Daily Archives: April 9, 2025

Artificial Intelligence in Cyber Security
Authors:-Karmvirsinh Jadeja, Chirag Chauhan, Prof. Mansi Gosai

Abstract-The development of Artificial Intelligence (AI) has found some uncommon inertia from technological advancement. The world of AI appears everywhere and raises questions of admiration and censure. Its increasing usage has both pros and cons in the domain of cyber security, and it is a regular item in the development and operational processes of advanced technologies. This paper is a deep dive into the use of AI in cyber security, focusing on its advantages, challenges, and discriminating negative impacts. It also studies AI-based models that can enhance or compromise safety concerning different infrastructures and cyber networks. The paper critiques the participation of AI in postulating cyber security applications, suggests ways to chalk out the birth of new technologies against the threats and weaknesses generated from AI, and comments on the socio-economic implications of AI interfering with cyber security.

DOI: 10.61137/ijsret.vol.11.issue2.295

Published by: Kajal Tripathi

Skin Disease Detection Using Image Processing and Machine Learning
Authors:-Assistant Professor Punashri Patil, Prathvish Shetty, Nikhil Shinde, Ashirwad Swami, Sahil Hanwate

Abstract-Skin diseases affect millions worldwide, making early and accurate diagnosis essential for effective treatment. Traditional diagnostic methods rely on manual visual inspection, which can be subjective and prone to errors due to variations in expertise and environmental factors. Misdiagnosis or delayed treatment can lead to severe complications, especially in conditions like melanoma. This paper presents an automated approach to skin disease detection using image processing and machine learning. The proposed system enhances image quality through preprocessing, extracts crucial features, and classifies skin conditions using algorithms like Support Vector Machine (SVM) and Convolutional Neural Networks (CNN). Experimental results demonstrate high classification accuracy, highlighting AI’s potential in dermatology for faster and more consistent diagnoses. By integrating artificial intelligence into dermatological assessments, this research aims to bridge the gap between conventional diagnosis and AI-assisted solutions, making skin disease detection more accessible, precise, and efficient.

DOI: 10.61137/ijsret.vol.11.issue2.294

Published by: Kajal Tripathi

Enhancing Cloud Security Using Blockchain-Based Authentication
Authors:-Assistant Professor Mrs. Punashri Patil, Yash Chavhan, Savi Dhoble, Tejas Patil

Abstract-Cloud computing is essential for modern enterprises, providing scalable and cost-efficient solutions for data storage and processing. However, security challenges such as unauthorized access, data breaches, and insider threats persist. Traditional authentication methods like passwords and two-factor authentication (2FA) have inherent vulnerabilities, including phishing attacks, credential theft, and centralized failures [1][2]. Blockchain-based authentication offers a decentralized, tamper-proof security mechanism that eliminates single points of failure and enhances trust. Existing research has explored blockchain’s role in cloud security, but challenges such as scalability, computational overhead, and latency remain [3]. This paper presents an optimized blockchain-based authentication model that enhances access control while addressing these limitations. Our approach leverages decentralized identity management, smart contract-based access control, and an efficient consensus mechanism to improve security, reduce computational overhead, and ensure seamless authentication. This model enhances security, scalability, and performance in cloud environments, making it a viable alternative to traditional authentication systems.

DOI: 10.61137/ijsret.vol.11.issue2.293

Published by: Kajal Tripathi

Interpretable AI for Intelligent Event Detection and Anomaly Classification in Healthcare Monitoring Systems
Authors:-Assistant Professor Mrs.K.S.R.Manjusha, D.Ashok Kumar, M.Harish, M.Hari Sathvik, M.Vinsy, A.Sri Sai Keerthi.

Abstract-Artificial intelligence (AI) is transforming healthcare by automating the detection and classification of events and anomalies, enhancing patient monitoring and intervention. In this context, events refer to abnormalities caused by medical conditions such as seizures or falls, while anomalies are erroneous data resulting from sensor faults or malicious attacks. AI-based event and anomaly detection (EAD) enables early identification of critical issues, reducing false alarms and improving patient outcomes. The advancement of Medical Internet of Things (MIoT) devices has further facilitated real-time data collection, AI-driven processing, and transmission, enabling remote monitoring and personalized healthcare. However, ensuring the transparency and explainability of AI systems is crucial in medical applications to foster trust and understanding among healthcare professionals. This work presents an online EAD approach utilizing a lightweight autoencoder (AE) on MIoT devices to detect abnormalities in real time. The detected abnormalities are then explained using Kernel SHAP, a technique from explainable AI (XAI), and subsequently classified as either events or anomalies using an artificial neural network (ANN). Extensive simulations conducted on the Medical Information Mart for Intensive Care (MIMIC) dataset demonstrate the robustness of the proposed approach in accurately detecting and classifying events, regardless of the proportion of anomalies present.

DOI: 10.61137/ijsret.vol.11.issue2.292

Published by: vikaspatanker

DeepSpineNet: Advanced Deep Learning for Multi-Class Spine X-Ray Condition Classification
Authors:-Assistant Professor Mrs.K.S.R.Manjusha, D.Ashok Kumar, M.Harish, M.Hari Sathvik, M.Vinsy, A.Sri Sai Keerthi.

Abstract-Addressing the complexchallenges of automated spine X-rayanalysis, our research introduces Deep Spine, a deep learning model designed for the multi-class classification of diverse spine conditions. Utilizing Convolutional Neural Networks (CNNs), Deep Spine demonstrates exceptional proficiency in identifying a range of spinal abnormalities, including Scoliosis, Osteochondrosis, Osteoporosis, Spondylolisthesis, Vertebral Compression Fractures (VCFs),Disability, Other, and Healthy cases. Trained on a Kaggle dataset, Deep Spine achieves high accuracy and robustness, ensuring reliable performance in classifying spinal conditions. The incorporation of transfer learning techniques further enhances its generalization capability, enabling the model to adapt effectively across different datasets. This approach not only strengthens its diagnostic accuracy but also highlights its potential for automated diagnosis and decision support in musculoskeletal radiology. This research contributes to the evolving intersection of artificial intelligence and medical imaging, demonstrating the transformative potential of deep learning in spine X-ray analysis. By leveraging AI-driven advancements, Deep Spine offers a promising step toward enhancing clinical outcomes, improving diagnostic precision, and revolutionizing spinal healthcare.

DOI: 10.61137/ijsret.vol.11.issue2.291

Published by: vikaspatanker

AI-Powered Ransomware Defence: Cutting-Edge Machine Learning Techniques for Threat Detection
Authors:-Assistant Professor Mrs.P.Satyavathi, M.Naga Sai Ganesh, N.V.Gowtham Kumar, V.S.V.Satya Yaswanth, G.Satya Nandini, V.Giri Sathvika.

Abstract-The increasing frequency and sophistication of ransomware attacks, there is a growing need for dynamic and effective detection and mitigation strategies. Traditional signature-based approaches often fall short in identifying new and evolving ransomware variants. This paper explores the application of machine learning techniques for ransomware detection, aiming to enhance the accuracy and adaptability of detection mechanisms. It provides a comprehensive analysis of various machine learning methods and algorithms, evaluating their effectiveness in identifying ransomware patterns. The findings offer valuable insights into the advancement of cybersecurity solutions, emphasizing resilience and proactive defense against the ever-evolving ransomware threat landscape.

DOI: 10.61137/ijsret.vol.11.issue2.290

Published by: vikaspatanker

Rapid Depression Detection Using Extreme Learning Machine: An AI-Driven Approach
Authors:-Assistant Professor Mrs.G.V.Rajeswari, Ch.Harikiran, K.L.Rishitha, K.H.Venkat Ganesh, B.Raj Kumar, V.L.Apoorva.

Abstract-Depression is one of the most prevalent psychological and mental health disorders, affecting a significant number of people worldwide. In recent years, Extreme Learning Machine (ELM) techniques have gained preference for addressing various health-related disease detection and prediction challenges. ELM is a single hidden layer feed-forward neural network (SLFN) that offers significantly faster convergence compared to traditional machine learning (ML) methods while delivering promising results. Although numerous studies have explored the application of ML models for depression detection, limited research has focused on utilizing ELM for this purpose. This study implements Extreme Learning Machine (ELM) alongside other ML techniques for depression detection, comparing their performance. The results demonstrate that ELM outperforms other methods, achieving the highest accuracy of 91.73%.

DOI: 10.61137/ijsret.vol.11.issue2.289

Published by: vikaspatanker

AI-Powered Fraud Detection: Secure Online Transaction Monitoring Using Machine Learning
Authors:-Mrs.G.Tejasri Devi, T.Sai Srinath, G.Naga Kastusi, V.Anshitha, G.Janitha Sree, G.Jashwitha

Abstract-Fraud detection remains one of the most critical challenges in financial transactions, driving on going research and the adoption of advanced technologies such as machine learning. Financial transaction fraud detection aims to explore and compare various machine learning approaches to assess their effectiveness, challenges, and potential future developments comprehensively.This paper begins by highlighting the importance of fraud detection in financial transactions, emphasizing the widespread impact of fraudulent activities on individuals, businesses, and the overall economy. While traditional fraud detection methods have been valuable, they often struggle to counter increasingly sophisticated and evolving fraudulent schemes. As a result, more advanced techniques are required to enhance detection accuracy.Machine learning-based approaches have emerged as a promising solution, enabling algorithms to analyse vast amounts of transactional data and identify patterns indicative of potential fraud. In particular, supervised learning techniques—such as logistic regression, decision trees, and support vector machines—have gained significant popularity in fraud detection due to their ability to classify transactions as legitimate or fraudulent based on historical data.

DOI: 10.61137/ijsret.vol.11.issue2.288

Published by: vikaspatanker