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The dynamometer card is a main method to analyze downhole working conditions of the beam pumping unit in actual operation. For computer based diagnosis mode, a method based on 16-directions chain codes and K-means clustering is proposed in this paper. First, the 16-directions chain codes are used to recreate boundary contour curve of the dynamometer card; then seven feature vectors which can accurately...
Over the past few years, the dimensionality of functional MRI (fMRI) effects the analysis of brain data. In the field of machine learning and statistical analysis, classification of objects plays a significant role. Machine learning classifiers are used to discover the class of new data points from a set of data points. The application of learning techniques on fMRI data alleviates to cognitive state...
Lower limb bones or lower limb component related to the torso with pelvic ankle interference can be fractured. Fractures can be detected automatically take advantage x-ray images performed using feature extraction methods. Feature Extraction helpful to know existence and location of fracture with x-ray images. This research apply Gray Level Co-Occurrence Matrix (GLCM) and K-Means Clustering Algorithm...
Nowadays the activity recognition based on multiple wearable sensors is still a challenging task due to the diversity of human activities. The application of unsupervised classification is helpful to discovery new activity classes and improve the activity classification model. Therefore, a new multi-sensor activity recognition scheme using the two-dimensional principal component analysis (2DPCA) and...
In this paper, we create a semi-supervised methodology for financial fraud detection in bank wire transactions based on a clustering-based-isolation-forest (CBiForest) algorithm. To test this hybrid model, we experiment on wire transaction data of twelve months from China Everbright Bank. The result of abnormal users is proved to be reliable and outperforms other clustering algorithms. Furthermore,...
Accurate network and phase connectivity models are crucial to distribution system analytics, operations and planning. Although network connectivity information is mostly reliable, phase connectivity data is typically missing or erroneous. In this paper, an innovative phase identification algorithm is developed by clustering of voltage time series gathered from smart meters. The feature-based clustering...
Trait is a particular characteristic that can produce a particular type of behavior. The email which is written communication medium among the people is the source, to identif1y the writing traits of the person. This paper proposes a novel approach to identify the writing traits of person from their email communication. The proposed technique is combination of an unsupervised k-means clustering algorithm...
responds from academic questionnaire generally contains many comments, advice and suggestions. This responds is not processed systematically due to lack of method to process. whereas such information might be very useful as additional source in decision making. Opinion mining is well suited to address the issue. The objective of this study is to develop opinion classification system using Maximum...
Soft computing techniques have emerged as a highly synergistic, computationally appealing, and conceptually unified framework supporting intelligent system design and analysis. The key contributing technologies of soft computing are neural network computing, fuzzy inference system, genetic algorithm or fusion of these techniques. This work proposes a method of analyzing cardiac signal (ECG) to diagnose...
Static security mechanisms such as firewalls can provide a reasonable level of security, but dynamic mechanisms like Intrusion Detection Systems (IDSs) should also be used. Different intrusion detection techniques can be employed to search for attack patterns in the observed data. Misuse detection and anomaly detection are the most commonly used techniques. But they have their own disadvantages. To...
An underwater target classifier can be trained only with the available limited instances of different ship and submarine emanations but in complex real world conditions, the classifier may encounter corrupted versions of the trained instances as well as novel occurrences such as targets belonging to an entirely different class. Most of the state-of-the art underwater target classifiers assign observed...
Classification of Network Traffic is one of most important issue in network management and detection of Intrusion attacks play a vital role in it. To have a holistic picture of the network intrusion detection, selection of appropriate feature is very important; it reduces analysis effort and time too. Data mining can be very fruitful for feature selection and intrusion detection. In this paper, Tcpdump...
The corpus callosum is one of the most important structures in human brain. Most of the neurological disorders reflect directly or indirectly on the morphological features of Corpus Callosum. The mid-sagittal brain Magnetic Resonance images fully describe the anatomical structure of corpus callosum. Often considered challenging task of segmenting Corpus Callosum from Magnetic Resonance images has...
Breast Cancer is one of the major health concerns of women all over the world. Computer Aided Detection (CAD) aids radiologists for the early detection of abnormalities in the breast masses. Abnormalities in the breast may be cancerous or non cancerous. This work proposes an effective CAD system that considerably reduces the misclassification rates of these abnormalities. 60 mammogram images were...
Condition monitoring and fault diagnosis of rotating machinery are very significant and practically challenging fields in industries for reducing maintenance costs. Fault diagnosis may be interpreted as a classification problem; therefore artificial intelligence-based classifiers can be efficiently used to classify normal and faulty machine conditions. K-means clustering is one of the methods applied...
This paper presents an innovative idea for the classification of individual drivers. The classification is based on each driver's driving features like, ratio of indicators to turns, number of brakes, number of time horn used, average gear, average speed, maximum speed and gear. K-means and hierarchical clustering is used to separate out the slow, normal and fast driving styles based on recorded data...
In recent years there is an apparent shift in research from content based image retrieval (CBIR) to automatic image annotation in order to bridge the gap between low level features and high level semantics of images. Automatic Image Annotation (AIA) techniques facilitate extraction of high level semantic concepts from images by machine learning techniques. Many AIA techniques use feature analysis...
Image searching is most interesting in the field of the computer vision. Every day many digital images are coming into the web. User is attracted to automatic image retrieval from this large dataset. Many methods which are introduced in this last ten years for image retrieval based on the similarity, size of database, image classification, similar group of images finding, performance of retrieval...
Image retrieval has been popular for several years. There are different system designs for content based image retrieval (CBIR) system. This paper propose a novel system architecture for CBIR system which combines techniques include content-based image and color analysis, as well as data mining techniques. To our best knowledge, this is the first time to propose segmentation and grid module, feature...
Hand gesture recognition is a growing and very vast field of research. Numerous work have been done and a lot of work still remains to be done for providing a intuitive, innovative and natural way of non verbal communication, which is more familiar to human beings. Gesture Recognition is widely used in sign language, alternative computer interfaces, Immersive game technology etc. The aim of this paper...
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