2692-4048ISSN (Online)
2770-0070ISSN (Print)
The classification of mushrooms as either deadly or edible stays a important challenge due to their similar appearances, which can lead to fatal poisonings. The primary difficulty lies in identifying complex patterns in mushroom appearances, such as cap shape, color, and gill structure, which complicate accurate classification. Traditional approaches and even some machine learning (ML) models fail to capture these subtle but important distinctions, leading to misclassifications. To address this issue, this paper proposed a One-Dimensional Convolutional Neural Network (1D-CNN) approach aimed at improving the accurate of mushroom classification. By effectively recognizing complex patterns in the mushroom data set, the proposed approach greatly improves classification accuracy. The model performance evaluated utilizing Precision, Accuracy, Recall, and F1-Score that achieved high scores of 100% across all metrics. These results highlight the strength of deep learning (DL) method, specifically 1D-CNNs, in recognizing with learning complex data patterns. This shows a clear advancement over traditional ML methods and ensemble techniques, establishing the 1D-CNN as a highly reliable tool for mushroom classification that can help reduce mushroom poisoning incidents.
Read MoreDoi: https://doi.org/10.54216/FPA.200201
Vol. 20 Issue. 2 PP. 01-12, (2025)
Chronic diseases (CDs) have become as significant as communicable diseases due to their rising mortality rates and long-term effects. Coronary artery disease (CAD), one of the most common NCDs, is increasingly concerning due to its impact on both death rates and overall health. Managing CAD typically requires professional care and lifestyle changes, which may be inaccessible to some patients due to financial constraints or difficulty in modifying their habits. However, remote health solutions, like mobile applications, could help CAD patients improve their condition and lower risks. In Iraq, the willingness of CAD patients to use mHealth apps has not been explored. This study examines existing mHealth readiness models and incorporates additional factors that consider the needs of CAD patients and the Iraqi context. This will be achieved by adapting a questionnaire based on expert feedback and distributing it to CAD patients in Iraq.
Read MoreDoi: https://doi.org/10.54216/FPA.200202
Vol. 20 Issue. 2 PP. 13-25, (2025)
Social media has supplanted conventional media as one of the most important venues for information exchange. Because of the internet's accessibility and simplicity, news on community media tends to spread quicker and simpler than a conventional news source. Still, not all of the information shared on ‘social media’ is true and/or comes from untrustworthy sources. Fake news may readily be manufactured and disseminated throughout ‘social media’, and this counterfeit news has the potential to mislead or misinform readers. Though several physical fact-inspection websites have been built to determine if the news is reliable, they cannot keep up with the amount of rapidly circulated internet information, particularly on social media. Twitter, being one of the most well-known continuing news sources, also happens to be one of the most dominating news disseminating media. Topic models facilitate the detection of the most relevant vocabulary and concept within a text corpus. This paper proposes a model for recognizing fake news messages from twitter posts using a novel data-mining algorithm. Here initially the twitter dataset is collected preprocessing is done by using word embedding. ‘Term Frequency Inverse Document Frequency ‘(TF-IDF)’ and Latent Semantic Analysis (LSA) do feature extraction. Feature selection is based on the Adaptive Whale Optimized Wrapper (AWOW) method. We proposed Fine-tuned Weighted Probabilistic Bayesian Neural Network (FWP-BNN) for the classification of the normal and the fake news. The proposed method is compared with existing approaches and the metrics are evaluated. The efficacy of the suggested technique in recognizing fake tweets is shown by test findings on a large miscellaneous events dataset.
Read MoreDoi: https://doi.org/10.54216/FPA.200203
Vol. 20 Issue. 2 PP. 26-37, (2025)
This research introduces a cutting-edge deep learning-based agricultural engineering illness diagnosis approach. Convolutional neural networks (CNNs) and improved methods improve accuracy and efficiency. The recommended solution includes network settings, convolution processes, and sharing strategies to reduce dimensions. These methods reduce the network's processing power so it can concentrate on disease characteristics. The model employs dropout regularization, attention processes, and multi-scale feature extraction to enhance sickness prediction. The technology also utilizes photographs and sensor data to adapt to agricultural circumstances. The performance test shows that the suggested technique outperforms traditional machine learning and mixed models in F1 score (95%), accuracy (95%), precision (94%), memory (96%), and correctness (94%). It has high discriminative power with an AUC-ROC score of 0.98. The model uses computers well: two hours to train, two seconds to derive conclusions, and 65% of the CPU at all times. Real-time farming could benefit from its use. The suggested technique can properly and reliably diagnose illnesses due to its low overfitting rate and excellent generalization potential. The precision agriculture technique will enhance crop health management and productivity.
Read MoreDoi: https://doi.org/10.54216/FPA.200204
Vol. 20 Issue. 2 PP. 38-52, (2025)
This paper presents a novel machine-learning framework designed to personalize Cognitive Behavioral Therapy (CBT) for adult patients by leveraging a multi-dimensional, adaptive approach. The proposed system integrates historical clinical data, real-time behavioral indicators, and contextual factors to generate a comprehensive psychological profile for each adult patient. A reinforcement learning mechanism underpins therapy selection, allowing the model to iteratively refine treatment strategies based on individual responses and therapeutic outcomes. An embedded optimization process enables dynamic adaptation of interventions, improving predictive accuracy and fostering patient-centered care. The framework incorporates a multi-factor assessment model that synthesizes psychological, behavioral, and physiological variables to enhance therapeutic effectiveness, sustainability, and responsiveness to change. Comparative evaluations demonstrate that this approach outperforms traditional CBT planning methods, as well as existing deep learning, hybrid, and reinforcement-based models, in terms of accuracy, interpretability, computational efficiency, and patient outcome optimization for adults. Furthermore, the system emphasizes fairness and equity in treatment personalization, supporting real-time clinical decision-making while minimizing ineffective therapeutic pathways. This research underscores the transformative potential of machine learning in mental health care by enabling scalable, data-driven, and continuously improving interventions tailored to the nuanced needs of adult patients undergoing CBT.
Read MoreDoi: https://doi.org/10.54216/FPA.200205
Vol. 20 Issue. 2 PP. 53-64, (2025)
Advancements in high-speed communication networks, such as 5G and 6G, display the shortcomings of earlier Radio Frequency (RF) systems due to their limited access to the electromagnetic spectrum. Optical Wireless Communication (OWC) gives access to an unlimited optical spectrum that can address the demands in 6G networks. One key thing about Free Space Optical (FSO) is that it uses the near-infrared spectrum to transfer large amounts of data over several kilometers. FSO systems can be found in a large number of places, ranging from home and outdoor use to important roles in the military and in medical settings. These systems, however, struggle to transmit signals clearly and reliably when the distance is very long due to effects of the atmosphere. One solution to these problems is to rely on advanced channel modeling and using Multiple-Input Multiple-Output (MIMO) schemes, as they improve reliability and efficiency. The latest research efforts are centered on Massive MIMO-FSO networks that make use of spatial diversity to fight atmospheric fading and guarantee a sturdier connection. Importantly, Machine Learning (ML) is transforming the way research is carried out. Channel estimation, turbulence prediction, signal demodulation, and adaptive modulation can now be done using ML, which reduces the need for many calculations and makes things run more smoothly. Using information from data, ML helps optimize FSO systems in different channel conditions. This study provides a review of how machine learning is applied in Massive MIMO-FSO systems. It sorts out highlighting current strategies, explaining their strengths, weaknesses, and how to use them. The main goal of this review is to give an in-depth look at how ML-assisted optical wireless systems can fulfill the needs of future communication networks.
Read MoreDoi: https://doi.org/10.54216/FPA.200206
Vol. 20 Issue. 2 PP. 65-76, (2025)
Surveillance cameras play a pivotal role in educational institutions. They monitor the educational process, detect violations, and protect students from potential injuries or dangers. Continuous recording generates a massive amount of video data. Human observers spend significant time and effort reviewing the footage. Reviewing aims to detect and quickly address abnormal events. Abnormal events are rare in educational environments. Observers may become bored during continuous monitoring. This may cause fatigue and loss of attention. To overcome these challenges, this paper proposes an intelligent system that combines summarization and abnormal event detection in surveillance video. It is divided into two stages: The first stage starts with the extraction of static, feature-based key frames that highlight the video's most significant content. In the second stage, Convolutional Autoencoder (CAE) network used to detect abnormal events from the key frames generated by the summary stage. The proposed system produces two separate videos: a general summary and a dedicated abnormal events video sent to the relevant individuals. The proposed system was tested on some benchmark datasets. The experimental results demonstrated that the proposed system was effective in reducing browsing time and effort, as well as in detecting abnormal events within an educational context.
Read MoreDoi: https://doi.org/10.54216/FPA.200207
Vol. 20 Issue. 2 PP. 77-92, (2025)
The advancement and expansion of artificial intelligence (AI) has revolutionized traditional education paradigms. The ability of language models to process human language has revolutionized the field of artificial intelligence. This had led to the integration of language models such as Generative AI (GAI) into learning as it can understand and process human language efficiently. Fusion of these models has significantly enhanced education and research development leading to academic progress. There is gap in the learning needs of the students. Traditional teaching methods often fail to provide personalised adaptive environments and hence to fill this gap this research focusses on integration of AI tools in classrooms. The objective of this paper is to explore and analyze the applications of integration of generative AI strategies in teaching and to examine the impact from educators’ perspective. The objective of the study is to evaluate the effectiveness of GAI powered integration in teaching and learning by analyzing the feedback scores gathered by students and teachers of an undergraduate course. Data was collected and analyzed using standard mean comparisons. Results of the analysis demonstrate that generative AI assisted teaching facilitated adaptive learning, automated content generation, enhanced student engagement and the quality of dynamic learning when compared with conventional strategies. Using quantitative analysis, the study validates GAI fusion, and the data is analyzed using standard mean scores. The improvement performance of students and educators feedback for traditional and GAI is 56.63% and 54.41% respectively, which suggests a positive shift of moving from traditional to GAI approaches. This strong score shows the GAI approach is more effective and student-centered. The results reveal that though challenges exist, strategic guided integration of GAI significantly enhances pedagogical factors of education and thus plays a crucial role in shaping AI education as AI models evolve.
Read MoreDoi: https://doi.org/10.54216/FPA.200208
Vol. 20 Issue. 2 PP. 93-102, (2025)