Search results for: color and texture features

Authors: Nhat Khoa Tran, Yee Cheong Lam, Chee Yoon Yue, Ming Jen Tan

Abstract:

Metallic micro parts are playing an important role in micro-fabrication industry. Recently, we have demonstrated a new deformation mechanism for micro-formability of polycrystalline materials. Different depressed micro-features smaller than the grain size have been successfully fabricated on 6061 aluminum alloy (AA6061) substrates with good fidelity. To further verify this proposed deformation mechanism that grain size is not a limiting factor, we demonstrate here that in addition of depressed features, protruded micro-features on a polycrystalline substrate can similarly be fabricated.

Keywords: Deformation mechanism, grain size, microfabrication, polycrystalline materials.

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Authors: Xiang Yu, Chuntao Feng, Lu Yang, Meiyang Song, Wenhao Zhou

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The existing two-dimensional micro-Doppler features extraction ignores the correlation information between the spatial and temporal dimension features. For the range-Doppler map, the time dimension is introduced, and a frequency modulation continuous wave (FMCW) radar human fall detection algorithm based on time-varying range-Doppler features is proposed. Firstly, the range-Doppler sequence maps are generated from the echo signals of the continuous motion of the human body collected by the radar. Then the three-dimensional data cube composed of multiple frames of range-Doppler maps is input into the three-dimensional Convolutional Neural Network (3D CNN). The spatial and temporal features of time-varying range-Doppler are extracted by the convolution layer and pool layer at the same time. Finally, the extracted spatial and temporal features are input into the fully connected layer for classification. The experimental results show that the proposed fall detection algorithm has a detection accuracy of 95.66%.

Keywords: FMCW radar, fall detection, 3D CNN, time-varying range-Doppler features.

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Authors: Awais Ahmad Khan, Emad Abouel Nasr, H. M. A. Hussein, Abdulrahman Al-Ahmari

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Sheet-metal parts have been widely applied in electronics, communication and mechanical industries in recent decades; but the advancement in sheet-metal part design and manufacturing is still behind in comparison with the increasing importance of sheet-metal parts in modern industry. This paper presents a methodology for automatic extraction of some common 2D internal sheet metal features. The features used in this study are taken from Unipunch ™ catalogue. The extraction process starts with the data extraction from STEP file using an object oriented approach and with the application of suitable algorithms and rules, all features contained in the catalogue are automatically extracted. Since the extracted features include geometry and engineering information, they will be effective for downstream application such as feature rebuilding and process planning.

Keywords: Feature Extraction, Internal Features, Punch Shapes, Sheet metal, STEP.

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Authors: H. B. Kekre, Tanuja K. Sarode, Bhakti Raul

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In this paper we propose segmentation approach based on Vector Quantization technique. Here we have used Kekre-s fast codebook generation algorithm for segmenting low-altitude aerial image. This is used as a preprocessing step to form segmented homogeneous regions. Further to merge adjacent regions color similarity and volume difference criteria is used. Experiments performed with real aerial images of varied nature demonstrate that this approach does not result in over segmentation or under segmentation. The vector quantization seems to give far better results as compared to conventional on-the-fly watershed algorithm.

Keywords: Image Segmentation, , Codebook, Codevector, data compression, Encoding

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Authors: M. Analoui, M. Fadavi Amiri

Abstract:

The design of a pattern classifier includes an attempt to select, among a set of possible features, a minimum subset of weakly correlated features that better discriminate the pattern classes. This is usually a difficult task in practice, normally requiring the application of heuristic knowledge about the specific problem domain. The selection and quality of the features representing each pattern have a considerable bearing on the success of subsequent pattern classification. Feature extraction is the process of deriving new features from the original features in order to reduce the cost of feature measurement, increase classifier efficiency, and allow higher classification accuracy. Many current feature extraction techniques involve linear transformations of the original pattern vectors to new vectors of lower dimensionality. While this is useful for data visualization and increasing classification efficiency, it does not necessarily reduce the number of features that must be measured since each new feature may be a linear combination of all of the features in the original pattern vector. In this paper a new approach is presented to feature extraction in which feature selection, feature extraction, and classifier training are performed simultaneously using a genetic algorithm. In this approach each feature value is first normalized by a linear equation, then scaled by the associated weight prior to training, testing, and classification. A knn classifier is used to evaluate each set of feature weights. The genetic algorithm optimizes a vector of feature weights, which are used to scale the individual features in the original pattern vectors in either a linear or a nonlinear fashion. By this approach, the number of features used in classifying can be finely reduced.

Keywords: Feature reduction, genetic algorithm, pattern classification, nearest neighbor rule classifiers (k-NNR).

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Authors: C. S. Sastry, Saurabh Jain, Ashish Mishra

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Image retrieval is a topic where scientific interest is currently high. The important steps associated with image retrieval system are the extraction of discriminative features and a feasible similarity metric for retrieving the database images that are similar in content with the search image. Gabor filtering is a widely adopted technique for feature extraction from the texture images. The recently proposed sparsity promoting l1-norm minimization technique finds the sparsest solution of an under-determined system of linear equations. In the present paper, the l1-norm minimization technique as a similarity metric is used in image retrieval. It is demonstrated through simulation results that the l1-norm minimization technique provides a promising alternative to existing similarity metrics. In particular, the cases where the l1-norm minimization technique works better than the Euclidean distance metric are singled out.

Keywords: l1-norm minimization, content based retrieval, modified Gabor function.

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Authors: J. L. Liu, L. Wang, B. Zhu, J. Zhou, W. D. Gao

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Fabric textures are very common in our daily life. However, the representation of fabric textures has never been explored from neuroscience view. Theoretical studies suggest that primary visual cortex (V1) uses a sparse code to efficiently represent natural images. However, how the simple cells in V1 encode the artificial textures is still a mystery. So, here we will take fabric texture as stimulus to study the response of independent component analysis that is established to model the receptive field of simple cells in V1. We choose 140 types of fabrics to get the classical fabric textures as materials. Experiment results indicate that the receptive fields of simple cells have obvious selectivity in orientation, frequency and phase when drifting gratings are used to determine their tuning properties. Additionally, the distribution of optimal orientation and frequency shows that the patch size selected from each original fabric image has a significant effect on the frequency selectivity.

Keywords: Fabric Texture, Receptive Filed, Simple Cell, Spare Coding.

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Authors: Metehan Makinacı

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The objective of this paper, is to apply support vector machine (SVM) approach for the classification of cancerous and normal regions of prostate images. Three kinds of textural features are extracted and used for the analysis: parameters of the Gauss- Markov random field (GMRF), correlation function and relative entropy. Prostate images are acquired by the system consisting of a microscope, video camera and a digitizing board. Cross-validated classification over a database of 46 images is implemented to evaluate the performance. In SVM classification, sensitivity and specificity of 96.2% and 97.0% are achieved for the 32x32 pixel block sized data, respectively, with an overall accuracy of 96.6%. Classification performance is compared with artificial neural network and k-nearest neighbor classifiers. Experimental results demonstrate that the SVM approach gives the best performance.

Keywords: Computer-aided diagnosis, support vector machines, Gauss-Markov random fields, texture classification.

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Authors: Farhad H. Mustafa, Mohamad S. Jaafar , Asaad H. Ismail, Ahamad F. Omar, Zahra A. Timimi, Hend A. A. Houssein

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The process of laser absorption in the skin during laser irradiation was a critical point in medical application treatments. Delivery the correct amount of laser light is a critical element in photodynamic therapy (PDT). More amounts of laser light able to affect tissues in the skin and small amount not able to enhance PDT procedure in skin. The knowledge of the skin tone laser dependent distribution of 635 nm radiation and its penetration depth in skin is a very important precondition for the investigation of advantage laser induced effect in (PDT) in epidermis diseases (psoriasis). The aim of this work was to estimate an optimum effect of diode laser (635 nm) on the treatment of epidermis diseases in different color skin. Furthermore, it is to improve safety of laser in PDT in epidermis diseases treatment. Advanced system analytical program (ASAP) which is a new approach in investigating the PDT, dependent on optical properties of different skin color was used in present work. A two layered Realistic Skin Model (RSM); stratum corneum and epidermal with red laser (635 nm, 10 mW) were used for irradiative transfer to study fluence and absorbance in different penetration for various human skin colors. Several skin tones very fair, fair, light, medium and dark are used to irradiative transfer. This investigation involved the principles of laser tissue interaction when the skin optically injected by a red laser diode. The results demonstrated that the power characteristic of a laser diode (635 nm) can affect the treatment of epidermal disease in various color skins. Power absorption of the various human skins were recorded and analyzed in order to find the influence of the melanin in PDT treatment in epidermal disease. A two layered RSM show that the change in penetration depth in epidermal layer of the color skin has a larger effect on the distribution of absorbed laser in the skin; this is due to the variation of the melanin concentration for each color.

Keywords: Photodynamic therapy, Realistic skin model, Laser, Light penetration, simulation, Optical properties of skin, Melanin.

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Authors: D. Zare, M. Alirezaei, S.M. Nassiri

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Iran is one of the greatest producers of date in the world. However due to lack of information about its viscoelastic properties, much of the production downgraded during harvesting and postharvesting processes. In this study the effect of temperature and moisture content of product were investigated on stress relaxation characteristics. Therefore, the freshly harvested date (kabkab) at tamar stage were put in controlled environment chamber to obtain different temperature levels (25, 35, 45, and 55 0C) and moisture contents (8.5, 8.7, 9.2, 15.3, 20, 32.2 %d.b.). A texture analyzer TAXT2 (Stable Microsystems, UK) was used to apply uniaxial compression tests. A chamber capable to control temperature was designed and fabricated around the plunger of texture analyzer to control the temperature during the experiment. As a new approach a CCD camera (A4tech, 30 fps) was mounted on a cylindrical glass probe to scan and record contact area between date and disk. Afterwards, pictures were analyzed using image processing toolbox of Matlab software. Individual date fruit was uniaxially compressed at speed of 1 mm/s. The constant strain of 30% of thickness of date was applied to the horizontally oriented fruit. To select a suitable model for describing stress relaxation of date, experimental data were fitted with three famous stress relaxation models including the generalized Maxwell, Nussinovitch, and Pelege. The constant in mentioned model were determined and correlated with temperature and moisture content of product using non-linear regression analysis. It was found that Generalized Maxwell and Nussinovitch models appropriately describe viscoelastic characteristics of date fruits as compared to Peleg mode.

Keywords: Stress relaxation, Viscoelastic properties, Date, Texture analyzer.

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Authors: Vahid Khorramshahi, Alireza Behrad, Neeraj K. Kanhere

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In this paper we present a new method for over-height vehicle detection in low headroom streets and highways using digital video possessing. The accuracy and the lower price comparing to present detectors like laser radars and the capability of providing extra information like speed and height measurement make this method more reliable and efficient. In this algorithm the features are selected and tracked using KLT algorithm. A blob extraction algorithm is also applied using background estimation and subtraction. Then the world coordinates of features that are inside the blobs are estimated using a noble calibration method. As, the heights of the features are calculated, we apply a threshold to select overheight features and eliminate others. The over-height features are segmented using some association criteria and grouped using an undirected graph. Then they are tracked through sequential frames. The obtained groups refer to over-height vehicles in a scene.

Keywords: Feature extraction, over-height vehicle detection, traffic monitoring, vehicle tracking.

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Authors: Dai Zhimei, Hua Chen

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The exploration of urban spatial evolution is an important part of urban development research. Therefore, the evolutionary modern Yangzhou urban spatial texture was taken as the research object, and Spatial Syntax was used as the main research tool, this paper explored Yangzhou spatial evolution law and its driving factors from the urban street network scale, district scale and street scale. The study has concluded that at the urban scale, Yangzhou urban spatial evolution is the result of a variety of causes, including physical and geographical condition, policy and planning factors, and traffic conditions, and the evolution of space also has an impact on social, economic, environmental and cultural factors. At the district and street scales, changes in space will have a profound influence on the history of the city and the activities of people. At the end of the article, the matters needing attention during the evolution of urban space were summarized.

Keywords: Space Syntax, spatial texture, urban space, Yangzhou.

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Authors: Gholam Hossein Naseri, Manucher Tamizi

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Light is one of the most important qualitative and symbolic factors and has a special position in architecture and urban development in regard to practical function. The main function of light, either natural or artificial, is lighting up the environment and the constructional forms which is called lighting. However, light is used to redefine the urban spaces by architectural genius with regard to three aesthetic, conceptual and symbolic factors. In architecture and urban development, light has a function beyond lighting up the environment, and the designers consider it as one of the basic components. The present research aims at studying the function of light and color in architectural view and their effects in buildings.

Keywords: Architectural View , Color , Light

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Authors: Jun Wan, Zehua Chen, Yingwu Chen, Zhidong Bai

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Classification is an interesting problem in functional data analysis (FDA), because many science and application problems end up with classification problems, such as recognition, prediction, control, decision making, management, etc. As the high dimension and high correlation in functional data (FD), it is a key problem to extract features from FD whereas keeping its global characters, which relates to the classification efficiency and precision to heavens. In this paper, a novel automatic method which combined Genetic Algorithm (GA) and classification algorithm to extract classification features is proposed. In this method, the optimal features and classification model are approached via evolutional study step by step. It is proved by theory analysis and experiment test that this method has advantages in improving classification efficiency, precision and robustness whereas using less features and the dimension of extracted classification features can be controlled.

Keywords: Classification, functional data, feature extraction, genetic algorithm, wavelet.

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Authors: Syong-Yang Chung, Xin-An Chen

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This study aims to explore the less well-known animation “Final Flight of the Osiris”, consisting of an initial exploration of the film color, storyline, and the simulacrum meanings of the roles, which leads to a further exploration of the light-shadow contrast and the psychological images presented by the screen colors and the characters. The research is based on literature review, and all data was compiled for the analysis of the visual vocabulary evolution of the characters. In terms of the structure, the relational study of the animation and the historical background of that time came first, including The Wachowskis’ and Andy Jones’ impact towards the cinematographic version and the animation version of “The Matrix”. Through literature review, the film color, the meaning and the relevant points were clarified. It was found in this research that “Final Flight of the Osiris” separates the realistic and virtual spaces by the changing the color tones; the "self" of the audience gradually dissolves into the "virtual" in the simulacra world, and the "Animatrix" has become a virtual field for the audience to understand itself about "existence" and "self".

Keywords: The Matrix, The Final Flight of Osiris, Wachowski sisters, simulacrum.

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Authors: Junseok Ham, Chansun Jung, Junhyung Park, Jihye Ryeo, Ilju Ko

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It is hard to express emotion through only speech when we watch a character in a movie or a play because we cannot estimate the size, kind, and quantity of emotion. So this paper proposes an artificial emotion model for visualizing current emotion with color and location in emotion model. The artificial emotion model is designed considering causality of generated emotion, difference of personality, difference of continual emotional stimulus, and co-relation of various emotions. This paper supposed the Emotion Field for visualizing current emotion with location, and current emotion is expressed by location and color in the Emotion Field. For visualizing changes within current emotion, the artificial emotion model is adjusted to characters in Hamlet.

Keywords: Emotion, Artificial Emotion, Visualizing, EmotionModel.

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Authors: Noor A. Draman, Campbell Wilson, Sea Ling

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Content-based music retrieval generally involves analyzing, searching and retrieving music based on low or high level features of a song which normally used to represent artists, songs or music genre. Identifying them would normally involve feature extraction and classification tasks. Theoretically the greater features analyzed, the better the classification accuracy can be achieved but with longer execution time. Technique to select significant features is important as it will reduce dimensions of feature used in classification and contributes to the accuracy. Artificial Immune System (AIS) approach will be investigated and applied in the classification task. Bio-inspired audio content-based retrieval framework (B-ACRF) is proposed at the end of this paper where it embraces issues that need further consideration in music retrieval performances.

Keywords: Bio-inspired audio content-based retrieval framework, features selection technique, low/high level features, artificial immune system

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Authors: Michela Lecca

Abstract:

System MEMORI automatically detects and recognizes rotated and/or rescaled versions of the objects of a database within digital color images with cluttered background. This task is accomplished by means of a region grouping algorithm guided by heuristic rules, whose parameters concern some geometrical properties and the recognition score of the database objects. This paper focuses on the strategies implemented in MEMORI for the estimation of the heuristic rule parameters. This estimation, being automatic, makes the system a highly user-friendly tool.

Keywords: Automatic object recognition, clustering, content based image retrieval system, image segmentation, region adjacency graph, region grouping.

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Authors: K.Mala, V.Sadasivam, S.Alagappan

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Advances in clinical medical imaging have brought about the routine production of vast numbers of medical images that need to be analyzed. As a result an enormous amount of computer vision research effort has been targeted at achieving automated medical image analysis. Computed Tomography (CT) is highly accurate for diagnosing liver tumors. This study aimed to evaluate the potential role of the wavelet and the neural network in the differential diagnosis of liver tumors in CT images. The tumors considered in this study are hepatocellular carcinoma, cholangio carcinoma, hemangeoma and hepatoadenoma. Each suspicious tumor region was automatically extracted from the CT abdominal images and the textural information obtained was used to train the Probabilistic Neural Network (PNN) to classify the tumors. Results obtained were evaluated with the help of radiologists. The system differentiates the tumor with relatively high accuracy and is therefore clinically useful.

Keywords: Fuzzy c means clustering, texture analysis, probabilistic neural network, LVQ neural network.

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Authors: Sara Sharifi Rad, Hassan Haghighi

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Due to important issues, such as deadlock, starvation, communication, non-deterministic behavior and synchronization, concurrent systems are very complex, sensitive, and error-prone. Thus ensuring reliability and accuracy of these systems is very essential. Therefore, there has been a big interest in the formal specification of concurrent programs in recent years. Nevertheless, some features of concurrent systems, such as dynamic process creation, scheduling and starvation have not been specified formally yet. Also, some other features have been specified partially and/or have been described using a combination of several different formalisms and methods whose integration needs too much effort. In other words, a comprehensive and integrated specification that could cover all aspects of concurrent systems has not been provided yet. Thus, this paper makes two major contributions: firstly, it provides a comprehensive formal framework to specify all well-known features of concurrent systems. Secondly, it provides an integrated specification of these features by using just a single formal notation, i.e., the Z language.

Keywords: Concurrent systems, Formal methods, Formal specification, Z language

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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: Michela Lecca

Abstract:

System MEMORI automatically detects and recognizes rotated and/or rescaled versions of the objects of a database within digital color images with cluttered background. This task is accomplished by means of a region grouping algorithm guided by heuristic rules, whose parameters concern some geometrical properties and the recognition score of the database objects. This paper focuses on the strategies implemented in MEMORI for the estimation of the heuristic rule parameters. This estimation, being automatic, makes the system a self configuring and highly user-friendly tool.

Keywords: Automatic Object Recognition, Clustering, Contentbased Image Retrieval System, Image Segmentation, Region Adjacency Graph, Region Grouping.

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Authors: I. Fathollahi, J. Hesari, S. Azadmard, S. Oustan

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The relationships between Proteolysis and soluble calcium levels with hardness of cheese texture were investigated in Iranian UF white cheese during 90 d ripening. Cheeses were sampled in interior and exterior. Results showed that levels of proteolysis, soluble calcium and hardness of cheese texture changed significantly (p< 0.05) over ripening. Levels of proteolysis and hardness were significantly (p< 0.05) different in interior and exterior zones of cheeses. External zones of cheeses became softer and had higher levels of proteolysis compared to internal zones during ripening. The highest correlation coefficient (r2= 0.979; p<0.01) was observed between hardness and levels of pH 4.6-soluble nitrogen in exterior zones of cheese. These result showed that proteolysis can contribute to textural softening during ripening of Iranian UF white cheese.

Keywords: Calcium, Proteolysis, Softening, Ultrafiltration, White cheese.

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Authors: P. Chaijak, M. Lertworapreecha, C. Sukkasem

Abstract:

A huge of dark color palm oil mill effluent (POME) cannot pass the discharge standard. It has been identified as the major contributor to the pollution load into ground water. Here, lignin-degrading yeast isolated from a termite nest was tested to treat the POME. Its lignin-degrading and decolorizing ability was determined. The result illustrated that Galactomyces sp. was successfully grown in POME. The decolorizing test demonstrated that 40% of Galactomyces sp. could reduce the color of POME (50% v/v) about 74-75% in 5 days without nutrient supplement. The result suggested that G. reessii has a potential to apply for decolorizing the dark wastewater like POME and other industrial wastewaters.

Keywords: Decolorization, palm oil mill effluent, ligninolytic enzyme, yeast, termite.

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Authors: M. Dehghan, R. Al-Mahaidi, I. Sbarski

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An industrial epoxy adhesive used in Carbon Fiber Reinforced Polymer (CFRP) strengthening systems was modified by dispersing multi-walled carbon nanotubes (MWCNTs). Nanocomposites were fabricated using the solvent-assisted dispersion method and ultrasonic mixing. Thermogravimetric analysis (TGA), dynamic mechanical analysis (DMA) and tensile tests were conducted to study the effect of nanotubes dispersion on the thermal and mechanical properties of the epoxy composite. Experimental results showed a substantial enhancement in the decomposition temperature and tensile properties of epoxy composite, while, the glass transition temperature (T_g) was slightly reduced due to the solvent effect. The morphology of the epoxy nanocomposites was investigated by SEM. It was proved that using solvent improves the nanotubes dispersion. However, at contents higher than 2 wt. %, nanotubes started to re-bundle in the epoxy matrix which negatively affected the final properties of epoxy composite.

Keywords: Carbon Fiber Reinforced Polymer, Epoxy, Multi-Walled Carbon Nanotube, Glass Transition Temperature.

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Authors: V. Gomathi, Dr. K. Ramar, A. Santhiyaku Jeevakumar

Abstract:

Facial expression analysis plays a significant role for human computer interaction. Automatic analysis of human facial expression is still a challenging problem with many applications. In this paper, we propose neuro-fuzzy based automatic facial expression recognition system to recognize the human facial expressions like happy, fear, sad, angry, disgust and surprise. Initially facial image is segmented into three regions from which the uniform Local Binary Pattern (LBP) texture features distributions are extracted and represented as a histogram descriptor. The facial expressions are recognized using Multiple Adaptive Neuro Fuzzy Inference System (MANFIS). The proposed system designed and tested with JAFFE face database. The proposed model reports 94.29% of classification accuracy.

Keywords: Adaptive neuro-fuzzy inference system, Facialexpression, Local binary pattern, Uniform Histogram

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Authors: Somnath Dey, Debasis Samanta

Abstract:

Iris-based biometric authentication is gaining importance in recent times. Iris biometric processing however, is a complex process and computationally very expensive. In the overall processing of iris biometric in an iris-based biometric authentication system, feature processing is an important task. In feature processing, we extract iris features, which are ultimately used in matching. Since there is a large number of iris features and computational time increases as the number of features increases, it is therefore a challenge to develop an iris processing system with as few as possible number of features and at the same time without compromising the correctness. In this paper, we address this issue and present an approach to feature extraction and feature matching process. We apply Daubechies D4 wavelet with 4 levels to extract features from iris images. These features are encoded with 2 bits by quantizing into 4 quantization levels. With our proposed approach it is possible to represent an iris template with only 304 bits, whereas existing approaches require as many as 1024 bits. In addition, we assign different weights to different iris region to compare two iris templates which significantly increases the accuracy. Further, we match the iris template based on a weighted similarity measure. Experimental results on several iris databases substantiate the efficacy of our approach.

Keywords: Iris recognition, biometric, feature processing, patternrecognition, pattern matching.

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Authors: Zhaoan Wang

Abstract:

Research in predictive maintenance modeling has improved in the recent years to predict failures and needed maintenance with high accuracy, saving cost and improving manufacturing efficiency. However, classic prediction models provide little valuable insight towards the most important features contributing to the failure. By analyzing and quantifying feature importance in predictive maintenance models, cost saving can be optimized based on business goals. First, multiple classifiers are evaluated with cross-validation to predict the multi-class of failures. Second, predictive performance with features provided by different feature selection algorithms are further analyzed. Third, features selected by different algorithms are ranked and combined based on their predictive power. Finally, linear explainer SHAP (SHapley Additive exPlanations) is applied to interpret classifier behavior and provide further insight towards the specific roles of features in both local predictions and global model behavior. The results of the experiments suggest that certain features play dominant roles in predictive models while others have significantly less impact on the overall performance. Moreover, for multi-class prediction of machine failures, the most important features vary with type of machine failures. The results may lead to improved productivity and cost saving by prioritizing sensor deployment, data collection, and data processing of more important features over less importance features.

Keywords: Automated supply chain, intelligent manufacturing, predictive maintenance machine learning, feature engineering, model interpretation.

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Authors: Adel Sabry Eesa, Adnan Mohsin Abdulazeez Brifcani, Zeynep Orman

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Feature selection has been used in many fields such as classification, data mining and object recognition and proven to be effective for removing irrelevant and redundant features from the original dataset. In this paper, a new design of distributed intrusion detection system using a combination feature selection model based on bees and decision tree. Bees algorithm is used as the search strategy to find the optimal subset of features, whereas decision tree is used as a judgment for the selected features. Both the produced features and the generated rules are used by Decision Making Mobile Agent to decide whether there is an attack or not in the networks. Decision Making Mobile Agent will migrate through the networks, moving from node to another, if it found that there is an attack on one of the nodes, it then alerts the user through User Interface Agent or takes some action through Action Mobile Agent. The KDD Cup 99 dataset is used to test the effectiveness of the proposed system. The results show that even if only four features are used, the proposed system gives a better performance when it is compared with the obtained results using all 41 features.

Keywords: Distributed intrusion detection system, mobile agent, feature selection, Bees Algorithm, decision tree.

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Authors: Piercarlo Dondi, Luca Lombardi, Marco Porta

Abstract:

In the last years numerous applications of Human- Computer Interaction have exploited the capabilities of Time-of- Flight cameras for achieving more and more comfortable and precise interactions. In particular, gesture recognition is one of the most active fields. This work presents a new method for interacting with a virtual object in a 3D space. Our approach is based on the fusion of depth data, supplied by a ToF camera, with color information, supplied by a HD webcam. The hand detection procedure does not require any learning phase and is able to concurrently manage gestures of two hands. The system is robust to the presence in the scene of other objects or people, thanks to the use of the Kalman filter for maintaining the tracking of the hands.

Keywords: Gesture recognition, human-computer interaction, Time-of-Flight camera.

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