International Journal of Information Technology and Computer Science (IJITCS)
ISSN: 2074-9007 (Print)
ISSN: 2074-9015 (Online)
DOI: https://doi.org/10.5815/ijitcs
Website: https://www.mecs-press.org/ijitcs
Published By: MECS Press
Frequency: 6 issues per year
Number(s) Available: 143
IJITCS is committed to bridge the theory and practice of information technology and computer science. From innovative ideas to specific algorithms and full system implementations, IJITCS publishes original, peer-reviewed, and high quality articles in the areas of information technology and computer science. IJITCS is a well-indexed scholarly journal and is indispensable reading and references for people working at the cutting edge of information technology and computer science applications.
IJITCS has been abstracted or indexed by several world class databases: Scopus, Google Scholar, Microsoft Academic Search, CrossRef, Baidu Wenku, IndexCopernicus, IET Inspec, EBSCO, VINITI, JournalSeek, ULRICH's Periodicals Directory, WorldCat, Scirus, Academic Journals Database, Stanford University Libraries, Cornell University Library, UniSA Library, CNKI Scholar, J-Gate, ZDB, BASE, OhioLINK, iThenticate, Open Access Articles, Open Science Directory, National Science Library of Chinese Academy of Sciences, The HKU Scholars Hub, etc..
IJITCS Vol. 18, No. 3, Jun. 2026
REGULAR PAPERS
FPGA Implementation of a Fault-tolerant Encryptor Based on Reconfigurable Fredkin Gates
As classical computation approaches its fundamental limits related to power dissipation, reversible logic, which theoretically achieves zero energy loss, is becoming a critical technology for future low-power and quantum computing. However, most research in this field remains theoretical, lacking practical, hardware-verified implementations. This paper bridges this gap by presenting the complete hardware implementation and rigorous fault-tolerance validation of a reversible encryptor based on extended Fredkin gates. First, we detail the full realization of the encryptor on an Altera Cyclone IV Field-Programmable Gate Array. This implementation is not just a simulation but a complete, interactive hardware prototype, featuring real-time data input via a standard keyboard and output to a video graphics array monitor. Second, since functional verification is insufficient for cryptographic hardware and exhaustive testing is computationally infeasible, we introduce a novel validation methodology. This core contribution utilizes a metaheuristic ant colony optimization algorithm, not for synthesis, but for the intelligent generation of an optimal and compact set of test vectors. This test set is designed to achieve maximum fault coverage for the industry-standard "stuck-at fault" model. The algorithm successfully generated a minimal test set achieving 100% coverage for the considered single stuck-at fault model. We then experimentally validated this methodology by manually injecting a stuck-at fault into the hardware description language, recompiling the faulty circuit into the device, and confirming that the metaheuristically generated test vector successfully detected the physical fault. Thus, this work demonstrates the full cycle from theory to a practically validated and reliable hardware implementation of a reversible system.
[...] Read more.Part-of-speech Tagging for Marathi using Maximum Entropy Markove Model
Part-of-Speech (POS) tagging is an essential and important pre-processing activity for many Natural Language Processing (NLP) applications, this is particularly more evident for morphologically rich languages such as Marathi. This research investigates POS tagging for Marathi using the Maximum Entropy Markov Model (MEMM). MEMM combines the strengths of conditional probability modelling and sequence prediction, allowing the integration of rich contextual features. Features used include word forms, suffixes, prefixes, and neighboring tags, effectively tackling the challenges presented by inflectional variations and ambiguity in Marathi. Experimental results demonstrate that the MEMM-based POS tagger achieves an accuracy of 83.72%. This performance marks a notable advancement in Marathi POS tagging, given the linguistic diversity and the scarcity of annotated data. Error analysis enhances the issues like ambiguity in homonyms and out-of-vocabulary words, providing methods for further improvement through enriched datasets and sophisticated modelling techniques. This study enhances NLP applications such as machine translation, spell checking, and sentiment analysis for Indian languages and offers a solid foundation for future research in Marathi POS tagging.
[...] Read more.Cost Denigration Based Data Center Allocation Policy Using Modified Parallel PSO Optimization Technique for Multi-cloud Framework
Most of the existing data center allocation mechanisms contribute either user centric or service provider centric not for both ends but in reality, both have different objectives. For example, the objective of a user is minimization of cost, response time as well as processing time whereas the objective of service provider is to maximize the profit and processing time and minimization of response time, bandwidth, energy consumption and computing overhead with subject to effective resource utilization and load balancing. To address this challenge, this paper introduces a Cost Denigration-Based Data Center Allocation Policy (CD-BDAP) utilizing Particle Swarm Optimization (PSO), which simultaneously considers economic cost, response time, and energy consumption in the selection of data centers. In contrast to conventional PSO-based broker policies, CD-BDAP integrates a workload similarity-aware allocation strategy by calculating a dissimilarity index among user requests, thereby facilitating enhanced consolidation and energy efficiency. A weighted objective function is developed to balance user-centric metrics (cost and response time) with provider-centric metrics (profit and energy consumption), explicitly capturing their trade-offs. The proposed mechanism is assessed utilizing CloudAnalyst, which is constructed on CloudSim. The experimental results indicate that CD-BDAP achieves a reduction in VM cost, a decrease in response time, and an enhancement in energy efficiency, while simultaneously increasing the overall profit for service providers. The findings suggest that the integration of energy-aware cost modeling and workload similarity into PSO-based allocation can enhance both economic and performance efficiency in the selection of cloud data centers. The outcomes of CD-BDAP are compared with the existing PSO-based mechanisms and found enhanced performance.
[...] Read more.Scholarship Award Recommendation System Using Predictive Modeling and Ensemble Learning
Education is crucial for personal and economic growth, but financial challenges in developing countries hinder equitable academic success. NGOs administer scholarship programs to empower underprivileged individuals, a crucial step towards the attainment of Sustainable Development Goal 4, which aims to provide inclusive and equitable quality education for all. This study proposes a novel Scholarship Award Recommendation System that leverages predictive modelling and ensemble learning to identify deserving students for scholarship awards. The system utilizes a robust ensemble model that combines the strengths of Quadratic Discriminant Analysis (QDA), Random Forest (RF), and Extra Trees (ET) to predict students' academic performance. Additionally, we incorporate answers from the General Mental Health Questionnaire (GHQ-12). The GHQ-12 responses are pre-processed using a binary scoring approach (0-0-1-1) and integrated as predictive variables alongside academic and demographic features. We apply this framework to a case study of Nigerian university students in partnership with Springtime Development Foundation. The results indicate that incorporating GHQ-12 features significantly enhances prediction accuracy, with QDA, RF, and ET achieving accuracy scores of 0.90, 0.86, and 0.89, respectively. Statistical analysis using a t-test confirms the relevance of GHQ-12 features, with a p-value of 0.0013 establishing a significant correlation between student performance and mental health status. The study showed the effectiveness of the ensemble model to accurately predict students’ academic performance. It highlights the significance of incorporating variables from the (GHQ-12) into the predictive model, indicating mental health as a crucial factor for predicting academic performance which in turn enhances the performances of the Classification Models considered.
[...] Read more.Understanding Gen Z's Engagement with Conversational AI: A Modified TAM Study on WhatsApp's Meta AI
The rapid integration of conversational Artificial Intelligence (AI) into instant messaging platforms has transformed how younger generations interact with digital technology. This study investigates Generation Z's engagement with Meta AI on WhatsApp by employing a modified Technology Acceptance Model (TAM) that partitions End-User Computing Satisfaction (EUCS) dimensions to ensure measurement validity. Specifically, 'content' and 'accuracy' reflect Perceived Usefulness, while 'format' and 'timeliness' reflect Perceived Ease of Use. A quantitative survey involving 272 Generation Z respondents in Indonesia was analyzed using Covariance-Based Structural Equation Modeling (CB-SEM) with AMOS. The results reveal that Perceived Ease of Use significantly influences both Perceived Usefulness and Attitude Toward Using. Consequently, Perceived Usefulness acts as a partial, rather than full, mediator between ease of use and user attitude. Furthermore, Attitude Toward Using emerged as a powerful determinant of Actual System Use, with the proposed model explaining 89% of its variance (R2 = 0.89). These findings suggest a synergistic effect for "digital natives": while an intuitive format and fast response times directly foster positive attitudes, the epistemic quality and accuracy of the AI remain the dominant drivers of sustained engagement. This study contributes theoretically by validating a robust, multicollinearity-resistant modified TAM for conversational AI, providing practical insights for developers to maintain frictionless interfaces while prioritizing algorithmic accuracy to enhance user adoption.
[...] Read more.TriGuard-Net: A Blockchain-enabled Hybrid Encryption and Ensemble Deep Learning Framework for Secure and Intelligent IoT DDoS Detection and Mitigation
In the Internet of Things (IoT) environment, a Distributed Denial-of-Service (DDoS) attack in the network causes poor performance and resource-limited issues to users. Existing systems do not provide real-time adaptability, leading to delayed mitigation. Also, centralized storage systems suffer from breaches and tampering. To tackle these issues, a secure and intelligent IoT DDoS detection and mitigation framework is presented that utilizes hybrid encryption, blockchain storage, ensemble deep learning (DL), and reinforcement learning (RL) to improve the accuracy, security, and efficiency of IoT networks against several cyber-attacks. The developed technique collects data from a dataset and pre-processes it for handling missing values and normalizes it for further analysis. Secondly, a hybrid encryption method combining Homomorphic Encryption (HE) and ChaCha20 is adopted for data encryption with optimal key selection using Dingo Optimizer (DOX). Then, the encrypted data is securely stored in blockchain through off-chain storage and on-chain hash storage to ensure data integrity and tamper-proof security. DDoS attack detection is performed using an ensemble model called TriGuard-Net that combines AlexNet, LSTM, and PSPNet, with optimizing hyperparameters using Fire Hawks Optimizer (FHO). Finally, an RL-based mitigation system using Deep Q-Network (DQN) helps in real-time attack mitigation and enhances IoT security. Experimental results reveal that the presented model offers superior performance by achieving an accuracy of 99%, a kappa score of 98%, an R2 Score of 97%, an MCC of 98%, a Jaccard Score of 98%, and a Hamming Loss of 0.006, thereby outperforming other current models.
[...] Read more.Context-oriented Framework for Determining Requirements Change Documentation Approaches
Requirements change management is one of the core business analyst's activities, directly affecting change impact analysis, stakeholder communication, and the long-term system maintainability. While research on this topic examines in detail change processes, tracking methods, and change type classification, the problem of systematically documenting requirements changes remains underexplored. Existing research lacks a unified classification of change documentation approaches and context-sensitive recommendations for their selection, which limits their effectiveness in managing requirements.
To address this gap, this study develops a context-oriented framework for selecting approaches to requirements change documentation. The framework integrates three components: a conceptual model based on the Baseline–Delta–Target State triad, a taxonomy of documentation approaches, and a context-driven selection mechanism grounded in empirical evidence. A systematic literature review was combined with an analysis of the survey of 324 practicing business analysts from Ukrainian and international companies. Statistically significant associations between selected project context attributes and documentation practices were identified using the Chi-square test of independence and Cramer's V, while additional dimensions were supported through evidence from the literature.
The framework incorporates six documentation approaches: Full Target State, Delta-only, Target-driven Delta, Delta-driven Target, Parallel Use, and Hybrid Cycle. Four contextual dimensions emerge as key factors: project, environment, resources, and stakeholders. To support context-based selection of the change documentation approach, a matrix was developed that integrates the identified dependencies.
The results position requirements change documentation as a context-sensitive knowledge management mechanism rather than a universal procedural standard.
Adaptive Swarm-Optimized Ensemble Learning for Generalizable Heart Disease Risk Prediction
Machine learning (ML) has made it much easier to find and estimate the risk of early stage of cardiovascular illnesses by making it possible to analyses massive, various clinical datasets quickly and easily. In these kinds of datasets, demographic information, lifestyle characteristics, medical history, and diagnostic measurements are all included. These are all things that may not be easy to see through standard clinical examination. This study examines heart disease prediction through a series of hybrid ML models that integrate neighborhood-based classifiers, swarm intelligence-driven optimization, and ensemble learning, motivated by existing obstacles. There are four hybrid models being proposed: MSMO-KE and MSMO-KM, which combine Modified Spider Monkey Optimization (MSMO) with K-Nearest Neighbour classifiers that use Euclidean and Minkowski distance measures, respectively. There are also two ensemble variants, MSMO-KECB and MSMO-KMCB, which add CatBoost as a final prediction layer. To make sure it is strong and can be used in other situations, the proposed framework is tested on three separate cardiovascular datasets using a cross-validation method. The experimental findings show that the performance is always better than the baseline and the best models that are already used. The MSMO-KMCB model performs the best overall out of all the approaches tested. It has a cross-validated accuracy of 98.2% on Dataset-3 while keeping a high sensitivity. The comparative research demonstrates that the proposed MSMO-based ensemble models surpass current methodologies in predictive accuracy and recall, underscoring their promise for dependable and efficient heart disease risk prediction in clinical decision-support systems.
[...] Read more.A Hybrid Attribute Enhanced Graph Neural Framework for Mitigating Data Sparsity and Cold Start Recommendations
Recommender system commonly suffers from data sparsity and cold-start problems, where user-item interactions hinder reliable preference learning. While recent Graph Neural Network based models such as LightGCN and NGCF effectively capture higher-order collaborative signals, they primarily rely on interaction-derived embeddings and remain sensitive to sparse environments. This paper Attribute Enabled Graph Neural Framework (AE-GNF) proposes a Semantic-Aware Graph Refinement Framework that integrates attribute-driven representation learning with graph-based collaborative propagation to address these limitations. The proposed method first encodes heterogeneous user and item attributes using semantic embedding modules to generate informative initial representations independent of interaction density. Dense sematic embeddings are generated using modality specific neural encoders including transformer based text encoder for descriptive attributes, a recurrent attention network for behavioral interaction sequences and temporal contextual feature encoder for metadata signals. These embeddings are then refined through a normalized graph propagation mechanism that jointly models structural connectivity and semantic similarity, enabling robust higher-order preference learning. Unlike conventional recommenders, the framework preserves attribute semantics during message passing and enables inductive cold-start recommendations, where embeddings for newly introduced users or items are generated directly from attributes without requiring prior interaction edges. Experimental evaluation conducted on publicly available benchmark datasets including MovieLens-1M, Amazon Electronics, Amazon Books, and Amazon Prime Movies and LastFM360 demonstrates consistent performance improvements over Matrix Factorization, content-based models, GraphSAGE and Neural Graph Collaborative Filtering (NGCF). Results show notable gains in ranking accuracy, diversity and robustness under varying sparsity levels. The proposed AE-GNF achieves improved recommendation performance reducing RMSE by 3.5 to 6.2% and improving NDCG@10 by 6-11% compared to graph-based baselines across benchmark datasets. The findings confirm that integrating semantic attribute encoding with graph refinement provides a scalable and effective solution for next-generation recommendation systems operating in sparse and heterogeneous environments.
[...] Read more.Optimizing Sinusitis Detection with Harmony Search-grey Wolf Feature Selection and Modified ANN Classification
Sinusitis is an inflammation of the paranasal sinus mucosa, which is an infection caused by a bacterium, fungus or virus. Therefore, for earliest and accurate prediction of sinusitis from Computed Tomography (CT) image, this research introduces a novel Artificial Intelligence (AI) based technique. The developed research is initiated with preprocessing using a Gabor filter to improve the quality of an image. After, segmentation using Gaussian Mixture Model (GMM) is exploited for effective isolation of sinus regions affected by inflammation. For acquiring the crucial features from the segmented regions, Gray-Level Co-occurrence Matrix (GLCM) based feature extraction is utilized which offers clinically meaningful features that improve transparency. Consequently, the hybrid Harmony Search Algorithm (HSA)-Grey Wolf Optimizer (GWO) feature selection is utilized to choose the most relevant features. This hybrid method outperforms traditional selection techniques by effectively identifying the most discriminative and non-redundant features, enhancing classification accuracy while reducing computational complexity. For accurate classification of sinusitis into various severity levels, the modified Artificial Neural Network (ANN) is employed. Unlike end-to-end deep learning models, this modular approach allows for fine-grained control at each stage, ensuring that critical medical insights are not lost in abstraction. This structured pipeline allows each phase to be optimized individually, improving transparency, reliability and ultimately, diagnostic performance. The performance of the research is analyzed via python software and it reveals that the developed classifier achieves an accuracy of 96.41%.
[...] Read more.Transformer Framework Enhanced by Large Language Models for Image-based Multi-class Malware Detection
With the rapid proliferation of electronic devices, the volume and sophistication of malware have surged, posing critical cybersecurity threats. Traditional malware detection approaches face challenges such as limited generalization, unbalanced datasets, and high computational costs. To address these issues, this study introduces the LLM-Powered Transformer Framework for Multi-Class Malware Detection, an image-based approach integrating Large Language Models (LLMs) and transformer architectures with Convolutional Neural Networks (CNNs). The proposed framework enhances malware classification by leveraging data visualization, balanced sampling, and data augmentation techniques, achieving over 98.86% accuracy across four open-source datasets. Furthermore, this study makes two key contributions: first, it provides granular insights into malware classification performance using confusion matrix analysis, aiding cybersecurity professionals in refining detection strategies. Second, the balanced sampling approach eliminates the need for additional datasets, minimizes hardware overhead, and dynamically adjusts sampling weights for optimal learning. Additionally, data augmentation techniques mitigate overfitting, enhancing the model's adaptability to diverse malware variants. Comparative analysis with state-of-the-art methods demonstrates the proposed framework's efficiency in achieving high accuracy while maintaining computational feasibility. These advancements establish a robust foundation for real-world malware detection and cybersecurity applications.
[...] Read more.Multi-model Fusion for Emotion Detection in Text: A Stacking and Majority Voting Approach
Emotion detection from text plays a pivotal role in applications such as sentiment analysis, social media insights, and customer experience management. This study introduces a multi-model fusion approach for emotion detection using the Kaggle Emotion Text Dataset, a widely recognized benchmark that captures a variety of emotions across diverse textual inputs. The proposed framework employs a combination of machine learning classifiers, including Random Forest (RF), Logistic Regression (LR), Decision Trees (DT), Stochastic Gradient Descent (SGD) and Support Vector Machine (SVM). To maximize predictive performance, these models are integrated using two ensemble strategies: Stacking and Majority Voting. Stacking combines base models with a meta-classifier, enabling the system to learn intricate patterns in the data, while Majority Voting provides a simpler yet effective method for decision consolidation by leveraging collective model predictions. Performance evaluation is conducted using metrics such as accuracy, precision, recall, F-measure, False Positive Rate (FPR), and False Negative Rate (FNR). The results demonstrate that the Stacking approach achieves the highest accuracy of 99.92%, with precision of 99.68 %, recall of 99.19% and f-measure of 99.43%, respectively with Micro FPR of 0.0001, Micro FNR of 0.0007, Macro FPR of 0.0002 and Macro FNR of 0.0081. Majority Voting, while slightly less accurate, excels in reducing FPR and FNR, making it a valuable alternative in scenarios where minimizing misclassification is critical. This work underscores the potential of ensemble learning in addressing the complexities of emotion detection in text. The integration of diverse classifiers enhances prediction robustness and highlights the trade-offs between model complexity and real-world feasibility. By delivering a comprehensive evaluation and actionable insights, this single-author study contributes to advancing the field of emotion analysis and its practical applications.
[...] Read more.Design and Implementation of a Web-based Document Management System
One area that has seen rapid growth and differing perspectives from many developers in recent years is document management. This idea has advanced beyond some of the steps where developers have made it simple for anyone to access papers in a matter of seconds. It is impossible to overstate the importance of document management systems as a necessity in the workplace environment of an organization. Interviews, scenario creation using participants' and stakeholders' first-hand accounts, and examination of current procedures and structures were all used to collect data. The development approach followed a software development methodology called Object-Oriented Hypermedia Design Methodology. With the help of Unified Modeling Language (UML) tools, a web-based electronic document management system (WBEDMS) was created. Its database was created using MySQL, and the system was constructed using web technologies including XAMPP, HTML, and PHP Programming language. The results of the system evaluation showed a successful outcome. After using the system that was created, respondents' satisfaction with it was 96.60%. This shows that the document system was regarded as adequate and excellent enough to achieve or meet the specified requirement when users (secretaries and departmental personnel) used it. Result showed that the system developed yielded an accuracy of 95% and usability of 99.20%. The report came to the conclusion that a suggested electronic document management system would improve user happiness, boost productivity, and guarantee time and data efficiency. It follows that well-known document management systems undoubtedly assist in holding and managing a substantial portion of the knowledge assets, which include documents and other associated items, of Organizations.
[...] Read more.Advanced Applications of Neural Networks and Artificial Intelligence: A Review
Artificial Neural Network is a branch of Artificial intelligence and has been accepted as a new computing technology in computer science fields. This paper reviews the field of Artificial intelligence and focusing on recent applications which uses Artificial Neural Networks (ANN’s) and Artificial Intelligence (AI). It also considers the integration of neural networks with other computing methods Such as fuzzy logic to enhance the interpretation ability of data. Artificial Neural Networks is considers as major soft-computing technology and have been extensively studied and applied during the last two decades. The most general applications where neural networks are most widely used for problem solving are in pattern recognition, data analysis, control and clustering. Artificial Neural Networks have abundant features including high processing speeds and the ability to learn the solution to a problem from a set of examples. The main aim of this paper is to explore the recent applications of Neural Networks and Artificial Intelligence and provides an overview of the field, where the AI & ANN’s are used and discusses the critical role of AI & NN played in different areas.
[...] Read more.Cardiotocography Data Analysis to Predict Fetal Health Risks with Tree-Based Ensemble Learning
A sizeable number of women face difficulties during pregnancy, which eventually can lead the fetus towards serious health problems. However, early detection of these risks can save both the invaluable life of infants and mothers. Cardiotocography (CTG) data provides sophisticated information by monitoring the heart rate signal of the fetus, is used to predict the potential risks of fetal wellbeing and for making clinical conclusions. This paper proposed to analyze the antepartum CTG data (available on UCI Machine Learning Repository) and develop an efficient tree-based ensemble learning (EL) classifier model to predict fetal health status. In this study, EL considers the Stacking approach, and a concise overview of this approach is discussed and developed accordingly. The study also endeavors to apply distinct machine learning algorithmic techniques on the CTG dataset and determine their performances. The Stacking EL technique, in this paper, involves four tree-based machine learning algorithms, namely, Random Forest classifier, Decision Tree classifier, Extra Trees classifier, and Deep Forest classifier as base learners. The CTG dataset contains 21 features, but only 10 most important features are selected from the dataset with the Chi-square method for this experiment, and then the features are normalized with Min-Max scaling. Following that, Grid Search is applied for tuning the hyperparameters of the base algorithms. Subsequently, 10-folds cross validation is performed to select the meta learner of the EL classifier model. However, a comparative model assessment is made between the individual base learning algorithms and the EL classifier model; and the finding depicts EL classifiers’ superiority in fetal health risks prediction with securing the accuracy of about 96.05%. Eventually, this study concludes that the Stacking EL approach can be a substantial paradigm in machine learning studies to improve models’ accuracy and reduce the error rate.
[...] Read more.Performance of Machine Learning Algorithms with Different K Values in K-fold Cross-Validation
The numerical value of k in a k-fold cross-validation training technique of machine learning predictive models is an essential element that impacts the model’s performance. A right choice of k results in better accuracy, while a poorly chosen value for k might affect the model’s performance. In literature, the most commonly used values of k are five (5) or ten (10), as these two values are believed to give test error rate estimates that suffer neither from extremely high bias nor very high variance. However, there is no formal rule. To the best of our knowledge, few experimental studies attempted to investigate the effect of diverse k values in training different machine learning models. This paper empirically analyses the prevalence and effect of distinct k values (3, 5, 7, 10, 15 and 20) on the validation performance of four well-known machine learning algorithms (Gradient Boosting Machine (GBM), Logistic Regression (LR), Decision Tree (DT) and K-Nearest Neighbours (KNN)). It was observed that the value of k and model validation performance differ from one machine-learning algorithm to another for the same classification task. However, our empirical suggest that k = 7 offers a slight increase in validations accuracy and area under the curve measure with lesser computational complexity than k = 10 across most MLA. We discuss in detail the study outcomes and outline some guidelines for beginners in the machine learning field in selecting the best k value and machine learning algorithm for a given task.
[...] Read more.Machine Learning based Wildfire Area Estimation Leveraging Weather Forecast Data
Wildfires are increasingly destructive natural disasters, annually consuming millions of acres of forests and vegetation globally. The complex interactions among fuels, topography, and meteorological factors, including temperature, precipitation, humidity, and wind, govern wildfire ignition and spread. This research presents a framework that integrates satellite remote sensing and numerical weather prediction model data to refine estimations of final wildfire sizes. A key strength of our approach is the use of comprehensive geospatial datasets from the IBM PAIRS platform, which provides a robust foundation for our predictions. We implement machine learning techniques through the AutoGluon automated machine learning toolkit to determine the optimal model for burned area prediction. AutoGluon automates the process of feature engineering, model selection, and hyperparameter tuning, evaluating a diverse range of algorithms, including neural networks, gradient boosting, and ensemble methods, to identify the most effective predictor for wildfire area estimation. The system features an intuitive interface developed in Gradio, which allows the incorporation of key input parameters, such as vegetation indices and weather variables, to customize wildfire projections. Interactive Plotly visualizations categorize the predicted fire severity levels across regions. This study demonstrates the value of synergizing Earth observations from spaceborne instruments and forecast data from numerical models to strengthen real-time wildfire monitoring and postfire impact assessment capabilities for improved disaster management. We optimize an ensemble model by comparing various algorithms to minimize the root mean squared error between the predicted and actual burned areas, achieving improved predictive performance over any individual model. The final metric reveals that our optimized WeightedEnsemble model achieved a root mean squared error (RMSE) of 1.564 km2 on the test data, indicating an average deviation of approximately 1.2 km2 in the predictions.
[...] Read more.PDF Marksheet Generator
The Marksheet Generator is flexible for generating progress mark sheet of students. This system is mainly based in the database technology and the credit based grading system (CBGS). The system is targeted to small enterprises, schools, colleges and universities. It can produce sophisticated ready-to-use mark sheet, which could be created and will be ready to print. The development of a marksheet and gadget sheet is focusing at describing tables with columns/rows and sub-column sub-rows, rules of data selection and summarizing for report, particular table or column/row, and formatting the report in destination document. The adjustable data interface will be popular data sources (SQL Server) and report destinations (PDF file). Marksheet generation system can be used in universities to automate the distribution of digitally verifiable mark-sheets of students. The system accesses the students’ exam information from the university database and generates the gadget-sheet Gadget sheet keeps the track of student information in properly listed manner. The project aims at developing a marksheet generation system which can be used in universities to automate the distribution of digitally verifiable student result mark sheets. The system accesses the students’ results information from the institute student database and generates the mark sheets in Portable Document Format which is tamper proof which provides the authenticity of the document. Authenticity of the document can also be verified easily.
[...] Read more.Cloud Computing: A review of the Concepts and Deployment Models
This paper presents a selected short review on Cloud Computing by explaining its evolution, history, and definition of cloud computing. Cloud computing is not a brand-new technology, but today it is one of the most emerging technology due to its powerful and important force of change the manner data and services are managed. This paper does not only contain the evolution, history, and definition of cloud computing, but it also presents the characteristics, the service models, deployment models and roots of the cloud.
[...] Read more.A Systematic Review of Natural Language Processing in Healthcare
The healthcare system is a knowledge driven industry which consists of vast and growing volumes of narrative information obtained from discharge summaries/reports, physicians case notes, pathologists as well as radiologists reports. This information is usually stored in unstructured and non-standardized formats in electronic healthcare systems which make it difficult for the systems to understand the information contents of the narrative information. Thus, the access to valuable and meaningful healthcare information for decision making is a challenge. Nevertheless, Natural Language Processing (NLP) techniques have been used to structure narrative information in healthcare. Thus, NLP techniques have the capability to capture unstructured healthcare information, analyze its grammatical structure, determine the meaning of the information and translate the information so that it can be easily understood by the electronic healthcare systems. Consequently, NLP techniques reduce cost as well as improve the quality of healthcare. It is therefore against this background that this paper reviews the NLP techniques used in healthcare, their applications as well as their limitations.
[...] Read more.Markov Models Applications in Natural Language Processing: A Survey
Markov models are one of the widely used techniques in machine learning to process natural language. Markov Chains and Hidden Markov Models are stochastic techniques employed for modeling systems that are dynamic and where the future state relies on the current state. The Markov chain, which generates a sequence of words to create a complete sentence, is frequently used in generating natural language. The hidden Markov model is employed in named-entity recognition and the tagging of parts of speech, which tries to predict hidden tags based on observed words. This paper reviews Markov models' use in three applications of natural language processing (NLP): natural language generation, named-entity recognition, and parts of speech tagging. Nowadays, researchers try to reduce dependence on lexicon or annotation tasks in NLP. In this paper, we have focused on Markov Models as a stochastic approach to process NLP. A literature review was conducted to summarize research attempts with focusing on methods/techniques that used Markov Models to process NLP, their advantages, and disadvantages. Most NLP research studies apply supervised models with the improvement of using Markov models to decrease the dependency on annotation tasks. Some others employed unsupervised solutions for reducing dependence on a lexicon or labeled datasets.
[...] Read more.Accident Response Time Enhancement Using Drones: A Case Study in Najm for Insurance Services
One of the main reasons for mortality among people is traffic accidents. The percentage of traffic accidents in the world has increased to become the third in the expected causes of death in 2020. In Saudi Arabia, there are more than 460,000 car accidents every year. The number of car accidents in Saudi Arabia is rising, especially during busy periods such as Ramadan and the Hajj season. The Saudi Arabia’s government is making the required efforts to lower the nations of car accident rate. This paper suggests a business process improvement for car accident reports handled by Najm in accordance with the Saudi Vision 2030. According to drone success in many fields (e.g., entertainment, monitoring, and photography), the paper proposes using drones to respond to accident reports, which will help to expedite the process and minimize turnaround time. In addition, the drone provides quick accident response and recording scenes with accurate results. The Business Process Management (BPM) methodology is followed in this proposal. The model was validated by comparing before and after simulation results which shows a significant impact on performance about 40% regarding turnaround time. Therefore, using drones can enhance the process of accident response with Najm in Saudi Arabia.
[...] Read more.Design and Implementation of a Web-based Document Management System
One area that has seen rapid growth and differing perspectives from many developers in recent years is document management. This idea has advanced beyond some of the steps where developers have made it simple for anyone to access papers in a matter of seconds. It is impossible to overstate the importance of document management systems as a necessity in the workplace environment of an organization. Interviews, scenario creation using participants' and stakeholders' first-hand accounts, and examination of current procedures and structures were all used to collect data. The development approach followed a software development methodology called Object-Oriented Hypermedia Design Methodology. With the help of Unified Modeling Language (UML) tools, a web-based electronic document management system (WBEDMS) was created. Its database was created using MySQL, and the system was constructed using web technologies including XAMPP, HTML, and PHP Programming language. The results of the system evaluation showed a successful outcome. After using the system that was created, respondents' satisfaction with it was 96.60%. This shows that the document system was regarded as adequate and excellent enough to achieve or meet the specified requirement when users (secretaries and departmental personnel) used it. Result showed that the system developed yielded an accuracy of 95% and usability of 99.20%. The report came to the conclusion that a suggested electronic document management system would improve user happiness, boost productivity, and guarantee time and data efficiency. It follows that well-known document management systems undoubtedly assist in holding and managing a substantial portion of the knowledge assets, which include documents and other associated items, of Organizations.
[...] Read more.Cardiotocography Data Analysis to Predict Fetal Health Risks with Tree-Based Ensemble Learning
A sizeable number of women face difficulties during pregnancy, which eventually can lead the fetus towards serious health problems. However, early detection of these risks can save both the invaluable life of infants and mothers. Cardiotocography (CTG) data provides sophisticated information by monitoring the heart rate signal of the fetus, is used to predict the potential risks of fetal wellbeing and for making clinical conclusions. This paper proposed to analyze the antepartum CTG data (available on UCI Machine Learning Repository) and develop an efficient tree-based ensemble learning (EL) classifier model to predict fetal health status. In this study, EL considers the Stacking approach, and a concise overview of this approach is discussed and developed accordingly. The study also endeavors to apply distinct machine learning algorithmic techniques on the CTG dataset and determine their performances. The Stacking EL technique, in this paper, involves four tree-based machine learning algorithms, namely, Random Forest classifier, Decision Tree classifier, Extra Trees classifier, and Deep Forest classifier as base learners. The CTG dataset contains 21 features, but only 10 most important features are selected from the dataset with the Chi-square method for this experiment, and then the features are normalized with Min-Max scaling. Following that, Grid Search is applied for tuning the hyperparameters of the base algorithms. Subsequently, 10-folds cross validation is performed to select the meta learner of the EL classifier model. However, a comparative model assessment is made between the individual base learning algorithms and the EL classifier model; and the finding depicts EL classifiers’ superiority in fetal health risks prediction with securing the accuracy of about 96.05%. Eventually, this study concludes that the Stacking EL approach can be a substantial paradigm in machine learning studies to improve models’ accuracy and reduce the error rate.
[...] Read more.Machine Learning based Wildfire Area Estimation Leveraging Weather Forecast Data
Wildfires are increasingly destructive natural disasters, annually consuming millions of acres of forests and vegetation globally. The complex interactions among fuels, topography, and meteorological factors, including temperature, precipitation, humidity, and wind, govern wildfire ignition and spread. This research presents a framework that integrates satellite remote sensing and numerical weather prediction model data to refine estimations of final wildfire sizes. A key strength of our approach is the use of comprehensive geospatial datasets from the IBM PAIRS platform, which provides a robust foundation for our predictions. We implement machine learning techniques through the AutoGluon automated machine learning toolkit to determine the optimal model for burned area prediction. AutoGluon automates the process of feature engineering, model selection, and hyperparameter tuning, evaluating a diverse range of algorithms, including neural networks, gradient boosting, and ensemble methods, to identify the most effective predictor for wildfire area estimation. The system features an intuitive interface developed in Gradio, which allows the incorporation of key input parameters, such as vegetation indices and weather variables, to customize wildfire projections. Interactive Plotly visualizations categorize the predicted fire severity levels across regions. This study demonstrates the value of synergizing Earth observations from spaceborne instruments and forecast data from numerical models to strengthen real-time wildfire monitoring and postfire impact assessment capabilities for improved disaster management. We optimize an ensemble model by comparing various algorithms to minimize the root mean squared error between the predicted and actual burned areas, achieving improved predictive performance over any individual model. The final metric reveals that our optimized WeightedEnsemble model achieved a root mean squared error (RMSE) of 1.564 km2 on the test data, indicating an average deviation of approximately 1.2 km2 in the predictions.
[...] Read more.Performance of Machine Learning Algorithms with Different K Values in K-fold Cross-Validation
The numerical value of k in a k-fold cross-validation training technique of machine learning predictive models is an essential element that impacts the model’s performance. A right choice of k results in better accuracy, while a poorly chosen value for k might affect the model’s performance. In literature, the most commonly used values of k are five (5) or ten (10), as these two values are believed to give test error rate estimates that suffer neither from extremely high bias nor very high variance. However, there is no formal rule. To the best of our knowledge, few experimental studies attempted to investigate the effect of diverse k values in training different machine learning models. This paper empirically analyses the prevalence and effect of distinct k values (3, 5, 7, 10, 15 and 20) on the validation performance of four well-known machine learning algorithms (Gradient Boosting Machine (GBM), Logistic Regression (LR), Decision Tree (DT) and K-Nearest Neighbours (KNN)). It was observed that the value of k and model validation performance differ from one machine-learning algorithm to another for the same classification task. However, our empirical suggest that k = 7 offers a slight increase in validations accuracy and area under the curve measure with lesser computational complexity than k = 10 across most MLA. We discuss in detail the study outcomes and outline some guidelines for beginners in the machine learning field in selecting the best k value and machine learning algorithm for a given task.
[...] Read more.Advanced Applications of Neural Networks and Artificial Intelligence: A Review
Artificial Neural Network is a branch of Artificial intelligence and has been accepted as a new computing technology in computer science fields. This paper reviews the field of Artificial intelligence and focusing on recent applications which uses Artificial Neural Networks (ANN’s) and Artificial Intelligence (AI). It also considers the integration of neural networks with other computing methods Such as fuzzy logic to enhance the interpretation ability of data. Artificial Neural Networks is considers as major soft-computing technology and have been extensively studied and applied during the last two decades. The most general applications where neural networks are most widely used for problem solving are in pattern recognition, data analysis, control and clustering. Artificial Neural Networks have abundant features including high processing speeds and the ability to learn the solution to a problem from a set of examples. The main aim of this paper is to explore the recent applications of Neural Networks and Artificial Intelligence and provides an overview of the field, where the AI & ANN’s are used and discusses the critical role of AI & NN played in different areas.
[...] Read more.Multi-Factor Authentication for Improved Enterprise Resource Planning Systems Security
Universities across the globe have increasingly adopted Enterprise Resource Planning (ERP) systems, a software that provides integrated management of processes and transactions in real-time. These systems contain lots of information hence require secure authentication. Authentication in this case refers to the process of verifying an entity’s or device’s identity, to allow them access to specific resources upon request. However, there have been security and privacy concerns around ERP systems, where only the traditional authentication method of a username and password is commonly used. A password-based authentication approach has weaknesses that can be easily compromised. Cyber-attacks to access these ERP systems have become common to institutions of higher learning and cannot be underestimated as they evolve with emerging technologies. Some universities worldwide have been victims of cyber-attacks which targeted authentication vulnerabilities resulting in damages to the institutions reputations and credibilities. Thus, this research aimed at establishing authentication methods used for ERPs in Kenyan universities, their vulnerabilities, and proposing a solution to improve on ERP system authentication. The study aimed at developing and validating a multi-factor authentication prototype to improve ERP systems security. Multi-factor authentication which combines several authentication factors such as: something the user has, knows, or is, is a new state-of-the-art technology that is being adopted to strengthen systems’ authentication security. This research used an exploratory sequential design that involved a survey of chartered Kenyan Universities, where questionnaires were used to collect data that was later analyzed using descriptive and inferential statistics. Stratified, random and purposive sampling techniques were used to establish the sample size and the target group. The dependent variable for the study was limited to security rating with respect to realization of confidentiality, integrity, availability, and usability while the independent variables were limited to adequacy of security, authentication mechanisms, infrastructure, information security policies, vulnerabilities, and user training. Correlation and regression analysis established vulnerabilities, information security policies, and user training to be having a higher impact on system security. The three variables hence acted as the basis for the proposed multi-factor authentication framework for improve ERP systems security.
[...] Read more.Early Formalization of AI-tools Usage in Software Engineering in Europe: Study of 2023
This scientific article presents the results of a study focused on the current practices and future prospects of AI-tools usage, specifically large language models (LLMs), in software development (SD) processes within European IT companies. The Pan-European study covers 35 SD teams from all regions of Europe and consists of three sections: the first section explores the current adoption of AI-tools in software production, the second section addresses common challenges in LLMs implementation, and the third section provides a forecast of the tech future in AI-tools development for SD.
The study reveals that AI-tools, particularly LLMs, have gained popularity and approbation in European IT companies for tasks related to software design and construction, coding, and software documentation. However, their usage for business and system analysis remains limited. Nevertheless, challenges such as resource constraints and organizational resistance are evident.
The article also highlights the potential of AI-tools in the software development process, such as automating routine operations, speeding up work processes, and enhancing software product excellence. Moreover, the research examines the transformation of IT paradigms driven by AI-tools, leading to changes in the skill sets of software developers. Although the impact of LLMs on the software development industry is perceived as modest, experts anticipate significant changes in the next 10 years, including AI-tools integration into advanced IDEs, software project management systems, and product management tools.
Ethical concerns about data ownership, information security and legal aspects of AI-tools usage are also discussed, with experts emphasizing the need for legal formalization and regulation in the AI domain. Overall, the study highlights the growing importance and potential of AI-tools in software development, as well as the need for careful consideration of challenges and ethical implications to fully leverage their benefits.
Accident Response Time Enhancement Using Drones: A Case Study in Najm for Insurance Services
One of the main reasons for mortality among people is traffic accidents. The percentage of traffic accidents in the world has increased to become the third in the expected causes of death in 2020. In Saudi Arabia, there are more than 460,000 car accidents every year. The number of car accidents in Saudi Arabia is rising, especially during busy periods such as Ramadan and the Hajj season. The Saudi Arabia’s government is making the required efforts to lower the nations of car accident rate. This paper suggests a business process improvement for car accident reports handled by Najm in accordance with the Saudi Vision 2030. According to drone success in many fields (e.g., entertainment, monitoring, and photography), the paper proposes using drones to respond to accident reports, which will help to expedite the process and minimize turnaround time. In addition, the drone provides quick accident response and recording scenes with accurate results. The Business Process Management (BPM) methodology is followed in this proposal. The model was validated by comparing before and after simulation results which shows a significant impact on performance about 40% regarding turnaround time. Therefore, using drones can enhance the process of accident response with Najm in Saudi Arabia.
[...] Read more.Detecting and Preventing Common Web Application Vulnerabilities: A Comprehensive Approach
Web applications are becoming very important in our lives as many sensitive processes depend on them. Therefore, it is critical for safety and invulnerability against malicious attacks. Most studies focus on ways to detect these attacks individually. In this study, we develop a new vulnerability system to detect and prevent vulnerabilities in web applications. It has multiple functions to deal with some recurring vulnerabilities. The proposed system provided the detection and prevention of four types of vulnerabilities, including SQL injection, cross-site scripting attacks, remote code execution, and fingerprinting of backend technologies. We investigated the way worked for every type of vulnerability; then the process of detecting each type of vulnerability; finally, we provided prevention for each type of vulnerability. Which achieved three goals: reduce testing costs, increase efficiency, and safety. The proposed system has been validated through a practical application on a website, and experimental results demonstrate its effectiveness in detecting and preventing security threats. Our study contributes to the field of security by presenting an innovative approach to addressing security concerns, and our results highlight the importance of implementing advanced detection and prevention methods to protect against potential cyberattacks. The significance and research value of this survey lies in its potential to enhance the security of online systems and reduce the risk of data breaches.
[...] Read more.Cloud Computing: A review of the Concepts and Deployment Models
This paper presents a selected short review on Cloud Computing by explaining its evolution, history, and definition of cloud computing. Cloud computing is not a brand-new technology, but today it is one of the most emerging technology due to its powerful and important force of change the manner data and services are managed. This paper does not only contain the evolution, history, and definition of cloud computing, but it also presents the characteristics, the service models, deployment models and roots of the cloud.
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