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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Ramkumar, M. Siva Arun, M. Gopan, Gokul Giri, Jayant Al-Qawasmi, Khaled Sns College of Technology Dept of Ece India Saveetha University Department of Mechanical Engineering Chennai602105 India YeshwantraoChavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India Saveetha University Department of Vlsi Microelectronics TN Chennai602105 India Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331509828

In the digital era, global logistics and supply chain management are undergoing a revolutionary transformation, driven by advancements in technology and interconnected systems. This evolution aims to enhance operational efficiency, reduce costs, and increase overall supply chain visibility. However, existing supply chain systems often suffer from challenges such as lack of real-time data, limited visibility, inefficiencies, and difficulties in tracking inventory across complex networks. These issues lead to delays, increased operational costs, and reduced customer satisfaction. The proposed framework, Revolutionizing Supply Chains using IoT (RSC-IoT), integrates cutting-edge Internet of Things (IoT) technologies to address these challenges. By embedding sensors and connected devices throughout the supply chain, RSC-IoT facilitates real-time data collection, enabling smarter decision-making, accurate demand forecasting, and enhanced inventory management. This system also improves visibility, helping stakeholders track goods, anticipate disruptions, and optimize resource allocation. The use of RSC-IoT will lead to improved supply chain resilience, faster response times, and increased operational efficiency. The system's ability to monitor and control key metrics, such as transportation times, inventory levels, and production schedules, offers unprecedented control and insight. The findings from the implementation of RSC-IoT reveal significant improvements in logistics performance. Enhanced real-time tracking, reduced lead times, and optimized resource utilization contribute to a more agile and cost-efficient global supply chain, demonstrating the transformative potential of IoT in logistics management. © 2025 IEEE.

关键词： Customer satisfaction

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Effective Classification of Synovial Sarcoma Cancer Using Structure Features and Support Vectors

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computers, Materials & Continua 2022年第8期72卷 2521-2543页

作者： P.Arunachalam N.Janakiraman Junaid Rashid Jungeun Kim Sovan Samanta Usman Naseem Arun Kumar Sivaraman A.Balasundaram Department of Electronics and Communication Engineering K.L.N.College of EngineeringPottapalayam630612Tamil NaduIndia Department of Computer Science and Engineering Kongju National UniversityCheonan 31080Korea Department of Mathematics Tamralipta MahavidyalayaTamluk721636West Bengal School of Computer Science University of SydneyNSW 2006Australia School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India School of Computer Science and Engineering Centre for Cyber Physical SystemsVellore Institute of TechnologyChennai600127India

In this research work,we proposed a medical image analysis framework with two separate releases whether or not Synovial Sarcoma(SS)is the cell structure for *** this framework the histopathology images are decomposed into a third-level sub-band using a two-dimensional Discrete Wavelet ***,the structure features(SFs)such as PrincipalComponentsAnalysis(PCA),Independent ComponentsAnalysis(ICA)and Linear Discriminant Analysis(LDA)were extracted from this subband image representation with the distribution of wavelet *** SFs are used as inputs of the Support Vector Machine(SVM)***,classification of PCA+SVM,ICA+SVM,and LDA+SVM with Radial Basis Function(RBF)kernel the efficiency of the process is differentiated and compared with the best classification ***,data collected on the internet from various histopathological centres via the Internet of Things(IoT)are stored and shared on blockchain technology across a wide range of image distribution across secure data IoT *** to this,the minimum and maximum values of the kernel parameter are adjusted and updated periodically for the purpose of industrial application in device ***,these resolutions are presented with an excellent example of a technique for training and testing the cancer cell structure prognosis methods in spindle shaped cell(SSC)histopathological imaging *** performance characteristics of cross-validation are evaluated with the help of the receiver operating characteristics(ROC)curve,and significant differences in classification performance between the techniques are *** combination of LDA+SVM technique has been proven to be essential for intelligent SS cancer detection in the future,and it offers excellent classification accuracy,sensitivity,specificity.

关键词： Principal components analysis independent components analysis linear discriminant analysis support vector machine blockchain technology IoT application industry application

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Leveraging OpenStack-based Private Cloud for Intrusion Detection Using Deep Learning Techniques

Leveraging OpenStack-based Private Cloud for Intrusion Detec...

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Women in Computing (InCoWoCo), International Conference for

作者： Rahul Roshan G Vishal S Salanke Nagasundari S Department of Computer Science and Engineering Research Center for Information Security Forensics and Cyber Resilience PES University Bangalore India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

OpenStack is a cloud platform that offers infrastructure services, providing access to computing, storage, and network resources. However, due to its open-source nature, the cloud environment can be vulnerable to security threats. OpenStack security measures are generally applied to specific services, rather than being integrated comprehensively across the entire cloud environment. Despite offering flexibility and scalability, the widespread use of OpenStack calls for robust security solutions. Current intrusion detection systems often struggle to adapt to constantly evolving traffic patterns and attack vectors in the OpenStack environment. This study introduces a two-stage deep learning framework to create a robust Intrusion Detection System (IDS) for OpenStack-based private clouds. The proposed framework efficiently analyzes network traffic data in these clouds by implementing dimensionality reduction and anomaly detection techniques. It utilizes a network-based IDS, trained on two publicly available datasets, which enables a broader detection range of known attack patterns. The results indicate that the OpenStack IDS significantly enhances OpenStack's security, addressing the limitations of generic IDS solutions.

关键词： Deep learning Dimensionality reduction Cloud computing Scalability Current measurement Intrusion detection Telecommunication traffic Traffic control Vectors security

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Comprehensive Review of Capsule Networks with a Case Study on Potato Leaf Disease Detection Using CapsNet and Attention Mechanism 8th

Comprehensive Review of Capsule Networks with a Case Stud...

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8th International Symposium on Intelligent Informatics, ISI 2023

作者： Shenbaga Moorthy, Rajalakshmi Arikumar, K.S. Prathiba, Sahaya Beni Pabitha, P. Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Chennai600 116 India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai603 203 India Centre for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India Department of Computer Technology Anna University Madras Institute of Technology Campus Chennai600044 India

ISBN: (纸本)9789819721467

Accurate solutions are needed for a variety of computer vision applications, including medical imaging, object detection, and recognition. Such complicated challenges are beyond the capabilities of artificial intelligence and machine learning, which mainly rely on data and algorithms to learn. The path to Deep Learning (DL) is thus paved. Even though deep learning is incredibly effective at solving complex problems, it is invariant, meaning that it ignores the spatial relationship between the features and instead searches for features only. This can cause the model’s performance to suffer. Additionally, because the model is invariant, gathering a large volume of training data is a difficult undertaking. To address this, the Capsule Network (CapsNet), whose performance outperforms Deep Learning techniques, was introduced. Despite its success, applications of CapsNet and working remain a mystery. Thus, the performance of CapsNet across many applications and obstacles is examined in this study together with the predecessors of CapsNet. Additionally, this paper explores the use of Capsule Networks with an attention mechanism (CapsNet-ATM) for predicting potato leaf diseases. The performance of CapsNet-ATM in this crucial agricultural application is thoroughly assessed and contrasted with the well-known models VGG-16 and VGG-19, providing insight into the system’s efficacy and promise as a reliable tool for the detection and prediction of potato leaf disease. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Discrete ripplet-II transform feature extraction and metaheuristic-optimized feature selection for enhanced glaucoma detection in fundus images using least square-support vector machine

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Multimedia Tools and Applications 2024年 1-33页

作者： Sharma, Santosh Kumar Muduli, Debendra Rath, Adyasha Dash, Sujata Panda, Ganapati Shankar, Achyut Dobhal, Dinesh Chandra Department of Computer Science and Engineering C.V. Raman Global University Odisha India Department of Information Technology School of Engineering and Technology Nagaland University Dimapur India Department of Cyber Systems Engineering WMG University of Warwick CoventryCV74AL United Kingdom University Centre for Research & amp Development Chandigarh University Punjab Mohali140413 India School of Computer Science Engineering Lovely Professional University Punjab Phagwara144411 India Center of Research Impact and Outcome Chitkara University Punjab Rajpura India Department of Computer Science & amp Engineering Graphic Era Deemed to be University Uttarakhand Dehradun248002 India

Recently, significant progress has been made in developing computer-aided diagnosis (CAD) systems for identifying glaucoma abnormalities using fundus images. Despite their drawbacks, methods for extracting features such as wavelets and their variations, along with classifier like support vector machines (SVM), are frequently employed in such systems. This paper introduces a practical and enhanced system for detecting glaucoma in fundus images. The proposed model adresses the chanallages encountered by other existing models in recent litrature. Initially, we have employed contrast limited adaputive histogram equalization (CLAHE) to enhanced the visualization of input fundus inmages. Then, the discrete ripplet-II transform (DR2T) employing a degree of 2 for feature extraction. Afterwards, we have utilized a golden jackal optimization algorithm (GJO) employed to select the optimal features to reduce the dimension of the extracted feature vector. For classification purposes, we have employed a least square support vector machine (LS-SVM) equipped with three kernels: linear, polynomial, and radial basis function (RBF). This setup has been utilized to classify fundus images as either indicative of glaucoma or healthy. The proposed method is validated with the current state-of-the-art models on two standard datasets, namely, G1020 and ORIGA. The results obtained from our experimental result demonstrate that our best suggested approach DR2T+GJO+LS-SVM-RBF obtains better classification accuracy 93.38% and 97.31% for G1020 and ORIGA dataset with less number of features. It establishes a more streamlined network layout compared to conventional classifiers. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Radial basis function networks

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Across Images and Graphs for Question Answering 40

Across Images and Graphs for Question Answering

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Wen, Zhenyu Qian, Jiaxu Qian, Bin Yuan, Qin Qin, Jianbin Xuan, Qi Yuan, Ye Institute of Cyberspace Security Zhejiang University of Technology Hangzhou China Binjiang Cyberspace Security Institute of ZJUT Hangzhou China University of Science and Technology of China Department of Electronic Engineering and Information Science China School of Computing Newcastle University Newcastle Upon Tyne United Kingdom School of Computer Science and Technology Beijing Institute of Technology Beijing China School of Computer Shenzhen University Shenzhen China

ISBN: (纸本)9798350317152

Cross-source query serves as a proxy for scene understanding to support many web applications such as rec-ommendation systems, e-commerce, and e-learning applications. In this paper, we propose SVQA that semantically combines the knowledge from available images and graphs to answer the complex question. To this end, we design a graph-based method to unify various data sources into one representation. We then develop a complex question parse method that utilizes the structure of languages to transform the query into a query graph. A graph query engine that performs the query graph over the unified data source while optimizing the query process. To evaluate the proposed system, we build a vanilla dataset called MVQA and show that the state-of-the-art (SOTA) VQA models fail to perform our task. The comprehensive evaluations show that the proposed SVQA is able to reason implicit relationships over multiple images and external knowledge to correctly answer a complex query. We hope that our first attempt provides researchers with a fresh taste of multimodal data analysis. © 2024 IEEE.

关键词： Query processing

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Efficient Deep CNN Model for COVID-19 Classification

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computers, Materials & Continua 2022年第3期70卷 4373-4391页

作者： Walid El-Shafai Amira A.Mahmoud El-Sayed M.El-Rabaie Taha E.Taha Osama F.Zahran Adel S.El-Fishawy Mohammed Abd-Elnaby Fathi E.Abd El-Samie Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Coronavirus(COVID-19)infection was initially acknowledged as a global pandemic in Wuhan in *** Health Organization(WHO)stated that the COVID-19 is an epidemic that causes a 3.4%death *** X-Ray(CXR)and computerized Tomography(CT)screening of infected persons are essential in diagnosis *** are numerous ways to identify positive COVID-19 *** of the fundamental ways is radiology imaging through CXR,or CT *** comparison of CT and CXR scans revealed that CT scans are more effective in the diagnosis process due to their high ***,automated classification techniques are required to facilitate the diagnosis *** Learning(DL)is an effective tool that can be utilized for detection and classification this type of medical *** deep Convolutional Neural Networks(CNNs)can learn and extract essential features from different medical image *** this paper,a CNN architecture for automated COVID-19 detection from CXR and CT images is *** activation functions as well as three optimizers are tested and compared for this *** proposed architecture is built from scratch and the COVID-19 image datasets are directly fed to train *** performance is tested and investigated on the CT and CXR *** activation functions:Tanh,Sigmoid,and ReLU are compared using a constant learning rate and different batch *** optimizers are studied with different batch sizes and a constant learning ***,a comparison between different combinations of activation functions and optimizers is presented,and the optimal configuration is ***,the main objective is to improve the detection accuracy of COVID-19 from CXR and CT images using DL by employing CNNs to classify medical COVID-19 images in an early *** proposed model achieves a classification accuracy of 91.67%on CXR image dataset,and a classification accuracy of 100%on CT dataset with training times of 58 min and 46 min on CXR an

关键词： COVID-19 image classification CNN DL activation functions optimizers

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Adaptive System-Level Fault Diagnosis of Bijective Connection Networks

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IEEE Transactions on Reliability 2025年第2期74卷 2916-2926页

作者： Huang, Yanze Lin, Limei Xu, Li Hsieh, Sun-Yuan School of Mathematics and Statistics Fujian Normal University College of Computer and Cyber Security Fuzhou 350117 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan 701 Taiwan

As the multiprocessor systems are becoming largescale, fault-diagnosis is crucial to ensure the reliability of multiprocessor systems. In order to improve the self-diagnosis capability of a multiprocessor system, a pessimistic fault diagnosis scheme such as t/s-diagnosis allows some fault-free processors to be mistakenly identified as faulty. All faulty processors in a t/s-diagnosable multiprocessor system (t ≤ s) should be identified into a set with size up to s, when the total amount of faulty processors in the system does not exceed t. This article focuses on the t/s-diagnosis for the n-dimensional bijective connection network Xn. An adaptive t/s-diagnosis algorithm APDMM*t/s of complexity O(M(log2 M)²) under the comparison model is proposed, where M is the total amount of nodes in Xn. Then, the correctness of algorithm APDMM*t/s is proved by the fault-tolerant properties of the network itself. Moreover, we calculate the t/s-diagnosability of Xn by theoretical method in mathematics, which is (Formula presented) for 2 ≤ y ≤ n under comparison model, where (Formula presented). Furthermore, we apply algorithm APDMM*t/s on the hypercube and the real world network WSN-DS to verify our main results, and analyze the experimental outcomes in terms of true positive rate, false positive rate, accuracy and precision. The experimental results reveal the advantage and high performance of our algorithm APDMM*t/s. Besides, we compare the t/s-diagnosability of Xn with traditional accurate diagnosability, and it turns out that as n gets larger, the t/s-diagnosability of Xn is significantly better than traditional accurate diagnosability.

关键词： adaptive fault diagnosis bijective connection network reliability t/s-diagnosability

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Trust delegation-based secure mobile cloud computing framework

Trust delegation-based secure mobile cloud computing framewo...

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2023 IEEE International Conference on Emerging Trends in engineering, Sciences and technology, ICES and T 2023

作者： Hasan, Muhammad Zulkifl Hussain, Muhammad Zunnurain Sarwar, Nadeem Ullah, Zaka Alam, Intakhab Irshad, Asma University of Central Punjab Faculty of Computer Science and Information Technology Lahore Pakistan Bahria University Lahore Campus Department of Computer Science Pakistan Air University Aerospace and Aviation Campus Department of Computer Science and Cyber Security Kamra Pakistan University of Sialkot Department of Information Technology Sialkot Pakistan University of the Punjab School of Biochemistry and Biotechnology Lahore Pakistan

ISBN: (纸本)9781665455602

Nowadays, practically everyone has a smartphone with them. These smart devices store several types of personal information, such as images, files, credit card pins, etc., and are used for communication purposes. Consider the possibility that someone could gain access to it without our knowledge. How could they possibly use it? One of the most critical security concerns is data and privacy in terms of stolen data and malicious file behaviour. However, private and delicate data are required for these practical ***, connected phones' other weak points are their limited battery life, slow processor speed, and unpatched vulnerabilities. To overcome such limitations, mobile cloud computing (MCC) is introduced, which analyzes data in the cloud and returns the findings to the smartphone. However, MCC increased the effectiveness of several programs. MCC also considered several types of issues, such as real-Time communication data confidentiality. Further, safety, speed, and customer satisfaction are the three fundamental parameters of mobile cloud services that will be highlighted in this article. Finally, a model concept is also elaborate for demonstration purposes to employ the trust-based assignment system to enhance safety ratios, showing the perfection of the five-portable cloud computing architecture. © 2023 IEEE.

关键词： Smartphones

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Expert Phishing Detection System

Expert Phishing Detection System

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2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications technology, IAICT 2024

作者： Alsarhan, Ayoub Al-Aiash, Issa Al-Fraihat, Dimah Aljaidi, Mohammad Abu Laila, Dena Abd Al Hakem Department of Information Technology Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa13133 Jordan Department of Cybersecurity Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa13133 Jordan Department of Software Engineering Faculty of Information Technology Isra University Amman11622 Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa13110 Jordan Department of Cyber Security Faculty of Information Technology Zarqa Technical Intermediate College Zarqa Jordan

ISBN: (纸本)9798350353464

Phishing attacks are a threat that continuously emphasizes the need for effective detection methods. This paper presents a machine learning detection method that employs a random forest classifier, which is an adaptable algorithm that can handle a variety of feature sets. The dataset used in this work offers a range of features extracted from various URLs, addressing elements such as URL length, domain structure, and keyword presence. We deployed a computational approach for feature engineering. The random forest classifier is employed for both prediction and to assess feature importance, guiding an automated feature selection process. By doing this, the model’s performance is optimized, and the most important features are utilized. The findings of this study show the effectiveness of our method with an impressive accuracy of 98.45% on a test set. The classifier exhibits balanced precision and recall for both phishing and legitimate instances. Moreover, the automated feature selection process improves the interpretation of the model, emphasizing key indicators of phishing attacks. The proposed approach not only produces outstanding outcomes but also offers a flexible and scalable approach to address the dynamically evolving threat landscape of phishing attempts. ©2024 IEEE.

关键词： cyber-security Phishing attacks Phishing URLs

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