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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Selvanarayanan, Raveena Surendran, R. Gomathi, T. Kartheesan, L. Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Department of Computer Science and Engineering Chennai India Sathyabama Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Vel Tech Rangarajan Dr.Sangunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331534950

The article proposes an innovative Hybrid Vision Transformer and CNN architecture for the precise identification of Overripe Coffee Berry Disease (OCBD) in coffee farms. The suggested model adeptly integrates the advantages of Vision Transformers and CNNs, allowing it to capture both the global and local characteristics of coffee berries. Experimental findings indicate that the Hybrid model outperforms conventional CNN-based methods, with an impressive accuracy of 96.7% on a varied dataset. The model's resilience to fluctuating illumination conditions and obstructions makes it a viable alternative for practical applications in coffee plantation management. © 2024 IEEE.

关键词： Image segmentation

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A Scheme Library-Based Ant Colony Optimization with 2-Opt Local Search for Dynamic Traveling Salesman Problem

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computer Modeling in engineering & sciences 2023年第5期135卷 1209-1228页

作者： Chuan Wang Ruoyu Zhu Yi Jiang Weili Liu Sang-Woon Jeon Lin Sun Hua Wang College of Software Henan Normal UniversityXinxiang453007China College of Computer and Information Engineering Henan Normal UniversityXinxiang453007China School of Computer Science and Engineering South China University of TechnologyGuangzhou510006China School of Computer Science Guangdong Polytechnic Normal UniversityGuangzhou510665China Department of Military Information Engineering Hanyang UniversityAnsan15588South Korea College of Engineering and Science Institute for Sustainable Industries and Liveable CitiesVictoria UniversityMelbourneVIC 8001Australia

The dynamic traveling salesman problem(DTSP)is significant in logistics distribution in real-world applications in smart cities,but it is uncertain and difficult to *** paper proposes a scheme library-based ant colony optimization(ACO)with a two-optimization(2-opt)strategy to solve the DTSP *** work is novel and contributes to three aspects:problemmodel,optimization framework,and ***,in the problem model,traditional DTSP models often consider the change of travel distance between two nodes over time,while this paper focuses on a special DTSP model in that the node locations change dynamically over ***,in the optimization framework,the ACO algorithm is carried out in an offline optimization and online application framework to efficiently reuse the historical information to help fast respond to the dynamic *** framework of offline optimization and online application is proposed due to the fact that the environmental change inDTSPis caused by the change of node location,and therefore the newenvironment is somehowsimilar to certain previous *** way,in the offline optimization,the solutions for possible environmental changes are optimized in advance,and are stored in a mode scheme *** the online application,when an environmental change is detected,the candidate solutions stored in the mode scheme library are reused via ACO to improve search efficiency and reduce computational ***,in the algorithm design,the ACO cooperates with the 2-opt strategy to enhance search *** evaluate the performance of ACO with 2-opt,we design two challenging DTSP cases with up to 200 and 1379 nodes and compare them with other ACO and genetic *** experimental results show that ACO with 2-opt can solve the DTSPs effectively.

关键词： Dynamic traveling salesman problem(DTSP) offline optimization and online application ant colony optimization(ACO) two-optimization(2-opt)strategy

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The Federated Fielder: CNNs and the Future of Cricket Umpire Analysis

The Federated Fielder: CNNs and the Future of Cricket Umpire...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Suryavanshi, Ankita Mehta, Shiva Chaudhary, Preeti Joshi, Kireet Jain, Vishal Chitkara University Chitkara University Institute of Engineering and Technology Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India Sharda University Computer Science and Engineering Sharda School of Engineering and Technology U.P. Greater Noida India

ISBN: (纸本)9798350361537

As we move into a landscape where sports analysis is becoming more critical, the demand for action recognition systems that are real-time and high-accuracy is also increasing. It is vital in complex cricket games where umpire signals have taken center stage in decision-making. This research suggests a new way of discovering the invisible actions of cricket umpires. The method of FLAN associated with CNNs for the confident detection of two types of umpire signals will enable strides in automating umpiring in tennis. The very point of our solution is that five different clients use it. Each client gives us information about its climate characteristics that can be used in a compact universal model that does not need to be updated after every client response. The application of this option preserves the confidentiality of data that must be privately protected. We give a detailed breakdown of the aggregated outcomes, using macro, Micro, and weighted averages as an example, by collecting local data insights and improving the overall model. In the presence of five direct customers, the on-site trial resulted in macro averages varying between 83.99% and 90.31%. The same study also produced micro averages ranging from 83.99% to 90.28%, and weighted averages tended to match macro averages. This connotes the reliability and dependability of our system. Throughout the development, federated averaging became a solid method for enhancing a global dataset through local discrepancies of input data for the improvement of model accuracy altogether with secure privacy. © 2024 IEEE.

关键词： Cricket (Sports)

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Advanced transformer model with fine-grained correlation fusion for Multimodal Emotion analysis 2

Advanced transformer model with fine-grained correlation fus...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Krishnan, Sundara Rajulu Navaneetha Sangeetha, M. Srinivasulu, Senduru Surendran, R. Dhanalakshmi Srinivasan University Trichy Department of Computer Science and Engineering Samayapuram Campus Tiruchirappalli India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Computer Science and Engineering Avadi Chennai India Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Chennai India

ISBN: (纸本)9798331534950

In recent days, Emotion recognition has garnered a lot of attention because of its important applications in human-computer interaction. Human emotion is expressed through a variety of verbal and nonverbal languages, including text, audio, and visual. Current multimodal emotion analysis techniques typically perform poorly because they don't fully utilize the link between various modalities. An advanced transformer model with fine-grained correlation fusion (AT-FGCF) is presented in this research to identify emotion in text, audio, and visual modalities. To enhance the representation of the input audio and video, dense hierarchical feature maps are captured using the Dense Swin Transformer (DSwinT) model. Also, an Emotion- Aware Cognitive BERT (EAC-BERT) is introduced to enhance text representation. Lastly, a fine-grained correlation fusion is carried out to take advantage of correlations between fine-grained features from multimodal data. Our experimental findings show that the suggested method is successful in reaching radical outcomes across two benchmark datasets. That is the datasets IEMOCAP, and MELD all have average accuracy improvements of about 88.35%, and 84.80% respectively. © 2024 IEEE.

关键词： Emotion Recognition

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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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Design and implementation of power socket soldering robot arm system 4

Design and implementation of power socket soldering robot ar...

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2024 4th International Conference on Artificial Intelligence and Industrial technology Applications, AIITA 2024

作者： Liu, Jingqiao Wu, Shuqin Lin, Zicheng Zhang, Huimin Fan, Yong Xie, Wu School of Mechanical and Electrical Engineering Guilin University of Electronic Technology Guilin541004 China School of Architecture and Transportation Engineering Guilin University of Electronic Technology Guangxi Guilin541004 China School of Computer Science and Information Engineering Guangxi Normal University Guilin541004 China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China

In order to solve the problem of high cost and low efficiency in manual welding of power socket welding components, a soldering robot arm system was developed. The gripper, 5-DOF manipulator, clamp, support, and control system were designed. The coordinate system of the robot arm was constructed, and the working space of the robot arm was analyzed by using the MATLAB robot toolbox. It was concluded that the x0 coordinate range of the welding working area was [175mm, 215mm], the y0 coordinate range was [55mm, -55mm], and the z0 coordinate was 90mm. At the same time, the motion trajectory of the robot arm was determined. Using Raspberry PI as a controller, the control system is developed by programming Python language. The prototype is made, and a welding test is carried out. The test shows that the system has completed the welding operation, and the welding effect is good. © Published under licence by IOP Publishing Ltd.

关键词： MATLAB

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Target tracking method of Siamese networks based on the broad learning system

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CAAI Transactions on Intelligence technology 2023年第3期8卷 1043-1057页

作者： Dan Zhang C.L.Philip Chen Tieshan Li Yi Zuo Nguyen Quang Duy Navigation College Dalian Maritime UniversityDalianChina Innovation and Entrepreneurship Education College Dalian Minzu UniversityDalianChina Computer Science and Engineering College South China University of TechnologyGuangzhouChina Department of Computer and Information Science Faculty of Science and TechnologyUniversity of MacaoMacaoChina School of Automation Engineering University of Electronic Science and Technology of ChinaChengduChina Faculty of Navigation Vietnam Maritime UniversityHaiphongVietnam

Target tracking has a wide range of applications in intelligent transportation,real‐time monitoring,human‐computer interaction and other ***,in the tracking process,the target is prone to deformation,occlusion,loss,scale variation,background clutter,illumination variation,etc.,which bring great challenges to realize accurate and real‐time *** based on Siamese networks promotes the application of deep learning in the field of target tracking,ensuring both accuracy and real‐time ***,due to its offline training,it is difficult to deal with the fast motion,serious occlusion,loss and deformation of the target during ***,it is very helpful to improve the performance of the Siamese networks by learning new features of the target quickly and updating the target position in time *** broad learning system(BLS)has a simple network structure,high learning efficiency,and strong feature learning *** at the problems of Siamese networks and the characteristics of BLS,a target tracking method based on BLS is *** method combines offline training with fast online learning of new features,which not only adopts the powerful feature representation ability of deep learning,but also skillfully uses the BLS for re‐learning and re‐*** broad re‐learning information is used for re‐detection when the target tracking appears serious occlusion and so on,so as to change the selection of the Siamese networks search area,solve the problem that the search range cannot meet the fast motion of the target,and improve the *** results show that the proposed method achieves good results on three challenging datasets and improves the performance of the basic algorithm in difficult scenarios.

关键词： broad learning system siamese network target tracking

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Polarization image defogging based on detail recovery generative adversarial network 3

Polarization image defogging based on detail recovery genera...

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3rd International Conference on Signal Image Processing and Communication, ICSIPC 2023

作者： Yang, Cheng Li, Yang School of Computer Science and Information Engineering Hefei University of Technology AnHui Hefei China

ISBN: (纸本)9781510670945

Most of the existing polarization image based defogging methods use a priori and assumptions to recover images, and although these methods have made great progress, the a priori or assumptions are not always reliable in practical scenarios, which limits the performance of defogging methods. In this paper, we design a two branch network to learn image features;the defogging network uses the fusion module to adaptively assign weights to different path features to ensure the defogging effect while focusing on the haze region;the detail recovery network uses convolutional layers and smoothly expanding convolution to expand the perceptual field and fully obtain local and global feature information;finally, the features obtained from the detail recovery network and the defogging network are fused to improve the defogging Finally, the features obtained from detail recovery network and defogging network are fused to improve the defogging effect. The experimental results show that this method can reconstruct clear images in foggy environment with 2db improvement in PSNR and 0.016 improvement in SSIM, and the quality of reconstructed images is better than existing algorithms. © 2023 SPIE.

关键词： Generative adversarial networks

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Sentimental Technique Implementation on Textual Data 6

Sentimental Technique Implementation on Textual Data

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Karthik, Baggam Sai Bharat, Dhavileswarapu N.S.S.V.V. Durga Alam, Md Afroz Sai, Bandari Tharun Rana, Mrinalini Sharma, Smita Yadav, Suman Avdhesh School of Computer Science and Engineering Lovely Professional University Punjab Kapurthala India Amity School of Engineering and Technology Department of Computer Science and Engineering Amity University Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350304480

The increasing amount of real-time or near-real-time data in the big data age provides organizations with opportunities to make data-driven decisions. However, social media data, such as that generated by Twitter, is typically unstructured and difficult to manage. This paper proposes an efficient text data pre-processing approach and develops an algorithm for training decision tree, random forest, SVM, and logistic regression classifiers on a dataset. The algorithm weights the sentiment score based on the hashtag and cleaned text weights, and the most efficient data pre-processing strategy is identified using a novel technology called the hash vectorizer. The effectiveness of the algorithm is tested against manually sorted sentiments and sentiments created before text data pre-processing, and the resultsshow its accuracy is comparable to human-annotated feelings. This research contributes to the existing body of knowledge by providing a precise method for cleaning social media data and a new approach to sentiment analysis. © 2023 IEEE.

关键词： Decision tree Hashing Vectorizer Hybrid model Logistic Regression Random Forest Sentimental Analysis SVM

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Multi-hop emergency message dissemination through optimal cooperative forwarder in grid-based 5G-VANETs

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4461-4476页

作者： Ullah, Sami Abbas, Ghulam Waqas, Muhammad Abbas, Ziaul Haq Halim, Zahid Telecommunications and Networking Research Center GIK Institute of Engineering Sciences and Technology Topi23460 Pakistan Department of Computer Science Shaheed Benazir Bhutto University Sheringal Upper Dir Pakistan Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and Technology Topi23460 Pakistan School of Engineering Edith Cowan University PerthWA6027 Australia Department of Computer Engineering College of Information Technology University of Bahrain Zallaq32038 Bahrain Faculty of Electrical Engineering GIK Institute of Engineering Sciences and Technology Topi23460 Pakistan

Emergency message (EM) dissemination is a significant process in vehicular ad hoc networks (VANETs) and plays a vital role in road safety. Nonetheless, EMs’ dissemination while avoiding broadcast storms poses a considerable challenge. Moreover, selecting optimal cooperative forwarders is also challenging because of unreliability, high end-to-end delay, and packet loss caused by localization errors. Besides, the conventional broadcast-based schemes usually choose forwarder vehicles based on distance, neglecting the influence of links’ qualities. To that end, we propose multi-hop EM dissemination (MEMD) through optimal cooperative forwarder in grid-based fifth-generation (5G) VANET environment. To ensure reliable EMs’ dissemination, we incorporate vehicle-to-everything communication instead of the conventional short-range vehicle-to-vehicle communication. The proposed scheme obtains vehicle positions by integrating GPS measurements with a map-matching technique to avoid vehicle localization errors. Instead of a single parameter, the proposed scheme selects an optimal cooperative forwarder by using our proposed fitness function, which considers different mobility and social parameters. The proposed scheme categorizes EMs’ dissemination into single-hop and multi-hop broadcasting to avoid unnecessary rebroadcasting and communication congestion in the network. Simulation results demonstrate that for varying density, the proposed scheme achieves an average increase of 12%, 9%, and 8.9% in coverage, and 7.95%, 4%, and 3.4% in PDR, and an average reduction of 13.5%, 7.2%, and 12.7% in E2E delay, and 20.9%, 11.3%, and 4% in rebroadcast ratio, as compared to LCL, REMD, and EMDZoI, respectively. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Vehicular ad hoc networks

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