To facilitate the computer-aided medical applications, this paper tries to build better intelligent diagnosis systems with the help of swarm intelligence method. As to the clinical data, a built-in graph structure is constructed with training samples being mapped as labeled vertices and test samples being unlabeled vertices. On the basis of the iterative label propagation algorithm, this paper first introduces a confidence-based random walk learning model, where unlabeled vertices that consistently show high probability (above the confidence threshold) in belonging to one class is treated as labeled vertices in the next iteration. Later motivated by the swarm intelligence, this model is further improved by treating the labeled vertices as real ants in nature and the predefined classes as different ant colonies. A novel labeled ant random walk algorithm is introduced by incorporating the history information of random walk in the form of aggregation pheromone. The proposed algorithms are evaluated with a synthetic data as well as some real-life clinical cases in terms of diagnostic accuracy. Experimental results show the potentiality of the proposed algorithms.
Traditional security models and single security technology cannot keep up with the change of complicated network structure and varied intrusion measure. Network security manage based on optimization has traits of low manage cost, high agility, wide applicability. This paper introduces network security management based on non-optimum system theory into new network security model, and emphases on its structure, control strategy and implementation. This paper has also come at sub-optimum principle of the networks system optimization, established the conception of sub-optimum thresholds and put forward three theorems about non-optimum parameters. Finally, according to the previous practice of optimization, kind of method has been developed to approach the sub-optimum optimum from non-optimum.
High-dimensional data present significant challenges such as inadequate sample size, abundance of noise, and the curse of dimensionality, which make many traditional classification algorithms inapplicable. To provide valid inference for such data, it requires finding a noise-free low-dimensional representation that preserves both the underlying manifold structure and discriminative information. However, the existing methods often fail to take full consideration of these requirements. In this article, we introduce a robust supervised spline embedding (RS
Summary The classification of gene expression data is significantly important for medical diagnosis. In recent years, compressive sensing emerges as a popular sparse learning method and has been applied in different areas. It is featured with the sparse representation of data with a few atoms in the dictionary. However, the traditional compressive sensing model only focuses on the relationship among different samples but neglects the relationship among different genes. In order to take into account of the both kinds of correlation, we propose a novel bidirectional compressive sensing model for the classification of gene expression data. Under this model, we develop a novel Bi‐ADMM algorithm with three different variants to solve the optimization problem. The promising experimental results on the real‐world gene expression datasets demonstrate both the effectiveness and efficiency of our proposed approach.
The paper was aimed at ensuring the stable operation of the photovoltaic power generation system (PVPGS) and improving the accuracy of automatic mismatch detection. Consequently, this paper presents a PVPGS-oriented mismatch detection system based on wireless sensing technology (WSN). Firstly, the photovoltaic array (PVA) is constructed using a microcontroller, power management chip, nRF24L01, temperature sensor, voltage, and current sensor. Then, a fault detection and localization (FDL) scheme based on the Hampel algorithm is optimized, and Matlab/Simulink implements the PVA simulation model. Finally, several typical mismatch faults are simulated to verify the feasibility of the proposed FDL scheme using the measured voltage and current data. The empirical findings corroborate that the proposed FDL scheme can automatically and regularly collect photovoltaic (PV) electrical characteristic data and quickly and accurately identify and position a mismatch. In the case of a PVA open-circuit fault, the output current loss of the PVA is equal to the sum of the current of the open-circuit fault string in the array during normal operation. When the PVA is short-circuited, the PVA output voltage loss equals the sum of the output voltages of the faulty components in the most serious fault string under normal operation. Overall, the classification accuracy of the proposed FDL scheme is 97.556%. Lastly, the experiment reveals that the classification accuracy of the proposed FDL scheme is 100% for array aging, shadow, and the open circuit. Therefore, the research proposal has a good application prospect.
Dictionary learning is often incorporated in classification method, which can obtain a new representation under the learned dictionary to achieve better classification performance. In this paper, we propose a novel Batch Dictionary Learning model with augmented orthogonal matching pursuit classification. Batch Dictionary Learning model is capable of improving the dictionary by removing the redundancy of over-complete dictionary, thus the learned optimal dictionary is more suitable for classification. To solve the optimization target, we improve the traditional orthogonal matching pursuit (OMP) algorithm and propose an augmented orthogonal matching pursuit algorithm (AOMP) to solve the objective function. Superior experimental results demonstrate that our proposed model outperform the other state-of-the-art classification algorithms on real-world dataset.
A power-system protection device built using Internet-of-Things (IoT) technologies in an intelligent environment. IoT supports electrical and physical parameters monitoring. One of the characteristics that must be checked is electricity usage from electronic gadgets. It is a complex problem to design energy-efficient IoT methods. IoT gets more complicated because of its vast size, and current wireless sensor network approaches cannot be used directly to IoT. Information gathering on the area is monitored by intelligent cellular terminals, intelligent security tools, and other multi-source sensing equipment. That is the foundation for the combined analysis and evaluation of security risk extensive data by cloud computing and edge computing. The IoT-based Power safety tools management (IoT-PSTM) system has been developed to integrate it into intelligent settings, such as smart homes or smart cities, to safeguard electrical equipment. It is meant to increase power security by quickly disconnecting in failure events such as leaking current. The system allows for real-time monitoring and alerting of events using a sophisticated data-concentration architecture communication interface. The goal is to progress and merge several technologies technically and integrate them into a personal safety system to increase security, preserve their availability, eliminate mistakes, and reduce the time required for scheduled or ad hoc interventions. Real-time data transmission, instant data processing from diverse sources, local intelligence in low-power embedded systems, interaction with many on-site users, sophisticated user interfaces, portability, and wearability are the main difficulties for the research project. This article offers a comprehensive explanation of the design and execution of the proposed system and the test findings. The results denote the higher performance of the suggested IoT-PSTM system with IoT module and enhanced performance of 94.7%.
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