A Novel Method to Improve Model fitting for Stock Market Prediction

2017 IEEE 7th International Advance Computing Conference (IACC)

et al. 2017

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Clustering is a useful data exploratory method with its wide applicability in multiple fields. However, data clustering greatly relies on initialization of cluster centers that can result in large intra-cluster variance and dead centers, therefore leading to sub-optimal solutions. This paper proposes a novel variance based version of the conventional Moving K-Means (MKM) algorithm called Variance Based Moving K-Means (VMKM) that can partition data into optimal homogeneous clusters, irrespective of cluster initialization. The algorithm utilizes a novel distance metric and a unique data element selection criteria to transfer the selected elements between clusters to achieve low intra-cluster variance and subsequently avoid dead centers. Quantitative and qualitative comparison with various clustering techniques is performed on four datasets selected from image processing, bioinformatics, remote sensing and the stock market respectively. An extensive analysis highlights the superior performance of the proposed method over other techniques.

Section: Stock Marketmentioning

confidence: 99%

Variance Based Moving K-Means Algorithm

Vijay¹,

Raghunath²,

2017 IEEE 7th International Advance Computing Conference (IACC)

et al. 2017

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“…3). The initialization with priors also reduces the need for sizeable labeled training datasets for effective training which is especially advantageous for this task or other applications [25,10] as it can be expensive and time-consuming to generate keypoint annotations. • Real-time Identification: The proposed system performs the computation and memory demanding SHDL network processes along with the activity classification technique on the cloud while keeping short-term navigation onboard.…”

Section: Introductionmentioning

confidence: 99%

Eye in the Sky: Real-Time Drone Surveillance System (DSS) for Violent Individuals Identification Using ScatterNet Hybrid Deep Learning Network

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Patil

Omkar

2018

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109

Drone systems have been deployed by various law enforcement agencies to monitor hostiles, spy on foreign drug cartels, conduct border control operations, etc. This paper introduces a real-time drone surveillance system to identify violent individuals in public areas. The system first uses the Feature Pyramid Network to detect humans from aerial images. The image region with the human is used by the proposed ScatterNet Hybrid Deep Learning (SHDL) network for human pose estimation. The orientations between the limbs of the estimated pose are next used to identify the violent individuals. The proposed deep network can learn meaningful representations quickly using ScatterNet and structural priors with relatively fewer labeled examples. The system detects the violent individuals in real-time by processing the drone images in the cloud. This research also introduces the aerial violent individual dataset used for training the deep network which hopefully may encourage researchers interested in using deep learning for aerial surveillance. The pose estimation and violent individuals identification performance is compared with the state-ofthe-art techniques.

“…In addition, the vast arrays of network parameters can only be learned with the help of powerful computational resources and large training datasets. These may not be available for many applications such as stock market prediction [9], medical imaging [2] etc. The third class of models combine the concepts from both of the above-mentioned models to learn shallow or deep feature hierarchies from low-level hand-crafted descriptors.…”

Section: Introductionmentioning

confidence: 99%

Generative Scatternet Hybrid Deep Learning (G-Shdl) Network with Structural Priors for Semantic Image Segmentation

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Kingsbury

2018

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This paper proposes a generative ScatterNet hybrid deep learning (G-SHDL) network for semantic image segmentation. The proposed generative architecture is able to train rapidly from relatively small labeled datasets using the introduced structural priors. In addition, the number of filters in each layer of the architecture is optimized resulting in a computationally efficient architecture. The G-SHDL network produces state-of-the-art classification performance against unsupervised and semi-supervised learning on two image datasets. Advantages of the G-SHDL network over supervised methods are demonstrated with experiments performed on training datasets of reduced size.