Detection of Face Emotion and Music Recommendation System using Machine Learning

Ali, Danish; Huque, Md. Tahmidul; Godhuli, Jafreen Jafor; Ahmed, Naeem

doi:10.51584/ijrias.2022.71101

Cited by 5 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, the shoe's GPS module can be enhanced to provide more precise location data, which can be used to develop better navigation and mapping applications. Also can be add new available techniques to the review process, which may include edge sensitivity, Voice alerts, Object identification using artificial intelligence, computer vision, machine learning [30], [31]. Regular weather forecasts can be generated in time to alert users to rain and storms in advance.…”

Section: Future Workmentioning

confidence: 99%

Iot Based Intelligent and Multi-Functional Shoe for the Visually Impaired Peoples

2023

IRJMETS

View full text Add to dashboard Cite

Peoples who are visually impaired typically have several challenges while engaging with their surroundings. The major challenge for visually impaired people is figuring out how to get where they want to go. People with good eyesight need to help these visually impaired peoples. Intelligent and multi-functional shoe is a great system that helps the visually impaired peoples finds their way when they walk. So, a special shoe has been made to help visually impaired people walk safely without worrying about running into other people or solid objects. This research paper proposes the design and development of an intelligent and multi-functional shoe for the visually impaired peoples. This system is based on Internet of Things (IoT) technology that incorporates various sensors to assist them in navigating their surroundings. The intelligent and multi-functional shoe has a Ultrasonic sensor (HC-SR04), an IR sensor, and a 6DOF Accelerometer Gyroscope GY-521 (MPU-6050) to detect obstacles, count the number of steps taken by the wearer, and improve their mobility and independence. Additionally, a GPS Module GY-NEO-6M V2 is used to track the wearer's location in real-time, enabling caregivers or family members to monitor their location remotely. The data collected by the sensors are transmitted to a central device via Wi-Fi or Bluetooth, providing real-time monitoring of the wearer's activities. This research paper also provides a comprehensive overview of the implementation details and the results obtained during the testing of the intelligent and multi-functional shoe. The proposed Internet of Things (IoT) based intelligent and multi-functional shoe is expected to provide a low-cost and effective solution for addressing the challenges faced by the visually impaired peoples in navigating their surroundings, and also improve the quality of life for the visually impaired peoples.

show abstract

Section: Future Workmentioning

confidence: 99%

Iot Based Intelligent and Multi-Functional Shoe for the Visually Impaired Peoples

2023

IRJMETS

View full text Add to dashboard Cite

show abstract

Music Recommendation System Using Machine Learning

Kaur,

Mehta,

Mansha

et al. 2023

2023 International Conference on Advanced Computing &Amp; Communication Technologies (ICACCTech)

View full text Add to dashboard Cite

Multimodal Data Integration for Early Alzheimer’s Detection Using Random Forest and Support Vector Machines

Muhammad Nadeem,

Wei Zhang,

Sarwat Aslam

et al. 2024

Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol

View full text Add to dashboard Cite

Alzheimer's is a very challenging brain disease to recognize, diagnose, and treat correctly when it appears in its earliest forms. The primary contribution of this research study is about machine learning models, techniques, and approaches. In contrast, Random Forest and Support Vector Machine (SVM) are particularly suitable for identifying and staging Alzheimer's disease stages using multimodal data sources. In this paper, the aim was to develop well-performing predictive models to help diagnose Alzheimer's disease at an early stage by combining neuroimaging data (MRI/PET images), imaging-based biomarkers, both structural and functional measures from MRI(P) /PET image analysis along with subject-specific demographics like age using clinical features in a probabilistic fashion obtained from the Alzheimer's Disease Neuro-Imaging Initiative (ADNI) database. The methodology focuses on data pre-processing, feature selection, and model building using supervised learning algorithms. The accuracy of the Random Forest model is 78%, having a high performance in classifying some classes while showing different marks of performances across other courses. SVM reached an accuracy of 61%, or the model's performance is good in some classes and not reliable to identify samples from the others. The findings of this study underscore the capabilities and limits of these machine learning models in identifying Alzheimer’s disease and highlight the importance of feature engineering, data pre-processing, and model tuning to increase performance and correct class unevenness and misclassification.

show abstract

Detection of Face Emotion and Music Recommendation System using Machine Learning

Cited by 5 publications

References 12 publications

Iot Based Intelligent and Multi-Functional Shoe for the Visually Impaired Peoples

Iot Based Intelligent and Multi-Functional Shoe for the Visually Impaired Peoples

Music Recommendation System Using Machine Learning

Multimodal Data Integration for Early Alzheimer’s Detection Using Random Forest and Support Vector Machines

Contact Info

Product

Resources

About