{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":2,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":37,"n_cols":5,"rows_kept":37,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[2,4,2,5],[3,2,3,3],[4,2,4,3],[5,2,5,3],[6,2,6,3],[9,2,9,3],[9,4,9,5],[10,2,10,3],[10,4,10,5],[11,2,11,3],[11,4,11,5],[12,2,12,3],[13,2,13,3],[14,2,14,3],[15,2,15,3],[16,2,16,3],[17,2,17,3],[18,2,18,3],[24,2,24,3],[25,2,25,3],[27,2,27,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNISOBE600200032841"]
[null,null,"Autore",null,"D' Angelo, Edoardo"]
[null,null,"Titolo",null,"The Outer Metric in Joseph of Exeter's Ylias and Odo of Magdeburg's Ernestus / Edoardo D'Angelo"]
[null,null,"Pubbl/distr/stampa",null,"s. l., : Brepols, 1993"]
[null,null,"Descrizione fisica",null,"p. 113-134 : 9 tav. ; 24 cm"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Estr. da : \"The Journal of Medieval latin\", (1993)3"]
[null,"2.","Record Nr.",null,"UNINA9910874692703321"]
[null,null,"Autore",null,"Sharma Neha"]
[null,null,"Titolo",null,"Data Management, Analytics and Innovation : Proceedings of ICDMAI 2024, Volume 1 / / edited by Neha Sharma, Amol C. Goje, Amlan Chakrabarti, Alfred M. Bruckstein"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024"]
[null,null,"ISBN",null,"9789819732425"]
[null,null,"Edizione",null,"[1st ed. 2024.]"]
[null,null,"Descrizione fisica",null,"1 online resource (664 pages)"]
[null,null,"Collana",null,"Lecture Notes in Networks and Systems, , 2367-3389 ; ; 997"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Computational intelligence"]
[{"$nulls":4},"Engineering - Data processing"]
[{"$nulls":4},"Big data"]
[{"$nulls":4},"Computational Intelligence"]
[{"$nulls":4},"Data Engineering"]
[{"$nulls":4},"Big Data"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Contents -- About the Editors -- A Comprehensive Literature Review on Emerging Potentials of Machine Learning Algorithms on Geospatial Platform for Medicinal Plant Cultivation Management in Existing Scenario -- 1 Introduction -- 2 Literature Review -- 2.1 Overview on Medicinal Plant Cultivation (MPC) Management -- 2.2 Descriptive Machine Learning (M/L) Techniques for Geospatial Data Analysis -- 2.3 Findings from Literature -- 3 Proposed Framework for Medicinal Plant Cultivation Using Machine Learning Approach on Geospatial Platform -- 3.1 Phases of Medicinal Plant Cultivation -- 3.2 Machine Learning Algorithms -- 3.3 Performance Evaluation -- 4 Conclusion -- References -- Facial Features Recognition and Classification Using Machine Learning Model -- 1 Introduction -- 2 Related Works -- 3 Datasets -- 4 Proposed Method -- 4.1 Training the Machine Learning Model -- 4.2 Real-Time Detection Using Haar Cascades -- 5 Results and Discussion -- 6 Future Scope and Conclusion -- References -- An Intelligent System for Prediction of Lung Cancer Under Machine Learning Framework -- 1 Introduction -- 2 Literature Survey -- 3 Methodology -- 3.1 Dataset Collection -- 3.2 Feature Selection -- 3.3 Split the Dataset -- 3.4 Model Building -- 4 Result Analysis -- 4.1 Confusion Matrix -- 5 "]
[{"$nulls":4},"Conclusion -- References -- Identification of Misinformation Using Word Embedding Technique Word2Vec, Machine Learning, and Deep Learning Models -- 1 Introduction -- 2 Related Work -- 2.1 Background Work -- 3 Proposed Methodology -- 3.1 Dataset Description -- 4 Results and Discussion -- 4.1 Data Collection and Cleaning -- 4.2 Data Visualization -- 4.3 Feature Extraction -- 4.4 Model Selection -- 4.5 Performance Measure -- 5 Conclusion and Future Scope -- References -- Multiscript Handwriting Recognition Using RNN Transformer Architecture -- 1 Introduction."]
[{"$nulls":4},"2 Related Works -- 3 Proposed Method -- 3.1 Recurrent Neural Network (RNN) -- 3.2 Transformer Architecture -- 4 Result and Discussion -- 5 Conclusion and Future Work -- References -- Enhancing Agriculture Productivity with IoT-Enabled Predictive Analytics and Machine Learning -- 1 Introduction -- 2 Literature Survey -- 3 Agile Agriculture Prototype -- 3.1 SAPS (Smart Agricultural Production System) -- 3.2 ACIS (Automated Crop Irrigation System) -- 4 Agile Agriculture Workflow -- 5 Simulation -- 6 Conclusion -- References -- Gender Gaps in the Context of Cryptocurrency Literacy: Evidence from Survey Data in Europe and Asia -- 1 Introduction -- 2 Data -- 3 Results and Discussion -- 4 Conclusion -- References -- An Optimized Machine Learning Model for Crop Yield Predication by Applying Weighted Ensemble Technique -- 1 Introduction -- 2 Literature Survey -- 3 Proposed Optimized Ensemble Model (OEM) -- 3.1 Weight -- 3.2 Optimized Weight Calculation -- 3.3 A Weighted Optimized Ensemble Model Algorithm (OEM) -- 4 Experimental Setup -- 5 Result and Discussion -- 6 Conclusion -- References -- Crop Recommendation and Irrigation System Using Machine Learning with Integrated IoT Devices -- 1 Introduction -- 2 Literature Review -- 3 Problems with the Existing Methods -- 4 Proposed Methodology -- 5 Crop Recommendations Flow -- 6 Irrigation Flow -- 7 Implementation -- 8 Comparative Analysis -- 9 Results -- 10 Conclusion -- References -- Toward Space-Efficient Semantic Querying with Graph Databases -- 1 Introduction -- 2 Literature Survey -- 3 Methodology/Proposed Approach -- 3.1 Knowledge Graph Creation -- 3.2 Opportunity Identification -- 4 Challenges -- 5 Conclusion -- References -- Enhanced Artificial Neural Networks for Prostate Cancer Detection and Classification -- 1 Introduction -- 2 Literature Survey -- 3 Proposed Method -- 4 Results and Discussion."]
[{"$nulls":4},"5 Conclusion -- References -- Agricultural Indicators as Predictors of Annual Water Quality: An Analysis of Interconnectedness and Prediction Using Machine Learning -- 1 Introduction -- 2 Literature Review -- 3 Materials and Methods -- 3.1 Data -- 3.2 Machine Learning Methods -- 4 Experiments and Results -- 4.1 Insights on Water Quality -- 4.2 Linear Regression -- 4.3 Prediction -- 5 Limitations -- 6 Conclusions and Future Work -- References -- Predicting Mental Health Disorders in the Technical Workplace: A Study on Feature Selection and Classification Algorithms -- 1 Introduction -- 2 Background -- 3 Data Exploration -- 4 Modeling of Feature Selection -- 4.1 Feature Selection Using LASSO -- 4.2 Feature Selection Using RFECV -- 4.3 Feature Selection Using RFE -- 5 Performance Evaluations -- 5.1 LASSO-Based Classification -- 5.2 Classification with RFECV -- 5.3 Classification with RFE -- 6 Results and Discussions -- 7 Conclusion -- References -- Enhancing the Detection of Fake News in Social Media: A Comparison of Support Vector Machine Algorithms, Hugging Face Transformers, and Passive Aggressive Classifier -- 1 Problem Description -- 2 Introduction -- 3 Survey of Literature -- 3.1 Support Vector Machine (SVM) [1-4] -- 3.2 Hugging Face Transformers [6, 7] -- 3.3 Passive Aggressive Classifier [8] -- 4 Comparison "]
[{"$nulls":4},"of Various Algorithms -- 4.1 Proposed Comparison -- 4.2 Proposal for the New Work -- 5 Methodology -- 6 Algorithm Summary -- 7 Execution Environment -- 7.1 Powerful Processing -- 7.2 Scalability -- 7.3 Flexibility -- 7.4 Ease of Use -- 7.5 Cost-Effective -- 8 Inference Based on Test Results and Outputs from Table 1 -- 8.1 Hugging Face Model -- 8.2 Passive Aggressive Classifier -- 8.3 Support Vector Machine (SVM) -- 9 Model Fine-Tuning for Hugging Face Model -- 10 Conclusion -- References."]
[{"$nulls":4},"A Multifactor Authentication Framework for Usability in Education Sectors in Uganda -- 1 Introduction -- 2 Related Works -- 3 Research Methodology -- 4 Results and Discussion of Findings -- 4.1 Username/Passwords -- 4.2 Face Recognition -- 4.3 Fingerprint Authentication -- 5 Framework Design for E-MuAF -- 6 E-Assessment Multifactor Authentication Framework -- 7 Conclusion -- References -- Rank Prediction for Indian Universities Based on National Institutional Ranking Framework -- 1 Introduction -- 2 Literature Review -- 3 Problem Statement -- 4 Research Method -- 4.1 NIRF Ranking Parameters and Sub-Parameters -- 4.2 ML Algorithms Used -- 5 Result and Discussion -- 6 Conclusion -- References -- Research Paper Summarization Using Extractive Approach -- 1 Introduction -- 2 Literature Review -- 3 Design and Implementation -- 4 Results and Discussion -- 5 Conclusion -- References -- Safarnaama: User Experience-Based Travel Recommendation System -- 1 Introduction -- 2 Review of Literature -- 2.1 Gaps in Literature Survey -- 3 Design of Safarnaama -- 3.1 Architectural Design -- 3.2 Data Description -- 3.3 Cold Start Problem -- 3.4 User Interface Design -- 3.5 Algorithms and Methods Used -- 4 Performance Analysis -- 4.1 Data Collection and Pre-processing -- 4.2 Formula for Custom Recommendations -- 4.3 App Development -- 4.4 Feedback Analysis -- 4.5 Result Analysis -- 5 Conclusion and Future Scope -- References -- Handling Missing Data in Longitudinal Anthropometric Data Using Multiple Imputation Method -- 1 Introduction -- 2 Literature Review -- 3 Materials and Methods -- 3.1 Data Source -- 3.2 Imputation Methods -- 4 Pre-processing and Analysis -- 5 Experimental Results and Evaluation -- 5.1 Comparative Analysis of Imputation Methods -- 6 Conclusion -- References."]
[{"$nulls":4},"From Pixels to Insight: Enhancing Metallic Component Defect Detection with GLCM Features and AI Explainability -- 1 Introduction -- 2 Methods and Methodology -- 2.1 Dataset -- 2.2 Feature Extraction -- 2.3 Training and Testing -- 3 Explainability and Shapley Additive Explanations (SHAP) Plots -- 3.1 Explainability -- 3.2 SHAP and SHAP Plots -- 3.3 Results and Discussion -- 4 Conclusions -- References -- Predicting Chronic Kidney Disease Progression Using Classification and Ensemble Learning -- 1 Introduction -- 2 Comprehensive Review -- 3 Proposed Framework -- 4 Experiment and Performance Evaluation -- 5 Performance Evaluation Metrics -- 6 Conclusions -- References -- Analyzing UNO Statistics on Land Use of Agricultural Practices by Using k-Means Clustering and SARIMA: Irrigated, Organic, and Overall Agricultural Activities on a Global Scale -- 1 Introduction -- 2 Literature Review -- 3 Data and Methods -- 4 Analysis -- 4.1 Silhouette Score -- 4.2 Limitations of k-Means Clustering -- 4.3 k-Means Clustering -- 4.4 SARIMA Prediction -- 5 Alternative Explanations for the Observed Results -- 6 Conclusion -- References -- Fruit and Vegetable Segmentation with Decision Trees -- 1 Introduction -- 2 Related Literature -- 3 Data Acquisition -- 4 Image Preprocessing -- 4.1 Image Segmentation -- 4.2 Feature Extraction -- 5 Classification -- 6 Results -- 7 Summary and Conclusion -- References -- Chatbot Development Simplified: An In-Depth Look "]
[{"$nulls":4},"at JIGYASABOT Platform and Alternatives -- 1 Introduction -- 2 Literature Survey -- 3 Problem Statement -- 4 Proposed Solution -- 4.1 Architecture -- 4.2 Survey Platforms Ratings and Results -- 5 Building Chatbots with JIGYASABOT -- 5.1 Design of JIGYASABOT Chatbot -- 6 Comparative Market Analysis -- 7 Use Cases and Innovations -- 8 Conclusion -- 9 Future Scope -- References."]
[{"$nulls":4},"Guarding the Gateway: Data Privacy and Security in Metaverse Tourism."]
[null,null,null,"Sommario/riassunto","This book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence, and data analytics, along with advances in network technologies. The book is a collection of peer-reviewed research papers presented at 8th International Conference on Data Management, Analytics and Innovation (ICDMAI 2024), held during 19–21 January 2024 in Vellore Institute of Technology, Vellore, India. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry. The book is divided into two volumes."]