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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 756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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Exploring the landscape of network security: a comparative analysis of attack detection strategies

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Journal of Ambient Intelligence and Humanized Computing 2024年第8期15卷 3211-3228页

作者： Rajesh Kanna, P. Santhi, P. Department of Computer Science and Engineering Bannari Amman Institute of Technology Tamil Nadu Erode India TIFAC-CORE in Cyber Security Amrita School of Engineering Amrita Vishwa Vidyapeetham Tamil Nadu Coimbatore India

The field of computer networking is experiencing rapid growth, accompanied by the swift advancement of internet tools. As a result, people are becoming more aware of the importance of network security. One of the primary concerns in ensuring security is the authority over domains, and network owners are striving to establish a common language to exchange security information and respond quickly to emerging threats. Given the increasing prevalence of various types of attacks, network security has become a significant challenge in the realm of computing. To address this, a multi-level distributed approach incorporating vulnerability identification, dimensioning, and countermeasures based on attack graphs has been developed. Implementing reconfigurable virtual systems as countermeasures significantly improves attack detection and mitigates the impact of attacks. Password-based authentication, for instance, can be susceptible to password cracking techniques, social engineering attacks, or data breaches that expose user credentials. Similarly, ensuring privacy during data transmission through encryption helps protect data from unauthorized access, but it does not guarantee the prevention of other types of attacks such as malware infiltration or insider threats. This research explores various techniques to achieve effective attack detection. Multiple research methods have been utilized and evaluated to identify the most suitable approach for network security and attack detection in the context of cloud computing. The analysis and implementation of diverse research studies demonstrate that the based signature intrusion detection method outperforms others in terms of precision, recall, F-measure, accuracy, reliability, and time complexity. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Network security

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Electronic Medical Records Management and Administration: Current Trends, Issues, Solutions, and Future Directions

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SN computer Science 2024年第5期5卷 460页

作者： Abdulkadir, Umar Waziri, Victor Onomza Alhassan, John Kolo Ismaila, Idris Department of Cyber Security Science Federal University of Technology Minna Nigeria Department of Computer Science Federal University of Technology Minna Nigeria

Electronic Medical Records (EMR) is often used to refer to as electronic personal health (EPH) records or electronic healthcare records (EHR). These are considered vivacious assets of health facilities and patients. The relevance of the EMRs has motivated diverse innovations in the collecting, organizing, managing and administering for purpose of treatment primarily and other reasons. There are various concerns raised about legitimate usages, reproducibility, accuracy, and privacy breaches of EMRs across majority of health and medical facilities globally. This phenomenon became popular due to its support of electronic devices, wireless links, transmission and storage of data in the cloud, and communication across a gateway (or central-point). Therefore, physicians and medical facilities were empowered to undertake virtual consultations to patients through telemedicine applications for the remote treatment and diagnosis. Internet of Things (IoT) systems, medical wearable objects, and sensors were the basic components that collect and transmit patient data on real-time basis to a base station or centralized servers managed by hospitals. However, there is the need to improve creation and adoption of EMRs not without understanding their roles and shortfalls as well possible means of improvement. Consequently, this study conducts a systematic literature review on electronic medical records management and administration under current trends, issues, solutions and future directions. To this end, eighty-one (81) peer-reviewed articles including conferences and journals papers were included in the final stage of the study after applying Preferred Reporting Items for Systematic Review and Meta-analysis Protocols (PRIMA-P) methodology of records selection. The contributions of this study include: the recognition of the informed consent strategy as the best solution to illegitimate access to patient electronic records;the use of permissioned access strategies provided by blockc

关键词： Access permission scheme Data administration Medical records Privacy security

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TU-IR Apple Image Dataset: Benchmarking, Challenges, and Asymmetric Characterization for Bruise Detection in Application of Automatic Harvesting

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第1期2卷 105-124页

作者： Das, Dipak Hrishi Roy, Sourav Dey Saha, Priya Bhowmik, Mrinal Kanti Government Degree College Department of Information Technology Santirbazar 799144 India Department of Computer Science and Engineering Suryamaninagar 799022 India Department of Cyber Security and Digital Forensics Agartala799001 India

With the blooming interest in computer vision-based technologies for future automation of food producers, there is a need for incorporating an automatic bruise detection module in robotic apple harvesting because of decreasing accessibility and growing labor costs. Although numerous studies have been published for automatic quality inspection of fruit and other agricultural products, there is a lack of publicly available image-based datasets for quality inspection/automatic detection of bruises. Toward the aim of developing a bruise detection system for apple harvesting, especially at night time, this article describes the designing issues (i.e., protocol) and creation of a new infrared imaging-based dataset titled 'TU-IR Apple Image Dataset,' which contains 1375 infrared images of apples defining four major categories of bruises (i.e., fresh, slight, moderate, and severe). Along with the infrared images, ground truths (in the form of binary masks) and measurements of suspicious bruised regions are defined. This study also investigates the efficiency of infrared imaging technology for automatic bruise detection in apples by performing an analysis of temperature-based, intensity-based, texture-based, shape-based, and deep convolutional neural network-based features. The classification performance was evaluated using eight different feature sets. Based on the experimental results, considering the most outer-performed classifier, deep convolutional neural networks as a fixed feature extraction method were found to provide the highest prediction performance for discriminating between fresh and three categories of bruises in apples with an average accuracy, specificity, and sensitivity of 93.87%, 80.57%, and 92.02%, respectively. © 2023 IEEE.

关键词： Feature extraction

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Effectiveness of a touch-based hand therapeutic application for hand dexterity in children with handwriting difficulties-a pilot study

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Multimedia Tools and Applications 2024年 1-35页

作者： Smitha, John Renumol, V.G. School of Computer Science and Technology Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India School of Engineering Cochin University of Science and Technology Kochi India

Handwriting is an important skill for children during their academic years. It is the coordination of perceptual-motor and cognitive abilities. Some children have difficulties in handwriting, which is an indication of issues in their fine motor skills. Nowadays, technology-based instructions are available to develop fine motor skills in children with handwriting disabilities. This paper describes the details of a newly developed software application namely ‘HanDex’ for children with handwriting difficulties and the experiments conducted to test its effectiveness. The application comprises six hand therapeutic activities which were designed based on the oscillation theory of handwriting. Initially, in the first phase, the experiments were conducted in nine primary school children from various schools, aged 8–12 years. Then, in the second phase, the software training was given to six upper primary school children (10–12 years) with handwriting issues. The results of using this application were promising. So one more study was conducted in another set of nine children (8–11 years) with handwriting difficulties. The participants were assessed by their special educators, class teachers and parents. After the pretest, they received a total of 18 handwriting practice sessions using this new application. At the end of the training sessions, a post test was conducted, to measure the progress in handwriting. The handwriting rubrics with eight handwriting parameters such as letter formation, placement, letter sizing, spacing of letters, legibility, speed, neatness, and spacing of words were used for handwriting assessment. The findings from the study suggested that touch-based hand therapeutic activities were beneficial for children with writing difficulties to improve their handwriting performance and tripod grip. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Children with handwriting difficulties Hand Dexterity Handwriting performance and tripod grip Tablet computer ‘HanDex’ Software

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Effective Tool Augmented Multi-Agent Framework for Data Analysis

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Data Intelligence 2024年第4期6卷 923-945页

作者： Xilin Zhang Zhixin Mao Ziwen Chen Shen Gao School of Automation Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Computer Science and Engineering(School of Cyber Security) University of Electronic Science and Technology of ChinaChengdu 611731China

Data analysis tasks aim to provide insightful analysis for given data by incorporating background knowledge of the represented phenomenon, which in turn supports decision-making. While existing large language models(LLMs) can describe data trends, they still lag behind human data analysts in terms of integrating external knowledge and in-depth data analysis. Therefore, we propose a multi-agent data analysis framework based on LLMs. The framework decomposes the data analysis task into subtasks by employing three different agents. By empowering agents with the ability to utilize data search tools, the framework enables them to search for arbitrary relevant knowledge during the analysis process, leading to more insightful analysis. Moreover, to enhance the quality of the analysis results, we propose a multi-stage iterative optimization method that iteratively performs data analysis to form more in-depth conclusions. To validate the performance of our framework, we apply it to three real-world problems in the research development of higher education in China data. Experimental results demonstrate that our approach can achieve more insightful data analysis results compared to directly using LLMs alone.

关键词： Exploratory data analysis Multi-agent Large language models

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Novel method for ranking batsmen in Indian Premier League

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Data Science and Management 2023年第3期6卷 158-173页

作者： M.K.Manju Abin Oommen Philip Department of Computer Science&Engineering Toch Institute of Science&TechnologyErnakulamKerala682313India Department of CSE-Cyber Security Indian Institute of Information TechnologyKottayam686635India

Sports analytics have benefited immensely from the growth and popularity of artificial intelligence and machine *** techniques enable sports analysts to evaluate player performance more effectively.A literature review of player performance evaluation methods shows the need to develop a new performance evaluation index for Twenty20(T20)cricket.A novel framework was proposed to evaluate batsman strength based on individual performance,role in the team,and team ***,proposed ranking systems are derived from static networks,that is,the aggregation of game results over ***,the scores of the players(or teams)fluctuate over ***,defeating a renowned player during peak performance is more rewarding than defeating the same player during other *** account for this,we propose a new method and apply it to the T20 format Indian Premier *** method serves three main purposes:First,it creates a new performance index for players to rank them more accurately and ***,the players are clustered based on their *** the third phase,a social network analysis approach is applied to visualize and analyze crickets as a network to gain better insights into players’team *** novel approach is a helpful index for sports coaches,analysts,cricket fans,and managers to evaluate player performance and rank for future aspects.

关键词： Indian Premier League Social network analysis Artificial intelligence Machine learning

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MODELGUARD: Information-Theoretic Defense Against Model Extraction Attacks 33

MODELGUARD: Information-Theoretic Defense Against Model Extr...

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33rd USENIX security Symposium, USENIX security 2024

作者： Tang, Minxue Dai, Anna DiValentin, Louis Ding, Aolin Hass, Amin Gong, Neil Zhenqiang Chen, Yiran Li, Hai Department of Electrical and Computer Engineering Duke University United States Cyber Security Lab Accenture

ISBN: (纸本)9781939133441

Malicious utilization of a query interface can compromise the confidentiality of ML-as-a-Service (MLaaS) systems via model extraction attacks. Previous studies have proposed to perturb the predictions of the MLaaS system as a defense against model extraction attacks. However, existing prediction perturbation methods suffer from a poor privacy-utility balance and cannot effectively defend against the latest adaptive model extraction attacks. In this paper, we propose a novel prediction perturbation defense named MODELGUARD, which aims at defending against adaptive model extraction attacks while maintaining a high utility of the protected system. We develop a general optimization problem that considers different kinds of model extraction attacks, and MODELGUARD provides an information-theoretic defense to efficiently solve the optimization problem and achieve resistance against adaptive attacks. Experiments show that MODELGUARD attains significantly better defensive performance against adaptive attacks with less loss of utility compared to previous defenses. © USENIX security Symposium *** rights reserved.

关键词： Differential privacy

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AttrLeaks on the Edge: Exploiting Information Leakage from Privacy-Preserving Co-inference

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Chinese Journal of Electronics 2023年第1期32卷 1-12页

作者： WANG Zhibo LIU Kaixin HU Jiahui REN Ju GUO Hengchang YUAN Wei School of Cyber Science and Engineering Wuhan University School of Cyber Science and Technology Zhejiang University Department of Computer Science and Technology Tsinghua University

Collaborative inference(co-inference) accelerates deep neural network inference via extracting representations at the device and making predictions at the edge server, which however might disclose the sensitive information about private attributes of users(e.g.,race). Although many privacy-preserving mechanisms on co-inference have been proposed to eliminate privacy concerns, privacy leakage of sensitive attributes might still happen during inference. In this paper, we explore privacy leakage against the privacy-preserving co-inference by decoding the uploaded representations into a vulnerable form. We propose a novel attack framework named AttrL eaks, which consists of the shadow model of feature extractor(FE), the susceptibility reconstruction decoder,and the private attribute classifier. Based on our observation that values in inner layers of FE(internal representation) are more sensitive to attack, the shadow model is proposed to simulate the FE of the victim in the blackbox scenario and generates the internal ***, the susceptibility reconstruction decoder is designed to transform the uploaded representations of the victim into the vulnerable form, which enables the malicious classifier to easily predict the private attributes. Extensive experimental results demonstrate that AttrLeaks outperforms the state of the art in terms of attack success rate.

关键词： Deep learning Privacy Collaboration Transforms Feature extraction Prediction algorithms Iron

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Endogenous security of FQn Networks: Adaptive System-Level Fault Self-Diagnosis

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IEEE Transactions on Reliability 2024年第3期73卷 1659-1668页

作者： Lin, Yuhang Lin, Limei Huang, Yanze Xu, Li Hsieh, Sun-Yuan Fujian Normal University College of Computer and Cyber Security Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou350117 China Fujian University of Technology School of Computer Science and Mathematics Fuzhou350118 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan

Endogenous security has the ability to discover, eliminate, and solve internal security problems and hidden dangers within the network, and is a superior technology to ensure future network security. The t/k-diagnosis strategy, as a strong and adaptive self-diagnosis strategy, is an important part for ensuring endogenous security. Moreover, the folded hypercube (FQn) as a data transmission network (e.g., optical network) topology offers new potential for the construction of large-scale, high data throughput, and low-latency systems, such as computing network, human-cyber-physical systems, and smart grid. However, there are few studies on endogenous security based on FQn networks. Therefore, this article designs an adaptive system-level fault self-diagnosis strategy, namely Fast t/k-Diagnosis Under Maeng-Malek Model (Ftk-DIAG-MM*) to diagnosis the faulty vertices in FQn network under the Maeng-Malek model (MM∗ mod). Then, we provide a proof of the algorithm correctness theoretically by the fault tolerance of FQn network. It is derived by theoretical derivation that the t/k-diagnosability inherent to the FQn network is (n+1)(k+1)-k(k+3)/2. The simulation experiments demonstrate that the designed Ftk-DIAG-MM∗ strategy can correctly diagnose all vertices within the range allowed by the diagnosability, and still has a great performance when it exceeds the range allowed by the diagnosability. It greatly enhances the fault diagnosis capability of FQn network in the circumstance of misdiagnosing a few vertices, which provides an important theoretical basis for the reliability and endogenous security of FQn networks. © 1963-2012 IEEE.

关键词： Topology

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