Data integration using DEMATEL to optimize protocols

Quintana Churches Janneth Ximena; Machado Maliza Messiah Elias; Stefany Lizbeth Ocana Lliguin; Yusupov Sherzod Abdusalamovich

doi:https://doi.org/10.54216/JCIM.140117

Metaheuristic Optimization Review | 2024

Stem Cells and Regenerative Medicine: A Review of Artificial Intelligence Techniques for Stem Cell Differentiation

Following this background, this review discusses the advanced technologies such as AI, micro-fluids, and automated platforms that this differentiation protocol could help achieve in regenerative medicine. Stem cell research, essential in tissue engineering, disease modeling, and drug development, is challenging through heterogeneity, scalability, and reproducibility, as observed in differentiation procedures. Machine learning and deep learning patterns have become more effective in predicting cellular behavior, tracking cellular stations, and optimizing differentiation methods for current stem cell technology. These methods also reduce observer bias, increase the throughput of high-throughput screening, and advance modeling to precise therapeutic applications. At the same time, microfluidic and automated systems provide nearly perfect control over differentiation stimuli, recreating the in vivo conditions with the ability to control spatial and temporal gradients. This integration between AI and microengineering has promoted 3D culture systems, lab-on-a-chip technologies, and biosensors, enabling reproducible and efficient differentiation results. There is still much to accomplish, such as the problem of obtaining uniform stem cell populations or decoding the tissue context. This field incorporates several interdisciplinary advancements such as stimuli-responsive systems and computational modeling; it envisages new horizons in regenerative medicine, transforming stem cell-based therapeutic technologies to their optimum level for personalized medicine and other advanced tissue engineering applications.

groups

Nima Khodadadi mail -

Benyamin Abdollahzadeh mail

link https://doi.org/10.54216/MOR.010102

Volume & Issue

Vol. Volume 1 / Iss. Issue 1

Details open_in_new

Metaheuristic Optimization Review | 2024

Improving Tuberculosis Diagnosis and Forecasting Through Machine Learning Techniques: A Systematic Review

Tuberculosis (TB) is ranked as one of the leading causes of death from infectious diseases in the present world, causing important health and economic consequences in the different developing countries. The practices of traditional diagnostic approaches, although still expected, are associated with relativity, slowness, and organs, besides being confined to visual observations and touch. The new and increased capacity in advanced machine learning is a promising area that has shown potential in improving the diagnosis of TB, as well as identifying drug resistance and disease management. This review presents various aspects of using ML in diagnosing and managing TB disease based on its various categories of models, including deep learning, hybrid approach, and the metabolomics approach. Some of these methods have been very effective, with high diagnostic performance improvements in sensitivity, specificity and accuracy; Furthermore, ML has been used to analyze the molecular picture of TB and to find drug targets of the disease toward future targeted therapies. As seen with the integration of ML, substantial benefits are provided by the solutions proposed. However, questions concerning the quality of data, interpretations of ML models and ethical problems hinder further application. This review concludes with the idea that ML can transform the diagnosis and management of TB and calls for more investment in developing this field to overcome these barriers to global health.

groups

Abdelaziz Rabehi mail -

Pushan Kumar Dutta mail

link https://doi.org/10.54216/MOR.010103

Volume & Issue

Vol. Volume 1 / Iss. Issue 1

Details open_in_new

Metaheuristic Optimization Review | 2024

Innovations in Machine Learning Models for Hepatitis Diagnosis and Disease Progression Prediction

Chronic liver disease (CLD) is a group of conditions for which up to half of the global population remains at risk and causes serious complications: liver cirrhosis and liver cancer. Therefore, early diagnosis and proper treatment of these diseases enhance the prognosis of patients suffering from CLD. This review paper explores how machine learning (ML) techniques are used in practice to diagnose, prognosis, and treat chronic liver diseases. Continuing with more specific examples of collected data from the results of several studies, their more comprehensive implementation is expected to improve the respective management processes and the detection of liver disease in patients more accurately. The review further discusses the various ML methods, including supervised and unsupervised learning, neural network, and ensemble learning, also applied to the estimation of risk felt by the patients, suggesting a course of treatment or how far the disease has progressed. While the inclusion of ML technology in the field of Hepatology is progressing well, some issues like model diversity, applicability of models, and concerns about ethics still pose challenges. This paper points out the importance of working in teams from various fields to develop appropriate mechanisms for dealing with these issues and adequately use ML for clinics. In conclusion, the results indicate that there is a possibility that ML will change the management of chronic liver diseases, which in turn will lead to the development of innovative treatment methods and better patient management.

groups

Marwa M. Eid mail -

Wei Hong Lim mail

link https://doi.org/10.54216/MOR.010104

Volume & Issue

Vol. Volume 1 / Iss. Issue 1

Details open_in_new

Metaheuristic Optimization Review | 2024

Machine Learning Approaches for Malaria Risk Prediction and Detection: Trends and Insights

The current review summarizes the latest trends in malaria literature, emphasizing transmission ecology, new diagnostics and treatment. It stresses the additional focus on the transmission, according to the spatiotemporal models and predictive analytics, which help identify periods and the locations with the most significant risk, noting that these processes should consider the environmental factors. The change in the diagnostic approach, especially the introduction of artificial intelligence techniques such as deep learning, has improved the rate and precision at which malaria parasites are diagnosed in resource-limited countries where time is of the essence. Furthermore, there have been significant advances in drug discovery due to machine learning applications that have made it quicker to find new antimalarial drugs in the face of drug resistance. Despite these developments, there are still problems such as drug resistance, socio-economic disparities, and the environment that are being altered and still require an integrated and transdisciplinary approach. Combining these determinants is indispensable for eliminating these challenges and further promoting global efforts to control malaria.

groups

Ahmed Mohamed Zaki mail -

Khaled Sh. Gaber mail -

Faris H. Rizk mail -

Mahmoud Elshabrawy Mohamed mail

link https://doi.org/10.54216/MOR.010105

Volume & Issue

Vol. Volume 1 / Iss. Issue 1

Details open_in_new

Metaheuristic Optimization Review | 2024

Machine Learning in Public Health Forecasting and Monitoring the Zika Virus

The Zika virus is a severe public health threat all across the world, owing to its spreading mechanism through Aedes mosquitoes and its ability to result in extreme neurological diseases, which include the congenital Zika syndrome and the Guillain-Barré syndrome, amongst others. Conventional monitoring techniques often fail because many asymptomatic cases render early diagnosis challenging. Machine learning (ML) techniques can be seen as a constructive development in addressing this challenge, which entails predicting and tracking the spread of diseases such as Zika through extensive and complex datasets. Data analytic ML systems also enhance early warning systems and situational uplift by using data from social media, climate history, and genetics. This helps reasonably to predict the mosquito population biologically and the environmental factors that favor the spread of the virus for a more practical approach from the public health sector. Over and above, some issues are still pending, especially regarding the quality of data, understanding the models and how to apply such models within the current health systems. These factors must be solved to implement ML successfully in surveillance practice. This review provides an overview of the issue, stating the potential of machine learning applications in the development of public health, whose actions focus on Zika and other diseases transmitted by vectors.

groups

El-Sayed M. El-kenawy mail -

Marwa M. Eid mail -

Laith Abualigah mail

link https://doi.org/10.54216/MOR.010201

Volume & Issue

Vol. Volume 1 / Iss. Issue 2

Details open_in_new

Metaheuristic Optimization Review | 2024

Leveraging Machine Learning Algorithms for Early Detection and Prediction of Dengue Fever

This paper explores the potential of machine learning (ML) in revolutionizing the screening and prognosis of dengue fever, a pervasive viral illness transmitted through the bite of infected mosquitoes prevalent in tropical and subtropical areas. Historically, traditional approaches to monitoring outbreaks have been hampered by a lack of precision and timing, creating an opportunity for machine learning to rectify datasets and uncover patterns that enhance accuracy. The paper introduces Random Forests, Support Vector Machines, Neural Networks, and combined classification models, along with their advantages, disadvantages, and the potential for incorporating external data such as climatic factors, population data, real-time Twitter data, etc. The results demonstrate significant increases in accuracy from the models, but it is clear that their applicability is contingent on localized approaches suitable for the regions. This underscores the importance of the quality and completeness of data used in the models. Current research indicates that data availability and the refinement of these models require a collective approach. The work underscores the potential of ML to redefine the paradigm of outbreak management in dengue and other vector-borne diseases, offering hope for improved public health worldwide.

groups

Marwa M. Eid mail -

Christos Gatzoulis mail -

Osama Al Abedallat mail

link https://doi.org/10.54216/MOR.010202

Volume & Issue

Vol. Volume 1 / Iss. Issue 2

Details open_in_new

Metaheuristic Optimization Review | 2024

A Review of Machine Learning Techniques for Early Detection of Alzheimer's disease

This review aims to discuss the use of AI and ML in diagnosing and managing neurodegenerative diseases, with particular emphasis on AD and MCI. Emerging innovations present in depth the effectiveness of using ML models such as SVM, random forests, CNNs, and new frameworks such as quantum-classical neural networks on data obtained from MRI imaging, EEG signals, genetic makers and sociodemographic data. Widely used research findings demonstrate that these tools offer seemingly higher detection rates, sensitivity, and specificity than traditional diagnostic techniques for identifying and diagnosing early-stage illnesses. Some of them are techniques based on analyzing EEG time-frequency bands, combining MRI and PET data integration approaches, and creating telemedicine services to overcome geographical barriers. Furthermore, interpretable AI models improve clinical relevance by providing decision and trust support among practitioners. While these achievements are notable, the following limitations need to be noted, thus making it easier to establish the generalizability of the results and ways of using datasets that are free from bias and difficulties associated with applying AI in clinical settings. There are pressing questions regarding patients' rights and privacy, the issue of homogenization and standardization of data, and the distribution and accessibility of AI tools across industries as well as within the same region. More studies should be conducted to expand AI applications, use a more diverse dataset, and promote cooperation between representatives of various fields of science to ensure that technological advancement meets clinical demands. It also includes new methods like Vision Transformers and Quantum Computing Enhanced Deep Learning to overcome diagnostic issues in time-consuming and multi-parametric data analysis. These gaps can be closed with the help of AI and ML to enhance diagnostic accuracy, select the right treatment strategy, and risk assessment for the long-term management of NDs. In conclusion, this review similarly reaffirms how stunning AI's role is in improving future neurodegenerative disease care. For this reason, the deployment process must be done sensibly to enhance the patient's value most appropriately.

groups

El-Sayed M. El-kenawy mail

link https://doi.org/10.54216/MOR.010203

Volume & Issue

Vol. Volume 1 / Iss. Issue 2

Details open_in_new

Metaheuristic Optimization Review | 2024

Optimizing AI Models for COVID-19 Detection and Forecasting: A Comprehensive Study

This systematic review explores the use of artificial intelligence (AI) and machine learning (ML) during the COVID-19 disease outbreak. AI/ML models may interpret medical images, auditory input, and patient records to diagnose early enough, thus enhancing the likelihood of positive patient outcomes. Coupled with optimization algorithms, deep learning methods have predicted COVID-19 from chest X-rays and CT scans with unprecedented high accuracy. This review, therefore, synthesizes the existing literature and looks at the significant emphases, gaps, and potential trends of applying AI in diagnosing COVID-19 and forecasting outbreaks. Further, the advancement of AI and ML in this domain needs to be known to enhance global preventive diagnostic techniques for future pandemics.

groups

Abdelhameed Ibrahim mail

link https://doi.org/10.54216/MOR.010204

Volume & Issue

Vol. Volume 1 / Iss. Issue 2

Details open_in_new

Metaheuristic Optimization Review | 2024

Utilizing Machine Learning for Predicting Lyme Disease Trends and Enhancing Diagnostic Accuracy

The present research investigates the role of machine learning models in forecasting the course of Lyme disease and improving diagnostics by looking for environmental, host and anthropogenic factors contributing to the rise and fall of the tick population and disease outbreaks. With the popularization of ecological models and artificial intelligence-based techniques such as neural networks and random forests, it has become possible to efficiently and accurately over various risk maps that relate to ticks' location and distribution, which is an essential aspect of improving public health management issues. These models integrate climate and demographic data as well as host-pathogen interaction data and help understand the distribution of high-risk areas and the dynamics of the diseases, thus facilitating the management of tick-borne illness. This approach also illustrates the significance of predictive diagnostics for early disease detection, allowing for interventions and preventive measures only on relevant population sub-groups. Ultimately, this study considers the possibilities machine learning offers in managing Lyme disease, articulating the implications of these conclusions for the preparedness for health emergencies on a more global scale.

groups

Ahmed El-Sayed Saqr mail -

Ahmed M. Elshewey mail

link https://doi.org/10.54216/MOR.010205

Volume & Issue

Vol. Volume 1 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2024

Data integration using DEMATEL to optimize protocols

The first aim of this paper is to solve the problem of protocol optimization by the means of data integration through DEMATEL (Decision Evaluation and Laboratory Testing Method). The research addresses one key question: how can complexities management protocols be extended in relations with systems where interactions as well as feedback of multiple factors make the process full of uncertainties and hard to analyse? In the present setting, where there is transformation of information systems and critical processes are interwoven, there is a need for proper design of viable protocols to avert redundancy and improve effectiveness of operation. This appraisal is especially important considering the challenge of handling massive data volumes and risk management decision making in complex scenarios. Using DEMATEL pens out a systematic procedure in this research to disentangle the complexity of the interrelations of the variables and accomplish the task of identifying and ordering the key elements affecting protocol performance. The results also show that the methodology enables one to have a good perspective of several factors and the procedures followed in establishing the protocols also enhance the concerned decision making. The main contribution of the study lies in providing a robust and adaptable tool that can be used to optimize protocols in various areas, from logistics to network management, offering a theoretical and practical framework of great value for the advancement of research and practice in complex systems management.

groups

Quintana Churches Janneth Ximena mail -

Machado Maliza Messiah Elias mail -

Stefany Lizbeth Ocana Lliguin mail -

Yusupov Sherzod Abdusalamovich mail

link https://doi.org/10.54216/JCIM.140117

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new