scholarly journals Sensor Fusion Basketball Shooting Posture Recognition System Based on CNN

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Jingjin Fan ◽  
Shuoben Bi ◽  
Guojie Wang ◽  
Li Zhang ◽  
Shilei Sun
Keyword(s):  
Neural Networks ◽  
Sensor Fusion ◽  
Recognition System ◽  
Recall Rate ◽  
Accuracy Rate ◽  
Average Precision ◽  
Posture Recognition ◽  
Deep Learning Model ◽  
Precision Rate ◽  
Basketball Shooting

In recent years, with the development of wearable sensor devices, research on sports monitoring using inertial measurement units has received increasing attention; however, a specific system for identifying various basketball shooting postures does not exist thus far. In this study, we designed a sensor fusion basketball shooting posture recognition system based on convolutional neural networks. The system, using the sensor fusion framework, collected the basketball shooting posture data of the players’ main force hand and main force foot for sensor fusion and used a deep learning model based on convolutional neural networks for recognition. We collected 12,177 sensor fusion basketball shooting posture data entries of 13 Chinese adult male subjects aged 18–40 years and with at least 2 years of basketball experience without professional training. We then trained and tested the shooting posture data using the classic visual geometry group network 16 deep learning model. The intratest achieved a 98.6% average recall rate, 98.6% average precision rate, and 98.6% accuracy rate. The intertest achieved an average recall rate of 89.8%, an average precision rate of 91.1%, and an accuracy rate of 89.9%.

scholarly journals Convolutional Neural Network for Iris Recognition

2021 ◽  
Author(s):  
Wael Alnahari
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Iris Recognition ◽  
Recognition System ◽  
High Accuracy ◽  
Accuracy Rate

Abstract In this paper, I proposed an iris recognition system by using deep learning via neural networks (CNN). Although CNN is used for machine learning, the recognition is achieved by building a non-trained CNN network with multiple layers. The main objective of the code the test pictures’ category (aka person name) with a high accuracy rate after having extracted enough features from training pictures of the same category which are obtained from a that I added to the code. I used IITD iris which included 10 iris pictures for 223 people.

scholarly journals Implementation of Simple Additive Weighting (SAW) Method and Profile Matching for Employee Selection

2021 ◽  
Vol 10 (01) ◽  
pp. 022-029
Author(s):  
Andi Pratomo Wiyono ◽  
Muhammad Aziz Muslim ◽  
Muhammad Aswin
Keyword(s):  
Selection Process ◽  
Confusion Matrix ◽  
Employee Selection ◽  
Recall Rate ◽  
Accuracy Rate ◽  
Profile Matching ◽  
Simple Additive Weighting ◽  
A Company ◽  
Precision Rate ◽  
F Measure

Employees are an important element in a company that determines the progress of a company. With good quality employees in a company, it is easier to achieve desired goals of a company. Conventional (manual) recruitment method is vulnerable to non-technical factors such as frequent duplicate data or invalid data. In such condition, a Decision Support System (DSS) will be helpful in making decision process valid and reliable. In this paper, a Simple Addictive Weighting (SAW) method and Profile Matching were proposed to solve employee selection problem. This research was conducted at UPT Career Development and Entrepreneurship Universitas Brawijaya Malang, using data collected from written test selection in 2019. The effectiveness of both methods is analyzed by means of confusion matrix. SAW method give Accuracy rate of 94.7%, Precision rate of 87.5%, Recall rate of 91.3% and F-measure rate of 89.4%. On the other hand, Profile Matching method obtained the Accuracy rate of 90.4.7%, Precision rate of 81.4%, Recall rate of 81.4% and F-measure rate of 81.4%. From these results, it can be concluded that both methods have a high accuracy value accompanied by a high precision value when used for the selection process. This system can also reduce the bias of the same data very well, as can be seen from the high Recall and F-measure rates.

scholarly journals Farsi document image recognition system using word layout signature

2019 ◽  
pp. 1477-1488
Author(s):  
CEM ERGÜN ◽  
SAJEDEH NOROZPOUR
Keyword(s):  
Image Retrieval ◽  
Image Recognition ◽  
Recognition System ◽  
Recall Rate ◽  
Document Image ◽  
Keyword Spotting ◽  
Connected Component ◽  
Component Layout ◽  
Precision Rate

In this paper, a new representation of Farsi words is proposed to present the keyword spotting problems in Farsi document image retrieval. In this regard, we define a signature for each Farsi word based on the word connected component layout. The mentioned signature is shown as boxes, and then, by sketching vertical and horizontal lines, we construct a grid of each word to provide a new descriptor. One of the advantages of this method is that it can be used for both handwritten and machine-printed texts. Finally, to evaluate the performance of our system in comparison to other methods, a database that contains 19,582 printed Farsi words is examined, and after applying this approach, a recall rate of 98.1% and a precision rate of 94.3% are obtained.

scholarly journals Content Based Image Retrieval using Feature Extraction Technique

2019 ◽  
Vol 9 (2) ◽  
pp. 2527-2532
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Recall Rate ◽  
Distance Metrics ◽  
Average Precision ◽  
Feature Extraction Method ◽  
Retrieval Technique ◽  
Precision Rate ◽  
Feature Extraction Technique

Now days the image processing can be used in various areas such as in Agriculture, in Health care system also for security purpose. In case of Crime investigation the image processing can be used to identify the particular suspect from an available dataset for that purpose an image retrieval technique is presented in this paper. For image retrieval number of techniques is available. In earlier days Block Truncation Coding is used but due its some disadvantage feature extraction method is used. Using DDBTC technique two features are derived. The first feature as Color Co-occurrence Features (CCF) obtained using color quantizes features such as Bit Pattern Feature (BPF) is derived from Bitmap image. The five different distance metrics are used to measure the similarity between two images. The simulated results shows proposed Technique can shows the better result in the form of Average Precision rate (APR) and Average Recall Rate (ARR) as compared to other techniques.

scholarly journals Mobile Anomaly Detection Based on Improved Self-Organizing Maps

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Chunyong Yin ◽  
Sun Zhang ◽  
Kwang-jun Kim
Keyword(s):  
Anomaly Detection ◽  
Mobile Devices ◽  
Clustering Algorithms ◽  
Recall Rate ◽  
Accuracy Rate ◽  
Optimal Method ◽  
Self Organizing Maps ◽  
Different Characteristics ◽  
Precision Rate ◽  
Self Organizing

Anomaly detection has always been the focus of researchers and especially, the developments of mobile devices raise new challenges of anomaly detection. For example, mobile devices can keep connection with Internet and they are rarely turned off even at night. This means mobile devices can attack nodes or be attacked at night without being perceived by users and they have different characteristics from Internet behaviors. The introduction of data mining has made leaps forward in this field. Self-organizing maps, one of famous clustering algorithms, are affected by initial weight vectors and the clustering result is unstable. The optimal method of selecting initial clustering centers is transplanted from K-means to SOM. To evaluate the performance of improved SOM, we utilize diverse datasets and KDD Cup99 dataset to compare it with traditional one. The experimental results show that improved SOM can get higher accuracy rate for universal datasets. As for KDD Cup99 dataset, it achieves higher recall rate and precision rate.

scholarly journals Facial Expression Recognition System using Convolutional Neural Networks

2019 ◽  
Vol 8 (2S4) ◽  
pp. 603-607
Keyword(s):  
Neural Networks ◽  
Facial Expression ◽  
Convolutional Neural Networks ◽  
Facial Expression Recognition ◽  
Recognition System ◽  
Expression Recognition ◽  
Outward Appearance ◽  
Adequate Information ◽  
Difficult Issue ◽  
Precision Rate

Facial expression recognition has been a functioning exploration territory in the previous ten years, with developing application regions including symbol activity, neuromarketing and amiable robots. The acknowledgment of outward appearances isn't a simple issue for AI techniques, since individuals can change altogether in the manner they demonstrate their looks. Indeed, even pictures of a similar individual in a similar outward appearance can shift in splendor, foundation and present, and these varieties are underscored if thinking about various subjects (due to varieties fit as a fiddle, ethnicity among others). Albeit outward appearance acknowledgment is contemplated in the writing, few works perform reasonable assessment abstaining from blending subjects while preparing and testing the proposed calculations. Thus, outward appearance acknowledgment is as yet a difficult issue in PC vision. In this work, we propose a straightforward answer for outward appearance acknowledgment that utilizes a blend of Convolutional Neural Network and explicit picture pre-handling steps. Convolutional Neural Networks accomplish better precision with huge information. Be that as it may, there are no openly accessible datasets with adequate information for outward appearance acknowledgment with profound structures. Subsequently, to handle the issue, we apply some pre-preparing systems to extricate just demeanour explicit highlights from a face picture and investigate the introduction request of the examples amid preparing. An investigation of the effect of each picture pre-preparing task in the precision rate is displayed. The proposed strategy: accomplishes aggressive outcomes when contrasted and other outward appearance acknowledgment techniques – going up to 92% precision - it is quick to prepare, and it takes into consideration ongoing outward appearance acknowledgment with standard PCs.

scholarly journals TapChain: A Rule Chain Recognition Model Based on Multiple Features

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Keyu Jiang ◽  
Hanyi Zhang ◽  
Weiting Zhang ◽  
Liming Fang ◽  
Chunpeng Ge ◽  
...  
Keyword(s):  
Recall Rate ◽  
Security Model ◽  
Correction Algorithm ◽  
Accuracy Rate ◽  
Accurate Identification ◽  
Multiple Features ◽  
Recognition Model ◽  
Model Based ◽  
Iot Devices ◽  
Precision Rate

Trigger-action programming (TAP) is an intelligent tool, which makes it easy for users to make intelligent rules for IoT devices and applications. Unfortunately, with the popularization of TAP and more and more rules, the rule chain from multiple rules appears gradually and brings more and more threats. Previous work pays more attention to the construction of the security model, but few people focus on how to accurately identify the rule chain from multiple rules. Inaccurate identification of rule chains will lead to the omission of rule chains with threats. This paper proposes a rule chain recognition model based on multiple features, TapChain, which can more accurately identify the rule chain without source code. We design a correction algorithm for TapChain to help us get the correct NLP analysis results. We extract 12 features from 5 aspects of the rules to make the recognition of the rule chain more accurate. According to the evaluation, compared with the previous work, the accuracy rate of TapChain is increased by 3.1%, the recall rate is increased by 1.4%, and the precision rate can reach 88.2%. More accurate identification of the rule chain can help to better implement the security policies and better balance security and availability. What’s more, according to the rule chain that TapChain can recognize, there is a new kind of rule chain with threats. We give the relevant case studies in the evaluation.

Edge Features and Geometrical Properties Based Approach for Vehicle License Plate Detection and Localization

2012 ◽  
Vol 4 (2) ◽  
pp. 63-75
Author(s):  
Hinde Anoual ◽  
Sanaa El Fkihi ◽  
Abdelilah Jilbab ◽  
Driss Aboutajdine
Keyword(s):  
Recognition System ◽  
Recall Rate ◽  
License Plate ◽  
Flow Management ◽  
Geometrical Properties ◽  
Time Record ◽  
License Plate Detection ◽  
Edge Features ◽  
Detection And Localization ◽  
Precision Rate

Frequently, a need exists to identify vehicle license plates (VLP) for security. The extracted information from VLP is used for enforcement, access-control, and flow management, e.g., to keep a time record for automatic payment calculations or fight crime, making license plate detection crucial and inevitable in the VLP recognition system. This paper presents a robust method to detect and localize license plates in images. Specifically, the authors examine Moroccans’ VLPs. The proposed approach is based on edge features and characteristics of license plate characters. Various images including Moroccans’ VLPs were used to evaluate the proposed method. The experimental results show that the authors’ system can efficiently detect and localize the VLP in the images. Indeed, the recall/precision curve proves that 95% precision rate is obtained for recall rate value equals to 81%. In addition, the standard measure of quality is equal to 87.44%.

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