Brain tumor is a disease characterized by uncontrolled growth of abnormal cells inthe brain. The brain is responsible for regulating the functions of all other organs,hence, any atypical growth of cells in the brain can have severe implications for itsfunctions. The number of global mortality in 2020 led by cancerous brains was estimatedat 251,329. However, early detection of brain cancer is critical for prompttreatment and improving patient’s quality of life as well as survival rates. Manualmedical image classification in diagnosing diseases has been shown to be extremelytime-consuming and labor-intensive. Convolutional Neural Networks (CNNs) hasproven to be a leading algorithm in image classification outperforming humans. Thispaper compares five CNN architectures namely: VGG-16, VGG-19, AlexNet, EffecientNetB7,and ResNet-50 in terms of performance and accuracy using transferlearning. In addition, the authors discussed in this paper the economic impact ofCNN, as an AI approach, on the healthcare sector. The models’ performance isdemonstrated using functions for loss and accuracy rates as well as using the confusionmatrix. The conducted experiment resulted in VGG-19 achieving best performancewith 97% accuracy, while EffecientNetB7 achieved worst performance with93% accuracy. / Hjärntumör är en sjukdom som kännetecknas av okontrollerad tillväxt av onormalaceller i hjärnan. Hjärnan är ansvarig för att styra funktionerna hos alla andra organ,därför kan all onormala tillväxt av celler i hjärnan ha allvarliga konsekvenser för dessfunktioner. Antalet globala dödligheten ledda av hjärncancer har uppskattats till251329 under 2020. Tidig upptäckt av hjärncancer är dock avgörande för snabb behandlingoch för att förbättra patienternas livskvalitet och överlevnadssannolikhet.Manuell medicinsk bildklassificering vid diagnostisering av sjukdomar har visat sigvara extremt tidskrävande och arbetskrävande. Convolutional Neural Network(CNN) är en ledande algoritm för bildklassificering som har överträffat människor.Denna studie jämför fem CNN-arkitekturer, nämligen VGG-16, VGG-19, AlexNet,EffecientNetB7, och ResNet-50 i form av prestanda och noggrannhet. Dessutom diskuterarförfattarna i studien CNN:s ekonomiska inverkan på sjukvårdssektorn. Modellensprestanda demonstrerades med hjälp av funktioner om förlust och noggrannhetsvärden samt med hjälp av en Confusion matris. Resultatet av det utfördaexperimentet har visat att VGG-19 har uppnått bästa prestanda med 97% noggrannhet,medan EffecientNetB7 har uppnått värsta prestanda med 93% noggrannhet.

Cette étude porte sur les défis à l’intégration des pharmaciens diplômés à l’étranger au marché du travail du Québec. L’objectif vise à comprendre le processus d’intégration au Québec et la contribution des différents acteurs qui sont impliqués au processus de reconnaissance des diplômes étrangers dans le cas des pharmaciens. La question de recherche est « Comment les multi-acteurs de la coalition intersectorielle contribuent-ils au processus de reconnaissance des diplômes étrangers dans le cas des pharmaciens? Le modèle conceptuel d’analyse tourne autour de la contribution des acteurs impliqués dans le processus d’intégration et le cadre conceptuel mis à profit se base sur la théorie des coalitions intersectorielles. La coalition intersectorielle soutient l’idée que la coopération entre différents secteurs permettrait d’aborder les questions de manière plus globale afin de répondre à une complexification des problématiques concernées. L’intégration des professionnels immigrants est reconnue pour sa complexité et sa multidimensionnalité. La coalition apporte une réponse globale et satisfaisante aux défis de l’intégration des professionnels immigrants facilitant ainsi l’ouverture à la diversité professionnelle. Pour atteindre cet objectif et répondre à la question de recherche, l’approche employée est de nature qualitative puisque des entrevues ont été réalisées avec une vingtaine des représentants des différents organismes qui interviennent dans le processus d’intégration des immigrants formés à l’étranger. / This study focuses on the challenges of integrating pharmacists with foreign graduates into the Quebec labor market. The objective is to understand the process of integration in Quebec and the contribution of the various actors who are involved in the process of recognition of foreign diplomas in the case of pharmacists. The research question is “How do the multi-actors of the intersectoral coalition contribute to the process of recognition of foreign diplomas in the case of pharmacists? The conceptual analysis model revolves around the contribution of the actors involved in the integration process and the conceptual framework used is based on the theory of intersectoral coalitions. The intersectoral coalition supports the idea that cooperation between different sectors would make it possible to address the issues in a more global way in order to respond to the increasing complexity of the issues concerned. The integration of immigrant professionals is recognized for its complexity and multidimensionality. The coalition provides a comprehensive and satisfactory response to the challenges of integrating immigrant professionals, thus facilitating openness to professional diversity. To achieve this objective and answer the research question, the approach used is of a qualitative nature since interviews were carried out with about twenty representatives of the various organizations involved in the process of integrating immigrants trained abroad.

Les changements climatiques et les phénomènes météorologiques extrêmes sont devenus des moteurs importants de changements de la biodiversité, posant une menace pour la perte d’habitat et l’extinction d’espèces. Comprendre l’état actuel de la biodiversité et identifier les zones hautement adaptées (still strugling with this expression, high suitability for who or what?) sont essentiels afin de lutter contre la perte de biodiversité et guider les processus décisionnels en lien avec les études scientifiques (added scientifiques, as in scientific surveys), les mesures de protection et les efforts de restauration. Les modèles de distribution des espèces (MDE ou SDM en anglais) sont des outils statistiques permettant de prédire la distribution géographique potentielle d’une espèce en fonction de variables environnementales et des données recueillies à cet endroit. Cependant, les MDE conventionnels sont souvent confrontés à des limitations dues à la résolution spatiale et à la couverture restreinte des variables environnementales, lesquelles sont obtenues suite à des mesures au sol ou à l’aide de stations météorologiques. Pour mieux comprendre la distribution des espèces à des fins de conservation, le défi GeoLifeCLEF 2022 a été organisé. Cette compétiion comprend un vaste ensemble de données composé de 1,6 million géo-observations liées à la présence de 17 000 espèces végétales et animales. L’objectif principal de ce défi est d’explorer le potentiel des données de télédétection afin de prédire la présence d’espèces à des géolocalisations spécifiques. Dans ce mémoire, nous étudions diverses techniques d’apprentissage automatique et leur performance en lien avec le défi GeoLifeCLEF 2022. Nous explorons l’efficacité d’algorithmes bien connus en apprentissage par transfert, établissons un cadre d’apprentissage non supervisé et étudions les approches d’apprentissage auto-supervisé lors de la phase d’entraînement. Nos résultats démontrent qu’un ajustement fin des encodeurs pré-entraînés sur différents domaines présente les résultats les plus prometteurs lors de la phase de test. / Climate change and extreme weather events have emerged as significant drivers of biodiversity changes, posing a threat of habitat loss and species extinction. Understanding the current state of biodiversity and identifying areas with high suitability for different species are vital in combating biodiversity loss and guiding decision-making processes for protective measures and restoration efforts. Species distribution models (SDMs) are statistical tools for predicting a species' potential geographic distribution based on environmental variables and occurrence data. However, conventional SDMs often face limitations due to the restricted spatial resolution and coverage of environmental variables derived from ground-based measurements or weather station data. To better understand species distribution for conservation purposes, the GeoLifeCLEF 2022 challenge was introduced. This competition encompasses a large dataset of 1.6 million geo-observations linked to the presence of 17,000 plant and animal species. The primary objective of this challenge is to explore the potential of remote sensing data in forecasting species' presence at specific geolocations. In this thesis, we investigate various machine learning techniques and their performance on the GeoLifeCLEF 2022 challenge. We explore the effectiveness of standard transfer learning algorithms, establish an unsupervised learning framework, and investigate self-supervised learning approaches for training. Our findings demonstrate that fine-tuning pre-trained encoders on different domains yields the most promising test set performance results.

Brain tumor is a disease characterized by uncontrolled growth of abnormal cells in the brain. The brain is responsible for regulating the functions of all other organs, hence, any atypical growth of cells in the brain can have severe implications for its functions. The number of global mortality in 2020 led by cancerous brains was estimated at 251,329. However, early detection of brain cancer is critical for prompt treatment and improving patient’s quality of life as well as survival rates. Manual medical image classification in diagnosing diseases has been shown to be extremely time-consuming and labor-intensive. Convolutional Neural Networks (CNNs) has proven to be a leading algorithm in image classification outperforming humans. This paper compares five CNN architectures namely: VGG-16, VGG-19, AlexNet, EffecientNetB7, and ResNet-50 in terms of performance and accuracy using transfer learning. In addition, the authors discussed in this paper the economic impact of CNN, as an AI approach, on the healthcare sector. The models’ performance is demonstrated using functions for loss and accuracy rates as well as using the confusion matrix. The conducted experiment resulted in VGG-19 achieving best performance with 97% accuracy, while EffecientNetB7 achieved worst performance with 93% accuracy. / Hjärntumör är en sjukdom som kännetecknas av okontrollerad tillväxt av onormala celler i hjärnan. Hjärnan är ansvarig för att styra funktionerna hos alla andra organ, därför kan all onormala tillväxt av celler i hjärnan ha allvarliga konsekvenser för dess funktioner. Antalet globala dödligheten ledda av hjärncancer har uppskattats till 251329 under 2020. Tidig upptäckt av hjärncancer är dock avgörande för snabb behandling och för att förbättra patienternas livskvalitet och överlevnadssannolikhet. Manuell medicinsk bildklassificering vid diagnostisering av sjukdomar har visat sig vara extremt tidskrävande och arbetskrävande. Convolutional Neural Network (CNN) är en ledande algoritm för bildklassificering som har överträffat människor. Denna studie jämför fem CNN-arkitekturer, nämligen VGG-16, VGG-19, AlexNet, EffecientNetB7, och ResNet-50 i form av prestanda och noggrannhet. Dessutom diskuterar författarna i studien CNN:s ekonomiska inverkan på sjukvårdssektorn. Modellens prestanda demonstrerades med hjälp av funktioner om förlust och noggrannhets värden samt med hjälp av en Confusion matris. Resultatet av det utförda experimentet har visat att VGG-19 har uppnått bästa prestanda med 97% noggrannhet, medan EffecientNetB7 har uppnått värsta prestanda med 93% noggrannhet.

The principle of trained immunity represents innate immune memory due to sustained, mainly epigenetic, changes triggered by endogenous or exogenous stimuli in bone marrow (BM) progenitors (central trained immunity) and their innate immune cell progeny, thereby triggering elevated responsiveness against secondary stimuli. BM progenitors can respond to microbial and sterile signals, thereby possibly acquiring trained immunity-mediated long-lasting alterations that may shape the fate and function of their progeny, for example, neutrophils. Neutrophils, the most abundant innate immune cell population, are produced in the BM from committed progenitor cells in a process designated granulopoiesis. Neutrophils are the first responders against infectious or inflammatory challenges and have versatile functions in immunity. Together with other innate immune cells, neutrophils are effectors of peripheral trained immunity. However, given the short lifetime of neutrophils, their ability to acquire immunological memory may lie in the central training of their BM progenitors resulting in generation of reprogrammed, that is, “trained”, neutrophils. Although trained immunity may have beneficial effects in infection or cancer, it may also mediate detrimental outcomes in chronic inflammation. Here, we review the emerging research area of trained immunity with a particular emphasis on the role of neutrophils and granulopoiesis.

This thesis investigates the possibilities of conditionally generating English sentences based on keywords-framing content and different difficulty levels of vocabulary. It aims to contribute to the field of Conditional Text Generation (CTG), a type of Natural Language Generation (NLG), where the process of creating text is based on a set of conditions. These conditions include words, topics, content or perceived sentiments. Specifically, it compares the performances of two well-known model architectures: Sequence-toSequence (Seq2Seq) and Autoregressive (AR). These are applied to two different tasks, individual and combined. The Common European Framework of Reference (CEFR) is used to assess the vocabulary level of the texts. In the absence of openly available CEFR-labelled datasets, the author has developed a new methodology with the host company to generate suitable datasets. The generated texts are evaluated on accuracy of the vocabulary levels and readability using readily available formulas. The analysis combines four established readability metrics, and assesses classification accuracy. Both models show a high degree of accuracy when classifying texts into different CEFR-levels. However, the same models are weaker when generating sentences based on a desired CEFR-level. This study contributes empirical evidence suggesting that: (1) Seq2Seq models have a higher accuracy than AR models in generating English sentences based on a desired CEFR-level and keywords; (2) combining Multi-Task Learning (MTL) with instructiontuning is an effective way to fine-tune models on text-classification tasks; and (3) it is difficult to assess the quality of computer generated language using only readability metrics. / I den här studien undersöks möjligheterna att villkorligt generera engelska meningar på så-kallad “naturligt” språk, som baseras på nyckelord, innehåll och vokabulärnivå. Syftet är att bidra till området betingad textgenerering, en underkategori av naturlig textgenerering, vilket är en metod för att skapa text givet vissa ingångsvärden, till exempel ämne, innehåll eller uppfattning. I synnerhet jämförs prestandan hos två välkända modellarkitekturer: sekvenstill-sekvens (Seq2Seq) och autoregressiv (AR). Dessa tillämpas på två uppgifter, såväl individuellt som kombinerat. Den europeiska gemensamma referensramen (CEFR) används för att bedöma texternas vokabulärnivå. I och med avsaknaden av öppet tillgängliga CEFR-märkta dataset har författaren tillsammans med värdföretaget utvecklat en ny metod för att generera lämpliga dataset. De av modellerna genererade texterna utvärderas utifrån vokabulärnivå och läsbarhet samt hur väl de uppfyller den sökta CEFRnivån. Båda modellerna visade en hög träffsäkerhet när de klassificerar texter i olika CEFR-nivåer. Dock uppvisade samma modeller en sämre förmåga att generera meningar utifrån en önskad CEFR-nivå. Denna studie bidrar med empiriska bevis som tyder på: (1) att Seq2Seq-modeller har högre träffsäkerhet än AR-modeller när det gäller att generera engelska meningar utifrån en önskad CEFR-nivå och nyckelord; (2) att kombinera inlärning av multipla uppgifter med instruktionsjustering är ett effektivt sätt att finjustera modeller för textklassificering; (3) att man inte kan bedömma kvaliteten av datorgenererade meningar genom att endast använda läsbarhetsmått.

Auto-completion is a task that requires an algorithm to give suggestions for completing sentences. Specifically, the history of live chat and the words already typed by the agents are provided to the algorithm for outputting the suggestions to finish the sentences. This study aimed to investigate if the above task can be handled by fine-tuning a pre-trained T5 model on the target dataset. In this thesis, both an English and a Portuguese dataset were selected. Then, T5 and its multilingual version mT5were fine-tuned on the target datasets. The models were evaluated with different metrics (log perplexity, token level accuracy, and multi-word level accuracy), and the results are compared to those of the baseline methods. The results on these different metrics show that a method based on pre-trained T5 is a promising approach to handle the target task. / Automatisk komplettering är en uppgift som kräver en algoritm för att ge förslag på hur man kan slutföra meningar. Specifikt levereras historien om livechatt och de ord som redan har skrivits av agenterna till algoritmen för att mata ut förslagen för att avsluta meningarna. Denna studie syftade till att undersöka om ovanstående uppgift kan hanteras genom att finjustera en förtränad T5-modell på måldatamängden. I denna avhandling valdes både en engelsk och en portugisisk datamängd. Därefter finjusterades T5 och dess flerspråkiga version mT5 på måldatauppsättningarna. Modellerna utvärderades med olika mätvärden (log-perplexitet, precision på ordnivå och flerordsnivå), och resultaten jämförs med baslinjemetoderna. Resultaten på dessa olika mätvärden visar att en metod baserad på en förtränad T5 är ett lovande tillvägagångssätt för att hantera uppgiften.

Vitiligo is a skin disease where the pigment cells that produce melanin die or stop functioning, which causes white patches to appear on the body. Although vitiligo is not considered a serious disease, there is a risk that something is wrong with a person's immune system. In recent years, the use of medical image processing techniques has grown, and research continues to develop new techniques for analysing and processing medical images. In many medical image classification tasks, deep convolutional neural network technology has proven its effectiveness, which means that it may also perform well in vitiligo classification. Our study uses four deep convolutional neural networks in order to classify images of vitiligo and normal skin. The architectures selected are VGG-19, ResNeXt101, InceptionResNetV2 and Inception V3. ROC and AUC metrics are used to assess each model's performance. In addition, the authors investigate the economic benefits that this technology may provide to the healthcare system and patients. To train and evaluate the CNN models, the authors used a dataset that contains 1341 images in total. Because the dataset is limited, 5-fold cross validation is also employed to improve the model's prediction. The results demonstrate that InceptionV3 achieves the best performance in the classification of vitiligo, with an AUC value of 0.9111, and InceptionResNetV2 has the lowest AUC value of 0.8560. / Vitiligo är en hudsjukdom där pigmentcellerna som producerar melanin dör eller slutar fungera, vilket får vita fläckar att dyka upp på kroppen. Även om Vitiligo inte betraktas som en allvarlig sjukdom, det finns fortfarande risk att något är fel på en persons immun. Under de senaste åren har användningen av medicinska bildbehandlingstekniker vuxit och forskning fortsätter att utveckla nya tekniker för att analysera och bearbeta medicinska bilder. I många medicinska bildklassificeringsuppgifter har djupa konvolutionella neurala nätverk bevisat sin effektivitet, vilket innebär att den också kan fungera bra i Vitiligo klassificering. Vår studie använder fyra djupa konvolutionella neurala nätverk för att klassificera bilder av vitiligo och normal hud. De valda arkitekturerna är VGG-19, RESNEXT101, InceptionResNetV2 och Inception V3. ROC- och AUC mätvärden används för att bedöma varje modells prestanda. Dessutom undersöker författarna de ekonomiska fördelarna som denna teknik kan ge till sjukvårdssystemet och patienterna. För att träna och utvärdera CNN modellerna använder vi ett dataset som innehåller totalt 1341 bilder. Eftersom datasetet är begränsat används också 5-faldigt korsvalidering för att förbättra modellens förutsägelse. Resultaten visar att InceptionV3 uppnår bästa prestanda i klassificeringen av Vitiligo, med ett AUC -värde på 0,9111, och InceptionResNetV2 har det lägsta AUC -värdet på 0,8560.

The aim of this qualitative interpretive research was to explore the experiences of HIV counsellors and how these experiences influence the counsellors' psychological wellbeing. The complexities of the context within which HIV pre and post test counselling occurs form the day-to-day real ity of barely trained volunteer counsellors whose task it is to counsel, inform and educate people at grass roots concerning HIV. The guiding question of the current research pertained to the experience of HIV counselors regarding the influence of their work on their own well-being. A case study design was used. In depth interviews were conducted with nine Sotho speaking HIV counselors working in primary healthcare clinics in the Sedibeng region of Gauteng. Additional data was collected through observation. Data was initially coded, using axial coding; this was followed by thematic analysis. The focus was .on the psychological well-being of the volunteer HIV counsellors. The data indicated that the participants were not overwhelmed by the many stressors of their challenging occupations. They succeeded in developing their own ways of stress relief especially through practising their spiritual beliefs and other means like participating in community activities and meaningful relationships of significant other. They experienced personal growth and empowerment in general, but especially in the field of health and sexuality. The female participants were increasingly able to negotiate safer sex. Participants' lives were enriched through amongst others the regard they received from their communities, and being in a position to give information and advice that they gained from the training and exposure to information. The participants experienced feelings of self-worth in that they were able to contribute to their communities and thereby adding meaning to their own existence. It became clear that their character strengths such as wisdom, courage, humanity, justice and transcendence enabled them to function and grow in their difficult situation. The research highlighted that the inner strengths and virtues of the volunteer counsellors enable them to persist, in challenging work conditions and socio-economic circumstances. Difficulties facing volunteer HIV counsellors that became clear are the lack of support and recognition they have to contend with. It is therefore recommended that more attention should be given by the relevant stakeholders to strengthen the support and to make more resources available to them. / Thesis (M.A. (Psychology))--North-West University, Vaal Triangle Campus, 2010.

The aim of this qualitative interpretive research was to explore the experiences of HIV counsellors and how these experiences influence the counsellors' psychological wellbeing. The complexities of the context within which HIV pre and post test counselling occurs form the day-to-day real ity of barely trained volunteer counsellors whose task it is to counsel, inform and educate people at grass roots concerning HIV. The guiding question of the current research pertained to the experience of HIV counselors regarding the influence of their work on their own well-being. A case study design was used. In depth interviews were conducted with nine Sotho speaking HIV counselors working in primary healthcare clinics in the Sedibeng region of Gauteng. Additional data was collected through observation. Data was initially coded, using axial coding; this was followed by thematic analysis. The focus was .on the psychological well-being of the volunteer HIV counsellors. The data indicated that the participants were not overwhelmed by the many stressors of their challenging occupations. They succeeded in developing their own ways of stress relief especially through practising their spiritual beliefs and other means like participating in community activities and meaningful relationships of significant other. They experienced personal growth and empowerment in general, but especially in the field of health and sexuality. The female participants were increasingly able to negotiate safer sex. Participants' lives were enriched through amongst others the regard they received from their communities, and being in a position to give information and advice that they gained from the training and exposure to information. The participants experienced feelings of self-worth in that they were able to contribute to their communities and thereby adding meaning to their own existence. It became clear that their character strengths such as wisdom, courage, humanity, justice and transcendence enabled them to function and grow in their difficult situation. The research highlighted that the inner strengths and virtues of the volunteer counsellors enable them to persist, in challenging work conditions and socio-economic circumstances. Difficulties facing volunteer HIV counsellors that became clear are the lack of support and recognition they have to contend with. It is therefore recommended that more attention should be given by the relevant stakeholders to strengthen the support and to make more resources available to them. / Thesis (M.A. (Psychology))--North-West University, Vaal Triangle Campus, 2010.