Irrigation Iot Sensor Data Analytics Using Bio-Inspired Data Mining Techniques

Maysaa H.; Saif M.; Deshinta Arrova

doi:https://doi.org/10.54216/FPA.210223

Journal of Artificial Intelligence and Metaheuristics | 2025

Hierarchical Clustering of Global COVID-19 Statistics: Comparative Insights from Pandemic Indicators

Hierarchical clustering is applied in this research to study world COVID-19 data up to January 2025 and partition the primary clusters of countries based on epidemiological criteria. Total cases, deaths, recoveries, active cases, tests, population, and per-million were the data explored and were standardized and thereafter analyzed employing agglomerative hierarchical clustering with Ward linkage. The assessment yielded an average Silhouette of 38.5%, Davies–Bouldin value of 0.87, and Calinski–Harabasz value of 77.6, reflecting cluster validity in separation. The application of dendrograms and PCA projections to plot identified four clusters, reflecting differences in the severity of COVID-19 impacts and responses. Clustering analysis revealed that the high-burden clusters accounted for almost 45% of global death, while low-burden clusters were predominant in over 40% of nations with fewer than 100,000 accumulated instances. The outcomes illustrate hierarchical clustering as an unsupervised learning approach to analyzing epidemiological data and give quantitative estimates to facilitate comparative public health interventions across communities.

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Noor Razzaq Abbas mail -

Ghassan AL-Thabhawee mail -

Isam Bahaa Aldallal mail -

Mostafa Abotaleb mail -

Klodian Dhoska mail

link https://doi.org/10.54216/JAIM.100202

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2025

Optimizing CO2 Emission Forecasting from Vehicles Using Deep Learning and Football Optimization Algorithm

Accurate prediction of CO2 emissions from vehicles is essential for environmental regulation and sustainable transport design. Existing models often suffer from limited accuracy due to suboptimal hyperparameter configurations. This s tudy a ims t o e nhance C O2 e mission f orecasting b y c ombining d eep l earning with advanced metaheuristic optimization. An attention-based Encoder LSTM (EALSTM) model is trained on Canadian vehicle emissions data, with hyperparameters tuned using a novel Football Optimization Algorithm (FbOA), inspired by cooperative team dynamics in football. Comparative evaluation against eight other optimizers shows that FbOA achieves the best performance. The optimized EALSTM model yields an RMSE of 0.00349, MAE of 0.00010, and R2 of 0.984, outperforming all alternatives. These results demonstrate the effectiveness of domain-inspired metaheuristics in improving prediction accuracy. The proposed FbOA-EALSTM framework offers a scalable, accurate solution for emissions modeling and supports data-driven environmental policy and intelligent vehicle technologies.

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Omnia M. Osama mail -

El-Sayed M. El-Rabaie mail -

Marwa M. Eid mail

link https://doi.org/10.54216/JAIM.100203

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2025

DenseNet201-Based Deep Transfer Learning Framework for Brain Tumor Classification in MRI Scans

The classification of brain tumors is crucial in the context of early intervention, as the appropriate and timely diagnosis can significantly influence the treatment plan and patient outcomes. Radiologists have long relied on their own judgment and have read these medical images through their own eyes, which is often subjective, time-consuming, and inter-observer variability is also likely to occur. Applications built on artificial intelligence (AI), or more specifically, deep learning (DL)-based algorithms, have radically changed the medical imaging field over the last couple of years and could potentially be used to automate the diagnosis process, offering prompt, trustworthy, and unbiased assessments. Despite such developments, most existing systems that rely on AI are constrained, especially when it comes to classification accuracy and robustness across different datasets. To overcome these problems, the article in this chapter presents a more effective DL model with a specifically designed architecture that aims to improve the classification of brain tumors. The specified methodology is based on preprocessing and data normalization steps that reduce noise and level out the data intensity, enabling effective feature extraction from the MRI images. This will increase the accuracy of the later classification. The primary component of the proposed methodology is an adapted version of DenseNet-201, designed explicitly for the four class brain tumor classification. To achieve optimal performance, the conventional output layer of DenseNet-201 was replaced with a Global Average Pooling (GAP) layer, designed to address the issues of vanishing gradients and overfitting commonly encountered during the training of deep networks. The architectural adjustment helps to combine the features and increase the overall generalization capacity of the model. The model was thoroughly tested using two datasets: one publicly available dataset on Figshare and a locally available dataset comprising a total of 3,504 T1-weighted contrast-enhanced MRI (T1-w MRI) images. The results of the experiment provided the proposed model with a general accuracy of 100 percent, which was higher than that of the existing comparative methods. Such results support the idea that complex architectural adjustments with the broader preprocessing strategy can be effective, and why deep neural networks can be viewed as trustworthy diagnostic tools in clinical neuro-oncology, potentially achieving extremely high accuracy.

groups

Doaa Sami Khafaga mail

link https://doi.org/10.54216/JAIM.100204

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2025

Quantum-Inspired Machine Learning: Bridging Classical and Quantum Algorithms

Integration of quantum-inspired algorithms in machine learning has opened up new horizons for improving predictive performance, efficiency, and scalability across a broad spectrum of application domains. This paper presents a comparative investigation between traditional machine learning techniques and quantum-inspired models. Experimental experiments demonstrate that quantum-inspired approaches exhibit higher accuracy, training effectiveness, and stability on difficult datasets than traditional methods. Results point towards higher convergence rates, shorter runtime, and enhanced generalization capacity in quantum-inspired models, realized in the form of enhanced accuracy, precision, recall, and F1-scores. Receiver operating characteristic (ROC) and precision–recall analyses further confirm the superior discriminative power of quantum-inspired approaches. Results point toward the potential of quantum-inspired machine learning as an interface between conventional algorithms and the new frontier of quantum computing with a stepping stone to future-proof intelligent systems.

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Ahmed Hamid Elias mail -

Dhurgham Abbas Mohsin Albojwaid mail -

Ahmed younus abdulkadhim mail -

Raad S. Alhumaima mail -

Laith Farhan mail

link https://doi.org/10.54216/JAIM.100205

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

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International Journal of Neutrosophic Science | 2026

On Convex Combinations of Starlike and Convex Functions Associated with the Epicycloid Domain

This paper introduces the class Mε,4L, defined through a convex combination of starlike and convex functions associated with a four-cusped epicycloid domain, where the parameter satisfies 0 ≤ ε ≤ 1. Unlike earlier studies that focused on circular or conic domains, this work extends the geometric framework to epicycloidal domains. Within this framework, sharp estimates for the first coefficients are obtained, together with the Fekete-Szeg¨o inequality and the second Hankel determinant evaluations. These findings extend several classical results for starlike and convex functions and offer new perspectives on analytic function theory related to epicycloidal domains.

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Nur Athirah Hani Senin mail -

Yuzaimi Yunus mail -

Nur Hazwani Aqilah Abdul Wahid mail -

Rashidah Omar mail

link https://doi.org/10.54216/IJNS.270121

Volume & Issue

Vol. Volume 27 / Iss. Issue 1

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International Journal of Neutrosophic Science | 2026

Extending One-Way ANOVA to Neutrosophic Sets: A Method for Uncertainty-Based Decision Making

Classical statistical methods assume that data are precise and free from uncertainty, which may not hold in many real-world applications. Neutrosophic statistics provides a flexible framework for handling indeterminacy, vagueness, and inconsistency in data. In this paper, we propose a new formulation of one-way analysis of variance (ANOVA) within the neutrosophic framework. The method treats membership, indeterminacy, and non-membership components separately, with explicit F -tests for each, and employs a maximum-based decision rule to determine significance. We also compare the proposed method with the classical one-way ANOVA. The results demonstrate that the neutrosophic ANOVA is more sensitive in detecting group differences, particularly in cases where the classical approach yields smaller F -values and may fail to reject the null hypothesis. These findings highlight the potential of neutrosophic ANOVA as a more robust alternative to classical ANOVA for analyzing data with inherent uncertainty and indeterminacy.

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Sasiwimon Iwsakul mail -

Ronnason Chinram mail

link https://doi.org/10.54216/IJNS.270122

Volume & Issue

Vol. Volume 27 / Iss. Issue 1

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American Journal of Business and Operations Research | 2025

Predictability of Stock Price Fluctuations with an Application of Agricultural Companies Data

The research aimed to predict the fluctuations in closing Stock Price of four agricultural companies listed on the Iraq Stock Exchange using daily closing Stock Price data from 11/3/2015 to 15/3/2025. The symmetric and asymmetric ARCH model was applied to the research data. The results of the GARCH models showed that the closing price behavior of the companies (Al-Ahliyah for Agricultural Production, Middle East for Fish, Iraqi for Meat Production and Marketing) achieved a GARCH (1,1) rank, indicating that the effect of past error variance (ARCH) was of rank 1, in addition to the conditional variance element GARCH also being of rank 1. Meanwhile, the results showed that the closing prices for the Iraqi Seed Production Company were of rank GARCH (1,2). The results indicated that the first-order variance parameter was greater than one for all agricultural companies, suggesting that the fluctuations in stock closing prices exhibit a slight upward trend, which aligns with the logic of financial behavior in financial markets.

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Ahmad Hussein Battal mail -

Abdulrazaq Shabeeb mail -

Bha Aldan Abdulsattar Faraj mail -

Wisam Al-Anezi mail -

Faisal Ghazi Faisal mail

link https://doi.org/10.54216/AJBOR.130104

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

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American Journal of Business and Operations Research | 2025

The Relationship between Foreign Direct Investment and Employment Growth in Southeast Asia

This study examines the relationship between Foreign Direct Investment (FDI) and employment growth in Southeast Asia, focusing on Malaysia, Vietnam, and Indonesia. It uses panel data from 2004 to 2023 and applies frameworks based on Neoclassical and Endogenous Growth theories using Excel and STATA software. The results indicate that job creation is strongly influenced by foreign direct investment, especially in the industrial and service sectors, with Vietnam showing the strongest correlation. These findings suggest that FDI can help countries boost economic development. This research provides valuable guidance for policymakers to attract targeted investments and promote sustainable employment opportunities.

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Farkhod Abdurakhmonov mail -

Shakhnoza Medetbaeva mail

link https://doi.org/10.54216/AJBOR.130105

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2026

Application of Wireless Body Area Networks and Wearable Sensors for Monitoring Sports People Health

Health reconnaissance frameworks are currently a more significant issue and examination subject. A few applications, like military, home consideration, medical clinic, athletic preparation, and the crisis control framework, have been laid out for wellbeing observation research. Competitors' lives require a lot of activity and exercise for wellness and wellbeing. The capacity to screen the imperative indications of the competitor that mirror the physical and physiological state of the individual, particularly during an apprenticeship, is fundamental both for the competitor and for the mentor to keep away from overtraining, wounds, and sickness or to change the power and time as per the information estimated — wearable checking gadgets associated with remote correspondence advances. In the model, utilizing remote innovations implies that devices utilized by competitors discuss information with other remote hubs progressively and make a small correspondence organization. The utilization of remote sensor correspondence and the need to impart between sensors has prompted the formation of wireless sensor networks (WSN) and wireless body area networks (WBANs). This paper presented a wireless sensor network-based athlete health monitoring (WSN-AHM) method and concentrated on their growth phases. Since it is a remote and versatile wellbeing reconnaissance arrangement, it can give medical care specialist organizations a valuable remote checking device to diminish the expense of their administrations. WSNs and their correspondence advancements and principles can be utilized in these reconnaissance applications, accentuating wearing exercises through the entire and relative show of realities on well-known correspondence conventions.

groups

May Kamil Al-Azzawi mail -

Saad Hameed Abid mail

link https://doi.org/10.54216/FPA.210222

Volume & Issue

Vol. Volume 21 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2026

Irrigation Iot Sensor Data Analytics Using Bio-Inspired Data Mining Techniques

Recently, irrigation management has been considered one of the most significant areas of research in smart vertical farming. Hence, it is essential to optimize freshwater usage for smart vertical farming management due to the lack of freshwater sources. It is observed that the soil moisture level and temperature data need to be appropriately examined and analyzed to predict the water irrigation level in a smart farming platform. Hence, in this work, the Internet of Things (IoT) sensors have been utilized to collect and monitor the soil moisture level, ambient temperature level, and humidity level data effectively. Besides, the collected sensor information has been analyzed and predicted to recognize the appropriate utilization of the optimum level of freshwater using Grey Wolf optimizer integrated recurrent network models. Therefore, this approach successfully analyzes the sensors' data and predicts the required level of irrigation based on motor ON and OFF conditions. The generated data from the sensor has been evaluated using the Keras model using the python language, and the performance is assessed based on the accuracy ratio. This model obtained a maximum of (0.995%) accuracy in forecasting the optimum irrigation level. The proposed system will utilize less voltage to minimize the power consumption ratio up to 35% in the irrigation process with 99.5% accuracy in forecasting the optimum irrigation level.

groups

Maysaa H. Abdulameer mail -

Saif M. Ali mail -

Deshinta Arrova Dewi mail

link https://doi.org/10.54216/FPA.210223

Volume & Issue

Vol. Volume 21 / Iss. Issue 2

Details open_in_new