Tinku Singh
@cbnu.ac.kr
Postdoctoral Researcher at School of Information and Communication Engineering
Chungbuk National University
My name is Tinku Singh and I am currently working as a Postdoctoral Researcher at Chungbuk National University, Cheongju, South Korea. I completed the Master’s in Technology (M.Tech) in Computer Science and Engineering in 2016 from Maharshi Dayanand University, Rohtak, Haryana, India and Ph.D. from the Indian Institute of Information Technology Allahabad (IIITA), India in 2023.
The potential of machine learning and deep learning algorithms in solving problems from different areas has always intrigued me, so I follow this as a research area of interest. I have published papers in international journals, conferences and pre-print servers. My main research includes Big data analytics, Machine learning, and Deep learning in different domains. I have also gained outreach experience in delivering tutorial sessions, hands-on sessions and organizing several workshops and conferences on international platforms.
EDUCATION
PhD from Indian Institute of Information Technology, Allahabad India
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Engineering, Computer Science, Artificial Intelligence
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Tinku Singh, Majid Kundroo, and Taehong Kim
MDPI AG
Soil moisture estimation is crucial for agricultural productivity and environmental management. This study explores the integration of Wireless Sensor Networks (WSNs) with machine learning (ML) and deep learning (DL) techniques to optimize soil moisture estimation. By combining data from WSN nodes with satellite and climate data, this research aims to enhance the accuracy and resolution of soil moisture estimation, enabling more effective agricultural planning, irrigation management, and environmental monitoring. Five ML models, including linear regression, support vector machines, decision trees, random forests, and long short-term memory networks (LSTM), are evaluated and compared using real-world data from multiple geographical regions, which includes a dataset from NASA’s SMAP project, supplemented by climate data, which employs both active and passive sensors for data collection. The outcomes demonstrate that the LSTM model consistently outperforms other ML algorithms across various evaluation metrics, highlighting the effectiveness of WSN-driven approaches to soil moisture estimation. The study contributes to the advancement of soil moisture monitoring technologies, offering insights into the potential of WSNs combined with ML and DL for sustainable agriculture and environmental management practices.
Tinku Singh, Vinarm Rajput, Nikhil Sharma, Satakshi, and Manish Kumar
Springer Science and Business Media LLC
Tinku Singh, Ayush Sinha, Satakshi Singh, O. P. Vyas, and Manish Kumar
Springer Science and Business Media LLC
Tinku Singh, Riya Kalra, Suryanshi Mishra, Satakshi, and Manish Kumar
Springer Science and Business Media LLC
Tinku Singh, Shivam Gupta, Satakshi, and Manish Kumar
Springer Science and Business Media LLC
Satakshi Singh, Suryanshi Mishra, Tinku Singh, and Shobha Thakur
IGI Global
The chapter will present state-of-the-art water assessment and treatment methods as well as the current issues and challenges of the domain. Modern techniques for water evaluation and treatment will be covered in this chapter, along with the current problems and difficulties facing the industry.
Manish Kumar, Tinku Singh, Manish Kumar Maurya, Anubhav Shivhare, Ashwin Raut, and Pramod Kumar Singh
Institute of Electrical and Electronics Engineers (IEEE)
The quality assessment of water is a challenging task due to extensive experimental requirements. However, the process of water quality monitoring can be automated with the help of Internet of Things (IoT) devices using sensor probes. This article presents the IoT infrastructure-based river water quality monitoring and assessment. Experiments were performed to assess the water quality during different months and seasons for the Ganga River and Sangam (confluence of Ganga and Yamuna rivers) at Prayagraj, Uttar Pradesh, India. The data samples were collected for 15 months continuously using the Libelium smart water kit. The smart water IoT (SWIoT) kit was equipped with sensors to assess specific parameters like pH, dissolved oxygen, temperature, conductivity, and oxidation–reduction potential. An algorithm is also presented that harnesses principal component analysis and factor analysis for feature selection and weight assignment for river water quality assessment. Further water quality is quantified using the water quality index that helps to categorize the water quality for different usages. The results corroborate that the water quality of the Ganga River was found to be better than the Sangam site most of the time, owing to the higher level of pollution in Yamuna River. Additionally, the water quality of both rivers was found to be suitable for irrigation and fisheries but not for drinking purposes, considering the average oxygen levels.
Tinku Singh, Vinarm Rajput, Satakshi, Umesh Prasad, and Manish Kumar
Springer Science and Business Media LLC
Tinku Singh, Riya Khanna, Satakshi, and Manish Kumar
Springer Science and Business Media LLC
Suryanshi Mishra, Tinku Singh, Manish Kumar, and Satakshi
Springer Science and Business Media LLC
Tinku Singh, Vinarm Rajput, Nikhil Sharma, Satakshi, and Manish Kumar
Springer Nature Singapore
Tinku Singh, Siddhant Bhisikar, Satakshi, and Manish Kumar
Springer Nature Singapore
Ayush Sinha, Tinku Singh, Ranjana Vyas, Manish Kumar, and O. P. Vyas
Springer Nature Singapore
Tinku Singh, Nikhil Sharma, Satakshi, and Manish Kumar
Informa UK Limited
Abstract Objective The air quality index (AQI) forecasts are one of the most important aspects of improving urban public health and enabling society to remain sustainable despite the effects of air pollution. Pollution control organizations deploy ground stations to collect information about air pollutants. Establishing a ground station all-around is not feasible due to the cost involved. As an alternative, satellite-captured data can be utilized for AQI assessment. This study explores the changes in AQI during various COVID-19 lockdowns in India utilizing satellite data. Furthermore, it addresses the effectiveness of state-of-the-art deep learning and statistical approaches for forecasting short-term AQI. Materials and methods Google Earth Engine (GEE) has been utilized to capture the data for the study. The satellite data has been authenticated against ground station data utilizing the beta distribution test before being incorporated into the study. The AQI forecasting has been explored using state-of-the-art statistical and deep learning approaches like VAR, Holt-Winter, and LSTM variants (stacked, bi-directional, and vanilla). Results AQI ranged from 100 to 300, from moderately polluted to very poor during the study period. The maximum reduction was recorded during the complete lockdown period in the year 2020. Short-term AQI forecasting with Holt-Winter was more accurate than other models with the lowest MAPE scores. Conclusions Based on our findings, air pollution is clearly a threat in the studied locations, and it is important for all stakeholders to work together to reduce it. The level of air pollutants dropped substantially during the different lockdowns.
Tinku Singh, Shivam Gupta, Satakshi, and Manish Kumar
Elsevier BV
Tinku Singh, Nikhil Sharma, Vinarm Rajput, Suryanshi Mishra, Satakshi, and Manish Kumar
IEEE
Human health is severely endangered by the novel coronavirus (COVID-19). It is viewed as the worst global health threat humans have faced since the second world war and the WHO recognized it as a pandemic on March 11, 2020. This pandemic led several nations to adopt statewide lockdowns, while the industrial, construction, and transportation activities in several nations were disrupted, which lead to a significant shift in air pollutants. The lockdown, however, significantly impacted the environment and air quality in distinct cities. There are numerous ground stations deployed by pollution control organizations to monitor and collect the air pollutants data, but it is not feasible to set up a ground station in every city. In places where ground stations are not available for data collection, Google Earth Engine (GEE) satellite captured data can be used for data analysis. This study aimed to analyze the changes in air pollutants during the different lockdowns in India, such as nitrogen dioxide(NO2), sulfur dioxide(SO2), and carbon monoxide(CO) that contribute significantly to air pollution. In India, lockdowns were imposed during different periods of 2020, 2021, and 2022, according to COVID-19 waves. The air pollutants data during different waves have been analyzed and compared with the pre-COVID year (2019) data for the same duration. According to the study results, $N$ O2 and $S$ O2 were drastically reduced, but only a minor reduction in CO. Delhi, Jaipur, Ahmedabad, and Mumbai were among the major cities that saw the largest reduction, which was up to 60%.
Tinku Singh, Riya Khanna, Satakshi, and Manish Kumar
IEEE
Because of the massive increase in data collection and storage that has occurred in recent years, big data applications are increasingly becoming the focus of attention. The difficulty of classification with imbalanced datasets is one of the complexities that make extracting meaningful information difficult, and the key impact arises from its existence in a variety of real-world applications. Because of the variety as well as the veracity of such obtained data, big data is impacted by an imbalance of classes. Furthermore, in real-world data applications, samples from one class, which is the core concern, are frequently vastly dominated by samples from other classes. In this study, we have proposed an approach using block-level undersampling and synthetic data point generation to deal with imbalanced big data. Furthermore, the performance of Random Forest and Decision Tree algorithms in dealing with imbalanced datasets in the big data context has been evaluated. Extensive experiments have been performed utilizing Apache Spark Cluster in the development of the different discussion methods. The proposed technique can handle massive datasets while still offering the assistance required to accurately categories classes with a comparatively less number of instances.
Tinku Singh, Siddhant Bhisikar, Satakshi, and Manish Kumar
IEEE
With the ever-increasing usage of biometric systems in today's scenario, there's a need for accurate identification of fingerprints. The fingerprint data is associated with various important services in today's scenario like aadhar verification, mobile unlocking, biometric attendance and the security aspects of the devices and accounts. To utilize the fingerprint data, it needs to be analyzed efficiently. The fingerprint identification process needs to look at various challenges like even if the fingerprint is rotated, altered, or not completely available still a correct prediction is required. Many deep learning algorithms like Convolution Neural Network (CNN), Inception V3 and Capsule Network have been implemented in this segment still there is a need to design the algorithm for the same with higher accuracy. In this work, a Modified Capsule Network is proposed for effective fingerprint identification. The experiments were performed utilizing the biometric Sokoto Coventry Fingerprint (SOCOFing) dataset extracted from Kaggle. The proposed model achieves better accuracy than the state-of-the-art models in this segments. It approaches more than 99 % accuracy for classifying fingerprints into fingers, hands, and gender class category.
Tinku Singh, Durgesh Kumar Srivastava, and Alok Aggarwal
IEEE
Multicore architecture of CPU is popular because of its performance; the challenge for the Multicore environment are-writing the effective code that can exploit the parallelism, measuring the performance in terms of CPU individual core utilization. The effective code using multithreading (parallel code) leads to performance speedup. Various multithreading applications are getting developed now days to utilize the CPU cores. In this paper, tools are developed, one by using C# console viz. application for measuring the performance of the CPU cores individually. Performance is measured in terms of load on each core in percentage. Second tool is designed using windows C# viz. application for plotting the graph with respect to time of CPU load in percentage. By both the tools performance is measured while quicksort is getting executed in the serial and parallel for a large number of data elements. Experiment is done on dual core and quad core CPU and results are stored in the table. Comparison graphs are drawn for running time of quicksort as well as CPU individual core utilization. The result shows parallel version of quicksort better utilize the CPU individual cores compared to its sequential version. It exploits more parallelism that leads the better CPU utilization.