scholarly journals Depression Detection Using Stacked Autoencoder From Facial Features And NLP

IJOSTHE ◽  
2020 ◽  
pp. 1-7
Author(s):  
Ravi Kumar ◽  
Santosh Kumar Nagar ◽  
Anurag Shrivastava
Keyword(s):  
Language Processing ◽  
Detection System ◽  
Research Work ◽  
Hybrid Approach ◽  
Recognition System ◽  
Mental Illnesses ◽  
Automated System ◽  
Facial Features ◽  
Depression Detection ◽  
Textual Features

Depression has become one of the most common mental illnesses in the past decade, affecting millions of patients and their families. However, the methods of diagnosing depression almost exclusively rely on questionnaire-based interviews and clinical judgments of symptom severity, which are highly dependent on doctors’ experience and makes it a labor-intensive work. This research work aims to develop an objective and convenient method to assist depression detection using facial features as well as textual features. Most of the people conceal their depression from everyone. So, an automated system is required that will pick out them who are dealing with depression. In this research, different research work focused for detecting depression are discussed and a hybrid approach is developed for detecting depression using facial as well as textual features. The main purpose of this research work is to design and propose a hybrid system of combining the effect of three effective models: Natural Language Processing, Stacked Deep Auto Encoder with Random forest (RF) classifier and fuzzy logic based on multi-feature depression detection system. According to literature several fingerprint as well as fingervein recognition system are designed that uses various techniques in order to reduce false detection rate and to enhance the performance of the system. A comparative study of different recognition technique along with their limitations is also summarized and optimum approach is proposed which may enhance the performance of the system. The result analysis shows that the developed technique significantly advantages over existing methods.

scholarly journals A Review on Depression Detection Among Adolescent by Face

2020 ◽  
Vol 6 (1) ◽  
pp. 4
Author(s):  
Ravi Kumar ◽  
Santosh Kumar Nagar ◽  
Anurag Shrivastava
Keyword(s):  
Convenient Method ◽  
Symptom Severity ◽  
Research Work ◽  
Mental Illnesses ◽  
Automated System ◽  
Facial Features ◽  
Clinical Judgments ◽  
The Past ◽  
Depression Detection ◽  
Intensive Work

Depression has become one of the most common mental illnesses in the past decade, affecting millions of patients and their families. However, the methods of diagnosing depression almost exclusively rely on questionnaire-based interviews and clinical judgments of symptom severity, which are highly dependent on doctors’ experience and makes it a labor-intensive work. Our study aims to develop an objective and convenient method to assist depression detection using facial features in adolescent. Most of the adolescent are totally unaware that they may be having depression. If at all they are aware of it, some adolescents conceal their depression from everyone. So, an automated system is required that will pick out the adolescents who are dealing with depression. In this paper, different research work focused for detecting depression are discussed.

scholarly journals Implementasi Algoritma Hard Melokalisasi Fitur Wajah Pada Citra Warna

2018 ◽  
Vol 10 (2) ◽  
Author(s):  
Ujang Juhardi
Keyword(s):  
Face Recognition ◽  
Data Collection ◽  
Face Detection ◽  
Detection System ◽  
Early Stage ◽  
Recognition System ◽  
Image Resolution ◽  
Level Of Detail ◽  
Facial Features ◽  
The Face

AbstrakPendeteksian wajah (face detection) adalah salah satu tahap awal yang sangat penting dalam sistem pengenalan wajah (face recognition) yang digunakan dalam identifikasi biometrik. Sejauh ini, kendala utama yang dihadapi dalam sistem pendeteksian wajah berkisar pada masalah ukuran (resolusi citra), Resolusi citra merupakan tingkat detailnya suatu citra. Semakin tinggi resolusinya semakin tinggi pula tingkat detail dari citra tersebut. Haar like Feature merupakan metode yang lazim digunakan dalam pendeteksian obyek khususnya pendeteksian wajah dan fitur-fiturnya. Prinsip Haar-like features adalah mengenali obyek berdasarkan nilai sederhana dari fitur tetapi bukan merupakan nilai piksel dari image obyek tersebut. Metode ini memiliki kelebihan yaitu komputasinya sangat cepat, karena hanya bergantung pada jumlah piksel dalam persegi bukan setiap nilai piksel dari sebuah image. Untuk mengimplementasikan dan menganalisis kecepatan hasil algoritma haar dalam melokalisasikan fitur wajah penelitian ini menggunakan software MATLAB R2012b agar dapat mengetahui bagaimana cara menganalis dan mengimplementasikan serta mendapatkan hasil menganalisis pengaruh resolusi citra dari algoritma haar dalam melokalisasikan fitur wajah(mata,hidung, dan mulut). Penelitian ini dilaksanakan secara mandiri baik pengambilan data skunder maupun proses pengolahan datanya, untuk metode pengumpulan data pada penelitian ini penulis menggunakan metode studi pustaka dan studi laboratorium. Disarankan dengan adanya penelitian ini, penulis berharap dapat memberikan kontribusi kepada peneliti yang lain untuk meneliti pengaruh-pengaruh lain yang mempengaruhi keberhasilan algoritma haar, sehingga algoritma haar dapat dikembangkan lebih baik lagi.Kata kunci: Pendektesian wajah,resolusi citra, haar like featureAbstractFace detection is one of the early stage is very important in a facial recognition system (face recognition) used in biometric identification. So far, the main obstacle in the face detection system revolves around the issue size (image resolution), the image resolution is the level of detail of an image. The higher the resolution the higher the level of detail of that image .Haar like Feature is a method commonly used in the detection of objects particularly the face detection and its features. Principle Haar-like features are simple to recognize objects based on the value of the feature but not the pixel values of the image of that object. This method has the advantage that the computation is very fast, because it depends on the number of pixels in a square instead of each pixel value of an image. To implement and analyze speed haar algorithm results in a localized facial features of this research using MATLAB R2012b software in order to know how to analyze and implement and get the results to analyze the effect of image resolution algorithm localizes haar in the facial features (eyes, nose, and mouth). This research was carried out independently both secondary data collection and processing of data, for the data collection method in this study the authors use the method of literature and laboratory studies. Suggested the presence of this study, the authors hope to contribute to fellow-researcher to examine other influences which affect the success haar algorithms, so the algorithm can be developed haar better.Keywords: face detection, image resolution, haar like feature

scholarly journals A computer vision based image processing system for depression detection among students for counseling

2019 ◽  
Vol 14 (1) ◽  
pp. 503 ◽  
Author(s):  
Sandhya Parameswaran Namboodiri ◽  
Venkataraman D
Keyword(s):  
College Students ◽  
Image Processing ◽  
Mental State ◽  
Automated System ◽  
Facial Features ◽  
Present System ◽  
College Level ◽  
Face Features ◽  
Depression Detection ◽  
Facial Images

Psychological problems in college students like depression, pessimism, eccentricity, anxiety etc. are caused principally due to the neglect of continuous monitoring of students’ psychological well-being. Identification of depression at college level is desirable so that it can be controlled by giving better counseling at the starting stage itself. The disturbed mental state of a student suffering from depression would be clearly evident in the student’s facial expressions.Identification of depression in large group of college students becomes a tedious task for an individual. But advances in the Image-Processing field have led to the development of effective systems, which prove capable of detecting emotions from facial images, in a much simpler way. Thus, we need an automated system that captures facial images of students and analyze them, for effective detection of depression. In the proposed system, an attempt is being made to make use of the Image processing techniques, to study the frontal face features of college students and predict depression. This automated system will be trained with facial features of positive and negative facial emotions. To predict depression, a video of the student is captured, from which the face of the student is extracted. Then using Gabor filters, the facial features are extracted. Classification of these facial features is done using SVM classifier. The level of depression is identified by calculating the amount of negative emotions present in the entire video. Based on the level of depression, notification is send to the class advisor, department counselor or university counselor, indicating the student’s disturbed mental state. The present system works with an accuracy of 64.38%. The paper concludes with the description of an extended architecture for depression detection as future work.

scholarly journals Comparitive Analysis of Segmentation Methods for Wheat Canopy Extraction

2021 ◽  
Author(s):  
Ankita Gupta ◽  
Lakhwinder Kaur ◽  
Gurmeet Kaur
Keyword(s):  
Water Stress ◽  
Detection System ◽  
Mean Shift ◽  
Research Work ◽  
Hybrid Approach ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Gradient Based ◽  
Comparative Results ◽  
Photosynthetic Activities

Global food demand is expected to be doubled by 2050, while natural resources are continuously under threat due to unpredictable climatic changes. This challenge can be tackled by increasing the yield of the crops and by reducing abiotic stresses such as water stress. Research shows that due to water stress the morphology and the structure of plant’s canopy changes. The first step in building early water stress detection system is to extract accurate area where photosynthetic activities of the plant are occurring. In this research work, comparative analysis of seven different segmentation algorithms viz., convolution gradient-based, watershed, mean-shift, k-means, Global static thresholding, Otsu thresholding and hybrid approach (combination of Global Static thresholding with k-means) has been analyzed in order to identify the most probable area of canopy where maximum photosynthetic signals can be captured. The comparison is done in terms of IoU metric. The comparative results indicate that the most appropriate method for wheat canopy segmentation is a hybrid approach, which achieves IoU score of 59.8 and its runner up algorithm is Global Static Thresholding with an IoU score 53.8.

scholarly journals Hierarchical Phoneme Classification for Improved Speech Recognition

2021 ◽  
Vol 11 (1) ◽  
pp. 428
Author(s):  
Donghoon Oh ◽  
Jeong-Sik Park ◽  
Ji-Hwan Kim ◽  
Gil-Jin Jang
Keyword(s):  
Speech Recognition ◽  
Language Processing ◽  
Confusion Matrix ◽  
Critical Factor ◽  
Recognition System ◽  
Classification Performance ◽  
Language Models ◽  
Successful Implementation ◽  
Phoneme Classification ◽  
Improved Performance

Speech recognition consists of converting input sound into a sequence of phonemes, then finding text for the input using language models. Therefore, phoneme classification performance is a critical factor for the successful implementation of a speech recognition system. However, correctly distinguishing phonemes with similar characteristics is still a challenging problem even for state-of-the-art classification methods, and the classification errors are hard to be recovered in the subsequent language processing steps. This paper proposes a hierarchical phoneme clustering method to exploit more suitable recognition models to different phonemes. The phonemes of the TIMIT database are carefully analyzed using a confusion matrix from a baseline speech recognition model. Using automatic phoneme clustering results, a set of phoneme classification models optimized for the generated phoneme groups is constructed and integrated into a hierarchical phoneme classification method. According to the results of a number of phoneme classification experiments, the proposed hierarchical phoneme group models improved performance over the baseline by 3%, 2.1%, 6.0%, and 2.2% for fricative, affricate, stop, and nasal sounds, respectively. The average accuracy was 69.5% and 71.7% for the baseline and proposed hierarchical models, showing a 2.2% overall improvement.

scholarly journals Recommendation Systems for Education: Systematic Review

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1611
Author(s):  
María Cora Urdaneta-Ponte ◽  
Amaia Mendez-Zorrilla ◽  
Ibon Oleagordia-Ruiz
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Formal Education ◽  
Research Work ◽  
Hybrid Approach ◽  
Recommendation Systems ◽  
Educational Resources ◽  
Future Research ◽  
Collaborative Approach ◽  
Developmental Approach

Recommendation systems have emerged as a response to overload in terms of increased amounts of information online, which has become a problem for users regarding the time spent on their search and the amount of information retrieved by it. In the field of recommendation systems in education, the relevance of recommended educational resources will improve the student’s learning process, and hence the importance of being able to suitably and reliably ensure relevant, useful information. The purpose of this systematic review is to analyze the work undertaken on recommendation systems that support educational practices with a view to acquiring information related to the type of education and areas dealt with, the developmental approach used, and the elements recommended, as well as being able to detect any gaps in this area for future research work. A systematic review was carried out that included 98 articles from a total of 2937 found in main databases (IEEE, ACM, Scopus and WoS), about which it was able to be established that most are geared towards recommending educational resources for users of formal education, in which the main approaches used in recommendation systems are the collaborative approach, the content-based approach, and the hybrid approach, with a tendency to use machine learning in the last two years. Finally, possible future areas of research and development in this field are presented.

HUMAN FACE RECOGNITION FROM A SINGLE FRONT VIEW

1992 ◽  
Vol 06 (04) ◽  
pp. 571-593 ◽  
Author(s):  
CHING-WEN CHEN ◽  
CHUNG-LIN HUANG
Keyword(s):  
Face Recognition ◽  
Active Contour Model ◽  
Facial Feature ◽  
Recognition System ◽  
Facial Features ◽  
Human Face Recognition ◽  
Front View ◽  
Deformable Template ◽  
Face Recognition System ◽  
Feature Values

This paper presents a face recognition system which can identify the unknown identity effectively using the front-view facial features. In front-view facial feature extractions, we can capture the contours of eyes and mouth by the deformable template model because of their analytically describable shapes. However, the shapes of eyebrows, nostrils and face are difficult to model using a deformable template. We extract them by using the active contour model (snake). After the contours of all facial features have been captured, we calculate effective feature values from these extracted contours and construct databases for unknown identities classification. In the database generation phase, 12 models are photographed, and feature vectors are calculated for each portrait. In the identification phase if any one of these 12 persons has his picture taken again, the system can recognize his identity.

scholarly journals Evolution of Semantic Similarity—A Survey

2021 ◽  
Vol 54 (2) ◽  
pp. 1-37
Author(s):  
Dhivya Chandrasekaran ◽  
Vijay Mago
Keyword(s):  
Natural Language ◽  
Semantic Similarity ◽  
Language Processing ◽  
Hybrid Methods ◽  
Research Work ◽  
Similarity Measures ◽  
Text Data ◽  
Knowledge Based ◽  
Open Research ◽  
Research Problems

Estimating the semantic similarity between text data is one of the challenging and open research problems in the field of Natural Language Processing (NLP). The versatility of natural language makes it difficult to define rule-based methods for determining semantic similarity measures. To address this issue, various semantic similarity methods have been proposed over the years. This survey article traces the evolution of such methods beginning from traditional NLP techniques such as kernel-based methods to the most recent research work on transformer-based models, categorizing them based on their underlying principles as knowledge-based, corpus-based, deep neural network–based methods, and hybrid methods. Discussing the strengths and weaknesses of each method, this survey provides a comprehensive view of existing systems in place for new researchers to experiment and develop innovative ideas to address the issue of semantic similarity.

Intelligent recognition system for viewpoint variations on gait and speech using CNN-CapsNet

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
G. Merlin Linda ◽  
N.V.S. Sree Rathna Lakshmi ◽  
N. Senthil Murugan ◽  
Rajendra Prasad Mahapatra ◽  
V. Muthukumaran ◽  
...  
Keyword(s):  
Spatial Information ◽  
Intelligent System ◽  
Gait Recognition ◽  
Research Work ◽  
Recognition System ◽  
Multimodal Fusion ◽  
Content Type ◽  
Surveillance Videos ◽  
Sensor Devices ◽  
Intelligent Recognition

PurposeThe paper aims to introduce an intelligent recognition system for viewpoint variations of gait and speech. It proposes a convolutional neural network-based capsule network (CNN-CapsNet) model and outlining the performance of the system in recognition of gait and speech variations. The proposed intelligent system mainly focuses on relative spatial hierarchies between gait features in the entities of the image due to translational invariances in sub-sampling and speech variations.Design/methodology/approachThis proposed work CNN-CapsNet is mainly used for automatic learning of feature representations based on CNN and used capsule vectors as neurons to encode all the spatial information of an image by adapting equal variances to change in viewpoint. The proposed study will resolve the discrepancies caused by cofactors and gait recognition between opinions based on a model of CNN-CapsNet.FindingsThis research work provides recognition of signal, biometric-based gait recognition and sound/speech analysis. Empirical evaluations are conducted on three aspects of scenarios, namely fixed-view, cross-view and multi-view conditions. The main parameters for recognition of gait are speed, change in clothes, subjects walking with carrying object and intensity of light.Research limitations/implicationsThe proposed CNN-CapsNet has some limitations when considering for detecting the walking targets from surveillance videos considering multimodal fusion approaches using hardware sensor devices. It can also act as a pre-requisite tool to analyze, identify, detect and verify the malware practices.Practical implicationsThis research work includes for detecting the walking targets from surveillance videos considering multimodal fusion approaches using hardware sensor devices. It can also act as a pre-requisite tool to analyze, identify, detect and verify the malware practices.Originality/valueThis proposed research work proves to be performing better for the recognition of gait and speech when compared with other techniques.

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