3 10 24 https://repository.horizon.ac.id/files/original/e2fdf36779a4d8edc98ff3ec75773ebf.pdf e673fc6f831e3ecd1a335f9caac2342c Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 22, NO.3 2024 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Consistency, local stability, and approximation of Shapash explanation Subject The topic of the resource Explainable artificial intelligence Interpretable artificial intelligence Interpretable machine learning Model explanation Predictive analytics Description An account of the resource Consistency, scalability, and local stability properties ensure that a model or method produces reliable and predictable outcomes. The Shapash helps users understand how the model makes its decisions. With machine learning (ML) system, healthcare experts can identify individuals at higher risk and implement interventions to reduce the occurrence and severity of disease. ML had achieved higher prediction accuracy even though the accuracy of their prediction depends on the quality and quantity of the data used for training. Despite the wider application and higher accuracy of different ML for disease prediction, the explanation of their predictive outcome is much more important to the healthcare professional, the patient, and even their developers. However, most of the ML systems do not explain their outcomes. To address the explainability issue various techniques such as local model agnostic explanation (LIME), and shapley additive explanation (SHAP) have been proposed over the recent years. Furthermore, the consistency, local stability, and approximation of the explanation remained one of the research topics in ML. This study investigated the consistency, stability, and approximation of LIME and SHAP in predicting heart disease (HD). The result suggested that LIME and SHAP generated a similar explanation (distance=0.35), compared to the active coalition of variable (ACV) explanation (distance=0.43). Creator An entity primarily responsible for making the resource Tsehay Admassu Assegie1, Bommy Manivannan2, Komal Kumar Napa3, Bindu Kolappa Pillai Vijayammal4, Rajkumar Govindarajan3, Sangeetha Murugan2, Atinkut Molla Mekonnen5 Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Feb 2, 2024 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT Explainable artificial intelligence Interpretable artificial intelligence Interpretable machine learning Model explanation Predictive analytics https://repository.horizon.ac.id/files/original/9ca25b0e4ec739efb1581144a6453013.pdf 4dd83338be53fff73eb91b207345cbf3 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 22, NO.3 2024 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A survey on the dataset, techniques, and evaluation metric used for abstractive text summarization Subject The topic of the resource Abstractive summarization Attention mechanism Automatic text summarization Deep learning Extractive summarization Transformers Description An account of the resource Whenever there is too much information out there, it is desirable to summarize. If humans are trying to create the summary, it will take lot of time. Now to make the problem of summarizing information easier and more effortless one can automate the summarization process which can reduce the time taken in creating summary. This is called as automatic summarization. The two ways of summarization are extractive summarization and abstractive summarization. Extractive summarization and its applications have been the subject of extensive research and have received state of art solution. But abstractive summarization still is a progressive field as it is difficult to create abstractive summary as humans do. Also, it is still a question i.e., how to evaluate the quality of a summary? Therefore, this paper is a comprehensive survey on the dataset used with its details and statistics, analysis of various abstractive summarization techniques and important parameters for evaluating the quality of summary. Deep leaning based models have given new direction in this field. The author also focuses on problems and challenges faced in the generation of summary which are opening the future research scope in this domain. Creator An entity primarily responsible for making the resource Shivani Sharma1, Gaurav Aggarwal1, Bipin Kumar Rai2 Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Feb 8, 2024 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT Abstractive summarization Attention mechanism Automatic text summarization Deep learning Extractive summarization Transformers https://repository.horizon.ac.id/files/original/8b91b88044506cc39180215f1c10c702.pdf ce091e7f03cfebd0f0ba6c15fa7cb4ea Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 22, NO.3 2024 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Igniting transistor to control three-phase alternating current motor servo Subject The topic of the resource Controlled Igniting Position Rate Torque Description An account of the resource The background of research try to use an open loop system, without feedback, by building torque in the target position. Theoretical basis manages the turning round of servo motor change direct currents to alternating currents by adjusting the rate input used. Methodology in simple terms, the flowchart of code processing from igniting transistors to managing the turning round of a three-phase alternating current (AC) servo motor. The result of the transistor ignition time with room phasor pulse width modulation (PWM) to set the ignition of a three-phase AC servo motor in the process of igniting as ordered, for manage position only igniting transistors in target position, and building torque at there. Discussion, igniting transistors to manage the turning round of servo motors, allows motion of servo motor, allows motion of servo motor to be added faster, accurately, soft, and steady. In conclusion, in this case the researcher uses it more specifically by turning on the transistor only at the target position, so that the torque generated is only at the target position, the servo motor motion from the start position immediately ends at the target position. Suggestions, this system igniting can be applied at a motion high-velocity system like a missile system controlled. Creator An entity primarily responsible for making the resource Syafruddin Rustam1, Andrew Ghea Mahardika2, Givy Devira Ramady1, Arif Rakhman3, Rahmad Hidayat1, Muchamad Sobri Sungkar3 Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Feb 2, 2024 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT Controlled Igniting Position Rate Torque https://repository.horizon.ac.id/files/original/ebaeea563559553e054a01bb91410f35.pdf 1c4eb6f4d835bb874921f83a0a165fe3 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 22, NO.3 2024 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Sclera boundary localization using circular hough transform and a modified run-data based algorithm Subject The topic of the resource Biometrics Circular hough transfrom Compound local binary pattern Modified run-data algorithm Sclera boundary localization Description An account of the resource Security challenges over the years has led to the need for an improvement in the traditional security approaches. This led to the advent of biometrics. Recently, among the biometric approaches, sclera has been an area of imense study. This is due to its accuracy; however, segmentation of the sclera has been a limiting factor to the application of this biometric trait. Several approaches have been proposed in literature but there is still the need to improve the segmentation accuracy. This study proposes the use of circular hough transform and a modified run-data based algorithm. The study also presented a sclera recognition system using the compound local binary pattern for features extraction and Manhattan distance for classification. The system produced a segmentation accuracy of 99.9% for sclera blood vessels, periocular and iris (SBVPI) sclera database and 100% for manually captured sclera database. The system produced an accuracy of 99.98 for SBVPI sclera database and 99.99% for manually captured sclera database. Creator An entity primarily responsible for making the resource Tunde Taiwo Adeniyi1, Oladele Tinuke Omolewa1, Jide Kehinde Adeniyi2 Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Feb 13, 2024 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT Biometrics Circular hough transfrom Compound local binary pattern Modified run-data algorithm Sclera boundary localization