Journal of Recent Innovations in Computer Science and Technology
Journal of Recent Innovations in Computer Science and Technology(JRICST) is a double-blind peer-reviewed journal published Quarterly, four (4) Times a year, focusing on theories, methods, and applications in Computer Science and innovation in Technology. It provides a challenging forum for researchers, industrial professionals, engineers, managers, and policymakers working in the field to contribute and disseminate innovative new work on Computer Science and innovation in Technology. Journal of Recent Innovations in Computer Science and Technology(JRICST) Publisher: Academics Achievers Education And Research Foundation (AAERF), India Address: Umrapur, Ibrahimpur, District Raebareli, Pin-229212 Email: admin@academicsachievers.in Editor in Chief: Dr. Animesh Srivastava ISSN (Online): 3050-7030 ISSN (Print): 3050-7022 Manuscript Turnaround Time Editorial Review: 2-4 days. Time to First Decision (Peer Review Decision): 2–3 weeks Overall estimated time from submission to decision: 4-6 weeks. Revision Time: 1-2 Weeks Starting Year of the Journal: 2024 If submitted within a reasonable timeframe of the issue, the journal’s review and publication process takes approximately 6–8 weeks. Frequency: Published quarterly every year in (January, April, July, and October). Acceptance Rate: Paper Acceptance Rate of 2025 is 33.33% Details. DOI: 10.70454/JRICST Coverage Areas: The topics covered by this journal include, but are not limited to, the following topics: Artificial Intelligence (AI) and Machine Learning (ML) Artificial Intelligence in Healthcare Blockchain Technology Cloud Computing Cyber-Physical Security Cybersecurity Data Science and Big Data Emerging Technologies Human-Computer Interaction (HCI) Information System Internet of Things (IoT) Networking and Communication Quantum Computing Robotics and Automation Software Engineering Web Development Types of Contributions: The Journal accepts the following categories of papers: 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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Detection of Mozi IoT Botnet Using Autoencoder-Based Feature Learning and Hashing
The detection method described in this paper uses autoencoder-based feature learning to identify abnormal traffic patterns indicative of a Mozi infection and employs hashing techniques to track and enumerate the P2P botnet nodes. The Internet of Things (IoT) has emerged as a game-changer in today’s world, influencing numerous industries and lifelines. Detection of IoT botnets has multiple implications for the security and availability of IoT ecosystems. Mozi is a P2P IoT botnet with characteristics such as fast spreading, persistence, and misuse of weak device configurations. Traditional signature and rule-based detection schemes are often unable to detect such dynamic threats, due to their poor generalization capabilities. In this paper, we present an efficient Mozi botnet detection scheme that leverages auto encoder-based feature learning and a hashing technique to achieve fast, scalable detection. Therefore, this paper proposes a method to detect botnets using an autoencoder and a hash function for large-scale data retrieval. The algorithm uses the autoencoder to learn what is normal, then it hashes what it learned to succinctly summarize the learned information, and it compares hash codes in real time to detect anomalies, including IoT botnet-related anomalies. The proposed method achieves high detection accuracy, and is robust to the evolving attack strategies of Mozi botnet, better than traditional methods with the low computation and storage overhead, which can be well applied in IoT infrastructure.
Copyright (c) 2026 Nitesh Kumar Saxena, Dr. Bhupender Singh Rawat (Author) https://creativecommons.org/licenses/by/4.0
Adaptive Machine Learning Strategies for Detecting Malicious URLs
The proliferation of online services has brought even greater exposure to cyber-attacks, especially in the form of phishing and malicious URL-based threats that impersonate legitimate websites to steal user credentials and financial data. Traditional blacklisting and rule-based security solutions are not able to keep up with the changing phishing tactics, posing a challenge for developing intelligent and adaptive detection solutions. Here, a holistic machine learning based framework for malicious URL classification using supervised learning models is being proposed. Four classifiers, namely K-Nearest Neighbor (KNN), Kernel Support Vector Machine (SVM), Decision Tree, Random Forest have been trained and tested on a comprehensive dataset which contains lexical, domain based and host based URL attributes. Model efficiency is improved using the preprocessing based on standardization and the automated feature extraction. A comparative analysis based on confusion matrices and accuracy indicates that Random Forest classifier gives the best results among other classifiers with highest accuracy of 96.82%. The results demonstrate that some method is more robust to different phishing scenarios. In this work, we present an efficient, scalable and low cost detection approach to facilitate real-time cyber security system, which constitutes a practical solution to enhancing online security against emerging malicious URL attacks.
Copyright (c) 2026 hemendra sharma Shanker (Author) https://creativecommons.org/licenses/by/4.0
Security of AI Models using Paillier cryptosystem
This paper demonstrates the practical operation of the Paillier cryptosystem in securing direct retrogression conclusion while conserving data sequestration. A customer- garçon armature is used, where sensitive input data is translated on the customer side using Paillier’s cumulative homomorphic encryption and reused on the garçon without revealing the raw values. The translated data is subordinated to a direct retrogression model (with a predefined weight and bias), using homomorphic operations to cipher translated prognostications directly. After decryption, the works corresponded exactly to the expected direct results, thus confirming the correctness of the calculations in the translated space. A performance test carried out on a dataset of 30 numerical values showed an average of the encryption and decryption times of 0.31 seconds and 0.28 seconds respectively. Nevertheless, the performance is quite effective for small to medium-scale datasets. Such a system yields a highly secure sequestration- conserving output for sensitive operations like healthcare analytics or fiscal modelling. The findings indicate that the model keeps computational consistency even when the data is encrypted, thus enabling precise and reliable predictions that do not compromise data security. Also, the simplicity and availability of the perpetration using Python and the PHE library make it a practical choice for real- world deployments. While presently limited to direct models due to the cumulative nature of the Paillier scheme, the approach presents a strong case for extending sequestration- conserving ways to broader classes of machine literacy algorithms. This exploration highlights the eventuality of homomorphic encryption in enabling secure and secure AI systems, especially in scripts where confidentiality is consummate.
Copyright (c) 2026 Brook Hilemriam, Asamene Kelelom, Beer Singh (Author) https://creativecommons.org/licenses/by/4.0
AI-based Diagnostic System for Chest X-rays: A Multi-Labeled Classification Approach using Deep Learning
Although chest X-rays (CXRs) are still a vital diagnostic tool for detecting thoracic disease, their interpretation can be challenging because of their multilevel findings and contradictory visual patterns. As a result, we examine how well deep convolutional neural networks (CNNs) with transfer learning perform automated multi-label classification of CXRs. Extensive preprocessing and augmentation techniques were used to address class imbalance and normalise image quality using the CheXpert dataset. Under consistent experimental conditions, several CNN architectures, including CustomNet, DenseNet121, ResNet50, InceptionV3, and VGG16, were trained and evaluated. With an AUROC of 0.78 and an accuracy of 87% on test data, DenseNet121 performs significantly better than all other models, according to a comparative analysis of AUROC and accuracy. Additional evaluation by disease category on an individual basis showed excellent performance for pleural effusion (AUROC 0.93) and lung opacity (AUROC 0.91). These results indicate the promise of Dense Net-based architectures to deliver accurate, automated diagnostic assistance to clinical radiology. The work emphasizes the utility of transfer learning in enhancing generalization with sparse labeled data and offers pragmatic guidance to model choice in the analysis of medical images.
Copyright (c) 2026 Deepak Raj Sharma (Author) https://creativecommons.org/licenses/by/4.0
Disaster Damage Assessment Using Deep Learning and Satellite Imagery
This paper focuses on deep learning strategies for the assessment of satellite images for the overall assessment of disaster. The study primarily examines the ability to correctly identify those places that can be affected by various classes of natural disasters. By imbuing a wide range of satellite images with seamless integration, a new disaster detection system is designed assisted by a set of models, a prime example of which includes the Convolutional Neural Networks (CNNs). The above-mentioned detection system has demonstrated competency in the semantic segmentation and examination of satellite data of increased interest in both urban and countryside vistas. In the context of a city, the CNN model, supported by three advanced convolutional layers, max-pooling layers, and a double fully connected layer configuration, was painstakingly trained on an unparalleled dataset. This dataset consists of thousands of unique image patches before and after disastrous events. These represent various disastrous events that happened all over the world, thus enabling direct and comparative consideration of the pre- and post-disaster landscape. The procedures that are reported here can raise the level of performance and reliability of the practices in the Disaster Management field. It has presented an approach to analyze disaster (effect) efficiently and in depth considering recent technology on satellite image.
Copyright (c) 2026 Rakesh Pandey (Author) https://creativecommons.org/licenses/by/4.0
Secure and Compressed Data Transmission Using ECC Key Sharing, DNA Cryptography, and LZ77 Compression
In the evolving landscape of digital communication, the need for secure, lightweight, and efficient data protection mechanisms is more critical than ever, particularly for bandwidth-constrained and privacy-sensitive applications. This paper proposes a novel hybrid cryptographic framework that synergistically combines three powerful techniques: Elliptic Curve Cryptography (ECC), DNA-based cryptography, and LZ77 data compression. The framework utilizes ECC for secure key sharing between communication parties due to its strong security and low computational overhead. Once the session key is established, DNA cryptographic techniques are applied for the encryption and decryption of sensitive data, exploiting the high parallelism, randomness, and vast encoding capacity of DNA sequences. To further enhance transmission efficiency, the plaintext is first compressed using the LZ77 algorithm, reducing redundancy before encryption. This layered approach not only ensures high security and resistance to cryptanalytic attacks but also achieves significant data compression, making it suitable for secure data transmission in constrained environments such as Internet of Things (IoT) and telemedicine. Experimental results demonstrate that the proposed scheme maintains confidentiality, integrity, and performance, while significantly optimizing storage and transmission requirements.
Copyright (c) 2026 Asamene Kelelom, Addisu Oumer, Pawan Kumar, Beer Singh (Author) https://creativecommons.org/licenses/by/4.0
AI-Driven Online Exam Proctoring: An Enhanced Machine Learning Approach
The proliferation of an online learning environment has opened up tremendous potential in the sphere of education, but has also posed significant problems to examination integrity. Conventional services of online proctoring, i.e., manual webcam supervision and lockdown browsers, failed to provide fairness since they were either inefficient or rather effortless to manipulate. This paper introduces an AI-based online exam proctoring (OEP) model that combines both visual and audio channels to identify cheating behavior, such as reading notes or using cell phones, muttering, or turning away the view on the examination. The research builds on a previously established framework that uses Support Vector Machines (SVMs), and tries different alternatives by using Random Forests (RF), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) models. A comparative analysis shows that the baseline system displayed an average True Detection Rate (TDR) of 87% under 2% False Alarm Rate (FAR) whereas the enhanced model with CNN-based visual processing and LSTM educated speech detection produced an improved system performance to 94% TDR at the same FAR limitation. The results indicate the potential of advanced ML to circumvent the drawbacks of earlier solutions and point to a way forward that results in scalable, fair, and privacy-sensitive proctoring solutions.
Copyright (c) 2025 hemendra sharma Shanker, Vinay Kumar Pant (Author) https://creativecommons.org/licenses/by/4.0
Cloud Gaming: Redefining the Future of Entertainment beyond Conventional PCs
Cloud gaming, as a paradigm in which games are rendered and streamed over remote servers, promises to transform the digital entertainment sector, as it eliminates the reliance on powerful local hardware. The paper discusses the trend of the traditional PC/ console game to modern cloud-based systems with essential performance indicators such as latency, bandwidth demands, and user experience. The mixed-method approach is used, which relies on case studies of the most successful platforms, including Google Stadia, NVIDIA GeForce Now, and Xbox Cloud Gaming, as well as on empirical reports of performance and user experience. The results indicate that cloud gaming can greatly enhance accessibility especially among users with low-end platforms or when in an emerging market, yet real-time responsiveness is limited due to the network infrastructure Consumer preference to traditional gaming remains despite its disruptive potential, given that it benefits offline play, lower-latency performance, and ownership models . The research paper concludes that the future of 5G, edge computing and AI-based optimizations will play an instrumental role in solving latency and bandwidth issues. Cloud gaming, on the whole, turns out to be a disruptive technology that can widen the access and define the future of the entertainment industry.
Copyright (c) 2025 Krishna Kumar, Manish Kumar Jha (Author) https://creativecommons.org/licenses/by/4.0
Sustainable It Services: Advancing the Impact of Green Computing Practices
The needs of the growing information technology industry have made data centers, digital platforms and cloud computing achieve a never-before-seen growth, generating an enormous energy demand and environmental footprint. This paper follows the fundamental ideas of green computing best practices and goes further to detail how they can be converted to sustainability in IT services. With the second wave of green IT, the priority remains on cost savings and energy efficiency but second place to be addressed is the need to combine the ecological responsibility, business value, customer value as well as the societal value. It is through the critiquing of current approaches like virtualization, workload management, green power utilization, policy, and industry-driven, that this paper discusses the need to make sustainability part of the IT services. It also assesses trade-offs, offers best practice and reflects on what practices are most effective in terms of aligning IT operations with long-term corporate social responsibility and the triple bottom line framework.
Copyright (c) 2025 Nitesh Kumar Saxena, Dr.Ankita Tandon, Dr. Deepesh Tiwari (Author) https://creativecommons.org/licenses/by/4.0
Development of a Smart Women Safety ID with Real-Time Gas Detection and Crime-Aware Emergency Alerts System
In many parts of the world, women’s safety in public, educational, and professional settings is still a major concern. Conventional safety measures frequently depend on wearable technology or smartphone apps, which aren’t always reliable in an emergency or covert enough to keep potential criminals from spotting them. This study proposes a clever and affordable women’s safety ID card that offers both proactive and re active protection to overcome these drawbacks. This cutting-edge gadget incorporates essential technologies like a Bluetooth module (HC-05) for short-range device pairing, a SIM900A GSM module for emergency communication, a GPS module for location tracking, and an MQ2 gas sensor for hazardous gas detection. To ensure smooth operation at companies, educational institutions, and schools, the system is controlled by an Arduino Uno (ATmega328P) and housed in a standard ID card form factor. To detect the presence of dangerous compounds, including LPG, alcohol, and an aesthetic gases, labelled gas sensor data was used to train a supervised machine learning model, more precisely, a Random Forest Classifier. The model produced dependable real-time predictions with a high classification accuracy of 98.75% and strong precision, recall, and F1 scores. Furthermore, the system classifies areas as red zones by using historical crime data, such as location, crime type, and coordinates. The Haversine distance method is used to assess the user’s real-time GPS data and calculate how close they are to these high-risk areas. An automated alert is set off if the user goes inside a predetermined danger radius of four kilo metres. A manual SOS button is another feature of the safety system that allows users to send emergency alerts via SMS with their position and threat kind right away, even when there is no internet connectivity. Only authorized users, like guardians or institution leaders, can access critical data, track user movement, and receive alerts thanks to the secure dashboard and user authentication offered by the supporting mobile and online application. The interface, which was constructed with Gradio and Folium, provides real-time viewing of red zone boundaries, position, and gas detection status. This integrated system is an effective instrument to increase women’s safety because it not only guarantees situational awareness but also makes quick emergency reaction possible. Because of its scalable architecture, open-source. technologies, and low-cost hardware, it can be widely implemented in a variety of industries. The Women Safety ID Card is a step forward in utilizing artificial intelligence and embedded systems to create safer environments and give women the freedom, security, and self-assurance they need to go about their everyday lives.
Copyright (c) 2025 Tanima Bhowmik, Pratyusha Chatterjee, Debajyoti Mitra, Rajnandini Banerjee (Author) https://creativecommons.org/licenses/by/4.0
Enhancing Organizational Performance and Strategic Forecasting Through Business Intelligence Technique
In this paper, show how BI applications can lead to sales forecasting and organizational performance improvements based on the case of a retail store. To detect patterns, performance issues and actionable results, a relatively simple business intelligence model based on descriptive, diagnostic and predictive analytics was applied. Although descriptive analytics revealed regional sales differences, diagnostic analytics found that too much discounting reduces profit. The comparison analysis confirmed that the proposals enable more profit, better discount management and improved performance in general when BI-based decision making is applied versus traditional methods. The present work evaluates five leading BI tools, namely Power BI, Tableau, Qlik Sense, Looker and SAP Business Objects based on their scalability, data integration and visualization features. In addition, future sales were predicted with the help of predictive models such as ARIMA, Prophet, etc., which contributed to inventory control and strategic alignment. The results highlight that BI solutions are core enablers of data-driven decision making, operational efficiency, and even continuous growth. With cloud and AI integration, BI provides real-time analytics to enable organizations stay competitive, agile, and responsive in today’s business landscape.
Copyright (c) 2025 Sonam Srivastava (Author) https://creativecommons.org/licenses/by/4.0
Enhancing Crop Yields with IoT Driven Smart Agriculture Systems
As global food demand increases amid climate change and dwindling natural resources, there is an urgent need to adopt more intelligent and sustainable farming practices. Without real-time awareness and optimization, traditional ways of farming methods do not make full use of resources and bring about below-average crop results. With Internet of Things (IoT), farmers can automate tasks, watch over operations all the time, and rely on data for making decisions. The goal of this paper is to propose and examine an IoT framework for smart agriculture meant to improve environmental conditions and crop production efficiency. There are sensors in the system that measure moisture, temperature, humidity, and light, plus LoRa and 5G connections for communication, edge units for processing, and a cloud system for detailed analysis and visualization. Through the mobile and web portal, farmers can receive immediate feedback and useful information to improve their farming. Using this system on real farms resulted in better scheduling of irrigation, better crop health, and higher overall yields. Results conclude that IoT can lower expenses for farmers at the same time as producing a larger crop, making it a realistic and flexible solution for current agriculture in remote and under-resourced regions.
Copyright (c) 2025 Parveen Kumar (Author) https://creativecommons.org/licenses/by/4.0
Deep Learning Based Text Extraction from Video Using CNN, LSTM, and Transformer Models
This study offers a deep learning-based method for text extraction from video frames, addressing issues like motion blur, variable text orientations, and background noise. Traditional optical character recognition (OCR) methods like Tesseract suffer from these problems, while contemporary deep learning models offer notable advancements. The suggested model uses Convolutional Neural Networks (CNNs) to identify text regions, Transformer-based models to increase recognition accuracy, and Long Short-Term Memory (LSTM) networks to maintain sequences. Several tests demonstrate that by striking a balance between accuracy and real-time functionality, the CNN + LSTM architecture performs better than conventional OCR algorithms. The results show that transformer-based methods have the highest accuracy but the highest computational cost. deep learning models like CNN, LSTM, and Transformers can handle contextual recognition, temporal sequencing, and spatial detection, they are particularly well suited for video text extraction. This hybrid approach, in contrast to traditional OCR, guarantees high accuracy even in video frames that are noisy, blurry, or multilingual.
Copyright (c) 2025 Manender Dutt, Ritu Sharma (Author) https://creativecommons.org/licenses/by/4.0
Machine Learning Based Method for Forecasting Crop Yield
Applications of machine learning are revolutionizing data processing and decision-making, which is having a significant effect on the global economy. Given the worldwide food supply crisis, agriculture is one of the industries where the effects are most noticeable. This paper focuses on crop yield prediction based on pattern analysis with the help of the machine learning approach, which focuses on data acquisition, preprocessing, and assessment. Taking the Crop Yield Prediction Dataset as a solution, the most potential factors, including rainfall, temperature, and pesticide, have been identified to have the most influential factors in creating better prediction models. Among these, Decision Trees, Random Forest, Support Vector Machine (SVM), Artificial Neural Network (ANN), Naïve Bayes, and Long Short-Term Memory (LSTM)are the most common and are checked for their effectiveness. It brings out facts that are instrumental in analysis to improve the yields on farms and come up with possible recommendations on precision farming and sustainable agriculture. This paper aims to provide insights that can help improve farm yields.
Copyright (c) 2025 Vikash Sawan, Renu Kumari, Mrinmoy Kayal (Author) https://creativecommons.org/licenses/by/4.0
Enhancing AI Decision-Making: Sensitivity Analysis, Hyperparameter Optimization, Multi-Agent Collaboration, and AI-Human Comparisons
Artificial intelligence (AI) has significantly influ enced decision-making processes across various domains, in cluding law, healthcare, and autonomous systems. Despite its advancements, AI models face several critical challenges, in cluding sensitivity to input variations, hyperparameter tuning complexities, coordination issues in multi-agent environments, and fundamental differences in decision-making compared to human cognition. This study investigates four key dimensions of AI decision-making: (1) the impact of input perturbations on AI-generated responses, (2) the role of hyperparameter tuning in optimizing AI performance, (3) the effectiveness of multi agent AI collaboration in ethical and strategic dilemmas, and (4) a comparative analysis of AI and human reasoning in real world scenarios. The findings indicate that AI models exhibit response inconsistencies with minor input rewording, hyperpa rameter tuning significantly alters model accuracy and coherence, multi-agent AI systems struggle with consensus-building, and AI decision-making lacks ethical and emotional depth compared to human reasoning. This study highlights the need for robust AI training methodologies, structured decision-making protocols in multi-agent AI systems, and enhanced explainability frameworks to improve AI’s effectiveness and reliability in real-world appli cations.
Copyright (c) 2025 Manish Kumar, Dr Jugnesh Kumar (Author) https://creativecommons.org/licenses/by/4.0
Variational Autoencoder Model for Image Processing Methods in Game Design
This paper investigates the use of Variational Autoencoders (VAEs) as a deep learning-based generative framework for AI-assisted image processing in game design, focusing on the procedural generation of stylized visual assets. Its application in AI-assisted image generation in game design for the generation of diverse stylized visual assets is explored in this paper. In order to learn stylistic consistent content and generate new art for the Ethereal Monsters game, we propose a deep learning-based generative approach using VAEs to learn latent representations of existing game art. Pre-processing of a curated dataset of 10,000 game sprites spans parsing colour palette and sprite patterns, creating an adapted palette for less sparse variants of sprites, and creating training and testing sets through pooling sprites into images and grouping images for a generation. A convolutional VAE architecture is trained, its (re)construction loss and visual fidelity are evaluated, a prospective error correction test is performed, and the results are analysed. We show that the VAE model can effectively capture the main features of 2D game sprites and if iterated numerous times not only does it produce an endless number of variations, but it also keeps the game-specific aesthetic properties. It is compared with existing generative methods and improved visual coherence is found, whilst diversity is saturated. It adds to the exploration of AI-driven creativity in game design, in particular an increasing number of ways to generate assets and prototypes in a scalable way.
Copyright (c) 2025 Riya Sharma, Mr.Kapil Kumar (Author) https://creativecommons.org/licenses/by/4.0
Facial Recognition and Object Detection using Machine learning
Facial recognition and object detection are critical computer vision problems used in security, surveillance, autonomous systems, and human-computer interaction. This study investigates the of machine learning techniques. Use of facial recognition and object detection in deep learning has been develop to high level using machine learning. In enhancing the performance and enhancing the generalization of the model, this studies implements a recognition system on VGG16 model with data augmentation. To feed into the AI model, 1800 images were extracted from 17 classes and pre-processed, normalized and augmented also through rotation and flipping. The VGG16 model architecture was used and then trained by using categorical cross entropy loss function and Adam optimizer. The model achieved a validation accuracy of 76.39% within five epochs, signifying the model’s potential on various facial variations. Some of the issues that were pointed out include shortcoming such as misidentification and low quality images. Further researches propose to expand the dataset used for the study, to employ more complex base architectures such as ResNet, and to augment data pre-processing to improve recognition accuracy.
Copyright (c) 2025 Abhishek Kumar, Kapil Kumar (Author) https://creativecommons.org/licenses/by/4.0
Object Detection in Autonomous Driving with Sensor-Based Technology Using YOLOv10
The creation of intelligent transportation systems, such as autonomous driving and traffic monitoring, is dependent on precise vehicle recognition. Autonomous vehicles detect and recognize objects in real-time, such as pedestrians, other vehicles, traffic signs, and obstacles. This paper improves the object detection ability of autonomous vehicles (AVs') by integrating technologies including YOLOv10 and multi-modal sensor fusion. This paper takes a deep learning algorithm with sensor technology, about important issues in the areas of response time, real-time processing, and detection accuracy. They have used YOLOv10's architectural and optimization strategies along with a comprehensive methodology that integrates data from LiDAR, radar, and cameras to construct a trustworthy perception system for dynamic and flexible driving settings. According to the experimental results, YOLOv10 outperformed both previous versions and competing object detection models with a significantly high accuracy of 96.8%, while maintaining a processing speed of 80 frames per second. Additionally, YOLOv10 had a significantly higher recall of 94.1%, and an accuracy of 95.4%, indicating its increased effectiveness at pedestrian and obstacle identification in the autonomous driving domain. With explicit attention to accounting for occlusions and poor lighting, the authors created a strong and scalable framework for deep learning to bridge the gap between theory and application in autonomous driving. Furthermore, the extension to address these issues enhances reliability and safety in autonomous systems and will ultimately aid the development and adoption of broader autonomous technology systems.
Copyright (c) 2025 Riya Saini, Mr. Kapil Kumar (Author) https://creativecommons.org/licenses/by/4.0
Human Skin Disease Detection and Classification Using Ensemble Learning
Skin disorders are thought to be common in humans and carry several invisible risks, including the potential to cause psychological sadness, lower self-esteem, and, in more serious cases, skin cancer. Medical professionals must diagnose these skin conditions, but doing so requires highly sophisticated diagnostic tools because they have trouble seeing clearly while examining images of the conditions. This paper focuses on skin disease detection and classification using ensemble learning. This is done using Multiple Skin Disease Detection and Classification data sets from the ISIC Archive through employing the bagging, boosting, and stacking methodologies for better diagnosis. To compare the proposed ensemble of CNN models and individual CNN models, the observations were made. This strategy is useful for dermatologists to identify skin diseases at an early stage or at the first instance to prevent further deterioration of the skin health of their patients.
Copyright (c) 2025 Vimal Kumar, Jeetu Rani (Author) https://creativecommons.org/licenses/by/4.0
REAL-TIME OBJECT DETECTION IN AUTONOMOUS VEHICLES USING DEEP LEARNING
Object detection is a crucial component of autonomous driving technology. Accurate and real-time detection of every object on the road is required to ensure the safe operation of vehicles at high speeds. In recent years, there has been a lot of research into how to balance detection speed with accuracy. Real-time object detection is one of the important technologies applied to autonomous vehicles that allow vehicles to move safely through traffic. This paper focuses on the use of deep learning, the YOLOv8 algorithm in object detection of self-driving cars. The real-world data set of real driving scenarios involved includes streets, roads, and intersections/squares. The powerful interaction of the model with the deep learning algorithms defines the objects and allows for a fast decision-making process applied in autonomous systems. The metrics used to assess the models include detection rates, accuracy of the bounding die placement, and accuracy of the objects’ detection. The outcome is beneficial in refining the object detection methods and advancing the perception capability for self-driven vehicles as well as making driving automation safer.
Copyright (c) 2025 Kapil Kumar, Rakhi Bhardwaj, Kamal Kant Verma (Author) https://creativecommons.org/licenses/by/4.0
ARTIFICIAL INTELLIGENCE AND CLOUD-BASED COLLABORATIVE PLATFORMS FOR MANAGING EMERGENCY OPERATIONS
Emergency management operations increasingly depend on cutting-edge technological solutions to support better disaster response, resource coordination, and recovery. This research uses artificial intelligence (AI) and cloud-based collaborative platforms to enhance emergency management in pre-disaster, disaster, and post-disaster phases. AI predictive abilities allow for early risk estimation, enhancing disaster forecast accuracy by 47% for wildfires and 42% for earthquakes. In emergencies, real-time data analysis and AI automated response cut response times from 12–24 hours to 2–6 hours, boosting situational awareness and resource allocation by 55%. Cloud platforms enable real-time sharing of data between emergency responders, which promotes the number of individuals contacted within the initial 48 hours by 200% and cuts down on incident costs by 60%. The research highlights a gap in AI-based decision-making systems and the scalability of the cloud, especially in developing countries. It suggests more interdisciplinary research to create standardized AI models for emergency management. The results underscore that AI and cloud platforms improve disaster response effectiveness, resource optimization, and cost savings and overcome data security, privacy, and system integration issues.
Copyright (c) 2025 Sandeep Kumar, Anurag Semwal (Author) https://creativecommons.org/licenses/by/4.0
Enhancing Voice Assistant Systems through Advanced AI and NLP Techniques
In the rapidly evolving digital age, voice assistants have become an indispensable tool for enhancing user interaction with technology. This paper explores the design, development, and functionality of a Python-based voice assistant system, leveraging cutting-edge advancements in Artificial Intelligence (AI), Natural Language Processing (NLP), and Machine Learning. The voice assistant is designed to bridge the gap between human commands and machine execution by employing robust speech recognition techniques and advanced contextual understanding. Unlike existing models, the proposed system integrates tone and mood recognition to offer personalized responses and recommendations, thereby elevating user experience. The research delves into significant challenges in the field, such as multi-language adaptability, mood inference, and offline processing capabilities, offering innovative solutions that enhance system reliability and efficiency. By incorporating Python libraries and APIs, the assistant performs diverse tasks, from executing basic commands like opening applications and retrieving weather updates to advanced functionalities like personalized news delivery and automated emotional support. Testing revealed an impressive accuracy rate of 91.87%, demonstrating its practical viability and effectiveness. The findings underscore the growing importance of voice assistants as a transformative technology in the fields of home automation, accessibility, and intelligent systems. This paper aims to contribute to the body of knowledge in AI and NLP, addressing current limitations and setting a foundation for future developments in voice assistant technology.
Copyright (c) 2025 Rahul Kumar Singh, Sakshi Kathuria, Pankaj Saraswat, Ashok Kumar (Author) https://creativecommons.org/licenses/by/4.0
Internet of Underwater things (IoUT): A Systematic Review Research
The development of intelligent systems for the monitoring, exploration, and administration of underwater environments is made possible by the Internet of Underwater Things (IoUT), which is a revolutionary development in marine and environmental research. This thorough research examines the advancements, challenges, and promise of IoUT with a focus on its applications in domains such as resource extraction, the science of oceanography underwater tracking, and tracking the environment. IoUT systems require customised approaches in processing information, conservation of energy, connectivity, and sensor that is being tested design because to the particular difficulties of underwater settings. The new protocols and methods designed for underwater applications—such as acoustic, optical, and electromagnetic communications—as well as the incorporation of artificial intelligence (AI) and machine learning (ML) technologies for improved data processing and taking decisions are covered in this study. Furthermore, we draw attention to the urgent issues surrounding data security, environmentally friendly interaction, and installation expenses while providing suggestions for future lines of inquiry and technical advancements.
Copyright (c) 2025 Pawan Kumar, Shashank Jha, prashant, Ritik Kumar Singh (Author) https://creativecommons.org/licenses/by/4.0
Attack and Anomaly Detection in IoT Sensors Using Machine Learning Approaches
The extensive usage of IoT sensors significantly improved the collection and monitoring of data within various application domains, such as smart agriculture and industrial automation. On the other hand, the great dependence on IoT sensors makes systems vulnerable to hacks and anomalies. In this paper, we explore machine learning approaches that can be used to protect Internet of Things sensor networks against attacks and anomalies. Due to the limited resources available to IoT devices, traditional security measures fall short. There is, therefore, a need to develop more intelligent smart detection systems. This paper examines the capabilities of machine learning in identifying patterns of anomalies in IoT sensor data. Carried out on a dataset of simulated IoT environments, the research presents the stages of data pre-processing, exploratory data analysis, and feature engineering. In addition, three models; Logistic Regression, Decision Tree, and Random Forest were constructed and tested. The results show that it is possible to use machine learning algorithms for anomaly detection in IoT domains, thereby presenting the possibilities for improving IoT security and reliability. The findings of this study are important in that they highlight how advanced analytics can help organizations deal with IoT environments.
Copyright (c) 2025 Ashish Seth (Author) https://creativecommons.org/licenses/by/4.0
Potato Leaf Disease Detection Method is based on a CNN model with a Genetic Algorithm
This paper puts forward an approach that combines Convolutional Neural Networks (CNNs) and Genetic Algorithm (GA) to detect accurately and quickly the diseases that affect potato leaves swiftly and accurately. The model of the CNN is employed for automated feature extraction from the images of leaves which are pivotal in differentiating between the healthy and the infected leaves. Optimization of CNN hyper parameters, like learning rate, the number of layers, and dimensions of filters, is not only time-consuming but also challenging to find optimal values, and genetic algorithms for the optimal values of the parameters of the Convolutional Neural Network. A genetic algorithm uses an iterative search process that aims to optimize a population of models of Convolutional Neural Networks over several generations. It achieves this through the use of optimal models to perform crossover and mutation hence efficiently searching for the optimal configuration of hyperparameters. The above combine will improve the performance of the model which leads to better performance in detecting the variety of diseases attacking the potato leaves, such as early blight and late blight. In this paper CNN model utilizing a genetic algorithm attains an accuracy of 98.3% in 50 epochs.
Copyright (c) 2025 Vikash Sawan, Renu Kumari, Kumari Jugnu (Author) https://creativecommons.org/licenses/by/4.0
IoT Based Weather Monitoring System for Tourists
The rapid expansion of tourism has necessitated the provision of weather information to guarantee the safety, convenience, and improved travel experiences of travelers. An innovative solution is provided by an IoT-based weather monitoring system, which provides precise, current meteorological data that is specifically designed for visitors. The IoT has greatly impacted and improved many aspects of our daily lives and the same is likely to transform our travels. There is nothing worse than going for a trip and forgetting that the weather in the new location is different. When it comes to tourists, the role of weather is that it may greatly affect their schedule and activities while increasing or decreasing their level of comfort. As a result, there is the need to develop the Internet of Things (IoT) for a tourist-driven weather checking system. Wireless capability allows passengers to send their data to an online server and other related data like accurate local weather updates are processed and relayed back. This system is unique in that it is capable of observing and mapping weather at a given location and can be accessed remotely from any other location. IoT refer to a system of systems in which all the devices are interconnected through the internet to form a complex network. The many sensors that are placed strategically collect data that is then sent to a cloud-based platform where weather forecasts can be quickly and accurately delivered.
Cyber Attack Detection in an Internet of Things Employing Random Forest
The Internet of Things is a vast system of interconnected devices. These gadgets are becoming increasingly commonplace in vital applications. As a result, cybercriminals are directing more of their attention toward the IoT. To protect the IoT from intrusion, we employ Random Forest, a popular machine-learning algorithm for classification tasks, in this work. To implement the proposed method, labeled data depicting both normal IoT device operation and malicious attacks must be collected. Our approach is 95% accurate. Because the advantages of using random forests can be used with smaller datasets and normal computational resources, we believe our method is a good one for detecting and avoiding IoT attacks. This paper introduces a method for detecting cyber-attacks in IoT environments. It uses the Random Forest (RF) algorithm, which is well-known for its accuracy and resilience in classification tasks. To better protect the Internet of Things (IoT) from ever-changing cyber threats, this method offers a dependable and scalable solution.
Copyright (c) 2024 PRASHANT Bajpai (Author) https://creativecommons.org/licenses/by/4.0
Load Balancing in Wireless Mobile Ad Hoc Networks
Ad hoc networks are made up of groups of similar nodes, such as computers or embedded devices. Each node works on its own and talks to the others through a wireless channel. Networks like these are made up of groups of nodes that are either next to each other or connected by another wireless node. Nodes are grouped together along their wireless links to make clusters. Nodes that can talk to other nodes in their transmission range are called cluster heads. This can use cluster heads to send packets to the right nodes in your cluster. In an Ad Hoc network, the idea behind it is that nodes don't always move in the same way. Cluster Heads are picked by different algorithms in different ways. Some of these rules make it clear that some nodes are more important than others. Because of the extra work and faster energy loss that could happen because of more contacts, these nodes may be taken out of the network. Load-balancing across Cluster Heads is necessary to make sure that every node has a fair chance to be a Cluster Head. We Proposed that a number of changes should be made to current algorithms in order to fix the uneven spread of nodes among Cluster Heads and make nodes in a network that uses the fuzzy algorithm last longer.Load balancing in Wireless Mobile Ad Hoc Networks (MANETs) ensures equitable distribution of network traffic among nodes and resources. Due to the ongoing mobility and dynamic nature of nodes in MANETs, this aspect holds significant importance.
Copyright (c) 2024 Rahul Bhandari (Author) https://creativecommons.org/licenses/by/4.0
Review of Techniques and Algorithms of Load Balancing in Cloud Computing
Cloud computing has transformed the IT industry by enabling convenient access to data, programs, and files over the Internet. A variety of load balancing algorithms, including Round Robin, ESCE, Min-Min, Max-Min, and Throttled, are covered in the discussion along with their effects on throughput, fault tolerance, scalability, and overhead. The literature review stresses the dynamic nature of the cloud computing landscape by highlighting important contributions from scholars. Additionally, the study identifies issues in cloud services, such as automated service provisioning, virtual machine migration, energy management, stored data management, and the growth of small data centers. Even though cloud computing has become widely used, these difficulties highlight the continued need for innovation in the industry.
Copyright (c) 2024 Naman Chauhan, Dr. Gesu Thakur, Dr. Ankush Joshi, Vikash Kumar, Anuj Kumar (Author) https://creativecommons.org/licenses/by/4.0
Describing the Research Initiative: Unmanned Aircraft Education in Technology
The Structure of the drone, which includes the frame, propeller, engine, power system, electronic control, and communication systemis covered as the first aspect of this article about drones and their applications. One device is called an aircraft. In addition to this other name for a UAV (unmanned aerial vehicle), it is necessary to mention that any drone can be referred to as “unmanned” if it is capable of flying without the presence of a pilot. It can be estimated that shortly, because they are so much loved by the masses and due to their low-cost nature and ease at which one can find them, their use will soar up greatly. The most important danger associated with drone use is when it falls from a great height. This may be caused by depletion in battery bad weather (such as rain or cold) or striking against something (for example trees, houses, or electricity lines).
Copyright (c) 2024 Vikash Kumar, Dr. Ankush Joshi, Dr. Gesu Thakur, Naman Chauhan, Anuj Kumar, Bhuwneshwar Pratap Singh (Author) https://creativecommons.org/licenses/by/4.0