2692-4048ISSN (Online)
2770-0070ISSN (Print)
The E-Government Development Index (EGDI) represents the performance and reality of e-government. The importance of maintaining and planning for the enhancement of such an index enables the policymakers to understand, process, and develop the right plans and strategies for it. In this paper, the Auto Regressive Integrated Moving Average (ARIMA) has been utilized to build predictive models. The time-series data collected from the UN survey versions for the years 2003, 2005, 2008, 2010, 2012, 2014, 2016, 2018, 2020, 2022, and 2024 for the countries of Iraq and KSA. The necessary data maintenance was implemented, then analyzed, covering the inspection of their temporal behavior. Afterwards, two individual data sets were created for both countries under study, containing 253 months. The optimal values ​​for the ARIMA models were determined by implementing the data transformation, including the autocorrelation function (ACF) and partial autocorrelation function (PACF). 80% of the dataset is used for training, and 20% is used for testing. The data residuals analyzed by ACF, PACF, and the Ljung-Box test were performed for the residuals independence check. Nine metrics were utilized for model evaluation and ruthlessness. By using ARIMA models, the e-government performance (EGDI) has been predicted for the next five years for Iraq and KSA. The ARIMA models for both Iraq and KSA showed high performance, where the RMSE value for the Iraq model was (0.0054) and the MAE value was (0.0031) compared to the RMSE value (0.0481) and the MAE value (0.0093) for the KSA model. The Iraq arima model has better quality of the prediction in absolute terms. On the other hand, the ARIMA model for KSA was better in terms of predicted trends with an accuracy of 98.44% compared to 97.39% for the Iraq model.
Read MoreDoi: https://doi.org/10.54216/FPA.190211
Vol. 19 Issue. 2 PP. 134-150, (2025)
In autonomous navigation, the ability to detect 3D objects from a Bird’s-Eye View (BEV) perspective is essential. Nevertheless, many obstacles remain before LiDAR and camera data can be effectively combined. We propose CL-FusionBEV, a novel framework for sensor fusion that enhances Three-dimensional object recognition in the BEV domain. This method structures LiDAR point clouds for improved spatial feature extraction while converting camera data into BEV format via an implicit learning technique. An implicit fusion network and a multi-modal cross-attention mechanism facilitate seamless sensor interaction, ensuring comprehensive feature integration. Additionally, a self-attention mechanism of BEV enhances broad-scale reasoning and data extraction, improving the detection of occluded and distant objects. By efficiently synchronising data from several sensors, the suggested method improves feature uniformity and resolves spatial inconsistencies. It further leverages adaptive feature selection to enhance robustness against sensor noise and varying conditions. We evaluate CL-FusionBEV on the nuScenes dataset, achieving achieved a 73.3% mAP and a 75.5% NDS on the nuScenes benchmark, with vehicle and pedestrian detection accuracies of 89% and 90.7%, respectively. Our model demonstrates superior robustness in challenging conditions such as low visibility and dense urban environments. CL-FusionBEV maintains high efficiency with real-time inference, making it suitable for deployment in autonomous systems. Extensive experiments show our strategy routinely beats cutting-edge techniques, especially in detecting small and distant objects. By addressing key sensor fusion challenges in the BEV domain, CL-FusionBEV offers a notable advancement in Three-dimensional object recognition, ensuring high accuracy, efficiency, and reliability for real-world driving scenarios.
Read MoreDoi: https://doi.org/10.54216/FPA.190202
Vol. 19 Issue. 2 PP. 15-27, (2025)
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%).
Read MoreDoi: https://doi.org/10.54216/FPA.190201
Vol. 19 Issue. 2 PP. 01-14, (2025)
One of the significant challenges in modern healthcare is the early and accurate detection of health anomalies, especially in the case of life-threatening diseases such as breast cancer. This paper investigates the comparative efficacy of ML models and statistical methods for the classification of breast tumors as benign or malignant using the Breast Cancer Wisconsin (Diagnostic) Dataset. The dataset, comprising various tumor cell attributes, was preprocessed with Principal Component Analysis (PCA) to enhance model training efficiency. The first 11 principal components retained 95% of the total variance, ensuring minimal information loss while reducing dimensionality. We compared the performance of several machine learning algorithms, including Logistic Regression, Support Vector Machines (SVM), Artificial Neural Networks (ANN), Decision Trees (DT), Random Forests (RF), Naïve Bayes (NB), and K-Nearest Neighbors (KNN). Among them, Logistic Regression, SVM, and ANN achieved near-perfect classification accuracy with balanced precision-recall metrics, where the accuracy rates were all more than 98%. XGBoost and Random Forest were also very impressive as advanced models, while simple models like Decision Trees and Naïve Bayes proved to be less potent and were unable to manage class imbalances and complex data patterns. Our main findings are essentially reflective of the transformative role machine learning would play in healthcare; for instance, enhancing the accuracy of diagnosis, optimizing clinical workflow, and promoting decision-making. These insights are made actionable for practitioners in healthcare to promote the adoption of reliable ML solutions for breast cancer detection. In the future, real-time data integration, external validation, and hybrid modeling approaches must be considered to further enhance the practical utility of these findings.
Read MoreDoi: https://doi.org/10.54216/FPA.190203
Vol. 19 Issue. 2 PP. 28-44, (2025)
Network intrusions are becoming more common, resulting in significant privacy violations, financial losses, and the illegal transfer of sensitive information. Numerous intrusion strategies pose a threat to data, computer resources, and networks. While hackers may focus on obtaining trade secrets, private information, or confidential data that can then be disclosed for illegal purposes, each type of intrusion aims to achieve a distinct objective. False attack detection by security systems and changing threat environments create challenges such as delayed identification of true attacks and long-term financial harm. This paper presents a novel hybrid optimization algorithm-assisted deep learning model for accurately identifying intrusion types and enhancing network security. Initially, input information is composed from openly obtainable datasets. The input data is cleaned, normalized, and standardized to produce accurate results. An improved synthetic minority oversampling technique (ISMOTE) for data balance reduces the method's overfitting problem. Then, the Chaotic Bat Artificial Bee Colony optimization algorithm (CBABCOA) is used to identify critical features and reduce feature dimensionality issues. HSVM-XGBoost (Hybrid Kernel Support Vector Machine-Extreme Gradient Boosting) is used for intrusion detection and classification. The Chaotic Binary Horse Optimization Algorithm (CBHOA) is used for hyper parameter tuning. This method makes use of the CIC UNSW-NB15 Augmented dataset, the CICIDS 2019 data set, and the NSL-KDD information set. The proposed method achieves better than the other method.
Read MoreDoi: https://doi.org/10.54216/FPA.190204
Vol. 19 Issue. 2 PP. 45-63, (2025)
Wind energy is one of the fastest-growing sustainable, clean, and renewable sources, attracting significant attention and investment from many countries. However, given the substantial capital investment required for wind power plants, understanding the proposed plants’ performance becomes critical before implementation. This assessment is most effectively conducted using refined wind power predictability models and precise wind velocity data. Accurate wind forecasts are essential for informed decision-making and effective wind energy utilization. In this study, three advanced Machine Learning (ML) regression methods were applied to the TNWind dataset to predict the power output of wind turbines. The dataset variables included date and time (measured at 10-minute intervals), low-voltage active power (in kW), wind speed (in m/s), the theoretical wind power curve (in kWh), and wind direction. To predict wind power output, six supervised ML models were trained, including Random Forest Regressor (RF), Extreme Gradient Boosting Regressor (XGB), Gradient Boosting Regressor (GB), Support Vector Machine Regressor (SVR), K-Neighbors Regressor (KN), and Linear Regressor. The analysis revealed that the Random Forest model outperformed the others, achieving exceptional performance metrics: an R2 value of 0.97, an MAE of 0.17 and an MSE of 0.07. The analysis to identify the outcomes for wind power generation from machine learning proves that renewable energies are more capable and are a lucrative investment.
Read MoreDoi: https://doi.org/10.54216/FPA.190205
Vol. 19 Issue. 2 PP. 64-81, (2025)
The examination of commercial bank deposits together with their influencing factors relies on econometric analyses in this paper. The econometric model for commercial bank deposit base factors used a multiple linear regression (LS) method because the data came from time series that included multiple variables. The research used 74 economic indicators spanning an eight-year period and collected those indicators in monthly intervals. The dependent variable was the deposit volume (y), while the independent variables were the inflation rate (x1), the minimum wage (x2), the number of individuals using digital banking services (x3), the average interest rate on term deposits (x4), and the per capita GDP (x5). Our analysis, based on data from the Central Bank of the Republic of Uzbekistan, indicates that the selected independent variables are significantly related to the growth of the deposit base. The implementation of multiple linear regression (LS) answered Gauss-Markov assumption tests successfully while the Durbin-Watson test and Shapiro-Wilk test along with the Breusch-Pagan test evaluated the statistical import of the obtained results. The key findings indicate that a 1% increase in the inflation rate leads to a 1.06% decrease in the deposit volume; a 1% increase in the minimum wage results in a 0.32% increase in the deposit volume; a 1% increase in the number of individuals using digital banking services leads to a 0.59% increase in the deposit volume; a 1% increase in the average interest rate on term deposits results in a 0.81% increase in the deposit volume; and a 1% increase in per capita GDP causes a 0.79% increase in the deposit volume. Banks should concentrate their efforts on fighting inflation while developing their digital systems because these strategies build a better deposit base, which boosts interbank rivalry and supports economic stability.
Read MoreDoi: https://doi.org/10.54216/FPA.190206
Vol. 19 Issue. 2 PP. 82-91, (2025)
Melanoma Skin cancer is a serious type of cancer affecting people globally in order to improve survival rates, it is crucial to detect the infection at an early stage. Old Traditional methods for cancer detection make use of biopsies, which were time-consuming and involved complex procedures, which delayed diagnosis. However, accurate diagnosis is challenging due its complex imaging techniques. With the advancements in technology, particularly in deep learning techniques like CNN, have significantly improved the accuracy and efficiency of melanoma skin cancer detection. This research paper presents a Novel Hybrid deep learning architecture that combines Convolution Neural Networks (CNNs) and Vision Transformers (ViT) for automated classification of skin lesions into binary categories: Malignant (cancerous) and Benign (Non-cancerous). The proposed model influences CNN's superior ability to extract local features alongside ViT's capability to extract global features. This hybrid architecture was trained and evaluated on ISIC 2020 challenging Dataset of dermatological images representing excellent performance with an accuracy of 94%, with a precision of 91%, recall (sensitivity) of 90%, and an F1 score of 91% after 25 epochs. The model's robustness is further authorized through confusion matrix analysis, which forms a strong classification capability across various melanoma presentations. The proposed hybrid approach offers a more efficient and less complex approach in the automatic detection and identification of melanoma skin cancer, thus increasing the chances of successful early intervention and improving patient outcomes, thus making it suitable for Clinical use and sets a foundation for future developments in automated skin cancer detection systems. In comparison to other advanced networks, this model displays superior performance.
Read MoreDoi: https://doi.org/10.54216/FPA.190207
Vol. 19 Issue. 2 PP. 92-101, (2025)
This study aimed to identify the most relevant factors influencing the effectiveness of self-assessment and external evaluation processes in higher education in Ecuador. Through an analytical approach, the DEMATEL method integrated with neutrosophic logic was employed to evaluate interactions, prioritize these factors, and enhance information fusion in decision-making. The methodology allowed for the incorporation of inherent uncertainty and subjectivity in evaluation, generating a more adaptive and robust model for integrating multiple sources of information. The results revealed that key factors included the clarity of quality indicators, institutional commitment to continuous improvement, training of evaluators, and institutional infrastructure. Furthermore, the study highlighted that the fusion of internal and external evaluation data is crucial for a comprehensive quality assessment. The most influential factors within the system were identified as the impact of evaluation results on decision-making and infrastructure quality. Findings indicate that improving educational quality in Ecuador requires strengthening data integration mechanisms, ensuring coherence between self-assessment and external evaluation, and optimizing the interaction between different quality assurance processes. It is recommended to enhance information fusion strategies in quality assurance policies to improve the efficiency and accuracy of evaluation processes in higher education.
Read MoreDoi: https://doi.org/10.54216/FPA.190208
Vol. 19 Issue. 2 PP. 102-108, (2025)
Cuneiform script, among the earliest writing systems, poses a distinct challenge for classification because of its complex symbols and varied linguistic contexts. This study investigates the use of Convolutional Neural Network (CNN) architectures for the classification of cuneiform symbols. The preprocessing includes resizing the cuneiform images to a uniform dimension and categorizing them into training, validation, and testing sets. A modified CNN model has been introduced. The CNN model demonstrates a lower parameter count in comparison to other deep learning models, which frequently necessitate significant storage capacity. The results from the CLI dataset indicate that the proposed CNN model reached an impressive accuracy of 99.55%, This study enhances computational approaches for the analysis of ancient scripts and underscores the significance of utilizing deep learning techniques within the fields of historical linguistics and digital humanities.
Read MoreDoi: https://doi.org/10.54216/FPA.190209
Vol. 19 Issue. 2 PP. 109-117, (2025)
The sustainable development of automobile enterprises in Uzbekistan is a topic that deserves our attention and analysis. In this study, we focus on utilizing fusion analysis to identify and understand the factors that play a crucial role in the sustainable growth of these enterprises. By examining and fusion, multiple dimensions, such as economic, environmental, social and technological factors, we aim to provide valuable insights and recommendations for promoting sustainable within the automobile industry in Uzbekistan. Through fusion analysis, we examine how economic factors, such as market demand, production efficiency, and financial viability, influence the sustainable development of the country and automobile enterprises.
Read MoreDoi: https://doi.org/10.54216/FPA.190210
Vol. 19 Issue. 2 PP. 118-133, (2025)
Glaucoma is a dangerous eye illness that greatly reduces the sharpness of a person's vision. If not caught early enough, this retinal disorder can damage the optic nerve head (ONH) and cause permanent blindness. Automated glaucoma diagnosis now has tool support thanks to recent advances in deep learning besides the convenience of computing resources. The low reliability of generic convolutional neural networks has prevented their widespread usage in medical procedures, even if deep learning has improved illness diagnosis using medical pictures. While there has been a rise in the use of deep learning for glaucoma classification, very few studies have tested whether or not the models are easy to understand and interpret, which bodes well for their future use. Medical picture feature extraction using Vision Transformers is showcased in this study utilising an EvoTransform: Advanced Evolutionary Algorithm Integration in Transformer Networks named as (EvoTAEA). Combining the powers of Convolutional Neural Networks with Vision Transformers, the suggested EAT Former architecture takes advantage of their data pattern recognition in addition adaptability capabilities. The classification accuracy is enhanced by using the Wild Geese Migration Optimizer (WGMO) to fine-tune the parameters of the proposed feature extraction. The design makes use of new parts, such as the Multi-Scale Region Aggregation, Global and Local Interaction, and Enhanced EA based Transformer blocks with Feed-Forward networks. For dynamically simulating non-standard places, it also presents the Modulated Deformable MSA module. Important components of the Vision Transformer (ViT) model are covered in the study, including patch-based processing, Multi-Head Attention mechanism, and positional context inclusion. In order to give an inductive bias, it presents the Multi-Scale Region Aggregation module, which combines data from several receptive fields. The MSA-based global module is improved by the Global and Local Interaction module, which adds a local path for extracting discriminative local info. An approach to glaucoma diagnosis that integrates ResNet-50, DenseNet-201, and Xception is suggested in the study. Two publicly available datasets, ORIGA and ACRIMA, are used to evaluate the trials. This model can help with the automated diagnosis of glaucoma using fundus pictures.
Read MoreDoi: https://doi.org/10.54216/FPA.190212
Vol. 19 Issue. 2 PP. 151-169, (2025)
As optimization tasks become increasingly complex, particularly in feature selection, there is a growing need for algorithms capable of robustly balancing exploration and exploitation. In this work, we propose the Binary Swordfish Movement Optimization Algorithm (BSMOA), inspired by the synchronized and agile movements of swordfish. BSMOA employs adaptive parameters to navigate high-dimensional search spaces through dynamic exploration, exploitation, and elimination stages. Extensive experiments on benchmark datasets demonstrate that BSMOA outperforms state-of-the-art algorithms, including bHHO, bGWO, and bPSO, regarding average error, feature reduction, and computational efficiency. Key contributions of BSMOA include its improved balance between global and local search and its ability to achieve stable and accurate feature selection. This work has broad implications for applications in machine learning, engineering design, and other optimization domains, providing a reliable tool for tackling challenging binary optimization problems.
Read MoreDoi: https://doi.org/10.54216/FPA.190213
Vol. 19 Issue. 2 PP. 170-186, (2025)
In the context of dynamic and highly diverse IoT (Internet of Things), the nature of threats and the amount of data that needs to be processed by IDSs (Intrusion Detection System) have become much greater and represent considerable problems for modern security systems. This work presents a new method called a Hybrid Deep Learning-Evolutionary Algorithm with an Ensemble Strategy (HDLE-EASE) for improving intrusion detection in IoT systems. Our method combines the spatial feature extraction capability of CNN (Convolutional Neural Networks) and temporal feature extraction of LSTM (Long Short-Term Memory) networks with the optimization feature of GA to optimize model parameters. We further incorporate a composite of boosting-bagging hybrid to enhance the stability and reliability of the intrusion detection mechanism. As privacy and scalability are critical issues in IoT networks, we propose a federated learning approach, allowing for model training on IoT networks while preserving data privacy. Furthermore, the presented approach includes a reinforcement-learning module for the capability of adapting to newly emerge and changing security threats. Initial tests show that the detection accuracy and model optimization capabilities of HDLE-EASE significantly outperform other methods, while its adaptability makes the tool a promising one for developing a holistic solution to protect IoT systems against a wide range of attacks.
Read MoreDoi: https://doi.org/10.54216/FPA.190214
Vol. 19 Issue. 2 PP. 187-193, (2025)
Data imbalance is a common problem in machine learning, specifically in classification, in which examples in a dominant class outnumber examples in a minority class many times over. Besides, such a problem keeps a model unable to discover meaningful patterns for a minority class —hence, such a problem reduces model performance specifically in terms of Recall and F1-Score. In current work, activity is performed in overcoming data imbalance problem in sentence classification model of documents of assurance certificate for halal with a combination of over-sampling and under-sampling techniques, namely Adaptive Synthetic (ADASYN) and Tomek Links. Text Classification technique is adopted in classifying sentences regarding assurance of halal in documents of assurance certificate for halal Text Classification; since incorrect classification of such sentences is not preferable, therefore, it is important to make sure no information about halal product is missed out. Over-sampling techniques considered include the SMOTE, Borderline SMOTE, ADASYN, and SMOTENC, and under-sampling techniques include the Random Under-Sampler, Near Miss, and Tomek Links. As comparative result, best performance gain in terms of Accuracy (0.759), F1-Score (0.748), Recall (0.759), and Precision (0.768) is generated with ADASYN. In our use case, ADASYN + Tomek Links is effective; recall is important in case of classification of documents for assurance certificate for halal and therefore, we cannot miss any relevant sentences. The proposed approach remarkably enhances the accuracy level for halal-related sentence identification and can be adopted in the halal product checking systems in industries with a halal feature.
Read MoreDoi: https://doi.org/10.54216/FPA.190215
Vol. 19 Issue. 2 PP. 194-210, (2025)
Identifying Autism early in children is vital for ensuring more precise developmental support and effective therapeutic interventions. Traditional diagnostic approaches are frequently delayed, and data privacy concerns limit the availability of broad, multi-institutional datasets required for effective machine learning models. To address these limitations, this study proposes a CNN-LSTM-based autism detection model for children using Federated Learning (FL). In the model, temporal and spatial information is extracted from the facial CNNs are highly adept at using convolutional filters to extract spatial features from images. LSTM networks are a specific type of Recurrent Neural Network (RNN) that is ideal for processing time-series or sequences because it can identify long-term relationships in sequential data. This architecture uses CNN layers to extract spatial information from important indications that are important for detecting ASD, like eye patterns, gestures, and facial expressions. After that, these features are sent to LSTM layers, which examine the time-dependent and sequential behavioral patterns associated with autism. Federated Learning allows the locally to train the model on its own dataset locally, sharing only model updates with a central server, thereby preserving data privacy while promoting diverse data contributions. According to experimental results using the proposed techniques, the federated CNN-LSTM model performs 4.3% better than the conventional centralized models because it has less overfitting and is more resilient to a range of data distributions. The model’s performance metrics further highlight its reliability, accuracy, precision, recall, and F1-Score values reaching 98.90%, 97.80%, 98.05%, and 98%, respectively, showing its potential for reliable ASD detection in children across diverse populations.
Read MoreDoi: https://doi.org/10.54216/FPA.190216
Vol. 19 Issue. 2 PP. 211-223, (2025)
Leukemia is a very dangerous kind of malignancy troubling the blood or bone marrow in all age categories, both in adults and children. The deadly and threatening kind of leukemia is named Acute Lymphoblastic Leukemia (ALL). The accurate and automated ALL diagnosis of blood cancer is complex work. Medical experts and hematologists in the bone marrow and blood samples detect it by employing a high-quality microscope. The manual classification is observed as tiresome and is restricted by varying expert considerations and other attributes. Presently, the Convolutional Neural Networks (CNNs) have become an acceptable mechanism for analyzing the medical image. However, for attaining outstanding performance, conventional CNNs normally demand large data sources for better training. Thus, to alleviate the existing complexities, we implemented an effective ALL detection system using deep learning. At first, the necessitated images are aggregated from global resources of data. Further, the garnered images are inputted into the Optimized Trans-Res-Unet+ (OTRUnet+)-based segmentation model. Here, the Fitness-aided Position Updating in the Social engineering Algorithm (FPUSA) for improving the segmentation process’s efficacy optimally tunes the OTRUnet+ technique parameters. In addition, the segmented images are taken to perform the classification process using the Adaptive Multi-Dilated Residual Attention Network (AMDRAN); here several parameters are optimally tuned by the same FPUSA to enrich the classification process. Finally, the suggested AMDRAN technique offered the ALL classified output. The effectiveness of the designed ALL detection system is explored with several existing systems to display its enhanced performance over other models
Read MoreDoi: https://doi.org/10.54216/FPA.190217
Vol. 19 Issue. 2 PP. 224-252, (2025)
One of the most important sectors for providing for daily human requirements is agriculture. At the same time, digitization has a big impact on a number of businesses, making it simpler to carry out a number of challenging tasks. In order to help the farmer and the consumer, technology and digitization must be adopted. Utilizing technology and routine monitoring, diseases can be identified and eliminated, increasing agricultural output. This paper suggests a system for recognizing and categorizing plant illnesses, initially focused on five separate classes: two fruit classes, one vegetable class, one edible pulse class, and one-grain class. The Plant Village and UCI ML Repository Dataset, which is well known as a freely accessible, accepted standard, and reliable data source, was used for this purpose. Based on them, a CNN model is prepared for analyzing them with an accuracy upto 95.42%. Image segmentation will also play a role in calculating precise amount of infection followingly, a good interface is must to utilize it in a proper way for a user which can be provided in the form of app, a feature that every user requires on daily basis.
Read MoreDoi: https://doi.org/10.54216/FPA.190218
Vol. 19 Issue. 2 PP. 253-264, (2025)
Elderly health has always been a matter of concern for the medical doctors and researchers to come up with advanced recovery techniques. With the rise in population of elderly people and mostly residing alone at home in solitude has motivated many researchers to work on remedial measures for the biggest safety risk faced by them which is elderly fall prevention and mitigating thereby causes of injuries. In this paper, an intelligent deep learning and computer vision based elderly fall recognition system is designed which utilizes advanced spatial-channel decoupled downsampling in You Only Look Once version 10 (YOLOv10), pytorch, darknet and cascaded CNN technologies for the fall detection. The results after testing manifest that the accuracy of the proposed system to recognize and detect the elderly fall is quite assuring, the values of accuracy and mean Average Precision (mAP50) coming out to be 92.46% and 94.1% respectively after the model validation. Moreover, the system displays a real time performance as it can process approximately 45 frames of images per second that realizes a real-time identification of elderly fall patterns. As compared to previous models, the proposed model is much more efficient and has shown promising results.
Read MoreDoi: https://doi.org/10.54216/FPA.190219
Vol. 19 Issue. 2 PP. 265-277, (2025)
Plant diseases are considered a real threat to food security due to the losses incurred by individuals and countries. Early detection is one of the real solutions that can help reduce the size of these losses, but early detection is still bleeding. This study presents the development of a Convolutional Neural Network (CNN) model for classification with a new architecture and optimal performance suitable for real-time applications for the detection of fruit diseases (figs, oranges, grapes). The developed CNN model balanced accuracy and FLOPs using Squeeze-Excitation (SE) and adaptive-average pool layers. After implementing new data developed from Iraqi farms, the CNN model achieved optimal performance compared to the most famous models such as VGG16, ResNet, EfficientNet, and AlexNet.
Read MoreDoi: https://doi.org/10.54216/FPA.190220
Vol. 19 Issue. 2 PP. 278-287, (2025)
Predicting rainfall proves critical for businesses to organize their water resources, make agricultural choices, and prevent disasters. Therefore, proposed model presents a novel approach, namely Heuristic Intelligence towards Enhancing Rainfall Prediction with Artificial Neural Networks (HI2NN) to enhance rainfall prediction by designing heuristic Intelligence combined with Improved Artificial Neural Networks (IANNs). The proposed HI2NN framework leverages heuristic optimization techniques to fine-tune ANN parameters to improve prediction accuracy. Prediction accuracy is computed through our designed custom accuracy metric. The methodology uses historical weather information to extract complex non-linear patterns, which neural models generate from the designed big dataset. The accuracy level of rainfall predictions using our methodology achieves 92%, which demonstrates superior performance than traditional approaches that include random forest and decision tree and XGBoost models. The new forecasting systems develop higher reliability through collaborative efforts between heuristic algorithms and neural networks as described in this research work targeting challenging meteorological forecasts.
Read MoreDoi: https://doi.org/10.54216/FPA.190221
Vol. 19 Issue. 2 PP. 288-303, (2025)
Precise assessment of software development effort (SDE) is essential for efficient project planning and resource distribution. Conventional methods frequently encounter difficulties in generalizing across different project areas because of disparate data attributes. This research presents an innovative approach that combines transfer learning with hybrid deep learning models to tackle these difficulties. The platform utilizes pre-trained Random Forest and LSTM models, enhanced using Jaya optimization, to improve prediction accuracy and adapt effectively to new datasets. Transfer learning is utilized to extract reusable patterns and features from source domains, facilitating effortless adaption to target domains with minimum retraining. Extensive experiments on various benchmark datasets illustrate the proposed framework's enhanced performance regarding accuracy, scalability, and robustness relative to leading techniques. This study emphasizes the capability of transfer learning to transform SDE estimates, providing a scalable and domain-adaptive approach for intricate software projects.
Read MoreDoi: https://doi.org/10.54216/FPA.190222
Vol. 19 Issue. 2 PP. 304-314, (2025)
In medical diagnosis and prognosis, symptoms provided by patients play a critical role in identifying diseases. Machine learning offers a powerful approach to analyzing and predicting illnesses based on these symptoms. In particular, classification algorithms are widely used to analyze input data and predict disease outcomes. A key factor in effective classification is the selection of relevant attributes, which directly affects the accuracy of the prediction. This research emphasizes the importance of proper feature extraction techniques in the context of disease prediction using biomedical signal analysis. Effective analysis requires both the extraction of critical features and the elimination of irrelevant data. The aim of this study is to explore existing approaches to disease prediction based on biomedical signal analysis. We focus on feature extraction from pre-processed data, which aids in distinguishing between different biomedical signals recorded by medical devices. Our objective is to identify biomedical cues that differentiate various health conditions. Examples of such signals include electroencephalogram (EEG), electrocardiogram (ECG), and electrogastrogram (EGG). Understanding how these signals differ between healthy and diseased states is crucial for accurate disease prediction. This research investigates diseases such as heart disease, kidney failure, and lung infections, considering how variations in biomedical signals can be used to predict the likelihood of severe illness. We continue to seek advancements in predicting and mitigating future health risks
Read MoreDoi: https://doi.org/10.54216/FPA.190223
Vol. 19 Issue. 2 PP. 315-327, (2025)
The complex developing nature of urban infrastructure necessitates intelligent solutions for optimizing smart city operations. Based on this research paper, a multi-modal fusion framework that integrates real-time traffic and environmental sensor data with advanced machine learning algorithms to enhance decision-making for urban traffic management and pollution control is proposed. A hybrid AI model is proposed, with a combination of CNNs for the estimation of image-based traffic density, LSTM networks for the time-series environmental prediction, and RL for adaptive control of traffic signals. The system proposed integrates sensor data in real-time from cameras, GPS, LiDAR, and nodes for environmental monitoring to create an optimized control strategy. The model has been deployed on edge computing devices, such as Raspberry Pi, to enable the real-time processing and reduce the latency. Security layer based on block chain for data integrity protection and tamper proofing within smart city networks. The suggested system shows high improvements in congestion reduction, better accuracy in air pollution forecasting, and energy efficiency in urban management. It will be validated using simulation with SUMO and MATLAB and real-world sensor data that the sensor fusion approach outperforms the conventional fixed-rule strategies of traffic management. This work allows for cost-effective, large-scale smart city deployment that would reduce traffic delay and urban air pollution while securing data and being computationally efficient. The low-latency decision-making approach with edge-AI makes it fit for real-time urban governance. Unlike traditional models that process either traffic or environmental data in silos, the work presented herein integrates multi-source sensor data with edge computing and blockchain security for a unified AI-driven fusion approach, thus building a robust framework for next-generation smart city intelligence.
Read MoreDoi: https://doi.org/10.54216/FPA.190224
Vol. 19 Issue. 2 PP. 328-340, (2025)
Accurate disease prediction is essential for enabling preventive healthcare and reducing the burden of chronic illnesses. This study introduces an innovative multi-disease prediction framework leveraging machine learning and optimization techniques to address limitations in precision and scope present in prior research. Specifically, we focus on predicting five major diseases—diabetes, heart disease, kidney disease, liver disease, and breast cancer—by employing the Social Group Optimization (SGO) algorithm to fine-tune the Random Forest (RF) classifier's hyperparameters.The proposed SGO-optimized RF model optimizes seven critical parameters - n_estimators, max_depth, min_samples_split, min_samples_leaf, max_features, bootstrap, and criterion simultaneously, significantly enhancing the model's performance. Our approach, applied to five disease datasets, achieves notable accuracy improvements: 98.25 When tested on diverse datasets, the model achieves exceptional accuracy: 98.25% for breast cancer, 84.62% for liver disease, 93.44% for heart disease, 82.47% for diabetes, and 100% for chronic kidney disease. On average, the SGO-optimized RF outperforms existing methods with a 2.3% accuracy improvement across datasets. This research highlights the transformative potential of SGO-based optimization in advancing the accuracy and reliability of predictive models. The results demonstrate the framework's applicability in clinical scenarios, providing precise and actionable insights that support early diagnosis and improve patient outcomes.
Read MoreDoi: https://doi.org/10.54216/FPA.190225
Vol. 19 Issue. 2 PP. 341-366, (2025)
Multi-label data stream classification plays a crucial role in various applications, including recommendation systems, real-time monitoring systems, smart cities, social media analysis, and healthcare. Its ability to classify constantly generated, potentially unbounded data at a high rate is of utmost importance. Besides accommodating multiple labels, data streams may evolve due to concept drift and bias toward particular classes due to class imbalance. This research introduces the multi-label classification model based on Hoeffding inequality (ML-kNN-H). The proposed model aims to process multi-label data streams, handle concept drift, and class imbalance. ML-kNN-H removes instances introducing errors based on a dynamic value computed from the Hoeffding inequality instead of a fixed value, thereby enhancing the model's efficiency and applicability to different types of data streams. Several experiments have been conducted to assess the model's performance in the presence of concept drift (abrupt and gradual drift) and class imbalance. Particularly, it has been evaluated against six kNN multi-label classifiers on ten datasets: synthetic and real world. The results indicate that ML-kNN-H outperformed the other classifiers on benchmark datasets in terms of Subset Accuracy, Accuracy, Hamming Score, and F-score, except in running time. Statistical analysis has also been utilized to measure the significance of the ML-kNN-H compared to the state-of-the-art classifiers.
Read MoreDoi: https://doi.org/10.54216/FPA.190226
Vol. 19 Issue. 2 PP. 367-378, (2025)
As increasingly Arabic textual data becomes accessible through the Intranet and Internet services, there is an important requirement for technologies and devices to handle the related data. Named Entity Recognition (NER) is an Information Extraction task that became a major part of several other Natural Language Processing (NLP) tasks. NER for Arabic has been obtaining improving attention, but possibilities for development in performance are even accessible. In recent decades, the Arabic NER (ANER) task has been confined to great effort to increase its performance. The ANER difficult task is to collect vast corpora or immense white gazetteers/lists that address probably the majority of Arabic language challenges like complexity, orthography, and ambiguity. Recently, deep learning (DL) has been the most typically applied NER model in the Arabic language and others. DL methods utilize the features of words and text to identify NEs. This paper presents a Secretary Bird Optimization Algorithm for Enhancing Fusion Deep Learning in Arabic Named Entity Recognition (SBOFDL-ANER) model. The main intention of the SBOFDL-ANER technique is to develop an effective method for NER in Arabic text. At first, the text pre-processing stage is applied to clean and transform the raw text into a structured format for analysis. Next, the word embedding method has been implemented by the Word2Vec method. Besides, the proposed SBOFDL-ANER technique designs ensemble models such as deep belief network (DBN), elman recurrent neural network (ERNN), and multi-graph convolutional networks (MGCN) for the process of classification. Eventually, the secretary bird optimization algorithm (SBOA) implements the hyperparameter choice of ensemble models. A wide-ranging simulation was applied to verify the performance of the SBOFDL-ANER method. The experimental outcomes demonstrated that the SBOFDL-ANER model highlighted improvement over other existing methods
Read MoreDoi: https://doi.org/10.54216/FPA.190227
Vol. 19 Issue. 2 PP. 379-391, (2025)
In the digital age, e-commerce platforms are critical components of the global economy, facilitating seamless transactions and interactions between businesses and consumers. The digital infrastructure of these institutions is frequently attacked, either to hack or disrupt online services, leading to significant financial losses and damage to reputation. The most famous of these attacks are DDoS attacks, which lead to an increase in the volume of traffic to the platform's website beyond the capacity of the servers, thus causing the platform to respond slowly and crash and customers to be unable to access it. The increase in these attacks causes significant material damage to institutions, whether in the loss of revenues or the cost of responding to attacks. This work presents a robust DDoS attacks early detection model that can be adopted on e-commerce platforms using a lightweight one-dimension Convolutional neural network. The proposed model leverages the efficiency of deep learning with the lightweight architecture to analyze network traffic in real time, identifying patterns indicative of an impending DDoS attack. The balance between high detection accuracy with computational efficiency makes it suitable for real-time implementation in diverse e-commerce environments. DNN is trained on a comprehensive dataset of network traffic, encompassing both normal and attack scenarios, to ensure it can distinguish between legitimate traffic spikes and malicious activity. DDoS Evaluation Dataset CIC-DDoS2019 and CICIDS2017 are used in the experimental and accuracy achieved 0.98 and 0.99 in these two datasets respectively.
Read MoreDoi: https://doi.org/10.54216/FPA.190228
Vol. 19 Issue. 2 PP. 392-401, (2025)
The Internet of Things (IoT) devices and technologies are more effective in the medical sector. It includes the combination of numerous interrelated sensor, systems, and devices for gathering, examining, and conveying health-related information for medicinal uses. In the healthcare field, Blockchain (BC) technology embraces huge latent for increasing the security and confidentiality of data. BC-aided intrusion detection on IoT healthcare methods is a new technique for increasing the privacy and security of complex health data. Patients have superior control throughout their information’s growth, granting or revoking access as needed, but healthcare employees will modernize data sharing and certify the reliability of significant data. On the other hand, deep learning (DL) is excellent for transforming healthcare analytics, presenting rapid and tremendously precise estimations of medicinal circumstances. This paper presents a Blockchain-Assisted Deep Learning Model for Enhancing Healthcare Data Security with Metaheuristic Optimization Techniques (BCDL-HDSMOT) model. The main intention of the BCDL-HDSMOT technique is to develop an effective method for enhancing data security in the medical sector. At first, the blockchain technique is applied in healthcare to enhance data security, interoperability, and transparency while ensuring patient privacy and efficient record management. Next, the data pre-processing stage employs min-max normalization to clean, transform, and organize input data into a suitable quality for analysis. Besides, the black widow optimization algorithm (BWOA) has been deployed for the feature selection process to select the relevant features from input data. For the classification process, the proposed BCDL-HDSMOT technique designs a versatile long-short-term memory (VLSTM) method. At last, the improved seagull optimization algorithm (ISOA)--based hyperparameter selection process is performed to optimize the classification results of the VLSTM method. The experimental evaluation of the BCDL-HDSMOT algorithm can be tested on a benchmark dataset. The wide-ranging outcomes highlight the significant solution of the BCDL-HDSMOT approach to the cyberattack detection process.
Read MoreDoi: https://doi.org/10.54216/FPA.190229
Vol. 19 Issue. 2 PP. 402-417, (2025)
Kidney cancer is a lethal cancerous and very dangerous disease caused by genetic renal disease or by kidney tumors, and some patients might survive since there is no technique for earlier diagnosis of kidney tumor. Earlier diagnosis of kidney tumor assists physicians to begin proper treatment and therapy for the patient, which prevent kidney cancers and renal transplantation. Accurate classification of kidney tumor is vital for prediction and treatment planning. However, manual classification by pathologists could be subjective and time-consuming, and there can be considerable inter-observer variability. With the development of artificial intelligence (AI), automated tools enabled by machine learning (ML) and deep learning (DL) methods could predict cancers. This study designs a new white shark optimizer with an ensemble majority voting based kidney cancer classification (WSO-EMVKCC) technique on pathology images. The presented WSO-EMVKCC technique intends to identify the different grades of kidney cancer using DL and ensemble voting concepts. To accomplish this, the presented WSO-EMVKCC technique employs a deep convolutional neural network based Xception technique for the feature extraction process. Moreover, the WSO model has been used for the optimal hyperparameter tuning of the Xception approach. Furthermore, an ensemble majority voting classifier including three ML techniques like long short-term memory (LSTM), sparse autoencoder (SAE), and gated recurrent unit (GRU) models are employed for kidney cancer classification. The stimulation validation of the WSO-EMVKCC model is performed on the open access histology image database from Kaggle repository. The stimulated values illustrate the promising performance of the WSO-EMVKCC algorithm over other DL techniques.
Read MoreDoi: https://doi.org/10.54216/FPA.190230
Vol. 19 Issue. 2 PP. 418-433, (2025)