Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
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Item Prevention of webshell attack using machine learning techniques(Grenze Scientific Society, 2021) Satish, Y.C.; Naik, P.M.; Rudra, B.Webshell is a web vulnerability and a security threat to any user or a server that can be accessed by attackers to control our system. And also, they may use our system as a command control device to attack other systems. It is difficult to monitor and identify such threats because attackers always tried to attack in different methods and new technologies. However, we can detect the webshell with Machine Learning Techniques with better accuracy; all we need is more number of samples. With this project, we presented a PHP based webshell detecting model. We used different ML algorithms: Logistic Regression(LR), Random Forest(RF), Support Vector Machine(SVM) and K-Nearest Neighbour(KNN). Addition to this PHP file's standard statistical features, we also added an opcode sequence from the PHP files, consisting of the TF-IDF Vector and the Hash Vector. Depending upon these features, we trained with different machine learning models(SVM, RF, LR, KNN). In these models, we got better results with Random Forest having an accuracy of 96.45\% with a false-positive rate of 3.5\%, which is good results compared to several popular detection techniques. © Grenze Scientific Society, 2021.Item Flower Phenotype Recognition and Analysis using YoloV5 Models(Grenze Scientific Society, 2022) Naik, P.M.; Rudra, B.Real time detection of flowers based on their growth cycle is called as phenotype. It is one of the most important methods for judging the maturity of flowers and to estimate their yield. Traditional method involves flower detection and classification of varied species. In this paper, we introduce a new dataset based on flower phenotype. The dataset has images of flowers, classified into bud, fresh and stale. The work will help for identification and localization of classes based on flower phenotype. The detection of flowers at various stages of their life will be more important to harvesting in floriculture field. We propose a state of art deep learning-based approach using YOLOV5 model is used for identifying flowers based on flower phenotype. The images are subsequently augmented using rotation transformation, color balance transformation, brightness transformation and blur processing. The augmented images are used for preparing training sets. Using different versions of YoloV5 the flower image dataset is trained and tested. Primarily, flower phenotype considered has stages like bud formation, blossoming and stale flower. © Grenze Scientific Society, 2022.Item Optimizing Object Detection in YOLOv5 using Adaptive Genetic Algorithm: A Study on Population Sizes for Enhanced Accuracy(Institute of Electrical and Electronics Engineers Inc., 2024) Naik, P.M.; Rudra, B.In recent years, deep learning-based object detection models like YOLOv5 have demonstrated remarkable efficiency in accurately identifying objects within images. However, achieving optimal performance still presents challenges. Many researchers have employed hybrid models to enhance the mean average precision (mAP) in the YOLOv5 architecture. However, only a few have explored hyperparameter-based optimization techniques to improve mAP. We use an adaptive genetic algorithm-based approach for analyzing Arecanut X-ray images to improve detection accuracy within the YOLOv5 framework. Balancing population size and computational efficiency is crucial for effective exploration and exploitation of the solution space in genetic algorithms without incurring unnecessary computational costs. This research investigates the influence of various population sizes on mAP using a genetic algorithm. Our findings indicate that a population size of 50 is ideal for achieving the best mAP among the explored sizes, when evolved for 300 generations. © 2024 IEEE.Item Classification of Arecanut X-Ray Images for Quality Assessment Using Adaptive Genetic Algorithm and Deep Learning(Institute of Electrical and Electronics Engineers Inc., 2023) Naik, P.M.; Rudra, B.The traditional approach for analyzing the quality of arecanuts is based on their external appearance. However, using machine learning and deep learning techniques, automated classifications were performed. But the true quality can only be analyzed when the internal structure of the arecanut is examined. Therefore, we use the X-ray imaging technique to determine the internal quality of arecanuts. We prepared a novel dataset of arecanut X-ray images and used a YOLOv5 based deep learning architecture for classification. The present study employs an adaptive genetic algorithm based approach for hyperparameter optimization to enhance the mean average precision (mAP) using a light weight model generated using a ghost network and a feature pyramid network (FPN). We have achieved the highest mAP of 97.84% using our method with a lower model size of 15 MB. Our method has excelled in detecting the arecanut compared to cutting-edge object detection algorithms such as YOLOv3, YOLOv4, Detetron, YOLOv6, YOLOv8, and YOLOX. We also acknowledged the performance enhancement using the adaptive genetic algorithm on the Pascal VOC 2007 image dataset. Despite of significant computational requirements for executing genetic algorithms, we proved that genetic algorithms can boost mAP. Additionally, the methodology developed in this investigation produced multiple models with the best mAP featuring optimized hyperparameters. This methodical strategy is helpful for the design of an automatic, non-destructive, integrated X-ray image based classification system. This system has the potential to revolutionize the quality assessment of arecanuts by offering a more efficient evaluation method. © ; 2023 The Authors.Item Deep learning-based arecanut detection for X-ray radiography: improving performance and efficiency for automated classification and quality control(Taylor and Francis Ltd., 2025) Naik, P.M.; Rudra, B.X-ray radiography is a valuable, non-destructive tool and can be used to examine the internal components or quality attributes of agricultural commodities, including arecanut. The true quality of an arecanut can be determined using destructive methods through visual inspection. However, dissected arecanuts do not have a shelf life. There is no non-destructive method available for grading arecanuts. We employ X-ray imaging as an aid to conduct internal examinations of arecanuts, allowing for thorough inspection without causing damage. A custom X-ray image dataset of arecanuts is created for automated interpretation of grades. We developed a hybrid arecanut grading model using YOLOv5s architecture incorporating the Stem, the GhostNet and the Transformer blocks. The proposed hybrid architecture outperforms in comparison with state-of-the-art models with a mean average precision (mAP) of 97.30%. The proposed 9.5 MB lightweight model can easily fit into X-ray devices, making it ideal for detecting arecanut grades in the industry. This method could transform the standards of quality inspection for arecanut. Its incorporation could establish a new industry benchmark for unparalleled quality assessment using X-ray technology as a non-destructive tool. © 2024 Informa UK Limited, trading as Taylor & Francis Group.