On the Audio-Visual Emotion Recognition using Convolutional Neural Networks and Extreme Learning Machine

The advances in artificial intelligence and machine learning concerning emotion recognition have been enormous and in previously inconceivable ways. Inspired by the promising evolution in human-computer interaction, this paper is based on developing a multimodal emotion recognition system. This research encompasses two modalities as input, namely speech and video. In the proposed model, the input video samples are subjected to image pre-processing and image frames are obtained. The signal is pre-processed and transformed into the frequency domain for the audio input. The aim is to obtain Mel-spectrogram, which is processed further as images. Convolutional neural networks are used for training and feature extraction for both audio and video with different configurations. The fusion of outputs from two CNNs is done using two extreme learning machines. For classification, the proposed system incorporates a support vector machine. The model is evaluated using three databases, namely eNTERFACE, RML, and SAVEE. For the eNTERFACE dataset, the accuracy obtained without and with augmentation was 87.2% and 94.91%, respectively. The RML dataset yielded an accuracy of 98.5%, and for the SAVEE dataset, the accuracy reached 97.77%. Results achieved from this research are an illustration of the fruitful exploration and effectiveness of the proposed system

Pulsed Eddy current signal processing using wavelet scattering and Gaussian process regression for fast and accurate ferromagnetic material thickness measurement

Testing the structural integrity of pipelines is a crucial maintenance task in the oil and gas industry. This structural integrity could be compromised by corrosions that occur in the pipeline wall. They could cause catastrophic accidents and are very hard to detect due to the presence of insulation and cladding around the pipeline. This corrosion manifests as a reduction in the pipe wall thickness, which can be detected and quantified by using Pulsed Eddy Current (PEC) as a state-of-the-art Non-Destructive Evaluation technique. The method exploits the relationship between the natural log transform of the PEC signal with the material thickness. Unfortunately, measurement noise reduces the accuracy of the technique particularly due to its amplified effect in the log-transform domain, the inherent noise characteristics of the sensing device, and the non-homogenous property of the pipe material. As a result, the technique requires signal averaging to reduce the effect of the noise to improve the prediction accuracy. Undesirably, this increases the inspection time significantly, as more measurements are needed. Our proposed method can predict pipe wall thickness without PEC signal averaging. The method applies Wavelet Scattering transform to the log-transformed PEC signal to generate a suitable discriminating feature and then applies Neighborhood Component Feature Selection method to reduce the feature dimension before using it to train a Gaussian Process regression model. Through experimentation using ferromagnetic samples, we have shown that our method can produce a more accurate estimation of the samples’ thickness than other methods over different types of cladding materials and insulation layer thicknesses. Quantitative proof of this conclusion is provided by statistically analyzing and comparing the root mean square errors of our model with those from the inverse time derivative approach as well as other machine learning models

Enhanced Emotion Recognition in Videos: A Convolutional Neural Network Strategy for Human Facial Expression Detection and Classification

The human face is essential in conveying emotions, as facial expressions serve as effective, natural, and universal indicators of emotional states. Automated emotion recognition has garnered increasing interest due to its potential applications in various fields, such as human-computer interaction, machine learning, robotic control, and driver emotional state monitoring. With artificial intelligence and computational power advancements, visual emotion recognition has become a prominent research area. Despite extensive research employing machine learning algorithms like convolutional neural networks (CNN), challenges remain concerning input data processing, emotion classification scope, data size, optimal CNN configurations, and performance evaluation. To address these issues, we propose a comprehensive CNN-based model for real-time detection and classification of five primary emotions: anger, happiness, neutrality, sadness, and surprise. We employ the Amsterdam Dynamic Facial Expression Set – Bath Intensity Variations (ADFES-BIV) video dataset, extracting image frames from the video samples. Image processing techniques such as histogram equalization, color conversion, cropping, and resizing are applied to the frames before labeling. The Viola-Jones algorithm is then used for face detection on the processed grayscale images. We develop and train a CNN on the processed image data, implementing dropout, batch normalization, and L2 regularization to reduce overfitting. The ideal hyperparameters are determined through trial and error, and the model's performance is evaluated. The proposed model achieves a recognition accuracy of 99.38%, with the confusion matrix, recall, precision, F1 score, and processing time further quantifying its performance characteristics. The model's generalization performance is assessed using images from the Warsaw Set of Emotional Facial Expression Pictures (WSEFEP) and Extended Cohn-Kanade Database (CK+) datasets. The results demonstrate the efficiency and usability of our proposed approach, contributing valuable insights into real-time visual emotion recognition

Enhanced emotion recognition in videos: a convolutional neural network strategy for human facial expression detection and classification

The human face is essential in conveying emotions, as facial expressions serve as effective, natural, and universal indicators of emotional states. Automated emotion recognition has garnered increasing interest due to its potential applications in various fields, such as human-computer interaction, machine learning, robotic control, and driver emotional state monitoring. With artificial intelligence and computational power advancements, visual emotion recognition has become a prominent research area. Despite extensive research employing machine learning algorithms like convolutional neural networks (CNN), challenges remain concerning input data processing, emotion classification scope, data size, optimal CNN configurations, and performance evaluation. To address these issues, we propose a comprehensive CNN-based model for real-time detection and classification of five primary emotions: anger, happiness, neutrality, sadness, and surprise. We employ the Amsterdam Dynamic Facial Expression Set – Bath Intensity Variations (ADFES-BIV) video dataset, extracting image frames from the video samples. Image processing techniques such as histogram equalization, color conversion, cropping, and resizing are applied to the frames before labeling. The Viola-Jones algorithm is then used for face detection on the processed grayscale images. We develop and train a CNN on the processed image data, implementing dropout, batch normalization, and L2 regularization to reduce overfitting. The ideal hyperparameters are determined through trial and error, and the model's performance is evaluated. The proposed model achieves a recognition accuracy of 99.38%, with the confusion matrix, recall, precision, F1 score, and processing time further quantifying its performance characteristics. The model's generalization performance is assessed using images from the Warsaw Set of Emotional Facial Expression Pictures (WSEFEP) and Extended Cohn-Kanade Database (CK+) datasets. The results demonstrate the efficiency and usability of our proposed approach, contributing valuable insights into real-time visual emotion recognition

Affective computing for visual emotion recognition using convolutional neural networks

Affective computing is a developing interdisciplinary examination field uniting specialists and experts from different fields, going from artificial intelligence, nat-ural language processing, to intellectual and sociologies. The thought behind Af-fective Computing is to give PCs the aptitude of insight that will, in general, comprehend human feelings. Notwithstanding, these victories, the field needs hypothetical firm establishments and efficient rules in numerous regions, espe-cially so in feeling demonstrating and the development of computational models of feeling. This exploration manages Affective Computing to improve the exhibi-tion of Human-Machine Interaction. The focal point of this work is to distinguish the emotional state of a human utilizing deep learning procedure, i.e. Convolu-tional Neural Networks (CNN). The Warsaw Set of Emotional Facial Expression Pictures dataset has been utilized to build up a feeling acknowledgement model which will have the option to perceive five facial feelings, including happy, sad, anger, surprise and neutral. The proposed framework design and the strategy has been discussed in this paper alongside the experimental findings

Efficient pavement crack detection and classification using custom YOLOv7 model

It is crucial to detect and classify pavement cracks as part of maintaining road safety. The inspection process for identifying and classifying cracks manually is tedious, time-consuming, and potentially dangerous for inspectors. As a result, an efficient automated approach for detecting road cracks is essential for this development. Numerous issues, such as variations in intensity, uneven data availability, the inefficacy of traditional approaches, and others, make it challenging to accomplish. This research has been carried out to contribute towards developing an efficient pavement crack detection and classification system. This study uses state of the art deep learning algorithm, customized YOLOv7 model. Data from two sources, RDD2022, a publicly available online dataset, and the second set of data gathered from the roads of Malaysia have been used in this investigation. In order to have balanced data for training, many image preprocessing techniques have been applied to the data, such as augmentations, scaling, blurring, etc. Experimental results demonstrate that the detection accuracy of the YOLOv7 model is significant, 92% on the RDD2022 dataset and 88% on our custom dataset. This study reports the outcomes of experiments conducted on both datasets. RDD2022 achieved a precision of 0.9523 and a recall of 0.9545. On the custom dataset, the resulting values for precision and recall were 0.93 and 0.9158, respectively. The results of this study were compared to those of other recent studies in the same field in order toestablish a benchmark. Results from the proposed system were more encouraging and surpassed the benchmarking ones

Implementing critical thinking methods in Engineering Curriculum

It is the deliberate and skillful process of intentionally and intellectually applying, analyzing, synthesizing and/or evaluating knowledge received through observation, experience, reflection, reasoning and/or communication as a guide to beliefs and actions. The development of critical thinking abilities is emphasized in Islamic teachings and modern educational models and methodologies. When it comes to technological data, engineers are the experts. Engineers are becoming more and more reliant on critical thinking as issues become more complex. Critical thinking in engineering, particularly for students, is the subject of this paper, which emphasizes its importance

Deep learning based emotion recognition for image and video signals: matlab implementation

Emotion recognition utilizing pictures, videos, or speech as input is considered an intriguing issue in the research field over certain years. The introduction of deep learning procedures like the Convolutional Neural Networks (CNN) has made emotion recognition achieve promising outcomes. This book is carried out to develop an image and video-based emotion recognition model using CNN for automatic feature extraction and classification with Matlab sample codes. Five emotions are considered for recognition: angry, happy, neutral, sad, and surprise, compared to previous algorithms. Different pre-processing steps have been carried over data samples, followed by the popular and efficient Viola-Jones algorithm for face detection. Evaluating results using confusion matrix, accuracy, F1-score, precision, and recall shows that video-based datasets obtained more promising results than image-based datasets

Image-based disease detection and classification in Indian apple plant species by using deep learning

Plant diseases pose a significant danger to global food security, yet their timely diagnosis remains difficult across many regions of the world due to the lack of infrastructure. Traditional farming methods are insufficient to address the impending global food crises. As a result, agricultural product growth is critical, and new techniques and methods are required for efficient and sustainable farming practices that balance the supply chain according to customer demand. Even though India is one of the most agriculturally dependent countries, it nevertheless suffers from various agricultural shortages. Plant diseases that go unnoticed and untreated are one such deprivation. Developing a smart technique for plant disease detection is explored in this research. For this, we used deep learning to develop an intelligent system for image-based disease detection in Indian apple plant species. Specifically, this model uses a convolution neural network to identify diseases in apple plants. On the basis of 70% - 30% and 80 % - 20% dataset partition, the proposed model obtained an accuracy of 97.5 % and 98.4 %, respectively. The results obtained from this study illustrate the productive exploration along with the utility of the proposed model for future research by implementing various deep learning models and incorporating additional modules that will provide cure and preventative measures for the detected diseases

Localisation of inspection probes in a storage tank

Indoor Positioning System (IPS) has been widely used in today’s industry for the various purposes of locating people or objects such as inspection, navigation, and security. Many research works have been done to develop the system by using wireless technology such as Bluetooth and Wi-Fi. The techniques that can give some better performances in terms of accuracy have been investigated and developed. In this paper, ZigBee IEEE 802.15.4 wireless communication protocols are used to implement an indoor localization application system. The research is focusing more on analyzing the behaviour of Received Signal Strength Indicator (RSSI) reading under several conditions and locations by applying the Trilateration algorithm for localizing. The conditions are increasing the number of transmitters, experimented in the non-wireless connection room and wireless connection room by comparing the variation of RSSI values. Analysis of the result shows that the accuracy of the system was improved as the number of transmitters was increased