rss_2.0Sciendo RSS Feed for Applied Computer Systemshttps://sciendo.com/journal/ACSShttps://www.sciendo.comhttps://sciendo-parsed.s3.eu-central-1.amazonaws.com/64707a9171e4585e08a9e1cd/cover-image.jpghttps://sciendo.com/journal/ACSS140216https://sciendo.com/article/10.2478/acss-2024-0012<abstract> <title style='display:none'>Abstract</title> <p>The intersection of Artificial Intelligence (AI) and medical science has shown great promise in recent years for addressing complex medical challenges, including the early detection of Alzheimer’s disease (AD). Alzheimer’s disease presents a significant challenge in healthcare, and despite advancements in medical science, a cure has yet to be found. Early detection and accurate prediction of AD progression are crucial for improving patient outcomes. This study comprehensively evaluates four Machine Learning (ML) models and one Perceptron Model for early detection of AD using the Open Access Series of Imaging Studies (OASIS) dataset. The evaluated models include Logistic Regression, Random Forest, XGBoost, CatBoost, and a Multi-layer Perceptron (MLP). This study assesses the performance of each model, on metrics like accuracy, precision, recall, and AUC ROC. The MLP model emerges as the top performer, achieving an impressive accuracy of 95 %, highlighting its efficacy in accurately predicting AD status based on biomarker indicators. While other models, such as Logistic Regression (85 %), Random Forest (87 %), XGBoost (83 %), and CatBoost (89 %), demonstrate considerable accuracy, they are outperformed by the MLP model.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2024-00122024-08-15T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2024-0004<abstract> <title style='display:none'>Abstract</title> <p>In the pursuit of enhancing the efficiency of the inverse kinematics of SCARA robots with four degrees of freedom (4-DoF), this research delves into an approach centered on the application of Artificial Neural Networks (ANNs) to optimise and, hence, solve the inverse kinematics problem. While analytical methods hold considerable importance, tackling the inverse kinematics for manipulator robots, like the SCARA robots, can pose challenges due to their inherent complexity and computational intensity. The main goal of the present paper is to develop efficient ANN-based solutions of the inverse kinematics that minimise the Mean Squared Error (MSE) in the 4-DoF SCARA robot inverse kinematics. Employing three distinct training algorithms – Levenberg-Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG) – and three generated datasets, we fine-tune the ANN performance. Utilising diverse datasets featuring fixed step size, random step size, and sinusoidal trajectories allows for a comprehensive evaluation of the ANN adaptability to various operational scenarios during the training process. The utilisation of ANNs to optimise inverse kinematics offers notable advantages, such as heightened computational efficiency and precision, rendering them a compelling choice for real-time control and planning tasks. Through a comparative analysis of different training algorithms and datasets, our study yields valuable insights into the selection of the most effective training configurations for the optimisation of the inverse kinematics of the SCARA robot. Our research outcomes underscore the potential of ANNs as a viable means to enhance the efficiency of SCARA robot control systems, particularly when conventional analytical methods encounter limitations.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2024-00042024-08-15T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2024-0006<abstract> <title style='display:none'>Abstract</title> <p>Multiple Sclerosis (MS) is a central nervous system disease that causes ataxia and balance disorders. In ataxia, the first symptom is usually seen as gait disturbance. In gait ataxia, symptoms can be clinically defined by shortened stride length and irregular strides. Evaluation of gait disturbance in clinical cases is important for the detection of the first stage of ataxia. With the increasing amount of data, high-performance models can be produced, especially in the field of healthcare, with computer machine learning, deep learning and artificial intelligence methods. This study aimed to identify ataxia in individuals with Multiple Sclerosis (MS) by analysing images that encompass plantar pressure distribution signals. A total of 105 images, each containing plantar pressure distribution signals, were utilized to extract features through pre-trained EfficientNet architectures. Then the feature vectors obtained were classified by SVM, k-NN, and ANN methods. As a result of this study, the best classification performance was obtained with SVM classifier with 88.09 % Acc, 80.55 % Sen, 93.75 % Spe and 85.29 % F1 Score. The results show that the study will help the clinician in the detection of PwMS ataxia and will be a pioneer for future studies.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2024-00062024-08-15T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2024-0008<abstract> <title style='display:none'>Abstract</title> <p>UML diagrams are a base for the planning of development in most software projects. It is used for representing different artefacts during software development and project structure. The use case is one of the diagrams in Unified Modelling Language (UML), which allows describing the dynamic flow of the system. There are a lot of tools that are used for creating this diagram before starting the actual coding process, and the diagram needs to be specific and easily understandable. Meantime, the creation of a UML use case diagram from scratch for complex systems can be time-consuming and confusing for people, which needs to be optimised. The authors of the paper attempt to solve the addressed problem. Therefore, in this research paper a new definition for UML use case diagrams will be introduced, where the main question will be whether it is possible to formalise use case modelling by introducing pre-defined use case patterns. This is academic research and discussion, which is based on the analysis of advanced UML tools, which use case diagram templates contain. The solution to this research question contains an initial set of UML use case patterns, created by analysing of the existing use case diagram templates. Moreover, in order to validate work, the pre-defined patterns were demonstrated on a developed prototype. The operation principle of the prototype focused on giving the ability to the user to construct a use case diagram by the combination of pre-defined patterns. The prototype can be useful for the development/management process in case of correct implementation. It will allow decreasing spent time on the use case diagram creation as well as avoid creating anti-patterns.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2024-00082024-08-15T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2024-0013<abstract> <title style='display:none'>Abstract</title> <sec><title style='display:none'>Context</title> <p>The primary expectation from a software system revolves around its functionality. However, as the software development process advances, equal emphasis is placed on the quality of the software system for non-functional attributes like maintainability and performance. Tools are available to aid in this endeavour, assessing the quality of a software system from multiple perspectives.</p> </sec> <sec><title style='display:none'>Objective</title> <p>This study aims to perform a comprehensive analysis of a particular set of source code analytical tools by examining diverse perspectives found in the literature and documentations. Given the vast array of programming languages available today, selecting appropriate source-code analytical tools presents a significant challenge. Therefore, this analysis aims to provide general insights to aid in selecting a more suitable analytical tool tailored to specific requirements.</p> </sec> <sec><title style='display:none'>Method</title> <p>Seven prominent static analysis tools, namely SonarQube, Coverty, CodeSonar, Snyk Code, ESLint, Klocwork, and PMD, were chosen based on their prevalence in the literature and recognition in the software development community. To systematically categorise and organise their distinctive features and capabilities, a taxonomy was developed. This taxonomy covers crucial dimensions, including input support, technology employed, extensibility, user experience, rules, configurability, and supported languages.</p> </sec> <sec><title style='display:none'>Results</title> <p>The comparative analysis highlights the distinctive strengths of each tool. SonarQube stands out as a comprehensive solution with a hybrid approach supporting static and dynamic code evaluations, accommodating multiple languages and integrating with popular Integrated Development Environments (IDEs). Coverity excels in identifying security vulnerabilities and defects, making it an excellent choice for security -focused development. CodeSonar prioritises code security and safety, offering a robust analysis. Snyk Code and ESLint, focusing on JavaScript, emphasise code quality and standards adherence. Klocwork is exceptional in defect detection and security analysis for C, C++, and Java. Lastly, PMD specialises in Java, emphasising code style and best practices.</p> </sec> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2024-00132024-08-15T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2024-0009<abstract> <title style='display:none'>Abstract</title> <p>Generative AI is only a few years old but already being applied in Software Engineering (SE). This literature review examines the most popular SE sub-fields of such cases and research methods that are typically used. 117 studies starting from 2020 have been assessed, and literature review has shown that the most active research is ongoing in the code generation area. It is not clearly defined by researchers, but the majority of the methods can be assumed as experiments. It is concluded that researchers often do not define the used research method with exclusions such as literature review or opinion survey. However, different validation methods are highly valued and applied thoroughly.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2024-00092024-08-15T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2024-0015<abstract> <title style='display:none'>Abstract</title> <p>Knowledge elicitation is important in designing knowledge-based diagnosis systems. Various approaches such as interviews and questionnaires have been used to elicit knowledge from experts. These approaches elicit knowledge from individual experts separately. Medical practitioners have diverse knowledge and experience in the diagnosis and management of a particular disease. A major challenge is in producing a harmonised diagnosis from different practitioners, which will inform the level of agreement among them on the treatment of Sickle Cell Disease (SCD). Therefore, it is important to elicit and integrate knowledge from different medical practitioners in developing an effective diagnosis system. Thus, the Delphi technique was employed in this study to elicit domain knowledge in developing SCD diagnosis systems in African Traditional Medicine (ATM) since there is no gold standard for achieving diagnosis in ATM. A kappa value of 0.487 was achieved. This implies that the Herb sellers averagely agree in the ranking of the SCD symptoms. Therefore, to build an effective SCD diagnosis system, further work should be done by conducting more Delphi rounds to ensure that a high level of consensus is reached. The Delphi technique used in this study helped in the area of requirement elicitation of SCD diagnosis in ATM which could be used in the development of an SCD diagnosis system.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2024-00152024-08-15T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2024-0001<abstract> <title style='display:none'>Abstract</title> <p>Fuzzy Time Series (Fts) models are experiencing an increase in popularity due to their effectiveness in forecasting and modelling diverse and intricate time series data sets. Essentially these models use membership functions and fuzzy logic relation functions to produce predicted outputs through a defuzzification process. In this study, we suggested using a Second Order Type-1 fts (S-O T-1 F-T-S) forecasting model for the analysis of time series data sets. The suggested method was compared to the state-of-theart First Order Type 1 Fts method. The suggested approach demonstrated superior performance compared to the First Order Type 1 Fts method when applied to household consumption data from the Magene Regency in Indonesia, as measured by absolute percentage error rate (APER).</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2024-00012024-08-15T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2024-0007<abstract> <title style='display:none'>Abstract</title> <p>We study the feasibility of video transmission over optical fibre to optimise bandwidth with the implementation of HEVC codec features. We use simulation (Matlab and the OptiSystem software). Different values of the CRF are used to evaluate its impact on the visual quality and the size of the encoded file, as well as its influence on the video transmission performance. The simulation results show that by adjusting the CRF, the encoders can optimise the compression of the video data to reduce the file size while preserving an acceptable visual quality. This makes it possible to adapt the transmission to the bandwidth constraints of the optical fibre, by choosing higher CRF values to reduce the size of the files and save bandwidth, or lower values to maintain optimal quality when the bandwidth is sufficient. In addition, from the optical fibre point of view, the dispersion weakens and the eye opens, and it is observed that the length of the fibres is inversely proportional to the signal transmission quality. Thus, the judicious use of different CRF values can contribute to efficient and high-quality video transmission via optical fibre.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2024-00072024-08-15T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2024-0002<abstract> <title style='display:none'>Abstract</title> <p>Artificial Intelligence (AI) functionalities are increasingly being used in vehicle applications. While current product development models take the increasing proportion of software into account, the special requirements of artificial intelligence developments are hardly ever explicitly considered. The new requirements result both from increasing standardisation and regulation and from the iterative and explorative approach inherent in AI model development. This paper identifies the key adaptations to the standard automotive product development process that are required to cover the requirements of AI development. The adapted development model was trialled in two vehicle developments, the most important lessons learnt of which are summarised in this paper.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2024-00022024-08-15T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2024-0011<abstract> <title style='display:none'>Abstract</title> <p>One of the major areas of machine learning application is in medical diagnosis. Machine learning algorithms can detect patterns in patients’ data and generates a diagnosis based on those patterns. There are several machine learning classification algorithms each having different strengths and weaknesses, and this makes it difficult to determine the best one for classification problems. In this paper, machine learning techniques used to classify the clinical depression dataset are Fuzzy Logic, Neural Network, Neuro-Fuzzy System, and Genetic Neuro-Fuzzy System. A total of 134 clinical diagnosis first report depression datasets were used in arriving at prediction. The outcome of the experiment showed that the Genetic Neuro-Fuzzy model generated the best result with a prediction accuracy of 95 %, and cross-validation of 83.2 %. This shows that the model is robust and can make accurate prediction on new, unseen data. This research work will guide future researchers and practitioners to identify new directions for advanced development opportunities in using machine learning in depression diagnosis. It will help policymakers in the area of depression to make informed decisions, especially in the area of best machine learning technique for classification problem related to depression diagnosis. The research is limited to clinical depression diagnosis; future work could be expanded to compute the severity ranks of other depression-connected dysfunctions similar to diabetes, lungs, and cancer diseases.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2024-00112024-08-15T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2024-0010<abstract> <title style='display:none'>Abstract</title> <p>With the increase in the capabilities of robotic devices, there is a growing need for accurate and relevant environment maps. Current robotic devices can map their surrounding environment using a multitude of sensors as mapping sources. The challenge lies in combining these heterogeneous maps into a single, informative map to enhance the robustness of subsequent robot control algorithms. In this paper, we propose to perform map fusion as a post-processing step based on the alignment of the window of interest (WOI) from occupancy grid histograms. Initially, histograms are obtained from map pixels to determine the relevant WOI. Subsequently, they are transformed to align with a selected base image using the Manhattan distance of histogram values and the rotation angle from WOI line regression. We demonstrate that this method enables the combination of maps from multiple sources without the need for sensor calibration.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2024-00102024-08-15T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2024-0005<abstract> <title style='display:none'>Abstract</title> <p>Urine sediment examination (USE) is an essential aspect in detecting urinary system diseases, and it is a prerequisite for diagnostic procedures. Urine images are complex, containing numerous particles, which makes a detailed analysis and interpretation challenging. It is crucial for both patients and medical professionals to conduct urine analysis automatically, quickly and inexpensively, without compromising reliability. In this paper, we present a deep multi-modal fusion system, commonly employed in artificial intelligence, capable of automatically distinguishing particles in urine sediment. To achieve this objective, we first created a new dataset comprising erythrocytes, leukocytes, yeast, epithelium, bacteria, crystals, cylinders, and other particles (such as sperm). The data were gathered from urinalysis requests made between July 2022 and September 2022 at the biochemistry laboratory of Fethi Sekin Medical Center Hospital. A dataset containing 8509 images was compiled using the Optika B293PLi microscope with trinocular brightfield. We propose a 5-step process for detecting particles in the dataset using a multi-modal fusion deep learning model: i) The obtained images were augmented by applying affine transformation. ii) To distinguish images, we opted for ResNet18 and ResNet50 models, which yielded high performance in medical data. iii) Feature vectors from both models were fused to generate more consistent, accurate, and useful particle features. iv) We employed ReliefF, Neighborhood Component Analysis (NCA), and Minimum-Redundancy Maximum-Relevancy (mRMR) feature selection methods, widely used to determine features that maximise particle discrimination success. v) In the final step, Support Vector Machine (SVM) was utilised to distinguish the particles. The results demonstrate that the highest accuracy value achieved is 98.54 % when employing the ReliefF algorithm. Contributions of the study include eliminating standardisation differences in manual microscopy, achieving high accuracy in particle discrimination, offering an artificial intelligence-based system applicable in laboratory environments, and providing the dataset as educational and practical material for biochemistry professionals.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2024-00052024-08-15T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2024-0003<abstract> <title style='display:none'>Abstract</title> <p>An approach to speed up the DBSCAN algorithm is suggested. The planar clusters to be revealed are assumed to be tightly packed and correlated constituting, thus, a serpentine dataset developing rightwards or leftwards as time goes on. The dataset is initially divided into a few sub-datasets along the time axis, whereupon the best neighbourhood radius is determined over the first sub-dataset and the standard DBSCAN algorithm is run over all the sub-datasets by the best neighbourhood radius. To find the best neighbourhood radius, it is necessary to know ground truth cluster labels of points within a region. The factual speedup registered in a series of 80 000 dataset computational simulations ranges from 5.0365 to 724.7633 having a trend to increase as the dataset size increases.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2024-00032024-08-15T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2024-0014<abstract> <title style='display:none'>Abstract</title> <p>Early kidney stone detection is essential for the diagnosis and treatment of people who have kidney stones. The objective of this study is to employ deep learning algorithms for renal stone detection, addressing the critical need for early, accurate diagnosis, which can significantly improve patient outcomes and reduce healthcare costs. The paper thoroughly assesses a variety of models, including ResNet, DenseNet, and EfficientNet, for CT images. The limitations of manual identification procedures highlight the urgent need for a more effective automated approach, making this research necessary. Notably, the painstakingly improved DenseNet model achieves a peak accuracy of 0.86, demonstrating its potential superiority. These results convincingly demonstrate the revolutionary power of deep learning, which is poised to revolutionise the detection of renal stones. This fast, trustworthy, and non-invasive method has the potential to advance clinical procedures and significantly improve patient care.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2024-00142024-08-15T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2023-0023<abstract> <title style='display:none'>Abstract</title> <p>Living standards are rising due to a more developed society, and recreation, particularly tourism, is becoming more critical. Expanding the tourist industry is one of the most significant concerns in economic growth. Tourism revenue has helped increase residents’ income, leading to socio-economic development. In recent years, emerging Vietnamese tourism spots like Hon Son, Sapa, Hue, Phu Quoc in Vietnam, and others have consistently drawn travellers to visit and experience through social networking platforms. Tourism potential is tremendous, but foreign visitors’ information about tourist destinations still needs to be improved. This work proposes an approach to integrating machine learning algorithms into an information system to consult tourism traveling. Machine learning algorithms can classify question topics, predict user intent, and predict conversation scenarios to give appropriate responses. Our method is evaluated on the dataset, including 7319 samples on 11 topics collected from the TWCS dataset, using three algorithms: Bag of Words, BERT, and RoBERTa. BERT achieved the highest performance among the surveyed algorithms with 90 % in accuracy and 90.1 % in F1-Score. From the trained model, the team built a mobile application on Android to deploy the chatbot application with the Flutter framework based on Dart, an object-oriented programming language developed by Google using the concept of containers. The system’s functionality serves two primary user groups: administrators and application users. Administrators can utilize the application’s primary functions to manage content set up, and train a chatbot. Users can access information about locations, read location articles, check hotel prices, and use chatbots to find answers to their location-related questions. Administrators can also train the chatbot model to expand its knowledge.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2023-00232024-01-29T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2023-0025<abstract> <title style='display:none'>Abstract</title> <p>Image registration is an essential pre-processing step required for many image processing applications such as medical imaging and computer vision. The aim is to geometrically align two or more images of the same scene by establishing a mapping that relies on each point from one image to its corresponding point of another image. Scale invariant feature transform (SIFT) and speeded up robust features (SURF) are well-liked local features descriptors that have been extensively utilised for feature-based image registration due to their inherent properties such as invariance, changes in illumination, and noise. Moreover, the task of registration can be viewed as an optimization problem that can be solved by applying genetic algorithms (GAs). This paper presents an efficient feature image registration method based on combined local features and GAs. Firstly, the procedure consists of extracting the local features from the images by combining SIFT and SURF algorithms and matching them to refine the feature set data. Therefore, an adaptive GA based on fitness sharing and elitism techniques is employed to find the optimal rigid transformation parameters that best align the feature points by minimizing a distance metric. The suggested method is applied for registering medical images and the obtained results are significant compared to other feature-based approaches with reasonable computation time.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2023-00252024-01-29T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2023-0021<abstract> <title style='display:none'>Abstract</title> <p>The paper proposes an Energy-Harvested Transmitter-Initiated MAC Protocol for WSNs (ETI-MAC). ETIMAC takes advantage of the benefits of transmitter-initiated schemes and employs the low power listening (LPL) method with small preamble messages so that each sensor node in the network can predict its next sleep duration based on the harvested energy rate value, thereby lowering the duty cycle by making use of its accumulating residual energy. The simulation results show that the proposed protocol outperforms compared to the old PSEHWSN scheme.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2023-00212024-01-29T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2023-0026<abstract> <title style='display:none'>Abstract</title> <p>Worldwide websites publish enormous amounts of text, audio, and video content every day. This valuable information allows for the assessment of regional trends and general public opinion. Based on consumers’ online behavioural habits, businesses are showing them their chosen ads. It is difficult to carefully analyse these raw data to find valuable trends, especially for a language with limited resources like Urdu. There have not been many studies or efforts to create language resources for the Urdu language and analyse people’s sentiment, even though there are more than 169 million Urdu speakers in the world and a sizable amount of Urdu data is generated on various social media platforms every day. However, there has been relatively little research on sentiment analysis in Urdu. Researchers have primarily performed studies in English and Chinese. In response to this gap, we suggest an emotion analyser for Urdu, the primary language of Asia, in this research study. In this paper, we propose to assess sentiment in Urdu review texts by integrating a bidirectional long short-term memory (BiLSTM) model with a convolutional neural network (CNN). We contrast the CNN, LSTM, BiLSTM, and CNN-LSTM models with the CNN-BiLSTM model. With an accuracy rate of 0.99 %, the CNN-BiLSTM model performed better than the other models in an initial investigation.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2023-00262024-01-29T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2023-0020<abstract> <title style='display:none'>Abstract</title> <p>Content-based image retrieval system (CBIR) is a well-known and widely used system for image retrieval. Most of the current CBIR systems are either command-based or specific to applications. However, due to the availability of a good computing facility, a graphical way of retrieving images may prove to be very useful for both industrial and research purposes. This paper proposes a generic and user-friendly graphical user interface (GUI) for CBIR framework. With the proposed GUI, any user with or without knowledge of CBIR can operate and retrieve images of their choice among a huge number of images. The GUI gives a vast range of facilities for selecting options. The proposed GUI is implemented and verified on a well-known image database.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2023-00202024-01-29T00:00:00.000+00:00en-us-1