At present, deep learning is being used to compute data related to medical images with the purpose of improving disease-related research. Deep learning based medical image analysis includes various important tasks suc...
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ISBN:
(数字)9798350364729
ISBN:
(纸本)9798350364736
At present, deep learning is being used to compute data related to medical images with the purpose of improving disease-related research. Deep learning based medical image analysis includes various important tasks such as medical image reformation, quality enhancement in different types of images, segmentation and classification of images (computer-Aided Detection) CAD, registration to distinguish between normal cells and tumor cells. This article focuses on the hybrid approach for analyzing medical images that makes use of both clustering and classification via k-mean, VGG, RNN) techniques on different diseases datasets like cardio-vascular diseases, breast cancer and brain disease dataset. This paper utilizes predictive analytics in medical image data which is spread across a combination of clustering and classification. The objective of this study is to demonstrate that integrating these two methods will help improve accuracy as well as save time in diagnosing illnesses from image data by avoiding the process where labeling the data for feature detection takes place. Furthermore, this paper aims at presenting an overview of the methodology employed during investigation along with results emanating from this analysis highlighting how predictive analytics can be applied in healthcare.
Based to statistics, about 80 percent of the population follows incorrect sitting posture. From the year 2020, due to the Covid-19 curfew, the traditional work office of many professionals turned into Work from home a...
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The smart grid seamlessly integrates modern control, communication, and metering technologies, revolutionising the capture of large-scale, multi-type, high-dimensional data about electric power grid operations. Even w...
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A patient's therapy options and prognosis are both improved by detecting and diagnosing a brain tumor early. Brain tumors can be located and diagnosed with the help of magnetic resonance imaging (mRI). In real pra...
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(6G) conversation networks promise extra pace, coverage and capabilities than the current era (5G). To fulfill this promise, deep getting to know techniques can be used to allow better useful resource allocation choic...
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At present, the speedy growth of natural-language-processing(NLP) has produced considerable evolution for the topic of machine-translation. There have been many deep neural network-based machine-translation(mT) method...
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Recommendation systems have become increasingly important in various online applications. One of the significant types of research in machine learning (mL) is recommendation systems. A recommender system, alternativel...
Recommendation systems have become increasingly important in various online applications. One of the significant types of research in machine learning (mL) is recommendation systems. A recommender system, alternatively referred to as a recommendation system, is an information filtering system that supports users in identifying their “rating” or “preference” for an object and makes predictions based on that rating or preference. This research analyzes current recommendation systems applied in mL public instances to explore knowledge discovery. The suggestions systems are developed for musical, online dating, and restaurant applications. The recommendation system serves as a tool to assist users in discovering what they are interested in by providing them with appropriate ideas. To provide personalized recommendations to users, primarily employ collaborative, content-based, session-based, demographic, and hybrid filtering. Classification, clustering, and association rule discovery are the critical data mining and mL techniques most widely employed in recommendation systems. In general, the accuracy of the recent models lies at (75%, 99 % ) and the error rate occurs at (5%, 25 % ) for the mL public instances.
The paper "Delving into the Intricacies and Nuances of modern Nested Network Systems" investigates a novel approach to solving the problems inherent in today’s nested network systems. The suggested techniqu...
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People today see social networking as an essential part of business, communication, and getting information from one place to another. Each of these applications produces a lot of information sharing. many large organ...
People today see social networking as an essential part of business, communication, and getting information from one place to another. Each of these applications produces a lot of information sharing. many large organizations spend a lot of time processing, analyzing, and looking for patterns before deciding. This research analyzes the different recommender systems used in online social networks that help organizations make better decisions since a vast amount of information is generated daily. When the number of social networks continues to grow, it is expected to have a recommender system that gives more accurate and reliable results. This research also aims to analyze and compare the different design challenges of using the recommender system in social network applications. This research looks at and compares different datasets for recommender systems to find problems and possible opportunities in how they are designed. The analysis and challenges of distinct large-scale online social networks with the heterogeneity of information and structures are also addressed in this research.
State of mind is essential in every person's life to keep their life stable, calm and peaceful. The key factors that contribute towards them are mood, emotion and thoughts. monitoring them is key to a person's...
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