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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Saratkar, Saniya Chaudhari, Aarti Thute, Trupti Raut, Rohini Thakre, Gayatri Kumar, Hemant Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha442107 India

ISBN: (纸本)9798350391770

This paper evaluates the prediction of heart attacks with a fuzzy rule-based system (FRBS). Since heart disease is the world's leading cause of death, precise prediction models are crucial for prevention and early intervention. With its reputation for managing imprecise and ambiguous data, fuzzy logic presents a viable method for raising prediction accuracy in medical diagnostics. In order to capture the complexity and unpredictability of cardiovascular risk variables, fuzzy rules are used in this work to investigate the creation and assessment of a fuzzy rule-based system (FRBS) for heart attack prediction. The efficacy and dependability of the FRBS in identifying those at high risk of heart attack are shown via testing and comparison with conventional prediction models. The results highlight fuzzy logic's potential to improve cardiovascular predictive analytics. © 2024 IEEE.

关键词： Prediction models

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Detection of Lung Cancer Using CT-Scan Image - Deep Learning Approach 20

Detection of Lung Cancer Using CT-Scan Image - Deep Learning...

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20th OITS International Conference on Information technology, OCIT 2022

作者： Pal, Jashasmita Das, Subhalaxmi Tripathy, Jogeswar Radhakrishna Institute of Technology and Engineering Computer Science and Engineering Odisha India Odisha University of Technology and Research Computer Science and Engineering Odisha India

ISBN: (纸本)9781665493482

Cancer is a disease that comes in many forms and is the largest cause of death worldwide for men and women alike. Early detection of cancer has the highest chance of saving a person's life. Some of the procedures used to diagnose cancer include CT scans, bone scans, MRIs, PET (Positron Emission Tomography), ultrasound, and X-rays. Cancers such as lung cancer are among the deadliest worldwide, killing approximately five million people every year. This chapter focuses on lung cancer detection. The diagnosis of Cancer is usually a very difficult task in the biomedical and the bioinformatics field. Now, computed tomography (CT) scans can provide useful information for lung cancer diagnosis. In recent advances, deep learning approaches have improved to outperform humans in some tasks like classifying objects in images and also predicting better accuracy. Therefore, these techniques have been utilized in this model for the treatment of cancerous conditions. We detect lung cancer nodules from a given input and classify cancer as Adenocarcinoma, Large Cell Carcinoma, or Squamous Cell Carcinoma in our research. To detect the location of lung nodules, researchers used revolutionary deep learning approaches. In this paper basically, we used three deep learning case studies to diagnose lung cancer such as VGG16, INCEPTIONV3 and RESNET50 and also, we are discussing various measures for evaluating the performance of our model to get better accuracy. SS © 2022 IEEE.

关键词： Diagnosis

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Advancing Network Anomaly Detection: Comparative Analysis of Machine Learning Models

Advancing Network Anomaly Detection: Comparative Analysis of...

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International Conference on. Cryptology and Network Security with Machine Learning, ICCNSML 2023

作者： Pandey, Rashmikiran Pandey, Mrinal Nazarov, Alexey Department of Radio Engineering and Cybernetics Moscow Institute of Physics and Technology Moscow Russia Federal Research Center Computer Science and Control Russian Academy of Science Moscow Russia

ISBN: (纸本)9789819706402

In the rapidly evolving realm of cyber-security, the detection of network anomalies serves as a pivotal line of defense against a myriad of malicious activities and cyberthreats. This research undertakes the task of enhancing the accuracy and efficacy of network anomaly detection by employing a comparative analysis of various individual machine learning models. The study delves into the performance of distinct models, including Random Forest, Gradient Boosting, AdaBoost, neural networks, and SVM, meticulously scrutinizing their capabilities in detecting network anomalies. The crux of this study lies in its meticulous evaluation of each individual model on the revered NSL-KDD dataset—an established benchmark within the field of network intrusion detection. Through a systematic blend of rigorous mathematical frameworks, precise model implementations, and comprehensive experimental assessments, this research offers a deep understanding of the inner workings of each algorithm. The pivotal aspect of this study revolves around the comprehensive comparative analysis of these standalone models. Going beyond the mere quantifica-tion of accuracy, the exploration delves into aspects of precision, recall, F1-score, and more, shedding light on their diverse facets of performance. With achieved accuracies of 99.2419% for Random Forest, 99.5197% for Gradient Boosting, 86.6044% for AdaBoost, 84.00% for neural networks, and 87.00% for SVM, this research underlines the distinctive attributes and potential of each model in the context of network anomaly detection. As the study unravels the distinct strengths and limitations of each model, it contributes to the broader landscape of cyber-security by providing insights into the efficacy of individual machine learning approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Analysis of Indian and American poetry using topic modeling and Deep learning

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Materials Today: Proceedings 2022年 64卷 787-792页

作者： Praveen Kumar, K. Phani Kumar S, Venkatrama Lokesh Kumar, S.K. Department of CSE Vignan's Foundation for ScienceTechnology and Research A.P Guntur India Department of Computer Science and Engineering MLR Institute Technology Hyderabad India

Text classification is a supervised machine learning technique that assigns a set of predefined categories or classes to the given text corpora based on the content of the processed text using Natural language processing techniques. Text classification is widely used in numerus applications such as categorizing the sentiment of the tweets and reviews, classification of news and web pages into multiple categories and automatic classification of emails in to spam or not spam. Under the text categories poetry is a literary text and it is special when compared with the regular prose text. A very less focus is given to the task of classification of poetry by the research community. In this context, this work aimed to classify poetry using machine learning and deep learning models and to analyze the performance of the algorithms. To perform this task, poetry corpus is categorized into multiple classes using Latent Dirichlet Allocation a topic modeling technique. The classification task is carried using Multinomial Bayesian, SGD models under machine learning methods and LSTM, Bi-LSTM and CNN models under the deep learning methods. The results are evaluated with parameter accuracy. As a result of this experiment the best classification accuracy is achieved using CNN model with 87% by outperforming other models. This shows that for literary text classification CNN can be considered as a best classifier in comparative with the LSTM and Bi-LSTM models. © 2022

关键词： Classification (of information)

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CoChain: High Concurrency Blockchain Sharding via Consensus on Consensus 42

CoChain: High Concurrency Blockchain Sharding via Consensus ...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Li, Mingzhe Lin, You Zhang, Jin Wang, Wei Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Research Institute of Trustworthy Autonomous Systems Computer Science and Engineering Department China Hong Kong University of Science and Technology Computer Science and Engineering Department Hong Kong Institute of High Performance Computing and Agency for Science Technology and Research Singapore

ISBN: (纸本)9798350334142

Sharding is an effective technique to improve the scalability of blockchain. It splits nodes into multiple groups so that they can process transactions in parallel. To achieve higher parallelism and concurrency at large scales, it is desirable to maintain a large number of small shards. However, simply configuring small shards easily results in a higher fraction of malicious nodes inside shards, causing shard corruption and compromising system security. Existing sharding techniques hence demand large shards, at the expense of limited concurrency. To address this limitation, we propose CoChain: a blockchain sharding system that can securely configure small shards for enhanced concurrency. CoChain allows some shards to be corrupted. For security, each shard is monitored by multiple other shards. The latter reach a cross-shard Consensus on the Consensus results of their monitored shard. Once a corrupted shard is found, its subsequent consensus will be taken over by another shard, hence recovering the system. Via Consensus on Consensus, CoChain allows the existence of shards with more fraction of malicious nodes ( © 2023 IEEE.

关键词： Blockchain

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A Survey of Artificial Hummingbird Algorithm and Its Variants: Statistical Analysis, Performance Evaluation, and Structural Reviewing

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Archives of Computational Methods in engineering 2025年第1期32卷 269-310页

作者： Hosseinzadeh, Mehdi Rahmani, Amir Masoud Husari, Fatimatelbatoul Mahmoud Alsalami, Omar Mutab Marzougui, Mehrez Nguyen, Gia Nhu Lee, Sang-Woong Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Communication and Computer Engineering Faculty of Engineering Cihan University-Erbil Kurdistan Region Iraq Department of Electrical Engineering College of Engineering Taif University Taif21944 Saudi Arabia Department of Computer Engineering College of Computer Science King Khalid University Abha61413 Saudi Arabia School of Computer Science Duy Tan University Da Nang Viet Nam Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam13120 Korea Republic of

In the last few decades, metaheuristic algorithms that use the laws of nature have been used dramatically in numerous and complex optimization problems. The artificial hummingbird algorithm (AHA) is one of the metaheuristic algorithms that was invented in 2022 based on the foraging and migration behavior of the hummingbird for modeling and solving optimization problems. The algorithm initially starts with an initial random population of solutions. It then uses iterative processes and hummingbird position updates to balance exploration and exploitation toward the most optimal solutions. This paper has a detailed and extensive review of the AHA algorithm considering the aspects of hybrid, improved, binary, multi-objective, and optimization problems. In addition, a wide range of applications of AHA in various fields such as feature selection, image processing, scheduling, Internet of Things, classification, clustering, financial and economic issues, forecasting, wireless sensor networks, and many engineering challenges are explored. The statistical and numerical results showed that the AHA algorithm with deep learning methods, Levy flight, and opposition-based learning had the best performance. Also, the AHA algorithm is most widely used in solving multimodal optimization problems and continuous functions. © The Author(s) under exclusive licence to International Center for Numerical Methods in engineering (CIMNE) 2024.

关键词： Optimization

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Deep Transfer Learning Models for Mobile-Based Ocular Disorder Identification on Retinal Images

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computers, Materials & Continua 2024年第7期80卷 139-161页

作者： Roseline Oluwaseun Ogundokun Joseph Bamidele Awotunde Hakeem Babalola Akande Cheng-Chi Lee Agbotiname Lucky Imoize Department of Multimedia Engineering Kaunas University of TechnologyKaunas44249Lithuania Department of Computer Science Landmark UniversityOmu Aran251103Nigeria Department of Computer Science Faculty of Information and Communication SciencesUniversity of IlorinIlorin240003Nigeria Department of Telecommunication Science University of IlorinIlorin230003Nigeria Department of Library and Information Science Fu Jen Catholic UniversityNew Taipei City24205TaiwanChina Department of Computer Science and Information Engineering Fintech and Blockchain Research CenterAsia UniversityTaichung City41354TaiwanChina Department of Electrical and Electronics Engineering Faculty of EngineeringUniversity of LagosAkokaLagos100213Nigeria Department of Electrical Engineering and Information Technology Institute of Digital CommunicationRuhr UniversityBochum44801Germany

Mobile technology is developing *** phone technologies have been integrated into the healthcare industry to help medical ***,computer vision models focus on image detection and classification ***2 is a computer vision model that performs well on mobile devices,but it requires cloud services to process biometric image information and provide predictions to *** leads to increased *** biometrics image datasets on mobile devices will make the prediction faster,but mobiles are resource-restricted devices in terms of storage,power,and computational ***,a model that is small in size,efficient,and has good prediction quality for biometrics image classification problems is *** pre-trained CNN(PCNN)MobileNetV2 architecture combined with a Support Vector Machine(SVM)compacts the model representation and reduces the computational cost and memory *** proposed novel approach combines quantized pre-trained CNN(PCNN)MobileNetV2 architecture with a Support Vector Machine(SVM)to represent models efficiently with low computational cost and *** contributions include evaluating three CNN models for ocular disease identification in transfer learning and deep feature plus SVM approaches,showing the superiority of deep features from MobileNetV2 and SVM classification models,comparing traditional methods,exploring six ocular diseases and normal classification with 20,111 images postdata augmentation,and reducing the number of trainable *** model is trained on ocular disorder retinal fundus image datasets according to the severity of six age-related macular degeneration(AMD),one of the most common eye illnesses,Cataract,Diabetes,Glaucoma,Hypertension,andMyopia with one class *** the experiment outcomes,it is observed that the suggested MobileNetV2-SVM model size is *** testing accuracy for MobileNetV2-SVM,InceptionV3,and MobileNetV2 is 90.11%,86.88%,a

关键词： Retinal images ocular disorder deep transfer learning disease identification mobile device

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Data Driven Vibration Control:A Review

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IEEE/CAA Journal of Automatica Sinica 2024年第9期11卷 1898-1917页

作者： Weiyi Yang Shuai Li Xin Luo Chongqing Institute of Green and Intelligent Technology Chinese Academy of SciencesChongqing 400714 Chongqing School University of Chinese Academy of SciencesChongqing 400714China Faculty of Information Technology and Electrical Engineering University of OuluOulu 90570 VTT-Technology Research Center of Finland Oulu 90570Finlan College of Computer and Information Science Southwest UniversityChongqing 400715China

With the ongoing advancements in sensor networks and data acquisition technologies across various systems like manufacturing,aviation,and healthcare,the data driven vibration control(DDVC)has attracted broad interests from both the industrial and academic *** shaping(IS),as a simple and effective feedforward method,is greatly demanded in DDVC *** convolves the desired input command with impulse sequence without requiring parametric dynamics and the closed-loop system structure,thereby suppressing the residual vibration *** on a thorough investigation into the state-of-the-art DDVC methods,this survey has made the following efforts:1)Introducing the IS theory and typical input shapers;2)Categorizing recent progress of DDVC methods;3)Summarizing commonly adopted metrics for DDVC;and 4)Discussing the engineering applications and future trends of *** doing so,this study provides a systematic and comprehensive overview of existing DDVC methods from designing to optimizing perspectives,aiming at promoting future research regarding this emerging and vital issue.

关键词： Data driven vibration control(DDVC) data science designing method feedforward control industrial robot input shaping optimizing method residual vibration

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Circadian Effect on Luminance Contrast Sensitivity

Transactions of Japanese Society for Medical and Biological ...

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Transactions of Japanese Society for Medical and Biological engineering 2022年第6期60卷 158-163页

作者： Yoshikawa, Mayu Nagai, Midori Koida, Kowa Department of Electronics Engineering College of Engineering National Institute of Technology Tokyo College Japan Electronics-Inspired Interdisciplinary Research Institute Toyohashi University of Technology Japan Department of Computer Science and Engineering Toyohashi University of Technology Japan

All biological functions, including vision, areaffected by timeof day. It is known that theretinal response to visual stimuli changes according to circadian rhythms. However, the effects on visually guided behavior were poorly understood, and previous results have been inconsistent. The previous studies examined the effect of time on contrast sensitivity were also inconsistent, one reason for this could be the order effects, such as fatigue due to long measurement times. To overcome this issue, we applied a tablet PC based measurement system which made the measuring contrast sensitivity quick and easy. Observers participated in the experiment every morning, noon, evening, and night, and performed a task to detect a Gabor patch. The results showed that sensitivity improved from morning to night, and this trend was more pronounced in the mid-to high-frequency band. The improvement was reproduced even when measurements were taken once daily at random times. These results suggest that the improvement was not due to intraday learning or fatigue. We confirmed that our experimental system is useful for measuring the diurnal variation in contrast sensitivity, as it is quick and easy to use. © 2022, Japan Soc. of Med. Electronics and Biol. engineering. All rights reserved.

关键词： Fatigue of materials

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Hybrid Classification Method for Anomaly Detection in Injection Moulding Process 6

Hybrid Classification Method for Anomaly Detection in Inject...

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6th International Conference on Industrial Artificial Intelligence, IAI 2024

作者： Xuan, Ng Yao Ge, Hailin Feng, Lu Wen Nam, Doan Ngoc Chi Li, Xiang National University of Singapore Department of Electrical and Computer Engineering Singapore Singapore Institute of Manufacturing Technology Agency for Science Technology and Research Singapore National University of Singapore Department of Mechanical Engineering Singapore

ISBN: (纸本)9798350356618

Industrial and environmental challenges faced in injection moulding processes push manufacturing companies to embrace smart manufacturing and adopt machine learning to deal with anomaly detection. Challenges arisen from imbalanced dataset gives rise to the development of our novel hybrid classification method. A novel hybrid classification method for anomaly detection in injection moulding is proposed in this paper. The proposed method involves feature selection with K-Means Clustering to remove noise data prior to Multi-Layer Perceptron. Feature selection helps to reduce the high-dimension of the dataset. K-Means Clustering helps to remove noise data that will have similar patterns with data of another class label. Multi-Layer Perceptron is used to verify the training accuracy and to build train model. Real industrial dataset of injection moulding process from a local manufacturing company is used to demonstrate the effectiveness of the method in this paper. The proposed hybrid method outperforms other classical models such as Multi-Layer Perceptron (MLP), Decision Tree (DT) and CNN (Convolutional Neural Network). It successfully increases the failure detect rate and reduces false reject rate up to 90%. © 2024 IEEE.

关键词： Smart manufacturing

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