AIMC Topic: Algorithms

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A Parametric Design Method for Optimal Quick Diagnostic Software.

Sensors (Basel, Switzerland)
Fault diagnostic software is required to respond to faults as early as possible in time-critical applications. However, the existing methods based on early diagnosis are not adequate. First, there is no common standard to quantify the response time o...

Synthesizing Supervision for Learning Deep Saliency Network without Human Annotation.

IEEE transactions on pattern analysis and machine intelligence
Recently, the research field of salient object detection is undergoing a rapid and remarkable development along with the wide usage of deep neural networks. Being trained with a large number of images annotated with strong pixel-level ground-truth ma...

Streamlining Quality Review of Mass Spectrometry Data in the Clinical Laboratory by Use of Machine Learning.

Archives of pathology & laboratory medicine
CONTEXT.—: Turnaround time and productivity of clinical mass spectrometric (MS) testing are hampered by time-consuming manual review of the analytical quality of MS data before release of patient results.

Robust water-fat separation for multi-echo gradient-recalled echo sequence using convolutional neural network.

Magnetic resonance in medicine
PURPOSE: To accurately separate water and fat signals for bipolar multi-echo gradient-recalled echo sequence using a convolutional neural network (CNN).

Whale optimized mixed kernel function of support vector machine for colorectal cancer diagnosis.

Journal of biomedical informatics
Microarray technique is a prevalent method for the classification and prediction of colorectal cancer (CRC). Nevertheless, microarray data suffers from the curse of dimensionality when selecting feature genes of the disease based on imbalance samples...

Deep Learning for Sensor-Based Rehabilitation Exercise Recognition and Evaluation.

Sensors (Basel, Switzerland)
In this paper, a multipath convolutional neural network (MP-CNN) is proposed for rehabilitation exercise recognition using sensor data. It consists of two novel components: a dynamic convolutional neural network (D-CNN) and a state transition probabi...

Recognizing Physical Activity of Older People from Wearable Sensors and Inconsistent Data.

Sensors (Basel, Switzerland)
The physiological monitoring of older people using wearable sensors has shown great potential in improving their quality of life and preventing undesired events related to their health status. Nevertheless, creating robust predictive models from data...

Cell Identity Codes: Understanding Cell Identity from Gene Expression Profiles using Deep Neural Networks.

Scientific reports
Understanding cell identity is an important task in many biomedical areas. Expression patterns of specific marker genes have been used to characterize some limited cell types, but exclusive markers are not available for many cell types. A second appr...

Software Development Effort Estimation Using Regression Fuzzy Models.

Computational intelligence and neuroscience
Software effort estimation plays a critical role in project management. Erroneous results may lead to overestimating or underestimating effort, which can have catastrophic consequences on project resources. Machine-learning techniques are increasingl...

Multifocus Image Fusion Using Wavelet-Domain-Based Deep CNN.

Computational intelligence and neuroscience
Multifocus image fusion is the merging of images of the same scene and having multiple different foci into one all-focus image. Most existing fusion algorithms extract high-frequency information by designing local filters and then adopt different fus...