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

作者： Singh, Amit Kumar Kharbas, Vikas Kumar Vinay Kumar, S.B. Arka Jain University Department of Journalisim & Mass Communication Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India School of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350370249

This paper specializes in enhancing the safety and privacy of machine-mastering algorithms in agricultural automation. With the emergence of digital generation in agricultural production, system mastering algorithms have emerged as essential to offer precision control in farm control operations. However, their security and privacy may be compromised due to assault vectors inclusive of opposed inputs, facts leakage assaults, and facts poisoning attacks. Several ability solutions are studied, which include the introduction of statistics anonymization strategies, getting the right of entry to manage mechanisms, cryptographic strategies, and software program and hardware protection tactics. This paper analyses the one-of-a-kind security and privacy enhancement techniques and affords a comprehensive discussion of their effectiveness. Sooner or later, the authors advise a complete solution that encompasses all the security and privacy enhancement approaches to shield the gadget studying algorithms in agricultural automation effectively. © 2024 IEEE.

关键词： Differential privacy

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Data-Sparse Deep Learning for Studying Diabetic Retinopathy Lesions 5

Data-Sparse Deep Learning for Studying Diabetic Retinopathy ...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Singh, Devendra Bisht, Narendra Dobhal, Dinesh C. Pant, Janmejay Dhanik, Saurabh Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Graphic Era Hill University Department of Management Studies Bhimtal India Graphic Era Deemed to Be University Department of Computer Application Uttarakhand Dehradun India Graphic Era Hill University Department of Computer Science and Engineering Bhimtal Campus India Graphic Era Hill University School of Computing Bhimtal Campus Uttarakhand India

ISBN: (纸本)9798350383522

Diabetic retinopathy, a serious complication of diabetes, can result in permanent blindness if not promptly detected. The difficulty in assessing the stages of the disease using fundus images by experienced human specialists impedes early identification. This problem has prompted researchers to turn toward convolutional neural networks (CNNs) to simplify the evaluation process. The incorporation of CNNs offers hope for a more effective and accurate distinguishing of the stages of diabetic retinopathy, which could help prevent permanent loss of vision. The objective of such an effective screening mechanism is to simplify procedures, and thus allow for early intervention soon after onset so that the precious gift of sight will not be lost. We suggest a classification system below along these lines. © 2024 IEEE.

关键词： Convolutional Neural Networks Diabetic Retinopathy (DR) Efficient Net ResNet-50

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Exploring Sentiment Analysis Algorithms for Brand-Product Reputation Enhancement 15

Exploring Sentiment Analysis Algorithms for Brand-Product Re...

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

作者： Kumar, Ashwini Sharma, Sunil Nanduri, Sireesha Arka Jain University Department of Mechnical Engineering Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India School of Mangement - Pg Department of Management Karnataka Bangalore India

ISBN: (纸本)9798350370249

This paper affords an overview of sentiment analysis algorithms for boosting brand-product reputation. The point of interest is on supervised and unsupervised systems getting to know strategies, which might be usually used for predicting sentiment. Specifically, the authors examine 4 forms of algorithms - Naïve Bayes classifiers, support vector machines, recurrent neural networks, and sentiment analysis with deep mastering - and how they can be used for boosting emblem-product popularity. Moreover, the look evaluates the constraints of the algorithms, in addition to capacity destiny studies instructions. The authors finish that while sentiment analysis with deep getting to know can also be the maximum accurate choice, cautious consideration must be made while choosing an algorithm as specific algorithms may also produce distinctive outcomes. This paper presents an informative and comprehensive assessment of sentiment analysis algorithms and their usefulness for boosting logo-product reputation. © 2024 IEEE.

关键词： Support vector machines

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Exploring Dimensionality Reduction Techniques for On-line Analytical Processing 15

Exploring Dimensionality Reduction Techniques for On-line An...

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

作者： Kumar, Anup Inzimam Patel, Srinivasan Arka Jain University Department of Mechnical Engineering Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India School of Management - Pg Department of Management Karnataka Bangalore India

ISBN: (纸本)9798350370249

Exploring Dimensionality discount strategies for Analytical Processing (OLAP) is an area of research that makes a specialty of the use of diverse techniques to reduce the variety of dimensions needed to investigate a records set. That is high-quality because it reduces processing time, garage area requirements, and simplifies the statistics and outcomes acquired. Diverse strategies are to be had for reducing the wide variety of dimensions, and those are based totally upon the application and the associated information set. The strategies include central factor assessment (PCA), characteristic desire, weighted length reduction, and Locality Sensitive Hashing (LSH). This paper provides a comparative evaluation of those strategies and discusses their strengths and obstacles. The outcomes of this observation will help researchers and practitioners understand each method's talents and limitations while using it in OLAP packages. © 2024 IEEE.

关键词： Analytical Processing application Dimensionality Locality Observation Researchers Technique

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Improvised Text Classification by Hybrid Machine Learning Techniques

Improvised Text Classification by Hybrid Machine Learning Te...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Anitha, Cuddapah Kapruwan, Akanksha Shilpa, M. Dange, Jyoti Vekariya, Vipul Maranan, Ramya Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College School of Computing Department of Computer Science and Engineering Andhra Pradesh Tirupati517102 India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Rns First Grade College Department of Computer Application Bangalore Karnataka Channasandra560098 India Atharva College of Engineering Department of Information Technology Malad West Maharashtra Mumbai400095 India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Post Limda Gujarat Waghodia391760 India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350373783

To improve the performance of English-text classification, text classification systems aim to design appropriate algorithms for extracting features and auto-classifying texts with automatic semantic feature selection. Motivated by the efficiency of Attention mechanism (AM), the ways to use AMs at different levels in CNN and specific RNN organization are explored in this work and BLSTM based models to eliminate misinformation, obtain more accurate contextual information, and ultimately improve performance. Finally, all of the features are concatenated and fed into the softmax classifier. The BLSTM is primarily used to hold the sentence interdependencies, then CNN is selected to extract features for continuous modeling operations, and this model is evaluated by applying it to the ETC. Furthermore, the proposed integrated CNN and BLSTM with Attention mechanism (A-C-BLSTM) model achieves significant results through our evaluation and performs better than a wide range of baseline models. © 2024 IEEE.

关键词： Semantics

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DETR-DC5 Approaches for Improved Spatial Object Detection in Satellite Imagery

DETR-DC5 Approaches for Improved Spatial Object Detection in...

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Baskar, Radhika Dwibedi, Rajat Kumar Kumbhkar, Makhan Sah, Swati Patil, Harshal Firos, A. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Aarupadai Veedu Institute of Technology Vmrf Chennai India Indian Institute of Soybean Research Department of Computer Application Indore India School of Set Sharda University Greater Noida India Computer Science and Engineering Pune India Department of Computer Science and Engineering Rono-Hills Arunachal Pradesh Doimukh India

ISBN: (纸本)9798350394580

Satellite spatial object detection is crucial for urban planning, environmental monitoring, and catastrophe management. The DETR-DC5 (Data Efficient Transformer with DC5 Backbone) is a cutting-edge object detection model. This study introduces DETR-DC5 framework-based satellite spatial object detection improvements. DETR-DC5's strengths are used to improve feature extraction and post-processing. First, a specific feature augmentation technique extracts richer and more discriminative spatial features to let the model capture subtle object attributes. Second, a semi-supervised learning training technique is used to address the lack of labeled satellite imagery data and improve the model's generalization. A new satellite imagery post-processing refining method uses non-maximum suppression methods. This refinement stage reduces false positives and improves localization, especially in densely populated metropolitan regions and complicated natural surroundings. On benchmark satellite imagery datasets, the suggested methods outperform baseline DETR-DC5 models in spatial object detection. Experimental findings indicate significant improvements in precision, recall, and F1-score, proving the upgrades work. This study uses the DETR-DC5 architecture, targeted feature augmentation, semi-supervised training, and refined post-processing algorithms to recognize spatial objects in satellite data. These techniques have the potential to dramatically improve satellite-based object detection, which has implications for urban planning, environmental monitoring, and disaster response. © 2023 IEEE.

关键词： DETR-DC5 Feature Augmentation Satellite Imagery Semi-Supervised Learning Spatial Object Detection

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A range and radial velocity measurement method based on up-chirp and down-chirp LFM signal in pulsed radar

A range and radial velocity measurement method based on up-c...

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IET International Radar Conference 2023, IRC 2023

作者： Chen, Hao Lei, Yi Key Lab of Aperture Array and Space Application East China Research Institute of Electronic Engineering Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9781839539954

Range accuracy and radial velocity accuracy are important indexes in pulsed radar. However, in pulsed radar using linear frequency modulation (LFM) pulse, the range-Doppler coupling of LFM signal will lead to large range error in high-speed target measurement. The target's radial velocity is computed using range differentiation in traditional method, which is also not accurate. This paper presents a measurement method based on up-chirp and down-chirp LFM signal to get the target's accurate range and radial velocity simultaneously. Experiments confirm that the proposed method outperforms traditional method in both range accuracy and radial velocity accuracy. © The Institution of engineering & Technology 2023.

关键词： Chirp modulation

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An adaptive representation model for geoscience knowledge graphs considering complex spatiotemporal features and relationships

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science China Earth sciences 2023年第11期66卷 2563-2578页

作者： Yunqiang ZHU Kai SUN Shu WANG Chenghu ZHOU Feng LU Hairong LV Qinjun QIU Xinbing WANG Yanmin QI State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China Department of Automation Tsinghua UniversityBeijing 100084China School of Computer Science China University of GeosciencesWuhan 430074China Department of Electronic Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Computer Science University of Nottingham Ningbo ChinaNingbo 315100China

Geoscience knowledge graph(GKG)can organize various geoscience knowledge into a machine understandable and computable semantic network and is an effective way to organize geoscience knowledge and provide knowledge-related *** a result,it has gained significant attention and become a frontier in *** knowledge is derived from many disciplines and has complex spatiotemporal features and relationships of multiple scales,granularities,and ***,establishing a GKG representation model conforming to the characteristics of geoscience knowledge is the basis and premise for the construction and application of ***,existing knowledge graph representation models leverage fixed tuples that are limited in fully representing complex spatiotemporal features and *** address this issue,this paper first systematically analyzes the categorization and spatiotemporal features and relationships of geoscience *** this basis,an adaptive representation model for GKG is proposed by considering the complex spatiotemporal features and *** the constraint of a unified spatiotemporal ontology,this model adopts different tuples to adaptively represent different types of geoscience knowledge according to their spatiotemporal *** model can efficiently represent geoscience knowledge,thereby avoiding the isolation of the spatiotemporal feature representation and improving the accuracy and efficiency of geoscience knowledge *** can further enable the alignment,transformation,computation,and reasoning of spatiotemporal information through a spatiotemporal ontology.

关键词： Geoscience Knowledge graph Representation model Spatiotemporal features Spatiotemporal relationships

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Using analytic hierarchy process to evaluate deep learning for infrared target recognition

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Multimedia Tools and applications 2024年第38期83卷 86229-86245页

作者： Xu, Laixiang Cao, Bingxu Wei, Xinhong Li, Bei Su, Jingfeng Ning, Shiyuan Zhao, Junmin School of Computer and Data Science Henan University of Urban Construction Pingdingshan467036 China School of Information Engineering Luohe Vocational Technology College Henan University of Technology Luohe Institute of Technology Luohe462000 China School of Computer and Data Science Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Pingdingshan467036 China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Henan University of Urban Construction Pingdingshan467036 China Department of Software Information China Electronics Technology Group Corporation 36-th Research Institute Jiaxing314033 China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Research Center of Intelligent Campus Application Engineering of Henan Province Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Henan University of Urban Construction Pingdingshan467036 China

In order to promote the evaluation performance of deep learning infrared automatic target recognition (ATR) algorithms in the complex environment of air-to-air missile research, we proposed an analytic hierarchy process (AHP) evaluation system consisting of the target recognition ability, the generalization recognition ability, the anti-interference ability, the background suppression ability, and the cost metrics ability. According to detailed quantitative analysis and experimental results, we establish probability evaluation models and get their probability values. Compared with traditional evaluation systems, our proposed method implementation is showing high reliability. Similar superior performances are also achieved, such as quick, comprehensive, and scientific. Through the above research, the key technology of deep learning performance evaluation in automatic target recognition has been broken through, which provides some basis and guidance for the analysis of infrared imaging-guided weapon systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Infrared imaging

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Empirical Evaluation of Stock Market Prediction System using Intelligent Learning Scheme with Data Processing Logic 5

Empirical Evaluation of Stock Market Prediction System using...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Prabakar, S. Kumar, Ankit Jayakarthik, R. Venkatesh, D. Pratheeba, R.S. Khan, Bhola Isbr Business School Finance and Accounting Bangalore India Faculty of Science Swami Vivekanand Subharti University Department of Computer Application Meerut India Saveetha College of Liberal Arts and Sciences Simats Dept of Computer Science India Commerce School of Commerce and Management Mohan Babu University Tirupati India Velammal Institute of Technology Department of Computer Science and Engineering Panchetti601204 India Mjp Rohilkhand University Department of Regional Economics Bareilly India

ISBN: (纸本)9798350385649

The Stock Market Prediction System using Intelligent Learning Scheme (SMS-ILS) presented in this study integrates advanced methodologies to enhance the accuracy and reliability of stock market predictions. The system encompasses a preprocessing phase, addressing missing data and outliers, followed by feature extraction utilizing Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) to capture intricate temporal dependencies. A Deep Belief Network (DBN) is employed for classification, categorizing stocks based on learned representations from historical and technical indicators. Grey Wolf Optimization (GWO) optimizes the system's parameters, ensuring adaptability to dynamic market conditions. Notably, the system achieves an impressive accuracy of 97%, emphasizing its efficacy in making correct predictions. This multi-model approach, integrating LSTM, RNN, DBN, and GWO, provides a comprehensive and adaptive framework for navigating the complexities of stock market prediction. The study emphasizes the importance of choosing metrics aligned with specific goals and conducting thorough analysis to ensure the robustness of the evaluation results in the challenging and dynamic context of financial markets. © 2024 IEEE.

关键词： Long short-term memory

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