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Sentiment-Based Community Detection Using Graph Transformation 1

International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2022

作者： Meena, Shyam Sundar Tokekar, Vrinda Department of Computer Science and Engineering Shri Vaishnav Institute of Information Technology Shri Vaishnav Vidyapeeth Vishwavidyalaya Indore India Department of Information Technology Institute of Engineering and Technology Devi Ahilya Vishwavidyalaya Indore India

ISBN: (数字)9789819909810

ISBN: (纸本)9789819909803

There are a variety of social media platforms where people can review products and services depending on personal expertise. The user’s friends and followers may see each other’s ideas and sentiments, which may spread to more users in the future. Therefore, this research proposes a concept named "sentiment community". The purpose is to explore the feelings and interactions of users on social networking sites. We have used graphs for the modelling of these networks. The suggested sentiment community discovery approach alters the original graph based on sentiment scores of users. Experiments have been conducted on networks generated using the LFR model and Twitter’s data. In the experiments, the modularity score is used to determine the quality of the detected community structure. The communities were detected in each network with and without the sentiment concept. The results reveal that the inclusion of the sentiment score concept for community detection significantly improves the modularity score and quality of the resultant community structure. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Decoding and Analysing Consumer Feedback for Companies and Goods using Machine Learning 3

Decoding and Analysing Consumer Feedback for Companies and G...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Joel, M. Robinson Ebenezer, V. Jeyaraj, K. Arul Navaneethakrishnan, M. Arunadevi, R. Jenifa, D. Rejees Kings Engineering College Department of Information Technology Chennai India Department of Computer Science and Engineering Coimbatore India St. Joseph College of Engineering Sriperumbudur Department of Computer Science and Engineering Chennai India Technology Prince Shri Venkateshwara Padmavati Engineering College Department of Information Chennai India Psna College of Engineering and Technology Department of Ece Dindigul India

ISBN: (纸本)9781665462167

Over the past ten years, the number of internet users has increased tremendously. Due to the e- commerce industry's fast growth, practically all goods are now sold online. Before making a purchasing choice, the customer gets comprehensive information on the items they are interested in. Customer opinions and reviews assist businesses and other consumers in making wise decisions and enhancing the purchasing experience. It is getting harder to comprehend and keep track of every customer's wants since the number of individuals leaving evaluations is increasing quickly. Some evaluations, which make it more time-consuming and challenging to analyse for business, are purposefully overly lengthy, unclear, incomplete, and short. A customer Feedback Evaluation System is needed to address this issue since it will track every one of those evaluations and offer statistical data for improving the manufacturer's quality and functionality. The suggested approach uses machine learning to give each word a certain amount of weight. © 2023 IEEE.

关键词： Support vector machines

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Industrial Systems Optimization from Combining Internet of Things and Cloud Computing

Industrial Systems Optimization from Combining Internet of T...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Nachappa, M.N. Faujdar, Pramod Kumar Agarwal, Parag Department of Computer Science and Information Technology Bangalore India Mechanical Engineering Vivekananda Global University Jaipur India Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India

ISBN: (纸本)9798350383348

Business structures optimization from combining the net of things (IoT) and Cloud Computing is a subject of study that recognizes the vital function that the two technology can play collectively in optimizing industrial methods. IoT leverages numerous statistics resources;the cloud gives a repository where these statistics resources may be stored and maintained, and cloud computing evaluates and optimizes the records to discover potential efficiencies and enhance overall operational performance. This analysis results in becoming aware of and implementing process upgrades and reducing the production fee. As a result, the industrial gadget is in a position to use the statistics generated using the IoT and Cloud Computing to enhance operational efficiency and patron delight. ultimately, the combination of IoT and Cloud Computing resulted in reduced manufacturing fees and improved operational performance. Business structure optimization by combining the Internet of Factors (IoT) and Cloud Computing can bring about stepped forward performance and productivity of organizations and their production approaches. IoT in business settings presents agencies with correct and real-time facts from systems and machines, and cloud computing gives value-effective storage and management of large amounts of such facts. By combining records from one-of-a-kind linked devices, IoT and Cloud Computing may create a 'business Intelligence community,' which may be used to screen and optimize approaches. It permits improved selection-making, elevated production speed, and more advantageous product excellence. Additionally, with the aid of leveraging predictive analytics, the community can be used to assume ability issues in manufacturing techniques and diagnose and prevent them as nicely. This combination of IoT and Cloud Computing can ultimately assist businesses in growing operational efficiency, reducing fees, and gaining an aggressive advantage in the market.. © 2024 IEEE.

关键词： Business efficiency Intelligence resources statistics

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An Efficient Beamforming Design for Near-Field Beam Squint Mitigation in Wideband ELAA Systems 15

An Efficient Beamforming Design for Near-Field Beam Squint M...

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15th IEEE International Conference on Wireless Communications and Signal Processing, WCSP 2023

作者： Liu, Mengzhen Cai, Wenhao Li, Ming Liu, Qian Dalian University of Technology School of Information and Communication Engineering Dalian China Dalian University of Technology School of Computer Science and Technology Dalian China

ISBN: (纸本)9798350324662

In the future 6G wireless networks, the growing number of antennas and extra-wide mmWave frequency band will lead to near-field effect and beam squint effect. The generated beams at different frequencies will respectively focused at different locations referred to as the near-field beam squint effect, which will cause a severe performance degradation, remaining to be mitigated efficiently to enhance the data rates. We propose an efficient grouping beamforming design algorithm utilizing the near-field codebook to generate the analog beamformer. Specifically, we uniformly partition the entire large array into multiple small subarrays and design the beamforming of each subarray for the specific selected subcarrier in the whole bandwidth. The analog beamformer and digital beamformer are iteratively designed until a convergence solution is obtained. Simulation results illustrate the effectiveness of the proposed approach to mitigate near-field beam squint effect. © 2023 IEEE.

关键词： Beam squint extremely large antenna array (ELAA) hybrid beamforming near-field commmunications wideband

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On Parameterized Picture Fuzzy Discriminant information Measure in Medical Diagnosis Problem 1

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3rd International Conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

作者： Monika Sharma, Aman Bajaj, Rakesh Kumar Department of Mathematics Jaypee University of Information Technology Himachal Pradesh Waknaghat Solan173234 India Department of Computer Science and Engineering Jaypee University of Information Technology Himachal Pradesh Waknaghat Solan173234 India

ISBN: (数字)9789819986460

ISBN: (纸本)9789819986453

Decision-making processes related to the problems of pattern recognition, clustering, and expert and knowledge-based systems contain a lot of uncertainty in the form of imprecise, incomplete, and inexact information with partial contents where the notions of entropy, discriminant measure and similarity measure play a crucial role. In the present communication, a very recently proposed doubly-parameterized information tool is suitably applied for picture fuzzy sets. This bi-parameterized discriminant measure would give diversification in handling the inexact/incomplete information in terms of obtaining the degree of association and closeness in the data of various applications. Further, the introduced bi-parametric measure has been successfully applied in the principle of minimum discriminant information with the help of some illustrative discussions in the applied fields, e.g., "medical diagnosis". Additionally, for the validity and efficacy of the presented work, necessary characteristic comparison along with important remarks has been done. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Diagnosis

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SFA-Net: A Selective Features Absorption Network for Object Detection in Rainy Weather Conditions

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IEEE Transactions on Neural Networks and Learning Systems 2023年第8期34卷 5122-5132页

作者： Huang, Shih-Chia Hoang, Quoc-Viet Le, Trung-Hieu National Taipei University of Technology Department of Electronic Engineering Taipei10608 Taiwan National Taipei University of Technology International Graduate Program in Electrical Engineering and Computer Science Taipei10608 Taiwan Hung Yen University of Technology and Education Faculty of Information Technology Hungyen 17000 Viet Nam

In recent years, object detection approaches using deep convolutional neural networks (CNNs) have derived major advances in normal images. However, such success is hardly achieved with rainy images due to lack of visibility. Aiming to bridge this gap, in this article, we present a novel selective features absorption network (SFA-Net) to improve the performance of object detection not only in rainy weather conditions but also in favorable weather conditions. SFA-Net accomplishes this objective by utilizing three subnetworks, where the feature selection subnetwork is concatenated with the object detection subnetwork through the feature absorption subnetwork to form a unified model. To promote further advancement in object detection impaired by rain, we propose a large-scale rainy image dataset, named srRain, which contains both synthetic rainy images and real-world rainy images for training and testing purposes. srRain is comprised of 25 900 rainy images depicting diverse driving scenarios in the presence of rain with a total of 181 164 instances interpreting five common object categories. Experimental results display that our SFA-Net reaches the highest mean average precision (mAP) of 77.53% on a normal image set, 62.52% on a synthetic rainy image set, 37.34% on a collected natural rainy image set, and 32.86% on a published real rainy image set, surpassing current state-of-the-art object detectors and the combination of image deraining and object detection models while retaining a high speed. © 2012 IEEE.

关键词： Feature extraction

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A Machine Learning Based Automated Model for Managing Student Dropout 22

A Machine Learning Based Automated Model for Managing Studen...

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22nd IEEE/ACIS International Conference on Software engineering Research, Management and Applications, SERA 2024

作者： Ghosh, Partha Charit, Arnab Banerjee, Hindol Bandhu, Debanwesa Ghosh, Agniv Pal, Ankita Goto, Takaaki Sen, Soumya Academy of Technology Department of Computer Science and Engineering Adisaptagram India Toyo University Faculty of Information Sciences and Arts Saitama Japan A.K.Choudhury School of Information Technology University of Calcutta Kolkata India

ISBN: (纸本)9798350391343

Addressing the persistent challenge of student dropout, particularly prevalent in developing countries like India, Bangladesh, etc. are of paramount importance. Factors such as poverty, natural calamities, and early marriages exacerbate this issue. High student dropout rates can negatively impact a country by diminishing its economic productivity, increasing social inequalities, and perpetuating a cycle of poverty. Addressing dropout issues requires comprehensive strategies to ensure a skilled and educated workforce, fostering societal well-being and global competitiveness. This research focuses on analysing comprehensive data on students who have dropped out. Thereafter, a machine learning based methodology is used to discern the underlying causes of student attrition in various schools. Furthermore, it allows for efficient monitoring of the state's educational landscape, with the ability to drill down to granular levels when necessary to identify specific regional challenges. The effectiveness of this approach is validated through the utilization of real-world datasets. © 2024 IEEE.

关键词： Students

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A Real- Time Multi-Camera Auto-Adjustment Framework for Infrastructure Inspections

A Real- Time Multi-Camera Auto-Adjustment Framework for Infr...

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2024 IEEE/SICE International Symposium on System Integration, SII 2024

作者： Phuoc Le, Chuong Ellison, Charles Bunkley, Steven La, Hung Netchaev, Anton Lab Department of Computer Science and Engineering RenoNV89557 United States Information Technology Lab VicksburgMS39180 United States

ISBN: (纸本)9798350312072

In this paper, we proposed a novel multi-camera auto-adjustment framework for infrastructure inspections, specifically 3D point cloud mapping via lidar-camera fusion. Our method uses a single controller for consensus target brightness and adaptive lighting. In addition, a vignette correction filter was created to apply to images outputted from cameras to avoid dark strips on the point cloud data. This approach is designed to be a versatile, real-time friendly, and platform agnostic software stack. © 2024 IEEE.

关键词： Cameras

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Physical Layer Security of 6G Vehicular Networks with UAV Systems:First Order Secrecy Metrics,Optimization,and Bounds

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computers, Materials & Continua 2024年第9期80卷 3685-3711页

作者： Sagar Kavaiya Hiren Mewada Sagarkumar Patel Dharmendra Chauhan Faris A.Almalki Hana Mohammed Mujlid Smt.Chandaben Mohanbhai Patel Institute of Computer Applications Charotar University of Science and TechnologyChangaAnand388421India Electrical Engineering Department Prince Mohammad Bin Fahad UniversityP.O.Box 1664Al Khobar31952Kingdom of Saudi Arabia Department of Electronics and Communication Engineering Chandubhai S.Patel Institute of TechnologyCharotar University of Science and TechnologyChangaAnand388421India Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Kingdom of Saudi Arabia

The mobility and connective capabilities of unmanned aerial vehicles(UAVs)are becoming more and more important in defense,commercial,and research ***,their open communication makes UAVs susceptible toundesirablepassive attacks suchas eavesdroppingor ***,the inefficiencyof traditional cryptography-based techniques has led to the addition of Physical Layer Security(PLS).This study focuses on the advanced PLS method for passive eavesdropping in UAV-aided vehicular environments,proposing a solution to complement the conventional cryptography ***,we present a performance analysis of first-order secrecy metrics in 6G-enabled UAV systems,namely hybrid outage probability(HOP)and secrecy outage probability(SOP)over 2×2 Nakagami-m ***,we propose a novel technique for mitigating passive eavesdropping,which considers first-order secrecy metrics as an optimization problem and determines their lower and upper ***,we conduct an analysis of bounded HOP and SOP using the interactive Nakagami-m channel,considering the multiple-input-multiple-output configuration of the UAV *** findings indicate that 2×2 Nakagami-mis a suitable fadingmodel under constant velocity for trustworthy receivers and *** results indicate that UAV mobility has some influence on an eavesdropper’s intrusion during line-of-sight-enabled communication and can play an important role in improving security against passive eavesdroppers.

关键词： Physical layer security 3D positioning 6G communications UAV systems

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Bi-Static Inverse Synthetic Aperture Radar Imaging Based on 5G NR Signal 23

Bi-Static Inverse Synthetic Aperture Radar Imaging Based on ...

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23rd International Symposium on Electrical Apparatus and Technologies, SIELA 2024

作者： Lazarov, Andon Kostadinov, Todor Nikola Vaptzarov Naval Academy Information Technology Department Varna Bulgaria Burgas Free University Computer Science and Engineering Faculty Burgas Bulgaria

ISBN: (纸本)9798350382365

This study addresses Bi-static Inverse Synthetic Aperture Radar (BISAR) concept with a 5G NR waveform. BISAR scenario with uncooperative transmitter and specially designed receiver, and flying target as helicopter is discussed. The positions of the transmitter, receiver and flying target are defined by vector kinematic equations. Mathematical models of 5G NR waveform and BISAR signal reflected from a target with complex geometry are described. Two dimensional (2-D) image reconstruction is realized by applying correlation with an emitted waveform or Fourier transform on a range direction, and Fourier transform on an azimuth direction. Simulation experimental results are provided to illustrate the correctness of mathematical models and imaging algorithms. © 2024 IEEE.

关键词： Inverse synthetic aperture radar

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