scholarly journals Large-Scale Image Retrieval of Tourist Attractions Based on Multiple Linear Regression Equations

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yinping Song
Keyword(s):  
Feature Extraction ◽  
Linear Regression ◽  
Image Retrieval ◽  
Multiple Linear Regression ◽  
Large Scale ◽  
Image Feature ◽  
Index Structure ◽  
Regression Equations ◽  
Visual Dictionary ◽  
Low Dimensional

This paper presents an in-depth study and analysis of large-scale tourist attraction image retrieval using multiple linear regression equation approaches. This feature extraction method often relies on the partitioning of the grid and is only effective when the overall similarity of different images is high. The BOF model is borrowed from the method for text retrieval, which generally extracts the local features of an image by the scale-invariant feature transform algorithm and clusters them using k -means to obtain a low-dimensional visual dictionary and characterizes the image features with a histogram vector based on the visual dictionary. However, when there are many kinds of images, the dimensionality of the visual dictionary will be large and it is not convenient to construct the BOF model. The last fully connected layer is taken as the image feature, and it is dimensionalized by the principal component analysis method, and then, the low-dimensional feature index structure is constructed using the locality-sensitive hashing- (LSH-) based approximate nearest neighbor algorithm. The accuracy of our graph retrieval has increased by 8%. The advantages of feature extraction by a convolutional neural network and the high efficiency of a hash index structure in retrieval are used to solve the shortcomings of traditional methods in terms of accuracy and other aspects in image retrieval. The results show that compared with the above two algorithms, for most of the attractions, the method has a relatively obvious advantage in the accuracy of retrieval, and when there are few similar images of a particular attraction in the attraction image library, the accuracy of the query results is not much different from the first two methods.

scholarly journals An Advanced Relevance Feedback Method to Improve Performance of CBIR using Convolutional Neural Network and Comprehensive Values

2019 ◽  
Vol 9 (2) ◽  
pp. 5427-5438
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Activation Function ◽  
Image Feature ◽  
Similarity Measurement ◽  
Query Image ◽  
Image Production

Content-Based Image Retrieval (CBIR) is extensively used technique for image retrieval from large image databases. However, users are not satisfied with the conventional image retrieval techniques. In addition, the advent of web development and transmission networks, the number of images available to users continues to increase. Therefore, a permanent and considerable digital image production in many areas takes place. Quick access to the similar images of a given query image from this extensive collection of images pose great challenges and require proficient techniques. From query by image to retrieval of relevant images, CBIR has key phases such as feature extraction, similarity measurement, and retrieval of relevant images. However, extracting the features of the images is one of the important steps. Recently Convolutional Neural Network (CNN) shows good results in the field of computer vision due to the ability of feature extraction from the images. Alex Net is a classical Deep CNN for image feature extraction. We have modified the Alex Net Architecture with a few changes and proposed a novel framework to improve its ability for feature extraction and for similarity measurement. The proposal approach optimizes Alex Net in the aspect of pooling layer. In particular, average pooling is replaced by max-avg pooling and the non-linear activation function Maxout is used after every Convolution layer for better feature extraction. This paper introduces CNN for features extraction from images in CBIR system and also presents Euclidean distance along with the Comprehensive Values for better results. The proposed framework goes beyond image retrieval, including the large-scale database. The performance of the proposed work is evaluated using precision. The proposed work show better results than existing works.

scholarly journals PENGARUH TINGKAT PENDIDIKAN, MOTIVASI, DAN KOMPENSASI TERHADAP KINERJA PEGAWAI DI BRI CABANG SEMARANG AHMAD YANI

2019 ◽  
Vol 11 (02) ◽  
pp. 31-47
Author(s):  
Sopi Sopi ◽  
Zumrotun Nafi'ah
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Industrial Revolution ◽  
Employee Performance ◽  
Science And Technology ◽  
Regression Equations ◽  
Linear Regression Equation ◽  
Improve Performance ◽  
Independent Variables ◽  
Life Education

Education, motivation and compensation are important things that can improve performance. This study aims to explain whether there is an influence of education, motivation and compensation on employee performance. So that through the results of this study it is expected to be a reference for leaders in managing the organization. In this study there are three independent variables namely education, motivation and compensation and one dependent variable is employee performance. At present it is in the era of industrial revolution 4.0, which is marked by; big data / giant data, internet of think, labor knowledge, and long life education. Since the beginning of the life of mankind to an infinite period, it is largely determined by the mastery of science and technology. Science and technology can not be separated from the progress of education level. Education is the base of all changes both individually, as well as countries. Employee performance is determined by the education that is owned, as high as education, the higher the performance and vice versa. The population in this study are BRI CAB employees, SEMARANG A-YANI, 60 people and all of them are sampled. The results of the analysis using SPSS 23 program statistical tools obtained multiple linear regression equation Y = 0.505 X1 + 0.175 X2 + 0.408 X3 The results of multiple linear regression equations show that there is a positive and significant influence between education on employee performance at BRI CAB. A YANI SEMARANG (t count test 6.314> t table 0.05), motivation towards employee performance at BRI CAB. A YANI SEMARANG (tcount 2,160> t table 0,05), and compensation for employee performance at BRI CAB. A YANI SEMARANG (t test 5.108> ttable 0.05). While together (simultaneously) the influence of education, motivation and compensation has an effect on and significant on the performance of employees at BRI CAB. A YANI SEMARANG (count = 44,692> ftabel = 0.05). The influence of the two research variables is very strong with a correlation value of 69.0% for employee performance at BRI CAB. A YANI SEMARANG is influenced by the motivation and compensation education of the remaining 31.0% of the employees' performance at BRI CAB. A YANI SEMARANG is influenced by other variables that affect employee performance.

scholarly journals PENGARUH LINGKUNGAN KERJA DAN KOMPENSASI TERHADAP KINERJA PEGAWAI PADA DINAS PENDIDIKAN, PEMUDA DAN OLAHRAGA KABUPATEN MAJENE

2020 ◽  
Vol 9 (2) ◽  
pp. 121
Author(s):  
Sri Indira Hartawati ◽  
Meutia A Sahur
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Work Environment ◽  
Employee Performance ◽  
Linear Regression Analysis ◽  
Youth Sports ◽  
Multiple Linear Regression Analysis ◽  
Department Of Education ◽  
Test Reliability ◽  
Regression Equations

<p><em>This research was conducted at the Department of Education, Youth and Sports of Majene Regency with the title The Effect of Work Environment and Compensation on Employee Performance. The formulation of the problem used by researchers is How the influence of the Work Environment on Employee Performance at the Education and Youth Sports Office of Majene Regency, How the influence of Compensation on Employee Performance at the Education and Youth Sports Office of Majene Regency, which variables have more influence on Employee Performance at the Education and Youth Office Majene District Sports. The research method, namely the population and sample used in this study were all employees of the Department of Education and Youth Sports of Majene Regency, which amounted to about 50 people, while the analysis method used the Validity Test, Reliability Test, Multiple Linear Regression Analysis This analysis was used to determine how much influence it had. independent variables, namely: compensation (X1), and work environment (X2) on the dependent variable, namely Employee Performance (Y). Multiple linear regression equations, Partial Significance Test (t test) and Simultaneous Test (F test). The results obtained from this study are the work environment has a significant effect on employee performance at the Department of Education and Youth Sports of Majene Regency, compensation has an effect on employee performance at the Education and Youth Sports Office of Majene Regency. and the work environment has a more dominant influence on employee performance at the Department of Education and Youth Sport, Majene Regency.</em></p><p><strong><em>Keywords: </em></strong><em>Work Environment, Compensation, Employee Performance</em></p>

scholarly journals Deep Learning Triplet Ordinal Relation Preserving Binary Code for Remote Sensing Image Retrieval Task

2021 ◽  
Vol 13 (23) ◽  
pp. 4786
Author(s):  
Zhen Wang ◽  
Nannan Wu ◽  
Xiaohan Yang ◽  
Bingqi Yan ◽  
Pingping Liu
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Binary Code ◽  
Satellite Observation ◽  
Cross Entropy ◽  
Retrieval Task ◽  
Search Tasks ◽  
Low Dimensional

As satellite observation technology rapidly develops, the number of remote sensing (RS) images dramatically increases, and this leads RS image retrieval tasks to be more challenging in terms of speed and accuracy. Recently, an increasing number of researchers have turned their attention to this issue, as well as hashing algorithms, which map real-valued data onto a low-dimensional Hamming space and have been widely utilized to respond quickly to large-scale RS image search tasks. However, most existing hashing algorithms only emphasize preserving point-wise or pair-wise similarity, which may lead to an inferior approximate nearest neighbor (ANN) search result. To fix this problem, we propose a novel triplet ordinal cross entropy hashing (TOCEH). In TOCEH, to enhance the ability of preserving the ranking orders in different spaces, we establish a tensor graph representing the Euclidean triplet ordinal relationship among RS images and minimize the cross entropy between the probability distribution of the established Euclidean similarity graph and that of the Hamming triplet ordinal relation with the given binary code. During the training process, to avoid the non-deterministic polynomial (NP) hard problem, we utilize a continuous function instead of the discrete encoding process. Furthermore, we design a quantization objective function based on the principle of preserving triplet ordinal relation to minimize the loss caused by the continuous relaxation procedure. The comparative RS image retrieval experiments are conducted on three publicly available datasets, including UC Merced Land Use Dataset (UCMD), SAT-4 and SAT-6. The experimental results show that the proposed TOCEH algorithm outperforms many existing hashing algorithms in RS image retrieval tasks.

scholarly journals Pengaruh Pelatihan Dan Pengembangan Terhadap Kinerja Karyawan Departemen Produksi Pada PT. Kaltim Diamond Coal Site Loa Gagak Kutai Kartanegara

2021 ◽  
Vol 9 (2) ◽  
pp. 170
Author(s):  
Wijayanti Wijayanti Wijayanti
Keyword(s):  
Data Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Employee Performance ◽  
Training And Development ◽  
Test Reliability ◽  
Regression Equations ◽  
Production Department ◽  
Data Analysis Technique ◽  
Analysis Technique

The results of the analysis using multiple linear regression equations show the following regression equation: Y = 5.622 + 0.488 (X1) + 0.329 (X2). Data collection techniques are based on the source, including primary data through observation, interviews, questionnaires or questionnaires, and include documentation. Secondary data, among others, by searching for literature in the form of previous research journals, and textbooks that are related to the variables to be studied. The data analysis technique used the validity test, reliability test, and classical assumption test. As for the data analysis technique method using Multiple Linear Regression, Correlation Analysis, Analysis of Determination (R2), hypothesis testing using the F test, T test and variable test which have the most influence. The results showed that the Training (X1) and Development (X2) variables simultaneously had a significant effect on the Employee Performance (Y) of the Production Department of PT. Kaltim Diamond Coal Site Loa Gagak Kutai Kartanegara.“The results of multiple linear regression tests simultaneously found that the Training and Development variables had a significant effect on the Employee Performance of the Production Department“at PT. Kaltim Diamond Coal Site Loa Gagak”Kutai Kartanegara.”The results of”the multiple linear regression test partially found that training and development had a significant influence on the performance of the production department“employees at PT. Kaltim Diamond Coal Site Loa Gagak Kutai Kartanegara”. The results of the most influential variable test, it is known that the training variable is the variable that has the most dominant influence on the Employee Performance of the Production Department at PT. Kaltim Diamond Coal Site Loa Gagak Kutai Kartanegara.

scholarly journals . Reflection of individual typological properties in the tremorogram (Predicting the indicators of Rusalov’s test according to the indicators of the tremorogram)

2021 ◽  
Vol 11 (5) ◽  
pp. 213-222
Author(s):  
B. A. Lobasyuk ◽  
L. N. Akimova ◽  
A. N. Stoyanov ◽  
A. V. Zamkovaya
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Regression Coefficients ◽  
Regression Equations ◽  
Target Feature ◽  
Left Hand ◽  
Right Hand ◽  
Postural Load ◽  
Significant Regression ◽  
The Right

Rationale for choosing. An increase in physiological tremor (Tr) in emotionally saturated situations is reflected not only in fiction, but also in scientific literature. In other words, tremors and emotional responses are interconnected. Purpose. To investigate the reflection of individual-typological properties in a tremorogram using V. M. Rusalov’s mathematical modeling. Material and methods. Tr was recorded using a linear transducer. Tr was recorded under postural load (arms extended forward). The sensor was alternately placed on the outstretched right and left arms in front of oneself, under conditions of “eyes open” (operative rest). The analysis of the tremorogram (TrG) files was carried out after the end of the study using the "Analist - 2" software according to the half - period analysis algorithm. To study the individual psychological characteristics of the personality, we used the method of determining the properties of the temperament by V.M. Rusalov. Each of the many indicators of Rusalov’s test selected in the analysis was considered as a target feature (Y-s), and the amplitudes and frequencies of TrG were considered as influencing variables (sets of X - s) and multiple linear regression equations of the form were built: The parameters of the amplitude and frequency of EEG rhythms were used as Xs. Own research. In multiple regression analysis of the influence of TrG indices of the right hand on the indices of Rusalov’s test, 12 statistically significant regression coefficients were determined, and 11 statistically significant regression coefficients for the left hand. After obtaining the diagnostic equations of multiple linear regression, describing the influence of TrG indicators on the indicators of Rusalov’s test, an attempt was made, using these equations, to obtain the indicators of Rusalov’s test, using the tremor indicators. On average, the% discrepancy between the determined and predicted indicators was 97.42% for the right hand, and 101.98 for the left. Conclusions. 1. With the use of diagnostic equation, it was possible to predict the indicators of psychological testing according to Rusalov’s test by the indicators of tremor of the right and left hands.2. Influence of Rusalov’s test indicators on TrG indicators were less in modulus than the influence of TrG indicators on the indicators of Rusalov’s  test, i.e. did not participate in the control of the mechanisms of TrG generation.3. The results obtained indicate that tremor indicators contain information about the subject-activity and communicative aspects of temperament according to V. M. Rusalov.

scholarly journals Plant Diseases Recognition Based on Image Processing Technology

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Guiling Sun ◽  
Xinglong Jia ◽  
Tianyu Geng
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Image Recognition ◽  
Color Image ◽  
Regional Growth ◽  
Recognition System ◽  
Plant Diseases ◽  
Image Feature

A new image recognition system based on multiple linear regression is proposed. Particularly, there are a number of innovations in image segmentation and recognition system. In image segmentation, an improved histogram segmentation method which can calculate threshold automatically and accurately is proposed. Meanwhile, the regional growth method and true color image processing are combined with this system to improve the accuracy and intelligence. While creating the recognition system, multiple linear regression and image feature extraction are utilized. After evaluating the results of different image training libraries, the system is proved to have effective image recognition ability, high precision, and reliability.

Feature Extraction of Video Using Artificial Neural Network

2017 ◽  
Vol 11 (2) ◽  
pp. 25-40 ◽  
Author(s):  
Yoshihiro Hayakawa ◽  
Takanori Oonuma ◽  
Hideyuki Kobayashi ◽  
Akiko Takahashi ◽  
Shinji Chiba ◽  
...  
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Image Recognition ◽  
High Performance ◽  
Feature Space ◽  
Middle Layer ◽  
Image Feature ◽  
Consecutive Series ◽  
Form Analysis ◽  
Low Dimensional

In deep neural networks, which have been gaining attention in recent years, the features of input images are expressed in a middle layer. Using the information on this feature layer, high performance can be demonstrated in the image recognition field. In the present study, we achieve image recognition, without using convolutional neural networks or sparse coding, through an image feature extraction function obtained when identity mapping learning is applied to sandglass-style feed-forward neural networks. In sports form analysis, for example, a state trajectory is mapped in a low-dimensional feature space based on a consecutive series of actions. Here, we discuss ideas related to image analysis by applying the above method.

Effects of Hearing Levels at the Activator and Other Frequencies upon the Expected Levels of the Acoustic Reflex Threshold

1983 ◽  
Vol 48 (1) ◽  
pp. 11-17 ◽  
Author(s):  
Stanley A. Gelfand ◽  
Neil Piper ◽  
Shlomo Silman
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Clinical Applications ◽  
Hearing Level ◽  
Regression Equations ◽  
Acoustic Reflex ◽  
Reflex Threshold ◽  
Acoustic Reflex Threshold

Multiple linear regression equations were derived to define the expected levels of acoustic reflex thresholds (ARTs) at 500, 1000, and 2000 Hz predictable from hearing levels as 500–4000 Hz in 646 ears. When the hearing level at the activator frequency is ⩽50 dB HL, ARTs tend to be best defined by a constant value of 86–90 dB HL with small adjustments due to the loss at the activator or higher frequencies. When the hearing level at the activator frequency is 55–90 dB HL, the ART is best predicted by a constant plus the degree of loss at that frequency plus the loss at 4000 Hz. These data present the clinician with empirically derived bases for determining the effects of other frequencies on a given ART. Application of these findings permits the clinician to apply known relationships in determining whether a particular ART is representative of those expected for normal and/or cochlear impaired ears, elevated or depressed, without having to rely on vague intuitions of such relationships. Clinical applications are discussed.

Interpretation of Hydroxylic Solvent Effects Based on Correlations with Solvent Parameters. Reaction of Et3N with EtI

1994 ◽  
Vol 59 (4) ◽  
pp. 898-904 ◽  
Author(s):  
António R. T. Calado ◽  
Lídia M. V. Pinheiro ◽  
Lídia M. P. C. Albuquerque ◽  
Raquel M. C. Goncalves ◽  
Martí Rosés ◽  
...  
Keyword(s):  
Energy Density ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Solvent Effects ◽  
Rate Constants ◽  
Cohesive Energy ◽  
Regression Equations ◽  
Solvatochromic Parameters ◽  
Cohesive Energy Density ◽  
Solvent Parameters

Hydroxylic solvent effects on 20 rate constants, k, of the Menschutkin reaction of Et3N with EtI are unravelled and rationalized by means of multiple linear regression equations. To perform this analysis new k values in 2 mono- and 9 dialcohols are obtained. New values are also presented for the Kamlet and Taft solvatochromic parameters π*, α and β of 1-hexanol. The results show that the solvent dipolarity, polarizability and cohesive energy density are the main properties influencing the reactivity, for the set of studied solvents.

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