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: 145
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, SCImago, Google Scholar, CrossRef, Baidu Wenku, IndexCopernicus, IET Inspec, EBSCO, VINITI, JournalSeek, ULRICH's Periodicals Directory, WorldCat, 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. 5, Oct. 2026
REGULAR PAPERS
An Approach to Employ Content-based Recommendation Techniques in the Context of Cardiovascular Disease Detection and Prevention
Cardiovascular disease after post-COVID has become a life-threatening, deadly disease. A major percentage of the mortality rate occurring every year is due to heart-related diseases. India, being a middle-income nation, is facing a severe need for awareness and resources to reduce the untimely death, especially in the middle-aged population, due to cardiac arrest. Machine learning has been acting as an essential tool to predict an early occurrence of this fatal disease. There is a need to provide personalized recommendations to the patient if the patient is predicted to have heart disease. The paper aims at providing the various content-based recommendations to the patients based on 5 parameters: age, FBS, trestbps, chol, thalach, and CP so that the precautionary measures need to be taken by the person based on the recommendation provided by the model. The contribution of this work is summarized into three parts. a) A stacking ensemble-based meta-learner is developed to predict a person with heart disease. b) A machine learning pipeline is incorporated to automate the workflow of disease detection and c) A novel personalized recommendation method with respect to cardiovascular risk reduction and preventative measures providing the optimal solutions to the person suffering from heart disease and its prognosis. The proposed method is validated by providing the necessary recommendations to the patients, demonstrating significant risk reduction for individuals with high CVD risk. The result evaluation is done using a t-test showing a statistically significant level and an ROC curve with a value of 0.98 and a sensitivity analysis with a value of 0.91. The mean average precision (MAP) value is 0.75. Creating a machine learning-based model that forecasts the likelihood of heart disease onset is the aim of this research. The existence or absence of heart illness, given as a binary classification (0 = no heart disease, 1 = heart disease), is the outcome variable for this prediction task.
[...] Read more.A Secure and Efficient Mobile Agent Framework for Medical Data Protection Using Variable Threshold CRT and Lightweight Cryptography
Mobile agents are powerful advanced technology for distributing computing with intelligent and autonomous execution of assigned tasks. During the execution, a mobile agent utilizes a specific life cycle in a malicious environment. Collaboration of mobile agents for securing medical data like patient information, patient medical report etc. plays an important role. In the digital world, smart health care system is required. For secure accessing of patient information from one hospital to another hospital a framework is proposed based on the variable threshold Chinese remainder theorem with the present and pride lightweight cipher. The Chinese remainder theorem is used for secret sharing of key among hospitals. The VT-CRT approach dynamically modifies the threshold in response to system variables, improving adaptability and security while minimizing computing cost. Present and pride are used for encryption and decryption of electronic medical records. This is the collaborative approach of mobile agents for creation and recreation of secret keys among hospitals for authentication. The effectiveness of the healthcare system is evaluated by the ability to securely and effectively transmit medical information. The lightweight PRIDE and PRESENT encryption methods provide efficient, low-latency encryption that is ideal for resource-constrained medical devices. The framework is compared to popular cryptographic systems like AES, DES, and Blowfish in terms of time complexity, communication cost, flexibility, and memory usage. Experimental results show that the proposed framework improves security while maintaining optimal speed, making it a reliable solution for safe patient information sharing in medical applications.
[...] Read more.Enhanced Deep Learning Prediction Framework using Improved Golden Eagle and Fire Hawk Optimization
Predicting medical insurance costs is a difficult task that requires calculating future medical expenses for individuals or groups based on their personal and medical data. Deep learning is the robust technique that can extract complicated relationships and patterns from huge and varied data sources. In this article, we suggest a novel deep learning model to predict the cost of medical insurance for a specific person based on their age, BMI, sex, number of children and smoking status. First, the Z-score pre-processing technique is employed to remove the noisy data. Then the metaheuristic optimization algorithm modified Fire Hawk Optimization (BFHO) is introduced for feature selection (FS) to select the most relevant features data, thus decreasing the number of features. Additionally, the dynamic chunk-based max pooling (DCMP) technique is employed to improve the pooling layer in CNN network and the improved golden eagle optimization (IGEO) approach is utilized to enhance the weight of the CNN network. Finally, this improved CNN is used for the prediction of medical insurance based on the medical dataset. The predictive performance of the proposed approach is systematically evaluated and benchmarked against several traditional methods, including the Improved Whale Optimization Algorithm (IWOA), Fire Hawk Optimization (FHO), Improved Manta Ray Foraging Optimization (IMRFO), Honey Badger Algorithm (HBA), and Binary Grey Wolf Optimizer (BGWO). The experimental results show that the proposed model is the best approach for the cost prediction of medical insurance.
[...] Read more.Early Detection and Prediction of Osteopenia: A Pathway to Enhanced Bone Health using Machine Learning
Early identification of osteopenia, an easy indicator of osteoporosis, is essential for fracture preventative measures and the restoration of long-term bone wellness. This research sets up and assesses a machine learning screening framework designed for detecting the beginning of osteopenia, utilizing enhanced clinical datasets that include demographic, medical, and bone health variables. The Custom Weighted Ensemble Model, through a comparative analysis with different classification algorithms, obtained the highest accuracy of 98.69%, surpassing standard models including Random Forest and Gradient Boosting, both at 98.48%. The ensemble approach demonstrates enhanced precision and F1-scores, underscoring its effectiveness in balancing both precision and sensitivity compared with prior research which concentrates primarily on osteoporosis. This investigation demonstrates the initial stage of osteopenia detection along with it suggests that optimized ensemble strategies significantly improve predictive performance. The findings indicate the potential of data-driven models to enhance preventive bone health care and decrease the risk of future fractured bones. Early recognition of osteopenia—widely noticed as the impassive initial level for osteoporosis—is essential for safeguarding the health of bones and avoiding severe fractures. This paper presents a detailed review of existing methodologies for diagnosing and predicting osteopenia, with a focus on the comprehensive overview of machine learning (ML). By combining insights from significant studies on osteoporosis prediction and choosing a comprehensive dataset incorporating with demographic, medical, and bone health parameters, we reveal essential predictive factors. Additionally, we offer a state-of-the-art framework for creating reliable machine learning models specifically suited for early detection. This study highlights how data-driven innovation can improve clinical care, slow the progression of osteopenia, and pave the way for a future free of fractures.
[...] Read more.Predictive Risk Identification and Resource Optimization in Social Work: A Hybrid Semi-Supervised Learning Framework
Social work practice faces critical challenges in accurately identifying high-risk families for timely intervention due to limited labeled data. This research develops an intelligent system integrating supervised and semi-supervised learning for early warning, coupled with uncertainty-aware resource optimization. We propose a hybrid framework combining Self-training and Co-training leveraging 1,200 labeled and 13,800 unlabeled family cases. The proposed method achieves 85.6% accuracy and 82.4% recall on the high-risk class, with 95% confidence intervals, substantially outperforming supervised-only baselines. A multi-objective allocation model balances effectiveness, cost, and equity while incorporating prediction uncertainty via robust optimization. Sensitivity analysis over 500 bootstrap scenarios demonstrates service quality guarantees. Subgroup fairness audits show consistent recall across demographic groups (max gap 1.2%). The optimized allocation reduces manpower by 22% while improving intervention success from 68.5% to 84.2%. SHAP-based explainability and human oversight support ethical deployment in social services.
[...] Read more.Monitoring Student Learning Outcomes: Student Dropout Prediction Model Using Subgraph Matching Approach
Student dropout is still a crucial issue in many colleges and universities in countries including Indonesia despite various initiatives that have been implemented to reduce its rate. As a preventive effort, many educational institutions continuously monitor several factors that potentially affect student graduation and dropout rates. However, the increasing ratio of student numbers to administrative staff has made manual processes inefficient. This paper presents a novel method to monitor student learning outcomes using a subgraph matching approach to identify students who potentially drop out early or fail to graduate due to low learning achievement. This study emphasizes diagnostic accuracy in identifying students at risk of dropping out as the main focus of the proposed contribution. Performance assessment is directed at the model's ability to accurately distinguish between at-risk and not-at-risk students, particularly through evaluations that emphasize the minority class. This approach strengthens the model's role as the foundation of a more reliable early warning system relevant to supporting academic decision-making. In this research, the academic achievement of each student is represented as a graph that represents the relationship between each student and courses that have been enrolled in the previous semesters. By comparing the learning achievement pattern between each student and those students who have either "graduated" or "dropped out", the students with elevated dropout risk can be identified in a computationally efficient manner. The experiment findings showed that the proposed method can identify students with elevated dropout risk with 89.47% average accuracy. Graph-based approaches are capable of capturing structural relationships and complex interaction patterns among student activities that cannot be explicitly represented by other models. Although subgraph matching is known to have high computational complexity, the identification process can be accelerated through subgraph pattern constraints, preprocessing strategies, and search optimizations, thereby supporting claims of efficiency both theoretically and practically.
[...] Read more.Comprehensive Feature Fusion in Deep Learning Models for Robust Epileptic Seizure Detection from EEG Signals
Epilepsy is a prevalent neurological disorder characterized by recurrent epileptic seizures. After a stroke, it is a highly common neurological condition. For identifying seizures, the Electroencephalogram (EEG) is the gold-standard modality for capturing and recording the brain's electrical activity with high temporal resolution. The existing machine learning approaches have been leveraged before the emergence of deep learning. However, these models limited the performance as they included handcrafted features. The deep learning models perform the feature extraction automatically, which improves the classification accuracy compared with the conventional strategies. Therefore, in this article, an epileptic seizure detection approach on the basis of deep learning methods is developed to identify the abnormal brain functionalities in the initial stage. Initially, the EEG signal collection is carried out through standard benchmark databases. After that, the spectral and statistical features are directly extracted from the acquired EEG signals. Subsequently, the Short-Time Fourier Transform (STFT) is applied to transform the EEG signals into spectrograms, and then the features from those spectrograms are extracted via Vision Transformer (ViT). Furthermore, the Wave-based features are also extracted, which provide a comprehensive view of the brain activities. After that, a Coordinate Attention-based Feature Fusion mechanism is employed to fuse these diverse feature types, which captures complementary details from the EEG signals. The fused features are subjected to the Adaptive Dilated dense Recurrent neural Network with a Novel Activation Function (ADRNet-NAF) for the Epileptic seizure detection. The detection accuracy of the proposed model is improved by optimizing the hyperparameters of the ADRNet-NAF model using Modernized Exploration of Magnificent Frigatebird Optimization (MEMFO) during network training. The empirical analysis over the traditional methods is conducted to validate the Epileptic seizure detection performance of the proposed model using various measures. The proposed model achieves 94.98% accuracy, 89.30% sensitivity and 99.08% specificity.
[...] Read more.GreenCloud-RL: Carbon-Aware Multi-Cloud Scheduling with SLA Guarantees
We present GreenCloud-RL, a formally defined scheduler for multi-cloud fleets that jointly optimizes carbon emissions, operational cost, and service-level agreement (SLA) risk. The scheduling problem is modeled as a constrained Markov decision process (CMDP) in which expected cost–carbon reward is maximized subject to aggregate SLA-risk and power-cap constraints. The framework integrates (i) a physics-informed DVFS power model, (ii) queueing-theoretic latency predictors, (iii) probabilistic short-term forecasts of regional grid carbon intensity and electricity price with uncertainty penalties, and (iv) a dual-variable actor–critic algorithm that provably enforces constraint satisfaction online. We analyze the algorithm’s convergence under bounded rewards and provide distributed complexity bounds for large-scale multi-cloud control. Empirical evaluation on production-like workloads shows that GreenCloud-RL reduces SLA-violation rate by 37.6% and power-cap violations by 53.8%, while lowering mean tardiness to 1.9 s and improving throughput by 4.2% compared with strong baselines. A region×regime analysis (load ∈ {low, medium, high}; grid ∈ {clean, neutral, dirty}) demonstrates consistent reductions in both cost and carbon emissions without degrading SLA satisfaction. Post-hoc calibration improves reliability (ECE 4.8%→1.7%, lower Brier score), enabling trustworthy on-time probability estimates for risk-aware admission and tenant-tier guarantees. Scalability tests show a p95 scheduling latency of ≈41 ms at 100k jobs, ≈0.25 ms inference per decision, modest CPU/GPU overhead, and ≈5 s controller failover. These results establish, both theoretically and empirically, that multi-objective optimization of carbon, cost, and reliability is achievable in production-scale cloud environments while maintaining contractual SLA guarantees.
[...] Read more.Carbon Dioxide Emission Prediction for Trend Analysis using CAViaR Pine Cone Optimization Algorithm Enabled Attention-Based LSTM
Predicting Carbon dioxide (CO₂) emissions is essential for guiding environmental policies and climate strategies, yet accurate local-scale prediction remains challenging due to complex spatial and temporal factors. Existing approaches often focus on regional or global models and do not fully exploit high-resolution satellite imagery for local trend analysis. To overcome these limitations, Conditional Pine Cone Optimization enabled Attention-Based Long Short-Term Memory (CPCO_ALSTM) is devised for CO2 Emission prediction for Trend Analysis in Satellite Image. In this approach, satellite images of the query region acquired at different time intervals are used as input. Initially, a Kalman Filter is employed for noise reduction and temporal smoothing during preprocessing. Then, segmentation of satellite image is done using Dense-Res Recurrent Prototypical Network (DRRP-Net), wherein DRRP-Net is developed by combining Dense-Res-Inception Network (DRINet) and Recurrent Prototypical Network (RP-Net). Based on the segmented regions and extracted features, carbon emission estimation is carried out. The estimated CO₂ emissions are then validated against ground-truth emission data obtained from a reference database to assess prediction accuracy. For temporal trend analysis, an Attention-Based Long Short-Term Memory (ALSTM) model is utilized to capture long-range dependencies in emission patterns. The ALSTM parameters are optimally tuned using the proposed CPCO algorithm, which combines Pine Cone Optimization (PCO) with Conditional Autoregressive Value at Risk (CAViaR) to enhance convergence stability and prediction robustness. Additionally, CPCO_ALSTM has attained 96.35% of accuracy, 0.087 of normalized MSE, and 0.957 of Jaccard Coefficient.
[...] Read more.Explainable AI for Diabetes Nutrition: Time-Aware Seq2Seq Learning for Personalized 21-Meal Weekly Planning
Adequate nutrition, patient acceptance and consistent dietary adherence are required for optimal chronic glycaemic control in T2DM. Customised and clinically adapted meal planning may best support these factors. This work is situated in the field of Medical Informatics, combining explainable sequence modelling with clinically meaningful dietary restrictions, ensuring both prediction accuracy and health-oriented decision support for Type 2 Diabetes Mellitus (T2DM). In contrast to the traditional RNN based recommenders, the proposed approach explicitly incorporates adherence-aware assessment, interpretability and nutritional safety. In this paper, we propose CA-Seq2Seq-LSTM-Attn, a Constraint-Aware Seq2Seq-LSTM with Dot-Product Attention framework for generating customised seven-day meal plans (21 meals) from nutritional constraints and previous dietary behaviour. We use an LSTM-based Seq2Seq architecture in place of Transformer-based models because of its strong inductive bias for modelling short, structured temporal sequences (21 meals over 7 days), better generalisation on small high-variance dietary datasets, and lower computational complexity than data-hungry Transformer models. The model is a sequence-to-sequence Long Short-Term Memory (LSTM) architecture with attention for capturing temporal eating patterns in daily meal sequences. The constraint-aware decoding strategy ensures that the calorie, carbohydrate, sugar, protein limits, and the Glycaemic Index (GI) and Glycaemic Load (GL) thresholds are satisfied, which is crucial in the context of diabetes management. The attention mechanism makes the respective forecast more interpretable by pointing to important previous meals. We evaluate the system using enriched user-recipe interactions and comprehensive nutritional data from the Food.com dataset. The experimental results show a good ranking performance with NDCG@10 of 0.5026 and Recall@10 of 0.5909. The model also ensures a better diet consistency, as indicated by the lower variance in carbs (826.6690), weekday adherence (74.39%), intra-plan variety (0.7180), and high explainability (minimum perturbation-based faithfulness confidence reduction: −0.0288). Incorporating temporal sequence modelling, attention-based interpretability and constraint-aware decoding substantially improve clinical nutritional feasibility and recommendation accuracy. The proposed framework outperforms the traditional Collaborative Filtering (BPR-MF, NCF) and sequential deep learning baselines (GRU4Rec, SASRec) on all ranking and nutrition metrics. The proposed CA-Seq2Seq-LSTM-Attn framework is a promising decision-support tool for personalised dietary control in T2DM as it generates interpretable, nutritionally balanced and clinically matched meal recommendations.
[...] Read more.Two-stage GAN with Attention Gates for Brain MRI Inpainting: A Hybrid Framework for Preserving Diagnostic Features in Alzheimer's Disease Classification
Clinical brain MRI scans for Alzheimer's disease diagnosis often suffer from motion artifacts, signal dropout, and incomplete acquisitions. While deep learning methods like LaMa produce visually coherent inpainting, they may alter critical anatomical structures, and traditional methods such as OpenCV Telea maintain local continuity but introduce excessive smoothing. This study presents a dual-stage generative adversarial network with attention gates for medical image restoration, uniquely complemented by a novel gradient-weighted hybrid blending strategy that adaptively combines GAN outputs with classical inpainting based on local image gradients. The architecture employs an attention-enhanced U-Net generator and refinement network, supervised by patch and global discriminators through combined adversarial, reconstruction, perceptual, structural similarity, and gradient losses. Evaluation on 12,491 balanced Alzheimer's MRI scans across four severity stages with synthetic 10–30% occlusions shows that the proposed GAN reduces mean squared error by 48.7% versus LaMa and 41.3% versus OpenCV, achieving 17.44 dB PSNR and 0.9796 SSIM (computed over the brain region). The gradient-guided Hybrid-GAN maintains reconstruction quality with a total inference time of approximately 8.97 ms per image. Downstream VGG16 classification reveals Hybrid-GAN preserves diagnostic information most effectively, attaining 96.05% accuracy, only 0.96 points below the 97.01% baseline and surpassing all alternative methods. These results demonstrate that attention-driven generative models combined with structure-aware blending provide a practical and novel solution for artifact mitigation in neuroimaging diagnostics.
[...] Read more.A Dynamic Dual-Graph GCN-GRU Framework for Stock Movement Prediction
Stock movement prediction is a challenging task due to the complex temporal dynamics of financial time series and the interdependence among stocks. Most existing approaches either model each stock independently or rely on a single static graph, which limits their ability to capture heterogeneous and time-varying relationships in financial markets. To address this issue, this paper proposes a dynamic dual-graph GCN-GRU framework for next-day stock movement prediction. The proposed model integrates two complementary graph structures: a static industry graph to encode long-term sectoral relationships and a dynamic correlation graph to capture evolving co-movement patterns among stocks over time, where the dynamic graph is constructed in a causally consistent manner using only historical information available before the target prediction. For each graph, a graph convolutional network (GCN) is employed to learn relational stock representations, while a gated recurrent unit (GRU) is used to model temporal dependencies from sequential graph-based embeddings. The two graph-specific representations are integrated through an adaptive gated fusion mechanism. By jointly exploiting structural market information and temporal dynamics, the proposed framework provides a unified representation for next-day stock direction forecasting. Experiments are conducted on two public stock datasets constructed from the S&P 500 and VN30 markets using engineered features derived from daily trading data. The evaluation considers both standard classification metrics and finance-oriented indicators, including IC, Sharpe ratio, and maximum drawdown. The empirical results demonstrate the effectiveness of the proposed approach compared with conventional machine learning, sequence-based, and graph-based baselines. These findings suggest that combining dynamic relational modeling with temporal learning is a practical and reproducible forecasting framework for stock movement prediction in both developed and emerging markets.
[...] 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.Software Quality Attributes in Requirements Engineering
As software systems continue to grow more complex, evaluating software quality becomes increasingly critical. This study analyzes existing software quality models, including McCall, Boehm, FURPS, and ISO Systems and Software Engineering – Systems and Software Quality Requirements and Evaluation (SQuaRE), with a specific focus on the ISO/IEC 25010:2023 standard. The research aims to assess the completeness of these models and explore interdependencies among key quality attributes relevant to software requirements engineering. The paper identifies key characteristics and associated metrics based on ISO/IEC standards using comparative analysis and a literature review. Findings show that ISO/IEC 25010:2023 provides the most comprehensive structure, with Functional Suitability and Compatibility identified as essential due to their universally recommended metrics. Survey data from 328 practicing analysts in Ukraine and internationally demonstrate a gap between theoretical models and real-world requirements documentation practices, particularly for non-functional requirements. The identified dependencies between quality attributes enable a more integrated and structured approach to identifying and analyzing non-functional requirements in IT projects. The study emphasizes that software quality models must be tailored to project-specific goals and constraints, with attention to trade-offs and stakeholder needs during the requirements specification, prioritization, and validation processes. The findings support the adaptation of quality models to specific project constraints and emphasize the business analyst’s role in tailoring quality criteria for practical use in software development.
[...] 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.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.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.
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.IoT Driven HRES Smart Grid with Advanced Routing and IQKM Security Mechanism
Expansion of Internet of Things (IoT) technologies has greatly enhanced monitoring and management of energy systems, especially in Hybrid Renewable Energy Systems (HRES). This paper presents an IoT-based HRES smart grid framework with a modified Brain Storm Optimization (BSO) algorithm for routing optimization and an Improved Quantum Key Management (IQKM) is a quantum inspired protocol for better data security. The enhanced BSO algorithm, hosted in the cloud infrastructure, optimizes IoT sensor data routing paths, thus diminishing packet transmission latency and improving the network throughput. In contrast to conventional BSO techniques, the enhancement is through dynamic cluster refinement and adaptive node prioritization, designed specifically for real-time cloud-integrated energy systems. In order to protect sensitive energy transmission information, the IQKM protocol includes strong quantum-aided encryption processes and dynamic key creation. These enhancements directly counter the dangers of man-in-the-middle and replay attacks, which exceed capabilities of standard encryption approaches by facilitating low-latency, quantum-resistant communication between HRES nodes. Both Photovoltaic (PV) and wind-based energy sources are utilized by the system to provide power consistently, with cloud-based analytics and IoT sensors ensuring real-time monitoring. Experimental testing via the Adafruit platform reports a 23% Packet Delivery Ratio (PDR) enhancement and 17% encryption/decryption delay reduction compared to baseline and traditional routing algorithms. Such findings ensure the potential for stable, secure, and scalable grid performance by the proposed system.
[...] 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.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.