Detection of Parkinson39;s disease remains challenging due to the complexity and cost of diagnosis. Recently, different machine learning models have been proposed to detect Parkinson39;s. This paper proposes a nov...
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ISBN:
(纸本)9798350372977;9798350372984
Detection of Parkinson's disease remains challenging due to the complexity and cost of diagnosis. Recently, different machine learning models have been proposed to detect Parkinson's. This paper proposes a novel hybrid machine learning detection system to detect Parkinson's disease using a combination of both videos of freezing of gait and numerical sensor data. Three machine learning models (sensor model, video model, and a hybrid model using a combination of the data) have been developed and evaluated. Four machine learning algorithms were used for model development, which were Logistic Regression, K-neighbors, Bayesian Regression, and Random Forest. All the models were evaluated using standard performance metrics of accuracy and precision based on the prediction of 12 commonly used Parkinson's rating scales (Mini-Mental, NFo-GQ, H&Y, UPDRS-II, UPDRS-III, PIGD, Dyskinesia, HADS, HADS-A, HADS-D, FES-I, mini-BESTest). This is the first time to use all 12 rating scales as output of a machine learning model for Parkinson's disease detection. The results indicate that the hybrid model has a significantly better performance than the video or sensor models. The hybrid model achieved an average accuracy of approximately 94% for all twelve rating scales, while the sensor model had an accuracy of approximately 91% and the video model had an accuracy of approximately 89%. This research indicates that the hybrid model is accurate and reliable because of its ability to fully use two sets of clinical data to make a final decision.
Clustering is often utilised in wireless sensor networks to offer efficient and scalable operation. Typically, the goal of a clustering method is to produce a set of distinct groups that all meet a certain set of requ...
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Manual provisioning of infrastructure limited flexibility, scalability, and stability in distributed environments. The adoption of Infrastructure as Code (IaC) principles has dramatically enhanced deployment agility, ...
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Pressure transmitters, directly or indirectly, play a key role in many systems in nuclear reactors and industries worldwide. The Innovative sensor section(ISS) in Indira Gandhi Center for Atomic Research(IGCAR) develo...
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Blockchain technology is poised to transform the data storage and data interchange models. A blockchain is a distributed ledger of transactions stored across a network of nodes. This decentralized model provides immut...
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This paper presents a novel piezoelectrically driven, charge-sensing micro electric field sensor. The sensor operates by driving a beam that causes a shielding plate to oscillate periodically under an applied driving ...
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ISBN:
(纸本)9798350375145;9798350375138
This paper presents a novel piezoelectrically driven, charge-sensing micro electric field sensor. The sensor operates by driving a beam that causes a shielding plate to oscillate periodically under an applied driving voltage. When the sensor is placed in an external electric field, the periodic movement of the shielding electrode induces charges on the sensing electrode. These induced charges generate a measurable induced current in an external circuit, from which the magnitude of the external electric field can be deduced. Theoretical analysis combined with finite element simulation was first used to derive the theoretical output response of the sensor. The device was then fabricated using a commercial Silicon on Insulator (SOI) process. The proposed MEMS electric field sensor features a low driving voltage, effectively reducing power consumption. It also exhibits a linear output response and excellent sensitivity, greatly simplifying the sensor's signal processing circuitry and minimizing interference. These characteristics make it well-suited for industrial electric field measurement applications.
Wireless body area network (WBAN) is a unique technology where efficient energy operation is a key challenge in WBAN. The battery powered wireless sensor nodes are in large number that are spatially distributed nodes ...
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With the surge in distributed generation, particularly from renewable sources, Distribution System Operators (DSOs) face the challenge of evolving from traditional protection strategies to more proactive strategies. T...
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The proceedings contain 81 papers. The topics discussed include: research on distributedcomputing power scheduling based on ant path optimization;facial expression recognition in museum settings using an enhanced res...
ISBN:
(纸本)9798331530280
The proceedings contain 81 papers. The topics discussed include: research on distributedcomputing power scheduling based on ant path optimization;facial expression recognition in museum settings using an enhanced residual networks;LSD3K: a benchmark for smoke removal from laparoscopic surgery images;enhancing single image de-raining with a sparse spatial transformer;research on image recognition and intelligent management of accounting bills based on information technology;cloud-based flight test data management and application platform;design and simulation of biped robot based on ball screw;thinking about anti-drone strategies;RGembed: a knowledge graph embedding model integrating dual-prediction and graph attention networks;and improving the CodeGeeX model based on the relative convolutional multi-head attention method.
Integration complexity and intermittency of distributed energy resources (DERs) raise concerns about grid stability, security, and control of microgrids in various operating modes. MGs sustain reliability due to their...
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