Search results for: Distance learning

Authors: Q. Niu, X. H. Zhou, W. Liu

Abstract:

The cooling channels of injection mould play a crucial role in determining the productivity of moulding process and the product quality. It’s not a simple task to design high quality cooling channels. In this paper, an intelligent cooling channels design system including automatic layout of cooling channels, interference checking and assembly of accessories is studied. Automatic layout of cooling channels using genetic algorithm is analyzed. Through integrating experience criteria of designing cooling channels, considering the factors such as the mould temperature and interference checking, the automatic layout of cooling channels is implemented. The method of checking interference based on distance constraint algorithm and the function of automatic and continuous assembly of accessories are developed and integrated into the system. Case studies demonstrate the feasibility and practicality of the intelligent design system.

Keywords: Injection mould, cooling channel, automatic layout, interference checking.

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Authors: Ashly Joseph

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Cloud computing has become an essential component of enterprises and organizations globally in the current era of digital technology. The cloud has a multitude of advantages, including scalability, flexibility, and cost-effectiveness, rendering it an appealing choice for data storage and processing. The increasing storage of sensitive information in cloud environments has raised significant concerns over the security of such systems. The frequency of cyber threats and attacks specifically aimed at cloud infrastructure has been increasing, presenting substantial dangers to the data, reputation, and financial stability of enterprises. Conventional security methods can become inadequate when confronted with ever intricate and dynamic threats. Artificial Intelligence (AI) technologies possess the capacity to significantly transform cloud security through their ability to promptly identify and thwart assaults, adjust to emerging risks, and offer intelligent perspectives for proactive security actions. The objective of this research study is to investigate the utilization of AI technologies in augmenting the security measures within cloud computing systems. This paper aims to offer significant insights and recommendations for businesses seeking to protect their cloud-based assets by analyzing the present state of cloud security, the capabilities of AI, and the possible advantages and obstacles associated with using AI into cloud security policies.

Keywords: Machine Learning, Natural Learning Processing, Denial-of-Service attacks, Sentiment Analysis, Cloud computing.

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Authors: Fatima Muhammad Shitu

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In language learning, second language learners as well as Native speakers commit errors in their attempt to achieve competence in the target language. The realm of collocation has to do with meaning relation between lexical items. In all human language, there is a kind of ‘natural order’ in which words are arranged or relate to one another in sentences so much so that when a word occurs in a given context, the related or naturally co-occurring word will automatically come to the mind. It becomes an error, therefore, if students inappropriately pair or arrange such ‘naturally’ co–occurring lexical items in a text. It has been observed that most of the second language learners in this research group commit collocation errors. A study of this kind is very significant as it gives insight into the kinds of errors committed by learners. This will help the language teacher to be able to identify the sources and causes of such errors as well as correct them thereby guiding, helping and leading the learners towards achieving some level of competence in the language. The aim of the study is to understand the nature of these errors as stumbling blocks to effective essay writing. The objective of the study is to identify the errors, analyze their structural compositions so as to determine whether there are similarities between students in this regard and to find out whether there are patterns to these kinds of errors which will enable the researcher to understand their sources and causes. As a descriptive research, the researcher samples some nine hundred essays collected from three hundred undergraduate learners of English as a second language in the Federal College of Education, Kano, North- West Nigeria, i.e. three essays per each student. The essays which were given on three different lecture times were of similar thematic preoccupations (i.e. same topics) and length (i.e. same number of words). The essays were written during the lecture hour at three different lecture occasions. The errors were identified in a systematic manner whereby errors so identified were recorded only once even if they occur severally in students’ essays. The data was collated using percentages in which the identified numbers of occurrences were converted accordingly in percentages. The findings from the study indicate that there are similarities as well as regular and repeated errors which provided a pattern. Based on the pattern identified, the conclusion is that students’ collocation errors are attributable to poor teaching and learning which resulted in wrong generalization of rules.

Keywords: Collocations, errors, collocation errors, second language learning.

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Authors: Zhen-Hui Hu, Jyh-Shong Ju, Ming-Hwei Perng

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This paper presents a useful sub-pixel image registration method using line segments and a sub-pixel edge detector. In this approach, straight line segments are first extracted from gray images at the pixel level before applying the sub-pixel edge detector. Next, all sub-pixel line edges are mapped onto the orientation-distance parameter space to solve for line correspondence between images. Finally, the registration parameters with sub-pixel accuracy are analytically solved via two linear least-square problems. The present approach can be applied to various fields where fast registration with sub-pixel accuracy is required. To illustrate, the present approach is applied to the inspection of printed circuits on a flat panel. Numerical example shows that the present approach is effective and accurate when target images contain a sufficient number of line segments, which is true in many industrial problems.

Keywords: Defect detection, Image registration, Straight line segment, Sub-pixel.

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Authors: Ali Sarosh, Dong Yun-Feng, Muhammad Umer

Abstract:

Fully reusable spaceplanes do not exist as yet. This implies that design-qualification for optimized highly-integrated forebody-inlet configuration of booster-stage vehicle cannot be based on archival data of other spaceplanes. Therefore, this paper proposes a novel TIPSO-SVM expert system methodology. A non-trivial problem related to optimization and classification of hypersonic forebody-inlet configuration in conjunction with mass-model of the two-stage-to-orbit (TSTO) vehicle is solved. The hybrid-heuristic machine learning methodology is based on two-step improved particle swarm optimizer (TIPSO) algorithm and two-step support vector machine (SVM) data classification method. The efficacy of method is tested by first evolving an optimal configuration for hypersonic compression system using TIPSO algorithm; thereafter, classifying the results using two-step SVM method. In the first step extensive but non-classified mass-model training data for multiple optimized configurations is segregated and pre-classified for learning of SVM algorithm. In second step the TIPSO optimized mass-model data is classified using the SVM classification. Results showed remarkable improvement in configuration and mass-model along with sizing parameters.

Keywords: TIPSO-SVM expert system, TIPSO algorithm, two-step SVM method, aerothermodynamics, mass-modeling, TSTO vehicle.

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Authors: Hee-Jeong Ahn, Kee-Won Kim, Seung-Hoon Kim

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The knowledge of the relationship between characters can help readers to understand the overall story or plot of the literary fiction. In this paper, we present a method for extracting the specific relationship between characters from a Korean literary fiction. Generally, methods for extracting relationships between characters in text are statistical or computational methods based on the sentence distance between characters without considering Korean linguistic features. Furthermore, it is difficult to extract the relationship with direction from text, such as one-sided love, because they consider only the weight of relationship, without considering the direction of the relationship. Therefore, in order to identify specific relationships between characters, we propose a statistical method considering linguistic features, such as syntactic patterns and speech verbs in Korean. The result of our method is represented by a weighted directed graph of the relationship between the characters. Furthermore, we expect that proposed method could be applied to the relationship analysis between characters of other content like movie or TV drama.

Keywords: Data mining, Korean linguistic feature, literary fiction, relationship extraction.

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Authors: Phornpat Chewasoonthorn, Surat Kwanmuang

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Indoor positioning technologies have been evolved rapidly. They augment the Global Positioning System (GPS) which requires line-of-sight to the sky to track the location of people or objects. In this study, we developed an error correction method for an indoor real-time location system (RTLS) based on an ultra-wideband (UWB) sensor from Decawave. Multiple stationary nodes (anchor) were installed throughout the workspace. The distance between stationary and moving nodes (tag) can be measured using a two-way-ranging (TWR) scheme. The result has shown that the uncorrected ranging error from the sensor system can be as large as 1 m. To reduce ranging error and thus increase positioning accuracy, we present an online correction algorithm using the Kalman filter. The results from experiments have shown that the system can reduce ranging error down to 5 cm.

Keywords: Indoor positioning, ultra-wideband, error correction, Kalman filter.

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Authors: Peng Mei, Yan-Jun Qi, Yu Cui, Song Lu, He-Ping Zhang

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A lot of computer-based methods have been developed to assess the evacuation capability (EC) of high-rise buildings. Because softwares are time-consuming and not proper for on scene applications, we adopted two methods, fuzzy analytic hierarchy process (FAHP) and technique for order preference by similarity to an ideal solution (TOPSIS), for EC assessment of a high-rise building in Jinan. The EC scores obtained with the two methods and the evacuation time acquired with Pathfinder 2009 for floors 47-60 of the building were compared with each other. The results show that FAHP performs better than TOPSIS for EC assessment of high-rise buildings, especially in the aspect of dealing with the effect of occupant type and distance to exit on EC, tackling complex problem with multi-level structure of criteria, and requiring less amount of computation. However, both FAHP and TOPSIS failed to appropriately handle the situation where the exit width changes while occupants are few.

Keywords: Evacuation capability assessment, FAHP, high-rise buildings, TOPSIS.

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Authors: Shiuh-Jer Huang, Yu-Sheng Hsu

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On-board available parking space detecting system, parking trajectory planning and tracking control mechanism are the key components of vehicle backward auto-parking system. Firstly, pair of ultrasonic sensors is installed on each side of vehicle body surface to detect the relative distance between ego-car and surrounding obstacle. The dimension of a found empty space can be calculated based on vehicle speed and the time history of ultrasonic sensor detecting information. This result can be used for constructing the 2D vehicle environmental map and available parking type judgment. Finally, the auto-parking controller executes the on-line optimal parking trajectory planning based on this 2D environmental map, and monitors the real-time vehicle parking trajectory tracking control. This low cost auto-parking system was tested on a model car.

Keywords: Vehicle auto-parking, parking space detection, parking path tracking, intelligent fuzzy controller.

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Authors: S.I Sulaiman, T.K Abdul Rahman, I. Musirin, S. Shaari

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This paper presents performance analysis of the Evolutionary Programming-Artificial Neural Network (EPANN) based technique to optimize the architecture and training parameters of a one-hidden layer feedforward ANN model for the prediction of energy output from a grid connected photovoltaic system. The ANN utilizes solar radiation and ambient temperature as its inputs while the output is the total watt-hour energy produced from the grid-connected PV system. EP is used to optimize the regression performance of the ANN model by determining the optimum values for the number of nodes in the hidden layer as well as the optimal momentum rate and learning rate for the training. The EPANN model is tested using two types of transfer function for the hidden layer, namely the tangent sigmoid and logarithmic sigmoid. The best transfer function, neural topology and learning parameters were selected based on the highest regression performance obtained during the ANN training and testing process. It is observed that the best transfer function configuration for the prediction model is [logarithmic sigmoid, purely linear].

Keywords: Artificial neural network (ANN), Correlation coefficient (R), Evolutionary programming-ANN (EPANN), Photovoltaic (PV), logarithmic sigmoid and tangent sigmoid.

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Authors: Jutharat Sunprasert, Ekapong Hirunsirisawat, Narongrit Waraporn, Somporn Peansukmanee

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Metal Ship and Robotic Car is one of the hands-on activities in the course, the Fundamental of Engineering that can be divided into three parts. The first part, the metal ships, was made by using engineering drawings, physics and mathematics knowledge. The second part is where the students learned how to construct a robotic car and control it using computer programming. In the last part, the students had to combine the workings of these two objects in the final testing. This aim of study was to investigate the effectiveness of hands-on activity by integrating Science, Technology, Engineering and Mathematics (STEM) concepts to develop scientific and engineering skills. The results showed that the majority of students felt this hands-on activity lead to an increased confidence level in the integration of STEM. Moreover, 48% of all students engaged well with the STEM concepts. Students could obtain the knowledge of STEM through hands-on activities with the topics science and mathematics, engineering drawing, engineering workshop and computer programming; most students agree and strongly agree with this learning process. This indicated that the hands-on activity: “Metal Ship and Robotic Car” is a useful tool to integrate each aspect of STEM. Furthermore, hands-on activities positively influence a student’s interest which leads to increased learning achievement and also in developing scientific and engineering skills.

Keywords: Hands-on activity, STEM education, computer programming, metal work.

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Authors: Feng-Tsai Weng, Dong- Syuan Cai, Tsochu-Lin

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A vehicle gasoline engine converts gasoline into power so that the car can move, and lubricants are important for engines and also gear boxes. Manufacturers have produced numbers of engine oils, and gear oils for engines and gear boxes to SAE International Standards. Some products not only can improve the lubrication of both the engine and gear box but also can raise power of vehicle this can be easily seen in the advertisement declared by the manufacturers. To observe the lubrication performance, a multi-leveled (heavy duty) gear oil was added to a gasoline engine as the oil in the vehicle. The oil was checked at about every 10,000 kilometers. The engine was detailed disassembled, cleaned, and parts were measured. The wear of components of the engine parts were checked and recorded finally. Based on the experiment results, some gear oil seems possible to be used as engine oil in particular vehicles. Vehicle owners should change oil periodically in about every 6,000 miles (or 10,000 kilometers). Used car owners may change engine oil in even longer distance.

Keywords: Multi-level gear oil, engine oil, viscosity, abrasion.

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Authors: Sahil Sharma, Mukesh Gupta, Raj Kumar, N. S Bindra

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This paper presents an experimental investigation on the optimization and the effect of the cutting parameters on Material Removal Rate (MRR) in Plasma Arc Cutting (PAC) of EN-45A Material using Taguchi L ₁₆ orthogonal array method. Four process variables viz. cutting speed, current, stand-off-distance and plasma gas pressure have been considered for this experimental work. Analysis of variance (ANOVA) has been performed to get the percentage contribution of each process parameter for the response variable i.e. MRR. Based on ANOVA, it has been observed that the cutting speed, current and the plasma gas pressure are the major influencing factors that affect the response variable. Confirmation test based on optimal setting shows the better agreement with the predicted values.

Keywords: Analysis of variance, Material removal rate, plasma arc cutting, Taguchi method.

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Authors: Saeid Fazli, Hadis Askarifar, Maryam Sheikh Shoaie

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Human identification at a distance has recently gained growing interest from computer vision researchers. Gait recognition aims essentially to address this problem by identifying people based on the way they walk [1]. Gait recognition has 3 steps. The first step is preprocessing, the second step is feature extraction and the third one is classification. This paper focuses on the classification step that is essential to increase the CCR (Correct Classification Rate). Multilayer Perceptron (MLP) is used in this work. Neural Networks imitate the human brain to perform intelligent tasks [3].They can represent complicated relationships between input and output and acquire knowledge about these relationships directly from the data [2]. In this paper we apply MLP NN for 11 views in our database and compare the CCR values for these views. Experiments are performed with the NLPR databases, and the effectiveness of the proposed method for gait recognition is demonstrated.

Keywords: Human motion analysis, biometrics, gait recognition, principal component analysis, MLP neural network.

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Authors: Madina Hamiane, Amina Khunji

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Detecting changes in multiple images of the same scene has recently seen increased interest due to the many contemporary applications including smart security systems, smart homes, remote sensing, surveillance, medical diagnosis, weather forecasting, speed and distance measurement, post-disaster forensics and much more. These applications differ in the scale, nature, and speed of change. This paper presents an application of image processing techniques to implement a real-time change detection system. Change is identified by comparing the RGB representation of two consecutive frames captured in real-time. The detection threshold can be controlled to account for various luminance levels. The comparison result is passed through a filter before decision making to reduce false positives, especially at lower luminance conditions. The system is implemented with a MATLAB Graphical User interface with several controls to manage its operation and performance.

Keywords: Image change detection, Image processing, image filtering, thresholding, B/W quantization.

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Authors: Dinesh Rao, M.G.M. Khan, Sabiha Khan

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Calibration estimation is a method of adjusting the original design weights to improve the survey estimates by using auxiliary information such as the known population total (or mean) of the auxiliary variables. A calibration estimator uses calibrated weights that are determined to minimize a given distance measure to the original design weights while satisfying a set of constraints related to the auxiliary information. In this paper, we propose a new multivariate calibration estimator for the population mean in the stratified sampling design, which incorporates information available for more than one auxiliary variable. The problem of determining the optimum calibrated weights is formulated as a Mathematical Programming Problem (MPP) that is solved using the Lagrange multiplier technique.

Keywords: Calibration estimation, Stratified sampling, Multivariate auxiliary information, Mathematical programming problem, Lagrange multiplier technique.

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Authors: Haiyan. Li, Jitendra. Paliwal, Digvir S. Jayas, Noel D. G. White

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A study was undertaken to investigate the effect of liquid nitrogen aeration on mortalities of adult Cryptolestes furrugineus, rusty grain beetles, in a prototype cardboard grain bin equipped with an aeration system. The grain bin was filled with Hard Red Spring wheat and liquid nitrogen was introduced from the bottom of the bin. The survival of both cold acclimated and unacclimated C. furrugineus was tested. The study reveals that cold acclimated insects had higher survival than unacclimated insects under similar cooling conditions. In most cases, mortalities of as high as 100% were achieved at the bottom 100 cm of the grain bin for unacclimated insects for most of the trials. Insect survival increased as the distance from the bottom of the grain bin increased. There was no adverse effect of liquid nitrogen aeration on wheat germination.

Keywords: Cold acclimated, liquid nitrogen aeration, mortalities, rusty grain beetles.

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Authors: Seongcheol Kwon, Jeongmin Kim, Kwang Ryel Ryu

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We assume an IoT-based smart-home environment where the on-off status of each of the electrical appliances including the room lights can be recognized in a real time by monitoring and analyzing the smart meter data. At any moment in such an environment, we can recognize what the household or the user is doing by referring to the status data of the appliances. In this paper, we focus on a smart-home service that is to activate a robot vacuum cleaner at right time by recognizing the user situation, which requires a situation-aware model that can distinguish the situations that allow vacuum cleaning (Yes) from those that do not (No). We learn as our candidate models a few classifiers such as naïve Bayes, decision tree, and logistic regression that can map the appliance-status data into Yes and No situations. Our training and test data are obtained from simulations of user behaviors, in which a sequence of user situations such as cooking, eating, dish washing, and so on is generated with the status of the relevant appliances changed in accordance with the situation changes. During the simulation, both the situation transition and the resulting appliance status are determined stochastically. To compare the performances of the aforementioned classifiers we obtain their learning curves for different types of users through simulations. The result of our empirical study reveals that naïve Bayes achieves a slightly better classification accuracy than the other compared classifiers.

Keywords: Situation-awareness, Smart home, IoT, Machine learning, Classifier.

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Authors: Mojtaba Aghamiri Esfahani, Mohammad Karkon, Seyed Majid Hosseini Nezhad, Reza Hosseini-Ara

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In this study, the effect of uncertainty in elastic modulus of a plate on free vibration response is investigated. For this purpose, the elastic modulus of the plate is modeled as stochastic variable with normal distribution. Moreover, the distance autocorrelation function is used for stochastic field. Then, by applying the finite element method and Monte Carlo simulation, stochastic finite element relations are extracted. Finally, with a numerical test, the effect of uncertainty in the elastic modulus on free vibration response of a plate is studied. The results show that the effect of uncertainty in elastic modulus of the plate cannot play an important role on the free vibration response.

Keywords: Stochastic finite elements, plate bending, free vibration, Monte Carlo, Neumann expansion method.

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Authors: Ju-Na Hwang, Yong-Jun Park, Min-Hae Park, Kee-Joe Lim

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Many studies have been conducted on DC transmission. Of power apparatus for DC transmission, high voltage direct current (HVDC) cable systems are being evaluated because of the increase in power demand and transmission distance. Therefore, dc insulation characteristics of liquid silicone rubber (LSR), which has various advantages such as short curing time and the ease of maintenance, were investigated to assess its performance as a HVDC insulation material for cable joints. The electrical performance of LSR added to nano-aluminum trihydrate (ATH) were confirmed by measurements of the breakdown strength and electrical conductivity. In addition, field emission scanning electron microscope (FE-SEM) was used as a means of confirmation of nanofiller dispersion state. The LSR nanocomposite was prepared by compounding LSR filled nano-sized ATH filler. The dc insulation properties of LSR added to nano-sized ATH fillers were found to be superior to those of the LSR without a filler. 

Keywords: Liquid silicone rubber, Nanocomposite, Nano-ATH, HVDC insulation, Cable joints.

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Authors: Kittipob Kondee, Chutima Prommak

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In this paper, we propose an optimization technique that can be used to optimize the placements of reference nodes and improve the location determination performance for the multi-floor building. The proposed technique is based on Simulated Annealing algorithm (SA) and is called MSMR-M. The performance study in this work is based on simulation. We compare other node-placement techniques found in the literature with the optimal node-placement solutions obtained from our optimization. The results show that using the optimal node-placement obtained by our proposed technique can improve the positioning error distances up to 20% better than those of the other techniques. The proposed technique can provide an average error distance within 1.42 meters.

Keywords: Indoor positioning System, Optimization System design, Multi-Floor Building, Wireless Sensor Networks.

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Authors: Miguel Angel Garcia-Ruiz, Pedro Cesar Santana-Mancilla, Laura Sanely Gaytan-Lugo

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This paper describes the design and application of a short in-class project conducted in Algoma University’s Human-Computer Interaction (HCI) course taught at the Bachelor of Computer Science. The project was based on the Maker Movement (people using and reusing electronic components and everyday materials to tinker with technology and make interactive applications), where students applied low-cost and easy-to-use electronic components, the Arduino Uno microcontroller board, software tools, and everyday objects. Students collaborated in small teams by completing hands-on activities with them, making an interactive walking cane for blind people. At the end of the course, students filled out a Technology Acceptance Model version 2 (TAM2) questionnaire where they evaluated microcontroller boards’ applications in HCI classes. We also asked them about applying the Maker Movement in HCI classes. Results showed overall students’ positive opinions and response about using microcontroller boards in HCI classes. We strongly suggest that every HCI course should include practical activities related to tinkering with technology such as applying microcontroller boards, where students actively and constructively participate in teams for achieving learning objectives.

Keywords: Maker movement, microcontrollers, learning, projects, course, technology acceptance.

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Authors: Simon Samwel Msanjila

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Networked environments which provide platforms for business organizations are configured in different forms depending on many factors including life time, member characteristics, communication structure, and business objectives, among others. With continuing advances in digital technologies the distance has become a less barrier for business minded collaboration among organizations. With the need and ease to make business collaborate nowadays organizations are sometimes forced to co-work with others that are either unknown or less known to them in terms of history and performance. A promising approach for sustaining established collaboration has been establishment of trust relationship among organizations based on assessed trustworthiness for each participating organization. It has been stated in research that trust in organization is dynamic and thus assessment of trust level must address such dynamic nature. This paper assesses relevant aspects of trust and applies the assessed concepts to propose a semi-automated system for the management of Sustainability and Evolution of trust in organizations participating in specific objective in a networked organizations environment.

Keywords: Trust evolution, trust sustainability, networked organizations, dynamic trust.

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Authors: Mourad Moussa, Ali Douik, Hassani Messaoud

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In this paper, we present a comparative study between two computer vision systems for objects recognition and tracking, these algorithms describe two different approach based on regions constituted by a set of pixels which parameterized objects in shot sequences. For the image segmentation and objects detection, the FCM technique is used, the overlapping between cluster's distribution is minimized by the use of suitable color space (other that the RGB one). The first technique takes into account a priori probabilities governing the computation of various clusters to track objects. A Parzen kernel method is described and allows identifying the players in each frame, we also show the importance of standard deviation value research of the Gaussian probability density function. Region matching is carried out by an algorithm that operates on the Mahalanobis distance between region descriptors in two subsequent frames and uses singular value decomposition to compute a set of correspondences satisfying both the principle of proximity and the principle of exclusion.

Keywords: Image segmentation, objects tracking, Parzen window, singular value decomposition, target recognition.

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Authors: Ahmad Anzaar, Husain Shahnawaz, Chand Mukesh, S. C. Gupta, R. Gowri, H. L. Mandoria

Abstract:

Due to insufficient frequency band and tremendous growth of the mobile users, complex computation is needed for the use of resources. Long distance communication began with the introduction of telegraphs and simple coded pulses, which were used to transmit short messages. Since then numerous advances have rendered reliable transfer of information both easier and quicker. Wireless network refers to any type of computer network that is wireless, and is commonly associated with a telecommunications network whose interconnections between nodes is implemented without the use of wires. Wireless network can be broadly categorized in infrastructure network and infrastructure less network. Infrastructure network is one in which we have a base station to serve the mobile users and in the infrastructure less network is one in which no infrastructure is available to serve the mobile users this kind of networks are also known as mobile Adhoc networks. In this paper we have simulated the result for different scenarios with protocols like AODV and DSR; we simulated the result for throughput, delay and receiving traffic in the given scenario.

Keywords: Adhoc network, AODV, DSR. mobility.

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Authors: Ali Ganoun

Abstract:

This paper proposes evaluation of sound parameterization methods in recognizing some spoken Arabic words, namely digits from zero to nine. Each isolated spoken word is represented by a single template based on a specific recognition feature, and the recognition is based on the Euclidean distance from those templates. The performance analysis of recognition is based on four parameterization features: the Burg Spectrum Analysis, the Walsh Spectrum Analysis, the Thomson Multitaper Spectrum Analysis and the Mel Frequency Cepstral Coefficients (MFCC) features. The main aim of this paper was to compare, analyze, and discuss the outcomes of spoken Arabic digits recognition systems based on the selected recognition features. The results acqired confirm that the use of MFCC features is a very promising method in recognizing Spoken Arabic digits.

Keywords: Speech Recognition, Spectrum Analysis, Burg Spectrum, Walsh Spectrum Analysis, Thomson Multitaper Spectrum, MFCC.

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Authors: Alper Akın, İbrahim Aydoğdu

Abstract:

This study presents a hybrid metaheuristic algorithm to obtain optimum designs for steel space buildings. The optimum design problem of three-dimensional steel frames is mathematically formulated according to provisions of LRFD-AISC (Load and Resistance factor design of American Institute of Steel Construction). Design constraints such as the strength requirements of structural members, the displacement limitations, the inter-story drift and the other structural constraints are derived from LRFD-AISC specification. In this study, a hybrid algorithm by using teachinglearning based optimization (TLBO) and harmony search (HS) algorithms is employed to solve the stated optimum design problem. These algorithms are two of the recent additions to metaheuristic techniques of numerical optimization and have been an efficient tool for solving discrete programming problems. Using these two algorithms in collaboration creates a more powerful tool and mitigates each other’s weaknesses. To demonstrate the powerful performance of presented hybrid algorithm, the optimum design of a large scale steel building is presented and the results are compared to the previously obtained results available in the literature.

Keywords: Optimum structural design, hybrid techniques, teaching-learning based optimization, harmony search algorithm, minimum weight, steel space frame.

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Authors: Maryam Vafadar, Alireza Behrad, Saeed Akbari

Abstract:

Nowadays, with the emerging of the new applications like robot control in image processing, artificial vision for visual servoing is a rapidly growing discipline and Human-machine interaction plays a significant role for controlling the robot. This paper presents a new algorithm based on spatio-temporal volumes for visual servoing aims to control robots. In this algorithm, after applying necessary pre-processing on video frames, a spatio-temporal volume is constructed for each gesture and feature vector is extracted. These volumes are then analyzed for matching in two consecutive stages. For hand gesture recognition and classification we tested different classifiers including k-Nearest neighbor, learning vector quantization and back propagation neural networks. We tested the proposed algorithm with the collected data set and results showed the correct gesture recognition rate of 99.58 percent. We also tested the algorithm with noisy images and algorithm showed the correct recognition rate of 97.92 percent in noisy images.

Keywords: Back propagation neural network, Feature vector, Hand gesture recognition, k-Nearest Neighbor, Learning vector quantization neural network, Robot control, Spatio-temporal volume, Visual servoing

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Authors: Haider A Ramadhan, Khalil Shihab

Abstract:

Clustering techniques have been used by many intelligent software agents to group similar access patterns of the Web users into high level themes which express users intentions and interests. However, such techniques have been mostly focusing on one salient feature of the Web document visited by the user, namely the extracted keywords. The major aim of these techniques is to come up with an optimal threshold for the number of keywords needed to produce more focused themes. In this paper we focus on both keyword and similarity thresholds to generate themes with concentrated themes, and hence build a more sound model of the user behavior. The purpose of this paper is two fold: use distance based clustering methods to recognize overall themes from the Proxy log file, and suggest an efficient cut off levels for the keyword and similarity thresholds which tend to produce more optimal clusters with better focus and efficient size.

Keywords: Data mining, knowledge discovery, clustering, dataanalysis, Web log analysis, theme based searching.

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Authors: Aref Aasi, Sahar Ebrahimi Bajgani, Erfan Aasi

Abstract:

Breast cancer (BC) continues to be the most frequent cancer in females and causes the highest number of cancer-related deaths in women worldwide. Inspired by recent advances in studying the relationship between different patient attributes and features and the disease, in this paper, we have tried to investigate the different classification methods for better diagnosis of BC in the early stages. In this regard, datasets from the University Hospital Centre of Coimbra were chosen, and different machine learning (ML)-based and neural network (NN) classifiers have been studied. For this purpose, we have selected favorable features among the nine provided attributes from the clinical dataset by using a random forest algorithm. This dataset consists of both healthy controls and BC patients, and it was noted that glucose, BMI, resistin, and age have the most importance, respectively. Moreover, we have analyzed these features with various ML-based classifier methods, including Decision Tree (DT), K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGBoost), Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM) along with NN-based Multi-Layer Perceptron (MLP) classifier. The results revealed that among different techniques, the SVM and MLP classifiers have the most accuracy, with amounts of 96% and 92%, respectively. These results divulged that the adopted procedure could be used effectively for the classification of cancer cells, and also it encourages further experimental investigations with more collected data for other types of cancers.

Keywords: Breast cancer, health diagnosis, Machine Learning, biomarker classification, Neural Network.

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