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共 1443 条记录，以下是221-230 订阅

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Investing in Products with the Greatest Demand on Online Stores During the Pandemic 7th

Investing in Products with the Greatest Demand on Online Sto...

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7th International Conference on Inventive Systems and Control, ICISC 2023

作者： Hassya, Titan Hanif, Muhammad Fauzi Alvian Gaol, Ford Lumban Matsuo, Tokuro Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program—Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9789819916238

From the year 2019, every aspect of our lives has been negatively impacted as a result of the spread of a deadly disease known as COVID-19, particularly the health and business domains. The primary goal of this study is to develop a solution, or at the very least an overall analysis, to the readers on how to overcome the economic crisis during pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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The Adoption of Artificial Intelligence Technology in Parking Security System 7th

The Adoption of Artificial Intelligence Technology in Parkin...

引用

7th International Conference on Inventive Systems and Control, ICISC 2023

作者： Alhafizh, Muhammad Rifqi Adriawan, Albertus Baskara Yunandito Fernaldy, Darryl Gaol, Ford Lumban Matsuo, Tokuro Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program—Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9789819916238

Artificial intelligence (AI) refers to human-like intelligence exhibited by computers, robots, or other machines. In popular use, artificial intelligence refers to the ability of a computer or machine to mimic the ability of the human mind to learn from examples and experiences, recognize objects, understand and respond to language, make decisions, solve problems, and combine these and other abilities to perform functions that humans might perform. Artificial intelligence requires experience and data to smarten up the technology. The most important things in making artificial intelligence are learning, reasoning, and self-correction. At the learning stage, AI gives machines the ability to learn tasks without requiring a defined programming language. Then, the reasoning stage is the stage where the reason AI is applied in a technology. The self-correction stage is the stage where the AI is refining itself from and learning from experience in order to minimize errors or problems that exist. Then, this matter is concerned with the parking lot to be discussed. Parking can be interpreted as public facilities available in agencies or offices that serve to store vehicles. Vehicles entering the parking area become tens or even thousands, because it requires a parking system and management area. Such arrangements are capable of parking procedures and even other support systems such as adequate parking facilities and infrastructure, and another function is to create and develop parking systems in general to provide safety and comfort. The methodology obtained from this problem is checking the research model to check and find out the comparison of vehicle user’s Parking System Security between cars or motorcycles, as well as the usual parking lots visited. Then, we created a questionnaire. This study used questionnaires with the aim to get respondents’ results about the Parking Security System. Our sampling method is based on respondents aged 17–20 years. Questionnaires are su

关键词： Security systems

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Personalized Recommendation System for Online Learning: An Opportunity 8

Personalized Recommendation System for Online Learning: An O...

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8th International Conference on Business and Industrial Research, ICBIR 2023

作者： Murad, Dina Fitria Toha, Mohamad Mayatopani, Hendra Wijanarko, Bambang Dwi Heryadi, Yaya Dewi, Meta Amalya Leandros, Riyan Bina Nusantara University BINUS Online Learning Information Systems Department Jakarta Indonesia Universitas Terbuka Indonesia Pradita University Department of Information Systems Indonesia Bina Nusantara University BINUS Online Learning Computer Science Department Jakarta Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350399646

In the world of education, learning management systems are currently widely used. The massively open online courses are accessible either via the web or mobile. The Learning Management System (LMS) is one of the learning media that is widely used today in online learning. An LMS with the use of a personalized recommendation system can provide items tailored to the interests and behavior of its users. Items in the recommendation system can be in the form of recommendations for learning models, ways of learning, learning patterns, or learning stages according to the characteristics and profile of the user. The purpose of this study is to identify opportunities for better online learning by utilizing a customized recommendation system based on the history of user interaction with the system, using several factors as determinants or variables. The method used is a review conducted from several stages. The result of this is knowing the factors that influence the user, and the opportunities that exist in the system recommendations on personalized development. © 2023 IEEE.

关键词： Recommender systems

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A Systematic Literature Review of CNN Approaches in Classifying Brain Tumor 6

A Systematic Literature Review of CNN Approaches in Classify...

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6th International Conference on computer and Informatics Engineering, IC2IE 2023

作者： Paulindino, Aedentrisa Yasmanda Pardamean, Bens Elwirehardja, Gregorius Natanael Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350345162

The ability of Convolutional Neural Networks (CNNs) to accurately discriminate between normal and tumorous brain tissues has been promising. The review focuses on the different CNN models, pre-processing methods, data augmentation, and Transfer Learning (TL) strategies used in this research. This Systematic Literature Review (SLR) collected the data from Google Scholar. The results of this study indicate that open-source datasets from Kaggle and Brain MRI Images for Brain Tumor Detection are the most used datasets. However, limited data and imbalanced class problems remain common challenges across various datasets. To overcome those challenges, using a larger dataset, oversampling, Generative Adversarial Network (GAN), federated learning, and Self-Supervised Learning (SSL) to handle the imbalance are the potential solution. Additionally, popular CNN architectures for brain tumor classification extensively use pre-trained models such as VGG16, VGG19, DenseNet121, DenseNet201, GoogleNet, ResNet-50, and Inception-v3. TL strategies are preferred, allowing CNNs to leverage knowledge from large datasets, improving generalization even with limited labeled data. © 2023 IEEE.

关键词： brain tumor classification cnn mri pre-trained transfer learning

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Pap Smear Medical Image Classification Using Deep Learning: A Systematic Review 6

Pap Smear Medical Image Classification Using Deep Learning: ...

引用

6th International Conference on computer and Informatics Engineering, IC2IE 2023

作者： Selvano, Elvan Elwiehardja, Gregorius Natanael Pardamean, Bens Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta Indonesia

ISBN: (纸本)9798350345162

Cervical cancer has been known as one of the most prevalent medical disorders globally and a leading cause of death. Early detection, particularly through Pap tests, plays a vital role in its prevention. Previous studies have leveraged machine learning and deep learning techniques to classify the medical images obtained from Pap tests. In this study, a Systematic Literature Review methodology was used to examine 15 relevant papers that have been filtered from queries to Google Scholar which have gone through 4 stages of filtering that include: identification, screening, eligibility, and inclusion. This study addresses two research questions regarding the datasets and deep learning techniques for classifying pap smear images in recent years. The performance of the models was analyzed and potential areas for improvements are suggested. The findings of this study reveal that the Herlev University Hospital and SIPaKMed datasets are the most used. The methodologies used by researchers range from machine learning techniques, transfer learning using Convolutional Neural Networks, and utilize state-of-the-art models with novel optimizing methodology. While there are exciting opportunities in the field, challenges include model generalization and interpretability. © 2023 IEEE.

关键词： cervical cancer deep learning machine learning pap test

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Systematic Literature Review of Transformer Model Implementations in Detecting Depression 6

Systematic Literature Review of Transformer Model Implementa...

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6th International Conference on computer and Informatics Engineering, IC2IE 2023

作者： Nanggala, Kenjovan Pardamean, Bens Elwirehardja, Gregorius Natanael Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350345162

This systematic literature review explores the application of transformer models in early detection of human depression, encompassing text, audio, and video data modalities. Transformer architectures, notably BERT for text, have proven adept at capturing crucial contextual and linguistic patterns associated with depression. For audio and video data, hybrid approaches that combine transformer models with other architectures are prevalent. Key features considered include eye gaze, head pose, facial muscle movements, and audio characteristics such as MFCC and Log-mel Spectrogram, along with text embeddings. Performance comparisons underscore the superiority of text-based data in consistently delivering the most promising results, followed by audio and video modalities when utilizing transformer models. The fusion of multiple modalities emerges as an effective strategy for enhancing predictive accuracy, with the amalgamation of audio, video, and text data yielding the most precise outcomes. However, it is noteworthy that unimodal approaches also exhibit potential, with text data exhibiting superior performance over audio and video data. Nevertheless, several challenges persist in this research domain, including imbalanced datasets, the limited availability of comprehensive and diverse samples, and the inherent complexities in interpreting visual cues. Addressing these challenges remains imperative for the continued advancement of depression detection using transformer-based models across various modalities. © 2023 IEEE.

关键词： deep learning major depressive disorder multimodal transformer unimodal

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Edge-IoTDistilBERT: Fine-Tuning DistilBERT for Multi-Class Classification of Network Packet Attacks

Edge-IoTDistilBERT: Fine-Tuning DistilBERT for Multi-Class C...

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Advancement in Data science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Valentino Setiawan Benfano Soewito Computer Science Department BINUS Graduate Program - Master of Computer Science Jakarta Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

This exponential proliferation of IoT devices is creating an ever-growing demand for efficient cybersecurity solutions in resource-constrained environments. In this study, we propose Edge-IoTDistilBERT, a fine-tuned DistilBERT model, intended for the multi-class classification of network packet attacks within IoT ecosystems. Our proposed model was trained on the Edge-IIoT dataset, representative of all traffic classes and attack variants with high accuracy of up to 99.99%, outperforming state-of-the-art solutions. The new preprocessing pipeline of PCAP to text would transform the network traffic to a textual form allowing the model to learn the pattern in the dataset and making it able to generalize its content. Our fine-tuned model show a robust result on imbalanced dataset with two diffrent splits namely 80–20 and 70–30. The result shown by Edge-IoTDistilBERT place this model as a feasible effective cybersecurity solution for IoT networks, offering a good trade-off between high performance and resource constraints linked to edge devices.

关键词： Performance evaluation Adaptation models Accuracy Pipelines Transforms Telecommunication traffic Data models Robustness Internet of Things Information systems

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Stemming Algorithm for the Indonesian Language: A Scientometric View

Stemming Algorithm for the Indonesian Language: A Scientomet...

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2022 IEEE Creative Communication and Innovative Technology, ICCIT 2022

作者： Maesya, Aries Ramadhan, Arief Abdurachman, Edi Trisetyarso, Agung Zarlis, Muhammad Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665468763

Stemming is the process of cutting affixes, both prefixes and suffixes from a term to get the root of the word that has affixes. Stemming can be done in any language, especially in Indonesia Language. Indonesian which is rooted in Malay and Sanskrit has a bigger influence on Language. The algorithm of stemming that has developed is the one that is based on the dictionary of a root word. This kind of algorithm was started by Nazief- Adriani and it grew up to be Confix Stripping (CS), and then was perfected to be Enhanced Confix Stripping (ECS). A scientometric analysis is a mathematical method used to identify academic publications related to citations and scientific matters and is intended for use in libraries or other fields. In this research, the publication of which using stemming algorithm selected and collected using keywords related to Nazief Adriani and ECS. The publications are collected from *** (an online database for advance scientific research). From those publication we did the analysis bibliometric help by the VOS Viewer application using the analysis technique of co-authorship and citation technique, and all done. The result are, from 310 publications that have keywords Nazief Adriani and 119 publications containing the keyword ECS, it was found that the owning documents will be the reference sources of the next research in which the most citation and co-authorship are found in Indonesia. © 2022 IEEE.

关键词： Publishing

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Implementation of Random Forest Model with Hyperparameter Tuning for Diabetes Mellitus Classification

Implementation of Random Forest Model with Hyperparameter Tu...

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International Conference of computer and Informatics Engineering (IC2IE)

作者： Ajeng Wulandari Maria Susan Anggreainy Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduiate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

This research aims to classify Diabetes Mellitus (DM) using the Random Forest (RF) model by exploring feature selection techniques and hyperparameter tuning. DM is a metabolic disorder in the body due to bodily incompetence produces or delivers the finished hormone insulin causes blood sugar levels to rise above normal. High sugar levels in the bloodstream can cause heart disease, stroke, kidney disease, blindness, nerve damage in the feet, etc. So, diabetes must be detected as early as possible so that sufferers can receive early treatment. One of the machine learning models that can be used to create a diabetes detection model is the random forest (RF) model. The results from RF will be compared with other machine learning models such as K-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGBoost). The comparison results show that RF model is better and achieves the highest accuracy with an accuracy value of 97%.

关键词： Radio frequency Accuracy Forestry Nearest neighbor methods Diabetes Random forests Tuning Sugar Diseases Overfitting

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Prototype UX Design: Mobile Augmented Reality Application based on Gamification for Cultural Heritage Tourism 8

Prototype UX Design: Mobile Augmented Reality Application ba...

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8th International HCI and UX Conference in Indonesia, CHIuXiD 2022

作者： Hiererra, Siti Elda Meyliana Ramadhan, Arief Purnomo, Fredy Bina Nusantara University BINUS Graduate Program-Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665476645

This study aims to identify the processes of UX designing a mobile application, Augmented Reality based on Gamification for Cultural Heritage Tourism, and to provide the prototype design and the testing result;this research employs the Design Thinking Method to design the prototype and uses the System Usability Scale (SUS) to identify and validate the testing result;the author establishes two research questions for this study, then explains the answers through a clear description of the stages of making a prototype which consists of the empathize, define, ideate, prototype, and testing stages, then to find out the testing results, the author uses the SUS method, scoring 88.5 in the excellent and acceptable categories;the originality of the research, this study provides the Design Thinking method to investigate the process of UX designing and the SUS Score method to calculate the resting result, helps build a complete understanding of developing the prototype and validation through testing comprehensively. © 2022 IEEE.

关键词： Augmented reality

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