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
20
Scopus Publications
806
Scholar Citations
15
Scholar h-index
26
Scholar i10-index
Scopus Publications
Anti-epileptic medication induced disturbed calcium-vitamin D metabolism: A behavioral analysis using association rule mining technique Pradeep K Dabla, Kamal Upreti, Divakar Singh, Anju Singh, Vinod Puri, Adina E Stanciu, Nafija Serdarevic, Damien Gruson World Journal of Experimental Medicine, 2025 BACKGROUND There is a lack of study on vitamin D and calcium levels in epileptic patients receiving therapy, despite the growing recognition of the importance of bone health in individuals with epilepsy. Associations one statistical method for finding correlations between variables in big datasets is called association rule mining (ARM). This technique finds patterns of common items or events in the data set, including associations. Through the analysis of patient data, including demographics, genetic information, and reactions with previous treatments, ARM can identify harmful drug reactions, possible novel combinations of medicines, and trends which connect particular individual features to treatment outcomes. AIM To investigate the evidence on the effects of anti-epileptic drugs (AEDs) on calcium metabolism and supplementing with vitamin D to help lower the likelihood of bone-related issues using ARM technique. METHODS ARM technique was used to analyze patients’ behavior on calcium metabolism, vitamin D and anti-epileptic medicines. Epileptic sufferers of both sexes who attended neurological outpatient and in patient department clinics were recruited for the study. There were three patient groups: Group 1 received one AED, group 2 received two AEDs, and group 3 received more than two AEDs. The researchers analyzed the alkaline phosphatase, ionized calcium, total calcium, phosphorus, vitamin D levels, or parathyroid hormone values. RESULTS A total of 150 patients, aged 12 years to 60 years, were studied, with 50 in each group (1, 2, and 3). 60% were men, this gender imbalance may affect the study’s findings, as women have different bone metabolism dynamics influenced by hormonal variations, including menopause. The results may not fully capture the distinct effects of AEDs on female patients. A greater equal distribution of women should be the goal of future studies in order to offer a complete comprehension of the metabolic alterations brought on by AEDs. 86 patients had generalized epilepsy, 64 partial. 42% of patients had AEDs for > 5 years. Polytherapy reduced calcium and vitamin D levels compared to mono and dual therapy. Polytherapy elevated alkaline phosphatase and phosphorus levels. CONCLUSION ARM revealed the possible effects of variables like age, gender, and polytherapy on parathyroid hormone levels in individuals taking antiepileptic medication.
Enhanced Pneumonia Detection from Chest X-rays Using Machine Learning and Deep Neural Architectures Kamal Upreti, Anju Singh, Divakar Singh, Preety Shoran, Uma Shankar, Meenakshi Yadav, Rituraj Jain Aro the Scientific Journal of Koya University, 2025 Pneumonia is a major worldwide health concern, particularly for vulnerable groups such as babies and the elderly. Despite advances in medical imaging, diagnosing pneumonia using a chest X-ray remains difficult, due to the subtle presentation of symptoms and the variety in picture interpretation. This study utilizes modern machine learning can improve the accuracy and speed of diagnosing pneumonia using chest X-ray images. Utilizing a comprehensive dataset from the Kaggle online repository, consisting of over 5,000 annotated images, we evaluate the efficacy of various machine learning models including deep convolutional neural networks (CNN) and ensemble learning techniques. Our findings indicate that models like the Fuzzy opponent histogram filter combined with Logistic model trees (LMT) achieved the highest accuracy at 96.97%, while the deep learning-based Lenet (CNN) with LMT closely followed at 95.85%. The study aims to improve diagnostic precision, reduce interpretation discrepancies, and facilitate faster clinical decision-making by identifying the most effective machine learning approaches for real-world applications in healthcare settings.
EEG Based Depression Detection Using Machine Learning and Deep Neural Network Vaishali Darbar, Amit Kumar Jha, Divakar Singh, Kamini Maheshwar 2025 4th Opju International Technology Conference on Smart Computing for Innovation and Advancement in Industry 5 0 Otcon 2025, 2025 The research presents a modern approach to depression diagnosis through Electroencephalography signal analysis using deep neural network and other machine learning technologies. Department of Health reports that depression stands as a widespread and very damaging mental health problem which severely impacts the lifestyle quality of people. Subjective clinical methods used for diagnosis normally lead to inaccurate results or treatment delays. The research uses EEG signals to provide instant objective brain monitoring data which improves diagnosis accuracy and efficiency for depression identification. A process of performing initial EEG data analysis leads to feature extraction before creating a system based on machine learning with deep neural network algorithms to spot depressive conditions. Empirical research results demonstrate that this method works effectively thus offering promise as an invasive and dependable technology for the early identification of depression.
Phishing Websites Prediction Using Deep Learning Technique for Prevention of Financial Risk Swati Meshram, Amit Kumar Jha, Divakar Singh, Kamini Maheshwar Proceedings of the 2025 International Conference on Technology Enabled Economic Changes Intech 2025, 2025 Fraudulent attempts to get sensitive information including user data and financial information of victims occur through Phishing by using fake trustworthy entities. Online transactions currently experience exponential growth thus phishing attacks emerged as a critical threat which results in serious financial damages and diminished trust in digital systems. Blacklist-based detection together with heuristic rule-based approaches fail to protect users because these traditional anti-phishing methods cannot detect recent or complex phishing attacks. The proposed artificial neural network (ANN)-based deep learning model seeks to predict phishing websites with both excellent accuracy and dependable performance in this examination. The model uses URL and content and network-based features to detect legitimate websites from phishing websites effectively. Feature selection preprocessing techniques apply to the dataset which results in performance improvement and computational load optimization. Experimental tests validate that the proposed ANN system surpasses regular machine learning approaches in achieving superior precision along with recall and F1-score scores. This research proves deep learning possesses the capability to identify phishing threats ahead of time which supports cybersecurity initiatives and monetary risk reduction in digital environments.
Efficient Credit Card Fraud Detection Based on Multiple ML Algorithms Neha Ahirwar, Divakar Singh, Kamini Maheshwar 2024 IEEE 9th International Conference for Convergence in Technology I2ct 2024, 2024 The occurrence of credit card fraud has become a major worry in the current digital era, impacting financial institutions and customers alike. Effective and reliable fraud detection systems are in high demand as fraudulent actions continue to change. Sophisticated machine learning algorithms have become essential instruments in tackling this problem. The design and assessment of a credit card fraud detection system based on well-known machine learning algorithms— Random Forest, Logistic Regression, Decision Tree, and XGBoost—is thoroughly investigated in this study.The increasing number of digital transactions has given criminals additional chances to take advantage of holes in payment systems. As a result, the requirement for proactive and flexible fraud detection systems has increased significantly. In order to solve this urgent issue, this study investigates how well machine learning algorithms can detect fraudulent credit card transactions.The machine learning algorithms that are the subject of this study were selected due to their demonstrated efficacy in a number of fields, including the identification of credit card fraud. The models Random Forest, Logistic Regression, Decision Tree, and XGBoost are highly respected for their capacity to accurately predict complex patterns. A variety of real and fraudulent credit card transaction datasets are used to develop each method and evaluate its performance in a controlled setting.
Investigating new patterns in symptoms of covid-19 patients by association rule mining (arm) Anju Singh, Divakar Singh, Kamal Upreti, Vaibhav Sharma, Bhawani Singh Rathore, Jagdish Raikwal Journal of Mobile Multimedia, 2023 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.
Discovering patterns of live birth occurrence before in vitro fertilisation treatment using association rule mining Kamal Upreti, Divakar Singh, Anju Singh, Prashant Vats, Rishu Bhardwaj, Shreya Kapoor International Journal of Electronic Healthcare, 2023 According to estimates, in-vitro fertilisation (IVF) is credited for the delivery of over 9 million children globally, constituting it to be a highly remarkable as well as commercialised advanced healthcare treatment. Nonetheless, the majority of IVF treatments are now constrained by factors such as expense, access and most notably, labour-intensive, technically demanding processes carried out by qualified professionals. Advancement is thus crucial to maintaining the IVF market's rapid growth while also streamlining current procedures. This might also improve access, cost, and effectiveness while also managing therapeutic time efficiently and at a reasonable cost. IVF has become a renowned technique for addressing problems like endometriosis, poor embryo development, hereditary diseases of the parents, issues with the biological function, problems with counteracting agents that harm either eggs or sperm, the limited capacity of semen to penetrate cervical bodily fluid, and lower sperm count that lead to infertility in humans.
Prediction of Mechanical Strength by Using an Artificial Neural Network and Random Forest Algorithm Kamal Upreti, Manvendra Verma, Meena Agrawal, Jatinder Garg, Rekha Kaushik, Chinmay Agrawal, Divakar Singh, Rajamani Narayanasamy Journal of Nanomaterials, 2022 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.
Target association rule mining to explore novel paediatric illness patterns in emergency settings Pradeep Kumar Dabla, Kamal Upreti, Divakar Singh, Anju Singh, Jitender Sharma, Aashima Dabas, Damien Gruson, Bernard Gouget, Sergio Bernardini, Evgenija Homsak, Sanja Stankovic Scandinavian Journal of Clinical and Laboratory Investigation, 2022 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.
Message Passing Clustering Technique: A Review Snehlata Yadav, Divakar Singh Proceedings 2015 International Conference on Computational Intelligence and Communication Networks Cicn 2015, 2016
A survey of association rule hiding algorithms Vikram Garg, Anju Singh, Divakar Singh Proceedings 2014 4th International Conference on Communication Systems and Network Technologies Csnt 2014, 2014
Enhanced Pneumonia Detection from Chest X-rays Using Machine Learning and Deep Neural Architectures K Upreti, A Singh, D Singh, P Shoran, U Shankar, M Yadav, R Jain ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY 13 (1), 227-236 , 2025 2025 Citations: 1
Discovering hidden patterns: Association rules for cardiovascular diseases in type 2 diabetes mellitus VMDS Pradeep Kumar Dabla, Kamal Upreti, Dharmsheel Shrivastav World Journal of Methodology , 2024 2024 Citations: 3
Efficient Credit Card Fraud Detection Based on Multiple ML Algorithms N Ahirwar, D Singh, K Maheshwar 2024 IEEE 9th International Conference for Convergence in Technology (I2CT) , 2024 2024 Citations: 15
Device for testing physical and chemical properties of soil and plants using smart Sensors kumbhar sudhir, naryankar chandan, kartikeyan punita, ... YU Patent 6,297,973 , 2023 2023
Survey Paper on Agricultural Dataset for Improving Crop Yield Prediction using Machine Learning Algorithms A Tripathi, BS Rathore, D Singh International Journal of Computer Applications 184 (46), 28-34 , 2023 2023 Citations: 2
Discovering patterns of live birth occurrence before in vitro fertilization treatment using association rule mining B Rishu, V Prashant, A Singh, D Singh, U Kamal, K Shreya International Journal of Electronic Healthcare 13 (1) , 2023 2023 Citations: 1
Target association rule mining to explore novel paediatric illness patterns in emergency settings PK Dabla, K Upreti, D Singh, A Singh, J Sharma, A Dabas, D Gruson, ... Scandinavian journal of clinical and laboratory investigation 82 (7-8), 595-600 , 2022 2022 Citations: 22
Face Recognition System K Bhupendra, S Aman, A Farooq, D Singh, R Bhavani, C Madhav easy chair 9153 , 2022 2022
DISCOVERING PATTERNS OF CARDIOVASCULAR DISEASE AND DIABETES IN MYOCARDIAL INFARCTION PATIENTS USING ASSOCIATION RULE MINING A Singh, D Singh, S Shikha, U Kamal, M Manish, M Vimal, S Jitender, ... FOLIA MEDICA INDONESIANA 58 (3), 242–250 , 2022 2022 Citations: 7
Smart Glasses for Monitoring Eye Strain Using IOT V Manvendra, C Arti, Y Shailendra, R Ranjana, D Singh, D Nirendra, ... IN Patent 200,741 , 2022 2022
Investigating New Patterns in Symptoms of COVID-19 Patients by Association Rule Mining (ARM) A Singh, D Singh, K Upreti, V Sharma, BS Rathore, J Raikwal Journal of Mobile Multimedia, 1-28 , 2022 2022 Citations: 17
Prediction of mechanical strength by using an artificial neural network and random forest algorithm K Upreti, M Verma, M Agrawal, J Garg, R Kaushik, C Agrawal, D Singh, ... Journal of Nanomaterials 2022 (1), 7791582 , 2022 2022 Citations: 111
Women Aptitude Quotient Tools By Using Chatbot Technique With PHP S Tiwari, D Singh International Journal Of Creative Research And Thoughts (IJCRT) https://www … , 2021 2021
Taxonomy of Community Detection over social media S Shende, D Singh International Journal of Innovative Technology and Exploring Engineering … , 2019 2019 Citations: 2
Development of High Quality Color Image Compression using Block Transformation P Parashar, D Singh International Journal of Innovative Technology and Exploring Engineering … , 2019 2019
Design and analysis of speed control of bipolar stepper motor by using GA with PID approach technique MK Prajapati, D Singh International Journal of Research in Electronics and Computer Engineering 7 … , 2019 2019 Citations: 3
An improved optimized web page classification using firefly algorithm with nb classifier (wpcnb) K Bhatt, A Singh, D Singh International Journal of Computer Applications 146 (4), 15-21 , 2016 2016 Citations: 7
Analyzing educational data through EDM process: A survey T Dwivedi, D Singh International Journal of Computer Applications 136 (5), 13-15 , 2016 2016 Citations: 15
A Parallel Early Binding Recursive Ant Colony Optimization (PEB-RAC) Approach for Generating Optimized Auto Test Cases from programming Inputs Y Dubey, D Singh, A Singh International Journal of Computer Applications 136 (3), 11-17 , 2016 2016 Citations: 3
A survey and analysis of various agricultural crops classification techniques S Chouhan, D Singh, A Singh International Journal of Computer Applications 136 (11), 25-30 , 2016 2016 Citations: 12
MOST CITED SCHOLAR PUBLICATIONS
Prediction of mechanical strength by using an artificial neural network and random forest algorithm K Upreti, M Verma, M Agrawal, J Garg, R Kaushik, C Agrawal, D Singh, ... Journal of Nanomaterials 2022 (1), 7791582 , 2022 2022 Citations: 111
A survey report on text classification with different term weighing methods and comparison between classification algorithms A Patra, D Singh International Journal of Computer Applications 75 (7) , 2013 2013 Citations: 73
Performance evaluation of k-means and heirarichal clustering in terms of accuracy and running time N Singh, D Singh IJCSIT) International Journal of Computer Science and Information … , 2012 2012 Citations: 62
An algorithm to construct decision tree for machine learning based on similarity factor N Patel, D Singh International Journal of Computer Applications 111 (10) , 2015 2015 Citations: 35
Neural network approach for text classification using relevance factor as term weighing method A Patra, D Singh International Journal of Computer Applications 68 (17) , 2013 2013 Citations: 31
Classification algorithms on a large continuous random dataset using rapid miner tool P Sharma, D Singh, A Singh Electronics and Communication Systems (ICECS), 2015 2nd International … , 2015 2015 Citations: 24
Target association rule mining to explore novel paediatric illness patterns in emergency settings PK Dabla, K Upreti, D Singh, A Singh, J Sharma, A Dabas, D Gruson, ... Scandinavian journal of clinical and laboratory investigation 82 (7-8), 595-600 , 2022 2022 Citations: 22
A Survey on CBIR on the Basis of Different Feature Descriptor M Jain, D Singh British Journal of Mathematics & Computer Science 14 (6), 1 , 2016 2016 Citations: 21
MINING LUNG CANCER DATA AND OTHER DISEASES DATA USING DATA MINING TECHNIQUES: A SURVEY P Deoskar, D Singh, A Singh INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY (IJCET) 4 (2 … , 2013 2013 Citations: 20
An improved feature selection and classification using decision tree for crop datasets S Chouhan, D Singh, A Singh International Journal of Computer Applications 142 (13) , 2016 2016 Citations: 19
Association rule mining in the field of agriculture: a survey F Khan, D Singh International Journal of Scientific and Research Publications 329 , 2014 2014 Citations: 19
An Efficient Support Based Ant Colony Optimization Technique for Lung Cancer Data P Deoskar, D Singh, A Singh International Journal of Advanced Research in Computer and Communication … , 2013 2013 Citations: 19
Investigating New Patterns in Symptoms of COVID-19 Patients by Association Rule Mining (ARM) A Singh, D Singh, K Upreti, V Sharma, BS Rathore, J Raikwal Journal of Mobile Multimedia, 1-28 , 2022 2022 Citations: 17
A survey on association rule mining using Apriori based algorithm and hash based methods P Asthana, A Singh, D Singh International Journal of Advanced Research in Computer Science and Software … , 2013 2013 Citations: 16
Result Analysis Using Classification Techniques V Namdeo, A Singh, D Singh, RC Jain International Journal of Computer Applications 1 (22), 22-26 , 2010 2010 Citations: 16
Efficient Credit Card Fraud Detection Based on Multiple ML Algorithms N Ahirwar, D Singh, K Maheshwar 2024 IEEE 9th International Conference for Convergence in Technology (I2CT) , 2024 2024 Citations: 15
Analyzing educational data through EDM process: A survey T Dwivedi, D Singh International Journal of Computer Applications 136 (5), 13-15 , 2016 2016 Citations: 15
A Survey of Association Rule Hiding Algorithms V Garg, A Singh, D Singh Communication Systems and Network Technologies (CSNT), 2014 Fourth … , 2014 2014 Citations: 15
A novel technique of email classification for spam detection V Patidar, D Singh, A Singh International Journal of Applied Information Systems 5 (10), 15-19 , 2013 2013 Citations: 14
Optimizing network traffic by generating association rules using hybrid apriori-genetic algorithm SK Chadokar, D Singh, A Singh 2013 Tenth International Conference on Wireless and Optical Communications … , 2013 2013 Citations: 14
