LEADER 01086nas 2200361-a 450 001 9910273537203321 005 20200612163355.0 011 $a1541-3322 035 $a(OCoLC)50392663 035 $a(CKB)111079555746002 035 $a(CONSER)--2002212650 035 $a(EXLCZ)99111079555746002 100 $a20020812a20029999 --- a 101 0 $aeng 200 00$aNordic reach 210 $aNorwalk, CT $cSwedish Council of America/Swedish News$d2002- 300 $aTitle from cover. 531 0 $aNordic reach 606 $aSwedish Americans$vPeriodicals 606 $aScandinavian Americans$vPeriodicals 606 $aScandinavian Americans$2fast$3(OCoLC)fst01106373 606 $aSwedish Americans$2fast$3(OCoLC)fst01140023 608 $aPeriodicals.$2fast 615 0$aSwedish Americans 615 0$aScandinavian Americans 615 7$aScandinavian Americans. 615 7$aSwedish Americans. 676 $a305 712 02$aSwedish Council of America. 906 $aJOURNAL 912 $a9910273537203321 996 $aNordic reach$92113273 997 $aUNINA LEADER 03801nam 22006975 450 001 9910483492103321 005 20231110132058.0 010 $a9783030516581 010 $a303051658X 024 7 $a10.1007/978-3-030-51658-1 035 $a(CKB)4100000011631454 035 $a(MiAaPQ)EBC6455934 035 $a(DE-He213)978-3-030-51658-1 035 $a(Perlego)3481072 035 $a(EXLCZ)994100000011631454 100 $a20201202d2021 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aKey Concepts in the Study of Antisemitism /$fedited by Sol Goldberg, Scott Ury, Kalman Weiser 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Palgrave Macmillan,$d2021. 215 $a1 online resource (XVIII, 336 p. 5 illus., 4 illus. in color.) 225 1 $aPalgrave Critical Studies of Antisemitism and Racism,$x2946-4641 311 08$a9783030516574 311 08$a3030516571 320 $aIncludes bibliographical references and index. 327 $a1. Introduction- Kalman Weiser -- 2. Anti-Judaism- Jonathan Elukin -- 3. Anti-Semitism (Historiography)- Jonathan Judaken -- 4. Anti-Zionism- James Loeffler -- 5. Blood Libel- Hillel J. Kieval -- 6. The Catholic Church- Magda Teter -- 7.Conspiracy Theory- Jovan Byford -- 8.Emancipation- Frederick C. Beiser -- 9. Gender- Sara R. Horowitz -- 10. Ghetto- Daniel B. Schwartz -- 11. The Holocaust- Richard S. Levy -- 12. Jewish Self-hatred- Sol Goldberg -- 13. Nationalism- Brian Porter-Sz?cs -- 14. Nazism- Doris L. Bergen -- 15. Orientalism- Ivan Kalmar -- 16. Philosemitism- Maurice Samuels -- 17. Pogrom- Jeffrey Kopstein -- 18. Post-colonialism- Bryan Cheyette -- 19. Racism- Robert Bernasconi -- 20. Secularism- Lena Salaymeh & Shai Lavi -- 21. Sinat Yisrael- Martin Lockshin -- 22. Zionism- Scott Ury. 330 $aThis volume is designed to assist university faculty and students studying and teaching about antisemitism, racism, and other forms of prejudice. In contrast with similar volumes, it is organized around specific concepts instead of chronology or geography. It promotes conversation about antisemitism across disciplinary, geographic, and thematic lines rather than privileging a single methodological paradigm, a specific academic field, or an overarching narrative. Its twenty-one chapters by leading scholars in diverse fields address the relationship to antisemitism of concepts ranging from Anti-Judaism to Zionism. Each chapter not only traces the history and major scholarly debates around a key concept; it also presents an original argument, points to avenues for further research, and exemplifies a method of investigation. 410 0$aPalgrave Critical Studies of Antisemitism and Racism,$x2946-4641 606 $aHistoriography 606 $aHistory$xMethodology 606 $aReligion and sociology 606 $aJudaism 606 $aCritical criminology 606 $aHistoriography and Method 606 $aSociology of Religion 606 $aJudaism 606 $aCritical Criminology 615 0$aHistoriography. 615 0$aHistory$xMethodology. 615 0$aReligion and sociology. 615 0$aJudaism. 615 0$aCritical criminology. 615 14$aHistoriography and Method. 615 24$aSociology of Religion. 615 24$aJudaism. 615 24$aCritical Criminology. 676 $a305.8924 702 $aGoldberg$b Sol 702 $aUry$b Scott 702 $aWeiser$b Keith Ian$f1973- 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483492103321 996 $aKey concepts in the study of antisemitism$92597989 997 $aUNINA LEADER 12844nam 22006733 450 001 9911019835703321 005 20250320002814.0 010 $a9781394269969 010 $a139426996X 010 $a9781394269945 010 $a1394269943 010 $a9781394269952 010 $a1394269951 024 7 $a10.1002/9781394269969 035 $a(MiAaPQ)EBC31879137 035 $a(Au-PeEL)EBL31879137 035 $a(CKB)37200603100041 035 $a(OCoLC)1484697059 035 $a(CaSebORM)9781394269938 035 $a(OCoLC-P)1484697059 035 $a(Perlego)4787307 035 $a(EXLCZ)9937200603100041 100 $a20250116d2025 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMultimodal Data Fusion for Bioinformatics Artificial Intelligence 205 $a1st ed. 210 1$aNewark :$cJohn Wiley & Sons, Incorporated,$d2025. 210 4$d©2025. 215 $a1 online resource (406 pages) 311 08$a9781394269938 311 08$a1394269935 327 $aCover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Advancements and Challenges in Multimodal Data Fusion for Bioinformatics AI -- 1.1 Introduction -- 1.2 Literature Review -- 1.3 Results and Discussion -- Conclusion -- References -- Chapter 2 Automated Machine Learning in Bioinformatics -- 2.1 Introduction -- 2.2 Need of Automated Machine Learning -- 2.3 Automated ML in Various Areas of Bioinformatics -- 2.4 Major Obstacles for Automated ML in Various Areas of Bioinformatics -- 2.5 Applications of Automated ML in Various Areas of Bioinformatics -- 2.6 Case Study 1 -- 2.7 Conclusion and Future Directions -- References -- Chapter 3 Data-Driven Discoveries: Unveiling Insights with Automated Methods -- 3.1 Introduction -- 3.2 Important Functions in Bioinformatics Include Data Mining and Analysis -- 3.3 Deep Learning in Bioinformatics -- 3.4 Challenges and Issues -- 3.4.1 Data Requirements for Big Data Sets -- 3.4.2 Model Selection and Learning Strategy -- 3.5 Conclusion -- References -- Chapter 4 Comparative Analysis of Conventional Machine Learning and Deep Learning Techniques for Predicting Parkinson's Disease -- 4.1 Introduction -- 4.2 Symptoms and Dataset for PD -- 4.3 Parkinson's Disease Classification Using Machine Learning Methods -- 4.4 Parkinson's Disease Classification Using DL Methods -- 4.5 Conclusion -- References -- Chapter 5 Foundations of Multimodal Data Fusion -- Introduction -- What is Multimodal Data Fusion in Bioinformatics AI? -- Types of Data Modalities in Bioinformatics -- Challenges and Considerations in Multimodal Data Fusion -- Foundational Principles of Data Fusion -- Machine Learning and Deep Learning Techniques for Multimodal Data Fusion -- Feature Representation and Fusion -- Applications in Bioinformatics AI -- Evaluation Metrics and Validation Strategies -- Evaluation Metrics. 327 $aApproval Techniques -- Ethical and Legal Considerations -- Future Directions and Challenges -- Conclusion -- References -- Chapter 6 Integrating IoT, Blockchain, and Quantum Machine Learning: Advancing Multimodal Data Fusion in Healthcare AI -- 6.1 Introduction -- 6.2 Internet of Things (IoT) in Healthcare -- 6.3 Blockchain Technology in Healthcare -- 6.4 Quantum Machine Learning in Healthcare -- 6.5 Integration of IoT, Blockchain, and Quantum Machine Learning in Healthcare -- 6.6 Ethical and Regulatory Considerations in Healthcare Technology -- 6.7 Challenges and Future Directions in Healthcare Technology Integration -- 6.8 Results and Discussion -- 6.9 Conclusion -- References -- Chapter 7 Integrating Multimodal Data Fusion for Advanced Biomedical Analysis: A Comprehensive Review -- 7.1 Introduction -- 7.2 Multimodal Biomedical Analysis -- 7.3 Challenges in Data Fusion -- 7.4 Deep Learning Methods for Data Fusion -- 7.5 Case Studies and Applications -- 7.5.1 Neuro-Imaging and Genetic Data Fusion -- 7.5.2 Multi-Omics Data Fusion for Cancer Classification -- 7.5.3 Clinical and Wearable Sensor Data Fusion -- 7.6 Future Directions -- 7.7 Conclusion -- References -- Chapter 8 Machine Learning Approaches for Integrating Imaging and Molecular Data in Bioinformatics -- 8.1 Introduction -- 8.2 Background and Motivation -- 8.3 Machine Learning Basics -- 8.4 Approaches for Data Integration -- 8.5 Machine Learning Techniques for Imaging and Molecular Data -- 8.6 Applications -- 8.7 Challenges and Future Directions -- 8.8 Case Studies -- 8.9 Conclusion -- References -- Chapter 9 Time Series Analysis in Functional Genomics -- 9.1 Introduction -- 9.2 Foundations of Time Series Analysis in Functional Genomics -- 9.2.1 Definition and Concept -- 9.2.1.1 Time Series Data in Genomics -- 9.2.1.2 Key Terminology. 327 $a9.2.2 Challenges in Analyzing Functional Genomic Time Series Data -- 9.2.2.1 Noise and Variability -- 9.2.2.2 Data Preprocessing Considerations -- 9.3 Methodologies for Time Series Analysis -- 9.3.1 Overview of Existing Approaches -- 9.3.1.1 Classical Methods -- 9.3.1.2 Advanced Computational Techniques -- 9.3.2 Case Studies -- 9.3.2.1 Successful Applications -- 9.4 Applications of Time Series Analysis in Functional Genomics -- 9.4.1 Gene Expression Profiling -- 9.4.1.1 Identification of Temporal Patterns -- 9.4.1.2 Regulatory Network Inference -- 9.4.2 Functional Annotation -- 9.4.2.1 Enrichment Analysis -- 9.4.2.2 Pathway Analysis -- 9.4.3 Comparative Analysis -- 9.4.3.1 Contrasting Time Series Data Across Genomic Entities -- 9.5 Integration with Multimodal Data -- 9.5.1 Overview of Multimodal Data Fusion -- 9.5.2 Challenges and Opportunities in Integrating Time Series Data -- 9.5.2.1 Challenges in Integrating Time Series Data -- 9.5.2.2 Opportunities in Integrating Time Series Data -- 9.5.3 Case Studies on Successful Integration -- 9.5.3.1 Unveiling Temporal Interactions Across Multiple Modalities -- 9.5.3.2 Temporal Biomarkers in Disease Progression -- 9.6 Conclusion -- References -- Chapter 10 Review of Multimodal Data Fusion in Machine Learning: Methods, Challenges, Opportunities -- 10.1 Introduction -- 10.2 Related Work -- 10.2.1 Machine and Deep Learning Methods with Multimodal -- 10.2.2 Evaluation of Multimodal -- 10.3 Multimodal and Data Fusion -- 10.4 Applications, Opportunities, and Challenges -- 10.4.1 Audio-Visual Multimodality -- 10.4.2 Human-Machine Interaction (HML) -- 10.4.3 Understanding Brain Functionality -- 10.4.4 Medical Diagnosis -- 10.4.5 Smart Patient Monitoring -- 10.4.6 Remote Sensing and Earth Observations -- 10.4.7 Meteorological Monitoring -- 10.5 Conclusion and Future Directions -- 10.5.1 Conclusion. 327 $a10.5.2 Future Directions -- References -- Chapter 11 Recent Advancement in Bioinformatics: An In-Depth Analysis of AI Techniques -- 11.1 Introduction -- 11.2 AutoMLDL Methods -- 11.3 Application of AutoMLDL in Bioinformatics -- 11.3.1 Bioinformatics and the Categorization of Cardiovascular Diseases -- 11.3.2 Diagnostics of Coronavirus Disease and Bioinformatics -- 11.3.3 Genomic and Bioinformatic Correlation with Clinical Data and Progress of Disease -- 11.3.4 Bioinformatics in the Study of Drug Resistance -- 11.4 Advanced Algorithm in AutoMLDL for Bioinformatics -- 11.4.1 Optimization with Hybrid Harris Hawks along with Cuckoo Search Applying Chemo Bioinformatics -- 11.4.2 The Integration of Chemoinformatics and Bioinformatics with AI -- 11.5 Security and Privacy Issues in AutoMLDL -- 11.5.1 Security and Privacy -- 11.5.2 Open Issues -- 11.6 Conclusion and Future Works -- References -- Chapter 12 Future Directions and Emerging Trends in Multimodal Data Fusion for Bioinformatics -- 12.1 Introduction -- 12.2 Foundational Concepts -- 12.3 Current State of Multimodal Data Fusion in Bioinformatics -- 12.4 Emerging Trends in Data Fusion -- 12.5 Algorithms -- 12.5.1 Deep Learning Architectures for Data Fusion -- 12.5.2 Ensemble Methods for Heterogeneous Data Integration -- 12.5.3 Dimensionality Reduction and Feature Extraction -- 12.5.4 Multi-View Learning Algorithms -- 12.5.5 Federated Learning for Privacy-Preserving Data Fusion -- 12.6 Future Directions -- 12.7 Case Studies and Applications -- 12.8 Challenges and Opportunities -- 12.9 Conclusion -- References -- Chapter 13 Future Trends in Bioinformatics AI Integration -- Introduction -- What Is Multimodal Data Fusion? -- Types of Multimodal Data in Bioinformatics -- Challenges in Multimodal Data Fusion -- Multimodal Data Integration Approaches -- Feature Representation and Selection. 327 $aIntegration of Omics Data -- Clinical Applications -- Imaging Data Fusion -- Biological Network Integration -- Applications in Precision Medicine -- Computational Tools and Resources -- Future Directions and Challenges -- Conclusion -- References -- Chapter 14 Emerging Technologies in IoM: AI, Blockchain and Beyond -- 14.1 Introduction -- 14.1.1 Importance of the Internet of Medicine -- 14.2 Artificial Intelligence (AI) in Healthcare -- 14.2.1 Diagnostic Imaging and Radiology -- 14.2.2 Predictive Analytics and Personalized Medicine -- 14.2.3 Natural Language Processing (NLP) for Clinical Documentation -- 14.2.4 Virtual Health Assistants and Chatbots -- 14.2.5 Drug Discovery and Development -- 14.2.6 Operational Efficiency and Resource Management -- 14.2.7 Remote Patient Monitoring -- 14.2.8 Fraud Detection and Security -- 14.2.9 Ethical Considerations and Bias Mitigation -- 14.2.10 Regulatory Compliance -- 14.3 Blockchain in the Medical Landscape -- 14.3.1 Data Security and Integrity -- 14.3.2 Interoperability -- 14.3.3 Patient Empowerment -- 14.3.4 Supply Chain Management -- 14.3.5 Clinical Trials and Research -- 14.3.6 Smart Contracts -- 14.3.7 Identity Management -- 14.3.8 Credentialing and Certification -- 14.3.9 Data Sharing and Consent -- 14.3.10 Cybersecurity -- 14.4 Benefits of Using Technologies in IoM -- 14.4.1 Remote Monitoring and Telemedicine -- 14.4.2 Improved Diagnostics and Treatment -- 14.4.3 Genomic Medicine and Data Analytics -- 14.4.4 Automation and Robotics -- 14.4.5 Wearables and IoT Devices -- 14.4.6 Virtual Reality (VR) and Augmented Reality (AR) -- 14.4.7 Telehealth and Mobile Health (mHealth) -- 14.4.8 Blockchain for Healthcare Management -- 14.4.9 Data Analytics and AI in Research -- 14.4.10 Blockchain and Encryption -- 14.5 Integration of Cutting-Edge Technologies. 327 $a14.6 Beyond AI and Blockchain: Exploring Additional Technologies. 330 $aMultimodal Data Fusion for Bioinformatics Artificial Intelligence is a must-have for anyone interested in the intersection of AI and bioinformatics, as it delves into innovative data fusion methods and their applications in 'omics' research while addressing the ethical implications and future developments shaping the field today. Multimodal Data Fusion for Bioinformatics Artificial Intelligence is an indispensable resource for those exploring how cutting-edge data fusion methods interact with the rapidly developing field of bioinformatics. Beginning with the basics of integrating different data types, this book delves into the use of AI for processing and understanding complex "omics" data, ranging from genomics to metabolomics. The revolutionary potential of AI techniques in bioinformatics is thoroughly explored, including the use of neural networks, graph-based algorithms, single-cell RNA sequencing, and other cutting-edge topics. The second half of the book focuses on the ethical and practical implications of using AI in bioinformatics. The tangible benefits of these technologies in healthcare and research are highlighted in chapters devoted to precision medicine, drug development, and biomedical literature. The book addresses a wide range of ethical concerns, from data privacy to model interpretability, providing readers with a well-rounded education on the subject. Finally, the book explores forward-looking developments such as quantum computing and augmented reality in bioinformatics AI. This comprehensive resource offers a bird's-eye view of the intersection of AI, data fusion, and bioinformatics, catering to readers of all experience levels. 606 $aBioinformatics 606 $aArtificial intelligence$xBiological applications 615 0$aBioinformatics. 615 0$aArtificial intelligence$xBiological applications. 676 $a570.285 700 $aLilhore$b Umesh Kumar$01838638 701 $aKumar$b Abhishek$0977677 701 $aVyas$b Narayan$01837560 701 $aSimaiya$b Sarita$01841170 701 $aDutt$b Vishal$01841171 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911019835703321 996 $aMultimodal Data Fusion for Bioinformatics Artificial Intelligence$94420803 997 $aUNINA LEADER 02224oas 2200745 a 450 001 9910898157803321 005 20251028213014.0 011 $a1659-3049 035 $a(DE-599)ZDB2413643-8 035 $a(OCoLC)261341316 035 $a(CONSER) 2009263269 035 $a(CKB)1000000000366513 035 $a(EXLCZ)991000000000366513 100 $a20081009a20069999 sy 101 0 $aspa 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aMe?todos en ecologi?a y sistema?tica 210 $aSan Jose? $cInstituto Centroamericano para la Investigacio?n en Biologi?a y Conservacio?n (CIBRC)$d[2006]- 215 $a1 online resource 311 08$a1659-2182 517 1 $aMethods in ecology and systematics 517 1 $aMES 517 1 $aRevista MES 606 $aNatural history$zCentral America$vPeriodicals 606 $aBotany$zCentral America$vPeriodicals 606 $aEcology$zCentral America$vPeriodicals 606 $aBiotic communities$zCentral America$vPeriodicals 606 $aBiotic communities$2fast$3(OCoLC)fst00832828 606 $aBotany$2fast$3(OCoLC)fst00836869 606 $aEcology$2fast$3(OCoLC)fst00901476 606 $aNatural history$2fast$3(OCoLC)fst01034268 606 $aSciences naturelles$zAme?rique centrale$vPe?riodiques 607 $aCentral America$2fast 608 $aPeriodicals.$2fast 615 0$aNatural history 615 0$aBotany 615 0$aEcology 615 0$aBiotic communities 615 7$aBiotic communities. 615 7$aBotany. 615 7$aEcology. 615 7$aNatural history. 615 6$aSciences naturelles 712 02$aInstituto Centroamericano para la Investigacio?n en Biologi?a y Conservacio?n. 801 0$bOCLCS 801 1$bOCLCS 801 2$bOCLCS 801 2$bTXA 801 2$bOCLCQ 801 2$bOCLCF 801 2$bOCLCO 801 2$bCUS 801 2$bOCLCQ 801 2$bOCLCO 801 2$bOCLCQ 801 2$bCUS 801 2$bOCLCL 801 2$bSFB 906 $aJOURNAL 912 $a9910898157803321 996 $aMétodos en ecología y sistemática$91987439 997 $aUNINA