mohammed rizvi - Academia.edu (original) (raw)
Papers by mohammed rizvi
Journal of Engineering Research and Reports, 2023
This paper emphasizes the need for efficient energy consumption in buildings due to the increase ... more This paper emphasizes the need for efficient energy consumption in buildings due to the increase in pollution and economic growth, which has led to an increase in energy consumption worldwide. Therefore, a real-time energy management system is needed to overcome the deficiency of energy consumption and improve energy efficiency. Further to adopt modern architecture, we introduce artificial intelligence; to identify the factors involved in optimizing energy consumption in buildings is an important factor. The paper also suggests different machine learning (ML) based algorithms for data cleaning, processing, and analysis. This includes different studies that used AI-based techniques for real-time energy management systems, including reinforcement learning, rule-based approach, and mixed integer linear programming, to reduce energy consumption by 20%-30%. The use of AI in energy management in buildings holds great potential for world's scientific community. This technology enables data-driven decision-making, fosters energy conservation, and promotes advancements in the field of energy science, contributing to a greener and more sustainable future. The paper concludes that the use of AI-based approaches for energy-efficient systems can predict future energy demands by using previous data and building characteristics, making it more efficient than previous monitoring and alert systems. Mini-review Article
International Journal of Advanced Engineering Research and Science (IJAERS), 2023
Due to its ability to evaluate security threats in real-time and take appropriate action, artific... more Due to its ability to evaluate security threats in real-time and take appropriate action, artificial intelligence has emerged as a key component of cyber security. AI now has a bigger impact on spotting and stopping attacks that keep businesses on the cutting edge. Threat detection and prevention are the main focus of AI's role in cybersecurity. Artificial intelligence can detect trends and anomalies in network traffic and user behavior that may indicate a potential cyberattack through the use of machine learning algorithms and advanced data analysis. This allows security personnel to respond to potential attacks quickly and proactively. Through predictive modeling, AI can be used to prevent attacks. AI also can recognize potential threats before they occur and take action to avoid them by assessing past attacks and detecting similarities. Creating automated incident response systems is another important function of artificial intelligence in cybersecurity. These systems can evaluate data, identify potential risks, and then work to contain or mitigate the attack, minimizing damage and disruption. Businesses must employ artificial intelligence in cybersecurity to protect their networks and sensitive data from ever changing online threats. Because of its ability to analyze vast volumes of data in real time and automate incident response, AI is swiftly becoming into a key tool for efficient cybersecurity in today's digital environment. In this paper, we will discuss the role of AI in cybersecurity including its uses in threat detection and prevention.
Advances in Research, 2023
Power outage prediction and fault detection play crucial roles in ensuring the reliability and st... more Power outage prediction and fault detection play crucial roles in ensuring the reliability and stability of electrical power systems. Traditional methods for predicting power outages and detecting faults rely on rule-based approaches and statistical analysis, which often fall short of accurately capturing the complex patterns and dynamics of power systems. Deep learning algorithms, with their ability to learn automatically representations from large amounts of data, have emerged as promising solutions for addressing these challenges. In this literature review, we present an overview of deep learning algorithms applied to power outage prediction and fault detection. The purpose of this literature review is to explore the uses, effectiveness and advantages and disadvantages of utilizing deep learning algorithms in this domain. Various deep-learning models were explored in the context of power outage prediction and fault detection. Convolutional Neural Networks (CNNs) are effective in analyzing spatial dependencies and patterns in power system data, such as voltage levels and load distributions. Recurrent Neural Networks (RNNs), particularly Long Short Term Memory (LSTM) networks, excel in capturing temporal dependencies and patterns in time series data such as power demand and line currents. Generative Adversarial Networks (GANs) offer a unique approach by generating synthetic power system data for training purposes. This literature review involve collecting historical power system data from various sites. Deep-learning algorithms
International Research Journal of Modernization in Engineering Technology and Science, 2024
Smart grids have become a vital part of modern electricity management as they enable integration ... more Smart grids have become a vital part of modern electricity management as they enable integration with renewable energy sources, demand-response programs, and efficient load balancing. Machine learning offers a powerful toolkit to leverage data for accurate predictions and optimize real-time consumption decisions. This review evaluates the potential of machine learning algorithms for intelligent energy forecasting and load prediction in smart grids, aiming to revolutionize the future of power systems. The paper begins by providing an overview of smart grid components such as meters, sensors, communication networks, etc., before discussing the difficulties posed by traditional forecasting methods due to nonlinear energy patterns or uncertain renewable generation output. Subsequently, it examines how machine learning techniques can be applied in intelligent energy forecasting and load prediction tasks through support vector machines (SVM), neural networks (NNs), decision trees (DTs), or ensemble methods. This review emphasizes the potential of machine learning to enable intelligent energy forecasting and load prediction in smart grids. It discusses the importance of data preprocessing and feature selection techniques for improving model performance, as well as incorporating weather data, historical energy consumption patterns, and other relevant factors. Additionally, it highlights the advantages of this approach including optimized energy scheduling, reduced costs, improved grid stability, and enhanced renewable integration. However, several challenges remain such as issues related to data quality or scalability which should be addressed through further research into tailored machine-learning methods. By analyzing existing literature on this topic thoroughly, gaps in knowledge have been identified along with emerging trends that could guide future directions for exploration. Finally, leveraging advanced algorithms and harnessing the power of available data can help empower stakeholders to make informed decisions about their operations while paving the way toward sustainable efficiency in our shared energy future.
Journal of Engineering Research and Reports, 2023
This paper emphasizes the need for efficient energy consumption in buildings due to the increase ... more This paper emphasizes the need for efficient energy consumption in buildings due to the increase in pollution and economic growth, which has led to an increase in energy consumption worldwide. Therefore, a real-time energy management system is needed to overcome the deficiency of energy consumption and improve energy efficiency. Further to adopt modern architecture, we introduce artificial intelligence; to identify the factors involved in optimizing energy consumption in buildings is an important factor. The paper also suggests different machine learning (ML) based algorithms for data cleaning, processing, and analysis. This includes different studies that used AI-based techniques for real-time energy management systems, including reinforcement learning, rule-based approach, and mixed integer linear programming, to reduce energy consumption by 20%-30%. The use of AI in energy management in buildings holds great potential for world's scientific community. This technology enables data-driven decision-making, fosters energy conservation, and promotes advancements in the field of energy science, contributing to a greener and more sustainable future. The paper concludes that the use of AI-based approaches for energy-efficient systems can predict future energy demands by using previous data and building characteristics, making it more efficient than previous monitoring and alert systems. Mini-review Article
International Journal of Advanced Engineering Research and Science (IJAERS), 2023
Due to its ability to evaluate security threats in real-time and take appropriate action, artific... more Due to its ability to evaluate security threats in real-time and take appropriate action, artificial intelligence has emerged as a key component of cyber security. AI now has a bigger impact on spotting and stopping attacks that keep businesses on the cutting edge. Threat detection and prevention are the main focus of AI's role in cybersecurity. Artificial intelligence can detect trends and anomalies in network traffic and user behavior that may indicate a potential cyberattack through the use of machine learning algorithms and advanced data analysis. This allows security personnel to respond to potential attacks quickly and proactively. Through predictive modeling, AI can be used to prevent attacks. AI also can recognize potential threats before they occur and take action to avoid them by assessing past attacks and detecting similarities. Creating automated incident response systems is another important function of artificial intelligence in cybersecurity. These systems can evaluate data, identify potential risks, and then work to contain or mitigate the attack, minimizing damage and disruption. Businesses must employ artificial intelligence in cybersecurity to protect their networks and sensitive data from ever changing online threats. Because of its ability to analyze vast volumes of data in real time and automate incident response, AI is swiftly becoming into a key tool for efficient cybersecurity in today's digital environment. In this paper, we will discuss the role of AI in cybersecurity including its uses in threat detection and prevention.
Advances in Research, 2023
Power outage prediction and fault detection play crucial roles in ensuring the reliability and st... more Power outage prediction and fault detection play crucial roles in ensuring the reliability and stability of electrical power systems. Traditional methods for predicting power outages and detecting faults rely on rule-based approaches and statistical analysis, which often fall short of accurately capturing the complex patterns and dynamics of power systems. Deep learning algorithms, with their ability to learn automatically representations from large amounts of data, have emerged as promising solutions for addressing these challenges. In this literature review, we present an overview of deep learning algorithms applied to power outage prediction and fault detection. The purpose of this literature review is to explore the uses, effectiveness and advantages and disadvantages of utilizing deep learning algorithms in this domain. Various deep-learning models were explored in the context of power outage prediction and fault detection. Convolutional Neural Networks (CNNs) are effective in analyzing spatial dependencies and patterns in power system data, such as voltage levels and load distributions. Recurrent Neural Networks (RNNs), particularly Long Short Term Memory (LSTM) networks, excel in capturing temporal dependencies and patterns in time series data such as power demand and line currents. Generative Adversarial Networks (GANs) offer a unique approach by generating synthetic power system data for training purposes. This literature review involve collecting historical power system data from various sites. Deep-learning algorithms
International Research Journal of Modernization in Engineering Technology and Science, 2024
Smart grids have become a vital part of modern electricity management as they enable integration ... more Smart grids have become a vital part of modern electricity management as they enable integration with renewable energy sources, demand-response programs, and efficient load balancing. Machine learning offers a powerful toolkit to leverage data for accurate predictions and optimize real-time consumption decisions. This review evaluates the potential of machine learning algorithms for intelligent energy forecasting and load prediction in smart grids, aiming to revolutionize the future of power systems. The paper begins by providing an overview of smart grid components such as meters, sensors, communication networks, etc., before discussing the difficulties posed by traditional forecasting methods due to nonlinear energy patterns or uncertain renewable generation output. Subsequently, it examines how machine learning techniques can be applied in intelligent energy forecasting and load prediction tasks through support vector machines (SVM), neural networks (NNs), decision trees (DTs), or ensemble methods. This review emphasizes the potential of machine learning to enable intelligent energy forecasting and load prediction in smart grids. It discusses the importance of data preprocessing and feature selection techniques for improving model performance, as well as incorporating weather data, historical energy consumption patterns, and other relevant factors. Additionally, it highlights the advantages of this approach including optimized energy scheduling, reduced costs, improved grid stability, and enhanced renewable integration. However, several challenges remain such as issues related to data quality or scalability which should be addressed through further research into tailored machine-learning methods. By analyzing existing literature on this topic thoroughly, gaps in knowledge have been identified along with emerging trends that could guide future directions for exploration. Finally, leveraging advanced algorithms and harnessing the power of available data can help empower stakeholders to make informed decisions about their operations while paving the way toward sustainable efficiency in our shared energy future.