Showing papers by "Chandigarh University published in 2020"

Current Technology of Supercapacitors: A Review

Abstract: A supercapacitor is a solid-state device that can store electrical energy in the form of charges. It represents an advancement in the field of energy storage, as it overcomes many of the shortcomings of batteries. This paper presents an overview of the various types of supercapacitors, electrode materials, and electrolytes, and the future of supercapacitors. Due to their high storage capacity, supercapacitors are commonly used in portable electronic devices such as MP3 players and mobile phones, and in hybrid vehicles and other applications. In electrical and hybrid vehicles, supercapacitors are increasingly used as provisional energy storage for regenerative braking. Various materials are used in electrodes to boost the performance of the supercapacitor. This review presents details regarding the materials and electrolyte, and the improvements in the field of supercapacitors.

BlockSDN: Blockchain-as-a-Service for Software Defined Networking in Smart City Applications

Abstract: Smart cities have emerged as a hub of intelligent applications (e.g., intelligent transportation systems, smart parking, smart homes, and e-healthcare) to provide ambient-assisted living and quality of experience to wide communities of users. The smooth execution of these applications depends on reliable data transmission between various smart devices and machines. However, the exponential increase in data traffic due to the growing dependency of end users on smart city applications has created various bottlenecks (e.g., channel congestion, manual flow configurations, limited scalability, and low flexibility) on the conventional network backbone, which can degrade the performance of any designed solution in this environment. To mitigate these challenges, SDN emerges as a powerful new technology that provides global visibility of the network by decoupling the control logic from the forwarding devices. The abstraction of network services in SDN architecture provides more flexibility for network administrators to execute various applications. In SDN architecture, the decision making process is handled by a logically centralized controller, which may have a single point of failure. An adversary/ attacker can compromise the controller using different types of attacks (e.g., eavesdropping, man-in-the middle attack, and distributed denial of service) in order to gain total control of the network by updating the flow table entries at the data plane or hindering control plane operations. Therefore, to cope with the aforementioned challenges, new strategies and solutions are required for securing the SDN-enabled network architecture at different planes and their associated interconnections. In this article, various security issues and different attack vectors are discussed along with possible solutions. To mitigate various attacks, BlockSDN, a blockchain as a service framework, for SDN is proposed. The architecture of permissioned blockchain is presented followed by two attack scenarios, 1) a malware compromised switch at the data plane and 2) distributed denial of service attack at the control plane, to demonstrate the applicability of the BlockSDN framework for various future applications. Finally, the open issues and challenges with respect to the design of blockchain solutions for SDN in smart city applications are also discussed.

Investigating the influence of WEDM process parameters in machining of hybrid aluminum composites

Abstract: This article presents an experimental investigation to assess the influence of input process parameters of machinability of wire electrical discharge machining (WEDM) process for machining of tripl...

An efficient image encryption using non-dominated sorting genetic algorithm-III based 4-D chaotic maps

Abstract: Chaotic maps are extensively utilized in the field of image encryption to generate secret keys. However, these maps suffer from hyper-parameters tuning issues. These parameters are generally selected on hit and trial basis. However, inappropriate selection of these parameters may reduce the performance of chaotic maps. Also, these hyper-parameters are not sensitive to input images. Therefore, in this paper, to handle these issues, a non-dominated sorting genetic algorithm-III (NSGA) based 4-D chaotic map is designed. Additionally, to improve the computational speed of the proposed approach, we have designed a novel master-slave model for image encryption. Initially, computationally expensive operations such as mutation and crossover of NSGA-III are identified. Thereafter, NSGA-III parameters are split among two jobs, i.e., master and slave jobs. For communication between master and slave nodes, the message passing interface is used. Extensive experimental results reveal that the proposed image encryption technique outperforms the existing techniques in terms of various performance measures.

Lifestyle Modifications and Nutritional Interventions in Aging-Associated Cognitive Decline and Alzheimer's Disease.

Abstract: Alzheimer's disease (AD) is a type of incurable neurodegenerative disease that is characterized by the accumulation of amyloid-β (Aβ; plaques) and tau hyperphosphorylation as neurofibrillary tangles (NFTs) in the brain followed by neuronal death, cognitive decline, and memory loss. The high prevalence of AD in the developed world has become a major public health challenge associated with social and economic burdens on individuals and society. Due to there being limited options for early diagnosis and determining the exact pathophysiology of AD, finding effective therapeutic strategies has become a great challenge. Several possible risk factors associated with AD pathology have been identified; however, their roles are still inconclusive. Recent clinical trials of the drugs targeting Aβ and tau have failed to find a cure for the AD pathology. Therefore, effective preventive strategies should be followed to reduce the exponential increase in the prevalence of cognitive decline and dementia, especially AD. Although the search for new therapeutic targets is a great challenge for the scientific community, the roles of lifestyle interventions and nutraceuticals in the prevention of many metabolic and neurodegenerative diseases are highly appreciated in the literature. In this article, we summarize the molecular mechanisms involved in AD pathology and the possible ameliorative action of lifestyle and nutritional interventions including diet, exercise, Calorie restriction (CR), and various bioactive compounds on cognitive decline and dementia. This article will provide insights into the role of non-pharmacologic interventions in the modulation of AD pathology, which may offer the benefit of improving quality of life by reducing cognitive decline and incident AD.

BloCkEd: Blockchain-Based Secure Data Processing Framework in Edge Envisioned V2X Environment

Abstract: There has been an increasing trend of moving computing activities closer to the edge of the network, particularly in smart city applications (e.g., vehicle-to-everything – V2X). Such a paradigm allows the end user’s requests to be handled/processed by nodes at the edge of the network; thus, reducing latency, and preserving privacy of user data/activities. However, there are a number of challenges in such an edge computing ecosystem. Examples include (1) potential inappropriate utilization of resources at the edge nodes, (2) operational challenges in cache management and data integrity due to data migration between edge nodes, particularly when dealing with vehicular mobility in a V2X application, and (3) high energy consumption due to continuous link breakage and subsequent reestablishment of link(s). Therefore in this paper, we design a blockchain-based secure data processing framework for an edge envisioned V2X environment (hereafter referred to as BloCkEd ). Specifically, a multi-layered edge-enabled V2X system model for BloCkEd is presented, which includes the formulation of a multi-objective optimization problem. In addition, BloCkEd comprises an optimal container-based data processing scheme, and a blockchain-based data integrity management scheme, designed to minimize link breakage and reducing latency. Using Chandigarh City, India, as the scenario, we implement and evaluate the proposed approach in terms of its latency, energy consumption, and service level agreement compliance.

Taguchi S/N and TOPSIS Based Optimization of Fused Deposition Modelling and Vapor Finishing Process for Manufacturing of ABS Plastic Parts.

Abstract: Despite several additive manufacturing techniques are commercially available in market, Fused Deposition Modeling (FDM) is increasingly used by researchers and engineers for new product development. FDM is an established process with a plethora of advantages, but the visible surface roughness (SR), being an intrinsic limitation, is major barrier against utilization of fabricated parts for practical applications. In the present study, the chemical finishing method, using vapour of acetone mixed with heated air, is being used. The combined impact of orientation angle, finishing temperature and finishing time has been studied using Taguchi and ANOVA, whereas multi-criteria optimization is performed using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The surface finish was highly responsive to increase in temperature while orientation angle of 0° yielded maximum strength; increase in finishing time led to weight gain of FDM parts. As the temperature increases, the percentage change in surface roughness increases as higher temperature assists the melt down process. On the other hand, anisotropic behaviour plays a major role during tensile testing. The Signal-to-noise (S/N) ratio plots, and ANOVA results indicated that surface finish is directly proportionate to finishing time because a longer exposure results in complete layer reflowing and settlement.

Mechanical Strength Enhancement of 3D Printed Acrylonitrile Butadiene Styrene Polymer Components Using Neural Network Optimization Algorithm

Abstract: Fused filament fabrication (FFF), a portable, clean, low cost and flexible 3D printing technique, finds enormous applications in different sectors. The process has the ability to create ready to use tailor-made products within a few hours, and acrylonitrile butadiene styrene (ABS) is extensively employed in FFF due to high impact resistance and toughness. However, this technology has certain inherent process limitations, such as poor mechanical strength and surface finish, which can be improved by optimizing the process parameters. As the results of optimization studies primarily depend upon the efficiency of the mathematical tools, in this work, an attempt is made to investigate a novel optimization tool. This paper illustrates an optimization study of process parameters of FFF using neural network algorithm (NNA) based optimization to determine the tensile strength, flexural strength and impact strength of ABS parts. The study also compares the efficacy of NNA over conventional optimization tools. The advanced optimization successfully optimizes the process parameters of FFF and predicts maximum mechanical properties at the suggested parameter settings.

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.

Movie Recommender System Using Collaborative Filtering

Abstract: Movies are one of the sources of entertainment, but the problem is in finding the desired content from the ever-increasing millions of content every year. However, recommendation systems come much handier in these situations. The aim of this paper is to improve the accuracy and performance of a regular filtering technique. Although varieties of methods are used to implement a recommendation system, Content-based filtering is the simplest method. Which takes input from the users, rechecks his/her history/past behavior, and recommends a list of similar movies. In this paper, to prove the effectiveness, K-NN algorithms and collaborative filtering are used to mainly focus on enhancing the accuracy of results as compared to content-based filtering. This approach is based on cosine similarity using k-nearest neighbor with the help of a collaborative filtering technique, at the same time removing the drawbacks of the content-based filtering. Although using Euclidean distance is preferred, cosine similarity is used as the accuracy of cosine angle and the equidistance of movies remain almost the same.

DeTrAs: deep learning-based healthcare framework for IoT-based assistance of Alzheimer patients

Abstract: Healthcare 4.0 paradigm aims at realization of data-driven and patient-centric health systems wherein advanced sensors can be deployed to provide personalized assistance. Hence, extreme mentally affected patients from diseases like Alzheimer can be assisted using sophisticated algorithms and enabling technologies. Motivated from this fact, in this paper, DeTrAs: Deep Learning-based Internet of Health Framework for the Assistance of Alzheimer Patients is proposed. DeTrAs works in three phases: (1) A recurrent neural network-based Alzheimer prediction scheme is proposed which uses sensory movement data, (2) an ensemble approach for abnormality tracking for Alzheimer patients is designed which comprises two parts: (a) convolutional neural network-based emotion detection scheme and (b) timestamp window-based natural language processing scheme, and (3) an IoT-based assistance mechanism for the Alzheimer patients is also presented. The evaluation of DeTrAs depicts almost 10–20% improvement in terms of accuracy in contrast to the different existing machine learning algorithms.

Machine Learning Aspects and its Applications Towards Different Research Areas

Abstract: Machine learning is a branch of artificial intelligence that aims at enabling machines to perform their jobs skillfully by using intelligent software. The statistical learning methods constitute the backbone of intelligent software that is used to develop machine intelligence. Now a Day, a huge increase in demand for machine learning has been seen with the great number of available datasets. The knowledge acquisition mechanizing from experience improvement using computational methods comes under machine learning. There is need of knowledge specific to the domain by expert performance and number of AI expert systems has been produced by knowledge engineering. Its regular use has been seen in the industry in different domains. Due to the increase in use and applicability of machine learning, the systematic review on various aspects related to it has been presented in this paper. The paper started with giving a brief description of machine learning, and the use of different models of machine learning. The various types of machine learning algorithms that are used for various purposes like data mining, predictive analytics, image processing etc. has also presented in the comprehensive review. We have also given a review of different work done by various researchers in different application areas. It covered the use of machine learning in medical, social media, Travelling and robotics. The primary purpose behind its popularity in the different application is its ability to learn once and then it works automatically for any same type of data or input given to it.

Post-processing of ABS Replicas with Vapour Smoothing for Investment Casting Applications

Abstract: The need of customized products with tight dimensional tolerances, lower production cost and shorter lead times led to the development of additive manufacturing techniques like fused deposition modelling (FDM). The digitally fabricated ABS patterns prepared on FDM needs to be processed by vapour smoothing (VS) in order to reduce the surface roughness. The post-processing of FDM-based patterns/replicas with VS increases their density, which increases heat input and complexities in ash removal during burnout stage from ceramic shell in investment casting (IC). This study highlights the step-by-step procedure for controlling the density of master patterns/replicas after processing with VS for IC applications.

Deep neuro‐fuzzy approach for risk and severity prediction using recommendation systems in connected health care

Abstract: Internet of Things (IoT) and Data science have revolutionized the entire technological landscape across the globe. Because of it, the health care ecosystems are adopting the cutting‐edge t...