Search Results - computer program learning data processing.

Table of Contents: “...Learning Process in E-Learning -- chapter 3 Solving the Problems of Linguistically Diverse First-Year University Students Using Digital Learning -- chapter 4 Moving Enterprise Architecture from Professional Certificates to Academic Credentials -- chapter 5 Automatic Identification and Data Capture Techniques by Radio-Frequency Identification (RFID) Tags: Readifer Authentication and Ethical Aspects -- chapter 6 Artificial Intelligence in E-Learning -- chapter 7 Wireless Multimedia Sensor Networks: Cross-Layer Approach Protocols -- chapter 8 Survey of E-Learning Standards -- chapter 9 A Classification Perspective of Big Data Mining -- chapter 10 Streaming Data Classification in Clustered Wireless Sensor Networks -- chapter 11 Video Codecs Assessment over IPTV Using OPNET -- chapter 12 Brain Storm Optimization (BSO) for Coverage and Connectivity Problems in Wireless Sensor Networks -- chapter 13 An Exploratory Evaluation of the Awareness of E-Government Services among Citizens in Saudi Arabia -- chapter 14 An Internet of Things (IoT) Course for a Computer Science Graduate Program -- chapter 15 Modeling Guidelines of FreeRTOS in Event-B -- chapter 16 Generation of Use Case UML Diagram from User Requirement Specifications -- part Section ii coMMUnicAtion SYSteMS -- chapter 17 Design and Simulation of Adaptive Cognitive Radio Based on Software- Defined Radio (SDR) Using Higher-Ordifer Moments and Cumulants -- chapter 18 Effect of Black Hole Attacks on Delay-Tolerant Networks -- chapter 19 Proposed Adaptive Neural-Fuzzy Inference System (ANFIS) Identifier for M-ary Frequency Shift Keying (FSK) Signals with Low SNR -- chapter 20 New Image Scrambling Algorithm Depending on Image Dimension Transformation Using Chaotic Flow Sequences -- chapter 21 Efficient Two-Stage Sensing Method for Improving Energy Consumption in Cognitive Radio Networks -- chapter 22 Current Status of Information Security Based on Hybrid Crypto and Stego Systems -- chapter 23 The Status of Research Trends in Text Emotion Detection -- chapter 24 Reaction Automata Direct Graph (RADG) Design on Elliptic Curve Cryptography -- chapter 25 A Performance Comparison of Adaptive LMS, NLMS, RLS, and AFP Algorithms for Wireless Blind Channel Identification -- chapter 26 Design of New Algorithms to Analyze RC4 Cipher Based on Its Biases -- chapter 27 Extracting Implicit Feedback from Users? ...”
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Table of Contents: “...3.5.4 Machine learning approaches -- 3.5.5 Pathway-based approaches -- 3.5.6 Artificial neuronal network approaches -- 3.5.7 Deep learning machine approaches -- 3.5.8 Network modeling approach -- 3.5.8.1 Autoencoder approaches -- 3.5.8.2 Text mining approaches -- 3.6 Artificial intelligence algorithms for drug repurposing -- 3.7 Computational intelligence-based approaches to identify therapeutic candidates for repurposing against coronavirus -- 3.7.1 Network-based model -- 3.7.2 Structure-based approaches -- 3.7.3 Artificial intelligence approaches -- 3.8 Challenges in drug repurposing -- 3.9 Future perspectives of artificial intelligence-informed drug repurposing -- 3.10 Conclusion -- References -- 4 COVID-19: artificial intelligence solutions, prediction with country cluster analysis, and time-series forecasting -- 4.1 Introduction -- 4.1.1 Motivation for this study -- 4.1.2 Adverse impacts of COVID-19 outbreak -- 4.1.3 Chapter organization -- 4.1.4 Table of acronyms used in this chapter -- 4.2 Review of literature on COVID-19 pandemic -- 4.3 K-means clustering for COVID-19 country analysis -- 4.3.1 Cluster analysis: an overview -- 4.3.2 Dataset selection and preprocessing -- 4.3.3 Findings from COVID-19 country cluster data analysis -- 4.3.4 The results and discussions -- 4.4 Time-series modeling for COVID-19 new cases -- 4.4.1 Time-series modeling: an overview -- 4.4.2 Dataset description -- 4.4.3 Time-series exploration -- 4.4.4 Predictive analytics -- 4.5 Conclusion -- References -- Further reading -- 5 Graph convolutional networks for pain detection via telehealth -- 5.1 Introduction -- 5.2 Methodology -- 5.2.1 Features extraction -- 5.2.2 Graph-based modules -- 5.2.3 Frame-wise weight calculation -- 5.2.4 Classification -- 5.3 Experiments -- 5.3.1 Datasets -- 5.3.2 Experimental setting -- 5.4 Results and discussion -- 5.5 Conclusion....”
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