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3rd International Conference on Communications, Information, Electronic and Energy Systems, CIEES 2022

作者： Ambrews, Ashley Bryan Gubin Moung, Ervin Farzamnia, Ali Yahya, Farashazillah Omatu, Sigeru Angeline, Lorita Universiti Malaysia Sabah Faculty of Computing And Informatics Kota Kinabalu Sabah Malaysia Universiti Malaysia Sabah Faculty of Engineering Kota Kinabalu Sabah Malaysia Education Research Centre Higashi-Hiroshima739-0046 Japan Taylor's University School of Computer Science Faculty of Innovation And Technology Selangor Malaysia

ISBN: (数字)9781665491495

ISBN: (纸本)9781665491495

The World Health Organization reports that more than 10 million people worldwide die each year due to heart disease. In this project, an ensemble-based machine learning (ML) model is proposed for the prediction of heart disease. The proposed method was evaluated on two datasets: UCI heart disease and Framingham heart study. Various ML such as K-Nearest Neighbors, Naïve Bayes, Decision Tree, Support Vector Machine (SVM), Logistic Regression (LR) and Random Forest (RF) were carried out to predict the outcome of heart disease. Additionally, the Stacking and Voting ensemble approaches are also employed for heart disease prediction. All ML classifiers evaluated on both datasets have their performance metrics analyzed and compared. The results of all ML classifiers assessed on the Framingham dataset is inferior than that of the UCI heart disease dataset. For individual ML classifier, SVM achieved the highest accuracy for UCI heart disease with 91.09%, whereas LR achieved the highest accuracy for the Framingham heart study with 85.38%. However, it should be noted that RF trained on the UCI dataset is the best ML for prioritizing Sensitivity (92.52%). Finally, with the exception of Sensitivity, the Voting approach is strongly suggested because it outperforms the other classifiers noticeably where it achieved Accuracy (91.96%), F1 (91.69%), Sensitivity (91.72%), Specificity (90.77%), and Precision (92.40%) on UCI dataset. © 2022 IEEE.

关键词： Forecasting

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Combining DeepLabV3 with Attention Mechanisms for Accurate Brain Tumor Segmentation: Insights from BraTS 2020 and a Private Clinical Dataset

Combining DeepLabV3 with Attention Mechanisms for Accurate B...

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European Workshop on Visual Information Processing (EUVIP)

作者： Ramin Ranjbarzadeh Ayse Keles Pinar Akdemir Ozisik Harun Demirci Halil Kul Martin Crane Shokofeh Anari Malika Bendechache Faculty of Engineering and Computing School of Computing Dublin City University Ireland Department of Artificial Intelligence Engineering Faculty of Engineering and Natural Sciences Ankara Medipol University Ankara Türkiye Department of Neurosurgery School of Medicine Ankara Yildirim Beyazit University Ankara Türkiye Department of Accounting Economic and Financial Sciences South Tehran Branch Islamic Azad University Tehran Iran ADAPT Research Centre School of Computer Science University of Galway Ireland

ISBN: (数字)9798350391145

ISBN: (纸本)9798350391152

This study explores the effectiveness of the DeepLabV3 model and its variations, which include attention mechanisms, in accurately segmenting brain tumors. The study used both the publicly accessible BraTS 2020 dataset and a private clinical dataset obtained from Ankara Bilkent City Hospital, Turkiye. The research assesses the impact of attention mechanisms on segmentation performance for three tumor classes: Tumor Core, Edema, and Enhancing Tumor. These attention mechanisms are incorporated both inside the overall model and especially in the Atrous Spatial Pyramid Pooling (ASPP) block. The accuracy and dependability of the models are evaluated using key metrics such as Precision, Recall, Intersection over Union (IoU), and Dice coefficient. The findings suggest that attention mechanisms can improve the identification of features, but their impact is highly dependent on their setup and the specific dataset being used. The conventional DeepLabV3 model frequently achieves better results compared to its attention-enhanced variations, especially when dealing with difficult tumor types and datasets. This indicates the possibility of overfitting or misalignment in the priority of features. This study enhances our comprehension of how attention mechanisms can be effectively included in medical imaging models, emphasizing the importance of meticulous implementation and calibration to optimize their potential. The results offer crucial insights for the development of more precise and robust segmentation algorithms in medical diagnostics.

关键词： Measurement Image segmentation Visualization Attention mechanisms Accuracy Urban areas Information processing Brain modeling Medical diagnosis Overfitting

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In-Context Code-Text Learning for Bimodal Software engineering

arXiv

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arXiv 2024年

作者： Tang, Xunzhu Wang, Liran Liu, Yonghui Chai, Linzheng Yang, Jian Li, Zhoujun Tian, Haoye Klein, Jacques Bissyandé, Tegawendé F. University of Luxembourg Luxembourg Luxembourg School of Computer Science and Engineering Beihang University Beijing China Faculty of Information Technology Monash University Melbourne Australia School of Computing and Information Systems University of Melbourne Melbourne Australia SnT Centre for Security Reliability and Trust University of Luxembourg Luxembourg Luxembourg

Bimodal software analysis initially appeared to be within reach with the advent of large language models. Unfortunately, the complex interplay of natural language text and code in software engineering, presents unique challenges that prevent pretrained models to generalize to a variety of tasks. We postulate that in-context learning for the code-text bimodality is a promising avenue. This paper thus introduces a comprehensive study of in-context code-text learning, focusing on leveraging pretrained CodeLLAMA models. We consider a diverse dataset encompassing 23 software engineering tasks, which we transform in an in-context learning format. To effectively extract informative features, we propose a configurable prompt template. Our proposed pipeline, INCTRL, then unifies prompt learning across various software engineering tasks. Extensive evaluation on the study datasets demonstrates the superiority of INCTRL-models in few-shot performance, surpassing state-of-the-art models including the support model, CodeLLAMA. Typically, we observe that applied to the CodeLLAMA model, INCTRL brings improvements in terms of precision (at least about 12%) and recall (up to 93.88%) on various tasks. For example, on the task of program repair, INCTRL improves the BLEU score of CodeLLAMA by 85 points, while for clone detection, INCTRL achieves an improvement of 69 percentage points. Moreover, INCTRL-models offer state-of-the-art performance when using retrieval-augmented generation on individual downstream tasks. Finally, we qualitatively analyze the benefits of INCTRL over CodeLLAMA and open-source all models for broader impact. Copyright © 2024, The Authors. All rights reserved.

关键词： Open source software

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A Black-Box Attack on Code Models via Representation Nearest Neighbor Search

arXiv

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arXiv 2023年

作者： Zhang, Jie Ma, Wei Hu, Qiang Liu, Shangqing Xie, Xiaofei Le Traon, Yves Liu, Yang Noah's Ark Lab Huawei Canada School of Computer Science and Engineering Nanyang Technological University Singapore The Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg Luxembourg School of Computing and Information Systems Singapore Management University Singapore

Existing methods for generating adversarial code examples face several challenges: limited availability of substitute variables, high verification costs for these substitutes, and the creation of adversarial samples with noticeable perturbations. To address these concerns, our proposed approach, RNNS, uses a search seed based on historical attacks to find potential adversarial substitutes. Rather than directly using the discrete substitutes, they are mapped to a continuous vector space using a pre-trained variable name encoder. Based on the vector representation, RNNS predicts and selects better substitutes for attacks. We evaluated the performance of RNNS across six coding tasks encompassing three programming languages: Java, Python, and C. We employed three pre-trained code models (CodeBERT, GraphCodeBERT, and CodeT5) that resulted in a cumulative of 18 victim models. The results demonstrate that RNNS outperforms baselines in terms of ASR and QT. Furthermore, the perturbation of adversarial examples introduced by RNNS is smaller compared to the baselines in terms of the number of replaced variables and the change in variable length. Lastly, our experiments indicate that RNNS is efficient in attacking defended models and can be employed for adversarial training. Copyright © 2023, The Authors. All rights reserved.

关键词： Nearest neighbor search

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Fast Image matting based on matting pyramids and surrogate models

Fast Image matting based on matting pyramids and surrogate m...

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Robotics, Artificial Intelligence and Intelligent Control (RAIIC), International Conference on

作者： Yihui Liang Hongshan Gou Guisong Liu Sibo Huang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer University of Electronic Science and Technology of China Zhongshan Institute Zhongshan China College of Big data and Information Engineering Guiyang Institute of Humanities and Technology Guiyang China School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Chengdu China Network and Information Centre Huizhou University Huizhou China

Image matting is a foundation task in the field of computer vision. Pixel-pair-optimization-based image matting methods have attracted lots of attention due to their distinct advantages on parallelization and handling spatially disconnected foregrounds. However, these methods struggle when the computing time is limited. To address this issue, this study presents the image matting method based on matting pyramid and surrogate model (IMMPSM). IMMPSM speeds up the process of pixel pair optimization by replaces the evolutionary algorithm in the pyramid matting framework, which consumes a lot of computing time, with the efficient surrogate-model-based method. The constructed surrogate model is reused by the presented surrogate model share strategy to save computing time. Specifically, the constructed surrogate model is shared with the pixel pair optimization subproblem adjacent to current subproblem avoiding repeated computation on the surrogate model construction. Extensive experimental results show that IMMPSM can provide acceptable alpha mattes even under limited computing time.

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Feature Selection for Detecting ICMPv6-Based DDoS Attacks Using Binary Flower Pollination Algorithm

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computer Systems science & engineering 2023年第10期47卷 553-574页

作者： Adnan Hasan Bdair Aighuraibawi Selvakumar Manickam Rosni Abdullah Zaid Abdi Alkareem Alyasseri Ayman Khallel Dilovan Asaad Zebari Hussam Mohammed Jasim Mazin Mohammed Abed Zainb Hussein Arif National Advanced IPv6 Centre(NAv6) Universiti Sains MalaysiaPenang11800Malaysia Baghdad College of Economic Sciences University BaghdadIraq School of Computer Sciences Universiti Sains MalaysPenang11800Malaysia Information Technology Research and Development Center University of KufaNajafIraq College of Engineering University of Warith Al-AnbiyaaKarbalaIraq Faculty of Computing and Informatics Universiti Malaysia SabahSabahMalaysia Business Administration College of Administration and Financial SciencesImam Ja’afar Al-Sadiq UniversityBaghdad10001Iraq College of Computer Science and Information Technology University of AnbarRamadi AnbarIraq Department of Computer Science College of ScienceNawroz UniversityDuhok42001Kurdistan RegionIraq

Internet Protocol version 6(IPv6)is the latest version of IP that goal to host 3.4×10^(38)unique IP addresses of devices in the ***6 has introduced new features like Neighbour Discovery Protocol(NDP)and Address Auto-configuration ***6 needed several protocols like the Address Auto-configuration Scheme and Internet Control Message Protocol(ICMPv6).IPv6 is vulnerable to numerous attacks like Denial of Service(DoS)and Distributed Denial of Service(DDoS)which is one of the most dangerous attacks executed through ICMPv6 messages that impose security and financial ***,an Intrusion Detection System(IDS)is a monitoring system of the security of a network that detects suspicious activities and deals with amassive amount of data comprised of repetitive and inappropriate features which affect the detection rate.A feature selection(FS)technique helps to reduce the computation time and complexity by selecting the optimum subset of *** paper proposes a method for detecting DDoS flooding attacks(FA)based on ICMPv6 messages using a Binary Flower PollinationAlgorithm(BFPA-FA).The proposed method(BFPA-FA)employs FS technology with a support vector machine(SVM)to identify the most relevant,influential ***,The ICMPv6-DDoS dataset was used to demonstrate the effectiveness of the proposed method through different attack *** results show that the proposed method BFPAFA achieved the best accuracy rate(97.96%)for the ICMPv6 DDoS detection with a reduced number of features(9)to half the total(19)*** proven proposed method BFPA-FAis effective in the ICMPv6 DDoS attacks via IDS.

关键词： IPv6 ICMPv6 DDoS feature selection flower pollination algorithm anomaly detection

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A Comprehensive Analysis in Investigating the Impact of Big Data Analytics in Wireless Networks in Failure Prediction, Prevention and Recovery 2

A Comprehensive Analysis in Investigating the Impact of Big ...

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2nd International Conference on Advance computing and Innovative Technologies in engineering, ICACITE 2022

作者： Surya, S. Rajasekaran, P. Yuriy, Shvets Johar, Siti Sarawati Tripathi, Amrendra Magno Ventayen, Randy Joy Computer Science and Engineering School of Computing Kalasalingam University India Information Technology Dr. N.G.P Institute of Technology India Financial University under the Goverment of Russian Federation Institute of Control of Science Ras Moscow Russia Centre for General Studies and Co-curricular Universiti Tun Hussein Onn Malaysia Department of Social Science Parit Raja Johor Darul Takzim Batu Pahat86400 Malaysia School of Computer Science Upes Dehradun India Pangasinan State University Lingayen Campus Philippines

ISBN: (纸本)9781665437899

The current economic and competitive environment has enabled many businesses and industries to implement digital for enhancing the business operations and support in addressing the overall objectives. Dur to this there is a mounting avalanche information being shared between the individuals, companies and governments, the increase in the usage of mobile devices, internet, social media and other ecommerce services has supported in increasing the volume of data. This trend is expected to grow exponentially and hence there is a requirement to provide wireless network in an effective manner, the increased usage of data and information has resulted in facing various failures, like call drops for mobile users, low bandwidth for internet users etc. Hence, it is highly necessary to implement tools to understand the failure prediction, prevention and recovery on the network. Hence, this study is focused in investigating the impact of big data analysis in protecting the wireless networks from such failures and support in processing large volume of information in an effective manner, the big data analytics can also extract useful data will enable in addressing the concerned parties so as to make critical decisions. In this study, the researcher intends to conduct the survey using closed ended questionnaire from the individuals so as to measure the effectiveness of applying big data for failure prevention, protection and recovery in the wireless network. © 2022 IEEE.

关键词： Wireless networks

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Data engineering and AI-Powered Skin Cancer Identification for Healthcare Applications

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Procedia computer science 2024年 246卷 179-188页

作者： Imran Ahmed Misbah Ahmad Abdellah Chehri Gwanggil Jeon School of Computing and Information Science Anglia Ruskin University Cambridge UK Animal and Agriculture Department Hartpury University Gloucester UK Centre for Machine Vision Bristol Robotics Laboratory University of the West of England Bristol UK Department of Mathematics and Computer Science at the Royal Military College of Canada (RMC) Canada Department of Embedded Systems Engineering Incheon National University Incheon Korea

Skin cancer diagnosis, a critical task in the medical domain, can be revolutionized through the application of advanced deep-learning techniques. This work investigates the efficacy of Convolutional Neural Networks (CNNs) in the automated classification of skin cancer. The process begins with a comprehensive explanation of key CNN layers: Conv2D, MaxPool2D, Dropout, and Dense. The Conv2D layers employ learnable filters that transform localized image segments, while MaxPool2D contributes to downsampling, effectively reducing computational cost and overfitting risk. Integrating these layers enables the network to capture local and global characteristics, which is crucial for accurate classification. Adding Dropout layers enhances generalization and mitigates overfitting by introducing randomness during training. ReLU activation functions infuse non-linearity, and the Flatten layer facilitates the transition to fully connected layers. The proposed CNN architecture is meticulously designed considering filter counts, kernel sizes, and pooling dimensions. The trained model demonstrates promising performance by utilizing the HAM10000 dataset, encompassing diverse skin lesion images across seven classes. The CNN model’s parameters and architecture are systematically presented, offering insights into its design rationale. The model undergoes optimization with the Adam optimizer and annealing techniques to facilitate convergence. The model’s effectiveness is evaluated on validation and test datasets, demonstrating an accuracy of 78.55% and 76.49%, respectively, for skin cancer classification. Data augmentation strategies are introduced to enhance model generalization further. The results underscore CNN’s potential as a robust tool for automating skin cancer diagnosis, aligning with the broader trend of leveraging deep learning for medical image analysis

关键词： Image Processing Medical Imaging Deep learning Skin Cancer Data Analytics

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Federated Learning Framework for Consumer IoMT-Edge Resource Recommendation Under Telemedicine Services

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IEEE Transactions on Consumer Electronics 2024年

作者： Ahmed, Syed Thouheed Sivakami, R. Banik, Debajyoty Khan, Surbhi Bhatia Dhanaraj, Rajesh Kumar Vinoth Kumar, V. Mahesh, T.R. Almusharraf, Ahlam REVA University School of Computing and Information Technology Karnataka Bengaluru India Sona College of Technology Department of Computer Science and Engineering Salem636005 India Anurag University Telangana Hyderabad500088 India University of Salford School of Science Engineering and Environment United Kingdom Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India Chandigarh University University Centre for Research and Development Punjab Mohali India Symbiosis International Pune India Vellore Institute of Technology University School of Computer Science Engineering and Information Systems Vellore632014 India Princess Nourah Bint Abdulrahman University College of Business Administration Department of Management Riyadh11671 Saudi Arabia

Medical IoT devices and Telemedicine computation is the growing domain and further involving biomedical computation via machine learning ecosystem has generated an insightful results and analysis. The resources sharing and availability in computing and decision support suffer with a higher latency and energy consumption. In this manuscript, a novel TinyML based model for medical consumer devices resources allocation and resource sharing is discussed. The proposed framework is developed using Federated learning (FL) models for extracting the resource utilization patterns at individual user levels. These locally computed models are further facilitated with edge computation layer for locating resource patterns extraction. The technique is deployed on the dynamic server based resource pooling for effective analysis and resource scheduling and expanded to develop a reliable recommendation model for medical resource management. The framework has trained 128 clusters of 6400 rural and 12800 urban IoT devices samples for resource allocation and scheduling using telemedicine protocol (TelMED). The framework has secured an efficiency of 93.21% in urban user recommendation and 94.72% for rural users. © 1975-2011 IEEE.

关键词： Federated learning

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arXiv

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arXiv 2024年

作者： Ren, Yingying Li, Qiuli Guo, Yangyang Pedrycz, Witold Xing, Lining Liu, Anfeng Song, Yanjie School of Computer Electronics and Information Guangxi University Nanning China Guangxi Key Laboratory of Multimedia Communications and Network Technology China Key Laboratory of Parallel Distributed and Intelligent Computing Guangxi University China School of Systems Science Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey School of Electronic Engineering Xidian University Xian China School of Electronic Engineering Central South University Changsha China National Engineering Research Center of Maritime Navigation System Dalian Maritime University Dalian China

With the rapid development of the satellite industry, the information transmission network based on communication satellites has gradually become a major and important part of the future satellite ground integration network. However, the low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system and needs to be solved urgently. Effectively planning the task of satellite ground networking by reasonably scheduling resources is crucial for the efficient transmission of task data. In this paper, we hope to provide a task execution scheme that maximizes the profit of the networking task for satellite ground network planning considering feeding mode (SGNPFM). To solve the SGNPFM problem, a mixed-integer planning model with the objective of maximizing the gain of the link-building task is constructed, which considers various constraints of the satellite in the feed-switching mode. Based on the problem characteristics, we propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks. To obtain more high-quality solutions, different similarity evaluation methods are designed to assist the algorithm in intelligently screening individuals. The DSGAalso uses an adaptive crossover strategy based on similarity mechanism, which guides the algorithm to achieve efficient population search. In addition, a task scheduling algorithm considering the feed-switching mode is designed for decoding the algorithm to generate a highquality scheme. The results of simulation experiments show that the DSGA can effectively solve the SGNPFM problem. Compared to other algorithms, the proposed algorithm not only obtains higher quality planning schemes but also has faster algorithm convergence speed. The proposed algorithm improves data trans

关键词： Profitability

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