International Journal of Scientific Research in Computer Science, Engineering and Information Technology
International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT) is peer-reviewed, online international journal published Bimonthly by Shauryam Research Institute. IJSRCSEIT is a highly-selective journal, covering topics that appeal to a broad readership of various branches of Computer Science, Engineering, Information Technology and related fields. The Journal has many benefits all geared toward strengthening research skills and advancing academic careers. Journal publications are a vital part of academic career advancement. To maintain a high-quality journal, manuscripts that appear in the IJSRCSEIT Articles section have been subjected to a rigorous review process. Email addresses for submitting articles are ijsrcseit@gmail.com, editor@ijsrcseit.com JOURNAL INFORMATION Title : International Journal of Scientific Research in Computer Science, Engineering and Information Technology Frequency : Bimonthly E-ISSN : 2456-3307 Publisher : Shauryam Research Institute Refereed : Peer Review Copyright : Technoscience Academy, India Starting Year : 2016 Subject : Computer Science, Engineering and Information Technology Language : English Distribution : Open Access Publication Format : PDF & HTML -> Online Document Identifier Type : DOI (https://doi.org/10.32628/IJSRCSEIT ) Email Id : editor@ijsrcseit.com, ijsrcseit@gmail.com Mobile No. : +1 (404) 860-9920 (WhatsApp support only) Website : https://ijsrcseit.com
AI-Driven CRM: Integration of Cloud Architecture and Intelligent Automation in Enterprise Customer Management
This article examines the transformative impact of artificial intelligence on Customer Relationship Management (CRM) systems through the integration of cloud architecture, system integration, and process automation capabilities. It explores how cloud-based CRM platforms provide scalable infrastructure supporting advanced functionality while eliminating traditional deployment barriers. The discussion addresses critical integration challenges and methodologies, creating unified customer data ecosystems across enterprise applications. Particular attention is given to AI applications, including natural language processing for conversational interfaces, machine learning algorithms for lead scoring, and predictive analytics for customer behavior modeling. The article presents implementation frameworks balancing automation efficiency with appropriate human intervention points, highlighting organizational considerations beyond technical requirements. Case examples illustrate successful deployments across industries, including healthcare, financial services, and manufacturing, demonstrating business impact through enhanced customer experiences and operational efficiencies. Governance frameworks ensuring ethical AI implementation and data quality maintenance are examined alongside future development directions. In addition to identifying ongoing learning opportunities in this quickly developing field, the thorough investigation offers practitioners implementation advice.
Copyright (c) 2025 International Journal of Scientific Research in Computer Science, Engineering and Information Technology https://creativecommons.org/licenses/by/4.0
Understanding the Basics of AI Driven Cloud Services
Artificial Intelligence (AI) has transformed cloud computing into smarter, automated, and more efficient systems. AI-driven cloud services leverage machine learning, natural language processing, and predictive analytics to optimize resource allocation, enhance security, and streamline data management. These intelligent services improve scalability, reduce operational costs, and increase computational efficiency across industries. This paper presents the fundamental concepts of AI-driven cloud services, highlighting their key components, benefits, and real-world applications. It explores how AI enhances cloud operations through automated monitoring, self-healing infrastructures, and intelligent decision-making. The study also examines AI’s role in cloud security, data analytics, and personalized user experiences. Major providers such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud integrate AI tools for automated insights, fraud detection, and predictive maintenance. Despite its advantages, AI-based cloud computing faces challenges like data privacy, ethical concerns, and high computational demands. Addressing these through strong governance and innovative architectures can shape the future of AI-driven cloud ecosystems.
AI-Powered Regulatory Compliance and Fraud Detection in Automated Payment Systems: A Review of Current Approaches and Future Directions
The rapid digitalization of financial transactions demands robust, adaptive, and intelligent systems capable of ensuring regulatory compliance and preventing fraudulent activity in automated payment environments. This paper presents a comprehensive review of existing approaches to AI-driven compliance monitoring and fraud detection, analyzing methods across rule-based, machine learning (ML), natural language processing (NLP), and hybrid frameworks. Through a synthesis of literature and industry practices, we identify major gaps related to scalability, explainability, regulatory language interpretation, and real-time adaptability. Building upon these insights, we propose a conceptual AI-powered framework integrating ML-based anomaly detection with NLP-based compliance automation. The proposed model highlights future directions for real-time compliance assurance and proactive fraud mitigation in next-generation payment ecosystems.
Artificial Intelligence Applications in Travel Technology and Financial Management Systems
The integration of artificial intelligence (AI) within travel technology (TravelTech) and financial management systems (FinTech) is reshaping the global service ecosystem through automation, personalization, and predictive intelligence. This review explores the convergence of these two sectors, highlighting how AI enables intelligent travel planning, dynamic pricing, fraud detection, and automated financial processes. The study examines the evolution of data-driven systems that leverage machine learning, natural language processing, and predictive analytics to improve customer experience, operational efficiency, and decision-making. It further discusses the convergence of AI-enabled payment systems, customer profiling, and loyalty management frameworks that link travel behavior with financial transactions, thereby fostering unified digital ecosystems. Ethical and regulatory challenges such as data privacy, algorithmic bias, and model governance are addressed, emphasizing the need for transparency and accountability in AI deployment. Emerging trends including generative AI, digital twins, and autonomous systems are identified as transformative forces driving innovation across TravelTech and FinTech. The review concludes that the strategic adoption of AI requires strong governance, interoperability, and cross-sector collaboration to ensure sustainable, ethical, and scalable integration. Ultimately, the study provides a comprehensive framework for understanding how AI-driven convergence enhances value creation, efficiency, and resilience within the interconnected domains of travel and finance.
Enhancing Stock Market Forecasting with a Stacked LSTM Model Integrating Technical Indicators and Market Sentiment
Background: The accurate prediction of stock prices remains a significant challenge due to market volatility, non-linear patterns, and the influence of diverse factors like economic indicators and investor sentiment. Traditional models like ARIMA and shallow machine learning models often fail to capture these complexities. Methods: This study develops and evaluates a Stacked Long Short-Term Memory (LSTM) neural network model for stock market forecasting. The model integrates heterogeneous data sources, including technical indicators, macroeconomic variables, and sentiment scores derived from financial news headlines using FinBERT. The architecture employs two LSTM layers, dropout regularization, and Bayesian hyperparameter tuning. We trained the model on a diverse dataset of four major indices (S&P 500, NASDAQ, FTSE 100, Nikkei 225) from 2010 to 2024 and evaluated its performance in a zero-shot setting on the unseen Shanghai Stock Exchange (SSE) Composite index. Results: The proposed Stacked LSTM model significantly outperformed traditional benchmarks (ARIMA, GARCH, SVM). It achieved an R² of 0.956 and the lowest RMSE (105.18) on the test set. In the critical zero-shot cross-market evaluation on the SSE, the model demonstrated strong generalization with an R² of 0.93, whereas ARIMA and GARCH failed entirely (R² ≈ 0). The incremental addition of technical indicators and sentiment features progressively improved predictive accuracy, with sentiment reducing short-term prediction errors by approximately 15% during volatile periods. Conclusion: The results confirm that a deep Stacked LSTM architecture, enriched with multi- source features and robust regularization, provides a superior and generalizable framework for stock market forecasting, offering substantial improvements over conventional methods.
ABS-PoNR: Attribute-Based Signature scheme with Proof of Non-Revocation
In this paper, we introduce a novel Attribute-Based Signature scheme with Proof of Non-Revocation (ABS-PoNR) that enhances security and fine-grained access control in dynamic environments. Our scheme extends traditional attribute-based signatures by incorporating a non-revocation mechanism for attributes and unique user identifiers (UUIDs). It ensures that signatures remain valid only if the signer’s attributes and UUIDs have not been revoked. The proof of non-revocation allows verifiers to check the revocation status without compromising privacy and rejects signature if the proof is invalid or attributes are revoked. Utilizing bilinear pairing-based cryptography, the ABS-PoNR scheme enables efficient and secure attribute-based signing. Our security analysis is based on the Computational Diffie-Hellman (CDH) assumption and proves the unforgeability of the scheme which makes it computationally infeasible to forge valid signatures. We also discuss the efficiency and practical applicability of the ABS-PoNR technique in scenarios requiring dynamic attribute management and frequent updates to access rights. It marks a significant advancement in attribute-based cryptography.
Artificial Intelligence in Cancer Detection : A New Era of Precision Diagnostics
Artificial intelligence has revolutionized cancer detection by integrating advanced computational techniques with medical imaging and clinical data analysis. AI-driven diagnostic systems employ a systematic methodology encompassing data acquisition, image preprocessing, region of interest segmentation, feature extraction through convolutional neural networks, and sophisticated classification algorithms. These systems analyze diverse imaging modalities including mammograms, CT scans, MRI, colposcopic, and histopathological images, often achieving diagnostic accuracies exceeding 90% that match or surpass experienced clinicians. The integration of multimodal data combining imaging with genetic profiles, biomarkers, and patient histories enhances diagnostic precision and enables personalized risk assessment. AI's scalability addresses critical healthcare disparities by democratizing expert-level screening in underserved regions lacking specialized medical infrastructure. Despite challenges in data standardization, algorithmic interpretability, and regulatory approval, rigorous validation through cross-validation and clinical trials ensures safety and efficacy. AI represents a transformative partnership with human expertise, promising earlier detection, improved accuracy, and reduced global cancer mortality through accessible, consistent, and precise diagnostic capabilities.
A Survey on Master–Slave FPV Drone Systems for Military Reconnaissance and Coordinated Aerial Operations
Research on master-slave and swarm-based drone architectures has accelerated due to the growing need for intelligent and coordinated unmanned aerial vehicle systems. Conventional drones' dependability in dynamic or GPS-denied environments is limited by their reliance on GPS and centralized ground stations. The literature on First-Person View and master-slave UAV systems is reviewed in this paper, with an emphasis on developments in swarm intelligence, formation control, and communication protocols. Numerous studies show how to improve UAV autonomy and coordination by combining adaptive communication models, block chain security, and reinforcement learning. The Master-Slave FPV Drone System for Military Reconnaissance is also covered in the paper as a real-world case study that uses nRF24L01 transceivers, Arduino Pro Mini microcontrollers, and MPU6050 IMUs to accomplish GPS-independent coordination. In addition to outlining a future roadmap toward robust, AI-driven multi-drone networks for defence and surveillance applications, the survey identifies research gaps in the areas of energy efficiency, secure communication, and large-scale swarm synchronization.
Advancing Digital Advertising: A Technical Deep Dive into Real-Time Ad Injection and HLS Streaming
Over-the-Top (OTT) platforms have become more abundant, and at the same time, with the support of adaptive streaming protocols such as HTTP Live Streaming (HLS), a very dynamic but complex environment has emerged for digital advertising. There is high demand for scalable, targeted ad monetization, which would also be non-disruptive. Current methodologies not only face resistance from ad blockers but also introduce latency-induced buffering as well as fragmentation between legacy broadcast and modern digital ecosystems. This paper presents an original unified framework for real-time ad injection within HLS environments. They are three-fold: improved Server-Side Ad Insertion (SSAI) architecture capable of facilitating dynamic ad decisioning and seamless ad stitching, a hybrid model intended to connect advanced cloud-native ad servers to legacy broadcast infrastructures via standards-based workflows; technical innovations, among which are Low-Latency HLS (LL-HLS) optimizations for reducing buffering as well as opening up the transparency of the ad-tracking systems. Proof-of-concept results show a drastic reduction in the ad-failure rate, plus improved targeting accuracy and consistency of playbacks from different devices. In the end, this work acts as an easy-to-use and scalable pathway for media houses and content providers to maximize their revenues by ensuring continuous, seamless viewing experiences that put real-time HLS ad insertion at the heart of next-generation digital advertising.
Wearable AI System for Real Time Threat Detection and Dual Spectrum Analysis
The development of autonomous wearable perception devices with real-time environmental awareness has been sped up by the convergence of robotics, embedded systems, and artificial intelligence (AI). However, the majority of current assistive and surveillance systems are still limited by their static deployment, reliance on networks, and subpar performance in low-visibility scenarios. The theoretical underpinnings and literature review of a wearable artificial intelligence system for dual-spectrum analysis and real-time threat detection are presented in this research. Mounted on a small robotic arm platform, the device incorporates a dual-camera vision module that combines an RGB sensor for daylight and an infrared night-vision sensor for darkness. Optimized lightweight deep-learning models converted to TensorFlow Lite are used to handle the visual data fully on edge devices like the Google Coral Dev Board or Raspberry Pi 4. Users without cloud access can receive instant spatial alerts thanks to the design's ability to detect objects on-device and provide multimodal feedback (tactile and audio). Defense surveillance for autonomous threat identification and assistive navigation for visually impaired people are the two applications that the framework is intended for. This paper highlights research gaps in wearable edge-AI systems for adaptive multimodal perception by concentrating on the theoretical underpinnings and literature synthesis supporting the development.
Framework for Integrating Climate Data and Health Outcomes to Improve Mortality Risk Prediction Systems
Climate variability exerts profound effects on population health, with temperature extremes, air pollution, and shifting rainfall patterns significantly influencing morbidity and mortality risks. Traditional health surveillance systems often underrepresent the role of environmental determinants, limiting their predictive capacity for anticipating adverse health outcomes. Developing a framework that integrates climate data with health outcomes offers a promising pathway for advancing mortality risk prediction systems. Such a framework recognizes that environmental exposures—such as heatwaves, vector-breeding conditions, and deteriorating air quality—interact with demographic and health system vulnerabilities to amplify risks of premature death. The proposed framework emphasizes the incorporation of diverse climate datasets, including satellite observations, meteorological records, and long-term climate projections, alongside clinical and epidemiological data. By aligning these data streams, the framework enhances the capacity to identify at-risk populations and quantify exposure–response relationships with greater precision. Advanced analytical techniques, including machine learning, spatiotemporal modeling, and system dynamics, further enable the detection of nonlinear interactions and lagged effects that are often overlooked in conventional models. Importantly, the integration of climate information supports early warning systems by providing timely risk assessments that can inform public health planning and rapid response measures. The application of this integrative framework is particularly valuable for vulnerable regions where climate-sensitive diseases, resource constraints, and limited health infrastructure converge. Beyond forecasting, it supports targeted interventions such as heat action plans, pollution advisories, and resource allocation strategies designed to reduce preventable deaths. Ultimately, embedding climate data within health risk prediction systems shifts the paradigm from reactive to proactive public health, fostering resilience against both acute and chronic climate-related health challenges. This framework represents a critical step toward building comprehensive, data-driven strategies to mitigate mortality risks in the era of accelerating climate change.
Copyright (c) 2024 International Journal of Scientific Research in Computer Science, Engineering and Information Technology https://creativecommons.org/licenses/by/4.0
Cross-Border Framework for Effective Presentation to Investment Committees across Multiple Jurisdictions
Globalization of capital markets has intensified the need for investment committees to evaluate opportunities across multiple jurisdictions, each with distinct regulatory, cultural, and economic contexts. This develops a cross-border framework for effective presentation to investment committees, aimed at enhancing comparability, transparency, and decision-making efficiency in multi-jurisdictional environments. The framework addresses key challenges such as fragmented disclosure standards, heterogeneous risk perceptions, and jurisdiction-specific legal and governance requirements. The proposed approach is built on three core principles: clarity, comparability, and adaptability. Clarity is achieved through standardized financial metrics, explicit assumptions, and transparent documentation of methodologies. Comparability is enhanced by benchmarking opportunities against both global and local peers, ensuring that investment committees can evaluate relative performance across geographies. Adaptability ensures that presentations are tailored to regulatory environments and cultural decision-making norms, while maintaining consistency with overarching institutional strategies. The framework integrates multiple layers of analysis. A regulatory and compliance layer ensures adherence to both domestic mandates and international best practices such as IFRS, ISSB, and Basel standards. A financial and risk analytics layer captures macroeconomic trajectories, capital structures, sectoral opportunities, and scenario-based stress tests across diverse jurisdictions. ESG integration further strengthens the framework by embedding sustainability metrics and governance indicators aligned with international disclosure norms. Finally, technology-enabled tools—such as collaborative cloud platforms, real-time dashboards, and AI-driven analytics—facilitate cross-border consistency, transparency, and efficiency. This argues that adopting a harmonized yet adaptable presentation framework enhances investor confidence, reduces regulatory fragmentation, and improves the resilience of decision-making processes. By combining financial rigor, regulatory alignment, and technological innovation, the framework provides investment committees with a robust, forward-looking tool to navigate the complexities of global capital allocation.
Translating EU AI Act Provisions into U.S. Enterprise Frameworks for Ethical and Responsible AI Use
The European Union Artificial Intelligence Act represents a landmark regulatory framework that establishes comprehensive standards for artificial intelligence governance, risk management, and ethical deployment across diverse sectors. As organizations worldwide grapple with the complexities of responsible AI implementation, the question of how to translate these European provisions into practical frameworks for American enterprise environments becomes increasingly critical. This research examines the systematic translation of EU AI Act provisions into actionable governance structures, compliance mechanisms, and operational protocols specifically tailored for U.S. enterprise contexts. Through comparative analysis of regulatory environments, stakeholder interviews, and organizational case studies, this study identifies key adaptation strategies that enable American corporations to leverage European AI governance principles while maintaining compatibility with domestic legal frameworks and business practices. The research methodology employs a mixed-methods approach combining regulatory analysis, expert consultation, and empirical evaluation of implementation strategies across multiple industry sectors including healthcare, financial services, manufacturing, and technology. Primary data collection involved structured interviews with 47 enterprise AI governance specialists, compliance officers, and legal practitioners representing organizations with annual revenues exceeding $1 billion. Secondary analysis incorporated extensive review of regulatory documentation, industry standards, and academic literature spanning 2018-2023 to establish comprehensive understanding of evolving AI governance landscapes. Key findings reveal that successful translation of EU AI Act provisions requires systematic adaptation across five critical dimensions: risk assessment methodologies, compliance monitoring systems, stakeholder engagement protocols, documentation requirements, and audit frameworks. Organizations demonstrating highest implementation success rates employed phased deployment strategies, comprehensive stakeholder training programs, and robust technological infrastructure supporting automated compliance monitoring. Notably, enterprises that proactively established cross-functional AI governance committees prior to formal implementation achieved 34% faster deployment timelines and 28% lower compliance-related operational costs compared to organizations adopting reactive approaches. The study identifies significant challenges in translating European regulatory concepts into American business contexts, particularly regarding cultural differences in risk tolerance, regulatory enforcement expectations, and organizational hierarchy structures. However, organizations successfully navigating these challenges developed innovative hybrid frameworks that preserved core ethical principles while accommodating domestic operational requirements. These frameworks demonstrated measurable improvements in AI system transparency, stakeholder trust, and long-term operational sustainability. Practical implications for enterprise leadership include the development of comprehensive AI governance roadmaps, investment in specialized compliance technologies, and establishment of cross-border regulatory monitoring capabilities. The research concludes that organizations implementing systematic translation frameworks achieve superior outcomes in ethical AI deployment, regulatory compliance, and stakeholder confidence compared to ad-hoc approaches. These findings contribute to the growing body of knowledge surrounding international AI governance harmonization and provide actionable guidance for enterprise practitioners navigating complex regulatory landscapes.
Applying Predictive Analytics to Streamline Warranty Claims and Strengthen Organizational Risk Management
The contemporary business landscape demands sophisticated approaches to warranty claims management and organizational risk mitigation, particularly as product complexity increases and customer expectations evolve. This research examines the transformative potential of predictive analytics in streamlining warranty claims processes while simultaneously strengthening organizational risk management frameworks. Through comprehensive analysis of industry practices, technological implementations, and empirical evidence from multiple sectors, this study demonstrates how predictive modeling can fundamentally reshape warranty operations from reactive to proactive paradigms. The research methodology employed a mixed-methods approach, incorporating quantitative analysis of warranty claims data from diverse industries and qualitative assessments of organizational implementations of predictive analytics solutions. Data sources included warranty claims databases from automotive, electronics, and appliance manufacturers spanning 2019-2023, representing over 2.3 million individual claims transactions. Advanced machine learning algorithms, including random forests, neural networks, and gradient boosting models, were evaluated for their effectiveness in predicting claim likelihood, processing time optimization, and fraud detection capabilities. Key findings reveal that organizations implementing predictive analytics in warranty management achieved average cost reductions of 23-31% while improving customer satisfaction scores by 18-27%. The most significant improvements occurred in claim processing efficiency, where predictive models reduced average resolution times from 14.3 days to 6.8 days. Fraud detection capabilities improved dramatically, with false positive rates declining by 45% and fraud identification accuracy increasing to 94.7%. Risk management benefits extended beyond warranty operations, with organizations reporting enhanced inventory management, improved supplier relationship management, and more accurate financial forecasting capabilities. The study identifies several critical success factors for effective implementation, including data quality assurance, cross-functional collaboration, and continuous model refinement processes. Organizational readiness factors, including technological infrastructure, workforce capabilities, and change management strategies, emerge as crucial determinants of implementation success. The research also reveals significant challenges, particularly in data integration complexities, privacy concerns, and the need for specialized analytical expertise. Strategic recommendations include phased implementation approaches, emphasizing pilot programs in specific product lines before enterprise-wide deployment. Organizations should prioritize data governance frameworks, invest in employee training programs, and establish continuous improvement mechanisms for predictive models. The integration of artificial intelligence and machine learning capabilities with existing warranty management systems requires careful consideration of technical architecture, data security, and regulatory compliance requirements. The implications for organizational risk management extend far beyond warranty operations, encompassing strategic planning, operational efficiency, and competitive advantage considerations. Predictive analytics enables organizations to transform warranty costs from unavoidable expenses into strategic insights for product development, quality improvement, and customer relationship enhancement. This transformation represents a fundamental shift from cost center to value driver perspectives on warranty operations. Future research directions include investigation of emerging technologies such as Internet of Things integration, blockchain-based warranty tracking, and advanced artificial intelligence applications in predictive maintenance scenarios. The potential for real-time warranty analytics, enabled by connected products and continuous data streams, presents opportunities for even more sophisticated risk management capabilities.
Adaptive Load Balancing Techniques for Improved Cloud Service Performance
Cloud computing has emerged as a fundamental platform for delivering scalable, flexible, and cost-effective computing services. However, the rapid growth of users and heterogeneous workloads often leads to challenges such as uneven resource utilization, performance degradation, and service-level agreement (SLA) violations. Load balancing plays a crucial role in overcoming these issues by efficiently distributing incoming workloads across multiple servers to optimize resource usage, minimize response time, and ensure system reliability. This paper focuses on adaptive load balancing techniques that dynamically adjust to fluctuating workloads and varying system conditions, unlike traditional static approaches. By leveraging machine learning algorithms, heuristic models, and feedback-driven optimization strategies, adaptive techniques enable real-time decision-making for efficient task allocation. The study highlights key methods, including predictive workload analysis, adaptive VM migration, and hybrid balancing algorithms, that improve system throughput, reduce latency, and enhance overall cloud service performance. The findings demonstrate that adaptive load balancing is essential for achieving high availability, fault tolerance, and sustainable cloud operations, making it a promising approach for next-generation cloud infrastructures.
Intelligent and Adaptive Load Balancing in Cloud Environments: A Review
Cloud computing delivers scalable and flexible services, but fluctuating workloads and heterogeneous resources pose significant challenges to efficient performance. Load balancing is essential for distributing tasks evenly across resources to improve utilization, reduce response time, and maintain service-level agreements (SLAs). Traditional methods are often static and limited in adaptability, whereas intelligent and adaptive strategies leverage machine learning, heuristic optimization, and hybrid techniques to provide dynamic decision-making and resilience. This review explores recent advancements in intelligent and adaptive load balancing approaches, highlights key performance metrics such as throughput, latency, and energy efficiency, and identifies open issues related to scalability, heterogeneity, and integration with edge and multi-cloud systems. The study concludes that adaptive and intelligent load balancing is vital for optimizing cloud service performance and ensuring reliability in next-generation computing environments.
Next-Generation Recycling: Automated Waste Sorting Using Object Detection and Classification Models
In today’s world, appropriate waste sorting is a crucial aspect of effective waste management which is essential to the sustainability of the environment. Manual labour is a significant aspect of conventional waste sorting processes, which very often leads to human errors, inefficiencies, and is expensive to implement. With the rapid increase in waste generation, there is an increasing need for smarter and more reliable sorting systems. This project outlines a smart waste sorting system with the ability to automatically identify and sort waste items into the following categories: paper, plastic, glass, metal, and organic waste, using deep learning, specifically the VGG16 neural network. The system uses a well-balanced dataset made up of various waste items images to train the model, and makes use of image pre-processing, and augmentation to improve the weight. The system also integrates the YOLO (You Only Look Once) deep learning algorithm in order to create a real time sorting s ystem for improved functionality for practice. The collaborative use of VGG16 and YOLO not only increases accuracy, but also ensures speed and reliability. The general aim of the approach is to reduce human resources and sorting errors and promote more environmentally responsible waste disposal.
Fusion-Based Deep Learning Approach for Accurate Cardiovascular Disease Diagnosis Using ECG and Structured Data
Cardiovascular disease (CVD) continues to be a major global health issue with a significant burden in mortality and morbidity. Ideally, earlier and more accurate diagnosis will improve patient outcomes and reduce health system burdens. Common diagnosis methods of CVD often use specific clinical data or clinical ECG interpretations which frequently lack interpretability and reliance on expert opinion. This paper presents an advanced deep learning based system that fuses structured clinical data with ECG images to improve the accuracy of predicting heart disease. The MLP used patient history while DenseNet was applied to extract features from ECG images demonstrating a multimodal system. By using both modalities together, clinicians can be more accurate in diagnosing heart disease by moving beyond binary classification towards multi-disease classification, which eliminates many of the issues with binary classifications. A more accurate system using multimodal data will reduce false positives, and provide greater generalisability across the patient population. This research introduced a scalable and automatic system which can be adapted into clinical situations in a clinical to support earlier intervention and better data driven decision making in heart health. This framework is a major leap forward in intelligent cardiovascular diagnosis, providing a whole-of- systems solution that considers multimodal data.
Artificial Intelligence Driven Colorectal Cancer Classification via Deep Learning Technique
Collateral cancer is a serious concern for patients with primary tumors, as the development of secondary tumours can significantly reduce survival rates and increase the complexity of treatment. Colon polyps are a common precursor to colorectal cancer, and early detection is critical for successful treatment. Traditional methods of polyp detection include colonoscopy and biopsy, which can be invasive and time-consuming. Machine learning algorithms have shown promise in detecting polyps in colonoscopy images, and in this study, we explore the use of convolutional neural network to detect polyps in colonoscopy images. Early detection and prevention of collateral cancer are crucial for improving patient outcomes, but accurately predicting the risk of secondary tumors can be challenging due to the complexity of cancer progression and the multitude of factors that can contribute to tumour development. Machine learning algorithms have shown promise in predicting cancer outcomes based on patient data, and in this study, we explore the use of a convolutional neural network (CNN) to predict the likelihood of collateral cancer in patients with primary tumors. The study uses a dataset of colonoscopy images, including both positive and negative cases of polyps. A CNN model is developed using this dataset to classify images as either positive or negative for polyps. The model is trained using a supervised learning approach, where the network learns from labelled examples of images with and without polyps. The accuracy of the CNN model is compared to other polyp detection methods, such as traditional image analysis techniques and other machine learning algorithms.
Sentiment Analysis Using Machine Learning
Sentiment analysis has changed completely in 2024, with developments in deep learning and machine learning, and multimodal approaches. This paper reviews ten recent studies that explore various sentiment analysis techniques, including transformer-based models (GPT-4, LLAMA 3, FinBERT), conventional techniques for machine learning (Nave Bayes, Logistic Regression), and multimodal frameworks integrating text and images. The findings suggest that large language models (LLMs) perform well in Learning situations with zero-shot and few-shot but struggle with complex sentiment understanding. Traditional models like Logistic Regression remain competitive in financial sentiment prediction, while multimodal approaches such as M2SA and topic-oriented models excel in image-text sentiment analysis. This review highlights The advantages and disadvantages of different techniques, supplying information for researchers and practitioners in choosing the most suitable approach for their particular uses.
Cloud Pattern Analysis Using AI and Machine Learning from Satellite Images
Cloud patterns play a critical role in understanding atmospheric processes, influencing weather systems, climate patterns, and environmental changes. The analysis of cloud formations using satellite imagery has become a cornerstone in meteorological and climate studies, aiding in weather prediction, storm tracking, and monitoring long-term climate changes. Clouds impact the Earth's energy balance by modulating solar radiation and trapping heat, making their accurate analysis essential for understanding both short-term weather fluctuations and broader climate phenomena. However, analyzing cloud patterns poses significant challenges due to the complexity of cloud formations, their diverse types, and the influence of dynamic environmental factors such as temperature, humidity, and wind currents. Satellite imagery, with its high spatial and temporal resolution, provides an invaluable source of data for studying clouds globally, but the large volume of data generated requires efficient and automated approaches for interpretation. Cloud pattern analysis plays a pivotal role in advancing weather forecasting, climate change monitoring, and environmental research. They often fail to model fine-grained spatial variations effectively, incur high computational costs, and face challenges such as overfitting, data scarcity, and class imbalance. These limitations hinder their ability to deliver reliable and scalable solutions for real-time applications. Existing systems struggle with limited accuracy in complex scenarios, an inability to capture spatial variations effectively, high computational costs, overfitting issues, and insufficient generalization Considering these challenges, we propose a hybrid deep learning framework that integrates CNNs for spatial feature extraction with U-Net++, a powerful segmentation-based model designed to enhance cloud pattern recognition. This automated, robust, and scalable approach leverages AI and machine learning advancements to analyze satellite images effectively. It addresses diverse cloud types, adapts to varying environmental conditions, and processes large datasets in real-time, providing highly accurate cloud behavior predictions. The proposed methodology improves precision and generalization in cloud pattern analysis, offering a transformative solution for real-world meteorological and environmental applications.
Revolutionizing Attendance Tracking with Artificial Intelligence (AI) Technologies
Among the most promising biometric technologies for non-contact, real-time person identification, facial recognition is quickly rising to the top. Because of how efficient and convenient it is, its use has expanded greatly in fields including security systems, access control, and surveillance. Manual roll calls and fingerprint-based systems are two examples of the outdated attendance techniques used in academic institutions. These alternatives are typically seen to be obtrusive, time-consuming, and vulnerable to proxy attendance. This study suggests a face recognition-based AI attendance system to automate and simplify the attendance process in educational environments, which would solve these constraints. A pair of well-known algorithms for real-time image processing the Haar Cascade Classifier for face identification and the Local Binary Patterns Histograms (LBPH) method for face recognition are used by the suggested system. The solution is built using OpenCV, Python, and a graphical user interface made using Tkinter. Administrative tasks such as student record management, face sample generation, recognition model training, and attendance logging are all accessible via the interface. A safe login mechanism is built into the system to ensure that only authorized users may access it. It performed consistently well, recognized objects quickly, and accurately under typical indoor lighting circumstances. By automating the process, decreasing human error, and doing away with the possibility of proxy attendance, this system provides a viable and effective substitute for conventional attendance tracking methods. It is well-suited for use in educational settings including classrooms and training canters due to its modular and expandable architecture.
Value-Chain Automation in Beverage Logistics: Throughput, Capacity, and Cost Avoidance via Queueing Models
The beverage industry faces increasing demand for operational efficiency across complex supply chains. This study investigates the role of value-chain automation in enhancing throughput, capacity utilization, and cost avoidance through queueing theory models. Using a combination of discrete-event simulation and analytical modeling, the research evaluates multiple automation strategies, including real-time scheduling, automated inventory replenishment, and robotic handling at bottleneck points. Findings indicate that integrating queueing-based automation can increase throughput by up to 22%, reduce average system wait times by 18%, and mitigate operational costs associated with idle resources and delays. The results further highlight the importance of prioritization strategies and capacity allocation in multi-echelon beverage logistics networks. Implications are drawn for both strategic planning and operational management, providing a data-driven foundation for technology investment decisions in beverage supply chains.
Sustainable Funding Framework for Student-Led Policy Advocacy and Dialogue in Universities
Student-led policy advocacy and dialogue play a critical role in shaping higher education governance, fostering civic engagement, and influencing policy development. However, the sustainability of such initiatives is often undermined by limited financial resources, fragmented funding mechanisms, and lack of institutional support. This paper proposes a sustainable funding framework for student-led policy advocacy and dialogue in universities. Using a systematic review of over 100 scholarly articles, policy reports, and practitioner studies published between 2000 and 2024, the study identifies funding strategies, stakeholder engagement approaches, and institutional mechanisms that enhance financial sustainability, accountability, and long-term impact. The framework integrates multi-source financing, performance-linked funding, participatory decision-making, and monitoring mechanisms, providing guidance for university administrators, student organizations, and policymakers. Findings indicate that a data-informed, participatory, and diversified funding approach strengthens the capacity of student-led initiatives to sustain impactful advocacy and dialogue.
Product-Led Growth Accounting: Linking Feature Adoption to Revenue Recognition and CAC Payback Dynamics
This study investigates the relationship between product-led growth (PLG) initiatives and financial performance metrics in subscription-based software businesses. Focusing on feature adoption, revenue recognition, and customer acquisition cost (CAC) payback dynamics, the research develops a framework to link user engagement patterns with financial outcomes. Using a mixed-methods approach combining longitudinal user analytics, cohort-based revenue analysis, and regression modeling, the study quantifies the impact of specific product features on customer monetization and cost recovery timelines. Findings indicate that early adoption of high-value features accelerates revenue recognition, shortens CAC payback periods, and strengthens unit economics. The research contributes to both academic literature and practical finance and product management, providing actionable insights for SaaS companies aiming to optimize product-led monetization strategies.
Controls for Cross-Border Payments Operations: Correspondent Banking Risk Reduction via End-to-End Monitoring
Cross-border payments have become an essential component of global financial networks, enabling businesses, financial institutions, and individuals to transfer funds across jurisdictions. However, correspondent banking relationships, which facilitate these transfers, carry inherent operational, compliance, and financial risks, including fraud, money laundering, sanctions violations, and settlement delays. This paper investigates the implementation of end-to-end monitoring mechanisms as a control framework for reducing risk in correspondent banking operations. Leveraging a combination of process mapping, risk assessment matrices, automated transaction monitoring tools, and exception management protocols, the study identifies the most effective approaches for enhancing transparency, operational efficiency, and regulatory compliance. The research employs a mixed-methods approach, including case study analyses of major financial institutions, quantitative modeling of payment flows, and simulation experiments to evaluate the performance of different monitoring architectures. Results demonstrate that comprehensive end-to-end monitoring can reduce operational risk by up to 35%, improve anomaly detection by 42%, and significantly mitigate financial exposure to correspondent bank failures. The findings contribute to the theoretical understanding of risk management in cross-border payment systems and provide actionable recommendations for financial institutions seeking to optimize their controls while maintaining operational efficiency. Furthermore, the study highlights the importance of integrating real-time monitoring, standardized data protocols, and multi-layered exception handling in a manner that balances regulatory requirements with business objectives. Overall, the paper presents a structured framework for risk reduction in correspondent banking operations that can be adapted across diverse financial networks.
Brain Cancer Cataloguing using MRI with Optimization-based Deep Learning
Brain cancer remains a critical health challenge, requiring accurate diagnosis and classification for improved survival rates and treatment planning. In this work, we are discussing about deep learning system based on optimization for automatic brain cancer cataloguing using MRI data.. The methodology integrates preprocessing, data augmentation, deep convolutional neural networks (3D CNNs), and metaheuristic optimization (Particle Swarm Optimization, PSO) for hyperparameter tuning. In this approach we are using A collection of multimodal MRI images (T1, T1ce, T2, FLAIR) with identified tumor subregions was used for the experiments. When it came to tumor segmentation, our model's overall accuracy was 97.1%, its sensitivity was 96.3%, its specificity was 98.2%, and its Dice similarity coefficient was 0.92. Comparative findings demonstrate advantages over traditional CNN and SVM models. These results demonstrate the potential of optimization-driven deep learning models in clinical judgment when diagnosing brain cancer.
Deep Learning with Jaya Optimization for Accurate and Automated Detection of Paddy Leaf Diseases: Advancing Smart Agriculture through Image Processing and AI-Driven Crop Health Monitoring
Paddy farming plays a vital role in global food security, yet its productivity is severely affected by various leaf diseases, particularly sheath blight, blast, and brown spot. Traditional methods of disease detection, relying on manual observation and laboratory analysis, are time-consuming, error-prone, and unsuitable for large-scale monitoring. This study proposes a deep learning–based framework optimized with the Jaya algorithm for the accurate and automated detection of paddy leaf diseases. The approach leverages convolutional neural networks (CNNs) integrated with image processing techniques to extract discriminative features from leaf samples, ensuring reliable classification across multiple disease categories. To enhance performance, the Jaya optimization algorithm is employed for hyperparameter tuning, thereby improving convergence speed, model precision, and generalization to heterogeneous field conditions. Experimental results indicate that the optimized CNN model achieved an overall classification accuracy of 94.6%, with significant improvements in precision and recall compared to conventional machine learning methods such as KNN, ANN, and SVM. The proposed system is not only computationally efficient but also scalable, making it suitable for real-time deployment using mobile and IoT-enabled platforms. This research contributes to the advancement of smart agriculture by enabling farmers to adopt proactive crop health management strategies, thereby reducing yield losses and promoting sustainable food production.
A Comparative Study of Digital Transformation in HR in the Healthcare Industry: India v/s Dubai (UAE)
The present paper compares the ways in which digital transformation is changing human resources (HR) in the healthcare industries in Dubai, United Arab Emirates, and India. The paper maps policy drivers, HR digital tools and platforms, workforce up skilling initiatives, significant impediments, and organizational outcomes in each setting using a literature-based comparative method (policy papers, peer-reviewed studies, government announcements, and industry reports in this case). While Dubai exhibits quick, government-driven, centralized digital initiatives with a heavy emphasis on artificial intelligence, up skilling, and institutional preparation, India's transformation is marked by extensive national interoperability efforts and inconsistent adoption among public and commercial providers. The report ends with actionable suggestions for legislators and HR directors in the healthcare industry to hasten the digitalization of HR in a fair, safe, and people-centered manner.
Design and Implementation of a Secure QR Code-Based Attendance Management System for Higher Education
Attendance monitoring is a critical component of academic administration, directly influencing student participation, performance, and institutional accountability. Traditional manual methods, such as roll calls and registers, are inefficient, prone to errors, and vulnerable to fraudulent practices like proxy attendance. This paper presents the design and implementation of a secure QR code-based attendance management system tailored for higher education institutions. The proposed solution integrates a mobile application for students, a web interface for lecturers and administrators, and a cloud-enabled backend with PostgreSQL and AWS for secure data management. Advanced features, including dynamic QR codes, facial verification, and geolocation, are incorporated to ensure accuracy, prevent malpractice, and safeguard data integrity. Developed using an agile methodology, the system was evaluated through usability, performance, and security testing. Results indicate that the system reduced attendance marking time by more than 60% compared to manual methods, minimized recording errors, and improved transparency in reporting. User feedback highlighted increased efficiency and reduced administrative workload. The findings demonstrate the potential of QR code-based systems as cost-effective, scalable, and secure solutions for digital attendance management. Future work will explore the integration of learning management systems and predictive analytics for enhanced student engagement insights.