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On the Dominant Local Metric Dimension of Corona Product Graphs

IAENG International Journal of Applied Mathematics 2022年第4期52卷 1页

作者： Umilasari, Reni Susilowati, Liliek Slamin Prabhu, Savari Mathematics Department Airlangga University Indonesia Study Program of Informatics Jember University Indonesia Mathematics Department Rajalakshmi Engineering College Tamil Nadu Chennai602105 India

A nontrivial connected graph T which one of the vertex is v, v is said to distinguish two vertex u;t if the distance between v and u is different from v to t, where u,t (Formula Presented) V (T). Metric dimension is one topic in graph theory that uses the concept of distance. Combining the definition of the local metric dimension and dominating set, there is a new term, we called it dominant local metric dimension and symbolized as Ddiml(T). An ordered subset Wl = {w1,w2,…,wn}(Formula Presented)V (T) is called a dominant local resolving set of T if Wl is a local resolving set as well as a dominating set of T. The goal of this paper’s research is to determine precise values of dominant local metric dimension for the corona product graphs. n copies of the graphs P1,P2,…,Pn of P are made to constructed the corona of any two graph T and P. After that, we link the i-th vertex of T to the vertices of Pi, where n is an order of graph T. T corona P is symbolized by T(Formula Presented)P. © 2022,IAENG International Journal of Applied Mathematics. All Rights Reserved.

关键词： Graph theory

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Forecasting system using single exponential smoothing with golden section optimization 2

Forecasting system using single exponential smoothing with g...

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2nd International Conference on Vocational Education and Technology, IConVET 2019

作者： Widitriani, N.P.S. Parwita, W.G.S. Meinarni, N.P.S. Study Program of Informatics Engineering STMIK STIKOM Indonesia Denpasar-Bali80225 Indonesia

Puri Wira Mahkota Ltd is a distributor company in the automotive parts industry. Puri Wira Mahkota has an obstacles in preparing weekly order on stock to the primary production unit. They predict total sales of product in the coming period just by using an expectation from stock administration staff then it will be compared with sales data in the previous period without any calculations using a definitive formula. Forecasting calculation could support Puri Wira Mahkota to predict the quantity of product. The purpose of this research is to apply the calculation process that can predict the quantity of product should be produced by the primary production unit every month and also to prevent over stock and out of stock. The method used in the forecasting calculation is Single Exponential Smoothing. To optimize Single Exponential Smoothing we used the Golden Section. The principle of the Golden Section is to reduce the alpha area boundary so it will produce an ideal forecast value with the minimum MAPE (Mean Absolute Percentage Error) level. This research shows the results that the Golden Section finds the optimal forecasting value with a level of MAPE (Mean Absolute Percentage Error) of 43.39%. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Sales

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Maternal Health Risk Classification - A Comparison between Algorithm Decision Tree and k-Nearest Neighbor (kNN)

Maternal Health Risk Classification - A Comparison between A...

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Sustainable Emerging Innovations in Engineering and Technology (ICSEIET), International Conference on

作者： Maylia Fatmawati Mega Novita Nugroho Dwi Saputro Alok Singh Chauhan Noora Qotrun Nada Amit Sinha Bambang Agus Herlambang Shruti Gupta Department of Informatics Faculty of Engineering and Informatics Universitas PGRI Semarang Semarang Indonesia Postgraduate Program of Science Education Universitas PGRI Semarang Semarang Indonesia School of Computing Science & Engineering Galgotias University Greater Noida India Department of Computer Science & Engineering ABES Engineering College Ghaziabad Uttar Pradesh India

Maternal health is a critical concern, particularly for individuals who are pregnant and will shape the future generations. However, not all expectant mothers receive tailored attention and care for their unique health needs. Consequently, many mothers face high levels of health risks during pregnancy, highlighting the importance of classifying maternal health risks. Classification can help identify influential factors affecting maternal health and predict risk levels for individual mothers. In the field of data mining, various classification methods exist for analyzing complex datasets. This study focuses on two widely used algorithms: decision tree and k-Nearest Neighbor (kNN). By employing these algorithms, we aim to classify a dataset comprising 1,014 records of maternal health risk levels. Through cross-validation and T-tests, we rigorously evaluate the performance of the decision tree and kNN algorithms in this context. Our findings indicate that the kNN algorithm is more suitable for classifying maternal health risks, achieving a significantly higher accuracy of 71.50% compared to the decision tree algorithm's accuracy of 70.71%. This result establishes the superiority of the kNN algorithm in accurately classifying maternal health risks. These findings have implications for developing targeted interventions and tailored healthcare strategies to mitigate identified risks.

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Multi-parameter Study on Linguistic and Semantic Functions of Alzheimer's Disease

Multi-parameter Study on Linguistic and Semantic Functions o...

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作者： Liyuan Zhu Carolina Health Informatics Program University of North Carolina at Chapel Hill

With an expanding number of people suffering from Alzheimer's disease(AD),which not only threatens the happiness of patients' family,but also becomes a substantial burden for the healthcare system worldwide,much attention has been paid to the deficits of linguistic function over the course of AD,and connected language analysis has been utilized as a method of assessing expressive linguistic performance for a long *** previous studies focus on picture description tasks in AD,which could provide a simple approach to disease monitoring in clinical scenarios and ***-increasing evidence reveals that changes in cerebral blood flow(CBF) can be identified before detecting the clinical symptoms of *** vivo measurement of CBF via arterial spin labelling(ASL) magnetic resonance imaging(MRI) may have the potential to become a clinical tool for early detection and characterization of AD *** we review previous studies on picture description tasks and ASL-MRI,discuss the potential value of these tasks for AD diagnosis,and propose the importance of finding potential biomarkers for future research.

关键词： Alzheimer's disease linguistic function semantic function picture description task magnetic resonance imaging arterial spin labelling

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Adversarially Robust Feature Learning for Breast Cancer Diagnosis

arXiv

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arXiv 2024年

作者： Hao, Degan Arefan, Dooman Zuley, Margarita Berg, Wendie Wu, Shandong Intelligent Systems Program University of Pittsburgh Pittsburgh United States Department of Radiology University of Pittsburgh Pittsburgh United States Department of Bioengineering University of Pittsburgh Pittsburgh United States Department of Biomedical Informatics University of Pittsburgh Pittsburgh United States

Adversarial data can lead to malfunction of deep learning applications. It is essential to develop deep learning models that are robust to adversarial data while accurate on standard, clean data. In this study, we proposed a novel adversarially robust feature learning (ARFL) method for a real-world application of breast cancer diagnosis. ARFL facilitates adversarial training using both standard data and adversarial data, where a feature correlation measure is incorporated as an objective function to encourage learning of robust features and restrain spurious features. To show the effects of ARFL in breast cancer diagnosis, we built and evaluated diagnosis models using two independent clinically collected breast imaging datasets, comprising a total of 9,548 mammogram images. We performed extensive experiments showing that our method outperformed several state-of-the-art methods and that our method can enhance safer breast cancer diagnosis against adversarial attacks in clinical settings. © 2024, CC BY.

关键词： Deep learning

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Design of IOT based facial recognition door access control locker services at tourist attractions in Bali 2

Design of IOT based facial recognition door access control l...

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2nd International Conference on Vocational Education and Technology, IConVET 2019

作者： Widiartha, K.K. Ekayana, A.A.G. Study Program of Informatics Engineering STMIK STIKOM Indonesia Denpasar-Bali80225 Indonesia

Safety and comfort are things that everyone wants to get on a trip. Every tourist who wants a vacation to one of the tourist destinations will prioritize these two things. Leisure in travel is influenced by the availability of supporting facilities. From accommodation, restaurants to facilities provided by tourism actors in each tourism object. One problem is the lack of facilities in attractions such as luggage storage (lockers), many tourists in enjoying tourism objects carrying luggage such as backpacks;groceries to backpacks for tourists are classified as backpackers. Of course this will disrupt the comfort of traveling. In the research conducted, researchers wanted to design a system of goods storage services as a form of supporting industry 4.0 in the tourism environment. By designing an image of the storage media of goods by implementing access control for doors in each locker based on image processing supported by a microcontroller. The method used that focuses on facial recognition as image input using the PCA (Principal Component Analysis) method, which can measure the Euclidean distance in an object's face image. The design as a blueprint makes it easier to develop the next item of storage media (lockers).The results of this study indicate that the facial recognition method used is able to extract facial features and be recalibrated to activate the locker that is owned by the face object. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Principal component analysis

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Automatic Aircraft Shadow Removal from Remote Sensing Images Using Mask-ShadowGAN 8

Automatic Aircraft Shadow Removal from Remote Sensing Images...

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8th IEEE International Conference on Industrial Engineering and Applications, ICIEA 2021

作者： Ganyaporngul, Sirapavee Cooharojananone, Nagul Kruachottikul, Pravee Trakulwaranont, Donnaphat Satoh, Shin'ichi Chulalongkorn University Faculty of Science Department of Mathematics and Computer Science Bangkok Thailand Chulalongkorn University Technopreneurship and Innovation Management Program Bangkok Thailand National Institute of Informatics Tokyo Japan

ISBN: (纸本)9781665428958

Objects with shadow may cause a problem for image classification. For example, it can separate one object into many objects. It can also alter the size or shape of the object resulting in misclassification. In this paper, we focus on removing aircraft shadow from remote sensing images where the shadows occur on wings, bodies, and tails. Since it is very difficult to get shadow-free aircraft images and a shadow aircraft image of the same type for the training part, we adopted Mask-ShadowGAN for solving this issue. The benefit of the Mask-ShadowGAN algorithm is that, in the training part, the technique does not require the same images that have both shadow and shadow-free. In the experiment, we evaluated our proposed technique using RMSE and Jaccard similarity index for measurement. The experimental result shows that our technique shows promising results. We present both best and worst result based on sorted similarity index. © 2021 IEEE.

关键词： Remote sensing

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An Evaluation of Number of Pixels Change Rate (NPCR) in Symetric Cryptography Based on Data Encryption Standard (DES)

An Evaluation of Number of Pixels Change Rate (NPCR) in Syme...

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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Muhammad Rizky Naufal Christy Atika Sari Eko Hari Rachmawanto Lekso Budi Handoko Folasade Olubusola Isinkaye Wisam Subhi Talib Al-Dayyeni Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Department of Computer Science Ekiti State University Ekiti Nigeria School of Information Technologies and Engineering ADA University Baku Azerbaijan

Data Encryption Standard (DES) is a symmetric encryption algorithm that uses a single key to encrypt and decrypt information. In addition, the cipher text will be hidden inside an image using Stepic. DES Encryption use symmetric encryption to encrypt and decryption to decrypt. It will secure the symmetric encryption and maintain the speed of the encryption and decryption. The result of this study is the lowest error that we got as the Mean Square Error (MSE) is 0.00% and is inversely proportional with the Peak Signal to Noise Ratio (PSNR) and Avalanche with 79.36% and 34.91% in order that is not stable for result of Avalanche Effect (AVA). Inversely proportional to the length of the text that is hidden in the image, the longest text that is hidden, the more changes that we get in the image with the highest Unified Average Changing Intensity (UACI) and Number of Pixels Change Rate (NPCR) with the biggest image size with 43.47% for UACI and 99.87% for the NPCR.

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Context-dependent gene regulatory network reveals regulation dynamics and cell trajectories using unspliced transcripts

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Briefings in Bioinformatics 2023年第2期24卷 bbac633-bbac633页

作者： Tu, Yueh-Hua Juan, Hsueh-Fen Huang, Hsuan-Cheng The Bioinformatics Program Taiwan International Graduate Program Academia Sinica Taiwan Department of Life Science Center for Computational and Systems Biology National Taiwan University Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taiwan

Gene regulatory networks govern complex gene expression programs in various biological phenomena, including embryonic development, cell fate decisions and oncogenesis. Single-cell techniques are increasingly being used to study gene expression, providing higher resolution than traditional approaches. However, inferring a comprehensive gene regulatory network across different cell types remains a challenge. Here, we propose to construct context-dependent gene regulatory networks (CDGRNs) from single-cell RNA sequencing data utilizing both spliced and unspliced transcript expression levels. A gene regulatory network is decomposed into subnetworks corresponding to different transcriptomic contexts. Each subnetwork comprises the consensus active regulation pairs of transcription factors and their target genes shared by a group of cells, inferred by a Gaussian mixture model. We find that the union of gene regulation pairs in all contexts is sufficient to reconstruct differentiation trajectories. Functions specific to the cell cycle, cell differentiation or tissue-specific functions are enriched throughout the developmental process in each context. Surprisingly, we also observe that the network entropy of CDGRNs decreases along differentiation trajectories, indicating directionality in differentiation. Overall, CDGRN allows us to establish the connection between gene regulation at the molecular level and cell differentiation at the macroscopic level. © The Author(s) 2023.

关键词： Gaussian mixture model gene regulatory network single-cell RNA sequencing trajectory analysis

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AlexNet Architecture Based Convolution Neural Network for Realtime Audio to Text Translator of Bisindo Hand Sign

AlexNet Architecture Based Convolution Neural Network for Re...

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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Dhiki Sujatmiko Christy Atika Sari Eko Hari Rachmawanto Andi Danang Krismawan Bilal R. Altamer Mohamed Ayad Alkhafaji Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Study Program in Animation University of Dian Nuswantoro Semarang Indonesia Department of Computer Science Mosul University Presidency University of Mosul Mosul Iraq Department of Computer Science National University of Science and Technology DhiQar Iraq

Deafness is a condition that results in the loss of hearing function, hindering the reception of information such as oral communication that relies on auditory senses. Consequently, individuals with hearing impairment experience communication barriers and may have limited or no ability to respond. One solution is the use of sign language. In Indonesia, there are two known sign languages: Sibi and Bisindo. Both serve the same function but differ in their style of movement and expression. Bisindo is considered more flexible as it conveys meaning based on the Indonesian language. However, the universal understanding of this language solution is still limited among many people. Therefore, a program is needed to facilitate translation between deaf individuals who use sign language and their counterparts who do not communicate through sign language. CNN (Convolutional Neural Network) is a deep learning algorithm used for training visual input data recognition by computer systems. There are various CNN-based architectures, and one of them is AlexNet. Based on the author's testing, the AlexNet architecture proves to be suitable for real-time sign language translation. The evaluation of the system involved 7,800 datasets and 520 testing instances, with an average accuracy of 468 correct translations. When averaged, the system achieved a 90% accuracy rate, representing a 100% increase in accuracy compared to previous approaches.

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