Pattern Classification Techniques Research Articles

Pattern classification of bearing faults in PMSM based on time domain feature ensembles

This research aims to identify an effective feature-based pattern classification technique that uses vibration and current data to identify bearing conditions. The authors attempted non-conventional time-domain features to detect the bearing conditions in permanent magnet synchronous motors (PMSM). This work employs two case studies utilizing eight datasets from Paderborn University to identify the bearing conditions of three and twelve classes. In this work, support vector machine, k-nearest neighbor, random forest, decision tree, and naive Bayes classifiers with 10% holdout validation are applied to study 31 feature combinations. This study also examines the Henry gas solubility optimization technique for feature selection to identify the most discriminating features. The results indicate that four feature ensembles consisting of 2 to 5 features performed better with the support vector machine, k-nearest neighbor, and random forest classifiers. In contrast to previous relevant studies, the proposed features are useful in identifying PMSM-bearing conditions with the highest accuracy of 99.8% and 99% using current signals for 3 and 12 classes respectively for combined current operating conditions.

Development of a Wafer Defect Pattern Classifier Using Polar Coordinate System Transformed Inputs and Convolutional Neural Networks

Defect pattern analysis of wafer bin maps (WBMs) is an important means of identifying process problems. Recently, automated analysis methods using machine learning or deep learning have been studied as alternatives to manual classification by engineers. In this paper, we propose a method to improve the feature extraction performance of defect patterns by transforming the polar coordinate system instead of the existing WBM image input. To reduce the variability of the location representation, defect patterns in the Cartesian coordinate system, where the location of the distributed defect die is not constant, were converted to a polar coordinate system. The CNN classifier, which uses polar coordinate transformed input, achieved a classification accuracy of 91.3%, which is 4.8% better than the existing WBM image-based CNN classifier. Additionally, a tree-structured classifier model that sequentially connects binary classifiers achieved a classification accuracy of 94%. The method proposed in this paper is also applicable to the defect pattern classification of WBMs consisting of different die sizes than the training data. Finally, the paper proposes an automated pattern classification method that uses individual classifiers to learn defect types and then applies ensemble techniques for multiple defect pattern classification. This method is expected to reduce labor, time, and cost and enable objective labeling instead of relying on subjective judgments of engineers.

A lateralized alpha-band marker of the interference of exogenous attention over endogenous attention.

Scientists differentiate top-down from bottom-up visuospatial attention processes. While bottom-up attention is rapidly engaged by emerging demands from the environment, top-down attention in contrast reflects slow voluntary shifts of spatial attention. Several lines of research substantiate the idea that top-down and bottom-up attentional processes involve distinct functional systems. An increasing number of studies, however, argue that both attention systems share brain processing resources. The current study examines how salient visual events that engage bottom-up processes interfere with top-down attentional processes. Using neurophysiological recordings and multivariate pattern classification techniques, the authors show that these patterns of interference occur within the alpha-band of neural activity (8-12Hz), which implies that bottom-up and top-down attention processes share this narrow-band frequency brain resource. The results further demonstrate that patterns of alpha-band activity explains, in part, the interference between top-down and bottom-up attention at the behavioral level.

Research on Rain Pattern Classification Based on Machine Learning: A Case Study in Pi River Basin

For the purpose of improving the scientific nature, reliability, and accuracy of flood forecasting, it is an effective and practical way to construct a flood forecasting scheme and carry out real-time forecasting with consideration of different rain patterns. The technique for rain pattern classification is of great significance in the above-mentioned technical roadmap. With the rapid development of artificial intelligence technologies such as machine learning, it is possible and necessary to apply these new methods to assist rain classification applications. In this research, multiple machine learning methods were adopted to study the time-history distribution characteristics and conduct rain pattern classification from observed rainfall time series data. Firstly, the hourly rainfall data between 2003 and 2021 of 37 rain gauge stations in the Pi River Basin were collected to classify rain patterns based on the universally acknowledged dynamic time warping (DTW) algorithm, and the classifications were treated as the benchmark result. After that, four other machine learning methods, including the Decision Tree (DT), Long- and Short-Term Memory (LSTM) neural network, Light Gradient Boosting Machine (LightGBM), and Support Vector Machine (SVM), were specifically selected to establish classification models and the model performances were compared. By adjusting the sampling size, the influence of different sizes on the classification was analyzed. Intercomparison results indicated that LightGBM achieved the highest accuracy and the fastest training speed, the accuracy and F1 score were 98.95% and 98.58%, respectively, and the loss function and accuracy converged quickly after only 20 iterations. LSTM and SVM have satisfactory accuracy but relatively low training efficiency, and DT has fast classification speed but relatively low accuracy. With the increase in the sampling size, classification results became stable and more accurate. Besides the higher accuracy, the training efficiency of the four methods was also improved.

Study on Tomato Disease Classification based on Leaf Image Recognition based on Deep Learning Technology

The utilization of computer vision technology is of the utmost significance in the examination of plant diseases. Research utilizing image processing to investigate plant diseases necessitates the analysis of discernible patterns on plants. Recently, numerous image processing and pattern classification techniques have been employed in the construction of a digital vision system capable of recognizing and categorizing the visual manifestations of plant diseases. Given the abundance of algorithms formulated for the purpose of plant leaf image classification for the detection of plant diseases, it is imperative to assess the accuracy of each algorithm, as well as its potential to identify diverse disease types. The main objective of this study is to explore accurate deep learning architectures that are more effective in deploying and detecting tomato diseases, thus eliminating human error when identifying tomato diseases through visual observation. and get more widespread use. An initial model was constructed from the ground up using a convolutional neural network (CNN), which was trained with 22930 tomato leaf images, and then compared to VGG16, Mobile Net, and Inceptionv3 architectures through a fine-tuning process. The basic CNN model achieved a training accuracy of 90%, whereas the training accuracies of VGG16, Mobile Net, and Inceptionv3 were respectively observed to be 89%, 91%, and 87%. The VGG16 model has a greater computational complexity than other approaches due to its considerable quantity of predefined parameters. Despite to be simpler, MobileNet proved to be the most efficient in terms of accuracy and thus is the most suitable for this research, due to its lightweight structure, fast functioning and adaptability for mobile devices. In contrast to other architectures, the suggested CNN architecture exhibits shallower characteristics, facilitating faster training on the same dataset. This research will provide a solid foundation for future scholars to easily improve the categorization of plant diseases, which is to develop algorithms that are lighter, faster, easier to run, and have higher accuracy.

DPCF: A framework for imputing missing values and clustering data in drug discovery process

The advent of modern Internet of Things (IoT) architectures has led to ease in data collection and availability. The data generated from such architectures are of large volume and dimensionality. As a result, data missingness and data labeling are the commonly occurring problems in the data collection process when data volume is too large. Data clustering is a commonly used unsupervised pattern classification technique that helps in identifying the hidden structure of the datasets and clusters or groups similar data items together. In context to chemometrics, clustering techniques play a significant role in identifying the structure-property relationships and structure-activity relationships among different compounds in drug discovery process. The quantitative structure-property relationships (QSPR) are based on the similar property principle which states that if the compounds are clustered together based on structural descriptors, the compounds within the same cluster will have similar properties. The quantitative structure-activity relationships (QSAR) help in determining the empirical relationships between chemical structure and biological activity of a set of identical compounds. Hence, a variety of compounds can be divided into homogeneous subsets using cluster analysis. The effectiveness of any clustering algorithm mostly depends on how efficiently the initial cluster centers are identified. Traditional techniques that mostly rely on the random selection of initial cluster centers and different parameter settings may result in distinct clusters for the same dataset. Moreover, if the initial cluster centers selected are outliers then the clusters formed are of poor quality. The quality of each cluster can be determined by using distinct cluster validation indices (CVIs). This paper aims to solve the problems of data missingness as well as data labeling for unsupervised datasets. In continuation to the previous study where the NMVI (Nullify the Missing Values before Imputation) technique efficiently imputes the missing values in different datasets, in this paper the proposed DPCF (Data Partitioning-based Clustering Framework) framework utilize the NMVI technique to impute the missing values and a novel Z-Clust clustering algorithm is proposed that efficiently clusters the unlabeled data samples. The integration of the NMVI technique and Z-Clust clustering technique in the proposed framework makes it well suited for the analysis of unsupervised datasets having missing values. The performance evaluation of the proposed Z-Clust clustering technique is done by using five standard CVIs and the results are compared with the existing clustering techniques. The experimental results depict that the proposed Z-Clust clustering technique shows better cluster formation as compared to the existing clustering techniques. Henceforth, the proposed DPCF framework is well suited for the analysis of datasets without labels and having missing values.

Reconfiguration of Functional Dynamics in Cortico-Thalamo-Cerebellar Circuit in Schizophrenia Following High-Frequency Repeated Transcranial Magnetic Stimulation.

Schizophrenia is a serious mental illness characterized by a disconnection between brain regions. Transcranial magnetic stimulation is a non-invasive brain intervention technique that can be used as a new and safe treatment option for patients with schizophrenia with drug-refractory symptoms, such as negative symptoms and cognitive impairment. However, the therapeutic effects of transcranial magnetic stimulation remain unclear and would be investigated using non-invasive tools, such as functional connectivity (FC). A longitudinal design was adopted to investigate the alteration in FC dynamics using a dynamic functional connectivity (dFC) approach in patients with schizophrenia following high-frequency repeated transcranial magnetic stimulation (rTMS) with the target at the left dorsolateral prefrontal cortex (DLPFC). Two groups of schizophrenia inpatients were recruited. One group received a 4-week high-frequency rTMS together with antipsychotic drugs (TSZ, n = 27), while the other group only received antipsychotic drugs (DSZ, n = 26). Resting-state functional magnetic resonance imaging (fMRI) and psychiatric symptoms were obtained from the patients with schizophrenia twice at baseline (t1) and after 4-week treatment (t2). The dynamics was evaluated using voxel- and region-wise FC temporal variability resulting from fMRI data. The pattern classification technique was used to verify the clinical application value of FC temporal variability. For the voxel-wise FC temporary variability, the repeated measures ANCOVA analysis showed significant treatment × time interaction effects on the FC temporary variability between the left DLPFC and several regions, including the thalamus, cerebellum, precuneus, and precentral gyrus, which are mainly located within the cortico-thalamo-cerebellar circuit (CTCC). For the ROI-wise FC temporary variability, our results found a significant interaction effect on the FC among CTCC. rTMS intervention led to a reduced FC temporary variability. In addition, higher alteration in FC temporal variability between left DLPFC and right posterior parietal thalamus predicted a higher remission ratio of negative symptom scores, indicating that the decrease of FC temporal variability between the brain regions was associated with the remission of schizophrenia severity. The support vector regression (SVR) results suggested that the baseline pattern of FC temporary variability between the regions in CTCC could predict the efficacy of high-frequency rTMS intervention on negative symptoms in schizophrenia. These findings confirm the potential relationship between the reduction in whole-brain functional dynamics induced by high-frequency rTMS and the improvement in psychiatric scores, suggesting that high-frequency rTMS affects psychiatric symptoms by coordinating the heterogeneity of activity between the brain regions. Future studies would examine the clinical utility of using functional dynamics patterns between specific brain regions as a biomarker to predict the treatment response of high-frequency rTMS.

Maturation of large-scale brain systems over the first month of life.

The period immediately after birth is a critical developmental window, capturing rapid maturation of brain structure and a child's earliest experiences. Large-scale brain systems are present at delivery, but how these brain systems mature during this narrow window (i.e. first weeks of life) marked by heightened neuroplasticity remains uncharted. Using multivariate pattern classification techniques and functional connectivity magnetic resonance imaging, we detected robust differences in brain systems related to age in newborns (n = 262; R2 = 0.51). Development over the first month of life occurred brain-wide, but differed and was more pronounced in brain systems previously characterized as developing early (i.e. sensorimotor networks) than in those characterized as developing late (i.e. association networks). The cingulo-opercular network was the only exception to this organizing principle, illuminating its early role in brain development. This study represents a step towards a normative brain "growth curve" that could be used to identify atypical brain maturation in infancy.

Data Mining and Machine Learning Techniques for Credit Card Fraud Detection

In the recent era, everybody is dealing with digital data. In such a scenario, individual one heavily depends on credit card. Therefore, the demand of online transactions and usage of e-commerce sites are rising at the rapid rate. The online payments are the main cause of increasing crime rate heavily. Hence, it is the biggest challenge for the IT sector to identify and solve such critical problems. This critical issue can be tackled with the help of machine learning. This paper mainly emphasis on various data mining algorithms such as C4.5, CART algorithms, J48, Naïve Bayes algorithm, EM algorithm, Apriori algorithm, and SVM, and inform the accuracy and precision of the result. The machine learning finds the genuine and non-genuine transition using learning pattern matching and classification technique. The machine learning also normalized the data, identified the anomalies in transaction, and provided appropriate results.

Corrosion evaluation of carbon steel bars by magnetic non-destructive method

ABSTRACT Reinforced concrete structures undergo various degradation processes, mainly rebars corrosion. Recently, magnetic non-destructive tests have been used to study the steel components used in concrete structures. This paper proposes a non-destructive method, based on the electromagnetic induction technique, capable of identifying corrosion in steel bars. Therefore, through signals obtained from sinusoidal and triangular waveform excitations, magnetic hysteresis loops were determined in steel bars of 5.0, 6.3, 8.0, 10.0, 12.5 and 16.0 mm diameters at 0, 10 and 20 mm depths. Magnetic tests were performed in uncorroded steel bars and in bars that have been exposed to corrosion by the salt spray test. The magnetic responses, remanence, coercivity and the maximum values of hysteresis curves obtained were organised into classes for uncorroded and corroded bars of each steel diameter, totalling 12 classes. A statistical analysis of the experimental results has been performed, and a detailed analysis of the data has also been performed by using the pattern classification techniques, namely, principal component analysis, Karhunen–Loéve transformation, Gaussian classifier and artificial neural network. The results show that the investigated method has great potential of becoming an electromagnetic non-destructive technique for the identification of corroded steel bars in concrete structures.

Prediction of Breast Cancer Using Extremely Randomized Clustering Forests (ERCF) Technique

Breast cancer is a significant public health concern in both developed and developing countries. It is almost one in three cancers diagnosed in all women. Data mining and pattern recognition applications in conjunction have been proven to be quite useful and relevant to extract the information useful for the medical purpose. This research work reflects the work based on extremely randomized clustering forests (ERCF) technique which is nothing but a type of pattern recognition technique that may be implemented as the prediction model for breast cancer (BC). The accuracy achieved through ERCF has also been compared with that of k-NN (correlation) and k-NN (Euclidean) in this research work (where k-NN refers to k-nearest neighbours technique), and thereafter, final conclusions have been drawn depending upon the testing attributes. The results show that the accuracy of ERCF in the forecasting of breast cancer is so much larger than that of the exactness of k-NN (correlation) and k-NN (Euclidean). Hence, ERCF, a randomized technique for pattern classification, is best.

Fingerprint Presentation Attack Detection in Open-Set Scenario Using Transient Liveness Factor

Background: In recent history, fingerprint presentation attack detection (FPAD) proposal came out in a variety of ways. A close-set approach uses pattern classification technique that best suits to a specific context and goal. Openset approach works fine in wider context, which is relatively robust with new fabrication material and independent of sensor type. In both case results were promising but not too generalizable because of unseen condition not fitting into method used. It is clear, the two key challenges in FPAD system, sensor interoperability and robustness with new fabrication materials not addressed to date. Objective: To address above challenge a liveness detection model is proposed using live sample using transient liveness factor and one-class CNN. Methods: In our architecture, liveness is predicted by using the fusion rule, score level fusion of two decisions. Here, ‘n’ high quality live samples are initially trained for quality. We have observed that fingerprint liveness information is ‘transitory’ in nature, a variation in the different live sample is natural. Thus, each live sample has a ‘transient liveness’ (TL) information. We use no-reference (NR) image quality measure (IQM) as a transient value corresponding to each live sample. A consensus agreement is collectively reached in transient value to predict adversarial input. Further, live sample at server are trained with augmented inputs on the one-class classifier to predict the outlier. So, by using the fusion rule, score level fusion of consensus agreement and appropriately characterized negative cases (or outliers) predicts liveness. Results: Our approach uses high quality 30-live sample only, out of 90 images available in dataset to reduce learning time. We used Time Series images from LivDet competition 2015. It has 90-live images and 45-spoof images made from Bodydouble, Ecoflex and Playdoh of each person. Fusion rule results in 100% accuracy in recognising live as live. Conclusion: We have presented an architecture for liveness-server for extraction/updating transient liveness factor. Our work explained here a significant step forward towards generalized and reproducible process with a consideration towards the provision for the universal scheme as a need of today. The proposed TLF approach has a solid presumption; it will address dataset heterogeneity as it incorporates wider scope-context. Similar results with other dataset are under validation. Implementation seems difficult now but have several advantages when carried out during the transformative process.

Comparison of Feature Extraction Techniques for Pattern Classification

Pattern recognition is a challenging task in research field for the last few decades. Many researchers have worked on areas such as computer vision, speech recognition, document classification, and computational biology to tackle complex research problems. In this article, a pattern recognition problem for handwritten Malayalam character is presented. This system goes through two different stages of HCR namely, feature extraction and classification. Three feature extraction techniques – wavelet transform, zoning, division point – are used in this study. Among these, division is point is able to show best discriminative power using SVM classifier. All the experiments are conducted on size normalized and binarized images of isolated Malayalam characters.

Raman spectroscopy and surface-enhanced Raman spectroscopy (SERS) spectra of salivary glands carcinoma, tumor and healthy tissues and their homogenates analyzed by chemometry: Towards development of the novel tool for clinical diagnosis

In this study, two approaches to salivary glands studies are presented: Raman imaging (RI) of tissue cross-section and surface-enhanced Raman spectroscopy (SERS) of tissue homogenates prepared according to elaborated protocol. Collected and analyzed data demonstrate the significant potential of SERS combined with multivariate analysis for distinguishing carcinoma or tumor from the normal salivary gland tissues as a rapid, label-free tool in cancer detection in oncological diagnostics. Raman imaging allows a detailed analysis of the cell wall's chemical composition; thus, the compound's distribution can be semi-quantitatively analyzed, while SERS of tissue homogenates allow for detailed analysis of all moieties forming these tissues. In this sense, SERS is more sensitive and reliable to study any changes in the area of infected tissues. Principal component analysis (PCA), as an unsupervised pattern recognition method, was used to identify the differences in the SERS salivary glands homogenates. The partial least squares-discriminant analysis (PLS-DA), the supervised pattern classification technique, was also used to strengthen further the computed model based on the latent variables in the SERS spectra. Moreover, the chemometric quantification of obtained data was analyzed using principal component regression (PCR) multivariate calibration. The presented data prove that the PCA algorithm allows for 91% in seven following components and the determination between healthy and tumor salivary gland homogenates. The PCR and PLS-DA methods predict 90% and 95% of the variance between the studied groups (in 6 components and 4 factors, respectively). Moreover, according to calculated RMSEC (RMSEP), R2C (R2P) values and correlation accuracy (based on the ROC curve), the PLS-DA model fits better for the studied data. Thus, SERS methods combined with PLS-DA analysis can be used to differentiate healthy, neoplastic, and mixed tissues as a competitive tool in relation to the commonly used method of histopathological staining of tumor tissue.

Diabetes Prediction Using Feature Selection

Neural network is one of the pattern classification techniques, which has been widely used in many fields application. The multilayer perceptron in a training process has an accuracy impact. The selection of features is another factor that influences classification accuracy. The objective of this research is to optimize simultaneously parameters and subset of functionalities in order to increase multilayer perceptron accuracy. A genetic algorithm approach is presented in order to characterize selection and optimization parameters that serve as input to the multilayer perceptron classifier. The proposed algorithm can scan irrelevant information in PIMA database and obtain better accuracy. The results of this experience show that the proposed algorithm can provide a significant performance gain in terms of accuracy and training speed for the multilayer perceptron classification.

A Fuzzy based technique for Pattern Recognition & Classification

A pattern can either be seen genuinely or it tends to be watched numerically by applying calculations. Pattern classification is worried about the capacity to discover absolute names for a lot of perceptions. A pattern classification task was viewed as an example determination issue where a meager subset of test from the marked preparing set was picked. We proposed a versatile learning calculation using the least square capacity to address this issue. Utilizing these chose tests, which we call educational vectors, a classifier equipped for perceiving the test tests was built up. This epic calculation is a mix of looking through systems that, in light of forward looking through advances, yet Adaptive finds a way to address the blunders presented by before forward advances. This paper reviews cost-delicate fuzzy standard based frameworks for pattern classification. Weighted preparing patterns are utilized to build cost-touchy fuzzy principle-based frameworks. A fuzzy classification framework is built from a given arrangement of preparing patterns. It is accepted that a weight is appointed to each preparation pattern from the earlier. The heaviness of preparing patterns can be determined dependent on their dispersion.

Performance Evaluation of EMG Pattern Recognition Techniques While Increasing The Number of Movement Classes

:In the past few years of research done in the field of myoelectric control, many researchers have proposed several models imploying a combination of different features and classifiers to increase the movement classes, but all that work fails to explain if there is any correlation between multi-class classification and its accuracy. This paper focuses on finding the factors that decide the limit of movement classes that machine learning algorithms can accurately differentiate and to evaluate the performance of pattern classification techniques using the sEMG signal when the number of movement classes is increased while keeping the simplicity of the system. The results were obtained for eight channels sEMG signal using 7 independent time-domain features and four feature set combinations over 4 classifiers (Support Vector Machine(SVM), K-Nearest Neighbour(K-NN), Decision Tree(DT), and Naïve Bayes(NB)). Then the number of classes was increased in the manner of 5, 7, 10, 12, and 15 classes to determine the highest number of movement classes that the sEMG system with above-described features can classify efficiently. And the effect of increasing the number of movement classes on system accuracy was observed. The highest accuracies for all five class progression were obtained for SVM with the MFL feature, and for DT using MAV, it was successfully observed that the NB classifier had minimum performance depletion for the features used in this work

A pattern analysis based underwater video segmentation system for target object detection

Detecting and classifying the target objects in the underwater videos is the primary and essential operation in this modern era. The present works proposed the pattern extraction, segmentation and classification techniques for target object detection in underwater images. These techniques were found to be lacking with the following limitations, such as inefficient computation, inaccurate results, and increased cost-efficiency. Thus, this work targets to introduce a new pattern extraction based classification system for processing underwater images. As the first and foremost thing, the input underwater picture is preprocessed for eliminating the noise by applying the Laplacian Bellman filtering technique. After that, the histogram equalization is utilized to enhance the quality of the source picture. Once the image is noise-free, the patterns are extracted from it with the help of the likelihood gradient pattern technique. Subsequently, the label formation and blob extraction processes are performed in an orderly manner to track the target object accurately. In this work, the novelty is seen implemented in both the preprocessing and the feature analysis stages by developing a novel technique. The significant advantages of this work are: yielding the improved image quality, by being the efficient texture pattern extractor, and they also do not require any additional information and adjustments. While under simulation, the fulfilment attained in the proposed techniques and the appropriate literature methodologies are evaluated and validated with the performance measures like accuracy, sensitivity, specificity, average time, precision, F1-measure, and the filtering features like entropy, contrast, and the correlation.

Metacognitive Training Modulates Default-Mode Network Homogeneity During 8-Week Olanzapine Treatment in Patients With Schizophrenia.

BackgroundPrevious studies have revealed the efficacy of metacognitive training for schizophrenia. However, the underlying mechanisms of metacognitive training on brain function alterations, including the default-mode network (DMN), remain unknown. The present study explored treatment effects of metacognitive training on functional connectivity of the brain regions in the DMN.MethodsForty-one patients with schizophrenia and 20 healthy controls were scanned using resting-state functional magnetic resonance imaging. Patients were randomly assigned to drug plus psychotherapy (DPP) and drug therapy (DT) groups. The DPP group received olanzapine and metacognitive training, and the DT group received only olanzapine for 8 weeks. Network homogeneity (NH) was applied to analyze the imaging data, and pattern classification techniques were applied to test whether abnormal NH deficits at baseline might be used to discriminate patients from healthy controls. Abnormal NH in predicting treatment response was also examined in each patient group.ResultsCompared with healthy controls, patients at baseline showed decreased NH in the bilateral ventral medial prefrontal cortex (MPFC), right posterior cingulate cortex (PCC)/precuneus, and bilateral precuneus and increased NH in the right cerebellum Crus II and bilateral superior MPFC. NH values in the right PCC/precuneus increased in the DPP group after 8 weeks of treatment, whereas no substantial difference in NH value was observed in the DT group. Support vector machine analyses showed that the accuracy, sensitivity, and specificity for distinguishing patients from healthy controls were more than 0.7 in the NH values of the right PCC/precuneus, bilateral ventral MPFC, bilateral superior MPFC, and bilateral precuneus regions. Support vector regression analyses showed that high NH levels at baseline in the bilateral superior MPFC could predict symptomatic improvement of positive and negative syndrome scale (PANSS) after 8 weeks of DPP treatment. No correlations were found between alterations in the NH values and changes in the PANSS scores/cognition parameters in the patients.ConclusionThis study provides evidence that metacognitive training is related to the modulation of DMN homogeneity in schizophrenia.

IMPLEMENTASI ALGORITMA C4.5 UNTUK PENGOLAHAN DATA HUTAN LINDUNG (STUDI KASUS: DINAS KEHUTANAN SUMATERA UTARA)

Data Mining is an information discovery by extracting information patterns that contain trend searches in a very large amount of data and assist the process of storing data in making a decision in the future. In determining the pattern classification techniques are done collecting records (Training set).Forests are a very important role for national and state development. Because forests can provide maximum benefits. However, the current situation of Protected Forest has experienced a drastic reduction in area, for that reason utilizing Protected Forest data can produce information about Protected Forest that is a priority and which is not a priority to be reforested or handled first so that the forest function is correct in its use.C4.5 algorithm or commonly known as the decision tree method can provide rule data information to describe the processes associated with processing protected forest data. The characteristics of the classified data can be obtained clearly, both in the form of decision tree structures and in the form of rules. So that in the testing phase with Tanagra 1.4 software can assist in processing valid Protection Forest data.Keywords: Data Mining, Protection Forest, C4.5 Algorithm, Tanagra Version 1.4