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International Conference on Data Analytics and Management, ICDAM 2023

作者： Kumar, Ashish Mangotra, Abhishek Ailawadi, Ayush Jain, Rachna Arora, Monika School of Computer Science Engineering and Technology Bennett University Uttar Pradesh India Department of Information Technology Bhagwan Parshuram Institute of Technology Delhi India

ISBN: (纸本)9789819965465

In this study, sentiment analysis was done on a collection of bilingual Hindi and English tweets. Before performing sentiment analysis, we first preprocessed the dataset to improve its quality by removing hashtags, URLs, and mentions. To classify the tweets as good, negative, or neutral, we used a range of machine learning models, such as logistic regression, multinomial Naive Bayes, support vector machines, random forests, and decision trees. We compared the results and utilized the metrics of accuracy and precision to assess the performance of the models. The investigation showed that the hybrid model, which included logistic regression with support vector machines, produced the highest accuracy and precision scores. Hyperparameter adjustment was used to improve the logistic regression model’s performance even more. The best model was then used to predict sentiments on a new dataset of unlabeled Hindi-English tweets. The empirical findings demonstrated that the hybrid model outperformed the other machine learning models utilized in this study, achieving an accuracy of 61.7% and a precision of 62.1%. When compared with all forecasts that were truly positive, the precision score showed the percentage of true positives, while the accuracy score showed the percentage of accurate predictions. The results showed that a highly accurate and exact solution for sentiment analysis on Hindi-English tweets is a hybrid model combining logistic regression and a support vector model with hyperparameter tweaking. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Sentiment analysis

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Comparative Performance Analysis of Deep Learning Models for Lung Disease Prediction using Chest X-Ray Images 6

Comparative Performance Analysis of Deep Learning Models for...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Vasamsetti, Samuel Shreyas, G.S.S. Chemboli, Vaibhav Thota, Srikanth Department of Computer Science & Engineering GITAM School of Technology GITAM University Visakhapatnam India

ISBN: (纸本)9798350398496

In recent times, lung diseases like COVID-19, Tuberculosis, and Pneumonia have been identified as significant public health issues. Early detection is a crucial factor for effective treatment in such cases. Previous studies have provided binary classifications of these illnesses with a high accuracy of 98%. However, adding additional classes to the classification process has made it more complex. Therefore, this research aims to investigate Chest X-Ray images of patients to detect potential ailments and compare them to pre-trained architectures such as VGG16, EfficientNetB7, and others. Additionally, various normalization techniques are employed to pre-process the data and X-ray image properties. Deep learning technology has emerged as a reliable and practical approach to accurately identify such illnesses. Convolutional Neural Network (CNN) algorithms is employed to obtain detection findings to analyze the dataset containing scenarios. This research aims to create a better and simpler model to improve the detection process of these classes. Therefore, this research presents a comprehensive investigation of the detection of lung diseases using deep-learning techniques and explores the potential of pre-trained models in addressing the complexities involved in the detection of these illnesses. The findings of this research can be useful for improving the accuracy and efficiency of the diagnosis of lung diseases, leading to better patient diagnosis outcomes. © 2023 IEEE.

关键词： COVID-19

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Enhanced Hybrid Intrusion Detection System with Attention Mechanism using Deep Learning

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SN computer science 2024年第5期5卷 534页

作者： Chavan, Pundalik Hanumanthappa, H. Satish, E.G. Manoli, Sunil Supreeth, S. Rohith, S. Ramaprasad, H.C. School of Computer Science and Engineering REVA University Bengaluru 560064 India Nitte Meenakshi Institute of Technology Bengaluru 560064 India Department of ECE Nagarjuna College of Engineering and Technology Bengaluru India

The introduction of the Attention mechanism by the Internet of Things—or WSN-IoT—in the sector has greatly enhanced the intrusion detection mechanism capabilities, whereas the deep learning techniques, together with the attention mechanism, enhance the efficacy and efficiency of IDS within WSNs. The proposed “Enhanced Hybrid Intrusion Detection System with Attention Mechanism” (EHID-SCA) underlying insight of this is that the characteristics of WSN data validate this including Convolutional Neural Networks (CNNs) and other deep learning models into an effective and coherent architecture. The design of the deep hybrid network with attention incorporates Channel Attention and Spatial Attention as some of the main components. The model biased capacity is enlarged to choose and stress important spatial and channel information from the sensor input. The method is based on the consideration of these things and it cuts noise. Therefore, the proposed technique can be made to pave the way for enabling the Internet of Things intrusion detection systems to do the automatic extraction of useful information from sensor data through the utilization of deep learning along with the Attention mechanism. The attack might therefore be better situated for mitigation in the network design that allows analysis of the geographical and temporal context of events, which are then more properly termed as intrusion events. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Attention mechanism Deep learning Enhanced Hybrid Intrusion Detection Wireless sensor networks

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Radar radiation source signal sorting method based on meta learning 8

Radar radiation source signal sorting method based on meta l...

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8th International Conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Qi, Meibin Zhou, Dade Li, Kunyuan HeFei University of Technology School of Computer Science and Information Engineering Anhui Hefei China

ISBN: (纸本)9798350302455

Radar radiation source sorting and identification methods based on in-depth learning have received considerable attention in recent years, but there are time and resource constraints in practice. To solve this problem, this paper presents a method of radar radiation source signal sorting based on meta-learning. Data samples for signal sorting are generated by acquiring the three-dimensional entropy, image coefficient, high-order cumulants, bandwidth and carrier frequency characteristics of the signal. A signal classification model based on adaptive channel attention is constructed. Model-Agnostic Meta-Learning (MAML) meta-learning mechanism is used to enhance the generalization performance of the model to meet the task requirements of radar signal sorting under small sample conditions. The experimental results show that this method can effectively improve the low recognition rate of small sample radar radiation source signals, and achieve a good classification accuracy under the condition of a small sample of training data. © 2023 IEEE.

关键词： Deep learning

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A Study on the Detection of Breast Lumps Based on Attentional Mechanisms 2

A Study on the Detection of Breast Lumps Based on Attentiona...

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2nd International Conference on Biomedical and Intelligent Systems, IC-BIS 2023

作者： Zhou, Lanfeng Chen, Zhikun School of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai201418 China

ISBN: (纸本)9781510666733

Traditional networks cannot focus on which features are important when extracting features and use the same weighting for all features, so this paper investigates breast lump detection based on attention mechanisms. By adding three attention mechanisms, SE, ECA and CBAM, respectively, to the backbone network of YOLOv5s, the network's ability to extract breast lump features is enhanced for breast lump recognition. The experimental results show that adding SE or ECA or CBAM attention mechanisms alone to the YOLOv5s network, they are higher in accuracy, recall and mAP@0.5 than the original YOLOv5s network model, and the model with the addition of CBAM attention mechanism improves the accuracy, recall and mAP@0.5 by 3.1%, 2.6% and 3.1% respectively, which is better than the models with the addition of SE and ECA attention mechanisms. The proposed attention mechanism-based breast lump detection model can effectively detect breast lumps and provide help for early screening. © 2023 SPIE.

关键词： Object detection

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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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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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Poisoning attacks and countermeasures in intelligent networks:Status quo and prospects

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Digital Communications and Networks 2022年第2期8卷 225-234页

作者： Chen Wang Jian Chen Yang Yang Xiaoqiang Ma Jiangchuan Liu School of Computer Science and Information Engineering Hubei UniversityWuhanChina School of Electronic Information and Communications Huazhong University of Science and TechnologyWuhan430074China School of Computing Science at Simon Fraser University British ColumbiaCanada

Over the past years,the emergence of intelligent networks empowered by machine learning techniques has brought great facilitates to different aspects of human ***,using machine learning in intelligent networks also presents potential security and privacy threats.A common practice is the so-called poisoning attacks where malicious users inject fake training data with the aim of corrupting the learned *** this survey,we comprehensively review existing poisoning attacks as well as the countermeasures in intelligent networks for the first *** emphasize and compare the principles of the formal poisoning attacks employed in different categories of learning algorithms,and analyze the strengths and limitations of corresponding defense methods in a compact *** also highlight some remaining challenges and future directions in the attack-defense confrontation to promote further research in this emerging yet promising area.

关键词： Machine learning Poisoning attack Intelligent networks Security threat

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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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Hybrid Vision Transformer and CNN for Detection of Overripe Coffee Berry Disease (OCBD) in Coffee Plantation 2

Hybrid Vision Transformer and CNN for Detection of Overripe ...

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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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