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Evolutionary Algorithms and Soft computing Techniques (EASCT), International Conference on

作者： Jagadeesan S Deepakraj E Venkadeshan Ramalingam Ilayaraja Venkatachalam Manojkumar Vivekanandan Manjula R School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Information Technology Department University of Technology and Applied Sciences - Shinas Branch Sultanate of Oman School of Computer Science and Engineeing Vellore Institute of Technology Vellore India Department of Computer Science & Engineering School of Engineering and Applied Sciences SRM University-AP Amaravati Andhra Pradesh Department of Computing Technology College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India

Crop failure caused by disease-causing pests is an important problem. Farmers struggle with disease management and detection due to inadequate interventions. The goal is to create an automatic system to efficiently identify plant diseases from photos while reducing crop losses and increasing productivity. Machine learning algorithms offer a faster and cheaper alternative to visual inspection by experts. The main goal is to perform image analysis for early diagnosis and effective disease control. The use of CNN architecture for plant disease classification and detection offers a promising solution for plant health monitoring and risk mitigation. Given the threats to crop productivity and global food security, reliable methods for early detection and accurate classification are essential. CNNs enable efficient analysis of vast plant image databases, enabling accurate plant disease identification with speed and accuracy. The multi-layer CNN approach extracts feature and refines the representation, facilitating accurate prediction and accurate diagnosis. Transfer learning methods accelerate system development and allow adaptation to plant disease-specific databases. Combining computer vision algorithms with CNN architecture enables real-time monitoring, early disease detection and targeted intervention, reducing yield losses and improving crop management. This approach uses AI, image analysis, and plant pathology to solve the challenges of sustainable agriculture and plant diseases. System performance is measured by various performance metrics.

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Performer: A High-Performance Global-Local Model-Augmented with Dual Network Interaction Mechanism

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IEEE Transactions on Cognitive and Developmental Systems 2024年

作者： Tan, Dayu Hao, Rui Hua, Linfeng Xu, Qi Su, Yansen Zheng, Chunhou Zhong, Weimin Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China

In deep learning, Convolutional Neural Networks (CNNs) focus on local information through convolutional kernels, while transformers attend to global information using self-attention mechanisms. The union of these distinct approaches enables a more comprehensive extraction of image features. However, the feature map dimensions of CNN and Transformer differ, leading to dimension mismatch issues when combining these architectures. Additionally, the parameter size of the hybrid model integrating both architectures remains large, making it difficult to train. To further augmenting the interpretation of complex image patterns, we present Performer, a dual-network architecture that seamlessly combines CNNs and transformers, resulting in a novel and efficient representation learning model. In the Performer model, we innovate by devising a unique interaction methodology for CNN and transformer architectures to enhance the image feature extraction capabilities mutually. To counteract issue of dimensionality mismatch, we also introduce a refined transformer block, a advancement over the transformer block of ViT. To validate the effectiveness of Performer, we conduct extensive experiments on both classification and segmentation tasks. Performer achieve an accuracy of 83.37% on the ImageNet-200 dataset. For semantic segmentation, Performer excels on the CamVid and Hippocampus datasets. On CamVid, our model achieves a mean Intersection over Union (mIoU) of 63.27% and Pixel Accuracy of 92.11%, demonstrating superior performance in capturing fine details and handling complex scenes effectively. The code is available at https://***/hlfthh/ Performer. © 2016 IEEE.

关键词： Semantic Segmentation

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Assessing the Efficacy of Distributed Ledger technology for Immutable Data Logging and Fraud Prevention in IoT Retail Applications

Assessing the Efficacy of Distributed Ledger Technology for ...

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2024 International Conference on Advances in computing Research on science Engineering and technology, ACROSET 2024

作者： Ponnrajakumari, M. Elanangai, V. Ramprasad, S.R. Velpula, Vijaya Kumar Bhagyalakshmi, L. Prabha, A.G. Velammal Engineering College Dept. of Ece Chennai India St. Peter's Institute of Higher Education and Research. Avadi Department of Electrical and Electronics Engineering Chennai India School of Computing Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Mlr Institute of Technology Dept of Ece Hyderabad India Rajalakshmi Engineering College Dept. of Ece Chennai India

ISBN: (纸本)9798350388800

This paper presents a comprehensive framework for assessing the efficacy of Distributed Ledger technology (DLT) in IoT retail applications. The framework integrates five key algorithms: Data Validation Algorithm (DVA), Consensus Mechanism Algorithm (CMA), Fraud Detection Algorithm (FDA), Privacy-Preserving Algorithm (PPA), and Dynamic Smart Contract Algorithm (DSCA). Each algorithm addresses specific challenges, ensuring data integrity, achieving consensus, preventing fraud, preserving privacy, and enhancing smart contract adaptability. The ablation study evaluates the individual contributions of each algorithm, highlighting their significance in the overall framework. Comparative performance evaluations demonstrate the proposed framework's superiority, with higher scores across key parameters compared to existing assessment methods. The framework offers a robust solution for enhancing the security, integrity, and efficiency of DLT in IoT retail applications, addressing multifaceted challenges and requirements. © 2024 IEEE.

关键词： Distributed ledger

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Swallowing Assessment using High-Resolution Cervical Auscultations and Transformer-based Neural Networks 46

Swallowing Assessment using High-Resolution Cervical Auscult...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Anwar, Ayman Khalifa, Yassin Lucatorto, Erin Coyle, James L. Sejdic, Ervin University of Toronto Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada University of Pittsburgh Center for Research Computing and Information Technology Analytics PittsburghPA United States Cairo University Faculty of Engineering Systems and Biomedical Engineering Department Giza Egypt University of Pittsburgh School of Health and Rehabilitation Sciences Department of Communication Science and Disorders PittsburghPA United States University of Toronto North York General Hospital Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada

ISBN: (纸本)9798350371499

Swallowing assessment is a crucial task to reveal swallowing abnormalities. There are multiple modalities to analyze swallowing kinematics, such as videofluoroscopic swallow studies (VFSS), which is the gold standard method, and high-resolution cervical auscultation (HRCA), which is a noninvasive technique that uses a triaxial accelerometer attached to the patient's neck. Deep learning models play an essential role in data driven analysis of swallowing landmarks using VFSS and/or HRCA as input data. Most of these models utilize convolutional and recurrent neural networks. Here, we investigate the ability of transformers to analyze swallowing kinematics;specifically upper esophageal sphincter opening and laryngeal vestibule closure using HRCA signals. We tested the model using an independent test dataset to assess the generalizability of the proposed network. The proposed network achieved an average detection accuracy higher than 90% and 85% for both segmentation tasks, which outperform the hybrid neural networks from the literature, and the model obtained high-performance measures for the independent dataset, showing the transformers' ability to generalize on unseen data. © 2024 IEEE.

关键词： Recurrent neural networks

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Brain Tumor Detection via Transfer Learning and Ensemble Learning

Brain Tumor Detection via Transfer Learning and Ensemble Lea...

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IEEE Conference on Systems, Process & Control (ICSPC)

作者： Wei-Jie Hun Jit-Yan Lim Kian-Ming Lim Chin-Poo Lee Yong-Xuan Tan Pa-Pa-Min Faculty of Information Science & Technology (FIST) Multimedia University Melaka Malaysia School of Computer Science University of Nottingham Ningbo China Ningbo China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Jiangsu Province China

ISBN: (数字)9798350391398

ISBN: (纸本)9798350391404

Brain tumors present a significant challenge in medical diagnostics due to their complexity and variability. This research investigates the application of deep learning to classify different types of brain tumors, aiming to enhance diagnostic accuracy and efficiency. The proposed ensemble method leverages a hybrid model that combines fine-tuned pre-trained DenseNet121 and EfficientNet-B0 models, omitting their respective top layers. By integrating the strengths of both networks through additional fully connected layers, classification performance is significantly improved. The proposed ensemble method achieves an accuracy of ${9 9. 6 7 \%}$ on the Figshare Dataset-I and ${9 9. 3 9 \%}$ on the Kaggle Dataset-II.

关键词： Deep learning Accuracy Transfer learning Brain tumors Brain modeling Robustness Complexity theory Ensemble learning Medical diagnosis

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Supervised Machine Learning-Based Prediction of COVID-19

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computers, Materials & Continua 2021年第10期69卷 21-34页

作者： Atta-ur-Rahman Kiran Sultan Iftikhar Naseer Rizwan Majeed Dhiaa Musleh Mohammed Abdul Salam Gollapalli Sghaier Chabani Nehad Ibrahim Shahan Yamin Siddiqui Muhammad Adnan Khan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Information System College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of CIT Faculty of Applied StudiesKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science&Information Technology Superior UniversityLahore54000Pakistan Directorate of IT The Islamia University of BahawalpurBahawalpur63100Pakistan School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Science Minhaj University LahoreLahore54000Pakistan Department of Computer Science Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan

COVID-19 turned out to be an infectious and life-threatening viral disease,and its swift and overwhelming spread has become one of the greatest challenges for the *** yet,no satisfactory vaccine or medication has been developed that could guarantee its mitigation,though several efforts and trials are *** around the globe are striving to overcome the COVID-19 spread and while they are finding out ways for early detection and timely *** this regard,healthcare experts,researchers and scientists have delved into the investigation of existing as well as new *** situation demands development of a clinical decision support system to equip the medical staff ways to timely detect this *** state-of-the-art research in Artificial intelligence(AI),Machine learning(ML)and cloud computing have encouraged healthcare experts to find effective detection *** study aims to provide a comprehensive review of the role of AI&ML in investigating prediction techniques for the COVID-19.A mathematical model has been formulated to analyze and detect its potential *** proposed model is a cloud-based smart detection algorithm using support vector machine(CSDC-SVM)with cross-fold validation *** experimental results have achieved an accuracy of 98.4%with 15-fold cross-validation *** comparison with similar state-of-the-art methods reveals that the proposed CSDC-SVM model possesses better accuracy and efficiency.

关键词： COVID-19 CSDC-SVM artificial intelligence machine learning cloud computing support vector machine

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ECLIPSE: Expunging Clean-label Indiscriminate Poisons via Sparse Diffusion Purification

arXiv

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arXiv 2024年

作者： Wang, Xianlong Hu, Shengshan Zhang, Yechao Zhou, Ziqi Zhang, Leo Yu Xu, Peng Wan, Wei Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Information and Communication Technology Griffith University SouthportQLD4215 Australia

Clean-label indiscriminate poisoning attacks add invisible perturbations to correctly labeled training images, thus dramatically reducing the generalization capability of the victim models. Recently, defense mechanisms such as adversarial training, image transformation techniques, and image purification have been proposed. However, these schemes are either susceptible to adaptive attacks, built on unrealistic assumptions, or only effective against specific poison types, limiting their universal applicability. In this research, we propose a more universally effective, practical, and robust defense scheme called ECLIPSE. We first investigate the impact of Gaussian noise on the poisons and theoretically prove that any kind of poison will be largely assimilated when imposing sufficient random noise. In light of this, we assume the victim has access to an extremely limited number of clean images (a more practical scene) and subsequently enlarge this sparse set for training a denoising probabilistic model (a universal denoising tool). We then introduce Gaussian noise to absorb the poisons and apply the model for denoising, resulting in a roughly purified dataset. Finally, to address the trade-off of the inconsistency in the assimilation sensitivity of different poisons by Gaussian noise, we propose a lightweight corruption compensation module to effectively eliminate residual poisons, providing a more universal defense approach. Extensive experiments demonstrate that our defense approach outperforms 10 state-of-the-art defenses. We also propose an adaptive attack against ECLIPSE and verify the robustness of our defense scheme. Our code is available at https://***/CGCL-codes/ECLIPSE. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Revisiting the impact of dependency network metrics on software defect prediction

arXiv

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arXiv 2022年

作者： Gong, Lina Rajbahadur, Gopi Krishnan Hassan, Ahmed E. Jiang, Shujuan School Of Computer Science And Technology China University Of Mining And Technology Xuzhou China School Of Computer Science And Technology Nanjing University Of Aeronautics And Astronautics China School Of Computing Queen's University Canada

Software dependency network metrics extracted from the dependency graph of the software modules by the application of Social Network Analysis (SNA metrics) have been shown to improve the performance of the Software Defect prediction (SDP) models. However, the relative effectiveness of these SNA metrics over code metrics in improving the performance of the SDP models has been widely debated with no clear consensus. Furthermore, some of the common SDP scenarios like predicting the number of defects in a module (Defect-count) in Cross-version and Cross-project SDP contexts remain unexplored. Such lack of clear directive on the effectiveness of SNA metrics when compared to the widely used code metrics prevents us from potentially building better performing SDP models. Therefore, through a case study of 9 open source software projects across 30 versions, we study the relative effectiveness of SNA metrics when compared to code metrics across 3 commonly used SDP contexts (Within-project, Cross-version and Crossproject) and scenarios (Defect-count, Defect-classification (classifying if a module is defective) and Effort-aware (ranking the defective modules w.r.t to the involved effort)). We find the SNA metrics by themselves or along with code metrics improve the performance of SDP models over just using code metrics on 5 out of the 9 studied SDP scenarios (three SDP scenarios across three SDP contexts). However, we note that in some cases the improvements afforded by considering SNA metrics over or alongside code metrics might only be marginal, whereas in other cases the improvements could be potentially large. Based on these findings we suggest that the future work should: consider SNA metrics alongside code metrics in their SDP models;as well as consider Ego metrics and Global metrics, the two different types of the SNA metrics separately when training SDP models as they behave differently. © 2022, CC BY-NC-ND.

关键词： Forecasting

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RETRACTED ARTICLE: A multi-scale and rotation-invariant phase pattern (MRIPP) and a stack of restricted Boltzmann machine (RBM) with preprocessing for facial expression classification (Journal of Ambient Intelligence and Humanized computing, (2021), 12, 3, (3447-3463), 10.1007/s12652-020-02517-7)

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Journal of Ambient Intelligence and Humanized computing 2024年

作者： Sherly Alphonse, A. Shankar, K. Jeyasheela Rakkini, M.J. Ananthakrishnan, S. Athisayamani, Suganya Singh, A. Robert Gobi, R. Department of Information Technology Ponjesly College of Engineering Nagercoil India Department of Computer Applications Alagappa University Karaikudi India School of Computing Sastra Deemed To Be University Thanjavur India School of Computing Kalasalingam Academy of Research and Education Anand Nagar India Department of Computer Science Christ University Bengalore India

The Publisher has retracted this article in agreement with the Editor-in-Chief. The article was submitted to be part of a guest-edited issue. An investigation by the publisher found a number of articles, including this one, with a number of concerns, including but not limited to compromised editorial handling and peer review process, inappropriate or irrelevant references or not being in scope of the journal or guest-edited issue. Based on the investigation’s findings the publisher, in consultation with the Editor-in-Chief therefore no longer has confidence in the results and conclusions of this article. S. Ananthakrishnan, A. Robert Singh, and R. Gobi disagree with this retraction. M. J. Jeyasheela Rakkini did not explicitly state whether they agree or disagree with this retraction. The remaining authors did not respond to correspondence from the Publisher regarding this retraction. © Springer-Verlag GmbH Germany, part of Springer Nature 2024.

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Exploring the Problems, their Causes and Solutions of AI Pair Programming: A Study on GitHub and Stack Overflow

arXiv

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arXiv 2023年

作者： Zhou, Xiyu Liang, Peng Zhang, Beiqi Li, Zengyang Ahmad, Aakash Shahin, Mojtaba Waseem, Muhammad School of Computer Science Wuhan University Wuhan China School of Computer Science Central China Normal University Wuhan China School of Computing and Communications Lancaster University Leipzig Leipzig Germany School of Computing Technologies RMIT University Melbourne Australia Faculty of Information Technology University of Jyväskylä Jyväskylä Finland

With the recent advancement of Artificial Intelligence (AI) and Large Language Models (LLMs), AI-based code generation tools become a practical solution for software development. GitHub Copilot, the AI pair programmer, utilizes machine learning models trained on a large corpus of code snippets to generate code suggestions using natural language processing. Despite its popularity in software development, there is limited empirical evidence on the actual experiences of practitioners who work with Copilot. To this end, we conducted an empirical study to understand the problems that practitioners face when using Copilot, as well as their underlying causes and potential solutions. We collected data from 473 GitHub issues, 706 GitHub discussions, and 142 Stack Overflow posts. Our results reveal that (1) Operation Issue and Compatibility Issue are the most common problems faced by Copilot users, (2) Copilot Internal Error, Network Connection Error, and Editor/IDE Compatibility Issue are identified as the most frequent causes, and (3) Bug Fixed by Copilot, Modify Configuration/Setting, and Use Suitable Version are the predominant solutions. Based on the results, we discuss the potential areas of Copilot for enhancement, and provide the implications for the Copilot users, the Copilot team, and researchers. © 2023, CC BY.

关键词： Software design

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