One of the many essential elements of life is one's health. Our healthcare systems has to possess to stay as secure as well as successful as it can be hoping for a healthier society. The establishment of sophistic...
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ISBN:
(数字)9798350366846
ISBN:
(纸本)9798350366853
One of the many essential elements of life is one's health. Our healthcare systems has to possess to stay as secure as well as successful as it can be hoping for a healthier society. The establishment of sophisticated medical facilities which improve tracking of patients evaluation, and customized therapy has been brought feasible by the Internet Of Things(IoT), which is having an enormous impact upon the healthcare sector. In addition, healthcare facilities, pharmaceutical companies, diagnostic centres, and doctors might freely trade and maintain information and data about patients effectively with the use of blockchain-based technology. Blockchain-based tools are adequate to build confidence and recognized and access incorrect information provided by doctors. Blockchain innovations may therefor help with the privacy, efficiency, safety, and visibility of health related information trade in the medical sector. Through the help of such technology, hospitals can get precise as superior knowledge that will improve client analysis of data and help with the efficient handling of illness. Substantial studies and evaluations has been done to look into every potential as the use of blockchain technology possess to improve the current situation of the healthcare in general. The study seeks to provide an overview of the current state and advancement in the industries which promotes IoT- based healthcare systems. As a result, the study additionally explores into the possibilities of merging healthcare sensors, wearable gadgets, and various other modern medical tools that enable immediate time gathering of information. The article additionally dicusses the dangers and weakness that exist in that plag in IoT-based healthcare systems as well as its features, and also in addition to the results and remedies which currently exist to fix such issues and problems while safeguarding private information and data about patients. Lastly, this presents as well as examines and array for a
This research investigates the use of Fuzzy Set Theory and the Kappa Co-efficient in diagnosing urological disorders, with a special focus on utilizing the prostate cancer dataset received from Kaggle. The dataset com...
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ISBN:
(数字)9798350354379
ISBN:
(纸本)9798350354386
This research investigates the use of Fuzzy Set Theory and the Kappa Co-efficient in diagnosing urological disorders, with a special focus on utilizing the prostate cancer dataset received from Kaggle. The dataset comprises medical records from 100 patients, each with 10 variables. The data underwent several preprocessing steps, including cleaning, normalization, and encoding of categorical variables. Utilizing a Fuzzy Inference System (FIS) was used in order to convert input variables into fuzzy sets using membership functions and generate fuzzy rules. These rules were incorporated into the Mamdani theoretical framework to produce fuzzy outputs, which were then defuzzified into crisp values. The evaluation of the FIS was conducted using many criteria, such as accuracy, precision, recall, and F1-score. The study compared the FIS with Naive Bayes, TensorFlow, and Keras models. The results indicated that the FIS achieved superior performance, with an accuracy of 99%, outperforming the other models. The Kappa Co-efficient was also used to assess inter-rater agreement. This research demonstrates the effectiveness of Fuzzy Set Theory in diagnosing prostate cancer and highlights its potential for broader applications in medical diagnostics.
Diminishing the appearance of a fence in an image is a challenging research area due to the characteristics of fences(thinness, lack of texture, etc.) and the need for occluded background restoration. In this paper, w...
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Diminishing the appearance of a fence in an image is a challenging research area due to the characteristics of fences(thinness, lack of texture, etc.) and the need for occluded background restoration. In this paper, we describe a fence removal method for an image sequence captured by a user making a sweep motion, in which occluded background is potentially observed. To make use of geometric and appearance information such as consecutive images, we use two well-known approaches: structure from motion and light field rendering. Results using real image sequences show that our method can stably segment fences and preserve background details for various fence and background combinations. A new video without the fence, with frame coherence, can be successfully provided.
A novel concept of a measurement technology for the localization and determination of the size of gas bubbles is presented, which is intended to contribute to a further understanding of the dynamics of efficiency-redu...
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We study a class of convex-concave saddle-point problems of the form minx maxy(Kx, y) + fP(x) - h∗(y) where K is a linear operator, fP is the sum of a convex function f with a Lipschitz-continuous gradient and the ind...
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Wearable IoT devices that can register and transmit human voice can be invaluable in personal situations, such as summoning assistance in emergency healthcare situations. Such applications would benefit greatly from a...
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Cloud computing has revolutionized the information technology era. It offers high-speed computing, storage, and ICT resources on demand. One of the significant challenges in cloud computing is the impact of impatient ...
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We study the problem of approximate sampling from non-log-concave distributions, e.g., Gaussian mixtures, which is often challenging even in low dimensions due to their multimodality. We focus on performing this task ...
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This study aims to optimise computing for intricate jobs within the overlapping coverage of 6G network base stations. A multi-access edge computing network model is created by solving the issues of task offloading. Th...
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This paper presents an application of a mixture of Hidden Markov Models (HMMs) as a tool for verification of IoT fuel sensors. The IoT fuel sensors report the level of fuel in tanks of a petrol station, and are a key ...
This paper presents an application of a mixture of Hidden Markov Models (HMMs) as a tool for verification of IoT fuel sensors. The IoT fuel sensors report the level of fuel in tanks of a petrol station, and are a key component for monitoring system reliability (billing), safety (fuel/oil leak detection) and security (theft prevention). We propose an algorithm for learning a mixture of HMMs based on a continual learning principle, i.e. it adapts the model while monitoring a sensor over time, signalling unexpected or anomalous sensor reports. We have tested the proposed approach on a real-life data of 15 fuel tanks being monitored with the FuelPrime system, where it has shown a very good performance (average area under ROC curve of 0.94) of detecting anomalies in the sensor data. Additionally we show that the proposed method can be used for trend monitoring and present qualitative analysis of the short and long term learning performance. The proposed method has promising performance score, the resulting model has a high degree of explainability, limited memory and computation requirements and can be easily generalized to other domains of sensor verification.
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