International Journal of Mathematics, Statistics, and Computer Science
IJMSCS is published Open Access under a Creative Commons CC-BY 4.0 license. Authors retain full copyright, with the first publication right granted to the journal.
Hierarchical Curriculum Transfer for Swahili QA with mT5: SQuAD Pretraining and KenSwQuAD Extractive-to-Abstractive Refinement
The introduction of the Kencorpus Swahili Question Answering Dataset (KenSwQuAD) presents a compelling opportunity to advance Machine Reading Comprehension (MRC) for the Swahili language. However, the mixed composition of this dataset—comprising 67.6\% extractive and 32.4\% abstractive answers—introduces substantial hurdles for standard training pipelines. In this study, we benchmark the performance of the multilingual T5 (mT5) architecture on KenSwQuAD, deploying a robust Hierarchical Curriculum Learning strategy. We introduce a novel data restructuring technique that algorithmically partitions the corpus into distinct extractive and abstractive subsets, thereby enabling meticulously phased fine-tuning. By sequentially progressing from structural transfer via the English SQuAD, to morphological alignment on the Swahili extractive subset, and culminating in abstractive refinement, we establish that the mT5-base model can achieve a SacreBLEU score of 48.99 on extractive tasks. For abstractive reasoning, whilst the BLEU score is comparatively modest (15.52), the model attains a remarkable BERTScore F1 of 77.21\%, reflecting a profound degree of semantic comprehension. Furthermore, we evaluate the utility of structure-aware input formatting (context scaffolding) for successfully navigating extensive narrative contexts. Ultimately, our findings indicate that whilst modern transformer architectures can successfully internalise Swahili QA logic, a persistent fluency gap characterises the abstractive domain. This gap underscores the urgent necessity for more expansive, targeted datasets to reconcile semantic accuracy with lexical precision.
Copyright (c) 2026 Jared Ongaro, Benjamin Kikwai (Author) https://creativecommons.org/licenses/by/4.0
Machine Learning Approaches for Privacy-Preserving Data Management in Cloud-Based Health Insurance
Cloud-based health insurance systems require effective protection of sensitive user information while maintaining accurate and intelligent decision-making capabilities. However, existing approaches often face challenges related to privacy preservation, computational complexity, and reduced predictive performance under strict security constraints. This study proposes a privacy-preserving machine learning framework for secure data management in cloud-based health insurance environments. The framework integrates data cleaning, data masking, and secure feature transformation techniques to safeguard confidential information while preserving data utility. An optimized XGBoost classification model is employed to predict customer interest in insurance products. The proposed methodology includes missing value handling, duplicate removal, feature encoding, and secure dataset partitioning for model training and evaluation. Experimental results demonstrate strong predictive performance, achieving an accuracy of 97.7%, precision of 96.9%, recall of 97.4%, and F1-score of 97.1%, indicating a highly reliable and balanced classification system. The findings show that privacy-preserving transformations have minimal impact on predictive effectiveness while significantly enhancing data security. The proposed framework offers a practical, cost-effective, and scalable solution for secure cloud-based health insurance management, supporting privacy protection and efficient data-driven decision-making in modern healthcare insurance services.
Copyright (c) 2026 Moorthy N (Author) https://creativecommons.org/licenses/by/4.0
Understanding Behavioral Patterns in Digital Skills Learning Through Advanced Deep Learning Techniques
Gaining digital skills has become essential as digital technologies progressively permeate every aspect of knowledge and work, even for students studying non-technological subjects who frequently do not have formal exposure to computational tools and environments. Developing intelligent assistance systems tailored to different learner profiles requires an understanding of and ability to model their learning habits. To evaluate and predict the behavioral patterns of learners of digital skills, this paper proposes a hybrid deep learning framework that combines the benefits of recurrent neural networks (RNNs) for temporal sequence modeling and convolutional neural networks (CNNs) for static feature extraction. Digital education platforms offering skill-based instruction (e.g., data literacy, coding principles, digital collaboration) were used to collect a behavioral dataset of 2,000 students. Academic success, self-reported motivation, engagement metrics, and time and frequency-based indicators are some of the features. With a classification accuracy of 92.23%, the suggested a hybrid model significantly outperforms both independent architectures and standard machine learning benchmarks (such as logistic regression and SVM). According to behavioral data, studying at night, staying involved, and having a strong desire are all important indicators of success in online learning environments. The model demonstrates the viability of deep behavioral modeling for intelligent tutoring and adaptive feedback systems by enabling the generation of personalized, real-time learning implications. Particularly for disadvantaged or multidisciplinary learner groups, these results offer a methodological foundation for incorporating AI-driven learner modeling into scalable digital skills education systems.
Deep Learning, Learning Behavior Analysis, Digital Skills Education, Personalized Learning, Predictive Modeling, Educational Data Mining
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
On the Power of Heteroskedasticity Tests in Regression: A Monte Carlo Simulation Study
An important assumption for the classical linear regression model (CLRM) is homoskedasticity, which assumes that variances of the error vector of the regression model are constant. But if the variances are varying (not constant), this means that the problem of heteroskedasticity is present, and as a result there are some consequences to it, such as the Gauss-Markov theorem not being applied, the ordinary least squares (OLS) estimators no longer being BLUE (Best Linear Unbiased Estimator), the regression predictions being inefficient too, and the standard for the least squares estimator being wrong. Several diagnostic tests have been developed to detect the heteroskedasticity problem. The most common ones include Breusch-Pagan test, White test, and Goldfeld-Quandt test. In this paper we focus on four tests for heteroskedasticity, namely Goldfeld-Quandt (GQ), Glesjer, White, and ARCH tests. We explain each test and indicate the difference between each test. After that we used the Monte Carlo simulation study to apply and compare the performance between the four tests in three types of heteroskedasticity to determine which test has the best performance. The Monte Carlo simulation study has been conducted to evaluate these tests. Several scenarios of simulations were used, such as different sample sizes, different values of the coefficient of variation of the variance of the error, and others. The simulation results were close to each other, but for the first type of heteroskedasticity, the Goldfeld-Quandt test has the best performance and most power. While for the second and third types of heteroskedasticity, the Goldfeld-Quandt and ARCH tests have power close to each other.
ARCH; Goldfeld-Quandt test; Glesjer test; Heteroskedasticity; White test
Hybrid Neural Network Approaches to Produced Water Quality Forecasting and Management in Nigerian Oil Fields
The management of produced water is a significant challenge for environmental agencies and operating companies in Nigerian oil environments, where escalating water-cut ratios persist. This research proposes and compares different artificial neural network (ANN) models with the integration strategies of Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Wavelet Transform (WT) for forecasting the quality variables TDS, Oil & Grease, Chloride, and Heavy Metals. Data for six prominent oil fields in the Niger Delta for 2015-2023 have been employed for analyses with all three models: ANN-PSO, ANN-GA, and ANN-WT. These models have confirmed that ANN-PSO models predict with higher precision compared to all other models, with regression values above 0.94 for forecasting TDS. The values for Root Mean Square Error (RMSE) have been found to vary between 23% and 31% lower than ANNs. The model ANNE-WT has outperformed with a regression value of 0.92 for forecasting chloride levels with a mean absolute percentage error (MAPE) value of 4.7% for Seasonal components. These models offer oil field operators an adequate predictive system that can proactively optimize water treatment systems and facilitate regulatory compliance. These results help address the issue of water cut increase in a more sustainable manner within the mature oil field settings.
produced water, artificial neural networks, hybrid models, water quality forecasting, Nigerian oil fields, Niger Delta
Bayes Estimation of Lindley Distribution Under Entropy Loss Functions and Type II Censoring
This study presents Bayesian estimation for the parameters of the Lindley distribution under Type II censoring, employing entropy loss functions and conjugate priors. Markov Chain Monte Carlo (MCMC) methods are used to derive the posterior distributions, facilitating parameter estimation. The use of conjugate priors streamlines the computational process, enhancing the practicality of the Bayesian framework. Simulation results confirm the effectiveness of the Bayesian estimators under entropy loss, particularly in censored data scenarios. The methodology offers a valuable approach for researchers and practitioners working with reliability and lifetime data, demonstrating the efficiency and accuracy of the proposed Bayesian methods.
Prior, Posterior distribution, Loss function, Right censoring
Mobility Estimation-Based Clustering for Energy-Efficient Routing in IoT-Enabled VANETs
Combining Internet of Things (IoT) devices with Vehicular Ad hoc Networks (VANETs) offers substantial benefits for traffic management, transportation efficiency, and road safety. However, challenges related to energy consumption, routing efficiency, and stability remain significant obstacles, particularly as modern VANETs increasingly rely on Electric Vehicles (EVs) and Solar-Powered Roadside Units (SP-RSUs), which have limited energy budgets. Existing routing protocols often fail due to the impact of high vehicular mobility and restricted energy resources. This affects periodic rerouting, unstable communication, and decreases network lifetime. This paper suggests a Mobility Estimation-Based Clustering Routing (MEBCR) protocol to treat these issues. In a united framework, the proposed MEBCR merges a hybrid mobility estimation module, including the Kalman Filter and Gauss-Markov approaches, together with cluster formation and energy-aware routing strategies. This design is essential for sustaining the processes in energy-restricted environments, guaranteeing reliable communication and a prolonged network. By comparing with existing protocols, simulation outcomes show that MEBCR preserves 10-28% extra energy and 13-42% node survival. Additionally, it reduces cluster variations by approximately 48-60%. These outcomes confirm the efficiency and robustness of the proposed protocol, making it a suitable solution for green and intelligent transportation systems in next-generation networks.
Internet of Things, Vehicular Ad hoc Networks, Energy Consumption, Routing Efficiency, Mobility Estimation.
Enhancing Secure Cloud Storage for Synthetic Healthcare Data: Encryption and Scalable Privacy-Preserving Analysis
With the heightened reliance on cloud computing for storage and management of sensitive health care data, the aspects of data security and privacy have become a real focus. This paper proposes improvements on a cloud storage setting for synthetic healthcare data, enhanced by embedding Modified Identity-Based Encryption (MIBE) into the security of the patient before uploading to cloud storage. The proposed system provides scalability, privacy-preserving analysis, and compliance with healthcare laws. In order to treat missing values and create a standardized dataset, we introduce pre-processing techniques such as k-NN Imputation and Z-Score Method. With the MIBE encryption mechanism, the encryption key would be tied to the identity of the user; hence it would be a strong method to secure the data while reducing the burden of managing keys. The results show a linear increment in encryption time concerning the number of files, which took 2.23 seconds for 100 files and 19.02 seconds for 1000, proving the efficiency of the encryption mechanism. After performing 10 operations, the proposed model achieved a security score of 9.8 units, which validates the scalability of the proposed setup. This work gives insight into a secure setup for managing healthcare data on cloud platforms with proven gain on scalability. This addresses, therefore, an emerging concern on the data security and privacy of modern-day health care systems.
Modified Identity-Based Encryption (MIBE)
Bhanutheja Nagabhushana Reddy Kasinayakanahally
Copyright (c) 2026 Rudhrananth Baladhandapani, Sri Harsha Grandhi, Bhanutheja Nagabhushana Reddy Kasinayakanahally, Rama Krishna Mani Kanta Yalla, Soundarraj K (Author) https://creativecommons.org/licenses/by/4.0
Hemodynamic behavior analysis of Fusiform Aneurysms under Pulsatile Flow with Shear-Dependent Blood Rheology
Bio-mechanical and hemodynamical changes happen due to fusiform aneurysms which alter flow structure and shear stress distribution along the affected segment. This study applies three-dimensional CFD analysis to investigate the hemodynamic environment associated with fusiform aneurysms under physiologically realistic pulsatile blood flow. Numerical simulations are performed in COMSOL Multiphysics using finite element methods, incorporating both Newtonian and Carreau non-Newtonian rheology to evaluate the influence of blood viscosity behavior. Key hemodynamic quantities velocity fields, pressure distribution, WSS, OSI, TAWSS, and RRT are quantified to characterize the flow response to the elongated aneurysmal deformation. Peak systolic velocity is 0.87% lower in the Carreau model compared to the Newtonian model, while diastolic velocity decreases by 11.6%. Peak WSS at systole differs by approximately 9.75% between models, whereas the minimum TAWSS in the aneurysm sac is up to 9% lower under Carreau rheology. The maximum OSI in the sac increases by 0.87% for the non-Newtonian case, indicating greater flow reversal sensitivity, and the maximum RRT increases by roughly 2.6%, suggesting enlarged thrombus-prone zones. These findings demonstrate that the choice of rheological model significantly influences diastolic-phase hemodynamics and thrombus risk indicators, underscoring the importance of non-Newtonian modeling for accurate fusiform aneurysm assessment.
Fusiform aneurysm, CFD, Pulsatile blood flow, Carreau model, WSS, OSI, TAWSS, RRT
Copyright (c) 2026 Farjana Mitu, Chinmayee Podder, Ashish Kumar Saha (Author) https://creativecommons.org/licenses/by/4.0
A Study on the Numerical Accuracy of Galerkin, Modified Galerkin, Shooting and Homotopy Perturbation Methods in Solving Boundary Value Problems
This study analyzes the behavior of four popular numerical methods of solving boundary-value problems (BVPs): the Standard Galerkin method, the Modified Galerkin method, the Shooting method, and the Homotopy Perturbation Method (HPM). The BVP is commonly used in scientific and engineering practice in fluid and thermal transport, micro- and bio-fluidic systems, fluid-structure interaction, aerodynamics, and electromagnetic modelling. The evaluation of each technique was based on the comparison of its numerical results and the errors with the analytical result. The precision and consistency were reflected by detailed tables and graphic representations that point out solution behaviour and error patterns. The best accuracy was derived with the Shooting method, followed by the Modified Galerkin method. The modified Galerkin method was more adaptable and stable as compared to the standard Galerkin scheme, which showed bigger variations in its error outcomes. HPM, however, was observed to have some irregularities, especially at the mid-point part of the solution domain. Therefore, the general comparison of this paper explains how each numerical technique reacts to boundary-value problems and how much they can be applied in real computational settings.
Error Analysis, Boundary Value Problems (BVPs), Shooting Method, Ordinary Differential Equations (ODEs), Numerical Analysis.
Explainable AI for Prognostic Factor Identification in Colorectal Cancer: An Electronic Health Records Analysis
Colorectal cancer (CRC) prognosis remains challenging due to the disease's heterogeneity and the complex interplay of clinical, demographic, and molecular factors. This study leverages explainable artificial intelligence (XAI) and electronic health records (EHRs) to develop interpretable machine learning models for prognostic factor identification in CRC. Using a retrospective cohort of 8,247 patients, we extracted 1,247 features from EHRs, including demographic, laboratory, treatment, and natural language processing (NLP)-derived data. After rigorous feature selection, six machine learning models were evaluated, with XGBoost achieving the highest performance (C-index: 0.798, 95% CI: 0.785–0.811), significantly outperforming traditional Cox models (C-index: 0.742) and established prognostic scores. SHAP and LIME analyses identified both established prognostic factors (e.g., TNM stage, age) and novel predictors, such as temporal albumin trends and neutrophil-to-lymphocyte ratio (NLR), which accounted for 40% of the top prognostic features. Clinical validation by oncology experts confirmed the relevance and biological plausibility of these findings. The study demonstrates that XAI-enhanced models can improve prognostic accuracy while providing transparent, actionable insights, bridging the gap between complex machine learning outputs and clinical decision-making. These results highlight the potential of integrating comprehensive EHR data with XAI to advance precision oncology in CRC care.
Explainable AI, colorectal cancer, prognostic factors, electronic health records, machine learning, survival analysis.
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
An Intuitionistic Fuzzy Parameterized Decision-making Approach For Evaluating Site Suitability For Educational Institutions Using Hypersoft Settings
Intuitionistic fuzzy parameterization aims to evaluate the uncertain and ambiguous nature of parameters and sub-parameters during the decision-making process. In this parameterization, an intuitionistic fuzzy grade is assigned to parameters and sub-parameters to address ambiguity. In cases of hypersoft settings, this grade is attached to multi-argument tuples containing sub-parametric values. The integration of intuitionistic fuzzy parameterization with hypersoft settings offers a flexible framework for modeling uncertainties in decision-making scenarios. In this study, a new theoretical framework called the intuitionistic fuzzy parameterized hypersoft set (IFpHSS) is developed, which replaces existing concepts based on parameterization of intuitionistic fuzzy soft sets. The concepts of empty, universal, subset, and equal IFpHSS are introduced and clarified with examples. Aggregation operations such as complement, union, intersection, AND, and OR for IFpHSS are also explained. Using the idea of an intuitionistic fuzzy decision set, cardinality set, and reduced fuzzy set within the IFpHSS framework, a robust decision-support system is established, along with an intelligent algorithm. This algorithm is applied to evaluate different sites for the establishment of an educational institution, demonstrating its effectiveness for solving problems involving vagueness and uncertainties.
Intuitionistic Fuzzy Parameterized Hypersoft Set
Preparing for Future Crises in Education: AI and Regression-Based Modeling of Emergency Distance Learning Satisfaction among Moroccan Law Students
Higher education institutions around the world have been forced to use Emergency Distance Learning (EDL) in place of traditional classroom instruction due to the COVID-19 pandemic. Moroccan law students were impacted by this shift, experiencing issues with access, the caliber of their education, and their technical readiness. Using both traditional statistical techniques and advanced artificial intelligence (AI) methods, this study aims to identify and forecast the primary factors influencing students' satisfaction with EDL in Moroccan legal education. Using a thorough survey of 16,187 law students from public and private Moroccan universities, we investigate the effects of six predictor categories on overall satisfaction: technological readiness, accessibility, teacher quality, assessment methods, learning engagement, and awareness. We combine artificial intelligence models like Support Vector Regression (SVR), Random Forest (RF), LightGBM, and Multilayer Perceptron Regression (MLPR) with multiple linear regression and stepwise regression. To improve model correctness, features are chosen using Recursive Feature Elimination (RFE). Students expressed concerns about the fairness and transparency of online assessments and expressed a preference for in-person instruction despite significant technical readiness. This study highlights the importance of digital equality, pedagogical support, and intelligent analytics for strong higher education systems and provides evidence-based insights to improve the architecture of distance legal education in Morocco.
Emergency Distance Learning, Moroccan Law Students, Student Satisfaction, Machine Learning, Regression, Feature Selection, Legal Education
Comparative Analysis of AI Models for Effort Estimation in Western and Regional Environments
Artificial Intelligence rapidly alters business operations and workflow management strategies these days in various corporate sectors. AI can be effective in calculating the effort required for a software. Figuring that how much time work and resources some project will probably take requires a fairly decent amount of effort and is also one of the most crucial activities of software project management. When an estimate is met the confidence of companies increase and funds can be allocated nicely, ultimately helping in finishing projects quickly. This work evaluates different AI models for estimating software effort accurately in two distinct areas. Western environment encompasses nations such as US and UK, and mostly all developed places including Canada alongside other similar countries. These countries typically possess sophisticated technology and proficient labor with meticulous documentation practices. Regional environment encompasses areas namely South Asia and Africa alongside Middle East which undergo development challenges. These areas often face many problems such as weak digital infrastructure in various sectors and somewhat disorganized data sets which are not very helpful for estimation. Various AI models were tested including Linear Regression, Neural Networks random forest etc. in different areas to determine which ones worked nicely. Three measures were used namely Mean Absolute Error (MAE) Root Mean Squared Error (RMSE) and R² Score to assess AI model efficiency. Better accuracy stems from lower MAE and RMSE values while higher R² scores signify deeper understanding of data patterns. Neural Networks operate more effectively in Western regions owing largely to relatively cleaner data and markedly greater regularity. Random Forests and Decision Trees perform markedly better in regional areas plagued by messy data because they handle such info pretty well. Companies ought to select an AI model suited pretty well to their specific local conditions and the kind of data they possess. Finally in both the environments, that means deploying both the datasets for the environments, it was the hybrid technique that performed the best for predicting the effort of software. The hybrid model used for prediction give the lowest MAE of 0.22 and |RMSE of 0.38 with R2 of 0.9 for the Western regions. Similary even for the regional areas give the lowest MAE and RMSE of 0.4 and 0.55 respectively and R2 of 0.79.
Software Effort Estimation, Machine Learning, Software Predictions, Western Environments, AI Models
Privacy-Preserving Integration of GPT-4 and Natural Language Processing via Secure Multi-Party Computation for Healthcare Data
Integrating Generative Pre-Trained Transformer (GPT-4) in healthcare heralds a transformative era, automating routine tasks and empowering physicians to focus on complex patient care. GPT-4's role in patient education simplifies medical terminology, fosters a knowledgeable patient base, and promotes adherence to care plans. This study presents a secure framework for integrating GPT-4, Natural Language Processing (NLP), and patient data in healthcare using Secure Multi-Party Computation (MPC). The framework ensures secure data exchange among stakeholders in healthcare data analysis. Emphasizing security, privacy, and regulatory compliance, the study underscores collaboration with privacy, security, and healthcare experts. The amalgamation of GPT-4 and NLP advances research and care and upholds data protection standards. The revolutionary use of MPC marks a paradigm shift, promising enhanced patient outcomes and securing the future of healthcare.
Artificial Intelligence, Healthcare, GPT-4, Natural Language Processing, Secure Multi-Party Computation.
AQ-ResCon: Adaptive Quantum-Resistant Lattice-Based Key Agreement Protocol for Secure Distributed Container Orchestration in Edge Cloud Environments
In the rapidly evolving domain of smart healthcare, the integration of Docker containers and Kubernetes with Internet of Things (IoT) edge cloud orchestration has significantly enhanced the performance, scalability, and modularity of healthcare applications. However, despite the efficiency benefits provided by microservices architecture, substantial security vulnerabilities persist, particularly in the face of the threat of quantum computing. Quantum algorithms, such as Shor’s and Grover’s, pose a significant risk to conventional encryption schemes, potentially compromising the integrity and confidentiality of healthcare data distributed across edge cloud environments. To address these critical concerns, we propose AQ-ResCon: an Adaptive Quantum-Resistant Lattice-Based Key Agreement Protocol designed for secure and resilient distributed container orchestration. AQ-ResCon leverages the hardness of the Learning With Errors (LWE) problem in lattice-based cryptography to provide a robust defence against quantum attacks. We further introduce the AQ-ResCon Scheduler Algorithm, which efficiently manages and executes healthcare workloads across decentralised IoT-edge-cloud nodes, ensuring secure data flow and orchestration. Extensive testbed experiments were conducted to evaluate the performance and security effectiveness of AQ-ResCon under realistic containerised healthcare scenarios. Results demonstrate that AQ-ResCon achieves up to 36% improvement in scheduling efficiency, 41% reduction in key compromise rates, and 28% lower latency compared to existing quantum-vulnerable orchestration protocols. Additionally, the AQ-ResCon protocol maintained consistent performance under simulated quantum attack conditions, validating its adaptability and resilience. These outcomes confirm that AQ-ResCon is a viable, secure, and future-proof solution for safeguarding microservices-based healthcare applications against evolving quantum threats in edge cloud environments.
Quantum Distributed key attacks, Adaptive Quantum resistant Lattice based key agreement protocols, Healthcare, Docker Container, Edge Cloud Networks.
A rigorous and expressive algebraic framework for modeling systems with uncertainty, ambiguity, and parameter-dependent variability is provided by soft set theory. In this paper, we present a new binary operation on soft sets whose parameter domains have group-theoretic structure: the soft union–plus product. The operation is completely compatible with generalized concepts of soft equality and soft subsethood when specified formally inside an axiomatic framework. Key structural aspects such as closure, associativity, commutativity, idempotency, and distributivity are investigated in detail algebraically, includinng its behavior with respect to identity, absorbing, null, and absolute soft sets. The outcomes demonstrate that the operation creates a strong and cohesive algebraic system on the universe of soft sets while adhering to all algebraic limitations imposed by group-indexed domains. In addition to its theoretical importance, the operation provides a strong basis for a generalized soft group theory and reinforces the underlying algebraic architecture of soft set theory. Furthermore, it has significant potential for both abstract theoretical advancement and real-world applications due to its formal consistency with soft subset and equality relations, which improves its usefulness in domains like categorization, decision-making, and uncertainty-aware modeling.
Soft sets, Soft subsets, Soft equalities, Soft union-plus product
Hybrid Steganography for Enhanced Information Security
The constant integration of technology in society has made it easy for personal information privacy to be violated. In response to this challenge, this paper develops a steganographic technique using cryptography, colour models, and Genetic Algorithm (GA). The proposed method starts with the Advanced Encryption Standard (AES) encryption of the secret text. After that, the encrypted data is embedded in an input image through a two-fold hiding technique. Initially, the input-image is altered into the Hue Saturation Intensity (HIS) colour model, and a particular model is chosen for the next process. Then the image is segmented into blocks, and the secret text is then hidden employing the Least Significant Bit (LSB) technique on some randomly chosen bytes. To improve the hiding efficiency of the hiding process, a genetic algorithm (GA) is used to find the peak signal-to-noise ratio of the blocks. A particular block that achieves the maximum PSNR value is chosen as the block that is suitable for embedding. In the second stage, all blocks are hidden according to the result of a GA, while the byte distribution is as optimal as possible. The performance of the proposed method is determined using functions like PSNR and Mean Squared Error (MSE). The results indicate that the method is fast and effective at keeping the secret information reliable while also maintaining the output- image quality.
steganography, cryptography, colour model, genetic algorithm, data security.
EEG Feature Selection Techniques for Epileptic Seizure Detection: Performance and Evaluation Study
Epilepsy poses a significant challenge for people around the world, particularly in third-world countries. Hence, over 80 million people are suffering from this disease. Therefore, the detection of epileptic seizures plays a vital role in diagnosis by measuring the activity of the brain, like the Electroencephalography (EEG) instrument. Nowadays, Data variety, attributes and size are growing increasingly, causing high dimensions and high preprocessing, which requires high computational resources. Therefore, reduce the data dimension to eliminate posing high computational resources in data preprocessing in the classification techniques. To address these issues, different feature selection techniques are used for high-dimensional data reduction by selecting the most relevant features in the classification process, which aim to detect disease faster and accurately. These techniques include Mean Decrease in Impurity (MDI), Correlation Coefficient, Sequential Forward Selection (SFS), and Sequential Backwards Selection (SBS). The results have registered diverse classification accuracy ratios for the mentioned method when using random forest classification, where MDI has 98.313% to 98.1% accuracy ranges for the selected features, whilst it achieved 98.504% in the case of using all the features. Moreover, the highest percentage, 98.4% accuracy, was achieved with the correlation method when extracting two features. On the other hand, SFS has an accuracy range from 98.6% to 98.212% after extracting nine features. A satisfactory classification accuracy was maintained by the SBS method, with accuracy from 98.6% to 98.2% after deleting ten features. Classification performance results show their impact in reducing dataset dimension, complexity, and computational resources cost, respectively.
Feature Selection; Filter Methods; Wrapper Methods; Epileptic Seizure; RF Classification
Towards Accurate Energy Measurement Simulations in Cloud Computing Environments
Cloud computing has become an essential part of modern IT infrastructure, providing scalable and ondemand resources to users. As the demand for cloud services continues to grow, researchers and practitioners are facing the challange of optimizing energy consumption of data centers. Simulation tools are crucial in this endeavor, enabling a thorough understanding of these dynamic environments. In this paper, we underline the importance of realistic simulation in cloud environments, focusing on the energy consumption measurements accuracy from various resources using real world data. To address this, we integrate multiple energy metrics to enhance the replication of real-life scenarios. Our study highlights the need for advanced simulation tools for supporting energy dynamics of cloud environments.
Cloud Computing, Simulation accuracy, Energy Consumption, Workload simulation, PlanetLab Workload.
Transforming Enterprise Systems with Cloud, AI, and Digital Marketing
Transforming Enterprise Systems is impact concept when researching the ways in which enterprise systems are being impacted by the revolutionary effects of web technology, cloud computing, digital marketing, and machine learning, the objective of this study is to investigate the ways in which these trends are affecting enterprise systems. The purpose of this paper is to give businesses with practical advice by doing an analysis of the benefits, challenges, and integration strategies connected with the technologies being discussed. The findings of this research draw attention to the fact that these technologies have the potential to enhance the effectiveness, scalability, and competitiveness of businesses. The purpose of this paper is to provide a comprehensive understanding of the role that these technologies play in the enterprise systems that are currently in use. In addition to providing insights into the actual implementations of these technologies, the paper's objective is to convey this knowledge.
Transforming Enterprise Systems, Web Technology, Cloud Computing, Digital Marketing, Machine Learning, SMEs, Technologies Innovation, Operational Efficiency
Distance and similarity measures on interval-valued Q-neutrosophic soft environments and their applications
Decision-making problems are characterized by many issues of uncertainty and hiddenness, and this requires identifying and developing the mathematical tools used to deal with them more accurately. For this purpose, in this work we will present an innovative mathematical concept called interval valued-Q-neutrosophic soft sets (IV-Q-NSSs) as a hyper fuzzy extension of some previous models. To clarify this concept further, we will provide some illustrative examples. After that, we discovered the distance and similarity measures on IV-Q-NSSs. We proposed several types of these measures and illustrated them with several numerical examples. Based on these measures, we created an innovative algorithm based on these procedures to solve one of the problems of daily life.
Fuzzy set; neutrosophic set; soft set; Q- neutrosophic set, Q- neutrosophic soft set
Fundamentals of Fermatean Neutrosophic Soft Set with Application in Decision Making Problem
The classical set theory based on crisp sets is not able to deal with uncertainties which is a common feature of various real-world problems. This problem is solved using modified forms of sets such as fuzzy sets, Intuitionistic fuzzy sets, neutrosophic sets, soft sets and hypersoft sets and others along with their hybrids. In this paper, a modified hybrid of soft set named Fermatean Neutrosophic Soft set (FrNSS) is established. Basic entities of set theory including subsets, null set, universal set along with different operators are defined. With respect to these operators, the algebraic structures as monoid, semigroup and semiring are defined. Also, fermatean neutrosophic soft topological space and the cartesian product of fermatean neutrosophic soft sets and fermatean neutrosophic soft relation are defined to establish an application of this hybrid structure to decision-making problems.
Comparative Analysis of Euler and Order Four Runge-Kutta Methods in Adams-Bashforth-Moulton Predictor-Corrector Method
This study conducts a comparative analysis of the Euler and Runge-Kutta 4 methods within the Adams Predictor-Corrector framework for solving second-order ordinary differential equations (ODEs) and coupled differential equations. The Euler method, a basic first-order explicit scheme, and the Runge-Kutta 4 method, a higher-order explicit scheme, are widely utilized numerical methods for ODEs. However, their effectiveness varies based on the problem’s characteristics. We specifically investigate these methods integrated into the Adams Predictor-Corrector method, renowned for its stability and efficiency in solving initial value problems. Through both numerical experiments and theoretical analysis, we establish that the Runge-Kutta method demonstrates superior performance over the Euler method within this framework. Additionally, we observe that the 4th order of the Adams Predictor-Corrector method yields more accurate results than the 5th order for second-order ODEs, while the 5th order performs better for coupled ODEs. Overall, the Runge-Kutta method exhibits enhanced accuracy and stability compared to the Euler method in the context of the Adams Predictor-Corrector method.
Euler method, Runge-Kutta 4 method, Adams Predictor-Corrector method, Second-order ordinary differential equations (ODEs) and coupled differential equations.
A Dataset for recognition of Norwegian Sign Language
Machine learning is a powerful tool in developing sign language recognition models, significantly enhancing communication accessibility for the deaf and hard-of-hearing community. However, such models' creation relies on the availability of comprehensive datasets, which are currently scarce for specific sign languages, particularly for Norwegian Sign Language (NSL). This paper introduces a unique dataset created to address this gap. The dataset, comprising 24,300 images of 27 NSL letters, was captured under varying conditions to represent each sign comprehensively. A man and a woman performed the signs. The goal of creating this dataset was to provide a robust foundation for further research in NSL recognition.
Dataset; Sign Language; Norwegian Sign Language
Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
Stochastic Modelling on New Mixture Distribution with Properties and Their Application
In this article, the authors propose a new two-parameter continuous distribution. It is called a new mixture distribution because it comes from the unique combination of exponential and gamma distributions (MEGD). Some statistical properties of the distribution are derived, such as the moments, moments generating function, reliability analysis. Also, the distribution of order statistics is presented, and entropies are derived. The application of the maximum likelihood estimation technique to the performance of traditional parameter estimates. Two authentic data sets describing cancer patients' survival are used to empirically demonstrate the potential importance and usability of the proposed distribution. Comparing the new mixture distribution to some other competing distribution, the analysis's results demonstrated that it performed quite well.
Mixture Model, Moments, Order Statistics, Maximum Likelihood Estimation, Model Selection Technic AIC, BIC.
Enhancing Intrusion Detection in Wireless Sensor Networks through Machine Learning Techniques and Context Awareness Integration
Wireless sensor networks (WSNs) play an imperative role in the communication among independently implemented wired, localized, or mobile sensors in a cyber-physical system. Environmental monitoring, object identification, data acquisition, analysis, and transmission to the owner of the wireless sensor network are the primary research focuses in this work. Due to the flexibility of WSNs and the scarcity of resources in IoTs, both are frequently integrated. But the above integration makes these networks more open to outside factors and, thus, vulnerable to attacks like flooding, assaults, grayhole attacks, black hole attacks, and other scheduling problems that are typical in such circumstances. There are certain inherent properties of Wireless Sensor Networks (WSNs) that have made the detection of intrusion unsuitable, namely, false alarms, it has very high computing overhead and a poor detection ratio. This problem has arisen due to the network’s excess of data tightly connected in a dense manner and resource constraints of sensor nodes. For that purpose, this research recommends the utilization of machine learning techniques for intrusion detection in WSNs. The detection precision is enhanced by enhancing the use of Support Vector Machine (SVM ) combined with stochastic gradient descent (SGD). Moreover, the research proposes the integration of context knowledge, which is known as context awareness that takes into account user preferences and system characteristics or situations to improve the performance of the recommendation systems. In order to decentralize the computational load of the system, the first traffic data is reduced by principal component analysis (PCA) and singular value decomposition (SVD). The network risks observed are further categorised using an VG-IDS model. Worthy of note is the fact that the recommended WSN-DS algorithm could yield better results than other complex algorithms that were examined by using the WSN-DS dataset throughout the evaluation process; the accuracy rate of the proposed WSN-DS algorithm was 96%. It is seen in improvement of accuracy and recall and F1-measure rates to the improved figure of 98%, 96%, and 97% respectively.
Intrusion Detection; Internet of Things; Wireless Sensor Network; Accuracy; Machine Learning.
A Comprehensive Review of Artificial Intelligence Approaches in Kidney Cancer Medical Images Diagnosis, Datasets, Challenges and Issues and Future Directions
Various diseases are prevalent in global populations and can be attributed to human lifestyles, economic conditions, social factors, genetics, and other country-specific factors. Our study provides a detailed analysis of the current methods and uses of artificial intelligence (AI) in diagnosing kidney cancer. This literature review examines the most recent advancements and uses of AI in diagnosing cancer of the kidneys, emphasizing its significant influence on medical diagnosis and patient treatment. Evaluate their efficacy, explore prospective areas for future study and development, and identify obstacles and constraints, offering a fundamental review of critical advancements in AI, machine learning, and deep learning. AI-powered diagnostic tools expedite the analysis of intricate images and enhance the early identification of diseases, ultimately leading to improved patient results. Furthermore, the utilization of AI-driven image processing enables the customization of treatment regimens, thereby enhancing the efficiency of healthcare provision. This review primarily highlights the importance of conducting additional research and improving AI to transform the detection and treatment of kidney cancer.
Kidney cancer diagnoses, Deep learning, Machine learning, The Cancer Genome Atlas (TCGA) dataset, Classification, Segmentation.
Enhancing Wireless Security and Privacy: A 2-Way Identity Authentication Method for 5G Networks
The communication network would have potential uses dependent on the infrastructure once the fifth generation (5G) network is fully commercialized. Subscriber Identification Module (SIM) authentication is a well-known mechanism for safeguarding the confidentiality of wireless device users. Fifth-generation computer technologies are very advanced in certification based on geolocation, regular confirmation, and authentication mechanisms. The clonable authentication feature solves the issue of authentication using duplicate modules. In highly dynamic environments, the problem poses a risk to privacy protection, adaptiveness, self-organization, information leakage, and the reliability of services. The article introduces a 2-way identity authentication method (2WIAM) using a Physical Unclonable Function (PUF) to address the issue above. The suggested technique for detecting module clones depends on user-provided authentication and geolocation data. The 2-way mutual authentication used in the suggested technique is a clever way to combine the many wireless technologies accessible to a given network node and enable the creation of wireless on-demand service providers and networks. The initial stage involves immediate passcode authentication, and the second is verifying the identity of the mobile equipment and any shifts or copies in its physical position. A new-cross authentication is then carried out to determine the identity of the clone's physical counterpart. In prior authentication situations, the choice to provide authorization or ban the clone was made fairly and objectively. The experiment results show that the users' privacy is protected, enhanced adaptiveness and the amount of data lost is limited.
5G, Decision-Making, Location-Tracking, PUF, SIM Authentication.
A Comprehensive Review on Advancements in Artificial Intelligence Approaches and Future Perspectives for Early Diagnosis of Parkinson's Disease
Parkinson's disease (PD) is a neurological condition that generally strikes people in their average age of onset for PD a neurological disorder, is 55 and up. A wide variety of motor and non-motor symptoms can be observed in patients with PD. The medical community has made great strides in recent years, but Parkinson's disease still has no treatment or cure. Therefore, exploring possible ways for early PD identification is an intriguing scientific endeavor. Full symptoms may not appear for years due to the progressive nature of PD. Thus, early diagnosis is vital to enhance the patient's quality of life. Symptoms will usually worsen with time, so keep that in mind. Several neurodegenerative disorders share very similar symptoms, making early identification crucial for disease prediction. Many people are starting to pay attention to using Artificial Intelligence (AI) methods in medical diagnostics because they can process massive volumes of data and make reliable statistical predictions. This paper covers all the bases when it comes to artificial intelligence (AI) approaches to PD diagnosis, including the many deep and machine learning-based methods that have been deployed and how they have opened new avenues for research. Furthermore, the study explores the current situation and future possibilities of data-driven AI approaches to Parkinson's disease diagnosis. This study is an excellent resource as a review article for researchers interested in creating PD prediction models employing different AI-based modalities.
Parkinson's disease, Machine Learning, Artificial Intelligence, Deep Learning, Early Detection