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International Conference of computer science and Information Technology

作者： Abba Suganda Girsang Rezky Yunanda Muhammad Edo Syahputra Ezra Peranginangin Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350399080

Convolutional Neural network is state of the art of image recognition or image classification. However to build the robust model using CNN needs many parameters adjusted, and choosing the good combination hyperparameter which impacts taking much computation time. Genetic algorithm is one method metaheuristic which is robust for choosing the combinatorial possible hyperparameter. This model also uses ResNet-50 which is a pretrained model of convolutional neural network that consists 50 layers. By using a pretrained like ResNet-50, it will increase the performance model. CNN-ResNet with efficient genetic algorithm (EGA) to optimize the hyperparameter. The EGA algorithm utilizes transfer learning techniques in its algorithm so that the optimization process on CNN can achieve unified accuracy values quickly. The best performance model optimized using EGA outperformed the ResNet-50 model and the model optimized using GA and VLGA in classifying organic and inorganic materials. The accuracy value obtained from EGA is 97.53% with a loss of 0.08.

关键词： Image recognition Computational modeling Transfer learning Metaheuristics Inorganic materials Iron Classification algorithms

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Smart Home Security System Using Face Recognition Based on IoT- CNN

Smart Home Security System Using Face Recognition Based on I...

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Information Technology Research and Innovation (ICITRI), International Conference on

作者： M. Aldiki Febriantono Alvin Zuhair Khaeruddin Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Electrical Engineering Department University of Islam Blitar Blitar Indonesia Diploma III Electronic Technology Vocational Study Program University of Muhammadiyah Malang Malang Indonesia

Home security is a crucial aspect that requires careful attention, particularly when it comes to addressing theft concerns. Hence, implementing smart door technology equipped with facial recognition holds promising potential for enhancing home security. This study aims to develop a more secure and regulated home entry system by leveraging Internet of Things (IoT) technology and Machine Learning computer Vision for facial recognition. The system integrates IoT devices, such as cameras and automatic doors, wherein facial image data is captured by the camera and processed using the Convolutional Neural Network (CNN) algorithm to identify individuals. Once an individual is recognized, the system grants access to the home through an automated door. By relying on facial features, the system effectively restricts unauthorized access and safeguards homes against theft risks. Therefore, the advancement of a safer and more controlled home entry system utilizing IoT technology and Machine Learning computer Vision holds tremendous benefits for homeowners.

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Data engineering and AI-Powered Skin Cancer Identification for Healthcare Applications

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Procedia computer science 2024年 246卷 179-188页

作者： Imran Ahmed Misbah Ahmad Abdellah Chehri Gwanggil Jeon School of Computing and Information Science Anglia Ruskin University Cambridge UK Animal and Agriculture Department Hartpury University Gloucester UK Centre for Machine Vision Bristol Robotics Laboratory University of the West of England Bristol UK Department of Mathematics and Computer Science at the Royal Military College of Canada (RMC) Canada Department of Embedded Systems Engineering Incheon National University Incheon Korea

Skin cancer diagnosis, a critical task in the medical domain, can be revolutionized through the application of advanced deep-learning techniques. This work investigates the efficacy of Convolutional Neural Networks (CNNs) in the automated classification of skin cancer. The process begins with a comprehensive explanation of key CNN layers: Conv2D, MaxPool2D, Dropout, and Dense. The Conv2D layers employ learnable filters that transform localized image segments, while MaxPool2D contributes to downsampling, effectively reducing computational cost and overfitting risk. Integrating these layers enables the network to capture local and global characteristics, which is crucial for accurate classification. Adding Dropout layers enhances generalization and mitigates overfitting by introducing randomness during training. ReLU activation functions infuse non-linearity, and the Flatten layer facilitates the transition to fully connected layers. The proposed CNN architecture is meticulously designed considering filter counts, kernel sizes, and pooling dimensions. The trained model demonstrates promising performance by utilizing the HAM10000 dataset, encompassing diverse skin lesion images across seven classes. The CNN model’s parameters and architecture are systematically presented, offering insights into its design rationale. The model undergoes optimization with the Adam optimizer and annealing techniques to facilitate convergence. The model’s effectiveness is evaluated on validation and test datasets, demonstrating an accuracy of 78.55% and 76.49%, respectively, for skin cancer classification. Data augmentation strategies are introduced to enhance model generalization further. The results underscore CNN’s potential as a robust tool for automating skin cancer diagnosis, aligning with the broader trend of leveraging deep learning for medical image analysis

关键词： Image Processing Medical Imaging Deep learning Skin Cancer Data Analytics

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Amico: Message Exchange Service for Elderly People

Amico: Message Exchange Service for Elderly People

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Atsushi Isozaki Kazuki Fukae Kenichi Arai Toru Kobayashi Masahide Nakamura Computer and Information Science Program Graduate School of Engineering Nagasaki University Nagasaki Japan Division of Electrical Engineering and Computer Science Graduate School of Engineering Nagasaki University Nagasaki Japan Center of Mathematical and Data Sciences Kobe University Hyogo Japan

ISBN: (纸本)9781665492331

This study is related to a system that enables elderly people to communicate interactively with young people who use existing message exchange services by simply speaking to an avatar on a tablet PC, without having to perform complicated operations of the tablet PC. On the other hand, the young people side can bring in elderly people who could not previously participate in existing message exchange services groups. The effectiveness of the system was verified through demonstration experiments with two households consisting of elderly people in their 80s.

关键词： Economics Avatars Older adults Usability Smart phones Consumer electronics Commercialization

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Marine Aquaculture Information Extraction from Optical Remote Sensing Images via MDOAU2-net 12

Marine Aquaculture Information Extraction from Optical Remot...

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12th International Conference on Information science and Technology, ICIST 2022

作者： Kuang, Guanghu Wang, Jichao Fan, Jianchao Wang, Jun School of Computer Science and Engineering Chongqing Three Gorges University Wanzhou Chongqing404020 China Hong Kong Centre for Logistics Robotics NT Shatin Hong Kong National Mar. Environ. Monitor. Cent. Depart. of Marine Remote Sensing Dalian116023 China

ISBN: (数字)9781665485821

ISBN: (纸本)9781665485821

China has the largest aquaculture area in the world and is still expanding. Extracting the area of marine aquaculture can prevent the overexploitation of marine aquaculture and protect the marine environment. MDOAU-net has an excellent performance in marine aquaculture extraction of SAR images which drives researchers to explore the performance of MDOAU-net in optical remote sensing images. Unlike SAR images, optical remote sensing images needn't consider speckles noises problem. To suit optical remote sensing images, a new method named MDOAU2-net is proposed to accurately extract marine aquaculture areas, which could keep the discriminative character and filter fake objects with similar features. It follows the structure of the U-net and is contained by a multi-scale block and some offset convolution blocks. In experiments, using the images shot by GF-2 satellite as data and compared to other five networks to verify the validity of MDOAU2-net in optical remote sensing images of marine aquaculture extraction. © 2022 IEEE.

关键词： Image segmentation

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Liver Necrotic Mapping Using Diffusing Illumination Photoacoustic Imaging

Liver Necrotic Mapping Using Diffusing Illumination Photoaco...

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Photons Plus Ultrasound: Imaging and Sensing 2025

作者： Gao, Shang Hua, Yanbo Bhagavatula, Sharath K. Liu, Guigen Jonas, Oliver Zhang, Haichong K. Department of Robotics Engineering Worcester Polytechnic Institute MA United States Department of Biomedical Engineering Worcester Polytechnic Institute MA United States Department of Computer Science Worcester Polytechnic Institute MA United States Department of Radiology Brigham and Women’s Hospital Harvard Medical School MA United States

ISBN: (纸本)9781510683860

Liver cancer treatments often face challenges, particularly when tumors are considered surgically unresectable. Percutaneous radiofrequency ablation (RFA) offers a minimally invasive alternative but can have higher recurrence rates due to incomplete ablations. Standard non-contrast ultrasound imaging cannot distinguish ablation-induced necrosis, limiting ability to monitor treatment and ensure complete tumor coverage during the ablation procedure. Photoacoustic (PA) imaging, which uses light to generate ultrasound signals, has shown promise in distinguishing between ablated and normal tissues through spectroscopic PA (sPA) imaging. However, conventional light delivery methods are unsuitable for minimally invasive procedures due to bulky fiber bundles. Optical fibers, though suitable for less invasive insertion, have limited illumination volume because of the confined light delivery only at the fiber tip. This study introduces a diffusing fiber approach for effective light delivery within the liver, enhancing the PA excitation area compared to conventional tip illumination. The diffusing fiber was tested in both phantom and ex vivo liver tissue studies. Results demonstrated that the diffusing fiber provided a wider PA excitation area and more homogeneous illumination. In an ex vivo porcine liver, the diffusing fiber accurately mapped the ablated region with minimal deviation from gross pathological measurements. Our findings suggest that this diffusing fiber approach could significantly improve the accuracy of PA imaging for liver ablation, potentially reducing recurrence rates by ensuring complete tumor ablation and minimizing complications by enabling real time monitoring. This innovation paves the way for more reliable and effective minimally invasive treatments for liver cancer. © 2025 SPIE.

关键词： Photoacoustic microscopy

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Innovations in Social Informatic: A Patent Landscape Review

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Procedia computer science 2024年 234卷 1228-1235页

作者： Nur Afia Agung Purnomo Fairuz Iqbal Maulana Yogi Tri Prasetyo Ma. Janice J. Gumasing Elsa Rosyidah Satria Fadil Persada Institute for Research and Community Services Universitas Nahdlatul Ulama Sidoarjo Sidoarjo 61234 Indonesia English Language Education Universitas Nahdlatul Ulama Sidoarjo Sidoarjo 61234 Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department Bina Nusantara University Jakarta 11480 Indonesia School of Industrial Engineering and Engineering Management Mapúa University Manila 1002 Philippines International Bachelor Program in Engineering Yuan Ze University Taoyuan 32003 Taiwan

Patent intellectual property in social informatics with entrepreneurship support creates greater opportunities to improve human life. It has the potential to open enormous innovation opportunities in various fields, such as technology, health, services, and society. This research was conducted to review the patent landscape and the main trends of patents on social informatics in all countries. Researchers applied a patent landscape analysis using data on 487 patent documents over 16 years from the *** database. The study results show yearly patented development patterns related to social informatics. There is a royalty-free opportunity by using 15 patent discontinued. Patent related to social informatics was dominated by company ownership and inventors from the United States. The patent physics (G) section overwhelmed patents related to social informatics. There were 334 simple family patents on social informatics in nine jurisdictions; the United States was the highest.

关键词： entrepreneurship, patent, social informatics innovation

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The Deep Learning Approach For American Sign Language Detection

The Deep Learning Approach For American Sign Language Detect...

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Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Gusti Pangestu Yaya Heryadi Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program Computer Science. Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research (CERG) Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Sign language has importance rule to deal with communication process especially with impairments hearing people. Sign language detection also attract lot of researchers to join the challenge of research to detect and recognize the sign language in the field of computer science. Hence, there is still no any standard approach and method to recognize the meaning in every pose of sign language. This research proposed a mechanism to detect Alphabet American Sign Language by utilizing Convolutional Neural Network (CNN) process. The CNN approach was chosen based on the ability and capability to recognize image. In this research, MNIST dataset is used for traning and testing process. The proposed CNN architecture produced 97% of accuracy that outperform the previous research using the same dataset which made this architecture promising.

关键词： Deep learning computer science Image recognition Gesture recognition computer architecture Auditory system Assistive technologies

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A strictly predefined-time convergent and anti-noise fractional-order zeroing neural network for solving time-variant quadratic programming in kinematic robot control

arXiv

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arXiv 2025年

作者： Yang, Yi Li, Xiao Wang, Xuchen Liu, Mei Yin, Junwei Li, Weibing Voyles, Richard M. Ma, Xin Multi-Scale Medical Robotics Center The Chinese University of Hong Kong 999077 Hong Kong Department of Mechanical and Automation Engineering The Chinese University of Hong Kong 999077 Hong Kong Qingdao266580 China School of Mechanical Engineering Dalian Jiaotong University Dalian116028 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China School of Engineering Technology Purdue University West LafayetteIN47907 United States

This paper proposes a strictly predefined-time convergent and anti-noise fractional-order zeroing neural network (SPTC-AN-FOZNN) model, meticulously designed for addressing time-variant quadratic programming (TVQP) problems. This model marks the first variable-gain ZNN to collectively manifest strictly predefined-time convergence and noise resilience, specifically tailored for kinematic motion control of robots. The SPTC-AN-FOZNN advances traditional ZNNs by incorporating a conformable fractional derivative in accordance with the Leibniz rule, a compliance not commonly achieved by other fractional derivative definitions. It also features a novel activation function designed to ensure favorable convergence independent of the model’s order. When compared to five recently published recurrent neural networks (RNNs), the SPTC-AN-FOZNN, configured with 0 © 2025, CC BY.

关键词： Quadratic programming

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Deep-learning triage of 3D pathology data for improved disease detection while reducing pathologist workloads

Deep-learning triage of 3D pathology data for improved disea...

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Microscopy Histopathology and Analytics, Microscopy 2024 - Part of Optica Biophotonics Congress: Biomedical Optics

作者： Gao, Gan Wang, Fiona Brenes, David Song, Andrew H. Chow, Sarah S.L. Mahmood, Faisal Liu, Jonathan T.C. Department of Mechanical Engineering University of Washington SeattleWA98195 United States Department of Computer Science University of Washington SeattleWA98195 United States Department of Pathology Brigham and Women's Hospital Harvard Medical School BostonMA02115 United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA02114 United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA02142 United States Data Science Program Dana-Farber Cancer Institute BostonMA02215 United States Department of Bioengineering University of Washington SeattleWA98195 United States Department of Laboratory Medicine & Pathology University of Washington School of Medicine SeattleWA98195 United States

3D pathology can potentially improve disease detection, but the datasets are too large to review. We're developing a deep-learning-based triage method to identify the highest-risk 2D sections within 3D pathology d... 详细信息

关键词： Adversarial machine learning

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