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2nd International Conference on Computer, Communication and Control, IC4 2024

作者： Hasan, M D Ashfaqul Balasubadra, K. Vadivel, G. Arunfred, N. Ishwarya, M.V. Murugan, S. Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India R.M.D. Engineering College Kavaraipettai Department of Information Technology Tamil Nadu Chennai India University of Technology and Applied Science Department of Information Technology Dhofar Salalah Oman Srm Institute of Science and Technology Kattankulathur Faculty of Management Studies Tamil Nadu Chennai India Agni College of Technology Artificial Intelligence and Data Science Department Tamil Nadu Thazhambur India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

Microplastic pollution in water systems is growing, requiring novel detection and analysis methods. This research presents an Internet of Things (IoT)-driven image identification system using Convolutional Neural Networks (CNNs) to detect and quantify microplastics in water samples. The suggested method is more scalable and responsive due to IoT real-time data capture and remote monitoring of water infrastructure. An innovative CNN architecture for image processing allows the system to accurately identify micro plastics. The CNN model is trained and validated using a large dataset of micro plastic-containing water samples. The trained model can recognize various sizes, shapes, and colors of micro plastics, making it responsive to different environmental situations. The IoT architecture also allows image recognition modules in dispersed sensor nodes to cover water systems. Extensive studies prove the system can analyze vast amounts of image data quickly and reliably. Edge computing also minimizes latency and improves micro plastic analysis system responsiveness. The suggested IoT-driven image recognition method for continuous micro plastic pollution monitoring and evaluation in water systems seems promising. Scalability, realtime capabilities, and accuracy make it useful for environmental monitoring agencies and academics trying to reduce microplastics' influence on aquatic ecosystems. This system advances IoT applications in environmental and pollution management. © 2024 IEEE.

关键词： Large datasets

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Metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images

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Computers, Materials & Continua 2022年第11期73卷 4193-4210页

作者： Nagwan Abdel Samee El-Sayed M.El-Kenawy Ghada Atteia Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Noha E.El-Attar Tarek Gaber Adam Slowik Mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura 35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityEgypt School of Science Engineeringand EnvironmentUniversity of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailia41522Egypt Koszalin University of Technology Poland Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the *** of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals,a rapid,reliable,and automatic detection of COVID-19 is in extreme need to curb the number of *** analyzing the COVID-19 chest X-ray images,a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm *** lung region was segmented from the original chest X-ray images and augmented using various transformation ***,the augmented images were fed into the VGG19 deep network for feature *** the other hand,a feature selection method is proposed to select the most significant features that can boost the classification ***,the selected features were input into an optimized neural network for *** neural network is optimized using the proposed hybrid *** experimental results showed that the proposed method achieved 99.88%accuracy,outperforming the existing COVID-19 detection *** addition,a deep statistical analysis is performed to study the performance and stability of the proposed *** results confirm the effectiveness and superiority of the proposed approach.

关键词： Covid-19 feature selection dipper throated optimization particle swarm optimization deep learning

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The TAO of Blockchain intelligence for Intelligent Web 3.0

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IEEE/CAA Journal of Automatica Sinica 2023年第12期10卷 2183-2186页

作者： Juanjuan Li Fei-Yue Wang IEEE the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China

WE are in an exciting new intelligent era where various Web 3.0 systems emerge and flourish.[1]–[3].In this new epoch,the collaboration of data and knowledge,humans and machines,actual and virtual worlds is undergoing an unprecedented diversification and community-driven transformation,unveiling an open future full of boundless ***,the value of dispersed data extends far beyond passive storage and application.

关键词： Web TAO knowledge

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Research on CMUTs Design with Different Cell Arrangements

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Recent Patents on engineering 2024年第1期18卷 110-118页

作者： Cao, Xuewen Yu, Yuanyu Liu, Xin Ma, Jingyi Zhang, Hua Wang, Lijia Intelligent Manufacturing Institute Hebei Vocational University of Industry and Technology Shi Jiazhuang050091 China School of Artificial Intelligence Neijiang Normal University Neijiang641100 China State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau Macau 999078 China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau 999078 China

Background: Capacitive micromachined ultrasonic transducers (CMUTs) is a promising component of mechanical-electrical-acoustical conversion, which shows valuable applications in non-distructive testing and obstacle detection fields. In order to study the influence of dimension parameters on the directivity of CMUTs, the three-dimensional multi-physics coupling model is proposed for CMUTs in this paper. Methods: The model combines multiple physical fields through finite element method (FEM), which is more efficient and accurate. The influence of dimension parameters on the transducer is studied from two aspects: the pitch between adjacent cells and cell number, respectively. Results: When increasing the pitch, the directivity becomes better if the pitch is less than half of the wavelength, the grating lobe and side lobe are aroused. However, the bandwidth at-3dB is reduced by up to 71.1% at the same time. When increasing the cell number, the better the direcitvity, while the change of the cell number has little effect on the bandwidth of the array. However, 5.4% frequency shift was caused due to the effective mass change of the transducer. Conclusion: In this study, a 3D model based on FEM is proposed for the influence of dimension parameter on CMUTs. The simulation results indicate that the directivity can be enhanced by chang-ing the dimension. At the same time, it also introduces other performance issues, such as bandwidth decrease and frequency shift through this approach. Therefore, the simulation proposed the guidance of CMUTs performance optimization and patented technology application work in the future. © 2024, Bentham science Publishers. All rights reserved.

关键词： Finite element method

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Deep Learning Approaches for Pedestrian and Cyclist Detection in Mixed Traffic Environments 2

Deep Learning Approaches for Pedestrian and Cyclist Detectio...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Revathy, G. Shyamala, V. Lekashri, S. Mariyammal, M. Srinivasa Ramanujan Centre SASTRA Deemed University Department of CSE Thanjavur India C.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Cuddalore India Kings Engineering College Department of ECE Chennai India Chennai Institute of Technology Department of CSE Chennai India

ISBN: (纸本)9798331534950

One of the biggest challenges in contemporary metropolitan areas is making sure that bikes and pedestrians are safe in mixed-traffic situations. In a variety of changing traffic situations where non-motorized road users are at a significant danger, traditional detection technologies frequently exhibit poor accuracy. For precise pedestrian and bicycle monitoring and detection in mixed traffic situations, this study suggests a strong deep learning-based method. Our model successfully differentiates between bikes, pedestrians, and other kinds of vehicles by utilizing a convolutional neural network (CNN) architecture combined with region proposal networks (RPNs). Furthermore, to improve the model's resilience to occlusions and changing lighting, we use data augmentation approaches. Finally, we use transfer learning to adjust our model to various urban environments. Our approach outperforms conventional detection systems in terms of precision and recall rates, as demonstrated by extensive testing on real-world datasets. By increasing situational awareness and boosting safety for vulnerable road users, our method promises to make a substantial contribution to autonomous vehicles and traffic control systems. © 2024 IEEE.

关键词： Bicycles

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A Decision Support Framework for Sustainable Waste Disposal technology Selection

A Decision Support Framework for Sustainable Waste Disposal ...

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Lakkireddy, Venkateswara Reddy Mohana, R. Madana Ganesh, B. Rama Reddy, Lakkireddy Udanth Gochhait, Saikat Chogle, Shrish Hyderabad India Chaitanya Bharathi Institute of Technology Department of Artificial Intelligence and Data Science Hyderabad India Sri Venkatesa Perumal College of Engineering and Technology Department of Computer Science and Engineering Tirupathi India Ap University Dept. of Electronics and Communication Engineering Vit Amaravathi India Symbiosis Institute of Digital and Telecom Management Symbiosis International Deemed University India

ISBN: (纸本)9798350372229

Waste Disposal technology (WDT) selection is a primary issue in Municipal Solid Waste (MSW) that affects the development of the environmental and economic perspectives/aspects, particularly in developing countries. The selection of appropriate WDT is a complex Multi-Attribute Decision-Making (MADM) problem with both qualitative and quantitative elements. The existent MADM approaches with fuzzy sets (removal of uncertainty), different subjective weight methods (significance of attributes), and rank reversal phenomenon leads to improper selection of WDT due to the involvement of different opinions of decision-makers. To avoid this, a Decision Support Framework (DSF) was proposed for optimal WDT selection for the growth of economic and environmental development. The proposed DSF integrates Preference Selection Index (PSI) and a Modified-Comprehensive distance Based Ranking (M-COBRA) approaches to determine the significance of attributes and ranking the alternatives, respectively. The DSF is illustrated using a case study collected from Iran and compared with state-of-the-art MADM approaches. Further, the DSF is validated in terms of sensitivity analysis, rank reversal phenomenon, and Pearson's rank correlation coefficient to ensure the stability of ranking. © 2024 IEEE.

关键词： multi-attribute decision-making Municipal solid waste preference selection index rank reversal phenomenon sensitivity analysis waste disposal technology

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Triple-negative Breast Cancer Classification Algorithm based on High-dimensional Big data of Breast Ultrasound Radiomics

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Journal of engineering science and technology Review 2023年第1期16卷 85-91页

作者： Liu, Wen Yao, Hui Leng, Xiaoling Xue, Yutong Ma, Binlin College of Computer Science and Technology Xinjiang Normal University Urumqi830054 China Artificial Intelligence and Smart Mine Engineering Technology Center Xinjiang Institute of Engineering Urumqi830023 China Xinjiang Changsen Data Technology Co. Ltd Urumqi830011 China Department of Ultrasound The Affiliated Tumor Hospital of Xinjiang Medical University Urumqi830011 China RestonVA20191 United States

In computer-aided diagnosis, breast cancer classification accuracy of artificial intelligence (AI) method might be influenced by imbalanced classification samples. Model training efficiency also decreases with the increase in data size. To decrease the influences of sample classification imbalance and increase data size on breast cancer classification efficiency, this study proposed a triple-negative breast cancer classification algorithm (TNBCC) based on similarity query. Similar breast cancer feature data could be stored in a similar region by establishing the TNBCC index. A pruning algorithm was designed based on the TNBCC index, which decreases similarity measurement with nodes of the index tree significantly during data input and query. Users could find data similar to the query object quickly and accurately through the TNBCC index using pruning algorithm, and they could obtain the molecular subtype of the query object according to the searched data. Results demonstrate that, on the real dataset provided by the Affiliated Tumor Hospital of Xinjiang Medical University, the TNBCC algorithm decreases the influences of sample classification imbalance on accuracy effectively compared with the existing AI algorithm, and the classification accuracy reaches 91%. Given the mass high-dimensional data, it takes 49 s to build the TNBCC index tree, with an efficiency higher than that of the existing AI algorithm. This study provides a good reference to improve the performances of breast cancer classification algorithm. © 2023 School of science, IHU. All rights reserved.

关键词： Computer aided diagnosis

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An Al Powered Exam Proctoring: Comprehensive Monitoring for Integrity

An Al Powered Exam Proctoring: Comprehensive Monitoring for ...

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Knowledge engineering and Communication Systems (ICKECS), International Conference on

作者： Dhruv Mewada Shlok Gaikwad Bhavika Gharat Priyanshu Kamble Dept. of Artificial Intelligence&Data science Vidyavardhini’s College of Engineering and Technology Mumbai India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

In recent times, remote learning has experienced significant growth, yet the challenge of conducting academic examinations in a secure manner persists. Various approaches have been adopted by universities, such as collecting assignments susceptible to plagiarism or implementing remote proctoring with manual supervision. As our current lifestyle appears to be transitioning towards a new normal, it becomes imperative to explore effective solutions. This paper introduces a proposed remedy: the development of an AI-based integrated system designed to prevent cheating during examinations. The system not only detects fraudulent activities but also records evidence, ensuring both security and cost-effectiveness.

关键词： Knowledge engineering Technological innovation Distance learning Face recognition Plagiarism Speech recognition Manuals

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Application of Machine Learning Algorithms for Analyzing Sentiments Using Twitter dataset

Application of Machine Learning Algorithms for Analyzing Sen...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： Bhairavi Rewatkar Aditya Barhate Prateek Verma Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Sentiment analysis is a Natural Language Processing (NLP) approach used to determine whether a piece of text is negative, positive, or neutral based on the emotions it expresses. Despite advancements, several challenges persist in this field, including handling sarcasm, context ambiguity, and the presence of noisy data. Recent techniques, such as deep learning models and transformer-based approaches, have improved accuracy but still face limitations in computational efficiency and real-time analysis. This article employs Machine Learning (ML) techniques to analyze text and determine the sentiments of reviews shared on Twitter, aiming to address these challenges. The proposed system extracts attributes from the text, such as the sentiment polarity (whether the sentiment is negative or positive) and the topic or theme of the discussion. A comparison with prior work on sentiment analysis in tweets is presented, along with a comprehensive study of feature extraction and representation methods. The study evaluates the performance of six classifiers: Naïve Bayes, Decision Tree, Logistic Regression, Support Vector Machine (SVM), Gradient Boosting, and Random Forest, based on different feature extraction techniques. Using the Bag of Words technique, SVM achieved the highest accuracy of 94.58%, while using term frequency, Random Forest showed the highest accuracy of 94.99%. The developed algorithms for sentiment analysis in tweets are useful for analyzing social feedback, product surveys, and social media comments. Combining neural networks and deep learning improves the accuracy of sentiment analysis and provides opportunities to analyze various types of emotions, enabling proactive responses to public sentiment.

关键词： Support vector machines Deep learning Sentiment analysis Accuracy Machine learning algorithms Social networking (online) Reviews

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A Comprehensive Review of Supervised Learning Algorithms in Healthcare Applications

A Comprehensive Review of Supervised Learning Algorithms in ...

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artificial intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Aditya Barhate Abhay Tale Nayan Jikar Prateek Verma Praveen Kumar Prajyot Yesankar Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

Supervised learning has revolutionized the concept of personalization in treatment with the development of Precision Medicine. This review aims to provide a systematic analysis of the utilization of supervised learning in healthcare and its functions in diagnostics and prognoses, recommendations for treatment, and patient surveillance. Classification algorithms such as Logistic Regression, Support Vector Machines, and Decision Trees. Regression techniques such as Linear Regression, Polynomial Regression, and Neural Networks. Ensemble methods such as Bagging, Boosting, and Stacking. Deep learning techniques such as Convolutional Neural Networks, Recurrent Neural Networks, and Long Short-Term Memory are discussed for their efficiency and applicability in various healthcare applications. There are strengths highlighted by the review including enhanced diagnostic capabilities and patient-targeted therapy alongside weaknesses such as quality of data, interpretability as well as compatibility with existing healthcare systems. This review gives a more generalized approach to how supervised learning algorithms help to enhance healthcare results and identifies the likely topic that requires more research in the future.

关键词： Industries Deep learning Ethics Systematics Reviews Precision medicine Supervised learning Medical services Classification algorithms Tuning

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