This paper explores the design and development of e-Guro 1.0, a culture-based learning management system (CLMS) for the City College of Calamba. Using a qualitative approach, research begins with the exploratory analy...
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The remarkable miniaturization of Internet of Things (IoT)-based systems and the rise of distributed intelligence are promising research paradigms in the design of smart cities. IoT and distributed intelligence are co...
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This paper presents two hands-on, project-based courses on unmanned aerial systems recently offered by the Intelligent systems Engineering program at Indiana University. In Fall 2023, ENGR-E399/599 Autonomous Sports w...
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Hyperdimensional computing (HDC) is an emerging computing paradigm with significant promise for efficient and robust learning. In HDC, objects are encoded with high-dimensional vector symbolic sequences called hyperve...
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Decentralized systems are integral to various sectors, including public and private organizations. A key component of these systems is the dissemination protocol. Hyperledger Fabric, a prominent production-ready distr...
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User Experience (UX) refers to the individual experience of users when using software. An institution must analyze the user experience, especially for custom-built software. The goal of this study is to assess the UX ...
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Despite the extensive effort to improve intelligent educational tools for smart learning environments,automatic Arabic essay scoring remains a big research *** nature of the writing style of the Arabic language makes ...
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Despite the extensive effort to improve intelligent educational tools for smart learning environments,automatic Arabic essay scoring remains a big research *** nature of the writing style of the Arabic language makes the problem even more *** study designs,implements,and evaluates an automatic Arabic essay scoring *** proposed system starts with pre-processing the student answer and model answer dataset using data cleaning and natural language processing ***,it comprises two main components:the grading engine and the adaptive fusion *** grading engine employs string-based and corpus-based similarity algorithms *** that,the adaptive fusion engine aims to prepare students’scores to be delivered to different feature selection algorithms,such as Recursive Feature Elimination and ***,some machine learning algorithms such as Decision Tree,Random Forest,Adaboost,Lasso,Bagging,and K-Nearest Neighbor are employed to improve the suggested system’s *** experimental results in the grading engine showed that Extracting DIStributionally similar words using the CO-occurrences similarity measure achieved the best correlation ***,in the adaptive fusion engine,the Random Forest algorithm outperforms all other machine learning algorithms using the(80%–20%)splitting method on the original *** achieves 91.30%,94.20%,0.023,0.106,and 0.153 in terms of Pearson’s Correlation Coefficient,Willmot’s Index of Agreement,Mean Square Error,Mean Absolute Error,and Root Mean Square Error metrics,respectively.
Through their websites, a lot of businesses and people directly offer their services. The way in which these services are offered may affect users emotionally. A website that elicits pleasant emotions in its users can...
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In recent years, many cyber incidents have occurred in the maritime sector, targeting the information technology (IT) and operational technology (OT) infrastructure. Although several literature review papers have been...
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Background : Microorganisms are found in almost every environment, including soil, water, air and inside other organisms, such as animals and plants. While some microorganisms cause diseases, most of them help in biol...
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Background : Microorganisms are found in almost every environment, including soil, water, air and inside other organisms, such as animals and plants. While some microorganisms cause diseases, most of them help in biological processes such as decomposition, fermentation and nutrient cycling. Much research has been conducted on the study of microbial communities in various environments and how their interactions and relationships can provide insight into various diseases. Co-occurrence network inference algorithms help us understand the complex associations of micro-organisms, especially bacteria. Existing network inference algorithms employ techniques such as correlation, regularized linear regression, and conditional dependence, which have different hyper-parameters that determine the sparsity of the network. These complex microbial communities form intricate ecological networks that are fundamental to ecosystem functioning and host health. Understanding these networks is crucial for developing targeted interventions in both environmental and clinical settings. The emergence of high-throughput sequencing technologies has generated unprecedented amounts of microbiome data, necessitating robust computational methods for network inference and validation. Results : Previous methods for evaluating the quality of the inferred network include using external data, and network consistency across sub-samples, both of which have several drawbacks that limit their applicability in real microbiome composition data sets. We propose a novel cross-validation method to evaluate co-occurrence network inference algorithms, and new methods for applying existing algorithms to predict on test data. Our method demonstrates superior performance in handling compositional data and addressing the challenges of high dimensionality and sparsity inherent in real microbiome datasets. The proposed framework also provides robust estimates of network stability. Conclusions : Our empirical study show
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