IoT-Enabled monitoring system for Plant Health Growth

Aditi; Deepak S.; Kapil; Vipin; Sumit; Prabhat Kr.; Rahul

doi:https://doi.org/10.54216/JISIoT.140219

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.

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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

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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.

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El-Sayed M. El-kenawy mail -

Marwa M. Eid mail -

Laith Abualigah mail

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

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Vol. Volume 1 / Iss. Issue 2

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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

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Vol. Volume 1 / Iss. Issue 2

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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

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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.

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Abdelhameed Ibrahim mail

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

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Vol. Volume 1 / Iss. Issue 2

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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

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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.

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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

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Fusion: Practice and Applications | 2025

Fast Numeric Sign Detection Using Adaptive Thresholding and Geometry of Optimized Fingers

A strong sign language recognition system can break down the barriers that separate hearing and speaking members of society from speechless members. A novel fast recognition system with low computational cost for digital American Sign Language (ASL) is introduced in this research. Different image processing techniques are used to optimize and extract the shape of the hand fingers in each sign. The feature extraction stage includes a determination of the optimal threshold based on statistical bases and then recognizing the gap area in the zero sign and calculating the heights of each finger in the other digits. The classification stage depends on the gap area in the zero signs and the number of opened fingers in the other signs as well as the sequence in which the opened fingers appear for those that have the same number of opened fingers. The conducted test results showed the system’s high capability to classify all the digits; where both the precision and F-score percentages of the proposed model reached the desired optimal value (100%).

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Mela G. Abdul-Haleem mail -

Loay E. George mail

link https://doi.org/10.54216/FPA.190201

Volume & Issue

Vol. Volume 19 / Iss. Issue 2

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Journal of Cybersecurity and Information Management | 2024

Proposal for the use of the neutrosophic method of Shannon entropy

The authors suggest a new approach to create a composite index by fusing of information as well as applying Shannon Entropy but ready under one challenge that exists when it comes to understanding the complex nature of the exercising data and that is integrating information from different sources that are distinct and provide different facets of an estimation into one single best. In that sense, the modern world of information system is one which has to work with data which is in most cases erroneous and sometimes even contradictory, then the perspective of fusing at such information in a fruitful manner becomes very important. However, such information is not always available as it is only after the uncertainty of the relevant data has been difficult to make optimal use of decision making ability of a model. This paper goes some way to assisting with that issue and describes a framework that involves multiple information sources as well as how information entropy models the data uncertainty. The method put forward in the study employs the integration of information fusion with the application of Shannon entropy computation to come up with a composite index that best represents the particular system of interest in terms of its intricacy and the amount of uncertainty associated with it. Furthermore, the application of this composite index has practical implications in various areas, such as risk management, business decision-making and public policy evaluation, where precision in information integration is crucial to achieve effective results.

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Leonso Dagoberto Torres Torres mail -

Milena Elizabeth Alvarez Tapia mail -

Paul Orlando Piray Rodríguez mail -

Aymuxammedova Amina Kakajanovna mail

link https://doi.org/10.54216/JCIM.140118

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2025

IoT-Enabled monitoring system for Plant Health Growth

In current scenario, plant health monitoring plays a crucial role in effective health maintenance of plants in climate changes. Internet of Things (IoT) played an efficient role in realizing the remote and real-time monitoring of any physical things and activities through internet connectivity. In this study we have proposed a system that is able to monitor the plant health with the assimilation of wireless sensors and wireless network. The proposed system is able to log the sensor values on the plants on the cloud server through internet connectivity.

groups

Aditi Sharma mail -

Deepak S. Dharrao mail -

Kapil Joshi mail -

Vipin Tiwari mail -

Sumit Kumar mail -

Prabhat Kr. Srivastava mail -

Rahul Sharma mail

link https://doi.org/10.54216/JISIoT.140219

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new