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5th International Conference on Soft computing and Signal Processing, ICSCSP 2022

作者： Babu, Adithya Sathiya, R.R. Department of Computer Science and Engineering Amrita School of Computing AmritaVishwaVidyapeetham Coimbatore India

ISBN: (纸本)9789811986680

Cloud computing is currently the most dynamic and evolutionary segment in our computational world. Since it is widely deployed by many clients from across the industry, the cloud service providers need to keep the Quality of Service (QoS) up to the Service level Agreements (SLA). Therefore, proper management of its resource functioning is the key objective to make profit as well as to meet the client’s optimal amount of services needed to complete a job/task. In this paper we predict one of the most important resources required for the smooth functioning of the jobs with one of its kind LSTM architectures. The objective is obtaining better fidelity in less time with the real-world Google Cluster Trace Dataset 2011. For data preprocessing and extracting the essential features from the original high-dimensional workload data, we use a custom autoencoder. Long short-term memory (LSTM) is used as our deep learning algorithm to facilitate the highly variable time series workload prediction for all the jobs/tasks based on certain prediction lengths like per hour & day. For validating we compare with the previously used approaches of deep learning techniques using Facebook’s Prophet model and ARIMA models with the same dataset. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Decoding Tomorrow's Gold Prices: A Comparative Study of GRU and CNN-LSTM in the Iranian Market 10

Decoding Tomorrow's Gold Prices: A Comparative Study of GRU ...

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10th International Conference on Web Research, ICWR 2024

作者： Baradaran, Amir Hossein Bohlouli, Masoumeh Motlagh, Mohammad Reza Jahed Iran University of Science and Technology School of Electrical Engineering Tehran Iran Alzahra University Department of Computer Engineering Tehran Iran Iran University of Science and Technology School of Computer Engineering Tehran Iran

ISBN: (纸本)9798350394986

With the rising prominence of gold as a lucrative investment avenue in Iran, this research delves into predicting the future price of 18-carat gold. In pursuit of this objective, a comprehensive comparison is conducted between two neural network architectures: the Gated Recurrent Unit (GRU) as a single structure and a hybrid model combining Convolutional Neural Network (CNN) and Long Short-Term Memory Neural Network (LSTM). The evaluation criteria employed focus on error metrics to gauge the accuracy of price predictions. Results reveal that the CNN-LSTM hybrid neural network exhibits superior performance, showcasing lower error values in predicting the price of 18-carat gold in Iran. Consequently, the chosen model, CNN-LSTM, is employed to forecast the following day's gold prices, providing valuable insights for investors navigating the Iranian market. This research contributes to the ongoing discourse on gold investment strategies by highlighting the effectiveness of advanced neural network models in enhancing predictive accuracy. © 2024 IEEE.

关键词： Long short-term memory

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Intelligent IoT-Driven Advanced Predictive Maintenance System for Industrial Applications

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SN computer science 2025年第2期6卷 1-24页

作者： Dhinakaran, D. Edwin Raja, S. Velselvi, R. Purushotham, N. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai India Department of Computer Science and Engineering P.S.R Engineering College Sivakasi India Department of CSE School of Computing Mohan Babu University Tirupati India

Predictive Maintenance (PdM) aims to ensure the continuous operation of high-risk industrial systems. This challenge is especially critical in environments where equipment failure can cause major financial losses and disrupt operations. Traditional PdM approaches, while fairly effective, often fall short in accurately predicting failures due to their reliance on simple statistical methods and predefined rules. The proposed work introduces an Intelligent IoT-Driven Advanced Predictive Maintenance System (APdM) that addresses these limitations. By integrating advanced machine learning techniques, including supervised and reinforcement learning, with IoT technologies to enhance predictive accuracy as well as the operational efficiency. The system leverages Explainable AI (XAI) to ensure transparency in decision-making and employs federated learning for distributed model training, preserving data privacy across multiple edge devices. The proposed APdM system was evaluated using the Kaggle "Air Compressor Predictive Maintenance" Dataset, which provided extensive sensor data from heavy vehicle operations. Comparative analysis against four prevailing PdM approaches—Traditional PdM, Decision Support System-PdM, Genetic Algorithm-based PdM, and AI-based PdM—demonstrated the superior performance of our system. The results show that the APdM system, powered by intelligent IoT integration, diminishes the Mean Error Percentage (MEP) by up to 38.4% compared to traditional methods and decreases the Mean Absolute Error (MAE) by 36.7% relative to DSS-PdM. Additionally, the system achieves a 78% improvement in Symmetric Mean Absolute Percentage Error (SMAPE) over GA-PdM, underscoring its robust performance in real-world industrial scenarios. These outcomes validate the effectiveness of the proposed APdM system, marking a significant improvement in the field of predictive maintenance through the intelligent integration of IoT technologies. © The Author(s), under exclusive licence to S

关键词： Explainable AI IoT Machine learning Predictive maintenance Supervised and reinforcement learning

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Anisotropic leaky-like perturbation with subwavelength gratings enables zero crosstalk

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Light(science & Applications) 2023年第6期12卷 1153-1161页

作者： Md Faiyaz Kabir Md Borhan Mia Ishtiaque Ahmed Nafiz Jaidye Syed Z.Ahmed Sangsik Kim Department of Electrical and Computer Engineering Texas Tech UniversityLubbockTX 79409USA Department of Physics and Astronomy Texas Tech UniversityLubbockTX 79409USA School of Electrical Engineering Korea Advanced Institute of Science and TechnologyDaejeon 34141South Korea

Electromagnetic coupling via an evanescent field or radiative wave is a primary characteristic of light,allowing optical signal/power transfer in a photonic circuit but limiting integration density.A leaky mode,which combines both evanescent field and radiative wave,causes stronger coupling and is thus considered not ideal for dense *** we show that a leaky oscillation with anisotropic perturbation rather can achieve completely zero crosstalk realized by subwavelength grating(SWG)*** oscillating fields in the SWGs enable coupling coefficients in each direction to counteract each other,resulting in completely zero *** experimentally demonstrate such an extraordinarily low coupling between closely spaced identical leaky SWG waveguides,suppressing the crosstalk by≈40 dB compared to conventional strip waveguides,corresponding to≈100 times longer coupling *** leaky-SWG suppresses the crosstalk of transverse–magnetic(TM)mode,which is challenging due to its low confinement,and marks a novel approach in electromagnetic coupling applicable to other spectral regimes and generic devices.

关键词： crosstalk coupling enable

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PALCT: vehicle-to-vehicle communication based on pseudonym assignment and encryption scheme using delay minimisation cover tree algorithm

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International Journal of Vehicle Information and Communication Systems 2025年第2期10卷 132-147页

作者： Tandon, Righa Gupta, P.K. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science and Engineering Jaypee University of Information Technology HP Solan173 234 India Department of Data Science School of Computing Mohan Babu University AP Tirupati517 102 India

Vehicle-to-vehicle communication is one of the new paradigms of networking, which should be secure, fast, and efficient. In this paper, we propose a framework that implements the pseudonym-based authentication scheme in which communication among vehicles is encrypted by using a matrix array symmetric key (MASK), digital signature algorithm (DSA), and intelligent water drop (IWD). The proposed security scheme also ensures the handling of many security attacks like key-guessing, non-repudiation, replay, and modification. In the proposed scheme, to preserve the identity of the vehicle, we have provided the different pseudonyms for each vehicle in the network that ensure secure communication among vehicles. Furthermore, the proposed delay minimisation cover tree algorithm ensures the issue of timed delay during vehicle-to-vehicle communication. In this algorithm, we have used Dijkstra’s algorithm for finding the optimal shortest path during vehicular communication. The results obtained show that the proposed scheme is effective and efficient, as it reduces the time delay by 4% for 140 vehicle nodes and 28.4% for 1000 vehicle nodes. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Encryption algorithms

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A Novel Approach for Sign Language Video Generation Using Deep Networks 2

A Novel Approach for Sign Language Video Generation Using De...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Kumar, Sachin Deepa, B. Kavitha, T. Tamilselvi, M. Sathiyapriya, V. Natarajan, B. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India Srmist Ramapuram Campus Department of Electroncis and Communication Engineering Ramapuram India MA.M College of Engineering and Technology Department of Computer Science and Engineering Trichy India Roever Engineering College Department of Computer Science and Engineering Perambalur India Knowledge Institute of Technology Department of Computer Science and Engineering Salem India School of Computer Science and Engineering Vellore Institue of Technology Chennai Campus Chennai India

ISBN: (纸本)9798350373110

Sign language enhances the communication capabilities of the deaf-mute community, allowing for a deeper understanding of their needs and emotions. These languages are highly structured and visual, using gestures and various upper body movements such as those of the hands, face, eyes, and gaze. Researchers face numerous challenges in recognizing and translating the diverse variations in sign movements, which requires specialized expertise in computer vision and artificial intelligence. Sign language recognition and translation research has garnered global attention. This research work introduces a novel methodology for generating sign gesture videos from text inputs by integrating various intelligent techniques. The proposed model employs an enhanced generative adversarial network (GAN) to create sign videos from input sentences. Experiments with the proposed VideoGAN model using diverse sign language datasets from multiple countries have demonstrated its effectiveness. The research outcomes highlight its contribution to high-quality video production, with improved evaluation metrics underscoring the model's superior performance. © 2024 IEEE.

关键词： Generative adversarial networks

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DESIGN OF RF FEEDING NETWORK [1:8] USING UNEQUAL AMPLITUDE HYBRID COUPLER FOR LOW SLL X-BAND RADAR APPLICATIONS

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第5期84卷 1-19页

作者： Kothapudi, Venkata Kishore Sunayana, Lella Divya, Shaik Kumar, Vijay Pappula, Lakshman Gottapu, Srinivasa Kiran Guntur District Andhra Pradesh Vadlamudi522213 India Department of Communication Engineering School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Electrical Electronics and Communication Engineering GITAM School of Technology GITAM University Andhra Pradesh Visakhapatnam530045 India School of Science and Engineering Engineering and Computer Science Department Benedict College 1600 Harden St. ColumbiaSC29204 United States

The design simulation and manufacturing of an x-band frequency uneven amplitude 90° hybrid coupler are described in this paper. This hybrid coupler is used to create a feeder network with eight output ports operating at 9.3 GHz. The overall goal is to design and analyze an uneven power feeding network (1:8) for low SLL x-band radar applications employing a hybrid coupler. The specific goal is to create a hybrid coupler with equal amplitude, a hybrid coupler with uneven amplitude [Taylor’s amplitude = –17 dB], and a feeding network using hybrid couplers. The goal of this research is to improve the ability of a single transmitter to provide power to more than one antenna, all at a time either equally or unequally, and this can be achieved by designing a desired number of hybrid couplers and connecting them to the specific number of antennas at the output end and to the single transmitter at the input end. © 2025 by Begell House, Inc.

关键词： Transmitters

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Data-Driven Approach for Condition Monitoring and Improving Power Output of Photovoltaic Systems

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computers, Materials & Continua 2023年第3期74卷 5757-5776页

作者： Nebras M.Sobahi Ahteshamul Haque V S Bharath Kurukuru Md.Mottahir Alam Asif Irshad Khan Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah 21589Saudi Arabia Advance Power Electronics Research Lab Department of Electrical EngineeringJamia Millia IslamiaNew DelhiIndia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Increasing renewable energy targets globally has raised the requirement for the efficient and profitable operation of solar photovoltaic(PV)*** light of this requirement,this paper provides a path for evaluating the operating condition and improving the power output of the PV system in a grid integrated *** achieve this,different types of faults in grid-connected PV systems(GCPVs)and their impact on the energy loss associated with the electrical network are analyzed.A data-driven approach using neural networks(NNs)is proposed to achieve root cause analysis and localize the fault to the component level in the *** localized fault condition is combined with a parallel operation of adaptive neurofuzzy inference units(ANFIUs)to develop a power mismatch-based control unit(PMCU)for improving the power output of the *** develop the proposed framework,a 10-kW single-phase GCPV is simulated for training the NN-based anomaly detection approach with 14 deviation ***,the developed algorithm is combined with the PMCU implemented with the experimental setup of *** results identified 98.2%training accuracy and 43000 observations/sec prediction speed for the trained classifier,and improved power output with reduced voltage and current harmonics for the grid-connected PV operation.

关键词： Condition monitoring anomaly detection performance evaluation fault classification optimization

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A Machine Learning Approach for Detecting Fake News in Hindi 10

A Machine Learning Approach for Detecting Fake News in Hindi

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10th IEEE Uttar Pradesh Section International Conference on electrical, Electronics and computer engineering, UPCON 2023

作者： Nagesh, Shreyas Sampath, Aditya N Guruprasad, Pradhumna Salai Sanjay, S. Jeyakumar, G. Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Electrical and Electronics Engineering Coimbatore India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science Engineering Coimbatore India

ISBN: (纸本)9798350382471

Several solutions exist to detect whether English news articles are fake or not. However, in a country such as India, news articles are published in numerous regional languages too. There has been little research work done on regional languages such as Hindi. This work proposes a framework to detect Hindi fake news articles. A dataset of nearly 4500 news articles has been collected in Hindi, written using the Devanagari Script. These news articles are scraped using BeautifulSoup and Scrapy libraries. The IndicBERT and MuRIL libraries, developed earlier in Hindi, are used to generate the word embedding. SVM, Logistic Regression, and a simple CNN are used as classifiers. Extensive experiments have been methodically conducted in order to find the best combination to detect fake news in Hindi. The best combination was found to be MuRIL and SVM, yielding 98.58% classification accuracy. A standalone GUI running on a local host is built to demonstrate the working model. © 2023 IEEE.

关键词： Hindi Fake News IndicBERT Machine Learning MuRIL Natural Language Processing StanfordNLP

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Optimal Multi-Bitrate Video Caching and Processing in Edge computing: A Stackelberg Game Approach

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IEEE Internet of Things Journal 2025年第13期12卷 25059-25076页

作者： Guo, Min Zhang, Di Xing, Weiwei Shao, Xun Liu, Zhi Zhang, Yaoxue Beijing Jiaotong University School of Software Engineering Beijing100044 China Toyohashi University of Technology Department of Electrical and Electronic Information Engineering Aichi441-8580 Japan The University of Electro-Communications Department of Computer and Network Engineering Tokyo182-8585 Japan Tsinghua University Department of Computer Science and Technology Beijing100084 China

By caching and transcoding video files on edge servers, video edge caching (VEC) can alleviate network congestion and improve user experience. To achieve this, VEC needs to address resource allocation and pricing problems and develop caching and transcoding strategies. However, existing works have not effectively coordinated and resolved these issues. Towards this end, we propose a game-theoretic multi-bitrate video caching and processing method for VEC. Specifically, to address these interdependent issues concurrently, we first incorporate multidimensional strategies into the system model and formalize the profit maximization problems of the network service provider and multiple video providers (VPs) as a constrained multi-objective optimization problem. Second, we transform the problem into a Stackelberg game and identify a Nash equilibrium as the solution. To solve the game problem, we decompose the game process into two stages focusing on resource competition and price determination, respectively. Then, we address the problems of transcoding and caching strategies development, and resource allocation in parallel across varying resource price conditions. The transcoding strategy is determined based on the constraints imposed by delay, value, and the source video. For the caching strategy development and resource allocation, we transform them into two grouped knapsack problems and apply the "dominant strategy» from game theory to identify the "feasible strategy» of VPs. Finally, we determine the resource prices in a traversal manner. Theoretical analysis shows that our proposed method achieves the optimal solution. Experimental results show that our algorithm achieve a 6.04% increase in overall profit compared to the greedy strategy algorithm. © 2025 IEEE.

关键词： Costs

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