Accurate identification of animal species is necessary to understand biodiversity richness, monitor endangered species, and study the impact of climate change on species distribution within a specific region. Camera traps represent a passive monitoring technique that generates millions of ecological images. The vast numbers of images drive automated ecological analysis as essential, given that manual assessment of large datasets is laborious, time-consuming, and expensive. Deep learning networks have been advanced in the last few years to solve object and species identification tasks in the computer vision domain, providing state-of-the-art results. In our work, we trained and tested machine learning models to classify three animal groups (snakes, lizards, and toads) from camera trap images. We experimented with two pretrained models, VGG16 and ResNet50, and a self-trained convolutional neural network (CNN-1) with varying CNN layers and augmentation parameters. For multiclassification, CNN-1 achieved 72% accuracy, whereas VGG16 reached 87%, and ResNet50 attained 86% accuracy. These results demonstrate that the transfer learning approach outperforms the self-trained model performance. The models showed promising results in identifying species, especially those with challenging body sizes and vegetation.

In this paper, an initial work of a research is discussed which is to teach young autistic children recognizing human facial expression with the help of computer vision and image processing. This paper mostly discusses the initial work of facial expression recognition using a deep convolutional neural network. The Kaggle's FER2013 dataset has been used to train and experiment with a deep convolutional neural network model. Once a satisfactory result is achieved, the dataset is modified with pictures of four different lighting conditions and each of these datasets is again trained with the same model. This is necessary for the end goal of the research which is to recognize facial expression in any possible environment. Finally, the comparison between results with different datasets is discussed and future work of the project is outlined.

The primary sources of injuries in the workplace are improper manual material handling (MMH) motions, forklift collisions, slip, and fall. This research presents a Digital Twin (DT) framework to analyze fatigue in humans while performing lifting MMH activity in a laboratory environment for the purpose of reducing these types of injuries. The proposed methodology analyzes the worker’s body joints to detect biomechanical fatigue as a factor of change in back, elbow, and knee joint angles. Using the dynamic time warping (DTW) algorithm, the change in joint angles with time was analyzed. The variation in DTW parameters was evaluated using exponentially weighted moving average (EWMA) control charts for further analysis. A preliminary study considering two healthy male subjects performing seven experiments, each under an optical motion capture system was performed to test the model’s efficiency. Our contributions are twofold. First, we propose a model to detect biomechanical fatigue in the subjects performing MMH lifting activity as a change in joint angles. Secondly, the research also shows evidence that different individuals show signs of body fatigue via different body joints and showcases the need for a true personalized DT for an operator for fatigue assessment in an MMH environment.

Fire incidents are responsible for severe damage and thousands of deaths every year all over the world. Extreme temperatures, low visibility, toxic gases, and unknown locations of victims create difficulties and delays in rescue operations, escalating the risk of injury or death. It is time-critical to detect the victims trapped inside the burning sites for facilitating the rescue operations. This research work presents an audio-based automated system for victim detection in fire emergencies, investigating two machine learning (ML) methods: support vector machines (SVM) and long short-term memory (LSTM). The performance of these two ML techniques has been evaluated based on a variety of performance metrics. Our analyses show that both ML methods provide superior scream detection performance, with SVM slightly overperforming LSTM. Because of its lower complexity, SVM is a better candidate for real-time implementation in our autonomous embedded system vehicle (AESV).

Protection of endangered species requires continuous monitoring and updated information about the existence, location, and behavioral alterations in their habitat. Remotely activated camera or “camera traps” is a reliable and effective method of photo documentation of local population size, locomotion, and predator-prey relationships of wild species. However, manual data processing from a large volume of images and captured videos is extremely laborious, time-consuming, and expensive. The recent advancement of deep learning methods has shown great outcomes for object and species identification in images. This paper proposes an automated wildlife monitoring system by image classification using computer vision algorithms and machine learning techniques. The goal is to train and validate a Convolutional Neural Network (CNN) that will be able to detect Snakes, Lizards and Toads/Frogs from camera trap images. The initial experiment implies building a flexible CNN architecture with labeled images accumulated from standard benchmark datasets of different citizen science projects. After accessing satisfactory accuracy, new camera-trap imagery data (collected from Bastrop County, Texas) will be implemented to the model to detect species. The performance will be evaluated based on the accuracy of prediction within their classification. The suggested hardware and software framework will offer an efficient monitoring system, speed up wildlife investigation analysis, and formulate resource management decisions.

Firefighters face numerous challenges when entering burning structures to rescue trapped victims, assess the conditions of a residential structure, and extinguish the fire as quickly as possible. These challenges include extreme temperatures, smoke, toxic gases, explosions, and falling objects, which can hinder their efficiency and pose risks to their safety. Accurate information and data about the burning site can help firefighters make informed decisions about their duties and determine when it is safe to enter and evacuate, reducing the likelihood of casualties. This research presents unsupervised deep learning (DL) to classify the danger levels at a burning site and an autoregressive integrated moving average (ARIMA) prediction model to forecast temperature changes using the extrapolation of a random forest regressor. The DL classifier algorithms provide the chief firefighter with an awareness of the danger levels in the burning compartment. The prediction models forecast the rise in temperature from a height ranging from 0.6 m to 2.6 m and the changes in temperature over time at an altitude of 2.6 m. Predicting the temperature at this altitude is critical as the temperature increases faster with height, and elevated temperatures can weaken the building’s structural material. We also investigated a new classification method using an unsupervised DL autoencoder artificial neural network (AE-ANN). The prediction data analytical approach included using the autoregressive integrated moving average (ARIMA) with random forest regression implementation. The proposed AE-ANN model, with an accuracy score of 0.869, did not perform as well compared to previous work, with an accuracy of 0.989, at achieving high accuracy scores for the classification task using the same dataset. However, the random forest regressor and our ARIMA models are analyzed and evaluated in this work, while other research has not utilized this dataset, even though it is open-sourced. However, the ARIMA model demonstrated remarkable predictions of the trends of temperature changes in a burning site. The proposed research aims to classify fire sites into dangerous levels and predict temperature progression using deep learning and predictive modeling techniques. This research’s main contribution is using a random forest regressor and autoregressive integrated moving average models to predict temperature trends in burning sites. This research demonstrates the potential of using deep learning and predictive modeling to enhance firefighter safety and decision-making processes.