Search results for: Case-based reasoning; Breast cancer diagnosis; Genetic algorithm; Wrapper feature selection

Authors: J. Umamaheswari, G. Radhamani

Abstract:

This paper describes about the process of recognition and classification of brain images such as normal and abnormal based on PSO-SVM. Image Classification is becoming more important for medical diagnosis process. In medical area especially for diagnosis the abnormality of the patient is classified, which plays a great role for the doctors to diagnosis the patient according to the severeness of the diseases. In case of DICOM images it is very tough for optimal recognition and early detection of diseases. Our work focuses on recognition and classification of DICOM image based on collective approach of digital image processing. For optimal recognition and classification Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Support Vector Machine (SVM) are used. The collective approach by using PSO-SVM gives high approximation capability and much faster convergence.

Keywords: Recognition, classification, Relaxed Median Filter, Adaptive thresholding, clustering and Neural Networks

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Authors: K. Kemih, M. Benslama

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This paper investigates the problem of spreading sequence and receiver code synchronization techniques for satellite based CDMA communications systems. The performance of CDMA system depends on the autocorrelation and cross-correlation properties of the used spreading sequences. In this paper we propose the uses of chaotic Lu system to generate binary sequences for spreading codes in a direct sequence spread CDMA system. To minimize multiple access interference (MAI) we propose the use of genetic algorithm for optimum selection of chaotic spreading sequences. To solve the problem of transmitter-receiver synchronization, we use the passivity controls. The concept of semipassivity is defined to find simple conditions which ensure boundedness of the solutions of coupled Lu systems. Numerical results are presented to show the effectiveness of the proposed approach.

Keywords: About Chaotic Lu system, synchronization, Spreading sequence, Genetic Algorithm. Passive System

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Authors: N. V. Enwuru, B. Nkeki, E. A. Adekoya, O. A. Adebesin, B. O. Ojo, R. F. Peters, V. A. Aikhomu, U. E. Mendie, O. Akinloye

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Awareness about probiotic health benefits is increasing tremendously. However, cell viability is often low due to harsh conditions exposed during processing, handling, storage, and gastrointestinal transit. Thus, encapsulation is a promising technique that increases cell viability. The study aims to encapsulate the probiotic, evaluate its viability and cytotoxic activity of its postbiotic on the Michigan Cancer Foundation (MCF)-7 breast cancer cell line. Human and animal raw milk was sampled for lactic acid bacteria. Isolated bacteria were identified using conventional and VITEK 2 systems. The identified bacteria were encapsulated using the spray-drying method. The free and encapsulated probiotic cells were exposed to simulated gastric intestinal (SGI) fluid conditions and different storage conditions for their viability. The properties of the formed probiotic granules, their disintegration time, and the weight uniformity of the microcapsules were tested. Furthermore, the postbiotic of the free cells was extracted, and its cytotoxic effect on the MCF-7 breast cancer cell line was tested through [3-(4,5-dimethylthiazolyl-2)-2,5 diphenyltetrazolium bromide] (MTT) assay. The bacteria isolated were identified as Lactobacillus plantarum. The size of the formed probiotic granules ranges within 0.71-1.00 mm in diameter, and disintegration time ranges from 2.14 ± 0.045 to 2.91 ± 0.293 minutes, while the average weight is 502.1 mg. The viability of encapsulated cells stored at refrigerated condition (4oC) was higher than that of cells stored at room temperature (25 oC). The encapsulated probiotic cells exhibited better viability after exposure to SGI solution at different pH levels than free cells. The Postbiotic Metabolites (PM) of L. plantarum produced a cytotoxic effect that shows significant activity similar to 5FU, a standard antineoplastic agent. The inhibition concentration of 50% growth (IC50) of postbiotic metabolite was consistent with the IC50 of the positive control (Cisplatin). Lactobacillus plantarum postbiotic exhibited a cytotoxic effect on the MCF-7 breast cancer cell line and could be used as combined adjuvant therapy in breast cancer management. The microencapsulation technique protects the probiotics and maintains their viability.

Keywords: Cytotoxicity effect, encapsulation, postbiotic, probiotic.

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Authors: Yesubai Rubavathi Charles, Ravi Ramraj

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In order to retrieve images efficiently from a large database, a unique method integrating color and texture features using genetic programming has been proposed. Opponent color histogram which gives shadow, shade, and light intensity invariant property is employed in the proposed framework for extracting color features. For texture feature extraction, fast discrete curvelet transform which captures more orientation information at different scales is incorporated to represent curved like edges. The recent scenario in the issues of image retrieval is to reduce the semantic gap between user’s preference and low level features. To address this concern, genetic algorithm combined with relevance feedback is embedded to reduce semantic gap and retrieve user’s preference images. Extensive and comparative experiments have been conducted to evaluate proposed framework for content based image retrieval on two databases, i.e., COIL-100 and Corel-1000. Experimental results clearly show that the proposed system surpassed other existing systems in terms of precision and recall. The proposed work achieves highest performance with average precision of 88.2% on COIL-100 and 76.3% on Corel, the average recall of 69.9% on COIL and 76.3% on Corel. Thus, the experimental results confirm that the proposed content based image retrieval system architecture attains better solution for image retrieval.

Keywords: Content based image retrieval, Curvelet transform, Genetic algorithm, Opponent color histogram, Relevance feedback.

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Authors: Hassan Azarkish, Said Farahat, S.Masoud H. Sarvari

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In the present work, the performance of the particle swarm optimization and the genetic algorithm compared as a typical geometry design problem. The design maximizes the heat transfer rate from a given fin volume. The analysis presumes that a linear temperature distribution along the fin. The fin profile generated using the B-spline curves and controlled by the change of control point coordinates. An inverse method applied to find the appropriate fin geometry yield the linear temperature distribution along the fin corresponds to optimum design. The numbers of the populations, the count of iterations and time to convergence measure efficiency. Results show that the particle swarm optimization is most efficient for geometry optimization.

Keywords: Genetic Algorithm, Geometry Optimization, longitudinal Fin, Particle Swarm Optimization

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Authors: P. N. Hrisheekesha, Jaydev Sharma

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In this paper, a method based on Non-Dominated Sorting Genetic Algorithm (NSGA) has been presented for the Volt / Var control in power distribution systems with dispersed generation (DG). Genetic algorithm approach is used due to its broad applicability, ease of use and high accuracy. The proposed method is better suited for volt/var control problems. A multi-objective optimization problem has been formulated for the volt/var control of the distribution system. The non-dominated sorting genetic algorithm based method proposed in this paper, alleviates the problem of tuning the weighting factors required in solving the multi-objective volt/var control optimization problems. Based on the simulation studies carried out on the distribution system, the proposed scheme has been found to be simple, accurate and easy to apply to solve the multiobjective volt/var control optimization problem of the distribution system with dispersed generation.

Keywords: Dispersed Generation, Distribution System, Non-Dominated Sorting Genetic Algorithm, Voltage / Reactive powercontrol.

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Authors: Delowar Hossain, Genci Capi

Abstract:

This paper concerns with the problem of deep learning parameters tuning using a genetic algorithm (GA) in order to improve the performance of deep learning (DL) method. We present a GA based DL method for robot object recognition and grasping. GA is used to optimize the DL parameters in learning procedure in term of the fitness function that is good enough. After finishing the evolution process, we receive the optimal number of DL parameters. To evaluate the performance of our method, we consider the object recognition and robot grasping tasks. Experimental results show that our method is efficient for robot object recognition and grasping.

Keywords: Deep learning, genetic algorithm, object recognition, robot grasping.

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Authors: Salwa Hagag Abdelaziz, Naglaa Fathy Youssef, Nadia Abdel Latif Hassan, Rasha Wesam Abdel Rahman

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Breast cancer is considered as a substantial health concern and practicing mammography screening [MS] is important in minimizing its related morbidity. So it is essential to have a better understanding of breast cancer screening behaviors of women and factors that influence utilization of them. The aim of this study is to identify the factors that are linked to MS behaviors among the Egyptian women. A cross-sectional descriptive design was carried out to provide a snapshot of the factors that are linked to MS behaviors. A convenience sample of 311 women was utilized and all eligible participants admitted to the Women Imaging Unit who are 40 years of age or above, coming for mammography assessment, not pregnant or breast feeding and who accepted to participate in the study were included. A structured questionnaire was developed by the researchers and contains three parts; Socio-demographic data; Motivating factors associated with MS; and association between MS and model of behavior change. The analyzed data indicated that most of the participated women (66.6%) belonged to the age group of 40- 49.A high proportion of participants (58.1%) of group having previous MS influenced by their neighbors to practice MS, whereas 32.7 % in group not having previous MS were influenced by family members which indicated significant differences (P <0.05). Doctors and media shown to be the least influence of others to practice MS. Women with intention to have a future mammogram had higher OR (1.404) for practicing MS compared with women with no intention. Further studies are needed to examine the relation between Transtheoretical Model [TTM] and practicing MS.

Keywords: Breast cancer, mammography, screening behaviors.

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Authors: Huynh Quyet Thang, Le Hai Quan

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Interaction Model plays an important role in Modelbased Intelligent Interface Agent Architecture for developing Intelligent User Interface. In this paper we are presenting some improvements in the algorithms for development interaction model of interface agent including: the action segmentation algorithm, the action pair selection algorithm, the final action pair selection algorithm, the interaction graph construction algorithm and the probability calculation algorithm. The analysis of the algorithms also presented. At the end of this paper, we introduce an experimental program called “Personal Transfer System".

Keywords: interface agent, interaction model, user model.

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Authors: Jorge A. Ruiz-Vanoye, Ocotlán Díaz-Parra, Alejandro Fuentes-Penna, Daniel Vélez-Díaz, Edith Olaco García

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In this paper, we present the use of the discriminant analysis to select evolutionary algorithms that better solve instances of the vehicle routing problem with time windows. We use indicators as independent variables to obtain the classification criteria, and the best algorithm from the generic genetic algorithm (GA), random search (RS), steady-state genetic algorithm (SSGA), and sexual genetic algorithm (SXGA) as the dependent variable for the classification. The discriminant classification was trained with classic instances of the vehicle routing problem with time windows obtained from the Solomon benchmark. We obtained a classification of the discriminant analysis of 66.7%.

Keywords: Intelligent transportation systems, data-mining techniques, evolutionary algorithms, discriminant analysis, machine learning.

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Authors: Sajjad Farashi

Abstract:

Neurons in the nervous system communicate with each other by producing electrical signals called spikes. To investigate the physiological function of nervous system it is essential to study the activity of neurons by detecting and sorting spikes in the recorded signal. In this paper a method is proposed for considering the spike sorting problem which is based on the nonlinear modeling of spikes using exponential autoregressive model. The genetic algorithm is utilized for model parameter estimation. In this regard some selected model coefficients are used as features for sorting purposes. For optimal selection of model coefficients, self-organizing feature map is used. The results show that modeling of spikes with nonlinear autoregressive model outperforms its linear counterpart. Also the extracted features based on the coefficients of exponential autoregressive model are better than wavelet based extracted features and get more compact and well-separated clusters. In the case of spikes different in small-scale structures where principal component analysis fails to get separated clouds in the feature space, the proposed method can obtain well-separated cluster which removes the necessity of applying complex classifiers.

Keywords: Exponential autoregressive model, Neural data, spike sorting, time series modeling.

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Authors: Satish Kumar, Arun Kumar Gupta, Pankaj Chandna

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In the modern manufacturing systems, the use of thermal cutting techniques using oxyfuel, plasma and laser have become indispensable for the shape forming of high quality complex components; however, the conventional chip removal production techniques still have its widespread space in the manufacturing industry. Both these types of machining operations require the positioning of end effector tool at the edge where the cutting process commences. This repositioning of the cutting tool in every machining operation is repeated several times and is termed as non-productive time or airtime motion. Minimization of this non-productive machining time plays an important role in mass production with high speed machining. As, the tool moves from one region to the other by rapid movement and visits a meticulous region once in the whole operation, hence the non-productive time can be minimized by synchronizing the tool movements. In this work, this problem is being formulated as a general travelling salesman problem (TSP) and a genetic algorithm approach has been applied to solve the same. For improving the efficiency of the algorithm, the GA has been hybridized with a noble special heuristic and simulating annealing (SA). In the present work a novel heuristic in the combination of GA has been developed for synchronization of toolpath movements during repositioning of the tool. A comparative analysis of new Meta heuristic techniques with simple genetic algorithm has been performed. The proposed metaheuristic approach shows better performance than simple genetic algorithm for minimization of nonproductive toolpath length. Also, the results obtained with the help of hybrid simulated annealing genetic algorithm (HSAGA) are also found better than the results using simple genetic algorithm only.

Keywords: Non-productive time, Airtime, 2.5 D milling, Laser cutting, Metaheuristic, Genetic Algorithm, Simulated Annealing.

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Authors: Nisar Ahmed Memon, Anwar Majid Mirza, S.A.M. Gilani

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Segmentation is an important step in medical image analysis and classification for radiological evaluation or computer aided diagnosis. The CAD (Computer Aided Diagnosis ) of lung CT generally first segment the area of interest (lung) and then analyze the separately obtained area for nodule detection in order to diagnosis the disease. For normal lung, segmentation can be performed by making use of excellent contrast between air and surrounding tissues. However this approach fails when lung is affected by high density pathology. Dense pathologies are present in approximately a fifth of clinical scans, and for computer analysis such as detection and quantification of abnormal areas it is vital that the entire and perfectly lung part of the image is provided and no part, as present in the original image be eradicated. In this paper we have proposed a lung segmentation technique which accurately segment the lung parenchyma from lung CT Scan images. The algorithm was tested against the 25 datasets of different patients received from Ackron Univeristy, USA and AGA Khan Medical University, Karachi, Pakistan.

Keywords: Computer Aided Diagnosis, Medical ImageProcessing, Region Growing, Segmentation, Thresholding,

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Authors: Mahkameh S. Mostafavi

Abstract:

In this paper an algorithm is used to detect the color defects of ceramic tiles. First the image of a normal tile is clustered using GCMA; Genetic C-means Clustering Algorithm; those results in best cluster centers. C-means is a common clustering algorithm which optimizes an objective function, based on a measure between data points and the cluster centers in the data space. Here the objective function describes the mean square error. After finding the best centers, each pixel of the image is assigned to the cluster with closest cluster center. Then, the maximum errors of clusters are computed. For each cluster, max error is the maximum distance between its center and all the pixels which belong to it. After computing errors all the pixels of defected tile image are clustered based on the centers obtained from normal tile image in previous stage. Pixels which their distance from their cluster center is more than the maximum error of that cluster are considered as defected pixels.

Keywords: C-Means algorithm, color spaces, Genetic Algorithm, image clustering.

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Authors: S. M. Kamruzzaman, Md. Monirul Islam

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Although backpropagation ANNs generally predict better than decision trees do for pattern classification problems, they are often regarded as black boxes, i.e., their predictions cannot be explained as those of decision trees. In many applications, it is desirable to extract knowledge from trained ANNs for the users to gain a better understanding of how the networks solve the problems. A new rule extraction algorithm, called rule extraction from artificial neural networks (REANN) is proposed and implemented to extract symbolic rules from ANNs. A standard three-layer feedforward ANN is the basis of the algorithm. A four-phase training algorithm is proposed for backpropagation learning. Explicitness of the extracted rules is supported by comparing them to the symbolic rules generated by other methods. Extracted rules are comparable with other methods in terms of number of rules, average number of conditions for a rule, and predictive accuracy. Extensive experimental studies on several benchmarks classification problems, such as breast cancer, iris, diabetes, and season classification problems, demonstrate the effectiveness of the proposed approach with good generalization ability.

Keywords: Backpropagation, clustering algorithm, constructivealgorithm, continuous activation function, pruning algorithm, ruleextraction algorithm, symbolic rules.

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Authors: Mustafa Yorgancılar, Emine Atalay, Necdet Akgün, Ali Topal

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In this study, polymerase chain reaction based Inter-simple sequence repeat (ISSR) from DNA fingerprinting techniques were used to investigate the genetic relationships among barley crossbreed genotypes in Turkey. It is important that selection based on the genetic base in breeding programs via ISSR, in terms of breeding time. 14 ISSR primers generated a total of 97 bands, of which 81 (83.35%) were polymorphic. The highest total resolution power (RP) value was obtained from the F2 (0.53) and M16 (0.51) primers. According to the ISSR result, the genetic similarity index changed between 0.64–095; Lane 3 with Line 6 genotypes were the closest, while Line 36 were the most distant ones. The ISSR markers were found to be promising for assessing genetic diversity in barley crossbreed genotypes.

Keywords: Barley, crossbreed, genetic similarity, ISSR.

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Authors: Younis R. Elhaddad, Aiman S.Gannous

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The particular interests of this paper is to explore if the simple Genetic Algorithms (GA) starts with population of only two individuals and applying different crossover technique over these parents to produced 104 children, each one has different attributes inherited from their parents; is better than starting with population of 100 individuals; and using only one type crossover (order crossover OX). For this reason we implement GA with 52 different crossover techniques; each one produce two children; which means 104 different children will be produced and this may discover more search space, also we implement classic GA with order crossover and many experiments were done over 3 Travel Salesman Problem (TSP) to find out which method is better, and according to the results we can say that GA with Multi-crossovers is much better.

Keywords: Artificial intelligence, genetic algorithm, order crossover, travel salesman problem.

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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: K. Idir, H. Merouani, Y. Tlili.

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An effective method for the early detection of breast cancer is the mammographic screening. One of the most important signs of early breast cancer is the presence of microcalcifications. For the detection of microcalcification in a mammography image, we propose to conceive a multiagent system based on a dual irregular pyramid. An initial segmentation is obtained by an incremental approach; the result represents level zero of the pyramid. The edge information obtained by application of the Canny filter is taken into account to affine the segmentation. The edge-agents and region-agents cooper level by level of the pyramid by exploiting its various characteristics to provide the segmentation process convergence.

Keywords: Dual Pyramid, Image Segmentation, Multi-agent System, Region/Edge Cooperation.

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Authors: V. K. Banga, R. Kumar, Y. Singh

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In this paper, we present optimal control for movement and trajectory planning for four degrees-of-freedom robot using Fuzzy Logic (FL) and Genetic Algorithms (GAs). We have evaluated using Fuzzy Logic (FL) and Genetic Algorithms (GAs) for four degree-of-freedom (4 DOF) robotics arm, Uncertainties like; Movement, Friction and Settling Time in robotic arm movement have been compensated using Fuzzy logic and Genetic Algorithms. The development of a fuzzy genetic optimization algorithm is presented and discussed. The result are compared only GA and Fuzzy GA. This paper describes genetic algorithms, which is designed to optimize robot movement and trajectory. Though the model represents is a general model for redundant structures and could represent any n-link structures. The result is a complete trajectory planning with Fuzzy logic and Genetic algorithms demonstrating the flexibility of this technique of artificial intelligence.

Keywords: Inverse kinematics, Genetic algorithms (GAs), Fuzzy logic (FL), Trajectory planning.

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Authors: Theerawat Songyot, Tham Manjing, Bunyarit Uyyanonvara, Chanjira Sinthanayothin

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Corner detection and optical flow are common techniques for feature-based video stabilization. However, these algorithms are computationally expensive and should be performed at a reasonable rate. This paper presents an algorithm for discarding irrelevant feature points and maintaining them for future use so as to improve the computational cost. The algorithm starts by initializing a maintained set. The feature points in the maintained set are examined against its accuracy for modeling. Corner detection is required only when the feature points are insufficiently accurate for future modeling. Then, optical flows are computed from the maintained feature points toward the consecutive frame. After that, a motion model is estimated based on the simplified affine motion model and least square method, with outliers belonging to moving objects presented. Studentized residuals are used to eliminate such outliers. The model estimation and elimination processes repeat until no more outliers are identified. Finally, the entire algorithm repeats along the video sequence with the points remaining from the previous iteration used as the maintained set. As a practical application, an efficient video stabilization can be achieved by exploiting the computed motion models. Our study shows that the number of times corner detection needs to perform is greatly reduced, thus significantly improving the computational cost. Moreover, optical flow vectors are computed for only the maintained feature points, not for outliers, thus also reducing the computational cost. In addition, the feature points after reduction can sufficiently be used for background objects tracking as demonstrated in the simple video stabilizer based on our proposed algorithm.

Keywords: background object tracking, feature point reduction, low cost tracking, video stabilization.

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Authors: Ahmed Z. Gabr, Ahmed A. Helal, Hussein E. Said

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With the incessant increase of power systems capacity and voltage grade, the safety of grounding grid becomes more and more prominent. In this paper, the designing substation grounding grid is presented by means of genetic algorithm (GA). This approach purposes to control the grounding cost of the power system with the aid of controlling grounding rod number and conductor lengths under the same safety limitations. The proposed technique is used for the design of the substation grounding grid in Khalda Petroleum Company “El-Qasr” power plant and the design was simulated by using CYMGRD software for results verification. The result of the design is highly complying with IEEE 80-2000 standard requirements.

Keywords: Genetic algorithm, optimum grounding grid design, power system analysis, power system protection, single layer model, substation.

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Authors: Xiaochuan Chen, Jianguo Yang, Beizhi Li

Abstract:

Design for cost (DFC) is a method that reduces life cycle cost (LCC) from the angle of designers. Multiple domain features mapping (MDFM) methodology was given in DFC. Using MDFM, we can use design features to estimate the LCC. From the angle of DFC, the design features of family cars were obtained, such as all dimensions, engine power and emission volume. At the conceptual design stage, cars- LCC were estimated using back propagation (BP) artificial neural networks (ANN) method and case-based reasoning (CBR). Hamming space was used to measure the similarity among cases in CBR method. Levenberg-Marquardt (LM) algorithm and genetic algorithm (GA) were used in ANN. The differences of LCC estimation model between CBR and artificial neural networks (ANN) were provided. ANN and CBR separately each method has its shortcomings. By combining ANN and CBR improved results accuracy was obtained. Firstly, using ANN selected some design features that affect LCC. Then using LCC estimation results of ANN could raise the accuracy of LCC estimation in CBR method. Thirdly, using ANN estimate LCC errors and correct errors in CBR-s estimation results if the accuracy is not enough accurate. Finally, economically family cars and sport utility vehicle (SUV) was given as LCC estimation cases using this hybrid approach combining ANN and CBR.

Keywords: case-based reasoning, life cycle cost (LCC), artificialneural networks (ANN), family cars

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Authors: Parvinder S. Sandhu, Satish Kumar Dhiman, Anmol Goyal

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Fault-proneness of a software module is the probability that the module contains faults. A correlation exists between the fault-proneness of the software and the measurable attributes of the code (i.e. the static metrics) and of the testing (i.e. the dynamic metrics). Early detection of fault-prone software components enables verification experts to concentrate their time and resources on the problem areas of the software system under development. This paper introduces Genetic Algorithm based software fault prediction models with Object-Oriented metrics. The contribution of this paper is that it has used Metric values of JEdit open source software for generation of the rules for the classification of software modules in the categories of Faulty and non faulty modules and thereafter empirically validation is performed. The results shows that Genetic algorithm approach can be used for finding the fault proneness in object oriented software components.

Keywords: Genetic Algorithms, Software Fault, Classification, Object Oriented Metrics.

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Authors: Dima Stopel, Zvi Boger, Robert Moskovitch, Yuval Shahar, Yuval Elovici

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Computer worm detection is commonly performed by antivirus software tools that rely on prior explicit knowledge of the worm-s code (detection based on code signatures). We present an approach for detection of the presence of computer worms based on Artificial Neural Networks (ANN) using the computer's behavioral measures. Identification of significant features, which describe the activity of a worm within a host, is commonly acquired from security experts. We suggest acquiring these features by applying feature selection methods. We compare three different feature selection techniques for the dimensionality reduction and identification of the most prominent features to capture efficiently the computer behavior in the context of worm activity. Additionally, we explore three different temporal representation techniques for the most prominent features. In order to evaluate the different techniques, several computers were infected with five different worms and 323 different features of the infected computers were measured. We evaluated each technique by preprocessing the dataset according to each one and training the ANN model with the preprocessed data. We then evaluated the ability of the model to detect the presence of a new computer worm, in particular, during heavy user activity on the infected computers.

Keywords: Artificial Neural Networks, Feature Selection, Temporal Analysis, Worm Detection.

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Authors: G. Murugeswari, A. Suruliandi

Abstract:

This paper proposes a rotational invariant texture feature based on the roughness property of the image for psoriasis image analysis. In this work, we have applied this feature for image classification and segmentation. The fuzzy concept is employed to overcome the imprecision of roughness. Since the psoriasis lesion is modeled by a rough surface, the feature is extended for calculating the Psoriasis Area Severity Index value. For classification and segmentation, the Nearest Neighbor algorithm is applied. We have obtained promising results for identifying affected lesions by using the roughness index and severity level estimation.

Keywords: Fuzzy texture feature, psoriasis, roughness feature, skin disease.

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Authors: Manpreet Singh, Keerat Kaur, Bhavdeep Singh

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With the hardware technology advancing, the cost of storing is decreasing. Thus there is an urgent need for new techniques and tools that can intelligently and automatically assist us in transferring this data into useful knowledge. Different techniques of data mining are developed which are helpful for handling these large size databases [7]. Data mining is also finding its role in the field of biotechnology. Pedigree means the associated ancestry of a crop variety. Genetic diversity is the variation in the genetic composition of individuals within or among species. Genetic diversity depends upon the pedigree information of the varieties. Parents at lower hierarchic levels have more weightage for predicting genetic diversity as compared to the upper hierarchic levels. The weightage decreases as the level increases. For crossbreeding, the two varieties should be more and more genetically diverse so as to incorporate the useful characters of the two varieties in the newly developed variety. This paper discusses the searching and analyzing of different possible pairs of varieties selected on the basis of morphological characters, Climatic conditions and Nutrients so as to obtain the most optimal pair that can produce the required crossbreed variety. An algorithm was developed to determine the genetic diversity between the selected wheat varieties. Cluster analysis technique is used for retrieving the results.

Keywords: Genetic diversity, pedigree, nutrients.

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Authors: Dragan D. Stamenkovic, Vladimir M. Popovic

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Warranty is a powerful marketing tool for the manufacturer and a good protection for both the manufacturer and the customer. However, warranty always involves additional costs to the manufacturer, which depend on product reliability characteristics and warranty parameters. This paper presents an approach to optimisation of warranty parameters for known product failure distribution to reduce the warranty costs to the manufacturer while retaining the promotional function of the warranty. Combination free replacement and pro-rata warranty policy is chosen as a model and the length of free replacement period and pro-rata policy period are varied, as well as the coefficients that define the pro-rata cost function. Multiparametric warranty optimisation is done by using genetic algorithm. Obtained results are guideline for the manufacturer to choose the warranty policy that minimises the costs and maximises the profit.

Keywords: costs, genetic algorithm, optimisation, warranty.

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Authors: Pranee Suecharoen, Jaturat Kanpittaya

Abstract:

Background: The incidence of adverse reactions to iodinated contrast media has risen. The dearth of reports on reactions to the administration of iso- and low-osmolar contrast media should be addressed. We, therefore, studied the profile of adverse reactions to iodinated contrast media; viz., (a) the body systems affected (b) causality, (c) severity, and (d) preventability. Objective: To study adverse reactions (causes and severity) to iodinated contrast media at Srinagarind Hospital. Method: Between March and July, 2015, 1,101 patients from the Department of Radiology were observed and interviewed for the occurrence of adverse reactions. The patients were classified per Naranjo’s algorithm and through use of an adverse reactions questionnaire. Results: A total of 105 cases (9.5%) reported adverse reactions (57% male; 43% female); among whom 2% were iso-osmolar vs. 98% low-osmolar. Diagnoses included hepatoma and cholangiocarcinoma (24.8%), colorectal cancer (9.5%), breast cancer (5.7%), cervical cancer (3.8%), lung cancer (2.9%), bone cancer (1.9%), and others (51.5%). Underlying diseases included hypertension and diabetes mellitus type 2. Mild, moderate, and severe adverse reactions accounted for 92, 5 and 3%, respectively. The respective groups of escalating symptoms included (a) mild urticaria, itching, rash, nausea, vomiting, dizziness, and headache; (b) moderate hypertension, hypotension, dyspnea, tachycardia and bronchospasm; and (c) severe laryngeal edema, profound hypotension, and convulsions. All reactions could be anticipated per Naranjo’s algorithm. Conclusion: Mild to moderate adverse reactions to low-osmolar contrast media were most common and these occurred immediately after administration. For patient safety and better outcomes, improving the identification of patients likely to have an adverse reaction is essential.

Keywords: Adverse reactions, contrast media, computed tomography, iodinated contrast agents.

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Authors: R. Krishnamoorthi, N. Kannan

Abstract:

In this paper, a new algorithm for generating codebook is proposed for vector quantization (VQ) in image coding. The significant features of the training image vectors are extracted by using the proposed Orthogonal Polynomials based transformation. We propose to generate the codebook by partitioning these feature vectors into a binary tree. Each feature vector at a non-terminal node of the binary tree is directed to one of the two descendants by comparing a single feature associated with that node to a threshold. The binary tree codebook is used for encoding and decoding the feature vectors. In the decoding process the feature vectors are subjected to inverse transformation with the help of basis functions of the proposed Orthogonal Polynomials based transformation to get back the approximated input image training vectors. The results of the proposed coding are compared with the VQ using Discrete Cosine Transform (DCT) and Pairwise Nearest Neighbor (PNN) algorithm. The new algorithm results in a considerable reduction in computation time and provides better reconstructed picture quality.

Keywords: Orthogonal Polynomials, Image Coding, Vector Quantization, TSVQ, Binary Tree Classifier

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