Plant diseased leaf segmentation and recognition by fusion of superpixel, K-means and PHOG

Health care systems are merely designed to meet the needs of increasing population globally. People around the globe are affected with different types of deadliest diseases. Among the different types of commonly existing diseases, diabetes is a major cause of blindness, kidney failure, heart attacks, etc. Health care monitoring systems for different diseases and symptoms are available all around the world. The rapid development in the fields of Information and Communication Technologies made remarkable improvements in health care systems. Various Machine Learning algorithms are proposed which automates the working model of health care systems and enhances the accuracy of disease prediction. Hadoop cluster based distributed computing framework supports in efficient processing and storing of extremely large datasets in cloud environment. This work proposes the novel implementation of machine learning algorithms in hadoop based clusters for diabetes prediction. The results show that the machine learning algorithms can able to produce highly accurate diabetes predictive healthcare systems. Pima Indians Diabetes Database from National Institute of Diabetes and Digestive Diseases is used to evaluate the working of algorithm.

Diabetes prediction in healthcare systems using machine learning algorithms on Hadoop cluster

Block chain is a decentralized core architecture, which is widely used in emerging digital encryption currencies. It has attracted much attention and has been researched with the gradual acceptance of bitcoin. Block chaining technology has the characteristics of centralization, block data, no tampering and trust, so it is sought after by enterprises, especially financial institutions. This paper expounds the core technology principle of block chain technology, discusses the application of block chain technology, the existing regulatory problems and security problems, so as to provide some help for the related research of block chain technology. Intrusion detection is an important way to protect the security of information systems. It has become the focus of security research in recent years. This paper introduces the history and current situation of intrusion detection system, expounds the classification of intrusion detection system and the framework of general intrusion detection, and discusses all kinds of intrusion detection technology in detail. Intrusion detection technology is a kind of security technology to protect network resources from hacker attack. IDS is a useful supplement to the firewall, which can help the network system to quickly detect attacks and improve the integrity of the information security infrastructure. In this paper, intrusion detection technology is applied to block chain information security model, and the results show that proposed model has higher detection efficiency and fault tolerance.

Information security model of block chain based on intrusion sensing in the IoT environment

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UAV based wilt detection system via convolutional neural networks

The accurate ground-based cloud classification is a challenging task and still under development. The most current methods are limited to only taking the cloud visual features into consideration, which is not robust to the environmental factors. In this paper, we present the novel joint fusion convolutional neural network (JFCNN) to integrate the multimodal information for ground-based cloud classification. To learn the heterogeneous features (visual features and multimodal features) from the ground-based cloud data, we designed the proposed JFCNN as a two-stream structure which contains the vision subnetwork and multimodal subnetwork. We also proposed a novel layer named joint fusion layer to jointly learn two kinds of cloud features under one framework. After training the proposed JFCNN, we extracted the visual and multimodal features from the two subnetworks and integrated them using a weighted strategy. The proposed JFCNN was validated on the multimodal ground-based cloud (MGC) dataset and achieved remarkable performance, demonstrating its effectiveness for ground-based cloud classification task.

Multimodal Ground-Based Cloud Classification Using Joint Fusion Convolutional Neural Network

In recent years, the analysis of natural image has made great progress while the image of the intrinsic component analysis can solve many computer vision problems, such as the image shadow detection and removal. This paper presents the novel model, which integrates the feature fusion and the multiple dictionary learning. Traditional model can hardly handle the challenge of reserving the removal accuracy while keeping the low time consuming. Inspire by the compressive sensing theory, traditional single dictionary scenario is extended to the multiple condition. The human visual system is more sensitive to the high frequency part of the image, and the high frequency part expresses most of the semantic information of the image. At the same time, the high frequency characteristic of the high and low resolution image is adopted in the dictionary training, which can effectively recover the loss in the high resolution image with high frequency information. This paper presents the integration of compressive sensing model with feature extraction to construct the two-stage methodology. Therefore, the feature fusion algorithm is applied to the dictionary training procedure to finalize the robust model. Simulation results proves the effectiveness of the model, which outperforms compared with the other state-of-the-art algorithms.

Chen Qi

Papers

Single image shadow detection and removal based on feature fusion and multiple dictionary learning