Memory and other mental processes are both severely disrupted by Alzheimer's disease (AD), a neurodegenerative condition. It interferes with almost every cognitive process that the brain is capable of. This ultima...
Memory and other mental processes are both severely disrupted by Alzheimer's disease (AD), a neurodegenerative condition. It interferes with almost every cognitive process that the brain is capable of. This ultimately results in a smaller and less volumetric brain as time passes. There is currently no treatment that can reverse the effects of Alzheimer's disease. The only way for early detection and therapy to be possible is for the symptoms to be recognized promptly. Throughout the years, there has been a significant amount of research conducted on the application of machine learning to the diagnosis and classification of Alzheimer's disease. The performance of linear, polynomial, and RBF (Radial Basis Function) classification kernels in the context of AD classification using the Support Vector Machine (SVM) with the Principal Component Architecture (PCA) is investigated in this study. As a result, a framework is provided for the categorization of AD, which is comprised of the following steps: data input, model building, hyperparameter tuning, prediction on test data, performance evaluation, and selection of the most effective SVM kernel model and PCA for dimension reduction. ADNI and the Alzheimer's MRI Preprocessed dataset that is available on Kaggle are used to conduct an evaluation of the proposed framework. The performance of the model is evaluated using accuracy measure for both the datasets. Across both datasets, the SVM linear kernel with PCA achieved the maximum accuracy, which was 99.99 percent.
The cell of today is a typical instrument. It has become a significant "social item," as opposed to only a minor "specialized object," in the regular daily existences of its users. In this article,...
The cell of today is a typical instrument. It has become a significant "social item," as opposed to only a minor "specialized object," in the regular daily existences of its users. In this article, we look at a few ongoing improvements in the examination of mobile phone data. With the developing openness of enormous, anonymised datasets, this field of concentrate previously seemed decade prior and has since formed into an independent subject. The estimating investigation of record framework utilization for a gathering of mobile gadget users in different spots is introduced in this work. The purpose of this research is to refute the notion that mobile system behaviour can be modelled as being uniform across different environments. The research demonstrates that when participants travel between places, they obtain statistically substantially diverse sets of data. These findings call into question how mobile computing systems are normally evaluated, which often models a user's behaviour as being comparable to her behaviour at a single place over a longer length of time.
Silicosis, a debilitating occupational lung disease caused by inhaling crystalline silica, continues to be a significant global health issue, especially with the increasing use of engineered stone (ES) surfaces contai...
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Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtainedwith several modalities such as magnetic resonance (MR) and confocal microscopy need to be classified ...
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The use of IoT technology in healthcare systems has been adopted in patient monitoring because IoT technology has been adopted rapidly in the systems. This paper discusses on design and implementation of an IoT integr...
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ISBN:
(数字)9798350388916
ISBN:
(纸本)9798350388923
The use of IoT technology in healthcare systems has been adopted in patient monitoring because IoT technology has been adopted rapidly in the systems. This paper discusses on design and implementation of an IoT integrated smart assistive health care system to enhance the precision and speed of patient health assessment. In the proposed system, wearable sensors are used to capture respiratory rate, pulse rate, blood pressure, oxygen saturation, and body temperature with data to be sent to the cloud server as and when it is received. Some of the features are, however, small data transmission delays, high accuracy in the physiological signals and long durations of monitoring due to the low power consumption. The simulations showed that the response time falls below 500ms, and physicians retain up to a 2 % variance to clinical grade devices, which actualized robust and effective patient care. The incorporation of predicting capacities, thus providing ways of early identification of health complications, makes the system applicable in chronic disease treatments and telemedicine.
The study explores the intersection of robotics and neuro-cognitive rehabilitation to offer personalized assistance for individuals with neurological disorders, emphasizing autism spectrum disorder (ASD). The proposed...
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ISBN:
(数字)9798350372748
ISBN:
(纸本)9798350372755
The study explores the intersection of robotics and neuro-cognitive rehabilitation to offer personalized assistance for individuals with neurological disorders, emphasizing autism spectrum disorder (ASD). The proposed methodology integrates Genetic Algorithms (GAs) to optimize the performance of robotic interventions. The data collection phase involves compiling a comprehensive image dataset capturing facial expressions, gestures, and relevant visual cues associated with ASD. Leveraging robotic platforms designed for therapeutic interventions, feature extraction techniques identify intricate patterns within the data. Advanced algorithms, including GAs, classify the dataset into positive (ASD) and negative (non-ASD) categories. The framework introduces a Diagnosis Matrix for enhanced diagnostic precision, correlating observed robotic interactions with clinical assessments. An Ontology Knowledge base adapts responses based on evolving patient needs. The proposed method surpasses all others, achieving an accuracy of 95.08% and demonstrating superior precision, recall, and F1-score metrics. This indicates the efficacy of the proposed approach in achieving a well-balanced performance with high accuracy and robustness in correctly identifying positive instances. The results underscore the potential of the proposed method for classification tasks, showcasing its superiority in comparison to traditional SVM, CNN, and even a well-established deep learning architecture like VGG-16. The Receiver Operating Characteristic curve validates the model's discriminatory power.
In this work, we evaluate the effect of variation of mole fraction of Tin in doped titanium oxide on band gap. To achieve this, we resort to Matminer's "dielectric_constant"dataset and apply an algorithm...
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In Industry 5.0, the workers' well-being is the central pillar. Therefore, research on methods and technics to improve the workers' user experience in a human-robot collaborative environment is necessary. Whil...
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This paper introduces an advanced charging control system for a Multiple Impact Water Wheel (MIWW) used in pico-hydropower generation. The control system maximizes energy conversion efficiency by employing a Maximum P...
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ISBN:
(数字)9798350375589
ISBN:
(纸本)9798350375596
This paper introduces an advanced charging control system for a Multiple Impact Water Wheel (MIWW) used in pico-hydropower generation. The control system maximizes energy conversion efficiency by employing a Maximum Power Point Tracking (MPPT) algorithm, which dynamically adjusts the system's operation based on real-time voltage and current measurements. Unlike traditional methods that rely on monitoring rotational speed, this approach simplifies the system by eliminating the need for speed sensors, thereby reducing costs and enhancing reliability. The control system is structured into four main operational phases: the water turbine acceleration phase, optimal control (MPPT) phase, drooping characteristic suppression phase, and overvoltage protection phase. Each phase is designed to optimize distinct aspects of system performance, ensuring stable and efficient power generation under varying water flow conditions. Experimental validation was conducted by subjecting the system to three different water pressure levels (0.036 MPa, 0.048 MPa, and 0.042 MPa). The results demonstrate that the system successfully maintained stable voltage and current, even during rapid fluctuations in water pressure, confirming the effectiveness of the proposed MPPT algorithm. By addressing the challenges of droop characteristics and overvoltage conditions, the control system ensures the reliability and efficiency of pico-hydropower systems. This research contributes to the development of cost-effective, efficient, and sustainable energy solutions, especially in regions lacking conventional power infrastructure.
Indoor relocalization is vital for both robotic tasks like autonomous exploration and civil applications such as navigation with a cell phone in a shopping mall. Some previous approaches adopt geometrical information ...
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