LEADER 02847aam 2200637 a 450 001 996202459803316 005 20230828225627.0 035 $a(CKB)111026746735170 035 $a(MH)002628291-7 035 $a(SSID)ssj0000451484 035 $a(PQKBManifestationID)12147302 035 $a(PQKBTitleCode)TC0000451484 035 $a(PQKBWorkID)10462847 035 $a(PQKB)11616409 035 $a(EXLCZ)99111026746735170 100 $a19910906d1991 uy 0 101 0 $aeng 181 $ctxt 182 $cc 183 $acr 200 10$aComputers, freedom & privacy $ea comprehensive, edited transcript of the First Conference on Computers, Freedom & Privacy, held March 26-28, 1991 in Burlingame, California /$fJim Warren, Jay Thorwaldson & Bruce Koball, editors ; sponsored by Computer Professionals for Social Responsibility ; co-sponsors & cooperating organizations, the Institute of Electrical and Electronics Engineers, Inc. ... [et al.] ; conference chair, Jim Warren 210 $aLos Alamitos, Calif. $cIEEE Computer Society Press$dc1991 215 $a1 online resource (xii, 230 p. ) 300 $aCover title: Proceedings, the First Conference on Computers, Freedom & Privacy. 300 $a"IEEE Computer Society Press order number 2565"--Verso t.p. 300 $aIncludes index. 311 $a0-8186-2565-1 606 $aComputers$xLaw and legislation$zUnited States$vCongresses 606 $aPrivacy, Right of$zUnited States$vCongresses 606 $aFreedom of information$zUnited States$vCongresses 606 $aGovernment information$zUnited States$vCongresses 606 $aData protection$zUnited States$vCongresses 606 $aData protection$xLaw and legislation$zUnited States$vCongresses 608 $aConference proceedings.$2fast 615 0$aComputers$xLaw and legislation 615 0$aPrivacy, Right of 615 0$aFreedom of information 615 0$aGovernment information 615 0$aData protection 615 0$aData protection$xLaw and legislation 676 $a342.73/0858 676 $a347.302858 700 $aWarren$b Jim C.$cJr.,$f1936-$01062560 701 $aWarren$b Jim C.$cJr.,$f1936-$01062560 701 $aThorwaldson$b Jay$01062561 701 $aKoball$b Bruce$01062562 712 02$aComputer Professionals for Social Responsibility. 712 02$aInstitute of Electrical and Electronics Engineers. 801 0$bDLC 801 1$bDLC 801 2$bMH-L 906 $aBOOK 912 $a996202459803316 996 $aComputers, freedom & privacy$92526574 997 $aUNISA 999 $aThis Record contains information from the Harvard Library Bibliographic Dataset, which is provided by the Harvard Library under its Bibliographic Dataset Use Terms and includes data made available by, among others the Library of Congress LEADER 11813nam 22006375 450 001 9910841868303321 005 20250807145451.0 010 $a981-9984-76-9 024 7 $a10.1007/978-981-99-8476-3 035 $a(CKB)30597468300041 035 $a(MiAaPQ)EBC31200996 035 $a(Au-PeEL)EBL31200996 035 $a(DE-He213)978-981-99-8476-3 035 $a(EXLCZ)9930597468300041 100 $a20240227d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aArtificial Intelligence: Theory and Applications $eProceedings of AITA 2023, Volume 1 /$fedited by Harish Sharma, Antorweep Chakravorty, Shahid Hussain, Rajani Kumari 205 $a1st ed. 2024. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2024. 215 $a1 online resource (495 pages) 225 1 $aLecture Notes in Networks and Systems,$x2367-3389 ;$v843 311 08$a981-9984-75-0 320 $aIncludes bibliographical references and index. 327 $aIntro -- Preface -- Contents -- Editors and Contributors -- Control Techniques for Vision-Based Autonomous Vehicles for Agricultural Applications: A Meta-analytic Review -- 1 Introduction -- 2 State-of-The Art Studies -- 2.1 Target Detection in Autonomous Vehicle System -- 2.2 Vision-Based System -- 3 Mathematical Modeling of Autonomous System -- 4 Conclusion -- References -- Co-GA: A Bio-inspired Semi-supervised Framework for Fake News Detection on Scarcely Labeled Data -- 1 Introduction -- 2 Related Work -- 2.1 Supervised Fake News Detection Using Linguistic Content -- 2.2 Semi-supervised Fake News Detection Using Linguistic Content -- 2.3 Metaheuristics-Based Approaches for Feature Selection -- 2.4 Metaheuristics-Based Fake News Detection -- 3 Data -- 4 Proposed Methodology -- 4.1 Pre-processing -- 4.2 Feature Extraction -- 4.3 Bio-inspired Feature Selection -- 4.4 Multi-view Co-training Model -- 5 Results and Analysis -- 6 Future Research Directions -- 7 Conclusion -- References -- Kernel Methods for Conformal Prediction to Detect Botnets -- 1 Introduction -- 2 Related Works -- 2.1 Signature-Based and Heuristic-Based Botnet Detection -- 2.2 Machine Learning for Botnet Detection -- 2.3 Kernel Methods -- 2.4 Conformal Prediction -- 2.5 Deep Learning and Graph-Based Approaches -- 2.6 Challenges and Limitations -- 2.7 Motivation for the Proposed Approach -- 2.8 Emerging Trends and Research Directions -- 3 Methodology -- 3.1 Kernel Methods -- 3.2 Conformal Prediction -- 3.3 Proposed Approach: Kernel Methods for Conformal Prediction -- 3.4 Evaluation Metrics -- 3.5 Experimental Setup -- 4 Results -- 4.1 Dataset Description -- 4.2 Experimental Setup -- 4.3 Experimental Results -- 4.4 Analysis of Results -- 5 Conclusion -- References -- Biogas Generation from Animal Waste: A Case Study of Village Wazirpur -- 1 Introduction. 327 $a2 Biogas Production from Animal Waste -- 2.1 Factors Affecting Biogas Production -- 2.2 Sensors for Determining the Parameters Affecting Biogas Production -- 3 Area Under Study -- 4 Cost Analysis and Electricity Production -- 5 Conclusion -- References -- Volume of Imbalance Container Prediction using Kalman Filter and Long Short-Term Memory -- 1 Introduction -- 2 Problem Statement -- 3 Research Questions -- 4 KALSTM: A Hybrid Model -- 5 Results and Limitations -- 6 Conclusion -- References -- Modelling Stock Prices Prediction with Long Short-Term Memory (LSTM): A Black Box Approach -- 1 Introduction -- 2 Methodology Based on LSTM -- 3 Description of Datasets -- 4 Results and Discussions -- 5 Conclusion and Future Work -- References -- Agricultural Crop Yield Prediction for Indian Farmers Using Machine Learning -- 1 Introduction -- 2 Literature Survey -- 3 Methodology -- 3.1 Dataset -- 3.2 Methodology -- 4 Architecture -- 5 Result Analysis -- 6 Conclusion -- References -- Application of Artificial Intelligence on Camera-Based Human Pose Prediction for Yoga: A Methodological Study -- 1 Introduction -- 1.1 Scope -- 1.2 Challenges -- 1.3 Impact of Yoga [1] -- 2 Literature Review -- 3 Methodology -- 3.1 Research Process -- 3.2 Key Point Detection Methods -- 3.3 Implementation Methodology [12, 13] -- 4 Datasets and Metrics -- 5 Results -- 6 Conclusion -- 7 Future Potential Development -- References -- Predicting of Credit Risk Using Machine Learning Algorithms -- 1 Introduction -- 2 Review of Literature -- 2.1 Machine Learning Algorithms -- 2.2 Development of Credit Risk Model -- 3 Data and Methodology -- 3.1 Data -- 3.2 Variables -- 3.3 Machine Learning Models and Evaluation Parameters -- 3.4 Evaluation Parameters -- 3.5 Methodology -- 4 Empirical Findings -- 5 Conclusions and Implications -- References -- Study of Various Text Summarization Methods. 327 $a1 Introduction -- 2 Literature Review -- 3 Overview of Proposed Model -- 3.1 Proposed Methodology -- 3.2 Design of Model Architecture -- 3.3 Model Evaluation -- 4 Results -- 5 Conclusion -- References -- Investigations on Deep Learning Pre-trained Model VGG-19 Using Transfer Learning for Remote Sensing Image Classification on Benchmark Datasets -- 1 Introduction -- 2 Literature Review -- 3 Comparison of Performance Metrics of Machine Learning Methods on the PatterNet Dataset -- 4 Utilizing Pre-trained Models for Transfer Learning -- 5 Transfer Learning with Pre-trained Models Based on the Baseline ImageNet Dataset -- 6 Overview of VGG-19 -- 7 Enabling Efficient Feature Reuse and Information Flow in Deep Neural Networks for Superior Performance -- 8 Deep Learning Surpassing Traditional Machine Learning Techniques -- 9 Setting Up Experiments: Feature Extraction and Classification for Remote Sensing Images with a Pre-trained VGG-19 Model -- 9.1 Dataset Description -- 9.2 Assessment Metrics Utilized for Model Evaluation in Image Classification and Retrieval -- 9.3 Research Findings: Investigating Test Accuracy and Test Loss Scores on Benchmark Datasets Using VGG-19 Pre-trained Model -- 10 Summarizing the Feature Extraction with Transfer Learning Approach in Deep Learning -- References -- Complexity Analysis of Legal Documents -- 1 Introduction -- 2 Related Works -- 2.1 NER for Indian Legal Documents -- 2.2 Information Extraction -- 2.3 Summarising in Legal Domain -- 2.4 Complexity of Legal Documents -- 3 Methodology -- 3.1 Proposed Model -- 3.2 Analysis of Complexity -- 4 Result Analysis -- 5 Conclusion and Future Works -- References -- Predicting Virality of Tweets Using ML Algorithms and Analyzing Key Determinants of Viral Tweets -- 1 Introduction -- 2 Theoretical Background and Related Work -- 3 Methodology -- 4 Results and Discussion. 327 $a5 Conclusion, Limitations, and Future Scope -- References -- Review of Classification and Detection for Insects/Pests Using Machine Learning and Deep Learning Approach -- 1 Introduction -- 1.1 Pictorial Representation of Classification and Detection of Pests and Comparison Between ML and DL -- 2 Material -- 2.1 Dataset Collection -- 3 Literature Work -- 3.1 Review of Different Machine Learning and Deep Learning Techniques for the Classification of Pests -- 4 Conclusion -- References -- Sentiment Analysis of Product Reviews Using Deep Learning and Transformer Models: A Comparative Study -- 1 Introduction -- 2 Literature Review -- 3 Sentiment Analysis -- 3.1 Sentiment Analysis Based on Machine Learning -- 3.2 Sentiment Analysis Based on Deep Learning -- 3.3 Sentiment Analysis Based on Transformer-Based Models -- 4 Implementation -- 4.1 Dataset -- 4.2 Data Pre-processing -- 4.3 Classification Models -- 5 Results and Discussions -- 5.1 Hyper Parameters Used -- 5.2 Performance Evaluation -- 6 Conclusion -- References -- Effect of Variation in Pause Times Over MANET Routing Protocols -- 1 Introduction -- 2 MANET Routing Protocols and Literature Review -- 3 Environment Setup -- 4 Performance Metrics -- 5 Conclusions and Future Scope -- References -- DDCMR2: A Deep Detection and Classification Model with Resizing and Rescaling for Plant Disease -- 1 Introduction -- 2 Literature Review -- 3 Proposed Methodology and Implementation -- 3.1 Data Collection -- 3.2 Data Cleaning, Preprocessing, and Visualization -- 3.3 Cache, Shuffle, and Prefetch -- 3.4 Model Building -- 3.5 Hyperparameters Choice -- 4 Results and Discussion -- 5 Conclusion and Future Scope -- References -- Leveraging Natural Language Queries for Effective Video Analysis -- 1 Introduction -- 2 Related Work -- 3 Methodology and Models -- 3.1 Uni-Modal Encoder -- 3.2 Cross-Modal Encoder. 327 $a3.3 Query Generator -- 3.4 Query Decoder -- 4 Experimental Analysis and Outcomes -- 5 Conclusion -- References -- An Experimental Study to Perform Bioinformatics Based on Heart Disease Case Study Using Supervised Machine Learning -- 1 Introduction -- 2 Preliminaries -- 2.1 Machine Learning -- 2.2 Logistic Regression -- 2.3 Decision Tree -- 2.4 Support Vector Machine -- 3 Experimentation -- 3.1 Data Provenance -- 3.2 Flow Diagram of This Study -- 3.3 Correlation Matrix -- 3.4 Logistic Regression -- 3.5 Support Vector Machine (SVM) -- 3.6 Decision Tree -- 4 Results and Analysis -- 5 Conclusion -- References -- Empirical Analysis of Denoising Algorithms for CCTV Face Images -- 1 Introduction -- 2 Related Work -- 3 BM3D (Block-Matching and 3D Filtering) -- 3.1 Collaborative Filtering: It Takes Four Steps -- 3.2 Aggregation -- 3.3 Wiener Filtering Step -- 4 KSVD (k-Singular Value Decomposition) -- 5 WNNM (Weighted Nuclear Norm Minimization) -- 6 Results and Discussion -- 7 Conclusion -- References -- Content-Based Tagging and Recommendation System for Tamil Songs Based on Text and Audio Input -- 1 Introduction -- 2 Literature Survey -- 3 Proposed Methodology -- 3.1 Music Segmentation -- 3.2 Instrument Recognition -- 3.3 Lyric Collection and Translation -- 3.4 Lyric Tagging -- 3.5 Audio Prompt -- 3.6 Similarity-Based Retrieval -- 4 Datasets -- 4.1 MUSDB18 Dataset -- 4.2 Tamil Songs -- 4.3 AudioSet -- 5 Outcomes -- 5.1 Metrics for Evaluation -- 5.2 Summary of Metrics -- 6 Conclusions and Future Work -- References -- Multimodal Face and Ear Recognition Using Feature Level and Score Level Fusion Approach -- 1 Introduction -- 2 Literature Review -- 3 Proposed Methodology -- 3.1 Preprocessing -- 3.2 Feature Extraction (BSIF) -- 3.3 Feature Level Fusion -- 3.4 Score Level Fusion -- 4 Experimental Results and Discussion -- 4.1 GTAV Dataset. 327 $a4.2 FEI Face Database. 330 $aThis book features a collection of high-quality research papers presented at International Conference on Artificial Intelligence: Theory and Applications (AITA 2023), held during 11?12 August 2023 in Bengaluru, India. The book is divided into two volumes and presents original research and review papers related to artificial intelligence and its applications in various domains including health care, finance, transportation, education, and many more. 410 0$aLecture Notes in Networks and Systems,$x2367-3389 ;$v843 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aBig data 606 $aComputational Intelligence 606 $aArtificial Intelligence 606 $aBig Data 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 0$aBig data. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 615 24$aBig Data. 676 $a006.3 702 $aSharma$b Harish 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910841868303321 996 $aArtificial intelligence$9104454 997 $aUNINA