A Management of Food Supply Chain in Sustainable Smart Cities using Fire Hawk Optimization B Girirajan, B. Raveendra Naick, Hassan M. Al-Jawahry, Revanasiddappa, Rana Veer Samara Sihman Bharattej R 2nd IEEE International Conference on Data Science and Information System Icdsis 2024, 2024 Nowadays, the urban cities are facing increasing strain due to the swift increase in population inside metropolitan areas. It is anticipated that the centered strategy for smart cities will address both the ecological environment and urban life. The food business is one of major IoT application areas in smart cities. In smart cities, IoT technology aid in the real-time monitoring, analysis, and management of the food business. In this study, an Internet of Things (IoT) based Dynamical Food Chain of Supply for Smart Cities using Dynamic Vehicle Routing (IFSCDVR) using Fire Hawk Optimization (FHO) technique is suggested, which guarantees food quality while also offering intelligent vehicle routing and the ability to identify the reasons for contamination of food. This strategy would increase the effectiveness and precision of the supply chain network with the smallest possible dataset size. The findings demonstrate that the suggested system works better than the current methodologies. The proposed IFSCDVR-FHO achieved overall performance of 91.34% of accuracy, 89.56% of precision, 89.68% of recall and 90.21% of f1. score.
LoRa Architecture-Enabled Intelligent for Agriculture with Deep Learning Architecture K K, Anitha D, S S.Prabu, B B.Girirajan, Arun M Journal of Intelligent Systems and Internet of Things, 2024 The agricultural industry faces significant challenges in improving efficiency and productivity, particularly in monitoring crop health and environmental conditions. Traditional methods are often labor-intensive, time-consuming, and lack real-time data, leading to suboptimal decision-making. Recent advancements in Internet of Things (IoT) and Artificial Intelligence (AI) technologies offer promising solutions. Long Range (LoRa) communication, a type of low-power wide-area network (LPWAN), enables long-distance data transmission with minimal power consumption, making it ideal for rural and expansive agricultural areas. When combined with deep learning, which can analyze large volumes of data to generate predictive insights, these technologies have the potential to revolutionize agricultural practices by providing farmers with timely and accurate information to optimize crop management and resource utilization. This study introduces an intelligent mote for agricultural applications, leveraging Long Range (LoRa) communication and deep learning techniques to improve precision farming. Traditional agricultural monitoring methods are labor-intensive and lack real-time insights. To address this, the mote is equipped with sensors to monitor temperature, humidity, soil moisture, and light intensity, transmitting real-time data over long distances with minimal power consumption using LoRaWAN. The collected data is processed by deep learning models to predict crop yield and identify potential issues. Field tests demonstrated a 15% improvement in yield prediction accuracy and a 20% reduction in water usage compared to traditional methods. These results highlight the effectiveness of integrating LoRa and deep learning in enhancing agricultural resource management and productivity.
Modified Whale Optimization Algorithm for Task Scheduling in Cloud Computing B. Girirajan, Nijaguna G S, Pramodhini R, Myasar Mundher Adnan, Gandla Shivakanth 2nd International Conference on Integrated Circuits and Communication Systems Icicacs 2024, 2024 In recent years, an efficiency of task scheduling is evolved as a major challenge in cloud platforms. Especially, identifying the optimal resources for input tasks is the major challenges faced by the task scheduler. So, in this research, a Modified Whale Optimization Algorithm (MWOA) is proposed to improve the behaviour in task scheduling by applying the parameters such as resource allocation and load balancing. Capacity criteria based MWOA algorithm determines the effective Virtual Machine (VM) for execution of tasks in queue. An effectiveness of proposed WOA algorithm is analysed by the utilization of performance measures such as memory storage, execution time, cost and makespan. The attained results from the proposed WOA algorithm are outperformed in various predictable optimization algorithms such as Storm, Spark, Flink and Kafka. The results of proposed WOA algorithms demonstrates the better performances in minimum execution time of 612ms, and memory storage of 309Kb on Kafka platform.
Hybrid Optimization Algorithm for Effective Clustering algorithm and Routing Protocol in MANET B Girirajan, Hemalatha K. L, B. N. Manjunath, Suma S, M. Pushpavalli 2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques Easct 2023, 2023 Mobile Ad-Hoc Network (MANET) is self-configuring WSN which doesn't need any specific infrastructure due to their pure dynamic network. The efficient clustering and routing are growing attention in the background of limit energy resources and environment friendly transmitting behaviors. In this paper, Ant Bee Colony and Whale Optimization Algorithm (ABCWOA) algorithm is proposed for effective clustering and routing in MANET. The optimal cluster head and route paths are selected by utilizing the proposed optimization algorithm. The developed ABCWOA algorithm minimizes the nodes' energy utilization while increasing the data transmission in MANET. The performance of developed algorithm is estimated by utilizing performance measure of throughput, network lifetime, delay and energy consumption. The proposed algorithm attained the high throughput of 0.97 Mbps, 0.99 Mbps, 0.99 Mbps and 0.99 Mbps for 50, 100, 150 and 200 nodes which is superior than other existing algorithms like Particle Swarm Optimization (PSO), Cuckoo Search Optimization (CSO), Ant Bee Colony (ABC) optimization and Whale Optimization Algorithm (WOA).
Hybrid Optimization Based Convolutional Neural Network for Intrusion Detection System Sureka N, B. Girirajan, Komuravelly Sudheer Kumar, B. S. Deepa Priya, Sowmya M 2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques Easct 2023, 2023 Intrusion Detection System (IDS) is one of the common deep learning (DL) techniques that are used to find and identify outliers to prevent adversarial attacks, fraud, and network intrusions. This paper proposed a hybrid Particle Swarm Optimization and Grey Wolf Optimization (HPSOGWO) based Convolutional Neural Network (CNN) model for intrusion detection systems. The proposed HPSOGWO is evaluated on the NSL-KDD dataset which contains 5 different classes and 41 features. The PSO is good for global exploration and GWO is for local exploitation. The hybrid PSO and GWO algorithm achieves a better balance between exploration and exploitation and enhances convergence speed. The CNN is utilized to enhance the system's capability to identify and classify intrusion accurately and effectively. The proposed HPSOGWO-based CNN model attains better results by utilizing evaluation metrics like accuracy, precision, recall, specificity, and f1-score values about 0.9918, 0.9852, 0.9908, 0.9879 and 0.9767 correspondingly which is comparatively higher than existing techniques like Chicken Swarm Optimization based Deep Long Short-Term Memory (ChCSO based Deep LSTM), Deep Neural Network (DNN) and LSTM.
High Gain Converter with Improved Radial Basis Function Network for Fuel Cell Integrated Electric Vehicles Balasubramanian Girirajan, Himanshu Shekhar, Wen-Cheng Lai, Hariraj Kumar Jagannathan, Parameshachari Bidare Divakarachar World Electric Vehicle Journal, 2022 In a recent trend, electric vehicles (EV) have been facing various power quality issues, so fuel cells (FC) are considered the best choice for integrating EV technology to enhance performance. A fuel cell electric vehicle (FCEV) is a type of EV that uses a fuel cell combined with a small battery or super-capacitor to power its on-board electric motor. However, the power obtained from the FC system is much less and is not enough to drive the EV. So, another energy source is required to deliver the demanded power, which should contain high voltage gain with high conversion efficiency. The traditional converter produces a high output voltage at a high duty cycle, which generates various problems, such as reverse recovery issues, voltage spikes, and less lifespan. High switching frequency and voltage gain are essential for the propulsion of FC-based EV. Therefore, this paper presents an improved radial basis function (RBF)-based high-gain converter (HGC) to enhance the voltage gain and conversion efficiency of the entire system. The RBF neural model was constructed using the fast recursive algorithm (FRA) strategy to prune redundant hidden-layer neurons. The improved RBF technique reduces the input current ripple and voltage stress on the power semiconductor devices to increase the conversion ratio of the HGC without changing the duty cycle value. In the end, the improved RBF with HGC achieved an efficiency of 98.272%, vehicle speed of 91 km/h, and total harmonic distortion (THD) of 3.12%, which was simulated using MATLAB, and its waveforms for steady-state operation were analyzed and compared with existing methods.
Enhancement of teaching learning process by Blended Teaching Journal of Engineering Education Transformations, 2022
