A new intelligent hepatitis diagnosis system: PCA–LSSVM

This study is aimed at developing an intelligent algorithm based on deep learning and discussing its application for the classification and diagnosis of retinal vein occlusions (RVO) using fundus images. A total of 501 fundus images of healthy eyes and patients with RVO were used for model training and testing to investigate an intelligent diagnosis system. The images were first classified into four categories by fundus disease specialists: (i) healthy fundus (group 0), (ii) branch RVO (BRVO) (group 1), (iii) central RVO (CRVO) (group 2), and (iv) macular branch RVO (MBRVO) (group 3), before being diagnosed using the ResNet18 network model. Intelligent diagnoses were compared with clinical diagnoses. The specificity of the intelligent diagnosis system under each attention mechanism was 100% in group 0 and also revealed a high sensitivity of over 95%, F1 score of over 97%, and an accuracy of over 97% in this group. For the other three groups, the specificities of diagnosis ranged from 0.45 to 0.91 with different attention mechanisms, in which the ResNet18+coordinate attention (CA) model had the highest specificities of 0.91, 0.88, and 0.83 for groups 1, 2, and 3, respectively. It also provided a high accuracy of over 94% with a coordinate attention mechanism in all four groups. The intelligent diagnosis and classifier system developed herein based on deep learning can determine the presence of RVO and classify disease according to the site of occlusion. This proposed system is expected to provide a new tool for RVO diagnosis and screening and will help solve the current challenges due to the shortage of medical resources.

This paper presents an intelligent combustion fault diagnosis approach for V-type marine diesel engines with uneven firing intervals based on the instantaneous crankshaft speed (ICS). First, experimental ICSs of a 16-cylinder marine diesel engine under normal and different degrees of fault conditions are investigated to obtain the most sensitive fault features. A new polar representation method named cyclic-polar-map (CPM) is proposed to effectively demonstrate the fault features. The quantitative radii of the CPM are used to indicate the location and degree of fault. Then, a support vector machine (SVM)-based intelligent fault diagnosis system is developed for automatically detecting the combustion faults with the features extracted from the proposed CPM and the frequency analysis of ICS. In addition, for the SVM training set is not always available in practice, a dynamic model of the crankshaft system is developed to simulate different fault patterns. Finally, the efficiency of the proposed intelligent diagnosis approach is validated by experimental data. The results show that the proposed SVM-based intelligent diagnosis system using both CPM features and frequency domain features of the ICS can obtain good accuracies under different fault degrees at various speed and load conditions.

Aimed at the characteristics of huge systems, complicated distributed structures and difficult fault diagnosis of large equipments, an extensible system of distributed online monitoring and multi-level intelligent fault diagnosis based on hybrid wireless network techniques was presented and designed. The entire system includes four hierarchy's subsystems. The first subsystem is monitoring sensors and Zigbee network. It is responsible to gain various data of equipments in a non-intrusive mode. The second one is monitoring and transmission network. It consists of embedded system ARM920T and relative multiple single-chips, and is responsible to receive and integrate all information of the previous subsystem. The third one is online and offline monitoring subsystem based on wireless protocol 802.11b. The final one is multi-level intelligent diagnosis and management system. The results of practical operation show that, because of the using of hybrid wireless network, embedded system and multilevel intelligent diagnosis, the system may be flexibly deployed, have powerful capability of monitoring, diagnosis and management, and can implement expeditious and accurate fault positioning and diagnosis for large equipment system.

With the popularization of computers and various mobile intelligent terminals, intelligent teaching systems based on learners are becoming more and more popular among learners. The above phenomenon has greatly affected and changed the current teaching quality diagnosis methods and models. However, the author found through investigation that the current intelligent teaching quality diagnosis still has different degrees of deficiencies in the design and implementation. In response to the above problems, this paper proposes a teaching quality intelligent diagnosis model based on the combination of wireless sensor networks and fuzzy comprehensive evaluation algorithms. First of all, this article is based on the wireless sensor network to link various levels of intelligent teaching systems, and constructs the information transmission structure of the teaching intelligent diagnosis system. Secondly, this article uses fuzzy comprehensive evaluation and convolutional neural network algorithms to evaluate and excavate intelligent teaching information. Finally, the model successfully passed the simulation test and simulation application, which can provide intelligent diagnosis of teaching quality for modern intelligent teaching system.

A dynamic artificial intelligence (AI) ultrasonic intelligent assistant diagnosis system (dynamic AI) is a joint application of AI technology and medical imaging, which can conduct real-time synchronous dynamic analysis of nodules from multiple sectional views with different angles. This study explored the diagnostic value of dynamic AI for benign and malignant thyroid nodules in patients with Hashimoto thyroiditis (HT) and its significance in guiding surgical treatment strategies. Data of 487 patients (154 with and 333 without HT) with 829 thyroid nodules who underwent surgery were collected. Differentiation of benign and malignant nodules was performed using dynamic AI, and diagnostic effects (specificity, sensitivity, negative predictive value, positive predictive value, accuracy, misdiagnosis rate and missed diagnosis rate) was assessed. Differences in diagnostic efficacy were compared among AI, preoperative ultrasound based on the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS), and fine needle aspiration cytology (FNAC) diagnoses. The accuracy, specificity and sensitivity of dynamic AI reached 88.06%, 80.19%, and 90.68%, respectively; besides, there was consistency with postoperative pathological consequences (κ=0.690; P<0.001). The diagnostic efficacy of dynamic AI was equivalent between patients with and without HT, and there were no significant differences in sensitivity, specificity, accuracy, positive predictive value, negative predictive value, missed diagnosis rate, and misdiagnosis rate. In patients with HT, dynamic AI had significantly higher specificity and a lower misdiagnosis rate than did preoperative ultrasound based on the ACR TI-RADS (P<0.05). Compared with FNAC diagnosis, dynamic AI had a significantly higher sensitivity and a lower missed diagnosis rate (P<0.05). Dynamic AI possessed an elevated diagnostic worth of malignant and benign thyroid nodules in patients with HT, which can provide a new method and valuable information for the diagnosis and development of management strategy of patients.

A networking and intelligent online monitoring and fault diagnosis system for large-scale rotating machinery is developed according to requirements of an iron &amp; steel enterprise. On the aspect of networking, a mixed structure of C/S and B/S is adopted, and the system integrates local online monitoring and diagnosis, remote monitoring and diagnosis, and remote diagnosis center. On the aspect of intelligent diagnosis, a multi-symptom comprehensive parallel diagnosis technology is adopted based on expert system, neural network and fuzzy logic. Finally, main functional modules and its realization are introduced. Application shows that the system runs normally, and the expected objective is achieved.

Smart healthcare has undergone new opportunities and challenges with the arrival of the Industry 4.0 era. The intelligent imaging diagnosis system is a staple part of smart healthcare, helping doctors make clinical decisions. Nevertheless, intelligent diagnosis analysis is still confronted with the issue that it is challenging to extract effective features from the limited and high‐dimensional data, particularly in resting‐state data of amnesic mild cognitive impairment (aMCI). Furthermore, the intelligent imaging diagnosis system for aMCI is conductive to make timely predicting groups that may convert to Alzheimer’s disease (AD). To improve the system’s detection performance and reduce its data redundancy, we first develop an adaptive structure feature generation strategy (ASFGS) based on the Laplacian matrix and sparse autoencoder to obtain the structural features of brain functional network (BFN). Concurrently, we present a multiscale local feature detection strategy (MLFDS) to overcome the low utilization of local features of BFN. And finally, multiscale features, including structural features and multiscale local features, are fused by concatenation method to further improve the detection performance of aMCI system. Support vector machine based on radial basis function (RBF‐SVM) for small data learning is adopted to evaluate the effectiveness of the proposed features. Besides, we employ leave‐one‐out cross‐validation strategy to avoid the overfitting problem of classifier training process. The experiment results elucidate that the accuracy (ACC) and the area under the curve (AUC) in this work provide 86.57% and 86.36%, respectively, which outperforms the traditional methods and offers new insights for accuracy requirements of the aMCI system.

Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform

Transformer failures are often due to aging, deterioration or damage of the internal insulation materials. Combustible gases are also generated when the insulation materials are subjected to thermal or electrical stress. This study proposes a fault diagnosis system, which combines a multinomial logistic regression model and back-propagation neural networks, to determine the type of fault of a power transformer by analyzing the dissolved gases in the transformer. The compositions and amounts of the dissolved gases that are crucial or relevant to specific types of faults are selected by the multinomial logistic regression model as the inputs to the neural network to train the diagnosis system, so the diagnosis system can learn to diagnose the type of faults. The test results show that the recognition rate of the proposed intelligent fault type diagnosis system is about 10- 30% higher than those of a single-neural or multi-neural networks recognition system which does not incorporate the multinomial logistic regression model.

본 논문에서는 저압 배선 진단 시스템 개발에서 핵심 파트 중 하나인 지능형 차단 시스템 구축을 목표로 한다. 제안된 진단 시스템은 TFDR (Time-Frequency Domain Reflectometry) 알고리즘을 바탕으로 하여 실제 전압이 흐르는 배선에 대해 이상 거리 측정을 하게 된다. 그리고 배선으로부터 얻은 정보를 바탕으로 배선 이상의 종류를 분석하는 것이 지능형 차단 시스템의 목표이다. 효율적인 분석을 위해, 본 논문에서는 퍼지-베이시안 (Fuzzy-Bayesian) 알고리즘을 바탕으로 하여 시스템을 구성하였다. 실제 저압 배선에서 실험된 데이터를 바탕으로 한 실험을 통해 제안된 기술의 우수성을 입증하고자 한다. In this paper, we present the development of an intelligent diagnosis system for detecting faults of the transmission line. Based on the TFDR (Time-Frequency Domain Reflectometry), the fault detecting performs to measure the location of fault line. We analyze the reflected signal which is sent from the wire detecting system and classify the fault type of the wires by using intelligent diagnosis system. In order to analyze effectively, we construct the intelligent diagnosis system which is based on the fuzzy-bayesian algorithm. Finally, we provide the simulation results which are performed at transmission line to evaluate the feasibility and generality of the proposed method in this paper.

This study combines adaptive neuro-fuzzy inference system (ANFIS) with greedy forward feature selection to develop the intelligent diagnosis system. Two different membership functions (MFs), Trapezoidal and Gaussian, are adopted during the training process of ANFIS in order to compare the diagnosis accuracy of Trapezoidal MF with one of Gaussian MF. The comparison of ANFIS values with simulated data indices that adoption of both Trapezoidal and Gaussian MF in proposed system achieve satisfactory accuracy (>96%). Furthermore, the accuracy of ANFIS with Gaussian MF is above 98%. Hence, the intelligent diagnosis system can provide a preliminary result to physicians so that the doctor could quickly and accurately decide whether patient have cold or influenza.

This paper proposes an intelligent fitness diagnosis system (IFDS), which integrates the electroencephalogram (EEG) and electrocardiogram (ECG) biomedical signals and fitness data. IFDS detects the voltage and current produced by the users under different states of attention and meditation during exercise. Based on EEG, ECG, and fitness data, the extension method is applied to distinguish the physical and mental conditions of the users during exercise. The brainwave training system, designed by LabVIEW, analyzes theαandθwave bands of EEG, and plots the waveforms under different states of attention and meditation simultaneously to effectively improve the users' attention. Ten subjects were included to exercise for 15 times, lasting 3 min each. The accuracy of IFDS reaches 91%. Meanwhile, IFDS indirectly diagnoses the symptoms of some diseases in the users. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.

The traditional medical diagnosis methods of ADHD mainly rely on scale evaluation and interview observation. The diagnosis conclusion is subjective and extremely dependent on the doctor's experience level. There is an urgent need to improve diagnosis efficiency and improve the diagnosis standard through other technical means in the clinical process. We have designed and developed the ADHD intelligent auxiliary diagnosis system with software and hardware cooperation. The system performs a set of functional test tasks, uses a camera module to capture multimodal information such as facial expressions, eye movements, limb movements, language expressions and reaction abilities of children during task completion, and uses computer vision technology to automatically extract measurable characteristics. Finally, deep learning technology is used to detect children's specific behaviors in the video, which is complementary to the existing doctor's diagnosis basis. This system was deployed in the Department of Psychology of Children's Hospital of Zhejiang University in July 2019 and has been used in actual clinical diagnosis to date. It has completed the testing and evaluation of hundreds of ADHD children.

BackgroundDynamic artificial intelligence (AI) ultrasound intelligent auxiliary diagnosis system (Dynamic AI) is a joint application of AI technology and medical imaging data, which can perform a real-time synchronous dynamic analysis of nodules. The aim of this study is to investigate the value of dynamic AI in differentiating benign and malignant thyroid nodules and its guiding significance for treatment strategies.MethodsThe data of 607 patients with 1007 thyroid nodules who underwent surgical treatment were reviewed and analyzed, retrospectively. Dynamic AI was used to differentiate benign and malignant nodules. The diagnostic efficacy of dynamic AI was evaluated by comparing the results of dynamic AI examination, preoperative fine needle aspiration cytology (FNAC) and postoperative pathology of nodules with different sizes and properties in patients of different sexes and ages.ResultsThe sensitivity, specificity and accuracy of dynamic AI in the diagnosis of thyroid nodules were 92.21%, 83.20% and 89.97%, respectively, which were highly consistent with the postoperative pathological results (kappa = 0.737, p < 0.001). There is no statistical difference in accuracy between people with different ages and sexes and nodules of different sizes, which showed the good stability. The accuracy of dynamic AI in malignant nodules (92.21%) was significantly higher than that in benign nodules (83.20%) (p < 0.001). The specificity and positive predictive value were significantly higher, and the misdiagnosis rate was significantly lower in dynamic AI than that of preoperative ultrasound ACR TI-RADS (p < 0.001). The accuracy of dynamic AI in nodules with diameter ≤ 0.50 cm was significantly higher than that of preoperative ultrasound (p = 0.044). Compared with FNAC, the sensitivity (96.58%) and accuracy (94.06%) of dynamic AI were similar.ConclusionsThe dynamic AI examination has high diagnostic value for benign and malignant thyroid nodules, which can effectively assist surgeons in formulating scientific and reasonable individualized diagnosis and treatment strategies for patients.

This paper aims to develop and demonstrate intelligent diagnosis system that ensures effective supporting the decision-maker in determining the cause and potential solutions to unpredictable problems or events detected by monitoring factory-wide operating processes. The algorithm for accomplishing rapid, cost-effective, and high reliable diagnosis system is well-designed with C++ as an object-oriented programming language under Windows OS platform. A windows-based prototype system for intelligent diagnosis system with real-time status data collected from shop floor is also suggested and demonstrated by using C++ programming language and C4.5 learning algorithm.