Underwater wireless sensor network has been an area of interest for a few previous decades. UWSNs consists of tiny sensors responsible for monitoring different underwater events and transmit the collected data to the sink node. In the harsh and continuously changing environment of water, gaining better communication and performance is a difficult task as compared to networks available on land because of different underwater characteristics such as end-to-end delays, node movement, and energy constraints. In this paper, a novel routing technique named angle adjustment for vertical and diagonal communication was proposed, which doesn't use any location information of nodes. It is also efficient in terms of energy and end-to-end delays. In this approach, the source node evaluates the flooding zone based on the angle by using the basic formula for forwarding the packet to the sink. After evaluating the flooding zone, the angles of each node are compared and the packet is sent to the node closest to the vertical line. The proposed approach is evaluated with the help of NS-2 with AquaSim. The results show better results performance in data delivery, end-to-end delays, and energy consumption than DBR.
Agent Based modelling is widely used for presenting and evaluating a social phenomenon. Agent based modelling helps the researcher to analyze and evaluate a social model and its related hypothetical theories by simulating a real situation. This research presents a model for showing the behavior of an offender that is greatly influenced by volunteering of people on the offending tendencies. It is observed that how the offending behavior of someone urges others to do the same criminal act or violation of norms. And how can volunteering make the offender feel shameful of his doings and motivate others to volunteer in likewise situation in future. An agent based Model is presented and simulated to evaluate and validate the conceptualization of presented social dilemma. This model is simulated by asking some questions with exacting focus on the offending behavior of an agent. This study evaluates all the simulated results from the presented model to describe theoretical foundation spreading of offending or criminal behavior. Moreover, it validates the role of volunteering in the decrement of offending tendencies of the people as it presents an understandable situation in which offending of someone increases the offending tendencies of audience. Moreover the results of this research show that the volunteering decreases the offending tendencies of not just offender but also of the audience.
Skin cancer is among common and rapidly increasing human malignancies, which can be diagnosed visually. The diagnosis begins with preliminary medical screening and by dermoscopic examination, histopathological examination, and proceeding to the biopsy. This screening and diagnosis can be automated using machine learning tools and techniques. Artificial neural networks are helping a lot in medical diagnosis applications. In this research, skin images are classified into 7 different classes of skin cancer using deep learning methodology, then analyzed the results w.r.t to their respective precision, recall, support, and accuracy to find its practical applicability. This model is efficient in comparison to the detection of skin cancer with human eyes. Human eyes detection can be 79% accurate at most. Thus, having a scientific method of diagnosis can help the doctors and practitioners to accurately identify the cancer and its type. The model provides 80% accuracy on average for all 7 types of skin diseases, thus being more reliable than human eye examination. It will help the doctors to diagnose the skin diseases more confidently. The model has only 2 misclassified predictions for Basal cell carcinoma and Vascular lesions. However, Actinic keratosis diagnosis is most accurately predicted.
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