Acta Informatica Pragensia, 2024 (vol. 13), issue 2

Editorial

Innovations in Deep Learning and Intelligent Systems for Healthcare and Engineering Applications

Hakim Bendjenna, Lawrence Chung, Abdallah Meraoumia

Acta Informatica Pragensia 2024, 13(2), 165-167 | DOI: 10.18267/j.aip.247784  

This editorial summarises the special issue entitled “Future Trends of Machine Intelligence in Science and Industry”, which brings together several pieces of research that showcase the transformative impact of deep learning and intelligent systems across various domains, including healthcare, security and communication networks. By exploring advanced methodologies and innovative applications, this collection highlights significant strides in medical imaging, mental health diagnosis, biometric identification, smart grid management and adaptive e-learning. The featured articles delve into topics such as breast cancer detection using UNET...

Article

Interoperable IoRT for Healthcare: Securing Intelligent Systems with Decentralized Blockchain

Abdessamed Echikr, Ali Yachir, Chaker Abdelaziz Kerrache, Abdelkrim Kamel Oudjida, Zakaria Sahraoui

Acta Informatica Pragensia 2024, 13(2), 168-192 | DOI: 10.18267/j.aip.2333071  

Integration of the internet of things (IoT) and robotics into the internet of robotic things (IoRT) presents inherent security and trust challenges. This article introduces an innovative blockchain-centred framework designed to address these challenges. By harnessing the features of oneM2M and blockchain technology, the architecture enhances accessibility, security and data management within IoRT. Encompassing key aspects such as data flow, user authentication, health data management and security, the proposed framework ensures compliance with the oneM2M standard. A notable contribution is the integration of a private blockchain network on ZedBoard...

Efficient Contactless Palmprint Recognition System Based on Deep Rule‐Based Classification

Yacine Belhocine, Abdallah Meraoumia, Khediri Abderrazak, Mohammed Saigaa

Acta Informatica Pragensia 2024, 13(2), 193-212 | DOI: 10.18267/j.aip.2362707  

In recent years, as technology has advanced and more and more activities have become digitized, cybersecurity has become a top priority for governments around the world. Cybersecurity is essential for protecting computer systems, networks and data from cyberattacks that can have a negative impact on individuals, businesses and governments. Indeed, biometrics is a key means of cybersecurity that can help prevent unauthorized access, identity theft and unauthorized changes to data. This paper presents an innovative contactless palmprint recognition system, integrating two types of features to enhance accuracy and efficiency. Our approach employs two...

Deep Neural Network-Based Model for Breast Cancer Lesion Diagnosis in Mammography Images

Mohamed Amine Yakoubi, Nada Khiari, Amine Khiari, Ahlem Melouah

Acta Informatica Pragensia 2024, 13(2), 213-233 | DOI: 10.18267/j.aip.2452166  

Deep learning has made identifying breast cancer lesions in mammography images an easy task in modern medicine, which has helped improve the diagnosis efficiency, sensitivity and accuracy by precisely identifying breast cancer from mammography images, contributing to timely detection and maintaining consistent performance. This paper presents the steps and strategies to develop a deep learning (DL) model to detect lesions in mammography images, based on U-Net architecture for precise segmentation, which has been developed for biomedical image segmentation, and incorporating ResNet34 as its encoder to extract features. Next, we employ the FastAI library,...

Beyond Traditional Biometrics: Harnessing Chest X-Ray Features for Robust Person Identification

Farah Hazem, Bennour Akram, Tahar Mekhaznia, Fahad Ghabban, Abdullah Alsaeedi, Bhawna Goyal

Acta Informatica Pragensia 2024, 13(2), 234-250 | DOI: 10.18267/j.aip.2382449  

Person identification through chest X-ray radiographs stands as a vanguard in both healthcare and biometrical security domains. In contrast to traditional biometric modalities, such as facial recognition, fingerprints and iris scans, the research orientation towards chest X-ray recognition has been spurred by its remarkable recognition rates. Capturing the intricate anatomical nuances of an individual's rib cage, lungs and heart, chest X-ray images emerge as a focal point for identification, even in scenarios where the human body is entirely damaged. Concerning the field of deep learning, a paradigm is exemplified in contemporary generations, with...

Revolutionizing Historical Manuscript Analysis: A Deep Learning Approach with Intelligent Feature Extraction for Script Classification

Merouane Boudraa, Akram Bennour, Tahar Mekhaznia, Abdulrahman Alqarafi, Rashiq Rafiq Marie, Mohammed Al-Sarem, Ayush Dogra

Acta Informatica Pragensia 2024, 13(2), 251-272 | DOI: 10.18267/j.aip.2392560  

The automated classification of historical document scripts holds profound implications for historians, providing unprecedented insights into the contexts of ancient manuscripts. This study introduces a robust deep learning system integrating an intelligent feature selection method, elevating the script classification process. Our methodology, applied to the CLaMM dataset, involves preprocessing steps such as advanced denoising through non-local means and binarization using Canny edge detection. These steps, pivotal for image cleaning and segmentation, set the stage for subsequent in-depth analysis. To enhance feature detection, we employ the Harris...

Deep Learning Proactive Approach to Blackout Prevention in Smart Grids: An Early Warning System

Abderrazak Khediri, Ayoub Yahiaoui, Mohamed Ridda Laouar, Yacine Belhocine

Acta Informatica Pragensia 2024, 13(2), 273-287 | DOI: 10.18267/j.aip.2462184  

Blackout events in smart grids can have significant impacts on individuals, communities and businesses, as they can disrupt the power supply and cause damage to the grid. In this paper, a new proactive approach to an early warning system for predicting blackout events in smart grids is presented. The system is based on deep learning models: convolutional neural networks (CNN) and deep self-organizing maps (DSOM), and is designed to analyse data from various sources, such as power demand, generation, transmission, distribution and weather forecasts. The system performance is evaluated using a dataset of time windows and labels, where the labels indicate...

Deep Learning Approach for Predicting Psychodiagnosis

Zouaoui Samia, Khamari Chahinez

Acta Informatica Pragensia 2024, 13(2), 288-307 | DOI: 10.18267/j.aip.2432469  

Artificial intelligence methods, especially deep learning, have seen increasing application in analysing personality and occupational data to identify individuals with psychological and neurological disorders. Currently, there is a great need for effectively processing mental healthcare with the integration of artificial intelligence such as machine learning and deep learning. The paper addresses the pressing need for accurate and efficient methods for diagnosing psychiatric disorders, which are often complex and multifaceted. By exploiting the power of convolutional neural networks (CNN), we propose a novel CNN-based natural language processing method...

Review

Advancements in Breast Cancer Diagnosis: A Comprehensive Review of Mammography Datasets, Preprocessing and Classification Techniques

Hama Soltani, Issam Bendib, Mohamed Yassine Haouam, Mohamed Amroune

Acta Informatica Pragensia 2024, 13(2), 308-326 | DOI: 10.18267/j.aip.2442855  

Breast cancer, a pervasive global health concern, necessitates early detection for an improved prognosis. Mammography, a pivotal screening tool, faces challenges in interpretation, motivating the integration of advanced computational models. This paper offers a comprehensive examination of breast cancer classification through mammography, focusing on machine learning (ML) and deep learning (DL) approaches. The discussion encompasses widely used mammography datasets, preprocessing techniques, data augmentation and diverse classification algorithms. Noteworthy datasets include LAMIS-DMDB, EMBED and INbreast. Preprocessing involves denoising and contrast...

Miscellanea

Examining Adaptive E-Learning Approaches to Enhance Learning and Individual Experiences

Fateh Benkhalfallah, Mohamed Ridda Laouar, Mohamed Salah Benkhalfallah

Acta Informatica Pragensia 2024, 13(2), 327-339 | DOI: 10.18267/j.aip.2402461  

The concept of individualization has emerged as an essential advance in education, representing a paradigm shift adopted by educational systems worldwide. This paradigm evolution aims to optimize student performance by harnessing the potential of diverse e-learning platforms tailored to individual needs. These platforms enable students to acquire knowledge that matches their unique interests and skills. This not only fosters academic prowess but also proactive engagement of stakeholders invested in talent development endeavours. This paper attempts to provide a holistic overview of adaptive e-learning approaches using a recognized categorization framework....