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41st International Conference on Machine Learning, ICML 2024

作者： Lee, Nahyuk Min, Juhong Lee, Junha Kim, Seungwook Lee, Kanghee Park, Jaesik Cho, Minsu Department of Computer Science and Engineering POSTECH Pohang Korea Republic of Graduate School of Artificial Intelligence POSTECH Pohang Korea Republic of Department of Computer Science and Engineering Seoul National University Seoul Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Korea Republic of

Learning to assemble geometric shapes into a larger target structure is a pivotal task in various practical applications. In this work, we tackle this problem by establishing local correspondences between point clouds of part shapes in both coarse- and fine-levels. To this end, we introduce Proxy Match Transform (PMT), an approximate high-order feature transform layer that enables reliable matching between mating surfaces of parts while incurring low costs in memory and compute. Building upon PMT, we introduce a new framework, dubbed Proxy Match TransformeR (PMTR), for the geometric assembly task. We evaluate the proposed PMTR on the large-scale 3D geometric shape assembly benchmark dataset of Breaking Bad and demonstrate its superior performance and efficiency compared to state-of-the-art methods. Project page: https://***/pmtr. Copyright 2024 by the author(s)

关键词： 3D modeling

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Automated Denial of Service Detection Using Moth Flame Optimization With Machine Learning in Cloud Environment 2

Automated Denial of Service Detection Using Moth Flame Optim...

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2nd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2023

作者： Thillaivanan, Alagu Wategaonkar, Somnath Raghunath Duraisamy, Suganthi Mishra, Ravi Nagaraj, S. Singh, Kamlesh Shadan College of Engineering and Technology Peerancheru Hyderabad India Electronics and Telecommunication Bharati Vidyapeeth College of Engineering Maharashtra Navi Mumbai India Saveetha College of Liberal Arts and Sciences SIMATS Tamilnadu Chennai India Electronics and Telecommunication Engineering GHRIET Maharashtra Nagpur India Department of Computer Science and Engineering & IT Jain University Karnataka Bengaluru India Department of Computer Science and Engineering Graphic Era Hill University Dehradun Campus Uttarakhand Dehradun India

ISBN: (纸本)9798350348002

Denial of Service (DoS) attack detection refers to preventing and detecting malicious attempts to make network resources or services unavailable to its intended users. DoS attacks is been a main concern for organizations since they can disturb the accessibility of critical services and cause economic losses. In cloud environments, the mitigation and detection of such attacks were very challenging because of the dynamic and distributed nature of infrastructure. In this regard, Machine Learning (ML) methods are potential in identifying DoS attacks, by using network traffic features to find unusual anomalies or paradigms that may specify an attack. This research work introduces an automated Denial of Service Detection using Moth Flame optimization with Machine Learning (DoSD-MFOML) technique in cloud environment. The DoSD-MFOML technique recognizes DoS attacks and the MFO algorithm is used for feature selection purposes to attain improved results. The detection of DoS attacks takes place using extreme gradient boosting (XGBoost) classifier. Finally, the DoSD-MFOML technique employs grey wolf optimizer (GWO) algorithm for the parameter tuning procedure. The performance validation of the DoSD-MFOML method is tested on benchmark dataset and the outcomes are studied under several measures. The experimental outcome confirms the increased performances of the DoSD-MFOML technique for DoS attack detection purposes. © 2023 IEEE.

关键词： Machine learning

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Performance Evaluation of Coffee Bean Binary Classification Through Deep Learning Techniques 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Islamy, Fajrul Muchtar, Kahlil Arnia, Fitri Dawood, Rahmad Febriana, Alifya Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Master of Computer Science Program BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Coffee beans are one of the high-value commodities in Indonesia, but the sorting method for the quality of coffee beans still uses visual methods and sieves with mechanical machines. This study aims to provide an alternative to classifying coffee beans using Deep Learning algorithms, such as the ResNet-18 and MobileNetV2. The dataset used is USK-COFFEE which consists of two classes, the normal coffee bean class, and the defect coffee bean class. The models were trained using the balanced and imbalanced datasets, meaning that two scenarios were performed. From the results of this study, the accuracy value for the ResNet-18 model was 91.50% when trained on the balanced dataset and 90.87% on the imbalanced dataset. It outperformed the MobileNetV2 architecture, which only achieved 0.8125 and 0.8112 accuracy scores when trained on the balanced and imbalanced datasets, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： computer vision

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Hybrid Deep Learning for Breast Cancer Diagnosis: Evaluating CNN and ANN on BreakHis_v1_400X

Hybrid Deep Learning for Breast Cancer Diagnosis: Evaluating...

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Ogundokun, Roseline Oluwaseun Abdullahi, AbdulRahman Tosho Adenike, Ajiboye Raimot Awoniyi, Charles Akande, Hakeem Babalola Falajiki, Funmilola Adedayo-Ajayi, Victoria Landmark University Department of Computer Science Omu Aran Nigeria Kaunas University of Technology Department of Multimedia Engineering Lithuania Kwara State Polytechnic Institute of Information and Communication Technology Department of Computer Science Nigeria Kwara State Polytechnic Institute of Applied Sciences Department of Statistics Nigeria Bupa Insurance Services London United Kingdom Telecommunication Science University of Ilorin Nigeria

ISBN: (纸本)9798350358155

Recent medical research has significantly emphasized the need for early illness diagnosis due to the fast rise of the world's population. Since there are now more people in the world, the danger of dying from breast cancer is rising at an exponential rate. Breast cancer is currently the second deadliest form of cancer out of all the forms that are known. When it comes to illness diagnosis, having access to an automated disease detection system is very helpful to medical professionals since it enables them to provide dependable, effective, and quick reactions while also lowering the mortality rate. This research assesses the hybridization of two deep learning models, a convolutional neural network (CNN) and an artificial neural network (ANN) - the well-known BreakHis_v1_400X dataset (a dataset for breast cancer histopathological images) to work with. The performance of these approaches is evaluated based on several criteria, including accuracy, sensitivity, specificity, precision, false-positive rate, and F1 score. In addition, we evaluate these methods by calculating the area under the curve for accuracy and recall, as well as the receiver operating characteristic curve. According to the findings, the hybridized CNN-ANN achieved the most remarkable accuracy, precision, AUC, and F1 score, with respective values of 89.47%, 86.18%, 89.46%, and 89.08%. © 2024 IEEE.

关键词： Lung cancer

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Environmental Disinfection Based on Mobile Intelligent Networking Highly Efficient Ultraviolet Light Machines

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Aerosol and Air Quality Research 2024年第2期24卷 1-10页

作者： Chung, Chun-Yen Sung, Wen-Tsai Wang, Lin-Chi Siami, Lailatus Santos, John Paul Computer and Game Development Program Kun Shan University Tainan710303 Taiwan Department of Electrical Engineering National Chin-Yi University of Technology Taichung411030 Taiwan Department of Marine Environmental Engineering National Kaohsiung University of Science and Technology Kaohsiung81157 Taiwan Department of Environmental Engineering Chung Yuan Christian University Taoyuan32023 Taiwan Department of Civil Engineering Chung Yuan Christian University Taoyuan32023 Taiwan

Since 2019, the world has suffered from the outbreak of the "Coronavirus Disease (COVID)-19 pandemic. This study used the principle of Ultraviolet (UV)-C disinfection to produce a mobile high-power UV-C sterilizer. It focused on combining intelligent control systems, human sensors, and Internet of Things (IoT) systems to carry out disinfection tasks in unmanned indoor spaces. Because of its mobile nature, the sterilizer could be moved at any time in various learning, research, and activity rooms for disinfection. In addition to manually controlling the sterilizer, a Wi-Fi or Narrowband (NB)-IoT-enabled remote control was provided to start the disinfection process. In order to prevent ultraviolet light from harming the human body, sensors were employed self-acting to detect the presence of people and turn off the ultraviolet light when people were detected in the area. At the same time, a camera on the sterilizer would capture an image and notify the relevant personnel through a LINE notification, to prevent the human body from being exposed to ultraviolet light, causing harm to the teachers and students on campus. During operation, voice broadcast reminders and a status indicator could provide information about the operation status. After disinfection, users could view the disinfection process through a mobile device application or a webpage, which would automatically notify relevant personnel of the disinfection status through a LINE notification. In this way, the disinfection progress could be updated at anytime, anywhere and the current disinfection status in the classroom could be displayed in real-time, allowing teachers and students on campus to carry out teaching and learning in a safe and secure environment. © The Author(s).

关键词： Internet of things

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Physics-informed neural networks for modeling water flows in a river channel

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1001-1015页

作者： Nazari, Luis Fernando Camponogara, Eduardo Seman, Laio Oriel Department of Mathematics Federal Institute of Education Science and Technology Catarinense Rio do Sul89160-202 Brazil Department of Automation and Systems Engineering Federal University of Santa Catarina Florianopolis88040-900 Brazil Program in Applied Computer Science Universidade Do Vale Do Itajai Itajai88302-901 Brazil

The impacts incurred by floods regularly affect the planets population, inflicting social and economic problems. Optimal control strategies based on reservoir management may aid in controlling floods and mitigating the resulting damage. To this end, an accurate dynamic representation of water systems is needed. In practice, flood control strategies rely on hydrological forecasting models obtained fromconceptual or data-drivenmethods. Encouraged by recent works, this research proposes a novel surrogate model for water flow in a river channel based on physics-informed neural networks (PINNs). This approach achieved promising results regarding the assimilation of real-data measurements and the parameter identification of differential equations that govern the underlying dynamics. This article investigates PINN performance in a simulated environment built directly from a configuration of the Saint-Venant equations. The objective is to create a suitable model with high prediction accuracy and scientifically consistent behavior for use in real-Time applications. The experiments revealed promising results for hydrological modeling and presented alternatives to solve the main challenges found in conventional methods while assisting in synthesizing real-world representations. Impact Statement-The research seeks to contribute to the hydrological modeling area with a surrogate model based on physicsinformed neural networks (PINNs) to water flow in a watershed. In practice, thesemodels use conceptual or *** models to reach the precision provided by themethodology use large numbers of physical parameters. These parameters can demand deep knowledge about the environment and are possibly hard to identify in a complex basin. On the other hand, while data-driven methods do not require such knowledge about the dynamic system, they depend on a reliable and useful database to guarantee the accuracy of system *** introduce PINNs as a viable solution for

关键词： Floods

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Sentiment Analysis on Service Quality of an Online Healthcare Mobile Platform Using VADER and Roberta Pretrained Model 4th

Sentiment Analysis on Service Quality of an Online Healthcar...

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4th International Conference on Electronics, Biomedical engineering, and Health Informatics, ICEBEHI 2023

作者： Maulana, Fairuz Iqbal Adi, Puput Dani Prasetyo Lestari, Dian Purnomo, Agung Wangean, Daniel Anando Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Telecommunication Research Center National Research and Innovation Agency Jakarta11480 Indonesia Faculty of Medicine Universitas Airlangga Surabaya60132 Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9789819714629

The development of mobile technology has penetrated into the health sector. With the development of this technology, information about health can improve the quality of health independently. The integration of information technology into the healthcare sector, namely the advancement of mobile-based health services, has significantly revolutionised the accessibility of healthcare across various regions in Indonesia. Halodoc is the leading digital health company in Indonesia and has substantially changed the axis of health services in Indonesia by providing health information that is easy to understand, credible, and accessible to everyone. This research will analyze Halodoc’s service quality based on customer reviews from Google Play Store, which will be analyzed using sentiment analysis. Specifically, this research uses VADER (Valence Aware Dictionary and Sentiment Reasoner) and Roberta Pretrained Model to analyze the sentiment of user reviews on Halodoc mobile application. The results of the sentiment analysis can provide insight into users’ overall satisfaction with the platform’s service quality. Results show that 84% users leave reviews with Neutral Sentiment Analysis, 13% with positive comments, and the smallest number of Sentiment Analysis is negative with 3%, in this application. We create correlation matrix to see correlation coefficients between sets of variables and the wordcloud. This research contributes to the growing body of research on sentiment analysis in the healthcare industry and can inform the development of strategies to improve the quality of online healthcare services. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Quality control

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Hybrid Model Approaches Toward Movie Recommendation Systems and Their Comparisons

Hybrid Model Approaches Toward Movie Recommendation Systems ...

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International Conference on Data Analytics and Management, ICDAM 2023

作者： Parikh, Jolly Antony, Saji M. Jain, Rachna Chawla, Naman Mishra, Ashish de Albuquerque, Victor Hugo C. Neto, Aloísio Vieira Lira Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering New Delhi India Department of Electronics and Communication Engineering Bharati Vidyapeeth’s College of Engineering New Delhi India Department of Information Technology Bhagwan Parshuram Institute of Technology New Delhi India Department of Computer Science and Engineering Bhagwan Parshuram Institute of Technology New Delhi India Department of CSE Gyan Ganga Institute of Technology and Sciences Madhya Pradesh Jabalpur India Federal Institute of Education Science and Technology of Ceara Fortaleza Brazil Federal University of Ceará Fortaleza Ceará Fortaleza Brazil Graduation Program in Telecommunication Engineering Federal University of Ceará Fortaleza Ceará Fortaleza Brazil

ISBN: (纸本)9789819965465

In the contemporary digital era, individuals face an overwhelming challenge of searching for personalized content from a seemingly infinite pool of available options such as books, videos, articles, and movies. Simultaneously, digital content providers strive to attract and retain as many users on their platforms as possible for extended periods. This has led to the evolution of recommender systems, which aim to enhance user engagement and satisfaction by recommending content based on their preferences and interests. These systems employ sophisticated algorithms and data mining techniques to analyze user behavior and feedback, enabling them to make personalized recommendations to users, thereby improving their overall experience. In this paper, an investigation is conducted in which different models in machine learning with their combinations are evaluated to evaluate which model could give the best accuracy with actual ratings given by users. The models used are Lasso regression, XGBoost, K-nearest neighbor, surprise baseline only, SVD, SVDpp, and their combinations. The proposed ensembled model of XGBoost + surprise baseline + surprise KNN baseline + SVD + SVDpp, with the minimum value of RMSE as 1.111410 and MAPE value as 39.261054, is our best model and can serve as a reference for any future recommender system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Singular value decomposition

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A Relative Study on Half-Wavelength and Stepped Impedance Resonator based Microstrip Band-Pass Filter 1

A Relative Study on Half-Wavelength and Stepped Impedance Re...

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Bharathi, R. Sumathi, M.S. Bhagya, R. BMS Institute of Technology and Management Department of Computer Science and Engineering Bengaluru India BMS Institute of Technology and Management Department of Electronics and Telecommunication Engineering Bengaluru India RV College of Engineering Department of Electronics and Telecommunication Engineering Bengaluru India

ISBN: (纸本)9781665456876

In this paper an attempt is made to compare the electrical performance of two microstrip band-pass filters (BPF) designed using half-wavelength (λ/2) resonator and stepped impedance resonator (SIR). Coupled resonator-based design methodology is adopted to design the two BPFs. The BPFs have a fractional bandwidth of around 1.9 % at 4.195 GHz. The comparative study from simulated results shows that the BPF based on λ/2 resonator provides better insertion loss than the one based on SIR. On the other hand, SIR based BPF provides better spurious signal rejection and in-addition to miniaturization. © 2022 IEEE.

关键词： Bandpass filters

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A New Tree Structure for Local Diagnosis

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Journal of Information science and engineering 2024年第2期40卷 231-244页

作者： Chen, Meirun Li, Xiao-Yan Lin, Cheng-Kuan Pai, Kung-Jui School of Mathematics and Statistics Xiamen University of Technology Xiamen361024 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Computer Science National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Computer Science and Information Engineering Chung Cheng Institute of Technology National Defense University Taoyuan30010 Taiwan Department of Industrial Engineering and Management Ming Chi University of Technology New Taipei City243303 Taiwan

Diagnosability is an important parameter to measure the fault tolerance of a multiprocessor system. If we only care about the state of a node, instead of doing the global diagnosis, Hsu and Tan proposed the idea of local diagnosis. Chiang and Tan provided an extended star structure to diagnose a node under comparison model. In this work, we evaluate the local diagnosability better by proposing a tree structure around this node. We provide the corresponding algorithm to diagnose the node. Simulation results are presented for different failure probability of a node in the tree and different percentage of faulty nodes in the tree, showing the performance of our algorithm. © 2024 Institute of Information science. All rights reserved.

关键词： comparison model diagnosis algorithm fault diagnosis local diagnosability tree

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