Optimized Identification with Severity Factors of Gastric Cancer for Internet of Medical Things

The Internet of Medical Things (IoMT) emerges with the vision of the Wireless Body Sensor Network (WBSN) to improve the health monitoring systems and has an enormous impact on the healthcare system for recognizing the levels of risk/severity factors (premature diagnosis, treatment, and supervision of chronic disease i.e., cancer) via wearable/electronic health sensor i.e., wireless endoscopic capsule. However, AI-assisted endoscopy plays a very significant role in the detection of gastric cancer. Convolutional Neural Network (CNN) has been widely used to diagnose gastric cancer based on various feature extraction models, consequently, limiting the identification and categorization performance in terms of cancerous stages and grades associated with each type of gastric cancer. This paper proposed an optimized AI-based approach to diagnose and assess the risk factor of gastric cancer based on its type, stage, and grade in the endoscopic images for smart healthcare applications. The proposed method is categorized into five phases such as image pre-processing, Four-Dimensional (4D) image conversion, image segmentation, K-Nearest Neighbour (K-NN) classification, and multi-grading and staging of image intensities. Moreover, the performance of the proposed method has experimented on two different datasets consisting of color and black and white endoscopic images. The simulation results verified that the proposed approach is capable of perceiving gastric cancer with 88.09% sensitivity, 95.77% specificity, and 96.55% overall accuracy respectively.     

Security & Privacy in Software Defined Networks,Issues, Challenges and Cost of Developed Solutions: A Systematic Literature Review

International Journal of Wireless Information Networks - Due to fast development in digital systems, the traditional network architecture is becoming inadequate for the requirements of new...     

Critical perspective of design collaboration: A review

This study reviews the issue of collaboration with respect to the manner by which it has become increasingly important in promoting a contemporary design approach. Moreover, the study aims to critically review relevant core research articles and establish the perspective of design collaboration. Furthermore, the study uses a qualitative content analysis method on 94 selected research articles that discuss the concept of design collaboration. The content analysis finds four key themes, namely, teamwork, building information modeling framework, evidence-based design practice, and modality supported collaboration design, as the proposed subjects for the examination. Further analysis reveals that majority of articles on design collaboration have focused on interdisciplinary design collaboration and teamwork using digital modalities. Meanwhile, design collaboration concentrates on the manner by which multiple designers can perform various key cognitive design characteristics, such as links, functions, behavior, structure, frame, move, evaluation, abduction, induction, and deduction. Furthermore, the main contributions, recommendations, and implications of the article are graphically presented using a statistical graphmethod. Finally, the study concludes that a definitive framework is lacking on the constituent parameters of design collaboration.