Implementation and Analysis of Depression Detection Model using Emotion Artificial Intelligence

2019 ◽  
Vol 7 (4) ◽  
pp. 9-12
Author(s):  
Unnati Chawda ◽  
Shanu K Rakesh
Keyword(s):  
Artificial Intelligence ◽  
Detection Model ◽  
Depression Detection

A hierarchical depression detection model based on vocal and emotional cues

2021 ◽  
Vol 441 ◽  
pp. 279-290
Author(s):  
Yizhuo Dong ◽  
Xinyu Yang
Keyword(s):  
Emotional Cues ◽  
Detection Model ◽  
Model Based ◽  
Depression Detection

scholarly journals Network Intrusion Detection Model Based on Artificial Intelligence

2020 ◽  
Vol 1617 ◽  
pp. 012082
Author(s):  
Qingchuan Meng ◽  
Youzi Zhang ◽  
Fengzhi Wu ◽  
Xiaoming Chen
Keyword(s):  
Artificial Intelligence ◽  
Intrusion Detection ◽  
Network Intrusion Detection ◽  
Detection Model ◽  
Model Based ◽  
Network Intrusion

Alzheimer detection model applying artificial intelligence techniques

2018 ◽  
Author(s):  
David Ricardo Castillo SalazarAutores ◽  
Cesar Byron Guevara Maldonado ◽  
Hector Fernando Gomez Alvarado ◽  
Dario Xavier Castillo Salazar
Keyword(s):  
Artificial Intelligence ◽  
Artificial Intelligence Techniques ◽  
Detection Model

scholarly journals Hybrid Email Spam Detection Model Using Artificial Intelligence

2020 ◽  
Vol 10 (2) ◽  
pp. 316-322 ◽  
Author(s):  
Samira. Douzi ◽  
◽  
Feda A. AlShahwan ◽  
Mouad. Lemoudden ◽  
Bouabid. El Ouahidi
Keyword(s):  
Artificial Intelligence ◽  
Spam Detection ◽  
Detection Model ◽  
Email Spam

scholarly journals RLXSS: Optimizing XSS Detection Model to Defend Against Adversarial Attacks Based on Reinforcement Learning

2019 ◽  
Vol 11 (8) ◽  
pp. 177
Author(s):  
Yong Fang ◽  
Cheng Huang ◽  
Yijia Xu ◽  
Yang Li
Keyword(s):  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Learning Algorithms ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
Detection Model ◽  
Attack Model ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
Adversarial Model

With the development of artificial intelligence, machine learning algorithms and deep learning algorithms are widely applied to attack detection models. Adversarial attacks against artificial intelligence models become inevitable problems when there is a lack of research on the cross-site scripting (XSS) attack detection model for defense against attacks. It is extremely important to design a method that can effectively improve the detection model against attack. In this paper, we present a method based on reinforcement learning (called RLXSS), which aims to optimize the XSS detection model to defend against adversarial attacks. First, the adversarial samples of the detection model are mined by the adversarial attack model based on reinforcement learning. Secondly, the detection model and the adversarial model are alternately trained. After each round, the newly-excavated adversarial samples are marked as a malicious sample and are used to retrain the detection model. Experimental results show that the proposed RLXSS model can successfully mine adversarial samples that escape black-box and white-box detection and retain aggressive features. What is more, by alternately training the detection model and the confrontation attack model, the escape rate of the detection model is continuously reduced, which indicates that the model can improve the ability of the detection model to defend against attacks.

scholarly journals Task-State Heart Rate Variability Parameter- Based Depression Detection Model and Effect of Therapy on the Parameters

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 105701-105709 ◽  
Author(s):  
Yaowen Xing ◽  
Nini Rao ◽  
Mengmeng Miao ◽  
Quanchi Li ◽  
Qian Li ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Heart Rate Variability Parameter ◽  
Detection Model ◽  
Depression Detection ◽  
Effect Of Therapy

scholarly journals Application of Various Machine Learning Techniques in Sentiment Analysis for Depression Detection

2019 ◽  
Vol 8 (10S) ◽  
pp. 292-296
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Mental Disorder ◽  
Human Life ◽  
Geographic Location ◽  
Machine Learning Techniques ◽  
World Health ◽  
Detection Algorithms ◽  
Learning Techniques ◽  
Depression Detection

Depression is the world’s fourth leading disease and will be in the second in 2020 according to the statistics of World Health Organization.Depression affects many people irrespective of their age, geographic location, demographic or social position and more commonly affects females than males.Depression is a mental disorder which can impair many facets of human life. Though not easily detected it has intense and wide-ranging impressions. Although many researchers explored numerous techniques in predicting depression, still there is no improvement and the generations are facing higher rate of depression. It is believed that the depression detection algorithms can be more accurate and their performance can be better if they rely on artificial intelligence. On considering these factors, it is planned to perform a survey on the application of various machine learning techniques that have been used in the domain of sentimental analysis for depression detection.

scholarly journals Artificial Intelligence Model to Detect Real Contact Relationship between Mandibular Third Molars and Inferior Alveolar Nerve Based on Panoramic Radiographs

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1664
Author(s):  
Tianer Zhu ◽  
Daqian Chen ◽  
Fuli Wu ◽  
Fudong Zhu ◽  
Haihua Zhu
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Inferior Alveolar Nerve ◽  
Third Molars ◽  
Panoramic Radiographs ◽  
The Real ◽  
Detection Model ◽  
Cooperative Approach ◽  
Real Contact ◽  
Mandibular Third Molars

This study aimed to develop a novel detection model for automatically assessing the real contact relationship between mandibular third molars (MM3s) and the inferior alveolar nerve (IAN) based on panoramic radiographs processed with deep learning networks, minimizing pseudo-contact interference and reducing the frequency of cone beam computed tomography (CBCT) use. A deep-learning network approach based on YOLOv4, named as MM3-IANnet, was applied to oral panoramic radiographs for the first time. The relationship between MM3s and the IAN in CBCT was considered the real contact relationship. Accuracy metrics were calculated to evaluate and compare the performance of the MM3–IANnet, dentists and a cooperative approach with dentists and the MM3–IANnet. Our results showed that in comparison with detection by dentists (AP = 76.45%) or the MM3–IANnet (AP = 83.02%), the cooperative dentist–MM3–IANnet approach yielded the highest average precision (AP = 88.06%). In conclusion, the MM3-IANnet detection model is an encouraging artificial intelligence approach that might assist dentists in detecting the real contact relationship between MM3s and IANs based on panoramic radiographs.

Study on the winding quality for spiral HTS cable based on AI detection model

2022 ◽  
Author(s):  
Mingyang Wang ◽  
Haosheng Ye ◽  
Xueliang Wang ◽  
Zhuyong Li ◽  
Jie Sheng ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Image Data ◽  
Monitoring Strategy ◽  
Data Preparation ◽  
Critical Stage ◽  
Cable Structure ◽  
Detection Model ◽  
First Choice ◽  
Spiral Geometry

Abstract The development of high temperature superconducting (HTS) conductors is leading to the diverse structure designs of HTS cable. (RE)Ba2Cu3Ox (REBCO) tapes using spiral geometry has been a popular compact HTS cable structure, which is in the critical stage of engineering production and application. However, the winding quality of REBCO tapes is unstable for spiral HTS cables, because of the different winding methods like manual winding, device-assisted winding, or automatic winding. Although automatic winding will be the first choice for the actual applications by spiral HTS cables, the related winding quality is not monitored effectively yet. In this paper, we first discuss the possible influence of the winding quality on the critical current performance of spiral HTS cables. Then, an artificial intelligence (AI) based method is implemented to realize the detection model for the winding quality. From image data preparation to AI detection and postprocessing, the detection model provides the final results to show the winding intervals as a binary image. Through the intuitive analysis and the evaluation metrics, both error and correct winding conditions obtain acceptable detection results, and the correct one has a better performance. The identification of the winding intervals will help to determine the monitoring strategy for the spiral HTS cable fabrication.

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