Dr. Pankaj Dadheech
@skit.ac.in
Professor, Computer Science and Engineering
Deputy Head, Computer Science and Engineering
Swami Keshvanand Institute of Technology, Management & Gramothan (SKIT), Jaipur, Rajasthan
Dr. Pankaj Dadheech has more than 18 years of experience in teaching. He has Published 23 Patents & Granted 6 Patents at Intellectual Property India, Office of the Controller General of Patents, Design and Trade Marks, Department of Industrial Policy and Promotion, Ministry of Commerce and Industry, Government of India. He has Published & Granted 5 Australian Patents, 1 German Patent, 1 South African Patent & 1 USA Patent. He has also Registered & Granted 2 Research Copyrights at Registrar of Copyrights, Copyright Office, Department for Promotion of Industry and Internal Trade, Ministry of Commerce and Industry, Government of India. He has presented 62 papers in various National & International Conferences. He has 72 publications in various International & National Journals. He has published 9 Books & 35 Book Chapters. He is a member of many Professional Organizations like the ACM, CSI, IAENG & ISTE. He has been appointed as a Ph.D. Research Supervisor (CSE) at RTU, Kota.
EDUCATION
Dr. Pankaj Dadheech is currently working as a Professor & Deputy Head in the Department of Computer Science & Engineering (NBA Accredited), Swami Keshvanand Institute of Technology, Management & Gramothan (SKIT), Jaipur, Rajasthan, India (Accredited by NAAC A++ Grade).
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Engineering, Artificial Intelligence, Engineering, Information Systems
Scopus Publications
Scopus Publications
Ankit Kumar, Kamred Udham Singh, Pankaj Dadheech, Aditi Sharma, Ahmed I. Alutaibi, Ahed Abugabah, and Arwa Mohsen Alawajy
Elsevier BV
Binay Kumar Pandey, Asif Iqubal Shah, Rajdip Bhadra Chaudhuri, Pankaj Dadheech, Blessy Thankachan, Dharmesh Dhabliya, and A. Shaji George
IGI Global
Innovative legal and supply chain complexity solutions include machine learning in legal management and intelligent supply chain governance. Complex laws and supply chains necessitate data-driven initiatives. Effective supply chain governance needs transparency, accountability, and risk management. Complex data-rich legal administration requires machine learning. Legal research, document analysis, and predictive analytics benefit from machine learning. Supply chain governance requires compliance and risk management. Machine learning principles demonstrate lawyers can switch careers. Innovation in legal tech comes from AI, blockchain, and cybersecurity. This novel machine learning strategy for legal and change and training management requires careful planning and implementation.
H. R. Swapna, Emmanuel Bigirimana, R. Geetha, Mukundan Appadurai Paramashivan, A. Shaji George, Pankaj Dadheech, and Vikas Vyas
IGI Global
This study emphasizes the importance of adopting a consumer-centric approach to supply chain management, highlighting the role of data-driven analytics, including artificial intelligence and machine learning (AI/ML), in extracting actionable insights from consumer data. Such insights can enhance demand forecasting, personalization strategies, supply chain efficiency, customer satisfaction, and risk mitigation. This chapter looks into the developing landscape of supply chain management, emphasizing the importance of adopting a consumer-centric approach. It examines the role of data-driven analytics, including artificial intelligence and machine learning, in extracting actionable insights from consumer data. The chapter also discusses how such insights can enhance demand forecasting, personalization strategies, supply chain efficiency, customer satisfaction, and risk mitigation.
H. R. Swapna, S. Geetanjali, K. V. N. Lakshmi, Mukundan Appadurai Paramashivan, M. S. Nikhil, Pankaj Dadheech, and Vikas Vyas
IGI Global
This chapter employs a structured secondary research approach to comprehensively investigate the evolution of artificial intelligence (AI) in the context of business. It encompasses a rigorous literature review, data collection from reputable sources, and meticulous analysis. The study conducts comparative analyses across diverse industries, supplemented by real-world case studies, to illuminate the practical applications of AI. Additionally, the chapter explores ethical considerations and regulatory frameworks, synthesizing findings to address gaps in the existing literature. The research adheres to ethical guidelines and presents its insights in a clear and organized manner.
Sanjeet Singh, Geetika Madaan, H. R. Swapna, Prabjot Kaur, Digvijay Pandey, and Pankaj Dadheech
IGI Global
The development of AI has made it possible to envision “thinking robots” that are capable of learning and taking over human roles. Since the late 1970s, AI has shown considerable promise in improving human decision-making processes and, by extension, productivity across a wide range of business endeavours, thanks to its ability to recognise business patterns, understand business phenomena, seek information, and intelligently analyse data. While artificial intelligence has many practical applications, it is seldom applied in supply chain management (SCM). In order to realise AI's numerous potential benefits, this chapter explores the different areas of AI most suited to tackling real-world SCM difficulties. This chapter of the book achieves precisely that, analysing the history of AI's use in SCM applications and identifying its most potential future uses.
P. Vidya Sagar, M. Rajyalaxmi, A.V.V.S. Subbalakshmi, Sudhakar Sengan, Ravi Kumar Bommisetti, and Pankaj Dadheech
Taru Publications
The investigation for precision in Stock Market Forecasts (SMF) developments has led financial professionals to explore numerous modeling approaches. This paper investigates a new technique aimed at advancing the precision of Support Vector Machine (SMF) by combining the application of Random Forest (RF) methods with Stochastic Differential Equations (SDEs). Market deviations can be enhanced using the Geometric Brownian Motion (GBM) scheme, but this approach has problems because it denotes that market deviations will stay identical. To solve these challenges and develop how the GBM context more precisely corresponds to the unpredictable stock market factors, Random Forest (RF) is used for variable parameter prediction. The change in migration and deviation variables in GBM procedures has been defined using RF approaches in the present study using historical data from the stock market. It examines the performance of the RF-enhanced GBM method by comparing it to static variables in GBM, the Heston Model, and standard GBM while taking unpredictable chances into account. The quantitative metrics, such as the Sharpe Ratio (SR), Cumulative Returns (CR), and Maximum Drawdown (MD), were computed for a total of five stock markets in the research. When compared to the other models, the RF-enhanced GBM generally displayed a competitive edge in terms of risk-adjusted income and was highly accurate in terms of coming up with precise projections.
Tarun Jain, Ashish Kumar, Vivek Kumar Verma, Pankaj Dadheech, and Sanwta Ram Dogiwal
Taru Publications
In this paper, we embark on a groundbreaking journey to understand one of the most profound mysteries of the human mind: consciousness. Our approach is unique because we use a branch of mathematics called Topological Data Analysis (TDA) to explore the complex workings of the brain. Imagine trying to understand the shape and connections of a vast network of roads without a map; TDA helps us create that map for the brain’s activity related to consciousness. At the heart of our study is the belief that the patterns of how brain regions connect and communicate hold the key to understanding consciousness. By applying TDA, we’re able to see these patterns in a new light, revealing the intricate landscape of brain activity in various states of consciousness, such as waking, sleeping, and dreaming. We meticulously collected and analyzed brain activity data using advanced neuroimaging techniques. Then, using TDA, we mapped out the topological structures—essentially, the shapes and connections within this data—that correspond to different conscious experiences. This mathematical lens allowed us to uncover hidden patterns and relationships within the brain’s activity, offering fresh insights into how consciousness emerges from the complex interplay of neural signals. Our findings not only deepen our understanding of consciousness but also demonstrate the power of mathematical approaches in unlocking the secrets of the human mind. This research paves the way for new explorations into consciousness and offers novel perspectives on how mathematics can help decipher the intricate workings of the brain.
Vivek Kumar Verma, Gottumukkala K. Chaitanya Varma, Ch. Karthikeya Varma, Anita Shrotriya, and Pankaj Dadheech
Taru Publications
This research introduces a novel mathematical model designed to integrate Natural Language Processing (NLP), functional Magnetic Resonance Imaging (fMRI), and Electroencephalography (EEG) data, aiming to decode the complex neural mechanisms of semantic processing in the human brain. By leveraging the complementary strengths of each modality—NLP’s linguistic analysis, fMRI’s spatial resolution, and EEG’s temporal precision—the model provides a groundbreaking approach to understanding how semantic information is processed across different brain regions and over time. The core of the proposed model is a dynamic, multi-layered framework that utilizes advanced statistical methods and machine learning algorithms. At its foundation, the model employs vector space representations from NLP to quantify semantic similarity and contextuality in language. These representations are then mapped onto neural activation patterns captured by fMRI and EEG, using a series of transformation matrices that are optimized through machine learning techniques. The model uniquely incorporates time-series analysis to account for the temporal dynamics of EEG data, while spatial patterns from fMRI data are analyzed through convolutional neural networks, ensuring a comprehensive integration of multimodal neuroimaging data. Key to proposed approach is the application of Bayesian inference methods to fuse these diverse data sources, allowing for the probabilistic modeling of semantic processing pathways in the brain. This enables the prediction of neural responses to linguistic stimuli with unprecedented accuracy and detail. Theoretical implications of our model suggest significant advances in understanding the neural basis of language comprehension, offering new insights into the dynamic interplay between linguistic structures and neural processes.
Chour Singh Rajpoot, Gajanand Sharma, Praveen Gupta, Pankaj Dadheech, Umar Yahya, and Nagender Aneja
Informa UK Limited
Vivek Kumar Verma, Bhavna Saini, Tarun Jain, and Pankaj Dadheech
Taru Publications
Mental well-being is often seen as a fragile state, akin to a tightrope walk of balance. In our paper, “Cognitive Equilibrium and Instability: Lyapunov Stability Analysis in Mental Health Research,” we explore the nuanced dance between consistency and change in the realm of cognitive function. Drawing on Lyapunov Stability principles from the study of dynamic systems, we offer new perspectives on mental health mechanics. We propose that the mind is in a state of perpetual flux, constantly adjusting to a spectrum of influences to maintain cognitive balance. Our work involves identifying these points of balance and assessing their robustness using Lyapunov Stability Analysis. We examine how various factors, such as stress, environmental changes, and biological variations, can disrupt this balance, possibly leading to states of well-being or illness. Through our models, we illuminate the evolution and path of mental health conditions. Our methodology is a synthesis of theoretical models and real-world data, including neuroimaging, clinical, and psychological evaluations. Case studies in our paper demonstrate the application of our models to conditions like anxiety, depression, and bipolar disorder, revealing the fluid nature of these ailments. This work goes on to discuss the practical implications of our findings in the clinical setting. By identifying pivotal points of potential instability, our model serves as a tool for early identification of mental health concerns, guiding the creation of specific therapeutic interventions. Additionally, our work supports a tailored approach to mental health care, appreciating the individual cognitive patterns unique to each person. Our paper contributes to the burgeoning field of computational psychiatry, blending mathematical analysis with a perspective centered on the human experience. It sets the stage for future interdisciplinary research aimed at decoding the intricacies of the human psyche.
Shaymaa Hussein Nowfal, Ganga Rama Koteswara Rao, V. Velmurugan, Sudhakar Sengan, Ravi Kumar Bommisetti, and Pankaj Dadheech
Taru Publications
A combination of inherent unique stress-strain response features, Viscoelastic Materials (VM) provide an integral part in many different fields of engineering. Although practical, these materials’ highly complex time-dependent methods according to non-linear loading scenarios are impossible to model using conventional viscoelastic models such as Maxwell and Kelvin-Voigt. The present study introduces a framework that pushes over traditional Maxwell and Kelvin-Voigt approaches by employing fractional calculus in order to enhance the prediction of VM performance. The mathematical representation makes use of the Caputo fractional derivative for expressing an artificial viscoelastic polymer’s non-linear and time-dependent responses. Dynamic Mechanical Analysis (DMA) and Stress Relaxation Tests (SRT) proved the polymer possessed 1500 MPa fractional modulus and 0.65 fractional order, respectively. The resulting model involved more significant computing resources, but contrasting testing indicated that it accurately depicted stress relaxation and dynamic responses. As mentioned, the technique integrates mathematical and actual viscoelasticity for industrial uses while offering a precise basis for advanced material analysis.
Ghayth Almahadin, Maheswari Subburaj, Mohammad Hiari, Saranya Sathasivam Singaram, Bhanu Prakash Kolla, Pankaj Dadheech, Amol D. Vibhute, and Sudhakar Sengan
Springer Science and Business Media LLC
Senthil Kumar Nramban Kannan, Bhanu Prakash Kolla, Sudhakar Sengan, Rajendiran Muthusamy, Raja Manikandan, Kanubhai K. Patel, and Pankaj Dadheech
Springer Science and Business Media LLC
Priyanka Sharma and Pankaj Dadheech
Kyushu University
Rasheed Abdulkader, Firas Tayseer Mohammad Ayasrah, Venkata Ramana Gupta Nallagattla, Kamal Kant Hiran, Pankaj Dadheech, Vivekanandam Balasubramaniam, and Sudhakar Sengan
Elsevier BV
Kanak Giri, Mukesh Gupta, and Pankaj Dadheech
Springer Science and Business Media LLC
Honey Gocher, Swapnesh Taterh, and Pankaj Dadheech
Springer Science and Business Media LLC
Jyoti Ahlawat, Digvijay Pandey, Ravish Chaudhary, Nidhi Verma, Binay Kumar Pandey, Shailesh Somnath Parkhe, and Pankaj Dadheech
IGI Global
Nanomaterials reduce biodegradable pollutants before promoting standard levels. Thus, nanomaterials could efficiently and sustainably treat environmental contaminants. However, additional research is needed to determine the destiny of environment remediation nanomaterials. This review covers biological and plant-based bioremediation nanotechnologies. Nanomaterials reduce waste and harmful material degradation costs. Nanomaterials/nanoparticles immediately catalyse waste and toxic material breakdown, which is hazardous to microorganisms, and enable microorganisms degrade waste and toxic materials more efficiently and sustainably.
T. Shanthi, R. Anand, Binay Kumar Pandey, Vinay Kumar Nassa, Aakifa Shahul, A. S. Hovan George, and Pankaj Dadheech
IGI Global
Diabetic Retinopathy (DR) affects people who have diabetes mellitus for a long period (20 years). It is one of the most common causes of preventable blindness in the world. If not detected early, this may cause irreversible damage to the patient's vision. One of the signs and serious DR anomalies are exudates, so these lesions must be properly detected and treated as soon as possible. To address this problem, the authors propose a novel method that focuses on the detection and classification of Exudateas Hard and soft in retinal fundus images using deep learning. Initially, the authors collected the retinal fundus images from the IDRID dataset, and after labeling the exudate with the annotation tool, the YOLOV3 is trained with specific parameters according to the classes. Then the custom detector detects the exudate and classifies it into hard and soft exudate.
Binay Kumar Pandey, Poonam Devi, A. Shaji George, Vinay Kumar Nassa, Pankaj Dadheech, Blessy Thankachan, Pawan Kumar Patidar, and Sanwta Ram Dogiwal
IGI Global
This chapter provides an analysis of the various kinds of distracting noise that can be seen in degraded complex images, such as those found in newspapers, blogs, and websites. A complicated image that had been deteriorated as a result of noise such as salt and pepper noise, random valued impulse noise, speckle noise, and Gaussian noise, amongst others, was the result. There is an extraordinarily high demand for saving the text that can be read from complicated images that have been degraded into a form that can be read by computers for later use.
Ashi Agarwal, Binay Kumar Pandey, Poonam Devi, Sunil Kumar, Mukundan Appadurai Paramashivan, Ritesh Agarwal, and Pankaj Dadheech
IGI Global
An image plays a vital role in today's environment. An image is a visual representation of anything that can be used in the future for recollecting or memorizing that scene. This visual representation is created by recording the scene through an optical device like a camera or mobile phone. The image fusion process helps integrate relevant data of the different images in a process into a single image. Image fusion applications are wide in range, and so is the fusion technique. In general, pixel, feature, and decision-based techniques for picture fusion are characterised. This study's main thrust is the application and comparison of two approaches to the image fusion process: PCA (principal component analysis) and CNN (convolutional neural network).The study implemented a practical approach to MATLAB. The result of the study is that CNN is much more favorable in terms of image quality and clarity but less favorable in terms of time and cost.
K. S. Kalavathy, H. R. Swapna, Anitha Nallasivam, Digvijay Pandey, Darshan A. Mahajan, and Pankaj Dadheech
IGI Global
The purpose of this chapter is to examine the factors which influence the intention to use Facebook among Generation Y consumers and its influence on purchase decision making. A quantitative research methodology was used, and the data was collected from 404 respondents in Bangalore city. Partial least square structural equation model using R software was used to analyse data collected. The findings showed that perceived usefulness, perceived enjoyment, perceived credibility, and subjective norm have a significant influence on intention to use Facebook while perceived ease of use does not have a significant influence on intention to use Facebook. Perceived enjoyment has the highest influence on intention to use Facebook followed by subjective norm, perceived credibility, and perceived usefulness. The results of this study also indicated that intention to use Facebook has a significant positive effect on consumers. The findings of this study contribute to an understanding of the importance of the selected factors in affecting the intention to use Facebook.
Binay Kumar Pandey, Digvijay Pandey, Pankaj Dadheech, Darshan A. Mahajan, A. Shaji George, and A. Shahul Hameed
IGI Global
Autonomous systems that cannot adapt to real-world environments have proliferated due to rapid technological improvement. These programmes will free people from repetitive, inefficient chores. The monotonous, dusty, and dangerous conditions of the unmanned aerial vehicle (UAV) pose a severe threat to human health and safety. Autonomous systems improve supply chain, tracking, and hazardous climate control. This chapter proposes merging compression and track architecture to improve UAV performance. The UAV and pipe increase trackwheel friction. The UAV doesn't move in the tube. Cleaning commences when sensors detect the mouth. Dirt sensors prevent the washing process from working. This chapter focuses on drain cleaning automation. Device automation addresses mobility and space. This study supports this method for garbage disposal and filtering. The technology removes manual cleaning and relies on human control of system movement.
RECENT SCHOLAR PUBLICATIONS
Publications
He has presented 62 papers in various National & International Conferences. He has 72 publications in various International & National Journals. He has published 9 Books & 22 Book Chapters.
GRANT DETAILS
• Received a Grant of Rs. 4,00,000/- from All India Council for Technical Education (AICTE) for Organizing an International Conference on “Intelligent Computing, Communication and Information Security“ (ICCIS-2022) held on November 25-26, 2022 at Department of Computer Science & Engineering, Swami Keshvanand Institute of Technology, Management & Gramothan, Jaipur, Rajasthan, India.
• Surbhi Bhatia, Mohammad Alojail, Sudhakar Sengan, Pankaj Dadheech, Research Project on “Robotic Logical Observation Identifiers Names and Codesto Predicate Features and Semantic Relations Detection Using Unified Medical Language System” Subtitle: Automated Lionc Filter Based Concept Attribute and Relationship Detection through Metamap API of Project No. GRANT 159, Start Date: 01/02/2022 & End Date: 01/08/2022 of 6 Months Duration, Research Grant of 15,000 Saudi Riyal (Approx. 3,04,878.27 Indian Rupee), Research Domains: Semantic Relations Detection, Semantic Relation, Deep Learning, Medical Images, Sponsored by: The Deanship of Scientific Research (DSR), Vice Presidency for Graduate Studies and Scientific Research, King Faisal University (KFU), Ministry of Education, Saudi Arabia in the Session 2021-22.
• A Project on “Criminal Record Management System” of B.Tech. Computer Science & Engin
RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)
He has Published 23 Patents & Granted 6 Patents at Intellectual Property India, Office of the Controller General of Patents, Design and Trade Marks, Department of Industrial Policy and Promotion, Ministry of Commerce and Industry, Government of India. He has Published & Granted 5 Australian Patents, 1 German Patent, 1 South African Patent & 1 USA Patent. He has also Registered & Granted 2 Research Copyrights at Registrar of Copyrights, Copyright Office, Department for Promotion of Industry and Internal Trade, Ministry of Commerce and Industry, Government of India. He has presented 62 papers in various National & International Conferences. He has 72 publications in various International & National Journals. He has published 9 Books & 35 Book Chapters.
Industry, Institute, or Organisation Collaboration
1. Recognized as Gold Partner Faculty under Inspire-The Campus Connect Faculty Partnership Model of Infosys Limited.
2. Awarded as a Certificate of Recognition for Microsoft in Education Certificate in recognition of membership in the “Certified Microsoft Innovative Educator”.
3. IBM Certified Rational Application Developer (RAD) Version 6.0.
4. IBM Certified Solution Developer - WebSphere Integration Developer V6.2.
5. IBM Certified Academic Associate - DB2 9 Database and Application Fundamentals.