Search results for: Continuous speech recognition

Authors: Iwona Nowak, Jacek Smolka, Andrzej J. Nowak

Abstract:

The paper discusses a 3D numerical solution of the inverse boundary problem for a continuous casting process of alloy. The main goal of the analysis presented within the paper was to estimate heat fluxes along the external surface of the ingot. The verified information on these fluxes was crucial for a good design of a mould, effective cooling system and generally the whole caster. In the study an enthalpy-porosity technique implemented in Fluent package was used for modeling the solidification process. In this method, the phase change interface was determined on the basis of the liquid fraction approach. In inverse procedure the sensitivity analysis was applied for retrieving boundary conditions. A comparison of the measured and retrieved values showed a high accuracy of the computations. Additionally, the influence of the accuracy of measurements on the estimated heat fluxes was also investigated.

Keywords: Boundary inverse problem, sensitivity analysis, continuous casting, numerical simulation.

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Authors: Rachid Ahdid, Said Safi, Bouzid Manaut

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In this paper, we present a comparative study of three methods of 2D face recognition system such as: Iso-Geodesic Curves (IGC), Geodesic Distance (GD) and Geodesic-Intensity Histogram (GIH). These approaches are based on computing of geodesic distance between points of facial surface and between facial curves. In this study we represented the image at gray level as a 2D surface in a 3D space, with the third coordinate proportional to the intensity values of pixels. In the classifying step, we use: Neural Networks (NN), K-Nearest Neighbor (KNN) and Support Vector Machines (SVM). The images used in our experiments are from two wellknown databases of face images ORL and YaleB. ORL data base was used to evaluate the performance of methods under conditions where the pose and sample size are varied, and the database YaleB was used to examine the performance of the systems when the facial expressions and lighting are varied.

Keywords: 2D face recognition, Geodesic distance, Iso-Geodesic Curves, Geodesic-Intensity Histogram, facial surface, Neural Networks, K-Nearest Neighbor, Support Vector Machines.

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Authors: A. Guellati, F-M Lounis, N. Guemras, K. Daoud

Abstract:

Micro droplet formation is considered as a growing emerging area of research due to its wide-range application in chemistry as well as biology. The mechanism of micro droplet formation using two immiscible liquids running through a T-junction has been widely studied. We believe that the flow of these two immiscible phases can be of greater important factor that could have an impact on out-flow hydrodynamic behavior, the droplets generated and the size of the droplets. In this study, the type of the capillary tubes used also represents another important factor that can have an impact on the generation of micro droplets. The tygon capillary tubing with hydrophilic inner surface doesn't allow regular out-flows due to the fact that the continuous phase doesn't adhere to the wall of the capillary inner surface. Teflon capillary tubing, presents better wettability than tygon tubing, and allows to obtain steady and regular regimes of out-flow, and the micro droplets are homogeneoussize. The size of the droplets is directly dependent on the flows of the continuous and dispersed phases. Thus, as increasing the flow of the continuous phase, to flow of the dispersed phase stationary, the size of the drops decreases. Inversely, while increasing the flow of the dispersed phase, to flow of the continuous phase stationary, the size of the droplet increases.

Keywords: Microfluidic system, micro droplets generation, T-junction.

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Authors: Yalong Jiang, Zheru Chi

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In this paper, we study the factors which determine the capacity of a Convolutional Neural Network (CNN) model and propose the ways to evaluate and adjust the capacity of a CNN model for best matching to a specific pattern recognition task. Firstly, a scheme is proposed to adjust the number of independent functional units within a CNN model to make it be better fitted to a task. Secondly, the number of independent functional units in the capsule network is adjusted to fit it to the training dataset. Thirdly, a method based on Bayesian GAN is proposed to enrich the variances in the current dataset to increase its complexity. Experimental results on the PASCAL VOC 2010 Person Part dataset and the MNIST dataset show that, in both conventional CNN models and capsule networks, the number of independent functional units is an important factor that determines the capacity of a network model. By adjusting the number of functional units, the capacity of a model can better match the complexity of a dataset.

Keywords: CNN, capsule network, capacity optimization, character recognition, data augmentation; semantic segmentation.

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Authors: Paul W. Murray

Abstract:

This research explorers the relationship between leadership style and continuous improvement (CI) teams. CI teams have several features that are not always found in other types of teams, including multi-functional members, short time period for performance, positive and actionable results, and exposure to senior leadership. There is no one best style of leadership for these teams. Instead, it is important to select the best leadership style for the situation. The leader must have the flexibility to change styles and the skill to use the chosen style effectively in order to ensure the team’s success.

Keywords: Leadership style, Lean Manufacturing, Teams, Cross-functional.

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Authors: JongKuk Kim, HernSoo Hahn, Uei-Joong Yoo, MyungJin Bae

Abstract:

In this paper, we propose a method of alter duration in frequency domain that control prosody in real time after pitch alteration. If there has a method to alteration duration freely among prosody information, that may used in several fields such as speech impediment person's pronunciation proof reading or language study. The pitch alteration method used control prosody altered by PSOLA synthesis method which is in time domain processing method. However, the duration of pitch alteration speech is changed by the frequency domain. In this paper, we altered the duration with the method of duration alteration by Fast Fourier Transformation in frequency domain. Consequently, the intelligibility of the pitch and duration are controlled has a slight decrease than the case when only pitch is changed, but the proposed algorithm obtained the higher MOS score about naturalness.

Keywords: PSOLA, Pitch Alteration, Duration Control.

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Authors: El Sayed A. Sharara, A. Tsuji, K. Terada

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Call centers have been expanding and they have influence on activation in various markets increasingly. A call center’s work is known as one of the most demanding and stressful jobs. In this paper, we propose the fatigue detection system in order to detect burnout of call center agents in the case of a neck pain and upper back pain. Our proposed system is based on the computer vision technique combined skin color detection with the Viola-Jones object detector. To recognize the gesture of hand poses caused by stress sign, the YCbCr color space is used to detect the skin color region including face and hand poses around the area related to neck ache and upper back pain. A cascade of clarifiers by Viola-Jones is used for face recognition to extract from the skin color region. The detection of hand poses is given by the evaluation of neck pain and upper back pain by using skin color detection and face recognition method. The system performance is evaluated using two groups of dataset created in the laboratory to simulate call center environment. Our call center agent burnout detection system has been implemented by using a web camera and has been processed by MATLAB. From the experimental results, our system achieved 96.3% for upper back pain detection and 94.2% for neck pain detection.

Keywords: Call center agents, fatigue, skin color detection, face recognition.

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Authors: Sunitha S. L., V. Udayashankara

Abstract:

Hearing impairment is the number one chronic disability affecting many people in the world. Background noise is particularly damaging to speech intelligibility for people with hearing loss especially for sensorineural loss patients. Several investigations on speech intelligibility have demonstrated sensorineural loss patients need 5-15 dB higher SNR than the normal hearing subjects. This paper describes Discrete Hartley Transform Power Normalized Least Mean Square algorithm (DHT-LMS) to improve the SNR and to reduce the convergence rate of the Least Means Square (LMS) for sensorineural loss patients. The DHT transforms n real numbers to n real numbers, and has the convenient property of being its own inverse. It can be effectively used for noise cancellation with less convergence time. The simulated result shows the superior characteristics by improving the SNR at least 9 dB for input SNR with zero dB and faster convergence rate (eigenvalue ratio 12) compare to time domain method and DFT-LMS.

Keywords: Hearing Impairment, DHT-LMS, Convergence rate, SNR improvement.

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Authors: Z. Večeřa, P. Mikuška, J. Kellner, J. Navrátil, A. Langerová

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The submitted paper deals with the problems of trapping and enriching the gases and aerosols of the substances to be determined in the ambient atmosphere. Further, the paper is focused on the working principle of the miniaturized portable continuous concentrator we have designed and the possibilities of its application in air sampling and accumulation of organic and inorganic substances with which the air is contaminated. The stress is laid on trapping vapours and aerosols of solid substances with the comparatively low vapour tension such as explosive compounds.

Keywords: Detectors of explosives, portable continuousconcentrator, misuse of explosive, terrorism.

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Authors: Paulo S. Caparelli, Eduardo Costa, Alexsandro S. Soares, Hipolito Barbosa

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This paper presents an evolutionary method for designing electronic circuits and numerical methods associated with monitoring systems. The instruments described here have been used in studies of weather and climate changes due to global warming, and also in medical patient supervision. Genetic Programming systems have been used both for designing circuits and sensors, and also for determining sensor parameters. The authors advance the thesis that the software side of such a system should be written in computer languages with a strong mathematical and logic background in order to prevent software obsolescence, and achieve program correctness.

Keywords: Pattern recognition, evolutionary computation, biological signal, functional programming.

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Authors: Khadijat T. Bamigbade, Olufade F. W. Onifade

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The field of automatic facial expression analysis has been an active research area in the last two decades. Its vast applicability in various domains has drawn so much attention into developing techniques and dataset that mirror real life scenarios. Many techniques such as Local Binary Patterns and its variants (CLBP, LBP-TOP) and lately, deep learning techniques, have been used for facial expression recognition. However, the problem of occlusion has not been sufficiently handled, making their results not applicable in real life situations. This paper develops a simple, yet highly efficient method tagged Local Binary Pattern-Histogram of Gradient (LBP-HOG) with occlusion detection in face image, using a multi-class SVM for Action Unit and in turn expression recognition. Our method was evaluated on three publicly available datasets which are JAFFE, CK, SFEW. Experimental results showed that our approach performed considerably well when compared with state-of-the-art algorithms and gave insight to occlusion detection as a key step to handling expression in wild.

Keywords: Automatic facial expression analysis, local binary pattern, LBP-HOG, occlusion detection.

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Authors: Trien V. Do, Jong-Weon Lee

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This paper describes a 3D modeling system in Augmented Reality environment, named 3DARModeler. It can be considered a simple version of 3D Studio Max with necessary functions for a modeling system such as creating objects, applying texture, adding animation, estimating real light sources and casting shadows. The 3DARModeler introduces convenient, and effective human-computer interaction to build 3D models by combining both the traditional input method (mouse/keyboard) and the tangible input method (markers). It has the ability to align a new virtual object with the existing parts of a model. The 3DARModeler targets nontechnical users. As such, they do not need much knowledge of computer graphics and modeling techniques. All they have to do is select basic objects, customize their attributes, and put them together to build a 3D model in a simple and intuitive way as if they were doing in the real world. Using the hierarchical modeling technique, the users are able to group several basic objects to manage them as a unified, complex object. The system can also connect with other 3D systems by importing and exporting VRML/3Ds Max files. A module of speech recognition is included in the system to provide flexible user interfaces.

Keywords: 3D Modeling, Augmented Reality, GeometricModeling, Virtual Reality

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Authors: Mais Nijim, Rama Devi Chennuboyina, Waseem Al Aqqad

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Advances in spatial and spectral resolution of satellite images have led to tremendous growth in large image databases. The data we acquire through satellites, radars, and sensors consists of important geographical information that can be used for remote sensing applications such as region planning, disaster management. Spatial data classification and object recognition are important tasks for many applications. However, classifying objects and identifying them manually from images is a difficult task. Object recognition is often considered as a classification problem, this task can be performed using machine-learning techniques. Despite of many machine-learning algorithms, the classification is done using supervised classifiers such as Support Vector Machines (SVM) as the area of interest is known. We proposed a classification method, which considers neighboring pixels in a region for feature extraction and it evaluates classifications precisely according to neighboring classes for semantic interpretation of region of interest (ROI). A dataset has been created for training and testing purpose; we generated the attributes by considering pixel intensity values and mean values of reflectance. We demonstrated the benefits of using knowledge discovery and data-mining techniques, which can be on image data for accurate information extraction and classification from high spatial resolution remote sensing imagery.

Keywords: Remote sensing, object recognition, classification, data mining, waterbody identification, feature extraction.

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Authors: Amr Almaddah, Sadi Vural, Yasushi Mae, Kenichi Ohara, Tatsuo Arai

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In this paper, we propose a robust face relighting technique by using spherical space properties. The proposed method is done for reducing the illumination effects on face recognition. Given a single 2D face image, we relight the face object by extracting the nine spherical harmonic bases and the face spherical illumination coefficients. First, an internal training illumination database is generated by computing face albedo and face normal from 2D images under different lighting conditions. Based on the generated database, we analyze the target face pixels and compare them with the training bootstrap by using pre-generated tiles. In this work, practical real time processing speed and small image size were considered when designing the framework. In contrast to other works, our technique requires no 3D face models for the training process and takes a single 2D image as an input. Experimental results on publicly available databases show that the proposed technique works well under severe lighting conditions with significant improvements on the face recognition rates.

Keywords: Face synthesis and recognition, Face illumination recovery, 2D spherical spaces, Vision for graphics.

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Authors: Ameneh Zare, Shahin Nematzadeh, Shahla Raghibdoust, Iran Kalbassi

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Autism spectrum disorder is characterized by abnormalities in social communication, language abilities and repetitive behaviors. The present study focused on some grammatical deficits in autistic children. We evaluated the impairment of correct use of different Persian verb tenses in autistic children-s speech. Two standardized Language Test were administered then gathered data were analyzed. The main result of this study was significant difference between the mean scores of correct responses to present tense in comparison with past tense in Persian language. This study demonstrated that tense is severely impaired in autistic children-s speech. Our findings indicated those autistic children-s production of simple present/ past tense opposition to be better than production of future and past periphrastic forms (past perfect, present perfect, past progressive).

Keywords: Autism, Past, Persian Language, Present, Tense

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Authors: Lu Yang, Meiyang Song, Xiang Yu, Wenhao Zhou, Chuntao Feng

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To address the problem that the FM continuous-wave (FMCW) radar extracts human vital sign signals which are susceptible to noise interference and low reconstruction accuracy, a detection scheme for the sign signals is proposed. Firstly, an improved complete ensemble empirical modal decomposition with adaptive noise (ICEEMDAN) algorithm is applied to decompose the radar-extracted thoracic signals to obtain several intrinsic modal functions (IMF) with different spatial scales, and then the IMF components are optimized by a backpropagation (BP) neural network improved by immune genetic algorithm (IGA). The simulation results show that this scheme can effectively separate the noise, accurately extract the respiratory and heartbeat signals and improve the reconstruction accuracy and signal to-noise ratio of the sign signals.

Keywords: Frequency modulated continuous wave radar, ICEEMDAN, BP Neural Network, vital signs signal.

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Authors: M. K. Jindal, G. S. Lehal, R. K. Sharma

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Character segmentation is an important preprocessing step for text recognition. In degraded documents, existence of touching characters decreases recognition rate drastically, for any optical character recognition (OCR) system. In this paper we have proposed a complete solution for segmenting touching characters in all the three zones of printed Gurmukhi script. A study of touching Gurmukhi characters is carried out and these characters have been divided into various categories after a careful analysis. Structural properties of the Gurmukhi characters are used for defining the categories. New algorithms have been proposed to segment the touching characters in middle zone, upper zone and lower zone. These algorithms have shown a reasonable improvement in segmenting the touching characters in degraded printed Gurmukhi script. The algorithms proposed in this paper are applicable only to machine printed text. We have also discussed a new and useful technique to segment the horizontally overlapping lines.

Keywords: Character Segmentation, Middle Zone, Upper Zone, Lower Zone, Touching Characters, Horizontally Overlapping Lines.

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Authors: K. R. V. Manikantachari, Vasudevan Raghavan, K. Srinivasan

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Present study focuses on studying the oscillatory behavior of jet diffusion flames. At a particular jet exit velocity, the flames are seen to exhibit natural flickering. Initially the flickering process is not continuous. In this transition region as well as in the continuous flickering regime, the flickering displays multiple frequency oscillations. The response of the flame to the exit velocity profile of the burner is also studied using three types of burners. The entire range of natural flickering is investigated by capturing high speed digital images and processing them using a MATLAB code.

Keywords: Diffusion flames, Natural flickering, flickering frequency, intermittent flickering

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Authors: Yiqin Lin, Liang Bao, Qinghua Wu, Liping Zhou

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In this paper, we propose a direct method based on the real Schur factorization for solving the projected Sylvester equation with relatively small size. The algebraic formula of the solution of the projected continuous-time Sylvester equation is presented. The computational cost of the direct method is estimated. Numerical experiments show that this direct method has high accuracy.

Keywords: Projected Sylvester equation, Schur factorization, Spectral projection, Direct method.

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Authors: Jae Kyun Bang, Sung Jae Hwang, Chang Yong Ko, Chi Hyun Kim

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Prolonged immobilization leads to significant weakness and atrophy of the skeletal muscle and can also impair the recovery of muscle strength following injury. Therefore, it is important to minimize the period under immobilization and accelerate the return to normal activity. This study examined the effects of heat treatment and rest-inserted exercise on the muscle activity of the lower limb during knee flexion/extension. Twelve healthy subjects were assigned to 4 groups that included: (1) heat treatment + rest-inserted exercise; (2) heat + continuous exercise; (3) no heat + rest-inserted exercise; and (4) no heat + continuous exercise. Heat treatment was applied for 15 mins prior to exercise. Continuous exercise groups performed knee flexion/extension at 0.5 Hz for 300 cycles without rest whereas rest-inserted exercise groups performed the same exercise but with 2 mins rest inserted every 60 cycles of continuous exercise. Changes in the rectus femoris and hamstring muscle activities were assessed at 0, 1, and 2 weeks of treatment by measuring the electromyography signals of isokinetic maximum voluntary contraction. Significant increases in both the rectus femoris and hamstring muscles were observed after 2 weeks of treatment only when both heat treatment and rest-inserted exercise were performed. These results suggest that combination of various treatment techniques, such as heat treatment and rest-inserted exercise, may expedite the recovery of muscle strength following immobilization.

Keywords: Electromyography, Heat Treatment, Muscle, Rest-Inserted Exercise.

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Authors: Sunitha. S.L., V. Udayashankara

Abstract:

Background noise is particularly damaging to speech intelligibility for people with hearing loss especially for sensorineural loss patients. Several investigations on speech intelligibility have demonstrated sensorineural loss patients need 5-15 dB higher SNR than the normal hearing subjects. This paper describes Discrete Cosine Transform Power Normalized Least Mean Square algorithm to improve the SNR and to reduce the convergence rate of the LMS for Sensory neural loss patients. Since it requires only real arithmetic, it establishes the faster convergence rate as compare to time domain LMS and also this transformation improves the eigenvalue distribution of the input autocorrelation matrix of the LMS filter. The DCT has good ortho-normal, separable, and energy compaction property. Although the DCT does not separate frequencies, it is a powerful signal decorrelator. It is a real valued function and thus can be effectively used in real-time operation. The advantages of DCT-LMS as compared to standard LMS algorithm are shown via SNR and eigenvalue ratio computations. . Exploiting the symmetry of the basis functions, the DCT transform matrix [AN] can be factored into a series of ±1 butterflies and rotation angles. This factorization results in one of the fastest DCT implementation. There are different ways to obtain factorizations. This work uses the fast factored DCT algorithm developed by Chen and company. The computer simulations results show superior convergence characteristics of the proposed algorithm by improving the SNR at least 10 dB for input SNR less than and equal to 0 dB, faster convergence speed and better time and frequency characteristics.

Keywords: Hearing Impairment, DCT Adaptive filter, Sensorineural loss patients, Convergence rate.

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Authors: H. El Ayech, A. Trabelsi

Abstract:

In this article we are going to discuss the improvement of the multi classes- classification problem using multi layer Perceptron. The considered approach consists in breaking down the n-class problem into two-classes- subproblems. The training of each two-class subproblem is made independently; as for the phase of test, we are going to confront a vector that we want to classify to all two classes- models, the elected class will be the strongest one that won-t lose any competition with the other classes. Rates of recognition gotten with the multi class-s approach by two-class-s decomposition are clearly better that those gotten by the simple multi class-s approach.

Keywords: Artificial neural network, letter-recognition, Multi class Classification, Multi Layer Perceptron.

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Authors: George Karkavitsas, Maria Rangoussi

Abstract:

We present a genetic algorithm application to the problem of object registration (i.e., object detection, localization and recognition) in a class of medical images containing various types of blood cells. The genetic algorithm approach taken here is seen to be most appropriate for this type of image, due to the characteristics of the objects. Successful cell registration results on real life microscope images of blood cells show the potential of the proposed approach.

Keywords: Genetic algorithms, object registration, pattern recognition, blood cell microscope images.

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Authors: Md. Zahangir Alom, Mei-Lan Piao, Md. Ashraful Alam, Nam Kim, Jae-Hyeung Park

Abstract:

A new approach for facial expressions recognition based on face context and adaptively weighted sub-pattern PCA (Aw-SpPCA) has been presented in this paper. The facial region and others part of the body have been segmented from the complex environment based on skin color model. An algorithm has been proposed to accurate detection of face region from the segmented image based on constant ratio of height and width of face (δ= 1.618). The paper also discusses on new concept to detect the eye and mouth position. The desired part of the face has been cropped to analysis the expression of a person. Unlike PCA based on a whole image pattern, Aw-SpPCA operates directly on its sub patterns partitioned from an original whole pattern and separately extracts features from them. Aw-SpPCA can adaptively compute the contributions of each part and a classification task in order to enhance the robustness to both expression and illumination variations. Experiments on single standard face with five types of facial expression database shows that the proposed method is competitive.

Keywords: Aw-SpPC, Expressoin Recognition, Face context, Face Detection, PCA

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Authors: Min Ah Kang, Sangbae Jeong, Minsoo Hahn

Abstract:

This paper presents the source extraction system which can extract only target signals with constraints on source localization in on-line systems. The proposed system is a kind of methods for enhancing a target signal and suppressing other interference signals. But, the performance of proposed system is superior to any other methods and the extraction of target source is comparatively complete. The method has a beamforming concept and uses an improved time-frequency (TF) mask-based BSS algorithm to separate a target signal from multiple noise sources. The target sources are assumed to be in front and test data was recorded in a reverberant room. The experimental results of the proposed method was evaluated by the PESQ score of real-recording sentences and showed a noticeable speech enhancement.

Keywords: Beam forming, Non-stationary noise reduction, Source separation, TF mask.

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Authors: J. K. Adedeji, M. O. Oyekanmi

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This paper has critically examined the use of Machine Learning procedures in curbing unauthorized access into valuable areas of an organization. The use of passwords, pin codes, user’s identification in recent times has been partially successful in curbing crimes involving identities, hence the need for the design of a system which incorporates biometric characteristics such as DNA and pattern recognition of variations in facial expressions. The facial model used is the OpenCV library which is based on the use of certain physiological features, the Raspberry Pi 3 module is used to compile the OpenCV library, which extracts and stores the detected faces into the datasets directory through the use of camera. The model is trained with 50 epoch run in the database and recognized by the Local Binary Pattern Histogram (LBPH) recognizer contained in the OpenCV. The training algorithm used by the neural network is back propagation coded using python algorithmic language with 200 epoch runs to identify specific resemblance in the exclusive OR (XOR) output neurons. The research however confirmed that physiological parameters are better effective measures to curb crimes relating to identities.

Keywords: Biometric characters, facial recognition, neural network, OpenCV.

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Authors: Julio Molleda, Daniel F. García, Juan C. Granda, Francisco J. Suárez

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One of the main trouble in a steel strip manufacturing line is the breakage of whatever weld carried out between steel coils, that are used to produce the continuous strip to be processed. A weld breakage results in a several hours stop of the manufacturing line. In this process the damages caused by the breakage must be repaired. After the reparation and in order to go on with the production it will be necessary a restarting process of the line. For minimizing this problem, a human operator must inspect visually and manually each weld in order to avoid its breakage during the manufacturing process. The work presented in this paper is based on the Bayesian decision theory and it presents an approach to detect, on real-time, steel strip defective welds. This approach is based on quantifying the tradeoffs between various classification decisions using probability and the costs that accompany such decisions.

Keywords: Classification, Pattern Recognition, ProbabilisticReasoning, Statistical Data Analysis.

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Authors: Muhammad Raza Ali, Tim Morris

Abstract:

Skin color is an important visual cue for computer vision systems involving human users. In this paper we combine skin color and optical flow for detection and tracking of skin regions. We apply these techniques to gesture recognition with encouraging results. We propose a novel skin similarity measure. For grouping detected skin regions we propose a novel skin region grouping mechanism. The proposed techniques work with any number of skin regions making them suitable for a multiuser scenario.

Keywords: Bayesian tracking, chromaticity space, optical flowgesture recognition

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Authors: N. A. Samat, D. F. Percy

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The main aim of this study is to describe and introduce a method of numerical analysis in obtaining approximate solutions for the SIR-SI differential equations (susceptible-infectiverecovered for human populations; susceptible-infective for vector populations) that represent a model for dengue disease transmission. Firstly, we describe the ordinary differential equations for the SIR-SI disease transmission models. Then, we introduce the numerical analysis of solutions of this continuous time, discrete space SIR-SI model by simplifying the continuous time scale to a densely populated, discrete time scale. This is followed by the application of this numerical analysis of solutions of the SIR-SI differential equations to the estimation of relative risk using continuous time, discrete space dengue data of Kuala Lumpur, Malaysia. Finally, we present the results of the analysis, comparing and displaying the results in graphs, table and maps. Results of the numerical analysis of solutions that we implemented offers a useful and potentially superior model for estimating relative risks based on continuous time, discrete space data for vector borne infectious diseases specifically for dengue disease. 

Keywords: Dengue disease, disease mapping, numerical analysis, SIR-SI differential equations.

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Authors: Sasadhar Bera, Indrajit Mukherjee

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Ant colony optimization (ACO) and its variants are applied extensively to resolve various continuous optimization problems. As per the various diversification and intensification schemes of ACO for continuous function optimization, researchers generally consider components of multidimensional state space to generate the new search point(s). However, diversifying to a new search space by updating only components of the multidimensional vector may not ensure that the new point is at a significant distance from the current solution. If a minimum distance is not ensured during diversification, then there is always a possibility that the search will end up with reaching only local optimum. Therefore, to overcome such situations, a Mahalanobis distance-based diversification with Nelder-Mead simplex-based search scheme for each ant is proposed for the ACO strategy. A comparative computational run results, based on nine nonlinear standard test problems, confirms that the performance of ACO is improved significantly with the integration of the proposed schemes in the ACO.

Keywords: Ant Colony Optimization, Diversification Scheme, Intensification, Mahalanobis Distance, Nelder-Mead Simplex.

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