• Journal of Multimedia Information System
• /
• v.5 no.3
• /
• pp.147-154
• /
• 2018

Natural language processing (NLP) is an emerging research area in which we study how machines can be used to perceive and alter the text written in natural languages. We can perform different tasks on natural languages by analyzing them through various annotational tasks like parsing, chunking, part-of-speech tagging and lexical analysis etc. These annotational tasks depend on morphological structure of a particular natural language. The focus of this work is part-of-speech tagging (POS tagging) on Hindi language. Part-of-speech tagging also known as grammatical tagging is a process of assigning different grammatical categories to each word of a given text. These grammatical categories can be noun, verb, time, date, number etc. Hindi is the most widely used and official language of India. It is also among the top five most spoken languages of the world. For English and other languages, a diverse range of POS taggers are available, but these POS taggers can not be applied on the Hindi language as Hindi is one of the most morphologically rich language. Furthermore there is a significant difference between the morphological structures of these languages. Thus in this work, a POS tagger system is presented for the Hindi language. For Hindi POS tagging a hybrid approach is presented in this paper which combines "Probability-based and Rule-based" approaches. For known word tagging a Unigram model of probability class is used, whereas for tagging unknown words various lexical and contextual features are used. Various finite state machine automata are constructed for demonstrating different rules and then regular expressions are used to implement these rules. A tagset is also prepared for this task, which contains 29 standard part-of-speech tags. The tagset also includes two unique tags, i.e., date tag and time tag. These date and time tags support all possible formats. Regular expressions are used to implement all pattern based tags like time, date, number and special symbols. The aim of the presented approach is to increase the correctness of an automatic Hindi POS tagging while bounding the requirement of a large human-made corpus. This hybrid approach uses a probability-based model to increase automatic tagging and a rule-based model to bound the requirement of an already trained corpus. This approach is based on very small labeled training set (around 9,000 words) and yields 96.54% of best precision and 95.08% of average precision. The approach also yields best accuracy of 91.39% and an average accuracy of 88.15%.

• Information Systems Review
• /
• v.5 no.1
• /
• pp.19-32
• /
• 2003

This paper develops a business model analysis framework and suggests a step-wise approach to the exploration of new business models. Three generic business model components, Value Change Model, Value Connection Model, and Value Complement Model, are identified based on their value propositions, and a composite of them, called Value Combination Model, is proposed. In order to integrate various value propositions from these business models, a business model analysis framework is also suggested. In order to test its real-world applicability, a case study is performed on the WLAN services and the effectiveness of the framework as a business model analysis tool is demonstrated.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2015.05a
• /
• pp.220-220
• /
• 2015

Water, energy, and food security already became a risk that threatens people around the world. Increasing of resources demand, rapid urbanization, decreasing of natural resources and climate change are four major problems inducing resources' scarcity. Indeed, water, energy, and food are interconnected each other thus cannot be analyzed separately. That is, for simple example, energy needs water as source for hydropower plant, water needs energy for distribution, and food needs water and energy for production, which is defined as W-E-F nexus. Due to their complicated linkage, it needs a computer model to simulate and analyze the nexus. Development of a computer simulation model using system dynamics approach makes this linkage possible to be visualized and quantified. System dynamics can be defined as an approach to learn the feedback connections of all elements in a complex system, which mean, every element's interaction is simulated simultaneously. Present W-E-F nexus models do not calculate and simulate the element's interaction simultaneously. Existing models only calculate the amount of water and energy resources that needed to provide food, water, or energy without any interaction from the product to resources. The new proposed model tries to cope these lacks by adding the interactions, climate change effect, and government policy to optimize the best options to maintain the resources sustainability. On this first phase of development, the model is developed only to learn and analyze the interaction between elements based on scenario of fulfilling the increasing of resources demand, due to population growth. The model is developed using the Vensim, well-known system dynamics model software. The results are amount of total water, energy, and food demand and production for a certain time period and it is evaluated to determine the sustainability of resources.

• Journal of Navigation and Port Research
• /
• v.34 no.7
• /
• pp.559-566
• /
• 2010

The container port industry has been variously studied by many researchers, because the contemporary container transportation and container port industries play a pivotal role in globalization of the world economy. For container terminals, the productivity, affected by many factors, is an important target in measuring container terminal performance. Under this background, finding the critical factors affecting the productivity is necessary. Regression analysis can be used to identify which independent variables are related to the dependent variable, and explore the relationships of them. The aim of paper is to evaluate the factors affecting the productivity of Chinese major terminals by using a regression statistical analysis modeling approach, which is to establish the variable preprocessing model (VPM) and regression analysis model (RAM), by means of collecting the major Chinese container terminals data in the year of 2008.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2003.05a
• /
• pp.262-269
• /
• 2003

Recently, a multi facility, multi product and multi period industrial problem has been widely investigated in Supply Chain Management (SCM). One of the key issues in the current SCM research area involved reducing both production and distribution costs. The purpose of this study is to determine the optimum quantity of production and transportation with minimum cost in the supply chain network. We have presented a mathematical model that deals with real world factors and constructs. Considering the complexity of solving such model, we have applied the genetic algorithm approach for solving this model computational experiments using a commercial genetic algorithm based optimizer. The results show that the real size problems we encountered can be solved In reasonable time

• Journal of the Korean Operations Research and Management Science Society
• /
• v.31 no.2
• /
• pp.129-140
• /
• 2006

Quantity Flexibility contract coordinates individually motivated supplier and buyer to the systemwide optimal outcome by effectively allocating the costs of market demand uncertainty. The main feature of the contract is to couple the buyer's commitment to purchase no less than a certain percentage below the forecast with the supplier's guarantee to deliver up to a certain percentage above. In this paper we refine the previous models by adding some realistic features including the upper and lower limits of the purchase. We also incorporate purchase and canceling costs in a cost function to reflect the real world contracting process more accurately. To obtain the solution of the model, we derive a condition for extreme points using the Leibniz's rule and construct an algorithm for finding the optimal solution of the model. Several examples illustrating the algorithm show that the approach is valid and efficient.

• Journal of the Korea Society for Simulation
• /
• v.12 no.1
• /
• pp.59-71
• /
• 2003

Recently, a multi facility, multi product and multi period industrial problem has been widely investigated in Supply Chain Management (SCM). One of the key issues in the current SCM research area involves reducing both production and distribution costs. The purpose of this study is to determine the optimum quantity of production and transportation with minimum cost in the supply chain network. We have presented a mathematical model that deals with real world factors and constraints. Considering the complexity of solving such model, we have applied the genetic algorithm approach for solving this model using a commercial genetic algorithm based optimizer. The results for computational experiments show that the real size problems we encountered can be solved in reasonable time.

• Industrial Engineering and Management Systems
• /
• v.9 no.4
• /
• pp.303-311
• /
• 2010

In the last few years growing interest has been dedicated to supply chain management. Modeling complexity is added to supply chain coordination problem by accounting for reverse logistics activities. The objective of this paper is to extend inventory model of manufacturing factory with respect to the production of raw material of forward logistics and recycling material of reverse logistics. The proposed model is applied to a plastic recycling process plant located in Taiwan. The case study improvement scheme shows when the recycling rate of recycling material increases from 15% to 50%, the total inventory cost of manufacturing factory decreases by 12.82%, safety stock volume decreases by 41.19% and the reorder quantity is down by 50.96%. This paper finds whether the results of the model can reach the economic profit through quantitative analysis and encourages companies integrate reverse logistics into the supply chain system.

• Asia pacific journal of information systems
• /
• v.1 no.1
• /
• pp.63-86
• /
• 1991

In this paper, our concern is the artificial neural network-based patten classification, when can resolve the difficulties in the Autoregressive Moving Average(ARMA) model identification problem To effectively classify a time series into an approriate ARMA model, we adopt the Multi-layered Backpropagation Network (MLBPN) as a pattern classifier, and Extended Sample Autocorrelation Function (ESACF) as a feature extractor. To improve the classification power of MLBPN's we suggest an integrated neural network system which consists of an AR Network and many small-sized MA Networks. The output of AR Network which will gives the MA order. A step-by-step training strategy is also suggested so that the learned MLBPN's can effectively ESACF patterns contaminated by the high level of noises. The experiment with the artificially generated test data and real world data showed the promising results. Our approach, combined with a statistical parameter estimation method, will provide a way to the automation of ARMA modeling.

• International Journal of Computer Science & Network Security
• /
• v.22 no.2
• /
• pp.131-138
• /
• 2022

Soft sensors are used to anticipate complicated model parameters using data from classifiers that are comparatively easy to gather. The goal of this study is to use artificial intelligence techniques to design and build soft sensors. The combination of a Long Short-Term Memory (LSTM) network and Grey Wolf Optimization (GWO) is used to create a unique soft sensor. LSTM is developed to tackle linear model with strong nonlinearity and unpredictability of manufacturing applications in the learning approach. GWO is used to accomplish input optimization technique for LSTM in order to reduce the model's inappropriate complication. The newly designed soft sensor originally brought LSTM's superior dynamic modeling with GWO's exact variable selection. The performance of our proposal is demonstrated using simulations on real-world datasets.