Dr. Sanjiv Kumar Jain
@medicaps.ac.in
Assistant Professor (S.G.), EE, Engineering
Medi-Caps University, Indore
Dr. Sanjiv Kumar Jain is working as an Assistant Professor (SG) in the Department of Electrical Engineering, Medi-Caps University, Indore, India. He received his ME from the SGSITS, Indore, MP, India in 2008 and BE in Electrical Engineering from RGPV, Bhopal, MP, India in 2000. He completed his PhD from the Maulana Azad National Institute of Technology, Bhopal, India. He has qualified GATE in 2006. He received Best Teaching Award and Best Project Mentor Award at university level. He has published more than 25 SCI/ SCOPUS/ Conference articles.
EDUCATION
Ph.D., M.E., B.E.
RESEARCH INTERESTS
power electronics
power systems and IoT
AI and ML
Controls
renewable sources
electric vehicles
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
K. Manikandan, Vivek Veeraiah, Dharmesh Dhabliya, Sanjiv Kumar Jain, Sukhvinder Singh Dari, Ankur Gupta, and Sabyasachi Pramanik
IGI Global
The control and management of crop diseases has always been a focal point of study in the agricultural domain. The growth of agricultural planting areas has posed several obstacles in monitoring, identifying, and managing large-scale illnesses. Insufficient disease identification capacity in relation to the expanding planting area results in heightened disease intensity, leading to decreased crop production and reduced yield per unit area. Evidence indicates that the reduction in crop productivity resulting from illnesses often surpasses 40%, leading to both financial setbacks for farmers and a certain degree of impact on local economic growth. A total of 1406 photos were gathered from 50 image sensor nodes. These images consist of 433 healthy images, 354 images showing big spot disease, 187 images showing tiny spot disease, and 432 images showing rust disease. This chapter examines the cultivation of maize fields in open-air environments and integrates internet of things (IoT) technologies.
Ritu Tandon, Shweta Agrawal, Narendra Pal Singh Rathore, Abhinava K. Mishra, and Sanjiv Kumar Jain
Wiley
AbstractDeep learning is gaining importance due to its wide range of applications. Many researchers have utilized deep learning (DL) models for the automated diagnosis of cancer patients. This paper provides a systematic review of DL models for automated diagnosis of cancer patients. Initially, various DL models for cancer diagnosis are presented. Five major categories of cancers such as breast, lung, liver, brain and cervical cancer are considered. As these categories of cancers have a very high percentage of occurrences with high mortality rate. The comparative analysis of different types of DL models is drawn for the diagnosis of cancer at early stages by considering the latest research articles from 2016 to 2022. After comprehensive comparative analysis, it is found that most of the researchers achieved appreciable accuracy with implementation of the convolutional neural network model. These utilized the pretrained models for automated diagnosis of cancer patients. Various shortcomings with the existing DL‐based automated cancer diagnosis models are also been presented. Finally, future directions are discussed to facilitate further research for automated diagnosis of cancer patients.
Janjhyam Venkata Naga Ramesh, Ajay kushwaha, Tripti Sharma, A. Aranganathan, Ankur Gupta, and Sanjiv Kumar Jain
Springer Nature Singapore
Harinder Singh, Veera Talukdar, Huma Khan, Dharmesh Dhabliya, Rohit Anand, Abhra Pratip Ray, and Sanjiv Kumar Jain
CRC Press
Preeti Singh and Sanjiv Kumar Jain
IEEE
Promoting renewable energy is vital to meet the future energy requirements. Our study primarily focuses on developing a dynamic approach that utilizes a sophisticated algorithm to manage the bidirectional flow of energy between electric vehicles (EV’s) and the power-grid. This strategy takes into account multiple objectives and criteria, such as optimizing energy supply and demand. A management and regulatory algorithm balances production and consumption. The bidirectional control ensures a balanced flow of energy between a home, electric vehicles (EV’s), and the power-grid. Next, the system is simulated to evaluate the multi-objective optimization of the regulation algorithm for charging/ discharging modes of electric vehicles (EV’s).The goal is to ensure that energy demands are met. Batteries in vehicles store and release energy based on electrical demand. Genetic algorithms optimize storage. Many case studies employ weighted sums and normalized objective functions. Matlab simulates optimal values after calculation. Simulation follows regulatory algorithm setup and optimization. Thus, reversible energy flows, production, and consumption are readily managed.
Sanjiv Kumar Jain and Shweta Agrawal
Inderscience Publishers
Tao Hai, Jincheng Zhou, Dayang Jawawi, Dan Wang, Uzoma Oduah, Cresantus Biamba, and Sanjiv Kumar Jain
Springer Science and Business Media LLC
AbstractCloud computing is an extremely important infrastructure used to perform tasks over processing units. Despite its numerous benefits, a cloud platform has several challenges preventing it from carrying out an efficient workflow submission. One of these is linked to task scheduling. An optimization problem related to this is the maximal determination of cloud computing scheduling criteria. Existing methods have been unable to find the quality of service (QoS) limits of users- like meeting the economic restrictions and reduction of the makespan. Of all these methods, the Heterogeneous Earliest Finish Time (HEFT) algorithm produces the maximum outcomes for scheduling tasks in a heterogeneous environment in a reduced time. Reviewed literature proves that HEFT is efficient in terms of execution time and quality of schedule. The HEFT algorithm makes use of average communication and computation costs as weights in the DAG. In some cases, however, the average cost of computation and selecting the first empty slot may not be enough for a good solution to be produced. In this paper, we propose different HEFT algorithm versions altered to produce improved results. In the first stage (rank generation), we execute several methodologies to calculate the ranks, and in the second stage, we alter how the empty slots are selected for the task scheduling. These alterations do not add any cost to the primary HEFT algorithm, and reduce the makespan of the virtual machines’ workflow submissions. Our findings suggest that the altered versions of the HEFT algorithm have a better performance than the basic HEFT algorithm regarding decreased schedule length of the workflow problems.
Vivek Veeraiah, K. O. Thejaswini, R. Dilip, Sanjiv Kumar Jain, Aradhana Sahu, Sabyasachi Pramanik, and Ankur Gupta
IGI Global
The chapter describes how clinical data may be stored in digital formats, such as patient reports, as electronic health records, and how meaningful information from these records may be created using data analytics methods and tools that may assist patients and physicians to save time and money. The Apollo Hospital in Kolkata, West Bengal, India is the subject of this study. Apollo Hospital is the biggest hospital in West Bengal. It collects a huge quantity of heterogeneous data from various sources, including patient health records, lab test results, digital diagnostic supplies, healthcare insurance data, social media data, pharmaceutical data, gene expression records, transactions, and data from MY hospital's Mahatma Gandhi Memorial Medical College. Data analytics could be used to organise this data and make it retrievable. As a result, the term “big data” may be used. Big data is defined as exceptionally big datasets which may be analysed computationally to uncover trends, patterns, and relationships, visualisation, information privacy, and predictive analytics on a huge dataset.
Aida Khakimova, Oleg Zolotarev, Bhisham Sharma, Shweta Agrawal, and Sanjiv Kumar Jain
MDPI AG
This article address approaches to the development of methods for assessing the psychological state of social network members during the coronavirus pandemic through sentiment analysis of messages. The purpose of the work is to determine the psychological tension index by using a previously developed thematically ranked dictionary. Researchers have investigated methods to evaluate psychological tension among social network users and to forecast the psychological distress. The approach is novel in the sense that it ranks emojis by mood, considering both the emotional tone of tweets and the emoji’s dictionary meanings. A novel method is proposed to assess the dynamics of the psychological state of social network users as an indicator of their subjective well-being, and develop targeted interventions for help. Based on the ranking of the Emotional Vocabulary Index (EVI) and Subjective Well-being Index (SWI), a scheme is developed to predict the development of psychological tension. The significance lies in the efficient assessment of the fluctuations in the mental wellness of network users as an indication of their emotions and a prerequisite for further predictive analysis. The findings gave a computed value of EVI of 306.15 for April 2022. The prediction accuracy of 88.75% was achieved.
Sanjiv Kumar Jain, Sandeep Bhongade, Shweta Agrawal, Abolfazl Mehbodniya, Bhisham Sharma, Subrata Chowdhury, and Julian L. Webber
MDPI AG
In this study, the load frequency control of a two-area thermal generation system based on renewable energy sources is considered. When solar generation is used in one of the control areas, the system becomes nonlinear and complicated. Zero deviations in the frequencies and the flow of power through the tie lines are achieved by considering load disturbances. A novel grey wolf optimizer, which is a metaheuristic algorithm motivated by grey wolves is utilized for tuning the controller gains. The proportional, integral, and derivative gains values are optimized for the two-area Solar integrated Thermal Plant (STP). As the load connected to the system varies continuously with time, random load variation is also applied to observe the effectiveness of the proposed optimization method. Sensitivity analyses have also been adopted with the deviation in the time constants of different systems. Inertia constant variations of both areas are considered from −25% to +25%, with or without STP. The proposed algorithm shows good dynamic performance as shown from the simulation results in terms of settling time, overshoot values, and undershoot values. The power in the tie line achieves zero deviation quite rapidly in solar-based cases compared to those without STP.
Narendra B Mustare, Arockia Raj Abraham, R. Suguna, Sanjiv Kumar Jain, Saritha PS, and Ch. Raja
IEEE
With the rapid development of industry, the types and quantities of wastewater have increased rapidly, and the pollution to water bodies has become increasingly widespread and serious. As far as water system is concerned, the information of water quality system is both known and unknown because of incomplete data information and great uncertainty of systematic observation information, such as data error, data quality and quantity influence, etc. Therefore, judging from the clarity of system information, water quality pollution assessment is actually a problem with definite extension but uncertain connotation. In this paper, a water quality detection method based on grey clustering algorithm is proposed to evaluate the treatment effect of industrial wastewater by magnetic resin adsorption, and the effectiveness of the proposed method is verified by comparative experiments. The results show that grey clustering algorithm can deal with the measured data of turbidity, dissolved oxygen and conductivity of water quality parameters without changing the basic trend of the measured data, and at the same time, it can remove some noise signals to get a smoother data curve. Through the analysis of practical data and the comparison of traditional water quality evaluation models, the prediction algorithm predicts the change trend of water turbidity more effectively, and improves the accuracy of water turbidity control in the process of water treatment.
Abdulsattar Abdullah Hamad, Leelavathi R, N. Manikandan, M R Senkumar, P. Thejasree, and Sanjiv Kumar Jain
IEEE
Natural resources such as sand and stone in concrete are non-renewable resources in the short term, and unlimited exploitation and utilization will cause irreversible damage and impact on the ecological environment. Composite material is a kind of multiphase material, and its thermodynamic properties and failure mechanism are not only dependent on the macro service environment, but also closely related to the material properties, structural morphology and interface characteristics of each component. Multi-scale science is a basic, interdisciplinary and forward-looking science, which is used to study the phenomenon of coupling and correlation between different time or space scales. In this paper, a test method of mechanical properties of graphene concrete based on fuzzy control algorithm is proposed, which provides a reference for further research on mechanical properties of rockfill concrete. Judging from the correlation coefficient, the strength of mortar matrix should be based on the compressive strength when calculating the concrete micromechanics. Under the condition of the same mass mix ratio, the compressive strength of concrete increases with the strength of coarse aggregate used in the preparation of concrete.
P. S. Ramesh, S. Surendran, S. Kerthy, Ramalakshmi B, Uganya G, and Sanjiv Kumar Jain
IEEE
Due to the participation of people in online public opinion, users have their own preferences and ideas, which leads to strong chaos in online public opinion. Currently, ML (machine learning) algorithms ignore the chaotic characteristics of online public opinion, so the established model can't fully and accurately describe the changes of online public opinion, and the prediction accuracy needs to be further improved. In this paper, the SVM (support vector machine) algorithm is used to analyze the sentiment tendency of text and predict the trend of Internet public opinion, and the pre-model of sentiment tendency identification and Internet public opinion is established. On the basis of the traditional Gaussian kernel function, we add parameters to increase the displacement change and amplitude adjustment of the kernel function, so that it can accurately control the performance characteristics of the kernel function. The document's overall emotional tendency can be obtained through this accumulation. The results show that the recognition rate of this method is up to 95.828%, and the prediction error is controlled within the effective range (5.028%). This method overcomes the shortcomings of traditional emotional word weighting methods, such as low performance and inability to adapt to multi-viewpoint topics, and can get better recognition accuracy.
Shahanawaj Ahamad, Suryansh Bhaskar Talukdar, Rohit Anand, Veera Talukdar, Sanjiv Kumar Jain, and Arpit Namdev
Springer Nature Singapore
Shreashtha Varun, Sanjiv Kumar Jain, Sandeep Bhongade, and Shweta Agrawal
Inderscience Publishers
Deepika Saravagi, Shweta Agrawal, Manisha Saravagi, Sanjiv K. Jain, Bhisham Sharma, Abolfazl Mehbodniya, Subrata Chowdhury, and Julian L. Webber
Computers, Materials and Continua (Tech Science Press)
V. Vidya Chellam, S Praveenkumar, Suryansh Bhaskar Talukdar, Veera Talukdar, Sanjiv Kumar Jain, and Ankur Gupta
IEEE
Although blockchain technology was first created to manage financial ledgers, it has lately found use in a wide range of industries, including healthcare. Research innovation in this area will be boosted by the sharing of healthcare data. Nevertheless, patients who disclose their medical records face several privacy and security concerns. Here, the authors propose a blockchain-dependent system architectural design to demonstrate the prospect of blockchain technology in supporting (i) personal and examined medical data exchange and (ii) medical data access authorization processing.
B. Prabha, Sandeep Kaur, Jaspreet Singh, Praful Nandankar, Sanjiv Kumar Jain, and Harikumar Pallathadka
Elsevier BV
Shweta Agrawal, Sanjiv Kumar Jain, Shruti Sharma, and Ajay Khatri
MDPI AG
The COVID-19 pandemic has shattered the whole world, and due to this, millions of people have posted their sentiments toward the pandemic on different social media platforms. This resulted in a huge information flow on social media and attracted many research studies aimed at extracting useful information to understand the sentiments. This paper analyses data imported from the Twitter API for the healthcare sector, emphasizing sub-domains, such as vaccines, post-COVID-19 health issues and healthcare service providers. The main objective of this research is to analyze machine learning models for classifying the sentiments of people and analyzing the direction of polarity by considering the views of the majority of people. The inferences drawn from this analysis may be useful for concerned authorities as they work to make appropriate policy decisions and strategic decisions. Various machine learning models were developed to extract the actual emotions, and results show that the support vector machine model outperforms with an average accuracy of 82.67% compared with the logistic regression, random forest, multinomial naïve Bayes and long short-term memory models, which present 78%, 77%, 68.67% and 75% accuracy, respectively.
Moti Lal Rinawa, V. Shanmugasundaram, Rajneesh Sharma, Sanjiv Kumar Jain, Ankit, and Ravindra Manohar Potdar
Elsevier BV
Tao Hai, Jincheng Zhou, Ning Li, Sanjiv Kumar Jain, Shweta Agrawal, and Imed Ben Dhaou
Springer Science and Business Media LLC
AbstractCloud technology is not immune to bugs and issue tracking. A dedicated system is required that will extremely error prone and less cumbersome and must command a high degree of collaboration, flexibility of operations and smart decision making. One of the primary goals of software engineering is to provide high-quality software within a specified budget and period for cloud-based technology. However, defects found in Cloud-Based Bug Tracking software’s can result in quality reduction as well as delay in the delivery process. Therefore, software testing plays a vital role in ensuring the quality of software in the cloud, but software testing requires higher time and cost with the increase of complexity of user requirements. This issue is even cumbersome in the embedded software design. Early detection of defect-prone components in general and embedded software helps to recognize which components require higher attention during testing and thereby allocate the available resources effectively and efficiently. This research was motivated by the demand of minimizing the time and cost required for Cloud-Based Bug Tracking Software testing for both embedded and general-purpose software while ensuring the delivery of high-quality software products without any delays emanating from the cloud. Not withstanding that several machine learning techniques have been widely applied for building software defect prediction models in general, achieving higher prediction accuracy is still a challenging task. Thus, the primary aim of this research is to investigate how deep learning methods can be used for Cloud-Based Bug Tracking Software defect detection with a higher accuracy. The research conducted an experiment with four different configurations of Multi-Layer Perceptron neural network using five publicly available software defect datasets. Results of the experiments show that the best possible network configuration for software defect detection model using Multi-Layer Perceptron can be the prediction model with two hidden layers having 25 neurons in the first hidden layer and 5 neurons in the second hidden layer.
Tao Hai, Jincheng Zhou, S. R. Srividhya, Sanjiv Kumar Jain, Praise Young, and Shweta Agrawal
Springer Science and Business Media LLC
AbstractBlockchain is the latest boon in the world which handles mainly banking and finance. The blockchain is also used in the healthcare management system for effective maintenance of electronic health and medical records. The technology ensures security, privacy, and immutability. Federated Learning is a revolutionary learning technique in deep learning, which supports learning from the distributed environment. This work proposes a framework by integrating the blockchain and Federated Deep Learning in order to provide a tailored recommendation system. The work focuses on two modules of blockchain-based storage for electronic health records, where the blockchain uses a Hyperledger fabric and is capable of continuously monitoring and tracking the updates in the Electronic Health Records in the cloud server. In the second module, LightGBM and N-Gram models are used in the collaborative learning module to recommend a tailored treatment for the patient’s cloud-based database after analyzing the EHR. The work shows good accuracy. Several metrics like precision, recall, and F1 scores are measured showing its effective utilization in the cloud database security.
Abdul Quadir Md, Dibyanshu Jaiswal, Jay Daftari, Sabireen Haneef, Celestine Iwendi, and Sanjiv Kumar Jain
MDPI AG
The instances of privacy and security have reached the point where they cannot be ignored. There has been a rise in data breaches and fraud, particularly in banks, healthcare, and government sectors. In today’s world, many organizations offer their security specialists bug report programs that help them find flaws in their applications. The breach of data on its own does not necessarily constitute a threat or attack. Cyber-attacks allow cyberpunks to gain access to machines and networks and steal financial data and esoteric information as a result of a data breach. In this context, this paper proposes an innovative approach to help users to avoid online subterfuge by implementing a Dynamic Phishing Safeguard System (DPSS) using neural boost phishing protection algorithm that focuses on phishing, fraud, and optimizes the problem of data breaches. Dynamic phishing safeguard utilizes 30 different features to predict whether or not a website is a phishing website. In addition, the neural boost phishing protection algorithm uses an Anti-Phishing Neural Algorithm (APNA) and an Anti-Phishing Boosting Algorithm (APBA) to generate output that is mapped to various other components, such as IP finder, geolocation, and location mapper, in order to pinpoint the location of vulnerable sites that the user can view, which makes the system more secure. The system also offers a website blocker, and a tracker auditor to give the user the authority to control the system. Based on the results, the anti-phishing neural algorithm achieved an accuracy level of 97.10%, while the anti-phishing boosting algorithm yielded 97.82%. According to the evaluation results, dynamic phishing safeguard systems tend to perform better than other models in terms of uniform resource locator detection and security.
Eric Appiah Mantey, Conghua Zhou, S. R. Srividhya, Sanjiv Kumar Jain, and B. Sundaravadivazhagan
Frontiers Media SA
Blockchain is a recent revolutionary technology primarily associated with cryptocurrencies. It has many unique features including its acting as a decentralized, immutable, shared, and distributed ledger. Blockchain can store all types of data with better security. It avoids third-party intervention to ensure better security of the data. Deep learning is another booming field that is mostly used in computer applications. This work proposes an integrated environment of a blockchain-deep learning environment for analyzing the Electronic Health Records (EHR). The EHR is the medical documentation of a patient which can be shared among hospitals and other public health organizations. The proposed work enables a deep learning algorithm act as an agent to analyze the EHR data which is stored in the blockchain. This proposed integrated environment can alert the patients by means of a reminder for consultation, diet chart, etc. This work utilizes the deep learning approach to analyze the EHR, after which an alert will be sent to the patient's registered mobile number.