Permanent WRAP URL:http://wrap.warwick.ac.uk86204 Copyright and reuse:The Warwick Research Archive Portal (WRAP) makes this work by researchers of the University of Warwick available open access under the following conditions. Copyright © and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable the material made available in WRAP has been checked for eligibility before being made available.Copies of full items can be used for personal research or study, educational, or not-for profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way.Publisher's statement: "© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works." A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP URL' above for details on accessing the published version and note that access may require a subscription.For more information, please contact the WRAP Team at: wrap@warwick.ac.uk JOURNAL OF L A T E X CLASS FILES 1 Weighted Level Set Evolution Based on Local Edge Features for Medical Image SegmentationAlaa Khadidos, Victor Sanchez, Member, IEEE, and Chang-Tsun Li, Senior Member, IEEE Abstract-Level set methods have been widely used to implement active contours for image segmentation applications due to their good boundary detection accuracy. In the context of medical image segmentation, weak edges and inhomogeneities remain important issues that may hinder the accuracy of any segmentation method based on active contours implemented using level set methods. This paper proposes a method based on active contours implemented using level set methods for segmentation of such medical images. The proposed method uses a level set evolution that is based on the minimization of an objective energy functional whose energy terms are weighted according to their relative importance in detecting boundaries. This relative importance is computed based on local edge features collected from the adjacent region located inside and outside of the evolving contour. The local edge features employed are the edge intensity and the degree of alignment between the image's gradient vector flow field and the evolving contour's normal. We evaluate the proposed method for segmentation of various regions in real MRI and CT slices, X-ray images, and ultra sound images. Evaluation results confirm the advantage of w...

Intrusion detection and classification have gained significant attention recently due to the increased utilization of networks. For this purpose, there are different types of Network Intrusion Detection System (NIDS) approaches developed in the conventional works, which mainly focus on identifying the intrusions from the datasets with the help of classification techniques. Still, it is limited by the significant problems of inefficiency in handling large dimensional datasets, high computational complexity, false detection, and more time consumption for training the models. To solve these problems, this research intends to develop an innovative clustering-based classification methodology to precisely detect intrusions from the different types of IDS datasets. Here, the most recent and extensively used IDS datasets such as NSL-KDD, CICIDS, and Bot-IoT have been employed for detecting intrusions. Data preprocessing has been performed to normalize the dataset to eliminate irrelevant attributes and organize the features. Then, the data separation is applied by forming the clusters by using an intelligent Anticipated Distance-based Clustering (ADC) incorporated with the Density-Based Spatial clustering of applications with noise (DBScan) algorithm. It helps to find the distance and density measures for grouping the attributes into the clusters, which increases the efficiency of classification. Here, the most suitable optimal parameters are selected using the Perpetual Pigeon Galvanized Optimization (PPGO) technique. The extracted features are used for training and testing the dataset samples.Consequently, the Likelihood Naïve Bayes (LNB) classification approach is implemented to accurately predict the classified label as to whether normal or attack. During the evaluation, the performance of the proposed IDS framework is validated and compared using various evaluation metrics. Theresults show that the proposed ADC-DBScan-LNB model outperforms the other techniques with improved performance outcomes. INDEX TERMSNetwork IntrusionDetection System (NIDS), Density-based spatial clustering of applications with noise (DBSCAN), Anticipated Distance-based Clustering (ADC), Data Preprocessing, Likelihood Naïve Bayes (LNB), and IDS Datasets.

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Copies of full items can be used for personal research or study, educational, or not-for profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way.Publisher's statement: "© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works." A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP url' above for details on accessing the published version and note that access may require a subscription. ABSTRACTActive contours, or snakes, have been widely used for image segmentation purposes. However, high noise sensitivity and poor performance over weak edges are the most acute issues that hinder the segmentation accuracy of these curves, particularly in medical images. In order to overcome these issues, we propose a novel external force that integrates gradient vector flow (GVF) field forces and balloon forces based on a weighting factor computed according to local image features. The proposed external force reduces noise sensitivity, improves performance over weak edges and allows initialization with a single manually selected point. We evaluate the proposed external force for segmentation of various regions on real MRI and CT slices. Evaluation results show that the proposed approach leads to more accurate segmentation than snakes using traditional external forces.

The Industrial Internet of Things (IIoT) promises to deliver innovative business models across multiple domains by providing ubiquitous connectivity, intelligent data, predictive analytics, and decision-making systems for improved market performance. However, traditional IIoT architectures are highly susceptible to many security vulnerabilities and network intrusions, which bring challenges such as lack of privacy, integrity, trust, and centralization. This research aims to implement an Artificial Intelligence-based Lightweight Blockchain Security Model (AILBSM) to ensure privacy and security of IIoT systems. This novel model is meant to address issues that can occur with security and privacy when dealing with Cloud-based IIoT systems that handle data in the Cloud or on the Edge of Networks (on-device). The novel contribution of this paper is that it combines the advantages of both lightweight blockchain and Convivial Optimized Sprinter Neural Network (COSNN) based AI mechanisms with simplified and improved security operations. Here, the significant impact of attacks is reduced by transforming features into encoded data using an Authentic Intrinsic Analysis (AIA) model. Extensive experiments are conducted to validate this system using various attack datasets. In addition, the results of privacy protection and AI mechanisms are evaluated separately and compared using various indicators. By using the proposed AILBSM framework, the execution time is minimized to 0.6 seconds, the overall classification accuracy is improved to 99.8%, and detection performance is increased to 99.7%. Due to the inclusion of auto-encoder based transformation and blockchain authentication, the anomaly detection performance of the proposed model is highly improved, when compared to other techniques.

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In this paper, a data mining model on a hybrid deep learning framework is designed to diagnose the medical conditions of patients infected with the coronavirus disease 2019 (COVID-19) virus. The hybrid deep learning model is designed as a combination of convolutional neural network (CNN) and recurrent neural network (RNN) and named as DeepSense method. It is designed as a series of layers to extract and classify the related features of COVID-19 infections from the lungs. The computerized tomography image is used as an input data, and hence, the classifier is designed to ease the process of classification on learning the multidimensional input data using the Expert Hidden layers. The validation of the model is conducted against the medical image datasets to predict the infections using deep learning classifiers. The results show that the DeepSense classifier offers accuracy in an improved manner than the conventional deep and machine learning classifiers. The proposed method is validated against three different datasets, where the training data are compared with 70%, 80%, and 90% training data. It specifically provides the quality of the diagnostic method adopted for the prediction of COVID-19 infections in a patient.

This paper proposed an innovative mechanical design using the Rocker-bogie mechanism for resilient Trash-Collecting Robots. Mask-RCNN, YOLOV4, and YOLOv4-tiny were experimented on and analyzed for trash detection. The Trash-Collecting Robot was developed to be completely autonomous as it was able to detect trash, move towards it, and pick it up while avoiding any obstacles along the way. Sensors including a camera, ultrasonic sensor, and GPS module played an imperative role in automation. The brain of the Robot, namely, Raspberry Pi and Arduino, processed the data from the sensors and performed path-planning and consequent motion of the robot through actuation of motors. Three models for object detection were tested for potential use in the robot: Mask-RCNN, YOLOv4, and YOLOv4-tiny. Mask-RCNN achieved an average precision (mAP) of over 83% and detection time (DT) of 3973.29 ms, YOLOv4 achieved 97.1% (mAP) and 32.76 DT, and YOLOv4-tiny achieved 95.2% and 5.21 ms DT. The YOLOv4-tiny was selected as it offered a very similar mAP to YOLOv4, but with a much lower DT. The design was simulated on different terrains and behaved as expected.