Anupam Bhandari

Scopus Publications

An IoT-driven machine learning system for real-time smart crop recommendation and optimization in precision agriculture
Nitish Rupesh Sawant, Anuj Kumar, Sangeeta Pant, Ketan Kotecha
Discover Artificial Intelligence, 2026
Agriculture is essential to global food security and sustainable development. The use of machine learning (ML) and Internet of Things (IoT) has the potential to enhance crop production and improve resource management through enhanced precision agriculture. This article describes a smart crop recommender system developed to support both ML-based decision making and real-time environmental sensing. At the core of the system is a capacitive soil moisture sensor and a temperature-humidity sensor interfaced using an Arduino Nano. Using a nRF24L01 transceiver employing the 2.4 GHz Enhanced Shock Burst (ESB) protocol, the sensor information can be wirelessly transmitted. Data collected from the sensors are sent to an ESP32 module that posts the information to the Blynk web application through Wi-Fi, allowing real-time remote access. As the data source for the crop recommender model, the Blynk application provides the data needed to perform data analysis on the various ML algorithms (Random Forest, Bagging, Decision Tree, Gradient Boosting, and Enhanced Gaussian Naive Bayes [EGNB]) to determine the most suitable type of crop based on the current soil and environmental conditions. Based on the analysis, the EGNB model showed superiority with an accuracy of 99.55% with respect to precision, recall, and F1-score. This system provides an economically viable and expandable approach to smart agricultural practices, which integrates cutting-edge machine-learning modelling with digital tools for obtaining data in real-time (IoT). These capabilities allow users to receive and utilize actionable, data-driven guidance in real time when making decisions about crop production.
Correction: Optimized deep learning for brain tumor detection: a hybrid approach with attention mechanisms and clinical explainability (Scientific Reports, (2025), 15, 1, (31386), 10.1038/s41598-025-04591-3)
Aditya Jayesh Aiya, Nishant Wani, Mayur Ramani, Anuj Kumar, Sangeeta Pant, Ketan Kotecha, Ambarish Kulkarni
Scientific Reports, 2026
Hesitant bi-fuzzy multi-attribute information fusion with expert reliability
Shshank Chaube, Anuj Kumar, Manoj Kumar Singh, Chandan Singh Ujarari, Sawan Kumar Rawat
Scientific Reports, 2025
Selecting alternatives wisely is essential in any multi-attribute decision-making (MADM) process, and it requires the participation of multiple experts. Initial decision data and the contributing experts' reliability affect the preciseness of the decision outcomes. However, no expert possesses exhaustive knowledge, and their estimations may exhibit varying preferences. Many times, experts hesitate to give contradictory/ conflicting information. Hence, the information gathered from them includes the hesitancy of the expert and conflict. To account for expert influence while preserving the integrity of the original data during aggregation, this paper proposes a hesitant bi-fuzzy information fusion (HBFIF) method that integrates expert reliability (ER) into the decision-making framework in the conflicting framework. Initially, the impression of expert reliability in the decision-making process is examined, and a reliability metric is constructed based on the degree of similarity among expert opinions. The power average (PA) operator is then used with expert reliability to aggregate expert preferences while preserving as much of the original data as feasible. Subsequently, inspired by the TOPSIS method, the hesitant bi-fuzzy information fusion approach is developed to maintain experts' original opinions and risk preferences depending on their assessed reliability. Finally, the proposed HBFIF method is applied to the multi-attribute decision-making framework in selecting an efficient hydrogen storage method in automobiles to confirm its effectiveness and applicability, underscoring its practical relevance and implementation.
Repairing the inconsistent pairwise comparison matrix using a cosine distance and grey wolf optimiser-based framework in multi-criteria decision-making
Shalu Kaushik, Sangeeta Pant, Lokesh Kumar Joshi, Anuj Kumar, Ketan Kotecha, Ambarish Kulkarni
Scientific Reports, 2025
Verifying the consistency of the Pairwise Comparison Matrix (PCM) is essential in the Multi-Criteria Decision-Making (MCDM) process, as decision-makers cannot use an inconsistent PCM as a credible reference. To optimize a PCM that is inconsistent, the primary requirement is to minimize the difference between the original and substitute matrices while improving the Consistency Ratio (CR) of the original Matrix. In this article, we employ a novel framework that uses a novel distance formula focused on the Cosine Distance metric to address inconsistencies in the PCM. Additionally, we utilize a swarm intelligence-based Grey Wolf Optimizer (GWO) to address further and repair these inconsistencies in the PCM. GWO leverages the exploration and exploitation strategies of grey wolves to identify the best-optimized value that satisfies the CR threshold while aligning closely with the decision-makers' (DM) original judgments. Additionally, we have introduced the maximum correction range ε. This ε is very helpful in achieving consistency based on the preference level of the DM, to what extent the DM wants to change the correction range. The experimental results indicate that we achieve better outcomes for the special case matrix with a CR of 0.546487; we obtain the resultant Matrix with a CR of 0.073397 with a minimal correction range of E = 3 in the 94th iteration. Experimental results also demonstrate that the suggested novel framework successfully generates a consistent matrix with minimal deviation from the original Matrix and outperforms previously used algorithms, such as ANT-based Analytic Hierarchy Process (ANTAHP) and Particle Swarm Optimization (PSO).
Optimized deep learning for brain tumor detection: a hybrid approach with attention mechanisms and clinical explainability
Aditya Jayesh Aiya, Nishant Wani, Mayur Ramani, Anuj Kumar, Sangeeta Pant, Ketan Kotecha, Ambarish Kulkarni, Abdullah Al-Danakh
Scientific Reports, 2025
Brain tumor classification (BTC) from Magnetic Resonance Imaging (MRI) is a critical diagnosis task, which is highly important for treatment planning. In this study, we propose a hybrid deep learning (DL) model that integrates VGG16, an attention mechanism, and optimized hyperparameters to classify brain tumors into different categories as glioma, meningioma, pituitary tumor, and no tumor. The approach leverages state-of-the-art preprocessing techniques, transfer learning, and Gradient-weighted Class Activation Mapping (Grad-CAM) visualization on a dataset of 7023 MRI images to enhance both performance and interpretability. The proposed model achieves 99% test accuracy and impressive precision and recall figures and outperforms traditional approaches like Support Vector Machines (SVM) with Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP) and Principal Component Analysis (PCA) by a significant margin. Moreover, the model eliminates the need for manual labelling-a common challenge in this domain-by employing end-to-end learning, which allows the proposed model to derive meaningful features hence reducing human input. The integration of attention mechanisms further promote feature selection, in turn improving classification accuracy, while Grad-CAM visualizations show which regions of the image had the greatest impact on classification decisions, leading to increased transparency in clinical settings. Overall, the synergy of superior prediction, automatic feature extraction, and improved predictability confirms the model as an important application to neural networks approaches for brain tumor classification with valuable potential for enhancing medical imaging (MI) and clinical decision-making.
A Deep Reinforcement Learning Framework for Strategic Indian NIFTY 50 Index Trading
Raj Gaurav Mishra, Dharmendra Sharma, Mahipal Gadhavi, Sangeeta Pant, Anuj Kumar
AI Switzerland, 2025
This paper presents a comprehensive deep reinforcement learning (DRL) framework for developing strategic trading models tailored to the Indian NIFTY 50 index, leveraging the temporal and nonlinear nature of financial markets. Three advanced DRL architectures deep Q-network (DQN), double deep Q-network (DDQN), and dueling double deep Q-network (Dueling DDQN) were implemented and empirically evaluated. Using a decade-long dataset of 15-min interval OHLC data enriched with technical indicators such as the exponential moving average (EMA), pivot points, and multiple supertrend configurations, the models were trained using prioritized experience replay, epsilon-greedy exploration strategies, and softmax sampling mechanisms. A test set comprising one year of unseen data (May 2024–April 2025) was used to assess generalization performance across key financial metrics, including Sharpe ratio, profit factor, win rate, and trade frequency. Each architecture was analyzed in three progressively sophisticated variants incorporating enhancements in reward shaping, exploration–exploitation balancing, and penalty-based trade constraints. DDQN V3 achieved a Sharpe ratio of 0.7394, a 73.33% win rate, and a 16.58 profit factor across 15 trades, indicating strong volatility-adjusted suitability for real-world deployment. In contrast, the Dueling DDQN V3 achieved a high Sharpe ratio of 1.2278 and a 100% win rate but with only three trades, indicating an excessive conservatism. The DQN V1 model served as a strong baseline, outperforming passive strategies but exhibiting limitations due to Q-value overestimation. The novelty of this work lies in its systematic exploration of DRL variants integrated with enhanced exploration mechanisms and reward–penalty structures, rigorously applied to high-frequency trading on the NIFTY 50 index within an emerging market context. Our findings underscore the critical importance of architectural refinements, dynamic exploration strategies, and trade regularization in stabilizing learning and enhancing profitability in DRL-based intelligent trading systems.
Modified Wild Horse Optimizer for system reliability-redundancy allocation problems with cold- standby redundancy strategy
Sangeeta Pant, Manoj K. Singh, Anuj Kumar, Shshank Chaube, Mangey Ram
International Journal of System Assurance Engineering and Management, 2025
Bridging current and future innovations to unlock the potential of multifunctional materials for sustainable energy applications
Aparna Ashok, Jitendra Pal Singh, Anuj Kumar, Neeru Bhagat
Materials Advances, 2025
Recent advances in multifunctional energy materials, driven by machine learning and bio-inspired design, are transforming photovoltaics, energy storage, and thermoelectrics, accelerating progress toward sustainable energy solutions.
COST AND RELIABILITY OPTIMIZATION OF A COMPLEX SYSTEM USING MULTI-OBJECTIVE GREY WOLF OPTIMIZATION TECHNIQUE
Anuj Kumar, Negi, Ganga, Mangey Ram, Pant, Sangeeta, Dimri, Sushil Chandra
Reliability Theory and Applications, 2025
Modern engineering systems increasingly focus on multi-objective optimization. Nature-inspired optimization techniques have shown superior efficiency and effectiveness compared to many traditional methods across various parameters. This work demonstrates the reliability and cost optimization of a complex bridge system using the Multi-Objective Grey Wolf Optimization algorithm (MOGWO). The bridge system in question is a seriesparallel system. A key performance highlight is the use of an archive for search agents to generate a Pareto optimal front (PoF) with a minimal number of iterations. Among the various solutions in the PoF, the solution set that best meets the multi-objective criteria is preferred. Additionally, statistical analyses are conducted to further validate the competitiveness of the results.
An Overview and Comparison of Axiomatization Structures Regarding Inconsistency Indices’ Properties in Pairwise Comparisons Methods: A Decade of Advancements
Sangeeta Pant, Anuj Kumar, Jiří Mazurek
International Journal of Mathematical Engineering and Management Sciences, 2025
Mathematical analysis of the analytic hierarchy process (AHP) led to the development of a mathematical function, usually called the inconsistency index, which has the center role in measuring the inconsistency of the judgements in AHP. Inconsistency index is a mathematical function which maps every pairwise comparison matrix (PCM) into a real number. An inconsistency index can be considered more trustworthy when it satisfies a set of suitable properties. Therefore, the research community has been trying to postulate a set of desirable rules (axioms, properties) for inconsistency indices. Subsequently, various axiomatic frameworks for these functions have been proposed independently. However, the existing literature remains fragmented and lacks a unifying framework. Therefore, the objective of this article is twofold. Over the past decade (2014-2024), significant progress has been made in the axiomatization of inconsistency indices' properties. In this article, we first provide a comprehensive review of these advancements. We then critically evaluate and compare the aforementioned axiomatic structures, discussing future research directions.
An Efficient Method for Detecting Cyberbullying Using Supervised Machine Learning Techniques
Bansidhar Joshi, Bineet Kumar Joshi, Sangeeta Pant, Anuj Kumar, Hitesh Kumar Sharma
Procedia Computer Science, 2025
Benchmarking Modern Deep Learning Architectures for Scalable and Accurate Plant Disease Detection
Nitish Rupesh Sawant, Kashish Vinay Sagar, Aarya Deepak Jaiswal, Anuj Kumar, Bansidhar Joshi, Rahul Sharma
2025 9th International Conference on Computing Communication Control and Automation Icccbea 2025, 2025
Photovoltaic Module Parameter Estimation by Using Wild Horse Optimizer
Anuj Kumar, Tina Sharma, Manoj K. Singh, Sangeeta Pant, Hitesh Kumar Sharma
Procedia Computer Science, 2025
A Systematic Review on Physics-Based Optimization Techniques
Mohammed Akheel, Aditya Anjanikar, Soham Navale, Sangeeta Pant, Anuj Kumar, Neeru Bhagat
Advanced Metaheuristics for Scheduling in Distributed Manufacturing Systems, 2025
Selection of Power Grids Using the Single-Valued Ambiguous Fuzzy Entropy and Distance Measures
Shweta Goyal, Shweta Sachdeva, Sangeeta Pant, Anuj Kumar, B.P. Joshi, Birendra Kumar Saraswat
2025 7th International Conference on Information Systems and Computer Networks Iscon 2025, 2025
Nature-inspired optimization and multi-criteria decision-making in photovoltaic systems: a systematic review
Anuj Kumar, Tina Sharma, Sangeeta Pant, Shshank Chaube, Manoj K. Singh, Ketan Kotecha
Energy Systems, 2025
A comprehensive review of nature-inspired optimization techniques and their varied applications
Sridhar Subramanian, Niranjan Bhojane, Harsh Madhnani, Sangeeta Pant, Anuj Kumar, Ketan Kotecha
Nature Inspired Optimization Algorithms for Cyber Physical Systems, 2024
An overview of nature-inspired optimization techniques for smart cities
Nida Nasir Khan, Yashashree Mahale, Kunal Kulkarni, Sangeeta Pant, Anuj Kumar, Ketan Kotecha
Nature Inspired Optimization Algorithms for Cyber Physical Systems, 2024
Signature reliability analysis of complex consecutive k-out-of-n:W CMO system via UGF and SFA
Sadiya Naaz, Rashmi Khanna, Riya Rawat, Neha Negi, Mangey Ram, Akshay Kumar, Anuj Kumar
Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability, 2024
An Overview of Multi-Criteria Decision Analysis and the Applications of AHP and TOPSIS Methods
Shshank Chaube, Sangeeta Pant, Anuj Kumar, Shaurya Uniyal, Manoj Kumar Singh, Ketan Kotecha, Akshay Kumar
International Journal of Mathematical Engineering and Management Sciences, 2024
AHP-based multi-criteria decision-making approach for monitoring health management practices in smart healthcare system
Sangeeta Pant, Priya Garg, Anuj Kumar, Mangey Ram, Akshay Kumar, Hitesh Kumar Sharma, Yury Klochkov
International Journal of System Assurance Engineering and Management, 2024
Parameter Estimation of a Solar Module by Wild Horse Optimizer
Tina Sharma, Anuj Kumar, Manoj K. Singh, Sangeeta Pant
Lecture Notes in Networks and Systems, 2024
A Review Based on Various Applications to Find a Consistent Pairwise Comparison Matrix
Shalu Kaushik, Sangeeta Pant, Lokesh Kumar Joshi, Anuj Kumar, Mangey Ram
Journal of Reliability and Statistical Studies, 2024
Exploring the diverse applications of the analytic hierarchy process: A comprehensive review
Mathematics in Engineering Science and Aerospace, 2024
Advances in Nanostructures: Processing and Methodology to Grow Nanostructures
Advances in Nanostructures Processing and Methodology to Grow Nanostructures, 2024
Selection of Electrical Distribution System using SVAS based Einstein Averaging Operator
B.P. Joshi, Shweta Sachdeva, Sangeeta Pant, Shweta Goyal, Anuj Kumar, Pushpendra Singh Kharayat
2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering Icacite 2024, 2024
Reliability-Redundancy Optimization of an Overspeed Protection System of a Gas Turbine by Modified Wild Horse Optimizer
Shshank Chaube, Sangeeta Pant, B.P. Joshi, Anuj Kumar, Manoj K. Singh, Ketan Kotecha
2024 2nd International Conference on Disruptive Technologies Icdt 2024, 2024
Modified wild horse optimizer based approach for solving constraint reliability redundancy optimization problems
Nonlinear Studies, 2024
Probabilistic Hesitant Bifuzzy Set and its Application in Risk Assessment
Journal of Multiple Valued Logic and Soft Computing, 2024
Intuitionistic fuzzy reliability assessment of a wind turbine generator
Aayushi Chachra, Saksham Agarwal, Diksha Rawat, Aditya Gusain, Akshay Kumar, Mangey Ram, Anuj Kumar
Reliability Assessment and Optimization of Complex Systems, 2024
DevOps: A journey from microservice to cloud based containerization
Devops A Journey from Microservice to Cloud Based Containerization, 2023
An integrated image visibility graph and topological data analysis for extracting time series features
Manoj K. Singh, Shshank Chaube, Sangeeta Pant, Sanjeev Kumar Singh, Anuj Kumar
Decision Analytics Journal, 2023
Modified Wild Horse Optimizer for Constrained System Reliability Optimization
Anuj Kumar, Sangeeta Pant, Manoj K. Singh, Shshank Chaube, Mangey Ram, Akshay Kumar
Axioms, 2023
Trench termination in Ga2O3-based power device: a simulation-based study
Jyoti Verma, Sangeeta Pant, Suman Kumari, Vinod Belwanshi, Jasvir Dalal, Anuj Kumar
Applied Nanoscience Switzerland, 2023
Analytical hierarchy process for sustainable agriculture: An overview
Anuj Kumar, Sangeeta Pant
Methodsx, 2023
Cost Optimization and Reliability Parameter Extraction of a Complex Engineering System
Anuj Kumar, Sangeeta Pant, Mangey Ram
Journal of Reliability and Statistical Studies, 2023
Wastewater Treatment and Multi-Criteria Decision-Making Methods: A Review
Tina Sharma, Anuj Kumar, Sangeeta Pant, Ketan Kotecha
IEEE Access, 2023
Ambiguous Fuzzy Averaging Operator Based Decision-Making Approach with Application to Classification of Renewable Energy Sources
Bhagawati Prasad Joshi, Arvind Bhatt, Mehul Manu, Anuj Kumar, Sangeeta Pant, Pawan Joshi
2023 International Conference on Power Energy Environment and Intelligent Control Peeic 2023, 2023
Geometric Aggregation Operator for MCDM Method Based on AIFS Information: Application to Renewable-Energy-Source-Selection Problems
Bhagawati Prasad Joshi, Arvind Bhatt, Mehul Manu, Anuj Kumar, Sangeeta Pant, Shailendra Giri
2023 International Conference on Power Energy Environment and Intelligent Control Peeic 2023, 2023
A State-of-the-Art Survey on Analytical Hierarchy Process Applications in Sustainable Development
Sudheer Singh Rawat, Sangeeta Pant, Anuj Kumar, Mangey Ram, Hitesh Kumar Sharma, Akshay Kumar
International Journal of Mathematical Engineering and Management Sciences, 2022
Consistency Indices in Analytic Hierarchy Process: A Review
Sangeeta Pant, Anuj Kumar, Mangey Ram, Yury Klochkov, Hitesh Kumar Sharma
Mathematics, 2022
Analysis of a Stochastic Model with Rework System
Mangey Ram, Ganga Negi, Nupur Goyal, Anuj Kumar
Journal of Reliability and Statistical Studies, 2022
Multi-Objective Optimization of Complex Bridge Network by MOPSO-CD
Anuj Kumar, Sangeeta Pant, Mangey Ram, Ganga Negi
2022 International Conference on Advances in Computing Communication and Materials Icaccm 2022, 2022
Multi-Criteria Decision-Making Techniques for Complex Decision Making Problems
Mathematics in Engineering Science and Aerospace, 2022
Analyzing the fuzzy reliability of a skim milk powder production system using intuitionistic fuzzy number
Mathematics in Engineering Science and Aerospace, 2022
Availability-cost optimization of butter oil processing system by using nature inspired optimization algorithms
Anuj Kumar, Ganga Negi, Sangeeta Pant, Mangey Ram, Sushil Chandra Dimri
Reliability Theory and Applications, 2021
GWO: a review and applications
Ganga Negi, Anuj Kumar, Sangeeta Pant, Mangey Ram
International Journal of System Assurance Engineering and Management, 2021
Optimization of complex system reliability using hybrid grey wolf optimizer
Ganga Negi, , Anuj Kumar, Sangeeta Pant, Mangey Ram, , , and
Decision Making Applications in Management and Engineering, 2021
Optimization techniques for petroleum engineering: A brief review
Anuj Kumar, Mridul Vohra, Sangeeta Pant, Sanjeev Kumar Singh
International Journal of Modelling and Simulation, 2021
Analysis of trench termination in 4H-nSiC based power devices
Jyoti Verma, Sangeeta Pant, Anuj Kumar, Bhupendra Kumar Jangir, Jasvir Dalal, Mangey Ram
Materials Today Proceedings, 2021
An overview of few nature inspired optimization techniques and its reliability applications
Nitin Uniyal, Sangeeta Pant, Anuj Kumar
International Journal of Mathematical Engineering and Management Sciences, 2020
Gray wolf optimizer approach to the reliability-cost optimization of residual heat removal system of a nuclear power plant safety system
Anuj Kumar, Sangeeta Pant, Mangey Ram
Quality and Reliability Engineering International, 2019
Solution of nonlinear systems of equations via metaheuristics
Sangeeta Pant, Anuj Kumar, Mangey Ram
International Journal of Mathematical Engineering and Management Sciences, 2019
Multi-objective grey wolf optimizer approach to the reliabilitycost optimization of life support system in space capsule
Anuj Kumar, Sangeeta Pant, Mangey Ram, Shshank Chaube
International Journal of System Assurance Engineering and Management, 2019
Complex system reliability analysis and optimization
Advanced Mathematical Techniques in Science and Engineering, 2018
Image denoising based on wavelet transform using Visu thresholding technique
International Journal of Mathematical Engineering and Management Sciences, 2018
Flower pollination algorithm development: a state of art review
Sangeeta Pant, Anuj Kumar, Mangey Ram
International Journal of System Assurance Engineering and Management, 2017
System Reliability Optimization Using Gray Wolf Optimizer Algorithm
Anuj Kumar, Sangeeta Pant, Mangey Ram
Quality and Reliability Engineering International, 2017
On solving complex reliability optimization problem using multi-objective particle swarm optimization
A. Kumar, S. Pant, M. Ram, S.B. Singh
Mathematics Applied to Engineering, 2017
Availability and cost analysis of an engineering system involving subsystems in series configuration
Anuj Kumar, Sangeeta Pant, S.B. Singh
International Journal of Quality and Reliability Management, 2017
Reliability Optimization: A Particle Swarm Approach
Sangeeta Pant, Anuj Kumar, Mangey Ram
Management and Industrial Engineering, 2017
A modified particle swarm optimization algorithm for nonlinear optimization
Nonlinear Studies, 2017
Reliability optimization of complex systems using Cuckoo Search Algorithm
Anuj Kumar, Sangeeta Pant, S. B. Singh
Mathematical Concepts and Applications in Mechanical Engineering and Mechatronics, 2016
Wavelet variance, covariance and correlation analysis of BSE and NSE indexes financial time series
Anuj Kumar, Sangeeta Pant, Lokesh Kumar Joshi
International Journal of Mathematical Engineering and Management Sciences, 2016
Application of a multi-objective particle article swarm optimization technique to solve reliability optimization problem
Sangeeta Pant, Anuj Kumar, Amar Kishor, Dhiraj Anand, Suraj Bhan Singh
Proceedings on 2015 1st International Conference on Next Generation Computing Technologies Ngct 2015, 2016
MODWT based time scale decomposition analysis of BSE and NSE indexes financial time series
International Journal of Mathematical Analysis, 2011

Anupam Bhandari

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