Applied Intelligence and Informatics: Second International Conference, AII 2022, Reggio Calabria, Italy, September 1–3, 2022, Proceedings(1724 Communications in Computer and Information Science)

This book constitutes the refereed proceedings of the Second International Conference on Applied Intelligence and Informatics, AII 2022, held in Reggio Calabria, Italy, during September 1–3, 2022.  The 38 full papers included in this book were carefully reviewed and selected from 108 submissions. They were organized in topical sections as follows: ​Emerging Applications of AI and Informatics;  Application of AI and Informatics in Healthcare; Application of AI and Informatics in Pattern Recognition; and Application of AI and Informatics in Network, Security, and Analytics.

Table of Contents:
​Track 1: Emerging Applications of AI and Informatics.- A hybrid speed and radial distance feature descriptor using optical flow approach in HAR.- Innovative Soft Computing Techniques for the Evaluation of the Mechanical Stress State of Steel Plates.- An Indirect Approach to Forecast Produced Power on Photovoltaic Plants under Uneven Shading Conditions.- An FPGA-based Hardware Accelerator for the k-Nearest Neighbor Algorithm Implementation in Wearable Embedded Systems.- Designing Low-Power and High-Speed FPGA-based Binary Decision Tree Hardware Accelerators.- MEMS and AI for the Recognition of Human Activities on IoT Platforms.- Tackling the Linear Sum Assignment Problem with Graph Neural Networks.- A Non-Parametric Model for Forecasting Life Expectancy at Birth Using Gaussian Process.- Optimized Layout: A Genetic Algorithm for Industrial and Business Application.- Track 2: Application of AI and Informatics in Healthcare.- Explainable Deep Learning for Alzheimer Disease Classification and Localisation.- ML-based Radiomics analysis for Breast Cancer classification in DCE-MRI.- Decoding motor preparation through a deep learning approach based on EEG time-frequency maps.- A Novel Fuzzy Semi-supervised Learning Approach for the Classification of Colorectal Cancer (FSSL-CRCC).- The Effect of Drug on Neonatal-ICU Length of Stay: A Machine Learning Approach to Analyze.- iConDet2: An Improved Conjunctivitis Detection Portable Healthcare App Powered by Artificial Intelligence.- Ensemble Classifiers for a 4-way Classification of Alzheimer’s Disease.- Identification of Crown and Rump in First-Trimester Ultrasound Images using Deep Convolutional Neural Network.- A Pyramidal Approach for Emotion Recognition from EEG Signals.- Evaluation of Galvanic Skin Response (GSR) Signals Features for Emotion Recognition.- Track 3: Application of AI and Informatics in Pattern Recognition.- A Privacy-Preserving Federated-MobileNet for Facial Expression Detection from Images.- Television programs classification via Deep Learning using SSMI-CCN.- A Hybrid Deep Learning System to detect face-mask and monitor social distance.- Handwriting and Drawing for Depression Detection: A Preliminary Study.- Identifying synthetic voices qualities for conversational agents.- Identifying Colluding Actors in Social Communities by Reputation Measures.- Weakly Supervised Transfer Learning for Multi-label Appliance Classification.- Toward an Interoperable Catalogue of Multimodal Depression-related Data.- Explainable deep learning classification of respiratory sound for telemedicine applications.- Nonexclusive Classification of Household Appliances by Fuzzy Deep Neural Networks.- Applied Enhanced Q-NAS for COVID-19 Detection in CT Images.- Track 4: Application of AI and Informatics in Network, Security, and Analytics.- Beware the Sirens: Prototyping an Emergency Vehicle Detection System for Smart Cars.- Indoor Positioning and Navigation using Bluetooth Low Energy and Cloud Service in Healthcare Perspective.- Science Parks externalities on financial performance of small firms through business intelligence tools.- A novel approach to identifying DDoS traffic in the Smart Home network via Exploratory Data Analysis.- A Novel Framework to Detect Anomalous Nodes to Secure Wireless Sensor Networks.- A graph-based approach to detect anomalies based on shared attribute values.