International Journal of Science, Engineering and TechnologyA Systematic Review Of Deep Learning Approaches For Agricultural Monitoring Using Satellite Imagery
Authors: Vandana Birle, Dr. Dilip Singh Solanki
Abstract: The increasing demand for sustainable agricultural production has accelerated the adoption of advanced technologies for crop monitoring and farm management. Satellite imagery has emerged as a valuable source of large-scale and continuous agricultural data, while deep learning techniques have demonstrated remarkable capabilities in extracting meaningful information from complex remote sensing datasets. This systematic review examines recent advances in deep learning approaches applied to satellite imagery for agricultural monitoring. The review analyzes studies published between 2023 and 2026 focusing on crop classification, farmland segmentation, crop health assessment, disease detection, yield prediction, land-use mapping, and environmental stress monitoring. Various deep learning architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers, Generative Adversarial Networks (GANs), and hybrid models, are discussed in relation to their effectiveness in agricultural applications. The findings indicate that deep learning significantly improves the accuracy and efficiency of agricultural monitoring compared to conventional image processing methods. However, challenges related to data availability, model generalization, computational complexity, and real-time implementation remain significant barriers to widespread adoption. The review highlights emerging trends and identifies future research directions for developing intelligent, scalable, and sustainable agricultural monitoring systems through the integration of satellite imagery and artificial intelligence.
DOI: http://doi.org/10.5281/zenodo.21412141
Finite Element Analysis Based Design and Optimization of a Lightweight Electric Vehicle Chassis
Authors: Research Scholar Abhinav Singh Dangi, Associate Professor Dr. Arun Kumar Yadav
Abstract: The increasing demand for energy-efficient and sustainable transportation has accelerated the development of lightweight electric vehicles. Vehicle weight plays a significant role in determining battery performance, driving range, energy consumption and overall vehicle efficiency. This study focuses on the lightweight design and optimisation of an electric vehicle chassis using advanced structural analysis and lightweight material strategies. The research reviews the application of lightweight materials such as aluminium alloys, magnesium alloys, high-strength steel and composite materials for reducing vehicle mass while maintaining structural strength and safety. Finite Element Analysis (FEA) and topology optimisation techniques are studied to improve chassis stiffness, load distribution and durability, and the relationship between lightweighting, energy efficiency, sustainability and intelligent chassis technologies is examined. Twelve studies published between 1998 and 2026 are reviewed and organised into three themes covering lightweight materials and design strategies, chassis optimisation and structural analysis, and energy efficiency with intelligent chassis performance. The reviewed evidence is consolidated into comparative tables that map each study to its focus, method, principal finding and limitation, alongside a qualitative comparison of candidate lightweight materials and a summary of the structural analysis methods reported. Five research gaps are identified and mapped to corresponding research directions. The findings indicate that optimised lightweight chassis structures can reduce vehicle weight, improve energy efficiency, enhance battery performance and increase driving stability, contributing toward the development of efficient, safe and sustainable electric vehicle systems.
Efficient Hybrid CNN-Transformer Models for Benchmark-Driven Image Deepfake Detection and Analysis
Authors: Research Scholar Priya Mishra, Dr. Deepika Pathak
Abstract: The rapid evolution of deepfake generation technologies has created significant challenges for digital media authentication, cybersecurity and misinformation prevention. Modern deepfake images generated using advanced artificial intelligence techniques exhibit highly realistic visual characteristics, making manual identification increasingly difficult. This paper presents a comprehensive review of efficient hybrid CNN-transformer models for benchmark-driven image deepfake detection and analysis. The study critically examines recent advances in Convolutional Neural Networks (CNNs), transformer-based architectures, hybrid feature fusion frameworks and explainable AI techniques for identifying manipulated images, and analyses benchmark-driven evaluation strategies, multimodal feature representation, manipulation localisation and attention-based contextual learning. Twelve studies published in 2025 are reviewed and organised into three themes covering hybrid architectures, benchmark-driven feature analysis, and cybersecurity challenges. The reviewed evidence is consolidated into comparative tables that map each study to its focus, approach, principal finding and limitation, alongside a component-level comparison of CNN, transformer and hybrid designs and an assessment of modality coverage across the corpus. Major challenges including adversarial attacks, computational complexity, cross-domain generalisation, multimodal deepfakes and benchmark inconsistency are discussed, and seven research gaps are identified and mapped to corresponding future research directions focusing on explainable AI integration, multimodal fusion, computational optimisation and generalised feature learning.
Gold Price Forecasting for Investment Decisions: A Multi-Factor Predictive Analytics Approach
Authors: Research Scholar Govind Kumar Verma, Associate Professor Dr. Richa Pareek
Abstract: Gold has long been considered one of the most important investment assets and safe-haven instruments in global financial markets. Rising market volatility, inflationary pressure, geopolitical uncertainty and shifting economic conditions have made accurate gold price forecasting essential for investors, financial institutions and policymakers. This study proposes a multi-factor predictive analytics approach to gold price forecasting that supports investment decision-making and risk management. The framework integrates machine learning, deep learning, econometric modelling and managerial analytics techniques to analyse the impact of macroeconomic indicators such as interest rates, exchange rates, inflation, commodity prices, climate risks and market volatility on gold price movements. Twelve studies published between 2017 and 2026 are reviewed and organised into three themes covering deep learning approaches, econometric and multi-factor models, and data mining with investment decision support. The reviewed evidence is consolidated into comparative tables that map each study to its data context, method, principal finding and contribution to the proposed design, and seven research gaps are identified and mapped to corresponding research directions. The framework is specified through five macroeconomic factor blocks and five forecasting components including Long Short-Term Memory (LSTM) networks, hybrid neural networks, ARIMA, Support Vector Machines and adaptive learning mechanisms. The findings indicate that multi-factor predictive analytics frameworks can improve forecasting accuracy, support strategic investment planning and enhance investor decision-making in uncertain financial environments.
Corporate Valuation Based on Dividend Policy in the FMCG Sector: A Review of Predictive Analytics Approaches
Authors: Research Scholar Jyoti Wadhwani, Associate Professor Dr. Uttam Kumar Jha
Abstract: Corporate valuation is central to financial management and investment analysis, and it is especially consequential in the Fast-Moving Consumer Goods (FMCG) sector, where stable demand, recurring cash flows and sustained investor confidence make firm value highly sensitive to distribution decisions. Dividend policy is repeatedly identified in the literature as a determinant of shareholder wealth, stock price behaviour, profitability signalling and investment attractiveness, yet the evidence remains fragmented across variables, markets and estimation methods. This paper presents a structured review of contemporary research on the dividend policy-corporate valuation relationship and on the emerging use of predictive analytics in valuation modelling. Twelve recent primary studies published between 2025 and 2026 were identified, screened and classified into four themes: dividend policy and corporate financial performance; dividend policy, stock market behaviour and investor response; capital structure, ownership and governance effects; and ESG, macroeconomic and analytics-driven valuation. Each study is compared on context, the structural role assigned to dividend policy, reported findings and methodological limitations. The synthesis shows that dividend policy operates in three distinct structural roles across the literature-as a direct determinant, as a moderator and as a mediator-and that this inconsistency is a substantive source of conflicting results. It further shows that the reviewed studies rely almost exclusively on conventional regression and structural equation techniques, that ESG and macroeconomic variables are treated in isolation, and that the FMCG sector itself is under-examined despite its dividend-intensive profile. Drawing on these findings, the review derives seven research gaps and proposes an integrated conceptual framework in which financial performance, capital structure, ownership, market and ESG determinants feed a dividend policy core, which in turn feeds a predictive analytics layer combining regression, machine learning, time-series forecasting and explainable AI to generate valuation forecasts and decision-support outputs. The framework is offered as a research agenda rather than an estimated model, and the review concludes with prioritised directions for its empirical validation.
Hybrid Deep Learning Framework for Robust and Computationally Efficient Image-Based Deepfake Detection
Authors: Research Scholar Priya Mishra, Dr. Deepika Pathak
Abstract: The rapid advancement of artificial intelligence and generative models has significantly increased the creation and dissemination of deepfake images, posing severe threats to digital security, media authenticity and public trust. Conventional deepfake detection methods often suffer from limited generalisation capability, high computational complexity and reduced performance against advanced synthetic image generation techniques. This review paper presents a comprehensive analysis of hybrid deep learning frameworks for robust and computationally efficient image-based deepfake detection. The study critically examines existing architectures including Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), attention-based models and explainable artificial intelligence (XAI)-enabled frameworks, and investigates the role of preprocessing and feature enhancement techniques such as normalisation, edge detection, artifact analysis and feature fusion in improving detection accuracy. Eleven studies are reviewed and organised into three themes covering deep learning architectures, preprocessing and feature enhancement, and benchmarking with societal impact. The reviewed evidence is consolidated into comparative tables that map each study to its focus, approach, principal finding and limitation, alongside a comparison of architecture families and a summary of preprocessing techniques. The review identifies six research gaps related to computational efficiency, cross-dataset generalisation, model interpretability, preprocessing strategy, benchmark inconsistency and lightweight hybrid design, and maps each to a corresponding future research direction. Directions focusing on lightweight hybrid architectures, explainable frameworks, benchmark-driven evaluation and multimodal detection strategies are presented to support the development of reliable and scalable deepfake detection systems.
Two-Way Fluid-Structure Interaction Analysis of Composite Wind Turbine Blades for Improved Fatigue Resistance and Structural Durability
Authors: Research Scholar Rishabh Dev Singh, Associate Professor Dr. Banarsi Pandey
Abstract: The increasing demand for renewable energy has accelerated the development of large-scale wind turbine systems with improved aerodynamic efficiency, structural reliability and operational durability. Wind turbine blades are continuously subjected to complex aerodynamic loading, cyclic stresses, vibration and environmental conditions that significantly affect their structural integrity and fatigue performance. This study presents a two-way Fluid-Structure Interaction (FSI) analysis of composite wind turbine blades for improved fatigue resistance and structural durability. Advanced composite materials are used to design lightweight blade structures with enhanced stiffness and load-bearing capability, and two-way FSI simulations investigate the interaction between aerodynamic airflow and structural deformation under varying wind speed conditions. Structural analysis and fatigue simulations evaluate stress distribution, deformation characteristics, vibration response and fatigue life of the composite blade system. Twelve studies published between 2003 and 2021 are reviewed and organised into three themes covering blade design with aerodynamic optimisation, composite materials with structural analysis, and blade damage with monitoring and sustainability. The reviewed evidence is consolidated into comparative tables that map each study to its focus, method, principal finding and limitation, alongside an assessment of the coupling approaches the corpus employs and a summary of the load cases and failure drivers it addresses. Five research gaps are identified and mapped to corresponding research directions. The simulation results demonstrate that optimised composite blade configurations significantly improve aerodynamic stability, reduce stress concentration and enhance fatigue resistance relative to conventional designs, confirming that integrated two-way FSI analysis combined with advanced composite materials provides an effective computational approach for improving structural durability and long-term operational performance.
Aerodynamic Performance Evaluation of CFRP-Based Blended Winglets for Commercial Aircraft Using ANSYS Simulation
Authors: Satyaprakash Tiwari, Dr Alok Choudhary
Abstract: The increasing demand for fuel-efficient and environmentally sustainable commercial aircraft has encouraged the development of advanced aerodynamic technologies for drag reduction and performance enhancement. Winglets are widely used in modern aircraft to reduce induced drag, minimise wake turbulence and improve aerodynamic efficiency. This study focuses on the aerodynamic performance evaluation of Carbon Fibre Reinforced Polymer (CFRP)-based blended winglets for commercial aircraft using ANSYS simulation. A lightweight blended winglet model is developed using CFRP and glass-carbon composite materials to improve structural strength while reducing overall wingtip weight, and Computational Fluid Dynamics (CFD) analysis is performed to evaluate airflow characteristics, pressure distribution, lift generation, drag reduction and wake turbulence behaviour around the winglet structure. The performance of the optimised blended winglet is compared with a conventional wingtip configuration on aerodynamic efficiency, stability and drag characteristics. Twelve sources published between 2004 and 2023 are reviewed and organised into three themes covering commercial aircraft wing design and aerodynamic performance, composite materials and lightweight structures, and numerical simulation with safety and performance assessment. The reviewed evidence is consolidated into comparative tables that map each source to its focus, method, principal contribution and limitation, alongside an assessment of the disciplinary composition of the corpus and the aerodynamic evaluation parameters it addresses. Five research gaps are identified and mapped to corresponding research directions. The simulation results demonstrate that the CFRP-based blended winglet significantly reduces induced drag and wake turbulence while improving lift-to-drag ratio and overall flight performance.
Soft Sensor-Assisted Nonlinear Model Predictive Control for Pyrolysis Reactor Temperature Regulation
Authors: Ranjan Kumar
Abstract: Pyrolysis reactors are difficult to control because thermochemical decomposition is nonlinear, the reaction rates are high, and feedstock variability causes disturbances. Traditional proportional-integral-derivative (PID) controllers often perform poorly, causing overshoot, sluggish response, and suboptimal energy efficiency. To address these challenges, this study proposes a soft-sensor-enabled nonlinear model predictive control (NMPC) strategy for temperature regulation in a pyrolysis reactor. This method combines an Extended Kalman Filter (EKF) soft sensor with predictive control to estimate unmeasured disturbances and states. A nonlinear energy balance model considers the heat input, heat loss, reaction dynamics, and disturbances. The NMPC controller solves an optimization problem over the prediction horizon while satisfying the constraints on the control inputs and reactor temperature. Simulations show that it outperforms PID and traditional MPC controllers, achieving a 499.75°C steady-state temperature, 0.247°C tracking error, 0.014°C overshoot, and minimized RMSE and IAE values of 129.53°C and 68060, respectively. The EKF soft sensor achieved a 0.40°C RMSE, enabling disturbance compensation. This method provides comparable energy efficiency while improving stability and robustness. In conclusion, this strategy offers an accurate and efficient solution for nonlinear pyrolysis reactor temperature control under disturbance conditions.