Translating artificial intelligence into socio-economic insight: a hybrid deep learning approach to employee financial well-being
CNN; Deep learning; Employee financial stress; Financial well-being assessment; Organizational productivity; Societal economic stability; Wide & deep network
This study aims to translate recent advancements in hybrid artificial intelligence (AI) modeling into a functional tool for assessing individual financial well-being. The objective is to develop a system that aids organizations in understanding employees financial stress, with broader implications for enhancing workplace productivity and societal economic resilience. A deep learning pipeline was developed to classify individuals into three financial well-being categories: Financially Secure, Moderately Stable, and Financially At-Risk. The approach utilizes a structured dataset of 20,000 Indian individuals and implements 15 advanced deep learning models, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Gated Recurrent Units (GRU), Bidirectional Long Short-Term Memory (BiLSTM), and Wide & Deep networks. Model performance was assessed using standard evaluation metrics, including validation accuracy and ROC-AUC scores. Among the tested models, the hybrid Wide & Deep + CNN configuration yielded the highest performance, achieving a validation accuracy of 99.44% and a perfect ROC-AUC score of 1.0000. These results validate the models capacity for robust classification and real-world applicability to financial profiling. This study demonstrates a practical application of AI in financial decision support systems and contributes to organizational research by offering a scalable solution to assess and mitigate employee financial stress. The Author(s) 2026.
Uppal, Aakanksha; Srivastava, Anubha; Awasthi, Yashmita; Srivastava, Anjita; Kakkar, Barkha
Discover Artificial Intelligence;Volume;6;Issue;1;Article No.;248;
All Open Access; Gold Open Access; Green Open Access
https://doi.org/10.1007/s44163-026-00949-2 https://www.scopus.com/pages/publications/105034192001?origin=resultslist
Uppal A., Symbiosis International (Deemed University) Pune, Symbiosis Law School Noida Campus, Noida, Ghaziabad, India; Srivastava A., Institute of Technology & Science, Mohan Nagar, Ghaziabad, India; Awasthi Y., School of Commerce, Finance and Accountancy, Christ University, Bengaluru, India; Srivastava A., Bundelkhand University, Jhansi, India; Kakkar B., Institute of Technology & Science, Mohan Nagar, Ghaziabad, India
AI Meets the Edge: Optimizing Computation Through Intelligent Offloading
Artificial intelligence; Computation offloading; Edge computing; IoT; Reinforcement learning; Resource optimization; Smart cities
The chapter looks into the developing bond between artificial intelligence (AI) and edge computing. In particular, the idea of using AI to intelligently offload computations. As the number of latency-sensitive applications have increased and the use cases for smart devices has widened, resource allocation at the edge has become critical. We discuss AI-based methods that intelligently determine what and when to transfer compute-intense tasks from resource-constrained edge devices to nearby edge servers or cloud environments. Pragmatic methods, RL optimization procedures and ML research exercises are the main focus of the standardized testing. To illustrate real-world examples, smart cities, autonomous vehicles, and industrial IoT are further explored. This chapter focuses on the development of a new hybrid offloading framework, synthesizing some of the greatest qualities of the predictive analytic and real-time learning to put into practice. These challenges including device heterogeneity, network variability, privacy, etc., are elaborated. Finally, in the concluding chapter, we argue the need for open problems that inform the path toward a sustainable, secure, AI-enabled edge computing. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
Studies in Computational Intelligence;Volume;1267;pp.149-161
Springer Science and Business Media Deutschland GmbH
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1007/978-3-032-16711-8_7 https://www.scopus.com/pages/publications/105038040683?origin=resultslist
Uppal K., Christ University, Bengaluru, India; Vidushi, Christ University, Bengaluru, India; Shoran P., Christ University, Bengaluru, India
Geospatial Analysis of Groundwater Recharge Zones in Bengaluru
Drainage infrastructure; Land use land cover (LULC); Rainwater harvesting (RWH); Urban flooding
Urban flooding in cities like Bengaluru results from excessive rainfall overwhelming drainage systems, worsened by rapid urbanisation and the expansion of impervious surfaces. This study investigates the causes and consequences of urban flooding in Bengaluru, highlighting the decline in natural drainage and the encroachment of water bodies. Using QGIS, a geographic information system tool, spatial data from sources like NRSCs Bhuvan portal and USGS were analysed to identify flood-prone areas, drainage networks, and land use changes. The analysis revealed critical flooding zones such as Bellandur, Bommanahalli, and Mahadevapura. The study also emphasises the importance of implementing Best Management Practices (BMPs) and Rainwater Harvesting (RWH) strategies. Land Use and Land Cover (LULC) mapping, soil infiltration data, and rainfall patterns were assessed to understand urban hydrology. The findings stress the need for climate-resilient infrastructure, lake rejuvenation, and improved public awareness to mitigate future urban flood risks in Bengaluru. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
Uppaluri, Sirisha; Teena Sojan, V.V.; Pradhan, Arpan; Mani, Shibu K.; Sahoo, Kirti Kanta
Lecture Notes in Civil Engineering;Volume;847 LNCE;pp.155-166
https://doi.org/10.1007/978-981-95-8836-7_13 https://www.scopus.com/pages/publications/105040551633?origin=resultslist
Uppaluri S., Civil Engineering Department, Christ University, Bengaluru, India; Teena Sojan V.V., Civil Engineering Department, Christ University, Bengaluru, India; Pradhan A., Civil Engineering Department, Christ University, Bengaluru, India; Mani S.K., Civil Engineering Department, Christ University, Bengaluru, India; Sahoo K.K., School of Civil Engineering, Kalinga Institute of Industrial Technology Deemed to Be University, Bhubaneswar, India
Smart Farming with Ensemble Learning: A Soil-Driven Crop Suggestion Model for Sustainable Agriculture
Agriculture; Agronomic Insights; Crop Recommendation; Machine Learning; Soil Characteristics
The use of machine learning (ML) in agriculture has paved new avenues to improve decision making, especially in crop choice. The current research offers a data-driven crop recommendation system using a machine learning approach based on key soil and environmental factorsi.e., nitrogen (N), phosphorus (P), potassium (K), pH, temperature, humidity, and rainfall. A dataset of 2,200 soil records was processed using exploratory data analysis (EDA), normalization, and model training with algorithms such as Random Forest, Logistic Regression, and Gradient Boosting. Of these, Random Forest provided the best test accuracy of 99.32%, with high predictive ability and interpretability via feature importance measures. Violin and boxplots showed distinct feature separability among crop types, particularly in variables such as rainfall, temperature, and NPK concentrations, confirming the model's classification effectiveness. The practicability of the system is in its possible incorporation in IoT-based soil monitoring devices and cell advisory apps, delivering real-time, location-specific crop advice. This strategy enables farmers to make informed decisions, minimizes fertilizer waste, and promotes sustainable farming practices. The suggested system not only showcases technical strength but also fits well within the overall vision of smart farming and precision agriculture. Author(s) 2025.
Upret?, Kamal; S?ngh, Jaspreet; Alapatt, Bosco Paul
Turkish Journal of Engineering;Volume;9;Issue;4;pp.801-810
https://doi.org/10.31127/tuje.1688064 https://www.scopus.com/pages/publications/105019248029?origin=resultslist
Upret? K., Christ University, Department of Computer Science, Delhi NCR Campus, Ghaziabad, India; S?ngh J., Christ University, Department of Computer Science, Delhi NCR Campus, Ghaziabad, India; Alapatt B.P., Christ University, Department of Computer Science, Delhi NCR Campus, Ghaziabad, India
Leveraging unsupervised machine learning to optimize customer segmentation and product recommendations for increased retail profits
The retail sector's success hinges on understanding and responding adeptly to diverse consumer behaviours and preferences. In this context, the burgeoning volume of transactional data has underscored the need for advanced analytical methodologies to extract actionable insights. This research delves into the realm of unsupervised machine learning techniques within retail analytics, specifically focusing on customer segmentation and the subsequent recommendation strategy based on clustered preferences. The purpose of this study is to determine which unsupervised machine learning clustering algorithms perform best for segmenting retail customer data to improve marketing strategies. Through a comprehensive comparative analysis, this study explores the performance of multiple algorithms, aiming to identify the most suitable technique for retail customer segmentation. Through this segmentation, the study aims not only to discern and profile varied customer groups but also to derive actionable recommendations tailored to each cluster's preferences and purchasing patterns. 2024, IGI Global. All rights reserved.
Intersection of AI and Business Intelligence in Data-Driven Decision-Making, pp. 257-282.
Upreti G., Christ University, India; Natarajan A.K., Samarkand International University of Technology, Uzbekistan
Wave Height Forecasting over Ocean of Things Based on Machine Learning Techniques: An Application for Ocean Renewable Energy Generation
Data driven; forecasting; ocean environment; renewable energy; wave height
With the evolution and integration of information and communication technologies, the marine environment is being converted into a smart ocean of things. The only way to monitor the marine environment is to access marine information through satellites, radar, etc. Recently, many researchers have focused their interest on generating power from renewable energy. Among all the available renewable resources, ocean waves are attracting the interest of researchers for power generation. Therefore, this article focuses on designing a data-driven forecasting model for marine renewable energy generation applications. This article applies a novel Gini-impurity-index-based bidirectional long short-term memory model for selecting the best ocean/marine environmental factors to forecast wave height and ultimately predict power generation using the numerical model. This article presents short- and long-term forecasting results. In the experiment, four stations each are taken for both short- and long-term forecasting. The average root-mean-square error was approximately 0.17 for long-term forecasting and approximately 0.05 for short-term forecasting. 1976-2012 IEEE.
Upreti K.; Arora S.; Sharma A.K.; Pandey A.K.; Sharma K.K.; Dayal M.
IEEE Journal of Oceanic Engineering, Vol-49, No. 2, pp. 430-445.
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/JOE.2023.3314090 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184831253&doi=10.1109%2fJOE.2023.3314090&partnerID=40&md5=6dc61981c4cb1ec30143c984ac6132fe
Upreti K., CHRIST (Deemed to Be University) Delhi NCR, Department of Computer Science, Ghaziabad, 201003, India; Arora S., KIET Group of Institutions, Department of Computer Applications, Ghaziabad, 201206, India; Sharma A.K., Galgotias University, School of Computing Science and Engineering, Greater Noida, 203201, India; Pandey A.K., KIET Group of Institutions, Department of Information Technology, Ghaziabad, 201206, India; Sharma K.K., KIET Group of Institutions, Department of Information Technology, Ghaziabad, 201206, India; Dayal M., Bharati Vidyapeeth's College of Engineering, Department of Computer Science and Engineering, New Delhi, 110063, India
Investigation on the analysis of integration of IoT and AI technologies with information security for advanced education 4.0
A duopoly economy; Market share; Mathematical modeling; Ordinary differential equations
This research examines the integration of emerging technologies in the form of the Internet of Things and Artificial Intelligence in driving forward to the educational application of Education 4.0. The systematic meta-analysis study provides evidence in the transformative capability of these technologies regarding attendance, performance, and learning pathway. The systems implementation was in the form of IoT sensors to capture and record student attendance, while the use of Artificial Intelligence based on machine learning models such as Support Vector Machine, Artificial Neural Network, k-Nearest Neighbors, and Decision Tree generated a personalized recommendation for the academic improvement or sports activity to be participated as an extracurricular activity. The performance evaluation of these models was illustrated for accuracy to correctly predict student responses related to the provided recommendations. The findings of implementation suggest the systems significant impacts given the augmented performance achievement with respect to academics and sports is the result of the implementation. It was measured comparing students performance before and after system implementation to capture the interpretation of student improvement regarding the use of the implemented system. The findings indicated that the systems implementation contributed to the increase in academic improvement from 65% to 75% and sports performance from 55% to 70% depending on student response to the provided academical or extracurricular recommendations. Such findings confirm an overall improvement in performance based on the use of the presented system. Taru Publications.
Upreti K.; Dadhich P.; Gautam A.; Singh J.; Kushwah V.S.; Parashar J.; Gangwar D.; Ghosh S.
Journal of Discrete Mathematical Sciences and Cryptography, Vol-27, No. 4, pp. 1445-1454.
https://doi.org/10.47974/JDMSC-1998 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198055156&doi=10.47974%2fJDMSC-1998&partnerID=40&md5=0999640be2912d4a643241f5be000883
Upreti K., Department of Computer Science, CHRIST University, Delhi NCR Campus, Uttar Pradesh, India; Dadhich P., School of Computer Application, Manav Rachna International Institute of Research and Studies (MRIIRS), Haryana, Faridabad, India; Gautam A., School of Computer Application, Manav Rachna International Institute of Research and Studies (MRIIRS), Haryana, Faridabad, India; Singh J., Department of Computer Science and Engineering, Alliance School of Advanced Computing, Alliance University, Karnataka, Bangalore, India; Kushwah V.S., School of Computer Science & Engineering, VIT Bhopal University, Bhopal-Indore Highway, Madhya Pradesh, Bhopal, India; Parashar J., Department of Computer Science and Engineering, Dr. Akhilesh Das Gupta Institute of Professional Studies, Shashti Park, New Delhi, India; Gangwar D., Department of Management, Babu Banarasi Das Institute of Technology and Management, Dr. A.P.J. Abdul Kalam Technical University, Uttar Pradesh, Lucknow, India; Ghosh S., Department of Information Technology, Maharaja Agrasen Institute of Technology, Rohini, Delhi, India
Artificial Neural Networks for Enhancing E-commerce: A Study on Improving Personalization, Recommendation, and Customer Experience
AI-powered chatbots; Artificial intelligence; Artificial neural network (ANN); E-commerce; Feature selection; Machine learning
With e-commerce companies, artificial intelligence (AI) has emerged as a crucial innovation that allows companies to streamline processes, improve customer interactions, and increase operational capabilities. To provide tailored suggestions, address client care requests, and improve inventory control, AI systems may evaluate consumer data. Moreover, AI can improve pricing methods and identify fraudulent activity. Companies can actually compete and provide better consumer interactions with the growing usage of machine learning in e-commerce. This essay examines how AI is reshaping the e-commerce sector and creating fresh chances for companies to enhance their processes and spur expansion. AI technology which enables companies to enhance their procedures and offer a more individualized customer experiences has grown into a crucial component of the e-commerce sector. Purpose of providing product suggestions and improve pricing tactics, intelligent machines may examine consumer behavior, interests, and purchase history. Customer service employees will have less work to do as a result of chatbots powered by artificial intelligence handling client queries and grievances. AI may also aid online retailers in streamlining their inventory control by anticipating demands and avoiding overstocking. The use of AI technologies can also identify suspicious transactions and stop economic losses. AI is positioned to assume a greater part in the expansion and accomplishment of the e-commerce sector as it grows in popularity. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Upreti K.; Gangwar D.; Vats P.; Bhardwaj R.; Khatri V.; Gautam V.
Lecture Notes in Electrical Engineering, Vol-1115, pp. 141-153.
Upreti K., Department of Computer Science, CHRIST (Deemed to Be University), Delhi NCR Campus, Ghaziabad, India; Gangwar D., Department of Management, ADGITM, Delhi, India; Vats P., Department of CSE, SCSE, Manipal University Jaipur, Rajasthan, Jaipur, India; Bhardwaj R., Chitkara Business School, Chitkara University, Punjab, Rajpura, India; Khatri V., Bhagwan Parshuram Institute of Technology, Delhi, India; Gautam V., Warwick Manufacturing Group, Coventry, United Kingdom
Blockchain Empowered IVF: Revolutionizing Efficiency and Trust Through Smart Contracts
Blockchain technology; Data privacy; Ethereum; Healthcare; Hyperledger; IVF; Smart contracts
Couples who are having trouble becoming pregnant now have hope thanks to in vitro fertilization (IVF), a revolutionary medical advancement. However, the IVF procedure calls for a large number of stakeholders, intricate paperwork, and highly confidential management of information that frequently results in inaccuracies, mistakes, and worries about data confidentiality and confidence. In this study, the revolutionary potential of the blockchain and smart contracts enabling the treatment of IVF is investigated. The IVF procedure may be accelerated by utilizing smart contracts, resulting in improved effectiveness, openness, and confidence among everybody involved. The paper explores the primary advantages of using smart agreements in IVF, including automation, implementing obligations under contracts, doing away with middlemen, assuring confidentiality and anonymity, and enabling safe and auditable operations. The implementation of electronic agreements and blockchain-based technologies in the discipline of IVF is also investigated, along with the problems it may face and possible alternatives. This study offers insightful information about the use of intelligent agreements and blockchain technology in the field of IVF, accompanied by conducting an in-depth evaluation of the literature on the topic, research papers, and interviews with professionals. The results demonstrate the possibility of lower prices, more accessibility, higher success rates, and better patient experiences in the IVF field. In general, this study intends to illuminate how blockchain and smart contracts have revolutionized IVF technological advances, opening the door for a more effective, transparent, and reliable IVF procedure. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Upreti K.; Haque M.; Patil S.S.; Shukla S.; Rai A.K.; Vats P.
Lecture Notes in Electrical Engineering, Vol-1115, pp. 347-364.
Upreti K., Department of Computer Science, CHRIST (Deemed to be University), Delhi-NCR, Ghaziabad, India; Haque M., Dr. D.Y. Patil Vidyapeeths Centre For Online Learning, Dr. D.Y. Patil Vidyapeeth, Pune (Deemed to Be University), Maharashtra, Pimpri, India; Patil S.S., Dr. D.Y. Patil Vidyapeeths Centre For Online Learning, Dr. D.Y. Patil Vidyapeeth, Pune (Deemed to Be University), Maharashtra, Pimpri, India; Shukla S., Department of Computer Science and Engineering, Christ (Deemed to be University), Kengeri Campus, Bangalore, India; Rai A.K., Department of Management, Asian International University, Manipur, India; Vats P., Department of CSE, SCSE, Manipal University Jaipur, Rajasthan, Jaipur, India
Artificial Intelligence, Smart Contracts, and the Groundbreaking Potential of Blockchain technology: Unlock the Next Generation of Innovation
Blockchain; Cloud computing authentication; P2P networks; Security; Smart Contracts
The blockchain technology consists of blocks and is a decentralized network of nodes (miners). Each block is made up of three parts: the data, the hash, and the hash from the previous block. After data has been stored, it is extremely difficult to temper the data. Transactions are verified by miners, who are compensated with a commission for their labor. Readers will gain a comprehensive understanding of blockchain technology from this review article, including how it may be used in a variety of industries including supply chains, healthcare, and banking. Most individuals were already familiar with Bitcoin as one of the well-known blockchain applications. In this section, we'll discuss a few of the countless research publications on the cutting-edge applications of this technology. We'll talk about the challenges that come with actually using these applications as well. Blockchain is an industry that is growing thanks to its more recent applications in a number of fields, such as hospital administration, cryptocurrency use, and other places. Only the manner that blockchain works and runs makes it possible for these applications. 2023 IEEE.
Upreti K.; Haque M.; Vats P.; Kumar V.V.; Divakaran P.; Tiwari A.
2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2023, pp. 127-133.
Upreti K., Christ (Deemed to Be University), Dept. of Computer Science, Ghaziabad, India; Haque M., Pune (Deemed to Be University), Dr. D. y Patil Vidyapeeth's Centre for Online Learning, India; Vats P., Scse Manipal University Jaipur, Dept. of Cse, Rajasthan, Jaipur, India; Kumar V.V., Malla Reddy Engineering College for Women Autonomous Mysammaguda, Via Kompally, Telangana, India; Divakaran P., Himalayan University, Arunachal Pradesh, Itanagar, India; Tiwari A., School of Business Studies, Christ (Deemed to Be University) Delhi Ncr, Ghaziabad, India
Deep Dive Into Diabetic Retinopathy Identification: A Deep Learning Approach with Blood Vessel Segmentation and Lesion Detection
Deep learning; diabetic retinopathy; hemorrhages; lesions; microaneurysms; soft exudates
In the landscape of diabetes-related ocular complications, diabetic retinopathy stands as a formidable challenge, reigning as the leading cause of vision impairment worldwide. Despite extensive research, the quest for effective treatments remains an ongoing pursuit. This study explores the burgeoning domain of AI-driven approaches in ocular research, particularly focusing on diabetic retinopathy detection. It delves into various diagnostic methodologies, encompassing the detection of microaneurysms, identification of hemorrhages, and segmentation of blood vessels, primarily utilizing retinal fundus photographs. Our findings juxtapose conventional machine learning techniques against deep neural networks, showcasing the remarkable efficacy of Convolutional neural network (CNN) and Random Forest (RF) in segmenting blood vessels and the robustness of deep learning in lesion identification. As we navigate the quest for clearer vision, artificial intelligence takes center stage, promising a transformative leap forward in the realm of vision care. 2024 River Publishers.
Upreti K.; Kapoor A.; Hundekari S.; Upreti S.; Kaul K.; Kapoor S.; Tiwari A.
Journal of Mobile Multimedia, Vol-20, No. 2, pp. 495-524.
https://doi.org/10.13052/jmm1550-4646.20210 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195080854&doi=10.13052%2fjmm1550-4646.20210&partnerID=40&md5=3209980c2ad4abab645518b8af3a7de3
Upreti K., CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India; Kapoor A., Maharaja Surajmal Institute of Technology, Delhi, India; Hundekari S., MIT ADT University, Pune, India; Upreti S., Maharishi Markandeshwar (Deemed to be University), Haryana, Ambala, Mullana, India; Kaul K., Bharati Vidyapeeth College of Engineering, Rohini, India; Kapoor S., Dr. Akhilesh Das Gupta Institute of Technology and Management, New Delhi, India; Tiwari A., CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India
Discrete financial in sentimental analysis using exploring patterns and trends
Financial forecasting; Investment prediction; Market sentiment; Text mining
In todays rapidly evolving financial environment, its crucial for investors and decision-makers to effectively analyze stakeholder communications to gain valuable insights. This research conducts a comprehensive evaluation of a range of models that utilize machine learning, such as CNN (Convolutional Neural Network), LR (Logistic Regression), Doc2vec, and LSTM (Long Short-Term Memory), to determine their efficacy in interpreting investors sentiments and predicting business assessments and trading dynamics. The justification for preferring deep neural architectures compared to conventional data analysis lies in the challenge of handling extensive amounts of diverse and unorganized data. Deep learning techniques have shown impressive capacity in automatically detecting complex characteristics and unveiling concealed patterns within written records, rendering them well-suited for sentiment analysis in financial dialogue. This research questions the notion that depending exclusively on data from a solitary origin leads to persistently effective investment moves. In fact, stakeholder communication is impacted by numerous influential elements, leading to diverse sentiments and sentiments. Through our comparative assessment, we aim to illuminate how various deep learning models can adeptly capture the intricate nuances of sentiment within fiscal messaging. 2024, Taru Publications. All rights reserved.
Upreti K.; Kapoor A.; Tiwari A.; Gupta H.; Kushwah V.S.; Parashar J.; Gangwar D.; Kumar N.
Journal of Discrete Mathematical Sciences and Cryptography, Vol-27, No. 7, pp. 2053-2065.
https://doi.org/10.47974/JDMSC-2079 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211137430&doi=10.47974%2fJDMSC-2079&partnerID=40&md5=059651c51619672607a8d3cef3da36d6
Upreti K., Department of Computer Science, CHRIST University, Delhi NCR Campus Uttar Pradesh, 201003, India; Kapoor A., Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, Janakpuri, New Delhi, 110058, India; Tiwari A., School of Business and Management, CHRIST University, Delhi NCR Campus Uttar Pradesh, 201003, India; Gupta H., Department of Computer Engineering, Syracuse University, Woodridge, 13244, NJ, United States; Kushwah V.S., School of Computer Science & Engineering, VIT Bhopal University, Bhopal-Indore Highway, Madhya Pradesh, Bhopal, 466114, India; Parashar J., Department of Computer Science and Engineering, Dr. Akhilesh Das Gupta Institute of Professional Studies, Shashti Park, New Delhi, 110053, India; Gangwar D., Department of Management, Babu Banarasi Das Institute of Technology and Management, Dr. A.P.J Abdul Kalam Technical University, Uttar Pradesh, 226028, India; Kumar N., School of Business and Management, CHRIST University, Karnataka, Bangalore, 560029, India
Vigilance and surveillance reinforced using mathematical approaches in object tracking techniques
Bounding box; Deep learning convolutional network; Multiple object tracking; Single object tracking; Visual object tracking
Visual tracking is crucial to the study of object recognition and has been utilized in a variety of realistic settings, such as robotics, traffic monitoring, self-driving automobiles, forensics, and more. This research concentrates on techniques for counting the total number of individuals entering or exiting a space under the watchful eye of a camera. The techniques described here can detect the number of persons in a scene, both for a single individual and for many passers in front of the camera. With the aid of surveillance that use the centroid concept, an effective solution has been devised for monitoring. Secondly, in this study, object tracking methods utilising deep learning are also reviewed and analysed. This study also compares the effectiveness of various algorithms on the LaSOT, VOT2015, VOT2016, VOT2017 and OTB2015 tests. 2024, Taru Publications. All rights reserved.
Upreti K.; Kapoor S.; Ghosh S.; Jain D.; Kushwah V.S.; Parashar J.; Jain R.; Bloch T.
Journal of Discrete Mathematical Sciences and Cryptography, Vol-27, No. 4, pp. 1455-1465.
https://doi.org/10.47974/JDMSC-1999 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197745377&doi=10.47974%2fJDMSC-1999&partnerID=40&md5=f0ce59d7af5b3819c53e4c3f35e45691
Upreti K., Department of Computer Science, CHRIST University, Delhi NCR Campus Uttar Pradesh, India; Kapoor S., Department of Computer Science Engineering, Dr. Akhilesh Das Gupta Institute of Professional Studies, Shashti Park, New Delhi, India; Ghosh S., Department of Information Technology, Maharaja Agrasen Institute of Technology, Rohini Delhi, India; Jain D., Department of Computer Science & Engineering (Artificial Intelligence & Machine Learning), ABES Engineering College, Uttar Pradesh, Ghaziabad, India; Kushwah V.S., School of Computer Science & Engineering, VIT Bhopal University, Bhopal-Indore Highway, Madhya Pradesh, Bhopal, India; Parashar J., Department of Computer Science Engineering, Dr. Akhilesh Das Gupta Institute of Professional Studies, Shashti Park, New Delhi, India; Jain R., Department of Information Technology, Marwadi University, Gujarat, Rajkot, India; Bloch T., Department of Information Technology, Marwadi University, Gujarat, Rajkot, India
A SWOT analysis of integrating cognitive and non-cognitive learning strategies in education
academic achievement; cognitive learning; education system; non-cognitive learning; SWOT analysis
Students must receive the knowledge and skills they require for succeeding in a constantly changing world. Meeting each student's diverse needs, nevertheless, is difficult. For the purpose to promote student development and improve educational outcomes, this review study attempts to give a thorough conceptual framework for integrating both cognitive and non-cognitive learning methodologies. While non-cognitive learning focuses on social and interpersonal skills, emotional intelligence, and resilience, cognitive learning involves the acquisition of intellectual skills and critical thinking. Both types of education are essential for children's holistic development. Integrating non-cognitive and cognitive approaches in education sector has several advantages. It promotes a well-rounded education by offering a balanced approach that addresses the intellectual, emotional, and social elements of student progress. To support the suggested conceptual framework, a thorough analysis of recent research on the subject is conducted. The implementation of cognitive and non-cognitive learning in the present condition is examined through a bibliometric analysis, which identifies research trends and gaps. In addition, a SWOT analysis has been done to assess the advantages, disadvantages, opportunities, and threats related to these strategies. The issues and areas that require additional research and development are better understood due to this analysis. The research's conclusions demonstrate the importance of adopting a well-rounded educational strategy which considers various demands of students. The education system can encourage academic performance, critical thinking, socio-emotional well-being, and prepare students for success in a variety of spheres of life by integrating cognitive and non-cognitive learning. It also points out the research gaps and underlines the value of further study for enhancing comprehension and cognitive and non-cognitive learning methodologies' application. 2024 John Wiley & Sons Ltd.
Upreti K.; Kushwah V.S.; Vats P.; Alam M.S.; Singhai R.; Jain D.; Tiwari A.
European Journal of Education, Vol-59, No. 2
https://doi.org/10.1111/ejed.12614 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183906170&doi=10.1111%2fejed.12614&partnerID=40&md5=8881ce6fe1589b81fb159f3c46ac3fff
Upreti K., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India; Kushwah V.S., School of Computing Science & Engineering, VIT Bhopal University, Bhopal, India; Vats P., Department of CSE, SCSE, Manipal University Jaipur, Jaipur, India; Alam M.S., Department of Medical Laboratory Technology, College of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia; Singhai R., International Institute of professional Studies, Devi Ahilya University, Madhya Pradesh, Indore, India; Jain D., Computer science engineering-AIML and Engineering, ABES Engineering College, Ghaziabad, India; Tiwari A., School of Business Studies, CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India
Optimization Ensemble Learning Techniques for Reliable Crop Yield Prediction using ML
Decision Tree Regressor; Ensemble Learning; Gradient Boosting Regressor; Hyperparameter tuning; Outlier Analysis; R-squared Score; Random Forest Regressor
The agricultural sector's increasing reliance on technology has paved the way for advanced data-driven methodologies, with crop yield prediction emerging as a critical focus. This study dives into the complex landscape of crop yield prediction, employing a comprehensive approach that involves data preprocessing, model development, and performance evaluation. This research goes into enhancing crop yield prediction through a thorough data-driven approach. Beginning with comprehensive data preprocessing, including outlier analysis and feature scaling, the study ensures dataset integrity. Ensemble learning, employing Gradient Boosting Regressor, Random Forest Regressor and Decision Tree Regressor, captures intricate relationships within the dataset. Model performance, assessed through R-squared scores, demonstrates promising predictive capabilities. Subsequent outlier analysis and hyperparameter tuning yield substantial improvements, contributing valuable insights for agricultural decision-making. The research not only advances crop yield prediction but also offers practical guidance for integrating machine learning into agriculture, promising transformative outcomes for sustainable practices. The research also highlights how significant interpretability is to machine learning models so that stakeholders can comprehend and embrace them. This allows for a smooth integration of the models into current agricultural practices and encourages openness and reliability in decision-making. 2024 IEEE.
Upreti K.; Lingareddy N.; Deepika S.; Kumar N.; Parashar J.; Divakaran P.
Proceedings - 2024 1st International Conference on Technological Innovations and Advance Computing, TIACOMP 2024, pp. 431-436.
https://doi.org/10.1109/TIACOMP64125.2024.00078 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212043398&doi=10.1109%2fTIACOMP64125.2024.00078&partnerID=40&md5=80771159abaa3f5e48b09bb5179a808d
Upreti K., Christ (Deemed to Be University), Dept. of Computer Science, Delhi NCR, India; Lingareddy N., Vignan Institute of Technology and Science, Department of Cse, Telangana, India; Deepika S., Sreyas Institute of Engineering and Technology, Department of Cse, Telangana, Hyderabad, India; Kumar N., Christ (Deemed to Be University), School of Business and Management, Bangalore, India; Parashar J., Adgitm, Dept. of Computer Science & Engg, India; Divakaran P., Himalayan University, Arunachal Pradesh, Itanagar, India
Revolutionizing healthcare telemedicine's global technological integration
The pursuit of universal and high-quality healthcare services is a fundamental obligation of any responsible state, yet India faces persistent challenges in achieving this goal despite governmental efforts and policies. Notably, the 65th World Health Assembly emphasized universal health coverage (UHC) as pivotal for global public health advancement. Addressing this, a 2010 high-level expert group identified impediments in UHC implementation, highlighting issues such as health financing, infrastructure, skilled human resources, and access to medicines. This study focuses on exploring telemedicine's potential to mitigate these challenges and become instrumental in realizing universal health coverage in India. It aims to scrutinize government plans, critically assess policies on telemedicine implementation, and propose effective integration models, particularly in rural areas, to facilitate UHC. Additionally, the research aims to examine the role of AI, ML, deep learning, and neutral networks within telemedicine, envisaging their contribution to augmenting telemedicine's efficacy towards achieving universal health coverage in India. 2024, IGI Global. All rights reserved.
Upreti K.; Malik K.; Kapoor A.; Patel N.; Tiwari P.
Improving Security, Privacy, and Connectivity Among Telemedicine Platforms, pp. 71-92.
Upreti K., Christ University, India; Malik K., Christ University, India; Kapoor A., Maharaja Surajmal Institute of Technology, India; Patel N., Christ University, India; Tiwari P., Christ University, India
Development and Evaluation of an Artificial Intelligence-Based System for Pancreatic Cancer Detection and Diagnosis
Artificial intelligence; Deep learning; Machine learning; Pancreatic cancer; Validation studies
Due to its aggressive nature and late-stage manifestation, pancreatic cancer is a difficult illness to find and diagnose. The creation of a pancreatic cancer detection and diagnosis system based on artificial intelligence (AI) has the potential to increase early detection and improve treatment results. We have described the creation and assessment of an AI-based system in this paper that is intended for the identification of pancreatic cancer. A large dataset including a variety of medical pictures, including CT scans, MRI scans, and PET scans, as well as the related clinical information, was gathered for the study. With the help of the annotated dataset, a deep learning model built on convolutional neural networks was created. The proposed AI-based solution was then assessed using a separate test dataset made up of control cases and known pancreatic cancer patients. A significant effectiveness for the early diagnosis of the disease was shown by the systems excellent precision as well as sensitivity in identifying pancreatic tumors. The outcomes of this investigation demonstrate the promise of AI-based systems for pancreatic cancer detection and diagnosis. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Upreti K.; Mittal S.; Vats P.; Haque M.; Pawar V.; Haque M.
Communications in Computer and Information Science, Vol-1920, pp. 26-38.
Upreti K., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India; Mittal S., Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India; Vats P., SCSE, Manipal University Jaipur, Rajasthan, India; Haque M., Dr. D.Y. Patil Vidyapeeths Centre for Online Learning, Patil Vidyapeeth (Deemed to be University), Pune, India; Pawar V., Dr. D.Y. Patil Vidyapeeths Centre for Online Learning, Patil Vidyapeeth (Deemed to be University), Pune, India; Haque M., National Medical College and Teaching Hospital, Birganj, Nepal
Combatting Phishing Threats: An NLP-Based Programming Approach for Detection of Malicious Emails and Texts
AI-based expert systems and deep learning (ML); Cybersecurity; Decision trees; Email security; Feature extraction; Logistic regression; NLP-based programming (Natural Language Processing); Phishing; Text classification
Attackers are employing more advanced strategies to trick people into divulging private information or carrying out harmful deeds, and phishing is still a serious cybersecurity risk. We provide a new method in this study that combines algorithms based on AI-based expert systems and deep learning (ML) with the use of NLP-based programming (NLP) approaches to identify fraudulent emails and messages. Using a variety of datasets that include samples of both authentic and phishing messages, our approach preprocesses textual data, extracts relevant characteristics, and trains AI-based expert systems and deep learning models. Metrics including accuracy, precision, recall, and F1-score are used to assess the effectiveness of different AI-based expert systems and deep learning methods, such as logistic regression, random forests, decision trees, and neural networks, among others. To collect semantic information and increase detection accuracy, we also investigate the integration of sophisticated NLP-based techniques, such as word embeddings. The efficacy of our suggested strategy in reducing this common cybersecurity issue is highlighted by our results, which show promising performance in correctly recognizing phishing attempts. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Upreti K.; Patil H.P.; Bhende V.S.; Hundekari S.; Divakaran P.; Vats P.
Lecture Notes in Networks and Systems, Vol-1111 LNNS, pp. 83-94.
Upreti K., CHRIST(Deemed to Be University), Delhi NCR Campus, India; Patil H.P., Symbiosis Skills And Professional University, KiwalePune, India; Bhende V.S., AISSMS IOIT Pune, Pune, India; Hundekari S., School of Engineering and Technology, Pimpri Chinchwad University, Pune, India; Divakaran P., Himalayan University, Arunachal Pradesh, Itanagar, India; Vats P., Manipal University Jaipur, Rajasthan, Jaipur, India
A multi-model unified disease diagnosis framework for cyber healthcare using IoMT-cloud computing networks
Cloud computing; Disease diagnosis; Internet of Things; Machine learning; Smart healthcare
The past several decades of research into machine learning have been of great assistance to humanity in the diagnosis of a variety of ailments using various forms of automated diagnostic procedures. Machine learning, combined with smart health devices, has improved health monitoring, timely diagnoses, and treatment. This paper introduces a unified disease diagnosis framework, integrating cloud computing, machine learning, and IoT. The framework has three layers: physical (collects patient data), fog (intermediate layer with a domain identification unit to determine input and diagnosis type), and transmission (cloud server with a disease detection unit). The performance evaluation shows the robustness and efficiency of the model as compared to state-of-art models. 2023, Taru Publications. All rights reserved.
Upreti K.; Peng S.-L.; Kshirsagar P.R.; Chakrabarti P.; A.al-Alshaikh H.; Sharma A.K.; Poonia R.C.
Journal of Discrete Mathematical Sciences and Cryptography, Vol-26, No. 6, pp. 1819-1834.
https://doi.org/10.47974/JDMSC-1831 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174565731&doi=10.47974%2fJDMSC-1831&partnerID=40&md5=6be2e94199e9a649920b46b24629028f
Upreti K., Department of Creative Technologies and Product Design, National Taipei University of Business, Taipei City, Taiwan; Peng S.-L., Department of Creative Technologies and Product Design, National Taipei University of Business, Taipei City, Taiwan; Kshirsagar P.R., Department of Data Science, Tulsiramji Gaikwad Patil College of Engineering & Technology, Nagpur, India; Chakrabarti P., Department of Computer Science and Engineering, Sir Padampat Singhania University, Rajasthan, Udaipur, India; A.al-Alshaikh H., Department of Information Systems, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University Riyadh, Saudi Arabia; Sharma A.K., Department of Creative Technologies and Product Design, National Taipei University of Business, Taipei City, Taiwan; Poonia R.C., Department of Computer Science, CHRIST (Deemed to be University), NCR, Uttar Pradesh, Delhi, Ghaziabad, India
Blockchain Computing: Unveiling the Benefits, Overcoming Difficulties, and Exploring Applications in Decentralized Ledger Infrastructure
Blockchain-based applications; Distributed ledger; Intelligent medical care; Peer-to-peer system
The protocol known as blockchain, which is composed of blocks, utilizes a decentralized distributed system of nodes (miners). There are three parts to every block: information, which is represented by a hash, and the hash of a previous transaction. In order to regulate data after it has been stored, it is quite difficult to make changes. Mining is compensated for each encrypted function computation they carry out to verify the transaction. This research paper will provide a comprehensive understanding of blockchain-based technologies and how they are applied in a variety of industries, including those that deal with digital currencies, financial services, medical manufacturing, privacy, and a number of other fields. Digital money, notably the cryptocurrency Bitcoin, had previously been one of the most well-known network applications. As there have lately been several studies about the unique utilization of this sort of technology, we will discuss some of these academic works as well as the challenges encountered during the development of these kinds of applications. Blockchain technology is a quickly growing area of database technology that has recently found use in a wide range of industries, including the use of digital money, hospital administration, and other academic subjects. Because of how blockchain technology works and operates, these types of applications are now possible. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Upreti K.; Poonia R.C.; Verma A.; Divakaran P.; Mittal S.; Vats P.; Biswas S.S.
Lecture Notes in Networks and Systems, Vol-890, pp. 397-410.
Upreti K., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India; Poonia R.C., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India; Verma A., Department of CSE, ADGITM, New Delhi, India; Divakaran P., Himalayan University, Arunachal Pradesh, Itanagar, India; Mittal S., Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India; Vats P., Department of CSE, SCSE, Manipal University Jaipur, Rajasthan, Jaipur, India; Biswas S.S., Department of CSE, SCSE, Jamia Hamdard University, New Delhi, India