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Utilization of Waste Banks System in the COVID-19 Pandemic 4th

Utilization of Waste Banks System in the COVID-19 Pandemic

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4th International Conference on Inventive Computation and Information Technologies, ICICIT 2022

作者： Pradanang, Riyan Aditya, Bima Rayhan, Adhitya Winarlie, Michael Adrian Gaol, Ford Lumban Matsuo, Tokuro School of Information System Bina Nusantara University Alam Sutera Tangerang Indonesia Computer Science Binus Graduate Program Jakarta Indonesia Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9789811974014

People’s perspectives and behaviors altered in a variety of ways during the pandemic period, most notably in areas related to health, environment, and most notably waste management. Many people still prefer to work from home and this in turn leads to a large accumulation of garbage without having any proper means of disposal. As a result, the garbage area will serve as a breeding ground for illness. Garbage is usually composed of a residual waste generated from a product or object that has outlived its useful life, but can still be recycled into a valuable item. On this basis, the solution is to use a waste bank;it is a collection point for sorted garbage. It is claimed that using a waste bank can help in preventing the garbage accumulation. The main purpose of this study is to determine the contribution of the waste bank to end garbage accumulation. The information gathered through a review of the literature and direct interviews with multiple informants. According to the findings of the literature review, waste banks can provide a variety of benefits to users, as well as the community and surrounding environment. The proposed research study's findings show that the changes in community activity patterns contribute to an increase in garbage, and that the community plays an important role in waste control. From the performed research study, it is evident that m any individuals are interested about garbage banks and their benefits based on this presumption. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Recycling

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Biologically Interpretable Model for Precise Recurrence Prediction of Non-Small Cell Lung Cancer 46

Biologically Interpretable Model for Precise Recurrence Pred...

引用

46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Ai, Yang Du, Xiuju Liu, Jing Li, Yinhao Lin, Lanfen Li, Jingsong Chen, Yen-Wei Ritsumeikan University Graduate School of Information Science and Engineering Shiga Japan Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China Ritsumeikan University College of Information Science and Engineering Shiga Japan Zhejiang University College of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798350371499

The precise prediction of preoperative recurrence in non-small cell lung cancer (NSCLC) that is suitable for clinical application is still an open question. Recent advancements integrating genomic data with deep learning have shown promise in enhancing recurrence analysis in NSCLC patients. However, the lack of interpretability in the decision-making process of DNN models has hindered their clinical trustworthiness. In this paper, we propose a novel Biologically Informed Pathway-Aware Neural Network (BioPAN). By automatically extracting biological prior knowledge to guide the architecture of DNN models, we design a unified architecture of gene-pathway-biological process-disease. This approach endows each neuron with entity meaning and learns a multi-level view of biological pathways and processes related to recurrence for fully interpretable NSCLC recurrence prediction. We demonstrated that the proposed model well explains the molecular mechanisms linking genes to NSCLC recurrence, identifies several genes that significantly promote and inhibit recurrence, and elucidates the pivotal roles of various gene pathways at the biological process level. Moreover, it outperforms classical machine learning methods, provides fully interpretable biological information, and requires fewer parameters. Broadly, BioPAN enables clinical discovery and prediction, and may have general applicability across cancer types. © 2024 IEEE.

关键词： Lung cancer

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Metal-Oxide Thin-Film Transistors for Artificial Neural Networks 17

Metal-Oxide Thin-Film Transistors for Artificial Neural Netw...

引用

17th IEEE International Conference on Solid-State and Integrated Circuit technology, ICSICT 2024

作者： Hu, Yushen Lei, Tengteng Wong, Man The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Guangzhou HKUST Fok Ying Tung Graduate School Guangzhou China

ISBN: (纸本)9798350361834

The technology of thin-film transistors (TFTs) enables the construction of both single- and dual-gate devices. When built on metal oxide (MO) semiconductors with large energy bandgaps, TFTs with exceptionally low leakage current can be realized. This combination of materials, structures and electrical characteristics enables the deployment of MO TFTs in a wide range of applications. Presently reviewed are the deployment of a dual-gate TFT as a biomimetic electronic synapse and its application to the construction of artificial neural networks (ANNs). © 2024 IEEE.

关键词： Thin film transistors

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Detection of Narcotics Money Laundering in Suspicious Financial Transactions Using Convolutional Extreme Gradient Boosting (ConvXGB)

Detection of Narcotics Money Laundering in Suspicious Financ...

引用

Intelligent Cybernetics technology & Applications (ICICyTA), International Conference on

作者： Leonardus Verrell Sayoga Kinantra Winarno Antoni Wibowo Computer Science Department Binus Graduate Program – Master of Computer Science School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

With the development of narcotics problems that continue to increase, the Indonesian Government responds through the Badan Narkotika Nasional (BNN) with data showing the condition of narcotics tends to grow every year and is followed by the number of assets of money laundering (ML) crimes against narcotics cases. One of the banks in Indonesia as a reporting party for suspicious financial transactions (SFT) has obstacles in detecting narcotics ML due to complex and rarely found patterns. Some previous studies conducted experiments using Convolutional Neural Network (CNN), Extreme Gradient Boosting (XGBoost), and even a combination of both into Convolutional Extreme Gradient Boosting (ConvXGB), and improved model performance in several datasets. This paper designs a model using the ConvXGB algorithm by adopting the CNN architecture, LeNet-5, by applying several convolution layers and pooling layers as a baseline model for feature learning, and the XGBoost as feature classification. Three phases of research are the preprocessing phase by collecting data, transforming data, balancing data with a hybrid sampling technique, splitting data, and scaling data, followed by the implementation phase by creating a ConvXGB model, training and testing the model, then finally the evaluation phase by analyzing results and hyperparameter tuning. The dataset used is SFT from the bank during 2023. This ConvXGB has three convolution layers, a pooling layer, and a flattened layer. The performance test results are the accuracy value and F1-Score value of 99.11% each after hyperparameter tuning. By performing a hybrid model, the model performance results are better.

关键词： Training Representation learning Analytical models Convolution Government Boosting Data models Convolutional neural networks Tuning Testing

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SPD Manifold-Based Graph Neural Network for Fault Diagnosis of Harmonic Drive 3

SPD Manifold-Based Graph Neural Network for Fault Diagnosis ...

引用

3rd International Conference on Electronic Information Engineering and computer Communication, EIECC 2023

作者： Cheng, Wei Long, Zhuo Ma, Xiaoguang Li, Chuanwen School of Computer Science and Engineering Northeastern University Shenyang China School of Electrical and Information Engineering Changsha University of Science and Technology Changsha China Foshan Graduate School Northeastern University Foshan China

ISBN: (纸本)9798350359961

Harmonic drive (HD), as an essential component of industrial robots, is susceptible to damage due to product manufacturing and working conditions. Therefore, it is imperative to conduct a comprehensive test to accurately diagnose potential HD faults. In this paper, a SPD Manifold-based Graph Neural Network (SPD-GNN) is proposed to accurately identify different faults of HD. In the time-frequency domain, the high-definition vibration signals obtained from multiple sensors are segmented. The resulting multi-channel spatial covariance matrices (MSCM) act as vertices in a time-frequency graph. A SPD-GNN is then utilized to extract classification details while preserving discriminative capabilities. Subsequently, the SPD matrices are projected to the tangent space using a logarithmic mapping (LOG) layer. Ultimately, the data is input into a cross-entropy loss function for subsequent computation. The SPD-GNN uses graph convolutional techniques tailored for SPD matrices to capture HD features in the time-frequency domain. © 2023 IEEE.

关键词： fault diagnosis graph neural network harmonic drive multi-sensor symmetric positive definite manifold

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STFormer: Learning to Explore Spot Relationships for Spatial Transcriptomics Prediction from Histology of Colorectal Cancer 46

STFormer: Learning to Explore Spot Relationships for Spatial...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Zhan, Gan Du, Xiuju Liu, Jing Li, Yinhao Lin, Lanfen Li, Jingsong Chen, Yen-Wei Ritsumeikan University Graduate School of Information Science and Engineering Shiga Japan Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China Ritsumeikan University College of Information Science and Engineering Shiga Japan Zhejiang University College of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798350371499

Spatial transcriptomics (ST) offers insights into gene expression patterns within tumor microenvironments, but its widespread application is impeded by cost constraints. To address this, predicting ST from Histology emerges as a cost-effective alternative. However, current methods such as STNet, HistoGene, and Hist2ST exhibit limitations, either overlooking stain variation across datasets or failing to well explore inter-spot correlations in scenarios with limited Whole Slide Image (WSI) data. In response, we present STFormer, a deep learning approach that incorporates the Style-Aug module to enhance feature generalization through medically-irrelevant style transfer augmentation. Additionally, the Cross-WSI Transformer module is introduced to capture Cross-WSI spot relationships efficiently. Our experimental results, conducted on both internal and external datasets, demonstrate that STFormer surpasses existing methods by a substantial margin. This showcases its potential as a powerful tool for spatial transcriptomics predictions, addressing critical gaps in current methodologies. © 2024 IEEE.

关键词： WSI circuits

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StackFi: An Ensemble Learning-Based Model for WiFi Sensing Classification Tasks

StackFi: An Ensemble Learning-Based Model for WiFi Sensing C...

引用

Consumer Electronics and computer Engineering (ICCECE), 2021 IEEE International Conference on

作者： Jiaxing Liu The Graduate School of Computer Science and Technology University of Science and Technology of China Hefei China

The widespread adoption of WiFi devices and ubiquitous WiFi networks has fostered the development of the WiFi sensing domain. With the continuous progress in deep learning, a rising prevalence is observed in the application of deep learning techniques to the field of WiFi sensing. However, researchers often face the dilemma of choosing either convolutional neural networks, disregarding the temporal nature of Channel State Information data, or recurrent neural networks, overlooking the similarity between its subcarriers and the multiple channels in image data. We propose an ensemble learning-based model, StackFi, which combines two prevailing approaches in deep learning for processing Channel State Information (CSI) data. One approach analogizes CSI data to images, utilizing deep convolutional neural networks for feature extraction. The other treats CSI data as temporal data, employing recurrent neural networks for feature extraction. Our model extracts features from CSI data in WiFi signals, and these extracted features are then fed into a classifier. We gathered some commonly employed deep learning models in the field of WiFi sensing and contrasted them with StackFi using the public datasets UT-HAR and NTU-Fi HAR. Our model exhibited outstanding performance, achieving accuracy rates of 99.13% and 99.27% on these two public datasets, surpassing the performance of other models in the field.

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Triple-path transformer network for multi-channel speech enhancement 24

Triple-path transformer network for multi-channel speech enh...

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24th International Congress on Acoustics, ICA 2022

作者： Park, Kyeong Wan Lee, Geon Woo Kim, Hong Kook School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Korea Republic of AI Graduate School Gwangju Institute of Science and Technology Korea Republic of

This paper proposes a neural network-based end-to-end multi-channel speech enhancement model that operates in time domain. To this end, a triple-path transformer network (TPTN) is proposed to extract clean speech features by replacing the attentive recurrent neural networks (RNNs) with transformers in the triple-path attentive recurrent network (TPARN) framework. The proposed TPTN is composed of an intra-chunk transformer, an inter-chunk transformer, and an inter-channel transformer. Thus, a series of TPTNs are applied to the speech features converted from multi-channel speech signals so that extracted features are represented with local and global temporal dependencies as well as spatial dependencies. The performance of the proposed TPTN-based multi-channel speech enhancement model was evaluated on the CHiME3 dataset by measuring the short-time objective intelligibility (STOI) score and perceptual evaluation of speech quality (PESQ) score. It was shown from the experiment that the proposed TPTN-based model achieved similar STOI score to and higher PESQ score by 0.15 than the TPARN-based model, while the number of parameters of the proposed model was smaller than that of the TPARN-based model. © ICA *** rights reserved

关键词： Deep Learning End-to-end model Multi-channel Speech enhancement Transformer

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Analysis of Factors Influencing Decisions to Purchase Airline Tickets Online during the Covid-19 Pandemic

Analysis of Factors Influencing Decisions to Purchase Airlin...

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2023 International Conference on computer science, Information technology and Engineering, ICCoSITE 2023

作者： Lie, Yulius Perbangsa, Anzaludin Samsinga Pardamean, Bens Bina Nusantara University School of Information Systems Information System Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

The background of this research is the condition of the covid-19 pandemic which has an impact on online ticket sales. Meanwhile, when future of covid-19 pandemic is starting to become clearer, we are going to have a lot more infections but hopefully a lot fewer hospitalizations, people began to travel long distances that had been delayed by implementing health protocols. Airfare competition can influence the decision to buy airline tickets. Various things have been done to attract the attention of buyers. This research is a non-experimental quantitative research. Population in this research was people who have purchased plane tickets online during the covid-19 pandemic, and the sample were 113 respondents age between 15 and 64. Sampling technique used was simple random sampling. Analysis of the data used in this study using multicollinearity, heteroscedasticity, multiple linear, T-test, F-test, and coefficient of determination test. It was concluded that price, promotion, and lifestyle variables can influence online purchasing decisions. © 2023 IEEE.

关键词： Sales

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Multi-task RetinaNet for Mitosis Detection 25th

Multi-task RetinaNet for Mitosis Detection

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25th International Conference on Medical Image Computing and computer-Assisted Intervention , MICCAI 2022

作者： Wang, Ziyue Chen, Yang Fang, Zijie Bian, Hao Zhang, Yongbing Department of Computer Science Harbin Institute of Technology Harbin China Shenzhen International Graduate School Tsinghua University Beijing China

ISBN: (纸本)9783031336577

The count of mitotic cells is a key feature in tumor diagnosis. However, due to the variability of mitotic cell morphology, detecting mitotic cells in tumor tissues is a highly challenging task. At the same time, the performance of the trained models often declines when there is a vast difference between the source domain and the target domain. (i.e., the different tumor types and scanners). Therefore, it is necessary to develop algorithms for detecting mitotic cells with robustness in domain shift scenarios. Our work proposes a foreground detection and tumor classification task based on the baseline (Retinanet) and utilizes data augmentation to improve our model’s detection ability and domain generalization performance. We achieve excellent performance on the challenging preliminary test dataset (F1 score: 0.5809) and the Final test dataset (F1:0.6300). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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