Segmentation of images is a common task within medical image analysis and a necessary component of medical image segmentation. The segmentation of the liver and liver tumors is an important but challenging stage in screening and diagnosing liver diseases. Although many automated techniques have been developed for liver and tumor segmentation; however, segmentation of the liver is still challenging due to the fuzzy & complex background of the liver position with other organs. As a result, creating a considerable automated liver and tumour division from CT scans is critical for identifying liver cancer. In this article, deeply dense-network ResU-Net architecture is implemented on CT scan using the 3D-IRCADb01 dataset. An essential feature of ResU-Net is the residual block and U-Net architecture, which extract additional information from the input data compared to the traditional U-Net network. Before being fed to the deep neural network, image pre-processing techniques are applied, including data augmentation, Hounsfield windowing unit, and histogram equalization. The ResU-Net network performance is evaluated using the dice similarity coefficient (DSC) metric. The ResU-Net system with residual connections outperformed state-of-the-art approaches for liver tumour identification, with a DSC value of 0.97% for organ recognition and 0.83% for segmentation methods.

Wireless sensor networks are a collection of a number of tiny, low-cost, and resource-constrained sensor nodes which are commonly not tamper proof. As a result, wireless sensor networks (WSNs) are prone to a wide variety of physical attacks. In this paper, we deem a typical threat known as node replication attack or clone node attack, where an adversary creates its own low-cost sensor nodes called clone nodes and misinforms the network to acknowledge them as legitimate nodes. To instigate this attack, an adversary only needs to physically capture one node, and after collecting all secret credentials (ID, cryptographic keys, etc.), an adversary replicates the sensor node and deploys one or more clones of the compromised node into the network at strategic positions, damaging the whole network by carrying out many internal attacks. Detecting the node replication attack has become an imperative research topic in sensor network security, and designing detection schemes against node replication attack involves different threatening issues and challenges. In this survey, we have classified the existing detection schemes and comprehensively explore various proposals in each category. We will also take a glance at some technical details and comparisons so as to demonstrate limitations of the existent detections as well as effective contributions.

Oral mucosal lesions (OML) and oral potentially malignant disorders (OPMDs) have been identified as having the potential to transform into oral squamous cell carcinoma (OSCC). This research focuses on the human-in-the-loop-system named Healthcare Professionals in the Loop (HPIL) to support diagnosis through an advanced machine learning procedure. HPIL is a novel system approach based on the textural pattern of OML and OPMDs (anomalous regions) to differentiate them from standard regions of the oral cavity by using autofluorescence imaging. An innovative method based on pre-processing, e.g., the Deriche–Canny edge detector and circular Hough transform (CHT); a post-processing textural analysis approach using the gray-level co-occurrence matrix (GLCM); and a feature selection algorithm (linear discriminant analysis (LDA)), followed by k-nearest neighbor (KNN) to classify OPMDs and the standard region, is proposed in this paper. The accuracy, sensitivity, and specificity in differentiating between standard and anomalous regions of the oral cavity are 83%, 85%, and 84%, respectively. The performance evaluation was plotted through the receiver operating characteristics of periodontist diagnosis with the HPIL system and without the system. This method of classifying OML and OPMD areas may help the dental specialist to identify anomalous regions for performing their biopsies more efficiently to predict the histological diagnosis of epithelial dysplasia.

Preserving inter-actor connectivity is essential in most wireless sensor and actor network (WSAN) applications as nodes have to collaborate and coordinate their actions against the events reported by the sensors. However, failure of a critical (i.e., cut-vertex) node partitions inter-actor network into disjoint segments and thus hinder network operation. The prime objective of this paper is to analyze the performance of reactive connectivity restoration algorithms for delay-tolerant WSAN applications. First, we provide insights to the state-of-the-art reactive connectivity restoration algorithms. Then, we present a Nearest Non-critical Neighbor (NNN) algorithm; a localized and distributed reactive approach for reconnecting network partitions. In NNN, each actor periodically determine its criticality (i.e., cut-vertex or not) based on 2-hop information and exchange with its neighbors. In case of a critical actor failure, the neighbors detect and trigger a connectivity restoration procedure that involves controlled and coordinated node relocation. NNN prefer to displace non-critical nodes during relocation in order to minimize recovery overhead in terms of distance movement and message coordination. We analyze the performance of reactive schemes through theoretical analysis and simulations.