Dr. Shailendra Tiwari

EDUCATION

PhD CSE 2025

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Artificial Intelligence, Computer Science Applications, Computer Vision and Pattern Recognition

Scopus Publications

Stroke vision: A cutting-edge computationally sustainable fusion architecture of vision transformer for brain stroke detection
Dupakuntla Ushasree, Ganta Sai Meghanadh, Cherukupalli Mallikarjuna Rao, Vaishnavi Pisipapi, Ramy Riad Al-Fatlawy, Shailendra Tiwari, Tarang Bhatnagar
Aip Conference Proceedings, 2025
Design and comparative analysis of earthquake resistant buildings with and without bracing system
Ogirala Satya Durga Hima Bindu, Vivek Kumar Chandrakumar, Manoj Kumar Maharajan, Venkada Priya Sekar, Garapati Venkatasai Prasad, Hayidr Muhamed, Shailendra Tiwari, Lakshay Bareja
Aip Conference Proceedings, 2025
The application of synchro phasor data for fault detection during power swings
Vegesina Vijaya Rama Raju, Jakinapally Chandana, Susani Neha, Tappeta Kavya Sree, Guduru Guhanika, Akkineni Kalpana, Muntadar Muhsen, Shailendra Tiwari, Gaurav Pushkarna
Aip Conference Proceedings, 2025
Computational intelligence- driven design and optimization of polyurethane belt-type oil skimmer for sustainable manufacturing using solidworks 3D cad
Amandeep Singh Wadhwa, Shalom Akhai, Mahapara Abbass, Arti Chouksey, Shailendra Tiwari, Tanu Taneja
Using Computational Intelligence for Sustainable Manufacturing of Advanced Materials, 2025
This study focuses on developing a belt-type oil skimmer to effectively remove oil from water surfaces, promoting a green industry and reducing global pollution. The skimmer uses a belt mechanism that uses water and oil density differences to remove oil, achieving an efficiency of 62% to 92% depending on the oil type. The study uses SOLIDWORKS to create a detailed 3D model, adhering to industry best practices. The research extends beyond environmental protection to aquatic ecosystems, aligning with eco-friendly industrial practices and showcasing the impact of technical advancements on environmental challenges. This research demonstrates the potential of improving oil-water separation-dependent industrial operations and reducing water pollution.
Design of an Automated System for Finger Millet Disease Detection and Prediction Using Multimodal Fusion and Dynamic Graph Attention Networks
Shailendra Tiwari, Anita Gehlot, Himani Maheshwari, Venkata Ramana Rao P, Shailesh Mishra, Rajesh Singh, Sachin Kumar
Ssrg International Journal of Electrical and Electronics Engineering, 2025
There is a critical need to detect and predict diseases in Finger Millet due to crop yield and quality losses. Traditional methods mostly fail to provide appropriate and timely detection as they are driven by single data sources and can't adapt to the spatial and temporal dynamics of development in complex ways. Towards this, we develop a unified framework for disease detection in Finger Millet leaves that combines 1) multimodal fusion, 2) dynamic graph neural networks, and 3) temporal sequence modeling state-of-the-art techniques. Our overall framework is driven by three main models: the Multimodal Fusion Network with Attention, the Dynamic Graph Attention Network, and the Temporal Fusion Transformer. The MFNA model considers multiple data types, including RGB and multispectral images, which are fed into the model with IoT sensor data. CNN is utilised for feature extraction from images, and fully connected layers are applied to sensor data samples. Afterwards, it applies an attention mechanism to automatically weigh the importance of features from each modality and then applies a fusion layer to integrate these features for robust disease detection. DGAT builds a dynamic graph wherein nodes represent the different parts of the Finger Millet leaf, hence encoding the attributes pertaining to color, texture, and health status. It is inbuilt with self attention mechanisms within the graph that can adjust the importance of nodes and edges by considering factors such as spatial spreads of the disease with temporal updates for evolving patterns of the diseases. The TFT model generates temporal attention to process time-series data from IoT sensors and sequential image data handling long-term dependencies. The recurrent layers, either LSTM or GRU, deal with short-term dependencies, and the outputs are combined using a fusion module for disease progression and severity forecasting. Our framework integrates these models to give a complete solution that encapsulates spatial intricacies, robust feature extraction, and temporal dynamics of disease progression. This approach greatly improves accuracy and robustness in disease detection and prediction, thus allowing timely interventions in crop management. The proposed work will go so far as to revolutionize agriculture technology by rendering precise spatial identification, robust detection, and accurate forecasting of the crop, hence improving health and increasing productivity.
Design of an improved model for finger millet leaf disease detection with raspberry Pi using multimodal data acquisition and precision-aware CNN
Shailendra Tiwari, Anita Gehlot, Rajesh Singh, Bhekisipho Twala, Neeraj Priyadarshi
Results in Engineering, 2025
• Raspberry Pi Powered IoT : Affordable and versatile platform enabling IoT solutions with low power consumption and seamless integration. • Precision-Aware Learning : Focuses on optimizing model performance by balancing accuracy and computational efficiency. • Convolutional Neural Network (CNN) : Deep learning architecture designed for processing structured grid data, widely used in image and video analysis. • Real-Time Data Acquisition : Captures and processes live data streams with minimal latency for immediate analysis and decision-making. In this scenario, the rising prevalence of leaf diseases in finger millet poses serious threats to yield and hence food security. Most conventional methods have a serious limitation to precision, scalability, and adaptability; hence, accuracy in diagnosis and inefficiency in disease management are not rare. This work proposes a new Raspberry Pi-based IoT-enabled real-time data acquisition and a machine learning-driven framework that shows immense promise to improve substantially the accuracy and reliability of the leaf diseases in finger millet. Our proposed framework embeds the features of various advanced models and algorithms to compensate for the pitfalls of earlier contributions. The Multimodal Data Acquisition Model utilises both RGB and infrared cameras to capture holistic images of the leaf in real-time delays. This is further refined by the Adaptive Data Filtering Algorithm, which weeds out a lot of noise and irrelevant information from the data but keeps all critical features pertaining to diseases intact. Then, there is a convolutional feature extraction model, powered by a deep convolutional neural network that captures intricate details of leaf texture and lesions with a selective attention mechanism in the service of paying attention to disease-specific patterns to enhance the precision of extracted features. We propose a precision-aware convolutional neural network, P-CNN, specifically designed for the phase of classification with a handcrafted loss function that differentially penalizes misclassifications according to their agricultural impact sets. Further enrichment is provided by residual learning and precision calibration for sophisticated patterns of diseases and optimization in decision boundaries. Incremental Learning with Precision Feedback will ensure it adapts to new data, aided by Bayesian Inference in order to make confident decisions. Finally, post-processing will be done using an Error Correction with Precision Assurance model that refines the results of classification to give maximum accuracy and a Disease Severity Estimation Model, which assesses and prioritizes diseases interventions during the process.
Plant Disease Recognition Using Machine Learning Framework
Shailendra Tiwari, Anita Gehlot, Rajesh Singh, Nagendar Yamsani
Lecture Notes in Networks and Systems, 2025
Estimating the Effectiveness of Test-Driven Development to Reduce Bugs
Vamsi Krishna Gottipati, Sourabh Sanghi, Prem Nishanth Kothandaraman, Lucky Jha, Akshit Kohli, Shailendra Tiwari
2025 2nd International Conference on New Frontiers in Communication Automation Management and Security Iccams 2025, 2025
Modern technology developments and data research are used to figure out whether Test-Driven Development (TDD) works at reducing software problems. This research examines TDD, a programming process that involves creating test cases prior to implementing code, using an orderly Red-Green-Refactor cycle. Modern strategy utilizes two techniques: a mathematical modeling structure that assesses the quantity of defects experienced whether or not it utilizes TDD, and the organizational diagram which graphically depicts how TDD operates. The case presents a scenario along with 100 software functions to demonstrate that TDD can reduce the number of problems from 10% to 5%. This reduces the bug count by half. A regular testing-feedback loop helps this outcome by guaranteeing code correctness and encouraging early error investigation. The study shows that TDD improves program security and code quality and maintainability. The results presented here give an excellent rationale to add TDD to the way software engineers work now, and they also help the organization learn more about the way agile methods work recently.
Applications of Integrated Circuit Devices for improved Service Marketing Strategies
Arshi Naim, Praveen Kumar Malik, Sweety Jain, Shikha Agarwal, Shailendra Tiwari, Gotte Ranjith Kumar
Proceedings of 6th International Conference on 2025 Devices for Integrated Circuit Devic 2025, 2025
The contemporary research discovers the role of integrated circuit (IC) devices in improving the service delivery within the telecommunication industry to measure their impact on customer experience and network efficiency. The research focuses on the problem of optimizing technological infrastructure to meet the increasing demand for high-speed, reliable communication services. The objectives of the research are to understand the perceptions of telecommunication service providers about the contribution of IC devices to service performance and their effect on customer satisfaction. A qualitative research method was used, involving in-depth interviews with industry professionals and stakeholders from telecommunication service providers in northern India. The findings reveal a consensus on the importance of IC devices in driving network reliability, speed, and service innovation, while highlighting challenges in implementation and integration. These insights emphasize the strategic value of IC devices in shaping the future of telecommunication services, offering a competitive edge to service providers through technological excellence.
Bim-based Digital Construction for Green Building Performance Prediction and Evaluation
Jatinder Kaur, Mostafha Alwbaidy, Ravi Kant Prasad, Meenakshi Maindola, Shailendra Tiwari, Ruqaiya Khanam, Raman Kumar
Journal of Physics Conference Series, 2025
With the growing magnitude of climate change and the scarcity of resources, now more than ever, sustainable approaches to building construction are crucial. Green building practices will also enable energy savings, minimize pollution, and make buildings more comfortable for the people residing or working within them. It can be achieved through making predictions and verifications of the sustainability of a building. Building Information Modelling (BIM) is a digital technology that enables the design of detailed building models, simulation, and efficient management of data. The paper presents an analysis of the application of BIM in the construction of green buildings. It involves examining the amount of energy a building consumes, assessing a building’s energy use, air quality, and lighting, evaluating a building’s performance throughout its entire life, and real-time monitoring of a building using digital twins. The paper also examines critical tools, frameworks, and research that demonstrate how BIM is transforming the design and management of buildings. It also discusses some of the challenges, such as integrating data from various sources, ensuring that models are compatible, and addressing performance concerns. It also considers new opportunities, such as AI applications in BIM models, integration with IoT devices, and the development of smart systems that can make their own choices. All this demonstrates that BIM-based digital construction is making a significant contribution to the greening of the built environment.
LSTM-FFAM: utilization of long-short term memory recurrent neural network with feed forward attention mechanism for detection of replay attacks in wireless sensor networks
Roshan Singh, Monali Gulhane, Pancham Cajla, Amit Dutt, Shailendra Tiwari, G. Ramesh, Prashant Sharma, Mohammed Ihsan Habelalmateen, Darya Viktorovna
International Journal of Information Technology Singapore, 2025
Design of an iterative method for disease prediction in finger millet leaves using graph networks, dyna networks, autoencoders, and recurrent neural networks
Shailendra Tiwari, Anita Gehlot, Rajesh Singh, Bhekisipho Twala, Neeraj Priyadarshi
Results in Engineering, 2024
Assessing Big Data Analytics Performance in Industry 5.0 Operations: A Comparative Experiment
Meshcheryakova Tatiana Sergeevna, Shailendra Tiwari, Sorabh Lakhanpal, G. Prasanthi, Chandra Mohan
Bio Web of Conferences, 2024
Modelling and Development of Static Wireless Charging System for Electric Vehicles
Vinay Kumar Awaar, M.N Sandhya Rani, B.L. Narasimha Rao, U. Pavan Kumar, Shailendra Tiwari, Sathish Singarapu
E3s Web of Conferences, 2023
An analysis of work-family conflict resolution in the IT industry for employee sustainability
Neha, Yogita Sharma, Shweta Awasthi, Shailendra Tiwari, G. Karuna, Surendra Tripathi
E3s Web of Conferences, 2023
Average Memory Access Time Reduction in Multilevel Cache of Proxy Server
Y. Niranjan, S. Tiwari, R. Gupta
Proceedings of the 2013 3rd IEEE International Advance Computing Conference Iacc 2013, 2013

RECENT SCHOLAR PUBLICATIONS

Design of an improved model for finger millet leaf disease detection with raspberry Pi using multimodal data acquisition and precision-aware CNN
S Tiwari, A Gehlot, R Singh, B Twala, N Priyadarshi
Results in Engineering 25, 103969 , 2025
2025.0
Citations: 8
Design of an iterative method for disease prediction in finger millet leaves using graph networks, dyna networks, autoencoders, and recurrent neural networks
S Tiwari, A Gehlot, R Singh, B Twala, N Priyadarshi
Results in Engineering 24, 103301 , 2024
2024.0
Citations: 14
Review of Medication plan for patient data using Block chain Technology
TJ Shailendra Tiwari, Harsha Gupta
International Journal of Scientific Research & Engineering Trends 5 (4 … , 2019
2019.0
Design and Implementation of Page Replacement Algorithm for Web Proxy Caching
Y Niranjan, S Tiwari
International Journal of Computer Technology and Applications 4 (2), 221 , 2013
2013.0
Citations: 4
Average memory access time reduction in multilevel cache of proxy server
Y Niranjan, S Tiwari, R Gupta
2013 3rd IEEE International Advance Computing Conference (IACC), 44-47 , 2013
2013.0
Citations: 5
Design of an Automated System for Finger Millet Disease Detection and Prediction Using Multimodal Fusion and Dynamic Graph Attention Networks

MOST CITED SCHOLAR PUBLICATIONS

Design of an iterative method for disease prediction in finger millet leaves using graph networks, dyna networks, autoencoders, and recurrent neural networks
S Tiwari, A Gehlot, R Singh, B Twala, N Priyadarshi
Results in Engineering 24, 103301 , 2024
2024.0
Citations: 14
Design of an improved model for finger millet leaf disease detection with raspberry Pi using multimodal data acquisition and precision-aware CNN
S Tiwari, A Gehlot, R Singh, B Twala, N Priyadarshi
Results in Engineering 25, 103969 , 2025
2025.0
Citations: 8
Average memory access time reduction in multilevel cache of proxy server
Y Niranjan, S Tiwari, R Gupta
2013 3rd IEEE International Advance Computing Conference (IACC), 44-47 , 2013
2013.0
Citations: 5
Design and Implementation of Page Replacement Algorithm for Web Proxy Caching
Y Niranjan, S Tiwari
International Journal of Computer Technology and Applications 4 (2), 221 , 2013
2013.0
Citations: 4
Review of Medication plan for patient data using Block chain Technology
TJ Shailendra Tiwari, Harsha Gupta
International Journal of Scientific Research & Engineering Trends 5 (4 … , 2019
2019.0
Design of an Automated System for Finger Millet Disease Detection and Prediction Using Multimodal Fusion and Dynamic Graph Attention Networks