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6th International Conference on Contemporary Computing and informatics, IC3I 2023

作者： Ramaiah, Narayana Swamy Andrews, S. Kevin Shenbagharaman, A. Gowtham, M.S. Bhaskar, Bandi Tiwari, Mohit Sankalchand Patel University SPCE Dept of Computer Engineering Gujarat Visnagar India Dr. Mgr Educational and Research Institute Department of Computer Applications Tamil Nadu Chennai India National Engineering College Department of CSE Tamilnadu Kovilpatti India Karpagam Institute of Technology ECE Department Tamilnadu Coimbatore India School of CS & AI SR University Telangana Warangal India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350304480

Enhancing the Internet of Things (IoT) security is among the most pressing concerns confronting the information technology sector. With significant numbers of loT systems being created and established, it is difficult for these systems to interact securely without affecting performance. The difficulties arise since the majority of loT systems are resource restricted and so possess restricted processing capability. In, this study, we intend to study the enhancement of IoT security of anomaly detection and intrusion prevention. To enhance anomaly detection and intrusion prevention performance, a binary categorization of typical and unusual IoT traffic is created. In this paper, we carefully evaluate the specificity and complication of IoT security protection, and then discover that Artificial Intelligence (AI) approaches like Machine Learning (ML) and ensemble classifiers may offer new strong abilities to satisfy IoT security demands. This enhancement can be associated with ensemble learning methods that make use of a variety of learning processes with variable capacities. We were capable of improving the predictability of our predictions while decreasing the likelihood of classification errors through the combination of these methods. The outcomes of the experiments suggest that the architecture could enhance the effectiveness of the anomaly detection and intrusion prevention System, with an accuracy level of 0.9863. © 2023 IEEE.

关键词： Anomaly detection Artificial intelligence Internet of things Intrusion prevention Machine learning Security

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Efficient Time-Delay System Optimization with Auto-Configured Metaheuristics

Efficient Time-Delay System Optimization with Auto-Configure...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Senkerik, Roman Byrski, Aleksander Guzowski, Hubert Janku, Peter Kadavy, Tomas Oplatkova, Zuzana Kominkova Matusu, Radek Pluhacek, Michal Pekar, Libor Smolka, MacIej Viktorin, Adam Tomas Bata University A.I. Lab Faculty of Applied Informatics Department of Informatics and Artificial Intelligence Zlin Czech Republic Tomas Bata University Faculty of Applied Informatics Department of Automation and Control Engineering Zlin Czech Republic Institute of Computer Science Agh University of Science and Technology Krakow Poland

ISBN: (纸本)9798350337020

This paper presents an experimental study that compares the performance of four selected metaheuristic algorithms for optimizing a time delay system model. Time delay system models are complex and challenging to optimize due to their inherent characteristics, such as non-linearity, multi-modality, and constraints. The study includes an explanation of the choice and core functionality of the selected algorithms, which are both baseline and state-of-the-art variants of self-organizing migrating algorithm (SOMA), state-of-the-art variant from the Success-History-based Adaptive Differential Evolution family of algorithms, with emphasis on diverse search (DISH algorithm), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm. The hyperparameters of the metaheuristic algorithms were set using the iRace automatic algorithm configuration framework. The paper emphasizes the importance of metaheuristic algorithms in control engineering for time-delay systems to develop more effective and efficient control strategies and precise model identifications. The experimental results highlight the effectiveness of the state-of-the-art algorithms with specific adaptive mechanisms like population organization process, diverse search and adaptation mechanisms ensuring a gradual transition from exploration to exploitation. Overall, this study contributes to understanding the challenges and advantages of using metaheuristic algorithms in control engineering for time delay systems. The results provide valuable insights into the performance of modern metaheuristic algorithms and can help guide the selection of appropriate adaptive mechanisms of metaheuristics. © 2023 IEEE.

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Carrying Out CNN Channel Pruning in a White Box

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IEEE Transactions on Neural Networks and Learning Systems 2023年第10期34卷 7946-7955页

作者： Zhang, Yuxin Lin, Mingbao Lin, Chia-Wen Chen, Jie Wu, Yongjian Tian, Yonghong Ji, Rongrong Xiamen University Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Xiamen361005 China Youtu Laboratory Tencent Shanghai200233 China National Tsing Hua University Department of Electrical Engineering The Institute of Communications Engineering Hsinchu30013 Taiwan Peng Cheng Laboratory Shenzhen518000 China Peking University Department of Computer Science and Technology Beijing100871 China School of Informatics and the Institute of Artificial Intelligence Xiamen University Media Analytics and Computing Laboratory Department of Artificial Intelligence Xiamen361005 China

Channel pruning has been long studied to compress convolutional neural networks (CNNs), which significantly reduces the overall computation. Prior works implement channel pruning in an unexplainable manner, which tends to reduce the final classification errors while failing to consider the internal influence of each channel. In this article, we conduct channel pruning in a white box. Through deep visualization of feature maps activated by different channels, we observe that different channels have a varying contribution to different categories in image classification. Inspired by this, we choose to preserve channels contributing to most categories. Specifically, to model the contribution of each channel to differentiating categories, we develop a class-wise mask for each channel, implemented in a dynamic training manner with respect to the input image's category. On the basis of the learned class-wise mask, we perform a global voting mechanism to remove channels with less category discrimination. Lastly, a fine-tuning process is conducted to recover the performance of the pruned model. To our best knowledge, it is the first time that CNN interpretability theory is considered to guide channel pruning. Extensive experiments on representative image classification tasks demonstrate the superiority of our White-Box over many state-of-the-arts (SOTAs). For instance, on CIFAR-10, it reduces 65.23% floating point operations per seconds (FLOPs) with even 0.62% accuracy improvement for ResNet-110. On ILSVRC-2012, White-Box achieves a 45.6% FLOP reduction with only a small loss of 0.83% in the top-1 accuracy for ResNet-50. Code is available at https://***/zyxxμWhite-Box. © 2012 IEEE.

关键词： Visualization

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Breast Cancer Detection in Mammogram Images using Machine Learning Methods and CLAHE Algorithm 5

Breast Cancer Detection in Mammogram Images using Machine Le...

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5th International Conference on Contemporary Computing and informatics, IC3I 2022

作者： Srivastav, Gaurav Rashid, Mamoon Singh, Richa Gehlot, Anita Sharma, Neha Computer Science & Engineering Datta Meghe Institute of Higher Education & Research Maharastra India Vishwakarma University Faculty of Science & Technology Department of Computer Engineering Pune India Computer Science & Engineering Krishna Institute of Engineering & Technology Gaziabad India Uttaranchal University Uttaranchal Institute of Technology Uttrakhand Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

ISBN: (纸本)9798350398267

Breast cancer is one of the most common cancer types. This is the second-leading cause of cancer-related death in women. It ranked 2nd according to available data lung cancers is the only one causing more causalities. It's critical to receive a breast cancer diagnosis quickly. The MIAS data set is used in this study to examine machine learning-based categorization approaches used to study breast cancer. Important image data is fetched from mammograms. We choose eight distinct classifiers and assess each one's precision, recall, accuracy, and F-score. The analysis's findings were higher than 69.88%. © 2022 IEEE.

关键词： Machine learning

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Mitigation of Straggler in Virtual Machine Stack Using Supervised Learning Methodology 5

Mitigation of Straggler in Virtual Machine Stack Using Super...

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5th International Conference on Emerging Smart Computing and informatics, ESCI 2023

作者： Gaykar, Reshma S. Khanaa, V. Joshi, S.D. Bharath Institute of Higher Education and Research Department of Computer Science Chennai India Information Center Bharath Institute of Higher Education and Research Chennai India Bharati Vidyapeeth University Faculty of Engineering and Technology Pune India

ISBN: (纸本)9781665475242

Hadoop is an inexpensive analytical tool as compared to the other distributed storage in market as it does not need any standalone machines and works on group of commodity hardware. It is a distributed storage system along with this it achieves parallelization of larger data collections. With MapReduce, HDFS (Hadoop distributed file system) provides solution for the system where processing huge datasets is a requirement. Few of the main reasons of stragglers in assorted Hadoop clusters are load inconsistency during storing, resource friction throughout scheduling tasks, hardware downturn due to excessive usage, as well as software configuration issues while managing the cluster. Hadoop's performance lows down in a heterogeneous network due to the technical heterogeneity. We used a supervised machine learning (ML) technique to identify straggler nodes in an eminently distributed network in this article. The suggested technique identifies the proper slow-running job (Straggler) in the network and assign it to other node in the stack to complete the operation with quick succession. Virtual Machine (VM) identifier, network bandwidth consumption, number of processors and its load, memory load and other parameters included in the full data set are utilized for recognition. Various feature extraction methodologies have been utilized to develop its training system. The whole data set was processed for heterogenous features on the dataset. We analyzed our approach using our suggested classifier after doing comprehensive empirical work. As out-turn, the system outperforms using typical machine learning models in classification performance. © 2023 IEEE.

关键词： Heterogeneous networks

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CNN segmentation of skin melanoma in pre-processed dermoscopy images 2

CNN segmentation of skin melanoma in pre-processed dermoscop...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Kadry, Seifedine Verdú, Elena Damasevicius, Robertas Abualigah, Laith Singh, Vijendra Rajinikanth, Venkatesan Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Higher School of Engineering and Technology Universidad Internacional de la Rioja Logroño26006 Spain Department of Applied Informatics Vytautas Magnus University Kaunas53361 Lithuania Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India

Using medical data to improve diagnosis accuracy has recently become common practice in hospitals. A modern computing environment has enabled real-time diagnosis of medical data using Convolutional Neural Networks (CNNs). To extract and evaluate skin melanoma recorded with digital dermatoscopy images (DDI), we developed a CNN segmentation framework. In this proposal, four phases are proposed: (i) DDI collection and resizing, (ii) DDI enhancement using pre-processing techniques, (iii) CNN segmentation for lesion extraction, (v) Comparing the extracted sections to the ground truth images, and (v) Verifying whether the framework is valid. Using DDI pre-processed with (i) Traditional procedures, (ii) Otsu's thresholding, (iii) Kapur's thresholding, and (iv) Fuzzy-Tsallis thresholding, this proposal examines the different CNN segmentation schemes presented in the literature. For mining skin lesions, the Moth-Flame Algorithm (MFA) combined with tri-level thresholding achieves an optimal threshold for the DDI. With Fuzzy-Tsallis thresholding images, the VGG-UNet performs better than the alternatives. This framework helps to achieve better values of Jaccard (88.47±2.13%), Dice (93.08±1.17%), and Accuracy (98.64±0.71%) on the chosen DDI database. © 2024 Elsevier B.V.. All rights reserved.

关键词： Image enhancement

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Automatic liver-vessel examination from CT slice using Kapur's thresholding and watershed algorithm 2

Automatic liver-vessel examination from CT slice using Kapur...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Kadry, Seifedine Abualigah, Laith González Crespo, Rubén Verdú, Elena Damasevicius, Robertas Singh, Vijendra Rajinikanth, Venkatesan Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Higher School of Engineering and Technology Universidad Internacional de la Rioja Logroño26006 Spain Department of Applied Informatics Vytautas Magnus University Kaunas53361 Lithuania School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India

computerized medical image examination (CMIE) plays a significant role in modern hospitals to achieve the necessary tasks, like segmentation and classification. By segmenting an image, we can extract a particular section for examination. A two-dimensional computed tomography (CT) slice was used for liver-vessel examination (LiVE). A simple automatic technique for supporting LiVE is being developed in this research. A CT slice is collected, a 3D to 2D conversion is done, (ii) Kapur's tri-level thresholding and Hummingbird-Optimizer is used to enhance the CT slice, (iii) the watershed algorithm (WA) is used to extract the vessel, and (iv) the WA is compared and verified against the segmentation methods chosen. WA provides better segmentation results than other methods because it is an automatic approach. Using the chosen image database, the proposed technique achieves an overall segmentation accuracy of >97%. Other segmentation problems can be used in the future to verify the merit of this scheme. © 2024 Elsevier B.V.. All rights reserved.

关键词： Medical imaging

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COVID19 detection in chest x-ray using vision-transformer with different patch dimensions 2

COVID19 detection in chest x-ray using vision-transformer wi...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

computerized disease detection systems (CDDs) have proven effective for automatic screening in recent years. Among the standard procedures in hospitals for faster and more accurate diagnosis is medical imaging-based disease screening. We aim to develop a CDD that detects COVID-19 using chest X-rays pre-trained vision transformers (PVTs). This scheme includes the following steps: (1) collecting images and resizing them, (2) implementing PVT for feature extraction, and (3) binary classifying the results and validating the proposed schemes. To prove the merit of the developed scheme, 4800 images (2400 normal and 2400 COVID-19) are analyzed. MLP classifiers verify the PVT performance using patch sizes of 6, 12, and 24. A patch size 24 results in 97.5% accuracy for the proposed CDD system. When patch sizes are increased to 12, accuracy increases to over 98%. For this specific task, smaller patch sizes are more effective. These high-accuracy results demonstrate the effectiveness of the developed scheme for detecting COVID-19 in chest X-rays. © 2024 Elsevier B.V.. All rights reserved.

关键词： Diagnosis

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A Review on Leveraging Language Agnostic Automated Text Data Augmentation with Explainable Semantic Integrity

A Review on Leveraging Language Agnostic Automated Text Data...

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Advanced research in Computing (ICARC), International Conference on

作者： Sarith Wijesundera Suresh Michael Peiris Department of Computer Science and Engineering University of Westminster London UK School of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

Data Augmentation (DA) is an effective strategy to increase model generalisation. In Natural Language Processing (NLP), DA remains in its early stages, primarily due to the inherent sensitivity of textual data, which complicates the augmentation process. These complexities are further increased in multilingual contexts, as DA techniques effective in one language may not yield comparable results in others. As a result, manually identifying optimal DA techniques that preserve semantic integrity and are language agnostic remains a significant challenge. It emphasises the necessity for automating the text DA process with Explainable Artificial Intelligence (XAI), which offers a promising approach by calculating word importance scores to effectively guide the augmentation. In this paper, a comprehensive review of existing approaches in auto-text DA is presented, beginning with an exploration of the concept of auto-text DA, followed by a discussion on its cross-lingual applications, and concluding with the integration of XAI for semantic preservation. The paper highlights the critical need for further research to enhance the effectiveness and applicability of XAI in auto-text DA, enabling its use across diverse languages.

关键词： Sensitivity Explainable AI Reviews Semantics Transfer learning Data augmentation Search problems Natural language processing Multilingual Optimization

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JARVIS-Leaderboard:a large scale benchmark of materials design methods

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npj Computational Materials 2024年第1期10卷 2280-2296页

作者： Kamal Choudhary Daniel Wines Kangming Li Kevin F.Garrity Vishu Gupta Aldo H.Romero Jaron T.Krogel Kayahan Saritas Addis Fuhr Panchapakesan Ganesh Paul R.C.Kent Keqiang Yan Yuchao Lin Shuiwang Ji Ben Blaiszik Patrick Reiser Pascal Friederich Ankit Agrawal Pratyush Tiwary Eric Beyerle Peter Minch Trevor David Rhone Ichiro Takeuchi Robert B.Wexler Arun Mannodi-Kanakkithodi Elif Ertekin Avanish Mishra Nithin Mathew Mitchell Wood Andrew Dale Rohskopf Jason Hattrick-Simpers Shih-Han Wang Luke E.K.Achenie Hongliang Xin Maureen Williams Adam J.Biacchi Francesca Tavazza Material Measurement Laboratory National Institute of Standards and TechnologyGaithersburgMD20899USA Department of Materials Science and Engineering University of Toronto27 King’s College CirTorontoONCanada Department of Electrical and Computer Engineering Northwestern UniversityEvanstonIL 60208USA Lewis-Sigler Institute for Integrative Genomics Princeton UniversityPrincetonNJ 08544USA Ludwig Institute for Cancer Research Princeton UniversityPrincetonNJ 08544USA Department of Physics and Astronomy West Virginia UniversityMorgantownWV 26506USA Materials Science and Technology Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Center for Nanophase Materials Science Oak Ridge National LaboratoryOak RidgeTN 37831USA Computational Sciences and Engineering Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Department of Computer Science and Engineering Texas A&M UniversityCollege StationTX 77843USA Globus University of ChicagoChicagoIL 60637USA Data Science and Learning Division Argonne National LabLemontIL 60439USA Institute of Nanotechnology Karlsruhe Institute of TechnologyKaiserstraße 1276131 KarlsruheGermany Institute of Theoretical Informatics Karlsruhe Institute of TechnologyKaiserstraße 1276131 KarlsruheGermany Department of Chemistry and Biochemistry and Institute for Physical Science and Technology University of MarylandCollege ParkMD 20742USA Department of Physics Applied Physics and AstronomyRensselaer Polytechnic InstituteTroyNY12180USA Department of Materials Science and Engineering University of MarylandCollege ParkMD20742USA Department of Chemistry and Institute of Materials Science and Engineering Washington University in St.LouisSt.LouisMO63130USA School of Materials Engineering Purdue UniversityWest LafayetteIN47907USA Department of Mechanical Science and Engineering University of Illinois Urbana-ChampaignUrbanaIllinois 61801USA Materials Research Laboratory University of Illinois Urbana-ChampaignUrbanaIL 61801USA Theore

Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many *** science,in particular,encompasses a variety of experimental and theoretical approaches that require careful *** efforts have been developed previously to mitigate these ***,a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still *** work introduces JARVIS-Leaderboard,an open-source and community-driven platform that facilitates benchmarking and enhances *** platform allows users to set up benchmarks with customtasks and enables contributions in the form of dataset,code,and meta-data *** cover the following materials design categories:Artificial Intelligence(AI),Electronic Structure(ES).

关键词： rigorous perfect enable

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