Dr. Dilip Kumar Sharma
@juet.ac.in
Assistant Professor
Jaypee University of Engineering and Technology, Guna
RESEARCH INTERESTS
Information Theory, Data Science, Probability and Statistics
Scopus Publications
Scopus Publications
Luigi P.L. Cavaliere, K. Suresh Kumar, Dilip K. Sharma, Himanshu Sharma, Sujay M. Jayadeva, Makarand Upadhyaya, and Nadanakumar Vinayagam
Wiley
Haider Mehraj, Vinay K. Nassa, A.S.K. Reddy, K.V.D. Sagar, Dilip K. Sharma, Shyamasundar Tripathy, and Franklin J. Selvaraj
Wiley
K. Bhavana Raj, Kamakshi Mehta, Someshwar Siddi, M.K. Sharma, Dilip K. Sharma, Sunil Adhav, and José L.A. Gonzáles
Wiley
Luigi P.L. Cavaliere, S. Silas Sargunam, Dilip K. Sharma,, Y. Venkata Ramana, K.K. Ramachandran, Umakant B. Gohatre, and Nadanakumar Vinayagam
Wiley
Pankaj Prasad Dwivedi and Dilip Kumar Sharma
World Scientific and Engineering Academy and Society (WSEAS)
Cutting fluids are used in machining processes to increase the quality of machined surfaces, extend the life of tools, and lessen the effect of friction and heat on contact surfaces. The least costly, least hazardous to the environment, and least poisonous lubricant would be the perfect choice. It should also be resistant to low temperatures, have high lubricating qualities, be recyclable, and have stability against oxidation, hydrolysis, and heat. Its viscosity should also fall between the ideal range and not exceed it. Taking the needed properties of the cutting fluids into consideration, for the machining process choosing the best cutting fluid is essential. Five types of cutting fluids are examined in this paper that are often used in machining operations: canola oil, mineral oil, synthetic ester, PAG (Polyalkylene Glycol), and TMPTO (trimethylolpropane trioleate). In this study, the Multicriteria decision-making (MCDM) techniques were used to identify the best choice of cutting fluids based on several parameters, such as low temperature, toxicity, lubricating ability, hydrolytic stability, thermal stability, viscosity index, oxidative stability, and cost. The most popular TOPSIS methods and Shannon's Entropy were utilized to choose these cutting fluids optimally. The TOPSIS approach is used to calculate the final ranking, and Shannon’s entropy method is utilized to calculate the weight of the criterion. According to the result with the more lucid rating, PAG cutting fluid was shown to be the most effective, followed by synthetic ester in second place, as well as last place achieved by vegetable-based canola oil.
Pankaj Prasad Dwivedi and Dilip Kumar Sharma
Wiley
SummaryThe right to privacy refers to an individual's decision about how personal information can be gathered, utilized, and disseminated. Individual consent and openness are the most important foundations for gaining consumers' confidence, and this pushes businesses to use privacy‐enhancing techniques while developing systems. The purpose of a privacy‐aware design is to safeguard data in such a manner that it does not expand an adversary's current understanding of an individual beyond what would be permitted. When these data pieces are coupled with the plethora of source data accessible outside the system to identify a user, this becomes crucial. Individual privacy is protected by privacy rules all around the globe, but they are often complicated and ambiguous, making their translation into practical and technologically privacy‐friendly structures difficult. The main contribution of this article is that we use Shannon's entropy (SE) to construct an objective measure that may guide our major technical design choices. And for privacy‐aware architecture, simplifying the state‐of‐the‐art security approaches given in the literature.
Pankaj Prasad Dwivedi and Dilip Kumar Sharma
Elsevier BV
Pankaj Dwivedi and Dilip Kumar Sharma
IGI Global
This chapter's major goal is to examine the issue of real-world recognition to enhance species preservation. As it is a popular topic and a crucial one, the authors focus on identifying plant species. The examples are scanned specimens in traditional plant species identification, and the setting is plain. Real-world species recognition, on the other hand, is more difficult. They begin by looking at realistic species recognition and how it differs from traditional plant species recognition. Interdisciplinary teamwork based on the newest breakthroughs in technology and computer science is provided to cope with the difficult challenge. In this research, they offer a unique framework for deep learning as well as an effective data augmentation strategy. They crop the image before everyone is aware in terms of visual attention. Furthermore, they use it as a data augmentation technique. Attention cropping (AC) is the name given to a revolutionary data augmentation technique. To predict species from a significant quantity of information, fully convolutional neural networks (CNN) are constructed.
Pankaj Prasad Dwivedi and Dilip Kumar Sharma
IGI Global
Detecting disease on crops is an essential and time-consuming operation in agricultural techniques. It takes a significant amount of time and specialized effort. This research provides a clever and effective agricultural disease detection system based on information theory. In the present chapter, first information measures, ‘useful' information measures, and distance measures are defined and explained. The authors find out the distance measures between leaves of apple scab (AS) and apple black rot (ABR). Six leaves of AS and ABR are taken into consideration. After measuring the distance, the impact of disease in the leaves of AS and ABR has been noticed. It is shown that this measure can be embedded in most image classification techniques and is subject to reference transformation. Weak and strong information is also obtained. Finally, minimum and maximum distances are evaluated, and our findings indicate that the likelihood of illnesses in plant leaves is low when the information measure of leaves is low.
Arodh Lal Karn, Sharnil Pandya, Abolfazl Mehbodniya, Farrukh Arslan, Dilip Kumar Sharma, Khongdet Phasinam, Muhammad Nauman Aftab, Regin Rajan, Ravi Kumar Bommisetti, and Sudhakar Sengan
Springer Science and Business Media LLC
Pankaj Prasad Dwivedi and Dilip Kumar Sharma
AIP Publishing
Anjan Kumar, Sangeeta Singh, Mustafa K. A. Mohammed, and Dilip Kumar Sharma
World Scientific Pub Co Pte Ltd
The invention of novel light-harvesting materials is one of the primary reasons behind the acceleration of current scientific advancement and technological innovation in the solar sector. Organometal halide perovskite (OHP) has recently attracted a great deal of interest because of the high-energy conversion efficiency that has reached within a few years of its discovery and development. Modern machine learning (ML) technology is quickly advancing in a variety of fields, providing blueprints for the discovery and rational design of new and improved material properties. In this paper, we apply ML to optimize the material composition of OHPs, propose design methods and forecast their performance. Our ML model is built using 285 datasets that were taken from about 700 experimental articles. We have developed two different ML models to predict the bandgap and performance parameters of solar cell. In the first model, we employed three ML algorithms to investigate the relationship between bandgap and perovskite material composition. We estimated the performance characteristics using projected and actual bandgap. Second, ML models are used to predict the performance parameters employing the bandgap of perovskite and energy difference between electron transport layer (ETL) and hole transport layer (HTL) with perovskite as an input parameter. Simulation results suggest that the artificial neural network (ANN) technique, which predicts the bandgap by taking into consideration how cations and halide ions interact with one another, demonstrates a better degree of accuracy (with a Pearson coefficient of 0.91 and root mean square error of 0.059). The constructed ML model closely fits the theoretical prediction made by Shockley and Queisser, and that is almost hard for a person to discover from an aggregation of datasets.
Anjan Kumar, Sangeeta Singh, Dilip Kumar Sharma, Mohammed Al-Bahrani, Mohammed Ridha H. Alhakeem, Amit Sharma, and T. Ch. Anil Kumar
Royal Society of Chemistry (RSC)
Vapor treatment approach to enhance the power conversion efficiency and stability of FAPbI3 based perovskite solar cell.
Dinesh Rajassekharan, V Ramachandran, Dilip Kumar Sharma, Thirumoorthy Palanisamy, Y. Varatharaj Myilsam, and Ramesh Chandra Poonia
IEEE
Wireless Sensor Networks (WSNs) are widely used in diverse applications due to their cost-effectiveness and versatility. However, they face substantial difficulties because of their innate resource constraints and susceptibility to security attacks. A possible method to improve the security of WSNs is clustering-based intrusion detection and responding mechanisms. An in-depth analysis of the clustering-based intrusion detection and response method for WSNs is presented in this study. The suggested method efficiently uses data mining and machine learning techniques to identify unusual behaviour and probable intrusions. The system effectively analyses data inside clusters by grouping Sensor Nodes (SN) into clusters, allowing it to differentiate between legitimate patterns and insecure activity. The network may respond promptly to identified breaches and react to the responsive mechanism, which reduces their impact and protects network integrity. The proposed Mathematically Modified Gene Populated Spectral Clustering Based Intrusion Detection System and Responsive Mechanism (MMMMGPSC-IDS-RM) is compared with existing state-of-art techniques, and MMMMGPSC-IDS-RM outperforms with the highest detection rate of 96%.
M. Narendran, Hari Krishnan Andi, Dilip Kumar Sharma, K. Amarendra, S. Uma, and Ramesh Chandra Poonia
IEEE
Ovarian cancer prediction models or algorithms estimate a person's risk of getting the disease based on different variables, such as their medical history, genetics, and biomarkers. Early identification and intervention will enhance patient successive diagnosis outcomes. Tumour markers are chemicals frequently detected in higher concentrations than usual in cancer patient's blood, urine, or tissues. They could be certain chemicals or proteins linked to the presence of tumours or cancer kinds. Tumour markers are employed for diagnosis, prognosis, and treatment response monitoring. Applying information or models from one quantum job to enhance the performance of another requires quantum transfer learning. Transferring knowledge from one domain to another seeks to increase learning effectiveness in novel quantum contexts. The main goal of efficient Quantum Transfer Learning (QTL) is to minimize the resources (computer power, data, or time) necessary to transfer between tasks successfully. In this research work, QTL is used to predict Ovarian Cancer (OC) with the assistance of biomarkers. The Quantum Transfer Learning- Ovarian Cancer (QTL-OC) achieves 93.78% accuracy and outperforms the existing techniques.
Chhote Lal Prasad Gupta, Dinesh Rajassekharan, Dilip Kumar Sharma, Mohanraj Elangovan, Varatharaj Myilsamy, and Kamal Upreti
IEEE
Classifying malicious traffic in Wireless Sensor Networks (WSNs) is crucial for maintaining the network's security and dependability. Traditional security techniques are challenging to deploy in WSNs because they comprise tiny, resourceconstrained components with limited processing and energy capabilities. On the other hand, machine learning-based techniques, such as Deep Learning (DL) models like LSTMs, may be used to detect and categorize fraudulent traffic accurately. The classification of malicious traffic in WSNs is crucial because of security. To protect the network's integrity, data, and performance and ensure the system functions properly and securely for its intended use, hostile traffic categorization in WSNs is essential. Classifying malicious communication in a WSN using a Long Short-Term Memory (LSTM) is efficient. WSNs are susceptible to several security risks, such as malicious nodes or traffic that can impair network performance or endanger data integrity. In sequential data processing, LSTM is a Recurrent Neural Network (RNN) appropriate for identifying patterns in network traffic data.
M. Narendran, K. Amarendra, D. Revathy, M. Divyapushpalakshmi, Dilip Kumar Sharma, and Kamal Upreti
IEEE
Leukemia is typically diagnosed based on an abnormal blood count, frequently an elevated White Blood Cell (WBC) count. The diagnosis is established through bone marrow, replaced by neoplastic cells. Acute Lymphoblastic Leukemia (ALL) is a type of leukaemia that affects the blood and bone marrow. Leukaemia primarily affects children and adults around the world. Early leukaemia detection is critical for appropriately treating patients, especially children. This research aims to present a diagnostic method that uses computational intelligence and image processing algorithms to identify blast cells from ALL images. The medical image is prepared initially using the preprocessing and segmentation technique for efficient classification. In this research, the type is accomplished using Bidirectional Associative Memory Neural Networks (BAMNN), where the accuracy is 96.87%, the highest classification rate and outperforms the existing technique.
Hari Krishnan Andi, S. Senbagam, Dilip Kumar Sharma, Mayura Shelke, N K Senthil Kumar, and Ramesh Chandra Poonia
IEEE
In an era characterized by the proliferation of digital media, the need to efficiently use multimedia content has become paramount. This article discusses an innovative technique called “Fast Captioning (FC)” to improve media accessibility, especially for people with disabilities and others with time restrictions. Modern Machine Learning (ML) algorithms are incorporated into the framework, which speeds up video consumption while maintaining content coherence. The procedure includes extracting complex features like Word2Vec embeddings, part-of-speech tags, named entities, and syntactic relationships. Using annotated data, a ML model is trained to forecast semantic similarity scores between words and frames. The predicted scores seamlessly integrate into equations that calculate similarity, thus enhancing content comprehension. Through this all-encompassing approach, the article offers a comprehensive solution that balances the requirements of contemporary media with the accessibility requirements of people with disabilities, producing a more inclusive digital environment. Machine Learning-based Media Augmentation (ML-MA) has achieved the highest accuracy of 96%, and the captioning is accurate.
Pankaj Prasad Dwivedi and Dilip Kumar Sharma
Defence Scientific Information and Documentation Centre
The selection of military defense equipment, especially fighter aircraft, has a bearing on the readiness ofthe Indian Air Force to defend the country’s independence. This study analyses a collection of alternative fighteraircraft that are linked to several choice factors using a multiple-criterion decision-making analysis technique. Tohandle such scenarios and make wise design judgements, a variety of criterion decision analysis techniques can beused. In this study, we assess fifth-generation fighter aircraft that incorporate significant 21st-century technologicaladvancements. These aircraft represent the state-of-the-art in fleet planning operations to 2022. These are generallyequipped with quick-moving airframes, highly integrated computer systems, superior avionics features, networkingwith other battlefield elements, situational awareness, command, control, and other communication capabilities.The study proposes a strategy for the selection of the fifth-generation combat aircraft for the National Air Force.Because of the problems, the Army needed an application that could assist with decision-making for combat selection systems. Solving the decision problem for evaluating fifteen military fighter alternatives in terms of nine decision criteria is the main objective of this work. We use the Shannon entropy and VIKOR model for the Air Force’s fleet program to evaluate military fighter aircraft suitability. The entropy technique is used to compute the weight of the criteria, and then the VIKOR technique has been used to rank the fighter aircraft.
Kalaivani Pachiappan, Venkata Ramana Vandadi, Dilip Kumar Sharma, Amarendra Kothalanka, Saravanan Thangavel, and Sudhakar Sengan
Springer International Publishing
Angel Sajani Joseph, Arokia Jesu Prabhu Lazar, Dilip Kumar Sharma, Anto Bennet Maria, Nivedhitha Ganesan, and Sudhakar Sengan
Springer International Publishing
Sriram Veeraiya Perumal, Sudhakar Sengan, Dilip Kumar Sharma, Amarendra Kothalanka, Rajesh Iruluappan, and Arjun Subburaj
Springer International Publishing
Govinda Rajulu Ganiga, Kalvikkarasi Subramani, Dilip Kumar Sharma, Sudhakar Sengan, Kalaiyarasi Anbalagan, and Priyadarsini Seenivasan
Springer International Publishing
Pankaj Prasad Dwivedi, Dilip Kumar Sharma, and Appaji M. Ashwini
Springer International Publishing
Mani Yuvarasu, Allam Balaram, Subramanian Chandramohan, and Dilip Kumar Sharma
Informa UK Limited