-
Research Article
Enhancing Agricultural Diagnostics: Advanced Training of Pre-Trained CNN Models for Paddy Leaf Disease Detection
Issue:
Volume 10, Issue 1, June 2025
Pages:
1-13
Received:
6 March 2025
Accepted:
21 March 2025
Published:
31 March 2025
Abstract: Timely and precise identification of foliar diseases is essential in contemporary agriculture to avert crop loss, enhance productivity, and guarantee food security. Paddy, being one of the most extensively farmed and consumed staple crops globally, is especially vulnerable to several leaf diseases that can markedly diminish yield. Conventional illness detection techniques, which depend significantly on manual observation and expert evaluation, are frequently time-consuming, labor-intensive, and susceptible to discrepancies. These constraints need the implementation of automated and efficient disease detection technologies. This research investigates the utilization of a pre-trained EfficientNetB3 convolutional neural network for the identification and categorization of paddy leaf diseases. The model was trained and assessed on a rich and diverse dataset comprising annotated pictures of healthy and sick paddy leaves. The performance evaluation included conventional classification criteria like as accuracy, precision, recall, and F1-score to ensure a comprehensive assessment of the model's efficacy. The EfficientNetB3 model exhibited exceptional performance, with an overall accuracy of 96% in the detection and classification of prevalent paddy leaf diseases. This elevated accuracy signifies the model's proficiency in generalizing effectively across diverse illness categories and imaging settings. The findings underscore the capability of deep learning and computer vision methodologies to revolutionize agricultural operations by offering scalable, dependable, and instantaneous solutions for disease identification. The suggested approach facilitates early diagnosis, aiding farmers and agronomists in executing timely and precise treatments, hence minimizing crop loss and enhancing production. Moreover, the incorporation of AI-driven technologies into current agricultural frameworks fosters sustainable farming and strengthens the resilience of food production systems. The research highlights the significant influence of artificial intelligence on precision agriculture and establishes a basis for additional investigation into intelligent crop monitoring systems.
Abstract: Timely and precise identification of foliar diseases is essential in contemporary agriculture to avert crop loss, enhance productivity, and guarantee food security. Paddy, being one of the most extensively farmed and consumed staple crops globally, is especially vulnerable to several leaf diseases that can markedly diminish yield. Conventional illn...
Show More
-
Research Article
XSS-Net: An Intelligent Machine Learning Model for Detecting Cross-Site Scripting (XSS) Attack in Web Application
Emmanuel Osaze Oshoiribhor
,
Adetokunbo MacGregor John-Otumu*
Issue:
Volume 10, Issue 1, June 2025
Pages:
14-24
Received:
14 March 2025
Accepted:
25 March 2025
Published:
14 April 2025
Abstract: This research paper focuses on detecting Cross-Site Scripting (XSS) attacks, a prevalent web security threat where attackers inject malicious scripts into web applications to steal sensitive user data, hijack sessions, and execute unauthorized actions. Traditional rule-based and signature-based detection methods often fail against sophisticated and obfuscated XSS payloads, necessitating more advanced solutions. To address this, a machine learning-based model is developed to enhance XSS detection accuracy while minimizing false positives. The proposed approach utilizes feature extraction techniques, including Term Frequency-Inverse Document Frequency (TF-IDF) and n-grams, to analyze JavaScript payloads, while Principal Component Analysis (PCA) is employed for feature selection, reducing dimensionality and improving computational efficiency. A Logistic Regression classifier is trained on an XSS payload dataset from Kaggle, with data split into 80% for training and 20% for testing to ensure a robust evaluation. Hyperparameter tuning is performed using GridSearchCV, optimizing the model’s predictive capabilities. Experimental results demonstrate a 99.70% accuracy, with 100% recall and 99.36% precision, highlighting the model’s effectiveness in detecting XSS attacks while minimizing false alarms. The high recall score ensures all malicious payloads are identified, while the strong precision rate enhances reliability for real-world deployment. These findings underscore the potential of machine learning in strengthening web security frameworks, offering a scalable and efficient alternative to conventional detection systems. Future research should focus on enhancing resilience against adversarial attacks by integrating deep learning models such as Bidirectional LSTMs (BiLSTMs) and Transformer-based architectures. Additionally, deploying the model in real-time web security solutions could provide proactive defense mechanisms, ensuring robust protection against evolving XSS threats.
Abstract: This research paper focuses on detecting Cross-Site Scripting (XSS) attacks, a prevalent web security threat where attackers inject malicious scripts into web applications to steal sensitive user data, hijack sessions, and execute unauthorized actions. Traditional rule-based and signature-based detection methods often fail against sophisticated and...
Show More
-
Review Article
A Comprehensive Review of FPV Technology: Applications, Advantages, and Future Trends
Mojtaba Nasehi*
Issue:
Volume 10, Issue 1, June 2025
Pages:
25-31
Received:
25 February 2025
Accepted:
31 March 2025
Published:
28 April 2025
DOI:
10.11648/j.mlr.20251001.13
Downloads:
Views:
Abstract: FPV (First Person View) technology has revolutionized various fields, including unmanned aerial vehicles (UAVs), robotics, and renewable energy systems. This paper provides a detailed overview of FPV technology, focusing on its applications, advantages, and recent advancements. Initially developed for military and surveillance purposes, FPV technology has evolved to become more accessible and widely adopted in civilian sectors. The paper explores how FPV technology enhances user experience, improves efficiency, and offers environmental benefits. In recreational activities, FPV drones are widely used in drone racing and freestyle flying, providing an immersive and engaging experience. Professionally, FPV drones are employed for aerial photography, infrastructure inspection, and search and rescue operations, where the real-time video feed enables operators to make immediate decisions and adjustments, significantly improving task efficiency and safety. In robotics, FPV technology is used in teleoperated robots for industrial inspection and search and rescue missions, allowing operators to control devices with greater accuracy and confidence. In renewable energy systems, FPV technology is applied to floating photovoltaic (FPV) systems, which are solar panels installed on water bodies. These systems benefit from the cooling effect of water, improving performance and lifespan, and help conserve land for other uses, making them suitable for densely populated areas. Additionally, FPV systems reduce water evaporation, which is beneficial in water-scarce regions. Recent advancements in FPV technology include high-definition video, extended range, and integration with artificial intelligence (AI), which provide real-time analytics and decision-making support. Despite challenges such as regulatory constraints, technical issues, and ethical considerations, the future of FPV technology looks promising. Ongoing innovations and expanding applications in fields such as agriculture, environmental monitoring, and entertainment will further enhance the capabilities and utility of FPV systems. As the technology continues to evolve, it will play a crucial role in shaping the future of unmanned systems and renewable energy solutions.
Abstract: FPV (First Person View) technology has revolutionized various fields, including unmanned aerial vehicles (UAVs), robotics, and renewable energy systems. This paper provides a detailed overview of FPV technology, focusing on its applications, advantages, and recent advancements. Initially developed for military and surveillance purposes, FPV technol...
Show More
-
Research Article
An Improved Deep Learning Model for Word Embeddings Based Clustering for Large Text Datasets
Vijay Kumar Sutrakar*,
Nikhil Mogre
Issue:
Volume 10, Issue 1, June 2025
Pages:
32-43
Received:
14 March 2025
Accepted:
31 March 2025
Published:
29 April 2025
DOI:
10.11648/j.mlr.20251001.14
Downloads:
Views:
Abstract: In the rapid growth of textual data in various domains has increased the need for efficient clustering techniques capable of handling large-scale datasets. Traditional clustering methods often fail to capture semantic relationships and struggle with high-dimensional, sparse data. The present study shows an improved document clustering technique, i.e., WEClustering++, which enhances the existing WEClustering framework by integrating fine-tuned BERT based word embeddings. The proposed model incorporates advanced dimensionality reduction techniques and optimized clustering algorithms to improve clustering accuracy. In the present work, the BERT-large model, fine-tuned on domain-specific datasets is utilized. Seven benchmark datasets spanning various domains and sizes are considered. These datasets include collections of research articles, news articles, and other domain-specific texts. Experimental evaluations on multiple benchmark datasets demonstrate significant performance improvements in clustering metrics, including silhouette score, purity, and ARI. Results show a 45% and 67% increase in median silhouette scores for WEClustering_K++ (K-means-based) and WEClustering_A++ (Agglomerative-based) models, respectively. Result also shows an increase of median purity metrics of 0.4% and 0.8% is obtained for proposed WEClustering_K++ and WEClustering_A++ compared to the state of art model. Also, an increase of median ARI metrics of 7% and 11% is obtained for proposed WEClustering_K++ and WEClustering_A++ compared to the state of art model. These findings highlight the potential of fine-tuned word embeddings in bridging the gap between statistical clustering robustness and semantic understanding. The proposed approach is expected to contribute to advancements in large-scale text mining applications, including document organization, topic modelling, and information retrieval.
Abstract: In the rapid growth of textual data in various domains has increased the need for efficient clustering techniques capable of handling large-scale datasets. Traditional clustering methods often fail to capture semantic relationships and struggle with high-dimensional, sparse data. The present study shows an improved document clustering technique, i....
Show More
-
Research Article
Red Onion Seed Quality Classification Using Transfer Learning Approaches
Tarekegn Walle Yirdaw,
Ermias Melku Tadesse*,
Endalkachew Hiwote,
Abebaw Mebrate,
Ambaw Mulatu
Issue:
Volume 10, Issue 1, June 2025
Pages:
44-52
Received:
10 March 2025
Accepted:
2 April 2025
Published:
29 April 2025
DOI:
10.11648/j.mlr.20251001.15
Downloads:
Views:
Abstract: An essential vegetable that is grown all over the world and eaten in a variety of ways is the onion (Allium cepa L.). A common condiment used to improve food flavor is onion. Around the world, red onion seed (A. fistulosum) is cultivated in a variety of temperate and tropical settings. It is grown in China and Japan, among other places, worldwide. A. fistulosum is grown across Ethiopia in various regions. In 2012, 3,281,574 tons of output were obtained from 30,478 hectares of coverage. Allium fistulosum covers the Amhara area over 8000 hectors, which is 26% of our country. For export, red onion seed is separated based on quality. Red onion seed quality separation or categorization is essential to the trade process. It aids in making people marketable. In Ethiopia, this procedure is carried out manually, which has a number of drawbacks like being less effective, inconsistent, and prone to subjectivity. To address this problem, we use pre-trained transfer learning model VGG, GoogleNet, and ResNet50 for quality classification of red onion seed. The main procedures include image preprocessing, resizing, data augmentation, and prediction. The model trained on 470 datasets collected from different agricultural fields in south Gondar libo kemkem and fogera woreda. We use various augmentation strategies to expand the dataset. Ten percent of the dataset was set aside for testing, ten percent for validation, and eighty percent for training. For VGG19, VGG16, GoogleNet, and ResNet, the model's classification accuracy for the input image is 99%, 100%, 100%, and 86%, respectively.
Abstract: An essential vegetable that is grown all over the world and eaten in a variety of ways is the onion (Allium cepa L.). A common condiment used to improve food flavor is onion. Around the world, red onion seed (A. fistulosum) is cultivated in a variety of temperate and tropical settings. It is grown in China and Japan, among other places, worldwide. ...
Show More
