scholarly journals An Effective Interest Identification Technique to Enhance Sales Performance in Supermarkets

2019 ◽  
Vol 8 (12) ◽  
pp. 2574-2580
Keyword(s):  
Main Idea ◽  
Sales Performance ◽  
Svm Classifier ◽  
Camera System ◽  
Visual Words ◽  
Surveillance Camera ◽  
Speeded Up Robust Features ◽  
Identification Technique ◽  
Codebook Generation ◽  
The Given

Surveillance Camera System is installed in the supermarkets mainly for security purposes. But the main idea of this paper is to use this surveillance camera system to improve the sales performance by targeting a particular stimulus (child) through marketing promotions. The owner of the supermarket monitors the entire store with the help of the security camera system. The owner suddenly finds an abnormal action in a stimulus (child) on looking at a particular product. On observation of the stimulus head and arm movements, the owner concludes the stimulus interest on that product which the parents refuse to buy. This scenario is implemented in this paper using live video analytics which identifies the abnormality. Action recognition is a technique that is used in the classification of actions present in the given video. The Bag of Visual Words Model is implemented for recognizing the action made by the stimulus. This model includes feature extraction, codebook generation and classification. The features from the stimulus such as arm and head are extracted using Speeded up Robust Features (SURF) algorithm. Codebook generation is done by K-means clustering and the histogram of discriminative features is generated and fed as input to SVM classifier which recognizes the action made by the stimulus (child) in order to identify the child’s interest factor on a particular product.

Development Financing under Constraints: A Decade of African Development Bank. By Ime Ekop Ebong. Bonn-Bad Godesberg: Verlag Neue Sesellschaft GmbH. 1974. 172 pp.(special Issue No. 8 of the Vierteljahresberichte.)

1975 ◽  
Vol 14 (1) ◽  
pp. 149-152
Author(s):  
M.A. Behzad
Keyword(s):  
Main Idea ◽  
Development Projects ◽  
Development Bank ◽  
Special Issue ◽  
Investment Bank ◽  
African Development ◽  
Know How ◽  
African Development Bank ◽  
Basic Infrastructure ◽  
The Given

Development Financing under Constraints, as the author himself puts it, is 'aimed to recapitulate the spirit in which the African Development Bank was founded, describe how it later functioned and why it functioned the way it did'. The study is an excellent attempt to highlight economic cooperation and integ¬ration and to discuss its rationale in view of the given constraints. The main idea behind the establishment of an institution, like the African Develop¬ment Bank (ADB), was necessarily an 'all-African Investment Bank' to promote development projects. The newly independent nations of Africa, lacking as they are in the basic infrastructure, are beset with difficulties in surviving as economically viable units. As such, the need for a pooling of resources and for technical know-how is particularly imperative

scholarly journals MultiKOC: Multi-One-Class Classifier Based K-Means Clustering

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 134
Author(s):  
Loai Abdallah ◽  
Murad Badarna ◽  
Waleed Khalifa ◽  
Malik Yousef
Keyword(s):  
Clustering Algorithm ◽  
Main Idea ◽  
Molecular Classification ◽  
Positive Sample ◽  
Classification Problems ◽  
Multiple Cancer ◽  
Multiple Tumor ◽  
One Class Classifier ◽  
The Given

In the computational biology community there are many biological cases that are considered as multi-one-class classification problems. Examples include the classification of multiple tumor types, protein fold recognition and the molecular classification of multiple cancer types. In all of these cases the real world appropriately characterized negative cases or outliers are impractical to achieve and the positive cases might consist of different clusters, which in turn might lead to accuracy degradation. In this paper we present a novel algorithm named MultiKOC multi-one-class classifiers based K-means to deal with this problem. The main idea is to execute a clustering algorithm over the positive samples to capture the hidden subdata of the given positive data, and then building up a one-class classifier for every cluster member’s examples separately: in other word, train the OC classifier on each piece of subdata. For a given new sample, the generated classifiers are applied. If it is rejected by all of those classifiers, the given sample is considered as a negative sample, otherwise it is a positive sample. The results of MultiKOC are compared with the traditional one-class, multi-one-class, ensemble one-classes and two-class methods, yielding a significant improvement over the one-class and like the two-class performance.

scholarly journals Image Classification and Detection of Insulators using Bag of Visual Words and Speeded up Robust Features

2020 ◽  
Vol 6 (10) ◽  
pp. 7-13
Keyword(s):  
Nearest Neighbor ◽  
Training Image ◽  
Reference Image ◽  
Inspection System ◽  
Bag Of Visual Words ◽  
Visual Words ◽  
Speeded Up Robust Features ◽  
Matching Process ◽  
Computer Vision Technology ◽  
Security Surveillance

Electricalsubstation online monitoring in computer vision technology is based on image processingalgorithm to perform visual analysis.This paperpresents classification of ceramicand glass insulators through Bag of Visual Words and detection of these insulators by Point Feature Matching.The training image datasets are used for categorization by forming a visual vocabularywhile a new unlabeled image from testing image dataset is classify using nearest neighbor classification method for features descriptor. For detection we use Speeded up Robust Features for detecting position of insulator present in cluttered scene image. Matching process is done between test and reference image and decision is made based on similar features. Weconducted experiment on insulators to verify the superiority of our proposed method.The proposed method can be used in security, surveillance and inspection system.

scholarly journals Numerical Solutions for Multi-Term Fractional Order Differential Equations with Fractional Taylor Operational Matrix of Fractional Integration

Mathematics ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 96 ◽  
Author(s):  
İbrahim Avcı ◽  
Nazim I. Mahmudov
Keyword(s):  
Differential Equations ◽  
Numerical Solutions ◽  
Fractional Integration ◽  
Operational Matrix ◽  
Main Idea ◽  
System Of Equations ◽  
Algebraic Equations ◽  
Fractional Order Differential Equations ◽  
Fractional Differential ◽  
The Given

In this article, we propose a numerical method based on the fractional Taylor vector for solving multi-term fractional differential equations. The main idea of this method is to reduce the given problems to a set of algebraic equations by utilizing the fractional Taylor operational matrix of fractional integration. This system of equations can be solved efficiently. Some numerical examples are given to demonstrate the accuracy and applicability. The results show that the presented method is efficient and applicable.

Performance Analysis of Machine Learning Based Classifiers for the Diagnosis of Melanoma Cancer and Comparison

2018 ◽  
Vol 15 (2) ◽  
pp. 558-575
Author(s):  
A. Anto Spiritus Kingsly ◽  
B. Sankaragomathi
Keyword(s):  
Detection System ◽  
Noise Removal ◽  
Morphological Processing ◽  
Svm Classifier ◽  
Bag Of Visual Words ◽  
Data Set ◽  
Visual Words ◽  
Melanoma Cancer ◽  
Processing Step ◽  
Ann Classifier

Melanoma cancer is the most injurious form of cancer which affects the human. Skin cancer has quickly increased in western part of the country among the world. In this paper, diagnosing melanoma in premature stages a detection system has been designed which contains the following digital image processing techniques. First, dermoscopy image of skin is taken, and it is subjected to the pre-processing step for noise removal and post-processing step for image enhancement. Then the processed image undergoes image segmentation using Otsu method and Morphological processing. Second, features are extracted using feature extraction technique-ABCD parameter, GLCM, and FOS. Various feature combinations are given as the input to the KNN, SVM, ANN and Bag of Visual Words classifiers. KNN classifier is used to classify the data set into two classes, SVM classifier is used to classify the data set into three classes, ANN classifier is used to classify the data set based on the number of layers and Bag of Visual Words are used to classify the data set into two classes. Performance is analyzed based on the accuracy of the learning classifier output.

Discrimination of Common Ragweed (Ambrosia artemisiifolia) and Mugwort (Artemisia vulgaris) Based on Bag of Visual Words Model

2017 ◽  
Vol 31 (2) ◽  
pp. 310-319 ◽  
Author(s):  
Anton Ustyuzhanin ◽  
Karl-Heinz Dammer ◽  
Antje Giebel ◽  
Cornelia Weltzien ◽  
Michael Schirrmann
Keyword(s):  
Plant Species ◽  
Ambrosia Artemisiifolia ◽  
Training Data ◽  
Support Vector ◽  
Identification System ◽  
Bag Of Visual Words ◽  
Common Ragweed ◽  
Visual Words ◽  
Grid Sampling ◽  
Speeded Up Robust Features

Common ragweed is a plant species causing allergic and asthmatic symptoms in humans. To control its propagation, an early identification system is needed. However, due to its similar appearance with mugwort, proper differentiation between these two weed species is important. Therefore, we propose a method to discriminate common ragweed and mugwort leaves based on digital images using bag of visual words (BoVW). BoVW is an object-based image classification that has gained acceptance in many areas of science. We compared speeded-up robust features (SURF) and grid sampling for keypoint selection. The image vocabulary was built using K-means clustering. The image classifier was trained using support vector machines. To check the robustness of the classifier, specific model runs were conducted with and without damaged leaves in the trainings dataset. The results showed that the BoVW model allows the discrimination between common ragweed and mugwort leaves with high accuracy. Based on SURF keypoints with 50% of 788 images in total as training data, we achieved a 100% correct recognition of the two plant species. The grid sampling resulted in slightly less recognition accuracy (98 to 99%). In addition, the classification based on SURF was up to 31 times faster.

scholarly journals Selecting Pedagogical Protocols Using SOM

2006 ◽  
Vol 5 (1) ◽  
Author(s):  
Fernando Salgueiro ◽  
Guido Costa ◽  
Fernando Lage ◽  
Zulma Cataldi ◽  
Ramón García-Martínez
Keyword(s):  
Current Model ◽  
Main Idea ◽  
Initial Population ◽  
Computer Engineering ◽  
Human Tutor ◽  
Tutorial System ◽  
Epistemological Conception ◽  
The Given ◽  
Stated Problem ◽  
Selection Of

During the first semesters of Computer Engineering the amount of human tutors is insufficient: the students/tutors ratio is very high and there is a great difference in the acquired knowledge and backgrounds of the students. The main idea of this paper is to describe a system that could emulate the human tutor and provide to the student with a degree of flexibility for the selection of the most adequate tutorial type. This could be a feasible solution to the stated problem. But a tutorial system should not only emulate the human tutor but besides it should be designed from an epistemological conception of what teaching Basic Programming means specially in an Engineering course due to the profile and identity of the future engineer. The stated solution implement a series of artificial neural networks to determine if there is a relationship between the given initial population of students learning predilections and the different tutoring types. A series of experiences were carried out to validate the current model.

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