Search results for: combined classifier

Authors: V. Kabeer, N.K.Narayanan

Abstract:

This paper reports a new pattern recognition approach for face recognition. The biological model of light receptors - cones and rods in human eyes and the way they are associated with pattern vision in human vision forms the basis of this approach. The functional model is simulated using CWD and WPD. The paper also discusses the experiments performed for face recognition using the features extracted from images in the AT & T face database. Artificial Neural Network and k- Nearest Neighbour classifier algorithms are employed for the recognition purpose. A feature vector is formed for each of the face images in the database and recognition accuracies are computed and compared using the classifiers. Simulation results show that the proposed method outperforms traditional way of feature extraction methods prevailing for pattern recognition in terms of recognition accuracy for face images with pose and illumination variations.

Keywords: Face recognition, Image analysis, Wavelet feature extraction, Pattern recognition, Classifier algorithms

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Authors: Nermine Ahmed Hendy, Hania Farag

Abstract:

Emotion recognition is an important research field that finds lots of applications nowadays. This work emphasizes on recognizing different emotions from speech signal. The extracted features are related to statistics of pitch, formants, and energy contours, as well as spectral, perceptual and temporal features, jitter, and shimmer. The Artificial Neural Networks (ANN) was chosen as the classifier. Working on finding a robust and fast ANN classifier suitable for different real life application is our concern. Several experiments were carried out on different ANN to investigate the different factors that impact the classification success rate. Using a database containing 7 different emotions, it will be shown that with a proper and careful adjustment of features format, training data sorting, number of features selected and even the ANN type and architecture used, a success rate of 85% or even more can be achieved without increasing the system complicity and the computation time

Keywords: Classification, emotion recognition, features extraction, feature selection, neural network

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Authors: Hanfei Tuo, Yanzhong Li

Abstract:

This paper presented a novel combined cycle of air separation and natural gas liquefaction. The idea is that natural gas can be liquefied, meanwhile gaseous or liquid nitrogen and oxygen are produced in one combined cryogenic system. Cycle simulation and exergy analysis were performed to evaluate the process and thereby reveal the influence of the crucial parameter, i.e., flow rate ratio through two stages expanders β on heat transfer temperature difference, its distribution and consequent exergy loss. Composite curves for the combined hot streams (feeding natural gas and recycled nitrogen) and the cold stream showed the degree of optimization available in this process if appropriate β was designed. The results indicated that increasing β reduces temperature difference and exergy loss in heat exchange process. However, the maximum limit value of β should be confined in terms of minimum temperature difference proposed in heat exchanger design standard and heat exchanger size. The optimal βopt under different operation conditions corresponding to the required minimum temperature differences was investigated.

Keywords: combined cycle simulation, exergy analysis, natural gas liquefaction.

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Authors: I. Slim, H. Akkari, A. Ben Abdallah, I. Bhouri, M. Hedi Bedoui

Abstract:

Osteoporosis is a common disease characterized by low bone mass and deterioration of micro-architectural bone tissue, which provokes an increased risk of fracture. This work treats the texture characterization of trabecular bone radiographs. The aim was to analyze according to clinical research a group of 174 subjects: 87 osteoporotic patients (OP) with various bone fracture types and 87 control cases (CC). To characterize osteoporosis, Fractal and MultiFractal (MF) methods were applied to images for features (attributes) extraction. In order to improve the results, a new method of MF spectrum based on the q-stucture function calculation was proposed and a combination of Fractal and MF attributes was used. The Support Vector Machines (SVM) was applied as a classifier to distinguish between OP patients and CC subjects. The features fusion (fractal and MF) allowed a good discrimination between the two groups with an accuracy rate of 96.22%.

Keywords: Fractal, micro-architecture analysis, multifractal, SVM, osteoporosis.

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Authors: Asmaa M. Hussein, Amr Wassal, Ahmed Farouk Al-Sadek, A. F. Abd El-Rahman

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In this study, a multivariate analysis of potato spectroscopic data was presented to detect the presence of brown rot disease or not. Near-Infrared (NIR) spectroscopy (1,350-2,500 nm) combined with multivariate analysis was used as a rapid, non-destructive technique for the detection of brown rot disease in potatoes. Spectral measurements were performed in 565 samples, which were chosen randomly at the infection place in the potato slice. In this study, 254 infected and 311 uninfected (brown rot-free) samples were analyzed using different advanced statistical analysis techniques. The discrimination performance of different multivariate analysis techniques, including classification, pre-processing, and dimension reduction, were compared. Applying a random forest algorithm classifier with different pre-processing techniques to raw spectra had the best performance as the total classification accuracy of 98.7% was achieved in discriminating infected potatoes from control.

Keywords: Brown rot disease, NIR spectroscopy, potato, random forest.

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Authors: T. Yavuz, Y. E. Akansu, M. Sarıoglu, M. Ozmert

Abstract:

Vortex-shedding phenomenon of the flow around combined two bodies having various geometries and sizes has been investigated experimentally in the Reynolds number range between 4.1x103 and 1.75x104. To see the effect of the rotation of the bodies on the vortex shedding, the combined bodies were rotated from 0° to 180°. The combined models have a cross section composing of a main circular cylinder and an attached circular or square cylinder. Results have shown that Strouhal numbers for two cases were changed considerably with the angle of incidence, while it was found to be largely independent of Reynolds number at 150. Characteristics of the vortex formation region and location of flow attachments, reattachments, and separations were observed by means of the flow visualizations. Depending on the inclination angle the effects of flow attachment, separation and reattachment on vortex-shedding phenomenon have been discussed.

Keywords: Bluff body, vortex shedding, flow separation, flow reattachment

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Authors: Homayoon Zarshenas, Mahdi Bamdad, Hadi Grailu, Akbar A. Shakoori

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In this study we focus on improvement performance of a cue based Motor Imagery Brain Computer Interface (BCI). For this purpose, data fusion approach is used on results of different classifiers to make the best decision. At first step Distinction Sensitive Learning Vector Quantization method is used as a feature selection method to determine most informative frequencies in recorded signals and its performance is evaluated by frequency search method. Then informative features are extracted by packet wavelet transform. In next step 5 different types of classification methods are applied. The methodologies are tested on BCI Competition II dataset III, the best obtained accuracy is 85% and the best kappa value is 0.8. At final step ordered weighted averaging (OWA) method is used to provide a proper aggregation classifiers outputs. Using OWA enhanced system accuracy to 95% and kappa value to 0.9. Applying OWA just uses 50 milliseconds for performing calculation.

Keywords: BCI, EEG, Classifier, Fuzzy operator, OWA.

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Authors: R. Mallika, V. Saravanan

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This paper gives a novel method for improving classification performance for cancer classification with very few microarray Gene expression data. The method employs classification with individual gene ranking and gene subset ranking. For selection and classification, the proposed method uses the same classifier. The method is applied to three publicly available cancer gene expression datasets from Lymphoma, Liver and Leukaemia datasets. Three different classifiers namely Support vector machines-one against all (SVM-OAA), K nearest neighbour (KNN) and Linear Discriminant analysis (LDA) were tested and the results indicate the improvement in performance of SVM-OAA classifier with satisfactory results on all the three datasets when compared with the other two classifiers.

Keywords: Support vector machines-one against all, cancerclassification, Linear Discriminant analysis, K nearest neighbour, microarray gene expression, gene pair ranking.

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Authors: Sui Yan Wong, Keat Ping Yeoh, Chi Wai Hui

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In this study, supercritical steam is introduced to Combined Cycle Power Plant (CCPP) in an attempt to further optimize energy recovery. Subcritical steam is commonly used in the CCPP, operating at maximum pressures around 150-160 bar. Supercritical steam is an alternative to increase heat recovery during vaporization period of water. The idea of improvement using supercritical steam is further examined with the use of exergy, pinch analysis and Aspen Plus simulation.

Keywords: Exergy, pinch, combined cycle power plant, CCPP, supercritical steam.

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Authors: S. Anbumozhi, P. S. Manoharan

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Medical Image fusion plays a vital role in medical field to diagnose the brain tumors which can be classified as benign or malignant. It is the process of integrating multiple images of the same scene into a single fused image to reduce uncertainty and minimizing redundancy while extracting all the useful information from the source images. Fuzzy logic is used to fuse two brain MRI images with different vision. The fused image will be more informative than the source images. The texture and wavelet features are extracted from the fused image. The multilevel Adaptive Neuro Fuzzy Classifier classifies the brain tumors based on trained and tested features. The proposed method achieved 80.48% sensitivity, 99.9% specificity and 99.69% accuracy. Experimental results obtained from fusion process prove that the use of the proposed image fusion approach shows better performance while compared with conventional fusion methodologies.

Keywords: Image fusion, Fuzzy rules, Neuro-fuzzy classifier.

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Authors: Addin Osman, Anwar Ali Yahya, Mohammed Basit Kamal

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Datasets or collections are becoming important assets by themselves and now they can be accepted as a primary intellectual output of a research. The quality and usage of the datasets depend mainly on the context under which they have been collected, processed, analyzed, validated, and interpreted. This paper aims to present a collection of program educational objectives mapped to student’s outcomes collected from self-study reports prepared by 32 engineering programs accredited by ABET. The manual mapping (classification) of this data is a notoriously tedious, time consuming process. In addition, it requires experts in the area, which are mostly not available. It has been shown the operational settings under which the collection has been produced. The collection has been cleansed, preprocessed, some features have been selected and preliminary exploratory data analysis has been performed so as to illustrate the properties and usefulness of the collection. At the end, the collection has been benchmarked using nine of the most widely used supervised multiclass classification techniques (Binary Relevance, Label Powerset, Classifier Chains, Pruned Sets, Random k-label sets, Ensemble of Classifier Chains, Ensemble of Pruned Sets, Multi-Label k-Nearest Neighbors and Back-Propagation Multi-Label Learning). The techniques have been compared to each other using five well-known measurements (Accuracy, Hamming Loss, Micro-F, Macro-F, and Macro-F). The Ensemble of Classifier Chains and Ensemble of Pruned Sets have achieved encouraging performance compared to other experimented multi-label classification methods. The Classifier Chains method has shown the worst performance. To recap, the benchmark has achieved promising results by utilizing preliminary exploratory data analysis performed on the collection, proposing new trends for research and providing a baseline for future studies.

Keywords: Benchmark collection, program educational objectives, student outcomes, ABET, Accreditation, machine learning, supervised multiclass classification, text mining.

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Authors: Yang Xiaoliang, Liu Wei, Wang Hongbo, Zhao Yunfei

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Numerical investigation of the characteristics of an 80° delta wing in combined force-pitch and free-roll is presented. The implicit, upwind, flux-difference splitting, finite volume scheme and the second-order-accurate finite difference scheme are employed to solve the flow governing equations and Euler rigid-body dynamics equations, respectively. The characteristics of the delta wing in combined free-roll and large amplitude force-pitch is obtained numerically and shows a well agreement with experimental data qualitatively. The motion in combined force-pitch and free-roll significantly reduces the lift force and transverse stabilities of the delta wing, which is closely related to the flying safety. Investigations on sensitive factors indicate that the roll-axis moment of inertia and the structural damping have great influence on the frequency and amplitude, respectively. Moreover, the turbulence model is considered as an influencing factor in the investigation.

Keywords: combined force-pitch and free-roll, numericalsimulation, sensitive factors, slender delta wing, wing rock

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Authors: Murat Koklu, Yavuz Unal

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Data mining can be called as a technique to extract information from data. It is the process of obtaining hidden information and then turning it into qualified knowledge by statistical and artificial intelligence technique. One of its application areas is medical area to form decision support systems for diagnosis just by inventing meaningful information from given medical data. In this study a decision support system for diagnosis of illness that make use of data mining and three different artificial intelligence classifier algorithms namely Multilayer Perceptron, Naive Bayes Classifier and J.48. Pima Indian dataset of UCI Machine Learning Repository was used. This dataset includes urinary and blood test results of 768 patients. These test results consist of 8 different feature vectors. Obtained classifying results were compared with the previous studies. The suggestions for future studies were presented.

Keywords: Artificial Intelligence, Classifiers, Data Mining, Diabetic Patients.

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Authors: Yuka Kawakami, Tomoyuki Yokogawa, Hisashi Miyazaki, Sousuke Amasaki, Yoichiro Sato, Michiyoshi Hayase

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In this paper, we proposed a method for detecting consistency violation between state machine diagrams and a sequence diagram defined in UML 2.0 using SMV. We extended a method expressing these diagrams defined in UML 1.0 with boolean formulas so that it can express a sequence diagram with combined fragments introduced in UML 2.0. This extension made it possible to represent three types of combined fragment: alternative, option and parallel. As a result of experiment, we confirmed that the proposed method could detect consistency violation correctly with SMV.

Keywords: UML, model checking, SMV, sequence diagram.

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Authors: Han Xiao, Xiaoyan Zhu

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Margin-Based Principle has been proposed for a long time, it has been proved that this principle could reduce the structural risk and improve the performance in both theoretical and practical aspects. Meanwhile, feed-forward neural network is a traditional classifier, which is very hot at present with a deeper architecture. However, the training algorithm of feed-forward neural network is developed and generated from Widrow-Hoff Principle that means to minimize the squared error. In this paper, we propose a new training algorithm for feed-forward neural networks based on Margin-Based Principle, which could effectively promote the accuracy and generalization ability of neural network classifiers with less labelled samples and flexible network. We have conducted experiments on four UCI open datasets and achieved good results as expected. In conclusion, our model could handle more sparse labelled and more high-dimension dataset in a high accuracy while modification from old ANN method to our method is easy and almost free of work.

Keywords: Max-Margin Principle, Feed-Forward Neural Network, Classifier.

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Authors: Robert Niese, Ayoub Al-Hamadi, Bernd Michaelis

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In modern human computer interaction systems (HCI), emotion recognition is becoming an imperative characteristic. The quest for effective and reliable emotion recognition in HCI has resulted in a need for better face detection, feature extraction and classification. In this paper we present results of feature space analysis after briefly explaining our fully automatic vision based emotion recognition method. We demonstrate the compactness of the feature space and show how the 2d/3d based method achieves superior features for the purpose of emotion classification. Also it is exposed that through feature normalization a widely person independent feature space is created. As a consequence, the classifier architecture has only a minor influence on the classification result. This is particularly elucidated with the help of confusion matrices. For this purpose advanced classification algorithms, such as Support Vector Machines and Artificial Neural Networks are employed, as well as the simple k- Nearest Neighbor classifier.

Keywords: Facial expression analysis, Feature extraction, Image processing, Pattern Recognition, Application.

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Authors: S. Sabzchi Asl, N. Tahouni, M. H. Panjeshahi

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Benchmarking of a process with respect to energy consumption, without accomplishing a full retrofit study, can save both engineering time and money. In order to achieve this goal, the first step is to develop a conceptual-mathematical model that can easily be applied to a group of similar processes. In this research, we have aimed to identify a set of key parameters for the model which is supposed to be used for benchmarking of combined cycle power plants. For this purpose, three similar combined cycle power plants were studied. The results showed that ambient temperature, pressure and relative humidity, number of HRSG evaporator pressure levels and relative power in part load operation are the main key parameters. Also, the relationships between these parameters and produced power (by gas/ steam turbine), gas turbine and plant efficiency, temperature and mass flow rate of the stack flue gas were investigated.

Keywords: Combined cycle power plant, energy benchmarking, modelling, Retrofit.

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Authors: E. Šarauskis, L. Masilionyte, Z. Kriaučiūniene, K. Romaneckas

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All over the world, including the Middle and East European countries, sustainable tillage and sowing technologies are applied increasingly broadly with a view to optimising soil resources, mitigating soil degradation processes, saving energy resources, preserving biological diversity, etc. As a result, altered conditions of tillage and sowing technological processes are faced inevitably. The purpose of this study is to determine the seedbed topsoil hardness when using a combined sowing coulter in different sustainable tillage technologies. The research involved a combined coulter consisting of two dissected blade discs and a shoe coulter. In order to determine soil hardness at the seedbed area, a multipenetrometer was used. It was found by experimental studies that in loosened soil, a combined sowing coulter equally suppresses the furrow bottom, walls and soil near the furrow; therefore, here, soil hardness was similar at all researched depths and no significant differences were established. In loosened and compacted (double-rolled) soil, the impact of a combined coulter on the hardness of seedbed soil surface was more considerable at a depth of 2 mm. Soil hardness at the furrow bottom and walls to a distance of up to 26 mm was 1.1 MPa. At a depth of 10 mm, the greatest hardness was established at the furrow bottom. In loosened and heavily compacted (rolled for 6 times) soil, at a depth of 2 and 10 mm a combined coulter most of all compacted the furrow bottom, which has a hardness of 1.8 MPa. At a depth of 20 mm, soil hardness within the whole investigated area varied insignificantly and fluctuated by around 2.0 MPa. The hardness of furrow walls and soil near the furrow was by approximately 1.0 MPa lower than that at the furrow bottom

Keywords: Coulters design, seedbed, soil hardness, combined coulters, soil compaction.

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Authors: Tai-Yue Wang, Hui-Min Chiang, Su-Ni Hsieh, Yu-Min Chiang

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In many industries, control charts is one of the most frequently used tools for quality management. Hotelling-s T2 is used widely in multivariate control chart. However, it has little defect when detecting small or medium process shifts. The use of supplementary sensitizing rules can improve the performance of detection. This study applied sensitizing rules for Hotelling-s T2 control chart to improve the performance of detection. Support vector machines (SVM) classifier to identify the characteristic or group of characteristics that are responsible for the signal and to classify the magnitude of the mean shifts. The experimental results demonstrate that the support vector machines (SVM) classifier can effectively identify the characteristic or group of characteristics that caused the process mean shifts and the magnitude of the shifts.

Keywords: Hotelling's T2 control chart, Neural networks, Sensitizing rules, Support vector machines.

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Authors: Heeseung Choi, Raechoong Kang, Kyungtaek Choi, Jaihie Kim

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Fake finger submission attack is a major problem in fingerprint recognition systems. In this paper, we introduce an aliveness detection method based on multiple static features, which derived from a single fingerprint image. The static features are comprised of individual pore spacing, residual noise and several first order statistics. Specifically, correlation filter is adopted to address individual pore spacing. The multiple static features are useful to reflect the physiological and statistical characteristics of live and fake fingerprint. The classification can be made by calculating the liveness scores from each feature and fusing the scores through a classifier. In our dataset, we compare nine classifiers and the best classification rate at 85% is attained by using a Reduced Multivariate Polynomial classifier. Our approach is faster and more convenient for aliveness check for field applications.

Keywords: Aliveness detection, Fingerprint recognition, individual pore spacing, multiple static features, residual noise.

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Authors: M. Anidha, K. Premalatha

Abstract:

Feature Selection is significant in order to perform constructive classification in the area of cancer diagnosis. However, a large number of features compared to the number of samples makes the task of classification computationally very hard and prone to errors in microarray gene expression datasets. In this paper, we present an innovative method for selecting highly informative gene subsets of gene expression data that effectively classifies the cancer data into tumorous and non-tumorous. The hybrid gene selection technique comprises of combined Mutual Information and Fisher score to select informative genes. The gene selection is validated by classification using Support Vector Machine (SVM) which is a supervised learning algorithm capable of solving complex classification problems. The results obtained from improved Mutual Information and F-Score with SVM as a classifier has produced efficient results.

Keywords: Gene selection, mutual information, Fisher score, classification, SVM.

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Authors: Hoai-Vu Nguyen, Yongsun Choi

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Distributed denial-of-service (DDoS) attacks pose a serious threat to network security. There have been a lot of methodologies and tools devised to detect DDoS attacks and reduce the damage they cause. Still, most of the methods cannot simultaneously achieve (1) efficient detection with a small number of false alarms and (2) real-time transfer of packets. Here, we introduce a method for proactive detection of DDoS attacks, by classifying the network status, to be utilized in the detection stage of the proposed anti-DDoS framework. Initially, we analyse the DDoS architecture and obtain details of its phases. Then, we investigate the procedures of DDoS attacks and select variables based on these features. Finally, we apply the k-nearest neighbour (k-NN) method to classify the network status into each phase of DDoS attack. The simulation result showed that each phase of the attack scenario is classified well and we could detect DDoS attack in the early stage.

Keywords: distributed denial-of-service (DDoS), k-nearestneighbor classifier (k-NN), anti-DDoS framework, DDoS detection.

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Authors: Ahmed Abdi Hassan, Mustapha Muhammad Aliyu

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Turmeric (Curcuma longa) belongs to the ginger family and is used for food coloring mostly in Asian countries. It has long traditional medicinal value for the treatment of inflammations with excellent antioxidant properties. The purpose of this study is to investigate the efficiency of turmeric powder in the treatment of hyperthyroidism when combined with carbimazole antithyroid drug. The trial was conducted on 20 hyperthyroid patients but only 16 of them were successfully enrolled for the study. The 16 patients were divided into two equal groups where one group was treated with the only carbimazole while the other group was treated with a combined approach of carbimazole plus turmeric for 6 months consecutively. The result obtained is promising showing an average improvement of 99% in Thyroid-stimulating hormone (TSH) and 88%Thyroid Autoantibodies (TPOAb) in patients treated with the combined approach compared to those treated with the only carbimazole with an average of 3% and 18% of TSH and TPOAb improvement respectively. However, no major difference has been observed in both T4 and T3. Therefore, turmeric powder is a promising treatment if carefully and consistently combined with carbimazole antithyroid drug at very low amounts of 1.5 to 2 grams for at least 2 to 3 times a week.

Keywords: Thyroid, curcuminoids, turmeric, thyroxine, triiodothyronine, thyroid stimulating hormone, TPOAb.

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Authors: Fatih Hattatoglu, Abdulrezzak Bakiş

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In this study, fiberless reactive powder concrete (RPC) was produced with high pressure and flexural strength. C30/37 concrete was chosen as the control sample. In this study, 9 different cure types were applied to fiberless RPC. the most suitable combined cure type was selected according to the pressure and flexure strength. Pressure and flexural strength tests were applied to these samples after curing. As a result of the study, the combined cure type with the highest pressure resistance was obtained. The highest pressure resistance was achieved with consecutive standard water cure at 20 °C for 7 days – hot water cure at 90 °C for 2 days - drying oven cure at 180 °C for 2 days. As a result of the study, the highest pressure resistance of fiberless RPC was found as 123 MPa with water cure at 20 °C for 7 days - hot water cure at 90 °C for 2 days - drying oven cure at 180 °C for 2 days; and the highest flexural resistance was found as 8.37 MPa for the same combined cure type.

Keywords: Rigid pavement, reactive powder concrete, combined cure, pressure test, flexural test.

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Authors: Hai-bo Lu, Wei-qiang Liu

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A numerical study on the influence of forward-facing cavity length upon forward-facing cavity and opposing jet combined thermal protection system (TPS) cooling efficiency under hypersonic flow is conducted, by means of which the flow field parameters, heat flux distribution along the outer body surface are obtained. The numerical simulation results are validated by experiments and the cooling effect of the combined TPS with different cavity length is analyzed. The numerical results show that the combined configuration dose well in cooling the nose of the hypersonic vehicle. The deeper the cavity is, the weaker the heat flux is. The recirculation region plays a key role for the reduction of the aerodynamic heating.

Keywords: Thermal protection, hypersonic vehicle, aerodynamic heating, forward-facing cavity, opposing jet

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Authors: Loai AbdAllah, Ilan Shimshoni

Abstract:

Missing values in data are common in real world applications. Since the performance of many data mining algorithms depend critically on it being given a good metric over the input space, we decided in this paper to define a distance function for unlabeled datasets with missing values. We use the Bhattacharyya distance, which measures the similarity of two probability distributions, to define our new distance function. According to this distance, the distance between two points without missing attributes values is simply the Mahalanobis distance. When on the other hand there is a missing value of one of the coordinates, the distance is computed according to the distribution of the missing coordinate. Our distance is general and can be used as part of any algorithm that computes the distance between data points. Because its performance depends strongly on the chosen distance measure, we opted for the k nearest neighbor classifier to evaluate its ability to accurately reflect object similarity. We experimented on standard numerical datasets from the UCI repository from different fields. On these datasets we simulated missing values and compared the performance of the kNN classifier using our distance to other three basic methods. Our  experiments show that kNN using our distance function outperforms the kNN using other methods. Moreover, the runtime performance of our method is only slightly higher than the other methods.

Keywords: Missing values, Distance metric, Bhattacharyya distance.

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Authors: Samad Jafarmadar, Amin Habibzadeh

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A power and cooling cycle, which combines the organic Rankine cycle and the ejector refrigeration cycle supplied by waste heat energy sources, is discussed in this paper. 13 working fluids including wet, dry, and isentropic fluids are studied in order to find their performances on the combined cycle. Various operating conditions’ effects on the proposed cycle are examined by fixing power/refrigeration ratio. According to the results, dry and isentropic fluids have better performance compared with wet fluids.

Keywords: Combined power and refrigeration cycle, low temperature heat sources, organic rankine cycle, working fluids.

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Authors: Dewan Md. Farid, Jerome Darmont, Nouria Harbi, Nguyen Huu Hoa, Mohammad Zahidur Rahman

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In this paper, a new learning approach for network intrusion detection using naïve Bayesian classifier and ID3 algorithm is presented, which identifies effective attributes from the training dataset, calculates the conditional probabilities for the best attribute values, and then correctly classifies all the examples of training and testing dataset. Most of the current intrusion detection datasets are dynamic, complex and contain large number of attributes. Some of the attributes may be redundant or contribute little for detection making. It has been successfully tested that significant attribute selection is important to design a real world intrusion detection systems (IDS). The purpose of this study is to identify effective attributes from the training dataset to build a classifier for network intrusion detection using data mining algorithms. The experimental results on KDD99 benchmark intrusion detection dataset demonstrate that this new approach achieves high classification rates and reduce false positives using limited computational resources.

Keywords: Attributes selection, Conditional probabilities, information gain, network intrusion detection.

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Authors: Kaveh Mollazade, Hojat Ahmadi, Mahmoud Omid, Reza Alimardani

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Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. The aim of this work is to investigate the effectiveness of a new fault diagnosis method based on power spectral density (PSD) of vibration signals in combination with decision trees and fuzzy inference system (FIS). To this end, a series of studies was conducted on an external gear hydraulic pump. After a test under normal condition, a number of different machine defect conditions were introduced for three working levels of pump speed (1000, 1500, and 2000 rpm), corresponding to (i) Journal-bearing with inner face wear (BIFW), (ii) Gear with tooth face wear (GTFW), and (iii) Journal-bearing with inner face wear plus Gear with tooth face wear (B&GW). The features of PSD values of vibration signal were extracted using descriptive statistical parameters. J48 algorithm is used as a feature selection procedure to select pertinent features from data set. The output of J48 algorithm was employed to produce the crisp if-then rule and membership function sets. The structure of FIS classifier was then defined based on the crisp sets. In order to evaluate the proposed PSD-J48-FIS model, the data sets obtained from vibration signals of the pump were used. Results showed that the total classification accuracy for 1000, 1500, and 2000 rpm conditions were 96.42%, 100%, and 96.42% respectively. The results indicate that the combined PSD-J48-FIS model has the potential for fault diagnosis of hydraulic pumps.

Keywords: Power Spectral Density, Machine ConditionMonitoring, Hydraulic Pump, Fuzzy Logic.

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Authors: Hong Pan, Yaping Zhu, Liang Zheng Xia

Abstract:

We propose a fast and robust hierarchical face detection system which finds and localizes face images with a cascade of classifiers. Three modules contribute to the efficiency of our detector. First, heterogeneous feature descriptors are exploited to enrich feature types and feature numbers for face representation. Second, a PSO-Adaboost algorithm is proposed to efficiently select discriminative features from a large pool of available features and reinforce them into the final ensemble classifier. Compared with the standard exhaustive Adaboost for feature selection, the new PSOAdaboost algorithm reduces the training time up to 20 times. Finally, a three-stage hierarchical classifier framework is developed for rapid background removal. In particular, candidate face regions are detected more quickly by using a large size window in the first stage. Nonlinear SVM classifiers are used instead of decision stump functions in the last stage to remove those remaining complex nonface patterns that can not be rejected in the previous two stages. Experimental results show our detector achieves superior performance on the CMU+MIT frontal face dataset.

Keywords: Adaboost, Face detection, Feature selection, PSO

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