A new classification approach for time-varying power quality (PQ) signals using ensemble classifiers (EC) is proposed in this paper. To achieve high performance, existing expert systems require several signal features...
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Despite improved therapies and decades of research over the last century, hypertension remained the world's leading avoidable cause of death. Machine learning (ML) is a subfield of artificial intelligence that int...
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Despite improved therapies and decades of research over the last century, hypertension remained the world's leading avoidable cause of death. Machine learning (ML) is a subfield of artificial intelligence that integrates computer science, statistics, and decision theory to recognize complicated patterns in vast amounts of data. Because hypertension may induce a rapid rise in blood pressure, artificial intelligence approaches are used to anticipate the start of hypertension the study's main goal is to use artificial intelligence technologies like deep learning (DL) and machine learning (ML) to predict hypertension. this research will analyze the current state, challenges, and potential of adopting technologies to detect and predict hypertension diseases. thus, AI-integrated hypertension care has the ability to revolutionize clinical practice by introducing individualized approaches to prevention and treatment, such as the establishment of optimal and patient-specific blood pressure (BP) goals, and the development of interventions focusing on modifiable risk factors. Prior research has showed that machine learning has the ability to improve all areas of patient care, from research and development to everyday clinical practice and global health. the purpose of this paper is to illustrate the potential of artificial intelligence for treating hypertension by analyzing existing evidence.
Functional near-infrared spectroscopy (fNIRS) is a non-invasive, low-cost method used to study the brain's blood flow pattern. Such patterns can enable us to classify performed by a subject. In recent research, mo...
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ISBN:
(纸本)9781728180229
Functional near-infrared spectroscopy (fNIRS) is a non-invasive, low-cost method used to study the brain's blood flow pattern. Such patterns can enable us to classify performed by a subject. In recent research, most classification systems use traditional machine learning algorithms for the classification of tasks. these methods, which are easier to implement, usually suffer from low accuracy. Further, a complex pre-processing phase is required for data preparation before implementing traditional machine learning methods. the proposed system uses a Bi-Directional LSTM based deep learning architecture for task classification, including mental arithmetic, motor imagery, and idle state using fNIRS data. Further, this system will require less pre-processing than the traditional approach, saving time and computational resources while obtaining an accuracy of 81.48%, which is considerably higher than the accuracy obtained using conventional machine learning algorithms for the same data set.
Low light image enhancement is one of the challenging tasks in computer vision, and it becomes more difficult when images are very dark. Recently, most of low light image enhancement work is done either on synthetic d...
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In this paper, a patternrecognition algorithm is given based on centroids of fuzzy hyper-pyramid numbers which are special type fuzzy n-cell numbers. the specific calculation formula (which can be easy calculated by ...
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Parkinson's Disease is a locomotive disorder commonly found among elders and causes various physical prodromes in an affected personnel. Freezing of Gait is a prominent symptom and gait data may be used to identif...
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Deep Affine Normalizing Flows are efficient and powerful models for high-dimensional density estimation and sample generation. Yet little is known about how they succeed in approximating complex distributions, given t...
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the proceedings contain 93 papers. the topics discussed include: comparative study between the different MPPT techniques;sliding mode observer design for battery state of charge estimation;effect of PEF and HVED on po...
ISBN:
(纸本)9781728155951
the proceedings contain 93 papers. the topics discussed include: comparative study between the different MPPT techniques;sliding mode observer design for battery state of charge estimation;effect of PEF and HVED on polyphenol extraction from pomegranate peels;EVs charging and discharging model consisted of EV users behavior;comparison between LCL and LLCL filters for a grid connected inverter using selective harmonic modulation;fault patternrecognition in power distribution integrated network with renewable energy source;enhancing small-signal stability of intermittent hybrid distributed generations;modeling of the lightning current on towers connected to a malt grid;and thermal performance and adherence of local materials to the Moroccan building code requirements.
the power grid lines and equipment maintenance of power enterprises is a complicated construction process, the cost of which is affected by meteorological and geographical factors, and the influence mode is uncertain....
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the power grid lines and equipment maintenance of power enterprises is a complicated construction process, the cost of which is affected by meteorological and geographical factors, and the influence mode is uncertain. Using fuzzy clustering method and the threshold intervals of the objective function in clusters, this paper builds a predictive control model to control the project cost. this model uses relative fuzzy operator to build fuzzy matrix, construct correlation between factors, and describe the factors' effect. Extracting the cluster's eigenfunction, and defining the boundaries of various clusters, we determined the type of the predicted points and the range of the objective function. When the actual cost of the maintenance project is within the range calculated by the cost model, then it is normal. If the actual cost exceeds this range, then further analysis of all the aspects of the cost is needed to find out the reason.
Human pose estimation has drawn much attention recently, but it remains challenging due to the deformation of human joints, the occlusion between limbs, etc. And more discriminative feature representations will bring ...
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