Quantum Pulse: Journal of AI, Data Science & Systems Engineering (QP-AIDSE)
*]:pointer-events-auto scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]" dir="auto" tabindex="-1" data-turn-id="request-WEB:0a389327-a9c2-41a7-bc1f-6c55bdfa76cc-1" data-testid="conversation-turn-4" data-scroll-anchor="true" data-turn="assistant"> Quantum Pulse: Journal of AI, Data Science & Systems Engineering (QP-AIDSE) is an international, peer-reviewed journal committed to publishing high-quality research and review articles in Artificial Intelligence, Data Science, and Systems Engineering. Our mission is to provide a global platform for researchers, scholars, and professionals to share innovative ideas, experimental results, and practical solutions that advance technology and industry practices.
QUANTUM-INSPIRED MACHINE LEARNING FRAMEWORK FOR HEALTHCARE DECISION SUPPORT SYSTEMS
Healthcare decision support demands models that are accurate on heterogeneous clinical data, fast enough for the point of care, and transparent enough to earn clinician trust. Classical learners meet parts of this brief but struggle to capture the high-order correlations that bind physiological variables together, and they typically expose little of their internal reasoning. This paper introduces QIDSS, a quantum-inspired machine-learning framework that runs entirely on commodity classical hardware yet borrows the mathematical machinery of quantum computation—amplitude encoding, parameterised unitary evolution, and entanglement-style feature interaction—to enrich the representation of clinical evidence. A patient record is mapped into a normalised state vector, a shallow variational circuit emulated through tensor operations transforms that state, and a Born-rule measurement yields a calibrated risk estimate together with a per-feature attribution. Training proceeds by a parameter-shift gradient over the emulated circuit, combined with cross-entropy and an entanglement-regularisation term that discourages spurious correlations. We evaluate the framework on a consolidated clinical dataset and benchmark it against logistic regression, gradient-boosted trees, a deep neural network, and a simulator-based variational quantum classifier. QIDSS reaches 94.5% accuracy with matching gains in precision and recall while holding average decision latency to 9.2 ms, comfortably below the quantum baseline. Beyond the headline numbers, the framework surfaces the features that most influenced each verdict and routes low-confidence cases to clinician review. Implementation overhead, qubit-count scaling, and deployment constraints are examined candidly.
Copyright (c) 2026 Quantum Pulse: Journal of AI, Data Science & Systems Engineering (QP-AIDSE)
PRIVACY-PRESERVING EXPLAINABLE MODELS FOR EARLY DETECTION OF BANK FAILURES
This research investigates the obstacles associated with achieving a balance between privacy and explainability in the prediction of bank failures by employing a differentially private glass-box technique. Although accurate early warning systems are essential for maintaining financial stability, private banking data is often used by these systems. Traditional black-box models have a lot of power, but regulators don't trust them because they're hard to understand. However, models that are easy to understand yet could reveal private information are known as glass-box models. This includes rule-based classifiers and decision trees. To solve for this trade-off, the suggested design uses differential privacy techniques in understandable models. Integrating calibrated noise during model training, the approach formally guarantees privacy without sacrificing prediction accuracy. This approach helps those with a stake in the matter, such banks and regulators, understand the key risk factors that impact the patterns of bank failure forecasts. In terms of accuracy, experimental assessments show that privacy-preserving glass-box models can hold their own against non-private alternatives. The framework's robustness in the face of different privacy budgets is further evidence of its practical utility. Being honest and open when making large financial decisions is vital, as stated in the report. It provides a way for the legitimate and moral application of AI in financial analytics as well.
Explainable Artificial Intelligence (XAI)
AN ADVANCED DEEP LEARNING FRAMEWORK FOR BANKING FRAUD DETECTION USING GNNS AND AUTOENCODERS
An innovative deep learning framework for the detection of banking fraud employs autoencoders and Graph Neural Networks (GNNs) to identify intricate and dynamic fraudulent activities. The model was able to identify concealed account-transaction linkages by analyzing banking transactions as graph-structured data. This discloses network activity that is dubious. The autoencoder is able to identify anomalies through reconstruction error analysis, while the GNN is able to learn the relationships and dependencies between entities. Anomaly detection and relational learning enhance accuracy, minimize false positives, and facilitate the adaptation of fraud schemes. This renders the framework a real-time fraud detection solution for modern financial systems that is both scalable and robust.
DETECTION AND ATTRIBUTION OF CYBER-ATTACKS IN IOT-ENABLED CYBER-PHYSICAL SYSTEMS
This research focuses on Cyber-Physical Systems (CPS) that are facilitated by the Internet of Things (IoT). Their detection and attribution of cyberattacks are a primary concern, as these systems are widely used in critical infrastructures such as smart grids, healthcare, and industrial automation. As the number of devices connected and the variety of devices used increases, these systems are susceptible to sophisticated cyberattacks. The paper proposes an integrated framework for real-time malicious activity detection that employs anomaly detection techniques and machine learning. This framework is achieved by examining system interactions, device behavior, and network traffic. It also emphasizes the importance of attack attribution, which involves the utilization of source tracing, behavioral analysis, and pattern recognition to determine the origin and purpose of attacks. In comparison to conventional methods, the proposed model improves the reliability, security, and resilience of IoT-enabled CPS environments by enhancing detection accuracy, decreasing false positives, and improving attribution precisions.
CLOUD-ENABLED SECURE MULTIMEDIA CONTENT PROTECTION
This research, which focuses on the secure storage of multimedia content in the cloud, underscores the growing significance of protecting media files, such as audio, video, and images. The pervasive adoption of cloud computing has resulted in an increase in the transmission, storage, and sharing of multimedia files over remote servers, subjecting this content to piracy, illegal access, and data breaches. In order to ensure the privacy, authenticity, and integrity of data, the proposed methodology implements cutting-edge security protocols, such as encryption, digital watermarking, and access control methods. Furthermore, it employs a scalable cloud architecture to provide secure real-time content dissemination and cost-effective storage. The paper's findings indicate that the system effectively protects multimedia assets without compromising performance or usability. In modern cloud-based ecosystems, the framework provides a dependable method for content proprietors and service providers to store and transmit multimedia data.
REAL TIME DYNAMIC PRICING IN ONLINE RETAIL USING PREDICTIVE MACHINE LEARNING MODELS
Predictive machine learning algorithms are implemented by online businesses to promptly adjust prices in response to market trends, inventories, competitor prices, user behavior, and demand. Sophisticated algorithms, including regression models, decision trees, ensemble approaches, and deep learning networks, utilize real-time data, including browsing patterns, seasonal impacts, and promotion reactions, in conjunction with historical sales data to rapidly determine pricing and evaluate demand elasticity. This data-driven approach enables retailers to enhance their comprehension of their consumers, boost sales, and preserve their competitiveness in digital markets that are perpetually evolving. Automated pricing systems enable price customization, reduce human labor, and increase efficiency, all while scaling large product libraries. Strategic innovations, such as dynamic pricing that is predicated on predictive machine learning, improve the responsiveness, profitability, and consumer engagement of the e-commerce ecosystem.
EFFICIENT ROAD ACCIDENT SEVERITY CLASSIFICATION USING RF-RFE AND DEEP LEARNING FRAMEWORK
The purpose of this research is to enhance the precision and dependability of traffic accident severity predictions by introducing a robust framework for classifying road accident severity that incorporates Random Forest–Recursive Feature Elimination (RF-RFE) and a deep learning model. This work uses RF-RFE for optimal feature selection to reduce dimensionality and eliminate unnecessary variables from large accident datasets, with the goal of identifying the most important factors. The selected criteria are then fed into a deep learning system that can detect complex nonlinear relationships between variables including road conditions, weather, vehicle specs, and driver behaviors. Through the integration of deep neural network classification and machine learning-driven feature optimization, the suggested technique improves prediction accuracy, streamlines computations, and enables real-time decision-making. In terms of data categorization and resilience, the results show that the hybrid RF-RFE and deep learning methodology outperforms standard models. For this reason, it is a useful tool for traffic management authorities and lawmakers to use in their pursuit of safer roads and less severe accidents.
A DATA-DRIVEN APPROACH TO CROP YIELD PREDICTION USING MACHINE LEARNING
This paper aims to improve agricultural productivity and decision-making by presenting a data-driven approach to crop yield prediction using machine learning. The research utilizes historical data on weather, soil, crops, and farming to develop prediction models that accurately predict crop yields. The optimal approach is determined by employing and evaluating a variety of algorithms, including ensemble methods, decision trees, and regression models. We ensure the model's dependability and robustness by training and validating it on real-world datasets. The findings indicate that machine learning generates predictions that are considerably more precise than conventional statistical methodologies. The proposed system is beneficial to both farmers and lawmakers because it provides current information on sustainable agricultural planning, risk management, and resource allocation.
FACIAL RECOGNITION BASED ATTENDANCE SYSTEM USING OPENCV
The Facial Recognition Attendance System, a cutting-edge software program, employs Python and OpenCV to automate real-time attendance monitoring. This system replaces antiquated manual methods by reliably identifying individuals entering a monitored area using state-of-the-art facial recognition technology. The attendance procedure is simplified by the system's recording of the individual's arrival time after they have been detected and verified. This method enables secure and contactless check-in, while also reducing the probability of error and increasing efficiency. This system is exceptional for public events, business offices, and educational institutions that require dependable attendance surveillance. Its straightforward interface and rapid setup make it an excellent choice.
ADVANCED PREDICTIVE ANALYTICS FOR EARLY CARDIAC ARREST DETECTION IN NEWBORN INFANTS
A medical emergency that is both common and terrifying is unexpected cardiac arrest in infants. The most effective care and treatment for these infants can only be administered if they are discovered at an early stage. In recent years, scientists have been conducting research to identify potential biomarkers and symptoms of neonatal cardiac arrest in order to create more precise and efficient diagnostic tools for early detection. Echocardiography and computed tomography are two of the numerous imaging modalities that have the potential to identify cardiac arrest at an early stage. The objective of this research is to rapidly identify neonatal cardiac arrest in the CICU by constructing a cardiac machine learning model (CMLM) that employs statistical models. The integration of the neonate's physiological data enabled us to ascertain the frequency of cardiac arrests. Predictive models for cardiac arrest were developed using two statistical modeling techniques: logistic regression and support vector machines. The proposed procedure will be implemented by the CICU to expedite the detection of neonatal cardiac arrest. In the training (Tr) comparative zone, the proposed CMLA achieved a 0.912 delta-p, 0.894 FDR, 0.076 FOR, 0.859 prevalence threshold, and 0.842 CSI. The CMLA that was recommended in the testing (Ts) comparison zone had the following values: 0.896 delta-p, 0.878 FDR, 0.061 FOR, 0.844 prevalence threshold, and 0.827 CSI. As an outcome, neonatal cardiac arrest-related mortality and morbidity will be diminished.
BLOCKCHAIN-BASED SECURE E-VOTING SYSTEM WITH PRIVACY-PRESERVING AND SCALABLE ARCHITECTURE
Elections may be expedited, simplified, and enhanced through electronic voting. They are not frequently employed as a result of security, transparency, scalability, and voter confidentiality concerns. Our blockchain-based electronic voting system is impermeable, visible, and privacy-protective due to the use of advanced cryptographic algorithms and a permissioned distributed ledger. A permissioned blockchain that employs an expedited consensus method enhances throughput and minimizes latency during critical elections. Voter registration, voting, and tabulation are automated through the use of smart contracts. This mitigates centralization and manipulation. Privacy is safeguarded through encryption, decentralized identity (DID) frameworks, and verified methods. Comprehensive verification is facilitated by the protection of voter anonymity. In a hybrid architecture, scalability is enhanced and computational power is reduced through off-chain storage and on-chain validation. The proposed system is capable of withstanding unauthorized access, data manipulation, and duplicate voting, as evidenced by a comprehensive security and performance analysis. Furthermore, it surpasses voting systems that are founded on blockchain technology. The findings indicate that the secure digital election technology is both scalable and viable, thereby facilitating the implementation of transparent and dependable voting systems.
INTELLIGENT CYBER THREAT PREDICTION USING ML AND GENERATIVE AI APPROACHES
Cyberattacks are a threat to enterprises, financial institutions, and digital infrastructure due to the frequent and sophisticated nature of hackers. This research examines a variety of hacking prediction techniques, including traditional machine learning and contemporary generative AI models. Random forests, support vector machines, and decision trees were implemented to assess historical network data in order to identify malicious activity. While these systems are capable of recognizing established attack signatures, they may fail to recognize emerging threats. Transformer-based models and generative adversarial networks are two recent advancements in generative AI. These technologies allow computers to understand intricate behavioral patterns, simulate plausible attack scenarios, and predict new cyberthreats. Generative AI employs predictive models, adaptive learning, and comprehensive data analysis to identify anomalous activity, attack patterns, and preventative security measures. In terms of assault prediction system accuracy, scalability, and expertise, generative AI outperforms traditional machine learning, according to the study.
REAL-TIME CREDIT CARD FRAUD DETECTION USING INTEGRATED GNN AND AUTOENCODER MODELS
Autoencoders and Graph Neural Networks (GNN) are two advanced deep learning methods that will be employed in this study to enhance fraud detection in financial systems and prevent real-time credit card fraud. As internet banking and digital payments become more prevalent, financial institutions, such as banks, are hindered in their ability to promptly and precisely identify fraudulent transactions. The intricate connections between purchases and cardholders are not always detected by conventional fraud detection technologies. Because of this, the process of identifying fraud is more time-consuming and expensive. In the proposed method, Graph Neural Networks are employed to depict the connections between consumers, transactions, and merchants as connected graphs. This enables the computer to identify suspicious patterns and concealed connections between fraudulent activities. An anomaly detection system that is based on autoencoders is employed to identify any deviations from the norm that may indicate fraudulent activity. These two methods are combined to enhance the system's object detection capabilities and minimize the occurrence of false positives. Banks can promptly identify and prevent fraudulent transfers, despite the fact that the method is intended to operate in real time. Experiments demonstrate that the combination of deep learning and graph-based analysis is significantly more reliable and effective in detecting misconduct in contemporary financial networks.
EXPLAINABLE AI FOR STUDENT PERFORMANCE ANALYSIS IN CODING PLATFORMS
Explainable AI (XAI) simplifies and clarifies complex machine learning models. Grading students' code platform performance becomes increasingly personal. Many earlier machines are "black boxes," making assumptions without explanation. This keeps teachers and students in the dark. XAI makes these predictions more transparent, so everyone can understand how diverse factors affect the final output. The system analyzes students' coding habits, submission trends, error types, and problem-solving tactics to discover what motivates and hampers them. SHAP and LIME simplify difficult model selections by emphasizing student work's most important parts. This shows your strengths and weaknesses, enabling more targeted feedback and help. These findings help educators identify at-risk students and improve class preparation and delivery. Finally, XAI supports fair, accountable, and practical educational analytics. This makes learning on coding platforms more welcoming and supportive.
MACHINE LEARNING-BASED ANALYSIS OF STUDENT PERFORMANCE IN E-LEARNING
Machine learning allows for the monitoring of online student performance and the identification of learning difficulties. Student activity records, assignment submissions, assessment scores, and login frequency predict learning. Data preprocessing decreases missing values, noise, and class imbalance, enhancing model dependability. Random Forest, Logistic Regression, Decision Tree, and KNN are compared. Academic progress, learning consistency, and engagement can be tracked via feature extraction. The method suggests early identification of at-risk children for academic assistance. Experiments show ensemble models predict better than individual classifiers. We evaluate the model using confusion matrix analysis, F1-score, recall, accuracy, and precision. Examining major predictors enhances interpretation.
ADAPTIVE IMBALANCE LEARNING FOR ACCURATE HEALTH INSURANCE FRAUD DETECTION USING META-REINFORCEMENT LEARNING
A meta-reinforcement learning system that can react to high-class imbalances and changing fraud patterns would be perfect for health insurance fraud detection. The suggested method detects minority (fraud) classes by automatically determining sampling, weighting, and decision-making procedures. Meta-learning lets the system adapt to new fraud patterns without retraining. Reinforcement learning improves policy using sparse and delayed reward sets using real-world claims data. A hybrid deep representation module tracks claimants, suppliers, and recipients' complicated temporal and interpersonal links. The design reduces fraud detection false negatives with cost-sensitive learning. Experimental results on real-world and standard health insurance datasets show increased F1-score, recall, and precision over static classifiers and conventional resampling. This makes hostile adaptation and notion drift harder for fraudsters. Scalability comes via gradual improvements and online learning. The model's clear decision indicators help inspectors prioritize inquiries.
PREDICTIVE ANALYTICS FOR CYBER ATTACK DETECTION USING NEXT-GENERATION AI TECHNIQUES
Next-generation AI is used to build a predictive analytics framework to identify cyber threats. Real-time simulation of complicated, high-dimensional security data streams uses graph neural networks, deep learning, and transformer topologies. Behavioral analytics and temporal sequence models can detect attack tendencies before they cause severe damage. Combining network data, system logs, and human actions from several places helps understand threats. Mixed learning enhances stability with sparsely labeled data by combining supervised, self-supervised, and reinforcement learning. Online education and idea control prepare people for any attack. Federated AI and privacy-preserving learning allow remote companies to work safely. Numerous tests using real-world and benchmark datasets have shown high recognition rates and low false alarm rates. The design protects against APTs, zero-day vulnerabilities, and malware that changes shape. Clearly explained AI modules alert. This helps security experts make rapid decisions. Distributed training and edge-cloud coupling enable fast responses, enabling scalability.
AN INTELLIGENT MACHINE LEARNING FRAMEWORK FOR CYBERBULLYING DETECTION ON SOCIAL MEDIA PLATFORMS
The research's goal is to find inappropriate behavior on social media sites using machine learning. Online communication has been significantly enhanced by the expansion of social media. As a consequence, cyberbullying has increased, posing a threat to both physical and mental health. The objective of the project is to create a computer system capable of detecting cyberbullying in extensive social media datasets. Natural Language Processing (NLP) techniques, including sentiment analysis, feature extraction, and text preparation, are employed to identify abusive verbal patterns. Machine learning techniques, including Naïve Bayes, Random Forests, and Support Vector Machines, are employed to construct and evaluate classification models. The proposed method effectively distinguishes between non-bullying and bullying content in order to identify and eliminate detrimental communications. The findings indicate that cyberbullying detection methods are improved by machine learning. The online environment is more complex and responsible.
CYBER ATTACK PREDICTION USING TRADITIONAL MACHINE LEARNING AND GENERATIVE AI MODELS
The shift from simple machine learning to generative AI models for cyberattack prediction is examined in this research. The rapid advancement of digital technologies and the prevalence of cyber risks make it more important to anticipate and identify attacks in order to protect network infrastructures and information systems. Using out-of-date network data, several have employed classification and anomaly detection to forecast attacks. These techniques, however, don't always prevent sophisticated and evolving cyberattacks. By collecting additional data, enhancing model learning, and simulating fictitious assault scenarios, generative artificial intelligence enhances cyberattack prediction. This work enhances cybersecurity forecasts, adaptability, and proactive defense through the use of generative AI models and traditional machine learning.
DEEP NEURAL NETWORK-BASED ELECTRICITY THEFT DETECTION IN SMART GRID SYSTEMS
This research presents a novel method for detecting electrical trickery in smart grids using deep neural networks. Electricity theft has become a major problem for contemporary power distribution systems, resulting in financial losses and a decrease in the dependability of energy delivery. In order to examine a significant amount of smart meter data and find unusual consumption patterns linked to fraudulent behavior, the suggested study makes use of deep learning algorithms. To find complex patterns and distinguish between normal and abnormal electricity consumption behaviors, the software builds a deep neural network using previous energy usage data. Because the technique automatically pulls relevant information and adjusts to evolving larceny schemes, its detection accuracy is higher than that of traditional machine learning and rule-based systems. Experimental results show that the suggested model successfully detects electricity theft with excellent precision, recall, and accuracy. As a result, power providers may improve the security and effectiveness of smart grid systems while also reducing revenue losses.
A ROBUST SUPERVISED MACHINE LEARNING APPROACH FOR SPAM DETECTION IN DIGITAL COMMUNICATION
Email and message filtering systems perform better and are more dependable when supervised learning techniques are applied. This study examines an effective method for identifying spam. Because so many people communicate online, spam is a major issue for both businesses and individuals. However, traditional filtering techniques may not always be sufficient to identify emerging trends in trash. In order to distinguish between spam and legitimate messages, supervised machine learning techniques such Naïve Bayes, Support Vector Machine, Decision Tree, and Random Forest are employed in this work. We utilize a marked-up sample to train the models and identify key elements in the text. Preprocessing techniques that improve the model's performance include tokenization, stop-word removal, and feature extraction. Many classifiers are evaluated based on their F1-score, accuracy, precision, and recall using the recommended method. To determine the most effective technique for identifying spam, a comparison study is conducted. Supervised learning techniques significantly improve detection accuracy and reduce false positives, according to empirical evidence. Regular training can teach the algorithm to identify new types of spam.
DEEP LEARNING-BASED ABNORMAL TRAFFIC DETECTION USING BIG STEP CONVOLUTION AND ATTENTION BLOCKS
The security and reliability of the network are affected by the discovery of unusual traffic. The primary issues that arise while attempting to detect unusual traffic are addressed by an attention-based big-step convolutional neural network traffic detection model. The similarities between features and the one-dimensional nature of the detection model are the root causes of these issues. Preprocessing the raw data into a two-dimensional grayscale picture allows us to examine the patterns of network traffic. Images with several grayscale channels are created using histogram equalization. To enhance local features, an attention approach is employed by assigning varying weights to traffic factors. When it comes to getting various traffic characteristics, pooling-free convolutional neural networks are a great way to avoid the issues that normal convolutional neural networks have, such as overfitting and missing local information. The simulation trial made use of both real-world data collecting and a diversified public dataset. We compare the proposed model against the most recent two models as well as SVM, ANN, CNN, RF, and Bayes. In a testing environment, using multiple classes resulted in an accuracy percentage of 99.5%. The proposed model is quite good at spotting issues. F1 score, accuracy, and recall are all areas where the proposed technique outperforms the status quo. There is evidence that the model is resilient and good at locating objects, even when faced with a variety of challenging scenarios.
PREDICTIVE MODELING FOR CARDIO VASCULAR HEALTH: A SURVEY ON DEEP LEARNING APPROACHES
*]:pointer-events-auto scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]" dir="auto" tabindex="-1" data-turn-id="request-6952a95d-e2f8-8322-a989-82bffaa33e28-0" data-testid="conversation-turn-60" data-scroll-anchor="true" data-turn="assistant"> Cardiovascular disease is the leading cause of mortality worldwide, affecting millions of individuals and healthcare systems. It is imperative to promptly identify and accurately diagnose issues in order to guarantee the success of interventions and treatment strategies. Predictive modeling has consistently demonstrated its capacity to significantly improve the accuracy and efficiency of cardiac disease detection when combined with robust deep learning algorithms. Deep learning incorporates a diverse array of concepts and methodologies. Recurrent neural networks, hybrids, and convolutional neural networks are among the models that fall under this category. Their work frequently necessitates the use of electrocardiograms (ECGs), medical images, patient records, and other intricate cardiovascular data. The application of optimization techniques, such as evolutionary algorithms and particle swarm optimization, enhances model performance, convergence speed, and interpretability. This investigation concentrates on the most recent developments in deep learning algorithms for the prediction of cardiac health. It concentrates on essential techniques, performance metrics, practical applications, and frequently employed datasets. Some of the potential obstacles that may arise when utilizing deep learning for predictive modeling include data types, model interpretability, and privacy. The objective of this study is to assist academicians and practitioners by examining current patterns, potential solutions, and future opportunities for the identification and prevention of cardiovascular disease through the application of deep learning.
PROTECTING MEDICAL IMAGES: A ROBUST APPROACHTO ENCRYPTED DICOM DATA TRANSMISSION
*]:pointer-events-auto scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]" dir="auto" tabindex="-1" data-turn-id="request-6952a95d-e2f8-8322-a989-82bffaa33e28-8" data-testid="conversation-turn-58" data-scroll-anchor="true" data-turn="assistant"> To maintain confidentiality and integrity within the medical sector, it is essential that medical photographs are exchanged securely. In the realm of transmitting and archiving medical pictures, numerous professionals in the healthcare sector depend on the DICOM standard. Robust encryption solutions are essential for safeguarding private data against these threats. This paper investigates a comprehensive approach to encrypting interactions involving DICOM pictures. To guarantee data integrity and security, it tackles essential concerns and provides answers. This research analyzes contemporary encryption methods for safe DICOM image transmission, focusing on their effectiveness, practicality, and possible shortcomings.
DEEP FAKE CHALLENGES IN E-KYC:A REALISTIC DATASET FOR TRAINING AND TESTING VERIFICATION MODELS
Due to deepfake technology's broad availability, evaluating digital registration processes, such as eKYC verifications, has become more difficult. So, it's getting harder and harder to verify digital registration processes. For the purpose of testing and improving facial recognition systems against deepfake attacks, the eKYC-DF corpus is a specific dataset. Both applications are doable. There are a lot of fake face recordings in this sample that are part of the collection. These fake recordings sound almost identical to the real ones. Light, editing, and racial composition vary greatly between the recordings, making them easily identifiable from one another. Better identity verification methods can be developed by researchers and developers to protect consumers' trust in the internet and prevent unauthorized access to services. By fortifying the reliability of eKYC systems, they will improve consumer safety.
Synthetic Dataset and Identity Fraud Prevention
DYNAMIC WEB SERVICE SUGGESTION MODEL LEVERAGING USER PREFERENCES AND COLLABORATIVE LEARNING
*]:pointer-events-auto scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]" dir="auto" tabindex="-1" data-turn-id="request-6952a95d-e2f8-8322-a989-82bffaa33e28-6" data-testid="conversation-turn-54" data-scroll-anchor="true" data-turn="assistant"> In order to narrow down an extensive range of possible system objects to the ones that users desire, recommender systems employ a number of data mining techniques and algorithms. In contrast to a stripped-down paradigm where users may simply search for and purchase products, recommender systems entice users by providing a more comprehensive experience. By considering a user's past searches, purchases, and actions, among other things, recommender systems can provide more personalized choices. The algorithm takes into account the user's historical data as well as the data of other users to make predictions about the user's preferences, and then it suggests products that match those tastes. This recommender system study primarily deals with precision, scalability, setup time, and data insufficiency. What the user does determines the suggestions made by content-based filtering. Comparable to Collaborative Filtering, long-term models display consumer decision profiles. A significant improvement in efficiency can be achieved by modifying the precedence profile.
REVERSIBLE IMAGE DATA HIDING WITH CONTRAST ENHANCEMENT
A novel approach to digital image processing is described in this research as reversible data hiding (RDH). Keeping the PSNR number high isn't given much weight in the proposed strategy. Conversely, it enhances the visual appeal of a primary image by drawing more attention to the contrasts between its constituent layers. The two largest histogram bins can hold data. It becomes more simpler to apply the approach to equalize the histogram repeatedly in this way. There is additional data in the host image in addition to the message bits. Thanks to this, we can get the original photo back to its original state. Both sets of images prove that the strategy is effective. Using reversible data hiding (RDH) technology to enhance contrast appears to be a first. Even after adding several message bits to images to make them more readable, the study found that the images maintained their visual integrity. When compared to three other popular MATLAB methods, this one significantly boosts image contrast.
TRANSFORMING BLACK BOX MODELS INTO TRANSPARANT SYSTEMS THROUGH EXPLAINABLE AI METHODS
The fast integration of AI in key industries like healthcare, banking, and autonomous cars has led to a growing demand for methods that improve understanding and ensure accountability in machine learning models. Users often struggle to accept, trust, and collaborate with black box models, such as deep neural networks and ensemble approaches, because they do not clearly reveal how decisions are made, even though they generate accurate predictions. Explainable AI (XAI) offers a novel way to bridge this gap by transforming complex systems into more interpretable and observable forms. This research explores various XAI approaches, including data visualization tools, inherently interpretable models, and model-agnostic techniques such as SHAP and LIME. The enhanced understanding of feature importance, causal relationships, and decision pathways provided by XAI supports fairer algorithmic decision-making, more reliable outcomes, and easier debugging. The study also addresses challenges such as consistency, scalability, and the risk of oversimplification, emphasizing the need to balance transparency with fidelity. Explainable AI (XAI) plays a crucial role in converting “black box” models into transparent systems, thereby enabling ethical AI deployment in critical real-world applications and fostering effective collaboration between humans and AI systems.
DESIGNING AN ETHICAL DATA SCIENCE FRAMEWORK FOR RESPONSIBLE AND TRANSPARENT AI INTEGRATION
The growing use of data-centric systems and artificial intelligence (AI) in fields such as business, government, and healthcare has led to an increase in the number of ethical concerns around transparency, privacy, accountability, and environmental impact. This article introduces the Ethical Data Science Framework (EDSF) and formally presents it for publication. It comprehensively outlines ethical principles for the regulation of AI research and development. The EDSF is designed for practical implementation through a hierarchical structure that includes governance mechanisms, technological toolkits, documentation standards, and continuous monitoring processes. The framework is built upon five core pillars: fairness, accountability, transparency, privacy, and sustainability (FATPS). The study provides mathematical definitions of key concepts, measurement protocols, auditing methods, governance roles, artifact templates, and guidance on integrating CI/CD and infrastructure practices. Domain-specific case studies in credit scoring and healthcare diagnostics are presented to demonstrate its strengths and limitations. The appendix includes a governance checklist, pseudocode, evaluation methodologies, practical templates, and mathematical derivations. Keywords: Ethical AI, Fairness, Explainability, Differential Privacy, Governance, Model Cards, Datasheets, Responsible AI, Monitoring, Audit.
DEEP LEARNING-BASED AUTOMATED SYSTEM FOREARLY DETECTION OF PLANT DISEASES
*]:pointer-events-auto scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]" dir="auto" tabindex="-1" data-turn-id="request-6952a95d-e2f8-8322-a989-82bffaa33e28-1" data-testid="conversation-turn-44" data-scroll-anchor="true" data-turn="assistant"> Agriculture is an essential part of being human. It is possible that more than 60% of the population lives in agricultural regions in some way. Farmers consistently declined to increase crop production since the antiquated system could not identify illnesses in most agricultural goods. It is vital to recognize farming illnesses as soon as possible since they inhibit plant growth. Multiple Machine Learning (ML) methods have been developed for the purpose of identifying and classifying agricultural illnesses. Recent developments in Deep Learning (DL) and other forms of machine learning suggest that this area of research has the potential to grow considerably more nuanced. To reliably detect signs of agricultural diseases, the suggested approach employs a combination of deep and convolutional neural networks. We also use a number of efficiency indicators to evaluate these strategies. This post delves into the detailed examination of the deep learning algorithms that are utilized to predict the advent of crop diseases. If scientists are alert to certain knowledge gaps, they may be able to detect plant diseases long before any symptoms show up. The proposed approach will be used to build convolutional neural network technology that can identify plant diseases by analyzing their leaves.