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Panoptic Region Slicing Segmentation and Optimized Alexnet-Based CNN for Early Melanoma Diagnosis

SN computer science 2024年第6期5卷 1-9页

作者： Ganesh, V.N. Gulappagol, Laxmi Pushparani, M.K. Ganesh, K. Department of Electronics and Communication Engineering Alvas Institute of Engineering and Technology Department of Internet of Things and Cyber Security including Block chain Technology A J Institute of Engineering and Technology Department of Computer Science & amp Design Alvas Institute of Engineering and Technology

Early and accurate diagnosis of melanoma, a potentially life-threatening skin cancer, is crucial for improving patient outcomes. In this study, we propose a novel approach for melanoma detection, termed Panoptic Region Slicing Segmentation (PRS2) using an optimized convolution neural network (PRS2-OCNN) based on AlexNet. The proposed system integrates several advanced methods to enhance the accuracy and efficiency of melanoma identification. The initial step involves preprocessing the dermoscopic image using a 2D Fusion Filter, which enhances the image quality and prepares it for subsequent analysis. Next, the Panoptic Region Slicing Segmentation (PRS2) method is applied to emphasize the boundary regions, allowing for precise localization of melanoma-affected areas. To assess color variations within the segmented regions, we employ Threshold Histogram Evaluation (THE), which effectively characterizes melanoma-specific color patterns. The features extracted using Spread Spectral Menzies’s Feature Selection (SSMFS), reducing the dimensionality and improving the efficiency of the subsequent analysis. The core of our proposed approach lies in the optimized convolution neural network, derived from the influential AlexNet architecture. By fine-tuning the AlexNet-OCNN on the reduced feature set, we maximize its ability to accurately classify melanoma lesions based on their risk level. The PRS2-OCNN identifies melanoma classes according to their risk severity, aiding dermatologists in making informed decisions for timely and appropriate *** evaluations were conducted on a diverse and extensive dataset of dermoscopic images. The proposed system demonstrated superior performance compared to existing methods, exhibiting heightened detection accuracy by deeply analyzing the melanoma-affected regions. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： 2D fusion filter AlexNet Early diagnosis Melanoma Panoptic region slicing segmentation PRS2-OCNN

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Ensuring Privacy and Integrity in IoT Supply Chains through Blockchain and Homomorphic Encryption

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IEEE Internet of Things Journal 2025年第13期12卷 25547-25558页

作者： Din, Ikram Ud Almogren, Ahmad Han, Zhu Guizani, Mohsen The University of Haripur Department of Information Technology Haripur22620 Pakistan King Saud University College of Computer and Information Sciences Department of Cyber Security Department of Computer Science Riyadh11633 Saudi Arabia University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

Ensuring data security and privacy in Internet of Things (IoT) is increasingly critical due to the growing interconnectedness of devices and the sensitivity of the data they handle. This paper presents a novel approach to enhancing data security in IoT through the integration of homomorphic encryption and blockchain technology. We conduct simulations using the Kaggle Smart Home Dataset to evaluate the effectiveness of our proposed methodology on smart home devices and wearable technology. Our approach not only secures data transmission but also guarantees data integrity and privacy through decentralized verification and secure aggregation techniques. Specifically, our evaluation demonstrates an encrypted data transmission rate exceeding 99.5%, a complete absence of unauthorized access instances in the simulated environment, and a verified data integrity rate of over 99.8%. Additionally, our method supports real-time processing and scalability, making it suitable for various IoT applications, including smart contract applications in IoT for privacy and security in supply chain transactions. The study highlights the robustness of combining homomorphic encryption and blockchain to protect sensitive data throughout its lifecycle. © 2014 IEEE.

关键词： Data integrity

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A Survey of Machine Learning Applications in Medical Imaging for Neurodegenerative Disease Diagnosis 2

A Survey of Machine Learning Applications in Medical Imaging...

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2nd International Conference on computer Vision and Internet of Things, ICCVIoT 2024

作者： Nandhashree, K.R. Senthil Kumar, M. Susee, S.K. Department of Cyber Security SRM Valliammai Engineering College India Department of Computer Science and Engineering SRM Valliammai Engineering College India Department of Electronics and Communication Mohamed Sathak A J College of Engineering India

ISBN: (数字)9781837242535

ISBN: (纸本)9781837242672

The increasing prevalence of brain degenerative disorders particularly Alzheimer's and Parkinson's, have incurred a dire need for effective diagnostic methodologies. With advancements in medical imaging techniques and Artificial Intelligence (AI), transformative potential exists to enhance early detection and disease classification. This review examines the recent progress made in the use of diagnostic medical imaging and artificial intelligence (AI) techniques for the early detection of Alzheimer's disease and Parkinson's disease. Despite significant progress, a comprehensive overview is lacking. The review aims to summarize and synthesize existing methods, critically evaluate their strengths and limitations, and identify emerging trends. Multimodal imaging data, combining information from different sources like MRI, PET, and SPECT, has shown progress in improving diagnostic accuracy and providing insights into disease progression. Advanced AI techniques, such as transfer learning and generative adversarial networks, have further enhanced the performance of diagnostic models. Previous studies have predominantly focused on using standalone techniques, often leaving the integration of multi-modal imaging and deep learning methodologies that could lead to more robust diagnostic frameworks. This literature review addresses this gap by synthesizing current research on medical imaging modalities, AI applications, and their implications in diagnosing Alzheimer's and Parkinson's diseases. This review seeks to connect technological innovations with clinical applications to establish more robust and dependable diagnostic methods. © The Institution of engineering & Technology 2024.

关键词： Generative adversarial networks

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Reversible Data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

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IEEE Internet of Things Journal 2025年第13期12卷 23933-23945页

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： Cryptography Encryption security Data mining Adaptive coding Correlation Cloud computing Polynomials Image coding Streaming media

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cyber Threat Intelligence (CTI): An Analysis on the Use of Artificial Intelligence and Machine Learning to Identify cyber Hazards

Cyber Threat Intelligence (CTI): An Analysis on the Use of A...

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International Conference on Recent Developments in cyber security, ReDCySec 2023

作者： Kant, Neelima Amrita Department of Computer Science and Engineering School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida201306 India Department of Computer Science and Engineering School of Engineering and Technology Center for Cyber Security and Cryptology Sharda University Uttar Pradesh Greater Noida201306 India

ISBN: (纸本)9789819998104

Data transport volume and scope on networks are growing daily due to the quick advancements in network technology. It is challenging for cybersecurity specialists to keep track of every action taking place on the network because of the constantly growing density of networks. This circumstance has led to an increase in the complexity and intensity of threats and attacks. It is harder to detect and identify irregularities in network activities because of frequent and sophisticated cyberattacks. A well-crafted cybersecurity strategy now includes cyber threat intelligence (CTI), which is a crucial foundation. Automating the detection of cyberattacks as well as speedy attack type analysis and predication are all made possible by machine learning (ML), which offers a number of tools and techniques. The strategies for using machine learning (ML) to identify assaults are discussed in this article. Threat intelligence can help security teams defend against a constantly evolving threat environment before, during, and after an attack if used properly. By analyzing attackers and comprehending their tactics and goals, groups may create cyber defenses that are more effective, delicate, and resilient. However, due to two significant flaws, its usefulness is still in question. First, current methods are unable to detect unknown Indicator of Compromise (IoC), and second, they are unable to automatically produce categorized CTIs which renders CTI sharing. As a result, the objective of this paper is to present a complete analysis of cyber threat identification using intelligent techniques. Additionally, we covered the issues and solutions related to machine learning applications utilized in network assaults. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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Vision transformer for contactless fingerprint classification

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Multimedia Tools and Applications 2024年 1-21页

作者： Kaplesh, Pooja Gupta, Aastha Bansal, Divya Sofat, Sanjeev Mittal, Ajay Cyber Security Research Centre Punjab Engineering College Chandigarh160012 India STME SVKM’s NMIMS University Chandigarh160014 India Computer Science and Engineering Punjab Engineering College Chandigarh160012 India UIET Panjab University Chandigarh160014 India

Contactless fingerprint recognition has gained attention due to its data security and hygiene as compared to the contact-based counterpart. In the previous years, deep learning models, mainly Convolutional Neural Networks (CNNs), have been extensively applied in this field and have given promising results. Recently, their successor, Vision Transformers (ViTs) have outperformed CNNs in various computer vision tasks due to their reliance on self-attention mechanism that focusses on capturing global and context-aware relationships between the image patches without inherent spatial bias. Due to the underlying advantageous and innovative feature extraction process, we propose to adapt ViT model for contactless fingerprint classification. To the best of our knowledge, this study would be first of its kind that applies ViT for contactless fingerprint classification. The performance of the model when evaluated on publicly available datasets, namely ISPFDv1, ISPFDv2, and UNFIT, results in average accuracies of 96%, 92%, and 91%, respectively. The comparative analysis with state-of-the-art CNN models using equal error rate (EER) metric shows that the proposed model outperforms them in classifying contactless fingerprints with a significant margin. Additionally, the Local Interpretable Model-agnostic Explanations (LIME) technique is integrated with the proposed model to enhance interpretability and explainability of the predictions. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Vehicular Intrusion Detection System Based on Hybrid Quantum Neural Networks

Vehicular Intrusion Detection System Based on Hybrid Quantum...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhou, Jianhua Liu, Yueheng Li, Sifan Cao, Yue Chen, Wenkai Ahmed, Naveed Wuhan University School of Cyber Science and Engineering China Wuhan Cyber Security Association China Technology Co. Ltd China Prince Sultan University College of Computer and Information Sciences Riyadh Saudi Arabia

ISBN: (纸本)9798350351255

Traditional Deep Learning (DL) method is increasingly used in vehicular Intrusion Detection Systems (IDSs). However, there are still some limitations. It combines various models, resulting in a massive model size and numerous parameters, requiring more computational resources. Furthermore, real-time performance is crucial in vehicular IDS. Large models typically require more time for detection, failing to meet the timeliness requirements. Quantum computing offers parallel computing capabilities, prompting our proposal of a Hybrid Quantum Neural Network (HQNN)-based IDS to improve the timeliness of intrusion detection. This IDS employs HQNN to enhance feature extraction and speed up traditional convolution. It retains most of the structure of classical Convolutional Neural Network (CNN), comprising attention layers, batch normalization layers, and quantum circuits. Through quantum superposition and entanglement, certain complex non-linear functions are compressed. Furthermore, residual connections enable seamless gradient flow during back propagation. This accelerates computation and expedites convergence. Comparative experiments on the Car-hacking dataset show improved detection time and convergence speed, with exceptionally high accuracy in practical applications. © 2024 IEEE.

关键词： Network intrusion

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Personalized Educational Video Evaluation Combining Student's Cognitive and Teaching Style

Personalized Educational Video Evaluation Combining Student'...

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2023 IEEE International Conference on Systems, Man, and cybernetics, SMC 2023

作者： Weng, Jinta Dong, Haoyu Deng, Yifan Hu, Yue Wu, Hao Huang, Heyan School of Cyber Security University of Chinese Academy of Sciences China School of Computer Science and Cyber Engineering Guangzhou University China School of Computer Science and Technology Southeast Academy of Information Technology Beijing Institute of Technology China

ISBN: (纸本)9798350337020

AI-powered technologies, like ChatGPT and learning analytic technologies, have encouraged the sharing of online teaching resources and the transformation of teaching methods and learning pathways. However, the mixed resources and the result-oriented video evaluation repeatedly let students fall into an information trap and only appeal to students' attention to unsuitable resources. Inspired by human-computer interaction, a novel online video assessment LPSA(Linguistic- Presentative-scientific-Artistic) is proposed, integrated by cognitive style and teaching style, to realize more precise learning detection and teaching quality assessment. The LPSA evaluation consists of a four-level classification and eight secondary indexes quantified by machine learning algorithms. By automatically searching for an appropriate threshold of all secondary indexes, a real-time video assessment system is developed to certify its technical feasibility and pedagogical availability. The results show that the proposed AI-assisted assessment could realize practical pedagogical recommendations and real-time supervision. © 2023 IEEE.

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IoT-Based Plant Health Monitor Using NodeMCU and ESP8266 5

IoT-Based Plant Health Monitor Using NodeMCU and ESP8266

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Tandel, Rajat Ebenezer, V. Santhiya, P. Edwin, Bijolin Stewart Kirubakaran, S. Thanka, Roshni Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Data Sciences and Cyber Security Coimbatore India

ISBN: (纸本)9798331518097

Plant Monitor Using NodeMCU(ESP8266)is a technology that is advanced;it uses IoT-Internet of Things to measure and monitor different plant phenomena. This system, which is based on sensors that collect data on different factors such as soil type, temperature, humidity, and other vital metrics vital for plant growth, is also integrated. Bringing IoT into the picture, the system can remotely sense the metrics and having control over other factors which include watering, shading, and even security provision for the plants to make it thrive. Besides that, it will also send notifications to users, hence allowing them to engage in the monitoring process through mobile applications with support from their smartphones. The Smart Plant Monitoring System though creates an impact on the efficiency of plant care also impacts on water conservation, automation of plant care process and better agricultural practices. © 2024 IEEE.

关键词： Efficiency

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cyberbullying Detection in Hinglish Language

Cyberbullying Detection in Hinglish Language

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Kumar, Rajiv Rai, Vipin Joshi, Shivani Raj, Daksh Amrita Galgotias University Department of Computer Science & Engineering U.P. Greater Noida India Galgotias College of Engineering and Technology Department of Computer Science and Engineering U.P. Greater Noida India Sharda University Center for Cyber Security and Cryptology Computer Science & Engineering Sharda School of Engineering & Technology Greater Noida India

ISBN: (纸本)9798350378092

cyberbullying represents one of the most recent challenges stemming from the widespread adoption of social media platforms. With the surge in social media usage, the right to express oneself is being misused. Statistics reveal that approximately 36.7% of individuals believe that they have encountered cyberbullying at some point in their lives. These figures have more than doubled since 2007, and a noticeable increase occurred from 2018 to 2019, indicating an unfavorable trend. While there are existing solutions in the market aimed at mitigating this issue, many of them come with usage limitations or employ fewer effective algorithms. The primary objective of this work is to explore innovative methods for comprehending and automatically identifying instances of cyberbullying across online text messages and comments on different social media platforms. For achieving this goal, real-time data are gathered, including comments, headlines and text messages from trending posts on twitter, and devised a labelling study for categorizing cyberbullying. An analysis is presented of this labelled data, which includes an examination of the relationships between various features and both cyberbullying and cyberaggression. The core focus of this work is the early detection of cyberbullying as it originates, while being drafted in real-time. The aim of this work is to enhance the efficiency of cyberbullying detection by using various machine learning techniques and natural language processing. © 2024 IEEE.

关键词： Phishing

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