Abstract : Present a newborn class of an operator (℘≀,ϱ)-Paranormal and (℘≀,ϱ)-*-Paranormal Composition, Weighted Composition and Composite Multiplication operators on L^2 space are characterized. Also characterized (℘,≀,ϱ)-paranormal and (℘,≀,ϱ)-*-paranormal operators on Hardy space..
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Abstract : In today's modern world, the Internet is highly crucial. A larger number of wired and wireless sensors characterize the Internet of Things (IoT). These sensors are networked devices that create massive amounts of data, reaction time, latency, and security vulnerabilities. These problems create delay in decision making in some situation which is very extreme in case of Healthcare, Smart vehicles, Industry 4.0 etc., the data moving from the sensor devices have to reach the cloud and from there it has to reach back to receivers. Therefore, in order deal with such huge data we use fog computing, a well-known distributed architecture. Fog computing works with the aim to enhance the processing, intelligence, and accumulation of data closer to the Edge devices. The proposed framework helps in reduce latency as we place a Fog node device between the cloud and the edge device where data is generated and sent to the cloud and retrieved from the cloud. The framework is designed in such way that the different sensor devices can be placed on it and collects the sensor data from them. To test the framework functioning, without the fognode and with the fognode, comparing the latency with packet transfer rate from sensor devices to the cloud and vice versa. In this paper, we are considering two different case studies to test the proposed functioning of the framework with the following case studies i) Related to medical are like diabetes and cardiovascular illnesses are considered for prediction based on patient health records and ii) Vehicle theft where the owner get an alert of the theft of the vehicle within few seconds of the theft. In Medical case study initially, patient health data is acquired and stored using Sensor devices. A group of patient health records is first subjected to the unique rule-based clustering technique. Finally, we consider diabetes and cardiovascular diseases in our study. To test the performance of the suggested task, a rigorous experiment and study using healthcare data were conducted. Various Machine Learning (ML) algorithms are applied to patient health information, and the prediction result, as well as the accuracy of each algorithm, is generated. The findings show that the proposed work accurately predicts cardiac and diabetes problems, identifies which algorithm has the highest accuracy among them, and reduces burden on cloud. Devices having inbuilt sensors used in this work are pulse oximeter, smart watch, glucometer and thermometer. In Vehicle theft case study, there are numerous anti-theft solutions for smart vehicles based on GSM and cloud computing. The previous models have excessive latency and bandwidth. Fog computing is a creative new concept that combines existing solutions with GPS and GSM. IoT development for a fog- based vehicle anti-theft technology that uses a mobile application to pinpoint the exact position of a stolen vehicle. Low latency, low bandwidth, fast responsiveness, and high security are all advantages of fog computing. Thus with the use of the above proposed framework can reduce the latency and bandwidth in these applications and helps to avoid the delay decision making. Further this framework can be used to analyze and improve the performance and consumption of power in smart cities, smart homes and smart appliances..
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Abstract : The use of credit cards has grown significantly as the globe moves quickly toward digitization and cashless transactions. Additionally, there have been more fraud-related operations, which costs financial institutions a great deal of money. As a result, we must examine and distinguish between fraudulent and legitimate transactions. In this study, we wanted to implement the comprehensive model training procedure from beginning to end. As a result, we will have the best possible model to categorize the transaction into normal and aberrant varieties. Machine learning methods have been used in the fight against credit card fraud; however, fraud detection systems have yet to prove particularly effective. The relatively breakthrough of deep learning has been used in various fields to address complex challenges. In this study, we investigate several models for machine learning to spot fraudulent credit card activity. We compare the results produced by each model as well as their performance. The SMOTE methodology yields the most successful outcomes. It has been argued that under-sampling the majority class (the normal class) could effectively improve a classifier's sensitivity to the minority class. This research shows that, compared to just undersampling the majority class, the procedure we chose for oversampling the lower (abnormal) standard and undersampling the upper (normal) standard can enhance the analyzer's conduct..
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Abstract : Warehouse automation is the automating practice of inventory control in-and-out of warehouses for consumers with zeroed human support. An enterprise can exclude labor-intensive costs which implicate Intelligent Storage System (ISS), Clustering and Racking Systems (CRS), and data analytics. The enterprise can run CRS manually, but it is extremely complex and can lead to an error-prone. This paper thus introduces the computer vision, upon Artificial Intelligence (AI) based training models with digital images and deploys to on-the-site devices that detect and interpret visual perception of products from cameras. Machine learning with visual AI recorded images of containers/products is used to equip the system with cognitive skills in the warehouse. In brief, it recognizes what to place into the warehouse, what checks out, where it is its cluster, and where it may have been relocated. This helps prevent problems arise in the racking system, where it could over-sit beyond expiry and become worthless. Both supervised and unsupervised training approaches for CRS with digital images are simulated. Finally, as a result, the computer vision speedily tracks warehouse products regardless of RFID tags, which subsides the limitations of RFID tags like vanished tags, imperfectly assigned tags, broken or non-functioning tags and the cost of re-issue tags..
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Abstract : Many blind people around the world have had challenges reading books. To help them solve their problem and not depend on others, a writing method was developed by the inventor Louis Braille known as the Braille writing method. But with the development of technologies and computers and the need for an easier and less expensive way to help the blind solve the problem of reading any manuscript, even handwritten, and converting it into audio output. Therefore, in this paper we propose to design and implement a smart gara system to assist the visually impaired using a raspberry pi connected to a webcam interfaced that takes a picture of the text. With the use of OCR technology to convert an image to text through optical character recognition and extract text information from the image and convert it into speech. Then the sound is output through the speakers and amplifier. Whereas, the raspberry pi contains OCR (Optical Character Recognition) and a text to speech conversion unit (TTS engine), where the first performs optical character recognition and extracts text from the image, while the second converts it into speech and outputs it with headphones..
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