ABES Engineering College

About: ABES Engineering College is a based out in . It is known for research contribution in the topics: Routing protocol & Wireless network. The organization has 576 authors who have published 621 publications receiving 2165 citations.

Materials and Fabrication Methods for Electrochemical Supercapacitors: Overview

Abstract: The rapid economic development and immense growth in the portable electronic market create tremendous demand for clean energy sources and energy storage and conversion technologies. To meet this demand, supercapacitors have emerged as a promising technology to store renewable energy resources. Based on this, this review will provide a detailed and current overview of the various materials explored as potential electrodes and electrolytes in the development of efficient supercapacitors along with corresponding synthesis routes and electrochemical properties. In addition, this review will provide introductions into the various types of supercapacitors as well as fundamental parameters that affect supercapacitor performance. Finally, this review will conclude with presentations on the role of electrolytes in supercapacitors and corresponding materials along with challenges and perspectives to guide future development.

Diagnosis of carbonation induced corrosion initiation and progression in reinforced concrete structures using piezo-impedance transducers

Abstract: In addition to chloride induced corrosion, the other commonly occurring type of rebar corrosion in reinforced concrete structures is that induced by the ingress of atmospheric carbon dioxide into concrete, commonly referred to as ‘carbonation induced corrosion’. This paper presents a new approach for detecting the onset and quantifying the level of carbonation induced rebar corrosion. The approach is based on the changes in the mechanical impedance parameters acquired using the electro-mechanical coupling of a piezoelectric lead zirconate titanate (PZT) ceramic patch bonded to the surface of the rebar. The approach is non-destructive and is demonstrated though accelerated tests on reinforced concrete specimens subjected to controlled carbon dioxide exposure for a period spanning over 230 days. The equivalent stiffness parameter, extracted from the frequency response of the admittance signatures of the PZT patch, is found to increase with penetration of carbon dioxide inside the surface and the consequent carbonation, an observation that is correlated with phenolphthalein staining. After the onset of rebar corrosion, the equivalent stiffness parameter exhibited a reduction in magnitude over time, providing a clear indication of the occurrence of corrosion and the results are correlated with scanning electron microscope images and Raman spectroscopy measurements. The average rate of corrosion is determined using the equivalent mass parameter. The use of PZT ceramic transducers, therefore, provides an alternate and effective technique for diagnosis of carbonation induced rebar corrosion initiation and progression in reinforced concrete structures non-destructively.

A smart System for Fake News Detection Using Machine Learning

Abstract: Most of the smart phone users prefer to read the news via social media over internet. The news websites are publishing the news and provide the source of authentication. The question is how to authenticate the news and articles which are circulated among social media like WhatsApp groups, Facebook Pages, Twitter and other micro blogs & social networking sites. It is harmful for the society to believe on the rumors and pretend to be a news. The need of an hour is to stop the rumors especially in the developing countries like India, and focus on the correct, authenticated news articles. This paper demonstrates a model and the methodology for fake news detection. With the help of Machine learning and natural language processing, author tried to aggregate the news and later determine whether the news is real or fake using Support Vector Machine. The results of the proposed model is compared with existing models. The proposed model is working well and defining the correctness of results upto 93.6% of accuracy.

Internet of Things: Evolution, Concerns and Security Challenges

Abstract: The escalated growth of the Internet of Things (IoT) has started to reform and reshape our lives. The deployment of a large number of objects adhered to the internet has unlocked the vision of the smart world around us, thereby paving a road towards automation and humongous data generation and collection. This automation and continuous explosion of personal and professional information to the digital world provides a potent ground to the adversaries to perform numerous cyber-attacks, thus making security in IoT a sizeable concern. Hence, timely detection and prevention of such threats are pre-requisites to prevent serious consequences. The survey conducted provides a brief insight into the technology with prime attention towards the various attacks and anomalies and their detection based on the intelligent intrusion detection system (IDS). The comprehensive look-over presented in this paper provides an in-depth analysis and assessment of diverse machine learning and deep learning-based network intrusion detection system (NIDS). Additionally, a case study of healthcare in IoT is presented. The study depicts the architecture, security, and privacy issues and application of learning paradigms in this sector. The research assessment is finally concluded by listing the results derived from the literature. Additionally, the paper discusses numerous research challenges to allow further rectifications in the approaches to deal with unusual complications.