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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Md Jakaria Zobair Md. Atiqur Rahman Md. Shakhawath Hossain Nusrat Khan Md Atik Asif Khan Akash Md Hasan Imam Bijoy Computer Science and Engineering East West University Dhaka Bangladesh Electronic System Aalborg University Aalborg Denmark Computer Science and Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

One crucial use of machine learning in medical diagnostics is the detection of breast cancer, where prompt and precise identification can greatly enhance patient outcomes. Using histopathological images from the Breast Cancer Dataset (400X, 100X, 200X, and 40X magnification levels), we present a reliable deep learning method in this work that uses Vision Transformers (ViT) and Gated Recurrent Unit (GRU) for the categorization of breast cancer. We use a weighted loss function in conjunction with random oversampling approaches to tackle the problem of dataset imbalance, guaranteeing that the model learns from underrepresented classes impartially and efficiently. Outstanding accuracy rates of 98.76% for 400X, 97.84% for 100X, 98.54% for 200X, and 98.63% for 40X photos are attained by our method. These outcomes are supported by outstanding precision, recall, and F1 scores, which show how resilient our model is at all magnification settings. Our ViT-GRU based approach continuously performs better, demonstrating its potency in feature extraction and classification. This study highlights the potential of Vision Transformers in medical image analysis as well as the significance of using creative pre-processing and loss function techniques to address class imbalance. With the potential for increased diagnostic accuracy and dependability, the findings advance automated breast cancer detection.

关键词： Deep learning computer vision Accuracy Image analysis Transfer learning Transformers Breast cancer Reliability Medical diagnosis Medical diagnostic imaging

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Advswap: Covert Adversarial Perturbation with High Frequency Info-Swapping for Autonomous Driving Perception 27

Advswap: Covert Adversarial Perturbation with High Frequency...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Huang, Yuanhao Zhang, Qinfan Xing, Jiandong Cheng, Mengyue Yu, Haiyang Ren, Yilong Xiong, Xiao School of Transportation Science and Engineering Beihang University Beijing100191 China State Key Lab of Intelligent Transportation System Beijing100191 China Zhongguancun Laboratory Beijing100094 China University of Alberta Department of Electrical and Computer Engineering Edmonton Canada

ISBN: (纸本)9798331505929

Perception module of Autonomous vehicles (AVs) are increasingly susceptible to be attacked, which exploit vulnerabilities in neural networks through adversarial inputs, thereby compromising the AI safety. Some researches focus on creating covert adversarial samples, but existing global noise techniques are detectable and difficult to deceive the human visual system. This paper introduces a novel adversarial attack method, AdvSwap, which creatively utilizes wavelet-based highfrequency information swapping to generate covert adversarial samples and fool the camera. AdvSwap employs invertible neural network for selective high-frequency information swapping, preserving both forward propagation and data integrity. The scheme effectively removes the original label data and incorporates the guidance image data, producing concealed and robust adversarial samples. Experimental evaluations and comparisons on the GTSRB and nuScenes datasets demonstrate that AdvSwap can make concealed attacks on common traffic targets. The generates adversarial samples are also difficult to perceive by humans and algorithms. Meanwhile, the method has strong attacking robustness and attacking transferability. © 2024 IEEE.

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Black Hole Attacks Detection in Mobile Ad Hoc Networks (MANETs) using Bayesian Inference Method 11

Black Hole Attacks Detection in Mobile Ad Hoc Networks (MANE...

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11th International Conference on Electrical engineering, computer science and Informatics, EECSI 2024

作者： Afifah, Nurul Fadli, Muhammad Septian, Tri Wanda Sari, Winda Kurnia Hermansyah, Adi Marieska, Mastura Diana Sriwijaya University Faculty of Comp. Science Computer Engineering Dept. Palembang Indonesia Sriwijaya University COMNETS Research Group Faculty of Comp. Science Palembang Indonesia Sriwijaya University Faculty of Comp. Science Information System Dept. Palembang Indonesia

ISBN: (纸本)9798350355314

MANET has vulnerability in terms of security systems against black hole attacks. The main target of black hole as a malicious node is to absorbs all data. The aim of this research is to identify black hole attacks against MANE Tusing Bayesian inference. The methodology of this research involves several testing phases. In the first phase, all data packets sent by the source node to destination within the range of the black hole node are lost. The second and third testing phases showed that the Bayesian method was successful in detecting black holes by preventing data transmission. The results indicated that traffic in the MANET runs smoothly when the nodes are operating under normal conditions. However, when a black hole node is introduced, a significant number of data packets are lost. Implementing the Bayesian method offers a solution to this issue. © 2024 IEEE.

关键词： Mobile ad hoc networks

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Exploring the Relationships between Dynamic Capabilities, Digital Transformation, and IT-Business Alignment 12

Exploring the Relationships between Dynamic Capabilities, Di...

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12th International Conference on Cyber and IT Service Management, CITSM 2024

作者： Kosasi, Sandy Sukmana, Husni Teja Yuliani, I Dewa Ayu Eka Laipaka, Robertus Fitriani, Diana Madhiyono Information System Stmik Pontianak Pontianak West Kalimantan Indonesia Information Engineering Syarif Hidayatullah State Islamic University Jakarta Jakarta Indonesia Computer Science Stmik Pontianak Pontianak West Kalimantan Indonesia

ISBN: (纸本)9798331528799

Business and IT alignment is crucial to business organizations, navigating digital transformation, leveraging dynamic capabilities, facilitating smooth integration of digital technologies, and creating competitive added value in managing digital. Nevertheless, there is no literature discussing the influence of dynamic capabilities in doing business and IT alignment through digital transformation in managing digital business. The objective of the research is to explore and ensure the full impact of the digital transformation construction in bringing business and IT alignment together with every indicator of the effects of dynamic capacity with the availability and readiness to manage a digital business-convergence triangulation research method with explanatory design through follow-up explanation. The research population was taken from all the digital businesses that had run them for at least five years. 198 of the 238 respondents completed the questionnaire online through Google Forms using a random sampling technique with a response rate of 83.19%. The Likert scale is used to process and analyze data using the SEM-PLS method. The results of the research showed it turns out that the assumption of managing digital business is very resistant to the need to harmonize business and IT from the influence of digital transformation is not proven at all, and the existence of the linkage of dynamic capabilities in carrying out the alignment of business and IT. © 2024 IEEE.

关键词： digital businesses digital transformation dynamic capabilities IT-business alignment

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Differential Evolution with Arithmetic Optimization Algorithm Enabled Multi-Hop Routing Protocol

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computer systems science & engineering 2023年第5期45卷 1759-1773页

作者： Manar Ahmed Hamza Haya Mesfer Alshahrani Sami Dhahbi Mohamed K Nour Mesfer Al Duhayyim ElSayed M.Tag El Din Ishfaq Yaseen Abdelwahed Motwakel Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11845Egypt

Wireless Sensor Networks(WSN)has evolved into a key technology for ubiquitous living and the domain of interest has remained active in research owing to its extensive range of *** spite of this,it is challenging to design energy-efficient *** routing approaches are leveraged to reduce the utilization of energy and prolonging the lifespan of *** order to solve the restricted energy problem,it is essential to reduce the energy utilization of data,transmitted from the routing protocol and improve network *** this background,the current study proposes a novel Differential Evolution with Arithmetic Optimization Algorithm Enabled Multi-hop Routing Protocol(DEAOA-MHRP)for *** aim of the proposed DEAOA-MHRP model is select the optimal routes to reach the destination in *** accomplish this,DEAOA-MHRP model initially integrates the concepts of Different Evolution(DE)and Arithmetic Optimization Algorithms(AOA)to improve convergence rate and solution ***,the inclusion of DE in traditional AOA helps in overcoming local optima *** addition,the proposed DEAOA-MRP technique derives a fitness function comprising two input variables such as residual energy and *** order to ensure the energy efficient performance of DEAOA-MHRP model,a detailed comparative study was conducted and the results established its superior performance over recent approaches.

关键词： Wireless sensor network routing multihop communication arithmetic optimization algorithm fitness function

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Assessing the Effectiveness of Anomaly Detection in IoT Data Streams with Machine Learning

Assessing the Effectiveness of Anomaly Detection in IoT Data...

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International Conference on computer, Communication, Control and Information Technology (C3IT)

作者： Subir Panja Suman Das Arindrajit Pal Computer Science and Engineering Academy of Technology Adisaptagram Hooghly India Computer Science and Business System Academy of Technology) Adisaptagram Hooghly India

ISBN: (数字)9798350389128

ISBN: (纸本)9798350389135

The proliferation of cyber-attacks and network infiltration has emerged as significant apprehensions in the contemporary utilization of the Internet of Things (IoT). Researchers have introduced several machine learning algorithms, which exhibit deficiencies in terms of binary classification accuracy or multi-class classification performance. This study presents an anomaly-based approach that encompasses major eight distinct machine learning algorithms, (i) Decision Tree (DT), (ii) SVMPolynomial Kernal (SVM-P) (iii) SVM- RBF Kernel(SVM-R) (iv) SVM-Linear Kernal (SVM-L) (v) K-Nearest Neighbours (KNN) (vi) Gaussian Naive Bayes (GNB) (vii) Bernoulli Naive Bayes (BNB) and (viii) AdaBoost with Decision Tree (Ada-DT). The experiment utilized the dataset “UNSW 2018 IoT Botnet Final 10 best Testing” for our research. Performance measures, including as accuracy, precision, recall, and F1-Score are employed to assess and evaluate the performance of the each machine learning algorithm. The DT Classifier demonstrated exceptional accuracy of 99.99%, surpassing expectations in terms of precision, recall, and f1-score for both classes. The significant macro-average Receiver Operating Characteristic Area Under the Curve (ROCAUC) score of 0.91 exhibited by the model reveals its robust overall performance. In contrast, the GNB and BNB models had limits, as evidenced by their lower ROC-AUC scores.

关键词： Accuracy Machine learning algorithms Botnet Gaussian processes Nearest neighbor methods Bayes methods Internet of Things Decision trees Anomaly detection Testing

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An improved multi-attribute decision-making based network selection algorithm for heterogeneous vehicular network

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Frontiers of computer science 2022年第3期16卷 211-213页

作者： Lei NIE Bo LIU Peng LI Heng HE Libing WU School of Computer Science and Technology Wuhan University of Science and TechnologyWuhan 430065China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan 430065China School of Cyber Science and Engineering and School of Computer Science Wuhan UniversityWuhan 430072China Shenzhen Research Institute of Wuhan University Shenzhen 518057China

1 Introduction With sharply rising quantity of urban vehicles over the past few decades,traffic jams and safety have gradually become outstanding *** recent years,Vehicular Ad hoc Network(VANET)is appeared and utilized to solve traffic related *** is a type of self-organized and open-structured network,and provides Vehicle-to-Everything(V2X)*** all kinds of applications in VANET rely on efficient data transmission and interaction[1-3].

关键词： network sharply utilized

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Significance of Internet-of-Things Edge and Fog Computing in Education Sector

Significance of Internet-of-Things Edge and Fog Computing in...

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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Siddiqui, Shams Tabrez Khan, Mohammad Rafeek Khan, Zeba Rana, Nadim Khan, Haneef Alam, Md Imran College of Cs It Jazan University Department of Computer Science Jazan Saudi Arabia College of Cs It Jazan University Department of Computer and Network Engineering Jazan Saudi Arabia Computer and Information System Applied College Jazan University Jazan Saudi Arabia College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia

ISBN: (纸本)9798350347050

IoT gadgets include smart lights, ovens, and industrial data-collecting equipment. IDC expects 41.6 billion connected IoT devices to generate 79.4 ZB of data by 2025. Early IoT devices sent data to the cloud for analysis. When sending trillions of gigabytes to the cloud, the data pipeline clogs. Edge computing enables IoT devices to handle data locally instead of sending it to the cloud. Data is processed at the 'edge' of your local network, rather than transferred away. Technology's versatility makes it valuable in education. IoT edge computing has different requirements than non-IoT edge computing. IoT devices have limited data processing and storage capabilities, thus processing must occur off the device. The edge offers an environment to manage huge numbers of IoT devices and data. Many functions can be off-loaded to the edge, reducing device cost. In education, IoT edge computing architecture has gained importance due to real-time applications. In order to facilitate the analysis of educational system architecture, this paper seeks to provide an IoT Edge computing framework in the field of education. The framework will identify IoT-related issues and concerns. This study discusses IoT Edge computing in education, identifies limits, and makes recommendations. As well as the paper evaluates IoT Edge computing's performance in educational systems. © 2023 IEEE.

关键词： Edge computing

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FIMD: Fusion-Inspired Modality Distillation for Enhanced MRI Segmentation in Incomplete Multi-Modal Scenarios 24

FIMD: Fusion-Inspired Modality Distillation for Enhanced MRI...

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7th International Conference on Image and Graphics Processing, ICIGP 2024

作者： Yang, Rui Wang, Xiao Xu, Xin Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology China School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Provincial Key Laboratory of Multimedia and Network Communication Engineering Wuhan China

ISBN: (纸本)9798400716720

Magnetic Resonance Imaging (MRI) is an invaluable tool for brain tumor segmentation. However, in clinical practice, certain modalities might be unavailable, leading to potential performance degradation in prediction tasks. According to current implementations, different modalities are treated as independent entities during the feature extraction phase, ignoring their inherent complementary nature. In this paper, inspired by knowledge distillation techniques, we introduce the Fusion-Inspired Modality Distillation (FIMD) framework. FIMD leverages the output from a fused multi-modal model as a teacher to guide the training of individual modalities. Each modality benefits from the collective knowledge of all modalities, enhancing its performance. Our FIMD method, devoid of specific architectural constraints, seamlessly integrates into existing multi-modal brain tumor segmentation frameworks. Notably, extensive experiments on BraTS2020, BraTS2018, and BraTS2015 datasets indicate that FIMD can enhance the performance of current state-of-the-art (SOTA) algorithms on Dice by an average of 0.55-1.60%. Especially in the case of missing modalities, the effectiveness of the FIMD framework in enhancing the robustness and accuracy of brain tumor segmentation is demonstrated. © 2024 ACM.

关键词： Semantic Segmentation

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Optimal-Condition-Based Maintenance of Cold Standby system with Multiple Operating Units and Multiple Standby Units 14

Optimal-Condition-Based Maintenance of Cold Standby System w...

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14th International Conference on Reliability, Maintainability and Safety, ICRMS 2023

作者： Li, Yuxin Zhang, Yongfei Luo, Yuangeng He, Yugang Zhang, Xiaohong Wang, Jinhe School of Economics & Management Taiyuan University of Science & Technology Taiyuan Taiyuan China Division of Industrial and System Engineering Taiyuan University of Science & Technology 030024 China College of Computer Science and Technology Taiyuan University of Science & Technology Taiyuan030024 China

ISBN: (纸本)9798350329988

To ensure continuity and security in production processes, a redundancy structure involving multiple units for use and multiple units for standby (MUMS) is typically used. This paper proposes an offline state maintenance decision model for periodic switching and block replacement within the MUMS cold standby system. The concept of maintenance degree is introduced, and a model is established to represent the relationship among the maintenance effect, cost, and time, under the influence of the maintenance degree. By analyzing the alternate state transition characteristics of operating and standby units in the operation process, a steady probability model of the system state is obtained. Subsequently, a model for average rate costs in a limited scope is constructed to determine the optimal system switching period, preventive maintenance threshold, and maintenance degree. The model is validated using a hot roll system. The results show that the proposed framework can effectively reduce the cost rate and ensure system reliability. © 2023 IEEE.

关键词： Condition based maintenance

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