Showing papers by "Jaypee Institute of Information Technology published in 2021"

In silico investigation of phytoconstituents from Indian medicinal herb ' Tinospora cordifolia (giloy)' against SARS-CoV-2 (COVID-19) by molecular dynamics approach.

Abstract: The recent appearance of COVID-19 virus has created a global crisis due to unavailability of any vaccine or drug that can effectively and deterministically work against it. Naturally, different pos...

A Review on the Role of Machine Learning in Enabling IoT Based Healthcare Applications

Abstract: The Internet of Things (IoT) is playing a vital role in the rapid automation of the healthcare sector. The branch of IoT dedicated towards medical science is at times termed as Healthcare Internet of Things (H-IoT). The key elements of all H-IoT applications are data gathering and processing. Due to the large amount of data involved in healthcare, and the enormous value that accurate predictions hold, the integration of machine learning (ML) algorithms into H-IoT is imperative. This paper aims to serve both as a compilation as well as a review of the various state of the art applications of ML algorithms currently being integrated with H-IoT. Some of the most widely used ML algorithms have been briefly introduced and their use in various H-IoT applications has been analyzed in terms of their advantages, scope, and possible improvements. Applications have been divided into the domains of diagnosis, prognosis and spread control, assistive systems, monitoring, and logistics. In healthcare, practical use of a model requires it to be highly accurate and to have ample measures against security attacks. The applications of ML algorithms in H-IoT discussed in this paper have shown experimental evidence of accuracy and practical usability. The constraints and drawbacks of each of these applications have also been described.

Linguistic feature based learning model for fake news detection and classification

Abstract: Social media is used as a dominant source of news distribution among users. The world's preeminent decisions such as politics are acclaimed by social media to influence users for enclosing users' decisions in their favor. However, the adoption of social media is much needed for awareness but the authenticity of content is an unknown factor in the current scenario. Therefore, this research work finds it imperative to propose a solution to fake news detection and classification. In the case of fake news, content is the prime entity that captures the human mind towards trust for specific news. Therefore, a linguistic model is proposed to find out the properties of content that will generate language-driven features. This linguistic model extracts syntactic, grammatical, sentimental, and readability features of particular news. Language driven model requires an approach to handle time-consuming and handcrafted features problems in order to deal with the curse of dimensionality problem. Therefore, the neural-based sequential learning model is used to achieve superior results for fake news detection. The results are drawn to validate the importance of the linguistic model extracted features and finally combined linguistic feature-driven model is able to achieve the average accuracy of 86% for fake news detection and classification. The sequential neural model results are compared with machine learning based models and LSTM based word embedding based fake news detection model as well. Comparative results show that features based sequential model is able to achieve comparable evaluation performance in discernable less time.

Antiviral Peptides: Identification and Validation.

Abstract: Despite rapid advances in the human healthcare, the infection caused by certain viruses results in high morbidity and mortality accentuate the importance for development of new antivirals. The existing antiviral drugs are limited, due to their inadequate response, increased rate of resistance and several adverse side effects. Therefore, one of the newly emerging field "peptide-based therapeutics" against viruses is being explored and seems promising. Over the last few years, a lot of scientific effort has been made for the identification of novel and potential peptide-based therapeutics using various advanced technologies. Consequently, there are more than 60 approved peptide drugs available for sale in the market of United States, Europe, Japan, and some Asian countries. Moreover, the number of peptide drugs undergoing the clinical trials is rising gradually year by year. The peptide-based antiviral therapeutics have been approved for the Human immunodeficiency virus (HIV), Influenza virus and Hepatitis virus (B and C). This review enlightens the various peptide sources and the different approaches that have contributed to the search of potential antiviral peptides. These include computational approaches, natural and biological sources (library based high throughput screening) for the identification of lead peptide molecules against their target. Further the applications of few advanced techniques based on combinatorial chemistry and molecular biology have been illustrated to measure the binding parameters such as affinity and kinetics of the screened interacting partners. The employment of these advanced techniques can contribute to investigate antiviral peptide therapeutics for emerging infections.

A Survey on Supply Chain Security: Application Areas, Security Threats, and Solution Architectures

Abstract: The rapid improvement in the global connectivity standards has escalated the level of trade taking place among different parties. Advanced communication standards are allowing the trade of all types of commodities and services. Furthermore, the goods and services developed in a particular region are transcending boundaries to enter into foreign markets. Supply chains play an essential role in the trade of these goods. To be able to realize a connected world with no boundary restrictions in terms of goods and services, it is imperative to keep the associated supply chains transparent, secure, and trustworthy. Therefore, some fundamental changes in the current supply chain architecture are essential to achieve a secure trade environment. This article discusses the supply chain’s security-critical application areas and presents a detailed survey of the security issues in the existing supply chain architecture. Various emerging technologies, such as blockchain, machine learning (ML), and physically unclonable functions (PUFs) as solutions to the vulnerabilities in the existing infrastructure of the supply chain have also been discussed. Recent studies reviewed in this work reveal a growing sentiment in the industry toward new and emerging technologies, such as Internet of Things (IoT), blockchain, and ML. While many organizations have already adopted IoT applications and artificial intelligence systems in their businesses, widespread adoption of blockchain remains distant. It has also been found that over the past decade, PUF-based authentication systems have gained much ground. However, a proper reference model for their implementation in complex supply chains is still missing.

Fast, Reliable, and Secure Drone Communication: A Comprehensive Survey

Abstract: Drone security is currently a major topic of discussion among researchers and industrialists. Although there are multiple applications of drones, if the security challenges are not anticipated and required architectural changes are not made, the upcoming drone applications will not be able to serve their actual purpose. Therefore, in this paper, we present a detailed review of the security-critical drone applications, and security-related challenges in drone communication such as DoS attacks, Man-in-the-middle attacks, De-Authentication attacks, and so on. Furthermore, as part of solution architectures, the use of Blockchain, Software Defined Networks (SDN), Machine Learning, and Fog/Edge computing are discussed as these are the most emerging technologies. Drones are highly resource-constrained devices and therefore it is not possible to deploy heavy security algorithms on board. Blockchain can be used to cryptographically store all the data that is sent to/from the drones, thereby saving it from tampering and eavesdropping. Various ML algorithms can be used to detect malicious drones in the network and to detect safe routes. Additionally, the SDN technology can be used to make the drone network reliable by allowing the controller to keep a close check on data traffic, and fog computing can be used to keep the computation capabilities closer to the drones without overloading them.

1-Soliton solutions of the (2 + 1)-dimensional Heisenberg ferromagnetic spin chain model with the beta time derivative

Abstract: This paper analytically explores a (2 + 1)-dimensional nonlinear model with the beta time derivative describing the wave propagation in the Heisenberg ferromagnetic spin chain. Particularly, after allocating the beta time derivative to the (2 + 1)-dimensional Heisenberg ferromagnetic spin chain (2D-HFSC) model, its 1-soliton solutions are formally derived through utilizing a group of systematic techniques such as the new Kudryashov and exponential methods. Some graphical representations in three-dimensional postures are considered to analyze the impact of the beta parameter on the dynamical behavior of the bright and dark solitons.

Disaster and Pandemic Management Using Machine Learning: A Survey

Abstract: This article provides a literature review of state-of-the-art machine learning (ML) algorithms for disaster and pandemic management. Most nations are concerned about disasters and pandemics, which, in general, are highly unlikely events. To date, various technologies, such as IoT, object sensing, UAV, 5G, and cellular networks, smartphone-based system, and satellite-based systems have been used for disaster and pandemic management. ML algorithms can handle multidimensional, large volumes of data that occur naturally in environments related to disaster and pandemic management and are particularly well suited for important related tasks, such as recognition and classification. ML algorithms are useful for predicting disasters and assisting in disaster management tasks, such as determining crowd evacuation routes, analyzing social media posts, and handling the post-disaster situation. ML algorithms also find great application in pandemic management scenarios, such as predicting pandemics, monitoring pandemic spread, disease diagnosis, etc. This article first presents a tutorial on ML algorithms. It then presents a detailed review of several ML algorithms and how we can combine these algorithms with other technologies to address disaster and pandemic management. It also discusses various challenges, open issues and, directions for future research.

Forecasting spread of COVID-19 using Google Trends: A hybrid GWO-Deep learning approach.

Abstract: The recent outbreak of COVID-19 has brought the entire world to a standstill. The rapid pace at which the virus has spread across the world is unprecedented. The sheer number of infected cases and fatalities in such a short period of time has overwhelmed medical facilities across the globe. The rapid pace of the spread of the novel coronavirus makes it imperative that its' spread be forecasted well in advance in order to plan for eventualities. An accurate early forecasting of the number of cases would certainly assist governments and various other organizations to strategize and prepare for the newly infected cases, well in advance. In this work, a novel method of forecasting the future cases of infection, based on the study of data mined from the internet search terms of people in the affected region, is proposed. The study utilizes relevant Google Trends of specific search terms related to COVID-19 pandemic along with European Centre for Disease prevention and Control (ECDC) data on COVID-19 spread, to forecast the future trends of daily new cases, cumulative cases and deaths for India, USA and UK. For this purpose, a hybrid GWO-LSTM model is developed, where the network parameters of Long Short Term Memory (LSTM) network are optimized using Grey Wolf Optimizer (GWO). The results of the proposed model are compared with the baseline models including Auto Regressive Integrated Moving Average (ARIMA), and it is observed that the proposed model achieves much better results in forecasting the future trends of the spread of infection. Using the proposed hybrid GWO-LSTM model incorporating online big data from Google Trends, a reduction in Mean Absolute Percentage Error (MAPE) values for forecasting results to the extent of about 98% have been observed. Further, reduction in MAPE by 74% for models incorporating Google Trends was observed, thus, confirming the efficacy of utilizing public sentiments in terms of search frequencies of relevant terms online, in forecasting pandemic numbers.

Traffic Jam Probability Estimation Based on Blockchain and Deep Neural Networks

Abstract: The exponential surge in the number of vehicles on the road has aggravated the traffic congestion problem across the globe. Several attempts have been made over the years to predict the traffic scenario accurately and consequently avoiding further congestion. Crowdsourcing has come forward as one of the most adopted methods for predicting traffic intensity using live data. However, the privacy concerns and the lack of motivation for the live users to help in the traffic prediction process have rendered existing crowdsourcing models inefficient. Towards this end, we present an advanced blockchain-based secure crowdsourcing model. Not only does our model ensure privacy preservation of the users, but by incorporating a revenue model, it also provides them with an incentive to participate in the traffic prediction process willingly. For accurate and efficient traffic jam probability estimation, our work proposes a neural network-based smart contract to be deployed onto the blockchain network. The results reveal that the proposed model is highly efficient in terms of attaining high participation and consequently obtaining highly accurate predictions.

Einstein's vacuum field equation: Painlevé analysis and Lie symmetries

Abstract: The current study deals with investigation of Einstein's vacuum field equation for exploring movable critical points. We employ first the Painleve analysis, and then we use the auto-Backlund transf...

Fast, Reliable, and Secure Drone Communication: A Comprehensive Survey

Abstract: Drone security is currently a major topic of discussion among researchers and industrialists. Although there are multiple applications of drones, if the security challenges are not anticipated and required architectural changes are not made, the upcoming drone applications will not be able to serve their actual purpose. Therefore, in this paper, we present a detailed review of the security-critical drone applications, and security-related challenges in drone communication such as DoS attacks, Man-in-the-middle attacks, De-Authentication attacks, and so on. Furthermore, as part of solution architectures, the use of Blockchain, Software Defined Networks (SDN), Machine Learning, and Fog/Edge computing are discussed as these are the most emerging technologies. Drones are highly resource-constrained devices and therefore it is not possible to deploy heavy security algorithms on board. Blockchain can be used to cryptographically store all the data that is sent to/from the drones, thereby saving it from tampering and eavesdropping. Various ML algorithms can be used to detect malicious drones in the network and to detect safe routes. Additionally, the SDN technology can be used to make the drone network reliable by allowing the controller to keep a close check on data traffic, and fog computing can be used to keep the computation capabilities closer to the drones without overloading them.

Gravitational search algorithm: a comprehensive analysis of recent variants

Abstract: Gravitational search algorithm is a nature-inspired algorithm based on the mathematical modelling of the Newton’s law of gravity and motion. In a decade, researchers have presented many variants of gravitational search algorithm by modifying its parameters to efficiently solve complex optimization problems. This paper conducts a comparative analysis among ten variants of gravitational search algorithm which modify three parameters, namely Kbest, velocity, and position. Experiments are conducted on two sets of benchmark categories, namely standard functions and CEC2015 functions, including problems belonging to different categories such as unimodal, multimodal, and unconstrained optimization functions. The performance comparison is evaluated and statistically validated in terms of mean fitness value and convergence graph. In experiments, IGSA has achieved better precision with balanced trade-off between exploration and exploitation. Moreover, triple negative breast cancer dataset has been considered to analysis the performance of GSA variants for the nuclei segmentation. The variants performance has been analysed in terms of both qualitative and quantitive with aggregated Jaccard index as performance measure. Experiments affirm that IGSA-based method has outperformed other methods.

Research on optimization of scientific research performance management based on BP neural network

Abstract: Higher education in my country needs to focus on the cultivation of innovative talents, independent innovation, technological development, cultural innovation, and the promotion of scientific and technological knowledge. This paper proposes a performance index system for university social science research based on BP neural network and the relevant theoretical knowledge is utilized to construct a university social science research performance evaluation model. The results show that the difference between the predicted value of each sample and its expected output value is not large, and the value of the prediction error is also relatively small, all less than 1. In this paper, the performance evaluation method of social science research in colleges and universities based on BP neural network is an evaluation method with high efficiency, strong operability and high accuracy. Therefore, the BP neural network model is utilized to evaluate and optimize the performance of social science research in colleges and universities. The established BP neural network model has very low error value and good generalization ability, which effectively proves that the training sample data can fit the neural network simulation ideally. In the same way, it shows that the output value of BP neural network can be very close to the input vector.

A mobile data offloading framework based on a combination of blockchain and virtual voting

Abstract: The emergence of mobile cloud computing enables mobile users to offload computation tasks to other resource‐rich mobile devices to reduce energy consumption and enhance performance. A direct peer‐to‐peer connection among mobile devices to offload computation tasks can be a highly promising solution to provide a fast mechanism, especially for deadline‐sensitive offloading tasks. The generic blockchain‐based system might fail in such a scenario due to it being a heavyweight mechanism requiring high power consumption in the mining process. To address these issues, in this article, we propose a directed acyclic graph‐enabled mobile offloading (DAGMO) algorithm. DAGMO model is empowered by traditional blockchain features and provides additional advantages to overcome the fundamental limitations of generic blockchain. A game‐theoretic approach is used to model the interactions between mobile devices. The numerical analysis proves the proposed model to enhance the overall welfare of the participating nodes in terms of computation cost and time.

A comprehensive study of spam detection in e-mails using bio-inspired optimization techniques

Abstract: Electronic mail is a medium of communication used frequently for conveying a variety of information. It has become an integral part of people's lives owing to its ease of access and capability to reach out to a large number of recepients without much hassle. This boon, however, has turned into a bane due to exploitation by marketers for publicizing their products, and scammers for fooling people into falling for their schemes. Such e-mails are usually termed as spam e-mails. The menace of spam e-mails requires attention because in addition to consuming resources like bandwidth and storage, they are time-consuming as their removal may require manual effort. This paper proposes a classification method based on k-Nearest Neighbours integrated with several bio-inspired optimization techniques to classify e-mails as spam or legitimate. The study first evaluates the performance of three distance metrics namely Euclidean, Manhattan, and Chebyshev, when utilized in k-Nearest Neighbours classification. Further, five bio-inspired algorithms namely, Grey wolf optimization, Firefly optimization, Chicken swarm optimization, Grasshopper optimization, and Whale optimization, have been explored and their performance is compared based on different evaluation measures like accuracy, precision, recall, speed of convergence to global optimum solution, F1-measure and computational time.

Analyzing one-day tour trends during COVID 19 disruption – applying push and pull theory and text mining approach

Abstract: Fear of COVID-19 and the quarantine lifestyle make travellers look for new ways of travelling to get rid of boredom and anxiety. People are looking for one-day tours to break the monotony. The stud...

Blended Learning Tools and Practices: A Comprehensive Analysis

Abstract: Blended learning incorporates online learning experiences and helps students for meaningful learning through flexible online information and communication technologies, reduced overcrowded classroom presence, and planned teaching and learning experience. This study has conducted surveys of various tools, techniques, frameworks, and models useful for blended learning. This article has prepared a comprehensive survey of student, teacher, and management experiences in blended learning courses during COVID-19 and pre-COVID-19 times. The survey will be useful to faculty members, students, and management to adopt new tools and mindsets for positive outcomes. This work reports on implementing and assessing blended learning at two different universities (University of Petroleum and Energy Studies, India, and Jaypee Institute of Information Technology, Noida, India). The assessments prepare the benefits and challenges of learning (by students) and teaching (by faculty) blended learning courses with different online learning tools. Additionally, student performance in the traditional and blended learning courses was compared to list the concerns about effectively shifting the face-to-face courses to a blended learning model in emergencies like COVID-19. As a result, it has been observed that blended learning is helpful for school, university, and professional training. A large set of online and e-learning platforms are developed in recent times that can be used in blended learning to improve the learner’s abilities. The use of similar tools (Blackboard, CodeTantra, and g suite) has fulfilled the requirements of the two universities, and timely conducted and completed all academic activities during pandemic times.

Blockchain Based Framework for Modeling and Evaluating 5G Spectrum Sharing

Abstract: The fifth-generation (5G) cellular technology aims at providing network services at high speed with reliable Quality of Service (QoS). To enable this, 5G deploys Massive Multi-Input Multi-Out-put (MIMO) to increase the capacity of a Base Station (BS) and the efficiency of the network. Provisioning guaranteed and reliable services to support MIMO requires effective resource management. Blockchain is a highly promising solution to enable multi-dimensional management of various resources such as spectrum allocation and user association. It can potentially mitigate spectrum under-utilization and can help in scaling up the deployment of different 5G services. In this article, we present a model for Blockchain-based multi-operator service provisioning for 5G users with Intra and Inter spectrum management among multiple telecom operators. In particular, we present a Blockchain-based implementation model for spectrum sharing between the operators to minimize spectrum under-utilization.

Lie Group Analysis of Nanofluid Slip Flow with Stefan Blowing Effect via Modified Buongiorno’s Model: Entropy Generation Analysis

Abstract: This article presents a detailed theoretical and computational analysis of alumina and titania-water nanofluid flow from a horizontal stretching sheet. At the boundary of the sheet (wall), velocity slip, thermal slip and Stefan blowing effects are considered. The Pak-Cho viscosity and thermal conductivity model is employed together with the non-homogeneous Buongiorno nanofluid model. The equations for mass, momentum, energy and nanoparticle species conservation are transformed via Lie-group transformations into a dimensionless system. The partial differential boundary value problem is therefore rendered into nonlinear ordinary differential form. With appropriate boundary conditions, the emerging normalized equations are solved with the semi-numerical homotopy analysis method (HAM). To consider entropy generation affects a second law thermodynamic analysis is also carried out. The impact of some physical parameters on the skin friction, Nusselt number, velocity, temperature and entropy generation number (EGM) are represented graphically. This analysis shows that diffusion parameter is a key factor to retards the friction and rate of heat transfer at the surface. Further, temperature of fluid decreases for the higher value of thermal slip parameter. In addition, EGM enhances with nanoparticles ambient concentration and Reynolds number. A numerical validation of HAM results is also included. The computations are relevant to thermodynamic optimization of nano-material processing operations.

A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets.

Abstract: Image segmentation is an essential phase of computer vision in which useful information is extracted from an image that can range from finding objects while moving across a room to detect abnormalities in a medical image. As image pixels are generally unlabelled, the commonly used approach for the same is clustering. This paper reviews various existing clustering based image segmentation methods. Two main clustering methods have been surveyed, namely hierarchical and partitional based clustering methods. As partitional clustering is computationally better, further study is done in the perspective of methods belonging to this class. Further, literature bifurcates the partitional based clustering methods into three categories, namely K-means based methods, histogram-based methods, and meta-heuristic based methods. The survey of various performance parameters for the quantitative evaluation of segmentation results is also included. Further, the publicly available benchmark datasets for image-segmentation are briefed.

Information diffusion modeling and analysis for socially interacting networks

Abstract: Social network analysis provides innovative techniques to analyze interactions among entities by emphasizing social relationships. Diffusion in the social network can be referred to spread of information among interconnected nodes or entities in a network. The rate and intensity of diffusion depend upon network topology and initialization of network parameters. Individual nodes act as source of motivation for others in the diffusion process. The epidemic model is one of the basic diffusion models that helps in analyzing the transmission of infectious disease from one person to another through social connections. This can be further generalized for other socially connected platforms involving information exchange. In our research, we have proposed a diffusion methodology for tracking the rate with which information spread over underlying social interaction structure, with variation in time and other social parameters. In addition to forward state transitions, recoverable transition is also proposed, which allows a node currently under influence of incoming information, to revert back to previous state of perception. The proposed model also assists in predicting the fraction of population getting diffused over real and large-scale complex network for specific temporal domain.

Dark and bright soliton solutions and computational modeling of nonlinear regularized long wave model

Abstract: In this article, the authors simulate and study dark and bright soliton solutions of 1D and 2D regularized long wave (RLW) models. The RLW model occurred in various fields such as shallow-water waves, plasma drift waves, longitudinal dispersive waves in elastic rods, rotating flow down a tube, and the anharmonic lattice and pressure waves in liquid–gas bubble mixtures. First of all, the tanh–coth method is applied to obtain the soliton solutions of RLW equations, and thereafter, the approximation of finite domain interval is done by truncating the infinite domain interval. For computational modeling of the problems, a meshfree method based on local radial basis functions and differential quadrature technique is developed. The meshfree method converts the RLW model into a system of nonlinear ordinary differential equations (ODEs), then the obtained system of ODEs is simulated by the Runge–Kutta method. Further, the stability of the proposed method is discussed by the matrix technique. Finally, in numerical experiments, some problems are considered to check the competence and chastity of the developed method.