Acta Informatica Pragensia, 2023 (vol. 12), issue 1

Editorial

Use of Deep Learning and Blockchain Technologies in Healthcare Industry

Mazin Abed Mohammed, Seifedine Kadry, Oana Geman

Acta Informatica Pragensia 2023, 12(1), 1-2 | DOI: 10.18267/j.aip.2131089  

This editorial summarises the special issue entitled “Deep Learning Blockchain-enabled Technology for Improved Healthcare Industrial Systems”, which deals with the intersection and use of deep learning and blockchain technologies in the healthcare industry. This special issue consists of eleven scientific articles.

Article

Optimized Negative Selection Algorithm for Image Classification in Multimodal Biometric System

Monsurat Omolara Balogun, Latifat Adeola Odeniyi, Elijah Olusola Omidiora, Stephen Olatunde Olabiyisi, Adeleye Samuel Falohun

Acta Informatica Pragensia 2023, 12(1), 3-18 | DOI: 10.18267/j.aip.1863147  

Classification is a crucial stage in identification systems, most specifically in biometric identification systems. A weak and inaccurate classification system may produce false identity, which in turn impacts negatively on delicate decisions. Decision making in biometric systems is done at the classification stage. Due to the importance of this stage, many classifiers have been developed and modified by researchers. However, most of the existing classifiers are limited in accuracy due to false representation of image features, improper training of classifier models for newly emerging data (over-fitting or under-fitting problem) and lack of an efficient...

Deep Residual Learning Image Recognition Model for Skin Cancer Disease Detection and Classification

Jamal Mustafa Al-Tuwaijari, Naeem Th. Yousir, Nafea Ali Majeed Alhammad, Salama Mostafa

Acta Informatica Pragensia 2023, 12(1), 19-31 | DOI: 10.18267/j.aip.1893744  

Skin cancer is undoubtedly one of the deadliest diseases, and early detection of this disease can save lives. The usefulness and capabilities of deep learning in detecting and categorizing skin cancer based on images have been investigated in many studies. However, due to the variety of skin cancer tumour shapes and colours, deep learning algorithms misclassify whether a tumour is cancerous or benign. In this paper, we employed three different pre-trained state-of-the-art deep learning models: DenseNet121, VGG19 and an improved ResNet152, in classifying a skin image dataset. The dataset has a total of 3297 dermatoscopy images and two diagnostic categories:...

Classification of Eye Images by Personal Details With Transfer Learning Algorithms

Cemal Aktürk, Emrah Aydemir, Yasr Mahdi Hama Rashid

Acta Informatica Pragensia 2023, 12(1), 32-53 | DOI: 10.18267/j.aip.1902798  

Machine learning methods are used for purposes such as learning and estimating a feature or parameter sought from a dataset by training the dataset to solve a particular problem. The transfer learning approach, aimed at transferring the ability of people to continue learning from their past knowledge and experiences to computer systems, is the transfer of the learning obtained in the solution of a particular problem so that it can be used in solving a new problem. Transferring the learning obtained in transfer learning provides some advantages over traditional machine learning methods, and these advantages are effective in the preference of transfer...

Comparative Analysis of Performance Metrics for Machine Learning Classifiers with a Focus on Alzheimer’s Disease Data

Sivakani Rajayyan, Syed Masood Mohamed Mustafa

Acta Informatica Pragensia 2023, 12(1), 54-70 | DOI: 10.18267/j.aip.1983232  

Alzheimer's disease is a brain memory loss disease. Usually, it will affect persons over 60 years of age. The literature has revealed that it is quite difficult to diagnose the disease, so researchers are trying to predict the disease in the early stage. This paper proposes a framework to classify Alzheimer's patients and to predict the best classification algorithm. The Bestfirst and CfssubsetEval methods are used for feature selection. A multi-class classification is done using machine learning algorithms, namely the naïve Bayes algorithm, the logistic algorithm, the SMO/SMV algorithm and the random forest algorithm. The classification accuracy...

Deep Learning Convolutional Neural Network for SARS-CoV-2 Detection Using Chest X-Ray Images

Ali Mohammed Saleh Ahmed, Inteasar Yaseen Khudhair, Salam Abdulkhaleq Noaman

Acta Informatica Pragensia 2023, 12(1), 71-86 | DOI: 10.18267/j.aip.2052909  

The COVID-19 coronavirus illness is caused by a newly discovered species of coronavirus known as SARS-CoV-2. Since COVID-19 has now expanded across many nations, the World Health Organization (WHO) has designated it a pandemic. Reverse transcription-polymerase chain reaction (RT-PCR) is often used to screen samples of patients showing signs of COVID-19; however, this method is more expensive and takes at least 24 hours to get a positive or negative response. Thus, an immediate and precise method of diagnosis is needed. In this paper, chest X-rays will be utilized through a deep neural network (DNN), based on a convolutional neural network (CNN), to...

Deep Learning Techniques for Quantification of Tumour Necrosis in Post-neoadjuvant Chemotherapy Osteosarcoma Resection Specimens for Effective Treatment Planning

T. S. Saleena, P. Muhamed Ilyas, V. M. Kutty Sajna, A. K. M. Bahalul Haque

Acta Informatica Pragensia 2023, 12(1), 87-103 | DOI: 10.18267/j.aip.2072649  

Osteosarcoma is a high-grade malignant bone tumour for which neoadjuvant chemotherapy is a vital component of the treatment plan. Chemotherapy brings about the death of tumour tissues, and the rate of their death is an essential factor in deciding on further treatment. The necrosis quantification is now done manually by visualizing tissue sections through the microscope. This is a crude method that can cause significant inter-observer bias. The suggested system is an AI-based therapeutic decision-making tool that can automatically calculate the quantity of such dead tissue present in a tissue specimen. We employ U-Net++ and DeepLabv3+,...

Longitudinal Investigation of Work Stressors Using Human Voice Features

Indhumathi Natarajan, Maheswaran Shanmugam, Samiappan Dhanalakshmi, Santhosh Easwaramoorthy, Sethuraja Kuppusamy, Saravanan Balu

Acta Informatica Pragensia 2023, 12(1), 104-122 | DOI: 10.18267/j.aip.2082917  

Stress is a part of everyone’s life. Any event or thought that makes you upset, furious or anxious can set it off. It will affect the human health mentally and physically and produce a negative impact on nervous and immune systems in our body. The human voice carries a lot of information about the person speaking. It also aids in determining a person's current state. In this proposed method, stress was detected using a deep learning model. Automatic stress detection is becoming an intriguing study topic as the necessity for communication between humans and intelligent systems rises. The hormone called cortisol can also be used to determine the...

Blood Pressure Estimation Using Emotion-Based Optimization Clustering Model

Vaishali Rajput, Preeti Mulay, Sharnil Pandya, Chandrashekhar Mahajan, Rupali Deshpande

Acta Informatica Pragensia 2023, 12(1), 123-140 | DOI: 10.18267/j.aip.2093292  

The features of human speech signals and emotional states are used to estimate the blood pressure (BP) using a clustering-based model. The audio-emotion-dependent discriminative features are identified to distinguish individuals based on their speech to form emotional groups. We propose a bio-inspired Enhanced grey wolf spotted hyena optimization (EWHO) technique for emotion clustering, which adds significance to this research. The model derives the most informative and judicial features from the audio signal, along with the person’s emotional states to estimate the BP using the multi-class support vector machine (SVM) classifier. The EWHO-based...

Multi-Class Skin Cancer Classification Using a Hybrid Dynamic Salp Swarm Algorithm and Weighted Extreme Learning Machines with Transfer Learning

Ramya Panneerselvam, Sathiyabhama Balasubramaniam

Acta Informatica Pragensia 2023, 12(1), 141-159 | DOI: 10.18267/j.aip.2112624  

Skin cancer is a significant healthcare problem with a high mortality rate worldwide. Skin lesions occur due to the abnormal growth of skin cells in humans. Failure of early prediction and proper lesion diagnosis may lead to a malignant stage. In recent times, different skin lesion images have appeared with high similarity. Hence, classification is a more challenging task with imbalances in the dataset. The proposed work is implemented as a hybrid model with a dynamic salp swarm algorithm (DSSA) with a weighted extreme learning machine (DSSA-WELM) that addresses the imbalances in the dataset and performs the classification with higher accuracy. GoogleNet...

Review

Survey on Security and Interoperability of Electronic Health Record Sharing Using Blockchain Technology

Reval Prabhu Puneeth, Govindaswamy Parthasarathy

Acta Informatica Pragensia 2023, 12(1), 160-178 | DOI: 10.18267/j.aip.1874769  

Blockchain is regarded as a significant innovation and shows a set of promising features that can certainly address existing issues in real time applications. Decentralization, greater transparency, improved traceability and secure architecture can revolutionize healthcare systems. With the help of advancement in computer technologies, most healthcare institutions try to store patient data digitally rather than on paper. Electronic health records are regarded as some of the most important assets in healthcare system and are required to be shared among different hospitals and other organizations to improve diagnosis efficiency. While sharing patients’...

Survey on Electronic Health Record Management Using Amalgamation of Artificial Intelligence and Blockchain Technologies

Krishna Prasad Narasimha Rao, Sunilkumar Manvi

Acta Informatica Pragensia 2023, 12(1), 179-199 | DOI: 10.18267/j.aip.1944357  

In the present times, the healthcare sector has seen an enormous growth in the usage of technology ranging from EHRs (electronic health records) to personal health trackers. Currently, there is a need for managing EHRs effectively with respect to storage, privacy and security measures. State-of-art technologies such as blockchain and artificial intelligence (AI) are applied in the healthcare domain. Innovation in AI is steadily advancing and is finding its place in different industries. The integration of blockchain and AI looks promising as there are several benefits. Blockchain can make the AI more secure and autonomous whereas AI can drive the blockchain...