Journal of Recent Innovation in Science and Technology
JRIST is meant by Journal of Recent Innovation in Science and Technology. It is an academic journal which focuses on cutting edge knowledge across science, engineering and technology. Through JRIST, scientists are able to foster their research activities, while academicians can also publish their work and industry people can share with the community by contributing articles or papers. Journal of Recent Innovation in Science and Technology(JRIST) Publisher: Academics Achievers Education And Research Foundation (AAERF), India Address: Umrapur, Ibrahimpur, District Raebareli, Pin-229212 Email: admin@academicsachievers.in Editor-In-Chief: Dr. Jaydip Kumar ISSN (Online): 3117-3926 ISSN (Print): 3117-535X Article Processing Charge (APC): No. of Issues Per Year: 4 | Frequency: Quarterly Frequency: Published quarterly every year in (March, June, September, and December). Starting Year of the Journal: 2025 Article Template Format: Click Here DOI: 10.70454/JRIST Coverage Areas: This journal is dedicated to publishing high-quality, original research and developments across a broad spectrum of disciplines. The coverage areas include, but are not limited to, the following topics: Computer Science and Information Technology Artificial Intelligence (AI) and Machine Learning (ML) Data Science and Big Data Analytics Cybersecurity and Information Assurance Internet of Things (IoT) Cloud and Edge Computing Blockchain and Distributed Ledger Technologies Software Engineering and Programming Languages Human-Computer Interaction (HCI) Computer Vision and Image Processing Natural Language Processing Quantum Computing Web and Mobile Application Development Networking and Communication Systems Computational Theory and Algorithms Mechanical Engineering Thermodynamics and Heat Transfer Fluid Mechanics and Hydraulics Computational Fluid Dynamics (CFD) Robotics and Mechatronics Materials Science and Engineering Manufacturing Technologies and Industrial Engineering CAD/CAM/CAE Systems Automotive Engineering Renewable Energy Systems and Sustainability Smart Materials and Structures Mechanical Design and Optimization Vibration and Acoustics Electrical and Electronics Engineering Power Systems and Energy Management Electrical Machines and Drives Control Systems and Automation Signal Processing and Embedded Systems Renewable and Smart Grid Technologies Microwave and RF Engineering Communication Systems and Wireless Networks VLSI and Microelectronics Instrumentation and Measurement Sensors and Actuators Electric Vehicles and Battery Management Systems AI and IoT Applications in Electrical Systems Civil and Environmental Engineering Structural Engineering and Earthquake Resistance Geotechnical Engineering Construction Materials and Technologies Transportation and Traffic Engineering Water Resources and Hydraulic Engineering Environmental Engineering and Sustainability Urban Planning and Smart Cities Building Information Modeling (BIM) Infrastructure Resilience and Risk Assessment Surveying and Remote Sensing Green Building and Energy-Efficient Design Waste Management and Recycling Technologies Original Research Articles: Presenting novel research findings and contributions to the field. Review Papers: Providing comprehensive surveys of existing literature on specific topics.
Academics Achievers Education And Research Foundation
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Stock Market Prediction Using Machine Learning and Deep Learning: A Systematic Literature Review
Stock market prediction has emerged as one of the most challenging and important research areas in finance and artificial intelligence. Accurate prediction of stock prices enables investors, financial institutions, and policymakers to make informed investment decisions and manage financial risks effectively. Traditional statistical forecasting models have shown limited performance due to the nonlinear, dynamic, and highly volatile nature of financial markets. In recent years, Machine Learning (ML), Deep Learning (DL), and Artificial Intelligence (AI) techniques have significantly improved prediction capabilities by learning complex relationships from historical market data, technical indicators, fundamental information, and textual sentiment extracted from news and social media. This review systematically examines recent developments in stock market prediction, focusing on traditional statistical models, machine learning algorithms, deep learning architectures, hybrid approaches, reinforcement learning, and transformer-based models. The paper also discusses commonly used datasets, evaluation metrics, challenges, and future research directions. The review provides researchers with a comprehensive understanding of current methodologies and emerging trends in intelligent financial forecasting.
Copyright (c) 2026 Anjali Parmar, Dr. Pharindra Kumar Sharma (Author) https://creativecommons.org/licenses/by/4.0
QOS-BASED WEB SERVICE RECOMMENDATION: A SYSTEMATIC LITERATURE REVIEW
Web service recommendation has become an essential research area due to the rapid growth of cloud computing and service-oriented architectures. Functional similarity alone is insufficient for selecting appropriate web services because multiple services often provide identical functionality with varying Quality of Service (QoS) characteristics. QoS-aware recommendation systems assist users in selecting services based on attributes such as response time, availability, reliability, throughput, and reputation. This review presents a comprehensive analysis of existing QoS-based web service recommendation techniques, including collaborative filtering, matrix factorization, clustering, deep learning, graph-based models, and hybrid recommendation approaches. The paper compares existing methodologies, identifies their strengths and limitations, discusses publicly available QoS datasets and evaluation metrics, and highlights current research challenges and future directions. The review serves as a valuable reference for researchers developing next-generation intelligent web service recommendation systems.
Copyright (c) 2026 Gajendra Singh, Shyamol Banerjee (Author) https://creativecommons.org/licenses/by/4.0
Review of Cloud-Based Public Security Video Investigation Systems: Architecture, Challenges, and Future Directions
Cloud computing has significantly transformed the way people investigate security video images by enhancing the analytical ability of surveillance technology, scaling and efficiency. Ordinary video surveillance systems have limitations in storage, real-time processing, and analysis of data, cloud computing addresses these issues quite satisfactorily. This paper explores different types of cloud computing models, Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS), and its applications in public security video studies. The concept of cloud-based video surveillance systems is mentioned along with the methods of data collection, cloud storage solutions, real-time processing, and analytics powered by artificial intelligence. The paper highlights the key benefits of cloud-based security systems which include improved scalability, cost efficiency, real-time monitoring, and AI-based threat detection. To ensure optimal performance, however, issues such as data privacy issues, cyber attacks, interoperability issues and dependency on constant internet connectivity need to be addressed. The paper also examines Indian case studies that have conducted cloud-based video analysis in the law enforcement system and smart city monitoring programs. By implementing the recently emerging technologies such as artificial intelligence, 5G, and edge computing, cloud-based public security systems can enhance the capability to prevent and respond to crimes. The essay discusses the future that should occur and highlights that regulatory frameworks, moral implementation of AI, and superior cloud protection measures are required.
Copyright (c) 2026 Pharindra Kumar Sharma, Sonia Chourasiya (Author) https://creativecommons.org/licenses/by/4.0
A Cloud-Based Deep Learning Framework for Real-Time Anomaly Detection in Public Surveillance Videos Using VGG16-LSTM
The purpose of this research is to design and train a deep learning-based surveillance network that can detect anomalous and suspicious behavior in real time using the Daily Crime Surveillance and Safety System (DCSASS). The system will enhance safety on the streets and enable detection of threats ahead through automation of anomaly detection of the video feeds. The methodology is systematic and organized and starts by gathering and authenticating video data of a high risk activity of 13 high risk classes of activities such as assault, robbery, arson and vandalism. An effective preprocessing pipeline was developed, including a Gaussian blur filter and brightness-contrast normalization to improve clarity of the frame and reduce noise. The videos were then split into frames and annotated by using official labels and separated into training and validation sets using stratified sampling to ensure class balance. A hybrid architecture was followed whereby the VGG16 convolutional neural network that was used as the frozen spatial feature extractor was integrated with the Long Short-Term Memory (LSTM) layer to learn temporal interrelations. The time intervals of the input feature vectors were converted to a repetition, simulating the motion context. The performance, such as the accuracy, loss, precision and recall, were modelled using the standard metrics. The proposed VGG16-LSTM model presented an accuracy of 95.05, low loss of 0.126, precision of 95.46 and recall of 93.11 indicating that this model is very reliable when it comes to detecting anomalies. In addition, object detection analysis yielded a precision of 85.7% and mAP 0.5 of 71.7%. The validity and scalability of the suggested framework were validated in comparison to the other existing models, including ResNet-50 and an earlier hybrid model.
An Integrated Model of Blended Learning Effectiveness
The blended learning has become a common practice in higher education, yet its success is commonly assessed with a single measure like student satisfaction, academic achievement, or technology acceptance. The current paper presents a multi-stakeholder model that can be explained with the help of the following problem statement: The proposed framework will combine the satisfaction of teachers, students, academic performance, and technological acceptance into one multi-level model of the effectiveness of blended learning. The study develops a composite measure, the Blended Learning Alignment Index (BLAI), to gauge the coherence among these dimensions by the method of measuring the alignment of teacher expectations, student experiences, and observed learning outcomes. The framework is designed to be mixed-methods in terms of survey data and academic records with digital learning indicators and to interpret the areas of alignment and mismatch with qualitative evidence. The suggested model has the potential to advance the field of blended learning because it provides a more comprehensive and interpretable way of evaluation and aids in making evidence-based decisions in the educational process.
Copyright (c) 2026 Sony Kumari (Author) https://creativecommons.org/licenses/by/4.0
Performance Enhancement of Renewable Energy System using Artificial Intelligence Control
Renewable energy systems are increasingly essential to sustainable power generation because they reduce dependence on fossil fuels and support low-carbon energy transition. However, the performance of solar, wind, and hybrid renewable systems is often limited by intermittency, nonlinear operating characteristics, environmental uncertainty, and integration challenges. These factors reduce energy extraction efficiency, degrade power quality, and complicate stable system operation under varying load and resource conditions. To address these limitations, this article proposes an artificial-intelligence-based control framework for renewable energy systems with emphasis on performance enhancement through adaptive supervision, predictive regulation, and multi-objective decision-making. The study presents a structured review of renewable energy system characteristics, major artificial intelligence techniques for control, and the need for intelligent supervisory operation in modern energy environments. A novel control strategy, termed the Predictive Adaptive Multi-Objective Energy Regulator (PAMER), is introduced to improve efficiency, power quality, stability, and reliability in solar, wind, and hybrid renewable configurations. The framework combines sensor-based monitoring, data preprocessing, intelligent state estimation, adaptive control prioritization, and constraint-aware energy management within a closed-loop architecture. A performance enhancement methodology is then developed to evaluate system behavior using indicators such as efficiency, settling time, harmonic distortion, voltage deviation, and reliability-related constraints. Illustrative comparative analysis shows that the proposed AI-based framework can provide higher energy utilization, faster dynamic response, reduced overshoot, lower total harmonic distortion, and improved voltage regulation compared with conventional control approaches. The results support the view that artificial intelligence can significantly strengthen renewable energy system control by enabling more adaptive and resilient operation under fluctuating conditions. The article also discusses computational, data-related, and implementation challenges, and highlights future research opportunities in real-time deployment, explainable intelligent control, and advanced hybrid optimization for next-generation renewable energy systems.
Copyright (c) 2026 Dr. Swati Singh (Author) https://creativecommons.org/licenses/by/4.0
Fish Disease Prediction Using Machine Learning on Water Quality Data
Fish diseases are a serious threat to the farming industry. To detect these diseases in the initial stage, a different machine learning model is proposed. This model has focused on identifying the fish diseases based on the water quality. To do this, we have used the “Aquaculture–Water Quality Dataset” data set havi15 physico-chemical parameters like PH, DO, hardness, solids, chloramines, iron, ammonia, nitrite, nitrate, phosphate, silica and the like. These attributes are linked with the existence of a disease. From the data set, we have created the Random Forest Classifier using the Python language and the Scikit library. The implementation has provided an efficiency of 89.25%. The metrics of precision, recall, and F1-score were employed to determine the efficiency. The evaluation has revealed that the proposed model has displayed the appropriate efficiency. The important feature analysis has shown that the dissolved oxygen , temperature ,PH, and ammonia are the critical factors used for the detection of the disease. Through the proposed method, a proper mechanism has been developed for detecting the fish diseases. This model can be used effectively to create proper system for the detection of fish diseases.
Copyright (c) 2026 Rajat Kumar, Roshni Prasad (Author) https://creativecommons.org/licenses/by/4.0
Cost-Sensitive Hybrid Ensemble Deep Model for Software Defect Prediction on NASA Datasets
Software defect prediction is a crucial task for improving software reliability and reducing maintenance cost in large-scale software systems. One of the major challenges in defect prediction is severe class imbalance, where defective modules are significantly fewer than non-defective ones. Traditional machine learning models often fail to prioritize defect detection, leading to biased performance. This paper proposes a cost-sensitive hybrid ensemble deep model for software defect prediction using NASA benchmark datasets. The proposed framework integrates cost-sensitive learning with ensemble deep classification to enhance minority defect detection while maintaining overall predictive stability. A comprehensive pre-processing pipeline and multi-metric evaluation strategy are employed, including accuracy, precision, recall, F1-score, and ROC-AUC. Experimental results across eight NASA datasets demonstrate improved defective module detection and strong generalization capability. The proposed model provides a scalable and practical framework for intelligent software quality assurance in industrial environments.
Copyright (c) 2026 Aditi gaur (Author) https://creativecommons.org/licenses/by/4.0
Enhancing Financial Fraud Detection Using a Hybrid Blockchain–AI Approach
Financial fraud is a serious problem in the digital economy, with billions being lost each year. Conventional fraud detection schemes are usually insufficient in real-time processing, false positives, and adaptive fraud pattern changes. This paper introduces a Hybrid system combining Block chain and Artificial Intelligence (AI) to improve fraud detection in financial transactions. Block chain provides data immutability and integrity, whereas AI models (machine learning, deep learning) monitor patterns of transactions for detecting anomalies. We compare existing methods, introduce a new architecture that is a hybrid of smart contracts and AI-driven fraud detection, and discuss some of the challenges and research directions Experimental results prove greater accuracy and safety compared to existing practices
Copyright (c) 2026 Narayan Jee, Sumit Kumar, Sarthika Dutt, Anjali Arora (Author) https://creativecommons.org/licenses/by/4.0
Neural Networks for Fashion Image Classification and Visual Search
In modern internet commerce and digital retail, fashion classification and visual search are very important jobs. They make it possible to quickly recommend products, find them, and keep track of inventories. Even though deep learning has come a long way, current CNN (convolutional neural network) methods still have trouble with class disparities, overlapping categories, and picking up on fine-grained visual details in manner datasets. To solve these problems, this study suggests Fashion ViT-SA, a hybrid neural network that combines a Vision Transformer (ViT-Base, Patch16) core with a Spatial Attention Module. The model uses the transformer's capacity to encode global context while also using spatial attention to highlight local clothing features, which improves discriminative representation. We used the Deep Fashion multi-modal Dataset and did some preprocessing, such as filtering categories, encoding labels, separating the data into groups, and adding data both online and offline to make sure the class distributions were even. Fashion ViT-SA was used to extract features that were then used to sort items into seven fashion types and to search for items visually based on their content using an Annoy-inspired approximate nearest neighbour index. We trained the model with weighted cross-entropy loss, improved it with Adam-W, and then tested it for accuracy, precision, recall, F1-score, and loss. Experimental findings indicate that Fashion ViT-SA attains 83% accuracy, surpassing a baseline CNN model by 13%, and delivers solid, real-time retrieval performance for visually analogous products. The research underscores the promise of hybrid transformer-based architectures in fashion AI applications, merging classification precision with scalable visual search, thus propelling both scholarly inquiry and practical e-commerce innovations.
Copyright (c) 2026 Kamal Kishor Rajak, Dr. Pharindra Kumar Sharma (Author) https://creativecommons.org/licenses/by/4.0
Feature Selection for Automatic Answer Extraction from Online Web Forums
This research proposes an efficient method for automatic answer quality classification in online web forums using a Multi-Task BERT-Based deep learning framework. The primary objective is to accurately categorize user responses into low, medium, and high-quality classes by leveraging advanced language representation and relevant content features. Starting with Stack Overflow forum data collection, the methodology moves on to comprehensive preprocessing, exploratory data analysis, and the extraction of syntactic and semantic features. Syntactic features include things like sentence length, punctuation use, code snippets, and TF-IDF vectors and contextual embeddings using spaCy. To reduce redundancy and increase the quality of the model input, we utilised feature selection with RFECV and the principle component analysis (PCA). A two-output-layer multi-task BERT architecture, which the model uses as its basis, can handle major classification and auxiliary tasks simultaneously, allowing it to increase generalisation. After seven training epochs, the trained model achieved remarkable results: 99.54% Training Accuracy, 92.13% Validation accuracy, 92.13% Precision, 92.13% Recall, and 92.13% F1-Score. In addition to providing fast inference, the model typically only takes 0.0071 milliseconds per sample for reactions. In large-scale community-driven QA platforms, these results validate the model's robustness, efficiency, and appropriateness for real-time applications.
Copyright (c) 2026 Hemant Sharma, Shyamol Banerjee (Author) https://creativecommons.org/licenses/by/4.0
Integrated Green Cloud Framework using Virtualization, Carbon-Aware Workload Migration, and Simulation for Sustainable Data Centers.
The unforeseen growth of digital services has escalated energy requirements in the world data centers, and an environmental sustainability issue is a hot topic in cloud computing. Conventional parallelism to workload scheduling fails to consider real time carbon intensity and regional grid efficiency thus resulting in high carbon emission even in optimally performed computational work. The proposed work suggests a green cloud synergetic framework, which integrates the virtualization, the carbon-aware workload migration, and the simulation-based decision model to plain the challenge of energy inefficiency and the environmental impact associated with the data center operations. This framework exploits a dynamic virtualization layer to achieve VM consolidation, as well as a carbon-conscious migrator that on real-time emissions data migrates workloads to utilise cleaner regional grids. CloudSim Plus-supported simulation environment was created to test the model in terms of its operation over the 24-hour period, with the testing against baseline and partial setup. The findings show the decreased energy usage of 36.6 percent and a decreased carbon emission of 52 percent in comparison with the conventional static allocation plan. The added migration events do not affect significantly the model as it continues to sustain high VM utilization (72.3%) and low SLA violations (8.1%). The effectiveness of the proposed framework compared to the available scheduling models is proven by the matter of fact that the former performs better in terms of various tracks of sustainability according to benchmarking. The paper presents a flexible and elastic approach to a sustainable cloud infrastructure and forms the basis of future-generation carbon-minded orchestration in the cloud.
Copyright (c) 2025 Pooja Jayprakash Patel (Author) https://creativecommons.org/licenses/by/4.0
Post Quantum Cryptography on Healthcare Security: Safeguarding Patient Data in Medical Systems
Quantum computers are capable of solving problems that classical computers cannot, particularly in the realm of cryptographic algorithms such as RSA, DSA, and ECC. Post quantum cryptography refers classical cryptographic algorithm that are designed to secure sensitive data. This research explores the application of post-quantum cryptographic techniques in healthcare systems, with a specific focus on integrating Quantum Key Distribution (QKD) with Genetic Algorithms (GA) for enhanced key optimization. As quantum computing advances, traditional cryptographic systems employed in health care are more and more vulnerable to being broken. Security of sensitive patient information—like electronic health records (EHRs), diagnostic data, and personally identifiable information— requires strong solutions that can handle future quantum attacks. Post-quantum cryptography (PQC) provides a hopeful way forward by employing quantum computer-resistant algorithms. This paper investigates the deployment of PQC methods—e.g., lattice-based, hash-based, and multivariate polynomial cryptography—inside healthcare systems to improve data confidentiality, integrity, and availability. It assesses the weaknesses of present encryption technologies and introduces PQC as a necessary update to protect patient information from sophisticated cyberattacks. The study also addresses the interoperability, computational overhead, and infrastructure readiness challenges of implementing PQC in healthcare. By implementing quantum-resilient cryptographic protocols, healthcare facilities are able to future-proof data protection practices and guarantee the safeguarding of patient data in an era governed by quantum computing.
Copyright (c) 2025 Shubham Kumar (Author) https://creativecommons.org/licenses/by/4.0
IOT BASED HYBRID SMART WATER METER FOR DOMESTIC UTILITIES
In the evolving world, automation plays an important role to make the life easier. Enhancing the properties to reduce the time and efforts of human or an organization. The automation is leading to betterment of the world but there are some fields where automation is not yet opted. The project “IOT Based Smart water and EB meter for Domestic Utilities” is to leverage automation in local monitoring of domestic utilities especially for government using Embedded System and Cloud Technology. A master-slave configured project using ESP32 as main controller to gather data in local and building a server to store the data in cloud. The project utilizes a hybrid working model to enhance the capability in future. The slave unit containing two ESP32 measuring water and electrical supply in home with the help of sensors. Once the values are taken. The slave communicates to Master ESP32. The Master ESP32 enabled with Wi-Fi interacts with the server to save values in the databases. As the project is enabled with Master-Slave Hybrid configuration it improves the upgradability. The main purpose of the project is to reduce the need of man power for measuring the Water and Electrical usage values in homes, more visibility on usage using statistical charts and tables through online, reducing the cost and more upgradability.
Copyright (c) 2025 Diksha Mishra (Author) https://creativecommons.org/licenses/by/4.0
A Transformer-Based Framework forDomain-Sensitive Amharic to English MachineTranslation with Character-Aware SubwordEncoding
This paper proposes a domain-adapted neural machine translation (NMT) system for Amharic-to-English translation, focusing on the issues of low-resource translation in a richly morphologically inflected language. We focus on the religious domain with the Tanzil corpus, a structured collection of Quranic verses which are translated into Amharic and English for coherence and semantic correspondence. To address the shortcomings of the traditional word-level tokenization of Amharic, we implement character-level subword tokenization using the SentencePiece model, which is better suited for rare and compound words. Our model harnesses a Transformer based encoder-decoder model together with multi-head attention and feedforward layers induction over the parallel corpus of poems in English and Amharic.The model achieved 59.03 BLEU score on the test set, greatly exceeding the classical RNN+Attention baselines which have been shown to have poor performance in low-resource settings. The strong score illustrates that an effectively tuned baseline Transformer model, in combination with domain-specific corpora and sophisticated subword methods, can perform well in translation tasks for under-resourced languages. The research provides a foundational, reproducible, and scalable framework that is linguistically-informed for Amharic-English translation, and it can be extended in the future, with additional extensions to other Semitic and morphologically rich languages. Our results highlight the value of domain adaptation and subword-aware architectures in advancing NMT for low-resource language communities.
Copyright (c) 2025 Ragini Rai (Author) https://creativecommons.org/licenses/by/4.0
INTELLIGENT DEEP LEARNING SYSTEM FOR MONITORING WORKERS IN HIGH RISK AREAS
One important issue that is quite relevant to the workplace in the fields of construction, mining, oil and gas, and heavy manufacturing is the problem of workplace safety as employees are often exposed to dangerous conditions. Other models of surveillance relying on manual oversight, or basic surveillance systems, typically lack real-time visibility, which results in action delays and unnecessary events. To solve this problem, this paper presents an intelligent monitoring system which combines both the deep learning techniques and the modern raised loss technologies. This proposed system uses convolutional neural networks (CNNs) and real-time video analytics to detect unsafe worker behaviors, the existence of personal protective equipment, and its use to determine potential risk situations. The system is capable of processing continuous data streams delivered by cameras and other environmental sensors to ensure hazards are recognized in time and allow supervisors to react as quickly as possible. However, the deep learning model adapts to changing work environments differently to traditional systems (this accounts for the reduction in false positives and the increase in the reliability of the safety measurements). In addition to the predictive analytics feature, the system can pinpoint high-risk cases before accidents happen, and ahead of time work to bring safety levels up. Not only does the framework improve worker protection, but it also decreases downtime and losses related to worker accidents. This research identifies intelligent deep learning systems as one solution that could help advance safety levels, increase efficiency in monitoring activities, and promote a culture of safety in high-risk workplaces.
Copyright (c) 2025 Bharath Kumar Nakka, Dilip Kumar (Author) https://creativecommons.org/licenses/by/4.0
An Intelligent Deep Learning-Based Framework for Suspicious Criminal Activity Detection in Surveillance Systems: Design, Implementation, and Evaluation
The rapid expansion of smart cities and widespread deployment of surveillance infrastructure have highlighted the need for intelligent systems capable of detecting suspicious and criminal activities in real-time. Traditional surveillance systems primarily rely on manual monitoring, which is error-prone and unable to scale with the increasing volume of video data. To address these challenges, this paper presents a novel deep learning-based framework that integrates convolutional neural networks (CNNs) for spatial feature extraction with Transformer encoders for temporal sequence modeling. The proposed system effectively captures both short-term motion anomalies and long-term behavioral patterns, thereby enhancing detection accuracy in diverse environments. Experimental evaluations on benchmark datasets such as UCF-Crime and Avenue demonstrate significant improvements over state-of-the-art approaches across metrics including accuracy, F1 score, and AUC. Furthermore, edge deployment on NVIDIA Jetson Xavier NX confirms the framework’s viability for real-time operations, achieving sub-300 ms inference latency without compromising detection quality. The framework is modular, interpretable, and scalable, making it suitable for integration into smart city surveillance ecosystems. In addition to technical contributions, ethical considerations such as fairness, transparency, and privacy are addressed to ensure responsible deployment of automated surveillance systems.
Copyright (c) 2025 Sunil Kumar Mishra, Dr. Rajat Kumar, Yogesh Kumar Sharma (Author) https://creativecommons.org/licenses/by/4.0
Systematic Review on Cybersecurity in the Internet of Vehicles (IoV)
The Internet of Vehicles (IoV) represents a critical evolution in intelligent transportation systems. It is a key advancement in intelligent transportation systems, where vehicles connect and communicate effectively with each other, utilizing great infrastructure that enables real-time data exchange and various advanced features. The paper spells out the attack vectors—such as MITM, DoS, spoofing and malware-based intrusions. However, it also poses some serious cybersecurity threats. This systematic literature review investigates the key security challenges in the IoV landscape and their countermeasures in VANETs through a comprehensive analysis of research conducted from 2015-2024. The review explores a range of security solutions that are currently being studied. Emphasis is focused on cryptographic protocols, machine learning based intrusion detection systems, and blockchain technologies, given their effectiveness, limitations, and suitability for resource-constrained vehicular environments. Even with the substantial progress, challenges remain in achieving real-time threat detection, ensuring interoperability, and developing lightweight yet effective security solutions. This review highlights these gaps and outlines future research directions, advocating for an integrated and adaptive cybersecurity framework according to the dynamic and heterogeneous nature of IoV networks.
Copyright (c) 2025 Nishu Kumari, Shashank Jha, Beer Singh, Pawan Kumar, Prashant (Author) https://creativecommons.org/licenses/by/4.0
A Hybrid Approach to Data Security Using Steganography and Blockchain for Tamper-Proof Communication
In the data security the cryptography and steganography could be usefull for data security individually. But individual these security techniques are not enough to provide sufficient data security. In this paper we have shows that steganography and blockchain are used together to hide encrypted data inside image. The proposed method will solve two major drawbacks of the current existing security techniques, the large size of ciphertext imbedded into an image and other is to provide integrity and authenticity are guaranteed by blockchain, because it provide unalterable ledger and cryptographic hashes. Tamper detection will guaranteed through verification of the extract messages’s hash against blockchain data.
Copyright (c) 2025 Prashant Prashant, Shashank Jha, Beer Singh, Gesu thakur (Author) https://creativecommons.org/licenses/by/4.0
Comparative Analysis of Deep fake Video and Audio Detection
Deepfake ("deep learning + fake" = DF) refers to the forged videos and audios generated using AI algorithms. While they can be a source of entertainment,theycanalsobeharmfulinvariousways.Manipulatingbothau- diosandvideosforharmfulpurposeshasbeen a concerning issue from the past more than 10 years. The ability todetect these videos and audios through AI detectors is a motivatingfactor in achieving the best results for the project. Thispapercontainscomparativestudyoftheexistingresearchondeepfakeissues showcasingAccuracy,F1Score,Barplotsandgraphsofthesame.Whileexplo- rationofdeepfakevideoshaveseveralapproachesanddatasetsavailable,theau- diodeepfakes has been relatively neglected. In this work, we propose theidea ofjointdeepfakevideoandaudiodetectionusingahybrid deep learning model ensembling ResNet50 and EfficientNet B0. The dataset comprising real and synthetic voice recordings was selected from the SceneFake repository on Kaggle. Key audio features, including Mel- frequency cepstral coefficients (MFCCs), spectral centroid, chroma, zero- crossing rate, and root mean square energy(RMSE),wereextractedusingthelibrosalibrary. RandomForestClassi- fierwastrainedtodetectaudiowhileDFDVdatasetwasutilizedtoextractfacial frames from videos.
Copyright (c) 2025 Harish Chandra Prasad, Dr. Arti Gautam Dinker (Author) https://creativecommons.org/licenses/by/4.0
A Comparative Study of Machine Learning Algorithms for Brain Tumor Detection
This study examines brain imaging to identify areas with tumors and categorizes these regions into three distinct types: meningioma, glioma, and pituitary tumors. This paper also compares different machine learning algorithms for the identification of brain tumors. The term "brain tumor" describes the excessive growth of cells in the brain, that can be either benign or malignant. In this study, machine learning algorithms have been utilized to both identify what kind of brain tumor in patients who have been diagnosed with one using multi-class classification and to detect whether brain tumors are present or absent by binary classification. There are two types of brain MRI pictures in the dataset used for the binary classification challenge: those containing tumors and those without. Several machine learning algorithms, including Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbor (KNN), Naïve Bayes (NB), Decision Tree (DT) classifier, and Random Forest classifier, have been applied to classify the MRI images. Accuracy, recall, precision, and F1-score were among the performance indicators used in a comparative analysis of machine learning algorithms. With an accuracy of 90.8%, recall of 95.2%, precision of 81.1%, and F1-score of 87.6% across 1,475 brain tumor images, the Random Forest classifier outperformed the other techniques.
Copyright (c) 2025 Vimmi Kochher (Author) https://creativecommons.org/licenses/by/4.0