The Role of Machine Learning and Metaheuristic Optimization in Enhancing Health Risk Prediction: A Review

Marwa; Shomona

doi:https://doi.org/10.54216/MOR.020101

American Journal of Business and Operations Research | 2025

Leveraging Time Lag-Based Diffusion Models to Predict Innovation Adoption for Optimized Product Development

The suggested models for the spread of technical breakthroughs make use of a phase structure to illustrate the steps involved in becoming familiar with the problem and making a choice. For it to portray genuine adopting conduct, a time-lag factor is included into the dispersion process. Depicts a two-step dissemination process by taking into account the reliance of adopting on the informed group of potential purchasers. Assuming that a prospective customer first becomes intrigued by an upcoming the item's availability and then accepts the novel idea at an ulterior point, a method of analysis for sales functions that incorporates time delay is proposed. The efficient propagation method for invention is shown using the various lag factors. Applying nonlinear regression modelling to worldwide shipping data of Acer PCs and Samsung smartphones experimentally validates the suggested models for mathematics. Several comparison models are used to evaluate the predicting abilities of the suggested models. By integrating a distributed time delay function into the implementation manage, a theoretical intergenerational diffusion model is created. To measure how long it takes for innovation to be eventually accepted, the distributed time lag function that follows the Erlang distributions is used. This framework incorporates switch and substituting, two forms of pragmatist shift behaviour. Using real shipping data of LCD (Liquid Crystal Display) computer monitors from consecutive generations, the predicted effectiveness of the suggested methods is examined and contrasted with well-established research. Here is the total accuracy of the approaches that have been proposed: When contrasted with more conventional models, MGDM 1 achieves a 99.33% accuracy rate, MGDM 2 a 99.81% rate, and MGDM 3 a 99.91% accuracy rate.

groups

Muddassar Sarfraz mail

link https://doi.org/10.54216/AJBOR.120202

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2024

Neutrosophic Model for Sentiment Data Analysis

Sentiment analysis has recently become popular in social, political and health related fields, but it has a common limitation of capturing the subjectivity involved in multiple human expressions. In this study, we tackle this concern by presenting a model that is constructed using neutrosophic logic which can incorporate indeterminacy in the evaluation of perceptions. Although some answers may be provided by the traditional methods, they fail to contain the uncertainties and contradictions which are characteristic of natural language, making them difficult to implement in complicated situations. In this methodological gap, the neutrosophic model is presented as a tool capable of overcoming these limitations by explicitly treating uncertainty and balancing definite, indeterminate, and contradictory elements. The integration of machine learning algorithms with neutrosophic techniques helps classify and visualize sentiments embedded in big volume of text data. The findings suggest that this methodology not only enhances the precision in the identification of emotional subtleties but also provides a hybrid platform for integrating imprecise information. His credits are based on the development of a theoretical model which advances the field of sentiment analysis and the development of real-life applications in customer services for example, political analytics and strategic decision making. This methodological advance demonstrates that incorporating neutrosophic logic into sentiment data analysis opens up new possibilities for understanding and modeling the complexities of human perceptions.

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Ned Vıto Quevedo Arnaız mail -

Genaro Vınıcıo Jordan Naranjo mail -

Diego Xavier Chamorro Valencia mail -

Joffre Joffre Paladines Rodríguez mail -

Anna Mixaylovna Aripova mail

link https://doi.org/10.54216/FPA.160214

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Enhancing DNP3 Security Using CNN Deep Learning Techniques

Industrial Automation and Control Systems (IACS) are necessary for enabling secure information exchange between smart devices; ensuring security in Industrial Control Systems (ICS) is of importance due to the presence of these devices at distant locations and their control over vital plant activities. Intelligent devices and hosts use protocols such as Modbus, DNP3, IEC 60870, IEC 61850, and others. This paper focuses on the analysis and development of techniques for detecting of network traffic within the industrial environment, more specifically anomalies in the application ZZZAlayer in the to the protocol called Distribution Network Protocol (DNP3) is an open-source protocol used in Supervisory Control and Data Acquisition (SCADA) systems and widely recognized as the standard for the water, sewage, and oil and gas industries. it is used in the realm of industrial automation; they are critical facilities for the population and must be secured against any security breaches. One of the main objectives of cyber attackers is related with these systems. In This paper presents an architecture that, classification system by Deep Learning algorithm with (CNN). The proposed model was evaluated using standard Intrusion Detection Dataset for DNP3, with 7326) and 86field. The CNN algorithm obtained the best results accuracy

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Amenah A. Jasim mail -

Khattab M. Ali Alheeti mail

link https://doi.org/10.54216/JCIM.150217

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Enhancing Malware Detection through Electromagnetic Side-Channel Analysis Using Random Forest Classifier

The continual increase of cyber dangers necessitates creative techniques to better the identification and mitigation of malware. This research provides a cutting-edge examination of employing the Random Forest Classifier in combination with electromagnetic side-channel analysis for finding malicious software. Electromagnetic side-channel analysis harnesses the accidental information leakage from electronic systems, giving it a formidable tool for studying the underlying workings of gadgets. This study reveals how these electromagnetic side-channel signals may be used to identify subtle and evasive malware activities. The paper goes into the theoretical basis of electromagnetic side-channel analysis and the actual application of the Random Forest Classifier in this setting. By analyzing electromagnetic emissions, a wide range of devices and systems can be scrutinized for the telltale signs of malware-induced behaviors. Experimental results illustrate the effectiveness of this approach, showcasing the model demonstrated high accuracy, with an accuracy rate of up to 97%, demonstrating its ability to effectively leverage electromagnetic side-channel information for malicious program detection for enhanced cybersecurity measures.

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Zaid M. Obaid mail -

Khattab M. Ali Alheeti mail

link https://doi.org/10.54216/JCIM.150218

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

A Digital Forensic Investigation of the Presence of Personally Identifiable Information (PII) in Refurbished Hard Drives

The last decade has seen a massive explosion of data, with a lot of Personally Identifiable Information (PII) flooding devices and the cyberspace. This has necessitated the growing call and global awareness for data protection, to ensure the responsible use of data, protect the privacy of data subjects, and prevent crimes such as identity theft and cybercrime. This paper investigated the presence of residual data and Personally Identifiable Information (PII) in refurbished hard drives bought from a retail shop. The study leveraged digital forensic tools to perform data recovery on refurbished hard drives, and analyses for presence of PII. The study adopted a modified form of the steps in Digital Investigation outlined by NIST IR 8354. Result of this study showed that one out of the 3 hard drives that were reportedly formatted and sanitized by the vendors had residual data with PII. Data recovered includes 28691 files with size on disk as 152.20GB, including PII and sensitive data. Digital Forensic tools used for this study includes EaseUS Data Recovery Wizard and Autopsy. The findings of this study are quite relevant to current studies in privacy and data protection, including recent legislations such as Nigeria Data Protection Act (NDPA), General Data Protection Regulation (GDPR), and others. The paper also presents a comprehensive and forensically sound software-based methodology focused on the recovery of deleted data from hard drives.

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Robinson Tombari Sibe mail -

Blossom U. Idigbo mail

link https://doi.org/10.54216/JCIM.150219

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Data-DrivenWeather Prediction: Integrating Deep Learning and Ensemble Models for Robust Weather Forecasting

Accurate weather forecasting is critical for sectors like agriculture, transportation, disaster management, and public safety. This paper presents a comprehensive methodology integrating traditional machine learning models, deep learning techniques, and ensemble learning approaches to enhance the precision and reliability of weather predictions. Using a combination of four datasets—two for classification and two for regression—the study evaluates various machine learning models such as Decision Trees, Support Vector Machines, and KNearest Neighbors, alongside ensemble methods like Bagging and AdaBoost. Additionally, deep learning models, particularly the Multilayer Perceptron (MLP), are employed to handle complex weather patterns. The Random Forest Regressor and Bagging Regressor consistently outperformed other models in terms of accuracy, precision, and F1-score. By comparing the performance of these models across different weather datasets, this research demonstrates the effectiveness of cross-validation and the importance of optimizing hyperparameters. The findings contribute valuable insights into enhancing the robustness and efficiency of weather forecasting systems, with potential applications in environmental monitoring, event planning, and climate change analysis.The findings indicate that Random Forest Regression consistently outperformed the other machine learning models evaluated. For ensemble learning, the Bagging Regressor was the top performer. In deep learning, the Multilayer Perceptron without cross-validation delivered outstanding performance. For the classification datasets, Random Forest achieved the highest accuracy, precision, and F-score. Our study also highlights the importance of cross-validation to prevent overfitting and ensure model robustness, as well as the impact of class imbalance on overall performance metrics.

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Hassan Al Sukhni mail -

Fatma Sakr mail -

Fadi yassin Salem Al jawazneh mail -

Mutasem K. Alsmadi mail -

Ibrahim A. Gomaa mail -

Shaimaa Abdallah mail

link https://doi.org/10.54216/JCIM.150220

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Analyzing the Effectiveness of Machine Learning Techniques in Detecting Attacks in a Big Data Environment

Protecting big data has become an extremely vital necessity in the context of cybersecurity, given the significant impact that this data has on institutions and clients. The importance of this type of data is highlighted as a basis for decision-making processes and policy guidance. Therefore, attacks on this data can lead to serious losses through illicit access, resulting in a loss of integrity, reliability, confidentiality, and availability of this data. The second problem in this context arises from the necessity of reducing the attack detection period and its vital importance in classifying malicious and non-harmful patterns. Structured Query Language Injection Attack (SQLIA) is among the common attacks targeting data, which is the focus of interest in the proposed model. The aim of this research revolves around developing an approach aimed at detecting and distinguishing patterns of loads sent by the user. The proposed method is based on training a model using random forest technology, which is considered one of the machine learning (ML) techniques while taking advantage of the Spark ML library that interacts effectively with big data frameworks. This is accompanied by a comprehensive analysis of the effectiveness of ML techniques in monitoring and detecting SQLIA. The study was conducted using the SQL dataset available on the Kaggle platform and showed promising results as the proposed method achieved an accuracy of 98.12%. While the proposed approach takes 0.046 seconds to determine the SQL type. It is concluded from these results that using the Spark ML library based on ML techniques contributes to achieving higher accuracy and requires less time to identify the class of request sent due to its ability to be distributed in memory.

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Omar Dhafer Madeeh mail -

Osamah M. Abduljabbar mail -

Huda Mohammed Lateef mail

link https://doi.org/10.54216/JCIM.150221

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Type-2 Neutrosophic Ontology for Automated Essays Scoring in Cybersecurity Education

Given the growing demand for cybersecurity education, the practice of protecting network and software systems from digital and electronic attacks, investing in educational cybersecurity helps significantly reduce the risk of data breaches and protect against security breaches, and given the urgent need and growing number of students worldwide, it is also a way to connect with and between customers by building trust-based relationships, especially regarding essays. Automated Essay Scoring (AES) is a scalable solution for grading large amounts of essays with multiple uses, making it ideal for cybersecurity certification programs, online courses, and standardized tests. In the field of educational cybersecurity, automated essay scoring poses unique challenges due to specialized terminology, persistent and evolving threats. These automated scoring systems use domain-defined ontologies to grade essays but struggle to manage uncertainties, such as ambiguous language and partially valid arguments, which can influence the accuracy of their scoring. Traditional ontologies often struggle to interpret such uncertainties, leading to inconsistent results. Type 2 neutrosophic clustering (T2NS) as a novel approach introduced in this paper is combined with an automated article scoring system based on the cybersecurity learning ontology to address these challenges. The main steps include extracting concepts relevant to this research area from the articles, formalizing the cybersecurity scoring criteria as ontological rules and extending the ontology using T2NS, as well as defining membership functions to measure uncertainty and inconsistency levels. This evaluation using benchmark datasets of cybersecurity articles shows that this approach significantly enhances the scoring reliability and robustness of the approach compared to the basic AES methods.  

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Huda Lafta Majeed mail -

Esraa Saleh Alomari mail -

Ali Nafea Yousif mail -

Oday Ali Hassen mail -

Saad M. Darwish mail -

Yu Yu Gromov mail

link https://doi.org/10.54216/JCIM.150222

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Text Categorization for Information Retrieval Using NLP Models

The paper presents the state-of-the-art natural language processing (NLP) models and methods, such as BERT and DistilBERT, to evaluate textual data and extract noteworthy insights. Preprocessing textual input, tokenization, and the implementation of deep learning architectures such as bidirectional LSTMs for classification tasks are all components of the approach that has been presented. To achieve the goal of producing accurate prediction models with the least amount of validation loss possible. Natural language processing (NLP) is a major focus of the manuscript in multiple areas such as sentiment analysis, language understanding, and text classification. The results show that our proposed NLP models perform exceptionally well. Long-term memory and natural language processing (NLP) go hand in hand. Therefore, these results demonstrate the value and relevance of our natural language processing approach to obtaining unstructured text data to improve and develop a variety of applications, such as chatbots, virtual assistants, and information retrieval systems, as well as to gain insights and help make better decisions, and the flexibility and generalizability of the models, while confirming their ability to handle a range of activities and textual materials. Excellent and accurate results were obtained in terms of validation, with the experimental models often exceeding the 99.85% accuracy benchmark. Another crucial factor to consider is that the average validation loss metrics for all tests remained remarkably low at 0.0058.

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Sundws M. Mohammed mail -

Vijay Madaan mail -

Rajaa Daami Resen mail -

Neha Sharma mail -

Oday Ali Hassen mail -

Jamal kh-madhloom mail

link https://doi.org/10.54216/JCIM.150223

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new

Metaheuristic Optimization Review | 2024

The Role of Machine Learning and Metaheuristic Optimization in Enhancing Health Risk Prediction: A Review

The present review aims to describe the impact of machine learning techniques in health risk prediction, including the progress, drawbacks, and potential development. ML approaches in health care have become more effective in risk prediction than simple regression techniques because of their accuracy, scalability, and personalization. A Statistical tool like Decision trees, Support vector machines, and neural networks allows or examine non-linear genetic and environmental interactions with lifestyle factors. The review's main points are the increase in relevance of more complex types of models like ANN-PSO, a combination of two algorithms for feature selection, higher prediction accuracy, and other fields, including healthcare. These innovations have shown a unique success rate in identifying diseases, including obesity, diabetes, and any cardiovascular diseases, for better prevention measures and avenues of cure. Nevertheless, there are several difficulties: inferior quality of the data, the question of privacy, and explaining the decision-making of the modern complex models. Solving these issues calls for effective data governance, explainable artificial intelligence, and a multi-disciplinary approach to create and deliver transparency and fairness. As mentioned in the review, feature importance analysis like SHAP also carries plenty of significance for enhancing model interpretability to chase positive alterations. Concerning the outlook, implementing ML in the current HC system will require investment in data platforms, clinician expertise, and broader, suitable systems. As a result, new opportunities for using ML in connection with population health, patient and client outcomes, and receiving individualized care point out the further evolution of the transforming impact of technology. This paper offers an understanding of how health risk prediction and the public health strategy might benefit from new applications of ML and how the moral and practical issues of this new application of the technology may be dealt with.

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Marwa Radwan mail -

Shomona Jacob mail

link https://doi.org/10.54216/MOR.020101

Volume & Issue

Vol. Volume 2 / Iss. Issue 1

Details open_in_new