Aim: This study contributes in the modelling of a high gain Saw-Tooth shaped fractal boundary square antenna (SFBS Antenna) in comparison with the square shaped microstrip patch antenna (MPA) in the 1.0 GHz to 10.0 GHz band of frequency. Materials and Methods: Group 1 is assumed to be SFBS Antenna with sample size (n = 26). Group 2 is a Square shaped MPA with sample size (n = 26). The substrate is FR4 Epoxy type. The confidence interval is 95%, the threshold is 0.05%, and the G power value is 80%. Results: The Saw-Tooth shaped fractal boundary square antenna has higher gain between 4.34 dB to 43.56 dB than the Square shaped micro strip patch antenna gain of 3.56 dB to 38.78 dB. The Saw-Tooth shaped fractal boundary square antenna has maximum gain at the frequency range of 10.0 GHz with significance 0.005. The square shaped microstrip patch antennas have maximum gain at 10.0 GHz. Conclusion: This study concluded that the Saw-Tooth shaped fractal boundary square antenna gives significantly better gain at frequency range of 10.0 GHz in comparison with the square shaped MPA.
Aim: This study's main goal is to precisely locate the sources of power supplies, forecast power rates, and alert consumers to high energy flow in order to reduce dangers to electronic devices and improve grid efficiency and stability. Methods and Materials: The proposed work included four groups. Group 1 refers to Deep Neural Network (DNN) algorithm used to, Group 2 refers to Long Short- Term Memory algorithm which is used to be more accurate when using datasets with longer sequences, Group 3 Gated Recurrent Unit (GRU) algorithm used for multi-source and multi-powered smart grid prediction with an astounding accuracy. Results: Achieving 99.03% accuracy using GUR algorithm in multi-source and multi-powered smart grid prediction. Conclusion: Outperforming LSTM in source and grid identification, the GRU algorithm shows promise for improving grid prediction.
<span lang="EN-US">Due to various killing diseases in the world, medical data clustering is a very challenging and critical task to handle and to take the proper decision from multidimensional complex data in an effective manner. The most familiar and suitable speedy clustering algorithm is K-means than other traditional clustering approaches. But K-means is extra sensitive for initialization of clustering centroid and it can easily surround. Thus, there is a necessity for faster clustering with an effective optimum clustering centroid. Based on that, this research paper projected an optimization-based clustering by hybrid fuzzy C-means (FCM) clustering on rainfall flow optimization technique (RFFO), which is the normal flow and behavior of rainfall flow from one position to another position. FCM clustering algorithm is used to cluster the given medical data and RFFO is used to produce optimum clustering centroid. Finally, the clustering performance is also measured for the proposed FCM clustering on RFFO technique with the help of accuracy, random coefficient, and Jaccard coefficient for medical data set and find the risk factor of a heart attack.</span>
Air quality prediction focuses mainly on these industrial areas.Industrial level usage of this project requires expensive sensors and huge amount of power supply.According to the World Health Organization (WHO), major air pollutants include particulate pollution, carbon monoxide (CO), Sulphur-di-oxide (SO2) and nitrogen oxide (NO2).In addition to these mentioned gases, PM or Particulate Matter and VOC or Volatile Organic Compounds components also cause serious threats.Long and short-term exposure to air suspended toxicants has a different toxicological impact on humans.Some of the diseases include asthma, bronchitis, some cardiovascular diseases, and long-term chronic diseases such as cancer, lung damage and in extreme cases diseases like pulmonary fibrosis.In this proposed system, an IoT prototype of a large-scale system which uses high-end and expensive sensors that measures the various air pollutants in the atmosphere is designed.Gas sensors are used in this prototype to record the concentration of the various pollutants that are encountered in the air on a regular basis.The framework uses stored data to train the model using multi-label classification with Random Forest algorithm, XG Boost algorithm in the local system.The real time data obtained using the different sensors is tested and the results obtained would be used to predict the possibilities of diseases such as asthma, lung cancer, ventricular hypertrophy etc. and the Air Quality Index (AQI) are calculated.In addition to this, preventive suggestions are also provided which is merely a cautionary message displayed on our LCD display to vacuum clean the room or mop the room thoroughly.
The detection of lung cancer through image processing is an important tool for diagnoses. Different methods were employed to detect the cancerous cell of a lung with image pre processing such as Gabor filter, image segmentation using watershed segmentation and feature extraction by using MATLAB. In this experiment, various Computed Tomography (CT) images of lung were used as input image and obtained the output image in JPEG format. The resultant output of this technique was evaluated. Based on the results, it shows that watershed segmentation technique is showed better results than other segmentation techniques for lung cancer cell identification. Keywords: CT Lung Cancer Image, Image Segmentation, Thresholding Operation, Watershed Method
This research study's objective is to develop a unique, high gain triangle-shaped Sierpinski fractal antenna utilizing a FR4 epoxy substrate at frequencies between 1.0 GHz and 3.0 GHz, and to evaluate its performance in comparison to a square-shaped microstrip patch antenna (MPA). There are 2 groups in this study. Group 1 refers to the triangle-shaped Sierpinski fractal antenna with 26 samples and Group 2 refers to square shaped microstrip patch antenna with 26 samples. The confidence interval is 95%, the threshold is 0.05%, and the G Power value is 80%. The observed gain from the novel triangle-shaped Sierpinski fractal antenna has 27.3709 dB to 68.6663 dB and the gain of the square shaped MP A have -21.0381 dB to - 0.1775 dB. The novel triangle-shaped Sierpinski fractal antenna's best frequency for maximum gain was 1.5 GHz, with a significance level of around 0.000423 (p 0.05). In this work, it is observed that the gain of novel triangle-shaped Sierpinski fractal antenna is significantly better than square shaped microstrip patch antenna for implantable biomedical applications.
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