This paper addresses the independent assumption issue in fusion process. In the last decade, dependency modeling techniques were developed under a specific distribution of classifiers or by estimating the joint distribution of the posteriors. This paper proposes a new framework to model the dependency between features without any assumption on feature/classifier distribution, and overcomes the difficulty in estimating the high-dimensional joint density. In this paper, we prove that feature dependency can be modeled by a linear combination of the posterior probabilities under some mild assumptions. Based on the linear combination property, two methods, namely, Linear Classifier Dependency Modeling (LCDM) and Linear Feature Dependency Modeling (LFDM), are derived and developed for dependency modeling in classifier level and feature level, respectively. The optimal models for LCDM and LFDM are learned by maximizing the margin between the genuine and imposter posterior probabilities. Both synthetic data and real datasets are used for experiments. Experimental results show that LCDM and LFDM with dependency modeling outperform existing classifier level and feature level combination methods under nonnormal distributions and on four real databases, respectively. Comparing the classifier level and feature level fusion methods, LFDM gives the best performance.
classify subjects with or without NAFLD, followed by applying the established cut-off of FIB-4 score on each subgroup to include patients with advanced fibrosis for treatment recommendations (Figure 1). 8 On the other hand, liver stiffness was assessed by transient elastography; a liver stiffness ≥9.6 kPa was classified as advanced fibrosis. Among the 392 subjects with a NAFLD ridge score ≥0.3, 323 had valid liver stiffness measurement (LSM) and 8 (2.5%) had LSM ≥9.6 kPa; 436 out of 530 subjects had valid LSM with a NAFLD ridge score <0.3; 7 (1.6%) had a liver stiffness ≥9.6 kPa. FIB-4 score is useful to screen patients at high risk of advanced fibrosis among suspected NAFLD patients, but probably less satisfactory in this population screening cohort with low prevalence of advanced fibrosis.We suggest further diagnostic testing for fibrosis, namely transient elastography, with or without a serum-based test.
The Amputees are prone to physical and mental problems. Due to the loss of their body parts, they mostly rely on others to perform their daily activities. In order to facilitate and improve the quality of life with independence and freedom to the amputees, prosthesis was developed. The notion for a low-cost microcontroller-based prosthetic arm is presented in this project. The idea is to change the perception of remote controls for actuating manually operated Prosthetic Arm. Well, this project provides a way to eradicate the buttons, joysticks and replace them with other advanced techniques that controls the complete Prosthetic Arm by the user gesture, voice command, flex sensors. The human commands are sensed with the help of a Bluetooth and flex sensors. A microcontroller is used in the transmitter section. It is coded in such a way that the required actions for the human gestures are done. These sensed signals are processed and then transmitted to the Arm at the receiver section. Thusthe prosthetic arm performs the required movement. Prosthesis is able to achieve actions like fully hold or release objects, bi-digital gripping pincer, or postures like thumb up and pointing with the index finger. Many studies use voice recognition to control medical devices and hand prostheses inreal-time, but its use has some limitations. Furthermore, somestudies take advantage of inertial sensors to control the hand prostheses. By reviewing the advantages and limitations for each control method, our project proposes a new synchronized control system, that combines voice recognition, inertial measurement, flex sensors based combination strategies to render the prosthetic hand more feasible, and easy to use
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.