AI-based Depression Detection using Profile Information DOI

Shaik Rasheeda Begum,

Saad Yunus Sait, Arul Saravanan Ramachandran

и другие.

Опубликована: Дек. 11, 2023

Depression is a severe mental health problem for people around the world, regardless of age, gender, or race. It cause psychological disability, and these disorders can have an impact on person's interpersonal connections, such as work environment family life, well their overall routines, irregular eating sleeping patterns. However, unfortunately, majority cases depression go undiagnosed and, therefore, untreated. Depression, when not detected at earlier stage, become illness may lead to suicide later stages. Consequently, it becomes crucial identify prevent stage. The data this study are collected through survey from undergraduates in consultation with psychiatrists professors.Further, Natural Language Processing(NLP) techniques Machine learning methodologies were used train evaluate efficiency proposed model. This looked various feature selection (FS) filter method Maximum Relevance Minimum Redundancy-mRMR, wrapper Recursive Feature Elimination-RFE, Boruta, Embedded method: Least Absolute Shrinkage Selection Operator-LASSO extract most significant features profile information user responsible forming depression. Adaboost model produced accuracy 94% considering all elements dataset. different techniques, applied, we found mRMR FS using Optuna Hypertuning 96%.

Язык: Английский

Stacking ensemble based hyperparameters to diagnosing of heart disease: Future works DOI Creative Commons
Alfredo Daza Vergaray, Juana Bobadilla Cornelio, Juan Carlos Herrera

и другие.

Results in Engineering, Год журнала: 2024, Номер 21, С. 101894 - 101894

Опубликована: Фев. 12, 2024

Heart disease is one of the most recurrent and worrying health problems today, due to its multiple complications, including: stroke, cardiac arrest, retinopathy, etc. Propose a method 4 Stacking models based on hyperparameters diagnose heart disease. In addition, web interface was developed with best model proposed in this study. First, dataset used from Disease Cleveland ICU, which 918 patient records 12 attributes. Therefore, paper composed following stages: Cleaning Pre-processing; Describe data; Training testing Cross validation; Calibration models; modelling evaluation, also compare different techniques predict using ensemble taking into account performance evaluation parameters. 1 (Logistic regression) oversampling AdaBoost-SVM hyperparameter test obtained higher Accuracy (88.24%), ROC Curve (92.00%), while too reached better Precision (88.54%), but algorithm achieved high value Sensitivity (88.14%) F1-Score (88.19%). Implementing stacking hyperparameters, it helps make an early diagnosis greater precision, decrease quantity deceases caused by it. combined method, improvement prediction observed, surpassing independent algorithms used.

Язык: Английский

Процитировано

12
New Insights into the Emerging Trends Research of Machine and Deep Learning Applications in Energy Storage: A Bibliometric Analysis and Publication Trends DOI Creative Commons
Samuel-Soma M. Ajibade, Abdelhamid Zaïdi, Asamh Saleh M. Al Luhayb

и другие.

International Journal of Energy Economics and Policy, Год журнала: 2023, Номер 13(5), С. 303 - 314

Опубликована: Сен. 16, 2023

The publication trends and bibliometric analysis of the research landscape on applications machine deep learning in energy storage (MDLES) were examined this study based published documents Elsevier Scopus database between 2012 2022. PRISMA technique employed to identify, screen, filter related publications MDLES recovered 969 comprising articles, conference papers, reviews English. results showed that count topic increased from 3 385 (or a 12,733.3% increase) along with citations high rate was ascribed impact, co-authorships/collaborations, as well source title/journals’ reputation, multidisciplinary nature, funding. top/most prolific researcher, institution, country, funding body are; is Yan Xu, Tsinghua University, China, National Natural Science Foundation respectively. Keywords occurrence revealed three clusters or hotspots learning, digital storage, Energy Storage. Further currently largely focused application machine/deep for predicting, operating, optimising design materials renewable technologies such wind, PV solar. However, future will presumably include focus advanced development, operational systems monitoring control techno-economic address challenges associated efficiency analysis, costing electricity pricing, trading, revenue prediction

Язык: Английский

Процитировано

22
Sentiment Analysis on E-Commerce Product Reviews Using Machine Learning and Deep Learning Algorithms: A Bibliometric Analysis, Systematic Literature Review, Challenges and Future Works DOI Creative Commons
Alfredo Daza Vergaray,

Néstor Daniel González Rueda,

Mirelly Sonia Aguilar Sánchez

и другие.

International Journal of Information Management Data Insights, Год журнала: 2024, Номер 4(2), С. 100267 - 100267

Опубликована: Июль 18, 2024

Язык: Английский

Процитировано

4
Optimizing Support Vector Machine Performance for Parkinson's Disease Diagnosis Using GridSearchCV and PCA-Based Feature Extraction DOI Creative Commons

Jumanto Jumanto,

Rofik Rofik,

Endang Sugiharti

и другие.

Journal of Information Systems Engineering and Business Intelligence, Год журнала: 2024, Номер 10(1), С. 38 - 50

Опубликована: Фев. 28, 2024

Background: Parkinson's disease (PD) is a critical neurodegenerative disorder affecting the central nervous system and often causing impaired movement cognitive function in patients. In addition, its diagnosis early stages requires complex time-consuming process because all existing tests such as electroencephalography or blood examinations lack effectiveness accuracy. Several studies explored PD prediction using sound, with specific focus on development of classification models to enhance The majority these neglected crucial aspects including feature extraction proper parameter tuning, leading low Objective: This study aims optimize performance voice-based through extraction, goal reducing data dimensions improving model computational efficiency. Additionally, appropriate parameters will be selected for enhancement ability identify both cases healthy individuals. Methods: proposed new applied an OpenML dataset comprising voice recordings from 31 individuals, namely 23 patients 8 participants. experimental included initial use SVM algorithm, followed by implementing PCA machine learning Subsequently, balancing SMOTE was conducted, GridSearchCV used best combination based predicted characteristics. Result: Evaluation showed impressive accuracy 97.44%, sensitivity 100%, specificity 85.71%. excellent result achieved limited 10-fold cross-validation rendering sensitive training data. Conclusion: successfully enhanced SVM+PCA+GridSearchCV+CV method. However, future investigations should consider number folds small dataset, explore alternative methods, expand generalizability. Keywords: GridSearchCV, Parkinson Disaese, SVM, PCA, SMOTE, Voice/Speech

Язык: Английский

Процитировано

3
Software Defect Prediction Based on a Multiclassifier with Hyperparameters: Future Work DOI Creative Commons
Alfredo Daza Vergaray

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104123 - 104123

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
A Hybrid Model for Improving Customer Lifetime Value Prediction Using Stacking Ensemble Learning Algorithm DOI Creative Commons

Amir Mohammad Haddadi,

H. Hamidi

Computers in Human Behavior Reports, Год журнала: 2025, Номер unknown, С. 100616 - 100616

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0
Explainable artificial intelligence model for the prediction of undrained shear strength DOI Creative Commons

Ho-Hong-Duy Nguyen,

Thanh‐Nhan Nguyen,

Thi-Anh-Thu Phan

и другие.

Theoretical and Applied Mechanics Letters, Год журнала: 2025, Номер unknown, С. 100578 - 100578

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0
Industrial applications of artificial intelligence in software defects prediction: Systematic review, challenges, and future works DOI
Alfredo Daza Vergaray,

Gonzalo Apaza-Perez,

Katherine Samanez-Torres

и другие.

Computers & Electrical Engineering, Год журнала: 2025, Номер 124, С. 110411 - 110411

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0
Systematic review of machine learning techniques to predict anxiety and stress in college students DOI Creative Commons
Alfredo Daza Vergaray, Nemías Saboya, Jorge Isaac Necochea-Chamorro

и другие.

Informatics in Medicine Unlocked, Год журнала: 2023, Номер 43, С. 101391 - 101391

Опубликована: Янв. 1, 2023

Anxiety is considered one of the most common pathologies that people go through frequently, this being main cause illness and disability in students since it more women with 7.7% than men 3.6%. Moreover, stress also causes some health-related problems, such as cardiovascular diseases mental disorders. The purpose study to gain a deeper understanding methodologies, attributes, selection algorithms, well techniques, tools or programming languages, metrics machine learning algorithms have been applied prediction anxiety college students. An exhaustive search 29 articles was performed, using keywords from 7 databases: ScienceDirect, IEEE Xplore, ACM, Scopus, Springer Link, InderScience Wiley 2019 2023. This article based on Preferred Reporting Items for Systematic Reviews Meta-Analyses (PRISMA) methodology, taking into account inclusion exclusion criteria. To then make synthesis findings studies about following aspects languages metrics. methodology used sequence steps, important attributes were age gender, do not use variable techniques; other hand, efficient techniques Support Vector Machine (SVM) Logistic regression (LR), language develop models Python finally essential determine effectiveness model Precision Accuracy. systematic review provides scientific evidence, results describing how help predict stress. For this, are compared perform broad analysis these Programming metrics, variables influential factors, which will medical fields detection

Язык: Английский

Процитировано

8
Improved playstore review sentiment classification accuracy with stacking ensemble DOI Creative Commons
Dwi Budi Santoso,

Aliyatul Munna,

Dewi Handayani Untari Ningsih

и другие.

Journal of Soft Computing Exploration, Год журнала: 2024, Номер 5(1), С. 38 - 45

Опубликована: Март 18, 2024

In today's digital era, user reviews on the Playstore platform are an invaluable source of information for developers, offering insights that critical service improvement. Previous research has explored application stacking ensemble methods, such as in context predicting depression among university students, to enhance prediction accuracy. However, these studies often do not explicitly detail data acquisition process, leaving a gap understanding applicability methods different domains. This aims bridge this by applying approach improve accuracy sentiment classification reviews, with clear exposition collection method. Utilizing Logistic Regression meta classifier, methodology is executed several stages. Initially, was collected from online loan applications Google Playstore, ensuring transparency process. The then classified using three basic models: Random Forest, Naive Bayes, and SVM. outputs models serve inputs model. A comparison each base model output subsequently carried out. test results review dataset demonstrated increase accuracy, precision, recall, F1 score compared single model, achieving 87.05%, which surpasses Forest (85.6%), Bayes (85.55%), SVM (86.5%). indicates effectiveness method providing deeper more accurate into sentiment, overcoming limitations previous addressing methods.

Язык: Английский

Процитировано

1