424 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Undergraduate Catalog of Studies, 2023-2024

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    Técnicas de Minería de datos aplicados a la agricultura: Estado del Arte y análisis bibliométrico

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    This research presents a bibliometric analysis of 106 journal and state-of-the-art articles indexed in Scopus and a systematic analysis of 83 selected papers. Areas of study are identified that include the prediction of crop yield and growth, the detection of plant diseases, and water and soil analysis related to different types of crops such as cereals (rice, barley, corn, wheat, soybeans); fruits (apple, cucumber); legumes (alfalfa, beans, peanuts); tubers, among others. Climatic variables, soil, water, topographic and edaphological conditions, and data mining techniques such as Neural Networks, Deep Learning, segmentation, association, and classification rules, among others, are examined to optimize the use of resources and make agricultural decisions based on data. In addition, the challenges and opportunities in this research area are highlighted as the future perspectives for developing advanced data mining solutions in the agricultural context. This analysis contributes to a better understanding of how data mining is transforming the farm sector academic and scientific community to drive efficiency, sustainability, and informed decision-making in food production.En esta investigación, se presenta un análisis bibliométrico de 106 artículos de revistas y estado del arte indexados en Scopus, junto con un análisis sistemático de 83 artículos seleccionados. Se identifican áreas de estudio que incluye la predicción de rendimiento y crecimiento de cultivos, la detección de enfermedades en plantas, análisis de agua y suelo, relacionados con diferentes tipos de cultivo como: cereales (arroz, cebada, maíz, trigo, soya); frutas (manzana, pepino); legumbres (alfalfa, frejol, cacahuate); tubérculos, entre otros. Se examinan variables climáticas, suelo, agua, condiciones topográficas, edafológicas y técnicas de minería de datos como, Redes Neuronales, Deep Learning, segmentación, reglas de asociación y clasificación, entre otras, para optimizar el uso de recursos y tomar decisiones agrícolas basadas en datos. Además, se destacan los desafíos y oportunidades en esta área de investigación, así como las perspectivas futuras para el desarrollo de soluciones de minería de datos avanzadas en el contexto agrícola. Este análisis contribuye a una mejor comprensión de cómo la minería de datos está transformando el sector agrícola, comunidad académica y científica, con el fin de impulsar la eficiencia, la sostenibilidad y la toma de decisiones informadas en la producción de alimentos

    Software Design Change Artifacts Generation through Software Architectural Change Detection and Categorisation

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    Software is solely designed, implemented, tested, and inspected by expert people, unlike other engineering projects where they are mostly implemented by workers (non-experts) after designing by engineers. Researchers and practitioners have linked software bugs, security holes, problematic integration of changes, complex-to-understand codebase, unwarranted mental pressure, and so on in software development and maintenance to inconsistent and complex design and a lack of ways to easily understand what is going on and what to plan in a software system. The unavailability of proper information and insights needed by the development teams to make good decisions makes these challenges worse. Therefore, software design documents and other insightful information extraction are essential to reduce the above mentioned anomalies. Moreover, architectural design artifacts extraction is required to create the developer’s profile to be available to the market for many crucial scenarios. To that end, architectural change detection, categorization, and change description generation are crucial because they are the primary artifacts to trace other software artifacts. However, it is not feasible for humans to analyze all the changes for a single release for detecting change and impact because it is time-consuming, laborious, costly, and inconsistent. In this thesis, we conduct six studies considering the mentioned challenges to automate the architectural change information extraction and document generation that could potentially assist the development and maintenance teams. In particular, (1) we detect architectural changes using lightweight techniques leveraging textual and codebase properties, (2) categorize them considering intelligent perspectives, and (3) generate design change documents by exploiting precise contexts of components’ relations and change purposes which were previously unexplored. Our experiment using 4000+ architectural change samples and 200+ design change documents suggests that our proposed approaches are promising in accuracy and scalability to deploy frequently. Our proposed change detection approach can detect up to 100% of the architectural change instances (and is very scalable). On the other hand, our proposed change classifier’s F1 score is 70%, which is promising given the challenges. Finally, our proposed system can produce descriptive design change artifacts with 75% significance. Since most of our studies are foundational, our approaches and prepared datasets can be used as baselines for advancing research in design change information extraction and documentation

    Identifying Relevant Features of CSE-CIC-IDS2018 Dataset for the Development of an Intrusion Detection System

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    Intrusion detection systems (IDSs) are essential elements of IT systems. Their key component is a classification module that continuously evaluates some features of the network traffic and identifies possible threats. Its efficiency is greatly affected by the right selection of the features to be monitored. Therefore, the identification of a minimal set of features that are necessary to safely distinguish malicious traffic from benign traffic is indispensable in the course of the development of an IDS. This paper presents the preprocessing and feature selection workflow as well as its results in the case of the CSE-CIC-IDS2018 on AWS dataset, focusing on five attack types. To identify the relevant features, six feature selection methods were applied, and the final ranking of the features was elaborated based on their average score. Next, several subsets of the features were formed based on different ranking threshold values, and each subset was tried with five classification algorithms to determine the optimal feature set for each attack type. During the evaluation, four widely used metrics were taken into consideration.Comment: 24 page

    Undergraduate Catalog of Studies, 2022-2023

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    The Utility of Decision Tree and Analytics Hierarchy Process in Prioritizing of Social Aid Distribution due to Covid-19 Pandemic in Indonesia

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    The Indonesian government provided various social assistance programs to local governments during Covid-19. One of the difficulties for the local governments in determining candidates for social aid is ensuring that the number of candidates is in balance with the available quota. Therefore, the local governments must select the most eligible candidates. This study proposes a priority model that can provide recommendations for candidates who meet the criteria for social assistance. The six parameters used in this study were: number of dependents, occupation, income, age, Covid status, and citizen status. The model operates in two stages, namely classification followed by ranking. The classification stage is conducted using a decision tree, while the ranking stage is performed conducted using the Analytical Hierarchy Process (AHP) algorithm. The decision tree separates two classes, namely, eligible and non-eligible. In addition, the classification process is also used to determine the dominant attributes and played a role in the modeling. The proposed model generates a list of the most eligible candidates based on our research. These are sorted by weight from greatest to most eligible using five dominant parameters: number of dependents, income, age, Covid status, and citizen status

    The State-of-the-Art in Air Pollution Monitoring and Forecasting Systems using IoT, Big Data, and Machine Learning

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    The quality of air is closely linked with the life quality of humans, plantations, and wildlife. It needs to be monitored and preserved continuously. Transportations, industries, construction sites, generators, fireworks, and waste burning have a major percentage in degrading the air quality. These sources are required to be used in a safe and controlled manner. Using traditional laboratory analysis or installing bulk and expensive models every few miles is no longer efficient. Smart devices are needed for collecting and analyzing air data. The quality of air depends on various factors, including location, traffic, and time. Recent researches are using machine learning algorithms, big data technologies, and the Internet of Things to propose a stable and efficient model for the stated purpose. This review paper focuses on studying and compiling recent research in this field and emphasizes the Data sources, Monitoring, and Forecasting models. The main objective of this paper is to provide the astuteness of the researches happening to improve the various aspects of air polluting models. Further, it casts light on the various research issues and challenges also.Comment: 30 pages, 11 figures, Wireless Personal Communications. Wireless Pers Commun (2023

    A Robust Cardiovascular Disease Predictor Based on Genetic Feature Selection and Ensemble Learning Classification

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    Timely detection of heart diseases is crucial for treating cardiac patients prior to the occurrence of any fatality. Automated early detection of these diseases is a necessity in areas where specialized doctors are limited. Deep learning methods provided with a decent set of heart disease data can be used to achieve this. This article proposes a robust heart disease prediction strategy using genetic algorithms and ensemble deep learning techniques. The efficiency of genetic algorithms is utilized to select more significant features from a high-dimensional dataset, combined with deep learning techniques such as Adaptive Neuro-Fuzzy Inference System (ANFIS), Multi-Layer Perceptron (MLP), and Radial Basis Function (RBF), to achieve the goal. The boosting algorithm, Logit Boost, is made use of as a meta-learning classifier for predicting heart disease. The Cleveland heart disease dataset found in the UCI repository yields an overall accuracy of 99.66%, which is higher than many of the most efficient approaches now in existence

    Undergraduate and Graduate Course Descriptions, 2023 Spring

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    Wright State University undergraduate and graduate course descriptions from Spring 2023
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