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5th International Conference on communication and Intelligent Systems, ICCIS 2023

作者： Jani, Mayur M. Panchal, Sandip R. Patel, Hemant H. Raiyani, Ashwin Information Technology Dr. Subhash University Gujarat Junagadh362001 India Electronics and Communication Engineering Dr. Subhash University Gujarat Junagadh362001 India Computer Science and Engineering Dr. Subhash University Gujarat Junagadh362001 India Information Management Nirma University Gujarat Ahmedabad382421 India

ISBN: (纸本)9789819720781

Speech is essential in communication because it allows people to convey their thoughts, feelings, and ideas in different languages. However, due to the complexities of multilingual speech, it might be difficult to recognize each word in its associated language correctly. Fortunately, thanks to technological improvements, various automatic speech-to-text tools are available that can translate diverse languages into the required output language, hence decreasing linguistic barriers during communication. This review article aims to offer an overview of the many applications, problems, and methodologies utilized in developing multilingual speech-to-text technology. The report will also look at possible areas for future advancement and development of this technology. Overall, the presentation will emphasize the critical role that multilingual speech-to-text technology may play in breaking down language barriers and facilitating cross-linguistic communication. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Speech recognition

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An Energy-Efficient Multi-swarm Optimization in Wireless Sensor Networks

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Intelligent Automation & Soft Computing 2023年第5期36卷 1571-1583页

作者： Reem Alkanhel Kalaiselvi Chinnathambi C.Thilagavathi Mohamed Abouhawwash Mona A.Al duailij Manal Abdullah Alohali Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electronics and Communication Engineering Kongu Engineering CollegePerundurai638060India Department of Information Technology M.Kumarasamy College of EngineeringKarur639113India Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Information Systems Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Wireless Sensor Networks are a group of sensors with inadequate power sources that are installed in a particular region to gather information from the *** energy-efﬁcient data gathering methods in large-scale Wireless Sensor Networks(WSN)is one of the most difﬁcult areas of *** every sensor node has aﬁnite amount of *** power is the most signiﬁcant source in the *** is a well-known technique for enhan-cing the power feature in *** the proposed method multi-Swarm optimiza-tion based on a Genetic Algorithm and Adaptive Hierarchical clustering-based routing protocol are used for enhancing the network’s lifespan and routing *** using distributed data transmission modiﬁcation,an adaptive hier-archical clustering-based routing algorithm for power consumption is presented to ensure continuous coverage of the entire *** begin,a hierarchical cluster-ing-based routing protocol is presented in terms of balancing node energy *** Multi-Swarm optimization(MSO)based Genetic Algorithms are proposed to select an efﬁcient Cluster Head(CH).It also improves the network’s longevity and optimizes the *** a result of the study’sﬁndings,the pro-posed MSO-Genetic Algorithm with Hill climbing(GAHC)is effective,as it increases the number of clusters created,average energy expended,lifespan com-putation reduces average packet loss,and end-to-end delay.

关键词： Clustering energy consumption genetic algorithm multi swarm optimization adaptive hierarchical clustering routing cluster head

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A Novel Phase Shifter for 5G Base Station Antenna 7

A Novel Phase Shifter for 5G Base Station Antenna

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7th World Conference on Computing and communication Technologies, WCCCT 2024

作者： Du, Lin Feng, Botao Ding, Xiao Gu, Wenzhe Sun, Sanshan Deng, Li Shenzhen University College of Electronics and Information Engineering Shenzhen518060 China Macau University of Science and Technology Faculty of Humanities and Arts 999078 China Huizhou University School of Computer Science and Engineering Huizhou516007 China Sichuan Normal University College of Physics and Electronic Engineering Sichuan China Beijing University of Posts and Telecommunications College of Information and Communication Engineering Beijing China

ISBN: (纸本)9798350372328

In this paper, a phase shifter for the base station antenna with low cost, ease of manufacturing and good performance on phase shift is presented. The phase shifter consists of a metal shell, a substrate, two dielectric plates, and two conductor lines. The phase shift is realized by moving the dielectric plates longitudinally, which cover the conductor lines. With the slots in dielectric materials, the slotted part of dielectric plates can function similarly to a quarter-wavelength impedance matching. Therefore, the function of this phase shifter is that the voltage standing wave ratio (VSWR) and power distribution ratio can be guaranteed when the dielectric plate is moved. The phase shifter exhibits a bandwidth of 1.7-2.1 GHz and the corresponding phase shift is 86.88° when the dielectric plate moves by 90 mm. Moreover, its good physical features including light weight, low cost, and easy fabrication make it well suitable for base station antenna applications. © 2024 IEEE.

关键词： Phase shifters

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Rethinking attribute localization for zero-shot learning

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Science China(information Sciences) 2024年第7期67卷 184-196页

作者： Shuhuang CHEN Shiming CHEN Guo-Sen XIE Xiangbo SHU Xinge YOU Xuelong LI School of Electronic Information and Communication Huazhong University of Science and Technology School of Computer Science and Engineering Nanjing University of Science and Technology School of Artificial Intelligence Optics and Electronics Northwestern Polytechnical University

Recent advancements in attribute localization have showcased its potential in discovering the intrinsic semantic knowledge for visual feature representations, thereby facilitating significant visual-semantic interactions essential for zero-shot learning(ZSL). However, the majority of existing attribute localization methods heavily rely on classification constraints, resulting in accurate localization of only a few attributes while neglecting the rest important attributes associated with other classes. This limitation hinders the discovery of the intrinsic semantic relationships between attributes and visual features across all *** address this problem, we propose a novel attribute localization refinement(ALR) module designed to enhance the model's ability to accurately localize all attributes. Essentially, we enhance weak discriminant attributes by grouping them and introduce weighted attribute regression to standardize the mapping values of semantic attributes. This module can be flexibly combined with existing attribute localization *** experiments show that when combined with the ALR module, the localization errors in existing methods are corrected, and state-of-the-art classification performance is achieved.

关键词： zero-shot learning attention mechanism attribute localization image classification

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A Novel CNN-Based IoT System Architecture for Real-Time Detection and Prevention of Animal Intrusion in Farmland 4

A Novel CNN-Based IoT System Architecture for Real-Time Dete...

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4th International Conference on Smart electronics and communication, ICOSEC 2023

作者： Ashokkumar, C. Kumar, M. Arun Krishnan, R. Santhana Priya, S. Mohanap Narayanan, K. Lakshmi Julie, E. Golden School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Chennai India PSNA College of Engineering and Technology Department of Information Technology Dindigul India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Scad Polytechnic College Department of Computer Science and Engineering Cheranmahadevi India Francis Xavier Engineering College Department of Electronics and Communication Engineering Tirunelveli India Anna University Regional Campus Department of Computer Science and Engineering Tirunelveli India

ISBN: (纸本)9798350300888

Animal intrusion is a significant challenge for farmers, causing extensive crop damage, human injuries, and substantial financial losses. Traditional animal movement monitoring and surveillance methods alone are insufficient to provide a permanent solution. To solve this problem, a novel system architecture that combines the latest convolutional neural network (CNN) algorithm and Internet of Things (IoT) technology is proposed. Our proposed system architecture integrates various components, including a Raspberry Pi as a central processing unit, cloud storage for efficient data management, and a GSM module for instant alert generation. To train the CNN algorithm, a comprehensive and diverse Animal Dataset consisting of various animal species commonly found in farmland areas is curated. The dataset encompasses a wide range of annotated images, enabling the CNN algorithm to accurately identify and classify animals. The Raspberry Pi serves as the core of the system, responsible for real-time image processing and analysis. Utilizing the power of the CNN algorithm, the Raspberry Pi processes the captured images from strategically placed surveillance cameras. When an animal intrusion is detected, the system promptly generates an alert via the integrated GSM module, providing immediate notifications to farmers and relevant authorities. Furthermore, the system leverages cloud storage to store and manage the collected data, facilitating easy access and retrieval for analysis and system improvement. This cloud-based approach enables scalability, allowing the system to handle large amounts of data efficiently. By integrating the CNN algorithm, IoT, Raspberry Pi, cloud storage, and GSM module, a comprehensive and robust framework for real-time detection and prevention of animal intrusion is provided. The system's ability to swiftly identify and alert farmers and authorities about potential threats minimizes crop damage, ensures human safety, and significantly reduces financial l

关键词： Signal detection

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Evaluation of Focused Web Crawlers in Real-World Scenarios 3

Evaluation of Focused Web Crawlers in Real-World Scenarios

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

作者： Dhabliya, Dharmesh Alawadi, Ahmed Hussein Rai, Vijay Kumar Velusudha, N.T. Sarathy, K. Vishwakarma Institute of Information Technology Department of Information Technology Pune India College of Technical Engineering The Islamic University Najaf Iraq Ies College of Technology Department of Computer Science & Engineering Bhopal India Prince Shri Venkateshwara Padmavathy Engineering College Electronics and Communication Engineering Chennai India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science & Engineering India

ISBN: (纸本)9798350342338

This study evaluates the practical performance of focused web crawlers, tools essential for extracting targeted data from the ever-expanding web. Moving beyond theoretical analysis, we configure crawlers for specific real-world scenarios such as academic research and social media monitoring, optimizing them for precise data retrieval across a dataset of 1,000 domains and 10 million URLs. Performance is measured through meticulous logging of resource use and data freshness, while quality is ensured via cross-verification with a benchmark dataset. Ethical compliance is rigorously maintained throughout the process. The comparative analysis of different crawlers provides a framework for their selection and deployment, culminating in actionable recommendations for their use in various industries, reflective of the complex demands faced by users in a dynamic digital landscape. © 2023 IEEE.

关键词： ASD prediction communication comparative analysis coverage data extraction diagnosis early detection efficiency ensemble techniques evaluation feature selection Focused web crawlers freshness hyperparameter tuning information retrieval machine learning model training multi-modal neuroimaging data neurodevelopmental disorder performance assessment predictive accuracy real-world scenarios repetitive behaviors social interaction validation

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FOG COMPUTING ARCHITECTURE FOR INTELLIGENT MOBILITY SYSTEMS: PROSPECTS AND OBSTACLES IN AN IOT FUTURE

FOG COMPUTING ARCHITECTURE FOR INTELLIGENT MOBILITY SYSTEMS:...

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2024 International Conference on Sustainable Computing and Integrated communication in Changing Landscape of AI, ICSCAI 2024

作者： Sarishma Mishra, Vinay Kumar Karthigaikumar, P. Malik, Suraj Vigenesh, M. Marathe, Vijay Department of Computer Science & Engineering Graphic Era Deemed to be University Uttarakhand Dehradun248002 India CCSIT TMU India Department of Electronics and Communication Engineering Karpagam College of Engineering Coimbatore641032 India School of Engineering and Technology IIMT University Meerut India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore641021 India Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350355338

In order to improve traffic management, protect our natural ecosystem, and boost safe driving, the automated mass transit idea was developed. Approach for this study have become more document, and the 5Vs of Big Data are used to characterize their data. Big data analytics must therefore be used if such data are to be completely utilized. The cloud storage facilities where data is handled are connected to the ITS equipment via the Internet of Vehicles (IoV). Huge data transfers from regionally dispersed products, notwithstanding, cause limited bandwidth, delays, and power usage. Additionally, using an unified method to handle big data produces high speed that lag time apps could perhaps accept. For real-time large data processing, fog computing is thought to be a potential tool. Fog software mainly evaluated cloud - based applications by distributing business applications at the program's periphery, which offers quicker answers to utility inquiries and conserves network services. Regrettably, in the changing world, it is difficult to apply cloud services and the logic design for real-time large data computation. This article suggests an innovative design for real-time big data analytics in the world. Three dimensions-intelligent intelligence, real-time big data analytics, and dimension-are combined in the suggested design. The ecosystem, big data features, the functional design for authentic big data analytics, and the several clever information technology platforms are also covered in detail in this article. More significantly, this article examines the advantages and disadvantages of assessment big data analytics and cloud technology into the surroundings. The important points and upcoming study path’s part addresses some topics that need to be taken into account for the suggested design to be implemented effectively. © 2024 IEEE.

关键词： Fog computing

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A Novel Framework in Scheduling Packets for Energy-Efficient Bandwidth Allocation in Wireless Networks 2

A Novel Framework in Scheduling Packets for Energy-Efficient...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Sivajothi, E. Jayaudhaya, J. Santhiya, S. Thapa, Naresh Kumar Kamatchi, S. Ganapathy, N. Bala Sundara Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India R.M. D. Engineering College Department of Electronics and Communication Engineering Tamil Nadu India M. Kumarasamy College of Engineerin Department of Computer Science and Engineering Tamil Nadu India R.M. K. Engineering College Department of Electronics and Communication Engineering Tamil Nadu India Saveetha School of Engineering Simats Department of Electronics and Communication Engineering Tamil Nadu India Panimalar Engineering College Department of Information Technology Tamil Nadu India

ISBN: (纸本)9781665456302

The research work provides a brand-new technique for distributing bandwidth in wireless networks that is also energy-efficient. To begin, look into how channel allocations, which ultimately determine the transmission rate for mobile terminals, affect the fundamental relationship that exists between energy use and transmission rates. The Fatality Selection Algorithm (FSA) and the Receiver Selection Algorithm (RSA) are two methodologies that control connection admittance to reduce the amount of energy used by each individual terminal. Furthermore, provide a corrective strategy for statistically satisfying the quality of service (QoS) criteria throughout the resource allocation process. Throughput, call blockage probability, and the energy consumption rate of each successfully sent bit are used to evaluate the efficiency of the recommended solutions. An extensive investigation into analysis and simulation is carried out in the case of Poisson and self-similar. © 2023 IEEE.

关键词： Quality of service

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Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

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computers, Materials & Continua 2022年第4期71卷 1473-1487页

作者： Pudi Sekhar T.J.Benedict Jose Velmurugan Subbiah Parvathy E.Laxmi Lydia Seifedine Kadry Kuntha Pin Yunyoung Nam Department of Electrical and Electronics Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Computer Applications Government Arts&Science CollegeKanyakumari629401India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126India Department of Computer Science and Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Department of ICT Convergence Soonchunhyang UniversityKorea Department of Computer Science and Engineering Soonchunhyang UniversityKorea

With the incorporation of distributed energy systems in the electric grid,transactive energy market(TEM)has become popular in balancing the demand as well as supply adaptively over the *** classical grid can be updated to the smart grid by the integration of information and communication Technology(ICT)over the *** TEM allows the Peerto-Peer(P2P)energy trading in the grid that effectually connects the consumer and prosumer to trade energy among *** the same time,there is a need to predict the load for effectual P2P energy trading and can be accomplished by the use of machine learning(DML)*** some of the short term load prediction techniques have existed in the literature,there is still essential to consider the intrinsic features,parameter optimization,*** *** this aspect,this study devises new deep learning enabled short term load forecasting model for P2P energy trading(DLSTLF-P2P)in *** proposed model involves the design of oppositional coyote optimization algorithm(OCOA)based feature selection technique in which the OCOA is derived by the integration of oppositional based learning(OBL)concept with COA for improved convergence ***,deep belief networks(DBN)are employed for the prediction of load in the P2P energy trading *** order to additional improve the predictive performance of the DBN model,a hyperparameter optimizer is introduced using chicken swarm optimization(CSO)algorithm is applied for the optimal choice of DBN parameters to improve the predictive *** simulation analysis of the proposed DLSTLF-P2P is validated using the UK Smart Meter dataset and the obtained outcomes demonstrate the superiority of the DLSTLF-P2P technique with the maximum training,testing,and validation accuracy of 90.17%,87.39%,and 87.86%.

关键词： Energy trading distributed systems power generation load forecasting deep learning peer-to-peer

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Learning Face Expression Features from Video Using Spatio-Temporal Feature Extractor and CNN-LSTM 7

Learning Face Expression Features from Video Using Spatio-Te...

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7th International Conference on Image information Processing, ICIIP 2023

作者： Gavade, Priyanka A. Bhat, Vandana S. Gavade, Anil B. Kle Technological University's Dr. Msscet Department of Computer Science And Engineering Belagavi India Sdm College of Engineering And Technology Department of Information Science And Engineering Dharwad India Gogte Institute of Technology Department of Electronics And Communication Engineering Belagavi India

ISBN: (纸本)9798350371406

The study of recognizing facial expressions (FER) has posed a significant challenge within the realm of computer vision over the past three decades. However, it continues to garner attention from researchers because of persistent challenges such as-blurring, illumination variation, and pose variation. Existing studies have predominantly focused on static image-based FER, neglecting temporal information. This research paper presents a systematic approach to identify and classify face expressions from videos by leveraging both spatial and temporal information. Datasets containing movie clips having spontaneous expression shot in the wild and the laboratory trained dataset, shot in a controlled environment are considered for evaluation, namely, RAVDESS, SAVEE, CK+ and AFEW. Performance metrics, such as Accuracy, Precision, and Recall, are employed to quantitatively assess the quality of the proposed Facial Expression Recognition (FER) system. Collectively, the findings validate the effectiveness of the proposed approach, solidifying its status as a significant advancement in the field of automatic facial expression recognition. © 2023 IEEE.

关键词： Long short-term memory

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