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Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28507
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Item Machine learning-based detection and classification of lung cancer(Elsevier, 2022) Dodia, S.; Annappa, A.Cancer is termed to be one of the life-threatening diseases in the world. Among various types of cancer, the highest mortality and morbidity rate recorded is from lung cancer. Computer-aided diagnosis (CAD) systems are used to identify lung cancer nodules. The development of reliable automated algorithms is important to provide doctors with a second opinion. A lung cancer diagnosis is performed in two steps: lung cancer nodule detection and classification. In nodule detection, from a given computed tomography (CT) scan, the nodules and nonnodules are identified. Once the nodules and nonnodules are identified, the next step is to classify the detected nodules as cancerous and noncancerous. This work explores various machine learning classifiers for lung cancer classification. A majority voting scheme is used to classify nodules. An in-depth analysis of different machine learning algorithms’ performance is presented in this work. © 2023 Elsevier Inc. All rights reserved.Item Hybrid Malicious Encrypted Network Traffic Flow Detection Model(Springer Science and Business Media Deutschland GmbH, 2023) Hublikar, S.; Shet, N.S.V.Encrypted communication technology has evolved as the network, and Internet applications have advanced. Malicious communication, on the other hand, employs encryption to bypass standard detection and security protection. The existing security prevention and detection technologies are unable to identify harmful communication that is encrypted. The growth of artificial intelligence (AI) in these days has enabled to employ machine learning (ML) as well as deep learning approaches to identify encrypted malicious communications without decryption, with remarkably precise detection outcomes. At this moment, research on detecting harmful encrypted traffic is mostly focused on analyzing the features of encrypted data and selecting neural network (NN) techniques. Hybrid ML is proposed in this study by merging two well-performing data mining algorithms with natural language processing tasks. Here, a new traffic flow detection method is performed by the hybrid ML technique. At first, the benchmark data is collected from public sources. The features are extracted using the convolutional layer of deep convolutional neural network (DCNN). Then, the weighted feature extraction is performed by grasshopper optimization algorithm (GOA). Employed the hybrid machine learning-based malicious detection with the “support vector machine (SVM) and neural network (NN)” is utilized in this model to detect the traffic affected by malicious activities, where the hidden neuron count of NN and kernel of SVM are tuning by the same GOA for increasing the accuracy and precision. This research provides findings from experiment, encouraging various researchers to develop the research as future work. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.