1,065 results for machine learning
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THE EVOLUTION AND IMPACT OF QSAR MODELS IN DRUG DISCOVERY: FROM LINEAR RELATIONSHIPS TO PERSONALIZED MEDICINE
…and biological activities into sophisticated tools that leverage machine learning, big data analytics, and systems biology to address the complexities of modern drug development. This transformation marks QSAR not only as a pivotal methodology in cheminformatics but as an essential framework for…
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ENHANCED STRATEGIES FOR BIOMEDICAL SIGNAL PROCESSING AND INTERPRETATION
…signal processing, with a strong focus on practical applications, machine learning, and artificial intelligence. The opening chapters introduce the fundamentals of signals and their processing, explaining the characteristics of analog, digital, and discrete signals, and the methods used to…
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ENHANCED APPROACH IN BIOMEDICAL SIGNAL PROCESS AND ANALYSIS
…signal processing, with a special emphasis on practical applications, machine learning, and artificial intelligence. The book begins with an introduction to the basics of signals and their processing, delving into different types of signals such as analog, digital, and discrete, and how they can be…
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Lu X
…in the series. The L u book series represents an exploration of comics as machine-interpretable information systems. By focusing specifically on crowd scenes in Little Lulu , Manouach's work engages with how computational systems can extract, classify, and interpret visual information from comics…
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Lu 0
…on our planet. The Lu book series represents an exploration of comics as machine-interpretable information systems. By focusing specifically on panels devoid of human presence in Little Lulu , Manouach's work engages with how computational systems can extract, classify, and interpret visual…
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Basic of AI
…prior technical expertise, it explains fundamental concepts such as machine learning, data, algorithms, neural networks, and intelligent systems using simple language and practical examples. Each chapter bridges theory with real-world applications, helping readers understand not only what AI is…
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Natural Language Processing
…used in the field. It then progresses into advanced topics such as deep learning models, transformer architectures, semantic analysis, and real-world applications including chatbots, machine translation, and sentiment analysis. With a structured flow, each chapter explains concepts with practical…
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Clinical Decision-Making Using Artificial Intelligence
…laboratory results, physiologic signals, and clinical text. Through machine learning and deep learning techniques, these systems identify patterns that may not be easily recognized by humans. This capability supports earlier diagnosis, more accurate risk stratification, and personalized treatment…
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Artificial Intelligence
…probabilistic reasoning, Bayes’ theorem, Bayesian networks, and machine learning fundamentals. Real-world examples, diagrams, and problem-solving models are included to help readers understand how AI systems operate in practical environments. Special emphasis is given to balancing theoretical…
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Quantum Computing for Robotics: Algorithms, Optimization, and Future Systems.
…system integration and forward-looking perspectives. It explores quantum machine learning models for robotic learning, outlines hybrid quantum-classical computer architectures required for real-world applications, and rigorously examines the ethical and societal implications of increasingly capable…
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THE CHEMICAL ALGORITHMIC INTELLIGENCE FRAMEWORK FROM QUANTUM MOLECULES TO REGULATORY COMPLIANCE A Unified Theory of Artificial Intelligence, Computational Chemistry, Algorithmic Design, and Chemical Law
…VAEs/GANs/diffusion models for molecular generation, and reinforcement learning for retrosynthesis planning and reaction optimization; (3) Engineering Applications, covering AI-driven drug discovery with multi-parameter optimization, computational materials science for batteries and catalysts, and…
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Introductory Algorithmic Thinking with Python Applications for Engineering and Science
The Philosophy: It is Not only About the Code Welcome to the study of Algorithmic Thinking. A common misconception is that programming is simply about memorizing syntax—learning where to put a semicolon or parenthesis. This is false. Programming is problem-solving . It is the art of translating a…
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DAIGS-Fusion: A Deterministic Multi-Organism Governance Architecture for Fusion Plasma Control
…traditional control methods, including PID loops, predictive models, and machine learning approaches. These systems must manage rapid state transitions, emergent instabilities, safety-critical constraints, and tightly coupled subsystems operating at extreme physical limits. DAIGS-Fusion reframes…
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Adaptive Closed-Loop Control of Sensory Nerve Stimulation in Neuromonitoring Systems A Predictive and Case-Specific Neuroengineering Approach
…real-time signal acquisition, adaptive control algorithms, and machine learning–based predictive models to actively modulate sensory nerve activation. Experimental validation demonstrates significant improvements in signal stability, amplitude preservation, latency accuracy, predictive detection…
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Modul Mata kuliah Intelligent Systems
…dan simulasi penyelesaian masalah. Mahasiswa diperkenalkan pada konsep machine learning dasar, evaluasi model, serta teknik validasi untuk meningkatkan akurasi dan efektivitas sistem. Dengan pendekatan berbasis proyek dan studi kasus, mahasiswa dilatih untuk mengidentifikasi masalah, memilih metode…
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Minds, Machines, and Markets: Bridging AI, Computer Technology, Finance, and Humanity in the Digital Intelligence Era
…professionals, and innovators, the book examines emerging trends such as machine learning, generative AI, fintech, blockchain, data analytics, digital governance, ethical AI, and sustainable technological development. It also highlights the growing importance of human-centered innovation…
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Artificial Intelligence: A Modern Approach
…book integrates various AI methodologies, including logic, probability, machine learning, and robotics, within this framework. It aims to provide both theoretical insights and practical applications, making it suitable for undergraduate courses and as a reference for professionals. The preface also…
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Introduction to Business Statistics through R software
…Physical sciences and including the new fields such as Data Science and Machine Learning. The data which form the basis for the statistical methods helps us to take scientific and informed decisions. Statistical methods deal with the collection, compilation, analysis and making inference from the…
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Big Data Analytics and Cloud Computing
…and statistical data analysis, later expanding into data mining, machine learning, and large-scale systems. Over the years, I have worked with technologies such as Hadoop Distributed File System (HDFS), MapReduce, Apache Spark, Kafka, and Flink, as well as cloud platforms like AWS, Google Cloud…
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Optimization Techniques and Evolutionary Algorithms
…introduction to optimization, especially as it applies to modern AI, machine learning, and engineering applications. It aims to bridge the gap between theory and prac- tice by offering a concise yet comprehensive overview of both foundational and advanced topics. Let me briefly share my own…
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