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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Shaik, Nazeer Kumar, B. Vinoth Bhati, Anita Kumar, Sandeep Singh, Jagendra Sahu, Dinesh Prasad SRIT-Autonomous Department of Computer Science Engineering Anantapur India Ksr Institute for Engineering and Technology Dept. of Electronics and Communication Engineering Tiruchengode India Jecrc University Department of Computer Applications Jaipur India Raj Kumar Goel Institute of Technology Department of Information Technology Ghaziabad India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350342338

This study proposes a novel approach for energy consumption evaluation in cloud computing data centers. The immense energy consumption of these data centers presents significant environmental and economic challenges. Traditional evaluation methods often fail to provide accurate estimations due to the complex dynamics of cloud computing environments. The research introduces an integrated model, combining Random Forest Regression and Support Vector Regression, along with a Gradient Boosting Regressor for iterative training of regression parameters. We measure the model's performance using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), replacing traditional relative deviation measures. The proposed approach aims to provide more accurate and efficient energy consumption evaluations, offering potential benefits in energy management, operational costs reduction, and minimizing environmental impact. © 2023 IEEE.

关键词： Cloud Computing Energy Consumption Evaluation Gradient Boosting Regressor Random Forest Regression Support Vector Regression

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Predicting Lifespan of Ground-to-Air Multipath Components in mmWave UAV Channels

arXiv

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arXiv 2025年

作者： Khawaja, Wahab Jacobsen, Rune H. Hussain, Sajid Guvenc, Ismail Dept. Electrical and Computer Engineering Aarhus University Aarhus8000 Denmark Dept. Computer Systems Engineering Mirpur Univ. Science and Technology AJK Mirpur10250 Pakistan

In mobile ground-to-air (GA) propagation channels, the birth and death of multipath components (MPCs) are frequently observed, and the wide-sense stationary uncorrelated scattering (WSSUS) assumption does not always hold. Several methods exist for tracking the birth and death of MPCs, however, to the best of knowledge of authors, there is no existing literature that addresses the prediction of the lifespan of the MPCs in non-WSSUS GA propagation channels. In this work, we consider the GA channel as non-WSSUS and individual MPCs across receiver positions are represented as time series based on the Euclidean distance between channel parameters of the MPCs. These time series representations, referred to as path bins, are analyzed using a semi-Markov chain model. The channel parameter variations and dependencies between path bins are used to predict the lifespan of path bins using weighted sum method, machine learning classifiers, and deep neural networks. For comparison, the birth and death of path bins are also modeled using a Poisson distribution and a Markov chain. Simulation results demonstrate that deep neural networks offer highly accurate predictions for the lifespan (including death) of MPC path bins in the considered GA propagation scenario. © 2025, CC BY.

关键词： Markov chains

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Intervention Analysis for Predicting Stock Market using Deep Learning Algorithms and Time Series Analysis

Intervention Analysis for Predicting Stock Market using Deep...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Krish Valecha Yash Pandloskar Sowmya Dadheech Pradnya Saval Dept. of Computer Science and Engineering (Data Science) Dwarkadas J. Sanghvi College of Engineering Mumbai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Stock market being intricate and volatile pose challenges for classical Time Series Analysis techniques in terms of generalization and computation on real-world data. In order to improve prediction capacities, new methods like intervention analysis and Deep Learning (DL) are required. The research seeks to unravel the complexities of stock price prediction modelling by comparing Time Series Analysis with Deep Learning augmented by intervention analysis. Upon evaluation on different datasets, the Deep Learning models, specifically the Recurrent Neural Network (RNN) performed better than the other models considered. RNN outperformed other models on Mean Squared Error (MSE) for Tesla and IBM data, with values 3.874 before intervention and 3.593 after intervention and 46.52 before intervention and 42.97 after intervention respectively, thus highlighting the advantages of Deep Learning models for predictions. The goal of investigating intervention analysis methods incorporated into predictive models is to create processes that can predict overall market trends and efficiently identify and address sudden deviations.

关键词： Deep learning Analytical models Recurrent neural networks Accuracy Computational modeling Time series analysis Predictive models Market research Data models Stock markets

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Machine Learning-based sEMG Signal Classification for Hand Gesture Recognition

Machine Learning-based sEMG Signal Classification for Hand G...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Parshuram N. Aarotale Ajita Rattani Dept. of Biomedical Engineering Wichita State University USA Dept. of Computer Science and Engineering Uni. of North Texas USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

EMG-based hand gesture recognition uses electromyographic (EMG) signals to interpret and classify hand movements by analyzing electrical activity generated by muscle contractions. It has wide applications in prosthesis control, rehabilitation training, and human-computer interaction. Using electrodes placed on the skin, the EMG sensor captures muscle signals, which are processed and filtered to reduce noise. Numerous feature extraction and machine learning algorithms have been proposed to extract and classify muscle signals to distinguish between various hand *** paper aims to benchmark the performance of EMG-based hand gesture recognition using novel feature extraction methods, namely, fused time-domain descriptors, temporal-spatial descriptors, and wavelet transform-based features, combined with the state-of-the-art machine and deep learning models. Experimental investigations on the Grabmyo dataset demonstrate that the 1D Dilated CNN performed the best with an accuracy of 97% using fused time-domain descriptors such as power spectral moments, sparsity, irregularity factor and waveform length ratio. Similarly, on the FORS-EMG dataset, random forest performed the best with an accuracy of 94.95% using temporal-spatial descriptors (which include time domain features along with additional features such as coefficient of variation (COV), and Teager-Kaiser energy operator (TKEO)).

关键词： Hands Deep learning Wavelet transforms Accuracy Biological system modeling Gesture recognition Muscles Feature extraction Electromyography Time-domain analysis

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A Simulation Model for EMC Compliance Assessment of Induction Cooktops for Cardiac Implanted Electronic Devices 35

A Simulation Model for EMC Compliance Assessment of Inductio...

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35th General Assembly and Scientific Symposium of the International Union of Radio science, URSI GASS 2023

作者： Campi, T. Cecilia, V. Mattei, E. Boumerdassi, W. Rotellini, M. Tatangelo, G. Feliziani, M. University of l'Aquila L'Aquila Italy Italian National Institute of Health Endocrine-metabolic Diseases and Aging Dept. of Cardiovascular Italy University of Rome Tor Vergata Dept. of Civil and Computer Science Engineering Italy

ISBN: (纸本)9789463968096

The increasing medical use of cardiac implanted electronic devices (CIEDs) for heart patients increases the risk of device malfunction or damage due to electromagnetic interference (EMI). In this work we focus on the EMI sources produced by induction cooktops operating at intermediate frequency (IF). A computational model is proposed to evaluate the induced voltage and the magnetic field in a pacemaker according to the ISO 14117 standard. The method is based on a finite element analysis (FEA) of the magnetic field configuration. A post-processing analysis was then performed with the approximated method to evaluate the maximum voltage induced in the standard-size loop of a pacing lead. © 2023 International Union of Radio science.

关键词： Electromagnetic compatibility

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Machine Learning Techniques for Identifying Child Abusive Texts in Online Platforms

Machine Learning Techniques for Identifying Child Abusive Te...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Tanjim Mahmud Tahmina Akter Mohammad Kamal Uddin Mohammad Tarek Aziz Mohammad Shahadat Hossain Karl Andersson Department of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Dept. of Computer Science and Engineering Port City International University Chittagong Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering & Technology Chittagong Bangladesh Dept. of Computer Science and Engineering University of Chittagong Chittagong Bangladesh Cybersecurity Laboratory Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This study addresses the critical issue of child abuse in digital communications by developing advanced machine learning and deep learning models for detecting child abusive texts in the Bengali language on online platforms. The primary goal of this research is to contribute to child abuse prevention by creating an effective tool for accurately identifying abusive content. Utilizing a combination of natural language processing (NLP) techniques and deep learning algorithms, this model distinguishes between abusive and non-abusive text. The dataset, comprising manually collected and labeled comments and posts from social media sites, is categorized into Positive, Neutral, and Negative classes. For model development, a variety of machine learning algorithms including RF, DT, and SVM, alongside deep learning approaches such as LSTM and CNN, were tested. The performance of the proposed model was assessed using precision, recall, and F1 score metrics. The findings reveal that the model performs with high accuracy in classifying abusive text in Bengali, offering significant potential as a tool for effective child abuse identification and prevention.

关键词： Deep learning Support vector machines Radio frequency Measurement Machine learning algorithms Social networking (online) Prevention and mitigation Computational modeling Natural language processing Long short term memory

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COVID-19 Diagnosis Algorithm Based on Convolutional Neural Networks

COVID-19 Diagnosis Algorithm Based on Convolutional Neural N...

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2024 International Conference on Electronics and Devices, Computational science, ICEDCS 2024

作者： Han, Xinxin Xiong, Xueguan Wang, Liuling Yang, Yuxin Lu, Dandan Southwest University of Science and Technology School of Computer Science and Technology Mianyang China Southwest University of Science and Technology School of National Defense Science Mianyang China Southwest University of Science and Technology School of Economics and Management Mianyang China Southwest University of Science and Technology School of Information Engineering Mianyang China Southwest University of Science and Technology Dept. Engineering Technology Center Mianyang China

ISBN: (纸本)9798331527624

This study investigates an automated COVID-19 diagnosis system based on convolution networks, which aims to improve the effectiveness and accuracy of diagnosis. In a systematic analysis of 1709 chest X-rays, the research team used the ResNet model of deep learning for training and evaluation. Based on a detailed analysis of the confusion matrix generated by the model, the study found that the weighted average accuracy of the model and the recall rate reached 94,04% with an F1 value of up to 0,9403. This data shows better performance of convolutional neural networks in automated diagnosis. The results of the study show that neural network technology can effectively relieve the workload of doctors and provide diagnostic support for future public health events. © 2024 IEEE.

关键词： COVID-19

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Federated Learning for Real-Time Decentralized Smile Detection in Virtual Reality Environments

Federated Learning for Real-Time Decentralized Smile Detecti...

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Metaverse Computing, Networking and Applications (MetaCom), IEEE International Conference on

作者： Salabat Khan Anwar Ghani Syed Shehreyar Ali Naqvi Murad Ali Khan Muhammad Faseeh Do Hyeun Kim Muhammad Sharif Dept. of Computer Engineering Big Data Research Center Jeju National University South Korea Dept. of Computer Engineering Jeju National University South Korea Dept. of Computer Science Comsats University Islamabad Pakistan

ISBN: (数字)9798331515997

ISBN: (纸本)9798331516000

Facial expression detection is paramount in real-world applications, including patient monitoring, photo selection, and facial protection within the metaverse and virtual reality environments. Among various expressions, detecting smiles poses a significant challenge. Traditional methods relying on low-level face descriptors are being superseded by advancements leveraging Convolutional Neural Networks (CNNs) to enhance smile detection performance. In this research, we introduce and implement three CNN architectures tailored for smile detection from facial images. Our proposed model incorporates a fully convolutional neural network featuring two deep convolutional layers dedicated to smile detection. Additionally, pre-trained LeNet and DenseNet CNNs are employed for this task. The effectiveness of our approach is demonstrated through extensive evaluations of the Hromada dataset, encompassing images with and without smiles within the virtual reality environment. Moreover, we delve into detailed discussions on the practicality and efficacy of CNNs in facial expression detection, explicitly focusing on smile detection in real-time scenarios. Our experiments are conducted within a federated learning framework, enabling model inference and optimization for privacy preservation and scalability of the smile detection system in large-scale virtual reality applications. Through this decentralized approach, we address challenges related to real-time detection, model inference, and collaboration in the metaverse.

关键词： Training Solid modeling Federated learning Metaverse Scalability Collaboration Virtual reality Real-time systems Convolutional neural networks Faces

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Electromagnetic Near-Field Codebook Design for General Antenna Arrays

Electromagnetic Near-Field Codebook Design for General Anten...

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Asilomar Conference on Signals, Systems & computers

作者： Miguel Rodrigo Castellanos Robert W. Heath Dept. of Electrical Engineering and Computer Science University of Tennessee Knoxville TN Dept. of Electrical and Computer Engineering University of California San Diego La Jolla CA

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

Precoding codebooks facilitate efficient channel state information acquisition in high-dimensional MIMO systems. Most codebook designs, however, are agnostic to the antenna array composition and the underlying gain pattern profiles. We leverage an electromagnetic characterization of the antenna array manifold to generalize the codebook design problem. Specifically, we formulate the codebook design problem in a manner that incorporates polarization, near-field propagation and the antenna array characteristics. Our proposed approach is also flexible enough to adapt to different antenna types and even to heterogenous arrays. Our numerical results demonstrate that the electromagnetic-based codebooks achieve higher beamforming gains than approaches that neglect antenna effects.

关键词： Manifolds computers Mutual coupling Precoding Adaptive arrays Receivers MIMO Numerical models Electromagnetics Design optimization

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Vehicle Detection and Speed Estimation Using Deep Learning

Vehicle Detection and Speed Estimation Using Deep Learning

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Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), IEEE International Conference on

作者： Vishnu Kumar Mishra Megha Mishra Rajesh Tiwari T Ram Kumar Tarun Dhar Diwan Raj Kumar Patra Dept. of Computer Science and Engineering Malla Reddy Engineering college for Women Hyderabad India Department of Computer Science and Engineering Shri Shankaracharya Technical Campus CSVTU Bhilai India Department of Computer Science and Engineering CMR Engineering College Hyderabad India Dept. of Computer Science and CoE Atal Bihari Vajpayee University Bilaspur Chhattisgarh India

ISBN: (数字)9798331505790

ISBN: (纸本)9798331505806

The use of deep learning algorithms for vehicle detection and speed estimate in traffic surveillance systems is investigated in this research study. Convolutional Neural Networks (CNNs) are the main tool used in this study to solve the problems related to precise and timely monitoring of vehicle movement. By improving the effectiveness and dependability of current systems, the suggested approach hopes to promote traffic management for smart transportation.

关键词： Deep learning YOLO Statistical analysis Vehicle detection Surveillance Estimation Traffic control Cameras Trajectory Automobiles

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