Support vector machine in chemistry / / Nianyi Chen ... [et al.]

• Record Nr.: UNINA9911096532003321
• Pubbl/distr/stampa: Singapore ; ; Hackensack, N.J., : World Scientific, c2004
• ISBN: 9786611934606; 9781281934604; 1281934607; 9789812794710; 9812794719
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (344p.)
• Altri autori (Persone): ChenNianyi
• Disciplina: 540.285631
• Soggetti: Chemistry - Data processing; Chemistry, Technical - Data processing; Machine learning
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Bibliographic Level Mode of Issuance: Monograph
• Nota di bibliografia: Includes bibliographical references (p. 319-327) and index.
• Nota di contenuto: 1. Introduction. 1.1. Support vector machine: data processing method for problems of small sample size. 1.2. Support vector machine: data processing method for complicated data sets in chemistry. 1.3. Underfitting and overfitting: problems of machine learning. 1.4. Theory of overfitting and underfitting control, ERM and SRM principles of statistical learning theory. 1.5. Concept of large margin - a basic concept of SVM. 1.6. Kernel functions: technique for nonlinear data processing by linear algorithm. 1.7. Support vector regression: regression based on principle of statistical learning theory. 1.8. Other machine learning methods related to statistical learning theory. 1.9. Some comments on the application of SVM in chemistry -- 2. Support Vector Machine. 2.1. Margin and optimal separating plane. 2.2. Interpretation by statistical learning therory. 2.3. Support vector classification. 2.4. Support vector regression. 2.5 V-SVM -- 3. Kernel functions. 3.1. Introduction. 3.2. Mercer kernel. 3.3. Properties of kernel. 3.4. Kernel selection -- 4. Feature selection using support vector machine. 4.1. Significance and difficulty of feature selection in chemical data processing. 4.2. SVM-BFS - application of wrapper ; method and floating search method. 4.3. SVM-RFE: application of optimal brain damage and recursive feature elimination. 4.4. Multitask learning. 4.5. Computer experiments: feature selection of artificially generated data set -- 5. Principle of atomic or molecular parameter-data processing method. 5.1. Two different strategies for structure-property relationship investigation. 5.2. Number of valence electrons of atoms. 5.3. Ionization potential of atoms. 5.4. Atomic radii and ionic radii. 5.5. Electronegativity. 5.6. Charge-radius ratio. 5.7. Topological parameters of molecules and 3-D molecular descriptors. 5.8. Atomic parameters for ionic systems. 5.9. Atomic parameters for covalent compounds. 5.10. Atomic parameters for metallic systems -- 6. SVM applied to phase diagram assessment and prediction. 6.1. Comprehensive assessment and computerized prediction of phase diagrams. 6.2.Atomic parameter-pattern recognition method for phase diagram prediction. 6.3. Prediction of intermediate compound formation. 6.4. Prediction of formation of extended solid solutions. 6.5. Prediction of melting types of intermediate compounds. 6.6. Modeling of melting points or decomposition temperature of intermediate compounds. 6.7. Prediction of crystal types of intermediate compounds. 6.8. Modeling of liquid-liquid immiscibility of inorganic systems. 6.9. SVM applied to intelligent database of phase diagrams.; 7. SVM applied to thermodynamic property prediction. 7.1. Significance of estimation of thermodynamic properties of chemical substances. 7.2. Modeling of enthalpy of formation of compounds. 7.3. Modeling of free energy of mixing of liquid alloy systems. 7.4 Prediction of activity coefficient of concentrated electrolyte solutions. 7.5. Regularity of the solubility of C[symbol] in organic solvents -- 8. SVM applied to molecular and materials design. 8.1. concepts of molecular design and materials design. 8.2. SVM applied to new compound synthesis problems. 8.3. SVM applied to the computerized prediction of properties of materials. 8.4. SVM applied to process design for materials preparation -- 9. SVM applied to structure-activity relationships. 9.1. Concept of Structure-Activity Relationships (SAR). 9.2. Brief Introduction to some of chemometric methods used in SAR. 9.3. Brief introduction to molecular descriptors used in SAR. 9.4 SAR of N-(3-Oxo-3,4-dihydro-2H-benzo[l,4]oxazine-6-carbonyl) guanidines. 9.5. SAR of triazole-derivatives. 9.6. SAR of the 5-hydroxytryptamine receptor antagonists. 9.7. QSAR of N-phenylacetamides as herbicides -- 10. SVM applied to data of trace element analysis. 10.1. Trace element science and chemical data processing. 10.2. SVM applied to trace element analysis of human hair. 10.3. SVM applied to trace elements analysis of cigarettes. 10.4. SVM applied to trace element analysis of tea -- 11. SVM applied to archeological chemistry of ancient ceramics. 11.1. SVM applied to archeological data processing. 11.2. Identification of Jun Wares of Song Dynasty. 11.3. Modeling of official Ru Wares. 11.4. Modeling of composition of Yue Wares. 11.5. Modeling of composition of blue and white porcelain samples. 11.6. Archeological research of ancient porcelain kilns. 11.7. Period discrimination of ancient samples -- 12. SVM applied to cancer research. 12.1. SVM applied to cancer epidemiology. 12.2. Carcinogenic and environmental behaviors of polycyclic aromatic hydrocarbons. 12.3. SVM applied to cancer diagnosis -- 13. SVM applied to some topics of chemical analysis. 13.1. Multivariate calibration in chemical analysis. 13.2. Retention indices estimation in chromatography. 13.3. Detection of hidden explosives -- 14. SVM applied to chemical and metallurgical technology. 14.1. Physico-chemical basis of modeling of chemical processes. 14.2. Characteristics of data processing for industrial process modeling. 14.3. Optimal zone: strategy of large ; margin search. 14.4. Application of strategy of large margin search. 14.5. Optimal control for target maximization or minimization. 14.6. Optimal control for problem of restricted response. 14.7. Materials properties estimation for production process. 14.8. Comprehensive strategy for industrial optimization.

Proceedings of the 4th International Conference on Cognitive and Intelligent Computing—Volume 1 : ICCIC-2024, 29–30 November, Hyderabad, India / / edited by Amit Kumar, Gheorghita Ghinea, Suresh Merugu

• Record Nr.: UNINA9911113358803321
• Autore: Kumar Amit
• Pubbl/distr/stampa: Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2026
• ISBN: 981-9501-40-7; 9789819501403
• Edizione: [1st ed. 2026.]
• Descrizione fisica: 1 online resource (754 pages)
• Collana: Cognitive Science and Technology, , 2195-3996
• Altri autori (Persone): KumarUthaya
• Disciplina: 006.3
• Soggetti: Computational intelligence; Machine learning; Artificial intelligence; Data mining; Internet of things; Computational Intelligence; Machine Learning; Artificial Intelligence; Data Mining and Knowledge Discovery; Internet of Things
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Review of AI Edge Computing Frameworks for Mitigating Incorrect Sensor Readings in Judicious Sensor Fusion Systems -- AI and ML Techniques for 5G and Next Generation Wireless Applications -- Optimal Design of Strength and Penetration Depth of Electric Field into ; The Thin Paper Film of The Coplanar Capacitive Sensor for Paper Moisture Measurement Application -- Cardiovascular Disease Diagnosis Using Machine Learning Techniques: A comprehensive review -- Enhanced Communication Among Hear-ing Impaired through Gestures and Facial Expression on 3D Avatar -- Diabetic Retinopathy Diagnosis using Image processing and Deep Learning Methods -- Transformative Learning: Exploring Biblical Studies with AI-Powered Chatbot -- Security and Privacy Challenges in cloud computing: analysis and framework for Risk Mitigation -- Anomaly Detection in Industrial Application through Machine Learning: A Case Study of Da Nang’s Water Supply Plan -- Torque Control of Power Assisted Steering System (APES) Based on GA_PI Controller -- Leveraging machine learning for cardiovascular disease diagnosis -- RSXBoost: A Hybrid Approach to Crop Yield Forecasting with Literature Insights and Simulation Results -- Enhancing Leaf Disease Detection with Vision Transformers: A Literature Survey and Preprocessing Analysi.
• Sommario/riassunto: This book presents original, peer-reviewed select articles from the International Conference on Cognitive & Intelligent Computing (ICCIC-2024), held on November 29–30, 2024, at Hyderabad in India. The proceedings has cutting edge research outcome related to machine learning in control applications, soft computing, pattern recognition, decision support systems, text analytics and NLP, statistical learning, neural network learning, learning through fuzzy logic, learning through evolution (evolutionary algorithms), reinforcement learning, multi-strategy learning, cooperative learning, planning and learning, multi-agent learning, online and incremental learning, scalability of learning algorithms, inductive learning, inductive logic programming, Bayesian networks, support vector machines, case-based reasoning, multi-agent systems, human–computer interaction, data mining and knowledge discovery, knowledge management and networks, data intensive computing architecture, medicine, health, bioinformatics, and systems biology, industrial and engineering applications, security applications, smart cities, game playing and problem solving, intelligent virtual environments, economics, business, and forecasting applications. Articles in the book are carefully selected on the basis of their application orientation. The content is expected to be especially useful for professionals, researchers, and research students working in the area of cognitive and intelligent computing.