The Internet of Things (IoT) has been increasingly applied in various applications in recent years. In IoT, many tasks are performed for a load operation, such as creating a cluster, preserving convergence/connectivity issues, etc. However, the energy consumption rate is also high due to more traffic in dense networks. Generally, a traditional IoT node's battery power capacity is limited due to a short-range cycle. To address the energy shortage problem, researchers have tackled it through the Solar Powered (SP) energy harvesting technique. This method provides abundant energy to the IoT nodes at a lower cost. One issue arises from the target coverage area, which requires that each target must have at least one node for continuous monitoring in a given area. To address these issues, we have designed an effective solution called the Efficient Scheduling Target Coverage (ESTC) algorithm. This approach consists of various cover sets that work in an interleaving way. If only some node sets need to be active to satisfy coverage constraints, then there is no need to activate all sets simultaneously. ESTC provides robust coverage awareness with a perpetual network lifetime using scheduling techniques. Furthermore, the proposed work also promotes a green IoT network.

Abstract Artificial intelligence and deep learning have aided ocular disease through experiments including automatic illness recognition from images of the iris, fundus, or retina. Automated diagnosis systems (ADSs) provide services for the benefit of humanity and are essential in the early detection of harmful diseases. In fact, early detection is essential to avoid total blindness. In real life, several diagnostic tests such as visual ocular tonometry, retinal exam, and acuity test are performed, but they are conclusively time demanding and stressful for the patient. To consume time and detect the retinal disease earlier, an efficient prediction method is designed. In this proposed model, the first process is data collection that consists of a retinal disease dataset for testing and training. The second process is pre‐processing, which executes image resizing and noise filter for feature extraction. The third step is feature extraction, which extracts the image's form, size, color, and texture for classification with CNN based on Inception‐ResNet V2. The classification process is done by using the SVM with the extracted features. The prediction of diseases is classified such as normal, cataract, glaucoma, and retinal disease. The suggested model's performance is assessed using performance indicators such as accuracy, error, sensitivity, precision, and so forth. The suggested model's accuracy, error, sensitivity, and precision are 0.96, 0.962, 0.964, and 0.04, respectively, higher than existing techniques such as VGG16, Mobilenet V1, ResNet, and AlexNet. Thus, the proposed model instantly predicts retinal disease.

Abstract An outlier has a significant impact on data quality and the efficiency of data mining. The outlier identification algorithm observes only data points that do not follow clearly defined meanings of projected behaviour in a data set. Several techniques for identifying outliers have been presented in recent years, but if outliers are located in areas where neighbourhood density varies substantially, it can result in an imprecise estimate. To address this problem, we provide a ‘Relative Density‐based Outlier Factor (RDOF)’ algorithm based on the concept of mutual proximity between a data point and its neighbours. The proposed approach is divided into two stages: an influential space is created at a test point in the first stage. In the later stage, a test point is assigned an outlier‐ness score. We have conducted experiments on three real‐world data sets, namely the Johns Hopkins University Ionosphere, the Iris Plant, and Wisconsin Breast Cancer data sets. We have investigated three performance metrics for comparison: precision, recall, and rank power. In addition, we have compared our proposed method against a set of relevant baseline methods. The experimental results reveal that our proposed method detected all (i.e., 100%) outlier class objects with higher rank power than baseline approaches over these experimental data sets.

The importance of outlier detection is growing significantly in a various fields, such as military surveillance,tax fraud detection, telecommunications, terrorist activities, medical and commercial sectors. Focusing on this has resulted in the growth of several outlier detection algorithms, mostly based on distance or density strategies. But for each approach, there are inherent weaknesses. The distance-based techniques have a local density issue, while the density-based method has a low-density pattern issue. In this article, we present an unsupervised density-based outlier detection algorithm to address these shortcomings. In the proposed approach, each object is assigned a local outlying degree, which indicates how much one point in its locality deviates from the other. The local outlying degree focuses explicitly on the concept of local density, which is defined as a relative measure of the local density of the object to the local density of its neighbour. The proposed approach uses a measure of k nearest neighbour kernel density (NKD) to estimate the density. Besides, our proposed algorithm used three different categories of nearest neighbours, k nearest neighbour (kNN), reverse nearest neighbour (RNN), and shared nearest neighbour (SNN) to make our systems more flexible in modeling different local data patterns. Formal analysis and extensive experiments on artificial and UCI machine learning repository datasets show that this technique can achieve better outlier detection performance.