Second International Conference on Artificial Intelligence and Fuzzy Logic Systems (AIFZ 2016)
, April 2~3, 2016, Chennai, India
Selection of Best Alternative in Manufacturing and Service Sector Using Multi grade Decision Approach - a Review
Karuppanna Prasad N
and Sekar K
Pricol India Ltd, India and
National Institute of Technology, India
Modern manufacturing organizations tend to face versatile challenges due to globalization, modern lifestyle trends and rapid market requirements from both locally and globally placed competitors. The organizations faces high stress from dual perspective namely enhancement in science and technology and development of modern strategies. In such an instance, organizations were in a need of using an effective decision making tool that chooses out optimal alternative within a shorter period of time. This paper explores a usage of MOORA an effective decision making tool used for selecting best alternatives in cumbersome circumstance. The usage of MOORA was studied up in two fold manner initially MOORA had been compared with other MCDM and MADM approaches to identify its advantage for selecting optimal alternative. On other side, the paper highlights scope and gap of using MOORA approach through intensify examining of case studies. Examination reveals an existence of huge scope in using MOORA for other manufacturing and service applications.
Neuro Fuzzy Controller Design for Linear Inverted Pendulum
Meenakshi R and Sravan Bharadwaj C, VIT University, India
In this paper, artificial intelligence method is used for designing a robust controller for the single stage linear inverted pendulum system using neural networks and fuzzy logic algorithm technique. The controller is designed for stabilizing the pendulum in the inverted position. A comparative study of the performance of the system with neuro fuzzy controller with the classical PID controller and pole placement controller is presented.
OCR-The 3 Layered Approach for Decision Making State and Identification of Telugu Hand Written and Printed Consonants and Conjunct Consonants by Using Advanced Fuzzy Logic Controller
B.Rama and Santosh Kumar Henge, Kakatiya University, India
Optical Character recognition is the method of digitalization of hand and type written or printed text into machine-encoded form and is superfluity of the various applications of envision of human°«s life. In present human life OCR has been successfully using in finance, legal, banking, health care and home need appliances. India is a multi cultural, literature and traditional scripted country. Telugu is the southern Indian language, it is a syllabic language, symbol script represents a complete syllable and formed with the conjunct mixed consonants in their representation. Recognition of mixed conjunct consonants is critical than the normal consonants, because of their variation in written strokes, conjunct maxing with pre and post level of consonants. This paper proposes the layered approach methodology to recognize the characters, conjunct consonants, mixed- conjunct consonants and expressed the efficient classification of the hand written and printed conjunct consonants. This paper implements the Advanced Fuzzy Logic system controller to take the text in the form of written or printed, collected the text images from the scanned file, digital camera, Processing the Image with Examine the high intensity of images based on the quality ration, Extract the image characters depends on the quality then check the character orientation and alignment then to check the character thickness, base and print ration. The input image characters can classify into the two ways, first way represents the normal consonants and the second way represents conjunct consonants. Digitalized image text divided into three layers, the middle layer represents normal consonants and the top and bottom layer represents mixed conjunct consonants. Here recognition process starts from middle layer, and then it continues to check the top and bottom layers. The recognition process treat as conjunct consonants when it can detect any symbolic characters in top and bottom layers of present base character otherwise treats as normal consonants. The post processing technique applied to all three layered characters. Post processing of the image: concentrated on the image text readability and compatibility, if the readability is not process then repeat the process again. In this recognition process includes slant correction, thinning, normalization, segmentation, feature extraction and classification. In the process of development of the algorithm the pre-processing, segmentation, character recognition and post-processing modules were discussed. The main objectives to the development of this paper are: To develop the classification, identification of deference prototyping for written and printed consonants, conjunct consonants and symbols based on 3 layered approaches with different measurable area by using fuzzy logic and to determine suitable features for handwritten character recognition.
An Intelligent Approach to Predict Flow Discharge in River Chenab Pakistan
Tanzila Saba, Prince Sultan University Riyadh KSA
River water flow forecast in general and particularly in floods is of worth importance for monitoring operations of floods in canals and rivers. The River Chenab is one of the largest rivers in Pakistan and has a historical recording of heavy floods. Due to heavy floods, in time warning is mandatory to save lives and property. Accordingly, this paper presents an intelligent model to predict in advance an alarming water flow from Chenab River. ANN using standard learning algorithm is trained for this task. Additionally, in order to avoid over fitting problem, a cross validation method is applied. Inputs to the neural network are taken from the daily discharge values and the output layer composed of three neurons to represent number of predicted days. Moreover, trial and error approach is adopted to select appropriate number of inputs for time-series data. Two architecture of neural network are evaluated and compared to find the most suitable one. The results thus achieved reveal well in time warning to the surroundings to secure flood victims. However, during low discharge, neural network miscalculates.
An Approach to NLP to Facilitate NLG
Akshay Tambe, Noble Varghese, Sachin Kamath and Umesh Kulkarni, Vidyalankar College Marg, India
Artificial Intelligence is a term commonly used in the media, to describe research that aims to develop software and hardware with cognitive abilities similar to those of the human brain. This project is a research and development project in which we are working to enhance the Natural Language Processing (NLP) of a machine that can interpret, think, process and even learn from the experiences like a human. Based on this acquired knowledge, the machine can be made capable to generate an appropriate response with the desired outcome i.e. Natural Language Generation (NLG). Deep research work is required for the machine to think and store the information like a human brain. The project mainly focuses on storing information about words, processing the natural language input from the user, resolve its meaning or intended action and using it to communicate like humans. In every natural language, words are inter-dependent and contribute in defining each other. The links between words can be created, updated or deleted as per the requirements and understanding of the machine towards the meaning of it in real life and reacting accordingly.
Segmentation and Labelling of Human Spine MR Images Using Fuzzy Clustering
Jiyo.S.Athertya and G.Saravana Kumar, IIT-Madras, India
Computerized medical image segmentation is a challenging area because of poor resolution and weak contrast. The predominantly used conventional clustering techniques and the thresholding methods suffer from limitations owing to their heavy dependence on user interactions. Uncertainties prevalent in an image cannot be captured by these techniques. The performance further deteriorates when the images are corrupted by noise, outliers and other artifacts. The objective of this paper is to develop an effective robust fuzzy C- means clustering for segmenting vertebral body from magnetic resonance images. The motivation for this work is that spine appearance, shape and geometry measurements are necessary for abnormality detection and thus proper localisation and labelling will enhance the diagnostic output of a physician. The method is compared with Otsu thresholding and K-means clustering to illustrate the robustness. The reference standard for validation was the annotated images from the radiologist, and the Dice coefficient and Hausdorff distance measures were used to evaluate the segmentation.
Mining Fuzzy Association Rules from Web Usage Quantitative Data
Ujwala Manoj Patil and J.B.Patil, R.C.P.I.T, India
Web usage mining is the method of extracting interesting patterns from Web usage log file. Web usage mining is subfield of data mining uses various data mining techniques to produce association rules. Data mining techniques are used to generate association rules from transaction data. Most of the time transactions are Boolean transactions, whereas Web usage data consists of quantitative values. To handle these real world quantitative data we used fuzzy data mining algorithm for extraction of association rules from quantitative Web log file. To generate fuzzy association rules first we designed membership function. This membership function is used to transform quantitative values into fuzzy terms. Experiments are carried out on different support and confidence. Experimental results show the performance of the algorithm with varied supports and confidence.
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