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Identification of Acute Myeloid Leukemia (AML) Subtypes: M1, M2, M3 on White Blood Cells Using Microscopic Images 25

Identification of Acute Myeloid Leukemia (AML) Subtypes: M1,...

引用

25th International Electronics Symposium, IES 2023

作者： Azzahra, Soniya Jasmine Sigit, Riyanto Yuniarti, Heny Hernaningsih, Yetti Imannurohma, Ade Politeknik Elektronika Negeri Surabaya Departemen of Informatics and Computer Engineering Surabaya Indonesia Airlangga University Dr. Soetomo General Academic Hospital Faculty of Medicine Departemen of Clinical Pathology Surabaya Indonesia

ISBN: (纸本)9798350314731

Leukemia is a form of blood cancer characterized by an overproduction of abnormal white blood cells that invade the body's immune system. Leukemias are divided into four main groups based on cell type and growth rate. Acute Myeloid Leukemia (AML) is one of them, namely leukemia that occurs in the myeloid series. AML itself is divided into several subtypes, according to the French-American-British (FAB), namely M0-M7. AML subtypes M1, M2, and M3 are influenced by the same cell type, namely myeloblast cells, so a more detailed analysis is needed to classify them. Therefore we need a system that can identify the presence of AML in the blood, this identification is done by classifying white blood cells on microscopic images. To detect the presence of AML in microscopic images, multiple processes will be used, including preprocessing, segmentation, feature extraction, and classification stages by applying the K-NN method. This classification procedure will result in a diagnosis of M1, M2, or M3 blood cells. At the end of phase 1 of this final project, pre-processing and segmentation processes and graphical user interface (GUI) designs have been carried out. This final project resulted in an accuracy of 80% for the identification of subtype M1, 70% for the identification of subtype M2, and 80% for the identification of subtype M3. © 2023 IEEE.

关键词： Graphical user interfaces

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A computer Vision System to Classify the Quality of Central Sulawesi Cocoa Beans Using Convolutional Neural Networks (CNN) 3

A Computer Vision System to Classify the Quality of Central ...

引用

3rd International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Angreni, Dwi Shinta Anshori, Yusuf Wirdayanti Arifin, Suci Ramadhani Humaira, Fitrah Maharani Universitas Tadulako Faculty of Engineering Departement of Information Technology Palu Indonesia Universitas Dipa Makassar Departement of Informatics Engineering Makassar Indonesia Politeknik Elektronika Negeri Surabaya Departemen of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350394559

The cocoa industry serves as a vital economic pillar, holding a pivotal role in the daily lives of farmers in Central Sulawesi. However, a prevailing challenge for cocoa cultivators in this region resides in the inherently subjective nature of cocoa bean quality assessment, often necessitating the involvement of a substantial human workforce. Addressing this challenge requires the implementation of computer vision technology capable of autonomously grading cocoa bean quality. Such a system not only expedites the evaluation process but also enhances the precision and consistency in determining the quality of the beans. The study employed Convolutional Neural Networks (CNN) methodology to analyze images of cocoa beans sourced from Central Sulawesi farmers. The research demonstrated a remarkable accuracy of 94.66%, accompanied by a minimal loss of 0.1195. The model demonstrates strong performance on both training and validation sets, indicating effective learning of relevant patterns. Consistent accuracy and loss trends across epochs signify stable training without notable overfitting or underfitting. The minimal gap between training and validation metrics reflects the model's effective generalization to unseen data. This technological integration promises to revolutionize cacao quality assessment, offering a sophisticated and efficient solution for the benefit of Central Sulawesi's cacao farming community. © 2023 IEEE.

关键词： computer vision

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Enhance Low-Resolution Face Recognition Accuracy Using Median and Mean Filtering Techniques Based on Convolutional Neural Networks (CNN) 18

Enhance Low-Resolution Face Recognition Accuracy Using Media...

引用

18th International Conference on Telecommunication Systems, Services, and Applications, TSSA 2024

作者： Kurnia, Dian Ade Risnanto, Slmaet Sugiyatno Kusuma, Ajeng Nisa Naufal, Yogacitra Bin Mohd, Othman Sekolah Tinggi Manajemen Informatika Dan Komputer Ikmi Departemen of Computer Science Cirebon Indonesia Unversitas Sangga Buana Faculty of Engineering Departemen of Informatics Engineering Bandung Indonesia Universitas Bhayangkara Jakarta Raya Fakulti Informatics Jakarta Indonesia Unversitas Sangga Buana Bandung Indonesia Universiti Teknikal Malaysia Fakulti Teknologi Maklumat Dan Komunikasi Melaka Malaysia

ISBN: (纸本)9798331527686

The aim of this study is to evaluate the effectiveness of median and mean filtering techniques in enhancing the performance of deep learning-based face recognition systems on low-resolution images. The main challenge in face recognition lies in the low quality of images due to various factors such as lighting, distance, and camera resolution. Median filtering replaces each pixel with the median value of its neighbors, effectively removing salt-and-pepper noise, while mean filtering replaces each pixel with the average value of its neighbors, resulting in smoother images but potentially blurring important details. The results show that median filtering is more effective in preserving edge details and reducing speckle noise, whereas mean filtering excels at producing overall smoother images. The combination of these techniques is expected to enhance the accuracy of face recognition on low-resolution images, contributing to the development of more reliable and precise face recognition systems © 2024 IEEE.

关键词： Wiener filtering

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The Application of Smart and Precision Agriculture (SPA) for Measuring Leaf Nitrogen Content of Oil Palm in Peat Soil Areas

The Application of Smart and Precision Agriculture (SPA) for...

引用

2023 International Conference on computer Science, Information Technology and engineering, ICCoSITE 2023

作者： Munir, Sirojul Seminar, Kudang Boro Sudradjat Sukoco, Heru Bogor Agricultural University Departemen of Computer Science Bogor Indonesia Nurul Fikri College of Technology Departemen of Informatics Depok Indonesia Bogor Agricultural University Departement of Mechanical and Biosystem Engineering Bogor Indonesia Bogor Agricultural University Department of Agronomy and Horticulture Bogor Indonesia Bogor Agricultural University Department of Computer Science Bogor Indonesia

ISBN: (纸本)9798350320954

One of the laborious tasks in operating and managing oil palm lands is measuring the nutrient content of leaves in a timely and periodic manner. It takes a manual process and takes a long time to get leaf samples that are representative of the observed oil palm area, especially in peat soil area. In addition, laboratory analysis of leaf samples is expensive and introduces creates significant time delays for queuing. This paper discusses methods for measuring leaf nitrogen levels in a timely manner based on imagery obtained by C-SAR Sentinel-1A using smart and precision agricultural approaches and applications. The intelligent computational model uses Random Forest Regression to calculate leaf nitrogen content based on spatial image processing and analysis. The calculated leaf nitrogen content is then used to examine and identify the variability of nitrogen content in a given oil palm area. Identification of nutrient variability is one of the approaches and goals of precision agriculture. Our computation model was developed using 553 leaf sampling units (LSU) and validated using 10-Fold validation method, resulting in correctness, MAPE, RMSE and R2 of 95.69%, 4.31%, 0.1119 and 93.28% respectively. The validated model was tested using a set of new data, which is not in the data samples for model development and validation, was resulting in correctness, MAPE, RMSE and R2 of 89.91%, 10.11%, 0.284, and 53.89% respectively. The visual maps can be produced and displayed to visualize variability of nitrogen contents in a peat soil area. This improves visibility and transparency in monitoring nutrient in timely and periodic manner. The future use of the proposed method is to support the application of smart and precision agriculture practices to recommend fertilizers types and dozes for oil palm in peat soil areas based on the variability of leaf nutrient contents. © 2023 IEEE.

关键词： Intelligent systems

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Identification of Acute Myeloid Leukemia (AML) Subtypes: M1, M2, M3 on White Blood Cells Using Microscopic Images

Identification of Acute Myeloid Leukemia (AML) Subtypes: M1,...

引用

International Electronics Symposium (IES)

作者： Soniya Jasmine Azzahra Riyanto Sigit Heny Yuniarti Yetti Hernaningsih Ade Imannurohma Departemen of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia Departemen of Clinical Pathology Faculty of Medicine Airlangga University Dr. Soetomo General Academic Hospital Surabaya Indonesia

Leukemia is a form of blood cancer characterized by an overproduction of abnormal white blood cells that invade the body’s immune system. Leukemias are divided into four main groups based on cell type and growth rate. Acute Myeloid Leukemia (AML) is one of them, namely leukemia that occurs in the myeloid series. AML itself is divided into several subtypes, according to the French-American-British (FAB), namely M0-M7. AML subtypes M1, M2, and M3 are influenced by the same cell type, namely myeloblast cells, so a more detailed analysis is needed to classify them. Therefore we need a system that can identify the presence of AML in the blood, this identification is done by classifying white blood cells on microscopic images. To detect the presence of AML in microscopic images, multiple processes will be used, including preprocessing, segmentation, feature extraction, and classification stages by applying the K-NN method. This classification procedure will result in a diagnosis of M1, M2, or M3 blood cells. At the end of phase 1 of this final project, pre-processing and segmentation processes and graphical user interface (GUI) designs have been carried out. This final project resulted in an accuracy of 80% for the identification of subtype M1, 70% for the identification of subtype M2, and 80% for the identification of subtype M3.

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A computer Vision System to Classify the Quality of Central Sulawesi Cocoa Beans Using Convolutional Neural Networks (CNN)

A Computer Vision System to Classify the Quality of Central ...

引用

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

作者： Dwi Shinta Angreni Yusuf Anshori Wirdayanti Suci Ramadhani Arifin Fitrah Maharani Humaira Departement of Information Technology Faculty of Engineering Universitas Tadulako Palu Indonesia Departement of Informatics Engineering Universitas Dipa Makassar Makassar Indonesia Departemen of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia

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Enhance Low-Resolution Face Recognition Accuracy Using Median and Mean Filtering Techniques Based on Convolutional Neural Networks (CNN)

Enhance Low-Resolution Face Recognition Accuracy Using Media...

引用

International Conference on Telecommunication Systems, Services, and Applications (TSSA)

作者： Dian Ade Kurnia Slmaet Risnanto Sugiyatno Ajeng Nisa Kusuma Yogacitra Naufal Othman Bin Mohd Departemen of Computer Science Sekolah Tinggi Manajemen Informatika dan Komputer IKMI Cirebon Indonesia Departemen of Informatics Engineering Faculty of Engineering Unversitas Sangga Buana Bandung Indonesia Fakulti Informatics Universitas Bhayangkara Jakarta Raya Jakarta Indonesia Unversitas Sangga Buana Bandung Indonesia Fakulti Teknologi Maklumat dan Komunikasi Universiti Teknikal Malaysia Melaka Malaysia

ISBN: (数字)9798331527686

ISBN: (纸本)9798331527693

关键词： Image resolution Accuracy Filtering Face recognition Noise Lighting Speckle Telecommunications Convolutional neural networks Telecommunication network reliability

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The Application of Smart and Precision Agriculture (SPA) for Measuring Leaf Nitrogen Content of Oil Palm in Peat Soil Areas

The Application of Smart and Precision Agriculture (SPA) for...

引用

computer Science, Information Technology and engineering (ICCoSITE), International Conference on

作者： Sirojul Munir Kudang Boro Seminar Sudradjat Heru Sukoco Departemen of Computer Science Bogor Agricultural University Bogor Indonesia Departemen of Informatics Nurul Fikri College of Technology Depok Indonesia Departement of Mechanical and Biosystem Engineering Bogor Agricultural University Bogor Indonesia Department of Agronomy and Horticulture Bogor Agricultural University Bogor Indonesia Department of Computer Science Bogor Agricultural University Bogor Indonesia

关键词： Visualization Soil measurements Computational modeling Oils Soil Data models Agriculture

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Smart Lighting System for Children's Therapy to Prevent Nyctophobia Syndrome at Bedtime 4

Smart Lighting System for Children's Therapy to Prevent Nyct...

引用

4th International Conference on Cybernetics and Intelligent System, ICORIS 2022

作者： Pramono, Andi Munir, Badrul Farchan, Muchammad Budiman, Satrio Arif Azis, Baskoro Putra, Wahyu Waskito Interior Design Department School of Design Bina Nusantara University Jakarta Indonesia Brawijaya University Departemen of Neurology Malang Indonesia Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Department of Electrical Engineering Surabaya Indonesia Brawijaya University Information System Computer Science Faculty Malang Indonesia

ISBN: (纸本)9781665453950

Everyone has a fundamental desire for sleep. By sleeping, humans can conserve energy and replenish their stamina to optimal levels. Lighting is one component that influences the quality of a person's sleep. One of them is a person who sleeps with the lights on, suffers from nyctophobia, has poor sleep quality, and may be responsible for several illnesses. This study aims to develop a smart lighting device that will aid in treating youngsters with nyctophobia to get used to sleeping with the lights off to improve their sleep quality. The case study methodology used in this study entails the development of a prototype. This method employs an oximeter as a heart rate detector and a sensor comparison as the measurement device. Arduino Uno circuit and heart rate sensor are the main devices in this research. At the same time, the NRF 24L01 is a communication medium between the two Arduino Uno boards. The experimental measurements in normal conditions indicate that the heart rate sensor was nearly the same as the value displayed by the oximeter. Similarly, after running for 1 minute, both devices produced almost identical results, with an average difference of 1 Beats Per Minute (BPM). According to experiments with a heart rate sensor, the switch-off condition can be activated if the heart rate hits 90 BPM. In this case, the light will switch off once the heart rate hits 70 BPM, which might be used for sleeping individuals. © 2022 IEEE.

关键词： Oximeters

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Smart Lighting System for Children's Therapy to Prevent Nyctophobia Syndrome at Bedtime

Smart Lighting System for Children's Therapy to Prevent Nyct...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Andi Pramono Badrul Munir Muchammad Farchan Satrio Arif Budiman Baskoro Azis Wahyu Waskito Putra Interior Design Department School of Design Bina Nusantara University Jakarta Indonesia Departemen of Neurology Brawijaya University Malang Indonesia Department of Electrical Engineering Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Information System Computer Science faculty Brawijaya University Malang Indonesia

ISBN: (纸本)9781665453967

关键词： Heart rate Phase measurement Prototypes Medical treatment Switches Turning Nanoscale devices

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