Showing papers in "International Journal of Advanced Research in Artificial Intelligence in 2012"
TL;DR: In this article, an integrated approach of remote sensing and spatial metrics with gradient analysis was used to identify the trends of urban land changes in Mysore, Karnataka, India, where the spatial and temporal dynamic pattern of the urbanization process of the megalopolis region considering the spatial data for the last four decades with 3 km buffer from the city boundary has been studied, which help in the implementation of location specific mitigation measures.
Abstract: Human-induced land use changes are considered the prime agents of the global environmental changes. Urbanisation and associated growth patterns (urban sprawl) are characteristic of spatial temporal changes that take place at regional levels. Unplanned urbanization and consequent impacts on natural resources including basic amenities has necessitated the investigations of spatial patterns of urbanization. A comprehensive assessment using quantitative methods and methodological understanding using rigorous methods is required to understand the patterns of change that occur as human processes transform the landscapes to help regional land use planners to easily identify, understand the necessary requirement. Tier II cities in India are undergoing rapid changes in recent times and need to be planned to minimize the impacts of unplanned urbanisation. Mysore is one of the rapidly urbanizing traditional regions of Karnataka, India. In this study, an integrated approach of remote sensing and spatial metrics with gradient analysis was used to identify the trends of urban land changes. The spatial and temporal dynamic pattern of the urbanization process of the megalopolis region considering the spatial data for the ?ve decades with 3 km buffer from the city boundary has been studied, which help in the implementation of location specific mitigation measures. The time series of gradient analysis through landscape metrics helped in describing, quantifying and monitoring the spatial configuration of urbanization at landscape levels. Results indicated a signi?cant increase of urban built-up area during the last four decades. Landscape metrics indicates the coalescence of urban areas occurred during the rapid urban growth from 2000 to 2009 indicating the clumped growth at the center with simple shapes and dispersed growth in the boundary region with convoluted shapes.
40 citations
TL;DR: Simulation and experimental results show that performance of the fuzzy controller with FPGA in a maximum power tracking of a photovoltaic array can be made use of in several photvoltaic products and obtain satisfied result.
Abstract: The cell has optimum operating point to be able to get maximum power. To obtain Maximum Power from photovoltaic array, photovoltaic power system usually requires Maximum Power Point Tracking (MPPT) controller. This paper provides a small power photovoltaic control system based on fuzzy control with FPGA technology design and implementation for MPPT. The system composed of photovoltaic module, buck converter and the fuzzy logic controller implemented on FPGA for controlling on/off time of MOSFET switch of a buck converter. The proposed maximum power point tracking controller for photovoltaic system is tested using model designed by Matlab/Simulink program with graphical user interface (GUI) for entering the parameters of any array model using information from its datasheet, Simulation and experimental results show that performance of the fuzzy controller with FPGA in a maximum power tracking of a photovoltaic array can be made use of in several photovoltaic products and obtain satisfied result. Keywords-Fuzzy Control; MPPT; Photovoltaic System; FPGA.
39 citations
TL;DR: The aim of this research is to analyze humans fingerprint texture in order to determine their Age & Gender, and correlation of RTVTR and Ridge Count on gender detection and back-propagation neural network to train the stored fingerprint.
Abstract: The aim of this research is to analyze humans fingerprint texture in order to determine their Age & Gender, and correlation of RTVTR and Ridge Count on gender detection. The study is to analyze the effectiveness of physical biometrics (thumbprint) in order to determine age and gender in humans. An application system was designed to capture the finger prints of sampled population through a fingerprint scanner device interfaced to the computer system via Universal Serial Bus (USB), and stored in Microsoft SQL Server database, while back-propagation neural network will be used to train the stored fingerprint. The specific Objectives of this research are to: Use fingerprint sensor to collect different individual fingerprint, alongside their age and gender, Formulate a model and develop a fingerprint based identification system to determine age and gender of individuals and evaluate the developed system.
35 citations
TL;DR: The current study proposes an Artificial Neural Networks (ANN) model to estimate soil moisture in paddy field with limited meteorological data and indicated that tight linear correlations between observed and estimated values of soil moisture were observed.
Abstract: In paddy field, monitoring soil moisture is required for irrigation scheduling and water resource allocation, management and planning. The current study proposes an Artificial Neural Networks (ANN) model to estimate soil moisture in paddy field with limited meteorological data. Dynamic of ANN model was adopted to estimate soil moisture with the inputs of reference evapotranspiration (ETo) and precipitation. ETo was firstly estimated using the maximum, average and minimum values of air temperature as the inputs of model. The models were performed under different weather conditions between the two paddy cultivation periods. Training process of model was carried out using the observation data in the first period, while validation process was conducted based on the observation data in the second period. Dynamic of ANN model estimated soil moisture with R 2 values of 0.80 and 0.73 for training and validation processes, respectively, indicated that tight linear correlations between observed and estimated values of soil moisture were observed. Thus, the ANN model reliably estimates soil moisture with limited meteorological data.
34 citations
TL;DR: This approach corrects typographical errors like inserting, deleting, and permutation in the Arabic language and allows a finer and better scheduling than Levenshtein.
Abstract: In this paper, we present a new approach dedicated to correcting the spelling errors of the Arabic language. This approach corrects typographical errors like inserting, deleting, and permutation. Our method is inspired from the Levenshtein algorithm, and allows a finer and better scheduling than Levenshtein. The results obtained are very satisfactory and encouraging, which shows the interest of our new approach.
29 citations
TL;DR: A fuzzy logic based- temperature control system, which consists of a microcontroller, temperature sensor, and operational amplifier, Analogue to Digital Converter, display interface circuit and output interface circuit, which uses fuzzy logic technique to achieve a controlled temperature output function.
Abstract: Fuzzy logic technique is an innovative technology used in designing solutions for multi-parameter and non-linear control models for the definition of a control strategy. As a result, it delivers solutions faster than the conventional control design techniques. This paper thus presents a fuzzy logic based- temperature control system, which consists of a microcontroller, temperature sensor, and operational amplifier, Analogue to Digital Converter, display interface circuit and output interface circuit. It contains a design approach that uses fuzzy logic technique to achieve a controlled temperature output function.
28 citations
TL;DR: In this paper, a detailed exploration on Brain Computer Interface (BCI) and its recent trends has been done in this paper, and the authors also lay suitable directions for future work.
Abstract: Detailed exploration on Brain Computer Interface (BCI) and its recent trends has been done in this paper. Work is being done to identify objects, images, videos and their color compositions. Efforts are on the way in understanding speech, words, emotions, feelings and moods. When humans watch the surrounding environment, visual data is processed by the brain, and it is possible to reconstruct the same on the screen with some appreciable accuracy by analyzing the physiological data. This data is acquired by using one of the non-invasive techniques like electroencephalography (EEG) in BCI. The acquired signal is to be translated to produce the image on to the screen. This paper also lays suitable directions for future work.
22 citations
TL;DR: This paper classifies human gender in Spatial Temporal reasoning using CASIA Gait Database using Support Vector Machine as a Classifier, the classification result is 97.63% accuracy.
Abstract: Biometrics technology already becomes one of many application needs for identification. Every organ in the human body might be used as an identification unit because they tend to be unique characteristics. Many researchers had their focus on human organ biometrics physical characteristics such as fingerprint, human face, palm print, eye iris, DNA, and even behavioral characteristics such as a way of talk, voice and gait walking. Human Gait as the recognition object is the famous biometrics system recently. One of the important advantage in this recognition compare to other is it does not require observed subject's attention and assistance. This paper proposed Gender classification using Human Gait video data. There are many human gait datasets created within the last 10 years. Some databases that widely used are University of South Florida (USF) Gait Dataset, Chinese Academy of Sciences (CASIA) Gait Dataset, and Southampton University (SOTON) Gait Dataset. This paper classifies human gender in Spatial Temporal reasoning using CASIA Gait Database. Using Support Vector Machine as a Classifier, the classification result is 97.63% accuracy.
21 citations
TL;DR: This work uses dynamic Bayesian network (DBN) to model the dynamic aspect of the decision, result of the application of a Medical Decision Support System (MDSS), which has to make dynamic decision on temporal data.
Abstract: The improvement of medical care quality is a significant interest for the future years. The fight against nosocomial infections (NI) in the intensive care units (ICU) is a good example. We will focus on a set of observations which reflect the dynamic aspect of the decision, result of the application of a Medical Decision Support System (MDSS). This system has to make dynamic decision on temporal data. We use dynamic Bayesian network (DBN) to model this dynamic process. It is a temporal reasoning within a real-time environment; we are interested in the Dynamic Decision Support Systems in healthcare domain (MDDSS).
20 citations
TL;DR: A model based on Fuzzy Expert System is proposed which enables the classification of learning disability into its various types and facilitates in simulating conditions which are otherwise imprecisely defined.
Abstract: The endeavor of this work is to support the special education community in their quest to be with the mainstream. The initial segment of the paper gives an exhaustive study of the different mechanisms of diagnosing learning disability. After diagnosis of learning disability the further classification of learning disability that is dyslexia, dysgraphia or dyscalculia are fuzzy. Hence the paper proposes a model based on Fuzzy Expert System which enables the classification of learning disability into its various types. This expert system facilitates in simulating conditions which are otherwise imprecisely defined.
19 citations
TL;DR: In this article, a cumulative multi-niching GA (CMN GA) was proposed to accelerate optimization problems that have computationally-expensive multimodal objective functions by never discarding individuals from the population, which makes use of the information from every objective function evaluation as it explores the design space.
Abstract: This paper presents a cumulative multi-niching genetic algorithm (CMN GA), designed to expedite optimization problems that have computationally-expensive multimodal objective functions. By never discarding individuals from the population, the CMN GA makes use of the information from every objective function evaluation as it explores the design space. A fitness-related population density control over the design space reduces unnecessary objective function evaluations. The algorithm’s novel arrangement of genetic operations provides fast and robust convergence to multiple local optima. Benchmark tests alongside three other multi-niching algorithms show that the CMN GA has a greater convergence ability and provides an order-of-magnitude reduction in the number of objective function evaluations required to achieve a given level of convergence.
TL;DR: This paper focuses on Discrete Wavelet Transform associated with the K means clustering for efficient plant leaf image segmentation and results of proposed approach gives better convergence when compare to existing segmentation method.
Abstract: This paper focuses on Discrete Wavelet Transform (DWT) associated with the K means clustering for efficient plant leaf image segmentation. Segmentation is a basic pre-processing task in many image processing applications and essential to separate plant leafs from the background. Locating and segmenting plants from the background in an automated way is a common challenge in the analysis of plant images. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. Image segmentation is a fundamental task in agriculture computer graphics vision. Although many methods are proposed, it is still difficult to accurately segment an arbitrary image by one particular method. In recent years, more and more attention has been paid to combine segmentation algorithms and information from multiple feature spaces (e.g. color, texture, and pattern) in order to improve segmentation results .The performance of the segmentation is analyzed by Jaccard, dice, variation of index and global consistency error method. The proposed approach is verified with real time plant leaf data base. The results of proposed approach gives better convergence when compare to existing segmentation method.
TL;DR: This paper proposes the use of soft computing, precisely adaptive neuro-fuzzy inference system for dynamic QoS-aware load balancing in 3GPP LTE and three key performance indicators are used to adjust hysteresis task of load balancing.
Abstract: ANFIS is applicable in modeling of key parameters when investigating the performance and functionality of wireless networks. The need to save both capital and operational expenditure in the management of wireless networks cannot be over-emphasized. Automation of network operations is a veritable means of achieving the necessary reduction in CAPEX and OPEX. To this end, next generations networks such WiMAX and 3GPP LTE and LTE-Advanced provide support for self-optimization, self-configuration and self-healing to minimize human-to-system interaction and hence reap the attendant benefits of automation. One of the most important optimization tasks is load balancing as it affects network operation right from planning through the lifespan of the network. Several methods for load balancing have been proposed. While some of them have a very buoyant theoretical basis, they are not practically implementable at the current state of technology. Furthermore, most of the techniques proposed employ iterative algorithm, which in itself is not computationally efficient. This paper proposes the use of soft computing, precisely adaptive neuro-fuzzy inference system for dynamic QoS-aware load balancing in 3GPP LTE. Three key performance indicators (i.e. number of satisfied user, virtual load and fairness distribution index) are used to adjust hysteresis task of load balancing.
TL;DR: The intent is to develop a generic methodology to solve all NP Complete problems via GAs thus exploring their ability to find out the optimal solution from amongst huge set of solutions.
Abstract: Subset Sum Problem (SSP) is an NP Complete problem which finds its application in diverse fields. The work suggests the solution of above problem with the help of genetic Algorithms (GAs). The work also takes into consideration, the various attempts that have been made to solve this problem and other such problems. The intent is to develop a generic methodology to solve all NP Complete problems via GAs thus exploring their ability to find out the optimal solution from amongst huge set of solutions. The work has been implemented and analyzed with satisfactory results. Keywords-subset sum problem; genetic algorithms; NP complete; heuristic search.
TL;DR: Speech recognition of Hausa Language is presented in this paper and a pattern recognition neural network was used for developing the system.
Abstract: Speech recognition is a key element of diverse applications in communication systems, medical transcription systems, security systems etc. However, there has been very little research in the domain of speech processing for African languages, thus, the need to extend the frontier of research in order to port in, the diverse applications based on speech recognition. Hausa language is an important indigenous lingua franca in west and central Africa, spoken as a first or second language by about fifty million people. Speech recognition of Hausa Language is presented in this paper. A pattern recognition neural network was used for developing the system.
TL;DR: In this paper, a comparison of different data imputation approaches used in filling missing data and proposes a combined approach to estimate accurately missing attribute values in a patient database is presented, which is likely to supply a value closer to the one that is missing for effective classification and diagnosis.
Abstract: This paper presents a comparison of different data imputation approaches used in filling missing data and proposes a combined approach to estimate accurately missing attribute values in a patient database. The present study suggests a more robust technique that is likely to supply a value closer to the one that is missing for effective classification and diagnosis. Initially data is clustered and z-score method is used to select possible values of an instance with missing attribute values. Then multiple imputation method using LSSVM (Least Squares Support Vector Machine) is applied to select the most appropriate values for the missing attributes. Five imputed datasets have been used to demonstrate the performance of the proposed method. Experimental results show that our method outperforms conventional methods of multiple imputation and mean substitution. Moreover, the proposed method CZLSSVM (Clustered Z-score Least Square Support Vector Machine) has been evaluated in two classification problems for incomplete data. The efficacy of the imputation methods have been evaluated using LSSVM classifier. Experimental results indicate that accuracy of the classification is increases with CZLSSVM in the case of missing attribute value estimation. It is found that CZLSSVM outperforms other data imputation approaches like decision tree, rough sets and artificial neural networks, K-NN (K- Nearest Neighbour) and SVM. Further it is observed that CZLSSVM yields 95 per cent accuracy and prediction capability than other methods included and tested in the study.
TL;DR: This research built a Web Mapping and Dempster-Shafer theory system and reveals that Poultry Diseases Warning System has successfully identified the existence of poultry diseases and the maps can be displayed as the visualization.
Abstract: In this research, the researcher built a Web Mapping and Dempster-Shafer theory as an early warning system of poultry diseases. Early warning is the provision of timely and effective information, through identified institutions, that allows individuals exposed to a hazard to take action to avoid or reduce their risk and prepare for effective response. In this paper as an example we use five symptoms as major symptoms which include depression, combs, wattle, bluish face region, swollen face region, narrowness of eyes, and balance disorders. Research location is in the Lampung Province, South Sumatera. The researcher’s reason to choose Lampung Province in South Sumatera on the basis that has a high poultry population. Our approach uses Dempster-Shafer theory to combine beliefs in certain hypotheses under conditions of uncertainty and ignorance, and allows quantitative measurement of the belief and plausibility in our identification result. Web Mapping is also used for displaying maps on a screen to visualize the result of the identification process. The result reveal that Poultry Diseases Warning System has successfully identified the existence of poultry diseases and the maps can be displayed as the visualization.
TL;DR: This paper proposes ELECTRE-Entropy for GDSS Modeling and proposes entropy weighting for each criteria under ELECTRE Method, one method in Multi-Attribute Decision Making (MADM).
Abstract: Application of Group Decision Support System (GDSS) can assist for delivering the decision of various opinions (preference) cancer detection based on the preferences of various expertise. In this paper we propose ELECTRE-Entropy for GDSS Modeling. We propose entropy weighting for each criteria under ELECTRE Method.ELECTRE is one method in Multi-Attribute Decision Making (MADM). Modeling of Group Decision Support Sytemapplyfor multi-criteria which the simulation data mutated genes that can cause cancer and solution recommended.
TL;DR: The suitability of Gumbel Model for software reliability data is illustrated using likelihood based inferential procedure: classical as well as Bayesian.
Abstract: In this paper, we have illustrated the suitability of Gumbel Model for software reliability data. The model parameters are estimated using likelihood based inferential procedure: classical as well as Bayesian. The quasi Newton- Raphson algorithm is applied to obtain the maximum likelihood estimates and associated probability intervals. The Bayesian estimates of the parameters of Gumbel model are obtained using Markov Chain Monte Carlo(MCMC) simulation method in OpenBUGS(established software for Bayesian analysis using Markov Chain Monte Carlo methods). The R functions are developed to study the statistical properties, model validation and comparison tools of the model and the output analysis of MCMC samples generated from OpenBUGS. Details of applying MCMC to parameter estimation for the Gumbel model are elaborated and a real software reliability data set is considered to illustrate the methods of inference discussed in this paper. I. INTRODUCTION A frequently occurring problem in reliability analysis is model selection and related issues. In standard applications like regression analysis, model selection may be related to the number of independent variables to include in a final model. In some applications of statistical extreme value analysis, convergence to some standard extreme-value distributions is crucial. A choice has occasionally to be made between special cases of distributions versus the more general versions. In this chapter, statistical properties of a recently proposed distribution is examined closer and parameter estimation using maximum likelihood as a classical approach by R functions is performed where comparison is made to Bayesian approach using OpenBUGS. In reliability theory the Gumbel model is used to model the
TL;DR: The main objective of the paper is to come out with a set of measures to evaluate agent’s characteristics in particular the reactive property, so that the quality of an agent can be determined.
Abstract: Agent technology is meant for developing complex distributed applications. Software agents are the key building blocks of a Multi-Agent System (MAS). Software agents are unique in its nature as it possesses certain distinctive properties such as Pro-activity, Reactivity, Social-ability, Mobility etc., Agent's behavior might differ for same input at different cases and thus testing an agent and to evaluate the quality of an agent is a tedious task. Thus the measures to evaluate the quality characteristics of an agent and to evaluate the agent behavior are lacking. The main objective of the paper is to come out with a set of measures to evaluate agent's characteristics in particular the reactive property, so that the quality of an agent can be determined. Keywords-Software Agent; Multi-agent system; Software Testing.
TL;DR: The Agent based Mobile Airline Search and Booking System is been developed that is built to work on the Android to perform Airline search and booking using Biometric and also possess agent learning capability to perform the search of Airlines based on some previous search pattern.
Abstract: The world globalization is widely used, and there are several definitions that may fit this one word. However the reality remains that globalization has impacted and is impacting each individual on this planet. It is defined to be greater movement of people, goods, capital and ideas due to increased economic integration, which in turn is propelled, by increased trade and investment. It is like moving towards living in a borderless world. With the reality of globalization, the travel industry has benefited significantly. It could be said that globalization is benefiting from the flight industry. Regardless of the way one looks at it, more persons are traveling each day and are exploring several places that were distant places on a map. Equally, technology has been growing at an increasingly rapid pace and is being utilized by several persons all over the world. With the combination of globalization and the increase in technology and the frequency in travel there is a need to provide an intelligent application that is capable to meeting the needs of travelers that utilize mobile phones all over. It is a solution that fits in perfectly to a user’s busy lifestyle, offers ease of use and enough intelligence that makes a user’s experience worthwhile. Having recognized this need, the Agent based Mobile Airline Search and Booking System is been developed that is built to work on the Android to perform Airline Search and booking using Biometric. The system also possess agent learning capability to perform the search of Airlines based on some previous search pattern .The development been carried out using JADE-LEAP Agent development kit on Android.
TL;DR: A Genetic Algorithm (GA) based approach for optimising resource scheduling in the VCIM system is proposed and a case study is given to demonstrate the robustness of the proposed approach.
Abstract: The concept of Virtual Computer-Integrated Manufacturing (VCIM) has been proposed for one and a half decade with purpose of overcoming the limitation of traditional Computer-Integrated Manufacturing (CIM) as it only works within an enterprise. VCIM system is a promising solution for enterprises to survive in the globally competitive market because it can exploit effectively locally as well as globally distributed resources. In this paper, a Genetic Algorithm (GA) based approach for optimising resource scheduling in the VCIM system is proposed. Firstly, based on the latest concept of VCIM system, a class of resource scheduling problems in the system is modelled by using agent-based approach. Secondly, GA with new strategies of handling constraint, chromosome encoding, crossover and mutation is developed to search for optimal solution for the problem. Finally, a case study is given to demonstrate the robustness of the proposed approach.
TL;DR: This research focuses on the feature extraction and classification stage of Arabic Optical Character Recognition, being as important as the segmentation stage, and fusing the features of these methods to get the most representative feature vector that maximizes the recognition rate.
Abstract: Optical character recognition systems improve human-machine interaction and are urgently required for many governmental and commercial departments. A considerable progress in the recognition techniques of Latin and Chinese characters has been achieved. By contrast, Arabic Optical Character Recognition (AOCR) is still lagging although the interest and research in this area is becoming more intensive than before. This is because the Arabic is a cursive language, written from right to left, each character has two to four different forms according to its position in the word, and most characters are associated with complementary parts above, below, or inside the character. The process of Arabic character recognition passes through several stages; the most serious and error-prone of which are segmentation, and feature extraction & classification. This research focuses on the feature extraction and classification stage, being as important as the segmentation stage. Features can be classified into two categories; Local features, which are usually geometric, and Global features, which are either topological or statistical. Four approaches related to the statistical category are to be investigated, namely: Moment Invariants, Gray Level Co-occurrence Matrix, Run Length Matrix, and Statistical Properties of Intensity Histogram. The paper aims at fusing the features of these methods to get the most representative feature vector that maximizes the recognition rate.
TL;DR: In this paper, a mathematical optimization method that reduces inter-features redundancy and maximizes relevance between each feature and the target variable is proposed to reduce inter-feature redundancy and maximize relevance.
Abstract: Filter selection techniques are known for their simplicity and efficiency. However this kind of methods doesn’t take into consideration the features inter-redundancy. Consequently the un-removed redundant features remain in the final classification model, giving lower generalization performance. In this paper we propose to use a mathematical optimization method that reduces inter-features redundancy and maximize relevance between each feature and the target variable.
TL;DR: Through simulation study and experiments with real remote sensing satellite images, the proposed method is validated in comparison to the conventional simple GA and it is found that the proposed clustering method is useful for preprocessing of the classifications.
Abstract: Clustering method for remote sensing satellite image classification based on Messy Genetic Algorithm: GA is proposed. Through simulation study and experiments with real remote sensing satellite images, the proposed method is validated in comparison to the conventional simple GA. It is also found that the proposed clustering method is useful for preprocessing of the classifications.
TL;DR: The recognition of different hand gestures through machine learning approaches and principal component analysis shows a close to One-hundred per cent (100%) classification result for three given hand gestures.
Abstract: This paper deals with the recognition of different hand gestures through machine learning approaches and principal component analysis. A Bio-Medical signal amplifier is built after doing a software simulation with the help of NI Multisim. At first a couple of surface electrodes are used to obtain the Electro-Myo-Gram (EMG) signals from the hands. These signals from the surface electrodes have to be amplified with the help of the Bio-Medical Signal amplifier. The Bio- Medical Signal amplifier used is basically an Instrumentation amplifier made with the help of IC AD 620.The output from the Instrumentation amplifier is then filtered with the help of a suitable Band-Pass Filter. The output from the Band Pass filter is then fed to an Analog to Digital Converter (ADC) which in this case is the NI USB 6008.The data from the ADC is then fed into a suitable algorithm which helps in recognition of the different hand gestures. The algorithm analysis is done in MATLAB. The results shown in this paper show a close to One-hundred per cent (100%) classification result for three given hand gestures.
TL;DR: By training a back propagation neural network to identify attack patterns, possible attacks can be identified from design scenarios presented to it and the result of performance of the neural network is presented.
Abstract: Security flaws in software applications today has been attributed mostly to design flaws. With limited budget and time to release software into the market, many developers often consider security as an afterthought. Previous research shows that integrating security into software applications at a later stage of software development lifecycle (SDLC) has been found to be more costly than when it is integrated during the early stages. To assist in the integration of security early in the SDLC stages, a new approach for assessing security during the design phase by neural network is investigated in this paper. Our findings show that by training a back propagation neural network to identify attack patterns, possible attacks can be identified from design scenarios presented to it. The result of performance of the neural network is presented in this paper.
TL;DR: An overview of techniques for Nearest Neighbour classification is presented; mechanisms for finding distance between neighbours using Cosine Distance, Earth Movers Distance and formulas are used to identify nearest neighbours, algorithm for classification in training and testing for identifying Melakarta raagas in Carnatic music.
Abstract: It is through experience one could as certain that the classifier in the arsenal or machine learning technique is the Nearest Neighbour Classifier. Automatic melakarta raaga identification system is achieved by identifying the nearest neighbours to a query example and using those neighbours to determine the class of the query. This approach to classification is of particular importance today because issues of poor run-time performance are not such a problem these days with the computational power that is available. This paper presents an overview of techniques for Nearest Neighbour classification focusing on; mechanisms for finding distance between neighbours using Cosine Distance, Earth Movers Distance and formulas are used to identify nearest neighbours, algorithm for classification in training and testing for identifying Melakarta raagas in Carnatic music. From the derived results it is concluded that Earth Movers Distance is producing better results than Cosine Distance measure.
TL;DR: A new lane bypass algorithm has been developed for route diversion resulting in smooth traffic flow on the urban road networks and genetic algorithms are utilized for the parameter optimization.
Abstract: Traffic congestion in urban areas is a very critical problem and increasing day-by-day due to increment in number of vehicles and un-expandable traffic infrastructure. Several intelligent control systems have been developed to deal with this issue. In this paper, a new lane bypass algorithm has been developed for route diversion resulting in smooth traffic flow on the urban road networks. Genetic algorithms are utilized for the parameter optimization in this approach. Finally, the results of the proposed approach are found satisfactory.
TL;DR: In this article, the authors proposed an automated method to detect clustered micro calcification (MC) utilizing two main methods, multi-branch standard deviation analysis for clustered MC detection and surrounding region dependence method for individual MC detection.
Abstract: Breast cancer is the most frightening cancer for women in the world. The current problem that closely related with this issue is how to deal with small calcification part inside the breast called micro calcification (MC). As a preventive way, a breast screening examination called mammogram is provided. Mammogram image with a considerable amount of MC has been a problem for the doctor and radiologist when they should determine correctly the region of interest, in this study is clustered MC. Therefore, we propose to develop an automated method to detect clustered MC utilizing two main methods, multi-branches standard deviation analysis for clustered MC detection and surrounding region dependence method for individual MC detection. Our proposed method was resulting in 70.8% of classification rate, then for the sensitivity and specificity obtained 79% and 87%, respectively. The gained results are adequately promising to be more developed in some areas.