Tropical cyclones (TC) are among the worst natural disasters, that cause massive damage to property and lives. The meteorologists track these natural phenomena using Satellite imagery. The spiral rain bands appear in a cyclic pattern with an eye as a center in the satellite image. Automatic identification of the cyclic pattern is a challenging task due to the clouds present around the structure. Conventional approaches use only image data to detect the cyclic structure using deep learning algorithms. The training and testing data consist of positive and negative samples of TC. But the cyclic structure's texture pattern makes it difficult for the deep learning algorithms to extract useful features. This paper presents an automatic TC detection algorithm using optical flow estimation and deep learning algorithms to overcome this draw-back. The optical flow vectors are estimated using the Horn-Schunck estimator, the Liu-Shen estimator, and the Lagrange multiplier. The deep learning algorithms take the optical flow vectors as input during the training stage and extract the features to identify the cyclone's circular pattern. The software used for experimental analysis is MATLAB 2021a. The proposed method increases the accuracy of detecting the cyclone pattern through optical flow vectors compared to using the pixel intensity values. By using proposed method 98% of accuracy will be achieved when compared with the existing methods.
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significant subfields in “Synthetic Aperture Radar (SAR)” research is considered to be target detection. Numerous studies have been conducted on target identification, with the majority of them favoring filter-oriented methods. The fundamental goal of radar systems is to “detect moving targets on the ground.” Decomposing a complex matrix into a structured sparse matrix and a low-rank matrix is a fundamental mathematics issue. Surveillance and reconnaissance rely heavily on “Ground Moving Target Indication (GMTI),” but it's not a simple task. The SAR ATI was first developed for calculating the radial velocity of ground-moving objects. Yet, overlapping stationary clutter can corrupt the recorded differential phase, resulting in mistakes in position and velocity calculations. The main concept of this paper is to propose a novel “Adaptive Simplified Fractional Fourier Transform (A-SFrFT)” using the intelligent meta-heuristic improvement. This adaptive SFrFT efficiently estimates the “Doppler parameters of the moving targets.” The improved “Harris Hawks Optimization (HHO)” termed Trio Updating HHO (TU-HHO) is used as the meta-heuristic algorithm that enhances the performance of the SFrFT-based target estimation. The mathematical analysis and simulation findings show that the suggested methods recommended strategy is successful.
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Data analytics involves examining raw data to extract insights and make informed decisions. It includes various techniques and processes to clean, transform, and model data to discover patterns, trends, correlations, and other meaningful information. It's used across industries for purposes like optimizing business operations, understanding customer behavior, improving healthcare outcomes, and more. Tools like statistical analysis, machine learning, data mining, and visualization aid in this process, allowing analysts to derive actionable insights from large and complex datasets.
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The Internet of Things (IoT) is a revolutionary technological concept that has transformed the way we interact with the world around us. It refers to a network of interconnected devices, objects, and systems that have the ability to collect, exchange, and process data over the internet without human intervention. These devices, often embedded with sensors and connectivity features, can range from everyday objects like smartphones and wearable devices to specialized equipment in industries, such as sensors in manufacturing plants or smart meters in energy grids. The power of IoT lies in its ability to enable seamless communication and collaboration between these devices, leading to unprecedented levels of automation, efficiency, and convenience in various aspects of our lives. Through IoT, devices can communicate with each other, share information, and perform tasks autonomously, creating a more connected and intelligent environment. This interconnectedness has given rise to a wide array of applications across numerous industries. In healthcare, IoT enables remote patient monitoring and personalized healthcare solutions. In agriculture, it facilitates precision farming techniques. Smart homes and cities utilize IoT to enhance energy efficiency, security, and overall quality of life. Industries like manufacturing and transportation benefit from IoT through predictive maintenance and optimized