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IEEE International Conference on Performance, computing and Communications (IPCCC)

作者： P.C. Sherimon Vinu Sherimon Yousuf Nasser Al Husaini Ambily Merlin Kuruvilla Ajit Mammen Varghese Harikrishnan D Faculty of Computer Studies Arab Open University Muscat Sultanate of Oman College of Computing and Information Sciences University of Technology and Applied Sciences Muscat Sultanate of Oman PG Department of Computer Applications and Artificial Intelligence Saintgits College of Applied Sciences Kerala India

Road accidents have increased with the increase in the vehicle population. There are several studies and research that focus on detecting road accidents. High-performance traffic accident detection systems are becoming ever more necessary because of the rise in traffic accidents. A vehicle accident detection system is developed in this study using the deep learning technique. A customized convolutional neural network is used for this study. The model uses pre-trained customized convolutional models for feature extractions from the images. In this research work a customized base model along with six custom hybrid models such as DenseNet-121, InceptionV3, Xception, MobileNetV2, VGG16 and ResNet-50 is used. The hybridized models use a standalone feature extractor module which uses transfer learning technique for feature extraction. The features from all the training images are extracted using the above six hybrid models. The selected features are then input to the customized base Deep Neural Network architecture. The model is tested using random Google images after being trained on a custom dataset. The performance of all the models were tested using the parameters Precision, Recall, Specificity, F1 Score along with Accuracy. It was found that the custom hybridized Resnet-50 showed the maximum performance compared with the other models in terms of accuracy.

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Emerging trends in hydrogen and synfuel generation: a state-of-the-art review

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Environmental Science and Pollution Research 2024年第30期31卷 42640-42671页

作者： Alhassan, Mansur Jalil, Aishah Abdul Owgi, Abdelrahman Hamad Khalifa Hamid, Muhamed Yusuf Shahul Bahari, Mahadi Bin Van Tran, Thuan Nabgan, Walid Hatta, Abdul Hakim Khusnun, Nur Farahain Binti Amusa, Abiodun Abdulhameed Nyakuma, Bemgba Bevan Faculty of Chemical and Energy Engineering Universiti Teknologi Malaysia Johor Skudai81310 Malaysia Centre of Hydrogen Energy Institute of Future Energy Universiti Teknologi Malaysia Johor Skudai81310 Malaysia Department of Chemistry Sokoto State University P. M. B 2134 Airport Road Sokoto Nigeria Faculty of Science Universiti Teknologi Malaysia Johor Skudai81310 Malaysia Institute of Applied Technology & Sustainable Development Nguyen Tat Thanh University 298-300A Nguyen Tat Thanh District 4 HCMC 755414 Viet Nam Departament d’Enginyeria Química Universitat Rovira i Virgili Av Països Catalans 26 Tarragona43007 Spain Department of Chemical Sciences Faculty of Science and Computing Pen Resource University P.M.B 0198 Gombe State Gombe Nigeria

The current work investigated emerging fields for generating and consuming hydrogen and synthetic Fischer-Tropsch (FT) fuels, especially from detrimental greenhouse gases, CO2 and CH4. Technologies for syngas generation ranging from partial oxidation, auto-thermal, dry, photothermal and wet or steam reforming of methane were adequately reviewed alongside biomass valorisation for hydrogen generation, water electrolysis and climate challenges due to methane flaring, production, storage, transportation, challenges and opportunities in CO2 and CH4 utilisation. Under the same conditions, dry reforming produces more coke than steam reforming. However, combining the two techniques produces syngas with a high H2/CO ratio, which is suitable for producing long-chain hydrocarbons. Although the steam methane reforming (SMR) process has been industrialised, it is well known to consume significant energy. However, coke production via catalytic methane decomposition, the prime hindrance to large-scale implementation of these techniques for hydrogen production, could be addressed by coupling CO with CO2 conversion to alter the H2/CO ratio of syngas, increasing the reaction temperatures in dry reforming, or increasing the steam content fed in steam reforming. Optimised hydrogen production and generation of green fuels from CO2 and CH4 can be achieved by implementing these strategies. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Steam reforming

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Multi-Criteria Decision-Making for Energy Management in Smart Homes Using Hybridized Neuro-Fuzzy Approach

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Distributed Generation and Alternative Energy Journal 2024年第1期39卷 83-109页

作者： Anbazhagu, U.V. Koti, Manjula Sanjay Muthukumaran, V. Geetha, V. Munrathnam, Meram Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur TN Chennai India Department of MCA Dayananda Sagar Academy of Technology and Management Bangalore560082 India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur Campus 603 203 India Department of Computer Science School of Applied Sciences REVA University Bangalore India Department of Mathematics Rajiv Gandhi University of Knowledge Technologies R.k.Valley Idupulapaya Andhra Pradesh Kadapa516330 India

The necessity for smart energy oversight solutions has arisen in response to the rising popularity of energy-efficient home automation and other energy-saving technologies. Optimizing smart home energy use using multi-criteria decision-making (MCDM) is a proven methodology. However, the procedure for making decisions and MCDM’s capacity to handle various criteria are typically limiting factors. Hybrid methods, which integrate multiple decision-making approaches like Fuzzy Logic (FL) and Modular Neural Networks (MNN), could potentially be able to circumvent these restrictions and boost energy management systems’ efficacy and precision. This investigation presents a hybrid Neuro-Fuzzy (H-NF) method for MCDM in regulating energy for smart homes by combining FL with an MNN. The suggested approach would optimize energy use in smart homes by considering several parameters, notably cost, ease of use, and environmental effects. In addition, this study aims to examine how the H-NF model fares in comparison to other methods of making important decisions in terms of several performance metrics. The suggested hybridized approach has the potential to deliver more precise and effective decision-making processes for energy management in smart homes, allowing users to optimize their energy consumption while preserving comfort and lowering environmental impact. © 2024 Taylor and Francis Inc.. All rights reserved.

关键词： Decision making

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A Netnographic-Based Semantic Analysis of Tweet Contents for Stress Management

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Computers, Materials & Continua 2022年第1期70卷 1845-1856页

作者： Jari Jussila Eman Alkhammash Norah Saleh Alghamdi Prashanth Madhala Mohammad Ayoub Khan HAMK Smart Research Unit Häme University of Applied SciencesHämeenlinna13100Finland Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia College of Computer and Information Sciences Princess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Faculty of Management and Business Tampere UniversityTampere33720Finland College of Computing and Information Technologies University of BishaBisha67714Saudi Arabia

Social media platforms provide new value for markets and research *** article explores the use of social media data to enhance customer value *** case study involves a company that develops wearable Internet of Things(IoT)devices and services for stress *** and semantic annotation for recognizing and categorizing the context of tweets are conducted to gain a better understanding of users’stress management *** aim is to analyze the tweets about stress management practices and to identify the context from the ***,we map the tweets on pleasure and arousal to elicit customer *** analyzed a case study of a marketing strategy on the Twitter *** in the marketing campaign shared photos and texts about their stress management *** learning techniques were used to evaluate and estimate the emotions and contexts of the tweets posted by the campaign *** computational semantic analysis of the tweets was compared to the text analysis of the *** content analysis of only tweet images resulted in 96%accuracy in detecting tweet context,while that of the textual content of tweets yielded an accuracy of 91%.Semantic tagging by Ontotext was able to detect correct tweet context with an accuracy of 50%.

关键词： Social media stress semantic analysis Twitter context recognition

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An Integration Of Wireless Communications And Artificial Intelligence For Autonomous Vehicles 3

An Integration Of Wireless Communications And Artificial Int...

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3rd International Conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Raghuram, Kadambari Altarawni, Ibrahim Vijayalakshmi, K. Kumar, Ajay Jadeja, Abhijeetsinh Chandra, Pankaj Jntuk Department of Computer Science and Engineering Andhra Pradesh Kakinada India Aqaba University of Technology Applied Computing and Artificial Intelligent Department Aqaba Jordan Ramapuram Campus Srmist India INurture Education Solution Pvt. Ltd It Academics Delhi India Sankalchand Patel University Faculty of Computer Science Shri C.J. Patel College of Computer Studies India Guru Ghasi das Viswavidyalaya Dept of Information Technology Chattisgarh Bilaspur India

ISBN: (纸本)9798350399264

The pinnacle of transportation is the development of autonomous driving, which, with the help of CAVs and related traffic management systems, can eventually lead to congestion- and accident-free driving. This vision has as of late prodded extraordinary examination interest in fields including IoV, LTE-V2X, and 5G. In any case, the huge volume of traffic information that CAVs produce makes issues for both the current organizations and the approaching 5G correspondence organizations. For outside network innovations, the VMBS fills in as both a client hub and an edge processing hub. For CAVs, it fills in as a base station and a data caching hub, melding correspondence and calculation. People offer both the VMBS-empowered handset and figuring for CAVs as well as the VMBS-helped wireless innovation for other wireless gadgets to achieve this. It is underlined and addressed that there are a number of research obstacles and open questions. Last but not least, the results of the simulation show that the planned VMBS-CCNA may significantly enhance throughput, latency, and the average amount of links. © 2023 IEEE.

关键词： Autonomous vehicles

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UNeLF: Unconstrained Neural Light Field for Self-Supervised Angular Super-Resolution

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Zhao, Mingyuan Sheng, Hao Chen, Rongshan Cong, Ruixuan Cui, Zhenglong Yang, Da Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang University Data Science and Intelligent Computing Laboratory Hangzhou International Innovation Institute Hangzhou311115 China Macao Polytechnic University Faculty of Applied Sciences 999078 China

Compared to supervised learning methods, self-supervised learning methods address the domain gap problem between light field (LF) datasets collected under varying acquisition conditions, which typically leads to decreased performance when differences exist in the distribution between the training and test sets. However, current self-supervised light field angular super-resolution (LFASR) techniques primarily focus on exploiting discrete spatial-angular features while neglecting continuous LF information. In contrast to previous work, we propose a self-supervised unconstrained neural light field (UNeLF) to continuously represent LF for LFASR. Specifically, any LF can be described as the camera pose for each sub-aperture image (SAI) and the two-plane that captures these SAIs. To describe the former, we introduce a SAIs-dependent pose optimization method to solve the issue that arises from the narrow baseline of most LF data, which hinders robust camera pose estimation. This mechanism reduces the number of trainable camera parameters from a quadratic to a constant scale, thereby alleviating the complexity of joint optimization. For the latter, we propose a novel adaptive two-plane parameterization strategy to determine the two-plane that captures these SAIs, facilitating refocusing. Finally, we jointly optimize the camera parameters, near-far planes and neural light field, efficiently mapping each adaptive two-plane parameterized ray to its correspondence color in a continuous manner. Comprehensive experiments demonstrate that UNeLF achieves faster training and inference with fewer computational resources while exhibiting superior performance on both synthetic and real-world datasets. © 1991-2012 IEEE.

关键词： Self-supervised learning

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Energy Efficient Cluster Based Clinical Decision Support System in IoT Environment

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Computers, Materials & Continua 2021年第11期69卷 2013-2029页

作者： C.Rajinikanth P.Selvaraj Mohamed Yacin Sikkandar T.Jayasankar Seifedine Kadry Yunyoung Nam Department of Electronics and Communications Engineering Erode Sengunthar Engineering College(Autonomous)Erode638057India Department of Information Technology SRM Institute of Science and TechnologyKattankulathur603203India Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Electrical Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India Faculty of Applied Computing and Technology(FACT) Noroff University CollegeKristiansand4608Norway Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

Internet of Things(IoT)has become a major technological development which offers smart infrastructure for the cloud-edge services by the interconnection of physical devices and virtual things among mobile applications and embedded *** e-healthcare application solely depends on the IoT and cloud computing environment,has provided several characteristics and *** research works reported that the energy consumption for transmission process is significantly higher compared to sensing and processing,which led to quick exhaustion of *** this view,this paper introduces a new energy efficient cluster enabled clinical decision support system(EEC-CDSS)for embedded IoT *** presented EECCDSS model aims to effectively transmit the medical data from IoT devices and perform accurate diagnostic *** EEC-CDSS model incorporates particle swarm optimization with levy distribution(PSO-L)based clustering technique,which clusters the set of IoT devices and reduces the amount of data *** addition,the IoT devices forward the data to the cloud where the actual classification procedure is *** classification process,variational autoencoder(VAE)is used to determine the existence of disease or *** order to investigate the proficient results analysis of the EEC-CDSS model,a wide range of simulations was carried out on heart disease and diabetes *** obtained simulation values pointed out the supremacy of the EEC-CDSS model interms of energy efficiency and classification accuracy.

关键词： Energy efficiency intelligent models decision support system IoT e-healthcare machine learning

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Decoupled structure-preserving discretization of incompressible MHD equations with general boundary conditions

arXiv

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arXiv 2024年

作者： Zhang, Yi Palha, Artur Brugnoli, Andrea Toshniwal, Deepesh Gerritsma, Marc School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China Delft Institute of Applied Mathematics Delft University of Technology Delft Netherlands ICA Université de Toulouse ISAE–SUPAERO INSA CNRS MINES ALBI UPS Toulouse France Faculty of Aerospace Engineering Delft University of Technology Delft Netherlands

In the framework of a mixed finite element method, a structure-preserving formulation for incompressible MHD equations with general boundary conditions is proposed. A leapfrog-type temporal scheme fully decouples the fluid part from the Maxwell part by means of staggered discrete time sequences and, in doing so, partially linearizes the system. Conservation and dissipation properties of the formulation before and after the decoupling are analyzed. We demonstrate optimal spatial and second-order temporal error convergence and conservation and dissipation properties of the proposed method using manufactured solutions, and apply it to the benchmark Orszag-Tang and lid-driven cavity test cases. Copyright © 2024, The Authors. All rights reserved.

关键词： Boundary element method

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Models of Battle Dynamics in Strategic Computer Games 16

Models of Battle Dynamics in Strategic Computer Games

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16th International Conference Management of Large-Scale System Development, MLSD 2023

作者： Laryushin, Ivan Dmitrievich Pashin, Dmitrii Sergeevich Koltochenko, Yaroslav Alekseevich Ereshko, Felix Ivanovich Gaponov-Grekhov Institute of Applied Physics Ras Plasma Physics Department Nizhny Novgorod Russia State University of Nizhny Novgorod Faculty of Physics National Research Lobachevsky Nizhny Novgorod Russia Moscow Institute of Physics and Technology Department of Mathematical Modeling of Complex Systems and Optimization Moscow Russia Federal Research Center 'Computer Science and Control' of the Ras Department of Information and Computing Systems Moscow Russia

ISBN: (纸本)9798350337907

A set of new models that describe the dynamics of battles in strategic computer games is presented. These models not only have descriptive functions, but also allow solving problems of optimizing army control under given conditions, as well as calculating the most efficient composition of the army for the planned operation within a computer game. © 2023 IEEE.

关键词： Computer games

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MSKI-Net: Towards modality-specific knowledge interaction for glioma survival prediction

MSKI-Net: Towards modality-specific knowledge interaction fo...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Su, Ran Xiao, Jin Cui, Hui Xuan, Ping Feng, Xikang Wei, Leyi Jin, Qiangguo Tianjin University School of Computer Software College of Intelligence and Computing Tianjin China La Trobe University Department of Computer Science and Information Technology Melbourne Australia Shantou University School of Engineering Department of Computer Science China Northwestern Polytechnical University School of Software China Macao Polytechnic University AIDD Faculty of Applied Science China Xiamen University School of Information Xiamen China Yangtze River Delta Research Institute Northwestern Polytechnical University Taicang China

ISBN: (纸本)9798350386226

Gliomas hold a prominent position in neurooncology due to their high malignancy and poor survival rates. Accurately predicting the prognosis and survival risk of glioma patients is crucial for clinical treatment. Recent advances in survival prediction methods emphasize the importance of integrating complementary information from diverse modalities while neglecting the significant modality gap between pathological images and genomic data. To address this issue, we propose a modality-specific knowledge interaction network (MSKI-Net), which integrates whole slide images (WSI), RNA-Seq gene expression data, and copy number variation (CNV) data for glioma survival analysis. The MSKI-Net consists of a modality-specific feature enhancement (MSFE) module, a modality-interactive cross-attention (MICA) module, and a modality-specific knowledge-guided representation learning (MSKR) module. The three modules collaborate by complementing modality-specific features with modality-agnostic knowledge to improve the learning capability of MSKI-Net. Furthermore, we construct a dataset named TCGAmm, which combines WSI, RNA-Seq, and CNV data from The Cancer Genome Atlas (TCGA) to address the issue of data scarcity. Extensive experiments demonstrate that MSKI-Net achieves superior performance in predicting the survival risk of glioma cancer. © 2024 IEEE.

关键词： Gene expression

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