False data injection cyber-physical threat is a typical integrity attack in modern smart grids. These days, analytical methods have been employed to mitigate false attacks (FDIAs), especially when large scale grids generate huge amounts of data. In this paper, novel method proposed detect FDIAs based on data-centric paradigm employing the margin setting algorithm (MSA). The performance demonstrated through simulation using six-bus power network wide area measurement system environment, as...

Blockchain is a technology that ensures data security by verifying database of records established in decentralized and distributed network. Blockchain-based approaches have been applied to secure the fields Internet Things, software engineering, healthcare systems, financial services, smart power grids. However, blockchain system still major concern. We took initiative present systematic study which sheds light on what defensive strategies are used effectively. Specifically, we focus aims...

Photos obtained via crowdsourcing can be used in many critical applications. Due to the limitations of communication bandwidth, storage, and processing capability, it is a challenge transfer huge amount crowdsourced photos. To address this problem, we propose framework, called <i>SmartPhoto</i> , quantify quality (utility) photos based on accessible geographical geometrical information (called <i>metadata </i> ) including smartphone's orientation, position, all related parameters built-in...

Abstract Efficiency, memory consumption and robustness are common problems with many popular methods for data analysis. As a solution, we present Random Bits Forest (RBF), classification regression algorithm that integrates neural networks (for depth), boosting width) random forests prediction accuracy). Through gradient scheme, it first generates selects ~10,000 small, 3-layer networks. These then fed into modified forest to obtain predictions. Testing datasets from the UCI (University of...

In this study, computer vision applicable to traditional agriculture was used achieve accurate identification of rice leaf diseases with complex backgrounds. The researchers developed the RiceDRA-Net deep residual network model and it identify four different diseases. disease test set a background named CBG-Dataset, new single constructed, SBG-Dataset, based on original dataset. Res-Attention module 3 × convolutional kernels denser connections compared other attention mechanisms reduce...

Based on the current research wine grape variety recognition task, it has been found that traditional deep learning models relying only a single feature (e.g., fruit or leaf) for classification can face great challenges, especially when there is high degree of similarity between varieties. In order to effectively distinguish these similar varieties, this study proposes multisource information fusion method, which centered SynthDiscrim algorithm, aiming achieve more comprehensive and accurate...

Abstract Vitiligo is a common depigmentary skin disease that may follow pattern of multifactorial inheritance. The essential factors its immunopathogenesis thought to be the selective destruction melanocytes. As new class microregulators gene expression, mi RNA have been reported play vital roles in autoimmune diseases, metabolic diseases and cancer. This study sought characterize different expression peripheral blood mononuclear cells ( PBMC ) patients with non‐segmental vitiligo NSV...

Integer-based vulnerability is an extremely serious bug for programs written in languages such as C/C++. However,in practice, very few software security tools can efficiently detect and accurately locate vulnerability. In addition, previous methods mainly depend on source code analysis recompilation which are impractical when protecting the program without code. this paper,we present design, implementation, evaluation of BRICK (binary run-time integer-based checker), a tool detecting...

Stock evaluation is a significant decision-making activity for investors. Due to the complexity of stock exchange market, information may be fuzzy and stochastic in meantime. Therefore, it essential conduct research on multi-criteria (FSMCDM ) methods. In this paper, at beginning, we propose interval neutrosophic probability from definition probability. Then, novel MCDM method proposed with based regret theory, which criteria values are numbers (INNs). The much better than methods using...

Bitcoin is a well-known cryptocurrency in which records of transactions are maintained by P2P network to create distributed ledger. Due the complex nature maintaining highly efficient, transparent and speed between nodes, security one primary concerns this system. The most prominent modern-day attacks system selfish mining double spending attacks. This survey organized around aspects pertaining countermeasures We selected total 20 studies from recent years as result systematic analysis...

In short, the ubiquitous power Internet of Things is connection various devices and people in system. Specifically, firstly, it uses sensor technology to collect device-related data. Then, data sent server via communication technology. Finally, processed stored through big data, cloud computing, artificial intelligence other technologies. Therefore, user supply company can grasp relevant each link grid, realize transparency sharing The system a large with many related equipments complicated...

Data stream processing has become a hot issue in recent years due to the arrival of big data era. There are three fundamental tasks: membership query, frequency query and Top-K query. While most existing solutions address these queries fixed windows, this paper focuses on more challenging task: answering sliding windows. different kinds by using algorithms, generic framework. In paper, we propose framework, namely Sliding sketches, which can be applied many for above queries, enable them...

A blockchain database containing files regarding transactions of cryptocurrency is sometime vulnerable to double spending attack. This type attack pertains a coin being spent more in that one transaction the network. paper motivated by goal create can withstand attacks. way, honest miners will be able safely and securely exchange cryptocurrency. There currently lack valuable prevention methods network therefore we designed novel countermeasure combat attacks on system. We proposed MSP...

LSM-tree is widely used as a write-optimized storage engine in many NoSQL systems. However, the periodical compaction operations cost I/O bandwidths and CPU resources of local server, resulting throughput drops system. To address this issue, paper proposes new scheme based on FaaS (Functions Service) architecture, which called Compaction. It utilizes elastic computing capability always pushes compactions to cluster. The cluster will perform actual operations, not affect processing server....

This paper describes the application of SVM to breast cancer diagnosis, which has shown good generalization. We take use non-symmetrical C-SVM solve problem unbalanced training examples. In order gain a fast searching method for parameters model, margin-based bound on generalization is more effective than traditional k-fold cross-validation. After feature subset selection by cross-entry filter, we even gained perfect prediction accuracy.

A wireless sensor network-based agricultural monitoring system was developed. Soil moisture and air temperature/humidity were monitored by nodes of the WSN. The collected information transmitted wirelessly to a gateway node which connected computer. Considering variable conditions under different applications, series field experiments conducted evaluate performance developed WSN system. communication distance, energy consumption data transmission tested three crop environments: greenhouse,...

In this paper, in order to improve the precision of short-term load forecasting, we propose a power forecasting method combined principal component analysis (PCA) with least squares support vector machine (LS-SVM). Firstly PCA extracts feature influence factors for load, and then LS-SVM constructs training model new variables extracted by PCA. After using PCA-LS-SVM paper proposed forecast one area, results show that can effectively eliminate redundant information among influential factors,...

The network link speed is increasing at an alarming rate, which requires all functions on routers/switches to keep pace. Bloom filter a widely-used membership check data structure in applications. It also faces the urgent demand of improving performance speed. To this end, paper proposes new variant called Ultra-Fast Filters, by leveraging SIMD techniques. We make three improvements for UFBF accelerate First, we develop novel hash computation algorithm can compute multiple parallel with use...

Blockchain as a novel technology which consists of peer-to-peer network, distributed consensus, cryptography and other fundamental knowledge. can help people build trust among untrusted people, will establish new application models in multiple areas. But there are several legacy problems obstruct the deployment development blockchain, biggest problem is blockchain performance that cannot process transactions much centralized server per second. We propose cascade structure for accelerate...

Intelligent environments is receiving lots of attention in the research community. With algorithms developed over years to model activity and pattern recognition, most are still not suitable for learning. Early was focused on home automation but recent moving towards an intelligent environment that capable identifying trends patterns make effective decisions. Making this feat a possibility requires extensive fields artificial intelligence, machine learning statistical These tools enable...

Activity recognition in smart environments is an important technology for assisted living and e-health. Recently there are growing interests applying machine learning algorithms to activity tasks. In this paper, we combine support vector (SVM) association rule improve the performance of based on streaming sensor data homes. The proposed approach allows us accurately identify boundaries, hence reducing errors system.

Support vector machine (SVM) is powerful to solve some problems such as nonlinear classification, function estimation and density estimation. To consider the chaotic fh (frequency hopping)-code's characters in dynamic system, forecasting model of support combination with Takens' delay coordinate phase reconstruction times established least squares for large-scale used local training this model. Finally, a fh-code series generated by Logistic-Kent mapping applied verify prediction Simulation...