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PSO-DBNet for Peak-to-Average Power Ratio Reduction Using Deep Belief Network

computer Systems science & engineering 2023年第5期45卷 1483-1493页

作者： A.Jameer Basha M.Ramya Devi S.Lokesh P.Sivaranjani D.Mansoor Hussain Venkat Padhy Department of Computer Science and Engineering Hindusthan Institute of TechnologyCoimbatore641032India Department of Computer Science and Engineering Hindusthan College of Engineering and TechnologyCoimbatore641032India Department of Electronics and Communication Engineering Kongu Engineering CollegeErode638060India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India School of Computing Science&Engineering VIT Bhopal UniversityBhopal466114India

Data transmission through a wireless network has faced various signal problems in the past *** orthogonal frequency division multiplexing(OFDM)technique is widely accepted in multiple data transfer patterns at various frequency bands.A recent wireless communication network uses OFDM in longterm evolution(LTE)and 5G,among *** main problem faced by 5G wireless OFDM is distortion of transmission signals in the *** transmission loss is called peak-to-average power ratio(PAPR).This wireless signal distortion can be reduced using various *** study uses machine learning-based algorithm to solve the problem of PAPR in 5G wireless *** transmit sequence(PTS)helps in the fast transfer of data in wireless *** is merged with deep belief neural network(DBNet)for the efficient processing of signals in wireless 5G *** indicates that the proposed system outperforms other existing ***,PAPR reduction in OFDM by DBNet is optimized with the help of an evolutionary algorithm called particle swarm ***,the specified design supports in improving the proposed PAPR reduction architecture.

关键词： 5G wireless network orthogonal frequency division multiplexing signal distortion peak to average power ratio partial transmit sequence deep belief network

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Efficient Meeting Insights: NLP-Enhanced Summarization of Voice and Text 15

Efficient Meeting Insights: NLP-Enhanced Summarization of Vo...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Brahmachari, K. Sankar Hanumesh, Mangaluru Anitha, G. Pattanaik, Balachandra Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India Wollega University School of Electrical Computer Engineering Nekemte Ethiopia

ISBN: (纸本)9798350370249

In order to improve meeting efficiency in the ever-changing workplace, research has mostly focused on sophisticated text summarization techniques such as machine learning, hybrid, semantic, and graph-based methods. These approaches have shown efficient in extracting key information from lengthy talks, hence dramatically improving decision-making and knowledge retention. However, their potential has been somewhat limited by a major concentration on processing material in a single language, which ignores the complex, multilingual environment of modern global enterprises. Our most recent research project tries to overcome this gap by combining these various summarizing approaches into acomplete, language-neutral ***, to address the specific difficulty of long meetings, when extracting insights might be especially difficult for those who are not there, we propose using the Longformer encoder-decoder paradigm. This methodology excels at distilling lengthy talks into digestible summaries, regardless of the original language, ensuring that critical insights are easily accessible to anybody, hence increasing inclusion and comprehension across multinational teams. © 2024 IEEE.

关键词： Semantics

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LiDAR-Based Elderly Fall Detection System for Indoor Environments Using Neural Network Algorithms 4

LiDAR-Based Elderly Fall Detection System for Indoor Environ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Vignesh, Puthucode Ganesh Nithin Kumar, S. Senthil Kumar, T. Jeyakumar, G. Amrita School of Computing Dept. of Computer Science and Engineering Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798331523923

Fall detection systems are critical in elderly care and healthcare monitoring, but traditional camera-based solutions raise significant privacy concerns. This research presents a novel approach utilizing LiDAR (Light Detection and Ranging) sensors for fall detection in indoor environments, addressing privacy issues while maintaining high accuracy. We introduce LiFall, a new dataset captured using a Velodyne VLP 16 Puck LiDAR sensor, comprising 12 scenes and over 500 frames simulating various fall scenarios (including back falls, bed falls, and walking falls), contributing to the scarcity of LiDAR-based fall detection datasets. Our methodology employs two advanced deep learning architectures: Annularly Convolutional Neural Networks (ACNN) and Dynamic Graph Convolutional Neural Networks (DGCNN). Evaluating these models on both the ModelNet40 dataset and our custom LiFall dataset, results show DGCNN outperforms ACNN, achieving an accuracy of 84% on the ModelNet40 dataset with a 70:30 train-test split. On the LiFall dataset, despite challenges with dataset imbalance and achieving lower detection accuracy even after data augmentation, our approach demonstrates the potential of LiDAR-based fall detection systems. Compared to other fall detection methods, our LiDAR-based system offers significant advantages beyond privacy preservation, such as robust performance in low-light conditions and the ability to capture detailed 3D spatial information. While our current implementation is limited to laboratory settings, future work will focus on real-world deployments and further addressing dataset imbalance issues to enhance overall system performance. This research contributes to the field by providing a privacy-preserving, high-performance alternative to camera-based fall detection systems, paving the way for more acceptable monitoring solutions in sensitive indoor environments. © 2025 IEEE.

关键词： Fall detection

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Benchmarking Indian E-Commerce Giants: Insights from Festive Sales Performance

Benchmarking Indian E-Commerce Giants: Insights from Festive...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Pranav, H. Harshini, Muppavarapu Sri Tarun, Rachuri Radha, D. Amrita School of Computing Dept of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331512965

This study presents a comparative analysis of the websites of three major e-commerce platforms in India: during the busy shopping sale of Diwali and Navratri. Using metrics derived from Lighthouse audits, the report evaluates the performance, accessibility, SEO, and adherence to best practices of each site. Key factors such as First Contentful Paint (FCP), Largest Contentful Paint (LCP), Speed Index, and overall user experience are examined. The analysis highlights the strengths and areas for improvement of each platform, with recommendations for enhancing load times, user accessibility, and search engine optimization. This comparison aims to provide actionable insights into optimizing the performance and usability of e-commerce websites. © 2025 IEEE.

关键词： Sales

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Next-Gen Parking Facilities: Deep Learning for Face and License Plate based Access 18

Next-Gen Parking Facilities: Deep Learning for Face and Lice...

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18th INDIAcom;11th International Conference on computing for Sustainable Global Development, INDIACom 2024

作者： Balasubramanian, K. Kannan, N. Sivakami, S. Senthil, S. Immaculate Rexi Jenifer, P. Murali, N. E. G. S. Pillay Engineering College Department of Computer Science and Engineering Nagapattinam India K. Ramakrishnan College of Technology Department of Computer Science and Engineering Trichy India School of Computing SASTRA Department of Computer Science and Engineering India

ISBN: (纸本)9789380544519

Parking congestion has become a major problem in today's metropolitan environments, resulting in lost time, higher emissions, and irritated drivers. There is a severe shortage of parking places in metropolitan areas because of the ever-growing number of the vehicles and the ever-growing urban population. This shortage has exacerbated traffic congestion and environmental pollution in addition to making it difficult and time-consuming to find a parking space. Conventional parking management systems are finding it harder and harder to handle these issues;they frequently depend on physical barriers or manual ticketing. Innovative approaches that incorporate automation and technology are becoming more prevalent to address this problem. Conventional parking management systems frequently depend on human intervention and physical infrastructure, which leads to inefficiencies and constrained scalability. In contrast, new opportunities for a more intelligent, effective, and user-friendly experience are made possible by the incorporation of deep learning methods and cutting-edge computer vision technology into parking management. This system makes use of two essential elements: automatically recognizing license plate numbers for vehicle identification and facial recognition technology for person identification. Through the smooth integration of these technologies, the system not only makes parking easier, but it also improves security and maximizes parking space usage. We will examine the essential elements, advantages, and possible effects of the Face and Number Plate-Based Smart Parking System in this survey. According to experimental findings, employing a face- and number-plate verification method can increase the efficiency of smart parking systems. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Deep learning Face recognition system Features based matching Slot allocation Smart parking system

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Revolutionizing Scene Text Recognition: Unleashing the Power of Dual Step Attention Mechanism in the Encoder 6th

Revolutionizing Scene Text Recognition: Unleashing the Power...

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6th International Conference on Recent Innovations in computing, ICRIC 2023

作者： Selvam, Prabu Sumathi, M. Maniappan, Varun Padmavathi, A. Natarajan, B. Syed Husain, S. Department of Computer Science and Engineering SRM Institute of Science & Technology Tiruchirapalli Campus Tiruchirapalli India School of Computing SASTRA Deemed University Thanjavur India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai601103 India Department of Electronics and Communication Engineering K. Ramakrishnan College of Engineering Tiruchirappalli India

ISBN: (纸本)9789819734412

Text recognition in natural scenes presents a significant challenge in computer vision despite its widespread applications in real-life scenarios. Deep learning advancements have notably enhanced the precision of scene text recognition. The prevailing framework for scene text recognition currently revolves around the encoder–decoder architecture, with Connectionist Temporal Classification (CTC) and attention mechanisms serving as popular text recognition techniques. However, existing methods rooted in the encoder–decoder architecture primarily integrate the attention mechanism into the decoder, limiting their capacity to identify characters accurately. Conversely, CTC-based methods exhibit faster processing times and impose fewer computational burdens, yet they are comparatively less effective than their attention-based counterparts. We introduce a new text recognition network based on a transformer-based network to address these limitations. Our proposed network introduces the Dual Step Attention Mechanism (DSAM), wherein the encoder's attention mechanism captures the overall text location within the image. In contrast, the decoder's attention mechanism precisely identifies the location of each character in the word image. In addition, we have employed a bidirectional encoding to capture the capture character sequence in both directions. To validate the efficacy of our approach, we conducted a comprehensive performance evaluation comparing our proposed network with state-of-the-art methods across six diverse text datasets, namely IIIT5K, SVT, IC13, IC15, SVTP, and CUTE. The results indicate that our model outperforms existing approaches across various metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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Denoising Graph Inference Network for Document-Level Relation Extraction

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Big Data Mining and Analytics 2023年第2期6卷 248-262页

作者： Hailin Wang Ke Qin Guiduo Duan Guangchun Luo School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Computing and Artificial Intelligence Southwestern University of Finance and EconomicsChengdu 611130China

Relation Extraction(RE)is to obtain a predefined relation type of two entities mentioned in a piece of text,e.g.,a sentence-level or a document-level *** existing studies suffer from the noise in the text,and necessary pruning is of great *** conventional sentence-level RE task addresses this issue by a denoising method using the shortest dependency path to build a long-range semantic dependency between entity ***,this kind of denoising method is scarce in document-level *** this work,we explicitly model a denoised document-level graph based on linguistic knowledge to capture various long-range semantic dependencies among *** first formalize a Syntactic Dependency Tree forest(SDT-forest)by introducing the syntax and discourse dependency ***,the Steiner tree algorithm extracts a mention-level denoised graph,Steiner Graph(SG),removing linguistically irrelevant words from the *** then devise a slide residual attention to highlight word-level evidence on text and ***,the classification is established on the SG to infer the relations of entity *** conduct extensive experiments on three public *** results evidence that our method is beneficial to establish long-range semantic dependency and can improve the classification performance with longer texts.

关键词： Relation Eextraction(RE) document-level denoising linguistic knowledge attention mechanism

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DiffMat:Latent diffusion models for image-guided material generation

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Visual Informatics 2024年第1期8卷 6-14页

作者： Liang Yuan Dingkun Yan Suguru Saito Issei Fujishiro Graduate School of Science and Technology Keio UniversityKanagawaJapan School of Computing Tokyo Institute of TechnologyTokyoJapan Department of Information and Computer Science Keio UniversityKanagawaJapan

Creating realistic materials is essential in the construction of immersive virtual *** existing techniques for material capture and conditional generation rely on flash-lit photos,they often produce artifacts when the illumination mismatches the training *** this study,we introduce DiffMat,a novel diffusion model that integrates the CLIP image encoder and a multi-layer,crossattention denoising backbone to generate latent materials from images under various *** a pre-trained StyleGAN-based material generator,our method converts these latent materials into high-resolution SVBRDF textures,a process that enables a seamless fit into the standard physically based rendering pipeline,reducing the requirements for vast computational resources and expansive *** surpasses existing generative methods in terms of material quality and variety,and shows adaptability to a broader spectrum of lighting conditions in reference images.

关键词： SVBRDF Diffusion model Generative model Appearance modeling

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A Survey on the Role of Complex Networks in IoT and Brain Communication

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computers, Materials & Continua 2023年第9期76卷 2573-2595页

作者： Vijey Thayananthan Aiiad Albeshri Hassan A.Alamri Muhammad Bilal Qureshi Muhammad Shuaib Qureshi Cybersecurity Department Faculty of ComputingEngineering and ScienceUniversity of SouthWalesWalesUK Department of Computer Science Faculty of Computing and Information Technology(FCIT)King Abdulaziz UniversityJeddah21589Saudi Arabia Computer Science Department Umm Al-Qura UniversityMakkah21955Saudi Arabia Department of Computer Science&IT University of Lakki MarwatLakki Marwat28420KPKPakistan Department of Computer Engineering Chungnam National UniversityDaejeon34134Republic of Korea

Complex networks on the Internet of Things(IoT)and brain communication are the main focus of this *** benefits of complex networks may be applicable in the future research directions of 6G,photonic,IoT,brain,etc.,communication *** data traffic,huge capacity,minimal level of dynamic latency,*** some of the future requirements in 5G+and 6G communication *** emerging communication,technologies such as 5G+/6G-based photonic sensor communication and complex networks play an important role in improving future requirements of IoT and brain *** this paper,the state of the complex system considered as a complex network(the connection between the brain cells,neurons,etc.)needs measurement for analyzing the functions of the neurons during brain ***,we measure the state of the complex system through *** 5G+/6G-based photonic sensor nodes,finding observability influenced by the concept of contraction provides the stability of *** IoT or any sensors fail to measure the state of the connectivity in the 5G+or 6G communication due to external noise and attacks,some information about the sensor nodes during the communication will be ***,neurons considered sing the complex networks concept neuron sensors in the brain lose communication and ***,affected sensor nodes in a contraction are equivalent to compensate for maintaining stability *** this compensation,loss of observability depends on the contraction size which is a key factor for employing a complex *** analyze the observability recovery,we can use a contraction detection algorithm with complex network *** survey paper shows that contraction size will allow us to improve the performance of brain communication,stability of neurons,etc.,through the clustering coefficient considered in the contraction detection *** addition,we discuss the scalability of IoT communication using 5G+/6G

关键词： Complex networks emerging communication IoT based on 6G systems neuroscience photonic technology

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Deep Convolutional Neural Network Based Churn Prediction for Telecommunication Industry

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computer Systems science & engineering 2022年第12期43卷 1255-1270页

作者： Nasebah Almufadi Ali Mustafa Qamar Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computing School of Electrical Engineering and Computer ScienceNational University of Sciences and Technology(NUST)IslamabadPakistan

Currently,mobile communication is one of the widely used means of ***,it is quite challenging for a telecommunication company to attract new *** recent concept of mobile number portability has also aggravated the problem of customer *** need to identify beforehand the customers,who could potentially churn out to the *** the telecommunication industry,such identification could be done based on call detail *** research presents an extensive experimental study based on various deep learning models,such as the 1D convolutional neural network(CNN)model along with the recurrent neural network(RNN)and deep neural network(DNN)for churn *** use the mobile telephony churn prediction dataset obtained from ***,containing the data for around 100,000 individuals,out of which 86,000 are non-churners,whereas 14,000 are churned *** imbalanced data are handled using undersampling and *** accuracy for CNN,RNN,and DNN is 91%,93%,and 96%,***,DNN got 99%for ROC.

关键词： Deep learning machine learning churn prediction convolutional neural network recurrent neural network

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