Search results for: feature clustering

Authors: Hyun-Koo Kim, Yonghun Kim, Yong-Hoon Kim, Ju Hee Lee, Myungho Song

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In this paper, we have described an efficient stereo matching method and pedestrian detection method using stereo types LWIR camera. We compared with three types stereo camera algorithm as block matching, ELAS, and SGM. For pedestrian detection using stereo LWIR camera, we used that SGM stereo matching method, free space detection method using u/v-disparity, and HOG feature based pedestrian detection. According to testing result, SGM method has better performance than block matching and ELAS algorithm. Combination of SGM, free space detection, and pedestrian detection using HOG features and SVM classification can detect pedestrian of 30m distance and has a distance error about 30 cm.

Keywords: advanced driver assistance system, pedestrian detection, stereo matching method, stereo long-wave IR camera

Procedia PDF Downloads 383

Authors: Gałka Aleksandra, Jelińska Justyna, Masiak Albert, Walentukiewicz Krzysztof

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An offline signature is well-known however not the safest way to verify identity. Nowadays, to ensure proper authentication, i.e. in banking systems, multimodal verification is more widely used. In this paper the online signature analysis based on dynamic time warping (DTW) coupled with machine learning approaches has been presented. In our research signatures made with biometric pens were gathered. Signature features as well as their forgeries have been described. For verification of authenticity various methods were used including convolutional neural networks using DTW matrix and multilayer perceptron using sums of DTW matrix paths. System efficiency has been evaluated on signatures and signature forgeries collected on the same day. Results are presented and discussed in this paper.

Keywords: dynamic time warping, handwritten signature verification, feature-based recognition, online signature

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Authors: Wei-Hsuan Hsu, Yi-Xuan Huang

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Nanoimprint lithography (NIL) possesses the advantages of sub-10-nm feature and low cost. NIL patterns the resist with physical deformation using a mold, which can easily reproduce the required nano-scale pattern. However, the variation of process parameters and environmental conditions seriously affect reproduction quality. How to ensure the quality of imprinted pattern is essential for industry. In this study, the authors used the electrowetting technology to assist mold filling in the NIL process. A special mold structure was designed to cause electrowetting. During the imprinting process, when a voltage was applied between the mold and substrate, the hydrophilicity/hydrophobicity of the surface of the mold can be converted. Both simulation and experiment confirmed that the electrowetting technology can assist mold filling and avoid incomplete filling rate. The proposed method can also reduce the crack formation during the de-molding process. Therefore, electrowetting technology can improve the process quality of NIL.

Keywords: electrowetting, mold filling, nano-imprint, surface modification

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Authors: Basit Shahzad, Abdullatif M. Abdullatif

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Recent advancements in Information and Communication Technologies (ICT) and easy access to Internet have made social media the first choice for information sharing related to any important events or news. On Twitter, trend is a common feature that quantifies the level of popularity of a certain news or event. In this work, we examine the impact of Twitter trends on real world events by hypothesizing that Twitter trends have an influence on print media in Pakistan. For this, Twitter is used as a platform and Twitter trends as a base line. We first collect data from two sources (Twitter trends and print media) in the period May to August 2016. Obtained data from two sources is analyzed and it is observed that social media is significantly influencing the print media and majority of the news printed in newspaper are posted on Twitter earlier.

Keywords: twitter trends, text mining, effectiveness of trends, print media

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Authors: Ali Cheraghian, Farshid Hajati, Soheila Gheisari, Yongsheng Gao

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In this paper, we present a novel 2.5D face recognition method based on Gabor Discrete Cosine Transform (GDCT). In the proposed method, the Gabor filter is applied to extract feature vectors from the texture and the depth information. Then, Discrete Cosine Transform (DCT) is used for dimensionality and redundancy reduction to improve computational efficiency. The system is combined texture and depth information in the decision level, which presents higher performance compared to methods, which use texture and depth information, separately. The proposed algorithm is examined on publically available Bosphorus database including models with pose variation. The experimental results show that the proposed method has a higher performance compared to the benchmark.

Keywords: Gabor filter, discrete cosine transform, 2.5d face recognition, pose

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Authors: S. Roy, R. Rekha, K. Sriram, G. Subhadra, R. Johana

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Cardiovascular disease remains a leading cause of death in most industrialized countries. Many chemical drugs are available in the market which targets different receptor proteins related to cardiovascular diseases. Of late the traditional herbal drugs are safer when compared to chemical drugs because of its side effects. However, many herbal remedies used in treating cardiovascular diseases have not undergone scientific assessment to prove its pharmacological activities. There are many natural compounds, nature derived and Natural product mimic compounds are available which are in the market as approved drug. In the most of the cases drug activity at the molecular level are not known. Here we have categorized those compounds with our experimental compounds in different classes based on the structural similarity and physicochemical properties, using a tool, Chemmine and has attempted to understand the mechanism of the action of a experimental compound, which are clustered with Simvastatin, Lovastatin, Mevastatin and Pravastatin. Target protein molecule for Simvastatin, Lovastatin, Mevastatin and Pravastatin is HMG-CoA reductase, so we concluded that the experimental compound may be able to bind to the same target. Molecular docking and atomic interaction studies with simvastatin and our experimental compound were compared. A pharmacophore modeling was done based on the experimental compound and HMG-CoA reductase inhibitor.

Keywords: molecular docking, physicochemical properties, pharmacophore modeling structural similarity, pravastatin

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Authors: Majid Forghani, Michael Khachay

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In genetics, the impact of neighboring amino acids on a target site is referred as the nearest-neighbor effect or simply neighbor effect. In this paper, a new method called wavelet particle decomposition representing the one-dimensional neighbor effect using wavelet packet decomposition is proposed. The main idea lies in known dependence of wavelet packet sub-bands on location and order of neighboring samples. The method decomposes the value of a signal sample into small values called particles that represent a part of the neighbor effect information. The results have shown that the information obtained from the particle decomposition can be used to create better model variables or features. As an example, the approach has been applied to improve the correlation of test and reference sequence distance with titer in the hemagglutination inhibition assay.

Keywords: antigenic variants, neighbor effect, wavelet packet, wavelet particle decomposition

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Authors: Lumindu Dilumka, Ranaweera I. D., Sudusinghe S. P., Sanduni Kanchana A. M. K.

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This study was motivated by the challenges experienced by parents and guardians in ensuring the safety of children in cyberspace. In the last two or three years, online education has become very popular all over the world due to the Covid 19 pandemic. Therefore, parents, guardians and teachers must ensure the safety of children in cyberspace. Children are more likely to go astray and there are plenty of online programs are waiting to get them on the wrong track and also, children who are engaging in the online education can be distracted at any moment. Therefore, parents should keep a close check on their children's online activity. Apart from that, due to the unawareness of children, they tempt to share their sensitive information, causing a chance of being a victim of phishing attacks from outsiders. These problems can be overcome through the proposed web-based system. We use feature extraction, web tracking and analysis mechanisms, image processing and name entity recognition to implement this web-based system.

Keywords: online education, cyber bullying, social media, face recognition, web tracker, privacy data

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Authors: Stylianos Kampakis

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This paper presents a new algorithm for neural networks called “Input Dropout with Aggressive Re-weighting” (IDAR) aimed specifically at datasets with many useless features. IDAR combines two techniques (dropout of input neurons and aggressive re weighting) in order to eliminate the influence of noisy features. The technique can be seen as a generalization of dropout. The algorithm is tested on two different benchmark data sets: a noisy version of the iris dataset and the MADELON data set. Its performance is compared against three other popular techniques for dealing with useless features: L2 regularization, LASSO and random forests. The results demonstrate that IDAR can be an effective technique for handling data sets with many useless features.

Keywords: neural networks, feature selection, regularization, aggressive reweighting

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Authors: Eric Chabrol, Sidonia B.G. Eckle, Renate de Boer, James McCluskey, Jamie Rossjohn, Mirjam H.M. Heemskerk, Stephanie Gras

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αβ and γδ T-cells are disparate T-cell lineages that, via their use of either αβ or γδ T-cell antigen receptors (TCRs) respectively, can respond to distinct antigens. Here we characterise a new population of human T-cells, term δβ T-cells, that express TCRs comprising a TCR-δ variable gene fused to a Joining-α/Constant-α domain, paired with an array of TCR-β chains. We characterised the cellular, functional, biophysical and structural characteristic feature of this new T-cells population that reveal some new insight into TCR diversity. We provide molecular bases of how δβ T-cells can recognise viral peptide presented by Human Leukocyte Antigen (HLA) molecule. Our findings highlight how components from αβ and γδTCR gene loci can recombine to confer antigen specificity thus expanding our understanding of T-cell biology and TCR diversity.

Keywords: new delta-beta TCR, HLA, viral peptide, structural immunology

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Authors: H. M. Anitha, Anusha N. Rao

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The current developments in technology and ideas have given entirely new dimensions to the field of research and education. New delivery methods are proposed which is an added feature to the engineering education. Particularly, more importance is given to new teaching practices such as Information and Communication Technologies (ICT). It is vital to adopt the new ICT methods which lead to the emergence of novel structure and mode of education. The flipped classroom, think pair share and peer instruction are the latest pedagogical methods which give students to learn the course. This involves students to watch video lectures outside the classroom and solve the problems at home. Students are engaged in group discussions in the classroom. These are the active learning methods wherein the students are involved diversely to learn the course. This paper gives a comprehensive study of past and present research which is going on with flipped classroom, thinks pair share activity and peer instruction.

Keywords: flipped classroom, think pair share, peer instruction, active learning

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Authors: César Garza

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In supervised binary classification and regression problems, it is well-known that learnability is equivalent to a uniform convergence of the hypothesis class, and if a problem is learnable, it is learnable by empirical risk minimization. For the general learning setting of unsupervised learning tasks, there are non-trivial learning problems where uniform convergence does not hold. We present here the task of learning centers of mass with an extra feature that “activates” some of the coordinates over the unit ball in a Hilbert space. We show that the learning problem is learnable under a stable RLM rule. We introduce a family of distributions over the domain space with some mild restrictions for which the sample complexity of uniform convergence for these problems must grow logarithmically with the dimension of the Hilbert space. If we take this dimension to infinity, we obtain a learnable problem for which the uniform convergence property fails for a vast family of distributions.

Keywords: statistical learning theory, learnability, uniform convergence, stability, regularized loss minimization

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Authors: Pavel Prochazka

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The study deals with transducers for measuring vibration displacements of rotating blade tips in turbomachines. In order to prevent major accidents with extensive economic consequences, it shows an urgent need for every low-pressure steam turbine stage being equipped with modern non-contact measuring system providing information on blade loading, damage and residual lifetime under operation. The requirement of measuring vibration and static characteristics of steam turbine blades, therefore, calls for the development and operational verification of both new types of sensors and measuring principles and methods. The task is really demanding: to measure displacements of blade tips with a resolution of the order of 10 μm by speeds up to 750 m/s, humidity 100% and temperatures up to 200 °C. While in gas turbines are used primarily capacitive and optical transducers, these transducers cannot be used in steam turbines. The reason is moisture vapor, droplets of condensing water and dirt, which disable the function of sensors. Therefore, the most feasible approach was to focus on research of electromagnetic sensors featuring promising characteristics for given blade materials in a steam environment. Following types of sensors have been developed and both experimentally and theoretically studied in the Institute of Thermodynamics, Academy of Sciences of the Czech Republic: eddy-current, Hall effect, inductive and magnetoresistive. Eddy-current transducers demand a small distance of 1 to 2 mm and change properties in the harsh environment of steam turbines. Hall effect sensors have relatively low sensitivity, high values of offset, drift, and especially noise. Induction sensors do not require any supply current and have a simple construction. The magnitude of the sensors output voltage is dependent on the velocity of the measured body and concurrently on the varying magnetic induction, and they cannot be used statically. Magnetoresistive sensors are formed by magnetoresistors arranged into a Wheatstone bridge. Supplying the sensor from a current source provides better linearity. The MR sensors can be used permanently for temperatures up to 200 °C at lower values of the supply current of about 1 mA. The frequency range of 0 to 300 kHz is by an order higher comparing to the Hall effect and induction sensors. The frequency band starts at zero frequency, which is very important because the sensors can be calibrated statically. The MR sensors feature high sensitivity and low noise. The symmetry of the bridge arrangement leads to a high common mode rejection ratio and suppressing disturbances, which is important, especially in industrial applications. The MR sensors feature high sensitivity, high common mode rejection ratio, and low noise, which is important, especially in industrial applications. Magnetoresistive transducers provide a range of excellent properties indicating their priority for displacement measurements of rotating blades in turbomachines.

Keywords: turbines, blade vibration, blade tip timing, non-contact sensors, magnetoresistive sensors

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Authors: Noha Seddik, Sherine Youssef, Mohamed Kholeif

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The automated epileptic seizure detection research field has emerged in the recent years; this involves analyzing the Electroencephalogram (EEG) signals instead of the traditional visual inspection performed by expert neurologists. In this study, a Support Vector Machine (SVM) that uses Weighted Permutation Entropy (WPE) as the input feature is proposed for classifying normal and seizure EEG records. WPE is a modified statistical parameter of the permutation entropy (PE) that measures the complexity and irregularity of a time series. It incorporates both the mapped ordinal pattern of the time series and the information contained in the amplitude of its sample points. The proposed system utilizes the fact that entropy based measures for the EEG segments during epileptic seizure are lower than in normal EEG.

Keywords: electroencephalogram (EEG), epileptic seizure detection, weighted permutation entropy (WPE), support vector machine (SVM)

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Authors: Strahinja Kovačević, Lidija Jevrić, Miloš Kuzmanović, Sanja Podunavac-Kuzmanović

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In this paper, various derivatives of benzimidazole have been evaluated against Gram-negative bacteria Escherichia coli. For all investigated compounds the minimum inhibitory concentration (MIC) was determined. Quantitative structure-activity relationships (QSAR) attempts to find consistent relationships between the variations in the values of molecular properties and the biological activity for a series of compounds so that these rules can be used to evaluate new chemical entities. The correlation between MIC and some absorption, distribution, metabolism and excretion (ADME) parameters was investigated, and the mathematical models for predicting the antibacterial activity of this class of compounds were developed. The quality of the multiple linear regression (MLR) models was validated by the leave-one-out (LOO) technique, as well as by the calculation of the statistical parameters for the developed models and the results are discussed on the basis of the statistical data. The results of this study indicate that ADME parameters have a significant effect on the antibacterial activity of this class of compounds. Principal component analysis (PCA) and agglomerative hierarchical clustering algorithms (HCA) confirmed that the investigated molecules can be classified into groups on the basis of the ADME parameters: Madin-Darby Canine Kidney cell permeability (MDCK), Plasma protein binding (PPB%), human intestinal absorption (HIA%) and human colon carcinoma cell permeability (Caco-2).

Keywords: benzimidazoles, QSAR, ADME, in silico

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Authors: Kourosh Modarresi

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The abundance of media channels and devices has given users a variety of options to extract, discover, and explore information in the digital world. Since, often, there is a long and complicated path that a typical user may venture before taking any (significant) action (such as purchasing goods and services), it is critical to know how each node (media channel) in the path of user has contributed to the final action. In this work, the significance of each media channel is computed using statistical analysis and machine learning techniques. More specifically, “Regularized Singular Value Decomposition”, and “Sparse Principal Component” has been used to compute the significance of each channel toward the final action. The results of this work are a considerable improvement compared to the present approaches.

Keywords: multimedia attribution, sparse principal component, regularization, singular value decomposition, feature significance, machine learning, linear systems, variable shrinkage

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Authors: Hyojin Lim, Cuong Nguyen Khac, Yeongyu Choi, Ho-Youl Jung

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In this paper, we propose a simple moving object detection, which is based on motion vectors obtained from pyramidal Lucas-Kanade optical flow. The proposed method detects moving objects such as pedestrians, the other vehicles and some obstacles at the front-side of the host vehicle, and it can provide the warning to the driver. Motion vectors are obtained by using pyramidal Lucas-Kanade optical flow, and some outliers are eliminated by comparing the amplitude of each vector with the pre-defined threshold value. The background model is obtained by calculating the mean and the variance of the amplitude of recent motion vectors in the rectangular shaped local region called the cell. The model is applied as the reference to classify motion vectors of moving objects and those of background. Motion vectors are clustered to rectangular regions by using the unsupervised clustering K-means algorithm. Labeling method is applied to label groups which is close to each other, using by distance between each center points of rectangular. Through the simulations tested on four kinds of scenarios such as approaching motorbike, vehicle, and pedestrians to host vehicle, we prove that the proposed is simple but efficient for moving object detection in parking lots.

Keywords: moving object detection, dynamic scene, optical flow, pyramidal optical flow

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Authors: Elham Allahmoradi, Bahman Allahmoradi, Ali Bonyadi Naeini

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Complex systems have many aspects. A variety of methods have been developed to analyze these systems. The most efficient of these methods should not only be simple, but also provide useful and comprehensive information about many aspects of the system. Matrix methods are considered the most commonly methods used to analyze and design systems. Each matrix method can examine a particular aspect of the system. If these methods are combined, managers can access to more comprehensive and broader information about the system. This study was conducted in four steps. In the first step, a process model of a real project has been extracted through IDEF3. In the second step, activity levels have been attained by writing a process model in the form of a design structure matrix (DSM) and sorting it through triangulation algorithm (TA). In the third step, sub-processes have been obtained by writing the process model in the form of an interface structure matrix (ISM) and clustering it through cluster identification algorithm (CIA). In the fourth step, a mixed model has been developed to provide a unified picture of the project structure through the simultaneous presentation of activities and sub-processes. Finally, the paper is completed with a conclusion.

Keywords: integrated definition for process description capture (IDEF3) method, design structure matrix (DSM), interface structure matrix (ism), mixed matrix model, activity level, sub-process

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Authors: C. Benjbara, A. Habbani

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Wireless networks are getting more and more used in every new technology or feature, especially those without infrastructure (Ad hoc mode) which provide a low cost alternative to the infrastructure mode wireless networks and a great flexibility for application domains such as environmental monitoring, smart cities, precision agriculture, and so on. These application domains present a common characteristic which is the need of coexistence and intercommunication between modules belonging to different types of ad hoc networks like wireless sensor networks, mesh networks, mobile ad hoc networks, vehicular ad hoc networks, etc. This vision to bring to life such heterogeneous networks will make humanity duties easier but its development path is full of challenges. One of these challenges is the communication complexity between its components due to the lack of common or compatible protocols standard. This article proposes a new patented routing protocol based on the OLSR standard in order to resolve the heterogeneous ad hoc networks communication issue. This new protocol is applied on a specific network architecture composed of MANET, VANET, and FANET.

Keywords: Ad hoc, heterogeneous, ID-Node, OLSR

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Authors: F. Saptouw

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This paper briefly reviews the range of curatorial interventions made to preserve and display the Timbuktu Manuscripts. The government of South Africa and Mali collaborated to preserve the manuscripts, and brief notes will be presented about the value of archives in those specific spaces. The research initiatives of the Tombouctou Manuscripts Project, based at the University of Cape Town, feature prominently in the text. A brief overview of the history of the archive will be presented and its preservation as a key turning point in curating the intellectual history of the continent. ­­­The strategies of preservation, curation, publication and digitization are presented as complimentary interventions. Each materialization of the manuscripts contributes something significant; the complexity of the contribution is dependent primarily on the format of presentation. This integrated reading of the manuscripts is presented as a means to gain a more nuanced understanding of the past, which greatly surpasses how much information would be gleaned from relying on a single media format.

Keywords: archive, curatorship, cultural heritage, museum practice, Timbuktu manuscripts

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Authors: B. Kishore Kumar, Rakesh Pogula, T. Kishore Kumar

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The steepness of an audio signal which is produced by the musical instruments, specifically percussive instruments is the perception of how high tone or low tone which can be considered as a frequency closely related to the fundamental frequency. This paper presents a novel method for silence removal and segmentation of music signals produced by the percussive instruments and the performance of proposed method is studied with the help of MATLAB simulations. This method is based on two simple features, namely the signal energy and the spectral centroid. As long as the feature sequences are extracted, a simple thresholding criterion is applied in order to remove the silence areas in the sound signal. The simulations were carried on various instruments like drum, flute and guitar and results of the proposed method were analyzed.

Keywords: percussive instruments, spectral energy, spectral centroid, silence removal

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Authors: Vesile Evrim, Aliyu Awwal

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Today as in the other domains, there are an enormous number of political transcripts available in the Web which is waiting to be mined and used for various purposes such as statistics and recommendations. Therefore, automatically determining the political orientation on these transcripts becomes crucial. The methodologies used by machine learning algorithms to do the automatic classification are based on different features such as Linguistic. Considering the ideology differences between Liberals and Conservatives, in this paper, the effect of Personality Traits on political orientation classification is studied. This is done by considering the correlation between LIWC features and the BIG Five Personality Traits. Several experiments are conducted on Convote U.S. Congressional-Speech dataset with seven benchmark classification algorithms. The different methodologies are applied on selecting different feature sets that constituted by 8 to 64 varying number of features. While Neuroticism is obtained to be the most differentiating personality trait on classification of political polarity, when its top 10 representative features are combined with several classification algorithms, it outperformed the results presented in previous research.

Keywords: politics, personality traits, LIWC, machine learning

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Authors: Jatin Kumar Choudhary, Naren Rayala, Abbas Eslami Kiasari, Fahimeh Jafari

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The Great Britain has one of the safest road networks in the world. However, the consequences of any death or serious injury are devastating for loved ones, as well as for those who help the severely injured. This paper aims to analyse the Great Britain's road safety situation and show the response measures for areas where the total damage caused by accidents can be significantly and quickly reduced. In this paper, we do an exploratory data analysis using STATS19 data. For the past 30 years, the UK has had a good record in reducing fatalities. The UK ranked third based on the number of road deaths per million inhabitants. There were around 165,000 accidents reported in the Great Britain in 2009 and it has been decreasing every year until 2019 which is under 120,000. The government continues to scale back road deaths empowering responsible road users by identifying and prosecuting the parameters that make the roads less safe.

Keywords: road safety, data analysis, openstreetmap, feature expanding.

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Authors: Jitendra Aswani

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In the first section of this study, impact of Global Financial Crisis (GFC) on the synchronization of fourteen Asian Stock Markets (ASM’s) of countries like Hong Kong, India, Thailand, Singapore, Taiwan, Pakistan, Bangladesh, South Korea, Malaysia, Indonesia, Japan, China, Philippines and Sri Lanka, has been analysed using the network science and its metrics like degree of node, clustering coefficient and network density. Then in the second section of this study by introducing the US stock market in existing network and developing a Minimum Spanning Tree (MST) spread of crisis from the US stock market to Asian Stock Markets (ASM) has been explained. Data used for this study is adjusted the closing price of these indices from 6th January, 2000 to 15th September, 2013 which further divided into three sub-periods: Pre, during and post-crisis. Using network analysis, it is found that Asian stock markets become more interdependent during the crisis than pre and post crisis, and also Hong Kong, India, South Korea and Japan are systemic important stock markets in the Asian region. Therefore, failure or shock to any of these systemic important stock markets can cause contagion to another stock market of this region. This study is useful for global investors’ in portfolio management especially during the crisis period and also for policy makers in formulating the financial regulation norms by knowing the connections between the stock markets and how the system of these stock markets changes in crisis period and after that.

Keywords: global financial crisis, Asian stock markets, network science, Kruskal algorithm

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Authors: A. Benatiallah, A. Hadjadj, D. Benatiallah, F. Abaidi, A. Harrouz

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Photovoltaic system is established as a reliable and economical source of electricity in rural and Sahara areas, especially in developing countries where the population is dispersed, has low consumption of energy and the grid power is not extended to these areas due to viability and financial problems. The production of energy by the photovoltaic system is very fluctuates and depend of meteorological conditions. Wind is a very important and often neglected parameter in the behavior of the solar module. The electric performances of a solar module to the silicon are very appreciable to the blows; in the present work we have studies the behavior of multi-crystal solar module according to the density of dust, and the principals electric feature of the solar module. An evaluation permits to affirm that a solar module under the effect of sand will collect a lower flux to the normal conditions.

Keywords: photovoltaic, multi-crystal module, experimental, effect of dust, performances

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Authors: Bin Liu

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Prokaryotic promoter, consisted of two short DNA sequences located at in -35 and -10 positions, is responsible for controlling the initiation and expression of gene expression. Different types of promoters have different functions, and their consensus sequences are similar. In addition, their consensus sequences may be different for the same type of promoter, which poses difficulties for promoter identification. Unfortunately, all existing computational methods treat promoter identification as a binary classification task and can only identify whether a query sequence belongs to a specific promoter type. It is desired to develop computational methods for effectively identifying promoters and their types. Here, a two-layer predictor is proposed to try to deal with the problem. The first layer is designed to predict whether a given sequence is a promoter and the second layer predicts the type of promoter that is judged as a promoter. Meanwhile, we also analyze the importance of feature and sequence conversation in two aspects: promoter identification and promoter type identification. To the best knowledge of ours, it is the first computational predictor to detect promoters and their types.

Keywords: promoter, promoter type, random forest, sequence information

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Authors: P. Halder, A. Zaman

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It is estimated that heart disease accounts for one in ten deaths worldwide. United States deaths due to heart disease are among the leading causes of death according to the World Health Organization. Cardiovascular diseases (CVDs) account for one in four U.S. deaths, according to the Centers for Disease Control and Prevention (CDC). According to statistics, women are more likely than men to die from heart disease as a result of strokes. A 50% increase in men's mortality was reported by the World Health Organization in 2009. The consequences of cardiovascular disease are severe. The causes of heart disease include diabetes, high blood pressure, high cholesterol, abnormal pulse rates, etc. Machine learning (ML) can be used to make predictions and decisions in the healthcare industry. Thus, scientists have turned to modern technologies like Machine Learning and Data Mining to predict diseases. The disease prediction is based on four algorithms. Compared to other boosts, the Ada boost is much more accurate.

Keywords: heart disease, cardiovascular disease, coronary artery disease, feature selection, random forest, AdaBoost, SVM, decision tree

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Authors: Hamza Nejib, Okba Taouali

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This paper presents two of the most knowing kernel adaptive filtering (KAF) approaches, the kernel least mean squares and the kernel recursive least squares, in order to predict a new output of nonlinear signal processing. Both of these methods implement a nonlinear transfer function using kernel methods in a particular space named reproducing kernel Hilbert space (RKHS) where the model is a linear combination of kernel functions applied to transform the observed data from the input space to a high dimensional feature space of vectors, this idea known as the kernel trick. Then KAF is the developing filters in RKHS. We use two nonlinear signal processing problems, Mackey Glass chaotic time series prediction and nonlinear channel equalization to figure the performance of the approaches presented and finally to result which of them is the adapted one.

Keywords: online prediction, KAF, signal processing, RKHS, Kernel methods, KRLS, KLMS

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Authors: N. Ghatwary, A. Ahmed, H. Jalab

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In this paper, an approach for the liver tumor detection in computed tomography (CT) images is represented. The detection process is based on classifying the features of target liver cell to either tumor or non-tumor. Fractional differential (FD) is applied for enhancement of Liver CT images, with the aim of enhancing texture and edge features. Later on, a fusion method is applied to merge between the various enhanced images and produce a variety of feature improvement, which will increase the accuracy of classification. Each image is divided into NxN non-overlapping blocks, to extract the desired features. Support vector machines (SVM) classifier is trained later on a supplied dataset different from the tested one. Finally, the block cells are identified whether they are classified as tumor or not. Our approach is validated on a group of patients’ CT liver tumor datasets. The experiment results demonstrated the efficiency of detection in the proposed technique.

Keywords: fractional differential (FD), computed tomography (CT), fusion, aplha, texture features.

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Authors: Samir Khene

Abstract:

Present and future industrial uses of high critical temperature superconductors require high critical temperatures TC and strong current densities JC. These two aims constitute the two motivations of the scientific research in this domain. The most significant feature of any superconductor, from the viewpoint of uses, is the maximum electrical transport current density that this superconductor is capable of withstanding without loss of energy. In this work, vortices pinning in conventional and high-TC superconductors will be studied. Our experiments on vortices pinning in single crystals and nanoparticles of YBa2Cu3O7- and La1.85 Sr0.15CuO will be presented. It will be given special attention to the study of the YBa2Cu3O7- nanoparticles doped by ferromagnetic nanoparticles of Y3Fe5O12. The ferromagnetism and superconductivity coexistence in this compound will be demonstrated, and the influence of these ferromagnetic nanoparticles on the variations of the critical current density JC in YBa2Cu3O7- nanoparticles as a function of applied field H and temperature T will be studied.

Keywords: ferromagnetism, superconductivity, coexistence, magnetic material

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