FA-KNN: Hybrid Algoritma Untuk Klasifikasi Penyakit Diabetes Melitus

Penanganan yang tepat dan tepat waktu dari Diabetes Melitus menjadi sangat penting karena penyakit ini dapat menyebabkan berbagai komplikasi serius. Komplikasi jangka panjang meliputi gangguan pada mata (retinopati), ginjal (nefropati), saraf (neuropati), jantung dan pembuluh darah (kardiovaskular), serta risiko luka yang sulit sembuh hingga amputasi pada ekstremitas. Penelitian ini bertujuan untuk menerapkan Firefly Algorithm (FA) atau algoritma kunang-kunang dan KNN dalam melakukan klasifikasi terhadap penyakit diabetes melitus, dimana FA akan digunakan untuk melakukan pencarian parameter yang paling optimal untuk KNN. Metode penelitian yang digunakan yaitu metode eksperimen dengan melakukan skenario perubahan pada jumlah populasi kunang-kunang dan juga perubahan nilai k-fold validation untuk melakukan pembagian dataset. Hasil akurasi terbaik didapatkan pada populasi 100 dan 150 dengan nilai k=5 yaitu sebesar 76.3% dengan parameter K pada KNN yang diperoleh yaitu 15 dan P adalah

Narrativizing Knowledge Graphs

Any natural language expression of a set of facts - that can be represented as a knowledge graph - will more or less overtly assume a specific perspective on these facts. In this paper we see the conversion of a given knowledge graph into natural language as the construction of a narrative about the assertions made by the knowledge graph. We, therefore, propose a specific pipeline that can be applied to produce linguistic narratives from knowledge graphs using an ontological layer and corresponding rules that turn a knowledge graph into a semantic specification for natural language generation. Critically, narratives are seen as necessarily committing to specific perspectives taken on the facts presented. We show how this most commonly neglected facet of producing summaries of facts can be brought under control

Making informed decisions in cutting tool maintenance in milling: A KNN based model agnostic approach

In machining processes, monitoring the condition of the tool is a crucial aspect to ensure high productivity and quality of the product. Using different machine learning techniques in Tool Condition Monitoring TCM enables a better analysis of the large amount of data of different signals acquired during the machining processes. The real time force signals encountered during the process were acquired by performing numerous experiments. Different tool wear conditions were considered during the experimentation. A comprehensive statistical analysis of the data and feature selection using decision trees was conducted, and the KNN algorithm was used to perform classification. Hyperparameter tuning of the model was done to improve the models performance. Much research has been done to employ machine learning approaches in tool condition monitoring systems, however, a model agnostic approach to increase the interpretability of the process and get an in depth understanding of how the decision making is done is not implemented by many. This research paper presents a KNN based white box model, which allows us to dive deep into how the model performs the classification and how it prioritizes the different features included. This approach helps in detecting why the tool is in a certain condition and allows the manufacturer to make an informed decision about the tools maintenance

Representing Imprecise Time Intervals in OWL 2

International audienceRepresenting and reasoning on imprecise temporal information is a common requirement in the field of Semantic Web. Many works exist to represent and reason on precise temporal information in OWL; however, to the best of our knowledge, none of these works is devoted to imprecise temporal time intervals. To address this problem, we propose two approaches: a crisp-based approach and a fuzzy-based approach. (1) The first approach uses only crisp standards and tools and is modelled in OWL 2. We extend the 4D-fluents model, with new crisp components, to represent imprecise time intervals and qualitative crisp interval relations. Then, we extend the Allen’s interval algebra to compare imprecise time intervals in a crisp way and inferences are done via a set of SWRL rules. (2) The second approach is based on fuzzy sets theory and fuzzy tools and is modelled in Fuzzy-OWL 2. The 4D-fluents approach is extended, with new fuzzy components, in order to represent imprecise time intervals and qualitative fuzzy interval relations. The Allen’s interval algebra is extended in order to compare imprecise time intervals in a fuzzy gradual personalized way. Inferences are done via a set of Mamdani IF-THEN rules

Overview and Evaluation of Conceptual Strategies for Accessing CPU-Dependent Execution Resources in Grid Infrastructures

The emergence of many-core and massively-parallel computational accelerators (e.g., GPGPUs) has led to user demand for such resources in grid infrastructures. A widely adopted approach for discovering and accessing such resources has, however, yet to emerge.  GPGPUs are an example of a larger class of computational resources, characterized in part by dependence on an allocated CPU. This paper terms such resources "CPU-Dependent Execution Resources" (CDERs). Five conceptual strategies for discovering and accessing CDERs are described and evaluated against key criteria, and all five strategies are compliant with GLUE 1.3, GLUE 2.0, or both. From this evaluation, two of the presented strategies clearly emerge as providing the greatest flexibility for publishing both static and dynamic CDER information and identifying CDERs that satisfy specific job requirements. Furthermore, a two-phase approach to job-submission is proposed for those jobs requiring access to CDERs. The approach is compatible with existing grid services.  Examples are provided to illustrate job submission under each strategy

Popularity, face and voice: Predicting and interpreting livestreamers' retail performance using machine learning techniques

Livestreaming commerce, a hybrid of e-commerce and self-media, has expanded the broad spectrum of traditional sales performance determinants. To investigate the factors that contribute to the success of livestreaming commerce, we construct a longitudinal firm-level database with 19,175 observations, covering an entire livestreaming subsector. By comparing the forecasting accuracy of eight machine learning models, we identify a random forest model that provides the best prediction of gross merchandise volume (GMV). Furthermore, we utilize explainable artificial intelligence to open the black-box of machine learning model, discovering four new facts: 1) variables representing the popularity of livestreaming events are crucial features in predicting GMV. And voice attributes are more important than appearance; 2) popularity is a major determinant of sales for female hosts, while vocal aesthetics is more decisive for their male counterparts; 3) merits and drawbacks of the voice are not equally valued in the livestreaming market; 4) based on changes of comments, page views and likes, sales growth can be divided into three stages. Finally, we innovatively propose a 3D-SHAP diagram that demonstrates the relationship between predicting feature importance, target variable, and its predictors. This diagram identifies bottlenecks for both beginner and top livestreamers, providing insights into ways to optimize their sales performance.Comment: 25 pages, 10 figure

CRDTs in highly volatile environments

Publisher Copyright: © 2022 The Author(s)The implementation of collaborative applications in highly volatile environments, such as the ones composed of mobile devices, requires low coordination mechanisms. The replication without coordination semantics of Conflict-Free Replicated Data Types (CRDTs) makes them a natural solution for these execution contexts. However, the current CRDT models require each replica to know all other replicas beforehand or to discover them on-the-fly. Such solutions are not compatible with the dynamic ingress and egress of nodes in volatile environments. To cope with this limitation, we propose the Publish/Subscribe Conflict-Free Replicated Data Type (PS-CRDT) model that combines CRDTs with the publish/subscribe interaction model, and, with that, enable the spatial and temporal decoupling of update propagation. We implemented PS-CRDTs in Thyme, a reactive storage system for mobile edge computing. Our experimental results show that PS-CRDTs require less communication than other CRDT-based solutions in volatile environments.publishersversionpublishe

Enhancing wettability prediction in the presence of organics for hydrogen geo-storage through data-driven machine learning modeling of rock/H2/brine systems

The success of geological H2 storage relies significantly on rock–H2–brine interactions and wettability. Experimentally assessing the H2 wettability of storage/caprocks as a function of thermos-physical conditions is arduous because of high H2 reactivity and embrittlement damages. Data-driven machine learning (ML) modeling predictions of rock–H2–brine wettability are less strenuous and more precise. They can be conducted at geo-storage conditions that are impossible or hazardous to attain in the laboratory. Thus, ML models were utilized in this research to accurately model the wettability behavior of a ternary system consisting of H2, rock minerals (quartz and mica), and brine at different operating geological conditions. The results revealed that the ML models accurately captured the wettability behavior at different geo-storage conditions by yielding less than 5% mean absolute percent error and above 0.95 coefficient of determination values. The partial dependency or sensitivity plots were generated to evaluate the impact of individual features on the trained models. These plots revealed that the models accurately captured the physics behind the problem. Furthermore, a mathematical equation is derived from the trained ML model to predict the wettability behavior without using any ML software. The accuracy of the predictions of the ML model can be beneficial for exactly predicting the H2 geo-storage capacities and assessing of H2 containment security of storage and caprocks for large-scale geo-storage projects

Automatic identification of ischemia using lightweight attention network in PET cardiac perfusion imaging

Ischemic disease, caused by inadequate blood supply to organs or tissues, poses a significant global health challenge. Early detection of ischemia is crucial for timely intervention and improved patient outcomes. Myocardial perfusion imaging with positron-emission tomography (PET) is a non-invasive technique used to identify ischemia. However, accurately interpreting PET images can be challenging, necessitating the development of reliable classification methods. In this study, we propose a novel approach using MS-DenseNet, a lightweight attention network, for the detection and classification of ischemia from myocardial polar maps. Our model incorporates the squeeze and excitation modules to emphasize relevant feature channels and suppress unnecessary ones. By effectively utilizing channel interdependencies, we achieve optimum reuse of interchannel interactions, enhancing the model's performance. To evaluate the efficacy and accuracy of our proposed model, we compare it with transfer learning models commonly used in medical image analysis. We conducted experiments using a dataset of 138 polar maps (JPEG) obtained from 15O_H2O stress perfusion studies, comprising patients with ischemic and non-ischemic condition. Our results demonstrate that MS-DenseNet outperforms the transfer learning models, highlighting its potential for accurate ischemia detection and classification. This research contributes to the field of ischemia diagnosis by introducing a lightweight attention network that effectively captures the relevant features from myocardial polar maps. The integration of the squeeze and excitation modules further enhances the model's discriminative capabilities. The proposed MS-DenseNet offers a promising solution for accurate and efficient ischemia detection, potentially improving the speed and accuracy of diagnosis and leading to better patient outcomes