Статьи журнала - International Journal of Information Technology and Computer Science
Все статьи: 1195
A Knowledge-Based System for Life Insurance Underwriting
Статья научная
The purpose of this work is to enhance the life insurance underwriting process by building a knowledge-based system for life insurance underwriting. The knowledge-based system would be useful for organizations, which want to serve their clients better, promote expertise capture, retention, and reuse in the organization. The paper identifies the main input factors and output decisions that life insurance practitioners considered and made on a daily basis. Life underwriting knowledge was extracted through interviews in a leading insurance company in Kenya. The knowledge is incorporated into a knowledge-based system prototype designed and implemented, built to demonstrate the potential of this technology in life insurance industry. Unified modelling language and visual prolog language was used in the design and development of the prototype respectively. The system's knowledge base was populated with sample knowledge obtained from the life insurance company and results were generated to illustrate how the system is expected to function.
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A Literature Survey of Topology Control and Its Related Issues in Wireless Sensor Networks
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Issues of Topology control (TC) have captured more attentions in Wireless Sensor Networks (WSN). While WSN applications are normally optimized by the underlying network topology. Now a day’s WSNs is one of the most interesting areas of research and are universally being used and deployed or implements to monitor the surrounding physical environments. A number of approaches have been invested in wireless sensor networking, such as topology directed routing, sensor coverage based TC and network connectivity based TC. Many schemes have proved to be able to provide a better network monitoring and communication performance with prolonged system lifetime. In this survey paper, it provides a view of the studies in the area of WSN with different topology issues. By summarizing previous achievements and analyzing existed problems, we provide some idea within this field and also point out some research direction for the future.
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A power system load forecasting method using wavelet neural network with a process of decomposition-forecasting-reconstruction and error analysis based on SPSS is presented in this paper. First of all, the load sequence is decomposed by wavelet transform into each scale wavelet coefficients of navigation. In this step, choosing an appropriate wavelet function decomposition of load is needed. In this paper, by comparing the signal-to-noise ratio (SNR) and the mean square error (MSE) of the different wavelet functions for load after processing; It is concluded that the most suitable wavelet function for the load sequence in this paper is db4 wavelet function. The scale of wavelet coefficients is obtained by load wavelet decomposition. In the process of wavelet coefficient of processing, the db4 wavelet function is used to decompose the original sequence in 3 scales; High frequency and low frequency wavelet coefficient is got through setting threshold. Secondly, these wavelet coefficients are used as the training sample of the input to the nonlinear regression neural network for processing, and then the forecasting result is obtained by the wavelet reconstruction. Finally, the actual and forecasting values are compared by SPSS with a comprehensive statistical charting capability, which is able to draw beautiful charts and is easy to edit.
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A Machine Learning Approach for Sentiment Analysis Using Social Media Posts
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Sentiment analysis on Twitter provides organizations and persons with quick and effective instrument to observe the public's perceptions of them and their competition. A modest number of assessment datasets have been produced in recent years to check the efficiency of sentiment analysis algorithms on Twitter. Researchers offer a review of eight publicly accessible as well as manually annotated assessment datasets for analyzing Twitter sentiment in this research. As a result of this evaluation, we demonstrate that is a widespread weakness of many when using these datasets performing at sentiment analysis the objective (entity) level is indeed the absence of different sentiment classifications across tweets as well as the objects contained in them.[1], As an example all of that "I love my iPhone but I despise my iPad." Could be marked with a made-by-mixing classify however the object iPhone contained within this Twitter post should be annotated with just a label with an optimism. To get around this restriction and enhance existing assessment We have datasets that provide STS-Gold a novel assessment of datasets in which tweets or objects (entities) remain tagged separately hence might show alternative opinion labels. Though research furthermore compares the various datasets on multiple characteristics such as an entire quantity of posts as well as vocabulary size and sparsity.[2] In addition, look at pair by pair relationships between these variables and how they relate to sentiment classifier performance on various data. In this study we used five different classifiers and compared them and, in our experiment, we found that the bagging ensemble classifier performed best among them and have an accuracy level of 94.2% for the GASP dataset and 91.3% for the STS-Gold dataset.
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The inaccurate detection of diabetes and hypertension causes’ time wastage and a cost burden due to higher amounts of medicine intake and health problems. The previous works did not investigate machine learning (ML)-based diabetic and hypertension patient prediction by using multiple characteristics. This paper utilizes ML algorithms to predict the presence of diabetes and hypertension in patients. By analyzing patient data, including medical records, symptoms, and risk factors, the proposed system can provide accurate predictions for early detection and intervention. This paper makes a list of eighteen characteristics that can be used for data set preparation. With a classification accuracy of 93%, the Support Vector Machine is the best ML model in our work and is used for the diabetic and hypertension disease prediction models. This paper also gives a new mobile application that alleviates the time and cost burden by detecting diabetic and hypertensive patients, doctors, and medical information. The user evaluation and rating analysis results showed that more than sixty five percent of users declared the necessity of the proposed application features.
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Статья научная
Software reuse refers to the development of new software systems with the likelihood of completely or partially using existing components or resources with or without modification. Reusability is the measure of the ease with which previously acquired concepts and objects can be used in new contexts. It is a promising strategy for improvements in software quality, productivity and maintainability as it provides for cost effective, reliable (with the consideration that prior testing and use has eliminated bugs) and accelerated (reduced time to market) development of the software products. In this paper we present an efficient automation model for the identification and evaluation of reusable software components to measure the reusability levels (high, medium or low) of procedure oriented Java based (object oriented) software systems. The presented model uses a metric framework for the functional analysis of the Object oriented software components that target essential attributes of reusability analysis also taking into consideration Maintainability Index to account for partial reuse. Further machine learning algorithm LMNN is explored to establish relationships between the functional attributes. The model works at functional level rather than at structural level. The system is implemented as a tool in Java and the performance of the automation tool developed is recorded using criteria like precision, recall, accuracy and error rate. The results gathered indicate that the model can be effectively used as an efficient, accurate, fast and economic model for the identification of procedure based reusable components from the existing inventory of software resources.
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Digital watermarking can be used as an important tool for the security and copyright protection of digital multimedia content. The present paper explores its applications as a quality indicator of a watermarked medical image when subjected to intentional (noise, cropping, alteration) or unintentional (compression, transmission or filtering) operations. The watermark also carries EPR data along with a binary mark (used for quality assessment). The binary mark is used as a No-Reference (NR) quality metrics that blindly estimates the quality of an image without the need of original image. It is a semi-fragile watermark which degrades at around the same rate as the original image and thus gives an indication of the quality degradation of the host image at the receiving end. In the proposed method, the original image is divided into two parts- ROI and non-ROI. ROI is an area that contains diagnostically important information and must be processed without any distortion. The binary mark and EPR are embedded into the DCT domain of Non-ROI. Embedding EPR within a medical image reduces storage and transmission overheads and no additional file has to be sent along with an image. The watermark (binary mark and EPR) is extracted from non-ROI part at the receiving end and a measure of degradation of binary mark is used to estimate the quality of the original image. The performance of the proposed method is evaluated by calculating MSE and PSNR of original and extracted mark.
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A Memetic-Based Approach for Web-Based Question Answering
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In this paper we proposed an evolutionary approach for answering open-domain factoid questions, which include searching among sentences that are candidate for the final answer with Memetic Algorithm (MA), and using lexical and syntactic features for calculating fitness of the sentences. Our main purpose is making a search engine with accurate answering ability, or a web-based Question Answering (QA) system. The Text Retrieval Conference (TREC) QA Tracks data are used to develop and evaluate the approach. The answering process begins with retrieving related documents from a search engine. Then, MA searches among all the sentences of these documents and finds the best one. Finally, one or more words will be extracted based on our hand-made patterns. The results of different approaches for local search, mutation, and crossover, and also different values for number of reproduction and retrieved documents are investigated in the empirical study section. The results are promising with sufficient retrieved documents, and we have obtained a threshold value for this variable. Using MA instead of examining all the sentences is a trade-off between lowering the process time and sacrificing the accuracy, but the results show that the Mametic-based approach is more efficient.
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A Method of Movie Business Prediction Using Back-propagation Neural Network
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Film industry is the most important component of Entertainment industry. Profit and Loss both are very high for this business. Before release of a particular movie, if the Production House or distributors gets any type of prediction that how the film will do business, then it can be helpful to reduce the risk. In this paper we have proposed, back propagation neural network for prediction about the business of a movie. Note that, this method is successfully applied in the field of Stock Market Prediction, Weather Prediction and Image Processing.
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A Mobile-Based Computer Controller via Android Technology
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The evolution of mobile devices, especially in these modern days, has drastically changed the face of business. A mobile phone device is often expected to offer computer-like functionality. These days, most mobile phone users find it somehow inconvenient to do some tasks using their computers. Most individuals prefer to change positions while sitting, stretching, and also feeling a bit more comfortable when browsing through their computers. It can be very impractical to be confined to the keyboard and mouse while sitting 5 or 10 feet from the computer. Hence, the proposed application is meant to turn the hand phone into a wireless keyboard and mouse with a touch-pad, through the wireless network. This prototype is proven to be able to perform most of the actions a normal computer keyboard and mouse can perform.
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A Mobile-Based Fuzzy System for Diagnosing Syphilis (Sexually Transmitted Disease)
Статья научная
The high rate at which Africans die of syphilis yearly has been majorly attributed to the uneven ratio of the patients to competent medical practitioners who provide Medicare. This mortality rate has always drawn the attention of researchers and different approaches had been used to bring the rate down. This paper provides a software solution that personifies the expert-like way of providing diagnostic service to patients who suffer this disease. It is capable of making approximate diagnosis based on uncertainties. The system has been structured into five components: user interface, fuzzification, knowledge base, inference engine and defuzzification. The user interface uses a graphic user interface based method of human-computer interaction while the fuzzification component has transformed crisp quantities into fuzzy quantities using both interval-valued and S-curve membership functions. The reasoning has been achieved using root sum square (RSS) method and transformation of fuzzy values to scalar ones was through weighted average method. This system was tested and found effective.
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Статья научная
Task assignment is one of the most fundamental combinatorial optimization problems. Solving the Task Assignment Problem is very important for many real time and computational scenarios where a lot of small tasks need to be solved by multiple processors simultaneously. In this paper a Heuristic and Parallel Algorithm for Task Assignment Problem is proposed. Results obtained for certain cases are presented and compared with the optimal solutions obtained by already available algorithms. It is observed that the proposed algorithm works much faster and efficient than the existing algorithms .The paper also demonstrates how the proposed algorithm could be extended to multiple distributed processors.
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A Modified T-S Model Fuzzy Adaptive Control System Based on Genetic Algorithm
Статья научная
According to the characteristics of the nonlinear,long time-delays and time-variation in the MSG wastewater treatment system based on three-phase fluidized bed bioreactor(FBBR),amodified T-S model fuzzy adaptive control system based on genetic algorithm(GA)is presented in this paper.In the system,firstly using GA to optimize the membership functions,then reducing the dimension of fuzzy controller and simplifying the rules by an integral unit. Moreover, adopt a prediction method to compensate the time-delay of system, which based on the theory of fuzzy. Finally, the method is verified by experiments.Simulation experimental results show that the method is feasible and effective, which provides an effective approach to solve the problem of process control with long time-delays,large inertia and time-variation.
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A Mono Master Shrug Matching Algorithm for Examination Surveillance
Статья научная
This paper proposes an unusual slant for Shrug recognition from Gesticulation Penetrated Images (GPI) based on template matching. Shrugs can be characterized with image templates which are used to compare and match shrugs. The proposed technique makes use of a single template to identify match in the candidates and hence entitled as mono master shrug matching. It does not necessitate erstwhile acquaintance of movements, motion estimation or tracking. The proposed technique brands a unique slant to isolate various shrugs from a given video. Additionally, this method is based on the reckoning of feature invariance to photometric and geometric variations from a given video for the rendering of the shrugs in a lexicon. This descriptor extraction method includes the standard deviation of the gesticulation penetrated images of a shrug. The comparison is based on individual and rational actions with exact definitions varying widely uses histogram based tracker which computes the deviation of the candidate shrugs from the template shrug. Far-reaching investigation is done on a very intricate and diversified dataset to establish the efficacy of retaining the anticipated method.
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A Morphological Analyzer for Reduplicated Manipuri Adjectives and Adverbs: Applying Compile-Replace
Статья научная
Finite-state implementations naturally denote concatenations of morphemes and are limited to modeling concatenative morphotactics. The non-concatenative structure, such as reduplication, in the computational morphology of many world languages cannot be handled completely by finite-state technology. This paper describes the non-concatenative phenomena of reduplication, occurs in the adjective and adverb word classes of Manipuri language using the formalism of finite-state morphology tools and techniques. The discussion covers the non-concatenative nature and the challenges in capturing the various reduplication phenomena exhibited by the two classes; then present a morphological analyzer of the reduplicated adjectives and adverbs. It has been implemented using XFST and LEXC with the application of compile-replace algorithm to the morphotactics description of the language, which includes finite-state operations other than concatenation, to capture reduplication phenomena.
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A Multi-Objective Optimization of Cloud Based SLA-Violation Prediction and Adaptation
Статья научная
Monitoring of Cloud services is vital for both service providing organizations and consumers. The service providers need to maintain the quality of service to comply their services with the QoS parameters defined in SLA's such as response time, throughput, delay through continuous monitoring of services. The dynamic monitoring involves prediction of SLA violations and subsequent adaptation of the service compositions. The task of adaptation is in fact the task of discovering another plausible composition in the face of services recorded to have generated QoS violations. QoS- Driven Utility based service composition approach considers the individual user's priorities for QoS parameters and determines the overall utility measure of the service composition for the end user. In this work we present the problem of service composition adaptation as a multi-objective assignment optimization problem, which in turn is a NP-hard problem. The evolutionary algorithm GA with Tabu has been formulated as a Memetic and Pareto optimal approach for the adaptation problem and analyzed for efficiency in solving the problem.
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A Multi-objective Mathematical Model for Job Scheduling on Parallel Machines Using NSGA-II
Статья научная
In the current industrial world, Time and cost are two the most important concepts affecting whole our planning, activities and scheduling. Effective use of these factors, will lead to increasing performance and profit. Solving the parallel-machine problem is one of the basic and important problems in industrial and service delivery systems. In this paper, a new mathematical multi-objective linear programming model is proposed for scheduling the parallel machines to minimize the total make-span and total machines cost. The proposed model is implemented in Matlab using the NSGA-II approach and the results are compared with MOPSO approach. The computational results show the effectiveness and superiority of the proposed model.
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A Multidimensional Cascade Neuro-Fuzzy System with Neuron Pool Optimization in Each Cascade
Статья научная
A new architecture and learning algorithms for the multidimensional hybrid cascade neural network with neuron pool optimization in each cascade are proposed in this paper. The proposed system differs from the well-known cascade systems in its capability to process multidimensional time series in an online mode, which makes it possible to process non-stationary stochastic and chaotic signals with the required accuracy. Compared to conventional analogs, the proposed system provides computational simplicity and possesses both tracking and filtering capabilities.
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A New Algorithm for Voting in Social Networks
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Regarding to increasing spread of internet in recent years, social networks attracted the attention of many people. A voting system is a set of rules that a community adopts to take collective decision. In this paper we study representative democracy voting and introduce an algorithm for finding a committee who are participated in voting rather than entire social network. In this model we use community detection techniques in order to obtain parties, and D’Hondt rule to clarifying proportion of each party in committee. We finally use analysis links webpages algorithms for finding important users in social network.
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A New Ant Colony Optimization Algorithm Applied to Optimizing Centralized Wireless Access Network
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The wireless access networks design problem is formulated as a constrained optimization problem, where the goal is to find a network topology such that an objective function is optimized, subject to a set of constraints. The objective function may be the total cost, or some performance measure like utilization, call blocking or throughput. The constraints may be bounds on link capacities, cost elements, or some network performance measure. However, the optimization problem is too complex. In this paper, we propose a novel Ant Colony Optimization (ACO) algorithm to finding the total cost of connecting the BSs to the MSCs, and connecting the MSCs to the LE called by the optimal centralized wireless access network. Numerical results show that performance of our proposed algorithm is much better than previous studies.
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