scholarly journals IMPROVED SYSTEM FOR RETRIEVAL OF COLOR LOGO IMAGES USING PSO, SOM AND RELEVANCE FEEDBACK TECHNIQUE

2021 ◽  
Vol 9 (1) ◽  
pp. 634-639
Author(s):  
Latika Pinjarkar, Manisha Sharma, Smita Selot
Keyword(s):  
High Precision ◽  
Relevance Feedback ◽  
New Technology ◽  
Self Organizing Map ◽  
Low Level ◽  
The Difference ◽  
Rf Performance ◽  
High Level ◽  
Self Organizing

The colour logo identification has one of the key problems of bridging the difference between low-level characteristics and high-level semantics, as the consumer wants to. Relevance feedback (RF) along with self-organizing map (SOM) have been successfully introduced to resolve this void. However, the efficiency of the automated map (SOM) based RF is always low when the feedback sample is limited. This paper proposed a new technology, namely the SOM-SOM-RF that combines SOM-based RF with warm particle optimization, to boost RF performance (PSO). The aim of this proposed technology is to increase SOM-based RF efficiency and also to minimise user encounters with the device by reducing its RF number. On 3000 colour logo pictures, the PSO-SOM-RF was tested. The findings from the tests revealed the high precision of the proposed PSO-SOM-RF.  

scholarly journals Static Profiling of Assembly Code Performance and Optimization Effectiveness using Instructions Performed and Program Latency

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1463-1468
Keyword(s):  
Assembly Language ◽  
Optimization Method ◽  
Program Optimization ◽  
Low Level ◽  
Code Performance ◽  
Execution Speed ◽  
The Difference ◽  
And Performance ◽  
High Level ◽  
Optimization And Performance

Software program optimization for improved execution speed can be achieved through modifying the program. Programs are usually written in high level languages then translated into low level assembly language. More coverage of optimization and performance analysis can be performed on low level than high level language. Optimization improvement is measured in the difference in program execution performance. Several methods are available for measuring program performance are classified into static approaches and dynamic approaches. This paper presents an alternative method of more accurately measuring code performance statically than commonly used code analysis metrics. New metrics proposed are designed to expose effectiveness of optimization performed on code, specifically unroll optimizations. An optimization method, loop unroll is used to demonstrate the effectiveness of the increased accuracy of the proposed metric. The results of the study show that measuring Instructions Performed and Instruction Latency is a more accurate static metric than Instruction Count and subsequently those based on it.

DISCOVERING STOCK TRADING PREFERENCES BY SELF-ORGANIZING MAPS AND DECISION TREES

2009 ◽  
Vol 18 (04) ◽  
pp. 603-611 ◽  
Author(s):  
CHIH-FONG TSAI ◽  
YUAH-CHIAO LIN ◽  
YI-TING WANG
Keyword(s):  
Individual Investors ◽  
Self Organizing Map ◽  
Tree Model ◽  
Stock Trading ◽  
Self Organizing Maps ◽  
Two Factors ◽  
Group A ◽  
Trading Activities ◽  
High Level ◽  
Self Organizing

Stock trading activities are always very popular in many countries. Generally, investors with various backgrounds have different preferences over the stocks they trade. In literature, a number of studies examine the institutions' holding preferences for certain stock characteristics when choosing the security portfolio. However, very few studies investigate the stock trading preferences of individual investors. In this paper, we focus on two factors which affect the portfolio choices of investors, which are stock characteristics and investor features. In particular, a self-organizing map (SOM) is used to group a certain number of clusters based on a chosen dataset. Then, the decision tree model is used to extract useful rules from the clusters which contain the most trading records in the sample. We find that if the investors are females, less wealthy, and make stock trades with lower frequencies, they will be more careful and conservative. On the other hand, if the investors are males, having a high level of wealth, and make stock trades very often, they tend to choose stocks with high EPS, high market-to-book, and high prices.

THE LEED-COMMERCIAL INTERIORS (V4) PROJECTS IN CALIFORNIA

2021 ◽  
Vol 16 (3) ◽  
pp. 57-72
Author(s):  
Svetlana Pushkar
Keyword(s):  
Green Building ◽  
Nonparametric Tests ◽  
Indoor Environmental Quality ◽  
Positive Correlation ◽  
Low Level ◽  
Impact Reduction ◽  
Environmental Product Declarations ◽  
The Difference ◽  
High Level ◽  
Building Standards

ABSTRACT This study analyzed the Leadership in Energy and Environmental Design for Commercial Interior Certified, Silver, and Gold projects version 4.0 (LEED-CIv4) in California for the period 2015‒2020. Nonparametric tests were used to assess category and credit achievement, i.e., the difference between possible and achieved points and the correlation between associated credits. The results show that most of the credits in the location and transportation category and the indoor environmental quality categories had a high level of achievement, a few credits in the energy and atmosphere and materials and resources categories had a high level of achievement, and all of the credits in the water efficiency category had a low level of achievement. Some associated credits, such as surrounding density and quality transit, had a high level of achievement and a positive correlation, whereas other associated credits, such as life-cycle impact reduction and environmental product declarations, had a low level of achievement and a positive correlation. If LEED-CIv4 credits meet the requirements of the California Green Building Standards Code 2016 (CGBSC 2016), then these credits typically have a medium/high level of achievement. If LEED-CIv4 credits exceed the requirements of CGBSC 2016, then these credits have a low level of achievement. Therefore, to improve the next version of LEED-CI, it is necessary to improve the local green codes.

scholarly journals S-SOM v1.0: a structural self-organizing map algorithm for weather typing

2021 ◽  
Vol 14 (4) ◽  
pp. 2097-2111
Author(s):  
Quang-Van Doan ◽  
Hiroyuki Kusaka ◽  
Takuto Sato ◽  
Fei Chen
Keyword(s):  
Pearson Correlation ◽  
Structural Similarity ◽  
Self Organizing Map ◽  
Weather Patterns ◽  
Weather Typing ◽  
Synoptic Weather Typing ◽  
Clustering Quality ◽  
Som Algorithm ◽  
The Difference ◽  
Self Organizing

Abstract. This study proposes a novel structural self-organizing map (S-SOM) algorithm for synoptic weather typing. A novel feature of the S-SOM compared with traditional SOMs is its ability to deal with input data with spatial or temporal structures. In detail, the search scheme for the best matching unit (BMU) in a S-SOM is built based on a structural similarity (S-SIM) index rather than by using the traditional Euclidean distance (ED). S-SIM enables the BMU search to consider the correlation in space between weather states, such as the locations of highs or lows, that is impossible when using ED. The S-SOM performance is evaluated by multiple demo simulations of clustering weather patterns over Japan using the ERA-Interim sea-level pressure data. The results show the S-SOM's superiority compared with a standard SOM with ED (or ED-SOM) in two respects: clustering quality based on silhouette analysis and topological preservation based on topological error. Better performance of S-SOM versus ED is consistent with results from different tests and node-size configurations. S-SOM performs better than a SOM using the Pearson correlation coefficient (or COR-SOM), though the difference is not as clear as it is compared to ED-SOM.

scholarly journals Automatic content-based image retrieval using hierarchical clustering algorithms

2021 ◽  
Author(s):  
Kambiz Jarrah
Keyword(s):  
Image Retrieval ◽  
Hierarchical Clustering ◽  
Relevance Feedback ◽  
Clustering Algorithms ◽  
User Interaction ◽  
Content Based Image Retrieval ◽  
Feature Weight ◽  
High Level ◽  
Tree Map ◽  
Self Organizing

The overall objective of this thesis is to present a methodology for guiding adaptations of an RBF-based relevance feedback network, embedded in automatic content-based image retrieval (CBIR) systems, through the principle of unsupervised hierarchical clustering. The main focus of this thesis is two-fold: introducing a new member of Self-Organizing Tree Map (SOTM) family, the Directed self-organizing tree map (DSOTM) that not only provides a partial supervision on cluster generation by forcing divisions away from the query class, but also presents an objective verdict on resemblance of the input pattern as its tree structure grows; and using a base-10 Genetic Algorithm (GA) approach to accurately determine the contribution of individual feature vectors for a successful retrieval in a so-called "feature weight detection process." The DSOTM is quite attractive in CBIR since it aims to reduce both user workload and subjectivity. Repetitive user interaction steps are replaced by a DSOTM module, which adaptively guides relevance feedback, to bridge the gap between low-level image descriptors and high-level semantics. To further reduce this gap and achieve an enhanced performance for the automatic CBIR system under study, a GA-based approach was proposed in conjunction with the DSOTM. The resulting framework is referred to as GA-based CBIR (GA-CBIR) and aims to import human subjectivity by automatically adjusting the search process to what the system evolves "to believe" is significant content within the query. In this engine, traditional GA operators work closely with the DSOTM to better attune the apparent discriminative characteristics observed in an image by a human user.

scholarly journals Self-Organizing Map vs Initial Centroid Selection Optimization to Enhance K-Means with Genetic Algorithm to Cluster Transcribed Broadcast News Documents

2019 ◽  
Vol 17 (3) ◽  
pp. 316-324
Author(s):  
Ahmed Maghawry ◽  
Yasser Omar ◽  
Amr Badr
Keyword(s):  
Genetic Algorithm ◽  
Self Organizing Map ◽  
Optimal Solutions ◽  
Algorithm Optimization ◽  
Chi Square ◽  
Text Documents ◽  
Data Set ◽  
Average Accuracy ◽  
The Difference ◽  
Self Organizing

A compilation of artificial intelligence techniques are employed in this research to enhance the process of clustering transcribed text documents obtained from audio sources. Many clustering techniques suffer from drawbacks that may cause the algorithm to tend to sub optimal solutions, handling these drawbacks is essential to get better clustering results and avoid sub optimal solutions. The main target of our research is to enhance automatic topic clustering of transcribed speech documents, and examine the difference between implementing the K-means algorithm using our Initial Centroid Selection Optimization (ICSO) [16] with genetic algorithm optimization with Chi-square similarity measure to cluster a data set then use a self-organizing map to enhance the clustering process of the same data set, both techniques will be compared in terms of accuracy. The evaluation showed that using K-means with ICSO and genetic algorithm achieved the highest average accuracy.

A 3D Engineering Model Retrieval Algorithm Based on Relevance Feedback and Features Combination

2013 ◽  
Vol 579-580 ◽  
pp. 340-344
Author(s):  
Ting Zhuang ◽  
Xu Tang Zhang ◽  
Zhen Xiu Hou
Keyword(s):  
Relevance Feedback ◽  
3D Model ◽  
3D Models ◽  
Semantic Knowledge ◽  
Retrieval Algorithm ◽  
Engineering Model ◽  
Feedback Information ◽  
Low Level ◽  
Model Retrieval ◽  
High Level

In order to reuse 3D models and design knowledge efficiently, a number of 3D model retrieval algorithms based on content features of models have been proposed in recent years. Although, the features-based methods have achieved some progress, there are two limitations stilly. The first, single content feature cant be suit for all kinds of 3D models; different features have different strengths and weakness. The second, semantic gap, the semantic of model is independent from low-level characteristics. For those two issues, we present a 3D engineering model retrieval algorithm based on relevance feedback and features combination in this paper. The proposed method takes advantage of multiple features by allying them with weights. In the retrieval process, our method utilizes the Particle Swarm Optimization to update the weights dynamically based on users relevance feedback information in order to narrowing the gap between high-level semantic knowledge and low-level content features. The Experiments, based on publicly available 3D model database Engineering Shape Benchmark (ESB) developed by Purdue University, suggested that the proposed approach has better retrieval ability than traditional ones.

Some results of an experiment to compare ley and arable rotations at Woburn

1958 ◽  
Vol 50 (3) ◽  
pp. 297-306 ◽  
Author(s):  
H. H. Mann ◽  
D. A. Boyd
Keyword(s):  
Farmyard Manure ◽  
Potato Yield ◽  
Early Years ◽  
Average Effect ◽  
Potato Root ◽  
Winter Cereal ◽  
Arable Crops ◽  
Low Level ◽  
The Difference ◽  
High Level

The effect of short leys and arable cropping on the yields of the following arable crops has been tested since 1937 at Woburn Experimental Farm, Bedfordshire. The leys were a 3-year grazed ley and 3 years of lucerne cut for hay and the arable cropping was potatoes, winter cereal and either a 1-year ley or a third tillage crop. The effects of these crop sequences were measured by test crops of potatoes and barley, uniformly treated except that 15 tons farmyard manure was applied on one-half of each plot for potatoes.Without farmyard manure, the yield of potatoes after the grazed ley was higher than after three tillage crops by an average of about 3 tons per acre; after lucerne it was about 2 tons per acre more than after the tillage crops and after the 1 -year ley under 1 ton per acre more. With farmyard manure the benefit from leys was less (about 2 tons per acre for both lucerne and the grazed ley). The average effect of the farmyard manure was about 2·8 tons except after the grazed ley, where the increase was only 1·6 tons per acre.Effects of the previous cropping on the yield of barley were small in the early years of the experiment, but in the last 5 years the yield after ley and lucerne has exceeded that after the tillage crops by about 15%.Part of the difference in potato yield between the ley and arable sequences can be attributed to the differential incidence of potato-root eelworm, which has reached a high level of infestation on some of the arable plots. Much of the remainder may be ascribed to the low level of basal manuring which has affected the yield of all plots, but particularly those under tillage crops. How far the observed differences can be explained by these considerations remains a matter for speculation, but may to some extent be clarified when further results are obtained from a revised scheme of cropping and manuring.

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