Search results for: product-development organisation pattern.

Authors: M. Bodner, M. Scampicchio

Abstract:

Fourier Transform Infrared (FT-IR) spectroscopy coupled with chemometrics was used to distinguish between butter samples and non-butter samples. Further, quantification of the content of margarine in adulterated butter samples was investigated. Fingerprinting region (1400-800 cm^–1) was used to develop unsupervised pattern recognition (Principal Component Analysis, PCA), supervised modeling (Soft Independent Modelling by Class Analogy, SIMCA), classification (Partial Least Squares Discriminant Analysis, PLS-DA) and regression (Partial Least Squares Regression, PLS-R) models. PCA of the fingerprinting region shows a clustering of the two sample types. All samples were classified in their rightful class by SIMCA approach; however, nine adulterated samples (between 1% and 30% w/w of margarine) were classified as belonging both at the butter class and at the non-butter one. In the two-class PLS-DA model’s (R² = 0.73, RMSEP, Root Mean Square Error of Prediction = 0.26% w/w) sensitivity was 71.4% and Positive Predictive Value (PPV) 100%. Its threshold was calculated at 7% w/w of margarine in adulterated butter samples. Finally, PLS-R model (R² = 0.84, RMSEP = 16.54%) was developed. PLS-DA was a suitable classification tool and PLS-R a proper quantification approach. Results demonstrate that FT-IR spectroscopy combined with PLS-R can be used as a rapid, simple and safe method to identify pure butter samples from adulterated ones and to determine the grade of adulteration of margarine in butter samples.

Keywords: Adulterated butter, margarine, PCA, PLS-DA, PLS-R, SIMCA.

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Authors: Hina Kausher, Sangita Srivastava

Abstract:

In India, there is a lack of standard, systematic sizing approach for producing readymade garments. Garments manufacturing companies use their own created size tables by modifying international sizing charts of ready-made garments. The purpose of this study is to tabulate the anthropometric data which cover the variety of figure proportions in both height and girth. 3,000 data have been collected by an anthropometric survey undertaken over females between the ages of 16 to 80 years from the some states of India to produce the sizing system suitable for clothing manufacture and retailing. The data are used for the statistical analysis of body measurements, the formulation of sizing systems and body measurements tables. Factor analysis technique is used to filter the control body dimensions from the large number of variables. Decision tree-based data mining is used to cluster the data. The standard and structured sizing system can facilitate pattern grading and garment production. Moreover, it can exceed buying ratios and upgrade size allocations to retail segments.

Keywords: Anthropometric data, data mining, decision tree, garments manufacturing, ready-made garments, sizing systems.

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Authors: Yi-Zhong Weng, Chien-Kang Huang, Yu-Feng Huang, Chi-Yuan Yu, Darby Tien-Hao Chang

Abstract:

Search for a tertiary substructure that geometrically matches the 3D pattern of the binding site of a well-studied protein provides a solution to predict protein functions. In our previous work, a web server has been built to predict protein-ligand binding sites based on automatically extracted templates. However, a drawback of such templates is that the web server was prone to resulting in many false positive matches. In this study, we present a sequence-order constraint to reduce the false positive matches of using automatically extracted templates to predict protein-ligand binding sites. The binding site predictor comprises i) an automatically constructed template library and ii) a local structure alignment algorithm for querying the library. The sequence-order constraint is employed to identify the inconsistency between the local regions of the query protein and the templates. Experimental results reveal that the sequence-order constraint can largely reduce the false positive matches and is effective for template-based binding site prediction.

Keywords: Protein structure, binding site, functional prediction

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Authors: Sanga Monthatipkul, Pio Iovenitti, Igor Sbarski

Abstract:

Bicycle usage for exercise, recreation, and commuting to work in Australia shows that pedal cycling is the fourth most popular activity with 10.6% increase in participants between 2001 and 2007. As with other means of transport, accident and injury becomes common although mandatory bicycle helmet wearing has been introduced. The research aims to develop a face surrogate made of sandwich of rigid foam and rubber sheets to represent human facial bone under blunt impact. The facial surrogate will serve as an important test device for further development of facial-impact protection for cyclist. A test procedure was developed to simulate the energy of impact and record data to evaluate the effect of impact on facial bones. Drop tests were performed to establish a suitable combination of materials. It was found that the sandwich structure of rigid extruded-polystyrene foam (density of 40 kg/m3 with a pattern of 6-mm-holes), Neoprene rubber sponge, and Abrasaflex rubber backing, had impact characteristics comparable to that of human facial bone. In particular, the foam thickness of 30 mm and 25 mm was found suitable to represent human zygoma (cheekbone) and maxilla (upper-jaw bone), respectively.

Keywords: Facial impact protection, face surrogate, cyclist, accident prevention

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Authors: V. Jafari, M. Jafari

Abstract:

When long-term changes in normal weather patterns happen in a certain area, it generally could be identified as climate change. Concentration of principal's greenhouse gases such as carbon dioxide, nitrous oxide, methane, ozone, and water vapor will cause climate change and perhaps climate variability. Main climate factors are temperature, precipitation, air pressure, and humidity. Extreme events may be the result of the changing of carbon dioxide concentration levels in the atmosphere which cause a change in temperature. Extreme events in some ways will affect the productivity of crop and dairy livestock. In this research, the correlation of milk production and temperature as the main climate factor in ChaharMahal and Bakhtiari province in Iran has been considered. The methodology employed for this study consists, collect reports and published national and provincial data, available recorded data on climate factors and analyzing collected data using statistical software. Milk production in ChaharMahal and Bakhtiari province is in the same pattern as national milk production in Iran. According to the current study results, there is a significant negative correlation between milk production in ChaharMahal and Bakhtiari provinces and temperature as the main climate change factor.

Keywords: ChaharMahal and Bakhtiari, climate change, impacts, Iran, milk production.

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Authors: C. Kalamani, K. Paramasivam

Abstract:

Test data compression is an efficient method for reducing the test application cost. The problem of reducing test data has been addressed by researchers in three different aspects: Test Data Compression, Built-in-Self-Test (BIST) and Test set compaction. The latter two methods are capable of enhancing fault coverage with cost of hardware overhead. The drawback of the conventional methods is that they are capable of reducing the test storage and test power but when test data have redundant length of runs, no additional compression method is followed. This paper presents a modified Run Length Coding (RLC) technique with Multilevel Selective Huffman Coding (MLSHC) technique to reduce test data volume, test pattern delivery time and power dissipation in scan test applications where redundant length of runs is encountered then the preceding run symbol is replaced with tiny codeword. Experimental results show that the presented method not only improves the test data compression but also reduces the overall test data volume compared to recent schemes. Experiments for the six largest ISCAS-98 benchmarks show that our method outperforms most known techniques.

Keywords: Modified run length coding, multilevel selective Huffman coding, built-in-self-test modified selective Huffman coding, automatic test equipment.

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Authors: N. Fujisaka

Abstract:

This sociological case study analyzes a sequence of student maltreatment in an elementary school in Japan, based on narratives from former students. Among various forms of student maltreatment, emotional maltreatment has received less attention. One reason for this is that emotional maltreatment is often considered part of education and is difficult to capture in surveys. To discuss the challenge of recognizing emotional maltreatment, it is necessary to consider the social background in which student maltreatment occurs. Therefore, from the perspective of the sociology of education, this study aims to clarify the process through which emotional maltreatment was embraced by students within a Japanese classroom. The research employs retrospective narrative data collected through interviews and autoethnography. The research results imply a pattern of emotional maltreatment that is challenging to differentiate from education. The finding is analyzed and discussed in conjunction with the cycle of violence theory and the deschooling theory. The cycle of violence theory explains how violence in a specific relationship can be tolerated. The deschooling theory provides a sociological explanation for how emotional maltreatment can be overlooked in society. Analyzing the case in association with these two theories highlights the characteristics of teachers’ behaviors that rationalize maltreatment as education and hinder students from escaping emotional maltreatment. This study deepens our understanding of the causes of student maltreatment and provides a perspective for future qualitative and quantitative research.

Keywords: Emotional maltreatment, education, student maltreatment, Japan.

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Authors: Guojian Cheng, Tianshi Liu, Jiaxin Han, Zheng Wang

Abstract:

The competitive learning is an adaptive process in which the neurons in a neural network gradually become sensitive to different input pattern clusters. The basic idea behind the Kohonen-s Self-Organizing Feature Maps (SOFM) is competitive learning. SOFM can generate mappings from high-dimensional signal spaces to lower dimensional topological structures. The main features of this kind of mappings are topology preserving, feature mappings and probability distribution approximation of input patterns. To overcome some limitations of SOFM, e.g., a fixed number of neural units and a topology of fixed dimensionality, Growing Self-Organizing Neural Network (GSONN) can be used. GSONN can change its topological structure during learning. It grows by learning and shrinks by forgetting. To speed up the training and convergence, a new variant of GSONN, twin growing cell structures (TGCS) is presented here. This paper first gives an introduction to competitive learning, SOFM and its variants. Then, we discuss some GSONN with fixed dimensionality, which include growing cell structures, its variants and the author-s model: TGCS. It is ended with some testing results comparison and conclusions.

Keywords: Artificial neural networks, Competitive learning, Growing cell structures, Self-organizing feature maps.

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Authors: Timothy Yoonsuk Lee, Jinhwan Yu, Somi Nah

Abstract:

Hollywood has produced various blockbusters on the subject of disasters. Entering the 2000s, disaster films began to be produced in the East Asian region as well, and as most of them were successful, disaster films have settled as a popular genre in the region. East Asian disaster films utilize a plot structure similar to Hollywood films but, at the same time, represent East Asian people-s unique value system. East Asian people-s social behavior pattern defined as collectivism is a characteristic that distinguishes this region from other cultural regions. In order to examine Confucian culture in disaster films on the premise of the difference, the author conducts this research as follows.This study first reviews the concepts disaster and disaster film, and understands the genre through analyzing the narrative structure and style. In addition, it discusses collectivism, a characteristic of the East Asian region distinguished from the West, and investigates Confucian culture in films and examines differences among Korean, Chinese and Japanese Confucianism. Films selected for this study are Tidal Wave (Korea, 2009), After Shock (China, 2006), and The Sinking of Japan (Japan, 2006). Using the characters in these films, we analyze how Confucian thought is described and reproduced.

Keywords: Confucianism, Disaster Film, East Asian Films, Three Basic Principles and Five Moral Disciplines in Human Relations

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Authors: R. Talebi, A. Abdel-Dayem, J. Johnson

Abstract:

Three-dimensional reconstruction of small objects has been one of the most challenging problems over the last decade. Computer graphics researchers and photography professionals have been working on improving 3D reconstruction algorithms to fit the high demands of various real life applications. Medical sciences, animation industry, virtual reality, pattern recognition, tourism industry, and reverse engineering are common fields where 3D reconstruction of objects plays a vital role. Both lack of accuracy and high computational cost are the major challenges facing successful 3D reconstruction. Fringe projection has emerged as a promising 3D reconstruction direction that combines low computational cost to both high precision and high resolution. It employs digital projection, structured light systems and phase analysis on fringed pictures. Research studies have shown that the system has acceptable performance, and moreover it is insensitive to ambient light. This paper presents an overview of fringe projection approaches. It also presents an experimental study and implementation of a simple fringe projection system. We tested our system using two objects with different materials and levels of details. Experimental results have shown that, while our system is simple, it produces acceptable results.

Keywords: Digital fringe projection, 3D reconstruction, phase unwrapping, phase shifting.

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Authors: P. Kamphikul, P. Krachodnok, R. Wongsan

Abstract:

This paper presents the gain improvement of a sector antenna for mobile phone base station by using the new technique to enhance its gain for microstrip antenna (MSA) array without construction enlargement. The curved woodpile Electromagnetic Band Gap (EBG) has been utilized to improve the gain instead. The advantages of this proposed antenna are reducing the length of MSAs array but providing the higher gain and easy fabrication and installation. Moreover, it provides a fan-shaped radiation pattern, wide in the horizontal direction and relatively narrow in the vertical direction, which appropriate for mobile phone base station. The paper also presents the design procedures of a 1x8 MSAs array associated with U-shaped reflector for decreasing their back and side lobes. The fabricated curved woodpile EBG exhibits bandgap characteristics at 2.1 GHz and is utilized for realizing a resonant cavity of MSAs array. This idea has been verified by both the Computer Simulation Technology (CST) software and experimental results. As the results, the fabricated proposed antenna achieves a high gain of 20.3 dB and the half-power beam widths in the E- and H-plane of 36.8 and 8.7 degrees, respectively. Good qualitative agreement between measured and simulated results of the proposed antenna was obtained.

Keywords: Gain Improvement, Microstrip Antenna Array, Electromagnetic Band Gap, Base Station.

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

Abstract:

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: Urvee B. Trivedi, U. D. Dalal

Abstract:

As wireless communication services grow quickly; the seriousness of spectrum utilization has been on the rise gradually. An emerging technology, cognitive radio has come out to solve today’s spectrum scarcity problem. To support the spectrum reuse functionality, secondary users are required to sense the radio frequency environment, and once the primary users are found to be active, the secondary users are required to vacate the channel within a certain amount of time. Therefore, spectrum sensing is of significant importance. Once sensing is done, different prediction rules apply to classify the traffic pattern of primary user. Primary user follows two types of traffic patterns: periodic and stochastic ON-OFF patterns. A cognitive radio can learn the patterns in different channels over time. Two types of classification methods are discussed in this paper, by considering edge detection and by using autocorrelation function. Edge detection method has a high accuracy but it cannot tolerate sensing errors. Autocorrelation-based classification is applicable in the real environment as it can tolerate some amount of sensing errors.

Keywords: Cognitive radio (CR), probability of detection (PD), probability of false alarm (PF), primary User (PU), secondary user (SU), Fast Fourier transform (FFT), signal to noise ratio (SNR).

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Authors: Sankalp Devanand, Prateek Agasimani, V. S. Shamith, Rohith Neeraje

Abstract:

Machine translation has witnessed significant advancements in recent years, but the translation of languages with distinct linguistic characteristics, such as English and Sanskrit, remains a challenging task. This research presents the development of a dedicated English to Sanskrit machine translation model, aiming to bridge the linguistic and cultural gap between these two languages. Using a variety of natural language processing (NLP) approaches including FastText embeddings, this research proposes a thorough method to improve word meaning retrieval. Data preparation, part-of-speech tagging, dictionary searches, and transliteration are all included in the methodology. The study also addresses the implementation of an interpreter pattern and uses a word similarity task to assess the quality of word embeddings. The experimental outcomes show how the suggested approach may be used to enhance word meaning retrieval tasks with greater efficacy, accuracy, and adaptability. Evaluation of the model's performance is conducted through rigorous testing, comparing its output against existing machine translation systems. The assessment includes quantitative metrics such as BLEU scores, METEOR scores, Jaccard Similarity etc.

Keywords: Machine translation, English to Sanskrit, natural language processing, word meaning retrieval, FastText embeddings.

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Authors: Chang Hsueh-Sheng, Chen Tzu-Ling

Abstract:

Earthquake is an unpredictable natural disaster and intensive earthquakes have caused serious impacts on social-economic system, environmental and social resilience. Conventional ways to mitigate earthquake disaster are to enhance building codes and advance structural engineering measures. However, earthquake-induced ground damage such as liquefaction, land subsidence, landslide happen on places nearby earthquake prone or poor soil condition areas. Therefore, this study uses spatial statistical analysis to explore the spatial pattern of damaged buildings. Afterwards, principle components analysis (PCA) is applied to categorize the similar features in different kinds of clustered patterns. The results show that serious landslide prone area, close to fault, vegetated ground surface and mudslide prone area are common in those highly damaged buildings. In addition, the oldest building might not be directly referred to the most vulnerable one. In fact, it seems that buildings built between 1974 and 1989 become more fragile during the earthquake. The incorporation of both spatial statistical analyses and PCA can provide more accurate information to subsidize retrofit programs to enhance earthquake resistance in particular areas.

Keywords: Earthquake disaster, spatial statistical analysis, principle components analysis, clustered patterns.

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Authors: Meng-Lung Lin, Chien-Min Chu, Chung-Hung Tsai, Chih-Cheng Chen, Chen-Yuan Chen

Abstract:

The study of tourist activities and the mapping of their routes in space and time has become an important issue in tourism management. Here we represent space-time paths for the tourism industry by visualizing individual tourist activities and the paths followed using a 3D Geographic Information System (GIS). Considerable attention has been devoted to the measurement of accessibility to shopping, eating, walking and other services at the tourist destination. I turns out that GIS is a useful tool for studying the spatial behaviors of tourists in the area. The value of GIS is especially advantageous for space-time potential path area measures, especially for the accurate visualization of possible paths through existing city road networks. This study seeks to apply space-time concepts with a detailed street network map obtained from Google Maps to measure tourist paths both spatially and temporally. These paths are further determined based on data obtained from map questionnaires regarding the trip activities of 40 individuals. The analysis of the data makes it possible to determining the locations of the more popular paths. The results can be visualized using 3D GIS to show the areas and potential activity opportunities accessible to tourists during their travel time.

Keywords: Tourist activity analysis, space-time path, GIS, geovisualization, activity-travel pattern.

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Authors: Dimitrios Koukopoulos, Yiannis Stamatiou

Abstract:

In this work, we present for the first time in our perception an efficient digital watermarking scheme for mpeg audio layer 3 files that operates directly in the compressed data domain, while manipulating the time and subband/channel domain. In addition, it does not need the original signal to detect the watermark. Our scheme was implemented taking special care for the efficient usage of the two limited resources of computer systems: time and space. It offers to the industrial user the capability of watermark embedding and detection in time immediately comparable to the real music time of the original audio file that depends on the mpeg compression, while the end user/audience does not face any artifacts or delays hearing the watermarked audio file. Furthermore, it overcomes the disadvantage of algorithms operating in the PCMData domain to be vulnerable to compression/recompression attacks, as it places the watermark in the scale factors domain and not in the digitized sound audio data. The strength of our scheme, that allows it to be used with success in both authentication and copyright protection, relies on the fact that it gives to the users the enhanced capability their ownership of the audio file not to be accomplished simply by detecting the bit pattern that comprises the watermark itself, but by showing that the legal owner knows a hard to compute property of the watermark.

Keywords: Audio watermarking, mpeg audio layer 3, hard instance generation, NP-completeness.

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Authors: Gulshan Sharma

Abstract:

This article presents the design of optimal automatic generation control (AGC) based on full state feedback control for a multi-area interconnected power system. An extra high voltage AC transmission line in parallel with a high voltage DC link is considered as an area interconnection between the areas. The optimal AGC are designed and implemented in the wake of 1% load perturbation in one of the areas and the system dynamic response plots for various system states are obtained to investigate the system dynamic performance. The pattern of closed-loop eigenvalues are also determined to analyze the system stability. From the investigations carried out in the work, it is revealed that the dynamic performance of the system under consideration has an appreciable improvement when a high voltage DC line is paralleled with an extra high voltage AC line as an interconnection between the areas. The investigation of closed-loop eigenvalues reveals that the system stability is ensured in all case studies carried out with the designed optimal AGC.

Keywords: Automatic generation control, area control error, DC link, optimal AGC regulator, closed-loop eigenvalues.

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Authors: Md. Emaj Uddin

Abstract:

Arrack is one of the forms of alcoholic beverage or liquor which is produced from palm or date juice and commonly consumed by the lower social class of all religious/ethnic communities in the north-western villages of Bangladesh. The purpose of the study was to compare arrack drinking patterns associated with socio-demographic status among the Muslim, Hindu, Santal, and Oraon communities in the Rasulpur union of Bangladesh. A total of 391 respondents (Muslim n-109, Hindu n-103, Santal n-89, Oraon n-90) selected by cluster random sampling were interviewed by ADP (Arrack Drinking Pattern) questionnaire. The results of Pearson Chi-Squire test revealed that arrack drinking patterns were significantly differed among the Muslim, Hindu, Santal, and Oraon communities- drinkers. In addition, the results of Spearman-s bivariate correlation coefficients also revealed that sociodemographic characteristics of the communities- drinkers were the significantly positive and negative associations with the arrack drinking patterns in the Rasulpur union, Bangladesh. The study suggests that further cross-cultural researches should be conducted on the consequences of arrack drinking patterns on the communities- drinkers.

Keywords: Arrack Drinking Patterns, Bangladesh, Community, Cross-Cultural Comparison, Socio-Demographic Status.

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Authors: Mohanad Alata, Mohammad Molhim, Abdullah Ramini

Abstract:

Fuzzy C-means Clustering algorithm (FCM) is a method that is frequently used in pattern recognition. It has the advantage of giving good modeling results in many cases, although, it is not capable of specifying the number of clusters by itself. In FCM algorithm most researchers fix weighting exponent (m) to a conventional value of 2 which might not be the appropriate for all applications. Consequently, the main objective of this paper is to use the subtractive clustering algorithm to provide the optimal number of clusters needed by FCM algorithm by optimizing the parameters of the subtractive clustering algorithm by an iterative search approach and then to find an optimal weighting exponent (m) for the FCM algorithm. In order to get an optimal number of clusters, the iterative search approach is used to find the optimal single-output Sugenotype Fuzzy Inference System (FIS) model by optimizing the parameters of the subtractive clustering algorithm that give minimum least square error between the actual data and the Sugeno fuzzy model. Once the number of clusters is optimized, then two approaches are proposed to optimize the weighting exponent (m) in the FCM algorithm, namely, the iterative search approach and the genetic algorithms. The above mentioned approach is tested on the generated data from the original function and optimal fuzzy models are obtained with minimum error between the real data and the obtained fuzzy models.

Keywords: Fuzzy clustering, Fuzzy C-Means, Genetic Algorithm, Sugeno fuzzy systems.

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Authors: P. Priakanth, P. Thangaraj

Abstract:

Due to the complex network architecture, the mobile adhoc network-s multihop feature gives additional problems to the users. When the traffic load at each node gets increased, the additional contention due its traffic pattern might cause the nodes which are close to destination to starve the nodes more away from the destination and also the capacity of network is unable to satisfy the total user-s demand which results in an unfairness problem. In this paper, we propose to create an algorithm to compute the optimal MAC-layer bandwidth assigned to each flow in the network. The bottleneck links contention area determines the fair time share which is necessary to calculate the maximum allowed transmission rate used by each flow. To completely utilize the network resources, we compute two optimal rates namely, the maximum fair share and minimum fair share. We use the maximum fair share achieved in order to limit the input rate of those flows which crosses the bottleneck links contention area when the flows that are not allocated to the optimal transmission rate and calculate the following highest fair share. Through simulation results, we show that the proposed protocol achieves improved fair share and throughput with reduced delay.

Keywords: MAC-layer, MANETs, Multihop, optimal rate, Transmission.

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Authors: Mahmoud Elmezain, Ayoub Al-Hamadi, Bernd Michaelis

Abstract:

Hand gesture is an active area of research in the vision community, mainly for the purpose of sign language recognition and Human Computer Interaction. In this paper, we propose a system to recognize alphabet characters (A-Z) and numbers (0-9) in real-time from stereo color image sequences using Hidden Markov Models (HMMs). Our system is based on three main stages; automatic segmentation and preprocessing of the hand regions, feature extraction and classification. In automatic segmentation and preprocessing stage, color and 3D depth map are used to detect hands where the hand trajectory will take place in further step using Mean-shift algorithm and Kalman filter. In the feature extraction stage, 3D combined features of location, orientation and velocity with respected to Cartesian systems are used. And then, k-means clustering is employed for HMMs codeword. The final stage so-called classification, Baum- Welch algorithm is used to do a full train for HMMs parameters. The gesture of alphabets and numbers is recognized using Left-Right Banded model in conjunction with Viterbi algorithm. Experimental results demonstrate that, our system can successfully recognize hand gestures with 98.33% recognition rate.

Keywords: Gesture Recognition, Computer Vision & Image Processing, Pattern Recognition.

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Authors: D. A. K. S. Gunaratna, N. D. Kodikara, H. L. Premaratne

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Automatic currency note recognition invariably depends on the currency note characteristics of a particular country and the extraction of features directly affects the recognition ability. Sri Lanka has not been involved in any kind of research or implementation of this kind. The proposed system “SLCRec" comes up with a solution focusing on minimizing false rejection of notes. Sri Lankan currency notes undergo severe changes in image quality in usage. Hence a special linear transformation function is adapted to wipe out noise patterns from backgrounds without affecting the notes- characteristic images and re-appear images of interest. The transformation maps the original gray scale range into a smaller range of 0 to 125. Applying Edge detection after the transformation provided better robustness for noise and fair representation of edges for new and old damaged notes. A three layer back propagation neural network is presented with the number of edges detected in row order of the notes and classification is accepted in four classes of interest which are 100, 500, 1000 and 2000 rupee notes. The experiments showed good classification results and proved that the proposed methodology has the capability of separating classes properly in varying image conditions.

Keywords: Artificial intelligence, linear transformation and pattern recognition.

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Authors: Chua Bee Seok, Shamsul Amri Baharuddin, Rosnah Ismail, Ferlis Bahari, Jasmine Adela Mutang, Lailawati Madlan, Asong Joseph

Abstract:

The aims of this study were to determine the factor structure and psychometric properties (i.e., reliability and convergent validity) of the Malaysian Multi-Ethnic Discrimination Scale (MMEDS). It consists of 71-items measure experience, strategies used and consequences of ethnic discrimination. A sample of 649 university students from one of the higher education institution in Malaysia was asked to complete MMEDS, as well as Perceived Ethnic and Racial Discrimination. The exploratory factor analysis on ethnic discrimination experience extracted two factors labeled ‘unfair treatment’ (15 items) and ‘Denial of the ethnic right’ (12 items) which accounted for 60.92% of the total variance. The two sub scales demonstrated clear reliability with internal consistency above .70. The convergent validity of the Scale was supported by an expected pattern of correlations (positive and significant correlation) between the score of unfair treatment and denial of the ethnic right and the score of Perceived Ethnic and Racial Discrimination by Peers Scale. The results suggest that the MMEDS is a reliable and valid measure. However, further studies need to be carried out in other groups of sample as to validate the Scale.

Keywords: Factor structure, psychometric properties, exploratory factor analysis.

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Authors: Ida Rosada, Nurliani

Abstract:

Today, the number of women who seek additional income to help her husband is increasing. They do that in order to be able to express themselves in the midst of the family and society. Nonetheless, housewives are in charge of managing family’s income and prepare food for the family. The objective of this research is 1) to analyze the effect of the dual role of women to household income and 2) to analyze the effect of the dual role to consumption patterns. The study used a qualitative approach, data collection techniques are through observation, interviews, and documentation on farming households. The data was analysed qualitative descriptively. The results found that: 1) The revenue contribution of women who play double role in the informal sector amounted to 34.07% (less than 50%). 2) The main reason that the respondents worked in the informal sector is to be able to send their children to school (34%) and to improve household economy condition (28%). 3) After earning additional income, respondents said that they can contribute to increase the family’s income and to cover the family shortage (82%); 4) Respondents’ opinion to changes in food consumption after performing the dual role is the ability to purchase and provide the desired food (44%) and changing patterns of consumption per day (30%).

Keywords: Dual role, the informal sector, consumption patterns, household income.

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

Abstract:

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: Gizelle K. Vianna, Gabriel V. Cunha, Gustavo S. Oliveira

Abstract:

Foliage diseases in plants can cause a reduction in both quality and quantity of agricultural production. Intelligent detection of plant diseases is an essential research topic as it may help monitoring large fields of crops by automatically detecting the symptoms of foliage diseases. This work investigates ways to recognize the late blight disease from the analysis of tomato digital images, collected directly from the field. A pair of multilayer perceptron neural network analyzes the digital images, using data from both RGB and HSL color models, and classifies each image pixel. One neural network is responsible for the identification of healthy regions of the tomato leaf, while the other identifies the injured regions. The outputs of both networks are combined to generate the final classification of each pixel from the image and the pixel classes are used to repaint the original tomato images by using a color representation that highlights the injuries on the plant. The new images will have only green, red or black pixels, if they came from healthy or injured portions of the leaf, or from the background of the image, respectively. The system presented an accuracy of 97% in detection and estimation of the level of damage on the tomato leaves caused by late blight.

Keywords: Artificial neural networks, digital image processing, pattern recognition.

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Authors: Sangamesh G. Sakri, Akash N. Patil, Sadashivappa M. Kotli

Abstract:

Agro based industries in India are considered as the micro, small and medium enterprises (MSME). In India, MSMEs contribute approximately 8 percent of the country’s GDP, 42 percent of the manufacturing output and 40 percent of exports. The toor dal (scientific name Cajanus cajan, commonly known as yellow gram, pigeon pea) is the second largest pulse crop in India accounting for about 20% of total pulse production. The toor dal milling industry in India is one of the major agro-processing industries in the country. Most of the dal mills are concentrated in pulse producing areas, which are spread all over the country. In Karnataka state, Gulbarga is a district, where toor dal is the main crop and is grown extensively. There are more than 500 dal mills in and around the Gulbarga district to process dal. However, the majority of these dal milling units use traditional methods of processing which are energy and capital intensive. There exists a huge energy saving potential in these mills. An energy audit is conducted on a dal mill in Gulbarga to understand the energy consumption pattern to assess the energy saving potential, and an economic analysis is conducted to identify energy conservation opportunities.

Keywords: Conservation, demand side management, load curve, toor dal.

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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: Hussin K. Ragb, Vijayan K. Asari

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In this paper, we present a pedestrian detection descriptor called Fused Structure and Texture (FST) features based on the combination of the local phase information with the texture features. Since the phase of the signal conveys more structural information than the magnitude, the phase congruency concept is used to capture the structural features. On the other hand, the Center-Symmetric Local Binary Pattern (CSLBP) approach is used to capture the texture information of the image. The dimension less quantity of the phase congruency and the robustness of the CSLBP operator on the flat images, as well as the blur and illumination changes, lead the proposed descriptor to be more robust and less sensitive to the light variations. The proposed descriptor can be formed by extracting the phase congruency and the CSLBP values of each pixel of the image with respect to its neighborhood. The histogram of the oriented phase and the histogram of the CSLBP values for the local regions in the image are computed and concatenated to construct the FST descriptor. Several experiments were conducted on INRIA and the low resolution DaimlerChrysler datasets to evaluate the detection performance of the pedestrian detection system that is based on the FST descriptor. A linear Support Vector Machine (SVM) is used to train the pedestrian classifier. These experiments showed that the proposed FST descriptor has better detection performance over a set of state of the art feature extraction methodologies.

Keywords: Pedestrian detection, phase congruency, local phase, LBP features, CSLBP features, FST descriptor.

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