A Quantized Kernel Learning Algorithm Using a Minimum Kernel Risk-Sensitive Loss Criterion and Bilateral Gradient Technique

Luo, Xiong; Deng, Jiankang; Wang, Weiping; Wang, Long; Zhao, Wenbing

doi:10.3390/e19070365

Cited by 38 publications

(17 citation statements)

References 43 publications

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“…With the supervised autonomous system design, we believe machine learning and artificial intelligence [161][162][163][164][165], in conjunction with multimodal sensing, will play much more important roles in the next generation autism diagnosis and treatment programs. Considering that good therapists are always in very high demand for the autism population, we envisage that a therapist could operate multiple supervised autonomous systems remotely at different sites, which could benefits more children with ASD while reducing the cost of autism care.…”

Section: Discussionmentioning

confidence: 99%

Technology-Facilitated Diagnosis and Treatment of Individuals with Autism Spectrum Disorder: An Engineering Perspective

Liu

Zhao

et al. 2017

Applied Sciences

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Abstract:The rapid development of computer and robotic technologies in the last decade is giving hope to perform earlier and more accurate diagnoses of the Autism Spectrum Disorder (ASD), and more effective, consistent, and cost-conscious treatment. Besides the reduced cost, the main benefit of using technology to facilitate treatment is that stimuli produced during each session of the treatment can be controlled, which not only guarantees consistency across different sessions, but also makes it possible to focus on a single phenomenon, which is difficult even for a trained professional to perform, and deliver the stimuli according to the treatment plan. In this article, we provide a comprehensive review of research on recent technology-facilitated diagnosis and treat of children and adults with ASD. Different from existing reviews on this topic, which predominantly concern clinical issues, we focus on the engineering perspective of autism studies. All technology facilitated systems used for autism studies can be modeled as human machine interactive systems where one or more participants would constitute as the human component, and a computer-based or a robotic-based system would be the machine component. Based on this model, we organize our review with the following questions: (1) What are presented to the participants in the studies and how are the content and delivery methods enabled by technologies? (2) How are the reactions/inputs collected from the participants in response to the stimuli in the studies? (3) Are the experimental procedure and programs presented to participants dynamically adjustable based on the responses from the participants, and if so, how? and (4) How are the programs assessed?

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Section: Discussionmentioning

confidence: 99%

Technology-Facilitated Diagnosis and Treatment of Individuals with Autism Spectrum Disorder: An Engineering Perspective

Liu

Zhao

et al. 2017

Applied Sciences

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“…This shows that it is challenging to design the activity detection algorithm that works in a crowded room with unpredictable orientation of the user. We plan to enhance our current algorithm with a machine-learning based approach [9,[41][42][43][44] by utilizing all joints in the upper extremity. Question 11 is: "If so, was this a problem for you?"…”

Section: Usability Survey Resultsmentioning

confidence: 99%

Design, Implementation, and Field Testing of a Privacy-Aware Compliance Tracking System for Bedside Care in Nursing Homes

Zhao

Reinthal

et al. 2017

ASI

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Lower back musculoskeletal disorders are pervasive in workplaces. In the United States alone, the total cost of such injuries exceed $100 billion a year. The lower-back injury rate in the healthcare sector is one of the highest among all industry sectors. A main risk factor for lower-back injuries is the use of improper body mechanics when doing lifting and pulling activities. In healthcare venues, nursing homes in particular, nursing assistants are on the front line to take care of patients. Even in places where ceiling-mounted lifting equipment is installed, they are still required to handle the patient for bedside care, such as sliding the sling underneath the patient, scooping up the patient, putting on compression socks, etc. To help nursing assistants get into the habit of using proper body mechanics, we designed and implemented a privacy-aware compliance tracking system (PACTS). PACTS can track a nursing assistant for possible violation of proper body mechanics while doing bedside care and provide realtime feedback via a smart wearable device such as a smart watch worn by the nursing assistant. The system was deployed in a local nursing home for an 80-day field study in six rooms with seven participating nursing assistants. The test exposed several issues with the original design of the system. The primary issue is how to balance the privacy requirement and the usability of the system. Overemphasizing the former would negatively impact the latter. This issue is partially resolved with a leasing mechanism where the system would automatically register a nursing assistant within the lease period once she or he has manually registered with the system.

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“…If one food source cannot be improved in continuous Limit iterations, the exploiting bee related to this food source will be changed into a scouter. In this situation, the food source is abandoned and the scouter search food as equation (16) x…”

Section: Swarm Intelligence Techniquesmentioning

confidence: 99%

A wireless sensor data-based coal mine gas monitoring algorithm with least squares support vector machines optimized by swarm intelligence techniques

Chen

Xie

Jin

et al. 2018

International Journal of Distributed Sensor Networks

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As the integral part of the new generation of information technology, the Internet of things significantly accelerates the intelligent sensing and data fusion in different industrial processes including mining, assisting people to make appropriate decision. These days, an increasing number of coal mine disasters pose a serious threat to people's lives and property especially in several developing countries. In order to assess the risks arisen from gas explosion or gas poisoning, wireless sensor data should be processed and classified efficiently. Due to the fact that the ''negative samples'' of coal mine safety data are scarce, least squares support vector machine is introduced to deal with this problem. In addition, several swarm intelligence techniques such as particle swarm optimization, artificial bee colony algorithm, and genetic algorithm are applied to optimize the hyper parameters of least squares support vector machine. Using the popular deep neural networks, convolutional neural network and long short-term memory model, as comparisons, a number of experiments are carried out on several UCI machine learning datasets with different features. Experimental results show that least squares support vector machine optimized by swarm intelligence techniques can effectively handle classification task on different datasets especially on those datasets with limited samples and mixed attributes. The application of least squares support vector machine optimized by swarm intelligence techniques on real coal mine data demonstrates that this algorithm can process the data accurately and timely, therefore can warn of the accidents early in mining workplace.

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A Quantized Kernel Learning Algorithm Using a Minimum Kernel Risk-Sensitive Loss Criterion and Bilateral Gradient Technique

Cited by 38 publications

References 43 publications

Technology-Facilitated Diagnosis and Treatment of Individuals with Autism Spectrum Disorder: An Engineering Perspective

Technology-Facilitated Diagnosis and Treatment of Individuals with Autism Spectrum Disorder: An Engineering Perspective

Design, Implementation, and Field Testing of a Privacy-Aware Compliance Tracking System for Bedside Care in Nursing Homes

A wireless sensor data-based coal mine gas monitoring algorithm with least squares support vector machines optimized by swarm intelligence techniques

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