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7th International Conference on Computational Intelligence and Intelligent Systems, CIIS 2024

作者： Chiura, Tafadzwa Blessing Van Der Haar, Dustin Academy of Computer Science and Software Engineering Faculty of Science University of Johannesburg Gauteng Johannesburg South Africa

ISBN: (纸本)9798400717437

Most of the research conducted in action recognition is mainly focused on general human action recognition, and most of the available datasets support studies in general human action recognition. In more specific contexts, such as basketball, datasets that are as comprehensive and publicly available are limited. This study proposes taking three popular and mature methods in the field of action recognition, namely Temporal Segment Networks (TSN), Two-Stream CNN using Inflated 3D-convolutional Neural Networks (I3D) and Pose-C3D, and applying them to the SpaceJam dataset, which is a basketball-specific action dataset. All three experiments used pre-trained ImageNet models and were fine-tuned on the SpaceJam dataset. TSN was the oldest of the methods but obtained the best results of the three experiments, scoring a top-1 and top-5 accuracy of 59% and 96%, respectively. I3D was second best, with a top-1 and top-5 accuracy of 41% and 85%, respectively. Pose-C3D came in third, scoring a top-1 and top-5 accuracy of 15% and 50%, respectively. The results show that the models cannot distinguish significantly between some actions, such as ball in hand, pass and dribble. The study shows that it is feasible for context-specific fine-grain action recognition, but more needs to be done to discriminate against similar actions. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Determination of Spherical Coordinates of Sampled Cosmic Ray Flux Distribution using Principal Components Analysis and Deep Encoder-Decoder Network

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Machine Graphics and Vision 2024年第2期33卷 29-45页

作者： Hachaj, Tomasz Piekarczyk, Marcin Bibrzycki, Lukasz Wąs, Jaroslaw Faculty of Electrical Engineering Automatics AGH University of Krakow Kraków Poland Faculty of Physics and Applied Computer Science AGH University of Krakow Kraków Poland

In this paper we propose a novel algorithm based on the use of Principal Components Analysis for the determination of spherical coordinates of sampled cosmic ray flux distribution. We have also applied a deep neural network with encoder-decoder (E-D) architecture in order to filter-off variance noises introduced by sampling. We conducted a series of experiments testing the effectiveness of our estimations. The training set consisted of 92 250 images and validation set of 37 800 images. We have calculated mean absolute error (MAE) between real values and estimations. When E-D is applied, the number of cases (estimations) where MAE © 2024 Institute of Information Technology, Warsaw University of Life sciences - SGGW. All rights reserved.

关键词： Cosmic rays

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Topology, Size, and Shape Optimization in Civil engineering Structures: A Review

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computer Modeling in engineering & sciences 2025年第2期142卷 933-971页

作者： Ahmed Manguri Hogr Hassan Najmadeen Saeed Robert Jankowski Faculty of Civil and Environmental Engineering Gdansk University of TechnologyGdansk80-223Poland Department of Computer Science ModelingElectronics and Systems EngineeringUniversity of CalabriaCosenza87036Italy Civil Engineering Department University of RaparinRania46012Iraq Civil Engineering Department Tishk International UniversityErbil44001Iraq

The optimization of civil engineering structures is critical for enhancing structural performance and material efficiency in engineering *** optimization approaches seek to determine the optimal design,by considering material performance,cost,and structural *** design approaches aim to reduce the built environment’s energy use and carbon *** comprehensive review examines optimization techniques,including size,shape,topology,and multi-objective approaches,by integrating these *** trends and advancements that contribute to developing more efficient,cost-effective,and reliable structural designs were *** review also discusses emerging technologies,such as machine learning applications with different optimization *** of truss,frame,tensegrity,reinforced concrete,origami,pantographic,and adaptive structures are covered and *** techniques are explained,including metaheuristics,genetic algorithm,particle swarm,ant-colony,harmony search algorithm,and their applications with mentioned structure *** and non-linear structures,including geometric and material nonlinearity,are *** role of optimization in active structures,structural design,seismic design,form-finding,and structural control is taken into account,and the most recent techniques and advancements are mentioned.

关键词： Structural optimization topology optimization size optimization shape optimization multi-objective optimization

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Evaluating the Performance of Clinical data using Machine learning Approach- An Ensemble Model 2

Evaluating the Performance of Clinical data using Machine le...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Abdullah, A. Sheik Vinod, A Aashish Pranav, A G Vishal Govindarajan, Yeshwanth Selvakumar, Subramanian Vellore Institute of Technology School of Computer Science and Engineering Chennai India Bahir Dar University Faculty of Electrical and Computer Engineering Ethiopia

ISBN: (纸本)9798350372892

In the rapidly evolving field of healthcare, accurate clinical predictions are paramount for effective disease management and treatment planning. This paper introduces a novel ensemble machine learning model that utilizes the combined strengths of decision trees, neural networks, random forests, and support vector machines, integrated through a sophisticated linear regression meta-learner. This advanced integration strategy enhances predictive accuracy by optimally weighting the contributions of each base model based on their predictive reliability and effectiveness. The ensemble model amalgamates diverse algorithms with a meta-learning approach to harness a broader spectrum of data insights, thereby overcoming the limitations of traditional single-model approaches. Comparative analysis reveals that the ensemble model significantly outperforms existing methods, achieving superior accuracy in clinical predictions. These results underscore the potential of ensemble approaches in revolutionizing predictive analytics in healthcare, offering a promising tool for improving diagnostic precision and patient outcomes. © 2024 IEEE.

关键词： Contrastive Learning

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Shadow Extraction and Elimination of Moving Vehicles for Tracking Vehicles

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computers, Materials & Continua 2023年第11期77卷 2009-2030页

作者： Kalpesh Jadav Vishal Sorathiya Walid El-Shafai Torki Altameem Moustafa HAly Vipul Vekariya Kawsar Ahmed Francis MBui Department of Electronics&Communication Engineering Faculty of Engineering and TechnologyParul Institute of Engineering and TechnologyParul UniversityVadodaraGujarat391760India Department Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Community CollegeKing Saud UniversityRiyadh11362Saudi Arabia Electronics and Communications Engineering Department College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria1029Egypt Department of Computer Engineering Faculty of Engineering and TechnologyParul Institute of Engineering and TechnologyParul UniversityVadodaraGujarat391760India Department of Electrical and Computer Engineering University of Saskatchewan57 Campus DriveSaskatoonSK S7N5A9Canada

Shadow extraction and elimination is essential for intelligent transportation systems(ITS)in vehicle tracking *** shadow is the source of error for vehicle detection,which causes misclassification of vehicles and a high false alarm rate in the research of vehicle counting,vehicle detection,vehicle tracking,and *** of the existing research is on shadow extraction of moving vehicles in high intensity and on standard datasets,but the process of extracting shadows from moving vehicles in low light of real scenes is *** real scenes of vehicles dataset are generated by self on the Vadodara–Mumbai highway during periods of poor illumination for shadow extraction of moving vehicles to address the above *** paper offers a robust shadow extraction of moving vehicles and its elimination for vehicle *** method is distributed into two phases:In the first phase,we extract foreground regions using a mixture of Gaussian model,and then in the second phase,with the help of the Gamma correction,intensity ratio,negative transformation,and a combination of Gaussian filters,we locate and remove the shadow region from the foreground *** to the outcomes proposed method with outcomes of an existing method,the suggested method achieves an average true negative rate of above 90%,a shadow detection rate SDR(η%),and a shadow discrimination rate SDR(ξ%)of 80%.Hence,the suggested method is more appropriate for moving shadow detection in real scenes.

关键词： Change illuminations ImageJ software intelligent traffic systems mixture of Gaussian model National Institute of Health vehicle tracking

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Exploring Deep Learning Methods for computer Vision Applications across Multiple Sectors:Challenges and Future Trends

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computer Modeling in engineering & sciences 2024年第4期139卷 103-141页

作者： Narayanan Ganesh Rajendran Shankar Miroslav Mahdal Janakiraman SenthilMurugan Jasgurpreet Singh Chohan Kanak Kalita School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaram522502India Department of Control Systems and Instrumentation Faculty of Mechanical EngineeringVSB-Technical University of OstravaOstrava70800Czech Republic Department of Computer Science and Engineering Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India Department of Mechanical Engineering and University Centre for Research&Development Chandigarh UniversityMohali140413India Department of Mechanical Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyAvadi600062India

computer vision(CV)was developed for computers and other systems to act or make recommendations based on visual inputs,such as digital photos,movies,and other *** learning(DL)methods are more successful than other traditional machine learning(ML)methods *** techniques can produce state-of-the-art results for difficult CV problems like picture categorization,object detection,and face *** this review,a structured discussion on the history,methods,and applications of DL methods to CV problems is *** sector-wise presentation of applications in this papermay be particularly useful for researchers in niche fields who have limited or introductory knowledge of DL methods and *** review will provide readers with context and examples of how these techniques can be applied to specific areas.A curated list of popular datasets and a brief description of them are also included for the benefit of readers.

关键词： Neural network machine vision classification object detection deep learning

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Employing Feature engineering for River Stage Forecasting to Improve Hybrid Model Performance 32

Employing Feature Engineering for River Stage Forecasting to...

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32nd International Conference on Software, Telecommunications and computer Networks, SoftCOM 2024

作者： Mihel, Anna Maria Pecnik, Spela Vrbancic, Grega Lerga, Jonatan Krvavica, Nino University of Rijeka Faculty of Engineering Department of Computer Engineering Rijeka Croatia University of Maribor Intelligent Systems Laboratory Faculty of Electrical Engineering and Computer Science Maribor Slovenia University of Rijeka Faculty of Civil Engineering Department of Hydrotechnics and Geotechnics Rijeka Croatia

ISBN: (纸本)9789532901382

Establishing early warning systems and efficient management of water resources in tidal reaches is crucial for achieving adequate flood protection. In tidal reaches, the river stage interacts non-linearly with tides (downstream) and discharge (upstream), making the modeling of river processes extremely complex. Data-driven methods have proven helpful for predicting the river stage parameter. This contrasts with existing numerical approaches, which suffer from several limitations. The aim of this study was to predict the river stage of the upstream part of the tidal river, where the influence of salinity is present during the summer months. Using the CNN-LSTM model trained on historical records of the discharge (station of the variable to be predicted), the river stage of three downstream stations, and the point of interest, the prediction was carried out up to 24 hours in advance. By incorporating feature engineering, we achieved an improvement in model results for the longest horizon, confirmed by standard performance metrics, and considered it efficient and effective for risk mitigation. Introducing feature engineering into the data preprocessing resulted in a predictive performance improvement of 0.98% for the Nash-Sutcliffe Efficiency (NSE) metric, 7.0% for the root mean squared error (RMSE) metric, and 7.25% for the mean absolute error (MAE) metric over the second and third-best scenarios using a spectrogram and time-series data. © 2024 University of Split, FESB.

关键词： Mean square error

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Leveraging Large Language Models to Identify the Values Behind Arguments 2nd

Leveraging Large Language Models to Identify the Values Be...

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2nd International Workshop on Value engineering in Artificial Intelligence, VALE 2024

作者： Senthilkumar, Rithik Appachi Homayounirad, Amir Siebert, Luciano Cavalcante Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands

ISBN: (纸本)9783031854620

Human values capture what people and societies perceive as desirable, transcend specific situations and serve as guiding principles for action. People’s value systems motivate their positions on issues concerning the economy, society and politics among others, influencing the arguments they make. Identifying the values behind arguments can therefore help us find common ground in discourse and uncover the core reasons behind disagreements. Transformer-based large language models (LLMs) have exhibited remarkable performance across language generation and analysis. However, leveraging LLMs in sociotechnical systems that assist with discourse and argumentation necessitates systematically evaluating their ability to analyse and identify the values behind arguments, an under-explored research direction. Using a multi-level human value taxonomy inspired by the Schwartz Theory of Basic Human Values, we present a systematic and critical evaluation of GPT-3.5-turbo in human value identification from a dataset of multi-cultural arguments, across the zero-shot, few-shot and chain-of-thought prompting strategies, carrying forward from prior research on this task which leveraged a fine-tuned BERT model. We observe that prompting strategies exhibit performance levels close to, but still behind fine-tuning for value classification. We also detail some challenges associated with value classification with LLMs, offering potential directions for future research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Human Values Large Language Models Prompting

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An ultra efficient 2:1 multiplexer using bar-shaped pattern in atomic silicon dangling bond technology

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Journal of Supercomputing 2024年第13期80卷 18347-18364页

作者： Rasmi, Hadi Mosleh, Mohammad Jafari Navimipour, Nima Kheyrandish, Mohammad Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou64002 Taiwan

As CMOS technology approaches its physical and technical limits, alternative technologies such as nanotechnology or quantum computing are needed to overcome the challenges of lithography, transistor scaling, interconnects, and miniaturization. This article introduces a novel nanotechnology that uses atomic-scale silicon dangling bonds (ASDB) to create high-performance, low-power, nanoscale logic circuits. DBs are atoms that can form basic logic gates on a silicon surface using a scanning tunneling microscope device. ASDB can also be an alternative to the existing complementary metal oxide semiconductor (CMOS) technology. The article also proposes a new bar-shaped pattern to design gates and logic circuits with ASDB nano tecnolgoy. The bar-shaped pattern improves the reliability of the output, reduces the area and power consumption, and solves the problem of interatomic energy effects of ASDB. The article demonstrates the efficiency of the bar-shaped pattern by implementing two-input gates such as AND, NAND, OR, NOR, XOR, XNOR, and a 2:1 multiplexer with ASDB. The article also uses a powerful tool called SiQAD to simulate and verify the performance of the proposed structures with ASDB. According to the simulation results, the proposed logic gates are more energy efficient, stable, and compact than the previous structures. They consume 35% and 24.34% less energy and have 14.18% more stability, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Logic gates

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Performance Analysis of Existing SDN Load Balancing Controller Using Mininet 1

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International Conference on Advanced engineering, Technology and Applications, ICAETA 2024

作者： Baniya, Pashupati Nand, Parma Bhushan, Bharat Hameed, Alaa Ali Jamil, Akhtar Agrawal, Atul Department of Computer Science and Engineering School of Engineeringand Technology IEC College of Engineering and Technology Greater Noida India Department of Computer Science and Engineering School of Engineering and Technology ITS Engineering College Greater Noida India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University Istanbul34396 Turkey Department of Computer Science National University of Computer and Emerging Sciences Islamabad44000 Pakistan

ISBN: (数字)9783031709241

ISBN: (纸本)9783031709234

Due to an increase in the load of network, load balancing service, i.e., a service that gives an equal volume of each task assignment to each of the servers in data centers, it is usually performed by the specialized hardware. Networks have to adjust due to the load and also as a result of dealing with faults and complexity which are provided in the implementation and configuration process. Network engineers or administrators must be aware of selecting controllers according to the network requirement, which can lead to performance issues, scalability challenges, reliability and fault tolerance and many more problems in the network. To tackle this problem, this research analyses four different controllers in an experiment: In-band, open flow, OVS, and remote controller, which are the key aspects. The paper shows how these controllers, which are detected through a comprehensive evaluation of network performance attributes (e.g., Throughput and Jitter), affect the overall system’s performance. This information provides beneficial information for the researchers and practitioners who wish to make informed choices whenever they select suitable controls for specific network needs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Remote control

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