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42nd IEEE International Conference on computer Design, ICCD 2024

作者： Hong, Dingze Hu, Jinlei Chen, Jianxi Feng, Dan Liu, Jian School of Computer Science and Technology Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System of MoE China The Fifth Electronics Research Institute of Miit China

ISBN: (纸本)9798350380408

Persistent Memory (PM) offers both byte-address ability and non-volatility, making it well-suited for accelerating B+-Tree indexes. However, existing persistent B+-Tree indexes face significant performance challenges due to high structural modification operation (SMO) overhead. SMOs often result in costly item migrations and increased tail latency, which severely degrade the overall performance. In this paper, we present SSTree, a high-performance B+- Tree index specifically optimized to address SMO overhead. SSTree introduces three key innovations: (i) efficient leaf node expansion using a list of subnodes to postpone expensive node splits, (ii) delegated fingerprints to speed up search operations across subnodes, and (iii) proactive subnode compaction that employs out-of-place updates to optimize item organization. Our evaluation demonstrates that SSTree delivers up to 4.38× higher write throughput and up to 62× lower tail latency compared to state-of-the-art persistent B+-Tree indexes. © 2024 IEEE.

关键词： Trees (mathematics)

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AI Bot to Increase the Accuracy and Efficiency of Hiring Process of Business Organizations

AI Bot to Increase the Accuracy and Efficiency of Hiring Pro...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Dilusha, N.H.A.S. Weerasinghe, E.J.K.D. Chamuditha, S.M.A. Wijekoon, K.W.M. Thelijjagoda, Samantha Giguruwa, Nishantha Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Computer System Engineering Malabe Sri Lanka College of Sustainability and Tourism Ritsumeikan Asia Pacific University Oita Prefecture Beppu Japan

ISBN: (纸本)9798331543617

Recruitment in most organizations is normally a rather inefficient process where it takes quite a long time to match the right candidate to a given job. In this research, AI-based hiring system is recommended to improve the efficiency and accuracy of the hiring process with the help of NLP and machine learning algorithms. Employment enabling processes such as CV filtering, skill evaluation and personality profiling as well as job matching are achieved by the system. An NLP-based tool is used for short listing of CVs, assessment quizzes are used to evaluate different skills and qualifications of the candidates and chatbot enables communication between candidates and HRs, while an AI bot is used to complete different document verifications' processes. Recruitment operation is made easier and efficient, and matching of candidates to the available jobs is done better since it is less time-consuming and energy-demanding, giving a solution that may be implemented on a large scale. Further improvements are to be made in the fine tuning of algorithms, enlargement of comprehensiveness of systems and the implementation of actual sample cases before the introduction into the marketplace. © 2024 IEEE.

关键词： Bot (Internet)

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Sentiment Analysis of Covid-19 Twitter Data using Deep Learning Algorithm 2

Sentiment Analysis of Covid-19 Twitter Data using Deep Learn...

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

作者： Valarmathi, B. Gupta, N. Srinivasa Karthick, V. Chellatamilan, T. Santhi, K. Chalicheemala, Dhanush Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Manufacturing School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Assistant Systems Engineer Tata Consultancy Services Chennai India Department of Analytics School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Information Technology School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

A key method used in the study of Natural Language Processing (NLP) is sentiment analysis, or emotion analysis, plays a pivotal role in text analysis. Its primary function is to discern and categorize the underlying emotions within textual content, classifying them as positive, neutral, or negative sentiments. A variety of textual inputs can be used with this process, including entire documents, individual sentences, and more. Understanding the sentiments expressed by individuals is of paramount importance to organizations in today's digital age. Clients and customers now have the means to express their thoughts and emotions with greater ease and immediacy than ever before. This wealth of sentiment data can offer valuable insights that organizations can leverage to enhance their products, services, and overall customer experience. The present study focuses on the sentiment analysis of Twitter data related to the COVID-19 pandemic, employing the Long Short-Term Memory (LSTM) algorithm. To improve the accuracy of sentiment analysis, a pre-processing procedure is introduced, which involves the use of the Neat Text module in Python to clean the tweets. In this research endeavor, a dataset comprising 1,79,107 COVID-19-related tweets is subjected to sentiment analysis using the proposed pre-processing module and LSTM. The results demonstrate an impressive accuracy rate of 96% in accurately determining the sentiments conveyed in these COVID-19 tweets. In contrast, the existing algorithm, based on Artificial Neural Network (ANN), achieved a significantly lower accuracy rate of 76%. This research not only showcases the effectiveness of LSTM and pre-processing in sentiment analysis but also highlights the significance of sentiment analysis in gaining valuable insights from vast amounts of textual data, especially when important events like the COVID-19 pandemic are involved. © 2024 Elsevier B.V.. All rights reserved.

关键词： COVID-19

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A Single Image Derain Method Based on Residue Channel Decomposition in Edge Computing

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Intelligent Automation & Soft Computing 2023年第8期37卷 1469-1482页

作者： Yong Cheng Zexuan Yang Wenjie Zhang Ling Yang Jun Wang Tingzhao Guan School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China School of Automation Nanjing University of Information Science and TechnologyNanjing210044China School of Geographical Sciences Nanjing University of Information Science&TechnologyNanjing210044China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing100101China School of Applied Technology Nanjing University of Information Science and TechnologyNanjing210044China

The numerous photos captured by low-price Internet of Things(IoT)sensors are frequently affected by meteorological factors,especially *** causes varying sizes of white streaks on the image,destroying the image texture and ruining the performance of the outdoor computer vision *** methods utilise training with pairs of images,which is difficult to cover all scenes and leads to domain *** addition,the network structures adopt deep learning to map rain images to rain-free images,failing to use prior knowledge *** solve these problems,we introduce a single image derain model in edge computing that combines prior knowledge of rain patterns with the learning capability of the neural ***,the algorithm first uses Residue Channel Prior to filter out the rainfall textural features then it uses the Feature Fusion Module to fuse the original image with the background feature *** results in a pre-processed image which is fed into Half Instance Net(HINet)to recover a high-quality rain-free image with a clear and accurate structure,and the model does not rely on any rainfall *** results on synthetic and real-world datasets show that the average peak signal-to-noise ratio of the model decreases by 0.37 dB on the synthetic dataset and increases by 0.43 dB on the real-world dataset,demonstrating that a combined model reduces the gap between synthetic data and natural rain scenes,improves the generalization ability of the derain network,and alleviates the overfitting problem.

关键词： Single image derain method edge computing residue channel prior feature fusion module

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Dipper Throated Optimization for Detecting Black-Hole Attacks inMANETs

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computers, Materials & Continua 2023年第1期74卷 1905-1921页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

In terms of security and privacy,mobile ad-hoc network(MANET)continues to be in demand for additional debate and *** more MANET applications become data-oriented,implementing a secure and reliable data transfer protocol becomes a major concern in the ***,MANET’s lack of infrastructure,unpredictable topology,and restricted resources,as well as the lack of a previously permitted trust relationship among connected nodes,contribute to the attack detection burden.A novel detection approach is presented in this paper to classify passive and active black-hole *** proposed approach is based on the dipper throated optimization(DTO)algorithm,which presents a plausible path out of multiple paths for statistics transmission to boost MANETs’quality of service.A group of selected packet features will then be weighed by the DTO-based multi-layer perceptron(DTO-MLP),and these features are collected from nodes using the Low Energy Adaptive Clustering Hierarchical(LEACH)clustering *** is a powerful classifier and the DTO weight optimization method has a significant impact on improving the classification process by strengthening the weights of key features while suppressing the weights ofminor *** hybridmethod is primarily designed to combat active black-hole *** the LEACH clustering phase,however,can also detect passive black-hole *** effect of mobility variation on detection error and routing overhead is explored and evaluated using the suggested *** diverse mobility situations,the results demonstrate up to 97%detection accuracy and faster execution ***,the suggested approach uses an adjustable threshold value to make a correct conclusion regarding whether a node is malicious or benign.

关键词： Black-hole attack mobile ad-hoc network optimization dipper throated optimization

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Integrated operating room planning and scheduling: an ILP-Based off-line approach for emergency responsiveness at a local hospital in Naples

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Soft Computing 2024年第20期28卷 11987-12003页

作者： Boccia, Maurizio Mancuso, Andrea Masone, Adriano Sterle, Claudio Department of Electrical Engineering and Information Technology University of Naples "Federico II" Via Claudio 21 Naples80125 Italy Institute for System Analysis and Computer Science "Antonio Ruberti" National Research Council of Italy via dei Taurini 19 Rome00185 Italy

This paper addresses a real-world Integrated Operating Room Planning and Scheduling (IORPS) problem encountered by a local hospital in Naples, characterized by stringent emergency management constraints, requiring treatment initiation within a 20-minute time-window. We tackle this problem by an original Integer Linear Programming formulation, capable of dealing with different operating room management strategies (open, block and block-modified). Our work differs from conventional cost-focused models by adopting a patient-oriented objective function, aligning with public hospitals obligations. The proposed method has been validated using real-world data provided by the hospital. The performed experimentation demonstrates the efficiency of the approach, capable of determining the optimal solution within an acceptable computation time that aligns with hospital requirements. Moreover, it also highlights the relevance of using our optimization approach to reduce delays in emergency responsiveness. This confirms its practical usage as a substitute for the current manual procedure. © The Author(s) 2024.

关键词： Operating rooms

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A lightweight point cloud analysis network based on graph walking 8

A lightweight point cloud analysis network based on graph wa...

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8th International Conference on Intelligent Informatics and Biomedical Sciences, ICIIBMS 2023

作者： Li, Wenju Ji, Qianqian Sha, Liye Cui, Liu Shanghai Institute of Technology School of Computer Science and Information Engineering Shanghai China Shanghai Precision Dosing & Weighing System Co Shanghai China

ISBN: (纸本)9798350369564

Based on the irregularity and permutation invariance of point clouds, graph neural networks have been widely used in 3D point cloud analysis tasks in recent years. However, these works suffer from insufficient feature extraction at long distances and high computational resource overhead. To this end, this paper proposes a graph walk based lightweight network for 3D point cloud analysis tasks. Specifically, after constructing the nearest neighbor graph in the downsampled point cloud, the MLP module in the original network was replaced by multiple nonparametric components to reduce the number of parameters of the model. Then, the graph walking mechanism was used to extract the long-distance features and geometric structure features of the point cloud. Finally, before point classification and segmentation, the residual pooling module is used to fuse the features to obtain the global features. Compared with the benchmark network DGCNN, the classification accuracy on the ScanObjectNN dataset is improved by 4.2%, the segmentation accuracy on the ShapeNetPart dataset is improved by 1.1%, and the number of model parameters is reduced by 33.3%. Experiments show that the design method has obvious effect on 3D point cloud analysis tasks. © 2023 IEEE.

关键词： Graph neural networks

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Design Thinking and Innovative Tool Use in Business Model

Design Thinking and Innovative Tool Use in Business Model

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2024 International Conference on Intelligent systems for Cybersecurity, ISCS 2024

作者： Singhi, Nitika Verma, Ankush Agrawal, Atul Khan, Irfan Computer Science Engineering Shri Vaishnav Vidhyapeeth Vishwavidyalava Indore India Data Analytics and Information System Prestige Institute of Management and Research Indore India Computer Science Engineering Sushila Devi Bansal College of Technology Indore India Computer Science Engineering Bansal Institute of Science and Technology Bhopal India

ISBN: (纸本)9798350375237

In past few year designs thinking has worked on several new effective problem and has generated innovative solution for new business model. For the scenario of today's demand, to better understand how design thinking can work on different perspectives in business models and how it can bring new innovative solutions for any problem is essential However, studies of existing literature explore the application, potential, and challenges of Design Thinking towards the comprehensive aim of this paper, which regards business model innovation as a fundamental subject of Design Thinking. The paper provides practical tools for exploring Design Thinking along with various critical aspects for the researchers to explore how Design Thinking can be applied to innovative business models. The main focus of this paper is to explain the basics of Design Thinking, how to frame the mind through different techniques used, the Design Techniques used for a business model, the methodology of a design thinker, and different tools like BPM, Blue Work Live, Mind Mapping, Brainstorming, and RICPS. © 2024 IEEE.

关键词： Mapping

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Deep Learning Techniques for Advanced Large-Scale Data Processing 15

Deep Learning Techniques for Advanced Large-Scale Data Proce...

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

作者： Sunil, M.P. Shekhawat, Jyoti Vanaja, T. Mohammed, Vazeer Ali Thaware, Shailesh J. Singh, Vaishali Karnataka Bangalore India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India Department of Computer & Information System Lewis University Illinois United States Department of Computer Engineering Vishwakarma Institute of Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350370249

Deep getting to know techniques have become increasingly more popular due to their potential to improve huge-scale records processing performance. Leveraging specialized hardware which includes GPUs, deep getting to know algorithms can attain mind-blowing pace will increase compared to traditional algorithms. further, deep gaining knowledge of fashions can discover complicated non-linear relationships in big datasets, permitting greater accurate overall performance and less fake predictions. Latest advances in deep mastering have enabled it to be implemented to remedy a plethora of information processing duties, from photo recognition to natural language processing. CNNs, RNNs, and GANs are a number of the maximum famous deep gaining knowledge of fashions used in tackling complex data processing obligations. agencies consisting of Google, fb, and Amazon use these sophisticated frameworks to build predictive models that help improve numerous programs. Deep getting to know strategies offer exquisite ability to take massive-scale information processing to the following stage. corporations are increasingly more becoming privy to the emerging opportunities, and actively investing in deep mastering to liberate the untapped strength of their information. ©2024 IEEE.

关键词： Large datasets

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Traffic sign recognition based on deformable convolutions 8

Traffic sign recognition based on deformable convolutions

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8th International Conference on Intelligent Informatics and Biomedical Sciences, ICIIBMS 2023

作者： Li, Wenju Yu, Jie Liye, Sha Liu, Cui Shanghai Institute of Technology School of Computer Science and Information Engineering Shanghai China Shanghai Precision Dosing & Weighing System Co Shanghai China

ISBN: (纸本)9798350369564

A traffic sign recognition model based on deformable convolution is proposed to address the low accuracy of existing traffic sign recognition algorithms for small and occluded targets. Firstly, replace some convolutions in the backbone network with deformable convolutions to adapt to different shape feature maps during feature extraction and improve the network's sensitivity to small targets;Secondly, in order to reduce the loss of information in the process of upsampling, the original Nearest-neighbor interpolation upsampling module is replaced by the sub-pixel convolution module in the feature fusion network to reduce the loss of information due to upsampling. Finally, design experiments on the TT100K dataset and test the advantages of this model in real-world scenarios. The experimental results show that the average accuracy of this model (mAP@0.5) It has reached 92.54%, with a Recall rate of 87.42%, which is 2.18% and 1.58% higher than the benchmark network YOLOv5n, respectively. The frame processing speed has reached 96.06. © 2023 IEEE.

关键词： Convolution

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