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EMG is a medical technique that measures muscle response or electrical activity in response to a nerve’s stimulation of the muscle [10]. In this paper we have classified multiple hand gestures using small number of electrodes. This report aims to review current use of EMG based control system and explain the existing challenges. The stages involved includes data acquisition, feature extraction, classification and controller. Some of the application includes Arm rehabilitation, game controller, wheel chair controllers, etc.

The main goal of this paper to evaluate the performance of the proposed long short-term memory (LSTM)- basis relative humidity (RH) prediction model. Computational physics learning theory is a subfield in artificial intelligence. In this paper, we use the real observed weather data of RH from synoptic station location as an important aspect and effect in weather and climate and using machine learning approach basis on the LSTM neural network, which is a type of recurrent neural network (RNN). We use a machine learning approach to compute the relative humidity basis on the LSTM model. The LSTM is trained by using two years of climate data (from January 2008 to December 2009). LSTM is capable of forecasting complex univariate relative humidity time series with strong no-stationarity. The result shows the usefulness of LSTM and out performs traditional forecasting methods in the challenging relative humidity record problem.

In recent years many of the people are suffering from diabetics which may result damaging the human eye sights by damaging the blood vessels of the retinal by form exudates around the optic disc. In this paper, we intend to build retinal exudates from fundus image to predict the severity of diabetics resulting in blood vessels tissue damages. In order to analysis the blood vessels damage and diabetic severity initially we use appropriate image pre-processing techniques to remove any noise from the retinal funds image, to remove the noise in this paper we use wavelet transform and first order Gaussian derivative and matched filter to segment the fundus image by rotating the original image by certain angle. The filtered data is stored in the matched filter bank, then by applying k-NN clustering technique to identify minimum value in each filter bank and marking such minimum value center of k-nearest neighbor value. Further, Support vector machine a supervised learning algorithm is applied to the identified k-nearest neighbor values thereby predicting the severity of blood vessel tissue damage from the fundus image.

Newly developed 3D reconstruction technology has many uses ranging from facial recognition, self-driving vehicles, and producing accurate models of samples. To utilize 3D reconstruction, a depth map of the sample must be generated from a series of images. This can be done using stereoscopic 3D, or in the case of a fixed camera (such as the 10ID On-Axis Microscope at NSLS-II used for this paper) through focus stacking. This paper will explain how focus stacking can be used to generate in-focus images at very shallow depth of fields, and merge these images to create depth maps and 3D models.

Computational Thinking is introduced as a problem-solving ability that is important for future generations to master. The mastery of computational thinking skills from an early age prepares children to anticipate competition and pursue success in the future. Robotics devices are widely advocated as interactive learning media to facilitate Computational Thinking development. Educational robotics have grown from ideas that represent critical stages of Computational Thinking. The application of robotics in teaching computational thinking skills is increasingly used and is evident in the literature. The paper reviews the development of an innovative robotics device in facilitating understanding of computational thinking in young children in terms of several aspects of computational thinking indicators. A review was also conducted to see the advantages and disadvantages of commercially available robotics devices. This paper was prepared by applying critical analysis methods to the literature published from 1952 to 2017 in international journals and proceedings. As a recommendation for future research, this paper proposes for an educational robotic development that facilitates the of computational thinking skills, especially for early childhood education.

Pattern analysis has been shown to have higher reliability for melanoma and nevus classification. The ABCD method, which is common to be used as a melanoma diagnosis method, has been shown to have inappropriate weighting for each parameter. In addition, pattern analysis has been shown to have a higher success for diagnosing melanoma. In this paper, we choose the Grey Level Co-occurrence Matrix (GLCM) as a texture parameter to represent the pattern of melanoma. We also choose Deep Neural Network (DNN) to retrieve information from the data set. DNN has the capability of analyzing data with a high level of abstraction. Therefore, we choose DNN as a method to classify melanoma and nevus. We use the International Skin Imaging Collaboration (ISIC) archive database as our training and validation data. We use 773 nevi with 870 melanoma images as training data and separate 200 Nevus and 200 Melanoma images as validation data. We achieve 81.75% diagnostic accuracy, 75.5% sensitivity, and 88% specificityPattern analysis has been shown to have higher reliability for melanoma and nevus classification. The ABCD method, which is common to be used as a melanoma diagnosis method, has been shown to have inappropriate weighting for each parameter. In addition, pattern analysis has been shown to have a higher success for diagnosing melanoma. In this paper, we choose the Grey Level Co-occurrence Matrix (GLCM) as a texture parameter to represent the pattern of melanoma. We also choose Deep Neural Network (DNN) to retrieve information from the data set. DNN has the capability of analyzing data with a high level of abstraction. Therefore, we choose DNN as a method to classify melanoma and nevus. We use the International Skin Imaging Collaboration (ISIC) archive database as our training and validation data. We use 773 nevi with 870 melanoma images as training data and separate 200 Nevus and 200 Melanoma images as validation data. We achieve 81.75% diagnostic accuracy, 75.5% sensitivity, and 88% specificity

A method to create mammography phantoms with radiopaque ink and an inkjet printer was developed. A mammography image was printed on several sheets of bond paper, modulated by a calibration function, creating a phantom image that can emulate the original object in a clinical image. The radiopaque ink on paper was stable, at least, over a month. The representation of the mammography image was visually equivalent, and the limitations of the method come from noise, alignment difficulties, and dynamic range compression.

With the rapid development of Internet social media, excavating the emotional tendencies of the short text information from the Internet, the acquisition of useful information has attracted the attention of researchers. At present, the commonly used can be attributed to the rule-based classification and statistical machine learning classification methods. Although micro-blog sentiment analysis has made good progress, there still exist some shortcomings such as not highly accurate enough and strong dependence from sentiment classification effect. Aiming at the characteristics of Chinese short texts, such as less information, sparse features, and diverse expressions, this paper considers expanding the original text by mining related semantic information from the reviews, forwarding and other related information. First, this paper uses Word2vec to compute word similarity to extend the feature words. And then uses an ensemble classifier composed of SVM, KNN and HMM to analyze the emotion of the short text of micro-blog. The experimental results show that the proposed method can make good use of the comment forwarding information to extend the original features. Compared with the traditional method, the accuracy, recall and F1 value obtained by this method have been improved.

By studying the defense scheme of Network attacks, this paper propose an effective traffic features selection algorithm based on k-means++ clustering to deal with the problem of high dimensionality of traffic features which extracted from cyber-attacks samples. Firstly, this algorithm divide the original feature set into attack traffic feature set and background traffic feature set by the clustering. Then, we calculates the variation of clustering performance after removing a certain feature. Finally, evaluating the degree of distinctiveness of the feature vector according to the result. Among them, the effective feature vector is whose degree of distinctiveness exceeds the set threshold. The purpose of this paper is to select out the effective features from the extracted original feature set. In this way, it can reduce the dimensionality of the features so as to reduce the space-time overhead of subsequent detection. The experimental results show that the proposed algorithm is feasible and it has some advantages over other selection algorithms.

Orthophoto map is one in which the line-work and symbols of a conventional map are replaced by the orthophotographic image. The development of current technology has introduced the Unmanned Aerial Vehicles (UAV) as an alternative to conventional photogrammetry in creating orthophoto map. This paper aims to show the potential of UAV in orthophoto map creation. UAV photogrammetry describes a photogrammetric measurement platform, which operates remotely controlled, semi-autonomously, or autonomously, without a pilot sitting in the vehicle. The large format aerial camera in conventional photogrammetry is replaced with small format digital camera in UAV photogrammetry. For general area, the amount of forward overlap and side overlap is 75% and 60% respectively. For forest area, the amount of forward overlap and side overlap is 85% and 70% respectively as the UAV flight altitude will be higher. The whole workflow is introduced in the paper, where 461 images were collected with DJI Phantom 4 Pro with camera model FC6310. The flight mission was completed using PIX4D Capture. An orthophoto map covering an area of 250458 m2 was produced using Agisoft PhotoScan. The output is an orthophoto map of UTHM Pagoh Campus with good quality that can portray the overview of the area and benefits the users with the most updated information.

Data analysis is an important issue for companies as it provides deep business insights thus facilitating their performance. Usually enterprise applications and platforms for Big Data processing live in the Java ecosystem. Therefore it is important to investigate how Java-based platforms for data analytics and machine learning work and the functionality they provide. The paper analyzes the most widely used machine learning libraries in the Java world - Deeplearning4j and Spark MLlib. Using data lakes is a common practice for storing large amount of data for data analytic tasks. Hadoop has proved to be the common choice for building data lakes. The paper will review Hadoop ecosystem components, their advantages and potential problems concerning machine learning tasks.

DNA microarray technology is used to analyze thousands of gene expression data simultaneously and a very important task for drug development and test, function annotation, and cancer diagnosis. Various clustering methods have been used for analyzing gene expression data. However, when analyzing very large and heterogeneous collections of gene expression data, conventional clustering methods often cannot produce a satisfactory solution. Clustering algorithm has been used as an alternative approach to identify structures from gene expression data. In this paper, we introduce a transform technique based on Singular Value Decomposition (SVD) to identify normalized matrix of gene expression data followed by Partitioning Around Medoids (PAM) to cluster and then displaying the best cluster according to Davis Bouldin Index (DBI) based on Agglomerative Hierarchical Clustering (AHC). Experimental study on standard dataset demonstrated the effectiveness of the algorithm in gene expression data.DNA microarray technology is used to analyze thousands of gene expression data simultaneously and a very important task for drug development and test, function annotation, and cancer diagnosis. Various clustering methods have been used for analyzing gene expression data. However, when analyzing very large and heterogeneous collections of gene expression data, conventional clustering methods often cannot produce a satisfactory solution. Clustering algorithm has been used as an alternative approach to identify structures from gene expression data. In this paper, we introduce a transform technique based on Singular Value Decomposition (SVD) to identify normalized matrix of gene expression data followed by Partitioning Around Medoids (PAM) to cluster and then displaying the best cluster according to Davis Bouldin Index (DBI) based on Agglomerative Hierarchical Clustering (AHC). Experimental study on standard dataset demonstrated the effectiveness of the algorithm in gene expression data.

Based on nonsubsampled contourlet transform (NSCT) and modified pulse coupled neural network (MPCNN), the paper proposes an effective method of image fusion. Firstly, the paper decomposes the source image into the low-frequency components and high-frequency components using NSCT, and then processes the low-frequency components by regional statistical fusion rules. For high-frequency components, the paper calculates the spatial frequency (SF), which is input into MPCNN model to get relevant coefficients according to the fire-mapping image of MPCNN. At last, the paper restructures the final image by inverse transformation of low-frequency and high-frequency components. Compared with the wavelet transformation (WT) and the traditional NSCT algorithm, experimental results indicate that the method proposed in this paper achieves an improvement both in human visual perception and objective evaluation. It indicates that the method is effective, practical and good performance.Based on nonsubsampled contourlet transform (NSCT) and modified pulse coupled neural network (MPCNN), the paper proposes an effective method of image fusion. Firstly, the paper decomposes the source image into the low-frequency components and high-frequency components using NSCT, and then processes the low-frequency components by regional statistical fusion rules. For high-frequency components, the paper calculates the spatial frequency (SF), which is input into MPCNN model to get relevant coefficients according to the fire-mapping image of MPCNN. At last, the paper restructures the final image by inverse transformation of low-frequency and high-frequency components. Compared with the wavelet transformation (WT) and the traditional NSCT algorithm, experimental results indicate that the method proposed in this paper achieves an improvement both in human visual perception and objective evaluation. It indicates that the method is effective, practical and good performance.

In the paper a solution proposal for indoor navigation and obstacle detection problem in hospital environment for a humanoid that is 150 cm tall was presented. As a medical assistant, the robot is expected to autonomously move between hospital rooms with extra care about people safety. The overview of existing solutions was made, mostly focused on Simultaneous Localization and Mapping (SLAM) applications including human-aware algorithms. Considering the robot size, a common approach for mobile robots utilizing single 2D laser scanner or RGB+Depth camera is not applicable as vertical field of view of scans is too narrow to detect all obstacles. We proposed a hybrid system combining laser scanner, multiple cameras and distance sensors. The hardware architecture and software design in Robot Operating System (ROS) were presented. The last part of the paper includes the future plans and proposals of improvements for the robot.

The human hand gesture can record and reproduce the posture and action information of the hand, which is of great significance to people’s production and life. This paper has improved the existing data gloves based on micro inertial technology, and integrates the magnetic field localization method into finger gesture measurements. The strap down inertial navigation technology and the magnetic localization technology are combined in this paper to make the advantages complement each other, and a low cost and high degree of freedom of data gloves are put forward to realize the way of data gloves in the past.

In order to solve the problem of how to reconstruct the original image under the condition of unknown sparse basis, this paper proposes an image reconstruction method based on blind compressed sensing model. In this model, the image signal is regarded as the product of a sparse coefficient matrix and a dictionary matrix. Based on the existing blind compressed sensing theory, the optimal solution is solved by the alternative minimization method. The proposed method solves the problem that the sparse basis in compressed sensing is difficult to represent, which restrains the noise and improves the quality of reconstructed image. This method ensures that the blind compressed sensing theory has a unique solution and can recover the reconstructed original image signal from a complex environment with a stronger self-adaptability. The experimental results show that the image reconstruction algorithm based on blind compressed sensing proposed in this paper can recover high quality image signals under the condition...

Iris is regarded as the most unique biometric identification. This paper proposes a new feature extraction based on iris texture patterns with Principal Component Analysis (PCA). PCA is used to store computing process in classification process. The focus of this paper was to compare the accuracy result of classification methods of distance measurement such as Euclidean Distance, City Block Distance, Chebyshev Distance, Canberra Distance and Bray-Curtis Distance. Accuracy test shows that PCA can be used in various classification methods that use distance measurement.Iris is regarded as the most unique biometric identification. This paper proposes a new feature extraction based on iris texture patterns with Principal Component Analysis (PCA). PCA is used to store computing process in classification process. The focus of this paper was to compare the accuracy result of classification methods of distance measurement such as Euclidean Distance, City Block Distance, Chebyshev Distance, Canberra Distance and Bray-Curtis Distance. Accuracy test shows that PCA can be used in various classification methods that use distance measurement.

This research paper aims to propose a hybrid of ant colony optimization (ACO) and k-nearest neighbor (KNN) algorithms as feature selections for selecting and choosing relevant features from customer review datasets. Information gain (IG), genetic algorithm (GA), and rough set attribute reduction (RSAR) were used as baseline algorithms in a performance comparison with the proposed algorithm. This paper will also discuss the significance test, which was used to evaluate the performance differences between the ACO-KNN, IG-GA, and IG-RSAR algorithms. This study evaluated the performance of the ACO-KNN algorithm using precision, recall, and F-score, which were validated using the parametric statistical significance tests. The evaluation process has statistically proven that this ACO-KNN algorithm has been significantly improved compared to the baseline algorithms. The evaluation process has statistically proven that this ACO-KNN algorithm has been significantly improved compared to the baseline algorithms. In ...

With the ever increasing of internet applications and social networking sites, people nowadays can easily express their feelings towards any products and services. These online reviews act as an important source for further analysis and improved decision making. These reviews are mostly unstructured by nature and thus, need processing like sentiment analysis and classification to provide a meaningful information for future uses. In text analysis tasks, the appropriate selection of words/features will have a huge impact on the effectiveness of the classifier. Thus, this paper explores the effect of the pre-processing strategies in the sentiment analysis of online movie reviews. In this paper, supervised machine learning method was used to classify the reviews. The support vector machine (SVM) with linear and non-linear kernel has been considered as classifier for the classification of the reviews. The performance of the classifier is critically examined based on the results of precision, recall, f-measure,...

This paper investigates a habituation based approach in detecting visual changes using video surveillance systems in a passive environment. Various techniques have been introduced for dynamic environment such as motion detection, object classification and behaviour analysis. However, in a passive environment, most of the scenes recorded by the surveillance system are normal. Therefore, implementing a complex analysis all the time in the passive environment resulting on computationally expensive, especially when using a high video resolution. Thus, a mechanism of attention is required, where the system only responds to an abnormal event. This paper proposed a novelty detection mechanism in detecting visual changes and a habituation based approach to measure the level of novelty. The objective of the paper is to investigate the feasibility of the habituation based approach in detecting visual changes. Experiment results show that the approach are able to accurately detect the presence of novelty as deviatio...

This paper investigates applications of non-negative matrix factorization as feature selection method to select the features from gray level co-occurrence matrix. The proposed approach is used to classify dementia using MRI data. In this study, texture analysis using gray level co-occurrence matrix is done to feature extraction. In the feature extraction process of MRI data, we found seven features from gray level co-occurrence matrix. Non-negative matrix factorization selected three features that influence of all features produced by feature extractions. A Naive Bayes classifier is adapted to classify dementia, i.e. Alzheimer’s disease, Mild Cognitive Impairment (MCI) and normal control. The experimental results show that non-negative factorization as feature selection method able to achieve an accuracy of 96.4% for classification of Alzheimer’s and normal control. The proposed method also compared with other features selection methods i.e. Principal Component Analysis (PCA).This paper investigates applications of non-negative matrix factorization as feature selection method to select the features from gray level co-occurrence matrix. The proposed approach is used to classify dementia using MRI data. In this study, texture analysis using gray level co-occurrence matrix is done to feature extraction. In the feature extraction process of MRI data, we found seven features from gray level co-occurrence matrix. Non-negative matrix factorization selected three features that influence of all features produced by feature extractions. A Naive Bayes classifier is adapted to classify dementia, i.e. Alzheimer’s disease, Mild Cognitive Impairment (MCI) and normal control. The experimental results show that non-negative factorization as feature selection method able to achieve an accuracy of 96.4% for classification of Alzheimer’s and normal control. The proposed method also compared with other features selection methods i.e. Principal Component Analysis (PCA).

Malaria is a global health problem, particularly in Africa and south Asia where it causes countless deaths and morbidity cases. Efficient control and prompt of this disease require early detection and accurate diagnosis due to the large number of cases reported yearly. To achieve this aim, this paper proposes an image segmentation approach via unsupervised pixel segmentation of malaria parasite to automate the diagnosis of malaria. In this study, a modified clustering algorithm namely enhanced k-means (EKM) clustering, is proposed for malaria image segmentation. In the proposed EKM clustering, the concept of variance and a new version of transferring process for clustered members are used to assist the assignation of data to the proper centre during the process of clustering, so that good segmented malaria image can be generated. The effectiveness of the proposed EKM clustering has been analyzed qualitatively and quantitatively by comparing this algorithm with two popular image segmentation techniques namely Otsu’s thresholding and k-means clustering. The experimental results show that the proposed EKM clustering has successfully segmented 100 malaria images of P. vivax species with segmentation accuracy, sensitivity and specificity of 99.20%, 87.53% and 99.58%, respectively. Hence, the proposed EKM clustering can be considered as an image segmentation tool for segmenting the malaria images.Malaria is a global health problem, particularly in Africa and south Asia where it causes countless deaths and morbidity cases. Efficient control and prompt of this disease require early detection and accurate diagnosis due to the large number of cases reported yearly. To achieve this aim, this paper proposes an image segmentation approach via unsupervised pixel segmentation of malaria parasite to automate the diagnosis of malaria. In this study, a modified clustering algorithm namely enhanced k-means (EKM) clustering, is proposed for malaria image segmentation. In the proposed EKM clustering, the concept of variance and a new version of transferring process for clustered members are used to assist the assignation of data to the proper centre during the process of clustering, so that good segmented malaria image can be generated. The effectiveness of the proposed EKM clustering has been analyzed qualitatively and quantitatively by comparing this algorithm with two popular image segmentation techniques nam...

At present, there are many relevant researches on data processing using classical pattern matching and its improved algorithm, but few researches on statistical data of link-layer bit stream. This paper adopts a frequent statistical method of link-layer bit stream data based on AC-IM algorithm for classical multi-pattern matching algorithms such as AC algorithm has high computational complexity, low efficiency and it cannot be applied to binary bit stream data. The method’s maximum jump distance of the mode tree is length of the shortest mode string plus 3 in case of no missing? In this paper, theoretical analysis is made on the principle of algorithm construction firstly, and then the experimental results show that the algorithm can adapt to the binary bit stream data environment and extract the frequent sequence more accurately, the effect is obvious. Meanwhile, comparing with the classical AC algorithm and other improved algorithms, AC-IM algorithm has a greater maximum jump distance and less time-consuming.

The novel concept of comprehensive weld inspection with use of three sources of data (radiogram image, image composed of laser profilometer 3D data, CCD camera image) and weld images fusion is presented in the paper. The algorithms of data processing aimed at detection and visualization of weld flaws are roughly described. The special attention was paid at presentation and explanation of algorithm for aligning and spatial correction of images composed of data delivered by three independent sources. The results yielded by proposed algorithms for preprocessing of weld images, weld detection, feature extraction, shape extraction, shape matching, filtering, distortion correction, etc. are illustrated with several figures included in the paper. The concise description of designed and implemented laboratory stands is included in the paper too.

This paper mainly introduces the research of human body state recognition technology based on multi-layer perceptron. By using the application environment of ASUS Xtion pro live infrared camera, this paper focuses on the daily behavior recognition of human body in small area. First is the depth image analysis, this paper extracts the characteristics of the postures that need to be classified and we need a large number of samples. Then the collected samples are trained and classified by the multi-layer perceptron model. Finally, the human state collected by the camera can be identified in real time.

This paper realizes the uncalibrated visual servoless control of the Six-DOF manipulator, where the intrinsic and extrinsic parameters of the camera and the robot manipulator model are not known, nor real-time computes the inverse of image Jacobian. And the neural network was used to optimize the trajectory of manipulator end in Cartesian space. Firstly, a biaxial parallel layoutt are proposed to establish the relationship between the moving component along the coordinate axis and the change of single image feature when the end of the robot moves in the Cartesian space. The neural network was used to fit the relationship established above, then the optimization model was established, and the parameter of PID controller were optimized by neural network. Finally, the simulation and comparison experiments are investigated, and the results indicate the controller posed in this paper is effective and stable.

This paper presents the concept and the modern advances in personalized medicine that rely on technology and review the existing tools for early detection of breast cancer. The breast cancer types and distribution worldwide is discussed. It is spent time to explain the importance of identifying the normality and to specify the main classes in breast cancer, benign or malignant. The main purpose of the paper is to propose a conceptual model for early detection of breast cancer based on machine learning for processing and analysis of medical big dataand further knowledge discovery for personalized treatment. The proposed conceptual model is realized by using Naive Bayes classifier. The software is written in python programming language and for the experiments the Wisconsin breast cancer database is used. Finally, the experimental results are presented and discussed.

Spectrum prediction technology is an effective way to solve the problems of processing latency, spectrum access, spectrum collision and energy consumption in cognitive radio networks. Spectral prediction technology is divided into three categories according to its nature, namely, spectral prediction method based on regression analysis, spectrum prediction method based on Markov model and spectrum prediction method based on machine learning. By analyzing and comparing the three kinds of prediction models, the author hopes to provide some reference for the later researchers. In this paper, the development situation, practical application and existent problems of three kinds of forecasting models are analyzed and summarized. On this basis, this paper discusses the development trend of the next step.

The aim of this paper is a comparison of the existing classification algorithms with different parameters, and selection those ones, which allows solving the problem of primary diagnosis of cervical intraepithelial neoplasia (CIN), as it characterizes the condition of the body in the precancerous stage. The paper describes a feature selection process, as well as selection of the best models for a multiclass classification. © 2017 Author(s).

This paper presents an image classification algorithm based on spatial sparse coding model and random forest. Firstly, SIFT feature extraction of the image; and then use the sparse encoding theory to generate visual vocabulary based on SIFT features, and using the visual vocabulary of SIFT features into a sparse vector; through the combination of regional integration and spatial sparse vector, the sparse vector gets a fixed dimension is used to represent the image; at last random forest classifier for image sparse vectors for training and testing, using the experimental data set for standard test Caltech-101 and Scene-15. The experimental results show that the proposed algorithm can effectively represent the features of the image and improve the classification accuracy. In this paper, we propose an innovative image recognition algorithm based on image segmentation, sparse coding and multi instance learning. This algorithm introduces the concept of multi instance learning, the image as a multi instance bag...

Traditional supervised classifiers neglect a large amount of data which not have sufficient follow-up information, only work with labeled data. Consequently, the small sample size limits the advancement of design appropriate classifier. In this paper, a transductive learning method which combined with the filtering strategy in transductive framework and progressive labeling strategy is addressed. The progressive labeling strategy does not need to consider the distribution of labeled samples to evaluate the distribution of unlabeled samples, can effective solve the problem of evaluate the proportion of positive and negative samples in work set. Our experiment result demonstrate that the proposed technique have great potential in cancer prediction based on gene expression.Traditional supervised classifiers neglect a large amount of data which not have sufficient follow-up information, only work with labeled data. Consequently, the small sample size limits the advancement of design appropriate classifier. In this paper, a transductive learning method which combined with the filtering strategy in transductive framework and progressive labeling strategy is addressed. The progressive labeling strategy does not need to consider the distribution of labeled samples to evaluate the distribution of unlabeled samples, can effective solve the problem of evaluate the proportion of positive and negative samples in work set. Our experiment result demonstrate that the proposed technique have great potential in cancer prediction based on gene expression.

In recent years, with the rapid development of deep learning, it has been widely used in the field of natural language processing. In this paper, I use the method of deep learning to achieve Chinese word segmentation, with large-scale corpus, eliminating the need to construct additional manual characteristics. In the process of Chinese word segmentation, the first step is to deal with the corpus, use word2vec to get word embedding of the corpus, each character is 50. After the word is embedded, the word embedding feature is fed to the bidirectional LSTM, add a linear layer to the hidden layer of the output, and then add a CRF to get the model implemented in this paper. Experimental results show that the method used in the 2014 People’s Daily corpus to achieve a satisfactory accuracy.

In this paper it is presented the initial study on the possibility of capturing the inner dynamic of Particle Swarm Optimization algorithm into a complex network structure. Inspired in previous works there are two different approaches for creating the complex network presented in this paper. Visualizations of the networks are presented and commented. The possibilities for future applications of the proposed design are given in detail. © 2016 Author(s).

Internet of things (IoT) becomes the backbone of the advanced countries and it has a real contribute to exchange the traditional style or way of practical life, even personal life into smart style, with (IoT) technology the life become more and more easy and professional. internet of things achieves various applications coordinate with sensors and standard protocols to apply what is called machine -to- machine connection (M2M), in this paper we will talk more about the concept of (M2M), the main component of internet of things and finally the common protocols that is used in network, in addition to that this work present an IOT operation with processing system using camera for capturing image and Xilinx system generator(XSG)models for designing image processing algorithms and the result of the processing is an image with black and white for edge detection and Thresholding models and gray color image for gray enhancement model.

This paper introduces HHL, a hierarchical variant of hybrid logic. First-order correspondence and a Hennessy-Milner like theorem relating (hierarchical) bisimulation and logical equivalence for HHLare presented. Combining hierarchical transition structures with the ability to refer to specific states at any level of description, this logic seems suitable to express and verify properties of hierarchical transition systems, a pervasive semantic structure in Computer Science., Dynamic logic combines logic with programs, which at a certain level of abstraction, can be regarded as behaviours changing the system state and, therefore, the truth value of formulas. This paper suggests a method for generating such logics for the domain of robot controllers and illustrates it with a logic for handling resource consumption.

In order to construct and control walking robots similar to humans, i.e., in anthropomorphic robotics, as well as the corresponding exoskeletons, it is necessary to know the physical human properties such as the corresponding geometry, mass, and inertial characteristics of the segments of the human body. In addition, the study of human locomotion is on the basis of the development and design of all possible devices aimed to help people, after some incident, say in traumatology, orthopedics, orthotics and prosthetics design. For that aim one often uses computer simulation and computer design approach. In the present work, we present a 16 segmental biomechanical geometrical model of the body of the averaged Bulgarian women and study the changes in vertical position in its mass center, as well as the inertial parameters of the body during walking. We do that using proper 3D CAD software. We have considered, in accordance with the literature, 8 distinct phases of the gait cycle. The study could be beneficial for disabled people who need orthopedic and prosthetic devices for rehabilitation. The model and the mass-inertial characteristics obtained may be used in studies of injuries, ergonomics, and sports. Furthermore, the results of this paper will be in the basis of further study of the gait dynamics of the average Bulgarian female. Despite the current computer realization and the specific data obtained are for the average Bulgarian women, the presented approach can be easily applied to a specific individual provided a set of relatively easily measurable anthropometric data for that individual are known.

Emotion classification rapidly gains popularity in research world, especially in healthcare field. EEG signal used to classify human emotional state might provide wider range of information rather using other types of signal, yet this data need more complex processes because of its dimensions and noises. ANN is one of many techniques used to classify human emotion, by using DEAP’s EEG dataset this research will provide higher recognition rate of 2-dimensional emotion classification, which is using EEG’s non-linear features, including every frequency bands in EEG signal except delta rhythm which produced when subjects are sleeping. In this research PCA played a big role in computational optimization and provide higher number of recognition rates up to 60,15%. To achieve this recognition rate, the system is given an additional part, which is BPNN as data train’s correction so that training rate will be on its maximum. Through this correlation, we will analyze the correlation of those additions for human emotional classification. And the result of this paper will give possibility of further research in human emotional classification using BPNN as correction factor of recognition rate.Emotion classification rapidly gains popularity in research world, especially in healthcare field. EEG signal used to classify human emotional state might provide wider range of information rather using other types of signal, yet this data need more complex processes because of its dimensions and noises. ANN is one of many techniques used to classify human emotion, by using DEAP’s EEG dataset this research will provide higher recognition rate of 2-dimensional emotion classification, which is using EEG’s non-linear features, including every frequency bands in EEG signal except delta rhythm which produced when subjects are sleeping. In this research PCA played a big role in computational optimization and provide higher number of recognition rates up to 60,15%. To achieve this recognition rate, the system is given an additional part, which is BPNN as data train’s correction so that training rate will be on its maximum. Through this correlation, we will analyze the correlation of those additions for human emo...

Multilayer Feedforward Neural Network is one of the linear or nonlinear mathematical model between the set of input vectors and their corresponding output vectors of the real-world problem. In this paper, MAMFFN has been used as a mathematical model as delimited as mathematical approach that develops the Multilayer Feedforward Neural Network with Backpropagation to overcome from the complexity of the conventional model. Also, the proposed model applied on various real-world problems and obtained results are compared with existing results on those problems.

The adoption of robotics has been becoming prominent in computational thinking literature. The modularity characteristics of lego robotics, for example, enables learners to construct their understanding of the abstraction of complex robotics parts. Educational organisations that adopt computing would be likely to introduce computational thinking as an integrated part of the regular curriculum. Organisations in the UK and the United States, for example, conceptualise the delivery of computing curricula in connection with the development of computational thinking skills that all students must learn in the 21st century. This paper is a literature review report aggregating publications from outstanding outlets. Focusing on investigating more deeply about the roles of learning computational thinking using robotics-based media, in hopes of finding new findings that can support the advance of 21st-century skills. The results show that computational thinking using robotics-based media can foster interest in the STEM field, which includes some skills like creative thinking, problem-solving, collaborative learning, algorithmic and critical thinking, abstraction, and debugging. The skills are essential for children to develop themselves in the 21st century, taking into account the learning material and the right learning approach.

There are many medical equipments being used by human as assistance to check some organs inside the body. The medical modalities are developed to obtain the most effective and efficient in terms of quality and cost. Research about infrared spectrum as the medical equipment is a highlight among scientists since it can be captured the blood vessel of humans. Infrared penetrates the human skin and be captured by camera. Vein has a pattern and it can be used as human identification system. However, the images need enhancement because of low contrast. Wavelet transforms such as Haar and Daubechies can enhance the quality of vein images. Hence the identification process can be conducted by using deep learning method. In this paper, we use one of convolutional neural networks (CNN) method called AlexNet structure as the deep learning method due to its high performance. As for the wavelet transforms, the Haar wavelet, Daubechies 2, Daubechies 4, and Daubechies 10 are selected for evaluation on palm vein images in the image preprocessing step. As a result, we found that the accuracy of the wavelet transforms and enhanced palm vein images are more than 92%. The highest accuracy can be achieved by applying Daubechies 10 wavelet transform with an accuracy of 93.92%±0.98334.

Continuous fibre reinforced plastics (CoFRPs) offer remarkable mechanical properties at low density and have thus drawn increasing attention in weight-sensitive industries over the last decades. Contrasting metals, manufacturing of CoFRPs consists of multiple steps, often comprising a forming process of a textile (draping). However, managing the inherently complex, anisotropic and non-linear material behaviour during textile forming and avoiding forming defects is a great challenge in serial production. To assess formability prior to manufacture, virtual process simulations can be applied. For optimum part quality, component design and applied process parameters must complement each other, which in turn requires a high number of optimisation iterations and quickly exceeds reasonable computation times. Considerable effort has been made with respect to obtaining optimum process parameters, however considering geometry adaptions to achieve manufacturability is rarely addressed. Deep Learning techniques using convolutional neural networks (CNN) are capable of learning complex system dynamics from supplied samples. In the work presented here, CNNs are used to rapidly predict textile forming results of variable component geometries. A large database of highly variant geometries and corresponding draping examples is generated, on which the CNNs are trained. The paper shows, that CNNs are capable of reproducing the underlying forming dynamics and that they generalise well to unknown test geometries. Contrasting traditional meta-model approaches, the presented method estimates not just a scalar part quality attribute, but predicts the complete shear strain field, which facilitates engineering interpretation. The method is demonstrated on different geometries ranging from simple shapes to complex geometries. Being computational inexpensive, CNNs give immediate feedback for real-time geometry iterations during component design. Thus, CNNs are considered a promising and time-efficient tool to reflect manufacturability during part and process design.

This paper presents a Fuzzy Decision Making (FDM) problem with the fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) method based on the new concept of the relationship between influenced and influencing factors to analyze the ranking by using the problems of transgender when the collected data’s are expressed in terms of trapezoidal fuzzy numbers. A numerical example is given from this proposed method.

Eye Gaze Tracking is emerging as a popular method to increase the efficiency as well as to increase the accessibility of computing systems. It has been already employed in various applications as well as many researches are going on to incorporate the concept in various other fields. There are various fields where the systems are employed ranging from ease of accessibility of computing systems [1] to performance of computer graphics processing by the method of foveated rendering [2] and as well as digital marketing analysis [3]. The application of eye gaze trackers is still limited due to its lack of availability as well as the cost of the eye gaze trackers which makes them still a scarce resource for majority of the population. This paper proposes an efficient system for Eye Gaze Tracking and draws a sharp contrast between the different open source software available for Eye Gaze Tracking on the market. The study draws the comparison between two of the most popular open source eye gaze tracking software available in the market. ITU Gaze Tracker [4] PyGame based Eye Tracker [5]. The components proposed for the system are easily available in the market.

In recent years, advances have been made in the field of machine learning and artificial intelligence (AI), primarily through developments in deep learning neural networks (DLNN). Challenges however exist with transitioning emerging DLNN algorithms directly for NDE applications. As a counterpoint to AI, intelligence augmentation (IA) refers to the effective use of information technology to enhance human intelligence. While attempting to replicate the human mind has encountered many obstacles over the years, IA has a much longer history of success. All forms of information technology, from writing cuneiform on clay tables to computers and smartphones today, have essentially been developed to enhance the information processing capabilities of the human mind. This paper introduces a series of best practices for intelligence augmentation in NDE, highlighting how the operator should interface with NDE data and algorithms. Algorithms clearly have a great potential to help alleviate the burden of ’big data’ in NDE; however, it is important that operators are involved in both secondary indication review, and the detection of rare event indications not addressed well by typical algorithms. Several past examples of transitioning algorithms for NDE applications are presented, emphasizing the successful interfacing of operator and software for optimal data review and decision making.

Remote satellite imaging provides vital information for observing number of applications such as, land region detection and urban area classification. This paper proposed a novel approach for Classification and extraction of texture features from high resolution satellite images dataset. Preprocessing is done for satellite sensing image using Hilbert matrix filter and Modified Hilbert matrix filter. Then the texture features are extracted from the Hilbert image and Modified Hilbert image using Gray Level Co-occurrence Matrix (GLCM). Finally, obtained features are classified using Decision tree and Random Forest and the accuracy, precision, recall and F-measure is analyzed for performance evaluation.

In this paper, a damage predicting form is developed using fuzzy logic techniques. An earthquake damages the building severely. In order to avoid and relieve devastation, it is essential to frame a disaster attentiveness plan according to the results of damage prediction. It is more coherent to deal with earthquake-related damage forecasting problems using fuzzy logic, as there be plenty of unclear and fuzzy concepts in damage predicting problem and relations involving concerning factors have confusing nature. The model is formulated by various parts “IF… THEN” approximate reasoning.

Blood pressure (BP) can reflect many physiological characteristics, and timely monitoring of it can somehow prevent hypertension, asphyxia and other diseases. With the development of clinical medical technology, the accuracy requirements of the intelligent physiological monitoring equipment for predicting the physiological characteristics of BP are gradually increasing. This paper proposes a BP prediction model based on Long Short Term Memory Networks (LSTM-NN), which makes full use of the efficient processing characteristics of LSTM for time series information and accurately predicts the systolic BP and diastolic BP. The primitive photoplethysmographic pulse wave (PPW) signal and actual BP data in different time periods and different states were collected on 6 adult goats simultaneously. The blood flow changes were stimulated by injection of adrenaline to obtain a wide range of raw data to improve the generalization ability of the model. The clinical features of each PPW cycle were introduced into the LSTM model for training and prediction to resolve actual systolic BP and diastolic BP. Comparing the model with the prediction effect of BP neural network (BP-NN) model, the result shows that the prediction accuracy of LSTM model is high and the robustness is strong. The maximum error values for systolic and diastolic pressure prediction are 1.05mmHg and 1.8mmHg, respectively.

Cancer is one of the most deadly diseases for humans. According to the WHO (2015), cancer is the causes of the death number two in the world by 13 % after cardiovascular disease. Cancer often causes death if treatment is too late. Therefore, early detection of cancer is necessary to avoid the spread of cancer. High-dimensional medical data is one of the obstacles to the application of machine learning techniques due to a negative effect on the process of analysis. Therefore, the selection features required to increase performance in the detection of cancer. This paper focuses on the comparison of feature selection on cancer data. We use Genetic Algorithm and Laplacian Score for cancer gene selection of features, coupled with the Support Vectors Machines for cancer classification. The results will show that Genetic Algorithm gives the best accuracy with the percentage of 98.69 % only using 40 features.

Breast cancer is one of the five highest in Malaysia and most affected by women. Computer-aided diagram typically used in conjunction with a mammogram to detect breast cancer cells of women. A detection cancerous cell by using image information is a challenge task because of the different intensity distribution in the breast-affected area. This can lead to wrong results and diagnosis. This paper proposes a method to detect cancer cells from a mammogram image. Matrix Laboratory (MATLAB) programming language is used during this entire analysis. Mammogram image is converted to grayscale and then is resized to 600 x 600 pixels. A median filter is used to remove the noises in the image. The thresholds values are investigated from the value-based grayscale histogram. The results clearly demonstrate the feasibility and effectiveness of the proposed approach of the range threshold value used for segmentation of the cancerous cell.

To improve recognition probabilities of galaxies double-peak or multi-peak structures in observed astronomical images, this paper proposes a series of image processing techniques for enhancing the resolution of galaxy image sequences. Considering significant difference in common images and astronomical observations, we designed solutions for automatic star searching and matching, image registration, and image stacking to incorporate information in several galaxy images. Simulated image sequence and real observed CCD frames are used to test the proposed method. Experiments show that benefit from high-precision star centering and image registration, more details and sharper profiles of stars are gained and the image resolutions are improved.