Search results for: vegetable oil extraction

Authors: Bing-Fei Wu, Yen-Lin Chen, Chung-Cheng Chiu

Abstract:

In this study, a new criterion for determining the number of classes an image should be segmented is proposed. This criterion is based on discriminant analysis for measuring the separability among the segmented classes of pixels. Based on the new discriminant criterion, two algorithms for recursively segmenting the image into determined number of classes are proposed. The proposed methods can automatically and correctly segment objects with various illuminations into separated images for further processing. Experiments on the extraction of text strings from complex document images demonstrate the effectiveness of the proposed methods.1

Keywords: image segmentation, multilevel thresholding, clustering, discriminant analysis

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Authors: Irene Perez-Alfaro, Nieves Murillo, Carlos Bernal, Daniel Gil-Hernandez

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Nowadays, the increasing applicability of internet of things (IoT) systems has changed the way that the world around is perceived. The massive interconnection of systems by means of sensing, processing and communication, allows multitude of data to be at our fingertips. In this way, countless advances have been made in different fields such as personal care, predictive maintenance in industry, quality control in production processes, security, and in everything imaginable. However, all these electronic systems have in common the need to be electrically powered. In this context, batteries and wires are the most commonly used solutions, but they are not a definitive solution in some applications, because of the attainability, the serviceability, or the performance requirements. Therefore, the need arises to look for other types of solutions based on energy harvesting and long-life electronics. Energy Harvesting can be defined as the action of capturing energy from the environment and store it for an instantaneous use or later use. Among the materials capable of harvesting energy from the environment, such as thermoelectrics, electromagnetics, photovoltaics or triboelectrics, the most suitable is the piezoelectric material. The phenomenon of piezoelectricity is one of the most powerful sources for energy harvesting, ranging from a few micro wats to hundreds of wats, depending on certain factors such as material type, geometry, excitation frequency, mechanical and electrical configurations, among others. In this research work, an exhaustive study is carried out on how different types of piezoelectric materials and electrical configurations influence the maximum power that a bimorph harvester is able to extract from mechanical vibrations. A series of experiments has been carried out in which the manufactured bimorph specimens are excited under fixed inertial vibrational conditions. In addition, in order to evaluate the dependence of the maximum transferred power, different load resistors are tested. In this way, the pure active power that achieves the maximum power transfer can be approximated. In this paper, we present the design of low-cost energy harvesting solutions based on piezoelectric smart materials with tunable frequency. The results obtained show the differences in energy extraction between the PZT materials studied and their electrical configurations. The aim of this work is to gain a better understanding of the behavior of piezoelectric materials, and the design process of bimorph PZT harvesters to optimize environmental energy extraction.

Keywords: Bimorph harvesters, electrical impedance, energy harvesting, piezoelectric, smart material.

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Authors: María L. Jalón, Feargal Brennan

Abstract:

A non-stationary stochastic optimization methodology is applied to an OWC (oscillating water column) to find the design that maximizes the wave energy extraction. Different temporal cycles are considered to represent the long-term variability of the wave climate at the site in the optimization problem. The results of the non-stationary stochastic optimization problem are compared against those obtained by a stationary stochastic optimization problem. The comparative analysis reveals that the proposed non-stationary optimization provides designs with a better fit to reality. However, the stationarity assumption can be adequate when looking at averaged system response.

Keywords: Non-stationary stochastic optimization, oscillating water column, temporal variability, wave energy.

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Authors: Jaedo Kim, Youngjoon Han, Hernsoo Hahn

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The purpose of this paper is to detect human in images. This paper proposes a method for extracting human body feature descriptors consisting of projected edge component series. The feature descriptor can express appearances and shapes of human with local and global distribution of edges. Our method evaluated with a linear SVM classifier on Daimler-Chrysler pedestrian dataset, and test with various sub-region size. The result shows that the accuracy level of proposed method similar to Histogram of Oriented Gradients(HOG) feature descriptor and feature extraction process is simple and faster than existing methods.

Keywords: Human detection, Projected edge descriptor, Linear SVM, Local appearance feature

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Authors: Vedansh Gupta, Allyson Lutz, Ameen Razavi, Fatemeh Shirazi

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The agriculture industry is especially vulnerable to forecasted water shortages. In the fresh and fresh-cut produce sector, conventional flume-based washing with recirculation exhibits high water demand. This leads to a large water footprint and possible cross-contamination of pathogens. These can be alleviated through advanced water reuse processes, such as membrane technologies including reverse osmosis (RO). Water reuse technologies effectively remove dissolved constituents but can easily foul without pre-treatment. Biological treatment is effective for the removal of organic compounds responsible for fouling, but not at the low temperatures encountered at most produce processing facilities. This study showed that the Microvi MicroNiche Engineering (MNE) technology effectively removes organic compounds (> 80%) at low temperatures (6-8 °C) from wash water. The MNE technology uses synthetic microorganism-material composites with negligible solids production, making it advantageously situated as an effective bio-pretreatment for RO. A preliminary technoeconomic analysis showed 60-80% savings in operation and maintenance costs (OPEX) when using the Microvi MNE technology for organics removal. This study and the accompanying economic analysis indicated that the proposed technology process will substantially reduce the cost barrier for adopting water reuse practices, thereby contributing to increased food safety and furthering sustainable water reuse processes across the agricultural industry.

Keywords: Biological pre-treatment, innovative technology, vegetable processing, water reuse, agriculture, reverse osmosis, MNE biocatalysts.

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Authors: S. Sarumathi, N. Shanthi, S. Vidhya

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In the present world, predicting rainfall is considered to be an essential and also a challenging task. Normally, the climate and rainfall are presumed to have non-linear as well as intricate phenomena. For predicting accurate rainfall, we necessitate advanced computer modeling and simulation. When there is an enhanced understanding of the spatial and temporal distribution of precipitation then it becomes enrichment to applications such as hydrologic, climatic and ecological. Conversely, there may be some kind of challenges occur in the community due to some application which results in the absence of consistent precipitation observation in remote and also emerging region. This survey paper provides a multifarious collection of methodologies which are epitomized by various researchers for predicting the rainfall. It also gives information about some technique to forecast rainfall, which is appropriate to all methods like numerical, traditional and statistical.

Keywords: Satellite Image, Segmentation, Feature Extraction, Classification, Clustering, Precipitation Estimation.

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Authors: P. Soda, G.Iannello

Abstract:

In this paper we address the issue of classifying the fluorescent intensity of a sample in Indirect Immuno-Fluorescence (IIF). Since IIF is a subjective, semi-quantitative test in its very nature, we discuss a strategy to reliably label the image data set by using the diagnoses performed by different physicians. Then, we discuss image pre-processing, feature extraction and selection. Finally, we propose two ANN-based classifiers that can separate intrinsically dubious samples and whose error tolerance can be flexibly set. Measured performance shows error rates less than 1%, which candidates the method to be used in daily medical practice either to perform pre-selection of cases to be examined, or to act as a second reader.

Keywords: Artificial neural networks, computer aided diagnosis, image classification, indirect immuno-fluorescence, pattern recognition.

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Authors: Meysam Mohamad pour, Fardad Farokhi

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In this paper in consideration of each available techniques deficiencies for speech recognition, an advanced method is presented that-s able to classify speech signals with the high accuracy (98%) at the minimum time. In the presented method, first, the recorded signal is preprocessed that this section includes denoising with Mels Frequency Cepstral Analysis and feature extraction using discrete wavelet transform (DWT) coefficients; Then these features are fed to Multilayer Perceptron (MLP) network for classification. Finally, after training of neural network effective features are selected with UTA algorithm.

Keywords: Multilayer perceptron (MLP) neural network, Discrete Wavelet Transform (DWT) , Mels Scale Frequency Filter , UTA algorithm.

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Authors: Alok Sharma, Dr. Harvinder S. Saini, Raviteja Tiruvury

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This paper outlines the application of Knowledge Management (KM) principles in the context of Educational institutions. The paper caters to the needs of the engineering institutions for imparting quality education by delineating the instruction delivery process in a highly structured, controlled and quantified manner. This is done using a software tool EDULOGIC+. The central idea has been based on the engineering education pattern in Indian Universities/ Institutions. The data, contents and results produced over contiguous years build the necessary ground for managing the related accumulated knowledge. Application of KM has been explained using certain examples of data analysis and knowledge extraction.

Keywords: Education software system, information system, knowledge management.

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Authors: Azita Behbahaninia, Seid Ahmad Mirbagheri

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The use of sewage sludge and effluents from wastewater treatment plants for irrigation of agricultural lands is on the rise particularly in peri-urban areas of developing countries. The reuse of nutrients and organic matter in treated wastewater and sewage sludge via land application is a desirable goal. However, trace or heavy metals present in sludge pose the risk of human or phytotoxicity from land application. Long-term use of sewage sludge, heavy metals can accumulate to phytotoxic levels and results in reduced plants growth and/or enhanced metal concentrations in plants, which consumed by animals then enter the food chain. In this research, the amount of heavy metals was measured in plants irrigated with wastewater and sludge application. For this purpose, three pilots were made in a Shush treatment plant in south of Tehran. Three plants species, spinach, lettuce and radish were selected and planted in the pilots.First pilot was irrigated just with wastewater of treatment plant and second pilot was irrigated with wastewater and sludge application .Third pilot was irrigated with simulated heavy metals solution equal 50 years of irrigation. The results indicate that the average of amount of heavy metals Pb, Cd in three plant species in first pilot were lower than permissible limits .In second pilot, Cadmium accumulations are high in three species plants and more than the standard limits. Concentration of Cd , Pb have exceed their permitted limits in plants in third pilot . It was concluded that the use of wastewater and sludge application in agricultural lands enriched soils with heavy metals to concentrations that may pose potential environmental and health risks in the long-term.

Keywords: Soil, contaminate, heavy metals, wastewater, sludge, plants.

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Authors: Mohammed Z. Khedher, Gheith A. Abandah, Ahmed M. Al-Khawaldeh

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Recognition of characters greatly depends upon the features used. Several features of the handwritten Arabic characters are selected and discussed. An off-line recognition system based on the selected features was built. The system was trained and tested with realistic samples of handwritten Arabic characters. Evaluation of the importance and accuracy of the selected features is made. The recognition based on the selected features give average accuracies of 88% and 70% for the numbers and letters, respectively. Further improvements are achieved by using feature weights based on insights gained from the accuracies of individual features.

Keywords: Arabic handwritten characters, Feature extraction, Off-line recognition, Optical character recognition.

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Authors: Rita Riekstina-Dolge, Zanda Kruma, Daina Karklina

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Aroma forming volatiles are important components of fermented beverages. The aim of current research is to evaluate the volatile compounds and phenolic compounds of commercial ciders. Volatile aroma compounds and TPC of seven commercial ciders were determined. Extraction of aroma compounds was performed using solid phase microextraction (DVB/Car/PDMS fibre). Analysis of volatile aroma compounds was made using a Perkin Elmer Clarus 500 GC/MS. Total phenol content (TPC) was determined according to the Folin-Ciocalteu spectrophotometric method and results were expressed as gallic acid equivalents. The highest volatile compounds were in apple ciders with pear flavor. The highest TPC and lower content of volatile compounds were detected in French ciders.

Keywords: cider, TPC, volatile compounds

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Authors: Z. Shaaban

Abstract:

This paper presents a new approach to tackle the problem of recognizing machine-printed Arabic texts. Because of the difficulty of recognizing cursive Arabic words, the text has to be normalized and segmented to be ready for the recognition stage. The new scheme for recognizing Arabic characters depends on multiple parallel neural networks classifier. The classifier has two phases. The first phase categories the input character into one of eight groups. The second phase classifies the character into one of the Arabic character classes in the group. The system achieved high recognition rate.

Keywords: Neural Networks, character recognition, feature extraction, multiple networks, Arabic text.

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Authors: Milad. A. Shalluf

Abstract:

The determination of sugars in foods is very significant. Their relation in fact, can affect the chemical and sensorial quality of the matrix (e.g., sweetness, pH, total acidity, microbial stability, global acceptability) and can provide information on food to optimize several selected technological processes. Three stages of ripeness (green, yellow and red) of tomatoes (Lycopersicon Esculentum cv. Elegance) at different harvest dates were evaluated. Fruit from all harvests were exposed to different of ozone doses (0.25, 0.50 and 1 mg O3/g tomatoes) and clean air for 5 day at 15 °C±2 and 90-95 % relative humidity. Then, fruits were submitted for extraction and analysis after a day from the finish of exposure of each stage. The concentrations of the glucose and fructose increased in the tomatoes which were subjected to ozone treatments.

Keywords: Post-harvest Treatment, Controlled Atmosphere Storage, Ozone, Tomatoes, Glucose, Fructose

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Authors: J. Y. Heo, H. G. Sung

Abstract:

Numerical simulations have been performed for assessment of compressibility correction of two-equation turbulence models suitable for large scale separation flows perturbed by pintle strokes. In order to take into account pintle movement, a sliding mesh method was applied. The chamber pressure, mass flow rate, and thrust have been analyzed, and the response lag and sensitivity at the chamber and nozzle were estimated for a movable pintle. The nozzle performance for pintle reciprocating as its insertion and extraction processes, were analyzed to better understand the dynamic performance of the pintle nozzle.

Keywords: Pintle, sliding mesh, turbulent model, compressibility correction.

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Authors: Endrick Barnacin, Jean-Luc Henry, Jimmy Nagau, Jack Molinié

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Palynology is a field of interest in many disciplines due to its multiple applications: chronological dating, climatology, allergy treatment, and honey characterization. Unfortunately, the analysis of a pollen slide is a complicated and time consuming task that requires the intervention of experts in the field, which are becoming increasingly rare due to economic and social conditions. In this context, the automation of this task is urgent. In this work, we compare classical feature extraction methods (Shape, GLCM, LBP, and others) and Deep Learning (CNN and Transfer Learning) to perform a recognition task over 80 regional pollen species. It has been found that the use of Transfer Learning seems to be more precise than the other approaches.

Keywords: Image segmentation, stuck particles separation, Sobel operator, thresholding.

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Authors: Xiangming Ge, Bing Pan, Wei He, Hao Chen, Yong Zhou, Jiayao Wu, Weijiang Chu

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How the extraction and penetration of spudcan affect the performance of the adjacent pile foundation supporting the jacket platform was studied in the program FLAC3D depending on a wind farm project in Bohai sea. The simulations were conducted at the end of the spudcan penetration, which induced a pockmark in the seabed. The effects of the distance between the pile foundation and the pockmark were studied. The displacement at the mudline arose when the pockmark was closer. The bearing capacity of this jacket platform with deep pile foundations has been less influenced by the process of spudcan penetration, which can induce severe stresses on the pile foundation. The induced rotation was also satisfied with the serviceability constraints.

Keywords: Offshore foundation, pile-soil interaction, spudcan penetration, FLAC3D.

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Authors: Nebi Gedik

Abstract:

Regardless of age or gender, cardiovascular illnesses are a serious health concern because of things like poor eating habits, stress, a sedentary lifestyle, hard work schedules, alcohol use, and weight. It tends to happen suddenly and has a high rate of recurrence. Machine learning models can be implemented to assist healthcare systems in the accurate detection and diagnosis of cardiovascular disease (CVD) in patients. Improved heart failure prediction is one of the primary goals of researchers using the heart disease dataset. The purpose of this study is to identify the feature or features that offer the best classification prediction for CVD detection. The support vector machine classifier is used to compare each feature's performance. It has been determined which feature produces the best results.

Keywords: Cardiovascular disease, feature extraction, supervised learning, support vector machine.

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Authors: A.Sankara Subramanian, G.Gurusamy, G.Selvakumar, P.Gnanasekar, A.Nagappan

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This paper presents an algorithm based on the wavelet decomposition, for feature extraction from the ECG signal and recognition of three types of Ventricular Arrhythmias using neural networks. A set of Discrete Wavelet Transform (DWT) coefficients, which contain the maximum information about the arrhythmias, is selected from the wavelet decomposition. After that a novel clustering algorithm based on nature inspired algorithm (Ant Colony Optimization) is developed for classifying arrhythmia types. The algorithm is applied on the ECG registrations from the MIT-BIH arrhythmia and malignant ventricular arrhythmia databases. We applied Daubechies 4 wavelet in our algorithm. The wavelet decomposition enabled us to perform the task efficiently and produced reliable results.

Keywords: Daubechies 4 Wavelet, ECG, Nature inspired algorithm, Ventricular Arrhythmias, Wavelet Decomposition.

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Authors: Adebisi. Elizabeth, Banjo

Abstract:

Language and culture are interwoven that they cannot be separated, for the knowledge of a language cannot be complete without having the culture of the language. Indigenous food is a cultural aspect of any language that is expected to be acquired by all the speakers of the language. Indigenous food is known to be vital right from early years, which is also attributed to the healthy living of the ancient people. However it is discovered that the indigenous food is almost being replaced by fast food products such as Indomie noodles, Spaghetti and Macaroni to the extent that majority of the young folks prefer the eating of the fast foods and cannot prepare the indigenous foods which are good for growth and healthy living of people. Therefore, there is need to revitalize and re-educate people on the indigenous food which is an aspect of inter-cultural education of any language to prevent it from being forgotten or neglected.

African foods are many, but this study focused on Nigerian food using some Yoruba dishes as a case study. Examples of Yoruba dishes are pounded yam and melon with vegetable and dried fish soup, beans pudding (moin moin) and pap (eko), water yam pudding with fish and meat (ikokore) and many more. The ingredients needed for the preparation of these indigenous foods contain some basic food nutrients which will be analyzed and their nutritional importance to human bodies will also be discussed.

The process of re- awakening the education of indigenous food to the present and up-coming generation should be via print and electronic media in form of advertisements on posters, billboards, calendars and in rhymes on television programs, radio presentations, video tapes and CD–ROM apart from classroom teaching and learning. Indigenous food is a panacea to healthy living and longevity, a prevention of diseases and a means of accelerated healing of the body through natural foods.

Keywords: Indigenous food, print and electronic media, nutritional values, re-awakening education.

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Authors: N. Reehana, A. Parveez Ahamed, D. Mubarak Ali, A. Suresh, R. Arvind Kumar, N. Thajuddin

Abstract:

Cyanobacteria play a vital role in the production of phycobiliproteins that includes phycocyanin and phycoerythrin pigments. Phycocyanin and related phycobiliproteins have wide variety of application that is used in the food, biotechnology and cosmetic industry because of their color, fluorescent and antioxidant properties. The present study is focused to understand the pigment at molecular level in the Cyanobacteria Oscillatoria terebriformis NTRI05 and Oscillatoria foreaui NTRI06. After extraction of genomic DNA, the amplification of C-Phycocyanin gene was done with the suitable primer PCβF and PCαR and the sequencing was performed. Structural and Phylogenetic analysis was attained using the sequence to develop a molecular model.

Keywords: Cyanobacteria, C-Phycocyanin gene, Phylogenetic analysis, Structural analysis.

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Authors: M. A. Ben Messaoud, A. Bouzid, N. Ellouze

Abstract:

Speech Segmentation is the measure of the change point detection for partitioning an input speech signal into regions each of which accords to only one speaker. In this paper, we apply two features based on multi-scale product (MP) of the clean speech, namely the spectral centroid of MP, and the zero crossings rate of MP. We focus on multi-scale product analysis as an important tool for segmentation extraction. The MP is based on making the product of the speech wavelet transform coefficients (WTC). We have estimated our method on the Keele database. The results show the effectiveness of our method. It indicates that the two features can find word boundaries, and extracted the segments of the clean speech.

Keywords: Speech segmentation, Multi-scale product, Spectral centroid, Zero crossings rate.

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Authors: Randa Boukhris Trabelsi, Alima Damak Masmoudi, Dorra Sellami Masmoudi

Abstract:

Nowadays, hand vein recognition has attracted more attentions in identification biometrics systems. Generally, hand vein image is acquired with low contrast and irregular illumination. Accordingly, if you have a good preprocessing of hand vein image, we can easy extracted the feature extraction even with simple binarization. In this paper, a proposed approach is processed to improve the quality of hand vein image. First, a brief survey on existing methods of enhancement is investigated. Then a Radon Like features method is applied to preprocessing hand vein image. Finally, experiments results show that the proposed method give the better effective and reliable in improving hand vein images.

Keywords: Hand Vein, Enhancement, Contrast, RLF, SDME

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Authors: C. R. Ríos-Soberanis, V. M. Moo-Huchin, R. J. Estrada-Leon, E. Perez-Pacheco

Abstract:

Polymers from renewable resources have attracted an increasing amount of attention over the last two decades, predominantly due to two major reasons: firstly environmental concerns, and secondly the realization that our petroleum resources are finite. Finding new uses for agricultural commodities is also an important area of research. Therefore, it is crucial to get new sources of natural materials that can be used in different applications. Ramon tree (Brosimum alicastrum) is a tropical plant that grows freely in Yucatan countryside. This paper focuses on the seeds recollection, processing and starch extraction and characterization in order to find out about its suitability as biomaterial. Results demonstrated that it has a high content of qualities to be used not only as comestible but also as an important component in polymeric blends.

Keywords: Biomaterials, biopolymer, starch, characterization techniques.

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Authors: Wan-Ting Lin, Chuen-Horng Lin, Tsung-Ho Wu, Yung-Kuan Chan

Abstract:

This study aims to segment objects using the K-means algorithm for texture features. Firstly, the algorithm transforms color images into gray images. This paper describes a novel technique for the extraction of texture features in an image. Then, in a group of similar features, objects and backgrounds are differentiated by using the K-means algorithm. Finally, this paper proposes a new object segmentation algorithm using the morphological technique. The experiments described include the segmentation of single and multiple objects featured in this paper. The region of an object can be accurately segmented out. The results can help to perform image retrieval and analyze features of an object, as are shown in this paper.

Keywords: k-mean, multiple objects, segmentation, texturefeatures.

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Authors: Nazar Zaki, Safaai Deris, Hany Alashwal

Abstract:

Detecting protein-protein interactions is a central problem in computational biology and aberrant such interactions may have implicated in a number of neurological disorders. As a result, the prediction of protein-protein interactions has recently received considerable attention from biologist around the globe. Computational tools that are capable of effectively identifying protein-protein interactions are much needed. In this paper, we propose a method to detect protein-protein interaction based on substring similarity measure. Two protein sequences may interact by the mean of the similarities of the substrings they contain. When applied on the currently available protein-protein interaction data for the yeast Saccharomyces cerevisiae, the proposed method delivered reasonable improvement over the existing ones.

Keywords: Protein-Protein Interaction, support vector machine, feature extraction, pairwise alignment, Smith-Waterman score.

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Authors: T. Thendral, M. S. Vijaya, S. Karpagavalli

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Tamil handwritten document is taken as a key source of data to identify the writer. Tamil is a classical language which has 247 characters include compound characters, consonants, vowels and special character. Most characters of Tamil are multifaceted in nature. Handwriting is a unique feature of an individual. Writer may change their handwritings according to their frame of mind and this place a risky challenge in identifying the writer. A new discriminative model with pooled features of handwriting is proposed and implemented using support vector machine. It has been reported on 100% of prediction accuracy by RBF and polynomial kernel based classification model.

Keywords: Classification, Feature extraction, Support vector machine, Training, Writer.

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Authors: T. Thendral, M. S. Vijaya, S. Karpagavalli

Abstract:

Tamil handwritten document is taken as a key source of data to identify the writer. Tamil is a classical language which has 247 characters include compound characters, consonants, vowels and special character. Most characters of Tamil are multifaceted in nature. Handwriting is a unique feature of an individual. Writer may change their handwritings according to their frame of mind and this place a risky challenge in identifying the writer. A new discriminative model with pooled features of handwriting is proposed and implemented using support vector machine. It has been reported on 100% of prediction accuracy by RBF and polynomial kernel based classification model.

Keywords: Classification, Feature extraction, Support vector machine, Training, Writer.

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Authors: Nidal F. Shilbayeh, Belal AbuHaija, Zainab N. Al-Qudsy

Abstract:

In this paper, we propose a new robust and secure system that is based on the combination between two different transforms Discrete wavelet Transform (DWT) and Contourlet Transform (CT). The combined transforms will compensate the drawback of using each transform separately. The proposed algorithm has been designed, implemented and tested successfully. The experimental results showed that selecting the best sub-band for embedding from both transforms will improve the imperceptibility and robustness of the new combined algorithm. The evaluated imperceptibility of the combined DWT-CT algorithm which gave a PSNR value 88.11 and the combination DWT-CT algorithm improves robustness since it produced better robust against Gaussian noise attack. In addition to that, the implemented system shored a successful extraction method to extract watermark efficiently.

Keywords: DWT, CT, Digital Image Watermarking, Copyright Protection.

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Authors: Sandeep Chandana, Rene V. Mayorga, Christine W. Chan

Abstract:

This article outlines conceptualization and implementation of an intelligent system capable of extracting knowledge from databases. Use of hybridized features of both the Rough and Fuzzy Set theory render the developed system flexibility in dealing with discreet as well as continuous datasets. A raw data set provided to the system, is initially transformed in a computer legible format followed by pruning of the data set. The refined data set is then processed through various Rough Set operators which enable discovery of parameter relationships and interdependencies. The discovered knowledge is automatically transformed into a rule base expressed in Fuzzy terms. Two exemplary cancer repository datasets (for Breast and Lung Cancer) have been used to test and implement the proposed framework.

Keywords: Knowledge Extraction, Fuzzy Sets, Rough Sets, Neuro–Fuzzy Systems, Databases

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