Dr Divakar Singh
@bubhopal.ac.in
Assistant Professor
Barkatullah University Bhopal
Singh is working as an Assistant Professor, at the University Institute of Technology, Barkatullah University, Bhopal, Madhya Pradesh, India. Till now 93 M.Tech and 1 Ph.D., dissertation guided by him and 110+ Research papers have been published. His Google Research Scholar total citations is 575, h-index is 13 and the i-index is 21. He has more than 22 years of experience in both teaching and research. He has worked as a member of the board of studies and as a Chairman, of the board of studies in the subject CSE/IT/Electronics in the Faculty of Engineering. He is a life member of ISTE and CSI and a member fellow IETE. His areas of interest include deep learning, image processing, nature-inspired algorithms, and soft computing.
EDUCATION
B.E, Mtech, Ph.D In Computer Science and Engineering
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Anju Singh, Divakar Singh, Kamal Upreti, Vaibhav Sharma, Bhawani Singh Rathore, and Jagdish Raikwal
River Publishers
Background: COVID-19 is a major public health emergency wreaking havoc on public health, happiness, and liberty of travel, as well as the worldwide economy. Scientists from all over the world are working to develop treatments and vaccines; the WHO has given emergency approval to eight vaccines from around the world. However, it is also seen that the efficiency of vaccines is not up to the mark in different age groups. COVID-19 symptoms come in many different shapes and sizes, so it’s important to learn about them as soon as possible so that medical attention and management can be easier. Method: The GitHub Data Repository-made COVID-19 patient data is available on the internet, which is used in this investigation. We have used the association rule mining method to look for common patterns in a targeted class or segment and then look at the symptoms based on them. Result: The result is that this study involves individuals with a median age of 52 years old. Few frequent symptoms like respiratory failure (1%), septic shock (1.4%), respiratory distress syndrome (1.8%), diarrhoea (1.8%), nausea (2%), sputum (3%), headache (5%), sore throat (8%), pneumonia (8%), weakness (7%), malaise/body pain (11%), cough (37%), fever (67%) and remaining diseases like myocardial infarction, cardiac failure, and renal illness (less than 1%) were present. If a patient had chronic disease, respiratory failure, and pneumonia, there was a higher risk of death; if a patient had a combination of chronic disease, respiratory failure, and pneumonia, respiratory failure in the age range of 45 to 84 years there was a higher risk of death. Patients having chronic conditions like pneumonia or renal disease symptoms that died as a result of the corona virus had more serious indication patterns than those without chronic diseases.
Pradeep Kumar Dabla, Kamal Upreti, Divakar Singh, Anju Singh, Jitender Sharma, Aashima Dabas, Damien Gruson, Bernard Gouget, Sergio Bernardini, Evgenija Homsak,et al.
Informa UK Limited
Abstract Background and aims To assess the hospitalized sick children admitted to the pediatric emergency department (ED) and to find new patterns of clinical and laboratory attributes using association rule mining (ARM). Methods In this observational study, 158 children with median (IQR) age 11 months and a PRISM III score of 5 (2–9) were enrolled. Hotspot data mining method was applied to assess clinical attributes, lab investigations and pre-defined outcome parameters of children and their association in sick hospitalized children aged 1 month to 12 years. Results We obtained 30 rules with value for outcome as discharge is given attributes as follows: duration of hospitalization > 4 days, lactate > 1.2 mmol/L, platelet = 3.67/μL, dur_ventil = 0 h, serum K = 5.2 mmol/L, SBP = 120 mmHg, pCO2 = 41.9 mmHg, PaO2 = 163 mmHg, age = 92 months, heart rate > 114–159 per minute, temperature > 98 °F, GCS (Glasgow Coma Scale) > 7–14, gas K = 4.14 mmol/L, gas Na = 138.1 mmol/L, BUN (Blood Urea Nitrogen) = 18.69 mg/dL, Diagnosis > 1–718, Creatinine = 1.2 mg/dL, serum Na = 148 mmol/L, shock = 2, Glucose = 144 mg/dL, Mg(i) > 0.23 meq/L, BUN > 6.54 mg/dL. Conclusion ARM is an effective data analysis technique to find meaningful patterns using clinical features with actual numbers in pediatric critical illness. It can prove to be important while analysing the association of clinical attributes with disease pattern, its features, and therapeutic or intervention success patterns.
Kamal Upreti, Manvendra Verma, Meena Agrawal, Jatinder Garg, Rekha Kaushik, Chinmay Agrawal, Divakar Singh, and Rajamani Narayanasamy
Hindawi Limited
Geopolymer concrete could be the best alternative to ordinary Portland cement concrete due to its higher performance in any severe condition. It reduces the carbon footprints to a very higher level. Machine learning methods are the future of the construction industry because it predicts the mechanical strengths of concrete mix design on the basis of their constituents without destructive test conduction. This study is aimed at developing the models to predict the mechanical strengths and validate them with the actual results. After the experimental investigation, we found the results of the mechanical (including compressive, splitting tensile, and flexural tensile) strength. The M2 mix of geopolymer concrete got the highest mechanical strengths whereas the M5 mix gets the lowest mechanical strengths among all the mix designs. The machine learning methods ANN (artificial neural network) and random forest are used to develop the models based on mixed experimental results. Mechanical strength results are taken as outputs, and mixed constituents are taken as inputs for training and testing. The performance of predicted results is checked based onR2, MAE (mean absolute error), RMSE (relative mean square error), RAE (relative absolute error), and RRSE (root-relative square error). Random forest models show the best prediction to the ANN models because it shows the negligible error between actual and predicted values. TheR2value is 1 of 12 predicted results out of 15 by the use of random forest methods. So it is most suitable to predict the strength of geopolymer concrete based on their constituent’s material quantity.
Prashant Parashar, , Dr. Divakar Singh, and
Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP
The upcoming era of social media will be highly equipped with the images and the videos. Images or multimedia content sharing and storing services are still costly for common man which is need to be resolved to cover all the users either middle class or high society users. Various online platforms have filled the gaps for freedom of expression for everyone. demand of the multimedia data sharing telecommunication networks has increased. images has changed the requirements for effective transmission and storage media. With the convenience of accessibility of press tools and digital image web exchange, there has been a dramatic increase. Image is the least component of multimedia information and includes a important portion of the velocity of communication for multimedia data Developments in image compression techniques have therefore developed potential requirement. For all pictures, a fundamental concept of image formation is that the pixels are linked and comprise extremely useless data afterwards. The primary objective of this job is to discover in the image reduced associated pixel intensities. In this work an adaptive frequency domain block processing for color image compression has developed and simulated.
Snehlata Yadav and Divakar Singh
IEEE
Clustering is grouping similar data items, features, observations etc. In to cluster. Clustering Problem has been addressed many times as it is one of the important step in data analysis in various application areas. This paper presents an overview of message passing data clustering technique with a goal of providing useful concepts which can be accessible to the community of clustering practitioners. Message passing clustering technique, its extensions, improvisation and usage in different application areas and recent advances are described.
Pooja Sharma, Divakar Singh, and Anju Singh
IEEE
Classification is widely used technique in the data mining domain, where scalability and efficiency are the immediate problems in classification algorithms for large databases. Now a day's large amount of data is generated, that need to be analyse, and pattern have to be extracted from that to get some knowledge. Classification is a supervised machine learning task which builds a model from labelled training data. The model is used for determining the class; there are many types of classification algorithms such as tree-based algorithms (C4.5 decision tree, j48 decision tree etc.), naive Bayes and many more. These classification algorithms have their own pros and cons, depending on many factors such as the characteristics of the data. We can measure the classification performance by using several metrics, such as accuracy, precision, classification error and kappa on the testing data. We have used a random dataset in a rapid miner tool for the classification. Stratified sampling is used in different classifier such as J48, C4.5 and naïve Bayes. We analysed the result of the classifier using the randomly generated dataset and without random dataset.
Saurabh Verma, Abhishek Singh, and Divakar Singh
IEEE
There has been a colossal increase in the use of credit cards as a medium of online transactions in the recent decades. Online payment is becoming a more popular and more convenient means for shopping and paying daily bills compared to the traditional ways.
Vinay Yadav and Divakar Singh
Springer India
Saurabh Verma, Abhishek Singh, Divakar Singh, and Vijay Laxmi
IEEE
In today's digital world internet became a popular source of online purchasing and plastic money facilitates the transaction of money. Online shopping has made the human life more easier and now user can feel the real shopping experience in virtual world of internet. As the popularity of e-commerce increases so the threats. Service providers and merchants who process credit card and debit card became the easy targets for computer hackers to steal information of cards and commit frauds. Nowadays merchants are providing many merchant facilitating employee to bring personnel devices and technology such as smart phones, laptop, etc. to simplify business and reduce capital expenditure, and opening a new site for fraud that can be committed by their own employees. In this paper we present the implementation of computer forensics to identify the source of for credit card fraud done by employee or internal people by USB devices.
Vikram Garg, Anju Singh, and Divakar Singh
IEEE
The significant development in field of data collection and data storage technologies have provided transactional data to grow in data warehouses that reside in companies and public sector organizations. As the data is growing day by day, there has to be certain mechanism that could analyze such large volume of data. Data mining is a way of extracting the hidden predictive information from those data warehouses without revealing their sensitive information. Privacy preserving data mining (PPDM) is the recent research area that deals with the problem of hiding the sensitive information while analyzing data. Association Rule Hiding is one of the techniques of PPDM to hide association rules generated by Association Rule Generation Algorithms. In this paper we will provide a comparative theoretical analysis of Algorithms that have been developed for Association Rule Hiding.
Surendra Kumar Chadokar, Divakar Singh, and Anju Singh
IEEE
Association rule mining is a technique of generating frequent item sets so that the analysis on the basis of these sets can be used for different application areas such as analysis of network traffic. Although the frequent sets generated using apriori algorithm provides less computational time and provides less frequent sets, but the technique that we are implemented here provides less computational time as compared as well generated less sets and provides less rules for the network traffics. These frequent sets are used for the analysis of traffic in the network so that the analysis of different spams or any unwanted issues can be detected easily.
Divakar Singh and Anju Singh
IEEE
The rapid development in computer technology for multimedia databases, digital media results in increase in the usage of digital images. Vast amount of data can be hidden in the form of digitized image, image mining is used to extract such kind of data and potential information from general collections of images. Image Clustering groups the images into classes of similar images without prior knowledge. Thus the search for the relevant information in the large space of image databases become more challenging and interesting too. This paper discusses the comparison between two partition clustering algorithm (K-Means and SOM) and one Hierarchical clustering algorithm using the texture as image features. The visual content of an image is analyzed in terms of low-level features extracted from the image. For texture feature extraction novel algorithm by pyramid-structured wavelet is presented. The SOM clustering algorithm produces better results, which is very much acceptable in image domain.
Anshu Shrivastava and Divakar Singh
IEEE
Association rules are the main technique for data mining. The Apriori algorithm is a classical algorithm in mining association rules. With the time a number of changes proposed in Apriori to enhance the performance in term of time and number of database passes. For the two bottlenecks of frequent item sets mining: the large multitude of candidate 2-itemsets, the poor efficiency of counting their support. This paper main focus lies in the generation of frequent patterns which is the most important task in explanation of the fundamentals of association rule mining. This is done by analyzing the implementations of the well known association rule mining algorithms Apriori and Proposed algorithm Set operation for Frequent Item using Transaction database.