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Single-Valued Neutrosophic Graph with Heptapartitioend Structure

Neutrosophic Sets and Systems 2025年 80卷 728-748页

作者： Fathi, Hanaa Myvizhi, M. Abdelhafeez, Ahmed Abdellah, Mohamed Refaat Eassa, Mohamed Sawah, Mohamed S. Elbehiery, Hussam College of Information Technology Amman Arab University Amman Jordan KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt Department of Computer Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan

This study applies single-valued neutrosophic sets, which extend the frameworks of fuzzy and intuitionistic fuzzy sets, to graph theory. We introduce a new category of graphs called Single-Valued Heptapartitioned Neutrosophic Graphs (SVHNG) and investigate their characteristics through comprehensive proofs and illustrative examples. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioend Neutrosophic Set Neutrosophic Graph Neutrosophic Sets SVHN-Graph

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Neurocognitive assessment under various human-robot teaming environments 46

Neurocognitive assessment under various human-robot teaming ...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Packy, Anna L. Jayahankar, Jayesh Teymourlouei, Arya Stone, Joshua Oh, Hyuk Katz, Garrett E. Reggia, James A. Gentili, Rodolphe J. University of Maryland Neuroscience and Cognitive Science Program College Park United States University of Maryland Department of Computer Science College Park United States University of Maryland Department of Electrical & Computer Engineering College Park United States University of Maryland Department of Kinesiology College Park United States Syracuse University Department of Electrical Engineering & Computer Science Syracuse United States

ISBN: (纸本)9798350371499

Human-robot teaming has become increasingly important with the advent of intelligent machines. Prior efforts suggest that performance, mental workload, and trust are critical elements of human-robot dynamics that can be altered by the robot's behavior. Most prior human-robot teaming studies used behavioral analyses, but a limited number used neural markers, without the use of physical robots and complex tasks. Here we combine behavioral and EEG cortical dynamics to examine cognitive-motor processes when individuals complete a complex task under various team environments with a robot. The results revealed that altering the robot quality affected both behavioral and EEG dynamics. Task completion with an experienced robot led to greater team performance and human trust along with lower mental workload compared to an inexperienced teammate or when individuals performed alone. EEG changes suggest that different attentional processes were engaged when humans worked with the robot and performed alone, and that visual processing was more prominent when teaming with an inexperienced teammate. This work can inform human cognitive-motor processes and the design of robotic controllers in human-robot teams. © 2024 IEEE.

关键词： Man machine systems

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Detection of fungal infection in dogs using deep learning techniques 4th

Detection of fungal infection in dogs using deep learning te...

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Sunaina Kaur, Sukhpreet Kaur, Sukhdeep Department of Computer Science and Engineering Chandigarh Engineering College-CGC Landran India

ISBN: (纸本)9781032911571

Fungal infections at the skin of puppies, including dermatophytosis, are commonplace and pose great health dangers to each the animals and their proprietors. Although these infections do no longer affect human skin immediately, rapid detection remains important for well-timed remedy and spreading prevention. The currently established diagnosis process frequently includes the visual confirmation of the infection by medical professionals and fungal culture and is often referred to as time-intensive and highly dependent on medical professionals’ qualifications. Over the past few years, deep learning has become quite a promising area of research, R&D, and deployment in multiple fields, including medical imaging. The current research develops a deep learning model for fungal infection detection on the skin of dogs based on a variety of architectures and variations of Convolutional Neural Networks to classify image patches as healthy and infected. Transfer learning for the model structuring is reviewed to determine the impact of pre-trained models on the model’s learning efficiency. © 2025 the Author(s).

关键词： Convolutional neural networks

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A Study of Contrastive Learning Methods for Bengali Social Analysis 6

A Study of Contrastive Learning Methods for Bengali Social A...

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6th International Conference on electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Maisha, Jannatim Swarnali, Farhana Hossain Iftikar, M. Saymon Islam Ahammed, Sarkar Bulbul Shah, Faisal Muhammad Ahsanullah University of Science and Technology Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350385779

Across various domains, Contrastive Learning (CL) has already proven to be a powerful technique but using the Bengali language in the domain of Natural Language Processing (NLP) its' application is still unexplored. In this research, we introduce the implementation of CL in Bengali NLP by presenting a comprehensive benchmark study on two distinct datasets: Bengali Hate Speech, and Rokomari Book Review. The efficiency of the supervised contrastive techniques is emphasized by our methodology. We have implemented a contrastive learning technique through Bangla Bert. The superiority of supervised contrastive learning techniques over traditional Cross Entropy (CE) methods has been showcased by our result. Detailed experiments reveal performance variations across datasets, models, and hyperparameters but ensure the superiority of CL techniques over CE. Our findings show the significance of contrastive learning methods in low-resource settings, contributing to the advancement of Bengali NLP research. © 2024 IEEE.

关键词： Learning systems

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Univariate Time Series Data Correction Method using LSTM Autoencoder with Temporal Distance 6

Univariate Time Series Data Correction Method using LSTM Aut...

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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Yun, Sohyeon Kim, Han-Joon University of Seoul Department of Electrical and Computer Engineering Seoul Korea Republic of

ISBN: (纸本)9798350344349

High-quality data is essential to increase the reliability of machine learning-based prediction models. For time series data, anomalies significantly reduce the accuracy of prediction models. In this paper, we propose a novel time series data correction method that converts abnormal values of univariate time series data into normal ones. For anomaly detection and correction, we utilize the LSTM Autoencoder model, where we propose a new weight function that considers temporal distance. Through experiments using the open NAB data, we show that our proposed method is superior to the recent conventional methods. © 2024 IEEE.

关键词： Anomaly detection

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Cricket Team Selection and Player Analysis using Data Analytics 6

Cricket Team Selection and Player Analysis using Data Analyt...

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Raajesh, Sanjay Martin, Noel Jiji, Jyothsna Nair, Aakash Haritha, H. Muthoot Institute of Technology and Science Department of Computer Science and Engineering India

ISBN: (纸本)9798350381689

The inception of the 'Cricket Team Selection and Player Analysis using Data Analytics' project arose from the imperative to introduce precision through data-driven methods in the process of selecting cricket teams. Recognizing the pivotal role of team composition in the sport, our project employs the K-means clustering algorithm to categorize players based on their historical performance data with the strike rate. This approach facilitates the streamlined creation of well-balanced teams, targeting cricket enthusiasts, coaches, and team selectors. The ultimate goal is to enhance their decision-making processes, optimizing team performance and strategy. The project adopts a comprehensive approach, heralding a new era in cricket by leveraging data. It enables the formation of playing elevens derived from historical player performance metrics, ensuring a more resilient and strategic selection process. The project also has a selection of top players based on the attributes provided by the user. The tangible outcome of this project is a tool designed to serve cricket stakeholders, aiding them in making informed decisions for improved performance on the field. Representing a significant stride towards a more data-driven approach to cricket, the 'Cricket Team Selection and Player Analysis using Data Analytics' project is poised to redefine team selection and strategy in the sport. Its applicability extends to a broad audience, promising to be a transformative force in the realm of cricket decision-making. © 2024 IEEE.

关键词： Support vector machines

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MOOD: Mindfulness fOr sOftware Developers 24

MOOD: Mindfulness fOr sOftware Developers

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18th ACM/IEEE International Symposium on Empirical Software engineering and Measurement, ESEM 2024

作者： Romano, Simone Scanniello, Giuseppe Marchetto, Alessandro Giorgini, Paolo Guidetti, Gloria Converso, Daniela Viotti, Sara Department of Computer Science University of Salerno Italy Department of Information Engineering and Computer Science University of Trento Italy Department of Psychology University of Turin Italy

ISBN: (纸本)9798400710476

Peopleware, which includes anything related to the role of people in Software Development (SD), has been arousing an increasing interest from both the software industry and research community. This interest is due to the current economic system that demands high-quality software products with a short time to market, staying on the budget. This exposes software developers to the risk of experiencing stress, burnout, and reduced motivation, leading, in turn, to reduced job performance, low-quality SD-related artifacts, and increased turnover. Mindfulness represents a promising intervention that might let developers do their best at work, limiting or even preventing the previously mentioned negative outcomes. This paper presents MOOD (Mindfulness fOr sOftware Developers), a research project whose overarching goal is to customize a well-known and validated group-based intervention program, Mindfulness-Based Stress Reduction (MBSR), in the context of SD-related tasks and assess whether it helps developers to improve their well-being and performance, as well as the quality of the SD-related artifacts they produce. © 2024 Owner/Author.

关键词： Budget control

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DL-Based Somnolence Detection for Improved Driver Safety and Alertness Monitoring 5

DL-Based Somnolence Detection for Improved Driver Safety and...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Neeraja, Peddinti. Kumar, R.G. Kumar, M. Sunil Liyakat, Kazi Kutubuddin Sayyad Vani, M. Sowmya School of Computing Department of Computer Application AP Tirupathi India Siddharth Institute of Engineering & Technology Computer Science and Engineering AP Puttur India School of Computing Department of Computer Science and Engineering AP Tirupathi India Brahmdevdada Mane Institute of Technology Department of Computer Science and Engineering Maharashtra Solapur India

ISBN: (纸本)9798350383522

This abstract explores the utilization of deep learning for detecting driver somnolence, aiming to enhance driver safety and alertness monitoring. It investigates the integration of computer vision, physiological signals, and machine learning algorithms. Key considerations include real-time detection, accuracy, scalability, and driver intervention mechanisms. By leveraging deep learning techniques, effective driver somnolence detection systems can contribute to preventing accidents and promoting safer roads. © 2024 IEEE.

关键词： Convolution neural network deep learning Drowsiness detection transfer learning

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High-Accuracy Transfer Learning-Based Power Quality Disturbance Recognition Under A Noisy Environment 28

High-Accuracy Transfer Learning-Based Power Quality Disturba...

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28th International electrical Power Distribution Conference, EPDC 2024

作者： Zarei, Mohammad Ebrahim Emadaleslami, Mahdi Abedi, Mohammad Mahdi Baghaee, Hamid Reza Haghifam, Mahmoud-Reza Tarbiat Modares University Department of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798350385502

The smart and sustainable development of power grids has brought about numerous power quality disturbances (PQD) that threaten the efficiency and reliability of the smart grids. Detecting and classifying these disturbances is crucial to taking appropriate corrective action to mitigate possible impacts. While machine learning (ML) algorithms are at the center of attention for most PQD detection problems, their performance is limited due to insufficient samples. Therefore, this paper proposes a new model based on continuous wavelet transform (CWT) and transfer learning to overcome the data challenge. In this regard, raw voltage signals are First transformed to 2D contour images using the CWT. Then, converted images are used to fine-tune a pre-trained model on the Imagenet dataset through transfer learning. The simulation results over 29 groups of PQD under different noise levels show its effectiveness over other ML models. Finally, our code is publicly available in a git repository for enthusiastic readers. © 2024 IEEE.

关键词： Smart power grids

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Wheeled Mobile Robot Modeling for Local Navigation Using System Identification 66

Wheeled Mobile Robot Modeling for Local Navigation Using Sys...

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2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023

作者： Lee, Cheng-Lung Paulik, Mark J. Krishnan, Mohan University of Detroit Mercy Department of Electrical & Computer Engineering and Computer Science DetroitMI48221 United States

ISBN: (纸本)9798350302103

In this work we investigate wheeled mobile robot (WMR) kinodynamic modeling using system identification techniques. The larger context is the development of a novel local navigation algorithm based on long kinodynamic trajectories which account for vehicle drivability. A high-fidelity physics-based differential-drive robot model is used to generate command velocity and ground truth data sets, which are subsequently analyzed using multiple system identification models. Parametric linear ARX and ARMAX models are considered. Experimental results using multiple training and validation data sets corre-sponding to different driving scenarios and vehicle loading are explored. The resulting model-generated WMR paths demonstrate excellent performance. © 2023 IEEE.

关键词： local navigation modeling System identification wheeled mobile robot

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