4,928 research outputs found

    Behavioral policies, evidence, and expertise

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    Text classification supervised algorithms with term frequency inverse document frequency and global vectors for word representation: a comparative study

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    Over the course of the previous two decades, there has been a rise in the quantity of text documents stored digitally. The ability to organize and categorize those documents in an automated mechanism, is known as text categorization which is used to classify them into a set of predefined categories so they may be preserved and sorted more efficiently. Identifying appropriate structures, architectures, and methods for text classification presents a challenge for researchers. This is due to the significant impact this concept has on content management, contextual search, opinion mining, product review analysis, spam filtering, and text sentiment mining. This study analyzes the generic categorization strategy and examines supervised machine learning approaches and their ability to comprehend complex models and nonlinear data interactions. Among these methods are k-nearest neighbors (KNN), support vector machine (SVM), and ensemble learning algorithms employing various evaluation techniques. Thereafter, an evaluation is conducted on the constraints of every technique and how they can be applied to real-life situations

    Climate Change and Critical Agrarian Studies

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    Climate change is perhaps the greatest threat to humanity today and plays out as a cruel engine of myriad forms of injustice, violence and destruction. The effects of climate change from human-made emissions of greenhouse gases are devastating and accelerating; yet are uncertain and uneven both in terms of geography and socio-economic impacts. Emerging from the dynamics of capitalism since the industrial revolution — as well as industrialisation under state-led socialism — the consequences of climate change are especially profound for the countryside and its inhabitants. The book interrogates the narratives and strategies that frame climate change and examines the institutionalised responses in agrarian settings, highlighting what exclusions and inclusions result. It explores how different people — in relation to class and other co-constituted axes of social difference such as gender, race, ethnicity, age and occupation — are affected by climate change, as well as the climate adaptation and mitigation responses being implemented in rural areas. The book in turn explores how climate change – and the responses to it - affect processes of social differentiation, trajectories of accumulation and in turn agrarian politics. Finally, the book examines what strategies are required to confront climate change, and the underlying political-economic dynamics that cause it, reflecting on what this means for agrarian struggles across the world. The 26 chapters in this volume explore how the relationship between capitalism and climate change plays out in the rural world and, in particular, the way agrarian struggles connect with the huge challenge of climate change. Through a huge variety of case studies alongside more conceptual chapters, the book makes the often-missing connection between climate change and critical agrarian studies. The book argues that making the connection between climate and agrarian justice is crucial

    Behavioral policies, evidence, and expertise

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    Website Phishing Detection Using Machine Learning Techniques

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    Phishing is a cybercrime that is constantly increasing in the recent years due to the increased use of the Internet and its applications. It is one of the most common types of social engineering that aims to disclose or steel users sensitive or personal information. In this paper, two main objectives are considered. The first is to identify the best classifier that can detect phishing among twenty-four different classifiers that represent six learning strategies. The second objective aims to identify the best feature selection method for websites phishing datasets. Using two datasets that are related to Phishing with different characteristics and considering eight evaluation metrics, the results revealed the superiority of RandomForest, FilteredClassifier, and J-48 classifiers in detecting phishing websites. Also, InfoGainAttributeEval method showed the best performance among the four considered feature selection methods

    An artificial intelligence-based decision support system for early and accurate diagnosis of Parkinson’s Disease

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    People with Parkinson’s Disease (PD) might struggle with sadness, restlessness, or difficulty speaking, chewing, or swallowing. A diagnosis can be challenging because there is no specific PD test. It is diagnosed by doctors using a neurological exam and a medical history. This study proposes several Machine Learning (ML) algorithms to predict PD. These ML algorithms include K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting algorithms (XGBoost), and their ensemble methods using publicly available PD dataset with 195 instances. The ML algorithms are used to predict and classify PD using homogeneous XGBoost ensemble techniques with reduced amount of entropy. Synthetic Minority Oversampling Technique (SMOTE) is utilized to handle imbalanced data, and 10-fold cross-validation is employed for evaluation. The results show that the homogeneous XGBoost-Random Forest outperforms other ML methods with 98% accuracy and Matthew’s correlation coefficient value 0.93

    Comparative Study on Multivariate Methods Using Chronic Kidney Disease

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    The human being is currently one of the most serious illnesses in the modern world, and accurate diagnosis is necessary as soon as possible. In this modern world, there are numerous diseases that exist. Chronic kidney disease is regarded as the most serious of these disorders in humans. There are several methods in the medical area for disease diagnosis, and the prediction criterion is also significant in the medical field for determining the consequences of the study in the future. Many statistical methods are employed in order to forecast the medical dataset and provide accurate and reliable findings. A lot of models are available in multivariate methods to predict the dataset. In this paper, the computational algorithms for detecting CKD using Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), and Logistic Regression (LR) are reviewed. The first, based on the association, inference for the study. Decision tree and logistic regression approaches are used to more correctly diagnose chronic renal disease based on the results of the association. Finally, the study came to the conclusion that greatest fit for forecasting chronic renal disease

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    A Critical Review Of Post-Secondary Education Writing During A 21st Century Education Revolution

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    Educational materials are effective instruments which provide information and report new discoveries uncovered by researchers in specific areas of academia. Higher education, like other education institutions, rely on instructional materials to inform its practice of educating adult learners. In post-secondary education, developmental English programs are tasked with meeting the needs of dynamic populations, thus there is a continuous need for research in this area to support its changing landscape. However, the majority of scholarly thought in this area centers on K-12 reading and writing. This paucity presents a phenomenon to the post-secondary community. This research study uses a qualitative content analysis to examine peer-reviewed journals from 2003-2017, developmental online websites, and a government issued document directed toward reforming post-secondary developmental education programs. These highly relevant sources aid educators in discovering informational support to apply best practices for student success. Developmental education serves the purpose of addressing literacy gaps for students transitioning to college-level work. The findings here illuminate the dearth of material offered to developmental educators. This study suggests the field of literacy research is fragmented and highlights an apparent blind spot in scholarly literature with regard to English writing instruction. This poses a quandary for post-secondary literacy researchers in the 21st century and establishes the necessity for the literacy research community to commit future scholarship toward equipping college educators teaching writing instruction to underprepared adult learners

    Methods to Improve Our Understanding of the Health and Welfare Status of Sheep (Ovis Aries) and the Influences of their Immediate Environment

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    Studies into the effective use of accelerometers in the automated assessment of sheep behaviour to improve welfare has increased exponentially with promising preliminary results. Previous research has focused primarily on explicit behaviour classification, for example, parturition and urination events, with a view to create a commercial tool that will provide health warnings for farmers. Yet the majority of trials have not been conducted in a farm environment. This study aims to provide essential primary research investigating environmental variables that may influence the behavioural patterns of a commercial flock. This vital information has been largely overlooked and crucial when considering tools that provide health warnings, due to the many factors that influence sheep behaviour such as weather, vegetation, soil type, land typography and breed (Hinch, 2017). The primary aim of this study was to assess the most appropriate model to predict the behaviours of commercial ewes. This was achieved by deploying accelerometers on a commercial flock and simultaneously collecting manual observations and video recordings of flock’s individual activity. The raw acceleration data was processed to create 6 variables. Behaviour classification was also evaluated using three ethograms, each with two mutually exclusive behavioural/postural states: 1. Head Position (head up/down), 2. Posture (standing/lying), 3. Activity (resting/grazing). Three Window setting (3, 5 and 7 seconds) and five machine learning algorithms 4 (Linear Discriminate Analysis (LDA), Classification and Regression Trees (CART), K Nearest Neighbour (KNN), Support Vector Machines (SVM) and Random Forest (RF)) were evaluated. Results indicated a RF with a 7 second window the optimal model across all ethograms. (Accuracy by ethogram; 1) 91.5%, 2) 91.0% and 3) 99.3%). The secondary aim of this study was to use a Linear Mixed Model (LMM) to investigate the influence of temperature and rainfall on grazing and resting behaviours. This was accomplished by using the initially developed model (RF) on data collected from an unsupervised commercial flock, recorded in a second trial. Results indicated that there was a significant positive relationship between grazing durations and rainfall (p.001), this finding conflicts with previous research observations and is yet unpublished. In addition, prior sheep behaviour research has suggested ‘foraging’ as the dominant activity, results from this trial indicate the dominant daily activity was resting (67% of daily activity). In conclusion this study highlights the difficultly of defining what ‘normal’ sheep behaviour is and that it is not viable to implement a ‘one-size fits all’ approach. Further research is required in the behavioural assessment for this particularly malleable species
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