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

作者： Pahuja, Saurav Ivucic, Gabriel Putze, Felix Cai, Siqi Li, Haizhou Schultz, Tanja University of Bremen Germany The Department of Electrical and Computer Engineering National University of Singapore Singapore University of Bremen Germany Csl and Hosts Mll The Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen China Kriston Ai Xiamen China

ISBN: (纸本)9798350337020

Electroencephalography (EEG) related research faces a significant challenge of subject independence due to the variation in brain signals and responses among individuals. While deep learning models hold promise in addressing this challenge, their effectiveness depends on large datasets for training and generalization across participants. To overcome this limitation, we propose a solution to the above limitation by increasing the size and quality of training data for subject-independent auditory attention decoding (AAD) using EEG with deep learning. Specifically, our method employs a Wasserstein Generative Adversarial Network (WGAN) to generate synthetic data, with Pearson correlation filtering the most realistic samples. We evaluated this method on a publicly available dataset of selective auditory attention experiments and showed superior performance in subject-independent AAD performance. The mixed training set, consisting of both real and artificial data generated by the WGAN+Pearson Correlation Coefficient, demonstrated approximately 4% improvement in AAD accuracy for a 1-second window. These results demonstrate that deep learning remains a viable approach to overcoming data scarcity in subject-independent AAD tasks based on EEG. Moreover, the proposed method has the potential to improve the generalization and reliability of EEG classification tasks. © 2023 IEEE.

关键词： Auditory attention decoding data scarcity EEG Generative adversarial network Pearson Correlation Coefficient

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Enhancing Breast Cancer Diagnosis with Vision Transformers: A Novel Approach using MaskGuided Attention for Ultrasound Image Classification

Enhancing Breast Cancer Diagnosis with Vision Transformers: ...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Deena Sivakumar Anamika Kumari Divya Dharshini K Samiksha S Hemachandran M Department of Computer Science and Engineering Sri Ramachandra Institute of Higher Education and Research Chennai Department of Computer Science (AI & Data Analytics) Sri Ramachandra Institute of Higher Education and Research Chennai

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Deep learning models, notably convolutional neural networks (CNNs), demonstrate great promise in medical image processing. Nonetheless, CNNs frequently encounter challenges in capture holistic context and long- range dependencies in high-resolution images, which are crucial for accurate medical diagnostics. A new state-of-the-art technique called Vision Transformers (ViTs) uses self-attention mechanisms for gathering global correlations between image patches, thereby addressing some of the inherent limitations of CNNs. This study investigates the application of ViTs for diagnosing breast cancer from ultrasound images of tumor. The findings suggest that ViTs not only achieve competitive accuracy in identifying breast cancer lesions but also offer superior interpretability through attention mechanism, which can aid clinicians in understanding model decisions. This paper highlights the potential of ViTs as a powerful tool in medical imaging, particularly for breast cancer diagnosis, and outlines future research directions for enhancing clinical applicability.

关键词： Deep learning computer vision Accuracy Attention mechanisms Ultrasonic imaging Transformers Breast cancer Convolutional neural networks Medical diagnosis Biomedical imaging

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A Hybrid Marine Predator Algorithm for Thermal-aware Routing Scheme in Wireless Body Area Networks

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Journal of Bionic engineering 2023年第1期20卷 81-104页

作者： Tao Hai Jincheng Zhou Mohammad Masdari Haydar Abdulameer Marhoon School of Computer and Information Qiannan Normal University for NationalitiesDuyun558000GuizhouChina Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun558000GuizhouChina Institute for Big Data Analytics and Artificial Intelligence(IBDAAI) Universiti Teknologi MARA40450Shah AlamSelangorMalaysia Department of Computer Engineering Islamic Azad UniversityUrmia BranchUrmiaIran Information and Communication Technology Research Group Scientific Research CenterAl-Ayen UniversityThi-QarIraq College of Computer Sciences and Information Technology University of KerbalaKarbalaIraq

Thermal-aware routing protocols in WBANs consider temperature factors in the routing process for preventing overheating of the tissues surrounding the sensor ***,providing an energy-efficient and thermal-aware routing in WBANs is a challenging *** deal with this problem,this article presents a novel temperature-aware routing protocol that applies Mamdani-based Fuzzy Logic Controllers(FLCs)for selecting the next forwarding node in routing data *** FLCs apply five important input factors such as the priority of the packet,and sensor node's remaining energy,temperature,distance,and link path ***,a new hybrid version of the Marine Predator Algorithm(MPA),named MPAOA is presented by combining the exploration and exploitation phases of the MPA and Arithmetic Optimization Algorithm(AOA).This algorithm is effectively applied for selecting the best possible set of fuzzy rules for FLCs and tuning their fuzzy *** experiments conducted in the Castalia simulator exhibit that the proposed temperature and priority-aware routing scheme can outperform other well-known routing schemes such as LATOR,TTRP,TAEO,ATAR,and EOCC-TARA in terms of metrics such as sensor nodes lifetime,the average temperature of the sensor nodes,and the percentage of the packets routed through non-overheated ***,it is shown that the MPAOA outperforms other algorithms such as Bat Algorithm(BA),Genetic Algorithm(GA),AOA,and MPA regarding the specified metrics.

关键词： WBANs Multi-hop routing Temperature Priority Fuzzy logic Bio-inspiration

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A Review and Bibliometric Analysis of the Current Studies for the 6G Networks

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computer Modeling in engineering & Sciences 2024年第9期140卷 2165-2206页

作者： Qusay M.Salih Md.Arafatur Rahman Ahmad Firdaus Mohammed Rajih Jassim Hasan Kahtan Jasni Mohamad Zain Ahmed Hussein Ali Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah(UMPSA)PekanPahang26600Malaysia Department of Medical Equipment Technology Engineering Al Salam University CollegeBaghdadIraq School of Mathematics and Computer Science University of WolverhamptonWolverhampton01902UK Department of Production Engineering and Metallurgy University of TechnologyBaghdadIraq Cardiff School of Technologies Cardiff Metropolitan UniversityCardiffCF52YBUK Institute for Big Data Analytics and Artificial Intelligence(IBDAAI) Kompleks Al-KhawarizmiUniversiti Teknologi MARAShah AlamSelangor40450Malaysia Department Computer Science Al-Salam University CollegeBaghdadIraq

The race to develop the next generation of wireless networks,known as Sixth Generation(6G)wireless,which will be operational in 2030,has already *** realize its full potential over the next decade,6G will undoubtedly necessitate additional improvements that integrate existing solutions with cutting-edge ***,the studies about 6G are mainly limited and scattered,whereas no bibliometric study covers the 6G ***,this study aims to review,examine,and summarize existing studies and research activities in *** study has examined the Scopus database through a bibliometric analysis of more than 1,000 papers published between 2017 and ***,we applied the bibliometric analysis methods by including(1)document type,(2)subject area,(3)author,and(4)country of *** study’s results reflect the research 6G community’s trends,highlight important research challenges,and elucidate potential directions for future research in this interesting area.

关键词： 6G bibliometric wireless networks review

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Prediction of Compressive Strength of Green Concrete by Artificial Neural Network 1st

Prediction of Compressive Strength of Green Concrete by Arti...

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1st International Conference on Advanced Communication and Intelligent Systems, ICACIS 2022

作者： Verma, Manvendra Upreti, Kamal Dadhich, Priyanka Ghosh, Soumi Khatri, Vishal Singh, Prashant Department of Civil Engineering GLA University Uttar Pradesh Mathura India Department of Computer Science and Engineering Akhilesh Das Gupta Institute of Technology and Management Delhi India Department of Big Data Analytics Asia Pacific Institute of Management Delhi India Department of Information Technology Maharaja Agrasen Institute of Technology Delhi India Department of Computer Science and Engineering Bhagwan Parshuram Institute of Technology Delhi India Department of Computer Science and Engineering Sunder Deep Engineering College Ghaziabad India

ISBN: (纸本)9783031250873

The concrete business has just come out with a brand-new product called geopolymer concrete. Because of its resilience in harsh environments and its high strength, it has the potential to revolutionise the whole construction industry. It is an excellent substitute for the traditional kind of concrete. It outperforms traditional concrete in terms of sustainability, ecology, durability, and economic viability. In this day and age, the use of machine learning strategies is not just the future of the research and development industry, but of all industries. These methods provide predictions about the outcomes by analysing the facts from the past. The process of determining the outcomes or worth of anything in the construction sector is very challenging, time consuming, and arduous. Without having to conduct destructive experiments or take samples, the strength of a mix design may now be more accurately predicted thanks to these procedures. By using deep learning and the random forest method, the purpose of this research is to predict the compressive strength of flyash-based geopolymer concrete. This will be accomplished by comparing the two algorithms with varying degrees of error and coefficient correlation. It has been shown via the use of simulated data that the random forest algorithm is the method that is most suited for making predictions about compressive strength. After the developing a model, the various errors were found for accuracy. The mean absolute error, root mean square error, relative absolute error, and root relative squared error are 3.46%, 5.94%, 64.32%, and 82.97%, respectively for the deep learning predicted compressive strength. The errors provide the proof of model accuracy to predict the compressive strength on the basis of ingredients proportions. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Compressive strength

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Fast Moving Consumer Goods Object Detection Using YOLOV8

Fast Moving Consumer Goods Object Detection Using YOLOV8

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Electronic Systems and Intelligent Computing (ICESIC), International Conference on

作者： A. Umamageswari Mukund. P.U Abhishek. V.B Y. Devi Vaishnavi Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering With Specialization in Big Data Analytics SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522995

The basis of this project is to investigate whether the YOLO, an object detection algorithm where 'You Only Look Once' constitutes the name, could be applied to develop FMCG management; together with the management of FMCG, it becomes the gateway to increasing retail precision, efficiency, and agility. Under real-time processing, the system detects and counts FMCG products on retail shelves with an accuracy rate of 92.5%. This accuracy ensures that the optimum stock levels are maintained and there will be no discrepancies. The system processes video feeds at 0.08 seconds per frame, thus allowing inventory data to be updated in real-time to enable quick, data-driven decisions. YOLO's flexibility ensures consistent performance across different store layouts and product types. This system integrates with other retail management tools and allows integrating inventory data in the real time. This means that it would be made available with invaluable insights which would prevent stock depletion while helping to curtail costs. This project takes YOLO ahead of just being a game changer in the FMCG inventory management while operating to reduce expenses and customer satisfaction due to product availability.

关键词： YOLO Training Accuracy Scalability Refining Streaming media Logic gates Real-time systems Mobile handsets Monitoring

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Customized Movie Recommendation for Enhanced Entertainment

Customized Movie Recommendation for Enhanced Entertainment

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Cybernation and Computation (CYBERCOM), International Conference on

作者： M.S. Minu Srikar Talapalli Saahithi Chevuri Medhasri T.S. Department of Computer Science Engineering SRM Institute of Science And Technology Ramapuram Chennai India Department of Computer Science Engineering with Specialization in Big Data Analytics SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

The surging popularity of online movie databases has created a challenge for viewers: choosing a film from a massive library can be overwhelming. In this paper, it proposes to design a new hybrid movie recommendation powered by machine learning techniques as a solution to improve user experience. Among those techniques, two are combined into this paper: content-based filtering, using the real features of a movie such as genre and a director, and collaborative filtering, which uses the rating provided by users. This hybrid approach thus improves the intrinsic weaknesses that reside in both these methods individually. On the other hand, content-based filtering tends to reduce exploration to movies of the exact type one is doing well at, whereas, inversely, collaborative filtering settings often mean that a user will be exposed only to films within his or her already-recognized taste profile. It is expected that the proposed hybrid system will solve both problems by balancing this line between exploring familiar cinematic territory and making new recommendations based on both user preferences and the entire collective intelligence of the user base. The approach would aim to enrich user satisfaction by facilitating the selection of movies and providing a richer and more diversified movie-viewing experience.

关键词： Hands Filtering Collaborative filtering Transforms Machine learning Motion pictures User experience Libraries Hamming distances Recommender systems

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Intelligent E-Commerce Chatbot for Personalized Product Recommendations Using Text Mining and Machine Learning

Intelligent E-Commerce Chatbot for Personalized Product Reco...

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Advances in Computation, Communication and Information Technology (ICaiCCIT), International Conference on

作者： M.S. Minu Adithya Kavya SreeRanjini Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering with Specialization in Big Data Analytics SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

Innovative technology solutions have been developed in response to the growing need for effective and customized client contact on e-commerce platforms. This work introduces an intelligent chatbot system that uses machine learning and text mining techniques to improve user satisfaction and answer customer concerns. Advanced Natural Language Processing (NLP) models have been trained on large datasets, allowing for precise user intent interpretation and pertinent product recommendations. A strong recommendation engine is included into the system architecture to aid in decision-making, ultimately enhancing the customer experience by providing individualized, real-time help. The study also highlights the significance of data privacy by using privacy-preserving techniques to safeguard user data. The system’s scalability and performance benefits are highlighted through thorough comparisons with current models. By showcasing the useful uses of machine learning and text mining in enhancing customer contact and service delivery, this study makes a substantial contribution to the development of ai-driven solutions in e-commerce.

关键词： Text mining Machine learning algorithms Scalability Systems architecture Machine learning Chatbots User experience Real-time systems Electronic commerce Time factors

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Machine Learning Model for Sentimental Analysis of Amazon Reviews

Machine Learning Model for Sentimental Analysis of Amazon Re...

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Electronic Systems and Intelligent Computing (ICESIC), International Conference on

作者： A. Umamageswari R.J. Pratishwaran M. Poojitha Reddy R. Yuvan Sankar Raj Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering With Specialization in Big Data Analytics SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522995

The goal of this project is to draw a deeper understanding of the subjective nature behind online product reviews, largely by examining a large dataset received from Amazon that contains numerous star ratings and comments. Since there is such a massive amount of data on the Amazon platform, it was perfect for the analysis. Our methodology is platform-agnostic and can be easily extended to other comparable datasets from other web platforms with comparable guarantees in achieving wide applicability. Our primary motivation is improving the dependability of online shopping. Since product reviews are currently widely used by many consumers as guides in making decisions to buy products, such reviews ought to be fair representations of the actual dependability and quality of the products. The goal of our effort is to identify patterns in the language and ratings of such reviews that would directly reflect the real customer sentiment and also the reliability of the product. Our approach has yielded already a rate of about 92%, which shows it has the potential greatly to raise the legitimacy of online reviews. We'd like to make our customers feel more confident and better equipped with information to make better and safer purchases on a website like Amazon and others. In the final analysis, we strive to make online shopping for our customers worldwide safer and more trustworthy.

关键词： Analytical models Sentiment analysis Reviews Stacking Stars Predictive models Boosting Ensemble learning Electronic commerce Random forests

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Towards Simulation-Based Circular Product Design

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Procedia CIRP 2023年 116卷 690-695页

作者： Gaurav Aher Jalil Boudjadar Devarajan Ramanujan Department of Mechanical & Production Engineering Aarhus University Aarhus 8200 Denmark Department of Electrical & Computer Engineering Aarhus University Aarhus 8200 Denmark Centre for Digitalisation Big Data and Data Analytics Aarhus University Aarhus 8200 Denmark

Assessing the circular economy (CE) performance of products has become an essential consideration in product design processes. While a growing body of literature is exploring the use of CE indicators to guide design processes, they typically assume broad design changes (e.g., material changes, end-of-life treatment) that may not accurately capture trade-offs in CE indicators, technical performance, and design feasibility. This paper aims to address the above gaps with the goal of incorporating CE assessment into simulation-based design. To this end, this paper defines a novel framework for using simulation-based design to analyze the effects of parametric changes in product design attributes on CE performance. First, the proposed framework describes the lifecycle information needed for computing resource-based, product-level CE indicators at the design stage. Depending on the set of selected CE indicators, corresponding simulation models are used to compute the effects of changing design parameters, and setting design/performance constraints, on the product's CE performance. Thus, the proposed framework enables the selection and optimization of product design attributes for achieving a specific level of CE performance, given specific technical performance requirements and other design constraints. The application of this framework is demonstrated using a case study conducted on a flywheel.

关键词： Circular economy Simulation engineering Design

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