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13th International Conference on Cloud Computing, Data science and engineering, Confluence 2023

作者： Puri, Naman Munjal, Geetika Amity School of Engineering and Technology Department of Computer Science Engineering Noida India

ISBN: (纸本)9781665462631

Pneumonia has been a concerning issue worldwide. This infectious disease has a higher mortality rate than Covid-19. More than two million individuals lost their lives in 2019 out of which almost 600,000 were infants less than 5 years of age. Globally, identification of the disease is done manually by radiologists, but this method is highly unreliable as its accuracy is not sufficiently good. With the evolution of computational resources, especially the computing power of GPUs, it has become possible to train very deep CNNs. This study involves a comparative analysis of neural networks for pneumonia recognition. The goal is to do binary image classification for pneumonia recognition on each of the three models, namely, a Sequential model using TensorFlow (built from scratch), ResNet50 and InceptionV3 and comparing their efficiency, to discover which model suits best for smaller datasets and which suits best for larger datasets. Dataset consists of 5856 anterior and posterior Chest X-Ray images labeled as either Normal or Pneumonic. © 2023 IEEE.

关键词： Deep learning

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Artificial Intelligence-Enabled Cooperative Cluster-Based Data Collection for Unmanned Aerial Vehicles

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computers, Materials & Continua 2022年第11期73卷 3351-3365页

作者： R.Rajender C.S.S.Anupama G.Jose Moses E.Laxmi Lydia Seifedine Kadry Sangsoon Lim Department of Computer Science Engineering Lendi Institute of Engineering&TechnologyDenkada535005India Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Computer Science Engineering Malla Reddy Engineering College500100India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Department of Computer Engineering Sungkyul UniversityAnyang14097Korea

In recent times,sixth generation(6G)communication technologies have become a hot research topic because of maximum throughput and low delay services for mobile *** encompasses several heterogeneous resource and communication standard in ensuring incessant availability of *** the same time,the development of 6G enables the Unmanned Aerial Vehicles(UAVs)in offering cost and time-efficient solution to several applications like healthcare,surveillance,disaster management,*** UAV networks,energy efficiency and data collection are considered the major process for high quality network *** these procedures are found to be challenging because of maximum mobility,unstable links,dynamic topology,and energy restricted *** issues are solved by the use of artificial intelligence(AI)and energy efficient clustering techniques for UAVs in the 6G *** this inspiration,this work designs an artificial intelligence enabled cooperative cluster-based data collection technique for unmanned aerial vehicles(AECCDC-UAV)in 6G *** proposed AECCDC-UAV technique purposes for dividing the UAV network as to different clusters and allocate a cluster head(CH)to each cluster in such a way that the energy consumption(ECM)gets *** presented AECCDC-UAV technique involves a quasi-oppositional shuffled shepherd optimization(QOSSO)algorithm for selecting the CHs and construct *** QOSSO algorithm derives a fitness function involving three input parameters residual energy of UAVs,distance to neighboring UAVs,and degree of *** performance of the AECCDC-UAV technique is validated in many aspects and the obtained experimental values demonstration promising results over the recent state of art methods.

关键词： 6G unmanned aerial vehicles resource allocation energy efficiency artificial intelligence clustering data collection

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Deep Learning Feature Extraction Architectures for Real-Time Face Detection

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SN computer science 2023年第5期4卷 645页

作者： Ravi Teja, B. Mythili, D. Duvva, Laxmiprasanna Bethu, Srikanth Garapati, Yugandhar Department of Computer Science and Engineering GITAM School of Technology GITAM Deemed-to-be University Telangana Hyderabad 502329 India Department of Computer Science and Engineering Vasavi College of Engineering Telangana Hyderabad 500089 India Department of Computer Science and Engineering CVR College of Engineering Telangana Hyderabad 501510 India

A Video Surveillance system can be used for a variety of purposes, including protection, secure data, crowd flux analytics and congestion analysis, individual recognition, anomalous activity detection, and so on. Video Surveillance systems play the key role in the human detection using the face features extraction. It helps in many applications like terrorists attack, thief identifying by detecting the face of the person but mostly failed in real-time aspect. In this context, we propose a method that significantly aids in the extraction and learning of features. To reduce the face recognition error, we use a bounding box regression model. To train the features, we utilized a CNN-based feature learning model with log-likelihood ratio calculations between inter- and intra-features. To increase the quality of video frames, we used a histogram redistribution image enhancement technique. Finally, a Background Subtracted Faster RCNN for video-based face recognition (BSF-RCNN-VFR) is used to discriminate the groups of detected faces. A comprehensive experiment is carried out on their datasets to demonstrate that the proposed solution performs better, and we compared the existing models with proposed models. We achieved 94.2 accuracy percentage. In this paper, the CNN models like AlexNet, ResNet and datasets like UADFV, Celeb-DF, FF++, DFDC, etc., accuracies are compared. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： CNN Deep Learning Feature extraction

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Stratification of Depressed and Non-Depressed Texts from Social Media using LSTM and its Variants 2

Stratification of Depressed and Non-Depressed Texts from Soc...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Kumar, Keerthan G.T. Anoop, R. Koolagudi, Shashidhar G. Rao, Trupthi Kodipalli, Ashwini Department of Information Science and Engineering Siddaganga Institute of Technology Tumkur India Department of Artificial Intelligence and Data Science Global academy of Technology Bengaluru India Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal India

This work examines the performance of various LSTM (long short-term memory) variants on social media text data. This study evaluates the performance of LSTM models with different architectures, namely, classic LSTM, Bidirectional LSTM, Stacked LSTM, gated recurrent unit (GRU), and bidirectional GRU, on a social network dataset comprising texts extracted from multiple social media platforms. We aim to identify the most effective LSTM variant of the five considered LSTM models for text analysis through a comparative study of the models' precision, recall, F1-score, and accuracy. The research findings show that the Classic LSTM and the GRU model perform better than the other models in accuracy. In contrast, the bidirectional models (Bidirectional LSTM and Bidirectional GRU) provide better precision scores than their respective primitive models. This research has significant implications for developing more efficient models for natural language processing applications. It offers beneficial insights into the implications involving the scrutiny of depression on social media platforms through text data analysis. © 2024 Elsevier B.V.. All rights reserved.

关键词： Long short-term memory

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Silver nano-particles' size prediction using deep learning model with green method

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Materials Today: Proceedings 2022年 69卷 1193-1199页

作者： Mathumathi, M. Vetriselvi, T. Matheswaran, P. Nandhini, R. Selvi, A. Department of Computer Science and Engineering K.Ramakrishnan College of Technology Trichy India Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Trichy India Department of Computer Science and Engineering M.Kumarasamy College of Engineering Karur India

When it comes to casting technologies and other production processes such as welding, DL models minimise the need for costly experimental inquiry. One of the most important criteria in nanotechnology is the measurement of particle size. They've received a lot of interest for their chemical, physical, and clinical uses because of their outstanding qualities. By adopting a green chemical reduction process, the nano-silver crystals are generated in the biopolymer interface without any agglomeration. Nano-metal production relies on precise control of particle size, and this approach provides that. For the prediction size of Ag-NPs via the green approach, a hybrid ideal of Long-Short Term Memory (LSTM) and Convolutional Neural Network (CNN) and model is presented in this research. Temperature, starch stabiliser, and AgNO3 concentration are among the experimental variables fed into the CNN-LSTM model. The size of a nano particle is the output of the CNN-LSTM model. Making three subsets of the experimental data for training, validation, and testing was one of the initial steps in creating an adapting model. The silver nanoparticles were identified using X-ray diffraction and transmission electron microscopy. Ag-NPs' crystallographic airplanes were found to be face-centered cubic, according to XRD analysis (fcc).When comparing with other existing classifiers, DNN achieved high performance on precision, i.e. 89.87%, and this is due to the presence of simple structure. The CNN and auto encoder achieved nearly 82% and 80% of accuracy, where LSTM achieved 77.25% of accuracy and proposed CNN-LSTM achieved 83.50% of recall. © 2022

关键词： Long short-term memory

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An Intelligent Optimization Technique Of Automatic Speech Recognition For Smart Homes 13

An Intelligent Optimization Technique Of Automatic Speech Re...

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13th International Conference on Research in ComputIng at Feminine, RIF 2024

作者： Aly, Rabab Hamed M. Hussein, Aziza I. The Higher Institute for Management and Information Technology Minya Egypt Electrical and Computer Engineering Department College of Engineering Effat University Jeddah Saudi Arabia Computer Science Department Faculty of Computer Science Nahda University Beni Suef Egypt

The creation of new approaches to the design and configuration of smart buildings relies heavily on AI tools and Machine Learning (ML) algorithms, particularly optimization techniques. The widespread use of electronic devices has sparked a strong desire to incorporate the Internet of Things (IoT) into houses, leading to the development of smart homes. As networked gadgets proliferate rapidly, this phenomenon is characterized by rapid proliferation. In smart buildings, smart cities, smart grids, and smart homes, interconnected electronic devices are becoming more popular. The objective of this paper is to enhance the functionality of home automation systems through the performance of speech recognition using the Bat-Salp Swarm Optimization (BSSO). This paper investigates the notion of (BSSO), a data analysis methodology that facilitates the automated construction of analytical models. The implementation of BSSO provides an enhancement to the feature selection process in speech recognition, providing an approximation solution that improves the accuracy of system decisions. The use of the BSSO technique improves the precision of the voice recognition system and also incorporates an Artificial Neural Network (ANN) for the classification part. The findings substantiated the efficacy of the employed methodology. © 2024 Copyright for this paper by its authors.

关键词： Bat Optimization Algorithm Salp Optimization Algorithm Smart homes Speech recognition

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Enabling Efficient Sparse Multiplications on GPUs with Heuristic Adaptability

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2024年第6期44卷 2226-2239页

作者： Xu, Jiaming Huang, Shan Li, Jinhao Huang, Guyue Xie, Yuan Wang, Yu Dai, Guohao Shanghai Jiao Tong University Qing Yuan Research Institute Shanghai China University of California Department of Electrical and Computer Engineering Santa BarbaraCA United States Hong Kong University of Science and Technology Department of Electronic and Computer Engineering China Tsinghua University Department of Electronic Engineering Beijing China

Sparse Matrix-Vector/Matrix Multiplication, namely SpMMul, has become a fundamental operation during model inference in various domains. Previous studies have explored numerous optimizations to accelerate it. However, to enable efficient end-to-end inference, the following challenges remain unsolved: (1) Incomplete design space and time-consuming preprocessing. Previous methods optimize SpMMul in limited loops and neglect the potential space exploration for further optimization, resulting in >30% waste of computing power. Additionally, the preprocessing overhead in SparseTIR and DTC-SpMM is 1000× larger than sparse computing. (2) Incompatibility between static dataflow and dynamic input. A static dataflow can not always be efficient to all input, leading to >80% performance loss. (3) Simplistic algorithm performance analysis. Previous studies primarily analyze performance from algorithmic advantages, without considering other aspects like hardware and data features. To tackle the above challenges, we present DA-SpMMul, a Data-Aware heuristic GPU implementation for SpMMul in multi-platforms. DA-SpMMul creatively proposes: (1) Complete design space based on theoretical computations and nontrivial implementations without preprocessing. We propose three orthogonal design principles based on theoretical computations and provide nontrivial implementations on standard formats, eliminating the complex preprocessing. (2) Feature-enabled adaptive algorithm selection mechanism. We design a heuristic model to enable algorithm selection considering various features. (3) Comprehensive algorithm performance analysis. We extract the features from multiple perspectives and present a comprehensive performance analysis of all algorithms. DA-SpMMul supports PyTorch on both NVIDIA and AMD and achieves an average speedup of 3.33× and 3.02× over NVIDIA cuSPARSE, and 12.05× and 8.32× over AMD rocSPARSE for SpMV and SpMM, and up to 1.48× speedup against the state-of-the-art open-source algo

关键词： Graphics processing unit

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Applying sentiment analysis in social web for smart decision support marketing

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Journal of Ambient Intelligence and Humanized Computing 2024年第3期15卷 1927-1936页

作者： Wu, Shih-Jung Chiang, Rui-Dong Chang, Han-Chi Department of Innovative Information and Technology Tamkang University New Taipei Taiwan Department of Computer Science and Information Engineering Tamkang University New Taipei Taiwan

Because of the rapid development of communication and service in Taiwan, competition among telecommunication companies has become ever fiercer. Differences in marketing strategy usually become the key factor in keeping existing customers while attracting new ones. Although electronic word-of-mouth (e-WOM) is one of the most important pieces of information to a consumer making a purchase decision, very few articles on opinion mining have discussed and compared the relationship between multifaceted word-of-mouth (WOM) and marketing strategy. In this paper, we use our Chinese opinion-mining system (Wu et al. in J Supercomput 73:2987–3001, 2017) not only to retrieve articles related to 4G and conduct reputation analysis but also to discuss the relation between WOM and marketing strategy. The results show that (1) e-WOM can immediately and directly reflect the results of marketing strategy, and (2) although users are primarily concerned with aspects of price, online speed, and signal quality, for most Taiwanese customers, price is the key in choosing a telecommunication company. Moreover, although this paper used 4G-related articles from June 2014 to June 2015 for analysis, the results are consistent with the Taiwanese telecommunication companies’ current marketing strategy of attracting customers through low pricing. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Sentiment analysis

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Blockchain based EHR system architecture and the need of blockchain inhealthcare

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Materials Today: Proceedings 2023年 80卷 2064-2070页

作者： Singh Chouhan, Arun Sanaullah Qaseem, Mohammad Mohammed Abdul Basheer, Qazi Asma Mehdia, Ms. Department of Computer Science School of Engineering Malla Reddy University India Nawab Shah Alam Khan College of Engineering and Technology India CSE Department Nawab Shah Alam Khan College of Engineering and Technology India

Purchases are concurrently safe and secure as well as credible because of making use of cryptographic guidelines. Recently, blockchain technology has become extremely cool and trendy and infiltrated various domain names, usually as a result of the appeal of cryptocurrencies. One field where blockchain technology possesses significant ability is medical care, because of the requirement for an extra patient-centric method to medical care systems and to link inconsonant devices and also boost the reliability of digital medical care files (EHRs). In this organized review, an analysis of cutting edge blockchain research study in the field of health care is carried out. It's own' prospective in the multi-stakeholder operated market like health and wellness has been responsible for several expenditures, researches, and executions. Electronic Health Records (EHR) devices typically made use of for the swap of wellness details amongst healthcare stakeholders have been criticised for centralising energy, failures and attack-points along with exchange records protectors. EHRs have battled when faced with multi-stakeholder as well as unit criteria while adhering to safety and security, privacy, reliable as well as other regulative constraints. Blockchain is promising among others to attend to the many EHR challenges, largely trustless and also protected swap of health-relevant information among stakeholders. This paper provides the demand for blockchain in medical care and also blockchain-based EHR device architecture. © 2021

关键词： Blockchain

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Sentirec - A Sentiment Analysis with Recommendation System 8

Sentirec - A Sentiment Analysis with Recommendation System

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Oswalt Manoj, S. Catherine Chandralekha, T. Dharshini, R.M. Hemadharsini, P. Sri Krishna College of Engineering and Technology Department of Computer Science and Business System Coimbatore India Sri Krishna College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350396638

The standard language is assessed, and the feelings transmitted by the individual are brought up. The purpose of sentiment analysis is to determine the polarity of a person's textual opinion. Most of the people use social media to market ourselves and share all knowledge, thoughts, ideas, and experiences with the rest of the world. Sentiment analysis has been studied by several internet companies that offer materials based on human emotions expressed through social media. Many people use social media to post and share their thoughts and feelings. The sentiment analysis standard assorts a sentence with all forms of emotions, each of which has an intensity. This study includes a sentiment analysis of diverse perspectives on social topics that are trending on social media, along with a recommendation system. This method recommends related content to viewers based on their current statement or issue. The algorithm also suggests customized video sets to users based on their social media activity, such as diverse perspectives on a particular social problem about which they actively express their thoughts on social media. NLP algorithms are utilized for sentiment analysis and recommendation systems. © 2023 IEEE.

关键词： Recommender systems

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