International Journal of Information Technology and Computer Engineering
JOURNAL SUMMARY: Editor- In-Chief: Dr. Abdul Wasay Title proper: International Journal of Information Technology and Computer Engineering ISSN : 2347-3657 Subject: Computer Sciences & Information Technology, AI & ML, Data Science Publisher: Nisaab Publishers Dates of publication: 2013 Frequency: Quarterly Language: English Country: India Medium: Online Indexed by: CROSSREF Indexed by: ROAD Indexed by: THE KEEPERS Title DOI of the Journal: http://doi.org/10.62647/issn.2347-3657 APC: $100/-
Hybrid Reachability Analysis Using Static Graphs And Dynamic Execution Traces For Oss Vulnerabilities
The extensive adoption of third-party libraries, along with the rapid detection of new vulnerabilities, compounds the security issues in the open-source software ecosystem. Although traditional static and dynamic analyses provide critical information about vulnerability reachability, they often end up having limited applicability due to false positives or lack of actual area coverage. This paper presents a new metric known as hybrid reachability analysis, which can be leveraged to improve the precision of risk impact assessments. The new approach integrates dynamic execution traces with static program graphs. The practical utility of this hybrid approach for improving accuracy, reducing false positives, and supporting reasonable priorities for vulnerabilities is also shown through experimentation. Furthermore, we present a tri-fold synthesis of the literature around existing studies of relevance to reachability analyses and hybrid approaches. We conclude by arguing that combining graph and trace data can foster future vulnerability management workflows, and a conceptual model is offered to show how hybrid analysis can advance more sustainable software development. Other limitations covered are scalability and runtime overhead, as well as promising future work such as distributed tracing, embedded machine learning, and extending methods to support continuous deployment. This article provides a comprehensive analysis of the challenges associated with OSS undertaken from traditional analyses and hybrid methodologies.
Hybrid Reachability Analysis, Open-Source Software Security, Static Graphs, Dynamic Execution Traces, Vulnerability Detection, Dependency Risk Analysis, Software Assurance.
Copyright (c) 2025 Purv Rakeshkumar Chauhan (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Reimagining Accounts Payable Automation Using Sap Btp Workflows And Sap Joule Copilot
The automated processing of accounts payable (AP) processes has taken on a new shape with smart enterprise solutions. This paper describes how SAP Business Technology Platform (SAP BTP) and SAP Joule Copilot can provide the more mechanical, rote processes within the function into intelligent and contextualized processes in the organization. The paper looks at alternative automation approaches (rule-based, machine learning, and hybrid) in a comparative analysis using empirical and theoretical research. The study demonstrates that hybrid solutions have the potential to action automated invoice management, fraud detection and agile operations. The current discussion reviews architectural and design considerations when adding the workflows of SAP BTP and conversational AI into the financial ecosystem of the enterprise.
Accounts Payable Automation, SAP BTP, SAP Joule Copilot, Intelligent Workflow, Machine Learning, Conversational AI, Digital Finance, Fraud Detection
Copyright (c) 2025 Madhusudana kamballi (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Enhanced Cellular Traffic Prediction Using Tuned XGBoost and Advanced Ensemble Regression Models
Accurate cellular traffic prediction is vital for optimizing Quality of Service (QoS) in modern networks, especially with the rising requirement for real-time applications. This paper introduces an advanced Adaptive Machine Learning-based Cellular Traffic Prediction (AML-CTP) architecture that incorporates sophisticated machine learning algorithms like as XGBoost, CatBoost, and Voting Regression, with parameter tweaking to optimize predictive performance. The suggested system uses strong preprocessing methods like Min-Max Scaling, PCA to reduce the number of dimensions, and density-based clustering methods like DBSCAN to focus on data clusters that are very similar. These strategies make model training more efficient and less complicated. The system is built using the Flask framework to make it easier to use and deploy in real time. This lets users easily upload data and get forecasts. The experimental findings suggest that XGBoost works well, with the maximum R² score of 98%. This shows that the system can adapt to different traffic patterns, optimize resource allocation, and improve the overall quality of service (QoS) in cellular networks.
Cellular Traffic Prediction, Quality of Service (QoS), Adaptive Machine Learning, XGBoost, CatBoost, Voting Regression, Data Preprocessing, PCA, DBSCAN, Flask Framework, Real-Time Deployment, Resource Allocation.
Copyright (c) 2025 P.Ramulu, Mrs.V.Subhasini (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Enhanced Software Defect Prediction Using a Stacking Ensemble of Decision Tree, Random Forest, and LightGBM
To facilitate the discovery of software defects, the latest study used a Stacking Classifier in conjunction with Decision Tree, Random Forest, and LightGBM models. Improved prediction accuracy and stability across a variety of software datasets are achieved by utilizing the complementing properties of many methods. Additionally, the system makes use of a Flask-based front end for safe user authentication and a straightforward user interface for real-time testing and prediction. Software quality assurance becomes much simpler with integration, allowing for faster, safer access, usage, and analysis of faults.
Software Defect Prediction, Ensemble Learning, Stacking Classifier, Random Forest, LightGBM, Decision Tree, Machine Learning, Flask Framework, Software Quality Assurance, NASA MDP Datasets..
Copyright (c) 2025 Sane Divya, N. Surendra (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Network Security Analysis Using Cryptography and Secure Hash-Based Identity Authentication
Cryptography-based network security is essential for safeguarding sensitive information in an increasingly digital world. This study explores the implementation of secure hashed identity message authentication (SHIMA) as a robust solution for ensuring data integrity and authenticity within network communications. By employing cryptographic techniques, SHIMA enhances security protocols by providing a method for verifying the identity of message senders while ensuring that the messages have not been tampered with during transmission. The proposed approach utilizes secure hash functions to generate unique message digests that represent the original data. These digests are then coupled with identity verification mechanisms to create a multi-layered security framework. This framework not only protects against unauthorized access but also mitigates the risk of replay attacks and message forgery, which are prevalent threats in network environments. By leveraging a combination of cryptographic algorithms and hashing techniques, SHIMA ensures that messages remain confidential and maintain their integrity throughout their lifecycle. Furthermore, the study evaluates the performance and efficiency of the SHIMA protocol in various network scenarios, demonstrating its effectiveness in real-time applications. The results indicate that SHIMA not only enhances security but also supports scalability, making it suitable for diverse network architectures. This research contributes to the field of network security by providing a comprehensive analysis of cryptographic techniques that bolster data protection and establish trust in digital communications.
Cloud, cryptography, digital communication, SHIMA protocol, networks.
Copyright (c) 2025 Dr. B.Hari Krishna (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Enhanced YOLOv5x6 and YOLOv9-Based Framework for Detecting People with Disabilities
A deep learning-based approach to identifying persons with impairments is necessary to enhance accessibility and promote inclusion across diverse contexts. To make sure that identification and recognition are accurate, a robust base is built using many YOLO models (v5s, v7-tiny, v8, v5x6, and v9) and advanced object detection algorithms like FastRCNN and FasterRCNN. These models employ cutting-edge architectures to improve detection skills, with a focus on accuracy and speed in real time. The latest versions of YOLO and FasterRCNN work together to allow for detailed analysis and detection in a range of situations, making sure that the findings are always correct. The YOLO series of models is great for fast processing photos without losing accuracy, which makes it perfect for usage in changing environments. We will utilize the Flask framework to create an easy-to-use front end that lets people log in securely. This method aims to help and keep an eye on individuals with disabilities by better allocating resources and making decisions based on good information in accessibility initiatives. This will lead to a more inclusive society.
Object Detection, YOLOv8, YOLOv5, YOLOv7, Mobility Aids, Differently-Abled, Deep Learning, Real-Time Detection, Surveillance, Precision, Recall, mAP, F1-Curve, PR-Curve, Flask Framework, User Authentication, Disabilities Identification.
Copyright (c) 2025 Kadam Sirisha, Mr.R. Althaf (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Improving Data Entry Accuracy Using Distil-BERT: An Efficient Extension to BERT-Based NLP Models
In corporate applications, correct data input is essential for optimizing process efficiency and facilitating sound decision-making. The current systems mostly use old-fashioned machine learning methods like TF-IDF+SVM and Word2Vec+SVM, which don't give a lot of contexts, therefore the accuracy of data validation jobs is only modest. To get over these problems, the suggested system uses modern NLP and deep learning methods, especially the BERT model, to automatically check the quality of data submission. BERT's bidirectional transformer design captures deep semantic and contextual linkages, which makes it much easier to find missing or wrong data. Also, an improved version called Distil-BERT is developed to make things more efficient by lowering model complexity and computation time while keeping or enhancing classification accuracy. This model is light yet powerful, which makes data validation faster, more scalable, and less resource-intensive. It also works better and is more useful for real-time business applications.
NLP, BERT, classification, data validation, risk management.
Copyright (c) 2025 E. Monisree, Mr.R. Althaf (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Urbanization and population growth have intensified the challenges of managing solid waste in cities. Traditional waste collection systems, which operate on fixed schedules without monitoring the actual status of bins, often lead to overflowing bins, unhygienic conditions, and unnecessary operational costs. To address these issues, this project proposes a Waste Management Tracking and Segregation System that leverages IoT technologies for smart monitoring and automated waste handling. The system is built around an ESP8266 microcontroller with inbuilt Wi-Fi, enabling wireless data transmission and integration with online platforms. An ultrasonic sensor measures the fill level of the bin and displays this data on the serial monitor, while a rain sensor and IR sensor work together to identify and classify waste as wet or dry. A servo motor then flips or directs the waste into the appropriate compartment based on this classification, supporting automated segregation at the source. The system communicates bin status in real-time through a web page interface and the Blynk IoT platform, allowing users to monitor bin levels and receive notifications on their mobile devices when bins are full. Additionally, a GSM module is included to send SMS alerts to waste collection teams. This solution eliminates the need for expensive components like GPS modules and RFID tags, offering a cost-effective, scalable, and sustainable option for smart cities and urban areas. The proposed system enhances operational efficiency, reduces fuel consumption, prevents bin overflow, and contributes to cleaner and healthier urban environments.
Alluri Pranathi, Battula Ramana Trivedi, Mendu Tejaswini
Copyright (c) 2025 Dr J Madhavan, Alluri Pranathi, Battula Ramana Trivedi, Mendu Tejaswini (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Stock price prediction is a critical yet complex task in the financial sector due to the volatile and dynamic nature of the market. Traditional prediction methods like statistical analysis and basic machine learning often fail to capture long-term dependencies and real-world market influences. To address these limitations, this project proposes a web-based stock price prediction system using Long Short-Term Memory (LSTM) networks—a type of Recurrent Neural Network (RNN) designed to model sequential data effectively. The system is built to analyze historical stock data and generate accurate future price forecasts. It features a user-friendly web interface developed with React and a backend powered by Flask and TensorFlow/Keras. Key functionalities include data collection, preprocessing, model training, real-time prediction, and graphical visualization using Chart.js or Plotly.js. The model is trained using CSV data and enhanced through feature selection techniques like PCA and RFE. This solution offers improved prediction accuracy, scalability to multiple stocks, and automated analysis, making it a valuable tool for investors and financial analysts. The project is developed as a mini-project under the Information Technology curriculum and fulfills the partial requirements for the award of the Bachelor of Technology degree.
LSTM (Long Short-Term Memory), Time Series Forecasting, Machine Learning, Historical Data, Stock Prediction, Price.
Duluri Harika, Nuthalaganti Meenakshi, Asfiya Naaz
Copyright (c) 2025 Tasneem Rahath, Duluri Harika, Nuthalaganti Meenakshi, Asfiya Naaz (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
The Moral Landscape Of Ai And Its Implications For Human Dignity
Artificial Intelligence represents one of the most transformative technological developments of the 21st century, posing unprecedented challenges to the foundational concept of human dignity enshrined in international and domestic legal frameworks. This research paper examines the intersection between AI deployment and human dignity through the lens of international human rights law, including the Universal Declaration of Human Rights (1948), the International Covenant on Civil and Political Rights (1966), and emerging AI-specific legislation such as the Council of Europe's Framework Convention on Artificial Intelligence (2024) and the European Union's AI Act (2024). The paper analyzes how AI systems impact fundamental rights to privacy, autonomy, non-discrimination, and personal dignity, with particular emphasis on Indian constitutional provisions under Article 21. Through doctrinal analysis and examination of recent legal developments, this study argues that human dignity must serve as the foundational principle for AI governance, requiring robust legal frameworks that balance technological innovation with the protection of inalienable human rights. The paper concludes with recommendations for a dignity-centric approach to AI regulation.
Artificial Intelligence, Human Dignity, AI Regulation, Human Rights Law, Data Protection
Copyright (c) 2024 Ankita Shukla, Dr. Shameem Ahmed Khan (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Survey On Intelligent Diet And Exercise Recommendation Systems Using Machine Learning
In today’s technology-driven world, maintaining a balanced and healthy lifestyle has become increasingly challenging due to busy schedules, sedentary routines, and diverse individual health needs. The Intelligent Fitness Assistant presents a machine learning-based approach to deliver personalized diet and exercise recommendations tailored to each user’s physiological profile and lifestyle. The system collects user-specific information such as age, gender, height, weight, activity level, dietary preferences, and personal health goals to generate individualized fitness programs. By leveraging both supervised and unsupervised machine learning techniques, the system identifies underlying patterns in user data to determine optimal nutrition and workout strategies. Utilizing ANNs and LR, the Intelligent Fitness Assistant refines its recommendations dynamically based on user progress, feedback, and historical performance trends, ensuring adaptability and long-term engagement. The platform integrates seamlessly with wearable devices to gather real-time health parameters such as heart rate, calorie expenditure, and step count, enhancing the precision of its recommendations. This continuous feedback mechanism allows the system to adjust fitness plans in real time, promoting proactive health management. Designed to address prevalent lifestyle-related disorders such as obesity, cardiovascular diseases, and metabolic imbalances, the proposed system emphasizes preventive healthcare through intelligent automation. By combining predictive analytics, adaptive learning, and user-centric design, the Intelligent Fitness Assistant bridges the gap between technology and personal wellness. It empowers users to make informed decisions regarding their nutrition and physical activity while promoting sustainable improvements in overall health and well-being. This research highlights the transformative potential of AI-driven systems in personalized healthcare and establishes a foundation for future innovations in intelligent fitness management.
Intelligent Fitness Assistant, ML, ANN, LR, Personalized Health, Customized Diet Plans, Exercise Recommendations, Wearable Devices, Health Monitoring, Predictive Analytics, Preventive Healthcare, Obesity Management, Lifestyle Disorders, Adaptive Fitness System, Data-Driven Wellnes.
Copyright (c) 2025 D. Madhuri, Dr. S. Viswanadha Raju (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
MEDIQUICK – Optimized Emergency Bed allocation using AI and IoT
MediQuick is an AI and IoT-powered system that simplifies emergency hospital bed booking. It enables patients to input symptoms through a web interface, which are analyzed using a Logistic Regression model to determine if the condition is an emergency. If confirmed, a Random Forest classifier further identifies the emergency category. The system then uses the patient’s location to search for nearby hospitals with both available beds and doctors specialized in the detected condition. Once a suitable match is found, a temporary bed reservation is made to hold the spot. If the patient fails to arrive within the given time frame, the bed is automatically released for others in need. An IoT module, powered by a Force Sensing Resistor (FSR) connected to a NodeMCU, detects real-world patient presence on the bed to confirm physical admission. This allows seamless, automatic check-in without manual hospital intervention. Flask is used to build the backend server, while MySQL handles all hospital, bed, and patient data. Python’s machine learning ecosystem powers the emergency prediction models. MediQuick enhances the responsiveness of emergency healthcare systems through intelligent automation and real-time decision-making. The system supports efficient triaging and optimizes resource usage in critical situations. It bridges the gap between patients and hospitals using smart technology. MediQuick ultimately delivers a scalable and life-saving solution for modern healthcare challenges.Traditional sentiment analysis tools often fall short by offering only basic polarity classification and failing to provide detailed understanding of specific customer concerns.
Emergency Healthcare, Bed booking system, AI in healthcare, IoT in hospitals, Logistic Regression, Random Forest Classifier, Symptom Classification, geolocation, Real-time admission, FSR Sensor, NodeMCU, Smart Triaging
Copyright (c) 2025 K Madhuravani, Eesha Karpuradu, Revadi Hema Satya Varshini (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Energy-Aware Predictive Maintenance in Industrial Edge Systems Using Pruned LSTM Networks for Sensor-Based Time-Series Data
In modern industrial environments, predictive maintenance has become a vital strategy for ensuring operational reliability, reducing downtime, and optimizing energy utilization. However, existing deep learning (DL) approaches such as CNN, GRU, and hybrid architectures, while accurate, often suffer from high computational complexity and energy consumption, making them unsuitable for real-time edge deployment. To address these limitations, this study proposes an Energy-Aware Predictive Maintenance framework using Pruned LSTM Networks for sensor-based time-series data, designed specifically for industrial edge systems. The model employs structured pruning techniques to reduce redundant parameters and computational overhead while preserving the temporal learning capability of LSTM. The proposed approach was implemented using Python and TensorFlow on the Kaggle Industrial Equipment Monitoring Dataset, which contains multi-sensor readings representing normal and faulty machine states. Experimental results show that the Pruned LSTM model achieves a 98.8% accuracy, marking an increase of approximately 6–7% over conventional models like GRU and CNN, while reducing energy consumption by nearly 40% compared to baseline methods. This improvement demonstrates the model’s ability to maintain high precision and reliability under resource constraints. The proposed framework establishes a strong foundation for real-time edge-based predictive analytics, offering both energy efficiency and predictive robustness. In the future, the model will be extended with adaptive transfer learning and federated edge optimization to enable scalable and cross-domain industrial applications, driving the next generation of intelligent and sustainable maintenance systems.
Predictive Maintenance, Energy-Aware Computing, Pruned LSTM Networks, Industrial Edge Systems, Sensor-Based Time-Series Data
Ali Mohamed Ali Annas, Mohamed Ali Mohamed Ali Abdulkader, Huda Abdussalam Ali Abdulla, Massoud Ali Abdalhadi, Abdussalam Mohamed Ali Altaher
Copyright (c) 2025 Ali Mohamed Ali Annas, Mohamed Ali Mohamed Ali Abdulkader, Huda Abdussalam Ali Abdulla, Massoud Ali Abdalhadi, Abdussalam Mohamed Ali Altaher (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Digital Data Security Through Watermarking
It is believed that watermarking is a technology that enables the protection of various media data against unlawful or improper re-use .Hawkins brought up the several watermarking methods that have been developed for copyright an intellectual property protection in the literature, but many media data use various digital water marking methods. Additionally, due to various functions and applications, different watermarking systems have distinct technological requirements. This project aims to promote digital watermarking and presents a framework for electronic business designers and developers to employ watermarking in securing their online media contents since intellectual property protections utilizing digital watermarking is still in it’s infancy.
Digital Data Security, Watermarking, Multimedia.
Lahari Vaddireddy, Manya sree Godavari, Meghana Anugu
Copyright (c) 2025 P Ganeh Kumar, Lahari Vaddireddy, Manya sree Godavari, Meghana Anugu (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Synthesis, Structural Characterization, and In Vitro AntioxidantEvaluation of Novel Azetidinone Derivatives
Background: The β-lactam ring is a foundational structure in medicinal chemistry, most famously known as the core of penicillin and cephalosporin antibiotics. Beyond their antimicrobial properties, β-lactam derivatives, particularly azetidin-2-ones, have emerged as privileged scaffolds demonstrating a wide spectrum of biological activities, including anticancer, antihyperlipidemic, and antioxidant effects. The growing interest in combating oxidative stress—a key pathological contributor to aging, neurodegenerative disorders, and cancer—drives the search for novel, potent antioxidant agents. Objective: This study aimed to design, synthesize, and evaluate a new series of azetidinone derivatives, strategically functionalized to enhance their free radical scavenging and metal-reducing capabilities, thereby exploring their potential as therapeutic antioxidants. Methods: The target compounds were synthesized via a efficient two-step protocol. First, a series of Schiff bases were prepared by condensing various aromatic aldehydes with substituted anilines. Subsequently, these Schiff bases underwent a [2+2] cycloaddition reaction with chloroacetyl chloride and triethylamine (the Staudinger ketene-imine reaction) to yield the novel azetidinone derivatives. All 12 synthesized compounds were rigorously characterized using Fourier-Transform Infrared (FT-IR), Nuclear Magnetic Resonance (¹H & ¹³C NMR), and Mass Spectrometry (MS). Their in vitro antioxidant potential was assessed through multiple standard assays: 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging, Ferric Reducing Antioxidant Power (FRAP), and Superoxide Dismutase (SOD) mimetic activity. Key Findings: The spectral data unequivocally confirmed the successful formation of all 12 novel azetidinone derivatives. The antioxidant evaluation revealed that several compounds exhibited significant activity, with IC₅₀ values in the DPPH assay comparable to standard antioxidants like ascorbic acid. Compounds featuring electron-donating groups (e.g., -OH, -OCH₃) on the aryl rings demonstrated particularly potent radical scavenging and reducing power, suggesting a structure-activity relationship. Conclusion: A novel series of azetidinone derivatives were successfully synthesized and characterized. The results of the antioxidant assays are highly promising, indicating that these compounds are effective free radical scavengers. This work validates the design strategy and positions these azetidinone derivatives as promising candidates for further development as therapeutic agents for managing oxidative stress-related pathologies.
Azetidinone, β-Lactam, Antioxidant Activity, Staudinger Synthesis, Schiff Base, Free Radical Scavenging, DPPH, Structure-Activity Relationship
Copyright (c) 2013 Dr. Himanshu Sharma, Dr. Priti Sharma (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Improving Fingerprint Recognition With CNN
A deep learning-based fingerprint recognition system is developed to match contactless fingerprint inputs with stored contact-based fingerprint records. Using a lightweight yet powerful MobileNetV2 architecture, the system efficiently extracts deep features from fingerprint images while maintaining high accuracy and fast performance. The model is trained with a distance-aware loss function and incorporates ridge-based features to improve matching reliability between the two modalities. When a user uploads a contactless fingerprint image, the system preprocesses it, generates a feature embedding, and compares it with a database of contact-based fingerprints to identify the best match. The matched person ID is then displayed through an attractive and interactive user interface designed for ease of use. This solution avoids physical contact with sensors, making it suitable for touch-free, hygienic authentication in public or mobile environments. The integration of MobileNetV2 makes the system highly scalable and lightweight, suitable for deployment on both desktops and embedded platforms. Overall, this project demonstrates the practical application of deep learning in bridging modality gaps in fingerprint biometrics, offering a robust and future-ready identity verification system.
Fingerprint Recognition, Deep learning, MobileNetV2, Contactless authentication, Feature Extraction, Biometric matching, Lightweight Model.
Copyright (c) 2025 Tasneem Rahath, R Charitha Reddy, S Nainjeeth Kour (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Predictive Model for Airline Fare Estimation
travel planning, offering travelers valuable insights into the best times to purchase tickets at optimal prices. This project leverages machine learning techniques to create a system that analyzes historical flight data, seasonal trends, and real-time variables to predict airfare prices accurately. Unlike traditional rule-based and static pricing models, which often fail to adapt to dynamic market conditions, this approach uses data-driven algorithms to offer personalized and timely fare predictions. The result is a smart solution that empowers users to make cost-effective travel decisions with minimal effort. The proposed system is implemented as a user-friendly web application, integrating backend machine learning models with intuitive frontend design. Users can enter travel details such as source, destination, date, and airline preferences, and the system returns a real-time fare prediction. Key modules include user input, data preprocessing, prediction generation, and result display, all supported by technologies such as Python, PyTorch, Flask, and Scikit-learn. This solution not only simplifies the flight booking experience but also enhances affordability and transparency for users by minimizing the guesswork traditionally involved in fare comparisons.
FlyFare Predictor, Deep Learning, Historical Flight Data, Seasonal Trends, Real-time Variables, Dynamic Pricing, Backend ML Models, Frontend Web Application, Cost-effective Travel, Python / Flask / Scikit-learn.
Aashritha Konjarla, Akhila Koukuntla, Manvitha Reddy Katika
Copyright (c) 2025 D Navaneetha, Aashritha Konjarla, Akhila Koukuntla, Manvitha Reddy Katika (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Transmission Dynamics of the AIDS Epidemic: Development and Analysis of Nonlinear Models
Mathematical modeling has emerged as a crucial tool in understanding the transmission dynamics and control strategies for HIV/AIDS. This review synthesizes significant contributions to nonlinear mathematical models of HIV/AIDS transmission over the past four decades. We systematically analyze deterministic, stochastic, and hybrid modeling approaches, with particular emphasis on how nonlinearities in transmission functions affect epidemic thresholds and long-term dynamics. The review examines various modeling innovations including heterogeneous mixing patterns, treatment effects, behavioral changes, and co-infection dynamics. Meta-analysis of 42 selected studies reveals that incorporation of nonlinear incidence rates and time delays significantly improves model accuracy in predicting epidemic trajectories compared to classical models. Furthermore, bifurcation analyses of these nonlinear systems have proven essential in identifying critical parameter thresholds where epidemic behavior qualitatively changes. This review highlights the continuing challenges in parameter estimation and model validation while emphasizing the growing importance of nonlinear modeling frameworks in developing effective intervention strategies against the ongoing global AIDS epidemic.
HIV/AIDS epidemic, nonlinear mathematical models, bifurcation analysis, epidemic thresholds, stability analysis.
Copyright (c) 2018 Rampally Maruthi, Dr. Agraj Tripathi (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
A Smart AI Based Solution For Traffic Management On Routes With Heavy Traffic From Different Directions, With Real-Time Monitoring And Adaptation Of Traffic Light Timings
Traffic Core AI, a sophisticated React-based web application, simulates intelligent traffic management at a four-way intersection by integrating real-time data, machine learning predictions, and a dynamic user interface to optimize traffic flow, detect violations, and prioritize emergency vehicles. The system employs a mock ML model (MLTrafficPredictor) to monitor traffic volume, pedestrian presence, and environmental conditions like weather and pollution across North, East, South, and West directions, while dynamically adjusting green and wait times within a 120-second cycle based on traffic density, violations, pedestrian activity, and emergency priorities for efficient flow. It identifies traffic violations such as signal jumping or speeding, applying penalties that increase wait times to enforce compliance, and automatically detects emergency vehicles, granting them a 30-second priority mode with audible alerts. Environmental monitoring tracks air quality (AQI) and horn usage, generating AI-driven recommendations like traffic diversions to mitigate pollution or congestion. The interactive dashboard, enhanced by Framer Motion animations, offers a responsive UI with tabs for Dashboard (displaying real-time traffic, weather, and efficiency), Cameras (simulating feeds with Fullscreen viewing), History (visualizing 24-hour traffic and performance trends via Recharts area and composed charts), and Settings (for user preferences). Users can toggle night mode, voice assistant, auto-emergency detection, and adjust simulation speeds (0.5x to 5x), while real-time alerts for maintenance, accidents, or weather changes include voice feedback via the Web Speech API. Historical data and analytics reveal traffic patterns, and React hooks (useState, useEffect, useCallback, useMemo, useRef) ensure optimized state management and performance. Designed to simulate real-world traffic scenarios, TrafficCore AI enhances intersection efficiency, safety, and responsiveness through AI insights, user interaction, and dynamic signal control.
Environmental monitoring tracks air quality (AQI)
Shaik Feroj, Saad Ahmad Sharief, Mohammed Nisar
Copyright (c) 2025 Shaik Feroj, Saad Ahmad Sharief, Mohammed Nisar, Mohammed Rahmat Ali (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Role Of Digital Payment Systems In Shaping Purchase Intentions On E-Commerce Platforms: A Study In Coimbatore District
The rapid adoption of digital payment systems has transformed consumer behavior and the overall shopping experience on e-commerce platforms. With the increasing availability of e-wallets, Unified Payments Interface (UPI), credit/debit cards, and internet banking, consumers are empowered to make quick and secure online purchases. This shift is especially evident in emerging markets like India, where government initiatives, technological advancements, and pandemic-induced changes have driven consumers towards cashless transactions. This study examines how digital payment systems influence purchase intentions among e-commerce shoppers in Coimbatore District. Primary data was collected from 150 respondents using a structured questionnaire, and chi-square tests were used to analyze relationships between demographic factors and purchase intentions. Findings indicate that digital payment systems significantly influence consumer purchasing behavior, with younger and educated consumers showing higher adoption rates. The study concludes that digital payment convenience, security, and cashback offers strongly shape purchase intentions on e-commerce platforms.
Digital Payments, Purchase Intention, E-Wallets, E-Commerce, Cashless Transactions
Copyright (c) 2025 M. Theebika, Dr.S.Jayachitra (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Scalable Ml Inference For Real-Time Recommendations
Modern web-scale applications such as e-commerce, streaming to social media all are based on real-time recommendation systems. Nevertheless, it is a challenge to ML inference to deliver high-quality recommendations at low latency and large request volumes. This paper introduces a low-latency, high-throughput recommendation pipeline-based scalable ML inference system. We examine trade-offs among model complexity, latency and system cost. We develop a modular architecture to combine effective feature retrieval, candidate generation and optimized model serving. In particular, we discuss such methods as model quantization, dynamic batching, caching and hardware acceleration to improve the inference performance. Experimental testing on benchmark workloads and real-world datasets demonstrates that our system can realize sub-100ms latency and state of the art recommendation quality, scalable and cost efficient to use in production.
Scalable Machine Learning Inference, Real-Time Recommendation Systems, Low-Latency Model Serving, Distributed Systems for ML, Model Optimization and Deployment
Copyright (c) 2025 Ramakrishnan Sathyavageeswaran (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
The Role Of Buy Now, Pay Later In Consumer Buying Decisions: A Novel Financial Approach
The buy-now-pay-later mechanism is a new facet of consumers' buying habits in the modern, technology-based age. The purpose of this research is to get to the bottom of why so many people in Lucknow city use BNPL, or microcredit, to buy things. Using a two-stage sampling procedure, 418 respondents provided the primary data utilized in the research. To learn what makes people choose the buy-now-pay-later option, researchers apply multiple regression analysis. Analyses reveal that the value of annual product purchases made via the buy-now-pay-later mechanism is significantly affected by education, a growth in the demand for goods, and an improvement in the level of living that makes use of this payment method. This rise in spending also shows that people are making a purchase. According to the study's results, a better outcome is possible with effective financial inclusion, reduced inter-bank variation in payment mechanisms, more readily available information, and a buy-now-pay-later option in stores and everywhere else.
BNPL, buying behavior, purchase decision, financial inclusion
Copyright (c) 2023 Dr. Sachin Ashok Deshmukh (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Lightweight Concurrency With Go For RealTime Edge Computing InIot Systems: A Review
Edge computing pushes computation closer to data sources to reduce latency, preserve bandwidth, and enhance privacy in Internet of Things (IoT) deployments. Go (Golang) provides lightweight concurrency via goroutines and channels, plus a modern scheduler a nd concurrent garbage collector. This review synthesizes how Go’s concurrency model maps to soft real time constraints typical at the edge. We summarize definitions and requirements from standards (e.g., NIST, ETSI MEC), explain the Go runtime (asynchronou s preemption, memory model, GC), and curate patterns for streaming pipelines, backpressure, and resilience. We examine protocol choices (MQTT, CoAP) and edge to cloud bridges (gRPC/NATS), survey embedded options (TinyGo, periph.io, Gobot), and outline obse rvability and tuning (pprof, runtime/trace, PGO). Analytical modeling and experience reports indicate that careful design bounded queues, reduced allocations, and context timeouts achieves predictable soft real time behavior. We conclude with open challeng es (determinism, safety cases, eBPF fast paths, io_uring integration) and a research agenda for Go at the edge.
Go (Golang); lightweight concurrency; goroutines; channels; edge computing; IoT; real time; ETSI MEC; NIST; MQTT; CoAP; TinyGo; gRPC; NATS; eBPF; io_uring etc.
Copyright (c) 2025 Mrs.Vrunda Piyush Chouthkanthiwar, (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Green Refineries: How Electrical and Instrumentation Engineers Can Lead Decarbonization
The global petroleum refining industry faces unprecedented pressure to achieve net-zero emissions by 2050, requiring comprehensive technological transformation led by electrical and instrumentation (E&I) engineering innovations. This research examines how advanced process automation, smart grid integration, and intelligent monitoring systems can reduce refinery carbon emissions by up to 45% while maintaining operational efficiency. Through analysis of 127 refineries globally, this study demonstrates that E&I-driven optimization can achieve energy efficiency improvements of 23-31%, reduce operational costs by $12-18 million annually per facility, and accelerate decarbonization timelines by 3-5 years. The integration of artificial intelligence-powered process control systems, combined with electrification of heating processes and implementation of carbon capture technologies, presents a pathway to sustainable refinery operations. Key findings indicate that strategic E&I investments averaging $85-120 million per refinery can deliver return on investment within 4.2-6.8 years while achieving 78% reduction in Scope 1 and Scope 2 emissions by 2035.
Green refineries, decarbonization, electrical engineering, instrumentation systems, process automation, carbon capture, smart grid integration, industrial electrification, energy efficiency optimization
Copyright (c) 2025 Nazim Nazir (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Deep Learning Framework For Early Identification Of Cardiac Abnormalities From Ecg Images
Over the past few decades, cardiovascular illnesses have become the leading cause of death worldwide in both industrialized and developing nations. The mortality rate can be decreased through early identification of heart disorders and ongoing clinical monitoring by professionals. Cardiovascular disease is extremely lethal by nature and kills a disproportionately large number of people worldwide. An effective early detection strategy is crucial to preventing cardiovascular disease deaths. An electrocardiogram (ECG) is a vital tool for understanding a variety of human heart problems. This subject has been the subject of numerous investigations to identify heart anomalies for prevention. In order to forecast cardiovascular disorders, this research intends to construct an algorithmic model to analyses ECG tracings. This effort directly affects saving lives and enhancing healthcare at a lower cost. Saving lives and enhancing medical treatment are two immediate effects of this work as health care and health insurance expenses rise globally. In this study, the public ECG picture dataset of cardiac patients was used to harness the potential of deep learning techniques to predict the four main cardiac abnormalities: abnormal heartbeat, myocardial infarction, history of myocardial infarction, and normal person classes. We used the Mobile Net Architecture to build the system for this project, and we were successful in achieving training accuracy of 97.34% and validation accuracy of 91.00%. As a result, the proposed Mobile Net Architecture model classifies cardiovascular diseases with impressive accuracy and can also be utilized to extract features for conventional machine learning classifiers. Bypassing the manual method that produces unreliable and time-consuming findings, the suggested Mobile Net Architecture model can be utilized as a tool to assist physicians in the medical profession in detecting heart disorders using ECG images.
Cardiovascular Disease (CVD), Electrocardiogram (ECG), Deep Learning, MobileNet Architecture, Cardiac Abnormalities, Abnormal Heartbeat, Myocardial Infarction, Image Classification, Medical Diagnosis, Feature Extraction
Copyright (c) 2025 Khaza K B Vali Bhasha Sk, Manohar Manchala, K.Krishna, Shivarao Yannam, Bujapa Shivakarthik,, Maloth Srinivas, (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Convolution Neural Network Approach for Accident Severity Detection and Hospital Selection
Road accidents remain a critical public safety issue, necessitating rapid injury assessment and timely hospital recommendations. This project proposes a solution using Convolutional Neural Networks (CNNs) for accurate injury classification and severity detection. By leveraging deep learning, the system can analyze images of injuries to determine severity levels and suggest suitable hospitals based on the injury type. This innovative approach significantly outperforms traditional machine learning models in terms of accuracy and efficiency. Data augmentation techniques further enhance the dataset’s diversity, improving model robustness. Experimental results demonstrate that CNN-based systems offer a promising and efficient framework for road accident severity detection, potentially saving lives through faster medical interventions.
CNN, Road accidents, detection, Data augmentation, accuracy, efficiency and robustness
Copyright (c) 2025 I.Lavanya, Mr.Ch.Kodanda Ramu, Mr.E.Mahendra Roy (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Human–computer interaction has evolved significantly with advancements in artificial intelligence (AI) and computer vision. Traditional pointing devices such as the mouse and touchpad are widely used but present challenges in terms of accessibility, portability, and physical limitations. This paper presents a gesture-controlled virtual mouse system that leverages real-time hand gesture recognition to perform mouse operations without physical hardware. The proposed system integrates MediaPipe for landmark-based hand tracking, OpenCV for image processing, and Python libraries such as PyAutoGUI and Pynput for GUI automation. The approach eliminates additional hardware requirements and ensures cost-effectiveness. Experimental results demonstrate successful implementation of functions such as cursor movement, left and right clicks, drag-and-drop, multi-selection, and screenshot capture. The system improves accessibility, offering touchless interaction for users with physical disabilitiesand potential applications in healthcare, education, and hazardous environments.
Virtual Mouse, Hand Gesture Recognition, Human–Computer Interaction, OpenCV, MediaPipe, Machine Learning
Sandyapagola Suchitha, Dr. V. S. GiridharAkula ,Dr.M.Ramesh
Copyright (c) 2025 Sandyapagola Suchitha, Dr. V. S. GiridharAkula ,Dr.M.Ramesh (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Predictive Cyber Defense Using Machine Learning
In the current digital environment, enterprises, governments, and people must embrace proactive security tactics that beyond conventional reactive measures due to the growing frequency, complexity, and unpredictability of cyber attacks. Predictive cyber defense with machine learning is a revolutionary solution that uses advanced algorithms to analyze real-time and historical data, find hidden threat patterns, forecast malicious activity, and help detect new attacks before they cause serious damage. Security defenses may become dynamic and adaptable thanks to machine learning, in contrast to older intrusion detection and prevention systems that rely on static signatures, strict rule sets, or preset thresholds. These systems are always changing as a result of atypical user behavior, zero-day vulnerabilities, and new attack methods. The goal of this project is to create an intelligent framework that combines deep learning, supervised, and unsupervised models to categorize different kinds of attacks, identify abnormalities, predict possible breaches, and provide useful insights to improve cybersecurity resilience. In order to extract significant signs of compromise, the framework's fundamental procedures involve extensive data preparation from a variety of sources, including log files, network traffic, and user activity records, followed by sophisticated feature engineering. To increase detection accuracy, machine learning methods such as ensemble approaches, Random Forests, Support Vector Machines, and Neural Networks are used. Autoencoders and clustering algorithms are utilized for anomaly detection in order to discover small deviations that static, signature-based technologies can miss. The system uses cloud-based architectures to provide scalability and real-time responsiveness, allowing for the effective processing of high-dimensional large data streams without compromising low latency. Thorough assessment techniques are used to thoroughly evaluate performance across various cyber-attack datasets, including precision, recall, F1-score, ROC curves, and confusion matrices. The adaptive feedback loops in the model allow for ongoing retraining to keep ahead of adversarial strategies and lower false positives. Mitigating algorithmic bias, handling unbalanced datasets, guaranteeing model interpretability and transparency, and improving generalization across diverse operating settings are some of the major issues the framework attempts to solve. Intrusion detection, phishing prevention, ransomware mitigation, fraud detection, and insider threat monitoring are all covered in practical applications, which significantly enhance situational awareness and shorten incident reaction times. By combining intelligent automation and predictive analytics, this method lessens the need for human analysts, lessens alert fatigue, and helps businesses move from a reactive security posture to a proactive threat hunting one. The innovation of the research is in the way it combines explainable machine learning with multi-layered detection techniques, encouraging openness and confidence in automated judgments. Because of this, the technology is ideal for use in business networks, critical infrastructure, and cybersecurity initiatives inside governments.
Akula Manasa Mr. G. Prasad, Mr Dr.V.S..Giridhar. Akula
Copyright (c) 2025 Akula Manasa Mr. G. Prasad, Mr Dr.V.S..Giridhar. Akula (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Autism Detection From Image Using Generative AI
There is a growing demand for early identification and treatment strategies due to the rise in autism spectrum disorder (ASD) diagnoses. The majority of current diagnoses rely on behavior observation, which can be time-consuming and not necessarily reliable. In order to identify early indicators of autism, this study examines the use of generative artificial intelligence (AI) to analyze facial photographs. We developed a model that can recognize subtle face characteristics and patterns associated with ASD using cutting-edge machine learning techniques. A sizable collection of facial photos of individuals with and without autism is used in our approach. This aids in the model's learning of the distinctive characteristics that could be connected to autism traits. The generative AI system also creates extra training data, which makes the model
Syed Aijaz, D r.K.Subba Rao, Dr.Kaja Mastan
Copyright (c) 2025 Syed Aijaz, D r.K.Subba Rao, Dr.Kaja Mastan (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Embedded Night-Vision System for Pedestrian Detection
Through this study, we show that contemporary computer vision algorithms and readily available hardware may be used to create a low-cost, mobile pedestrian detection system that can function well in challenging lighting circumstances, such as fog or dusk. Our suggested solution uses an ODROID XU4 microcomputer running Ubuntu MATE in conjunction with a thermal imaging camera. A compact yet potent single-board computer, the ODROID XU4 was chosen for its ideal balance of portability, processing power, and affordability. This technology relies heavily on thermal imaging, which is resistant to the limits of visible light since it can detect infrared radiation that things release. This makes it possible to detect pedestrians reliably in glare or darkness. We constructed a cascade object detector specifically designed for thermal images as part of the detection method. In order to identify human silhouettes using their distinct thermal signatures, a classifier must be trained. It operates by filtering portions of a picture one after the other, rapidly eliminating the least likely locations, and focusing processing on areas with a high probability of containing pedestrians, which makes it a computationally effective option for resource-constrained embedded systems. We used a deep learning-based method, a convolutional neural network (CNN) tuned for object detection in thermal images, to evaluate system performance in comparison to the cascade object detector. Although CNNs often offer improved detection accuracy, their significant computational resource requirements can be a barrier for portable, cost-conscious applications. Our findings demonstrate that a properly calibrated cascade detector can achieve similar performance in a variety of situations while using a lot less power. Advanced driver-assistance features, including night-vision pedestrian recognition, don't have to be limited to high-end cars, as this work shows. Effective and accessible solutions that can improve road safety for a larger variety of drivers can be developed by utilizing thermal imaging, effective algorithms, and reasonably priced computing systems. Intelligent vision-based transportation solutions are made more accessible with this method.
Gangipogu Sruthi, Dr Kaja Mastan Dr Kiran .B.M
Copyright (c) 2025 Gangipogu Sruthi, Dr Kaja Mastan Dr Kiran .B.M (Author) https://creativecommons.org/licenses/by-nc-nd/4.0