challenges, and future trends associated with their integration. Various industry applications, including supply chain management, financial services, healthcare, and government services, are discussed to highlight the transformative potential of this convergence. Additionally, the research delves into scalability, security, interoperability, data privacy, and compliance concerns, offering recommendations for organizations considering this integration. The study concludes by emphasizing this integration's immense possibilities for revolutionizing data management and collaboration in cloud-based systems.

Keywords: Blockchain, Cloud Services, Synergies, Applications, Security, Efficiency, Data Management Cite: Sunil Sukumaran Nair, "Blockchain and Cloud Services: Exploring the potential synergies and applications of Blockchain Technology in Cloud Computing", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 12, pp. 56-61, 2023, Crossref https://doi.org/10.17148/IJARCCE.2023.121209.

Abstract: Human resource management is a field that involves people's brains, bodies, psychological behaviours, codes of conduct, and other aspects of human behaviour. on. Every company has a human resources division. primarily committed to promoting improvement in the workers beginning with sourcing, screening, and hiring , on boarding, performance evaluation, and employee activities, learning & development, and engagement. In Technology innovation in the modern era is over. advancing technology and science in HR operations of the business. The advancements in AI and ML have worked to put them into practise in completing the HR procedures that could reduce rather than share the work done by the HR staff. Work of artificial intelligence towards streamlining the Managers' work. However, there is a different belief that, regardless of the sort of employment, artificial intelligence can replace the human worker. Work can be transformed and deviated towards automated systems with the added benefit of being done more efficiently and with fewer errors.

Keywords: Artificial intelligence, Recruitment process, Sourcing, Screening. Cite: Mrs. Shweta Shete, Mrs. Priyanka Koshti,"A Review Paper on the Use of AI in the Recruitment Process", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 12, pp. 62-66, 2023, Crossref https://doi.org/10.17148/IJARCCE.2023.121210.

Abstract: The ever-present threat that fires pose to human life, property, and the environment necessitates continuous evolution in fire protection technology. This study addresses the imperative need for advancements by focusing on early fire detection and the accurate monitoring of hazardous gasses, such as carbon monoxide (CO) and oxygen (O2) deficiency. Our research explores the integration of CO, O2 deficiency, and flame sensors into a comprehensive detection and monitoring system equipped with SMS alerting capabilities.Fires represent a significant risk, requiring proactive and sophisticated technological solutions. The study emphasizes the shortcomings of standalone detection systems, highlighting the limitations in providing a holistic approach to fire safety.The primary objective of this research is to develop and implement an integrated detection and monitoring system that surpasses the limitations of existing technologies. By incorporating CO, O2 deficiency, and flame sensors, the aim is to enhance the overall effectiveness of fire safety measures.In conclusion, the integration of CO, O2 deficiency, and flame sensors into a unified detection and monitoring system with SMS alerting capabilities represents a significant advancement in fire safety technology. By addressing the limitations of existing systems, this research contributes to a more comprehensive and effective approach to mitigating the risks associated with fires.

Keywords: Fire Protection, Carbon Monoxide Shortage, Oxygen Shortage, Detection, Fire Safety Cite: Dr Sowmya K. S., Vidhya Mahesh Hegde, Vishal Praveen, Yathish Chandrashekar, "Smart First Safety System with CO, O2 and Flame Detectors and SMS Notifications", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 12, pp. 67-72, 2023, Crossref https://doi.org/10.17148/IJARCCE.2023.121211.

Abstract: In recent years, the field of short-term predictions has witnessed substantial advancements due to the rapid growth in data-driven approaches. To contribute to this area of research, a novel model named the Spatial Feature Attention-based LSTM (Long Short-Term Memory) has been developed, aiming to enhance the accuracy and reliability of short-term predictions. The advent of deep learning techniques has revolutionized various domains and one such domain where these techniques have made significant strides in time series forecasting. Weather forecasting is crucial for various industries, including agriculture, transportation, and disaster management. The accuracy of short-term weather predictions significantly impacts decision-making and planning.

Keywords: LSTM Model, Short Term Prediction, Spatial Feature, Weather Forecasting. Cite: Prof. Nilam Honmane, Omkar Jadhav, Aditya Gavate, Sanika Nimse, Roshan Jadhav, "Weather Forecasting Using Spatial Feature Based LSTM Model", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 12, pp. 73-78, 2023, Crossref https://doi.org/10.17148/IJARCCE.2023.121212.

Abstract: In modern educational institutions, the effective management of student and staff complaints is crucial for maintaining a positive campus environment. This project proposes the development of a Campus Complaint Management System, a web-based tool designed to streamline the process of submitting, tracking, and resolving complaints within a college setting. The system incorporates user-friendly interfaces for both administrators and complainants, allowing for the efficient assignment of complaints to relevant departments or individuals responsible for resolution. Key features include a secure authentication system, detailed complaint submission forms, real-time status tracking, and a robust notification system to keep users informed about the progress of their complaints. The tool also includes a comprehensive dashboard for administrators to monitor complaint trends, track resolution times, and assess departmental performance. The implementation utilizes a chosen technology stack for backend, frontend, and database components, ensuring scalability and security. With a focus on user experience, the Campus Complaint Management System aims to enhance transparency, accountability, and overall satisfaction within the college community. Continuous improvement and feedback mechanisms are integrated to adapt the system to evolving campus needs, making it an indispensable component of a modern campus solution.

Keywords: Complaints, Ragging, Staff Management, Accountability. Cite: Prof. Shekhar Patle, Mayur Bachhav, Yash Jadhav, Supriya Jagadale, Pratik Kumbhar, "Efficient Campus Solutions: A Journal on Enhancing College Complaint Management", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 12, pp. 79-84, 2023, Crossref https://doi.org/10.17148/IJARCCE.2023.121213.

Abstract: Mobile cloud computing is changing quickly, posing new security challenges like data leaks and complex logins. This study looks for smart solutions to protect user data in the integrated world of mobile devices and cloud services. By combining mobile features with cloud resources, we can overcome device limitations and improve efficiency. The study suggests focusing on advanced security measures like encryption and dynamic authentication, along with global compliance, user education, and ongoing monitoring, to ensure a secure future for mobile cloud computing.

Keywords: Mobile Cloud Computing (MCC), Moblie Computing (MC), virtual private networks (VPNs), Mobile device management (MDM), Cloud Computing (CC), Augmented Reality (AR), RSA,Advanced Encryption Standard (AES). Cite: Dr Sindhu K, Kusumitha A, "Security challenges for mobile cloud computing", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 12, pp. 85-90, 2023, Crossref https://doi.org/10.17148/IJARCCE.2023.121214.

Abstract: Now a day Data science is emerging field , before data science we had statisticians. These statisticians are skilled person who are evaluate records and organizations hired them to research their standard overall performance and income. Data science is a booming field of study which has a multidimensional scope for all organizations and industries. Data Science has lots of scientific methods which includes statistical techniques, machine learning, artificial intelligence all together we all know from earlier time the mathematics can solve the once complex problems. It gives various information on emerging trends and patterns in a specific model . Data science provides with various methods to analyzed data, and make predictions on the data. The basic objective of this paper is to explore the techniques of data science , tools which are available as an open source for data science and various tools associated with it..–

Keywords: Machine Learning, Data science, Open source, Data science Tools ,Big data analytics, Structured data; Unstructured Data Cite: Mrs Anagha Abhijit Jawalkar,"Survey on data science: its technique, tools and Open issues", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 12, pp. 91-94, 2023, Crossref https://doi.org/10.17148/IJARCCE.2023.121215.

Abstract: The requirement for a strong and thorough fieldwork management system is critical in modern survey procedures, especially when evaluating the sincerity and diligence of officials collecting data from various locations. This project presents a Cross-Platform Fieldwork Management System for both Android and web platforms, with the goal of streamlining data gathering procedures during surveys in a variety of fields. This system's major goal is to examine the devotion and thoroughness of authorities entrusted with obtaining data from families in various locations. Managers are empowered by the suggested solution, which provides tools for tracking officials' activity, ensuring personal visits to defined regions, and closely monitoring the data they create

Keywords: Cross-platform Development, Fieldwork Management System, Data Collection Integrity, Operational Efficacy, Technological Innovation. Cite: Atharva Degwekar, Anshul Borkar, Chaitanya Khotele, Prashant Govardhan, "A Literature Survey on Field Survey Management System", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 12, pp. 95-101, 2023, Crossref https://doi.org/10.17148/IJARCCE.2023.121216.

Abstract: The importance of the level of security on digital media needs to be a special concern, especially when the information is sent. By using encryption or steganography methods where by inserting pieces of secret information in another media object. In steganography, data hiding or data embedding is known, which is data hiding that seems very familiar with encryption. However, data hiding in steganography and encryption is very different, where encryption performs data hiding by changing the arrangement of characters in the same media. While in steganography, data hiding is done by changing or exchanging some information that does not look important in the host media of the message carrier. With the method of using 24-bit Bitmap Digital Image media as input data for secret message carrier media. And the LSB (Least Significant Bit) method is by inserting secret message bits into low-level bits that have very little effect on the digital image visually that will be seen by the observer/reader of the message. So that the goal of inserting a secret message without being suspected by the observer / reader can be proven and the reading of the secret message can be read again after being inserted into the media.

Keywords: encryption, data hiding, embedding steganography. Cite: Sugiyatno, "INSERTING SECRET MESSAGES IN IMAGES USING THE STEGANOGRAPHY METHOD", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 12, pp. 102-110, 2023, Crossref https://doi.org/10.17148/IJARCCE.2023.121217.

Abstract: The objective of this study is to determine the elements that impact the choice of an aerospace defense company to engage in lobbying activities. The primary emphasis of this research is on accounting and financial variables that are unique to each firm. Public factors, such as the level of scrutiny the corporation faces from the public and the amount of money allocated to national defense, are also considered. From the analysis, it is found an inverse relationship between cash flow and lobbying. The study found a link between inventory turnover and subsequent lobbying. Also, public scrutiny and lobbying decisions are positively correlated.

Keywords: Aero defense, lobbying, scrutiny, financial metrics. Cite: Kamala S, Dr.A. Jayanthiladevi, "Defense Aerospace: An Industry Analysis", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 12, pp. 123-126, 2023, Crossref https://doi.org/10.17148/IJARCCE.2023.121219.

Keywords: iPhone, artificial intelligence, workspace, user experience, analytics tools Cite: Kamala S, Dr.A. Jayanthiladevi, "iPhone workspace in Artificial Intelligence: A Company Analysis", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 12, pp. 127-131, 2023, Crossref https://doi.org/10.17148/IJARCCE.2023.121220.

Abstract: Three types of machine learning were used in this paper: support vector machines (SVM), random forests, and the Naive Bayes. There are four main categorization metrics used to assess the efficiency of the system designed for the identification of insect pests. The four metrics covered here are accuracy, precision, recall, and F1-score. These results demonstrate that our enhanced SVM provides superior performance to the state-of-the-art approaches for automatic pest identification in crops.

Keywords: Machine Learning, Random Forests, SVM, Naive Bayes Cite: Dr. Vikrant Sharma, Dr. Jayanthiladevi,"CLOUD COMPUTING USING MACHINE LEARNING FOR AGRICULTURE APPLICATION", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 12, pp. 132-138, 2023, Crossref https://doi.org/10.17148/IJARCCE.2023.121221.

Abstract: At present, gas sensors are assuming a critical function in facilitating the shift from residential to industrial monitoring. Gas sensors are indispensable for a vast array of applications, including the detection of hazardous gases and the monitoring of environmental factors. An extensive range of semiconductor gas sensors that have attained an exceptional standing on the market on account of their rapid response time, dependability, affordability, and minimal maintenance needs are currently available for purchase. In the past, ceramic gas sensors were employed for gas detection. The gas sensors demonstrate oxidising and reducing properties preponderantly. Reducing sensors results in the formation of donor states, as opposed to oxidising sensors which generate acceptor states. Sensitivity, resistive, potentiometric, and amperometric sensors rank foremost.

Keywords: Gas Sensing, Monitoring, Gas Sensing Unit, Arduino UNO, Biogas Detection Sensor Cite: Dr.R.Jayakarthik, Dr.JayanthilaDevi,"IoT Based Gas sensors for Biogas Leakage Measurement", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 12, pp. 139-143, 2023, Crossref https://doi.org/10.17148/IJARCCE.2023.121222.

Abstract: In an age of ongoing machine learning and deep learning applications, energy-efficient AI clusters are valuable in that they greatly reduce carbon footprints. AI clusters have become indispensable for large models that have a long training time and require large amounts of data. AI clusters can be divided into two major parts for their operation: the first part is to train the model in the deep learning model. The second part is to store the massive amount of high-dimensional input data for our model. Using AI clusters in the deployment of cloud infrastructures, as well as high-speed storage solutions, has been integrated. Given the large computational costs of large-scale AI jobs, it is logical to optimize energy resources for both aspects individually. Little research has been done, however, on the relationship between storage and CPU energy optimization. Modern state-of-the-art AI systems mainly depend on the assignment of CPU-bound, disk-bound, or GPU-bound parts, connected over network links. While the usage of some of these elements can be diminished, usually the entire connection is cut off along the device chain, resulting in rapid degradation of the performance of the overall AI application. Energy-efficient and environmentally friendly technical implementation is highly significant. Artificial Intelligence is evolving rapidly, providing excellent solutions for many new challenges as well as making existing solutions even better. However, one of the main challenges is the enormous consumption of energy in the training process of AI.

Keywords: Energy-efficient AI Clusters, Carbon Footprint Reduction, Deep Learning Models, Large-scale Training, High-dimensional Data, Cloud Infrastructure Deployment, High-speed Storage, Computational Costs, Energy Optimization, CPU-bound Tasks, Disk-bound Tasks, GPU-bound Tasks, Network Links, Performance Degradation, Environmentally Friendly AI, AI Energy Consumption, AI Training Optimization, Sustainable AI, AI Deployment Challenges, Energy-efficient Storage.

Abstract: The banking sector is increasingly leaning on advanced technologies to optimize infrastructure services, particularly through the deployment of Artificial Intelligence (AI) and machine learning methodologies. This movement is driven by the need for enhanced decision-making capabilities, improved operational efficiencies, and robust risk management strategies. Federated Learning emerges as a pivotal framework within this context, allowing institutions to collaboratively train AI models without compromising sensitive customer data. This decentralized approach not only mitigates privacy risks, but also enriches the training datasets, ultimately yielding more accurate and reliable predictive models. Such models are crucial for applications that encompass fraud detection, customer segmentation, and algorithmic trading. Furthermore, the implementation of AI governance frameworks is instrumental in navigating the ethical and regulatory complexities that accompany the integration of AI in banking. Effective governance ensures that AI systems operate transparently, with accountability mechanisms in place to address potential biases and ethical dilemmas. This is particularly important in a sector that manages vast amounts of personal and financial information. By instituting comprehensive oversight policies, banks can foster trust among stakeholders while enhancing compliance with regulatory requirements. The synergy between Federated Learning and AI governance thus not only fortifies the technological backbone of banking IT but also aligns operational practices with ethical standards and consumer protection mandates. This interplay between collaborative AI initiatives and stringent governance encapsulates the future of banking infrastructure. As institutions embrace these innovative solutions, they position themselves to harness the full potential of data-driven insights while safeguarding customer interests. Consequently, the dual focus on optimizing infrastructure services through Federated Learning and enforcing AI governance emerges as a key strategic approach within the banking landscape. This comprehensive framework is essential for navigating the complexities of a rapidly evolving financial ecosystem, ultimately facilitating sustainable growth and enhancing competitive advantage in the marketplace.

Keywords: Autonomous agents, agent-based systems, real-time processing, credit risk assessment, credit scoring, financial decisioning, intelligent systems, machine learning, risk modeling, dynamic data analysis, automated decision-making, adaptive algorithms, transactional data, behavioral analytics, predictive modeling, data-driven insights, financial technology, AI in finance, risk evaluation, creditworthiness analysis.

Abstract: Alzheimer's disease (AD) represents a devastating neurodegenerative disorder with profound societal and individual implications. Despite advancements in understanding its clinical trajectory, current diagnostic approaches often lag behind the onset of irreversible neural damage, highlighting the critical need for innovative strategies that enable early detection. This study integrates genomic profiling with neurobiological pathway mapping to elucidate early biomarkers and mechanistic insights that precede cognitive decline. By merging data-driven techniques with molecular and cellular neuroscience, the research aims to bridge the gap between genetic predisposition and phenotypic manifestation of the disease. Central to this work is a multidisciplinary framework that synergizes large-scale genomic datasets with neural imaging and molecular pathway analyses. Genetic loci implicated in AD—such as APOE, PSEN1, and PSEN2—are examined alongside transcriptional networks and epigenomic modifications to identify signature patterns associated with preclinical disease states. Concurrently, neurobiological mapping sheds light on disruptions in synaptic signaling, neuroinflammatory cascades, and metabolic deficits in brain regions vulnerable to AD pathology. The integration of these domains permits an unprecedented resolution of the interplay between genetic architecture and neurobiological dysregulation, uncovering potential avenues for therapeutic intervention. This work advances translational science by proposing actionable biomarkers and computational models for risk stratification in asymptomatic populations. The findings carry broader implications for personalized medicine, particularly in enhancing predictive accuracy and tailoring preventive strategies to an individual’s genomic and neurobiological profile. By situating early detection within a systems biology context, the study underscores the importance of interdisciplinary analyses to dismantle the complexity of Alzheimer’s disease and foster novel avenues for clinical innovation.

Keywords: Alzheimer's Disease, Early Detection, Genomic Profiling, Neurobiological Pathways, Biomarker Discovery, Multi-omics Integration, Precision Medicine, Transcriptomics, Neuroimaging, Gene Expression Analysis, Epigenetics, Systems Biology, Neural Network Mapping, Pathway Enrichment Analysis, Disease Risk Stratification.

Abstract: In the contemporary era, characterized by the rapid expansion of the internet, social media, and mobile communication, substantial amounts of new data, sometimes called ‘big data’, are being generated every day. Machine Learning (ML) which is one important technique of AI, has the ability to extract significant information from big data. The financial industry continues to invest in machine learning models to better utilize big data. The most exciting Iceberg of big data occurs in the ‘pay-as-you-go’ market such as peer-to-peer lending platforms, where most data is generated by borrowers. Low Credit Scoring (CS) has been a critical problem for many individuals and small-sized businesses in emerging markets under financial exclusion. Traditional financial institutions rely heavily on fixed and well-structured information, restricting many creditworthy applicants from financial products. Peer-to-peer lenders often lower the entry barrier by adopting models on new data sources in the short-term, considering process efficiency. However, a significant percentage of applicants with no records on the platforms would not be able to access credit. The home-grown online lenders who best incorporate big data and machine learning are well positioned to succeed. In this work, a bootstrapping ensemble voting model was developed combining traditional credit scoring statistics with new data sources along with machine learning and ensemble techniques, which is proven to be capable of answering the inquiry well. Exploring more discriminative local data sources by clustering the online lending market and attention mechanisms could be future research agendas. Despite recent progress, credit scoring in peer-to-peer lending remains an open topic, and exploratory research is a rewarding direction. New localised lending patterns, data sources, and variables on credibility scoring for different platforms or markets deserve more attention, both in terms of theory and application. However, the problem still exists. An increasing amount of general and unstructured big data have the potential to yield actionable insights but requires extensible AI-powered platform solutions to efficiently aggregate, normalize, transform, and apply the data. In emerging markets, AI-powered credit scoring has traditionally been a luxury enjoyed only by wealthy groups and a specific number of well-known companies, limiting its extensive applications to the majority of people in need. Substantial investments and over-engineered solutions disallow small financial institutions to step in. In most cases, data themselves are not valid and informative, and lack transparency in terms of matching or separating. In addition, validation and explanation are very hard to obtain.

Keywords: Machine Learning for Credit Scoring, AI in Financial Inclusion, Big Data Credit Assessment, Alternative Credit Scoring Models, Predictive Analytics in Finance, Non-Traditional Data Sources, AI Credit Risk Modeling, Financial Behavior Analysis, Digital Lending Algorithms, Fair and Explainable AI in Credit, Credit Scoring for the Unbanked, Behavioral Credit Scoring, AI-Driven Risk Assessment, Open Banking Credit Models, Data-Driven Lending Solutions.

Abstract: Digital financial inclusion (DFI) through financial technology (FinTech) has emerged as a transformative force in empowering rural entrepreneurs by providing access to affordable financial services. This study investigates the role of FinTech in enhancing financial access, overcoming barriers, and fostering entrepreneurial outcomes in rural India. Using a mixed-methods approach with primary data from 170 respondents (rural entrepreneurs, FinTech users, and financial experts), the study assesses adoption rates, perceived benefits, barriers, and socioeconomic impacts. Statistical analyses (ANOVA, chi-square, t-tests, regression) reveal high FinTech adoption (70%), significant improvements in business growth (mean = 3.9), and persistent barriers like digital literacy (60%) and connectivity (50%). Regression analysis confirms that FinTech adoption significantly predicts entrepreneurial success (β = 0.42, p < 0.001). Key findings highlight the need for targeted policies to address digital divides and enhance financial literacy. Policy recommendations include subsidized FinTech training, rural infrastructure development, and public-private partnerships to scale DFI. This study underscores FinTech’s potential to drive inclusive economic growth for rural entrepreneurs.

Keywords: Digital Financial Inclusion, FinTech, Rural Entrepreneurs, Financial Access, Economic Empowerment

Abstract: Precision medicine seeks to provide individualized information-based care across a range of therapeutic areas, utilizing patient-specific clinical, biological, and lifestyle data. The clinical implementation of precision medicine remains nascent but has the potential to facilitate the discovery, development, and delivery of therapeutics that target disease subtypes and patient populations defined by their unique characteristics. It offers new opportunities for treatment at any stage of disease, from prevention in high-risk groups to rethinking indications for established products. Three interconnected developments enable the effective implementation of precision medicine: the creation of large and diverse biological, clinical, imaging, digital, and lifestyle datasets; the emergence of new transdisciplinary methods to derive knowledge from these datasets; and the establishment of new product development models that leverage the acquired knowledge to deliver more targeted, safer, and more efficacious therapeutics. The application of artificial intelligence (AI) to clinical, imaging, and lifestyle data, as well as new approaches to risk prediction and disease progression modeling, cohort assembly, and knowledge extraction from electronic health records are enabling more accurate stratification of complex diseases within oncology, rare diseases, cardio-metabolic conditions, infectious diseases, and neuropsychiatric disorders.

Keywords: Precision Medicine, Individualized Care, Patient-Specific Data, Clinical Data Integration, MultiOmics Analytics, Lifestyle And Digital Biomarkers, Disease Stratification, Risk Prediction Models, Disease Progression Modeling, Cohort Assembly, Electronic Health Records Analytics, Artificial Intelligence In Healthcare, Transdisciplinary Methods, Targeted Therapeutics, Clinical Decision Support, DataDriven Drug Development, Oncology And Rare Diseases, CardioMetabolic And Infectious Diseases, Neuropsychiatric Disorders, Personalized Treatment Pathways.

Abstract: Tax compliance constitutes a substantial challenge for national revenues and public services worldwide, particularly in a digital economy that enables rapid international transactions. Artificial intelligence (AI) can enhance automated risk scoring and tax auditing capabilities by bridging the gap between the rapid development of machine-learning methods and the pressing operational needs of tax administrations. The applicability of AI-based risk scoring and auditing methods in the tax domain has thus far remained largely unexplored in the literature, as has the evaluation and validation of the resulting systems. Motivation, design, methods, and specific foundations (data-driven evidence, risk-scoring models, and automated auditing techniques) are presented in these sections, along with considerations of data governance, privacy, and ethics. Evidence drawn from knowledge engineering and computational taxonomy outlines the data requirements, provenance, and quality for reliable AI applications for tax compliance, providing a foundation for subsequent sections on risk-scoring models, data-driven evidence, and automated tax auditing. Risk-scoring models identify the relevance of explainability, novelty detection, and machine-generated human-readable components, supported by privacy-preserving techniques and algorithmic transparency. Two key approaches are identified: supervised learning generates predictions for tax-relevant domains, whereas unsupervised and semi-supervised methods support hierarchical anomaly detection. These directions together address the completeness of AI auditing systems, complementing research on planning, knowledge representation, and evaluation of audit systems.

Keywords: Automated Tax Auditing. Artificial Intelligence; Classification and Regression; Data-Driven Audit Planning; Data Mining Technologies; Document Analysis; Natural Language Processing; Risk Scoring Models. Auditing Apparatus. Governance Framework.

Abstract: Research questions, methods, key findings, and implications are summarized with emphasis on AI-enabled big data analytics in smart energy management. The scope, limitations, and novelty are stated, and practical and theoretical contributions are outlined. The global push for net-zero carbon emissions by 2050 necessitates the decarbonization of energy systems, but the massive deployment of renewable generation introduces intermittency and variability. Consequently, demand–supply matching has emerged as a high-priority problem. Advanced data analytics is essential for smart energy management and artificial intelligence (AI) enables intelligent decision-making by using big data analytics. However, ensuring energy data privacy and security is vital for successful adoption of AI-based solutions. Future trends, such as the emergence of the metaverse, quantum computing, and 6G networks, will further boost demand for big data analytics and AI solutions. AI-enabled big data analytics covering data acquisition pipelines, quality, governance, storage, and processing frameworks will enable various smart management paradigms: demand response, renewable generation integration, storage management, microgrid management, and fault detection.

Keywords: AI, big data analytics, smart grids, demand response, renewable integration, cyber security, data governance.

Abstract: The integration of artificial intelligence (AI) with big data is widely perceived as a promising direction for smart healthcare diagnostics. Various definitions and conjectures sustain the power of such an amalgamation. Data-driven medicine constitutes the theoretical foundation and encompasses both mainstream and alternative AI paradigms. To capture the potential, a data ecosystem addressing the data supply, an arsenal of AI algorithms appropriate for diagnostic testing, and a big data infrastructure capable of handling large volumes are outlined. The data ecosystem focuses on knowledge dissemination, reproducibility, and the avoidance of data leakage effects. Moreover, a supporting diagnostic AI lifecycle emphasizes AI validation and evaluation in terms of accuracy, bias, fairness, and generalizability in a health-related context. The recent interweaving of AI and big data with clinical practice and healthcare activity has drawn considerable attention over the past few years, bottoming out at various facets. Despite an initial quest for solutions targeting real-world problems, several AI leaders have steered the discussion toward testing AI algorithms under their own terms, fostering some bewilderment. Data-driven medicine—medicine addressed towards its own data by applying data-centric solutions—has remained a shadowy concept because diagnostic testing or diagnostic examination is understood differently across the clinical landscape. Clinical practitioners specializing in a certain disease group commonly talk about diagnostic testing or core diagnostic tests for such diseases, whereas diagnostic pathology and forensic medicine are sometimes perceived as distinct specialties dealing with much less population-associated diseases.

Keywords: Integration of AI and Big Data for Smart Healthcare Diagnostics; Big Data; Artificial Intelligence; Decision Support Systems; Diagnostics; Healthcare Cloud; Machine Learning; Smart Healthcare.

Abstract: Cloud-Based Deep Learning for Real-Time Financial Risk Assessment and Market Forecasting Reviews key themes in real-time risk assessment and market forecasting in finance with cloud-based deep learning. Research and development directions for these real-time applications deployed in the cloud are discussed. State-of-the-art deep learning applications in the financial domain and their limitations are reviewed, providing insights for cloud engineering. Real-time risk assessment and market forecasting require cloud-based deep learning that does not reside on edge computing but rather leverages the scalable compute, storage, and orchestration resources of the cloud. Ingestion of structured and unstructured data, as well as the engineering of features for risk and forecasting signals, are foundational components of these cloud solutions. The cloud-based reinforcement-learning-driven risk assessment communications the risk of large losses and assists in strategic decision-making for high-net-worth individuals. Time-series modeling approaches deployed in the cloud achieve accurate predictions of future financial instrument price movements. With further improvements to achieve low-latency predictions, the ensemble forecasting of multiple correlated financial instruments provides information on future price movements and uncertainty quantification. Real-time risk assessment communications the risk of large losses and assists in supporting decisions for high-net-worth individuals. These communications utilize deep reinforcement learning for the risk assessment of personalized portfolios. Accurate predictions of price movements—a key component of speculative trading—are achieved with cloud-based architectures. State-of-the-art time-series modeling approaches based on recurrent neural networks, Transformers, and their hybrids are real-time solutions with low latency for Time-series modeling. Market forecasting models provide future price movements for correlated financial instruments, and the ensemble prediction framework supports simultaneous forecasts for multiple assets. Abundant information is conveyed by ensemble predictions with a probabilistic representation, yielding quantified uncertainty for prudent trading. Cloud-based computing is increasingly prevalent in diverse domains. Nevertheless, real-time risk assessment and market forecasting in finance with the prevalent cloud-based deep-learning approach remain largely unexplored.

Keywords: Cloud-Based Deep Learning, Real-Time Financial Risk Assessment, Market Forecasting Systems, Financial Decision Support, Cloud Computing in Finance, Scalable Financial AI Architectures, Structured and Unstructured Financial Data Ingestion, Feature Engineering for Risk Signals, Deep Reinforcement Learning in Finance, Personalized Portfolio Risk Assessment, High-Net-Worth Individual Decision Support, Time-Series Modeling in the Cloud, Low-Latency Financial Prediction, Recurrent Neural Networks in Finance, Transformer-Based Financial Models, Ensemble Market Forecasting, Correlated Asset Prediction, Probabilistic Forecasting and Uncertainty Quantification, Strategic Trading Decision Support, Cloud-Native Financial Analytics.

Abstract: Urban mobility is a major concern for large metropolitan areas. Cloud-enabled Artificial Intelligence (AI) technology may help to manage travel demand and offer a more sustainable urban mobility model for smart cities. An AI-driven, cloud-enabled architectural framework applied to traffic management and control business processes is proposed. It integrates four AI-based predictive models with real-time traffic control and incident-response systems. Different data-driven use cases, proved in two metropolitan areas, illustrate the applicability of this framework in real operational environments. Results show that this approach is capable of managing urban traffic in real time. In addition, it demonstrates how AI-based models can be developed, deployed, and operated within cloud environments, offering a decision-support capability. The central role of predictive traffic management systems in the cloud-enabled AI model for smart cities is assessed. Demand management processes and the integration of Mobility-as-a-Service platforms are also analyzed. These are mandatory steps to offer a sustainable traffic model for large metropolitan areas. Finally, other aspects such as environmental protection and energy consumption in urban mobility are examined. Traffic management by predictive systems enables a more accurate confrontation of real traffic conditions, improving the energy, environmental, and resilience contexts.

Keywords: Smart City Traffic Management, Cloud-Enabled Artificial Intelligence, AI-Driven Urban Mobility, Predictive Traffic Management Systems, Real-Time Traffic Control, Intelligent Incident Response, Urban Demand Management, Mobility-as-a-Service (MaaS), Cloud-Based Decision Support Systems, Data-Driven Traffic Optimization, Sustainable Urban Mobility Models, Metropolitan Traffic Analytics, AI Deployment in Cloud Environments, Traffic Prediction Models, Smart Transportation Architectures, Energy-Efficient Urban Mobility, Environmental Impact Reduction, Resilient Transportation Systems, Integrated Traffic Control Frameworks, AI-Enabled Smart Cities.