Yes / People start posting tweets containing texts, images, and videos as soon as a disaster hits an area. The analysis of these disaster-related tweet texts, images, and videos can help humanitarian response organizations in better decision-making and prioritizing their tasks. Finding the informative contents which can help in decision making out of the massive volume of Twitter content is a difficult task and require a system to filter out the informative contents. In this paper, we present a multi-modal approach to identify disaster-related informative content from the Twitter streams using text and images together. Our approach is based on long-short-term-memory (LSTM) and VGG-16 networks that show significant improvement in the performance, as evident from the validation result on seven different disaster-related datasets. The range of F1-score varied from 0.74 to 0.93 when tweet texts and images used together, whereas, in the case of only tweet text, it varies from 0.61 to 0.92. From this result, it is evident that the proposed multi-modal system is performing significantly well in identifying disaster-related informative social media contents.

The use of AI in the medical field is becoming more widespread, and research on its various applications is very popular. In biomedical image analysis, Convolutional Neural Networks (CNN), which are specialized in image processing, can analyze X-rays and detect signs of different diseases. However, to achieve that, CNNs require vast amounts of X-ray images with labels specifying the disease (labeled training data), which is not always available. One method to overcome this obstacle is the use of data augmentation. Data augmentation is manipulating images through flipping, rotating, or changing the saturation or brightness, among other methods. The purpose is to increase and diversify the training data to make the CNN model more robust. Our study aims to investigate the effects of different data augmentation techniques on the performance of a CNN model in detecting Pneumothorax. After fine-tuning our CNN model’s hyper-parameters, three data augmentation methods (color, geometric, and noise) and their combinations were applied to our model. We then tested and compared the effects of each data augmentation method on the accuracy of our model. Our study concluded that color augmentation performed the best compared to the other augmentation methods, while geometric augmentation had the worst performance. However, none of the augmentation methods significantly improved the original model’s performance, which can be attributed to the model’s configuration of hyper-parameters, leaving no room for improvement. / Användningen av AI inom det medicinska området blir mer utbredd och forskning om dess olika tillämpningar är mycket populär. Inom biomedicinsk bildanalys kan Convolutional Neural Networks (CNN), som är specialiserade på bildbehandling, analysera röntgenstrålar och upptäcka tecken på olika sjukdomar. Men för att uppnå det kräver CNN stora mängder röntgenbilder med etiketter som anger sjukdomen (märkta träningsdata), vilket inte alltid är tillgängligt. En metod för att övervinna detta hinder är användningen av dataförstärkning. Dataförstärkning är att manipulera bilder genom att bläddra, rotera eller ändra mättnad eller ljusstyrka, bland andra metoder. Syftet är att öka och diversifiera träningsdata för att göra CNN-modellen mer robust. Vår studie syftar till att undersöka effekterna av olika dataförstärkningstekniker på prestandan hos en CNN-modell vid detektering av pneumothorax. Efter att ha finjusterat vår CNN-modells hyperparametrar, tillämpades tre dataförstärkningsmetoder (färg, geometrisk och brus) och deras kombinationer på vår modell. Vi testade och jämförde sedan effekterna av varje dataförstärkningsmetod på noggrannheten i vår modell. Vår studie drog slutsatsen att färgförstärkning presterade bäst jämfört med andra förstärkningsmetoder, medan geometrisk förstärkning hade sämst prestanda. Ingen av förstärkningsmetoderna förbättrade dock den ursprungliga modellens prestanda avsevärt, vilket kan tillskrivas modellens konfiguration av hyperparametrar, vilket inte lämnar något utrymme för förbättringar.

The population of northern pike in the Baltic sea has seen a drasticdecrease in numbers in the last couple of decades. The reasons for this are believed to be many, but the majority of them are most likely anthropogenic. Today, many measures are being taken to prevent further decline of pike populations, ranging from nutrient runoff control to habitat restoration. This inevitably gives rise to the problem addressed in this project, namely: how can we best monitor pike populations so that it is possible to accurately assess and verify the effects of these measures over the coming decades? Pike is currently monitored in Sweden by employing expensive and ineffective manual methods of individual marking of pike by a handful of experts. This project provides evidence that such methods could be replaced by a Convolutional Neural Network (CNN), an automatic artificial intelligence system, which can be taught how to identify pike individuals based on their unique patterns. A neural net simulates the functions of neurons in the human brain, which allows it to perform a range of tasks, while a CNN is a neural net specialized for this type of visual recognition task. The results show that the CNN trained in this project can identify pike individuals in the provided data set with upwards of 90% accuracy, with much potential for improvement.

Indiana University-Purdue University Indianapolis (IUPUI) / The Convolutional Neural Network (CNN) is a mathematical model designed to distill input information into a more useful representation. This distillation process removes information over time through a series of dimensionality reductions, which ultimately, grant the model the ability to resist noise, and generalize effectively. However, CNNs often contain elements that are ineffective at contributing towards useful representations. This Thesis aims at providing a remedy for this problem by introducing Absolute Cosine Value Regularization (ACVR). This is a regularization technique hypothesized to increase the representational power of CNNs by using a Gradient Descent Orthogonalization algorithm to force the vectors that constitute their filters at any given convolutional layer to occupy unique positions in in their respective spaces. This method should in theory, lead to a more effective balance between information loss and representational power, ultimately, increasing network performance. The following Thesis proposes and examines the mathematics and intuition behind ACVR, and goes on to propose Dynamic-ACVR (D-ACVR). This Thesis also proposes and examines the effects of ACVR on the filters of a low-dimensional CNN, as well as the effects of ACVR and D-ACVR on traditional Convolutional filters in VGG-19. Finally, this Thesis proposes and examines regularization of the Pointwise filters in MobileNetv1.

Melanoma is a life threatening type of skin cancer with numerous fatal incidences all over the world. The 5-year survival rate is very high for cases that are diagnosed in early stage. So, early detection of melanoma is of vital importance. Except for several techniques that clinicians apply so as to improve the reliability of detecting melanoma, many automated algorithms and mobile applications have been developed for the same purpose.In this paper, deep learning model designed from scratch as well as the pretrained models Inception v3 and VGG-16 are used with the aim of developing a reliable tool that can be used for melanoma detection by clinicians and individual users. Dermatologists who use dermoscopes can take advantage of the algorithms trained on dermoscopical images and acquire a confirmation about their diagnosis. On the other hand, the models trained on clinical images can be used on mobile applications, since a cell phone camera takes images similar to them.The results using Inception v3 model for dermoscopical images achieved accuracy 91.4%, sensitivity 87.8% and specificity 92.3%. For clinical images, the VGG-16 model achieved accuracy 86.3%, sensitivity 84.5% and specificity 88.8%. The results are compared to those of clinicians, which shows that the algorithms can be used reliably for the detection of melanoma.

Evaluation using mouse subcutaneous tumour models is a key process in cancer drug development. Tumour material is implanted subcutaneously and tumour growth measured using callipers. However this methodology can have poor reproducibility and accuracy due to observer variation. Furthermore the physical pressure of using callipers can distress the mouse and lead to tumour damage. Non-invasive digital tumour imaging would reduce handling stresses and allow volume determination without physical contact. This thesis focusses on capturing 2D digital images of subcutaneous tumours, then using image processing and machine learning methods to determine 3D volume. The biggest challenge faced was lack of differentiation between tumour and surrounding skin, rendering tumour boundary identification difficult. Whilst image processing methods such as colour segmentation and edge detection were unsuccessful, machine learning proved more successful. Three convolutional neural networks, VGG-Face, VGG-19 and VGG-16 models were evaluated, with VGG-Face producing the best results. Using the layer FC7 before RELU activation for extraction in the VGC-Face model, a tumour recognition rate of 98.86% was achieved. This was increased to 100% through a semi-automatic step with detection repeated on cropped versions of negatively classified images. Finally, volume was determined through extracting image features using the VGG-Face model and conducting partial least squares regression (error of 0.1). This work has successfully demonstrated that with computational methods the volume of subcutaneous tumours can be evaluated through non-invasive digital imaging without need to have contact with the tumour itself, thus offering refinement benefits to the mice as well as eliminating observer bias.

DETECTION OF SNOW AVALANCHE DEBRIS FROM SATELLITE SYNTHETIC APERTURE RADAR (SAR) DATA Abstract This thesis engages with detection of snow avalanche debris at radar images taken with synthetic aperture radar on Sentinel-1 satellite. The aim is to find method for recognizing places at image where is the snow avalanche debris. A method is based on neural net principle, specifically on using pre-trained model of neural net VGG-19. According to results of neural net, training images are splitted into two cathegories: there is an avalanche and there is not. It is called binary classification. The result is statistical evaluation of success rate compared with other traditional methods. keywords: snow avalanche, Sentinel-1, neural net, VGG-19

The 2019 novel coronavirus has been proven to present several unique features on chest X-rays and CT-scans that distinguish it from imaging of other pulmonary diseases such as bacterial pneumonia and viral pneumonia unrelated to COVID-19. However, the key characteristics of a COVID-19 infection have been proven challenging to detect with the human eye. The aim of this project is to explore if it is possible to distinguish a patient with COVID-19 from a patient who is not suffering from the disease from posteroanterior chest X-ray images using synthetic minority over-sampling and transfer learning. Furthermore, the report will also present the mechanics of COVID-19, the used dataset and models and the validity of the results.

The Convolutional Neural Network (CNN) is a mathematical model designed to distill input information into a more useful representation. This distillation process removes information over time through a series of dimensionality reductions, which ultimately, grant the model the ability to resist noise, and generalize effectively. However, CNNs often contain elements that are ineffective at contributing towards useful representations. This Thesis aims at providing a remedy for this problem by introducing Absolute Cosine Value Regularization (ACVR). This is a regularization technique hypothesized to increase the representational power of CNNs by using a Gradient Descent Orthogonalization algorithm to force the vectors that constitute their filters at any given convolutional layer to occupy unique positions in Rⁿ. This method should in theory, lead to a more effective balance between information loss and representational power, ultimately, increasing network performance. The following Thesis proposes and examines the mathematics and intuition behind ACVR, and goes on to propose Dynamic-ACVR (D-ACVR). This Thesis also proposes and examines the effects of ACVR on the filters of a low-dimensional CNN, as well as the effects of ACVR and D-ACVR on traditional Convolutional filters in VGG-19. Finally, this Thesis proposes and examines regularization of the Pointwise filters in MobileNetv1.

Facial recognition technology has become a ubiquitous tool in security and personal identification. However, the rise of this technology has been accompanied by concerns over inherent biases, particularly regarding ethnic and gender. This thesis examines the extent of these biases by focusing on the influence of dataset imbalances in facial recognition algorithms. We employ a structured methodological approach that integrates AI-generated images to enhance dataset diversity, with the intent to balance representation across ethnics and genders. Using the ResNet and Vgg model, we conducted a series of controlled experiments that compare the performance impacts of balanced versus imbalanced datasets. Our analysis includes the use of confusion matrices and accuracy, precision, recall and F1-score metrics to critically assess the model’s performance. The results demonstrate how tailored augmentation of training datasets can mitigate bias, leading to more equitable outcomes in facial recognition technology. We present our findings with the aim of contributing to the ongoing dialogue regarding AI fairness and propose a framework for future research in the field.