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
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