International Journal of Mathematics, Statistics, and Computer Science
type: channel
description: International Journal of Mathematics, Statistics, and Computer Science
publisher: Nexora Academic Press
language: en
publication name: International Journal of Mathematics, Statistics, and Computer Science
issn: 2704-1069
copyright: 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.
items: Explainable AI for Prognostic Factor Identification in Colorectal Cancer: An Electronic Health Records Analysis
items: An Intuitionistic Fuzzy Parameterized Decision-making Approach For Evaluating Site Suitability For Educational Institutions Using Hypersoft Settings
items: Preparing for Future Crises in Education: AI and Regression-Based Modeling of Emergency Distance Learning Satisfaction among Moroccan Law Students
items: Comparative Analysis of AI Models for Effort Estimation in Western and Regional Environments
items: Privacy-Preserving Integration of GPT-4 and Natural Language Processing via Secure Multi-Party Computation for Healthcare Data
items: AQ-ResCon: Adaptive Quantum-Resistant Lattice-Based Key Agreement Protocol for Secure Distributed Container Orchestration in Edge Cloud Environments
items: Soft union-plus product of groups
items: Hybrid Steganography for Enhanced Information Security
items: EEG Feature Selection Techniques for Epileptic Seizure Detection: Performance and Evaluation Study
items: Towards Accurate Energy Measurement Simulations in Cloud Computing Environments
items: Transforming Enterprise Systems with Cloud, AI, and Digital Marketing
items: Distance and similarity measures on interval-valued Q-neutrosophic soft environments and their applications
items: Fundamentals of Fermatean Neutrosophic Soft Set with Application in Decision Making Problem
items: Comparative Analysis of Euler and Order Four Runge-Kutta Methods in Adams-Bashforth-Moulton Predictor-Corrector Method
items: A Dataset for recognition of Norwegian Sign Language
items: Stochastic Modelling on New Mixture Distribution with Properties and Their Application
items: Enhancing Intrusion Detection in Wireless Sensor Networks through Machine Learning Techniques and Context Awareness Integration
items: A Comprehensive Review of Artificial Intelligence Approaches in Kidney Cancer Medical Images Diagnosis, Datasets, Challenges and Issues and Future Directions
items: Enhancing Wireless Security and Privacy: A 2-Way Identity Authentication Method for 5G Networks
items: A Comprehensive Review on Advancements in Artificial Intelligence Approaches and Future Perspectives for Early Diagnosis of Parkinson's Disease
items: Stochastic Response of an Airfoil and Its Effects on Limit Cycle Oscillations’s Behavior Under Stall Flutter Regime
items: A Comprehensive Review of Artificial Intelligence Approaches in Omics Data Processing: Evaluating Progress and Challenges
items: Overview of Big Data Analytics in Modern Astronomy
items: Cluster Analysis on Longitudinal Data of Patients with Kidney Dialysis using a Smoothing Cubic B-Spline Model
items: Deep Learning for Plant Disease Detection
items: A New 4D Hidden Hyperchaotic System with Higher Largest Lyapunov Exponent and its Synchronization
items: Comparing Several Encrypted Cloud Storage Platforms
items: A Comparative Analysis of Machine Learning Algorithms for the Purpose of Predicting Norwegian Air Passenger Traffic
items: Face Mask Detection Using Haar Cascades Classifier To Reduce The Risk Of Coved-19
items: Hand-Sketchs based Parkinson's disease Screening using Lightweight Deep-Learning with Two-Fold Training and Fused Optimal Features

Explainable AI for Prognostic Factor Identification in Colorectal Cancer: An Electronic Health Records Analysis
type: item
description: 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.
subject: Articles
  topic: section
subject: Explainable AI, colorectal cancer, prognostic factors, electronic health records, machine learning, survival analysis.
  topic: keywords
creator: Amena Mahmoud
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
date: 2025-11-05
publication date: 2025-11-05
starting page: 457
ending page: 475
doi: 10.59543/ijmscs.v3i.15123

An Intuitionistic Fuzzy Parameterized Decision-making Approach For Evaluating Site Suitability For Educational Institutions Using Hypersoft Settings
type: item
description: 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.
subject: Articles
  topic: section
subject: Intuitionistic Fuzzy Parameterized Hypersoft Set
  topic: keywords
subject: Hypersoft Set Theory.
  topic: keywords
subject: Uncertainty Modeling
  topic: keywords
subject: Decision Support System
  topic: keywords
creator: Atiqe Ur Rahman
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
date: 2025-10-01
publication date: 2025-10-01
starting page: 442
ending page: 456
doi: 10.59543/ijmscs.v3i.15101

Preparing for Future Crises in Education: AI and Regression-Based Modeling of Emergency Distance Learning Satisfaction among Moroccan Law Students
type: item
description: 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.
subject: Articles
  topic: section
subject: Emergency Distance Learning, Moroccan Law Students, Student Satisfaction, Machine Learning, Regression, Feature Selection, Legal Education
  topic: keywords
creator: Houda Kaa
creator: Hanane Allioui
creator: Ilham Oumaira
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
date: 2025-07-23
publication date: 2025-07-23
starting page: 424
ending page: 441
doi: 10.59543/ijmscs.v3i.15129

Comparative Analysis of AI Models for Effort Estimation in Western and Regional Environments
type: item
description: 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 t…
subject: Articles
  topic: section
subject: Software Effort Estimation, Machine Learning, Software Predictions, Western Environments, AI Models
  topic: keywords
creator: Fahim Sardar
creator: Mohammad Ayub Latif
creator: Muhammad Khalid Khan
creator: Omar Al-Boridi
creator: Hassen Hamouda
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
date: 2025-07-24
publication date: 2025-07-24
starting page: 408
ending page: 423
doi: 10.59543/ijmscs.v3i.15097

Privacy-Preserving Integration of GPT-4 and Natural Language Processing via Secure Multi-Party Computation for Healthcare Data
type: item
description: 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.
subject: Articles
  topic: section
subject: Artificial Intelligence, Healthcare, GPT-4, Natural Language Processing, Secure Multi-Party Computation.
  topic: keywords
creator: Zainab Khalid Mohammed
creator: Mazin Abed Mohammed
creator: Mohd Khanapi Abd Ghani
creator: Salah A. Aliesawi
creator: Narjes Benameur
creator: Korhan Cengiz
creator: Abdullah Lakhan
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
date: 2025-07-12
publication date: 2025-07-12
starting page: 390
ending page: 407
doi: 10.59543/ijmscs.v3i.15181

AQ-ResCon: Adaptive Quantum-Resistant Lattice-Based Key Agreement Protocol for Secure Distributed Container Orchestration in Edge Cloud Environments
type: item
description: 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.
subject: Articles
  topic: section
subject: Quantum Distributed key attacks, Adaptive Quantum resistant Lattice based key agreement protocols, Healthcare, Docker Container, Edge Cloud Networks.
  topic: keywords
creator: Sajida Memon
creator: Abdullah Lakhan
creator: Quratual Ain Masoi
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
date: 2025-07-19
publication date: 2025-07-19
starting page: 377
ending page: 389
doi: 10.59543/ijmscs.v3i.15091

Soft union-plus product of groups
type: item
description: 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.
subject: Articles
  topic: section
subject: Soft sets, Soft subsets, Soft equalities, Soft union-plus product
  topic: keywords
creator: Zeynep Ay
creator: Aslıhan Sezgin
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
date: 2025-07-19
publication date: 2025-07-19
starting page: 365
ending page: 376
doi: 10.59543/ijmscs.v3i.14961

Hybrid Steganography for Enhanced Information Security
type: item
description: 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.
subject: Articles
  topic: section
subject: steganography, cryptography, colour model, genetic algorithm, data security.
  topic: keywords
creator: Nadia Mohammed Abdulmaged
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
date: 2025-06-20
publication date: 2025-06-20
starting page: 359
ending page: 364
doi: 10.59543/ijmscs.v3i.11135

EEG Feature Selection Techniques for Epileptic Seizure Detection: Performance and Evaluation Study
type: item
description: 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.
subject: Articles
  topic: section
subject: Feature Selection; Filter Methods; Wrapper Methods; Epileptic Seizure; RF Classification
  topic: keywords
creator: Ali Al Farawn
creator: Salam Al-Khammasi
creator: Ahmed Hazim Alhilali
creator: Nabeel Salih Ali
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
date: 2025-06-20
publication date: 2025-06-20
starting page: 345
ending page: 358
doi: 10.59543/ijmscs.v3i.14771

Towards Accurate Energy Measurement Simulations in Cloud Computing Environments
type: item
description: 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.
subject: Articles
  topic: section
subject: Cloud Computing, Simulation accuracy, Energy Consumption, Workload simulation, PlanetLab Workload.
  topic: keywords
creator: Hasanein D. Rjeib
creator: Gabor Kecskemeti
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
date: 2025-06-04
publication date: 2025-06-04
starting page: 338
ending page: 344
doi: 10.59543/ijmscs.v3i.14191

Transforming Enterprise Systems with Cloud, AI, and Digital Marketing
type: item
description: 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.
subject: Articles
  topic: section
subject: Transforming Enterprise Systems, Web Technology, Cloud Computing, Digital Marketing, Machine Learning, SMEs, Technologies Innovation, Operational Efficiency
  topic: keywords
creator: Redeer Avdal Saleh
creator: Subhi R. M. Zeebaree
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
date: 2025-03-05
publication date: 2025-03-05
starting page: 323
ending page: 337
doi: 10.59543/ijmscs.v3i.13883

Distance and similarity measures on interval-valued Q-neutrosophic soft environments and their applications
type: item
description: 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.
subject: Articles
  topic: section
subject: Fuzzy set; neutrosophic set; soft set; Q- neutrosophic set, Q- neutrosophic soft set
  topic: keywords
creator: Faisal Al-Sharqi
creator: Ashraf Al-Quran
creator: Mamika Ujianita Romdhini
creator: Zahari Md. Rodzi
creator: Hamiden Abd El- Wahed Khalifa
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
date: 2025-01-03
publication date: 2025-01-03
starting page: 313
ending page: 322
doi: 10.59543/ijmscs.v3i.12613

Fundamentals of Fermatean Neutrosophic Soft Set with Application in Decision Making Problem
type: item
description: 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.
subject: Articles
  topic: section
subject: Fermatean Neutrosophic Soft Set
  topic: keywords
subject: Uncertainty Modeling
  topic: keywords
subject: Algebraic Structures
  topic: keywords
subject: Decision-Making
  topic: keywords
creator: Muhammad Saeed
creator: Imrana Shafique
creator: Hatıra Gunerhan
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
date: 2025-01-02
publication date: 2025-01-02
starting page: 294
ending page: 312
doi: 10.59543/ijmscs.v3i.10625

Comparative Analysis of Euler and Order Four Runge-Kutta Methods in Adams-Bashforth-Moulton Predictor-Corrector Method
type: item
description: 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.
subject: Articles
  topic: section
subject: Euler method, Runge-Kutta 4 method, Adams Predictor-Corrector method, Second-order ordinary differential equations (ODEs) and coupled differential equations.
  topic: keywords
creator: A.O Akeju
creator: O.I Olatunji
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
date: 2025-01-02
publication date: 2025-01-02
starting page: 276
ending page: 293
doi: 10.59543/ijmscs.v3i.10471

A Dataset for recognition of Norwegian Sign Language
type: item
description: 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.
subject: Dataset
  topic: section
subject: Dataset; Sign Language; Norwegian Sign Language
  topic: keywords
creator: Benjamin Svendsen
creator: Seifedine Kadry
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2023-08-05
publication date: 2023-08-05
starting page: 1
ending page: 3
doi: 10.59543/ijmscs.v2i.8049

Stochastic Modelling on New Mixture Distribution with Properties and Their Application
type: item
description: 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.
subject: Articles
  topic: section
subject: Mixture Model, Moments, Order Statistics, Maximum Likelihood Estimation, Model Selection Technic AIC, BIC.
  topic: keywords
creator: M. Sakthivel
creator: P. Pandiyan
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2024-10-13
publication date: 2024-10-13
starting page: 259
ending page: 275
doi: 10.59543/ijmscs.v2i.9015

Enhancing Intrusion Detection in Wireless Sensor Networks through Machine Learning Techniques and Context Awareness Integration
type: item
description: 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 evalua…
subject: Articles
  topic: section
subject: Intrusion Detection; Internet of Things; Wireless Sensor Network; Accuracy; Machine Learning.
  topic: keywords
creator: Osamah Ahmed Mahmood
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2024-08-02
publication date: 2024-08-02
starting page: 244
ending page: 258
doi: 10.59543/ijmscs.v2i.10377

A Comprehensive Review of Artificial Intelligence Approaches in Kidney Cancer Medical Images Diagnosis, Datasets, Challenges and Issues and Future Directions
type: item
description: 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.
subject: Articles
  topic: section
subject: Kidney cancer diagnoses, Deep learning, Machine learning, The Cancer Genome Atlas (TCGA) dataset, Classification, Segmentation.
  topic: keywords
creator: Dhuha Abdalredha Kadhim
creator: Mazin Abed Mohammed
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2024-05-08
publication date: 2024-05-08
starting page: 199
ending page: 243
doi: 10.59543/ijmscs.v2i.9747

Enhancing Wireless Security and Privacy: A 2-Way Identity Authentication Method for 5G Networks
type: item
description: 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.
subject: Articles
  topic: section
subject: 5G, Decision-Making, Location-Tracking, PUF, SIM Authentication.
  topic: keywords
creator: Ali Darch Abed Dawar
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2024-03-09
publication date: 2024-03-09
starting page: 183
ending page: 198
doi: 10.59543/ijmscs.v2i.9073

A Comprehensive Review on Advancements in Artificial Intelligence Approaches and Future Perspectives for Early Diagnosis of Parkinson's Disease
type: item
description: 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.
subject: Articles
  topic: section
subject: Parkinson's disease, Machine Learning, Artificial Intelligence, Deep Learning, Early Detection
  topic: keywords
creator: Aiesha Mahmoud Ibrahim
creator: Mazin Abed Mohammed
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2024-01-26
publication date: 2024-01-26
starting page: 173
ending page: 182
doi: 10.59543/ijmscs.v2i.8915

Stochastic Response of an Airfoil and Its Effects on Limit Cycle Oscillations’s Behavior Under Stall Flutter Regime
type: item
description: In this work, I investigate the effect of noise on a classical two-degree-of-freedom pitch-plunge aeroelastic system. The inlet velocity of the flow is modeled as a stochastically varying parameter by the Ornstein-Uhlenbeck (OU) stochastic process. The system is a 2D airfoil, and the elastic problem is simulated using linear springs. I study the manifestation of Limit Cycle Oscillations (LCO) corresponding to the varying fluid velocity under the dynamic stall regime. The aim to delve into the unexplored facets of the classical pitch-plunge aeroelastic system, seeking a comprehensive understanding of how parametric noise influences the occurrence of LCO and expands the boundaries of its known behavior.
subject: Articles
  topic: section
subject: Aerodynamics, Aeroelasticity, Computational Fluid Mechanics, Stochastical Processes, Stochasticity, Stall Flutter
  topic: keywords
creator: Dimitrios Ketseas
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2024-01-05
publication date: 2024-01-05
starting page: 168
ending page: 172
doi: 10.59543/ijmscs.v2i.8663

A Comprehensive Review of Artificial Intelligence Approaches in Omics Data Processing: Evaluating Progress and Challenges
type: item
description: The primary objective of this study is to review and assess the best available research on omics-related artificial intelligence (AI) methods. Furthermore, it seeks to demonstrate the promise of AI approaches in omics data analysis and identify the critical problems that must be solved to achieve this potential fully. There are many moving parts when trying to make sense of a plethora of research through a literature review. Essential components include, for instance, clinical applications and literature collections. Other researchers have faced challenges, and the existing literature highlights them. Using a systematic strategy, we searched all relevant articles on omics and AI utilizing multiple keyword variations. We also seek additional research, such as guidelines, studies of comparison, and review studies. Challenges with AI, preprocessing, datasets, validation of models, and testbed applications arose when AI was used to analyze omics data. To solve these problems, several pertinent investigations were carried out. Our work offers unique insights into the intersection of omics and AI model fields, setting it apart from prior review articles. We anticipate that practitioners seeking an all-encompassing perspective on using AI in omics data processing would find this study's findings invaluable.
subject: Articles
  topic: section
subject: Artificial intelligence, cancer diagnosis, Machine Learning, Deep Learning, Quantum Computing, Multi-Omics, Omics dataset.
  topic: keywords
creator: Ali Mahmoud Ali
creator: Mazin Abed Mohammed
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2023-12-25
publication date: 2023-12-25
starting page: 114
ending page: 167
doi: 10.59543/ijmscs.v2i.8703

Overview of Big Data Analytics in Modern Astronomy
type: item
description: Astronomers are increasingly compelled to chart the universe with ever greater precision. Projects like the Sloan Digital Sky Survey (SDSS), Pan-STARRS, and the Large Synoptic Survey Telescope (LSST) generate approximately 100-200 Petabytes of data annually, presenting significant big data challenges in terms of storage, processing, and data transfer. The Square Kilometer Array (SKA), an ambitious project involving 130,000 antennas and 200 dishes spanning two continents, is scheduled to become operational in 2028. It will collect 160 terabytes of data per second, translating to 1 petabyte of data daily. Coping with this immense volume of data necessitates real-time processing and analysis, driving the need for efficient machine learning and image analysis algorithms. Astronomy stands as an ideal domain for big data analytics, pushing the boundaries of data analysis. This review paper will present intriguing applications for data scientists, exploring the challenges and recent technological advancements in big data analytics concerning astronomy. The paper will also critically assess the strengths and weaknesses of various approaches, methodologies, or tools used in big data analytics within the context of astronomy, supported by relevant case studies.
subject: Articles
  topic: section
subject: Big Data Analytics, Astronomy, Machine learning
  topic: keywords
creator: Muhammad Faaique
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2023-12-09
publication date: 2023-12-09
starting page: 96
ending page: 113
doi: 10.59543/ijmscs.v2i.8561

Cluster Analysis on Longitudinal Data of Patients with Kidney Dialysis using a Smoothing Cubic B-Spline Model
type: item
description: Longitudinal data analysis is gaining prominence, particularly in fields like medicine and economics. This research is centered around the collection and analysis of longitudinal data, with a specific focus on cluster analysis. The thesis places emphasis on the non-parametric cubic B-spline model, known for its smoothness, flexibility, and ability to capture intricate patterns and fluctuations in data due to continuity in its derivatives . To accomplish clustering, the penalization method was employed. It categorizes longitudinally balanced data by penalizing the pairwise distances between cubic B-spline model coefficients using a penalization function, such as the recently devised concave penalization function. The cubic spline penalty CSP, part of the pair distance penalty, employs the nonparametric pairwise grouping (NPG) method. Model selection criteria, like Bayesian Information Criteria (BIC), help determine the number of clusters. Optimization methods, including the alternative direction method of the multiplier ADMM algorithm, are applied to approximate solutions within the R statistical program . In a simulation study, balanced longitudinal data for 60 and 100 subjects were generated with ten replicates each. The experiments demonstrated the effectiveness of the CSP penalty function in the clustering process . For practical application, the study involved the analysis of data from kidney failure patients, collected from Ibn Sina Teaching Hospital for Dialysis in Mosul over seven consecutive months in 2023. The NPG aggregation method and CSP penalty functions were used, resulting in two groups based on the glomerular filtration rate of the kidneys. This rate determines the required dialysis frequency, either twice a week or thrice, according to medical criteria.
subject: Articles
  topic: section
subject: cubic B-spline, , cubic spline penalty CSP, ADMM algorithm, kidney failure, nonparametric pairwise grouping (NPG)
  topic: keywords
creator: Noor Nawzat Ahmed
creator: Suhail Najm Abdullah
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2023-11-26
publication date: 2023-11-26
starting page: 85
ending page: 95
doi: 10.59543/ijmscs.v2i.8337

Deep Learning for Plant Disease Detection
type: item
description: Agriculture, an essential bedrock of human survival, continually grapples with the menace of plant diseases, culminating in substantial yield reductions. While conventional detection techniques remain widespread, they often entail laborious efforts and are susceptible to inaccuracies, underscoring the pressing need for more efficient, scalable, and immediate solutions. Our research explores the transformative capabilities of Deep Learning (DL) models, primarily focusing on Convolutional Neural Networks (CNNs) and MobileNet architectures in the early and precise identification of plant ailments. We augmented our exploration by incorporating eXplainable Artificial Intelligence (XAI) through GradCAM, which elucidated the decision-making process of these models, providing a visual interpretation of disease indicators in plant images. Through rigorous testing, our CNN model yielded an accuracy of 89%, a precision and recall of 96%, and an F1-score of 96%. Conversely, the MobileNet design showcased an accuracy of 96% but recorded slightly lesser precision, recall, and F1-scores of 90%, 89%, and 89%, respectively. Such results amplify the transformative role of DL in redefining plant disease detection methodologies, presenting a formidable counterpart to conventional techniques and ushering in an era of heightened agricultural security.
subject: Articles
  topic: section
subject: Agriculture, Plant diseases, Automated disease detection, Deep Learning (DL), Convolutional Neural Networks (CNN), MobileNet.
  topic: keywords
creator: Munaf Mudheher Khalid
creator: Oguz Karan
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2023-11-23
publication date: 2023-11-23
starting page: 75
ending page: 84
doi: 10.59543/ijmscs.v2i.8343

A New 4D Hidden Hyperchaotic System with Higher Largest Lyapunov Exponent and its Synchronization
type: item
description: Recently, the introduction of high-dimensional systems with a larger Lyapunov exponent is very difficult and more complex. The paper introduces a new four-dimensional hyperchaotic system with a larger Lyapunov exponent compared with 20 works available in literature. Th proposed system will be derived from the Lorenz-like system via a state feedback control approach and consist of eight terms only. This system lacks equilibrium points but can generate hidden attractors. Two positive Lyapunov exponents (LE) indicating hyperchaotic behavior have been identified. The mathematical properties of this dissipative hyperchaotic system are both theoretically and numerically presented, encompassing Lyapunov exponents, Lyapunov dimension (Kaplan-Yorke dimension), Multistability, and Hybrid projective synchronization (HPS). Various dynamic behavior are observed such as hyperchaotic, chaotic, chaotic 2-tours, and periodic behaviors. The paper provides proof of the main results through theoretical analysis and numerical simulations conducted in MATLAB V2021.
subject: Articles
  topic: section
subject: Hidden attractors, Multistability, dissipative system, hybrid projective synchronization (HPS).
  topic: keywords
creator: Saad Fawzi Al-Azzawi
creator: Anmar M. Hasan
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2023-11-15
publication date: 2023-11-15
starting page: 63
ending page: 74
doi: 10.59543/ijmscs.v2i.8469

Comparing Several Encrypted Cloud Storage Platforms
type: item
description: Cloud services and cryptographic cloud storage systems have gained popularity in recent years due to their availability and accessibility. The present systems are nonetheless still ineffectual. They are the best since they demand a lot of trust from the user or the provider. To ensure they are not violating any End-User License Agreement (EULA) clauses, providers typically keep the ability to examine the files that have been saved, and some even keep the ability to share the data. It is simple to create a copy of every piece of data when a provider has access to go through it, which is considered an abuse. A typical user would have a very difficult time proving these claims because they have no method of finding any evidence supporting such claims. Due to the growing quantity of Machine Learning (ML) performed on personal user data for either tailoring advertisements or, in more severe cases, manipulating public opinion, this issue has only gotten worse in modern times. Due to the volume of users and files kept, cloud storage services are the ideal location for getting such information, whether personal or not. To retain complete anonymity, the user could take the simple step of adding a local layer of encryption. This will prevent the cloud provider from being able to decrypt the data. The requirement for ongoing key management, which becomes more challenging as the number of keys rises, is another drawback of this. To better understand normal behaviours and pinpoint potential weaknesses, this study aims to explore and assess the security of a few well-known existing cryptographic cloud storage options. Among the vendors investigated are Microsoft Azure, Tresorit, Amazon S3, and Google Cloud. Based on documentation particular to each service, this comparison was made. However, most providers frequently provide only a limited amount of information or don't go into detail about specific ideas or procedures (for instance, security in Google Cloud), leaving room for in…
subject: Articles
  topic: section
subject: Stroage systems, cloud computing, Google, Amazon S3, Cloud services, Cryptographic, Google Cloud, Microsoft Azure, Tresorit.
  topic: keywords
creator: Chinnadurai Manthiramoorthy
creator: K. Mohamed Sayeed Khan
creator: Noorul Ameen A
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2023-08-06
publication date: 2023-08-06
starting page: 44
ending page: 62
doi: 10.59543/ijmscs.v2i.7971

A Comparative Analysis of Machine Learning Algorithms for the Purpose of Predicting Norwegian Air Passenger Traffic
type: item
description: This analysis aims to provide an overview of potential machine learning algorithms that may aid the aviation industry in predicting future air passenger traffic flow, which can help increase stakeholder value as well as improve customer experiences. A review and discussion of the aviation industry’s past, current, and future challenges is provided, as well as an overview of machine learning algorithms, neural networks, and learning methods. Further, an overview and discussion of the architecture of the Long Short-Term Memory (LSTM) network, Support Vector Regression Machine (SVRM), and Random Forest (RF) algorithms is provided. The comparative analysis provides an overview and comparison of the performance of the LSTM, SVRM, and RF models based on Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). The dataset used includes the hourly number of passengers from scheduled flights at Oslo Airport Gardermoen for the period of January 1, 2009, to December 31, 2019, including the datetime features such as Time (hour), day, month, and year, as well as the weather features of air temperature and mean wind speed, with a total of 96185 samples. The Long Short-Term Memory model exhibited the highest generalization ability, with a performance evaluation on the testing dataset of 0.00445/0.06667 MSE/RMSE. Additionally, the performance of the SVRM and RF models on the testing dataset is 0.00511/0.07147 and 0.00543/0.07368 MSE/RMSE respectively. In addition to the performance, each of the models’ complexity, stability, and ability to predict the hourly and daily fluctuations of passengers are discussed.
subject: Articles
  topic: section
subject: Machine Learning, LSTM, SVRM, RF, Aviation
  topic: keywords
creator: Aleksander Stanulov
creator: Sahar Yassine
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2023-07-08
publication date: 2023-07-08
starting page: 28
ending page: 42
doi: 10.59543/ijmscs.v2i.7851

Face Mask Detection Using Haar Cascades Classifier To Reduce The Risk Of Coved-19
type: item
description: The global epidemic causes extensive damage. Several sectors, including tourism and recreation, have been halted. Since these projects require working together in close contact, it increases the danger of infection. To reduce the spread of COVID-19, keep a safe distance from others and always wear protective clothing. In light of this, we have developed COVID Vision, a system that uses Haar cascades as a classifier for a face mask detector. This will reduce reliance on employees while maintaining COVID-19 criteria. COVID Vision can determine if someone is wearing a mask or covering their lips live. According to the findings, the suggested system correctly displays the output result of human face detection within a range of 0.6 to 1.35 meters away, in lighting conditions ranging from medium to normal, and with facial angles falling within a range of ±40°. If they don't cover the face, face accessories are allowed on targeted photos. In conclusion, this approach may reduce the cost of real-time identity verification labor.
subject: Articles
  topic: section
subject: Face mask detection; Classification; Haar Cascades Classifier; Covid-19
  topic: keywords
creator: Imad Majed Zeebaree
creator: Omer Sedqi Kareem
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2023-07-21
publication date: 2023-07-21
starting page: 19
ending page: 27
doi: 10.59543/ijmscs.v2i.7845

Hand-Sketchs based Parkinson's disease Screening using Lightweight Deep-Learning with Two-Fold Training and Fused Optimal Features
type: item
description: Older people experience different Age-Associated Diseases (AAD), and appropriate diagnosis and treatment will help them to lead a peaceful life. Parkinson's disease (PD) is one of the ADD generally found in people aged >60 years. The clinical-level screening of PD is performed using different procedures, including the hand sketches, such as wave/spiral patterns. The proposed research implements Pretrained Lightweight Deep-Learning (PLDL) methods with two-fold training to detect the patterns accurately from hand sketches belonging to the healthy/PD class. The developed system consists of the following stages; (i) Image preprocessing, (ii) Data augmentation, (iii) Two-fold training to improve the detection accuracy, and (iv) Deep features selection with 50% drop-out and binary classification. This work considered the fused features of MobileNets to achieve better detection accuracy. The outcome of this research confirms that this procedure offers a satisfactory result in predicting PD from the considered test images. The K-Nearest Neighbor (KNN) classifier offered a detection accuracy of 100% with the chosen database.
subject: Articles
  topic: section
subject: Parkinson's disease, Hand sketches, Deep learning, MobileNet, Features selection, Classification.
  topic: keywords
creator: Venkatesan Rajinikanth
creator: Sahar Yassine
creator: Syed Ahmad Chan Bukhari
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2023-07-16
publication date: 2023-07-16
starting page: 9
ending page: 18
doi: 10.59543/ijmscs.v2i.7821