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anomaly-detection-in-logs

Using Unsupervised methods to identify anomalies in user behaviour through IP Profiling

anomaly-logs-detection

Anomaly Detection in logs data using Unsupervised Machine Learning Algorithms

capstoneproject-speech-emotion-machine-learning

datsets

voice

distracted-driver-detection

Trained MobilenetV2 model to recognize the distracted behaviors exhibited by drivers while driving. Deployed the Deep Learning model on the flask to make real-time predictions.

distracted-driver-detection-1

StateFarm dataset used to predict the class of the distracted driver using VGG-16, RESNET50, XCEPTION and MOBILE NET models

driver-drowsiness-dataset-d3s-

This page contains dataset for Driver's drwosiness detection.

drowsiness-detection

Constantly monitors the driver and plays an alert sound to wake the driver up if he/she is drowsy

drowsydriverdetection

This is a project implementing Computer Vision and Deep Learning concepts to detect drowsiness of a driver and sound an alarm if drowsy.

emotion-recognition-using-speech

Building and training Speech Emotion Recognizer that predicts human emotions using Python, Sci-kit learn and Keras

flask-graphql

fromgdrive

logbert

log anomaly detection via BERT

multimodal-emotion_recognition

ser-datasets

A collection of datasets for the purpose of emotion recognition/detection in speech.

speech-emotion-recognition

speech-emotion-recognition-1

这个项目将 RAVDESS 数据集切割成 1s 短语音，利用 openSMILE+CNN 进行训练，目标是将短语音分类到四种情感中，分别是：开心（happy）、悲伤（sad）、生气（angry）和中性（neutral）。最后准确率达到 76% 左右。

speechemotionrecognition

Classify and recognize emotions through voice signal in a foreign language