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13th International Conference on Software and computer Applications, ICSCA 2024

作者： Jatoth, Chandrashekar Patil, Sourabh Department Of Information Technology National Institute Of Technology Raipur India Department Of Computer Science And Engineering National Institute Of Technology Hamirpur India

ISBN: (纸本)9798400708329

A smart grid provides two-way information and energy flow between the power providers and consumers. In recent years, massive sensors and advanced metering infrastructure have been deployed, generating huge amounts of data. Cloud computing has been used to handle and compute expensive complex data. Generally, data is shared through a centralized entity, which requires expensive uploading and downloading, putting extra pressure on the wireless networks. Therefore, in the proposed work, a decentralized Blockchain network is considered. This work proposes offloading Blockchain transactions by smart grid environment to cloud resources and process allocation on cloud resources to optimize resource prices with a process collocation game-theoretic approach. We also present simulation results demonstrating optimized prices incurred to cloud users with the proposed solution. © 2024 ACM.

关键词： Game theory

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An EEG-Driven Framework for Emotion Recognition During Gameplay 5

An EEG-Driven Framework for Emotion Recognition During Gamep...

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5th International Conference on Advancements in Computational sciences, ICACS 2024

作者： Shahzad, Khurram Ali, Zohaib Rauf, Usman Rehman, Anees Ur Khan, Sheharyar Noorani, Sadam Hussain Comsat University Department of Elect & Comp Engineering Islamabad Pakistan Bahria University Department of Computer Science Islamabad Pakistan University of Engineering & Technology Department of Computer Engineering Taxila Pakistan

ISBN: (纸本)9798350394764

Gaming transcends mere entertainment, serving as a unique realm to explore human emotions. This research utilizes Brain-computer Interface (BCI) technology, specifically Electroencephalogram (EEG) signals, to unravel player emotions. Pioneering an innovative approach, our study decodes and interprets emotions exhibited during gaming experiences. Through meticulous analysis, we demonstrate the efficacy of EEG signals in revealing emotional states. Findings illuminate the dynamic interplay between the brain and gaming environments. Leveraging datasets like GAMEEMO, our study employs various data and feature extraction processes to detect emotions (boring, calm, horror, funny) with a accuracy of 98.21%, achieved by the Random Forest model. © 2024 IEEE.

关键词： Electroencephalography

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A Comprehensive Multi-modal Approach for Enhanced Product Recommendations Based on Customer Habits

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Journal of The Institution of Engineers (India): Series B 2024年第6期105卷 1537-1545页

作者： Bodapati, Jyostna Devi Veeranjaneyulu, N. Yenduri, Lakshmana Kumar Advanced Computer Science and Engineering Vignan’s Foundation for Science Technology and Research Andhra Pradesh Guntur India Department of IT Vignan’s Foundation for Science Technology and Research Vadlamudi522213 India San Francisco Bay Area United States

In response to the evolving demands and challenges in recommendation systems, a comprehensive and innovative approach is proposed for delivering personalized and accurate product recommendations while mitigating existing limitations. This approach combines cutting-edge methodologies from collaborative filtering, content-based recommendation systems, and state-of-the-art deep learning architectures to enhance the standard of recommendations and improve user contentment. The hybrid approach encompasses various modules, including data preprocessing, feature engineering, deep learning model development, recommendation generation processing, and post-processing. Utilizing the H &M Fashion dataset, a rich collection of product images and metadata, for experimentation, the dataset undergoes preprocessing to ensure data quality, and features are extracted from both text and images using transfer learning approaches. These features are then integrated into a unified representation using fusion techniques. Subsequently, a deep learning model is developed to generate personalized recommendations based on the integrated features. Empirical findings on benchmark H &M dataset demonstrate the superiority of the proposed hybrid approach over traditional content-based and collaborative-based models, with improvements in accuracy, recall, and F1 score. The hybrid model effectively combines different recommendation techniques, offering robustness, adaptability, and enhanced recommendation quality. This research contributes to the advancement of recommendation systems, offering insights and solutions for delivering tailored recommendations in the fashion retail industry. © The Institution of Engineers (India) 2024.

关键词： Recommender systems

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Real-Time CNN-Based Driver Distraction&Drowsiness Detection System

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Intelligent Automation & Soft Computing 2023年第8期37卷 2153-2174页

作者： Abdulwahab Ali Almazroi Mohammed A.Alqarni Nida Aslam Rizwan Ali Shah University of Jeddah College of Computing and Information Technology at KhulaisDepartment of Information TechnologyJeddahSaudi Arabia University of Jeddah College of Computer Science and EngineeringDepartment of Software EngineeringJeddahSaudi Arabia Department of Computer Science National College of Business Administration&EconomicsBahawalpur Campus63100Pakistan Department of Computer Science and Information Technology The Islamia University of BahawalpurRahim Yar Khan CampusPunjab64200Pakistan

Nowadays days,the chief grounds of automobile accidents are driver fatigue and *** the development of computer vision technology,a cutting-edge system has the potential to spot driver distractions or sleepiness and alert them,reducing *** paper presents a novel approach to detecting driver tiredness based on eye and mouth movements and object identification that causes a distraction while operating a motor *** the facial landmarks that the camera picks up and sends to classify using a Convolutional Neural Network(CNN)any changes by focusing on the eyes and mouth zone,precision is *** of the tasks that must be performed in the transit system is seat belt detection to lessen accidents caused by sudden stops or high-speed collisions with other cars.A method is put forth to use convolution neural networks to determine whether the motorist is wearing a seat belt when a driver is sleepy,preoccupied,or not wearing their seat belt,this system alerts them with an alarm,and if they don’t wake up by a predetermined time of 3 s threshold,an automatic message is sent to law enforcement *** suggested CNN-based model exhibits greater accuracy with 97%.It can be utilized to develop a system that detects driver attention or sleeps in real-time.

关键词： Deep learning convolutional neural network Tensorflow drowsiness and yawn detection seat belt detection object detection

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Generative AI — The End of Systematic Reviews in PhD Projects?

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ACM Inroads 2024年第2期15卷 48-50页

作者： Zamani, Sanaz Sinha, Roopak Department of Computer Science and Software Engineering Auckland University of Technology Auckland New Zealand School of Information Technology Deakin University Melbourne Australia

In this opinion piece, we question the efficacy of students conducting systematic reviews (SRs) at the very start of their PhDs, especially now that we are riding, or drowning in, the Generative AI wave. How would the ubiquitous availability of Generative AI tools shape PhD research and supervision, and how can we capitalize on such disruptions? We report our reflections about early-stage PhD research from 10+ PhD supervisions completed by Sinha, and a recently started PhD project with Zamani as the student. We conducted a systematic literature review following traditional methods for the first four months and then revisited the task using ChatGPT and other related tools. We opine that while SRs still have value for new students, Generative AI must be incorporated, with care, to significantly enhance student learning, reinvest time savings into other, often-ignored skills, and improve the quality (breadth and depth) of SRs. © 2024 Association for Computing Machinery. All rights reserved.

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Drought and Salinity Stress Classification in Soyabean Crops: Comparative Analysis of Machine Learning Models

Drought and Salinity Stress Classification in Soyabean Crops...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Madhumitaa, P.S. Ragavi, Chagamreddy Kiranmayi, Chinthamani Venkatesan, M. Prabhavathy, P. National Institute of Technology Department of Computer Science & Engineering Puducherry India Vellore Institute of Technology Department of Information Technology Vellore India

ISBN: (纸本)9798331540685

Soyabean cultivation encounters significant obstacles attributed to abiotic stresses like drought and salinity, especially in areas characterized by tropical climates. In this paper, we seek to fill gaps identified in previous research efforts, particularly the lack of comprehensive analysis regarding genotypic factors and the underutilization of morphological characteristics in machine learning algorithms for Soyabean genotype selection. We propose a novel approach to bridge these gaps by employing six morphological variables in machine learning models. Through the process of training the models using specific parameters, plants are categorized into either the Control group or the Stress Group, which comprises of genotypes facing drought or salinity stress. Various models such as Random Forest Classifier (RFC), Support Vector Machine (SVM), Gradient Boosting (GB), k-Nearest Neighbors (kNN) and Naive Bayes algorithms are included, offering an in-depth comparative analysis of the performance and feature importance under each model used. However, the Support Vector Machine was identified as the most accurate algorithm with an accuracy of 96.79% in stress classification of Soyabean genotypes. Incorporating morphological parameters in machine learning models enhances the accuracy and efficiency of Stress Classification process, increasing the sustainability of Soyabean production and its ability to withstand environmental adversities. © 2024 IEEE.

关键词： Abiotic

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Finite Difference Method for Neutrosophic Fuzzy Second Order Differential Equation under Generalized Hukuhara Differentiability

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Neutrosophic Sets and Systems 2023年 58卷 311-329页

作者： Kamal, Bassem Khalid, Huda E. Salama, A.A. El-Baghdady, Galal I. Department of Mathematics and Engineering Physics Faculty of Engineering Delta University for Science and Technology Egypt Telafer University Telafer Iraq Department of Mathematics and Computer Science Faculty of Science Port Saied University Egypt Department of Mathematics and Engineering Physics Faculty of Engineering Mansoura University Egypt

This paper solves the boundary value problem of the second-order differential equation under the neutrosophic fuzzy boundary condition. The proposed solution is approximated using the finite difference method but deneutrosophication utilizing strongly generalized Hukuhara differentiability. The error calculation is then processed by comparing the proposed numerical solution with the analytical solution presented in our earlier work [20]. © (2023). All Rights Reserved.

关键词： Boundary conditions

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Asynchronous Entanglement Provisioning and Routing for Distributed Quantum Computing 42

Asynchronous Entanglement Provisioning and Routing for Distr...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Yang, Lan Zhao, Yangming Huang, Liusheng Qiao, Chunming University of Science and Technology of China School of Computer Science and Technology China University at Buffalo Department of Computer Science and Engineering United States

ISBN: (纸本)9798350334142

In Distributed Quantum Computing (DQC), quantum bits (qubits) used in a quantum circuit may be distributed on multiple Quantum computers (QCs) connected by a Quantum Data Network (QDN). To perform a quantum gate operation involving two qubits on different QCs, we have to establish an Entanglement Connection (EC) between their host QCs. Existing EC establishment schemes result in a long EC establishment time, and low quantum resource *** this paper, we propose an Asynchronous Entanglement Routing and Provisioning (AEPR) scheme to minimize the task completion time in DQC systems. AEPR has three distinct features: (i). Entanglement Paths (EPs) for a given SD pair are predetermined to eliminate the need for runtime calculation;(ii). Entanglement Links (ELs) are created proactively to reduce the time needed create EL on demand;and (iii). For a given EC request, quantum swapping along an EP is performed by a repeater whenever two adjacent ELs are created, so precious quantum resources at the repeater can be released immediately thereafter for other ELs and ECs. Extensive simulations show that AEPR can save up to 76.05% of the average task completion time in DQC systems compared with the state-of-the-art entanglement routing schemes designed to maximize QDN throughput. © 2023 IEEE.

关键词： Quantum entanglement

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A Novel IoT-Enabled System for Real-Time Face Mask Recognition Based on Petri Nets

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IEEE Internet of Things Journal 2024年第4期11卷 6992-7001页

作者： Yang, Cheng-Ying Lin, Yi-Nan Shen, Victor R. L. Shen, Frank H. C. Wang, Chien-Chi University of Taipei Department of Computer Science Taipei100 Taiwan Ming Chi University of Technology Department of Electronic Engineering New Taipei City243 Taiwan Chaoyang University of Technology Department of Information Management Taichung City413 Taiwan National Taipei University Department of Computer Science and Information Engineering New Taipei City237 Taiwan Fu Jen Catholic University Department of Electronic Engineering New Taipei City242 Taiwan

Due to coronavirus disease 2019 (COVID-19), many countries have formulated pandemic prevention regulations, requiring the masses to wear a face mask before entering public places and taking public transportation. However, if the entrances of some places are manually controlled to check whether people are wearing a face mask or not, it becomes not only labor intensive but also time consuming. Therefore, this article aims to develop a face mask recognition system based on an edge computing platform. The traditional manual inspection control method is replaced by artificial intelligence (AI) technology to achieve automatic recognition and control. As an edge computing platform, Jetson Nano is an embedded system equipped with an AI platform, which can be used for object detection and image classification. Developed by Ultralytics LLC, the YOLOv5 model with PyTorch framework runs on the edge computing platform, featuring high speed, high precision, and small size. According to the model training results, the average precision (AP) reaches 95.41%, while the mean average precision (mAP) reaches 94.42%. The average single-class running time is 0.016 s, and the file size of the training model is 3.8 MB. The recognition distance is up to 8 m, and the maximum face rotation angle is 90°. In addition, a Petri net (PN) software tool, workflow Petri net designer (WoPeD), with graphical features based on mathematical theories, is used to verify the mask recognition system and ensures that the system has acceptable precision and recall values. © 2014 IEEE.

关键词： Petri nets

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A Decentralized Fog Architecture for Video Preprocessing in Cloud-based Video Surveillance as a Service 1

A Decentralized Fog Architecture for Video Preprocessing in ...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Divya, G. Swetha, K. Siva Shruthika, S. Guru Santhosh, P. Santhi, S. Kalaiselvi, S. National Engineering College Department of Information Technology Kovilpatti India National Engineering College Department of Computer Science and Engineering Kovilpatti India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372748

With the increasing demand for Cloud-based Video Surveillance as a Service (VSaaS), the efficient processing of vast amounts of video data poses significant challenges. The framework leverages Fog computing at the network edge, enabling real-time video analytics, object detection, and event recognition to be performed closer to the data source. By offloading intensive computational tasks from the central cloud to edge fog nodes, the framework reduces bandwidth consumption and minimizes processing latency. By integrating the state-of-the-art machine learning algorithms, automated video preprocessing with higher accuracy can be achieved. Moreover, the decentralized fog architecture enhances system responsiveness and ensures data privacy by processing sensitive information locally. The primary objective of this preprocessing is to reduce the volume of data transmitted to the cloud while preserving critical information for subsequent analysis. By leveraging fog computing, background subtraction, and intelligent video compression, a substantial reduction is achieved in data transmission, leading to more cost-effective and responsive video surveillance systems. © 2024 IEEE.

关键词： Network architecture

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