Journal of Global Research in Electronics and Communications(JGREC)
About JGREC The Journal of Global Research in Electronics and Communications (JGREC) is an international, peer-reviewed journal committed to publishing high-quality research in electronics, communication systems, and computing technologies. Scope & Focus Areas JGREC welcomes cutting-edge research and review articles in (but not limited to) the following areas: Networking & Communication Systems 5G, 6G, IoT, MIMO SDN & NFV Optical & Satellite Networks IoT & Edge Computing Networks Ad Hoc, Sensor & Mesh Networks Cognitive Radio & Spectrum Management Cybersecurity & Cryptographic Systems Network Security & Intrusion Detection Blockchain & Secure Communication Cryptographic Algorithms & Data Privacy Quantum Cryptography Signal Processing & AI in Communications Digital Signal & Image Processing AI & ML for Communication Networks Speech & Audio Processing Wireless Sensor Networks & Data Analytics High-Performance & Cloud Computing Cloud & Edge Computing Quantum Computing in Networking Distributed & Parallel Computing Data Center Networks Embedded & IoT Systems Real-Time Embedded Systems IoT Protocols & Architectures Automation & Smart Systems Publication Model JGREC follows an open-access model to ensure global knowledge-sharing while maintaining rigorous peer-review standards. The journal publishes monthly, accepting original research papers, review articles, technical reports, and industry case studies.
Employee Attrition Prediction through Machine Learning: Advancing Human Resource Analytics and Workforce Management
The term "employee attrition" describes a certain reduction in a company's workforce due to a wide variety of factors. Employee attrition may also be caused by various factors in companies. Nevertheless, the human resource managers have to be able to identify the signs of employee attraction at the initial stages. Organizational losses may be caused by employee attrition due to numerous reasons that include disruption of work and activities required to be done, re-employment and re-training cost, and loss of information. This study examines the accuracy of machine learning and deep learning models in forecasting employee turnover using the IBM HR Employee Attrition dataset. A number of models, including CNNs, were trained and evaluated against other methods, including DT, SVM, and LR. After comparing CNN model to more conventional machine learning algorithms, the experimental findings show that CNN model outperformed them with respect to accuracy (ACC) (92), precision (PRE) (96.67), recall (REC) (87), and F1-score (F1) (91.58). These findings indicate the efficiency of CNN to identify multifaceted trends in staff data, and it is a prospective HR analytics tool. These results demonstrate that predictive models based on deep learning have the potential to deliver actionable information to an organization to lower attrition and reinforce strategic human resource management.
Copyright (c) 2025 Dr. Prithviraj Singh Rathore (Author) https://creativecommons.org/licenses/by/4.0
A Survey on Property Insurance Claims Using Machine Learning Models in Finance Sector
The complexity of property insurance claims and the rising number of risk factors involved in financial risk prediction are factors that have made proper early claim prediction an essential requirement among insurers. Conventional means of assessment cannot process heterogeneous data, and increases of fraud attempts and the changing conditions of the market, which demonstrates the need to use more efficient and data-focused approaches. This paper considers the property insurance terrain within the finance industry and investigates how supervised and unsupervised machine learning methodologies can be used to enhance the accuracy of claim prediction and the operational decision-making process. The survey summarizes the important considerations in claims, key important ML approaches applied in risk assessment, and issues in early prediction, such as data imbalance, scarcity of early information, and large-scale heterogeneous data. Combining the results of recent research, this paper enables systematic knowledge on the effects of ML in improving claim assessments, fraud detection, and risk modeling in property insurance. The results also bring out the existing constraints that should be overcome in order to attain credible and scalable early claim prediction.
Copyright (c) 2025 Prof. (Dr.) Abid Hussain (Author) https://creativecommons.org/licenses/by/4.0
Thermodynamic Analysis of Hybrid Energy Systems: A Review of Modeling and Optimization Strategies
The rising demand of energy and the rising concerns of the environment have provided a great demand of efficient, reliable and sustainable hybrid energy solutions. Conventional single-source energy systems tend to be very difficult on intermittency, conversion loss, and sub-optimal performance. To gain insight into the role of energy and exergy techniques in enhancing the operation of multi-source arrangements, in this paper, an overview of thermodynamic analysis, optimization strategies and recent advances in Hybrid Energy Systems (HES) is provided. According to the review, the following factors play a role in increasing the reliability and operational efficiency because of energy flow modeling, subsystem interaction and load-matching strategies: Moreover, more intricate exergy-based assessment can be used to detect irreversibilities, which allows designing a better system. Recent works concerning the hybrid storage integration, dynamics of PV winds, fuel-cell assisted systems, and microgrid control structures are also reviewed. The results suggest the increased applicability of optimization techniques, such as multi-objective and intelligent control algorithms, to the issues of efficiency, cost, and environmental constraints. In general, the research paper offers a systematic insight into the thermodynamic-based enhancements that are imperative in the design of new, high-performance HES architectures.
Copyright (c) 2025 Ram Pratap Singh (Author) https://creativecommons.org/licenses/by/4.0
Optimizing Quality Across Manufacturing and Storage Systems Strategies for Efficiency and Integrity
The manufacturing and energy industry are typical complex large systems which cover a long cycle such as design, production chain, production or operation, after-sales, etc. This review is a thorough analysis of the current quality management practices in manufacturing and warehousing, which have undergone changes due to Industry 4.0. It combines articles about classical quality control models, conditions of utilization of statistical methods, and cutting-edge artificial intelligence-software-powered optimization tools. The evaluation shows that these technologies such as machine learning, deep learning, reinforcement learning, and digital twins will contribute to the processes becoming more stable, defects' catching being improved, and predictive maintenance being the most efficient. Other topics discussed include the impact of cloud and edge computing on the capacity of real-time decision-making to be enlarged and the decision-making to be more efficient. Besides, in warehousing, the review points out smart systems, RFID, blockchain, temperature–humidity monitoring, and automation as the main factors that contribute to operational excellence and sustainability. All in all, it is a matter of how integrated, data-driven, and environmentally friendly practices that are able to handle the quality, reliability, and resilience concerns of modern industrial systems.
Copyright (c) 2025 Dr. Nilesh Jain (Author) https://creativecommons.org/licenses/by/4.0
Optimized Histopathological Image Classification for Breast Cancer Using Deep Learning Model
A significant cause of death very early among women is due to breast cancer. Since invasive ductal carcinoma (IDC) and breast cancer in general continue to be among the most common and fatal illnesses affecting women, prompt identification is crucial. Convolutional neural networks (CNNs), in particular, are particularly notable in automating image processing of breast cancer to the point that the images do not need human interpretation. The proposed project enhances the detection of IDC by creating a reliable diagnostic algorithm, in which deep learning and histopathology image analysis are used. On a large-scale dataset of IDCs containing more than 277,000 image patches, it applied a full preprocessing pipeline, including Otsu thresholding, tissue masking, Hematoxylin channel extraction, CLAHE enhancement, Gaussian denoising and gamma correction to improve image quality. Images with low tissue content were eliminated and SMOTE has been utilized to work on the imbalance of classes. This was a fully fine-tuned ResNet50V2 that was pretrained on ImageNet, and then combined with own dense layers and trained based on Adam optimizer and binary classification. Accuracy (acc) of the model was 88.52%, and precision (pre), recall (rec), and F1-score (F1) were greater than 88%. The effect of various architectural and training setups was studied using ablation and confirmed the efficiency of the chosen model. The comparative analysis yielded better performance in comparison to the existing CNN, CNN-GRU, and DenseNet-based models. The results indicate the possibility of AI-based breast cancer detection to be used in clinical practice, decrease errors in diagnosis, and increase the rate of early diagnoses. Combined application of the state-of-the-art image enhancement, balanced data depiction, and comprehensive study of the ablation using pre-trained CNN models is novel. This adds a high-performing and interpretable, yet clinically relevant, solution to the early diagnosis of breast cancer by using histopathology.
Copyright (c) 2025 Anjali Choudhary, Prof Rajkumar Sharma (Author) https://creativecommons.org/licenses/by/4.0
Applications of Deep Learning Approaches for Defect Identification: Trends, Challenges, and Future Directions
Quality assurance in many fields relies on the ability to detect defects. This is especially true in the manufacturing, construction, electronics, and medical diagnostics sectors. As deep learning becomes more prevalent in industrial inspection systems, its revolutionary effect on defect identification is being felt in many different fields. This article summaries current methods for detecting defects in manufacturing, infrastructure, and biomedical imaging using deep learning. It examines methods such as CNNs, generative models, attention mechanisms, and emerging transformer and diffusion frameworks, focusing on their effectiveness in surface anomaly detection. The paper categorizes approaches into supervised, unsupervised, and semi-supervised models, examining their suitability under different data conditions and deployment scenarios. It also highlights key implementation challenges, including data imbalance, annotation complexity, dataset variability, and generalization across domains. There are also important items like the quality of the dataset, model interpretability, scalability, and real-time performance that are mentioned to ensure the successful implementation of AI in real-world scenarios. Future directions include the emergent technologies of domain-adaptive learning, explainable AI and the deployment of AI at the edge, where it could be applied to real-time inspection. The review summarizes the recent advances and highlights the methodologies to enhance the transparency and reliability of deep learning-based defect detection and the emergence of intelligent and high-performance adaptive inspection devices in industries.
An Intelligent System for Identifying Fraud Phone Calls Using Machine Learning Algorithms
Phone fraud, also known as spam and unwanted calls, is a major problem in the telecom industry, costing millions of dollars annually all around the globe even as technology goes further and further ahead. The rise of phone scams, also known as spam using a mobile phone or a telephone, has become a common security risk for businesses and individuals alike. Machine learning and artificial intelligence have shown promising results in analyzing and detecting fraudulent or harmful phone calls. This paper gives an efficient method to forecast fraud calls through Call Detail Records (CDR) based on a selection of different machine learning and clustering algorithms. Data cleaning, normalization, feature selection, and data balancing based on SMOTE were deemed applicable to the CDR dataset that includes fundamental features, including caller ID, called ID, duration, cost, destination, and call type. The various models that have been used include DB scan, SVM, GCN, and XGBoost which revealed patterns of fraudulent behavior. The proposed XGBoost model had the best accuracy score of 96.7 %, illustrating that it could better recognize fraud than the others.
Copyright (c) 2025 Sandeep Gupta (Author) https://creativecommons.org/licenses/by/4.0
Redundancy of Collisions and Data Repetition A State-of-the-Art AES Algorithms with SHA-512 to Support a High-Integrity IoT Storage
The secure data transmission through the internet is a growing field of work that is vital in streamlining service delivery in modern infrastructure. The study destroys the age-old dilemma of optimization of redundancy in the data security across storage servers that serve data to multiple users and ownership systems. Older hashing algorithms such as MD5 that is prone to collision issues and lack of enough key lengths and vulnerability in sharing keys are often found in conventional methodologies. In order to eliminate these constraints, a new and effective algorithmic method is suggested. This model applies an Advanced AES-based encryption system (which is mentioned in the sources as an Asymmetric Encryption System) to guarantee high-level privacy and security of data. At the same time, SHA-512 is built in to calculate hash, allowing secure detection of files at a faster rate than the older system and redundancy detection through chunking of files on a file-level. The proposed methodology was simulated using MATLAB, and it was shown to work better than the old encryption methods in terms of such parameters as block size, hash output security, and time complexity.
Copyright (c) 2025 Chandralok Kumar, Dr Arvind Kourav, Nehul Mathur (Author) https://creativecommons.org/licenses/by/4.0
Lightweight Machine Learning Techniques forHardware Trojan Detection in IoT Devices
Hardware trojans are malicious pieces of software that attempt to prevent the normal operation of a chip, and are carefully engineered not to be detected during the silicon design and verification phase before it is actually sold to a consumer. The military, business, and academics are all looking into this new threat. Consequently, as a defense during chip deployment, run-time hardware Trojan identification is vitally needed. This work focuses on hardware Trojans that affect processor performance. This study presents a machine learning-based approach to detecting hardware Trojans in IoT devices by exploiting the Hardware Trojan Dataset. To preserve the most relevant features, the dataset was subjected to a thorough preparation procedure that included data cleaning, augmentation, label encoding, normalization, and feature selection using PCA. A number of models were evaluated, including Logistic Regression, ResNet, Decision Tree, and Long Short-Term Memory (LSTM). With the highest accuracy, precision, recall, and F1-score of 95.25%, 95.25%, 95.27%, and 95.25%, respectively, the LSTM model fared better than the others. The outcomes demonstrate how well feature selection and sequential deep learning architectures work together to capture temporal relationships in power trace data. Overall, the suggested approach shows a strong and trustworthy foundation for improving IoT hardware security against Trojan assaults.
Copyright (c) 2025 Dr. Chintal Kumar Patel (Author) https://creativecommons.org/licenses/by/4.0
Reviewing Effect of GAI (Generative Artificial Intelligence) on Patient-Centered Medicine
The growing presence of Generative Artificial Intelligence (GenAI) in contemporary healthcare is changing the paradigm of patient-centered medicine by facilitating hyper-personalized, accessible, and high-efficiency care. GenAI has the potential to create human-like content and create synthetic data, and clinicians and patients’ equal recipients of the power of intelligent virtual assistants, automated documentation, and personalized communication. This review is the discussion of the technological development of GenAI tools, their uses in the delivery of personalized care, and how they transform clinical workflows. The three of the most significant areas of influence are improved patient-provider interaction, more efficient medical note-taking with the help of AI scribes, and constant patient contact with virtual health assistants. Although the possible advantages are high, issues of data privacy, chart bias, and medical responsibility are severe issues of concern. The paper also elaborates on ethical, legal, and social considerations of GenAI in the field of healthcare noting the requirements of trust, transparency, and fairness. An extensive literature review introduces an active research movement and defines the spheres of its betterment. The results underline the facts that responsible GenAI and its application can largely promote patients-centered medicine in case it is properly regulated by sound ethics and empirical confirmations. The future studies should concentrate on risk reduction and on the increased value of clinicality.
Generative Artificial Intelligence (GenAI)
Reinforcement Learning Approaches for Energy-Efficient Embedded Systems: A Survey
The increased usage of embedded systems in areas like mobile computing, biomedical applications, industrial automation, and Internet of Things (IoT) has exacerbated the need to operate the embedded systems intelligently with a focus on energy efficiency under tight computational and power limitations. Reinforcement Learning (RL) is a potential solution to optimize power consumption and system performance with adaptive and data-driven real-time decision-making. This article is an in-depth survey of RL-based approaches to embedded systems design, with a special focus on model-free and model-based learning, energy-aware learning, and other lightweight learning algorithms in resource-constrained systems. Important uses are Dynamic Voltage and Frequency Scaling (DVFS), CPU scheduling, real-time object detection and autonomous control of embedded robotics. Simulation environments like MATLAB/Simulink, OpenAI Gym, and Network Simulator 3 (NS-3), as well as common hardware platforms, like ARM Cortex-M, NVIDIA Jetson, and Texas Instruments MSP430. In literature, it is possible to identify the presence of significant achievements, including up to 47% power savings and latency reductions with Deep RL and adaptive Convolutional Neural Networks (CNNs). Nonetheless, there remain barriers to safe policy learning, deployment in real-time, and reliability in changeable environments. The paper ends with some of the main research findings, such as a scalable RL framework, energy-aware reward functions, and sophisticated simulation techniques on next-generation intelligent embedded systems.
Dynamic Voltage and Frequency Scaling (DVFS)
Copyright (c) 2025 Dr. Amit Jain (Author) https://creativecommons.org/licenses/by/4.0
Early Prediction of Heart Failure via Supervised Machine Learning Models: A Performance Analysis
Heart disease is one of the most important illnesses face today, and most of its victims die. Diagnosing and treating heart disease is no easy task. Being precise and efficient is essential for this difficult diagnostic procedure. Prevention of mortality was achieved with the early diagnosis of cardiac illness. The rising incidence of cardiac problems has made the prediction of their onset one of the most difficult medical jobs to date. The Cleveland Heart sickness dataset is utilized in this study to develop a Convolutional Neural Network (CNN) based model for the prediction of cardiovascular sickness. The dataset is split into two parts, with a ratio of 80:20, after undergoing essential preprocessing operations such as handling missing values, feature selection by correlation analysis, Min-Max normalization, and categorical encoding.6 Convolutional neural networks (CNNs) are designed to automatically extract hierarchical information for correct classification using their pooling, fully connected, and convolutional layers. With an AUC close to 1.0, a recall of 99.97%, a precision of 99.98%, and an F1-score of 99.96%, the CNN model demonstrates outstanding discriminative power in experimental assessments. Compared to more conventional models like Multi-Layer Perceptron, Logistic Regression, and Random Forest. Findings demonstrate CNN's dependability and robustness in identifying patterns of cardiac disease, lowering the rate of incorrect predictions, and bolstering clinical diagnosis; hence, CNN is a scalable option for practical healthcare applications.
Copyright (c) 2025 Mr. Sachin Manekar (Author) https://creativecommons.org/licenses/by/4.0
A Systematic Review of Salesforce CRM Adoption in the Insurance Domain: Benefits, Challenges, and Future Trends
Insurance business is characterized by a rich history of adopting changes in technology, which encompass actuarial risk models and online computerization. This systematic review examines the use of Salesforce Customer Relationship Management (CRM) in insurance companies, including its advantages, implementation difficulties and new emerging trends. Salesforce CRM has been amenable in terms of flexibility and scalability, and it has a set of cloud-based apps that enable businesses to streamline their operations in terms of sales, service and marketing all rolled into one in Salesforce CRM. The platform can allow consolidating the customer data, and thus customer-centered engagement can be realized with actions based on analytical insights of their behavior. It also improves the efficiency with which operations are carried out by streamlining workflows, scheduling tasks and implementing a real-time dashboard that guides informed decisions. However, the incorporation of futuristic technologies e.g., artificial intelligence (AI) and machine learning (ML) poses its problems, e.g., employee training and skilling and issues relating to algorithmic transparency. The trend of the future implies the increased use of Salesforce Financial Services Cloud, the application of AI-based tools, such as Salesforce Einstein, to gain predictive analytics, and the integration of blockchain to ensure higher security and transparency. Furthermore, stronger connectedness with Insurtech, application of IoT to perform risk assessment in real-time, and the emergence of low-code/ no-code tools might influence the next challenge of CRM-based insurance services that may correspond to changes in regulatory requirements.
Copyright (c) 2025 Mr. Himanshu Barhaiya (Author) https://creativecommons.org/licenses/by/4.0
A Lightweight and Robust SMS Spam Filtering Model for Mobile Networks
Mobile messaging has skyrocketed with the proliferation of mobile users, which has brought about an upsurge in SMS (Short Message Service) spam. Unwanted and sometimes dangerous spam text messages are a major obstacle to mobile communication. Using the 5,574 tagged messages (ham or spam) from the SMS Spam Collection dataset, this study aims to detect spam via machine learning. Preprocessing the raw text data involves stemming, removing special characters, lowercasing, and tokenizing. Then, features are extracted using TF-IDF and dimensionality is reduced using Principal Component Analysis (PCA). A few performance metrics are utilized to assess KNN and NB, two classification models, including accuracy, precision, recall, F1-score, ROC curve, and confusion matrix. When it came to spam detection, KNN had the best accuracy (95.3% and 98.5%, respectively), whereas NB was the best in precision (97.6%, minimizing false positives). Examining KNN and NB alongside other classifiers, like Decision Tree (DT) and Random Forest (RF), reveals that they outperform them. The study concludes that both models are highly effective for SMS spam filtering, with KNN preferred in high-recall applications and NB in precision-critical scenarios, making them suitable for mobile and resource-constrained environments.
Copyright (c) 2025 Mr. Ram Pratap Singh (Author) https://creativecommons.org/licenses/by/4.0
Comprehensive Analysis of Deep Learning Architectures for Temporal Prediction in Financial Markets
A set of data arranged by the time of the year is referred to as a time series, and the standard method of the presentation of the changes in the thing during time. To help in decision-making, time series forecasting tries to predict the future values of time series by attempting to predict how these values alter with time. Four large companies on the Nasdaq index are Amazon, Google, Microsoft and Apple. In this work, perform an analysis of architectural deep learning models in the prediction of stock prices on the financial market, based on its stock projections. This included the normalization of data, trimming of outliers and sentimenting of data since getting Ticker Stock via Yahoo Finance until April 2025. Four models were analyzed using traditional measures, including LSTM, GRU, RNN and ARIMA. RMSE, MSE and MAE were performance indicators. It has been shown that LSTM models can perform better than rival models, when required to model the long-term dependencies of time series data. Sentiment analysis and consideration of technology substantially boost the ability of the model to predict. It is also seen that deep learning models have the potential to predict financial time series, and it is possible to propose methods to enhance the present system of predicting market volatility.
Security and Privacy Management in Cloud Computing: A Review of Risk, Compliance, and Governance Strategies
Cloud computing has now emerged as a paradigm-shifting practice in the IT field, enabling organizations to realize scalability, cost-effective and flexible operations. Nevertheless, its implementation poses a vital issue of security, privacy, governance, and compliance. The study summarizes the most important risk management principles in the context of cloud, including the integration of technical controls, compliance frameworks, and governance to secure sensitive data. Risks that are discussed in the paper include information breaches, denial-of-service, phishing, and key exposure and access control, authentication, and intrusion detection techniques are discussed. The requirements of maintaining compliance to regulatory frameworks like GDPR, HIPAA, and PCI DSS, industry-based standards like ISO 27001 and SOC 2 are provided as a detailed analysis of why continuous monitoring and adaptive governance are needed. Moreover, cloud governance strategies are discussed as critical tools which are needed to bridge security with organizational goals: centralized, federated, and hybrid. The results showed that a successful adaptation can only be achieved through well-established governance and regulations, as well as the application of emerging technologies to improve monitoring and hazard prevention, such as AI and blockchain. The future looks toward the development of automated, AI-based models to enable response to changes in threats to achieve resilient and compliant cloud operations
Copyright (c) 2025 Dr. Manish Jain (Author) https://creativecommons.org/licenses/by/4.0
Towards Proactive Cloud Security: A Survey on ML and Deep Learning-Based Classification Techniques on Network Intrusion Systems
Cloud computing's scalability, flexibility, and on-demand resource availability have revolutionized contemporary IT architecture. However, complicated security and privacy issues are brought up by its dynamic, distributed, and multi-tenant nature. When it comes to dealing with these changing threats, conventional rule-based intrusion detection systems (IDS) frequently fail. With an emphasis on proactive and adaptable security measures, this study offers a thorough review of IDS based on machine learning (ML) and deep learning (DL) for cloud settings. The review examines the effectiveness of supervised and unsupervised ML models, as well as advanced DL architectures, in real-time threat detection and anomaly prediction. Behavior-based analysis, feature selection techniques, and predictive models such as Facebook Prophet are highlighted for their role in improving detection accuracy. Key datasets, evaluation metrics, and deployment tools are also discussed to offer practical insights into the implementation of intelligent IDS solutions. Emphasizing a shift from reactive defense to predictive, self-learning strategies, this survey outlines the potential of AI-driven IDS to enhance scalability, accuracy, and resilience in cloud security. The findings aim to support future research and development of next-generation intrusion detection frameworks for secure cloud computing.
Transaction Analysis of Categorizing Ethereum Addresses Based on Advanced Supervised Machine Learning Approach for Predictive Modeling
Ethereum has become one of the most significant cryptocurrencies in terms of transaction volume. Given Ethereum's recent rise, experts and the cryptocurrency community are eager to learn more about how Ethereum transactions behave. A machine learning system-based methodology exists for addressing Ethereum addresses to enable transaction classification. The preprocessing of CEAT dataset containing 4,371 entries with 15 features utilizes a systematic process that selects relevant features then handles missing data along with using SMOTE for class distribution balancing and converting categorical elements to numbers. The data is standardized with Min-Max Scaler to improve model performance. Exploratory data analysis is done by visualizations such as Heat map, Histogram and Pair plot to compute the features which are correlated. The four models of machine learning algorithms include Decision Tree (DT), LightGBM, Neighbors Classifier (KNN), and CatBoost Classifier, are trained with the best optimized hyperparameters. The classification reports, confusion matrices, and ROC curves are used for model evaluation for each model. Comparing these models, LightGBM has the highest accuracy of 91.99%, second is CatBoost Classifier with 91.23%, the Decision Tree is 85.16%, and the KNN model 78.91%. An important benefit of this study is that the results show that it is possible to use a machine-learning approach for classifying Ethereum addresses to enhance transaction security and avoid fraud in decentralized financial systems.
Copyright (c) 2025 Raja Lodhi, Raj Kumar Sharma (Author) https://creativecommons.org/licenses/by/4.0
Deep Learning-Powered AI System for Automated Detection of Common Ocular Diseases from Medical Images
Glaucoma, cataracts, diabetic retinopathy, and age-related macular degeneration are among the most common eye disorders globally, although numerous others can also lead to vision loss or impairment. Despite its critical importance, early detection is difficult to achieve because of the sensitivity of the symptoms and the need for skilled interpretation of medical pictures. Using fundus images from the ODIR dataset, this work suggests an AI system powered by deep learning for automated identification of common eye illnesses. A multi-class classification utilizing a fine-tuned VGG-16 convolutional neural network is part of the technique, along with data augmentation to rectify class imbalance and preprocessing methods like CLAHE and Gaussian denoising. The model achieved impressive classification performance, with a recall of 90.00%, an F1-score of 92.00%, a precision of 94.00%, and an accuracy of 98.27%. Examining the suggested model in comparison to DenseNet121, CNN, and VGG-16 + CNN reveals that it offers a better equilibrium between recall and precision. In terms of scalable and dependable eye disease screening, these findings validate the efficacy and therapeutic promise of the suggested method. In addition to highlighting the significance of deep learning integration in medical imaging workflows, the paper proposes future improvements that involve attention mechanisms and ensemble learning to increase sensitivity.
AI-Powered Presentation Assistants: A Review of Tools for Real-Time Feedback and Content Optimization
Artificial Intelligence (AI)-powered presentation assistants are revolutionizing how users create and deliver presentations by offering real-time, personalized feedback on speech, gestures, and content organization. This paper reviews and classifies these tools into five key categories: speech analysis tools, facial expression and gesture recognition systems, content structure and visual optimization platforms, real-time audience engagement analytics, and automatic summarization modules. The study examines core AI technologies such as CTENet for emotion detection, transformer-based models for semantic coherence, and explainable AI (XAI) methods to enhance feedback interpretability. Key evaluation criteria include real-time processing, personalization, voice integration, privacy, and accessibility. Applications are explored across education, corporate training, and public speaking, demonstrating the tools’ ability to provide unbiased, scalable, and time-efficient performance evaluations. Despite their strengths, challenges remain, including limited adaptability, insufficient qualitative feedback, and context-blind emotion recognition. The review makes reference to future objectives of increased inclusivity, user experience, and pedagogical effectiveness. In general, presentation assistance AI can be a beneficial tool in the workplace and in general education, as it demonstrates substantial potential to improve communication processes and streamline content delivery.
Copyright (c) 2025 Dr. Bal Krishna Sharma (Author) https://creativecommons.org/licenses/by/4.0
Advanced Deep Learning System for Financial News-Based Sentiment Analysis in Stock Price Prediction
The complex connection between news-driven mood and macroeconomic factors, as well as the intrinsic volatility of financial markets, makes stock price prediction a tough undertaking. This study recommends a Bi-direcstional Long Short-Term Memory (Bi-LSTM) model for analyzing the tone of financial news as a means to improve stock price prediction. The Financial Phrase Bank dataset, which contains 4,848 news headlines categorized as positive, negative, or neutral financial, is a good place to start when developing the approach. Before dividing the dataset into training and testing subsets, data must undergo preprocessing operations such as text cleaning, tokenization, normalization, and label encoding. From what can tell from the experiments, the Bi-LSTM gets an F1-score of 90.62%, a recall of 90.11%, and a precision of 91.13%. When compared to standard models like FinBERT and Random Forest, the Bi-LSTM performs far better. The results show that the suggested method is suitable for practical uses in financial forecasting since it combines sentiment analysis with sophisticated deep learning architectures to give a solid foundation for dealing with market unpredictability.
Human Body Detection in Disaster Environments: A Review of Framework Design and Development Strategies
The developments in intelligent systems and emerging technologies have greatly affected the disaster management processes by making the rescue operations quick, safe, and effective. Accuracy in detecting the human body in critical situations is crucial for minimizing response time and saving human lives. Various methods have been developed, including infrared and radar-based techniques, acoustic analysis, unmanned aerial vehicles, and robotics. These systems have been further enhanced with the incorporation of cloud computing, artificial intelligence (AI), and the Internet of Things (IoT), which enable real-time monitoring and decision-making, regardless of scale, as they can be easily deployed in changing, dynamic environments. In this study, deep learning (DL) frameworks and machine learning (ML) models have enhanced the accuracy of detection, particularly in complex or obstructed environments where conventional tools tend to fail. Combining dissimilar strategies for sensing creates a multimodal system, which is more reliable. The use of drones also expands to areas that are not accessible. A comprehensive review of existing approaches highlights the growing shift toward intelligent, adaptive, and cooperative frameworks in disaster response. This work emphasizes the importance of developing integrated detection technologies that not only improve efficiency and resilience but also contribute to sustainable and robust strategies for future disaster preparedness and management.
Copyright (c) 2025 Vivek Sharma, Dr.Manish Dixit (Author) https://creativecommons.org/licenses/by/4.0
Development in Machine Learning (ML) Algorithms for Fraudulent Identification in Banking Sector Via Credit Cards
The banking industry is seeing an alarming rise in credit fraud, highlighting the critical need for advanced and flexible fraud detection systems. Using state-of-the-art machine learning algorithms, this study systematically reduces the amount of false positives and increases accuracy in identifying credit card fraud. The CCFD dataset will be meticulously prepared to remove any instances of duplicate or missing data. The subsequent stage is to reduce the dimensionality using PCA. To fix the class disparity, the SMOTE method, which stands for Synthetic Minority Oversampling Technique. LR, NB, and XGBoost are three popular older models that are put side by side with the state-of-the-art CCNN. In trials, the CCNN achieved better results than the benchmark models in terms of F1-score (99.97), accuracy (99.96), precision (99.97), and recall (99.97). When compared to XGBoost (97.22%), NB (91.22%), and LR (94.44%), LR produces an exceptional outcome. The model can identify fraudulent transactions, which makes financial transaction systems more secure and dependable. at this exact moment. The XGBoost, CCNN, LR, NB, machine learning, financial sector, credit card fraud detection dataset, and overall financial industry are subjects that are linked to this topic.
SMOTE (Synthetic Minority Oversampling Technique
Next-Generation Cybersecurity The Role of AI and Quantum Computing in Threat Detection
The high rate at which cyber threats are increasing requires the adoption of new security systems using new technologies. This research work focuses on the disruptive contributions of Artificial Intelligence (AI) and Quantum Computing to modern cybersecurity. Artificial intelligence-based security solutions are better at identifying threats, automating response and making them more resistant to advanced attacks, such as advanced persistent threats (APTs) and zero-day vulnerabilities. Until then, Quantum Computing is both a curse and a blessing; an opportunity to implement radical cryptographic protocols and secure communication, but a threat to traditional cryptography. Quantum Machine Learning (QML) is an AI and quantum algorithm that improves intrusion prevention and anomaly detection. There is much to do, including exploring the capabilities of quantum hardware, addressing the challenges of applying quantum security to existing infrastructures, and navigating the ethical issues of AI-based cybersecurity, despite these changes being underway. The answers to these issues lie in the holistic approach (technology, policy and education). In this paper, I provide an overview of current developments, solutions, and future studies in AI and quantum-enhanced cybersecurity and how they can revolutionize threat detection and defense systems in the digital age.
Copyright (c) 2025 Jain Dr. Nilesh (Author) https://creativecommons.org/licenses/by/4.0
Cloud Security and Privacy: A Systematic Review of Threats, Solutions, and Future Direction
The protection of data saved, transferred, and processed in cloud settings has become a significant concern as cloud computing becomes the foundation of today's digital infrastructure. Cloud systems' multi-tenant design can be complicated, and standard security models may not take these factors into account. The current state of cloud security and privacy is thoroughly examined in this assessment, with data breaches, unsafe APIs, and violations of regulatory compliance being the most important issues. Covered are the primary methods of mitigation, such as secure key management, data encryption while in transit and at rest, and intrusion detection systems for real-time threat monitoring. The paper also speaks on the use of the latest privacy-preserving technologies like homomorphic encryption and biometric cryptosystems to preserve confidentiality and not reporting functionality. The risks and consequences of multi-tenancy and compliance are also discussed to determine the effects of shared infrastructures on data isolation and governance. With more enterprises moving workloads to the cloud, it is crucial to safeguard the data of the enterprise which includes its confidentiality, integrity, and availability. This review will provide an in-depth insight into the current security issues, research gaps, and future research trends in terms of adaptive security frameworks, standardization of security protocols, and integration of privacy-enhancing technologies towards fostering trustworthy and resilient cloud adoption.
Copyright (c) 2025 Hussain Prof. (Dr.) Abid (Author) https://creativecommons.org/licenses/by/4.0
Advancing Customer Experience with CRM Digital Transformation: Insights into Tools, Strategies and Challenges
The demand for faster, more accurate, and data-driven client experiences has increased the rate of digitalization in customer relationship management (CRM) systems. This analysis delves into the shift in focus from conventional customer relationship management (CRM) methods to those that make use of cutting-edge digital technologies like as AI, big data analytics, cloud computing, and the IoT. Such innovations allow companies to gather, process large amounts of data about the customer, streamline business processes, and provide individual interaction in various channels. The document provides some of the main strategies in CRM transformation such as the usage of omnichannel engagements, process automation, customer differentiations, and systems integration. It also examines the development of such technologies as chatbots operated by AI, mobile CRM, blockchain and social CRM that can enable organizational effectiveness and real-time responsiveness. On one hand, the positive effects of digital CRM cannot be overestimated, as it helps to enhance customer loyalty and streamline decision-making processes as well as improve the levels of information content, which is both extremely positive and detracts businesses by the critical nature of challenges like data security, complexity of integration, and change resistance. The paper combines both recent literature and practice which provide a thorough knowledge of the enablers and barriers to the adoption of digital CRM. It ends up suggesting the need to research further developments on cross-platform integration, other advanced strategies of personalization, and customer-focused innovation in order that future-ready and agile CRM systems are developed
Digital Transformation, Internet of Things (IoT)
Sparse Models vs. Dense Models: Efficiency Trade-offs in Foundation Models
As AI foundation models scale to billions of parameters, the dichotomy between sparse and dense architectures has grown fundamental to both research and deployment. Dense models, typified by classical transformer-based networks, attain high accuracy but at significant computational, memory, and energy costs. In contrast, sparse models, including static/dynamic pruning and Mixture-of-Experts (MoE) activate a subset of parameters, reducing computational overhead and enabling expansion of model capacity with near-constant inference cost. This paper conducts a state-of-the-art review and empirical comparison of sparse versus dense foundation models, including optimization strategies and hardware-aware efficiency. Drawing upon 20+ peer-reviewed sources and recent empirical benchmarks, it demonstrates that recent advances in sparse models achieve comparable or superior efficiency and generalization on language and vision benchmarks. It provides detailed methodological pipelines, LaTeX math, clean Python code, real dataset descriptions, and professional graphs comparing key metrics. The analysis also confronts societal, ethical, and interpretability consequences of increased sparsity. Finally, it recommends directions for robust, reproducible, and scalable model deployment in academic and enterprise settings.
Copyright (c) 2025 Mr. Vijay Kumar, Mr. Shubham Dwivedi, Ms. Pooja Koshti (Author) https://creativecommons.org/licenses/by/4.0
Explainable RL: Transparent Decision-Making in Complex Environments
Reinforcement learning (RL) has achieved remarkable progress in complex environments, underpinning breakthroughs across robotics, gaming, finance, and autonomous systems. Nonetheless, the “black-box” nature of modern RL policies particularly those based on deep learning has hindered their adoption in safety-critical, regulated, or ethically-sensitive domains due to a lack of transparency. Explainable RL (XRL) seeks to address this gap by generating human-interpretable rationales for agent actions and policy decisions. This paper presents a comprehensive review and new methodology for explainable RL. It critically examines diverse XRL methods, including model-agnostic post-hoc explainers, intrinsically interpretable architectures, reward decomposition, saliency mapping, and human-in-the-loop frameworks. Their novel system, XRL-Transp, integrates attention-based attribution and state-level policy summarization for transparent sequential decision-making. Empirical experiments are conducted on the OpenAI Gym CartPole and MinAtar Breakout benchmarks, with results demonstrating competitive performance and high user-rated interpretability. It discusses open challenges, evaluation protocols, and societal impacts, offering actionable recommendations for practical deployment and future work.
Copyright (c) 2025 Dr Prashant Kumar Shrivastava, Mr. Shailendra Singh Tomer, Mr. Kuldeep Tiwari (Author) https://creativecommons.org/licenses/by/4.0
A Comparative Review of Heuristic vs Metaheuristic Techniques in Cloud Resource Optimization
The flexibility, scalability, and on-demand services offered by cloud computing have fundamentally altered the way computer resources are accessed and handled. Nevertheless, dynamicity and heterogeneity of cloud environment present a major problem in the optimization of resources efficiently. A key problem in cloud computing is optimizing cloud resources, which aims to improve performance, reduce operating expenses, and guarantee effective use of computational resources including CPU, memory, storage, network bandwidth, and energy. A thorough review of cloud resource categories and the main distinctions between scheduling and resource allocation procedures is provided in this work. It investigates the use of heuristic approaches that provide quick and easy solutions for resource management in predictable situations, such as Shortest Job First (SJF), Round-Robin (RR), Min-Min, Max-Min, and First-Come-First-Served (FCFS). Additionally, because they offer scalable and reliable optimizations in dynamic and diverse cloud environments, the study highlights metaheuristic algorithms including Simulated Annealing (SA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). A comparative analysis highlights the strengths and limitations of both strategies in terms of computational overhead, accuracy, scalability, and convergence speed. The paper concludes by identifying key future directions, including hybrid models, AI-driven techniques, energy-aware optimization, privacy-preserving strategies, and cloud-edge synergy, to address the evolving demands of next-generation cloud systems.
The Role of Smart Cloud Systems in Quality Assurance: A Review on Artificial Intelligence (AI) and Machine Learning Techniques (ML)
Quality assurance (QA) is becoming more difficult as cloud computing grows into complex and dynamic systems. Smart cloud environments frequently require traditional QA approaches that are not sufficient to cover the real-time responsiveness issues, scalability expectations, and automation requirements. The present paper explores a revolutionary potential of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing QA frameworks towards cloud systems. It looks into the way some of the AI-based mechanisms like deep learning, natural language processing (NLP), and anomaly detection automate the creation of tests, enhance fault detection, and facilitate the incident management proactively. The ML would be helpful in reinforcing adaptive testing, predictive analytics, and smart resource allocation, amongst other factors, making the test much more specific, quick, and reliable. The research has an in-depth discussion of AI/ML-driven QA tools and methods and illustrates their use in such disciplines as healthcare, software engineering, manufacturing, and education. The comparative literature review also shows that they can be effective in the improvement of performance, minimization of manual efforts and attainment of service-level objectives. However, the paper also identifies ongoing challenges related to model transparency, generalizability, and regulatory compliance, especially in safety-critical systems. The findings confirm that AI and ML are not just auxiliary tools but essential components for building robust, autonomous, and cost-effective QA systems in the cloud. The paper concludes with suggestions for future research aimed at developing standardized, explainable, and regulation-compliant QA frameworks suitable for heterogeneous and rapidly evolving smart cloud ecosystem.
Copyright (c) 2025 Dr. Nilesh Jain (Translator) https://creativecommons.org/licenses/by/4.0