Browsing by Author "Singh, Paramjeet"

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    CottonLeafNet: cotton plant leaf disease detection using deep neural networks
    (Springer, 2023-03-18T00:00:00) Singh, Paramjeet; Singh, Parvinder; Farooq, Umar; Khurana, Surinder Singh; Verma, Jitendra Kumar; Kumar, Munish
    India is a cover crop region whereby agricultural production sustains a substantial proportion of the populace and upon which the whole Indian economy is heavily reliant. As per research, it provides subsistence for around 70% of rural households. In terms of agricultural output and exports, India ranks second and ninth, respectively. However, it accomplishes the first position globally in terms of cotton exports thereby adequately contributing to the economy of the country. However, it has been documented that various crops especially cotton plants are severely harmed by various pests, extreme climatic variations, nutrient inadequacy and toxicity, and so on. Cotton plant diseases cause a wide range of illnesses ranging from bacterial to nutritional deficiency giving a hard time for the human eye to recognize. However, most of the researchers have considered only a few types of cotton leaf diseases and excluded many. Keeping these constraints in consideration, this research seeks to aid the detection of these diseases by employing deep learning paradigms. The research begins with acquiring a near-balanced dataset with 22 leaf disease types including bacterial, fungal, viral, nutrient deficiency, etc. followed by data augmentation to boost the performance of the models. Many algorithms were tested, however, CNN happens to be very efficient and productive. The proposed model when evaluated on the test set achieves an accuracy of 99.39% with a negligible error rate, thus outperforming all the existing approaches by consuming less computational time. The outcome portrays that the proposed approach has the efficiency to be implemented in real-time detection systems to aid the precise detection of cotton leaf diseases to help the farmers in taking appropriate actions. � 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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    Feature Engineering and Ensemble Learning-Based Classification of VPN and Non-VPN-Based Network Traffic over Temporal Features
    (Springer, 2023-07-29T00:00:00) Abbas, Gazy; Farooq, Umar; Singh, Parvinder; Khurana, Surinder Singh; Singh, Paramjeet
    With the rapid advancement in technology, the constant emergence of new applications and services has resulted in a drastic increase in Internet traffic, making it increasingly challenging for network analysts to maintain network security and classify traffic, especially when encrypted or tunneled. To address this issue, the proposed strategy aims to distinguish between regular traffic and traffic tunneled through a virtual private network and characterize traffic from seven different applications. The proposed approach utilizes various ensemble machine learning techniques, which are efficient and accurate and consume minimal computational time for training and prediction compared to conventional machine and deep learning models. These models were applied for both the classification and characterization of network traffic, deriving efficient results. The extreme and light gradient boosting algorithms performed well in multiclass classification, while AdaBoost and Light GBM performed well in binary classification. However, when all the datasets were merged and categorized into two classes and various feature engineering methods were applied, the proposed system achieved an accuracy of more than 99%, with minimal error scores using light GBM with min�max scaling over stratified fivefold, thereby outperforming all existing approaches. This research highlights the efficiency and potential of the proposed model in detecting network traffic. � 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.