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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Kumaresh, P.S. Sivanantham, E. Shoba, L.K. Priya, W. Deva Mohankumar, N. Elango, B. K.L.N.College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Sivagangai India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chenagalpattu Chennai Tamil Nadu Kattankulathur India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Symbiosis Institute of Technology Maharashtra Nagpur India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

This research introduces unique techniques for improving preventative servicing of heavy equipment using a real-time leak-detecting system for hydraulic fluid. The technology employs a network of sensors across the gear to monitor the hydraulics in real time. These wireless sensors communicate data to a cloud-based platform, where cloud computing analytics are used for real-time analysis. The cloud-integrated sensor network quickly detects hydraulic fluid leakage to address the critical demand for quick maintenance interventions. Preventing possible breakdowns and avoiding downtime are two areas where real-time skills shine. The cloud-based design enables remote monitoring and administration, which is essential for a preventative maintenance approach. Multiple pieces of heavy equipment were used in virtual and real-world tests to evaluate the proposed solution. The results reveal that the system is useful for real-time detection of hydraulic fluid leaks, which can potentially increase heavy equipment's overall dependability and performance. The system is a viable option for a wide range of industrial uses due to the incorporation of cloud computing, which guarantees scalability, flexibility, and accessibility. © 2024 IEEE.

关键词： Wireless sensor networks

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Diverse Code Query Learning for Speech-Driven Facial Animation

arXiv

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arXiv 2024年

作者： Gu, Chunzhi Kuriyama, Shigeru Hotta, Katsuya The Department of Computer Science and Engineering Toyohashi University of Technology Toyohashi Japan The Faculty of Science and Engineering Iwate University Morioka Japan

Speech-driven facial animation aims to synthesize lip-synchronized 3D talking faces following the given speech signal. Prior methods to this task mostly focus on pursuing realism with deterministic systems, yet characterizing the potentially stochastic nature of facial motions has been to date rarely studied. While generative modeling approaches can easily handle the one-to-many mapping by repeatedly drawing samples, ensuring a diverse mode coverage of plausible facial motions on small-scale datasets remains challenging and less explored. In this paper, we propose predicting multiple samples conditioned on the same audio signal and then explicitly encouraging sample diversity to address diverse facial animation synthesis. Our core insight is to guide our model to explore the expressive facial latent space with a diversity-promoting loss such that the desired latent codes for diversification can be ideally identified. To this end, building upon the rich facial prior learned with vector-quantized variational auto-encoding mechanism, our model temporally queries multiple stochastic codes which can be flexibly decoded into a diverse yet plausible set of speech-faithful facial motions. To further allow for control over different facial parts during generation, the proposed model is designed to predict different facial portions of interest in a sequential manner, and compose them to eventually form full-face motions. Our paradigm realizes both diverse and controllable facial animation synthesis in a unified formulation. We experimentally demonstrate that our method yields state-of-the-art performance both quantitatively and qualitatively, especially regarding sample diversity. © 2024, CC BY-NC-ND.

关键词： Stochastic systems

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IoT and Cloud-based Solutions for Energy-Efficient Restroom Lighting Systems 4

IoT and Cloud-based Solutions for Energy-Efficient Restroom ...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Manthena, Kishore Varma Jothi, M. Visumathi, J. Mohankumar, N. Maheshwari, S. Sujatha, S. Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India School of Electrical and Electronics Engineering Srm Institute of Science and Technology SRM Nagar Tamil Nadu Trichy India Veltech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Information Technology Tamil Nadu Chennai India Nagpur Campus Maharashtra Pune India Sri Sai Ranganathan Engineering College Department of Information Technology Tamil Nadu Coimbatore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331540364

This research study introduces a new way to improve the energy efficiency of restroom lighting systems by combining cloud-based solutions with Internet of Things (IoT) technologies. Traditional toilet lighting systems waste much energy since flexible or optimized. Our suggested solution is to use IoT devices like ambient light sensors and motion detectors to regulate lighting in response to occupancy and daylight smartly. In real-time, advanced techniques examine trends in the data sent by these sensors and optimize the lighting schedule on a platform in the cloud. Our technology optimizes energy usage while simultaneously improving user comfort and convenience via the dynamic adjustment of lighting levels and the automatic switching of lights in unoccupied areas. Using simulations and real-world trials shows that our method is feasible and successful, with particular emphasis on improving sustainability in restrooms while significantly reducing energy use. Its findings highlight the promise of IoT and cloud computing in promoting sustainability in various settings and developing more energy-efficient building management systems. The challenges include providing continuous connection, maintaining sensor accuracy, controlling data security, seamlessly integrating technologies, and providing various user needs in diverse environments. © 2024 IEEE.

关键词： Cloud platforms

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Grasshopper Optimization Algorithm Based Spam Detection System Using Multi-Objective Wrapper Feature Selection and Neural Network Classification 2nd

Grasshopper Optimization Algorithm Based Spam Detection Syst...

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2nd International Conference on Emerging Technologies and Intelligent Systems, ICETIS 2022

作者： Ghaleb, Sanaa A. A. Mohamad, Mumtazimah Ghanem, Waheed A. H. M. Abdullahi, Akibu Mahmoud Nasser, Abdullah B. Saleh, Sami Abdulla Mohsen Arshad, Humaira Omolara, Abiodun Esther Abiodun, Oludare Isaac Ghetas, Mohamed Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Terengganu Malaysia Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Malaysia Faculty of Education Saber University of Lahej Lahej Yemen Faculty of Engineering University of Aden Aden Yemen Faculty of Education Aden University of Aden Aden Yemen Faculty of Computing and Informatics Albukhary International University Kedah Malaysia School of Technology and Innovation University of Vaasa Vaasa Finland School of Electrical and Electronic Engineering Universiti Sains Malaysia Nilai Malaysia Department of Computer Science Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science University of Abuja Abuja Nigeria Department of Computer Science Bingham University Nasarawa Nigeria Faculty of Computer Science Nahda University New Beni Seuf City Egypt

ISBN: (纸本)9783031252730

Email classification is essential to the trouble of email and pattern recognition. Nowadays, a number of unsolicited messages are circulated over the internet. While plenty of machine learning techniques are a success in detecting textual primarily based on totally unsolicited mail, this isn’t the case for messages spams, which can without difficulty avoid those textual-unsolicited mail detection systems. This paper proposes the introduction of MOBGOA constructed on the binary version of the GOA algorithm, for multi-objective selection of features purposes and the wrapper approach of selection of features and purpose of the EGOAMLP algorithm utilized this algorithm as the wrapper classifier. Spam assassin dataset was used to validate the performance of the MOB-EGOAMLP. The result uncovered that the new technique outflanks the wide selection of different methods in previously published works. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Feature Selection

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A Distributed Data Mining and Cloud Analysis for Predictive Gas Level, Dynamic Booking, and Smart Energy Optimization

A Distributed Data Mining and Cloud Analysis for Predictive ...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Sugumar, D. Ramadevi, R. Prasad, J. Mohankumar, N. Joy Winnie Wise, D.C. Velmurugan, S. Care College Of Arts And Science Department Of Commerce Tamil Nadu Trichy India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Saveetha Nagar Department Of Biomedical Engineering Tamil Nadu Chennai India Kpr Institute Of Engineering And Technology Department Of Electronics And Communication Engineering Tamil Nadu Coimbatore India B.V. Raju Institute Of Technology Department Of Electronics And Communication Engineering Telangana India Rajalakshmi Institute Of Technology Department Of Computer Science And Engineering Tamil Nadu Chennai India T.J.S. Engineering College Department Of Electronics And Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350364828

The state-of-the-art Internet of Things (IoT) innovation known as the Autonomous Gas Reservoir Management System (AGaRMS) totally changes the most common way of overseeing gas supplies. AGaRMS uses sensors that monitor gas levels and a distributed architecture that forecasts patterns in gas use to ensure that gas refills occur at the right time. Complex AI calculations examine verifiable information, current weather patterns, and usage patterns to estimate when gas will run out. This, then, prompts robotized booking sales to be transported off the supplier. Likewise, AGaRMS appeals to the savvy progression of energy, which empowers strong gas usage. The framework's flexibility and versatility make it reasonable for use in private and business settings, further developing production network productivity, reducing working expenses, and improving the client experience. The AGaRMS system offers a smart method for managing gas leaders that work with gathering effective power energy practices and streamlining gas provisioning processes. AGaRMS optimizes the gas supply chain and improves the user experience by catering to a wide range of residential and industrial applications with its flexible and scalable design. © 2024 IEEE.

关键词： Internet of things

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Enhanced Body Composition Estimation from 3D Body Scans

Enhanced Body Composition Estimation from 3D Body Scans

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18th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Feng, Boyuan Zheng, Yijiang Cheng, Ruting Feng, Shuya Vaziri, Khashayar Hahn, James Department of Computer Science George Washington University WashingtonDC United States Department of Computer Science and Engineering University of Connecticut StorrsCT United States Department of Surgery The George Washington University Medical Faculty Associates WashingtonDC United States

Accurate body composition assessment is essential for evaluating health and diagnosing conditions like sar copenia and cardiovascular disease. Approaches for accurately measuring body composition, such as Dual Energy X-ray Absorptiometry (DXA) and Magnetic Resonance Imaging (MRI), are precise but costly and limited in accessibility. Some studies have explored predicting body composition by using shapes since 3D scanning techniques allow for precise and efficient digital measurements of body shape. This study introduces an enhanced method using 3D body scanning integrated with a part-to-global Multilayer Perceptron (MLP) network that incorporates predefined high-level features for body composition prediction. For lean mass es timation, our method achieved a root mean square error (RMSE) of 2.85 kg. For fat mass estimation, the RMSE was 2.50 kg, and for bone mineral content (BMC), the RMSE was 193.50 g. These results represent substantial improvements over existing methods, highlighting the effectiveness and reliability of our approach in accurately predicting body composition metrics. © 2025 by SCITEPRESS– science and technology Publications, Lda.

关键词： Diagnosis

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Molecular Design for Drug Discovery with the Use of Generative Adversarial Networks 5

Molecular Design for Drug Discovery with the Use of Generati...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Sumathi, G. Juliet, P. Sudha Banu, M. Shafiya Sujitha, S. Daisylin Anbu Yuvaraj, S. Sujatha, S. Department of Information Technology Tamil Nadu Trichy India Meenakshi College of Engineering Department of Electronics and Instrumentation Engineering Tamil Nadu Chennai India Rajalakshmi Engineering College Artificial Intelligence and Machine Learning Tamil Nadu Chennai India Sri Sairam Institute of Technology Department of Chemistry Tamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Kattankulathur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350379945

This research study describes how Generative Adversarial Networks (GANs) are used to improve drug discovery molecular design. The goal is to maximize the discovery of potential drug candidates by making use of GANs' ability to produce a wide variety of chemical structures. The goal is to use better molecular design methods to speed up and improve the medication development process. The purpose of using GANs is to make it easier to build new molecular structures with certain features, such more potency and selectivity. This technology has the potential to bypass the drawbacks of conventional drug discovery techniques and speed up the process of exploring chemical space. It demonstrates that GANs have the potential to revolutionize drug development through computational simulations and validation tests. One potential way to find new treatments and solve medical problems that have so far gone unsolved is to include GANs into molecular design processes. Results from Drug Design Data Resource, Kaggle datasets shows that the molecular formula with id Activity-Based Learning (ABL) minimum value starts with 0.055 and maximum of 10, from the sample of 10 compounds the minimum affinity value is 0.075 and maximum of 3.42. In another data surveyed the drug discovery values show the minimum of the molecular weight is 356.3 and maximum is 471.5. © 2024 IEEE.

关键词： Generative adversarial networks

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Accuracy Prediction using Machine Learning Techniques for Indian Patient Liver Disease

Accuracy Prediction using Machine Learning Techniques for In...

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Computational Intelligence and Sustainable engineering Solutions (CISES), International Conference on

作者： Shylaja DS Mekala Karunya Oliviya J Suriya Prakash J Computer Science Engineering Faculty of Engineering and Technology Jain University Bengaluru India

One of the principal cause of mortality in the countryis liver disease. Each year, liver disease kills more than 2.4 percent of all Indians. As a result,for the early intervention of liver disease, anautomated programme with higher accuracy and reliability is necessary. Specific machine learning models such as SVM, Logistic Regression, KNN, and ANN are being created for this purpose in order to foretell liver disease with more precision, accuracy, and reliability.

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Optimizing Atmospheric Water Generation Through Advanced Weather Prediction Using Solar Energy Data 9

Optimizing Atmospheric Water Generation Through Advanced Wea...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Dar, Eman Rana, Khadija Hussain, Muhammad Zunnurain Zulkifl Hasan, Muhammad Khalid, Aqsa Javaid, Arslan Altaf, Jawad Sattar, Basit Chuhan, Saad Hussain Atif Yaqub, Muhammad Qureshi, Ali Moiz Mubarak, Zaima Information Technology University Computer Engineering Department Lahore Pakistan Bahria University Lahore Campus Dept. of Computer Science Lahore Pakistan University of Central Punjab Faculty of Information Technology Lahore Pakistan National College of Business Adm. & Economics Department of Computer Science Lahore Pakistan National College of Ireland Nci Department of Computer Science Ireland Umt Department of Ai Lahore Pakistan

ISBN: (纸本)9798350394474

This research focuses on enhancing the efficiency of Atmospheric Water Generation (AWG) systems through the development of an accurate weather prediction model. Leveraging solar energy data, the project aims to forecast key weather parameters such as solar radiation, temperature, and humidity. The objective is to optimize the performance of the AWG device across diverse weather conditions. By integrating advanced weather prediction, the study endeavors to enable proactive adjustments in the AWG operation, ensuring optimal water production. This research contributes to the sustainable utilization of solar energy for atmospheric water generation, addressing water scarcity challenges with a forward-thinking and adaptive approach. © 2024 IEEE.

关键词： Solar energy

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Multi-Blockchain Based Data Trading Markets With Novel Pricing Mechanisms

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IEEE/CAA Journal of Automatica Sinica 2023年第12期10卷 2222-2232页

作者： Juanjuan Li Junqing Li Xiao Wang Rui Qin Yong Yuan Fei-Yue Wang IEEE the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590China the School of Artificial Intelligence Anhui UniversityHefei 266114 the Qingdao Academy of Intelligent Industries Qingdao 230031China the School of Mathematics Renmin University of ChinaBeijing 100872China the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 the Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China

In the era of big data,there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive *** security and data pricing,however,are still widely regarded as major challenges in this respect,which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing *** this context,data recording and trading are conducted separately within two separate blockchains:the data blockchain(DChain) and the value blockchain(VChain).This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary ***,pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and ***,in regular data trading on VChain-S2D,two auction models are employed according to the demand scale,for dealing with users’ strategic *** incentive-compatible Vickrey-Clarke-Groves(VCG)model is deployed to the low-demand trading scenario,while the nearly incentive-compatible monopolistic price(MP) model is utilized for the high-demand trading *** temporary data trading on VChain-D2S,a reverse auction mechanism namely two-stage obscure selection(TSOS) is designed to regulate both suppliers’ quoting and users’ valuation ***,experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.

关键词： Auction data trading markets multi-blockchain pricing mechanisms

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