Boosted Stacking Ensemble Machine Learning Method for Wafer Map Pattern Classification

Recently,machine learning-based technologies have been developed to automate the classification of wafer map defect patterns during semiconductormanufacturing.The existing approaches used in the wafer map pattern classification include directly learning the image through a convolution neural network and applying the ensemble method after extracting image features.This study aims to classify wafer map defects more effectively and derive robust algorithms even for datasets with insufficient defect patterns.First,the number of defects during the actual process may be limited.Therefore,insufficient data are generated using convolutional auto-encoder(CAE),and the expanded data are verified using the evaluation technique of structural similarity index measure(SSIM).After extracting handcrafted features,a boosted stacking ensemble model that integrates the four base-level classifiers with the extreme gradient boosting classifier as a meta-level classifier is designed and built for training the model based on the expanded data for final prediction.Since the proposed algorithm shows better performance than those of existing ensemble classifiers even for insufficient defect patterns,the results of this study will contribute to improving the product quality and yield of the actual semiconductor manufacturing process.

A Pattern Classification Model for Vowel Data Using Fuzzy Nearest Neighbor

Classification of the patterns is a crucial structure of research and applications. Using fuzzy set theory, classifying the patterns has become of great interest because of its ability to understand the parameters. One of the problemsobserved in the fuzzification of an unknown pattern is that importance is givenonly to the known patterns but not to their features. In contrast, features of thepatterns play an essential role when their respective patterns overlap. In this paper,an optimal fuzzy nearest neighbor model has been introduced in which a fuzzifi-cation process has been carried out for the unknown pattern using k nearest neighbor. With the help of the fuzzification process, the membership matrix has beenformed. In this membership matrix, fuzzification has been carried out of the features of the unknown pattern. Classification results are verified on a completelyllabelled Telugu vowel data set, and the accuracy is compared with the differentmodels and the fuzzy k nearest neighbor algorithm. The proposed model gives84.86% accuracy on 50% training data set and 89.35% accuracy on 80% trainingdata set. The proposed classifier learns well enough with a small amount of training data, resulting in an efficient and faster approach.

Classification and Nomenclature of Plant Metallothionein-like Proteins Based on Their Cysteine Arrangement Patterns

随着植物基因组研究的进展 ,在基因文库和蛋白文库登录的植物类金属硫蛋白基因已超过 5 0个 ,接近金属硫蛋白总数的 1/ 3,而且有不断上升的趋势。鉴于目前植物类金属硫蛋白命名与分类随意性太大 ,很有必要建立一个统一合理的命名与分类法。对植物类金属硫蛋白一级结构进行详细分析后 ,发现该蛋白两端富含半胱氨酸的区域内半胱氨酸的排列方式颇具规律性 ,进而提出了以半胱氨酸排列方式为基础的分类及命名法 ,并阐述了采用这种方法的理由及其可行性。

Landscape Pattern Evaluation Based on Maximum Likelihood Classification——A Case Study of Irrigated Area of Hongsibao Town in China

By using maximum likelihood classification, several landscape indexes have been adopted to evaluate landscape structure of the irrigated area of Hongsibao Town, and landscape pattern and dynamic change of Hongsibao in 1989, 1999, 2003 and 2008 had been analyzed based on landscape patch, landscape type and transfer matrix. The results show that landscape pattern had changed obviously, patch number, fragmentation and dominance had increased, evenness had decreased, and landscape shape had become regular in the irrigated area of Hongsibao Town from 1989 to 2008. The primary landscape type in 1989 was grassland and in 2008 was sand, directly influenced by human activities.

Deep Learning-Based Action Classification Using One-Shot Object Detection

Deep learning-based action classification technology has been applied to various fields,such as social safety,medical services,and sports.Analyzing an action on a practical level requires tracking multiple human bodies in an image in real-time and simultaneously classifying their actions.There are various related studies on the real-time classification of actions in an image.However,existing deep learning-based action classification models have prolonged response speeds,so there is a limit to real-time analysis.In addition,it has low accuracy of action of each object ifmultiple objects appear in the image.Also,it needs to be improved since it has a memory overhead in processing image data.Deep learning-based action classification using one-shot object detection is proposed to overcome the limitations of multiframe-based analysis technology.The proposed method uses a one-shot object detection model and a multi-object tracking algorithm to detect and track multiple objects in the image.Then,a deep learning-based pattern classification model is used to classify the body action of the object in the image by reducing the data for each object to an action vector.Compared to the existing studies,the constructed model shows higher accuracy of 74.95%,and in terms of speed,it offered better performance than the current studies at 0.234 s per frame.The proposed model makes it possible to classify some actions only through action vector learning without additional image learning because of the vector learning feature of the posterior neural network.Therefore,it is expected to contribute significantly to commercializing realistic streaming data analysis technologies,such as CCTV.

Review of Remotely Sensed Imagery Classification Patterns Based on Object-oriented Image Analysis

With the wide use of high-resolution remotely sensed imagery, the object-oriented remotely sensed informa- tion classification pattern has been intensively studied. Starting with the definition of object-oriented remotely sensed information classification pattern and a literature review of related research progress, this paper sums up 4 developing phases of object-oriented classification pattern during the past 20 years. Then, we discuss the three aspects of method- ology in detail, namely remotely sensed imagery segmentation, feature analysis and feature selection, and classification rule generation, through comparing them with remotely sensed information classification method based on per-pixel. At last, this paper presents several points that need to be paid attention to in the future studies on object-oriented RS in- formation classification pattern: 1) developing robust and highly effective image segmentation algorithm for multi-spectral RS imagery; 2) improving the feature-set including edge, spatial-adjacent and temporal characteristics; 3) discussing the classification rule generation classifier based on the decision tree; 4) presenting evaluation methods for classification result by object-oriented classification pattern.

Multi-Valued Neuron with Sigmoid Activation Function for Pattern Classification

Multi-Valued Neuron (MVN) was proposed for pattern classification. It operates with complex-valued inputs, outputs, and weights, and its learning algorithm is based on error-correcting rule. The activation function of MVN is not differentiable. Therefore, we can not apply backpropagation when constructing multilayer structures. In this paper, we propose a new neuron model, MVN-sig, to simulate the mechanism of MVN with differentiable activation function. We expect MVN-sig to achieve higher performance than MVN. We run several classification benchmark datasets to compare the performance of MVN-sig with that of MVN. The experimental results show a good potential to develop a multilayer networks based on MVN-sig.

Multi-channel electromyography pattern classification using deep belief networks for enhanced user experience

An enhanced algorithm is proposed to recognize multi-channel electromyography(EMG) patterns using deep belief networks(DBNs). It is difficult to classify the EMG features because an EMG signal has nonlinear and time-varying characteristics.Therefore, in several previous studies, various machine-learning methods have been applied. A DBN is a fast, greedy learning algorithm that can find a fairly good set of weights rapidly, even in deep networks with a large number of parameters and many hidden layers. To evaluate this model, we acquired EMG signals, extracted their features, and then compared the model with the DBN and other conventional classifiers. The accuracy of the DBN is higher than that of the other algorithms. The classification performance of the DBN model designed is approximately 88.60%. It is 7.55%(p=9.82×10-12) higher than linear discriminant analysis(LDA) and 2.89%(p=1.94×10-5) higher than support vector machine(SVM). Further, the DBN is better than shallow learning algorithms or back propagation(BP), and this model is effective for an EMG-based user-interfaced system.

SVD-LSSVM and its application in chemical pattern classification

Pattern classification is an important field in machine learning; least squares support vector machine (LSSVM) is a powerful tool for pattern classification. A new version of LSSVM, SVD-LSSVM, to save time of selecting hyper parameters for LSSVM is proposed. SVD-LSSVM is trained through singular value decomposition (SVD) of kernel matrix. Cross validation time of selecting hyper parameters can be saved because a new hyper parameter, singular value contribution rate (SVCR), replaces the penalty factor of LSSVM. Several UCI benchmarking data and the Olive classification problem were used to test SVD-LSSVM. The result showed that SVD-LSSVM has good performance in classification and saves time for cross validation.

Improved Algorithm of Pattern Classification and Recognition Applied in a Coal Dust Sensor

To resolve the conflicting requirements of measurement precision and real-time performance speed,an im-proved algorithm for pattern classification and recognition was developed. The angular distribution of diffracted light varies with particle size. These patterns could be classified into groups with an innovative classification based upon ref-erence dust samples. After such classification patterns could be recognized easily and rapidly by minimizing the vari-ance between the reference pattern and dust sample eigenvectors. Simulation showed that the maximum recognition speed improves 20 fold. This enables the use of a single-chip,real-time inversion algorithm. An increased number of reference patterns reduced the errors in total and respiring coal dust measurements. Experiments in coal mine testify that the accuracy of sensor achieves 95%. Results indicate the improved algorithm enhances the precision and real-time ca-pability of the coal dust sensor effectively.

Encoding candlesticks as images for pattern classification using convolutional neural networks

Candlestick charts display the high,low,opening,and closing prices in a specific period.Candlestick patterns emerge because human actions and reactions are patterned and continuously replicate.These patterns capture information on the candles.According to Thomas Bulkowski’s Encyclopedia of Candlestick Charts,there are 103 candlestick patterns.Traders use these patterns to determine when to enter and exit.Candlestick pattern classification approaches take the hard work out of visually identifying these patterns.To highlight its capabilities,we propose a two-steps approach to recognize candlestick patterns automatically.The first step uses the Gramian Angular Field(GAF)to encode the time series as different types of images.The second step uses the Convolutional Neural Network(CNN)with the GAF images to learn eight critical kinds of candlestick patterns.In this paper,we call the approach GAF-CNN.In the experiments,our approach can identify the eight types of candlestick patterns with 90.7%average accuracy automatically in real-world data,outperforming the LSTM model.

FUZZY PARTITIONING OF FEATURE SPACE FOR PATTERN CLASSIFICATION BASED ON SUPERVISED C1USTERING

The choice of a fuzzy partitioning is crucial to the performance of a fuzzy system based on if-then rules. However, most of the existing methods are complicated or lead ,o too many subspaces, which is unfit for the applications of pattern classification. A simple but effective clustering approach is proposed in this paper, which obtains a set of compact subspaces and is applicable for classification problems with higher dimensional feature. Its effectiveness is demonstrated by the experimental results.

Progressive transductive learning pattern classification via single sphere

In many machine learning problems, a large amount of data is available but only a few of them can be labeled easily. This provides a research branch to effectively combine unlabeled and labeled data to infer the labels of unlabeled ones, that is, to develop transductive learning. In this article, based on Pattern classification via single sphere （SSPC）, which seeks a hypersphere to separate data with the maximum separation ratio, a progressive transductive pattern classification method via single sphere （PTSSPC） is proposed to construct the classifier using both the labeled and unlabeled data. PTSSPC utilize the additional information of the unlabeled samples and obtain better classification performance than SSPC when insufficient labeled data information is available. Experiment results show the algorithm can yields better performance.

Classification of 3D Film Patterns with Deep Learning

Researches on pattern recognition have been tremendously performed in various fields because of its wide use in both machines and human beings. Previously, traditional methods used to study pattern recognition problems were not strong enough to recognize patterns accurately as compared to optimization algorithms. In this study, we employ both traditional based methods to detect the edges of each pattern in an image and apply convolutional neural networks to classify the right and wrong pattern of the cropped part of an image from the raw image. The results indicate that edge detection methods were not able to detect clearly the patterns due to low quality of the raw image while CNN was able to classify the patterns at an accuracy of 84% within 1.5 s for 10 epochs.

Novel magnetic field computation model in pattern classification

Field computation, an emerging computation technique, has inspired passion of intelligence science research. A novel field computation model based on the magnetic field theory is constructed. The proposed magnetic field computation （MFC） model consists of a field simulator, a non-derivative optimization algo- rithm and an auxiliary data processing unit. The mathematical model is deduced and proved that the MFC model is equivalent to a quadratic discriminant function. Furthermore, the finite element prototype is derived, and the simulator is developed, combining with particle swarm optimizer for the field configuration. Two benchmark classification experiments are studied in the numerical experiment, and one notable advantage is demonstrated that less training samples are required and a better generalization can be achieved.

Antibiotic Prescribing Patterns in Adult Patients According to the WHO AWaRe Classification: A Multi-Facility Cross-Sectional Study in Primary Healthcare Hospitals in Lusaka, Zambia

Introduction: Indiscriminate prescribing and using of antibiotics have led to the development of antimicrobial resistance (AMR). To reduce this problem, the World Health Organization (WHO) developed the “Access”, “Watch”, and “Reserve” (AWaRe) classification of antibiotics that promotes antimicrobial stewardship (AMS). In Zambia, there are gaps in practice regarding prescribing of antibiotics based on the AWaRe protocol. This study assessed antibiotic prescribing patterns in adult in-patients in selected primary healthcare hospitals in Lusaka, Zambia. Materials and Methods: This retrospective cross-sectional study was conducted using 388 patient medical files from September 2021 to November 2021, five primary healthcare hospitals namely;Chawama, Matero, Chilenje, Kanyama, and Chipata. Data analysis was performed using the Statistical Package for Social Sciences version 23. Results: Of the selected medical files, 52.3% (n = 203) were for male patients. Overall, the prevalence of antibiotic use was 82.5% (n = 320) which was higher than the WHO recommendation of a less than 30% threshold. The most prescribed antibiotic was ceftriaxone (20.3%), a Watch group antibiotic, followed by metronidazole (17.8%) and sulfamethoxazole/trimethoprim (16.3%), both belonging to the Access group. Furthermore, of the total antibiotics prescribed, 41.9% were prescribed without adhering to the standard treatment guidelines. Conclusion: This study found a high prescription of antibiotics (82.5%) that can be linked to non-adherence to the standard treatment guidelines in primary healthcare hospitals. The most prescribed antibiotic was ceftriaxone which belongs to the Watch group, raising a lot of concerns. There is a need for rational prescribing of antibiotics and implementation of AMS programs in healthcare facilities in Zambia, and this may promote surveillance of irrational prescribing and help reduce AMR in the future.

Adaptive associative classification with emerging frequent patterns

In this paper, we propose an enhanced associative classification method by integrating the dynamic property in the process of associative classification. In the proposed method, we employ a support vector machine（SVM） based method to refine the discovered emerging ~equent patterns for classification rule extension for class label prediction. The empirical study shows that our method can be used to classify increasing resources efficiently and effectively.

Mapping Software Metrics to Module Complexity: A Pattern Classification Approach

A desirable software engineering goal is the prediction of software module complexity (a qualitative concept) using automatically generated software metrics (quantitative measurements). This goal may be couched in the language of pattern classification;namely, given a set of metrics (a pattern) for a software module, predict the class (level of complexity) to which the module belongs. To find this mapping from metrics to complexity, we present a classification strategy, stochastic metric selection, to determine the subset of software metrics that yields the greatest predictive power with respect to module complexity. We demonstrate the effectiveness of this strategy by empirically evaluating it using a publicly available dataset of metrics compiled from a medical imaging system and comparing the prediction results against several classification system benchmarks.

Classification of Attribute Mastery Patterns Using Deep Learning

It is very important to identify the attribute mastery patterns of the examinee in cognitive diagnosis assessment. There are many methods to classify the attribute mastery patterns and many studies have been done to diagnose what the individuals have mastered and or Montel Carl Computer Simulation is used to study the classification of the attribute mastery patterns by Deep Learning. Four results were found. Firstly, Deep Learning can be used to classify the attribute mastery patterns efficiently. Secondly, the complication of the structures will decrease the accuracy of the classification. The order of the influence is linear, convergent, unstructured and divergent. It means that the divergent is the most complicated, and the accuracy of this structure is the lowest among the four structures. Thirdly, with the increasing rates of the slipping and guessing, the accuracy of the classification decreased in verse, which is the same as the existing research results. At last, the results are influenced by the sample size of the training, and the proper sample size is in need of deeper discussion.

醋酸靛胭脂混合三明治染色法联合智能分光比色技术结肠镜下Pit pattern分型对结直肠病变的诊断价值

目的 探讨醋酸靛胭脂混合(AIM)三明治染色法联合智能分光比色技术(FICE)结肠镜下Pit pattern分型对结直肠病变的诊断价值。方法 选择2022年6月-2023年10月该院收治的100例结直肠病变患者作为研究对象,共222处病灶;分别采用普通内镜、FICE和AIM三明治染色+FICE进行检查,并记录Pit pattern分型的检出情况、病理学类型;计算不同模式下Pit pattern分型诊断的敏感度、特异度、阳性预测值(PPV)、阴性预测值(NPV)和准确度,采用Kappa检验评估不同模式下Pit pattern分型诊断与病理学检查的一致性,采用受试者操作特征曲线(ROC curve)评估诊断效能。结果 与普通内镜(74.32%)相比,FICE(92.34%)和AIM三明治染色+FICE (97.30%) Pit pattern分型检出与病理结果符合率更高,且AIM三明治染色+FICE高于FICE,差异均有统计学意义(P <0.05);与普通内镜相比,FICE和AIM三明治染色+FICE诊断结直肠肿瘤性病变的准确度更高,且AIM三明治染色+FICE高于FICE,差异均有统计学意义(P <0.05);与普通内镜相比,FICE和AIM三明治染色+FICE诊断早期结直肠癌的准确度更高,差异均有统计学意义(P <0.05);普通内镜、FICE和AIM三明治染色+FICE预测结直肠肿瘤性病变的曲线下面积(AUC)分别为0.815 (95%CI:0.711~0.859)、0.881 (95%CI:0.752~0.904)和0.933 (95%CI:0.793~0.961);普通内镜、FICE和AIM三明治染色+FICE预测早期结直肠癌的AUC分别为0.850 (95%CI:0.720~0.866)、0.938(95%CI:0.764~0.951)和0.947 (95%CI:0.803~0.972);AIM三明治染色+FICE预测结直肠肿瘤性病变和早期结直肠癌的Youden指数最大,分别为0.955和0.968。结论 AIM三明治染色+FICE下Pit pattern分型诊断结直肠肿瘤性病变和早期结直肠癌的准确度较高,可有效提高内镜的诊治质量。