A Menu Planning Model Using Hybrid Genetic Algorithm And Fuzzy Reasoning: A Study On Malaysian Geriatric Cancer Patients

Dewasa ini, terdapat banyak model perancangan menu yang menyediakan nasihat umum kepada pelanggan di pasaran. Namun, penyelesaian yang dijana daripada model ini biasanya sangat subjektif dan sukar untuk diwakili secara sistematik. Oleh itu, pemakanan yang betul bagi warga tua adalah penting untuk mengekalkan kesihatan dan kesejahteraan. Kajian ini menghasilkan model perancangan menu berasaskan ontologi menggunakan algoritma genetik hibrid dan penaakulan kabur terhadap pesakit kanser geriatrik di Malaysia. Kajian ini adalah bertujuan untuk mengemukakan perwakilan pelan diet berdasarkan ontologi pelan diet; mereka bentuk enjin perancangan dengan mengintegrasikan algoritma genetik dengan pencarian setempat untuk memperbaiki pelan menu; membangunkan pelan menu untuk pesakit tersebut dengan menggunakan mekanisme penaakulan kabur. Dengan tujuan untuk merancang menu yang sihat kepada pesakit, ontologi digunakan untuk mengklasifikasikan nutrien, jenis makanan, struktur pemakanan dan profil peribadi. Selain itu, algoritma genetik hibrid (HGA) digunakan untuk memastikan bahawa perancangan menu dapat memenuhi semua objektif dan kekangan yang telah ditetapkan. Tambahan pula, kawalan logik kabur (FLC) diaplikasikan dalam pemodelan fungsi keahlian set kabur bagi menganggarkan keperluan pemakanan. Nowadays, there are many diet recommendation models in the market that provide general advice to the clients. However, the generated menu plan from these models are usually very subjective and difficult to be represented systematically. Thus, proper nutrition for the elderly is important to maintain health and well-being, which can lead to fulfilling and independent lives. This research presents a study on ontology-based menu planning model using hybrid genetic algorithm and fuzzy reasoning for Malaysian geriatric cancer patients. The proposed work aims to produce a diet plan representation based on diet plan ontology; design a planning engine by integrating genetic algorithm with local search technique to enhance menu planning; and develop a menu planning approach to cater for Malaysian geriatric cancer patients using fuzzy reasoning mechanism. With the aim of planning healthy menu to patients, ontology is used to classify nutrients, food groups, meal structure and personal profile. Following that, hybrid genetic algorithm (HGA) is employed to ensure that the constructed menu satisfies all the objectives and predefined constraints. Furthermore, a fuzzy logic control (FLC) was applied in the modeling of membership functions of fuzzy sets for estimating nutrition needs

Adopting attention and cross-layer features for fine-grained representation

Fine-grained visual classification (FGVC) is challenging task due to discriminative feature representations. The attention-based methods show great potential for FGVC, which neglect that the deeply digging inter-layer feature relations have an impact on refining feature learning. Similarly, the associating cross-layer features methods achieve significant feature enhancement, which lost the long-distance dependencies between elements. However, most of the previous researches neglect that these two methods are mutually correlated to reinforce feature learning, which are independent of each other in related models. Thus, we adopt the respective advantages of the two methods to promote fine-gained feature representations. In this paper, we propose a novel CLNET network, which effectively applies attention mechanism and cross-layer features to obtain feature representations. Specifically, CL-NET consists of 1) adopting self attention to capture long-rang dependencies for each element, 2) associating cross-layer features to reinforce feature learning, and 3) to cover more feature regions, we integrate attention-based operations between output and input. Experiments verify that CLNET yields new state-of-the-art performance on three widely used fine grained benchmarks, including CUB-200-2011, Stanford Cars and FGVC-Aircraft. The url of our code is https://github.com/dlearing/CLNET.git

A New Hybrid Approach Based On Discrete Differential Evolution Algorithm To Enhancement Solutions Of Quadratic Assignment Problem

The Combinatorial Optimization Problem (COPs) is one of the branches of applied mathematics and computer sciences, which is accompanied by many problems such as Facility Layout Problem (FLP), Vehicle Routing Problem (VRP), etc. Even though the use of several mathematical formulations is employed for FLP, Quadratic Assignment Problem (QAP) is one of the most commonly used. One of the major problems of Combinatorial NP-hard Optimization Problem is QAP mathematical model. Consequently, many approaches have been introduced to solve this problem, and these approaches are classified as Approximate and Exact methods. With QAP, each facility is allocated to just one location, thereby reducing cost in terms of aggregate distances weighted by flow values. The primary aim of this study is to propose a hybrid approach which combines Discrete Differential Evolution (DDE) algorithm and Tabu Search (TS) algorithm to enhance solutions of QAP model, to reduce the distances between the locations by finding the best distribution of N facilities to N locations, and to implement hybrid approach based on discrete differential evolution (HDDETS) on many instances of QAP from the benchmark. The performance of the proposed approach has been tested on several sets of instances from the data set of QAP and the results obtained have shown the effective performance of the proposed algorithm in improving several solutions of QAP in reasonable time. Afterwards, the proposed approach is compared with other recent methods in the literature review. Based on the computation results, the proposed hybrid approach outperforms the other method

Quadratic Assignment Problem (Model, Applications, Solutions): Review Paper

n operations research, Quadratic Assignment Problem (QAP) is a significant combinatorial optimization problem. When the size of the QAP problem increases, it becomes impossible to solve the problem in polynomial time. Several practical problems such as hospital and campus layout, allocation of gates to airplanes in airports and electrical backboard wiring problems can bemodeled as QAP. The QAP model seeks to identify the optimal distribution of N facilities to N locations in a way that minimizes the total traveling cost based on the distance between every pair of a location and the amount of traffic between every pair of facilities of organizational units within a building. Against this background, there are two main approaches have been suggested to deal with QAP, and they are, the Exact and Approximate (Heuristic and Metaheuristic) approaches. The exact approach provides a global optimal solution for the small size of QAP, while the approximate approaches can find the optimal or a near-optimal solution at a reasonable time for large-sized QAP. The objectives of this study are as follows: (i) To analysis the QAP model, (ii) To conduct a comprehensive survey of the methods that have been used to solve the QAP model, (iii) To identify the issues and limitations of the methods in (ii), and (iv) to explore the best approach that can be used in enhancing the solutions of QAPmodel within a reasonable time based on the accuracy of algorithm. The results show that the hybrid metaheuristic approach has the capability of finding the best results within a reasonable time for the large sized problem

Classification of wood defect images using local binary pattern variants

This paper presents an analysis of the statistical texture representation of the Local Binary Pattern (LBP) variants in the classification of wood defect images. The basic and variants of the LBP feature set that was constructed from a stage of feature extraction processes with the Basic LBP, Rotation Invariant LBP, Uniform LBP, and Rotation Invariant Uniform LBP. For significantly discriminating, the wood defect classes were further evaluated with the use of different classifiers. By comparing the results of the classification performances that had been conducted across the multiple wood species, the Uniform LBP was found to have demonstrated the highest accuracy level in the classification of the wood defects

Identification of wood defect using pattern recognition technique

This study proposed a classification model for timber defect classification based on an artificial neural network (ANN). Besides that, the research also focuses on determining the appropriate parameters for the neural network model in optimizing the defect identification performance, such as the number of hidden layers nodes and the number of epochs in the neural network. The neural network's performance is compared with other standard classifiers such as Naïve Bayes, K-Nearest Neighbours, and J48 Decision Tree in finding their significant differences across the multiple timber species. The classifier's performance is measured based on the F-measure due to the imbalanced dataset of the timber species. The experimental results show that the proposed classification model based on the neural network outperforms the other standard classifiers in detecting many types of defects across multiple timber species with an F-measure of 84.01%. This research demonstrates that ANN can accurately classify the defects across multiple species while defining appropriate parameters (hidden layers and epochs) for the neural network model in optimizing defect identification performance

A Review On Automatic Text Summarization Approaches

It has been more than 50 years since the initial investigation on automatic text summarization was started.Various techniques have been successfully used to extract the important contents from text document to represent document summary.In this study,we review some of the studies that have been conducted in this still-developing research area.It covers the basics of text summarization,the types of summarization,the methods that have been used and some areas in which text summarization has been applied.Furthermore,this paper also reviews the significant efforts which have been put in studies concerning sentence extraction,domain specific summarization and multi document summarization and provides the theoretical explanation and the fundamental concepts related to it.In addition,the advantages and limitations concerning the approaches commonly used for text summarization are also highlighted in this study

Evaluation of texture feature based on basic local binary pattern for wood defect classification

Wood defects detection has been studied a lot recently to detect the defects on the wood surface and assist the manufacturers in having a clear wood to be used to produce a high-quality product. Therefore, the defects on the wood affect and reduce the quality of wood. This research proposes an effective feature extraction technique called the local binary pattern (LBP) with a common classifier called Support Vector Machine (SVM). Our goal is to classify the natural defects on the wood surface. First, preprocessing was applied to convert the RGB images into grayscale images. Then, the research applied the LBP feature extraction technique with eight neighbors (P=8) and several radius (R) values. After that, we apply the SVM classifier for the classification and measure the proposed technique's performance. The experimental result shows that the average accuracy achieved is 65% on the balanced dataset with P=8 and R=1. It indicates that the proposed technique works moderately well to classify wood defects. This study will consequently contribute to the overall wood defect detection framework, which generally benefits the automated inspection of the wood defects

Review On The Methods To Solve Combinatorial Optimization Problems Particularly:Quadratic Assignment Model

The quadratic assignment problem (QAP) is one of the fundamental combinatorial optimization problem (COPs) in the branch of optimization or operation research in mathematics,from the category of the Facilities Location Problems (FLPs).The quadratic assignment problem (QAP) be appropriate to the group of NP-hard issues and is measured as a challenging problem of the combinatorial optimization.QAP in Location Theory considers one of the problems of facilities tracing which the rate of locating a facility be determined by the spaces between facilities as well as the communication among the further facilities.QAP was presented in 1957 by Beckman and Koopmans as they were attempting to model a problem of facilities location.To survey the researcher’s works for QAP and applied,the mapped research landscape outlines literature into a logical classification and discovers this field basic characteristics represented on the motivation to use the quadratic assignment problem applied in hospital layout and campus planning.This survey achieved a concentrated each QAP article search in three key databases:Web of Science,Science Direct,and IEEE Xplore.Those databases are regarded extensive adequate in covering QAP and the methods utilized in solving QAP

A Pipeline To Data Preprocessing For Lipreading And Audio-Visual Speech Recognition

Studies show that only about 30 to 45 percent of English language can be understood by lipreading alone. Even the most talented lip readers are unable to collect a complete message based on lipreading only, although they are often very good at interpreting facial features, body language, and context to find out. As you can imagine, this technique affects the brain in different ways and becomes exhausting over a period of time. If a person who is deaf, uses language and is able to read lips, hearing people may not understand the challenges they are facing just to have a simple one-on-one conversation. The hearing person may be annoyed that they are often asked to repeat themselves or to speak more slowly and clearly. They could lose patience and break off the conversation. In our modern world, where technology connects us in a way never thought possible, there are a variety of ways to communicate with another person. Deaf people come from all walks of life and with different backgrounds. In this study, a lipreading model is being developed that is able to record, analyze, translate the movement of lips and display them into subtitles. A model is trained with GRID Corpus, MIRACL-VC1 and pre-trained dataset and with the LipNet model to build a system which deaf people can decode text from the movement of a speaker’s mouth. This system will help the deaf people understand what others are actually saying and communicate more effectively. As a conclusion, this system helps deaf people to communicate effectively with others