Search results for: Virtual machine

Authors: SeHun Lim, Sanghoon Kim, Sun-Tae Chung, Seongwon Cho

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Face authentication for access control is a face membership authentication which passes the person of the incoming face if he turns out to be one of an enrolled person based on face recognition or rejects if not. Face membership authentication belongs to the two class classification problem where SVM(Support Vector Machine) has been successfully applied and shows better performance compared to the conventional threshold-based classification. However, most of previous SVMs have been trained using image feature vectors extracted from face images of each class member(enrolled class/unenrolled class) so that they are not robust to variations in illuminations, poses, and facial expressions and much affected by changes in member configuration of the enrolled class In this paper, we propose an effective face membership authentication method based on SVM using class discriminating features which represent an incoming face image-s associability with each class distinctively. These class discriminating features are weakly related with image features so that they are less affected by variations in illuminations, poses and facial expression. Through experiments, it is shown that the proposed face membership authentication method performs better than the threshold rule-based or the conventional SVM-based authentication methods and is relatively less affected by changes in member size and membership.

Keywords: Face Authentication, Access control, member ship authentication, SVM.

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Authors: Lokesh Varshney, R. K. Saket

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This paper presents reliability indices evaluation of the rotor core magnetization of the induction motor operated as a self excited induction generator by using probability distribution approach and Monte Carlo simulation. Parallel capacitors with calculated minimum capacitive value across the terminals of the induction motor operated as a SEIG with unregulated shaft speed have been connected during the experimental study. A three phase, 4 poles, 50Hz, 5.5 hp, 12.3A, 230V induction motor coupled with DC Shunt Motor was tested in the electrical machine laboratory with variable reactive loads. Based on this experimental study, it is possible to choose a reliable induction machines operated as a SEIG for unregulated renewable energy application in remote area or where grid is not available. Failure density function, cumulative failure distribution function, survivor function, hazard model, probability of success and probability of failure for reliability evaluation of the three phase induction motor operating as a SEIG have been presented graphically in this paper.

Keywords: Residual magnetism, magnetization curve, induction motor, self excited induction generator, probability distribution, Monte Carlo simulation.

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Authors: Sidhartha Panda, Narayana Prasad Padhy

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This paper presents a systematic procedure for modelling and simulation of a power system installed with a power system stabilizer (PSS) and a flexible ac transmission system (FACTS)-based controller. For the design purpose, the model of example power system which is a single-machine infinite-bus power system installed with the proposed controllers is developed in MATLAB/SIMULINK. In the developed model synchronous generator is represented by model 1.1. which includes both the generator main field winding and the damper winding in q-axis so as to evaluate the impact of PSS and FACTS-based controller on power system stability. The model can be can be used for teaching the power system stability phenomena, and also for research works especially to develop generator controllers using advanced technologies. Further, to avoid adverse interactions, PSS and FACTS-based controller are simultaneously designed employing genetic algorithm (GA). The non-linear simulation results are presented for the example power system under various disturbance conditions to validate the effectiveness of the proposed modelling and simultaneous design approach.

Keywords: Genetic algorithm, modelling and simulation, MATLAB/SIMULINK, power system stabilizer, thyristor controlledseries compensator, simultaneous design, power system stability.

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Authors: Niousha Bagheri Khulenjani, Mohammad Saniee Abadeh

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Learning from very big datasets is a significant problem for most present data mining and machine learning algorithms. MicroRNA (miRNA) is one of the important big genomic and non-coding datasets presenting the genome sequences. In this paper, a hybrid method for the classification of the miRNA data is proposed. Due to the variety of cancers and high number of genes, analyzing the miRNA dataset has been a challenging problem for researchers. The number of features corresponding to the number of samples is high and the data suffer from being imbalanced. The feature selection method has been used to select features having more ability to distinguish classes and eliminating obscures features. Afterward, a Convolutional Neural Network (CNN) classifier for classification of cancer types is utilized, which employs a Genetic Algorithm to highlight optimized hyper-parameters of CNN. In order to make the process of classification by CNN faster, Graphics Processing Unit (GPU) is recommended for calculating the mathematic equation in a parallel way. The proposed method is tested on a real-world dataset with 8,129 patients, 29 different types of tumors, and 1,046 miRNA biomarkers, taken from The Cancer Genome Atlas (TCGA) database.

Keywords: Cancer classification, feature selection, deep learning, genetic algorithm.

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Authors: Emad E Abdallah, A.F. Otoom, ArwaSaqer, Ola Abu-Aisheh, Diana Omari, Ghadeer Salem

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As emails communications have no consistent authentication procedure to ensure the authenticity, we present an investigation analysis approach for detecting forged emails based on Random Forests and Naïve Bays classifiers. Instead of investigating the email headers, we use the body content to extract a unique writing style for all the possible suspects. Our approach consists of four main steps: (1) The cybercrime investigator extract different effective features including structural, lexical, linguistic, and syntactic evidence from previous emails for all the possible suspects, (2) The extracted features vectors are normalized to increase the accuracy rate. (3) The normalized features are then used to train the learning engine, (4) upon receiving the anonymous email (M); we apply the feature extraction process to produce a feature vector. Finally, using the machine learning classifiers the email is assigned to one of the suspects- whose writing style closely matches M. Experimental results on real data sets show the improved performance of the proposed method and the ability of identifying the authors with a very limited number of features.

Keywords: Digital investigation, cybercrimes, emails forensics, anonymous emails, writing style, and authorship analysis

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Authors: Kanika Sood, Sijie Shang

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A fundamental goal of education includes preparing students to become a part of the global workforce by making beneficial contributions to society. In this paper, we analyze student performance for multiple courses that involve different teaching pedagogies: a cooperative learning technique and an inquiry-based learning strategy. Student performance includes student engagement, grades, and attendance records. We perform this study in the Computer Science department for online and in-person courses for 450 students. We will perform correlation analysis to study the relationship between student scores and other parameters such as gender, mode of learning. We use natural language processing and machine learning to analyze student feedback data and performance data. We assess the learning outcomes of two teaching pedagogies for undergraduate and graduate courses to showcase the impact of pedagogical adoption and learning outcome as determinants of academic achievement. Early findings suggest that when using the specified pedagogies, students become experts on their topics and illustrate enhanced engagement with peers.

Keywords: Bag-of-words, cooperative learning, education, inquiry-based learning, in-person learning, Natural Language Processing, online learning, sentiment analysis, teaching pedagogy.

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Authors: Zhou Xiong, Kang Shao Bo, Yang Bo

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To date, high-performance structural steel has been widely used for columns in construction practices due to its significant advantages over conventional steel. However, the same design approach with conventional steel columns is still adopted in the design of high-performance steel columns. As a result, its superior properties cannot be fully considered in design. This paper conducts a test and finite element analysis on the overall stability behaviour of welded Q460GJ steel box columns. In the test, four steel columns with different slenderness and width-to-thickness ratio were compressed under an axial compression testing machine. And finite element models were established in which material nonlinearity and residual stress distributions of test columns were included. Then, comparisons were made between test results and finite element result, it showed that finite element analysis results are agree well with the test result. It means that the test and finite element model are reliable. Then, we compared the test result with the design value calculated by current code, the result showed that Q460GJ steel box columns have the higher overall buckling capacity than the design value. It is necessary to update the design curves for Q460GJ steel columns so that the overall stability capacity of Q460GJ box columns can be designed appropriately.

Keywords: Axial compression, Finite element analysis, Overall stability, Q460GJ steel, Welded box columns.

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Authors: Yi Yang, Jiasong Sun

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Almost every aspect of our daily lives is now intertwined with some degree of Human-Computer Interaction (HCI). HCI courses draw on knowledge from disciplines as diverse as computer science, psychology, design principles, anthropology and more. The HCI courses in the Department of Electronics at Tsinghua University, known as the Media and Cognition course, is constantly updated to reflect the most advanced technological advances, such as virtual reality, augmented reality and artificial intelligence-based interaction. For more than a decade, this course has used an interest-based approach to teaching, in which students proactively propose some research-based questions and collaborate with teachers, using course knowledge to explore potential solutions. Semantic communication plays a key role in facilitating understanding and interaction between users and computer systems, ultimately enhancing system usability and user experience. The advancements in AI-generated technology, which has gained significant attention from both academia and industry in recent years, are exemplified by language models like GPT-3 that generate human-like dialogues from given prompts. The latest version of the HCI course practices a semantic communication platform based on AI-generated techniques. We explored a student-centered model and proposed an interest-based teaching method. Students are no longer just recipients of knowledge, but become active participants in the learning process driven by personal interests, thereby encouraging students to take responsibility for their own education. One of the latest results of this teaching approach in the course "Media and Cognition" is a student proposal to develop a semantic communication platform rooted in artificial intelligence generative technologies. The platform solves a key challenge in communications technology: the ability to preserve visual signals. The interest-based approach emphasizes personal curiosity and active participation, and the proposal of an artificial intelligence-generated semantic communication platform is an example and successful result of how students can exert greater creativity when they have the power to control their own learning.

Keywords: Human-computer interaction, media and cognition course, semantic communication, retain ability, prompts.

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Authors: Mohamed A. Mahfouz, M. A. Ismail

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Biclustering is a very useful data mining technique for identifying patterns where different genes are co-related based on a subset of conditions in gene expression analysis. Association rules mining is an efficient approach to achieve biclustering as in BIMODULE algorithm but it is sensitive to the value given to its input parameters and the discretization procedure used in the preprocessing step, also when noise is present, classical association rules miners discover multiple small fragments of the true bicluster, but miss the true bicluster itself. This paper formally presents a generalized noise tolerant bicluster model, termed as μBicluster. An iterative algorithm termed as BIDENS based on the proposed model is introduced that can discover a set of k possibly overlapping biclusters simultaneously. Our model uses a more flexible method to partition the dimensions to preserve meaningful and significant biclusters. The proposed algorithm allows discovering biclusters that hard to be discovered by BIMODULE. Experimental study on yeast, human gene expression data and several artificial datasets shows that our algorithm offers substantial improvements over several previously proposed biclustering algorithms.

Keywords: Machine learning, biclustering, bi-dimensional clustering, gene expression analysis, data mining.

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Authors: Ingy A. El-Khouly, Khaled S. El-Kilany, Aziz E. El-Sayed

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The process of wafer fabrication is arguably the most technologically complex and capital intensive stage in semiconductor manufacturing. This large-scale discrete-event process is highly reentrant, and involves hundreds of machines, restrictions, and processing steps. Therefore, production control of wafer fabrication facilities (fab), specifically scheduling, is one of the most challenging problems that this industry faces. Dispatching rules have been extensively applied to the scheduling problems in semiconductor manufacturing. Moreover, lot release policies are commonly used in this manufacturing setting to further improve the performance of such systems and reduce its inherent variability. In this work, simulation is used in the scheduling of re-entrant flow shop manufacturing systems with an application in semiconductor wafer fabrication; where, a simulation model has been developed for the Intel Five-Machine Six Step Mini-Fab using the ExtendTM simulation environment. The Mini-Fab has been selected as it captures the challenges involved in scheduling the highly re-entrant semiconductor manufacturing lines. A number of scenarios have been developed and have been used to evaluate the effect of different dispatching rules and lot release policies on the selected performance measures. Results of simulation showed that the performance of the Mini-Fab can be drastically improved using a combination of dispatching rules and lot release policy.

Keywords: Dispatching rules, lot release policy, re-entrant flowshop, semiconductor manufacturing.

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Authors: Olfa.Ben Ahmed, Mahmoud. Mejdoub, Chokri. Ben Amar

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Recognizing human action from videos is an active field of research in computer vision and pattern recognition. Human activity recognition has many potential applications such as video surveillance, human machine interaction, sport videos retrieval and robot navigation. Actually, local descriptors and bag of visuals words models achieve state-of-the-art performance for human action recognition. The main challenge in features description is how to represent efficiently the local motion information. Most of the previous works focus on the extension of 2D local descriptors on 3D ones to describe local information around every interest point. In this paper, we propose a new spatio-temporal descriptor based on a spacetime description of moving points. Our description is focused on an Accordion representation of video which is well-suited to recognize human action from 2D local descriptors without the need to 3D extensions. We use the bag of words approach to represent videos. We quantify 2D local descriptor describing both temporal and spatial features with a good compromise between computational complexity and action recognition rates. We have reached impressive results on publicly available action data set

Keywords: Accordion, Bag of Features, Human action, Motion, Moving point, Space-Time Descriptor, SIFT, Video.

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Authors: Vidhu Mitha Goutham, Ruma Mukherjee

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The low availability of well-trained, unlimited, dynamic-access models for specific languages makes it hard for corporate users to adopt quick translation techniques and incorporate them into product solutions. As translation tasks increasingly require a dynamic sequence learning curve; stable, cost-free opensource models are scarce. We survey and compare current translation techniques and propose a modified sequence to sequence model repurposed with attention techniques. Sequence learning using an encoder-decoder model is now paving the path for higher precision levels in translation. Using a Convolutional Neural Network (CNN) encoder and a Recurrent Neural Network (RNN) decoder background, we use Fairseq tools to produce an end-to-end bilingually trained Spanish-English machine translation model including source language detection. We acquire competitive results using a duo-lingo-corpus trained model to provide for prospective, ready-made plug-in use for compound sentences and document translations. Our model serves a decent system for large, organizational data translation needs. While acknowledging its shortcomings and future scope, it also identifies itself as a well-optimized deep neural network model and solution.

Keywords: Attention, encoder-decoder, Fairseq, Seq2Seq, Spanish, translation.

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Authors: Azian Hariri, M. Z. M Yusof, A. M. Leman

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Experimental study was conducted to assess personal welding fumes exposure toward welders during an aluminum metal inert gas (MIG) process. The welding process was carried out by a welding machine attached to a Computer Numerical Control (CNC) workbench. A dummy welder was used to replicate welder during welding works and was attached with sampling pumps and filter cassettes for welding fumes sampling. Direct reading instruments to measure air velocity, humidity, temperature and particulate matter with diameter size 10µm or less (PM₁₀) were located behind the dummy welder and parallel to the neck collar level to make sure the measured welding fumes exposure were not being influenced by other factors. Welding fumes exposure during standing and sitting position with and without the usage of local exhaust ventilation (LEV) was investigated. Welding fume samples were then digested and analyzed by using inductively coupled plasma mass spectroscopy (ICP-MS) according to ASTM D7439-08 method. The results of the study showed the welding fume exposure during sitting was lower compared to standing position. LEV helped reduce aluminum and lead exposure to acceptable levels during standing position. However during sitting position reduction of exposure was smaller. It can be concluded that welder position and the correct positioning of LEV should be implemented for effective exposure reduction. 

Keywords: ICP-MS, MIG process, personal sampling, welding fumes exposure.

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Authors: Jacqueline R. T. Alipo-on, Francesca I. F. Escobar, Myles J. T. Tan, Hezerul Abdul Karim, Nouar AlDahoul

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Electrocardiogram (ECG) signal analysis and processing are crucial in the diagnosis of cardiovascular diseases which are considered as one of the leading causes of mortality worldwide. However, the traditional rule-based analysis of large volumes of ECG data is time-consuming, labor-intensive, and prone to human errors. With the advancement of the programming paradigm, algorithms such as machine learning have been increasingly used to perform an analysis on the ECG signals. In this paper, various deep learning algorithms were adapted to classify five classes of heart beat types. The dataset used in this work is the synthetic MIT-Beth Israel Hospital (MIT-BIH) Arrhythmia dataset produced from generative adversarial networks (GANs). Various deep learning models such as ResNet-50 convolutional neural network (CNN), 1-D CNN, and long short-term memory (LSTM) were evaluated and compared. ResNet-50 was found to outperform other models in terms of recall and F1 score using a five-fold average score of 98.88% and 98.87%, respectively. 1-D CNN, on the other hand, was found to have the highest average precision of 98.93%.

Keywords: Heartbeat classification, convolutional neural network, electrocardiogram signals, ECG signals, generative adversarial networks, long short-term memory, LSTM, ResNet-50.

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Authors: Masaumi Nakahara, Sou Watanabe, Hiromichi Ogi, Atsuhiro Shibata, Kazunori Nomura

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In the Japan Atomic Energy Agency, three types of experimental research, advanced reactor fuel reprocessing, radioactive waste disposal, and nuclear fuel cycle technology, have been carried out at the Chemical Processing Facility. The facility has generated high level radioactive liquid and solid wastes in hot cells. The high level radioactive solid waste is divided into three main categories, a flammable waste, a non-flammable waste, and a solid reagent waste. A plastic product is categorized into the flammable waste and molten with a heating mantle. The non-flammable waste is cut with a band saw machine for reducing the volume. Among the solid reagent waste, a used adsorbent after the experiments is heated, and an extractant is decomposed for its stabilization. All high level radioactive solid wastes in the hot cells are packed in a high level radioactive solid waste can. The high level radioactive solid waste can is transported to the 2nd High Active Solid Waste Storage in the Tokai Reprocessing Plant in the Japan Atomic Energy Agency.

Keywords: High level radioactive solid waste, advanced reactor fuel reprocessing, radioactive waste disposal, nuclear fuel cycle technology.

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Authors: Riad S. Jabri

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Pattern matching based on regular tree grammars have been widely used in many areas of computer science. In this paper, we propose a pattern matcher within the framework of code generation, based on a generic and a formalized approach. According to this approach, parsers for regular tree grammars are adapted to a general pattern matching solution, rather than adapting the pattern matching according to their parsing behavior. Hence, we first formalize the construction of the pattern matches respective to input trees drawn from a regular tree grammar in a form of the so-called match trees. Then, we adopt a recently developed generic parser and tightly couple its parsing behavior with such construction. In addition to its generality, the resulting pattern matcher is characterized by its soundness and efficient implementation. This is demonstrated by the proposed theory and by the derived algorithms for its implementation. A comparison with similar and well-known approaches, such as the ones based on tree automata and LR parsers, has shown that our pattern matcher can be applied to a broader class of grammars, and achieves better approximation of pattern matches in one pass. Furthermore, its use as a machine code selector is characterized by a minimized overhead, due to the balanced distribution of the cost computations into static ones, during parser generation time, and into dynamic ones, during parsing time.

Keywords: Bottom-up automata, Code selection, Pattern matching, Regular tree grammars, Match trees.

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Authors: L. A. Muhammad, M. S. Argungu

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Educational data mining (EDM) has recorded substantial considerations. Techniques of data mining in one way or the other have been proposed to dig out out-of-sight knowledge in educational data. The result of the study got assists academic institutions in further enhancing their process of learning and methods of passing knowledge to students. Consequently, the performance of students boasts and the educational products are by no doubt enhanced. This study adopted a student performance prediction model premised on techniques of data mining with Students' Essential Features (SEF). SEF are linked to the learner's interactivity with the e-learning management system. The performance of the student's predictive model is assessed by a set of classifiers, viz. Bayes Network, Logistic Regression, and Reduce Error Pruning Tree (REP). Consequently, ensemble methods of Bagging, Boosting, and Random Forest (RF) are applied to improve the performance of these single classifiers. The study reveals that the result shows a robust affinity between learners' behaviors and their academic attainment. Result from the study shows that the REP Tree and its ensemble record the highest accuracy of 83.33% using SEF. Hence, in terms of the Receiver Operating Curve (ROC), boosting method of REP Tree records 0.903, which is the best. This result further demonstrates the dependability of the proposed model.

Keywords: Ensemble, bagging, Random Forest, boosting, data mining, classifiers, machine learning.

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Authors: Endri Rama, Genci Capi, Shigenori Kawahara

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The mobile robot ability to navigate autonomously in its environment is very important. Even though the advances in technology, robot self-localization and goal directed navigation in complex environments are still challenging tasks. In this article, we propose a novel method for robot navigation based on rat’s brain signals (Local Field Potentials). It has been well known that rats accurately and rapidly navigate in a complex space by localizing themselves in reference to the surrounding environmental cues. As the first step to incorporate the rat’s navigation strategy into the robot control, we analyzed the rats’ strategies while it navigates in a multiple Y-maze, and recorded Local Field Potentials (LFPs) simultaneously from three brain regions. Next, we processed the LFPs, and the extracted features were used as an input in the artificial neural network to predict the rat’s next location, especially in the decision-making moment, in Y-junctions. We developed an algorithm by which the robot learned to imitate the rat’s decision-making by mapping the rat’s brain signals into its own actions. Finally, the robot learned to integrate the internal states as well as external sensors in order to localize and navigate in the complex environment.

Keywords: Brain machine interface, decision-making, local field potentials, mobile robot, navigation, neural network, rat, signal processing.

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Authors: Razib M. Othman, Safaai Deris, Rosli M. Illias, Hany T. Alashwal, Rohayanti Hassan, FarhanMohamed

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The most important property of the Gene Ontology is the terms. These control vocabularies are defined to provide consistent descriptions of gene products that are shareable and computationally accessible by humans, software agent, or other machine-readable meta-data. Each term is associated with information such as definition, synonyms, database references, amino acid sequences, and relationships to other terms. This information has made the Gene Ontology broadly applied in microarray and proteomic analysis. However, the process of searching the terms is still carried out using traditional approach which is based on keyword matching. The weaknesses of this approach are: ignoring semantic relationships between terms, and highly depending on a specialist to find similar terms. Therefore, this study combines semantic similarity measure and genetic algorithm to perform a better retrieval process for searching semantically similar terms. The semantic similarity measure is used to compute similitude strength between two terms. Then, the genetic algorithm is employed to perform batch retrievals and to handle the situation of the large search space of the Gene Ontology graph. The computational results are presented to show the effectiveness of the proposed algorithm.

Keywords: Gene Ontology, Semantic similarity measure, Genetic algorithm, Ontology search

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Authors: Cuneyt Yucelbas, Seral Ozsen, Sule Yucelbas, Gulay Tezel

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Due to the fact that there exist only a small number of complex systems in artificial immune system (AIS) that work out nonlinear problems, nonlinear AIS approaches, among the well-known solution techniques, need to be developed. Gaussian function is usually used as similarity estimation in classification problems and pattern recognition. In this study, diagnosis of breast cancer, the second type of the most widespread cancer in women, was performed with different distance calculation functions that euclidean, gaussian and gaussian-euclidean hybrid function in the clonal selection model of classical AIS on Wisconsin Breast Cancer Dataset (WBCD), which was taken from the University of California, Irvine Machine-Learning Repository. We used 3-fold cross validation method to train and test the dataset. According to the results, the maximum test classification accuracy was reported as 97.35% by using of gaussian-euclidean hybrid function for fold-3. Also, mean of test classification accuracies for all of functions were obtained as 94.78%, 94.45% and 95.31% with use of euclidean, gaussian and gaussian-euclidean, respectively. With these results, gaussian-euclidean hybrid function seems to be a potential distance calculation method, and it may be considered as an alternative distance calculation method for hard nonlinear classification problems.

Keywords: Artificial Immune System, Breast Cancer Diagnosis, Euclidean Function, Gaussian Function.

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Authors: U. Achutha Kini, S. S. Sharma, K. Jagannath, P. R. Prabhu, Gowri Shankar M. C.

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Aluminium matrix composites with alumina reinforcements give superior mechanical & physical properties. Their applications in several fields like automobile, aerospace, defense, sports, electronics, bio-medical and other industrial purposes are becoming essential for the last several decades. In the present work, fabrication of hybrid composite was done by Stir casting technique using Al 6061 as a matrix with alumina and silicon carbide (SiC) as reinforcement materials. The weight percentage of alumina is varied from 2 to 4% and the silicon carbide weight percentage is maintained constant at 2%. Hardness and wear tests are performed in the as cast and heat treated conditions. Age hardening treatment was performed on the specimen with solutionizing at 550°C, aging at two temperatures (150 and 200°C) for different time durations. Hardness distribution curves are drawn and peak hardness values are recorded. Hardness increase was very sensitive with respect to the decrease in aging temperature. There was an improvement in wear resistance of the peak aged material when aged at lower temperature. Also increase in weight percent of alumina, increases wear resistance at lower temperature but opposite behavior was seen when aged at higher temperature.

Keywords: Hybrid composite, hardness test, wear test, heat treatment, pin on disc wear testing machine.

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Authors: Mu-Yen Chen, Min-Hsuan Fan, Chia-Chen Chen, Siang-Yu Jhong

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In recent years, real estate prediction or valuation has been a topic of discussion in many developed countries. Improper hype created by investors leads to fluctuating prices of real estate, affecting many consumers to purchase their own homes. Therefore, scholars from various countries have conducted research in real estate valuation and prediction. With the back-propagation neural network that has been popular in recent years and the orthogonal array in the Taguchi method, this study aimed to find the optimal parameter combination at different levels of orthogonal array after the system presented different parameter combinations, so that the artificial neural network obtained the most accurate results. The experimental results also demonstrated that the method presented in the study had a better result than traditional machine learning. Finally, it also showed that the model proposed in this study had the optimal predictive effect, and could significantly reduce the cost of time in simulation operation. The best predictive results could be found with a fewer number of experiments more efficiently. Thus users could predict a real estate transaction price that is not far from the current actual prices.

Keywords: Artificial Neural Network, Taguchi Method, Real Estate Valuation Model.

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Authors: Tlili-Guiassa Yamina

Abstract:

Many natural language expressions are ambiguous, and need to draw on other sources of information to be interpreted. Interpretation of the e word تعاون to be considered as a noun or a verb depends on the presence of contextual cues. To interpret words we need to be able to discriminate between different usages. This paper proposes a hybrid of based- rules and a machine learning method for tagging Arabic words. The particularity of Arabic word that may be composed of stem, plus affixes and clitics, a small number of rules dominate the performance (affixes include inflexional markers for tense, gender and number/ clitics include some prepositions, conjunctions and others). Tagging is closely related to the notion of word class used in syntax. This method is based firstly on rules (that considered the post-position, ending of a word, and patterns), and then the anomaly are corrected by adopting a memory-based learning method (MBL). The memory_based learning is an efficient method to integrate various sources of information, and handling exceptional data in natural language processing tasks. Secondly checking the exceptional cases of rules and more information is made available to the learner for treating those exceptional cases. To evaluate the proposed method a number of experiments has been run, and in order, to improve the importance of the various information in learning.

Keywords: Arabic language, Based-rules, exceptions, Memorybased learning, Tagging.

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Authors: A.K. Baliarsingh, S. Panda, A.K. Mohanty, C. Ardil

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This paper presents a systematic approach for designing Unified Power Flow Controller (UPFC) based supplementary damping controllers for damping low frequency oscillations in a single-machine infinite-bus power system. Detailed investigations have been carried out considering the four alternatives UPFC based damping controller namely modulating index of series inverter (mB), modulating index of shunt inverter (mE), phase angle of series inverter (δB ) and phase angle of the shunt inverter (δE ). The design problem of the proposed controllers is formulated as an optimization problem and Real- Coded Genetic Algorithm (RCGA) is employed to optimize damping controller parameters. Simulation results are presented and compared with a conventional method of tuning the damping controller parameters to show the effectiveness and robustness of the proposed design approach.

Keywords: Power System Oscillations, Real-Coded Genetic Algorithm (RCGA), Flexible AC Transmission Systems (FACTS), Unified Power Flow Controller (UPFC), Damping Controller.

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Authors: Mohammad Hasan Raouf, Ebrahim Rasooli Anarmarzi, Hamid Lesani, Javad Olamaei

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This paper proposes the application of a hierarchical fuzzy system (HFS) based on multi-input power system stabilizer (MPSS) and also Static Var Compensator (SVC) in multi-machine environment.The number of rules grows exponentially with the number of variables in a conventional fuzzy logic system. The proposed HFS method is developed to solve this problem. To reduce the number of rules the HFS consists of a number of low-dimensional fuzzy systems in a hierarchical structure. In fact, by using HFS the total number of involved rules increases only linearly with the number of input variables. In the MPSS, to have better efficiency an auxiliary signal of reactive power deviation (ΔQ) is added with ΔP+ Δω input type Power system stabilizer (PSS). Phasor model of SVC is described and used in this paper. The performances of MPSS, Conventional power system stabilizer (CPSS), hierarchical Fuzzy Multi-input Power System Stabilizer (HFMPSS) and the proposed method in damping inter-area mode of oscillation are examined in response to disturbances. By using digital simulations the comparative study is illustrated. It can be seen that the proposed PSS is performing satisfactorily within the whole range of disturbances.

Keywords: Power system stabilizer (PSS), hierarchical fuzzysystem (HFS), Static VAR compensator (SVC)

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Authors: Muhibul Haque Bhuyan, Md. Refat Sarder

Abstract:

If a person can monitor his/her oxygen saturation level intermittently then he/she can identify his/her condition early and thus he/she can seek a doctor’s help. This paper reports the design, simulation, and implementation of a low-cost pulse oxygen saturation measurement device based on a reflective photoplethysmography (PPG) system using an integrated circuit sensor as the fundamental component of this health status checking device. The measurement of the physiological parameter is the blood oxygen saturation level (SpO₂) in the peripheral capillary. This work has been implemented using an Arduino Uno R3 microcontroller along with this sensor integrated circuit (IC). The system is designed in the Proteus environment and then simulated to check its performance. After that, the hardware implementation is performed. We used a clipping type optical sensor to sense the arterial oxygen saturation level of blood signal from the fingertips of an individual and then transformed it into the digital data in the microcontroller through its programming its instruction. The designed system was tested by measuring the SpO₂ level for several people of different ages, from 12 to 57 years of age. Besides, the same people were tested using a standard machine purchased from the market. Test results were found very satisfactory as the average percentage of error was very low, 1.59% only.

Keywords: Digital pulse oxygen saturation level, oximeter, measurement, design, simulation, implementation, proteus, Arduino Uno microcontroller.

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Authors: Dhurgham Al-Karawi, Nisreen Polus, Shakir Al-Zaidi, Sabah Jassim

Abstract:

The management of COVID-19 patients based on chest imaging is emerging as an essential tool for evaluating the spread of the pandemic which has gripped the global community. It has already been used to monitor the situation of COVID-19 patients who have issues in respiratory status. There has been increase to use chest imaging for medical triage of patients who are showing moderate-severe clinical COVID-19 features, this is due to the fast dispersal of the pandemic to all continents and communities. This article demonstrates the development of machine learning techniques for the test of COVID-19 patients using Chest X-Ray (CXR) images in nearly real-time, to distinguish the COVID-19 infection with a significantly high level of accuracy. The testing performance has covered a combination of different datasets of CXR images of positive COVID-19 patients, patients with viral and bacterial infections, also, people with a clear chest. The proposed AI scheme successfully distinguishes CXR scans of COVID-19 infected patients from CXR scans of viral and bacterial based pneumonia as well as normal cases with an average accuracy of 94.43%, sensitivity 95%, and specificity 93.86%. Predicted decisions would be supported by visual evidence to help clinicians speed up the initial assessment process of new suspected cases, especially in a resource-constrained environment.

Keywords: COVID-19, chest x-ray scan, artificial intelligence, texture analysis, local binary pattern transform, Gabor filter.

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Authors: Farhad Kolahan, A. Hamid Khajavi

Abstract:

Abrasive waterjet is a novel machining process capable of processing wide range of hard-to-machine materials. This research addresses modeling and optimization of the process parameters for this machining technique. To model the process a set of experimental data has been used to evaluate the effects of various parameter settings in cutting 6063-T6 aluminum alloy. The process variables considered here include nozzle diameter, jet traverse rate, jet pressure and abrasive flow rate. Depth of cut, as one of the most important output characteristics, has been evaluated based on different parameter settings. The Taguchi method and regression modeling are used in order to establish the relationships between input and output parameters. The adequacy of the model is evaluated using analysis of variance (ANOVA) technique. The pairwise effects of process parameters settings on process response outputs are also shown graphically. The proposed model is then embedded into a Simulated Annealing algorithm to optimize the process parameters. The optimization is carried out for any desired values of depth of cut. The objective is to determine proper levels of process parameters in order to obtain a certain level of depth of cut. Computational results demonstrate that the proposed solution procedure is quite effective in solving such multi-variable problems.

Keywords: AWJ cutting, Mathematical modeling, Simulated Annealing, Optimization

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Authors: Samira Vafaye Eslahi

Abstract:

BCI (Brain Computer Interface) is a communication machine that translates brain massages to computer commands. These machines with the help of computer programs can recognize the tasks that are imagined. Feature extraction is an important stage of the process in EEG classification that can effect in accuracy and the computation time of processing the signals. In this study we process the signal in three steps of active segment selection, fractal feature extraction, and classification. One of the great challenges in BCI applications is to improve classification accuracy and computation time together. In this paper, we have used student’s 2D sample t-statistics on continuous wavelet transforms for active segment selection to reduce the computation time. In the next level, the features are extracted from some famous fractal dimension estimation of the signal. These fractal features are Katz and Higuchi. In the classification stage we used ANFIS (Adaptive Neuro-Fuzzy Inference System) classifier, FKNN (Fuzzy K-Nearest Neighbors), LDA (Linear Discriminate Analysis), and SVM (Support Vector Machines). We resulted that active segment selection method would reduce the computation time and Fractal dimension features with ANFIS analysis on selected active segments is the best among investigated methods in EEG classification.

Keywords: EEG, Student’s t- statistics, BCI, Fractal Features, ANFIS, FKNN.

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Authors: Hikmat A. M. Abd-El-Jaber, Tengku M. T. Sembok

Abstract:

The information on the Web increases tremendously. A number of search engines have been developed for searching Web information and retrieving relevant documents that satisfy the inquirers needs. Search engines provide inquirers irrelevant documents among search results, since the search is text-based rather than semantic-based. Information retrieval research area has presented a number of approaches and methodologies such as profiling, feedback, query modification, human-computer interaction, etc for improving search results. Moreover, information retrieval has employed artificial intelligence techniques and strategies such as machine learning heuristics, tuning mechanisms, user and system vocabularies, logical theory, etc for capturing user's preferences and using them for guiding the search based on the semantic analysis rather than syntactic analysis. Although a valuable improvement has been recorded on search results, the survey has shown that still search engines users are not really satisfied with their search results. Using ontologies for semantic-based searching is likely the key solution. Adopting profiling approach and using ontology base characteristics, this work proposes a strategy for finding the exact meaning of the query terms in order to retrieve relevant information according to user needs. The evaluation of conducted experiments has shown the effectiveness of the suggested methodology and conclusion is presented.

Keywords: information retrieval, user profiles, semantic Web, ontology, search engine.

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