Automated Agricultural Crop Type Mapping Using Fusion of Transfer Learning and Tasmanian devil Optimization Algorithm on Remote Sensing Imagery

Daniel Arockiam; Azween Abdullah; Valliappan Raju

doi:https://doi.org/10.54216/JCIM.150206

Journal of Intelligent Systems and Internet of Things | 2025

Forecasting for Vaccinated COVID-19 Cases using Supervised Machine Learning in Healthcare Sector

Machine learning (ML)-based forecasting techniques have demonstrated significant value in predicting postoperative outcomes, aiding in improved decision-making for future tasks. ML algorithms have already been applied in various fields where identifying and ranking risk variables are essential. To address forecasting challenges, a wide range of predictive techniques is commonly employed. Research indicates that ML-based models can accurately predict the impact of COVID-19 on Jordan's healthcare system, a concern now recognized as a potential global health threat. Specifically, to determine COVID-19 risk classifications, this study utilized three widely adopted forecasting models: support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and linear regression (LR). The findings reveal that applying these techniques in the current COVID-19 outbreak scenario is a viable approach. Results indicate that LR outperforms all other models tested in accurately forecasting death rates, recovery rates, and newly reported cases, with LASSO following closely. However, based on the available data, SVM exhibits lower performance across all predictive scenarios.

groups

Ali Khraisat mail -

Mohd Khanapi Abd Ghani mail

link https://doi.org/10.54216/JISIoT.140214

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Fusion Model of Quantum Wavelet Transform and Neural Network for Video Coding on the Internet of Things Environment

Solving the video compression problem requires a multi-faceted approach, balancing quality, efficiency, and computational demands. By leveraging advancements in technology and adapting to the evolving needs of video applications, it is possible to develop compression methods that meet the challenges of the present and future digital landscape. To address these objectives, machine learning and AI approaches can be utilized to predict and remove redundancies more effectively, optimizing compression algorithms dynamically based on content. Still, state-of-the art neural network-based video compression models need large and diverse datasets to generalize well across different types of video content. Wavelets can provide both time (spatial) and frequency localization, making them highly effective for video compression. This dual localization allows wavelet transforms to handle both rapid changes in video content and slow-moving scenes efficiently, leading to better compression ratios. Yet, some wavelet coefficients may be more critical for maintaining visual quality than others. Inaccurate quantization can lead to noticeable degradation. For the first time, the suggested model combine Quantum Wavelet Transform (QWT) and Neural Networks (NN) for video compression. This fusion model aims to achieve higher compression ratios, maintain video quality, and reduce computational complexity by utilizing QWT’s efficient data representation and NN’s powerful pattern recognition and predictive capabilities. Quantum bits (qubits) can encode large amounts of information in their quantum states, enabling more efficient data representation. This is especially useful for encoding large video files. Furthermore, quantum entanglement allows for correlated data representation across qubits, which can be exploited to capture intricate details and redundancies in video data more effectively than classical methods. The experimental results reveal that QWT achieves a compression ratio of almost twice that of traditional WT for the same video, maintaining superior visual quality due to more efficient redundancy elimination.

groups

Iptehaj Alhakam mail -

Ali Abdullah Ali mail -

Oday Ali Hassen mail -

Saad M. Darwish mail -

Nur Azman Abu mail

link https://doi.org/10.54216/FPA.170219

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Machine Learning in Healthcare: A Comprehensive Review of Predictive Models for COVID-19 Transmission among Vaccinated Individuals

This review provides an in-depth exploration of machine learning (ML) applications in healthcare, focusing specifically on predictive models for COVID-19 transmission among vaccinated individuals. It underscores the pivotal role of ML in disease forecasting and prognosis, showcasing its potential to enhance healthcare outcomes in pandemic contexts. Key challenges of COVID-19, such as the high transmission rate of asymptomatic carriers and the effectiveness of containment strategies, are analyzed to highlight areas where ML can offer significant advantages. The study aims to develop an advanced forecasting model for COVID-19 transmission using diverse supervised ML regression techniques, including linear regression, LASSO, support vector machine, and exponential smoothing, applied to an extensive COVID-19 patient dataset. The insights generated from this review support efforts to combat COVID-19 and improve public health strategies, demonstrating ML's vital contribution to pandemic management and healthcare resilience.

groups

Ali Khraisat mail -

Mohd Khanapi Abd Ghani mail

link https://doi.org/10.54216/FPA.170220

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Multi-Label Diabetic Retinopathy Detection Using Transfer Learning Based Convolutional Neural Network

Retinopathy is a progressive and common retinal disease that most progressive diabetics suffer from and causes blood vessels in the retina to swell and leak blood and fluid. This condition requires timely diagnosis via medical experts to prevent causing visual loss among patients. To enhance the feasibility of checking many persons, diverse deep-learning schemes have recently been developed for diabetic retinopathy detection. In this paper, retinopathy image detection system based on diverse deep learning schemes (VGG-19, DenseNet-121, and EfficientNet-B6) has been presented. The implemented deep learning schemes with multi-label classification are trained and tested using the Asia Pacific Tele Ophthalmology Society (APTOS-2019) dataset, and the two combined datasets Indian Diabetic Retinopathy Image Dataset (IDRiD) and Messidor-2. The system outcomes of classification are exhibited as sensitivity, precision, F1Score, and accuracy measurements, and the system performance is compared with recently existing related systems. The attained outcomes indicate that the implemented EfficientNetB6 network outperforms peers’ schemes and related systems via realizing supreme accuracy using balanced multi-class retinopathy datasets.

groups

Raghad. H. Abood mail -

Ali. H. Hamad mail

link https://doi.org/10.54216/FPA.170221

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

MODRS: A Multi-Objective Deep Learning Algorithm for Optimizing Routing and Scheduling in LEO Satellite Networks

The demand for high-quality Direct-to-Home (D2H) television broadcasting services delivered via Low Earth Orbit (LEO) satellite constellations has surged in recent years. To address the growing needs of viewers, satellite communication must optimize the scheduling and routing of signals while balancing conflicting objectives. This research presents a novel approach named as Multi-Objective Deep Routing and Scheduling (MODRS) algorithm that is designed to tackle the challenges of signal latency minimization, bandwidth utilization maximization, and viewer demand satisfaction. The Multi-Objective Deep Neural Network (MODNN) is implemented in this paper to make intelligent routing and scheduling decisions for balancing multiple objectives. To enhance the learning process and provide training stability, the experience replay is used and the epsilon-greedy strategy is included to balance exploitation and exploration strategies. The Pareto-front concept is used for efficient D2H television broadcasting in the LEO satellite constellation. The experimental validation is conducted based on low-latency broadcasting, high-bandwidth utilization, viewer demand flexibility, adaptive signal strength and resource allocation efficiency. Using a series of simulated scenarios, this paper explores the versatility and robustness of MODRS, showcasing its exceptional performance in real-time, resource-efficient, disaster recovery, and rural broadcasting contexts. The findings indicate that MODRS is well-suited for a wide range of real-world applications, from low-latency broadcasting and disaster recovery to cost-effective rural expansion, enhancing the quality and accessibility of D2H television services. The MODRS algorithm emerges as a transformative solution for satellite communication optimization, ensuring viewer satisfaction and operational efficiency.

groups

Ali Jaber Almalki mail

link https://doi.org/10.54216/JISIoT.140216

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Smart Energy Transactions in Vehicle-to-Grid Networks: A Deep Q-Network Approach with Blockchain

Electric vehicles (EVs) have gained significant traction due to their environmental benefits and potential to revolutionize the transportation sector. Integrating EVs into the Vehicle-to-Grid (V2G) network presents an innovative solution for optimizing energy transactions and grid stability. However, managing energy transactions during peak hours poses a challenge. This research proposes a novel approach that combines the Deep Q-Network (DQN) algorithm with block chain technology to enhance energy transactions in the V2G network. In this study, a V2G network model is introduced consisting of EVs, charging stations, a grid control center, and a block chain infrastructure. The block chain ensures transparency, security, and decentralized energy transactions. The DQN algorithm learns optimal action policies based on current states and rewards, contributing to grid stability. To incentivize EV owners for peak-hour energy contributions, a block chain-enabled rewarding mechanism is implemented. The proposed methodology is rigorously evaluated through simulations conducted in a custom environment that emulates V2G network dynamics. Performance metrics such as load shifting efficiency, peak demand reduction, and energy efficiency are employed for comprehensive assessment. The proposed method showcases superior performance compared to traditional load shifting and demand response strategies. Furthermore, comparative analyses are conducted against different state-of-the-art methods, demonstrating the effectiveness of our approach. The results underscore the potential of integrating DQN-based energy management with block chain technology to achieve grid stability and incentivize sustainable energy behaviors. This research contributes to the advancement of smart grid technologies, paving the way for a more sustainable and efficient energy ecosystem.

groups

Ali Jaber Almalki mail

link https://doi.org/10.54216/FPA.170222

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

RETRACTED ARTICLE: On The Topological Space of Some n- Refined Neutrosophic Real Intervals and Its Open Sets For 𝟒≤𝒏≤𝟓

This paper is dedicated to studying for the first time the building of a topological space based on the intervals defined over 4-refined neutrosophic real numbers and 5-refined neutrosophic real numbers, where we define a special partial order relation on these rings, and we use it to study the structure of the corresponding intervals generated from this relation. Also, we characterize the formula of open sets through these two topological spaces with some illustrated examples.

groups

Jamal Oudetallah mail

link https://doi.org/10.54216/IJNS.250326

Volume & Issue

Vol. Volume 25 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2025

Spectral Radius Inequalities for Accretive-Dissipative Matrices

In this paper, we prove new spectral radius inequalities for sums, differences and commutators involving accretive-dissipative matrices of Hilbert space. Earlier well-known results used the spectral radius for its importance for general matrices. In our paper, we focus on some results related to spectral radius for special kind of matrices which are accretive-dissipative. A particular example is also presented in this work.

groups

Mona Sakkijha mail -

Shatha Hasan mail

link https://doi.org/10.54216/IJNS.250327

Volume & Issue

Vol. Volume 25 / Iss. Issue 3

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Intelligent Crop Disease Detection and Classification Using Deep Convolution Neural Network with Honey Badger Algorithm on Image Data

Cotton is the most significant cash crop in India. Each year cotton production is decreasing because of the attack of the disease. Plant diseases are usually produced by pathogens and pest insects and reduce the yield to a large scale if not controlled in time. The hour requires an effective plant disease diagnosis system that can assist the farmers in their farming and cultivation. Nevertheless, cotton production is harmfully affected by the presence of viruses, pests, bacterial pathogens, and so on. For the past decade or so, numerous image processing or deep learning (DL)--based automated plant leaf disease recognition techniques have been established but, unluckily, they infrequently focus on the cotton leaf diseases. Therefore, this article develops an Intelligent Detection and Classification of Cotton Leaf Diseases Using Transfer Learning and the Honey Badger Algorithm (IDCCLD-TLHBA) model with Satellite Images. The proposed IDCCLD-TLHBA technique intends to determine and classify various kinds of cotton leaf diseases using satellite imagery. In the IDCCLD-TLHBA technique, the wiener filtering (WF) model is used to reduce noise and enhance image quality for subsequent analysis. For feature extraction, the IDCCLD-TLHBA technique applies the MobileNetV2 model to capture relevant features from the satellite images while maintaining computational efficiency. In addition, the stacked long short-term memory (SLSTM) method is employed for the classification and recognition of cotton leaf diseases. Eventually, the honey badger algorithm (HBA) is used to optimally select the parameters involved in the SLSTM model to ensure a better configuration of the network to enhance results. The performance validation of the IDCCLD-TLHBA method is carried out against the benchmark dataset and the stimulated results highlight the better results of the IDCCLD-TLHBA model across the existing techniques.

groups

Daniel Arockiam mail -

Azween Abdullah mail -

Valliappan Raju mail

link https://doi.org/10.54216/JISIoT.140215

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Automated Agricultural Crop Type Mapping Using Fusion of Transfer Learning and Tasmanian devil Optimization Algorithm on Remote Sensing Imagery

At present, the application of remote sensing (RS) data achieved from satellite imagery or unmanned aerial vehicles (UAV) has become common for crop classification procedures, i.e. crop mapping, soil classification, or prediction of yield. The classification of food crop utilizing RS images (RSI) is one of the major applications of RS in farming. It contains the usage of aerial or satellite images for classifying and identifying dissimilar kinds of food crops developed in an exact region. This data is beneficial for estimation of yield, crop monitoring, and land management. Meeting the conditions for examining these data needs more refined techniques and artificial intelligence (AI) technologies, which deliver essential support. Recently, the usage of deep learning (DL) for crop type classification with RS images could help sustainable farming practices by providing appropriate and precise data on the kinds and features of crops. In this study, we offer an Automated Agricultural Crop Type Mapping Utilizing Fusion of Transfer Learning and Tasmanian Devil Optimization (AACTM-FTLTDO) algorithm on Remote Sensing Imagery. The primary goal of the AACTM-FTLTDO methodology is to accurately detect and classify crop types for more precise agricultural monitoring using remote sensing technologies. To accomplish that, the AACTM-FTLTDO model employs a fusion of transfer learning techniques involving three models such as SqueezeNet, CapsNet, and ShuffleNetV2 to capture diverse, multi-scale spatial and spectral features. For the crop type classification and detection process, the auto-encoder (AE) classifier can be employed. Eventually, the tasmanian devil optimization (TDO) technique was deployed to modify the hyperparameter of the AE technique for ensuring optimal model configurations and reducing computational complexity. A wide range of experimentation studies is made and the results are examined under numerous measures. The comparative study shows that the AACTM-FTLTDO technique performs better than existing approaches

groups

Daniel Arockiam mail -

Azween Abdullah mail -

Valliappan Raju mail

link https://doi.org/10.54216/JCIM.150206

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new