IJARAI Volume 1 Issue 1

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Paper 1: A Method for Chinese Short Text Classification Considering Effective Feature Expansion

Abstract: This paper presents a Chinese short text classification method which considering extended semantic constraints and statistical constraints. This method uses “HowNet” tools to build the attribute set of concept. when coming to the part of feature expansion, we judge the collocation between the attribute words of original text and the characteristics before and after expansion as the semantic constraints, and calculate the ratio between the mutual information of the original contents and the features before expansion versus the mutual information of the original contents and the features after expansion as statistical constraints, so as to judge whether feature expansion is effective with this two constraints , then rationally use various semantic relation word-pairs in short text classification. Experiments show that this method can use semantic relations in Chinese short text classification effectively, and improve the classification performance.

Author 1: Mingxuan liu,
Author 2: Xinghua Fan 2

Keywords: component; short text; classification; semantic relations; semantic constraints; statistical constraints; HowNet.

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Paper 2: A Sparse Representation Method with Maximum Probability of Partial Ranking for Face Recognition

Abstract: Face recognition is a popular topic in computer vision applications. Compressive sensing is a novel sampling technique for finding sparse solutions to underdetermined linear systems. Recently, a sparse representation-based classification (SRC) method based on compressive sensing is presented. It has been successfully applied in face recognition. In this paper, we proposed a maximum probability of partial ranking method based on the framework of SRC, called SRC-MP, for face recognition. Eigenfiaces, fisherfaces, 2DPCA and 2DLDA are used for feature extraction. Experiments are implemented on two public face databases, Entended Yale B and ORL. In order to show our proposed method is robust for face recognition in the real world, experiment is also implemented on a web female album (WFA) face database. We utilize AdaBoost method to automatically detect human face from web album images with complex background, illumination variation and image misalignment to construct WFA database. Furthermore, we compare our proposed method with the classical projection-based methods such as principal component analysis (PCA), linear discriminant analysis (LDA), 2DPCA and 2DLDA. The experimental results demonstrate our proposed method not only is robust for varied viewing angles, expressions, and illumination, but also has higher recognition rates than other methods.

Author 1: Yi-Haur Shiau
Author 2: Chaur-Chin Chen

Keywords: Compressive sensing; Face recognition; Sparse representation classification; AdaBoost.

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Paper 3: Adaptive Neuro-Fuzzy Inference System for Dynamic Load Balancing in 3GPP LTE

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.

Author 1: Aderemi A Atayero,
Author 2: Matthew K. Luka

Keywords: ANFIS; 3GPP; LTE; Neural Network; Fuzzy Logic; Load balancing; Virtual load.

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Paper 4: Estimation of soil moisture in paddy field using Artificial Neural Networks

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 R2 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.

Author 1: Chusnul Arif
Author 2: Masaru Mizoguchi
Author 3: Masaru Mizoguchi
Author 4: Ryoichi Doi

Keywords: soil moisture; paddy field; estimation method; artificial neural networks

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Paper 5: Dynamic Decision Support System Based on Bayesian Networks

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).

Author 1: Hela Ltifi
Author 2: Ghada Trabelsi
Author 3: Mounir Ben Ayed
Author 4: Adel M. Alimi

Keywords: Dynamic Decision Support Systems; Nosocomial Infection; Bayesian Network.

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Paper 6: An Intelligent Location Management approaches in GSM Mobile Network

Abstract: Location management refers to the problem of updating and searching the current location of mobile nodes in a wireless network. To make it efficient, the sum of update costs of location database must be minimized. Previous work relying on fixed location databases is unable to fully exploit the knowledge of user mobility patterns in the system so as to achieve this minimization. The study presents an intelligent location management approach which has interacts between intelligent information system and knowledge-base technologies, so we can dynamically change the user patterns and reduce the transition between the VLR and HLR. The study provides algorithms are ability to handle location registration and call delivery.

Author 1: N Mallikharjuna Rao
Author 2: M. M Naidu2
Author 3: P.Seetharam

Keywords: Baste Station; MSC; HLR; VLR; IMEI; MT; Fuzzy Logic; Fuzzy databases.

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Paper 7: Modified Genetic Algorithms Based Solution To Subset Sum Problem

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.

Author 1: Harsh Bhasin
Author 2: Neha Singla

Keywords: subset sum problem; genetic algorithms; NP complete; heuristic search.

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Paper 8: Solving the Resource Constrained Project Scheduling Problem to Minimize the Financial Failure Risk

Abstract: In practice, a project usually involves cash in- and out-flows associated with each activity. This paper aims to minimize the payment failure risk during the project execution for the resource-constrained project scheduling problem (RCPSP). In such models, the money-time value, which is the product of the net cash in-flow and the time length from the completion time of each activity to the project deadline, provides a financial evaluation of project cash availability. The cash availability of a project schedule is defined as the sum of these money-time values associated with all activities, which is mathematically equivalent to the minimization objective of total weighted completion time. This paper presents four memetic algorithms (MAs) which differ in the construction of initial population and restart strategy, and a double variable neighborhood search algorithm for solving the RCPSP problem. An experiment is conducted to evaluate the performance of these algorithms based on the same number of solutions calculated using ProGen generated benchmark instances. The results indicate that the MAs with regret biased sampling rule to generate initial and restart populations outperforms the other algorithms in terms of solution quality.

Author 1: Zhi Jie Chen

Keywords: RCPSP; cash availability; memetic algorithms; variable neighborhood search

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Paper 9: Spatial Metrics based Landscape Structure and Dynamics Assessment for an emerging Indian Megalopolis

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.

Author 1: Ramachandra T V
Author 2: Bharath H. Aithal
Author 3: Sreekantha S

Keywords: Landscape Metrics; Urbanisation; Urban Sprawl; Remote sensing; Geoinformatics; Mysore City, India.

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Paper 10: ELECTRE-Entropy method in Group Decision Support System Modelto Gene Mutation Detection

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.

Author 1: Ermatita
Author 2: Sri Hartati
Author 3: Retantyo Wardoyo
Author 4: Agus Harjoko

Keywords: component; Group decision support system(GDSS); Multi Atributte Decision making(MADM); Electre-entropy; preference.

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Paper 11: The Solution of Machines’ Time Scheduling Problem Using Artificial Intelligence Approaches

Abstract: The solution of the Machines’ Time Scheduling Problem (MTSP) is a hot point of research that is not yet matured, and needs further work. This paper presents two algorithms for the solution of the Machines’ Time Scheduling Problem that leads to the best starting time for each machine in each cycle. The first algorithm is genetic-based (GA) (with non-uniform mutation), and the second one is based on particle swarm optimization (PSO) (with constriction factor). A comparative analysis between both algorithms is carried out. It was found that particle swarm optimization gives better penalty cost than GA algorithm and max-separable technique, regarding best starting time for each machine in each cycle.

Author 1: Ghoniemy S
Author 2: El-sawy A. A.
Author 3: Shohla M. A.
Author 4: Gihan E. H.  Ali

Keywords: Machine Time Scheduling; Particle swarm optimization; Genetic Algorithm; Time Window.

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Paper 12: The study of prescriptive and descriptive models of decision making

Abstract: The field of decision making can be loosely divided into two parts: the study of prescriptive models and the study of descriptive models. Prescriptive decision scientists are concerned with prescribing methods for making optimal decisions. Descriptive decision researchers are concerned with the bounded way in which the decisions are actually made. The statistics courses treat risk from a prescriptive, by suggesting rational methods. This paper brings out the work done by many researchers by examining the psychological factors that explain how managers deviate from rationality in responding to uncertainty.

Author 1: Ashok A Divekar
Author 2: Sunita Bangal
Author 3: Sumangala D.

Keywords: Expected Value; Prospect; expected-value rule; risk-averse.

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