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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Shivhare, Mani Dhir, Saru Shrivastava, Santosh Kumar Augn Amity School of Engineering and Technology Greater Noida India Amity University Dept. of Information Technology Noida India G L Bajaj Institute of Technology and Management Dept. of Artificial Intelligence and Machine Learning Greater Noida India

ISBN: (纸本)9798331508685

The emergence of networked autonomous systems has brought a new wave of security threats. Because of not be able to adopt the traditional intrusion detection approaches with the new threats, AIML based cybersecurity defences have gain more attention in recent times. The current state of AIML-based intrusion and abuse detection is examined in this paper, with a focus on significant scientific advancements and the real-world obstacles preventing its broad deployment. As cyberattacks become more sophisticated, one thing has remained the same: Intrusion Detection Systems are a must for protecting digital infrastructures, and they need to be robust. This review summarises the awareness of IDS from traditional signature-based methods to an upcoming disruptive focus on Artificial Intelligence, especially ML and DL. We present a comparison of the advantages and disadvantages of these techniques, emphasizing challenges such as dataset limitations, adaptability to operate in real-time and detecting unseen attacks. Forthcoming, we examine new research directions (hybrid methods, ensemble learning and challenging advanced datasets) to improve the efficacy of IDS on ever more complicated network landscapes. © 2025 IEEE.

关键词： Network intrusion

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Botanical Bio Vision: Advancing Plant Leaf Disease Classifications with Convolutional Neural Network 6

Botanical Bio Vision: Advancing Plant Leaf Disease Classific...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Mythili, N. Madhuvarshini, V. Iphiya, Magdiel D. Dept. of Computer Science and Engineering St. Joseph 's Institute of Technology Old Mamallapuram Road 119 Chennai India

ISBN: (纸本)9798331522667

In recent years, the agricultural sector has encountered major challenges due to the widespread presence of plant leaf diseases, which pose serious risks to crop yields and food security. Advancements in artificial intelligence, particularly through Convolutional Neural Networks (CNNs), offer a promising solution for the precise and efficient classification of plant leaf diseases. This study delves into the development and application of a CNN model built using TensorFIow to classify a variety of plant leaf diseases. By leveraging an extensive dataset containing images of both healthy and diseased leaves, the model is trained to accurately recognize and distinguish between several disease types. This approach not only automates the process of disease identification but also significantly improves diagnostic precision and speed, enabling swift and targeted interventions. Experimental findings reveal the CNN model's effectiveness, achieving notable improvements over traditional image processing methods. This research highlights the potential for incorporating deep learning methods into agricultural practices, paving the way for more intelligent and resilient crop management systems. © 2025 IEEE.

关键词： Bacterial Count Dairy Farming Fat Content Machine learning Milk Quality Predictive Models Protein Levels Sustainability

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AI-Enhanced Virtual Trial Room using Augmented Reality 6

AI-Enhanced Virtual Trial Room using Augmented Reality

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Mythili, N. Harini, M. Janalyn maroula, L. Dept. of Computer Science and Engineering St. Joseph’s Institute of Technology Old Mamallapuram Road 119 Chennai India

ISBN: (纸本)9798331522667

The Virtual Trial Room is an innovative augmented reality (AR) system that enables users to virtually try on apparel, enhancing the online shopping experience by simulating a physical fitting room. This technology leverages advanced computer vision, 3D modeling and machine learning to deliver an accurate and immersive experience. Data augmentation techniques are utilized to expand the training dataset, ensuring more precise clothing placement on different body types. The results demonstrate that our integrated approach, which combines AR and machine learning, significantly outperforms traditional virtual try-on systems, achieving enhanced user satisfaction, fit accuracy and personalized recommendations. The convolutional neural network (CNN) used in the system accurately detects body and facial landmarks, allowing for real-time garment fitting and recommendations. © 2025 IEEE.

关键词： Convolutional neural networks

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Cryptography Innovations for Securing Data in the Quantum Computing Era: Integrating Machine Learning for Enhanced Security

Cryptography Innovations for Securing Data in the Quantum Co...

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2025 International Conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Temara, Sheetal Bhagyalakshmi, L. Suman, Sanjay Kumar Shakunthala, M. Chitra, N. Thulasi Golla, Kishor University of the Cumberlands Department of Computer and Information Sciences United States Rajalakshhmi Engineering College Dept. of ECE TN Chennai India St. Martin's Engineering College Dept. of Ece and Dean R&d Telangana Secunderabad India R.M.D. Engineering College Dept. of ECE TN Thiruvallur India Mlr Institute of Technology Dept. of CSE Hyderabad India St. Martin's Engineering College Dept. of CSE Telangana Secunderabad India

ISBN: (纸本)9798331513894

The rise of quantum computing introduces substantial risks to traditional cryptographic protocols, which are vulnerable to quantum decryption methods such as Shor's algorithm. To address these emerging threats, this paper proposes an innovative cryptographic framework that integrates machine learning (ML) techniques to enhance data security in the quantum computing era. Our approach leverages ML-driven anomaly detection, adaptive key management, and predictive analytics to create a flexible and resilient cryptographic defense. The system's anomaly detection module utilizes neural networks to identify potential quantum-based decryption attempts, while reinforcement learning optimizes key generation and distribution in response to detected threats. Experimental results demonstrate that the proposed ML-augmented framework significantly improves anomaly detection accuracy and reduces vulnerability to quantum decryption attempts by dynamically adjusting cryptographic parameters. These findings underscore the potential of machine learning to strengthen cryptographic systems, making them adaptable to the advanced threats posed by quantum computing. © 2025 IEEE.

关键词： Predictive analytics

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Underwater Image Enhancement by Using a Colour-Correction and Dehazing Approach

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Procedia computer Science 2025年 259卷 818-825页

作者： Shubhankar Nalawade Mohit Dua Shubhankar Nalawade Dept. of Computer Engineering National Institute of Technology Kurukshetra India Mohit Dua Dept. of Computer Engineering National Institute of Technology Kurukshetra India

Capturing clear images underwater is difficult because light gets absorbed and scattered, resulting in degraded image quality with reduced visibility and colour inaccuracy. This research paper proposes a comprehensive approach that combines colour correction and dehazing techniques to enhance such deteriorated underwater images. In the colour correction step, principles of colour constancy are utilized to address colour distortions caused by underwater conditions. This correction significantly improves colour accuracy and enhances the visual fidelity of the image by first converting the RGB image into CIELab colour space and then performing white balancing in the CIELab colour space. This step successfully eliminates colour casts and restores the natural colours of underwater scenes, leading to visually pleasing and accurate representations. In the dehazing step, our method utilizes a dark channel prior-based approach to remove the haze by estimating the overall atmospheric light and the transmission map. The dehazing step significantly reduces haze, enhancing the visibility of submerged objects and fine details. Our technique has been evaluated subjectively and objectively, and the results have demonstrated its efficacy in reducing haze, correcting colour cast, and preserving the natural appearance of the image. The enhanced results consistently achieve great results for RMSE, PNSR, SSIM, and UCIQE, surpassing several existing underwater enhancement methods.

关键词： Underwater Image Enhancement Colour Correction Dark Channel Prior

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Enhancing Livestock Supply Chains with Blockchain Traceability from Source to Market: A Survey 5

Enhancing Livestock Supply Chains with Blockchain Traceabili...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Dahariya, Tukendra Kumar Sharma, Suraj Ray, Niranjan Kumar Guru Ghasidas Vishwavidyalaya Dept. Computer Science & Engineering Chhattisgarh Bilaspur India Kalinga Institute of Industrial Technology School of Computer Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798331522100

This document provides an in-dept. analysis of how blockchain technology can greatly improve transparency, traceability, and accountability in fish and livestock supply chains, presenting the potential to transform this industry. Utilizing blockchain's decentralized framework, which guarantees a secure and unchangeable record of transactions, problems associated with inefficiencies, fraud, and data manipulation in conventional supply chains can be efficiently resolved. This survey analyzes the present condition of blockchain applications in fish and livestock management, emphasizing their advantages and drawbacks. Furthermore, it highlights significant technical and regulatory challenges that impede wider acceptance, while recognizing potential future research avenues that could enhance processes from the production stage to the retail stage. The results intend to assist stakeholders in developing supply chains that are more sustainable, efficient, and transparent, fostering trust and complying with regulatory requirements. © 2025 IEEE.

关键词： Records management

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AI-Powered Advertisement Design: A PIL-Based Approach for Quality and Performance Analysis 3

AI-Powered Advertisement Design: A PIL-Based Approach for Qu...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Chilupuri, Harshitha Ramesh, G. Praveen, J. Gude, Venkataramaiah Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Artificial Intelligence and Machine Learning Hyderabad India Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science and Engineering Hyderabad500090 India Gokaraju Rangaraju Institute of Engineering and Technology Department of Electrical and Electronics Engineering Telangana Hyderabad500 090 India Gp Technologies Llc United States

ISBN: (纸本)9798331515911

The paper presents the development of a Data Science solution that applies generative AI models in order to create high-quality images to be used in visual marketing and advertising. Using the Python library PIL (Pillow) along with machine learning frameworks, the solution combines predefined product images with the selected backgrounds, enhancing image synthesis techniques for more appealing and contextually relevant visuals. This leads to improved efficiency and effectiveness in the creation of content by automatically placing product images onto the background images used in marketing campaigns. The approach demonstrated a 98.06% SSIM (Structural similarity Index) score, indicating significant visual quality improvement. Initial results suggest a 15% enhancement in visual appeal compared to traditional image creation methods. © 2025 IEEE.

关键词： Data Science

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Deep Learning Techniques for Object Detection in Underwater Environments

Deep Learning Techniques for Object Detection in Underwater ...

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2025 IEEE International Conference on Computational, Communication and information technology, ICCCIT 2025

作者： Praveen, R.V.S. Maindola, Meenakshi Anandhi, R.J. Thandra, Nagarjuna Bansal, Saloni Sharma, Vishal LTIMindtree Limited Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India New Horizon College of Engineering Department of Information Science Engineering Bangalore India MLR Institute of Technology Dept. Computer Science and Engineering Telangana Hyderabad India GLA University Department of Computer Engineering and Applications Mathura India Lovely Professional University Phagwara India

ISBN: (纸本)9798331512965

The degradation of image quality caused by things like light absorption, scattering, and distortion makes object detection in underwater environments a unique challenge. Applications like marine research, environmental monitoring, and autonomous underwater vehicles (AUVs) are restricted in their effectiveness by traditional image processing techniques, which often struggle with these complexities. Recent years have seen the rise of deep learning techniques as a potent weapon in the fight against these obstacles. In order to accurately detect objects underwater, this paper investigates the use of deep learning models, specifically convolutional neural networks (CNNs). We explore cutting-edge models and methods like attention mechanisms, transfer learning, and hybrid deep learning architectures to improve detection performance in challenging underwater environments. Furthermore, we utilize data augmentation techniques and synthetic dataset generation to tackle the critical issue of limited labeled underwater datasets. From a detection accuracy and robustness perspective, the experimental results show that our suggested deep learning method is light years ahead of the competition. Marine exploration, autonomous underwater vehicle navigation, and underwater surveillance systems can all benefit from this study's new insights into how to better detect objects in real time while submerged. © 2025 IEEE.

关键词： Convolutional neural networks

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Advancing Wi-Fi Intrusion Detection Systems with Machine Learning Techniques for Enhanced Classification of Wireless Attacks

Advancing Wi-Fi Intrusion Detection Systems with Machine Lea...

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International Conference on Advancements in Computational Sciences (ICACS)

作者： Hasseeb Ahmad Bhutta Muhammad Talha Jahangir Areeb Ahmad Bhutta Computer Science Dept. National University of Sciences and Technology Islamabad Pakistan Computer Science Dept. MNS University of Engineering and Technology Multan Pakistan Computer Science Dept. Information Technology University Lahore Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

WiFi usability and scalability have contributed to an improvement in human living standards. However, this has also led to increased vulnerabilities and attack vectors within WiFi networks. Authentic users can face network failures and denial of service caused by attacks on wireless networks by some third-party. Wireless Intrusion Detection System (IDS) is needed to detect wireless attacks. Main of objective of wireless IDS is to monitor network traffic and classify if it is an attack or normal traffic. Modern anomaly based wireless intrusion detection systems use machine learning (ML) to learn from previous attacks in the dataset to learn patterns of the attack. Although this method is effective, but there is also downside to this approach. The time required to train and test these ML models are exceedingly high along with high computational costs. Big data is emerging technologies that is getting advanced day by day, due to which, network technology is increasing rapidly. For that reason, we need to discuss the issue, that is high computational costs. In our proposed solution, we used our custom oversampling technique BBOT (Balanced Boost Oversampling Technique) to rectify class imbalance, generating artificial samples for the classes, enhancing model performance. The results of the experiments show that the combination of feature selection, BBOT, and Decision Tree outperformed all other classifiers with reduced computational overhead, providing a practical and efficient solution for real-time intrusion detection in WiFi networks. When compared to XGBoost, the provided solution achieves a similar level of accuracy, but with significantly reduced training time.

关键词： Wireless communication Computational modeling Wireless networks Intrusion detection Machine learning Big Data Feature extraction Vectors Computational efficiency Wireless fidelity

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Disaster Management Based on Biodiversity Conservation Using Remote Sensing Data Analysis Using Machine Learning Model

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Remote Sensing in Earth Systems Sciences 2025年第1期8卷 191-199页

作者： Pokkuluri, Kiran Sree Mounika, Talla Devi, N. Durga Kishore, D. Ratna Balakiruthiga, B. Krishna, B. Murali Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Telangana Bhimavaram India Department of CSE-DS Mallareddy College of Engineering Hyderabad India Department of Computer Science and Engineering Aditya University Andhra Pradesh Surampalem India Dept of Information Technology Lakireddy Balireddy College of Engineering Mylavram Vijayawada India Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Artificial intelligence (AI) applications in forestry as well as wildlife domains have become more feasible due to the advancements in data science and digital and satellite technologies. However, there is a serious global threat to biodiversity due to the rapid expansion of urban, agricultural, and development initiatives. Therefore, the effective monitoring, management, and preservation of biodiversity as well as forest resources may be facilitated by integration of cutting-edge methods like AI in disciplines of forests as well as biodiversity. This study suggests a novel approach to disaster management based on biodiversity protection that makes use of machine learning models and remote sensing image analysis. In this case, the data for disaster management has been gathered and processed for normalisation and smoothing. The processed image features have been segmented using active contour Gaussian Q- histogram equalisation model and feature extracted using probabilistic Bayesian fuzzy transfer NN. Experimental analysis is carried out based on parameters like random accuracy, average precision, sensitivity, AUC, and normalized co-efficient. The proposed technique attained average precision was 95%, sensitivity was 97%, random accuracy was 98%, and normalized coefficient was 94%, AUC of 96%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Biodiversity conservation Disaster management Machine learning model Q- histogram equalisation Remote sensing

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