Search results for: algorithm recommendation

Authors: Levente Varga, Dávid Deritei, Mária Ercsey-Ravasz, Răzvan Florian, Zsolt I. Lázár, István Papp, Ferenc Járai-Szabó

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One of the major shortcomings of widely used scientometric indicators is that different disciplines cannot be compared with each other. The issue of cross-disciplinary normalization has been long discussed, but even the classification of publications into scientific domains poses problems. Structural properties of citation networks offer new possibilities, however, the large size and constant growth of these networks asks for precaution. Here we present a new tool that in order to perform cross-field normalization of scientometric indicators of individual publications relays on the structural properties of citation networks. Due to the large size of the networks, a systematic procedure for identifying scientific domains based on a local community detection algorithm is proposed. The algorithm is tested with different benchmark and real-world networks. Then, by the use of this algorithm, the mechanism of the scientometric indicator normalization process is shown for a few indicators like the citation number, P-index and a local version of the PageRank indicator. The fat-tail trend of the article indicator distribution enables us to successfully perform the indicator normalization process.

Keywords: citation networks, cross-field normalization, local cluster detection, scientometric indicators

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Authors: A. J. Regan, F. J. Lidgey, M. Betteridge, P. Georgiou, C. Toumazou, K. Hayatleh, J. R. Dibble

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Our goal is development of an algorithm capable of predicting the directional trend of the Standard and Poor’s 500 index (S&P 500). Extensive research has been published attempting to predict different financial markets using historical data testing on an in-sample and trend basis, with many authors employing excessively complex mathematical techniques. In reviewing and evaluating these in-sample methodologies, it became evident that this approach was unable to achieve sufficiently reliable prediction performance for commercial exploitation. For these reasons, we moved to an out-of-sample strategy based on linear regression analysis of an extensive set of financial data correlated with historical closing prices of the S&P 500. We are pleased to report a directional trend accuracy of greater than 55% for tomorrow (t+1) in predicting the S&P 500.

Keywords: financial algorithm, GPU, S&P 500, stock market prediction

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Authors: Younghyun Jeon, Seungjoo Maeng

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In the Ad-hoc network, the great interests regarding MIMO scheme leads to their combination, which is also utilized into its applicable network. We manage the field of the problem into Reverse-link MIMO Ad-hoc Network (RMAN) and propose the methodology to maximize the data rate with its power consumption using Node-Cooperative beamforming technique. Based on the result of mathematical optimization formulation, we design the algorithm to construct optimal orthogonal weight vector according to channel feedback and control its transmission power according to QoS-pricing value level. In simulation results, we show the validity of the proposed mathematical optimization result and algorithm which mean that the sum-rate of each link is converged into some point.

Keywords: ad-hoc network, MIMO, cooperative beamforming, transmit power

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Authors: Tirimisiyu Olaide Gbadamosi

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Individuals go into business for the sake of obtaining regular income, becoming self-employed. Although, there different kinds of business enterprises that female and male can go into, often times, some businesses are regarded more suitable for a particular sex and not the other. This means that there is some gender discrimination in the choice of business one goes into and by extension in entrepreneurship development. Apparently, gender attitudes and behaviors will have positive or negative effects on entrepreneurship development in a society or economy. This research work therefore intends to take a critical look at gender discrimination as they affect entrepreneurship development with particular reference to northern Nigeria in general, using Exceptional Production Services Limited Kaduna, Kaduna North Local Government area as a case study, and also to suggest the possible solution to unidentified problems and give recommendation where necessary. Statement of research problem: Entrepreneurship has generally been recognised as a good medium or strategy for economic development of an individual, a community and a nation. It is also a known a known fact that some gender discrimination are often used in the choice of business or even the decision to go into business. For example, some businesses are regarded as more suitable to men than women. The question here is, is this the right approach to economic development through entrepreneurship? Of what effect is this approach to entrepreneurship development? These and the other questions are what this research intends to find answers to and if possible make recommendations. Significance of the study: The findings of this study will provide a guide for anyone for the establishment of a business in Nigeria. The study will help any prospective entrepreneur to make the right decision of which business to go into and how to contend with gender related issues that might influence its success in business. Furthermore, it is hoped that the study will assist the government and her agencies in the process in developing entrepreneurship development programs. Conclusion: There has been growing recognition that various types of discrimination do not always affect women and men in the same way. Moreover, gender discrimination may be intensified and facilitated by all other forms of discrimination. It has been increasingly recognized that without gender analysis of all forms of discrimination in business, including multiple forms of discrimination, and, in particular, in this context, related intolerance, violations of the human rights of women might escape detection and remedies to address racism may also fail to meet the needs of women and girls. It is also important that efforts to address gender discrimination incorporate approaches to the elimination of all forms of discrimination. Recommendation: Campaigning and raising awareness among young men and women, parents, teachers and employers about gender stereotypical attitudes towards academic performances and the likely consequences of overall educational choices for employment and entrepreneurship opportunities, career progression and earnings.

Keywords: entrepreneurship, economic development, small medium enterprises, gender discrimination

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Authors: Zhihua Yu, Qi Zhang, Mingyu Zhang, Haolong Dai

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Distributed fiber-optic vibration sensors are gaining extensive attention, for the advantages of high sensitivity, accurate location, light weight, large-scale monitoring, good concealment, and etc. In this paper, a novel optical fiber distributed vibration sensing system is proposed, which is based on self-interference of Rayleigh backscattering with phase generated carrier (PGC) demodulation algorithm. Pulsed lights are sent into the sensing fiber and the Rayleigh backscattering light from a certain position along the sensing fiber would interfere through an unbalanced Michelson Interferometry (MI) to generate the interference light. An improved PGC demodulation algorithm is carried out to recover the phase information of the interference signal, which carries the sensing information. Three vibration events were applied simultaneously to different positions over 2000m sensing fiber and demodulated correctly. Experiments show that the spatial resolution of is 10 m, and the noise level of the Φ-OTDR system is about 10-3 rad/√Hz, and the signal to noise ratio (SNR) is about 30.34dB. This vibration measurement scheme can be applied at surface, seabed or downhole for vibration measurements or distributed acoustic sensing (DAS).

Keywords: fiber optics sensors, Michelson interferometry, MI, phase-sensitive optical time domain reflectometry, Φ-OTDR, phase generated carrier, PGC

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Authors: Nuwan Kuruwitaarachchi, M. K. M. Peiris, C. N. Madawala, K. M. A. R. Perera, V. U. N Perera

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Dealing with businesses with the foreign market always took a special place in a country’s economy. Political and social factors came into play making currency rate changes fluctuate rapidly. Currency rate prediction has become an important factor for larger international businesses since large amounts of money exchanged between countries. This research focuses on comparing the accuracy of mainly three models; Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Networks(ANN) and Support Vector Machines(SVM). series of data import, export, USD currency exchange rate respect to LKR has been selected for training using above mentioned algorithms. After training the data set and comparing each algorithm, it was able to see that prediction in SVM performed better than other models. It was improved more by combining SVM and SVR models together.

Keywords: ARIMA, ANN, FFNN, RMSE, SVM, SVR

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Authors: Rosa Figueroa, Christopher Flores

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Text categorization is the problem of assigning a new document to a set of predetermined categories, on the basis of a training set of free-text data that contains documents whose category membership is known. To train a classification model, it is necessary to extract characteristics in the form of tokens that facilitate the learning and classification process. In text categorization, the feature extraction process involves the use of word sequences also known as N-grams. In general, it is expected that documents belonging to the same category share similar features. The Smith-Waterman (SW) algorithm is a dynamic programming algorithm that performs a local sequence alignment in order to determine similar regions between two strings or protein sequences. This work explores the use of SW algorithm as an alternative to feature extraction in text categorization. The dataset used for this purpose, contains 2,610 annotated documents with the classes Obese/Non-Obese. This dataset was represented in a matrix form using the Bag of Word approach. The score selected to represent the occurrence of the tokens in each document was the term frequency-inverse document frequency (TF-IDF). In order to extract features for classification, four experiments were conducted: the first experiment used SW to extract features, the second one used unigrams (single word), the third one used bigrams (two word sequence) and the last experiment used a combination of unigrams and bigrams to extract features for classification. To test the effectiveness of the extracted feature set for the four experiments, a Support Vector Machine (SVM) classifier was tuned using 20% of the dataset. The remaining 80% of the dataset together with 5-Fold Cross Validation were used to evaluate and compare the performance of the four experiments of feature extraction. Results from the tuning process suggest that SW performs better than the N-gram based feature extraction. These results were confirmed by using the remaining 80% of the dataset, where SW performed the best (accuracy = 97.10%, weighted average F-measure = 97.07%). The second best was obtained by the combination of unigrams-bigrams (accuracy = 96.04, weighted average F-measure = 95.97) closely followed by the bigrams (accuracy = 94.56%, weighted average F-measure = 94.46%) and finally unigrams (accuracy = 92.96%, weighted average F-measure = 92.90%).

Keywords: comorbidities, machine learning, obesity, Smith-Waterman algorithm

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Authors: Abdulmajid Mukhtar Afat

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This paper aims at introducing nondeterministic finite automata with ε value which is used to perform some operations on languages. a program is created to implement the algorithm that converts nondeterministic finite automata with ε value (ε-NFA) to deterministic finite automata (DFA).The program is written in c++ programming language. The program inputs are FA 5-tuples from text file and then classifies it into either DFA/NFA or ε -NFA. For DFA, the program will get the string w and decide whether it is accepted or rejected. The tracking path for an accepted string is saved by the program. In case of NFA or ε-NFA automation, the program changes the automation to DFA to enable tracking and to decide if the string w exists in the regular language or not.

Keywords: DFA, NFA, ε-NFA, eclose, finite automata, operations on languages

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Authors: M. R. Ghasemi, R. Ghiasi, H. Varaee

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Surrogate model has received increasing attention for use in detecting damage of structures based on vibration modal parameters. However, uncertainties existing in the measured vibration data may lead to false or unreliable output result from such model. In this study, an efficient approach based on Monte Carlo simulation is proposed to take into account the effect of uncertainties in developing a surrogate model. The probability of damage existence (PDE) is calculated based on the probability density function of the existence of undamaged and damaged states. The kriging technique allows one to genuinely quantify the surrogate error, therefore it is chosen as metamodeling technique. Enhanced version of ideal gas molecular movement (EIGMM) algorithm is used as main algorithm for model updating. The developed approach is applied to detect simulated damage in numerical models of 72-bar space truss and 120-bar dome truss. The simulation results show the proposed method can perform well in probability-based damage detection of structures with less computational effort compared to direct finite element model.

Keywords: probability-based damage detection (PBDD), Kriging, surrogate modeling, uncertainty quantification, artificial intelligence, enhanced ideal gas molecular movement (EIGMM)

Procedia PDF Downloads 221

Authors: Foad Nazari, Seyed Mahmood Hosseini, Mohammad Hossein Abolbashari, Mohammad Hassan Abolbashari

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The present study is concerned with the optimal design of functionally graded plates using particle swarm optimization (PSO) algorithm. In this study, meshless local Petrov-Galerkin (MLPG) method is employed to obtain the functionally graded (FG) plate’s natural frequencies. Effects of two parameters including thickness to height ratio and volume fraction index on the natural frequencies and total mass of plate are studied by using the MLPG results. Then the first natural frequency of the plate, for different conditions where MLPG data are not available, is predicted by an artificial neural network (ANN) approach which is trained by back-error propagation (BEP) technique. The ANN results show that the predicted data are in good agreement with the actual one. To maximize the first natural frequency and minimize the mass of FG plate simultaneously, the weighted sum optimization approach and PSO algorithm are used. However, the proposed optimization process of this study can provide the designers of FG plates with useful data.

Keywords: optimal design, natural frequency, FG plate, hybrid meshless method, MLPG method, ANN approach, particle swarm optimization

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Authors: Senol Dogan, Gunay Karli

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Identification of relevant publications based on a Medline query is time-consuming and error-prone. An all based process has the potential to solve this problem without any manual work. To the best of our knowledge, our study is the first to investigate the ability of machine learning to identify relevant articles accurately. 5 different machine learning algorithms were tested using 23 predictors based on several metadata fields attached to publications. We find that the Boosted model is the best-performing algorithm and its overall accuracy is 96%. In addition, specificity and sensitivity of the algorithm is 97 and 93%, respectively. As a result of the work, we understood that we can apply the same procedure to understand cancer gene expression big data.

Keywords: prediction of publications, machine learning, algorithms, bariatric surgery, comparison of algorithms, boosted, tree, logistic regression, ANN model

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Authors: Rui Fonseca-Pinto, Emanuel Silva, Rui Rijo, Ricardo Martinho, Bruno Carreira

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The sedentary lifestyle is associated to the development of chronic noncommunicable diseases (obesity, hypertension, Diabetes Mellitus Type 2) and the World Health Organization, given the evidence that physical activity is determinant for individual and collective health, defined the Physical Activity Level (PAL) as a vital signal. Strategies for increasing the practice of physical activity in all age groups have emerged from the various social organizations (municipalities, universities, health organizations, companies, social groups) by increasingly developing innovative strategies to promote motivation strategies and conditions to the practice of physical activity. The adaptation of cities to the new paradigms of sustainable mobility has provided the adaptation of urban training circles and mobilized citizens to combat sedentarism. This adaptation has accompanied the technological evolution and makes possible the use of mobile technology to monitor outdoor training programs and also, through the network connection (IoT), use the training data to make personalized recommendations. This work presents a physical activity counseling platform to be used in the physical maintenance circuits of urban centers, the MOVIDA.polis. The platform consists of a back office for the management of circuits and training stations, and for a mobile application for monitoring the user performance during workouts. Using a QRcode, each training station is recognized by the App and based on the individual performance records (effort perception, heart rate variation) artificial intelligence algorithms are used to make a new personalized recommendation. The results presented in this work were obtained during the proof of concept phase, which was carried out in the PolisLeiria training circuit in the city of Leiria (Portugal). It was possible to verify the increase in adherence to the practice of physical activity, as well as to decrease the interval between training days. Moreover, the AI-based recommendation acts as a partner in the training and an additional challenging factor. The platform is ready to be used by other municipalities in order to reduce the levels of sedentarism and approach the weekly goal of 150 minutes of moderate physical activity. Acknowledgments: This work was supported by Fundação para a Ciência e Tecnologia FCT- Portugal and CENTRO2020 under the scope of MOVIDA project: 02/SAICT/2016 – 23878.

Keywords: physical activity, mHealth, urban training circuits, health promotion

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Authors: Martins Y. Otache, John J. Musa, Abayomi I. Kuti, Mustapha Mohammed

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The performance of an artificial neural network (ANN) is contingent on a host of factors, for instance, the network optimisation scheme. In view of this, the study examined the general implications of the ANN training optimisation algorithm on its forecast performance. To this end, the Bayesian regularisation (Br), Levenberg-Marquardt (LM), and the adaptive learning gradient descent: GDM (with momentum) algorithms were employed under different ANN structural configurations: (1) single-hidden layer, and (2) double-hidden layer feedforward back propagation network. Results obtained revealed generally that the gradient descent with momentum (GDM) optimisation algorithm, with its adaptive learning capability, used a relatively shorter time in both training and validation phases as compared to the Levenberg- Marquardt (LM) and Bayesian Regularisation (Br) algorithms though learning may not be consummated; i.e., in all instances considering also the prediction of extreme flow conditions for 1-day and 5-day ahead, respectively especially using the ANN model. In specific statistical terms on the average, model performance efficiency using the coefficient of efficiency (CE) statistic were Br: 98%, 94%; LM: 98 %, 95 %, and GDM: 96 %, 96% respectively for training and validation phases. However, on the basis of relative error distribution statistics (MAE, MAPE, and MSRE), GDM performed better than the others overall. Based on the findings, it is imperative to state that the adoption of ANN for real-time forecasting should employ training algorithms that do not have computational overhead like the case of LM that requires the computation of the Hessian matrix, protracted time, and sensitivity to initial conditions; to this end, Br and other forms of the gradient descent with momentum should be adopted considering overall time expenditure and quality of the forecast as well as mitigation of network overfitting. On the whole, it is recommended that evaluation should consider implications of (i) data quality and quantity and (ii) transfer functions on the overall network forecast performance.

Keywords: streamflow, neural network, optimisation, algorithm

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Authors: Sweeti, Anoop Kant Godiyal, Neha Singh, Sneh Anand, B. K. Panigrahi, Jayasree Santhosh

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This work is about developing a tool for visualization and segmentation of Electroencephalograph (EEG) signals based on frequency domain features. Change in the frequency domain characteristics are correlated with change in mental state of the subject under study. Proposed algorithm provides a way to represent the change in the mental states using the different frequency band powers in form of segmented EEG signal. Many segmentation algorithms have been suggested in literature having application in brain computer interface, epilepsy and cognition studies that have been used for data classification. But the proposed method focusses mainly on the better presentation of signal and that’s why it could be a good utilization tool for clinician. Algorithm performs the basic filtering using band pass and notch filters in the range of 0.1-45 Hz. Advanced filtering is then performed by principal component analysis and wavelet transform based de-noising method. Frequency domain features are used for segmentation; considering the fact that the spectrum power of different frequency bands describes the mental state of the subject. Two sliding windows are further used for segmentation; one provides the time scale and other assigns the segmentation rule. The segmented data is displayed second by second successively with different color codes. Segment’s length can be selected as per need of the objective. Proposed algorithm has been tested on the EEG data set obtained from University of California in San Diego’s online data repository. Proposed tool gives a better visualization of the signal in form of segmented epochs of desired length representing the power spectrum variation in data. The algorithm is designed in such a way that it takes the data points with respect to the sampling frequency for each time frame and so it can be improved to use in real time visualization with desired epoch length.

Keywords: de-noising, multi-channel data, PCA, power spectra, segmentation

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Authors: A. Cherifi, B. Bouazza, A. O. Dahmane, B. Yagoubi

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Transmission over Optical channels will introduce inter-symbol interference (ISI) as well as inter-channel (or inter-carrier) interference (ICI). To decrease the effects of ICI, this paper proposes equalizer for the Optical OFDM system based on the fractional Fourier transform (FrFFT). In this FrFT-OFDM system, traditional Fourier transform is replaced by fractional Fourier transform to modulate and demodulate the data symbols. The equalizer proposed consists of sampling the received signal in the different time per time symbol. Theoretical analysis and numerical simulation are discussed.

Keywords: OFDM, (FrFT) fractional fourier transform, optical OFDM, equalization algorithm

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Authors: A. Mansouri, F. Krim

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This paper presents two techniques for DC motor parameters identification. We propose a numerical method using the adaptive extensive recursive least squares (AERLS) algorithm for real time parameters estimation. This algorithm, based on minimization of quadratic criterion, is realized in simulation for parameters identification of DC motor autoregressive moving average with extra inputs (ARMAX). As advanced technique, we use genetic algorithms (GA) identification with biased estimation for high dynamic performance speed regulation. DC motors are extensively used in variable speed drives, for robot and solar panel trajectory control. GA effectiveness is derived through comparison of the two approaches.

Keywords: ARMAX model, DC motor, AERLS, GA, optimization, parameter identification, PID speed regulation

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Authors: Manpreet Kaur

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When legitimate credit card users are mistakenly labelled as fraudulent in numerous financial delated applications, there are numerous ethical problems. The innovative machine learning approach we have suggested in this research outperforms the current models and shows how to model a data set for credit card fraud detection while minimizing false positives. As a result, we advise using random forests as the best machine learning method for predicting and identifying credit card transaction fraud. The majority of victims of these fraudulent transactions were discovered to be credit card users over the age of 60, with a higher percentage of fraudulent transactions taking place between the specific hours.

Keywords: automated fraud detection, isolation forest method, local outlier factor, ML algorithm, credit card

Procedia PDF Downloads 90

Authors: N. D'Amico, E. Grossi, B. Colombo, F. Rigiroli, M. Buscema, D. Fazzini, G. Cornalba, S. Papa

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Purpose: To characterize, through a radiomic approach, the nature of nodules considered non-specific by expert radiologists, recognized in magnetic resonance mammography (MRm) with T1-weighted (T1w) sequences with paramagnetic contrast. Material and Methods: 47 cases out of 1200 undergoing MRm, in which the MRm assessment gave uncertain classification (non-specific nodules), were admitted to the study. The clinical outcome of the non-specific nodules was later found through follow-up or further exams (biopsy), finding 35 benign and 12 malignant. All MR Images were acquired at 1.5T, a first basal T1w sequence and then four T1w acquisitions after the paramagnetic contrast injection. After a manual segmentation of the lesions, done by a radiologist, and the extraction of 150 radiomic features (30 features per 5 subsequent times) a machine learning (ML) approach was used. An evolutionary algorithm (TWIST system based on KNN algorithm) was used to subdivide the dataset into training and validation test and to select features yielding the maximal amount of information. After this pre-processing, different machine learning systems were applied to develop a predictive model based on a training-testing crossover procedure. 10 cases with a benign nodule (follow-up older than 5 years) and 18 with an evident malignant tumor (clear malignant histological exam) were added to the dataset in order to allow the ML system to better learn from data. Results: NaiveBayes algorithm working on 79 features selected by a TWIST system, resulted to be the best performing ML system with a sensitivity of 96% and a specificity of 78% and a global accuracy of 87% (average values of two training-testing procedures ab-ba). The results showed that in the subset of 47 non-specific nodules, the algorithm predicted the outcome of 45 nodules which an expert radiologist could not identify. Conclusion: In this pilot study we identified a radiomic approach allowing ML systems to perform well in the diagnosis of a non-specific nodule at MR mammography. This algorithm could be a great support for the early diagnosis of malignant breast tumor, in the event the radiologist is not able to identify the kind of lesion and reduces the necessity for long follow-up. Clinical Relevance: This machine learning algorithm could be essential to support the radiologist in early diagnosis of non-specific nodules, in order to avoid strenuous follow-up and painful biopsy for the patient.

Keywords: breast, machine learning, MRI, radiomics

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Authors: Hassan S. Hussein, Rania A. Abul Seoud, Amr M. Refaat

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Scientific researchers face in the analysis of very large data sets that is increasing noticeable rate in today’s and tomorrow’s technologies. Hadoop and Spark are types of software that developed frameworks. Hadoop framework is suitable for many Different hardware platforms. In this research, a scientific Linux cluster for Big Data analysis (SLBD) is presented. SLBD runs open source software with large computational capacity and high performance cluster infrastructure. SLBD composed of one cluster contains identical, commodity-grade computers interconnected via a small LAN. SLBD consists of a fast switch and Gigabit-Ethernet card which connect four (nodes). Cloudera Manager is used to configure and manage an Apache Hadoop stack. Hadoop is a framework allows storing and processing big data across the cluster by using MapReduce algorithm. MapReduce algorithm divides the task into smaller tasks which to be assigned to the network nodes. Algorithm then collects the results and form the final result dataset. SLBD clustering system allows fast and efficient processing of large amount of data resulting from different applications. SLBD also provides high performance, high throughput, high availability, expandability and cluster scalability.

Keywords: big data platforms, cloudera manager, Hadoop, MapReduce

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Authors: Ogoh Andrew Enokela

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This paper dwelt on the need for the utilization of instructional materials with highlights on the type of instructional materials, selection, uses and their importance on the learning and teaching of Agricultural Science Education in developing countries. It further discussed the concept of improvisation with some recommendation in terms of availability, utilization on the teaching and learning of Agricultural Science Education.

Keywords: instructional materials, agricultural science education, improvisation, teaching and learning

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Authors: Sukhveer Singh, Sandeep Singh

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A bi-objective fuzzy transportation problem with the objectives to minimize the total fuzzy cost and fuzzy time of transportation without according priorities to them is considered. To the best of our knowledge, there is no method in the literature to find efficient solutions of the bi-objective transportation problem under uncertainty. In this paper, a bi-objective transportation problem in an uncertain environment has been formulated. An algorithm has been proposed to find efficient solutions of the bi-objective transportation problem under uncertainty. The proposed algorithm avoids the degeneracy and gives the optimal solution faster than other existing algorithms for the given uncertain transportation problem.

Keywords: uncertain transportation problem, efficient solution, ranking function, fuzzy transportation problem

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Authors: Wenjiang Deng, Tian Mou, Yudi Pawitan, Trung Nghia Vu

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Estimation of isoform-level gene expression from RNA-seq data depends on simplifying assumptions, such as uniform reads distribution, that are easily violated in real data. Such violations typically lead to biased estimates. Most existing methods provide a bias correction step(s), which is based on biological considerations, such as GC content–and applied in single samples separately. The main problem is that not all biases are known. For example, new technologies such as single-cell RNA-seq (scRNA-seq) may introduce new sources of bias not seen in bulk-cell data. This study introduces a method called XAEM based on a more flexible and robust statistical model. Existing methods are essentially based on a linear model Xβ, where the design matrix X is known and derived based on the simplifying assumptions. In contrast, XAEM considers Xβ as a bilinear model with both X and β unknown. Joint estimation of X and β is made possible by simultaneous analysis of multi-sample RNA-seq data. Compared to existing methods, XAEM automatically performs empirical correction of potentially unknown biases. XAEM implements an alternating expectation-maximization (AEM) algorithm, alternating between estimation of X and β. For speed XAEM utilizes quasi-mapping for read alignment, thus leading to a fast algorithm. Overall XAEM performs favorably compared to other recent advanced methods. For simulated datasets, XAEM obtains higher accuracy for multiple-isoform genes, particularly for paralogs. In a differential-expression analysis of a real scRNA-seq dataset, XAEM achieves substantially greater rediscovery rates in an independent validation set.

Keywords: alternating EM algorithm, bias correction, bilinear model, gene expression, RNA-seq

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Authors: Ahmed Fradi

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In a digital world in perpetual evolution and acceleration, data more and more voluminous, rich and varied, the new software solutions emerged with the Big Data phenomenon offer new opportunities to the company enabling it not only to optimize its business and to evolve its production model, but also to reorganize itself to increase competitiveness and to identify new strategic axes. Design and manufacturing industrial companies, like the others, face these challenges, data represent a major asset, provided that they know how to capture, refine, combine and analyze them. The objective of our paper is to propose a solution allowing geometric and topological information extraction from 3D CAD model (precisely STEP files) databases, with specific algorithm based on the programming paradigm MapReduce. Our proposal is the first step of our future approach to 3D CAD object retrieval.

Keywords: Big Data, MapReduce, 3D object retrieval, CAD, STEP format

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Authors: Rohit Mittal, Bright Keswani, Amit Mithal

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This paper provides a sense about the application of computational intelligence techniques used to develop computer games, especially car racing. For the deep sense and knowledge of artificial intelligence, this paper is divided into various sections that is optimization, imitation, innovation and combining approach of optimization and imitation. This paper is mainly concerned with combining approach which tells different aspects of using fitness measures and supervised learning techniques used to imitate aspects of behavior. The main achievement of this paper is based on modelling player behaviour and evolving new game content such as racing tracks as single car racing on single track.

Keywords: evolution algorithm, genetic, optimization, imitation, racing, innovation, gaming

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Authors: Jiaqi Dong, Qing-Hua Qin, Yi Xiao

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Some of the most fundamental challenges of elastic metamaterials (EMMs) optimization can be attributed to the high consumption of computational power resulted from finite element analysis (FEA) simulations that render the optimization process inefficient. Furthermore, due to the inherent mesh dependence of FEA, minuscule geometry features, which often emerge during the later stages of optimization, induce very fine elements, resulting in enormously high time consumption, particularly when repetitive solutions are needed for computing the objective function. In this study, a surrogate modelling algorithm is developed to reduce computational time in structural optimization of EMMs. The surrogate model is constructed based on a multilayer feedforward artificial neural network (ANN) architecture, trained with prepopulated eigenfrequency data prepopulated from FEA simulation and optimized through regime selection with genetic algorithm (GA) to improve its accuracy in predicting the location and width of the primary elastic band gap. With the optimized ANN surrogate at the core, a Nelder-Mead (NM) algorithm is established and its performance inspected in comparison to the FEA solution. The ANNNM model shows remarkable accuracy in predicting the band gap width and a reduction of time consumption by 47%.

Keywords: artificial neural network, machine learning, mechanical metamaterials, Nelder-Mead optimization

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Authors: H. E. Budak, B. Arslan-Ilkiz, N. Cakmakci, I. Gocek, U. K. Sahin, H. Acikgoz-Tufan, M. H. Arslan

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The aim of this study is to design a novel laundry algorithm for colored textiles which have significant decoloring problem. During the experimental work, bleached knitted single jersey fabric made of 100% cotton and dyed with reactive dyestuff was utilized, since according to a conducted survey textiles made of cotton are the most demanded textile products in the textile market by the textile consumers and for coloration of textiles reactive dyestuffs are the ones that are the most commonly used in the textile industry for dyeing cotton-made products. Therefore, the fabric used in this study was selected and purchased in accordance with the survey results. The fabric samples cut out of this fabric were dyed with different dyeing parameters by using Remazol Brilliant Red 3BS dyestuff in Gyrowash machine at laboratory conditions. From the alternative reactive-dyed cotton fabric samples, the ones that have high tendency to color loss were determined and examined. Accordingly, the parameters of the dyeing process used for these fabric samples were evaluated and the dyeing process which was chosen to be used for causing high tendency to color loss for the cotton fabrics was determined in order to reveal the level of improvement in color loss during this study clearly. Afterwards, all of the untreated fabric samples cut out of the fabric purchased were dyed with the dyeing process selected. When dyeing process was completed, an experimental design was created for the laundering process by using Minitab® program considering temperature, time and mechanical action as parameters. All of the washing experiments were performed in domestic washing machine. 16 washing experiments were performed with 8 different experimental conditions and 2 repeats for each condition. After each of the washing experiments, water samples of the main wash of the laundering process were measured with UV spectrophotometer. The values obtained were compared with the calibration curve of the materials used for the dyeing process. The results of the washing experiments were statistically analyzed with Minitab® program. According to the results, the most suitable washing algorithm to be used in terms of the parameters temperature, time and mechanical action for domestic washing machines for minimizing fabric color loss was chosen. The laundry algorithm proposed in this study have the ability of minimalizing the problem of color loss of colored textiles in washing machines by eliminating the negative effects of the parameters of laundering process on color of textiles without compromising the fundamental effects of basic cleaning action being performed properly. Therefore, since fabric color loss is minimized with this washing algorithm, dyestuff residuals will definitely be lower in the grey water released from the laundering process. In addition to this, with this laundry algorithm it is possible to wash and clean other types of textile products with proper cleaning effect and minimized color loss.

Keywords: color loss, laundry algorithm, textiles, domestic washing process

Procedia PDF Downloads 333

Authors: Heru Syah Putra, Aji Tri Pamungkas Nurcahyo, Chuang-Jan Chang

Abstract:

MOIL-SDK method refers to the spatial angle that forms a view with a different perspective from the Fisheye image. Visual Odometry forms a trusted application for extending projects by tracking using image sequences. A real-time, precise, and persistent approach that is able to contribute to the work when taking datasets and generate ground truth as a reference for the estimates of each image using the FAST Algorithm method in finding Keypoints that are evaluated during the tracking process with the 5-point Algorithm with RANSAC, as well as produce accurate estimates the camera trajectory for each rotational, translational movement on the X, Y, and Z axes.

Keywords: MOIL-SDK, intel realsense T265, Fisheye image, monocular visual odometry

Procedia PDF Downloads 119

Authors: Osman Turkoglu, Alvin Estilo, Ritu Gupta, Liliam Pineda-Salgado, Rajesh Pandey

Abstract:

Background: Hepatotoxicity can be linked to a variety of clinical symptoms and histopathological signs, posing a great challenge in the surveillance of suspected drug-induced liver injury (DILI) cases in the safety database. Additionally, the majority of such cases are rare, idiosyncratic, highly unpredictable, and tend to demonstrate unique individual susceptibility; these qualities, in turn, lend to a pharmacovigilance monitoring process that is often tedious and time-consuming. Objective: Develop a multifactorial algorithm to assist pharmacovigilance physicians in identifying high-risk hepatotoxicity cases associated with DILI from the sponsor’s safety database (Argus). Methods: Multifactorial selection criteria were established using Structured Query Language (SQL) and the TIBCO Spotfire® visualization tool, via a combination of word fragments, wildcard strings, and mathematical constructs, based on Hy’s law criteria and pattern of injury (R-value). These criteria excluded non-eligible cases from monthly line listings mined from the Argus safety database. The capabilities and limitations of these criteria were verified by comparing a manual review of all monthly cases with system-generated monthly listings over six months. Results: On an average, over a period of six months, the algorithm accurately identified 92% of DILI cases meeting established criteria. The automated process easily compared liver enzyme elevations with baseline values, reducing the screening time to under 15 minutes as opposed to multiple hours exhausted using a cognitively laborious, manual process. Limitations of the algorithm include its inability to identify cases associated with non-standard laboratory tests, naming conventions, and/or incomplete/incorrectly entered laboratory values. Conclusions: The newly developed multifactorial algorithm proved to be extremely useful in detecting potential DILI cases, while heightening the vigilance of the drug safety department. Additionally, the application of this algorithm may be useful in identifying a potential signal for DILI in drugs not yet known to cause liver injury (e.g., drugs in the initial phases of development). This algorithm also carries the potential for universal application, due to its product-agnostic data and keyword mining features. Plans for the tool include improving it into a fully automated application, thereby completely eliminating a manual screening process.

Keywords: automation, drug-induced liver injury, pharmacovigilance, post-marketing

Procedia PDF Downloads 136

Authors: Xiang Jianhong, Wang Cong, Wang Linyu

Abstract:

With the rise of medical IoT technologies, Wireless body area networks (WBANs) can collect fetal electrocardiogram (FECG) signals to support telemedicine analysis. The compressed sensing (CS)-based WBANs system can avoid the sampling of a large amount of redundant information and reduce the complexity and computing time of data processing, but the existing algorithms have poor signal compression and reconstruction performance. In this paper, a Joint block multi-orthogonal least squares (JBMOLS) algorithm is proposed. We apply the FECG signal to the Joint block sparse model (JBSM), and a comparative study of sparse transformation and measurement matrices is carried out. A FECG signal compression transmission mode based on Rbio5.5 wavelet, Bernoulli measurement matrix, and JBMOLS algorithm is proposed to improve the compression and reconstruction performance of FECG signal by CS-based WBANs. Experimental results show that the compression ratio (CR) required for accurate reconstruction of this transmission mode is increased by nearly 10%, and the runtime is saved by about 30%.

Keywords: telemedicine, fetal ECG, compressed sensing, joint sparse reconstruction, block sparse signal

Procedia PDF Downloads 112

Authors: Mustafa Engin Başoğlu, Bekir Çakır

Abstract:

The most important component affecting the efficiency of photovoltaic power systems are solar panels. Efficiency of these systems are significantly affected because of being low efficiency of solar panel. Therefore, solar panels should be operated under maximum power point conditions through a power converter. In this study, design boost converter with maximum power point tracking (MPPT) operation has been designed and performed with Incremental Conductance (Inc-Cond) algorithm by using direct duty control. Furthermore, it is shown that performance of boost converter with MPPT operation fails under low load resistance connection.

Keywords: boost converter, incremental conductance (Inc-Cond), MPPT, solar panel

Procedia PDF Downloads 1030