Chandigarh University

About: Chandigarh University is a education organization based out in Mohali, India. It is known for research contribution in the topics: Computer science & Chemistry. The organization has 1358 authors who have published 2104 publications receiving 10050 citations.

Moth Flame Optimization Based Energy Efficient Stable Clustered Routing Approach for Wireless Sensor Networks

Abstract: The widespread use of wireless sensor devices and their advancements in terms of size, deployment cost and user friendly interface have given rise to many applications of wireless sensor networks (WSNs). WSNs need to utilize routing protocols to forward data samples from event regions to sink via minimum cost links. Clustering is an efficient data aggregation method that effectively reduces the energy consumption by organizing nodes into groups. However, in clustering protocols, CH has to bear an additional load for coordinating various activities within the cluster. Therefore, proper CH selection and their load balancing using efficient routing protocol is a critical aspect for the long run operation of WSN. Clustering a network with proper load balancing is an NP-hard problem. To solve such problems having vast search area, optimization algorithm is the preeminent possible solution. In this paper, moth flame optimization (MFO) based threshold-sensitive energy-efficient clustering protocol (TECP) is proposed to extend the stability period of the network. Multi-hop communication between CHs and BS is utilized using MFO to achieve optimal link cost for load balancing of distant CHs and energy minimization. Analysis and simulation results demonstrate that the proposed methodology significantly outperforms existing protocols in terms of energy consumption, system lifetime and stability period.

Medical Diagnostic Systems Using Artificial Intelligence (AI) Algorithms: Principles and Perspectives

Abstract: Disease diagnosis is the identification of an health issue, disease, disorder, or other condition that a person may have. Disease diagnoses could be sometimes very easy tasks, while others may be a bit trickier. There are large data sets available; however, there is a limitation of tools that can accurately determine the patterns and make predictions. The traditional methods which are used to diagnose a disease are manual and error-prone. Usage of Artificial Intelligence (AI) predictive techniques enables auto diagnosis and reduces detection errors compared to exclusive human expertise. In this paper, we have reviewed the current literature for the last 10 years, from January 2009 to December 2019. The study considered eight most frequently used databases, in which a total of 105 articles were found. A detailed analysis of those articles was conducted in order to classify most used AI techniques for medical diagnostic systems. We further discuss various diseases along with corresponding techniques of AI, including Fuzzy Logic, Machine Learning, and Deep Learning. This research paper aims to reveal some important insights into current and previous different AI techniques in the medical field used in today’s medical research, particularly in heart disease prediction, brain disease, prostate, liver disease, and kidney disease. Finally, the paper also provides some avenues for future research on AI-based diagnostics systems based on a set of open problems and challenges.

An energy-aware cluster-based stable protocol for wireless sensor networks

Abstract: The widespread use of wireless sensor devices and their advancements in terms of size, deployment cost and user-friendly interface have given rise to many applications of wireless sensor networks (WSNs). WSNs need to utilize routing techniques to forward data samples from event regions to sink via minimum cost links. Clustering is a commonly used data aggregation technique in which nodes are organized into groups in order to reduce the energy consumption. However, in clustering protocols, cluster-head (CH) has to bear an additional load for coordinating various activities within the cluster. Therefore, proper CH selection and their load balancing using efficient routing protocol is a critical aspect for the long-run operation of WSN. In this paper, genetic algorithm (GA)-based threshold-sensitive energy-efficient routing protocol (TERP) is proposed to prolong network lifetime. Multi-hop communication between CHs and base station (BS) is utilized using GA to achieve optimal link cost for load balancing of distant CHs and energy minimization. The paper also considers stability-aware model of TERP named stable TERP (STERP) so as to extend the stability period (time interval from initial time to the death of first node) of the network. In STERP, energy-aware heuristics is applied for CH selection in order to improve the stability period. Analysis and simulation results demonstrate that the proposed methodology significantly outperforms existing protocols in terms of energy consumption, system lifetime and stability period.