Smart Artificial Firefly Colony Algorithm‐Based Support Vector Regression for Enhanced Forecasting in Civil Engineering

Chou, Jui‐Sheng; Pham, Anh-Duc

doi:10.1111/mice.12121

Cited by 105 publications

(44 citation statements)

References 72 publications

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“…Thus, the aim of the learning is to find the optimal hyperplane between training data for all classes. Kernel functions can be used to map the training set from input space to a high level feature space (Chou and Pham, 2015). Support vectors are the data points that lie closest to the decision surface.…”

Section: Support Vector Machinementioning

confidence: 99%

Imaging and machine learning techniques for diagnosis of Alzheimer’s disease

Mirzaei

Adeli

2016

Reviews in the Neurosciences

105

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AbstractAlzheimer’s disease (AD) is a common health problem in elderly people. There has been considerable research toward the diagnosis and early detection of this disease in the past decade. The sensitivity of biomarkers and the accuracy of the detection techniques have been defined to be the key to an accurate diagnosis. This paper presents a state-of-the-art review of the research performed on the diagnosis of AD based on imaging and machine learning techniques. Different segmentation and machine learning techniques used for the diagnosis of AD are reviewed including thresholding, supervised and unsupervised learning, probabilistic techniques, Atlas-based approaches, and fusion of different image modalities. More recent and powerful classification techniques such as the enhanced probabilistic neural network of Ahmadlou and Adeli should be investigated with the goal of improving the diagnosis accuracy. A combination of different image modalities can help improve the diagnosis accuracy rate. Research is needed on the combination of modalities to discover multi-modal biomarkers.

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Section: Support Vector Machinementioning

confidence: 99%

Imaging and machine learning techniques for diagnosis of Alzheimer’s disease

Mirzaei

Adeli

2016

Reviews in the Neurosciences

105

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“…SVM-based predictors are popular in different areas [9,19,23,24,32,38,39] including the classification mental states using physiological signals [6,8,12,30,37,40,55,74,83]. SMVs were used in our experiments because the size of a trained SVM model is often much smaller than the volume of training data required in order to be successful.…”

Section: Classificationmentioning

confidence: 99%

Stress Detection Using Wearable Physiological and Sociometric Sensors

Mozos

Sandulescu

Andrews

et al. 2016

Int. J. Neur. Syst.

148

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2 Mozos et al.Stress remains a significant social problem for individuals in modern societies. This paper presents a machine learning approach for the automatic detection of stress of people in a social situation by combining two sensor systems that capture physiological and social responses. We compare the performance using different classifiers including support vector machine, AdaBoost, and k-nearest neighbour. Our experimental results show that by combining the measurements from both sensor systems, we could accurately discriminate between stressful and neutral situations during a controlled Trier social stress test (TSST). Moreover, this paper assesses the discriminative ability of each sensor modality individually and considers their suitability for real time stress detection. Finally, we present an study of the most discriminative features for stress detection.

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“…In the exploration stage, the search space is extended by deploying a linear decrease of the gravitational constant GðtÞ de¯ned by Eqs. (12)- (14).…”

Section: Adaptive Gsamentioning

confidence: 99%

Gravitational Search Algorithm and Its Variants

Siddique

Adeli

2016

Int. J. Patt. Recogn. Artif. Intell.

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Gravitational search algorithm (GSA) is a nature-inspired conceptual framework with roots in gravitational kinematics, a branch of physics that models the motion of masses moving under the in°uence of gravity. In GSA, a collection of objects interacts with each other under the Newtonian gravity and the laws of motion. The performances of objects are measured by masses. All these objects attract each other by the gravity force, while this force causes a global movement of all objects toward the objects with heavier masses. The position of the object corresponds to a solution of the problem. The positions of the objects are updated every iteration and the best¯tness along with its corresponding object is stored. Heavier masses move slowly than lighter ones. The algorithm terminates after a speci¯ed number of iterations after which the best¯tness becomes the global¯tness for a particular problem and the positions of the corresponding object becomes the global solution of that problem. This paper presents a review of GSA and its variants.

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Smart Artificial Firefly Colony Algorithm‐Based Support Vector Regression for Enhanced Forecasting in Civil Engineering

Cited by 105 publications

References 72 publications

Imaging and machine learning techniques for diagnosis of Alzheimer’s disease

Imaging and machine learning techniques for diagnosis of Alzheimer’s disease

Stress Detection Using Wearable Physiological and Sociometric Sensors

Gravitational Search Algorithm and Its Variants

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