International Journal of Information Technology and Computer Science @ijitcs
Статьи журнала - International Journal of Information Technology and Computer Science
Все статьи: 1243
Improving the Prediction Rate of Diabetes using Fuzzy Expert System
Статья научная
The use of fuzzy logic in disease diagnosis is very common and beneficial as it incorporates the knowledge and experience of physician into fuzzy sets and rules. Most of the research proposed different systems for the diabetes diagnosis. But their accuracy of prediction is not accurate. So, the proposed system presents promising approach for accurately predicting the diabetes by considering the different parameters which are helpful in the diagnosis of diabetes. The proposed fuzzy verdict mechanism takes the information collected from the patients as inputs in the form of datasets. System considers both rules and physicians knowledge to provide the prediction rate of diabetes. Evaluation shows the approach results in better accuracy as compared to other prediction approaches.
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Статья научная
Prediction of customer churn has been one of the most interesting and challenging tasks facing most telecommunication companies recently. For the past decade, researchers together with practitioners have been working and designing models that can correctly make more accurate customer churn predictions (CCP). However, most of these models have less accuracy than expected which is hugely affecting the intended purpose. Consequently, most of these CCP models add little or nothing to the revenue growth of telecommunication industries. This work aims at improving the reliability of CCP in the telecommunication sector. To achieve this objective, a new attribute to be factored in CCP, known as the regional churn rate (RCR), is presented. Basically, RCR gives an idea about the rate of churning in a particular locality or region. Thus, a prediction model with a more accurate CCP using a support vector machine (SVM) classifier is proposed. The performance of the proposed model is critically evaluated using five metrics i.e. misclassification error, precision, recall, specificity and f-measure. At the same time, the performance of the proposed classifier (CCP with RCR) is compared with another SVM classifier which doesn’t consider RCR (CCP without RCR). Results show that the proposed model which considers the RCR of a customer’s location gives the highest accuracies for four performance metrics i.e. precision, recall, misclassification error and f-measure. Therefore, the proposed SVM-based CCP model gives a more clear indication as to whether a customer is a potential churner or not.
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Статья научная
Overcharging the passengers who are not familiar with the city for the services in railway stations and airport is a serious problem which can be addressed by installing prepaid counters. Prepaid counters encounter the problem of higher passenger frequency due to which the waiting time increases. The only alternative solution to this problem is queue formation for effective service. In most of the cases, only one counter for service is available due to which the queue length increases; thereby the passengers may lose patience and move away which may lead to the loss to the counter. In this paper, queuing model is used to solve this real case scenario in an optimal way. The experimental data were obtained from the counter and derived using Little’s Theorem and Single Server Queuing Model (M/M/1). Hence an attempt was made in studying the benefits of queuing model and optimizing it to minimize the waiting time thereby increasing the profit. This approach will help a busy prepaid autorickshaw counter to give service to passengers in a most feasible way.
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Статья научная
Incorporating information and communication technology (ICT), especially computer/mobile games into teaching and learning has been identified as a proven method of increasing primary grade students’ intrinsic motivation towards learning. However, in countries like Sri Lanka with teacher centric education cultures, the teacher still plays a significant role in the child’s education process. Therefore, it is imperative to look at the teachers’ willingness and inclination to integrate technology enhanced games in their classrooms. The purpose of this study is to investigate the teachers’ preparedness, attitude towards integrating mobile games in teaching and the issues faced by the teachers when trying to use technology in the Sri Lankan primary classroom. A questionnaire for assessing mobile game based learning readiness was designed and used as the research instrument to assess the inclination of teachers to incorporate mobile-based games for learning in their classroom. The survey was conducted involving primary school teachers in four Type 3 schools of Gampaha district in Sri Lanka. Type 3 schools have classes only up to grade 8. It was identified that the teachers in Type 3 schools of Gampaha district are moderately inclined towards incorporating mobile games into their day-to-day teaching activities.
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Статья научная
The usefulness of Collaborative filtering recommender system is affected by its ability to capture users' preference changes on the recommended items during recommendation process. This makes it easy for the system to satisfy users' interest over time providing good and quality recommendations. The Existing system studied fails to solicit for user inputs on the recommended items and it is also unable to incorporate users' preference changes with time which lead to poor quality recommendations. In this work, an Enhanced Movie Recommender system that recommends movies to users is presented to improve the quality of recommendations. The system solicits for users' inputs to create a user profiles. It then incorporates a set of new features (such as age and genre) to be able to predict user's preference changes with time. This enabled it to recommend movies to the users based on users new preferences. The experimental study conducted on Netflix and Movielens datasets demonstrated that, compared to the existing work, the proposed work improved the recommendation results to the users based on the values of Precision and RMSE obtained in this study which in turn returns good recommendations to the users.
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Information Security based on IoT for e-Health Care Using RFID Technology and Steganography
Статья научная
The Internet of Things (IoT) allows you to connect a broad spectrum of smart devices through the Internet. Incorporating IoT sensors for remote health monitoring is a game-changer for the medical industry, especially in limited spaces. Environmental sensors can be installed in small rooms to monitor an individual's health. Through low-cost sensors, as the core of the IoT physical layer, the RF (Radio Frequency) identification technique is advanced enough to facilitate personal healthcare. Recently, RFID technology has been utilized in the healthcare sector to enhance accurate data collection through various software systems. Steganography is a method that makes user data more secure than it has ever been before. The necessity of upholding secrecy in the widely used healthcare system will be covered in this solution. Health monitoring sensors are a crucial tool for analyzing real-time data and developing the medical box, an innovative solution that provides patients with access to medical assistance. By monitoring patients remotely, healthcare professionals can provide prompt medical attention whenever needed while ensuring patients' privacy and personal information are protected.
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Статья научная
The article presents a modern approach to analysing public opinion based on Ukrainian-language content from Telegram channels. This study presents a hybrid clustering approach that combines DBSCAN and K-means algorithms to analyse vectorised Ukrainian-language social media posts in order to detect public opinion trends. The methodology relies on a multilingual neural network–based text vectorisation model, which enables effective representation of the semantic content of posts. Experiments conducted on a corpus of 90 Ukrainian-language messages (collected between March and May 2025) allowed for the identification of six principal thematic clusters reflecting key areas of public discourse. Despite the small volume of the corpus (90 messages), the sample is structured and balanced by topic (news, vacancies, gaming), which allows you to test the effectiveness of the proposed methodology in conditions of limited data. This approach is appropriate in the case of the analysis of short texts in low-resource languages, where large-scale corpora are not available. A special advantage of this approach is the use of semantic vector representation and the construction of graphs of lexical co-occurrence networks (term co-occurrence networks), which demonstrate a stable topological structure even with small amounts of data. It allows you to identify latent topic patterns and coherent clusters that have the potential to scale to broader corpora. The authors acknowledge the limitations associated with sample size, but emphasise the role of this study as a pilot stage for the development of a universal, linguistically adaptive method for analysing public discourse. In the future, it is planned to expand the body to increase the representativeness and accuracy of the conclusions. The paper proposes a hybrid method of automatic thematic cluster analysis of short texts in social media, in particular Telegram. Vectorisation of Ukrainian-language messages is implemented using the transformer model multilingual-e5-large-instruct. A combination of HDBSCAN and K-means algorithms was used to detect clusters. More than 36,000 messages from three Telegram channels (news, games, vacancies) were analysed, and six main thematic clusters were identified. To identify thematic trends, a hybrid clustering approach was used, in which the HDBSCAN algorithm was used at the first stage to identify dense clusters and identify "noise" points, after which K-means were used to reclassify residual ("noise") embeddings to the nearest cluster centres. Such a two-tier strategy allows you to combine the advantages of flexible allocation of free-form clusters from HDBSCAN and stable classification of less pronounced groups through K-means. It is especially effective when working with fragmented short texts of social networks. To validate the quality of clustering, both visualisation tools (PCA, t-SNE, word clouds) and quantitative metrics were used: Silhouette Score (0.41) and Davis-Boldin index (0.78), which indicate moderate coherence and resolution of clusters. Separately, the high level of "noise" in HDBSCAN (34.2%) was analysed, which may be due to the specifics of short texts, model parameters, or stylistic fragmentation of Telegram messages. The results obtained show the effectiveness of combining modern vectorisation models with flexible clustering methods to identify structured topics in fragmented Ukrainian-language content of social networks. The proposed approach has the potential to further expand to other sources, types of discourse, and tasks of digital sociology. As a result of processing 90 messages received from three different channels (news, gaming content, and vacancies), six main thematic clusters were identified. The largest share is occupied by clusters related to employment (28.2%) and security-patriotic topics (24.7%). The average level of "noise" after the initial HDBSCAN clustering was 34.2%. Additional analysis revealed that post lengths varied significantly, ranging from short announcements (average of 10 words) to analytical texts (over 140 words). Visualisations (timelines, PCA, t-SNE, word clouds, term co-occurrence graphs) confirm the thematic coherence of clusters and reveal changes in thematic priorities over time. The proposed system is an effective tool for detecting information trends in the environment of short, fragmented texts and can be used to monitor public sentiment in low-resource languages.
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Статья научная
Human Genome Project has led to a huge inflow of genomic data. After the completion of human genome sequencing, more and more effort is being put into identification of splicing sites of exons and introns (donor and acceptor sites). These invite bioinformatics to analysis the genome sequences and identify the location of exon and intron boundaries or in other words prediction of splicing sites. Prediction of splice sites in genic regions of DNA sequence is one of the most challenging aspects of gene structure recognition. Over the last two decades, artificial neural networks gradually became one of the essential tools in bioinformatics. In this paper artificial neural networks with different numerical mapping techniques have been employed for building integrated model for splice site prediction in genes. An artificial neural network is trained and then used to find splice sites in human genes. A comparison between different mapping methods using trained neural network in terms of their precision in prediction of donor and acceptor sites will be presented in this paper. Training and measuring performance of neural network are carried out using sequences of the human genome (GRch37/hg19- chr21). Simulation results indicate that using Electron-Ion Interaction Potential numerical mapping method with neural network yields to the best performance in prediction.
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Integrating Non-Functional Properties in Model Driven Development: A Stepwise Refinement View
Статья научная
Most of the refinement approach is about functional property of systems. Non-functional properties are as important as functional one. Without an accurate approach for specifying and refining their behaviors, software models will be regarded as imperfect and imprecise, and as a result, software systems cannot be generated correctly. Therefore, how to model such behaviors and how to stepwise refine these behaviors automatically, have become two critical problems in Model Driven Development. In this paper we present an approach for Non-functional refinement in model driven development using high order transformation languages and traceability property of them. We extend the idea of model refinement to non-functional properties of software and propose a stepwise refinement framework with conformance checking between abstract and concrete descriptions of system model using model transformation. The approach is extendable to all quantitative and quantitative non-functional properties.
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Статья научная
Businesses today are dependent on custom enterprise software and web applications from independent software developers and software companies. This creates a lot of problems such as integration, interoperability, security, and system maintenance. Enterprise Application Integration (EAI) and Business-to-Business integration control several key technologies and swift advancement in technology to meet the increasing needs for integration in the enterprise which often results in a lot of challenges due to differences between one proprietary approach and another. This paper seeks to provide an approach of integrating independent applications and EAI systems by using Web services standards and open application development standards in dealing with the challenges faced in integrating applications. This will make it possible for organizations to add a new layer of abstraction that is open, standards-based, and easy to integrate with any new or existing system and also make easy for data discovery as well as building of new concepts from existing data. The combination of Service Oriented Architecture and Web services will be used to provide a rapid integration solution and also publishing services in a manner that new concepts can easily be built from existing services.
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Intelligent Adaptive Gain Backstepping Technique
Статья научная
In this research, intelligent adaptive backstepping control is presented as robust control for continuum robot. The first objective in this research is design a Proportional-Derivative (PD) fuzzy system to compensate the system model uncertainties. The second objective is focused on the design tuning gain adaptive methodology according to high quality partly nonlinear methodology. Conventional backstepping controller is one of the important robust controllers especially to control of continuum robot manipulator. The fuzzy controller is used in this method to system compensation. In real time to increase the system robust fuzzy logic theory is applied to backstepping controller. To approximate a time-varying nonlinear dynamic system, a fuzzy system requires a large amount of fuzzy rule base. The adaptive laws in this algorithm are designed based on the Lyapunov stability theorem. This method is applied to continuum robot manipulator to have the best performance.
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Intelligent Management of a Network of Smart Billboards on the IoT Platform in Industry 4.0
Статья научная
Artificial intelligence plays a special role in new technologies used to develop advertising and marketing. Artificial intelligence, which plays a special role in improving the effectiveness of advertising and marketing, has had its say in the business market, and this process continues. A quick search for any product in Internet search engines is an indispensable process for the marketing market. With the help of artificial intelligence, it is possible to present the required product or service in a timely manner, at a high level, taking into account the individual characteristics of the customer using virtual environments and street advertising. In the modern world of cyber-physical systems, machines created using intelligent algorithms facilitate human labor in almost all areas. Intelligent management of a network of smart billboards AI research in advertising and marketing has a positive impact on economic development. The article deals with the application of artificial intelligence in the field of advertising and the principle of their work. In this area, the processes of application of new technologies are studied. When preparing the article, scientific analysis of problems and their solutions, application of results, methodological system approach were used.
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Intelligent Mobile Application for Route Finding and Transport Cost Analysis
Статья научная
The explosive rate of increase in number of habitats and vehicles in different areas of the developing countries like Nigeria has motivated government of such world engage in both rural and urban road construction for ease of navigation. This brings stresses in navigating such roads with public traffic hence noise pollution to the environment. For effective autonomous geo-spatial navigation service, we propose a web based model implemented as intelligent mobile application for route finding and transport cost analysis. A case study observed on data collated from different areas within Ile-Ife and its surroundings shows that the system aid users in making decision regarding transportation alternatives. This study shows how to help people living in such parts of the world reach their destinations when navigating unknown routes with reduced transportation cost.
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Intelligent Vision Methodology for Detection of the Cutting Tool Breakage
Статья научная
In this paper, a new Intelligent system based on neurofuzzy for detecting and diagnostics the wear and damage of the milling cutter is presented. The compatibility between the computer vision and neurofuzzy techniques is introduced. The proposed approaches consists of capturing the milling cutter image, Fuzzy edge detection, Chain code technique for feature extraction and finally, apply the neural network on the feature. The results of the study are three different diagnostics models, The first is diagnostic model for the original profile of the perfect cutter, the second is model for the wearied profile and the third is model for the damage profile. Experimental test results show that the proposed system is reliable, practical and can be used for the easy distinguish between the wear and damage automatically.
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Interference Rejection in FH/BFSK System Using Blind Source Separation
Статья научная
This paper introduces a new approach based on blind source separation (BSS) to mitigate intentional interference in BFSK digital communication systems using frequency hopping spread spectrum technique. The use of BSS is possible thanks to adopting an adequate selection block to distinguish between the useful signal and other undesirable signals, hence, circumvent the problem of ambiguity of permutation. An analytical calculation of the probability of error to predict the performance is done. The simulation results showed the effectiveness of this approach, whatever the level of the JSR and without using the fast frequency hopping alternative or error-correcting codes.
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Internet Passport Authentication System Using Multiple Biometric Identification Technology
Статья научная
Electronic passports (e-Passports) have known a wide and fast deployment all around the world since the International Civil Aviation Organization (ICAO) the world has adopted standards whereby passports can store biometric identifiers. The purpose of biometric passports is to prevent the illegal entry of traveler into a specific country and limit the use of counterfeit documents by more accurate identification of an individual. The paper consider only those passport scenarios whose passport protocols base on public-key cryptography, certificates, and a public key infrastructure without addressing the protocols itself detailed, but this is no strong constraint. Furthermore assume the potential passport applier to use ordinary PCs with Windows or Linux software and an arbitrary connection to the Internet. Technological securities issues are to be found in several dimension, but below paper focus on hardware, software, and infrastructure as some of the most critical issues.
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Interpretation of Normal and Pathological ECG Beats using Multiresolution Wavelet Analysis
Статья научная
The Discrete wavelet transform has great capability to analyse the temporal and spectral properties of non stationary signal like ECG. In this paper, we have developed and evaluated a robust algorithm using multiresolution analysis based on the discrete wavelet transform (DWT) for twelve-lead electrocardiogram (ECG) temporal feature extraction. In the first step, ECG was denoised considerably by employing kernel density estimation on subband coefficients then QRS complexes were detected. Further, by selecting appropriate coefficients and applying wave segmentation strategy P and T wave peaks were detected. Finally, the determination of P and T wave onsets and ends was performed. The novelty of this approach lies in detection of different morphologies in ECG wave with few decision rules. We have evaluated the algorithm on normal and abnormal beats from various manually annotated databases from physiobank having different sampling frequencies. The QRS detector obtained a sensitivity of 99.5% and a positive predictivity of 98.9% over the first lead of the MIT-BIH Arrhythmia Database.
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Inverse Matrix using Gauss Elimination Method by OpenMP
Статья научная
OpenMP is an implementation program interface that might be utilized to explicitly immediate multi-threaded and it shared memory parallelism. OpenMP platform for specifications multi-processing via concurrent work between interested parties of hardware and software industry, governments and academia. OpenMP is not needs implemented identically by all vendors and it is not proposed for distributed memory parallel systems by itself. In order to invert a matrix, there are multiple approaches. The proposed LU decomposition calculates the upper and lower triangular via Gauss elimination method. The computation can be parallelized using OpenMP technology. The proposed technique main goal is to analyze the amount of time taken for different sizes of matrices so we used 1 thread, 2 threads, 4 threads, and 8 threads which will be compared against each other to measure the efficiency of the parallelization. The result of interrupting compered the amount of time spent in all the computing using 1 thread, 2 threads, 4 threads, and 8 threads. We came up with if we raise the number of threads the performance will be increased (less amount of time required). If we use 8 threads we get around 64% performance gained. Also as the size of matrix increases, the efficiency of parallelization also increases, which is evident from the time difference between serial and parallel code. This is because, more computations are done parallel and hence the efficiency is high. Schedule type in OpenMP has different behavior, we used static, dynamic, and guided scheme.
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Investigating into Automated Test Patterns in Erratic Tests by Considering Complex Objects
Статья научная
Software testing is an important activity in software development life cycle. Testing includes running a program on a set of test cases and comparing seen results with expected results. Automated testing encompasses all automation efforts across software testing lifecycle, with focus on automating system testing efforts and integration. Automated testing brings plenty of benefits that speeding up test running time, increasing accuracy of testing process and minimizing costs in different parts of system are three superior features of it. Maintenance and development of test automation tools are not as easy as traditional testing due to unexplored issues which need more examinations. Automated test patterns have been presented to mitigate some problems happening by automated testing and improve efficiency. This paper aims to investigate into automatic testing and automated test patterns. Also, demonstrates behaviour of applying an automated test pattern on a complex object. Results show during choosing an automated pattern to run, we should consider test structure especially level of test object complexity otherwise inconsistency may happen.
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Статья научная
This paper focuses on how students access web pages in a task specific information retrieval. An investigation on how students search the web for their current needs was carried out and students’ behavioural characteristics as they surf the internet to answer some given online multiple choice questions was collected. Twenty three students participated in the study and a number of behavioural characteristics were captured. Camtasia studio 7 was used to record their searching activity. The result shows that 328 web pages were visited by the students, and among the parameters captured, the time spent on the search task has a stronger correlation with the students’ performance than any other captured parameter. The time spent on a document can be used as a good implicit indicator to infer learner’s interest in a context based recommender system.
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