Energizing the Future: A Comprehensive Review and Evaluation of Artificial Intelligence based Solar Irradiance Forecasting Model with Swot Analysis of Solar Energy Gautam Kumar, Sandip Kumar Goyal 3rd International Conference on Electronics and Renewable Systems Icears 2025 Proceedings, 2025 Meeting the fast-growing need of energy is paramount; therefore, solar energy can contribute to playing a very important role in reducing greenhouse gas emissions. Solar radiation depends on several factors such as the availability of sunlight, cloud cover, and panel orientation, so it is the key to a solar energy system. Proper prediction of solar irradiance allows better reliability and efficiency of the system. AI and machine learning models, which are trained by use of historical data on weather, are increasingly applied for this purpose using techniques such as regression, neural networks, and ensemble methods. This review presents a review of the state-of-the- art models for solar irradiance forecasting, covering machine learning, numerical weather prediction, and hybrid approaches, by assessing their accuracy, strengths, and weaknesses. It also delineates the prospective future development possibilities while enunciating the role of interdisciplinary collaboration and technology in merging solar power into the main electricity system. Therefore, this work is going to be a core analysis for future researchers in finding optimum methods leading into medium- and long-term solar irradiance forecasting.
A Sensitive Collision Avoidance System Improved for Terrain Navigation on Elevated Terrain Anup Lal Yadav, Sandip Kumar Goyal 2025 2nd International Conference on Computational Intelligence Communication Technology and Networking Cictn 2025, 2025 This study introduces a specialized Collision Avoidance System designed specifically for navigating hilly terrains. It incorporates machine learning algorithms seamlessly integrated into an Arduino platform to improve safety measures by identifying potential collisions in challenging topographies. By harnessing machine learning methodologies, the system can detect and forecast obstacles in real-time, providing timely alerts and enabling proactive maneuvers to prevent collisions. The incorporation of Arduino technology streamlines the implementation process, offering a cost-efficient and adaptable solution tailored for hilly environments. Rigorous experimental assessments confirm the effectiveness and dependability of the proposed Collision Avoidance System, highlighting its potential to elevate safety standards in dynamic and intricate terrains.
Optimizing WBAN Lifetime with Hybrid Node Ranking and Energy-Aware Multi-Hop Routing Mitu Sehgal, Sandip Goyal, Sunil Kumar International Journal of Engineering Trends and Technology, 2024 Efficient clustering techniques were made possible by the necessity of optimizing network resources to extend the lifetime of densely deployed, large-scale Wireless Body Area Networks (WBN). This led to research on the development of these approaches. Clustering has shown to be a successful method for dividing a large-scale WBN into interconnected clusters, extending the networks’ lifespan and dependability. The nodes’ distances from the base station and one another can significantly affect how much energy they save and how long the network lasts. This research proposes a new technique that combines dynamic clustering, multi-CH selection, and node ranking. As per the Message Success Rate and Opportunistic Routing Scheme, the suggested method shows a mechanism of fuzzy ranking to rank forwarder nodes. This is done by listening to forwarder nodes’ participation in Wireless Body Networks (WBNs) and determining their positions with respect to the base station, that is depletion of information gathered. Because it takes minimum energy to replace the node, this advantage will significantly improve the network lifetime.
A Comparative Study on Reputation Management in Social Cloud: Validating Our Proposed Trust Architecture with Multiple Datasets Santosh Kumar, Sandip Kumar Goyal Proceedings 2024 6th International Conference on Computational Intelligence and Communication Technologies Ccict 2024, 2024 This paper presents a comparative study on reputation management in social cloud, focusing on validating a novel trust-based architecture through experimentation with multiple real-world datasets. The proposed architecture incorporates various components such as feedback collection, trust computation, and reputation propagation, aiming to improve reliability and robustness in managing user reputations within social clouds. We compare our approach with existing state-of-the-art methods and evaluate its effectiveness by analyzing results from four large-scale public datasets. Through extensive experiments, we demonstrate that our proposed architecture outperforms traditional approaches in terms of accuracy, efficiency, and adaptability. Additionally, we discuss insights gained from this research and outline future directions for improving trust and reputation management in social clouds.
An Efficient and Intelligent System for Controlling the Speed of Vehicle using Fuzzy Logic and Deep Learning Anup Lal Yadav, Sandip Kumar Goyal International Journal of Advanced Computer Science and Applications, 2024 —Vehicle collisions are a significant problem worldwide, causing injuries, fatalities, and property damage. There are several reasons for the collapse of vehicles such as rash driving, over speeding, less driving skills, increasing number of vehicles, drunk and drive, etc. However, over speeding is one of the critical factors out of all the reasons for vehicle collisions. To address the critical issues, the current article proposes a Fuzzy-based algorithm to prevent and control the speed of the vehicle. The major objective of the proposed system is to control the speed of the vehicle for proactive collision avoidance. Deep learning and fuzzy system provide better integrated approach for the controlling of the speed and avoid vehicle collision. Fuzzification of the speed variable provides an advanced or viable solution for speed control. The current research used RNN and other deep learning algorithm to predict the traffic and identify the traffic frequency. The traffic frequency in a time-series frame provides the frequency of the traffic within a time frame that can be detected by using involvement of IoT.
A Perceptive Collision Avoidance System Enhanced for Terrain Guidance in Elevated Terrain Situations Anup Lal Yadav, Sandip Kumar Goyal Proceedings International Conference on Computing Power and Communication Technologies Ic2pct 2024, 2024 The increasing number of vehicles on the roads has resulted in a higher risk of accidents and collisions. To address this critical issue, this research proposes an intelligent system for vehicle collision avoidance using the Internet of Things (IoT). The system aims to enhance road safety by providing real-time monitoring, analysis, and proactive collision avoidance mechanisms. The system operates in three main stages: perception, decision-making, and action. In the perception stage, sensor data from vehicles and infrastructure are collected and processed to gather relevant information about the surrounding environment. This includes detecting the presence, position, and velocity of nearby vehicles, pedestrians, and obstacles. In the decision-making stage, the collected data is analyzed and evaluated using machine learning techniques. The system generates real-time predictions and risk assessments based on the analyzed data to determine potential collision scenarios and prioritize the most critical situations. This system also has an over-speed detection feature that monitors speed and notifies the driver when the car exceeds a certain speed restriction.
An approach for implementation of cost effective automated data warehouse system International Journal of Computer Information Systems and Industrial Management Applications, 2020
An approach for implementation of cost effective automated data warehouse system International Journal of Computer Information Systems and Industrial Management Applications, 2019
Measuring throughput for fault tolerant based ACO algorithm under cloud computing: A comparison study International Journal of Engineering and Technology Uae, 2018
