G.L. Bajaj Institute of Technology and Management Greater Noida

Agriculture is the basis of every economy worldwide. Crop production is one of the major factors affecting domestic market condition in any country. Agricultural production is also a major prerequisite of economic development, be it any part of any country. It plays a crucial role as it even provides raw material, employment and food to different citizens. A lot of issues are responsible for estimated crop production varying in different parts of the world. Some of these include overutilization of chemical fertilizers, presence of chemicals in water supply, uneven distribution of rainfall, different soil fertility and others. Other than these issues one of the commonly faced challenges across the globe equally includes destruction of the major part of production due to diseases. After providing effective resources to the fields, major section of the production is diminished by the presence of diseases in the plants grown. This leads to focus on effective ways of detection of disease in plants. Presence of various diseases in plant is a major concern among farmers. Plant diseases acts as a major threat to small scale farmers as they lead to major destruction in overall food supply. To provide effective measures for detection and avoidance of the destruction requires an early identification of type of plant disease present. In recent time major work is being done for the identification of plant disease presents in varied parts of the world affection varied crops. Major work is being done in the domain of identification of causing factors of these diseases. Some of the diseases are marked by the presence of viruses while some are resultant of fungal infection. This becomes a major issue when the causing factor is not traceable before it has already spread to major production section. This paper brings a review on effective use of different imaging techniques and computer vision approaches for the identification and classification of plant diseases. Detection of Plant disease is initiated with image acquisition followed by pre-processing while using the process of segmentation. It is further accompanied by different techniques used for feature extraction along with classification. In this Paper we present the Current Trends and Challenges for detection of plant disease using computer vision and advance imaging technique.

The expanding Electric vehicle (EV) market is fueled by the need for more efficient and dependable ways to recharge the battery. By eliminating the necessity for direct physical interaction between vehicles and charge equipment, the Wireless Power Transfer (WPT) methodology eliminates the drawbacks and risks associated with the conventional conductive system. The innovative WPT technique replaces the conductive charging system to keep a similar power rating and efficiency. Numerous strategies have been created to improve the effectiveness and dependability of the WPT model. As a result, this review article thoroughly analyses current major research articles that describe WPT technologies for EV charging. The papers are classified based on various coupling types along with magnetic couplers and shielding, compensation, misalignment tolerance and control methods in WPT systems. In addition, the possible research gaps and the challenges associated with the existing works of WPT systems are discussed. The reviewed results are analyzed based on performance metrics and implementation tools attained using above classifications and EMF exposure references employed in the WPT system. The comparative effectiveness is presented in the tables, diagrams, as well as interconnections for ease of presentation and conceptual understanding. The core asset of this article lies in the fact that the findings offer a good "one-stop" resource including both aspects of the system and with regard to the power stage. This review article also emphasizes the potential and obstacles of inductive wireless EV battery chargers. A developer can find this study will contribute substantially to selecting an optimum design for the enhancement of the WPT system.

There are many different air quality indexes, which represent the global urban air pollution situation. Although the index proposed by USEPA gives an overall assessment of air quality, it does not include the combined effects (or synergistic effects) of the major air pollutants (Shenfeld, 1970; Ott and Thom, 1976; Thom and Ott, 1976; Murena, 2004). So an attempt is made to calculate the Air Quality Index based on Factor Analysis (NAQI) which incorporates the deficiencies of USEPA method. The daily, monthly and seasonal air quality indexes were calculated by using both these methods. It is observed that a significant difference exists between NAQI and EPAQI. However, NAQI followed the trends of EPAQI when plotted against time. Further, the indexes were used to rank various seasons in terms of air pollution. The higher index value indicates more pollution in relative terms. Moreover, the index may be used for comparing the daily and seasonal pollution levels in different sites.

Information about healthcare is derived from healthcare data. Healthcare data sharing helps make healthcare systems more efficient as well as improving healthcare quality. Patients should own and control healthcare information, one of their most valuable assets, instead of letting data be spread out among health care providers differ. This protects data from being shared between healthcare systems and privacy. Public ledger accompanied by a decentralized network of peer's compromises patient has been demonstrated to be able to achieve trusted, auditable computing by blockchain. The use of access control and cryptographic primitives are insufficient in addressing modern cyber threats all privacy and security concerns associated with a cloud-based environment. In this paper, the authors proposed a lightweight blockchain technique based on privacy and security for healthcare data for the cloud system. The cost-effectiveness of our system's smart contracts is evaluated, as well as the procedures used for data processing in order to encrypt and pseudonymize patient data.

The global COVID-19 (coronavirus disease 2019) pandemic, which was caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in a significant loss of human life around the world. The SARS-CoV-2 has caused significant problems to medical systems and healthcare facilities due to its unexpected global expansion. Despite all of the efforts, developing effective treatments, diagnostic techniques, and vaccinations for this unique virus is a top priority and takes a long time. However, the foremost step in vaccine development is to identify possible antigens for a vaccine. The traditional method was time taking, but after the breakthrough technology of reverse vaccinology (RV) was introduced in 2000, it drastically lowers the time needed to detect antigens ranging from 5-15 years to 1-2 years. The different RV tools work based on machine learning (ML) and artificial intelligence (AI). Models based on AI and ML have shown promising solutions in accelerating the discovery and optimization of new antivirals or effective vaccine candidates. In the present scenario, AI has been extensively used for drug and vaccine research against SARS-COV-2 therapy discovery. This is more useful for the identification of potential existing drugs with inhibitory human coronavirus by using different datasets. The AI tools and computational approaches have led to speedy research and the development of a vaccine to fight against the coronavirus. Therefore, this paper suggests the role of artificial intelligence in the field of clinical trials of vaccines and clinical practices using different tools.

Purpose The current study has drawn attention to investigating the impact of social media influencers’ (SMIs) authenticity on followers buying behavior by using followers who have an ongoing relationship with an influencer and are knowledgeable about the influencer. The study further intends to reveal the mediating effect of parasocial interaction on the relationship between SMI's authenticity and followers' purchase behavior. Design/methodology/approach The study has analyzed data from an online survey of 458 participants (Instagram followers) using structured equation modeling (CB-SEM) to investigate the relationship among authenticity attributes, parasocial interaction and followers' purchase behavior. Findings CB-SEM results reveal that authenticity attributes positively influence followers' buying behavior. The findings from mediation analysis specify that parasocial interaction mediates the relationship between authenticity attributes (sincerity, truthful endorsement and visibility) and buying behavior excluding expertise, uniqueness attributes. Practical implications The findings of the study reinforce the need to use authentic influencers by the marketers for the brand endorsements. Further, the findings of the study can benefit marketers in implementing strategic practice of social media influencer marketing. Originality/value The study overcomes the limitations of preceding studies by using Instagram followers who are well-informed about SMIs and have an ongoing relationship with them. This study has uniquely combined the behavioral data from real influencer campaigns with followers' assessment of an influencer's authenticity.

Sun flower (Helianthus annuus L.) is one of the important oil seed crops and potentially fit in agricultural system and oil production sector of India. Sunflower crop gets damaged by the impact of various diseases, insects and nematodes resulting in wide range of loss in production. Disease detection is possible through naked eye observation, but this method is unsuccessful when one has to monitor the large farms. As a solution to this problem, we developed and present a system for segmentation and classification of Sunflower leaf images. This research paper presents surveys conducted on different diseases classification techniques that can be used for sunflower leaf disease detection. Segmentation of Sunflower leaf images, which is an important aspect for disease classification, is done by using Particle swarm optimization algorithm. Satisfactory results have been given by the experiments done on leaf images. The average accuracy of classification of proposed algorithm is 98.0% compared to 97.6 and 92.7% reported in state-of-the-art methods.

For the past few years, the IoT (Internet of Things)-based restricted WSN (Wireless sensor network) has sparked a lot of attention and progress in order to attain improved resource utilisation as well as service delivery. For data transfer between heterogeneous devices, IoT requires a stronger communication network and an ideally placed energy-efficient WSN. This study uses deep learning architectures to provide a unique resource allocation method for wireless sensor IoT networks with energy efficiency as well as data optimization. EE (Energy efficiency) and SE (spectral efficiency) are two competing optimization goals in this case. The network’s energy efficiency has been improved because of a deep neural network based on whale optimization. The heuristic-based multi-objective firefly algorithm was used to optimise the data. This proposed method is applied to optimal power allocation and relay selection. The study is for a cooperative multi-hop network topology. The best resource allocation is achieved by reducing overall transmit power, and the best relay selection is accomplished by meeting Quality of Service (QoS) standards. As a result, an energy-efficient protocol has been created. The simulation results demonstrate the suggested model’s competitive performance when compared to traditional models in terms of throughput of 96%, energy efficiency of 95%, QoS of 75%, spectrum efficiency of 85%, and network lifetime of 91 percent.

Humans may make thousands of facial expressions throughout a discussion, varying in intricacy, passion, and significance. This paper discusses way of recognizing different emotions produced by humans using a software application that make use of Haar-Cascade Algorithm and a pre-trained dataset DeepFace. We have used DeepFace with the help of Which we have achieved roughly about 97 percent accuracy approximately. In this paper we also have tried to analyze the problem associated with previous methods. We have also made comparisons of different other technologies with deep face and compared their Accuracies. In paper we have carried out real-time emotion detection in a webcam which is able to detect the emotion of one person. It can be further upgraded to detect the emotion of multiple faces at a time.

Nowadays, neural networks (NN) are being utilized in different control problems because of their excellent ability to model any nonlinear process. NN is suitable for the process having a wide range of operating conditions. In this work, the neural network-based internal model control (NN-IMC) scheme has been considered as a secondary controller for the load frequency control (LFC) problem in the restructured electricity market in order to meet Poolco and bilateral transactions. The proposed control scheme has been implemented on a 75-bus, 15-generator power system. The test system is divided into four areas. It is seen that area frequency errors have been eliminated at a steady state in all cases, and Gencos/Discos shared the increase in demand as per their involvement in the frequency regulation market. The results show that the NN-IMC control scheme has good performance and improves system responses effectively. Further, the performance of the NN-IMC control scheme has also been compared with the fractional-order proportional-integral-derivative (FO-PID) control scheme It is observed that the performance of the FO-PID controller is superior to the NN-IMC scheme in terms of settling time and similar to the NN-IMC control scheme in terms of maximum overshoots/undershoots. The compliance of the NN-IMC scheme has also been checked with NERC standards. It is seen that the NN-IMC scheme also satisfied the CPS1 and CPS2 control standards.

For changing semantics, ontological and information presentation, as well as computational linguistics for Asian social networks, are one of the most essential platforms for offering enhanced and real-time data mapping, as well as huge data access across diverse big data sources on the web architecture, information extraction mining, statistical modeling and data modeling, database control, and so on. The concept of opinion or sentiment analysis is often used to predict or classify the textual data, sentiment, affect, subjectivity, and other emotional states in online text. Recognizing the message's positive and negative thoughts or opinions by examining the author's goals will aid in a better understanding of the text's content in terms of the stock market. An intelligent ontology and knowledge Asian social network solution can improve the effectiveness of a company's decision making support procedures by deriving important information about users from a wide variety of web sources. However, ontology is concerned primarily with problem-solving knowledge discovery. The utilization of Internet-based modernizations welcomed a significant effect on the Indian stock exchange. News related to the stock market in the most recent decade plays a vital role for the brokers or users. This article focuses on predicting stock market news sentiments based on their polarity and textual information using the concept of ontological knowledge-based Convolution Neural Network (CNN) as a machine learning approach. Optimal features are essential for the sentiment classification model to predict the stock's textual reviews' exact sentiment. Therefore, the swarm-based Artificial Bee Colony (ABC) algorithm is utilized with the Lexicon feature extraction approach using a novel fitness function. The main motivation for combining ABC and CNN is to accelerate model training, which is why the suggested approach is effective in predicting emotions from stock news.

Many criteria must be taken into account while selecting the best renewable energy source (RES), which necessitates a sophisticated multi-criteria decision-making (MCDM) procedure. Conflicting norms, as well as insufficient and inaccurate information, make this endeavour challenging. The theory of moderator-intuitionistic-fuzzy-set (MIFS), a generalization of intuitionistic fuzzy sets (IFSs) has been developed to handle these uncertainties. The MIFS also helps to get a higher degree of precision in the unpredictable behaviours due to the moderator parameter. In this paper, we suggest a technique for choosing the best RES inside MIFS architecture. Our strategy entails creating new aggregation operator namely the MIF Choquet Integral (MIFCI) by utilizing the Choquet operator into MIFS information. Finally, we provide an approach to address the RES-selection issues using the developed operator.

Fused deposition modelling (FDM) is the one among various additive manufacturing techniques which can fabricate component of multi material. However, the tensile strength of FDM component is naturally low and hence it is difficult to use for the engineering application. For enhancing the mechanical properties, this study explores a novel methodology by adding the high strength material in between matrix material as reinforcement in the additive structure. In this study, ABS and PLA based reinforced composites are prepared. ABS and PLA are used as matrix material whereas PETG is used for the reinforcement. Unidirectional tensile test is performed to check the effect of reinforcement on tensile strength of the fabricated component. Experimental results depict that addition of PETG reinforcement material improves the tensile strength of ABS by about 70% and tensile strength of PLA material is improved by about 8%. Computational analysis is also performed under similar boundary condition to physical model. It is observed that computational results are close approximation to the experimental results. The methodology proposed in this study could be very useful for the fabrication of high strength component by using low cost FDM technology.

In this study, an attempt was made to utilize waste products from industries to develop composite materials. In the present study, car scrap aluminium alloy wheels (SAAWs) was used as matrix material. Waste rice husk ash (RHA) was collected from a rice mill to utilize as a primary reinforcement material. Spent alumina catalyst (SAC) waste was used as a secondary reinforcement material. SAC was collected from the oil refinery industry. These wastes produced lots of soil and air pollution. However, by utilizing these wastes, some environment pollutions can be reduced. Car scrap aluminium alloy wheels (SAAWs) based composite material was developed using RHA as primary reinforcement material and SAC as a secondary reinforcement material by stir casting technique followed by squeeze pressure on the universal testing machine (UTM) in mushy zone. Microstructure behaviour shows a uniform distribution of RHA and SAC in a recycled aluminium alloy matrix. Mechanical properties such as hardness, ductility, compressive strength and tensile strength were improved using RHA and SAC as reinforcement material simultaneously in SAAWs matrix material. Thermal expansion behaviour, soil degradation test and corrosion loss were also observed to see the effect of agro-waste RHA and SAC in recycled aluminium alloy.

In the last few years, with increased population the most critical component of human life is healthcare. Compare to other deadly diseases, heart disease is one of the most lethal diseases, affecting the lives of millions of people worldwide. It is very important to detect heart disease must early so the loss of lives can be prevented. The availability of enormous amounts of data for medical diagnostics has aided in the development of complex learning-based models for automated early detection of cardiac problems. The classical machine learning approaches unable to generalize the new data sets which have not been seen in the training set. Therefore, the trained model has less accuracy in prediction stage. To minimize this issue, need to balance between training and testing datasets. This paper proposes a novel deep learning architecture using a 1D convolutional neural network for classification between healthy and non-healthy persons with balanced datasets to reduce the limitations of classical machine learning approach. Several clinical parameters are used for evaluating the risk contour in the patients which supports in early diagnosis. Various regularization methods are used to avoid overfitting in the proposed model. The proposed model achieves over 97% training accuracy and 96% test accuracy on the dataset. This is compared in detail with other machine learning algorithms using various performance parameters which proves the effectiveness of the proposed model.

Due to the fast advancement of Internet technology, the popularity of Online Social Networks (OSN) over the Internet is increasing day by day. In the modern world, people are using OSN to communicate with others around the world who may or may not know each other. OSN has become the most convenient means to transmit media (news/content) and gather or spread information in the world. The posts (contents) on OSN affect and impact people, and minds at least for some time. These contents are important because they play a crucial role in taking the decision. The posts which are available on the OSN may be information or just misinformation. The misinformation may be a type of fake news or rumour. This is very difficult for people to differentiate whether the posts are information or rumour. Therefore, the development of techniques that can prevent the transmission of false information or rumours that might harm society in any way is critical. In this paper, a model is developed based on the epidemic approach, for examining and controlling fake information dissemination in OSN. The proposed model illustrates how different misinformation debunking measures impact and how misinformation spreads among different groups. In this article, we explain that the proposed model will be able to recognize and eradicate fake news from OSN. The model is written as a system of differential equations. Its equilibrium and stability are also carefully examined. The basic reproduction number ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> ) is calculated, which is an important parameter in the study of message propagation in OSN. If <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> < 1, the propagation of rumor in the OSN will be minimal; nevertheless, if <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> > 1, the fake information/rumor will continue in OSN. The effects of disinformation of rumours in OSN in the real world are explored. In addition, the model covers the fake information/rumour dissemination control mechanism. The comparative study shows that the proposed model provides a better mechanism to prevent the dissemination of fake information in OSN in comparison to other previous models. Extensive theoretical study and computation analysis have also been used to validate the proposed model.

In today’s world, life without technology is not possible. Continuous advancement in patient health monitoring techniques, medical equipment’s or machines and other enhancing technologies is ongoing as per recent trends in specifically healthcare sector in order to reduce human efforts. Taking into consideration the serious nature of the above aforementioned problem, it is necessary to make some major improvements in the communication devices and systems with application-based technology in order to enhance their performance thereby saving medical costs and achieve other major advantages. The principal objective of this paper is to provide a system for remote and secure monitoring of healthcare information of patient suffering from virus and utilizing a mobile device as per the patient requirements. In this paper, a proposed model measure the temperature of the body, respiratory system especially lung sound and breathing activity, which are the main source of symptoms to understand the actual health condition of a person. And data sensed by the IoT sensor device used for measuring the real-time data body temperature, lung sounds, respiratory data, pulse rate and heartbeat.

Multi-core processing is extensively used in every sector for its performance efficiency, with the advent of multi-core architecture have to modify the existing primitive algorithms. This study analyses the feasibility of K-mean data-mining technique, which is applied to a hybrid cluster with multi-core programming. The algorithm is developed using Message Passing Interface (MPI) and C programming languages for the parallel processing of the sets and uses the CPU to its maximum power for the hybrid sets. The heterogeneous clusters are confirmed by the usage of MPICH2 (High performance and portability implementation of MPI). examined the algorithm for the huge dataset. The dataset is split into a number of cores and each of the cores estimates the number of dusters on the same dataset interdependent to each other. By this, assert the core processor time for communication is significant for huge datasets. Hence, the same dataset for two different processors takes different times even with identical speed and memory and also with different speeds and access times.

Purpose The current study aims to investigate the various consumption motives (hedonic, gain and normative) responsible for strengthening consumers' intentions toward purchase behavior for electric vehicle (EV). Design/methodology/approach A total of 411 valid survey responses were collected using a structured questionnaire. Data were analyzed using confirmatory factor analysis and structural equation modeling to investigate the empirical fit of the hypothesized framework. Findings The results of structural equation modeling revealed that all three motives were positively correlated with purchase intentions for EV. Hedonic motives were found to have the strongest influence on purchase intentions. In addition, gain and normative motives were also found to be significant predictors of EV buying behavior. Further analysis revealed a positive correlation between gain, normative and hedonic motives. Moreover, personal moral standards seem to have a significant and positive impact on the positive emotions associated with buying EV. Practical implications The results of current research can be useful for marketers while designing promotional strategies for all the high-involvement green products. Marketing professionals and policymakers can use these results to build effective marketing strategies for EVs and reduce greenhouse gas emissions resulting from personal vehicle use. Originality/value To the best of the authors' knowledge, this is the first study in the South Asian region that explores consumers' motives for EV purchase behavior. Further, this is among a few studies, which have attempted to investigate the impact of hedonic, gain and normative motives on green purchase behavior in the context of high involvement green products.

Many biometric authentication techniques have been defined over the years; of these techniques, Human Gait recognition has gathered popularity over the years due to its ability to recognize a person from a distance. As the data has grown in size the focus has shifted from basic Machine Learning algorithms to Deep Learning based approaches. This paper aims to review the various deep-learning approaches used in the discipline of gait identification. This review comprises recent trends in these deep learning approaches, Convolutional Neural networks, Capsule Networks, Recurrent Neural Networks, Autoencoders, Deep Belief Networks, and Generative Adversarial Networks.