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Enhancing Breast Cancer Segmentation with Attention Mechanisms in U-Net Architectures 9th

Enhancing Breast Cancer Segmentation with Attention Mechanis...

引用

9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Prasetyo, Simeon Yuda Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Breast cancer is still a major global health concern, as seen by the high incidence and death rates among women, which underscore its grave implications. Since treating this ailment necessitates quick identification and therapy planning, accurate segmentation is essential to medical imaging. The purpose of this work is to expand the choices for diagnosis and treatment by examining the effectiveness of Attention U-Net topologies in enhancing the accuracy of breast cancer segmentation. With a Dice coefficient of 97.99%, an Intersection over Union (IoU) of 93.41%, and an accuracy of 95.91% on the test set, the Attention U-Net outperforms both the standard U-Net and the Residual U-Net models when compared. These findings highlight the need of using deep learning frameworks to increase breast cancer segmentation accuracy and, as a result, provide improved patient care alternatives. As deep learning technologies evolve, the integration of Attention U-Net topologies may aid in breast cancer detection and therapy, resulting in better patient outcomes and aiding global efforts against the illness. © 2024 The Authors.

关键词： Lung cancer

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Sign Language Detection to Enhance Online Communication for Deaf and Mute Individuals through Deep Learning Models

Sign Language Detection to Enhance Online Communication for ...

引用

2024 International Conference on computer Engineering, Network and Intelligent Multimedia, CENIM 2024

作者： Ruslim, Gladys Jessica Salim, Nicholas Matthew Edbert, Ivan Sebastian Suhartono, Derwin Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350368802

Communicating online presents significant hurdles for deaf and mute people, who frequently confront challenges due to speech and hearing limitations. These limitations can make their digital interactions difficult and irritating, as many online platforms and communication tools are not designed with their special needs in mind. This study intends to improve these people's online communication experiences by creating an object detection and classification model that detects Sign Language using computer vision. Three deep learning models, VGG16, ResNet50, and MobileNet, are evaluated using the SIBI Sign Language Dataset. The comparison focused on determining the most effective approach for accurate sign language detection. The results show that the VGG16 model outperforms the other models by 0.9680 accuracy and 0.9694 F1 score. By incorporating such technologies, a critical gap in digital accessibility can be closed, allowing these individuals to participate more completely in the digital world. © 2024 IEEE.

关键词： Convolutional neural networks

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IoT Door Locking a Review 9

IoT Door Locking a Review

引用

9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Yulianto Rahayu, Anita School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

Traditional mechanical locks need a key for locking or unlocking the door, with the fundamental drawback of the possibility of losing the key. Researchers have proposed replacing the mechanical key with electronic one, also equipped with a sensor, in order to improve the fully mechanical system of door locks. The Internet of Things (IoT) scheme was also implemented for better control and monitoring via the cloud network. IoT schemes usually involve several components such as sensors, microcontrollers, and network components. The sensor is used to read the state of the environment. It can also be used to capture the biometric characteristics of the users authorized to access the system. For some types of sensors, it is not possible to connect them directly to a computer to collect the result of the reading of the sensor data. Thus, microcontrollers are needed. However, there are still a few papers that provide a summary of what the main component is and what other authors have proposed in their research. The aim of this study is to briefly summarize which sensor types, microcontrollers and communication protocols have recently been used to improve IoT-based door locks, especially for indoor environments. Whether the use of a single sensor or multiple sensors is also discussed. The types of microcontrollers used in recent research are also discussed. © 2024 IEEE.

关键词： Locks (fasteners)

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A Hybrid Deep Learning Techniques Using BERT and CNN for Toxic Comments Classification 9

A Hybrid Deep Learning Techniques Using BERT and CNN for Tox...

引用

9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Jessica, Adelia Sugiarto, Migel Sastrawan Jerry Achmad, Said Sutoyo, Rhio School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

Cyberbullying is a pervasive issue across all forms of media, affecting various demographics and platforms indiscriminately. From social media networks to online forums and comment sections on news sites, the harmful behavior of cyberbullying manifests in many forms, including harassment, threats, and demeaning comments. This ubiquity underscores the need for effective detection mechanisms. This paper explores the identification of toxic traits in online comments using advanced hybrid models, specifically BERT-CNN and BERT-LSTM. The research methodology involved constructing and configuring multiple layers within these models to optimize their ability to detect harmful content. For the BERT-CNN model, BERT's powerful language understanding capabilities are combined with CNN's strength in feature extraction through convolutional layers, capturing spatial hierarchies of features. In the BERT-LSTM model, BERT is integrated with LSTM layers to leverage their ability to learn long-term dependencies and sequential patterns in text. Various configurations of these models were tested, including adj ustments in the batch sizes and the length of the word tokens, to enhance performance in identifying toxic language. The best model which is BERT-CNN using the configuration 256 as the unit and 32 as the batch size achieved 94.48% accuracy in detecting toxic comments. The result from the model indicates that by harnessing BERT's contextual embeddings and the respective benefits of CNN's and LSTM's processing capabilities, it is possible to significantly reduce the frequency of cyberbullying through effective detection, ultimately fostering safer online environments across different media platforms. © 2024 IEEE.

关键词： Deep learning

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Comparison of LSTM and Transformer for Time Series Data Forecasting 7

Comparison of LSTM and Transformer for Time Series Data Fore...

引用

7th International Conference on Informatics and Computational sciences, ICICoS 2024

作者： Sonata, Ilvico Heryadi, Yaya Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350375886

Time series data is data that is collected periodically and has certain time intervals. Time series data is widely available in the fields of finance, meteorology, signal processing, health, and economics. Weather data, stock prices, and sales data are examples of time series data. Time series data analysis can be used to predict future conditions based on patterns and values from previous data through a forecasting process. These forecasting results are useful for identifying trends and data patterns for decision making. The rapid development of deep learning models can now be used as a method for forecasting time series data. Deep learning models that are suitable for forecasting include sequence-to-sequence models such as Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and Transformer. LSTM and Transformer are sequence-to-sequence models that are widely used for forecasting time series data. Research to compare the accuracy of the two models is still very limited. This article will discuss the use of the LSTM and Transformer models for the time series data forecasting process using Hewlett Packard stock price data from 1962 to 2022. The accuracy results show that the LSTM model outperforms the Transformer model in forecasting Hewlett Packard stock prices. © 2024 IEEE.

关键词： Data accuracy

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Stunting Prediction in Children Using Random Forest Algorithm 6

Stunting Prediction in Children Using Random Forest Algorith...

引用

6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Kanz, Ahmad Fauzan Johanes, Luvky Pratama Alam, Islam Nur Wulandhari, Lili Ayu Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798331539603

Stunted child growth and development caused by chronic malnutrition has remained a significant global public health problem worldwide, especially in low-and middle-income countries. Thus, it is essential to employ effective predictive models that can identify those at risk so as to implement appropriate interventions. In this research, a predictive model for childhood stunting based on artificial intelligence (AI) methods will be introduced. The machine learning algorithms and statistical techniques are employed in the model which integrates several socio-economic, demographic, dietary and health related factors to predict stunting among children under five years of age. This includes training the model using survey data that is nationally representative as well as longitudinal studies to ensure its reliability and applicability across populations with different characteristics. Some of the metrics used to assess the predictive performance include accuracy, sensitivity, specificity and area under receiver operating characteristic curve (AUC-ROC). Many interpretability techniques have been utilized to highlight the primary factors associated with an increased risk of stunting thereby becoming a valuable tool for policy makers and health care providers in planning focused interventions and resource allocation effectively. Utilizing AI, this predictive model presents a proactive strategy to address childhood stunting, ultimately enhancing child health and well-being globally. © 2024 IEEE.

关键词： Electronic health record

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Abstractive English Document Summarization Using BART Model with Chunk Method 9th

Abstractive English Document Summarization Using BART Model ...

引用

9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Wilman, Pascal Atara, Talia Suhartono, Derwin Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Particularly for the summarization task and the summary of relatively brief documents, abstractive document summarization has become a favored research topic in the field of natural language processing as it continues to advance. We recommend employing the enhanced abstractive summarization model, which integrates a pre-trained BART model from the CNN/Daily Mail dataset with chunk method processing. This model is capable of processing both brief and lengthy text input, including news articles and research articles, which are categorized as long documents. Our approach involves chunking the string into a list of sentences and encoding the text of a lengthy document written in English. Subsequently, we employ a BART model decoder to generate a summary by combining the results of each chunk text summarization, which then serves as the result summary. The performance of our chunking method is better when utilizing BART, which has been pre-trained on the CNN/Daily Mail dataset, compared to the one pre-trained on the XSum dataset. © 2024 The Authors.

关键词： Natural language processing systems

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Revolutionizing palm oil ripeness classification: Utilizing YOLOv8 for ultra-precise ripeness detection 9th

Revolutionizing palm oil ripeness classification: Utilizing ...

引用

9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Josdaan, Jordan Tamsil, Vianna Calista Harefa, Jeklin Jingga, Kenny Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

The palm oil industry has always been a pivotal sector on the world's vegetable crop industry, especially for southeast Asian countries such as Indonesia and Malaysia. These fast-growing sectors has led to challenges for assessing oil palm fruit maturity. In order to overcome these obstacles, this research suggests an automating the detection of oil palm fruit maturity through the use of computer vision. The six maturity clusters for oil fruit images are Rotten, Underripe, Unripe, Overripe, and Empty Bunch. Several different types of YOLOv8 models, including YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x, are evaluated in this study. The YOLOv8m emerges as the best option, which shows notable gains in accuracy metrics at the same time as processing speeds. For example, YOLOv8m processes at an average speed of about 14.68 iterations per second and achieves top-1 and top-5 accuracy of 0.9885 and 0.998, respectively. © 2024 The Authors.

关键词： Fruits

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Predicting Age and Gender Across Different Races Using Convolutional Neural Networks: A Deep Learning Approach

Predicting Age and Gender Across Different Races Using Convo...

引用

2024 International Conference on Information Technology and Computing, ICITCOM 2024

作者： Ardelia, Elvina Thenando, Jericho Gunawan, Alexander A.S. Syahputra, Muhammad E. School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350379839

Age and gender are two important attributes possessed by humans that serve as their identity in social life. Faking age and gender are criminal acts that are harmful especially for bad purposes. Therefore, artificial intelligence systems are needed which can predict the age and gender of humans with high accuracy. The model commonly used to detect images and perform visual-based tasks is CNN. Nowadays, there are many frameworks and other models that are the development of CNN models, one of them is DeepFace. The purpose of this research is to evaluate the CNN model and compare its accuracy with DeepFace using Mean Absolute Error metric for age and Exact Match Ratio metric for gender. The test data used is divided into 5 races, namely Asian, Black, Indian, White, and other races (like Middle Eastern, Latino, and Hispanic). The result of this research is the accuracy value for each race using CNN and DeepFace which states that DeepFace is superior for age prediction and CNN is superior for gender prediction. © 2024 IEEE.

关键词： Prediction models

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UI/UX Design Impact on E-Commerce Attracting Users 9th

UI/UX Design Impact on E-Commerce Attracting Users

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Hasan, Tsabbita Isnina Silalahi, Christine Irene Rumagit, Reinert Yosua Pratama, Galih Dea Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

In this era of digitalization, e-commerce platforms serve as vital transactional bridges between users and sellers, playing quite an important role on serving our interests and needs. With the efficiency and effectiveness offered, it's no wonder e-commerce platforms has become quite popular. This causes the competitive landscape in online marketing quite fierce, with e-commerce platforms striving to retain existing users while attracting new ones and expand their growth even more. Playing quite a huge role to this is the User Interface and User Experience (UI/UX) design, serving as one of the fundamentals in shaping positive user perceptions, leading to user engagement and dynamics with the platform. This research will discuss aspects that can attract users in terms of UI/UX design perspective. By employing a quantitative approach along with quantitative analytics, we aim to uncover the underlying factors that contribute to user attraction and retention. Through a comprehensive analysis of UI/UX design elements such as color theory, navigational ease, importance of homepage design, design priority, and user satisfaction, this study seeks to elucidate the key drivers of UI/UX design, enhancing user engagement and satisfaction. Through this research, our analysis on four well-known e-commerce platforms in Indonesia (Shopee, Tokopedia, Blibli, and Lazada) reveals that Tokopedia ranks highest in user satisfaction with a Mean Opinion Score (MOS) of 3.93 out of 5, followed by Shopee with 3.92, Blibli with 3.83 and lastly Lazada with 3.58. The result aligned with how Tokopedia has been applying the right design on aspects we deemed important leading to satisfactory user satisfaction. Thus, highlighting the importance of homepage design, navigational ease, and color theory in attracting and retaining users. The findings of this research validate the significance of these UI/UX elements and provide practical guidance for improving e-commerce design. © 2024 The Authors.

关键词： Marketplaces

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