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Mouth Tracking on Aerosol Suction Robot to Assist Dentist Surgery Using Deep Learning

Mouth Tracking on Aerosol Suction Robot to Assist Dentist Su...

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International Electronics Symposium (IES)

作者： Riyanto Sigit Bayu Sandi Marta Bayu Julianto Dept of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya (PENS) Surabaya Indonesia

COVID-19 disease is an unprecedented health condition and has never been diagnosed in humans before. According to scientific evidence, COVID-19 can spread from person to person through respiratory droplets, which are released when an infected individual coughs or sneezes. Those at the highest risk of contracting the disease include healthcare professionals, teachers, general practitioners, and dental hygienists. In dental practice, the transmission of COVID-19 often occurs through aerosols and splatters from the patient’s oral cavity. Aerosol may be generated during upper respiratory tract instrumentation of the oral and nasal cavities. Extra-oral suction (EOS) is a device for sucking patient aerosols during surgery or dental treatment. However, must move the tool manually to position the EOS chimney above the patient’s mouth. So that when the patient changes the position of his face, there is a possibility that some particles from the aerosol are not sucked in by Extra-oral Suction (EOS). Therefore, this research will build a system for tracking faces using Deep Learning on aerosol suction robots to assist dentist operations. To perform mouth detection, the MobileNET SSD method is used, and the centroid tracking algorithm is used to find the midpoint of the detected object, namely the human mouth, which will then enter the calculation process to look for ErrorX to obtain the tracking position of the human mouth. Mouth tracking on aerosol suction robots can be implemented automatically to assist dental health professionals in preventing the spread of COVID-19. To detect the mouth using the SSD MobileNet method with an accuracy rate of 94% from 815 testing images and the final results of the mouth tracking test, humans on the Extra-oral suction (EOS) robot get an accuracy of 93%.

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Classification Anterior and Posterior of Knee Osteoarthritis X-Ray Images Grade KL-2 Using Deep Learning with Random Brightness Augmentation

Classification Anterior and Posterior of Knee Osteoarthritis...

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2022 International Conference on computer engineering, Network and Intelligent Multimedia, CENIM 2022

作者： Supatman Yuniarno, Eko Mulyanto Purnomo, Mauridhi Hery Institut Teknologi Sepuluh Nopember FTI UMBY Dept. of Electrical Engineering Dept. of Informatics Surabaya60111 Indonesia Institut Teknologi Sepuluh Nopember Dept. of Electrical Engineering Dept. of Computer Engineering Surabaya60111 Indonesia

ISBN: (纸本)9781665476508

Osteoarthritis of the knee (KOA) is a narrowing of the joint space area (JSA) due to lack of fluid in the knee joint, resulting in pain when moving and when it is severe, the femur and tibia meet. Medical personnel and existing computer-based methods have been able to detect patients with X-rays but have not been able to detect where visually the posterior AJ is still wide while the anterior AJ is actually narrow, so that the patient still feels joint pain. A new approach is proposed for the classification of X-Ray Grade Kellgren-Lawrence (KL)-2 Osteoarthritis Initiative image datasets using Deep Learning Neural Networks (DCNN) by setting the Random Brightness Augmentation hyperparameter. The experimental results obtained X-Ray Grade KL-2 narrowing image classification 'Anterior View KOA' and 'Posterior View KOA' with training accuracy of 83.33% and validation accuracy of 54.69% at Random Brightness with a value of 30. © 2022 IEEE.

关键词： computer vision

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A Comparative Analysis between Sentiment Analysis Methods in Stock Prediction of IoT-based companies 7

A Comparative Analysis between Sentiment Analysis Methods in...

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7th World Symposium on Communication engineering, WSCE 2024

作者： Pasoi, Georgia E. Talattinis, Kyriacos A. Memos, Vasileios A. Goudos, Sotirios K. Sarigiannidis, Panagiotis G. Ishibashi, Yutaka Psannis, Konstantinos E. University of Macedonia Dept. of Applied Informatics Thessaloniki Greece Aristotle University of Thessaloniki Dept. of Physics Thessaloniki Greece University of Western Macedonia Dept. of Electrical and Computer Engineering Kozani Greece Aichi Sangyo University Faculty of Business Administration Dept. of Business Management Aichi444-0005 Japan

ISBN: (纸本)9798331542825

Predictions in financial markets have attracted much of scholars' interest. Since the stock market is generally defined as dynamic and volatile due to the affect of the well-known "Internet of Things"(IoT). It is a fact that IoT significantly impacts the financial market and stocks in various ways. Therefore, numerous researchers have attempted to detect patterns regarding the way financial markets react to various external factors. In the last decade, the integration of machine learning and sentiment analysis has appeared to be an effective approach for improving IoT-based stock price predictions in the financial field. This paper intends to propose an approach involving financial news as a source of information for forecasting the stock movement of noteworthy IoT-related companies, employing Big Data Analytics (BDA), Machine Learning, and Sentiment Analysis methods as well. A comparative analysis is conducted between lexicon-based and machine learning-based methods of sentiment analysis about their performance, particularly in the domain of IoT-related stock market prediction. © 2024 IEEE.

关键词： Decentralized finance

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Effect of Interest Variation and Prefix Length on Pending Interest Table (PIT) Performance in Named Data Networking (NDN) 9

Effect of Interest Variation and Prefix Length on Pending In...

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9th International Conference on Wireless and Telematics, ICWT 2023

作者： Khaderani, Helmi Ahmad Muhammadi, Ishlah Muhammad, Lutfi Razan Sucipto, Adi Kusumastuti, Luthfi Restu Nurhayati, Ade Ahdan, Syaiful Syambas, Nana Rachmana Bandung Institute of Technology Dept. of Telecommunication Engineering Bandung40132 Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung40132 Indonesia Universitas Teknokrat Indonesia Faculty of Engineering & Computer Science Lampung35132 Indonesia Telkom University Faculty of Engineering Jakarta Barat11710 Indonesia

ISBN: (纸本)9798350305029

Pending interest table (PIT) is one of the data structures on each named data network router. The speed of delivery of interest as well as the interest served at PIT are several important parameters for measuring PIT performance. This research was conducted to analyze the performance of PIT by measuring the round trip time (RTT) of sending data and sending interests from consumers to producers on the Palapa Ring Sumatra topology. Testing is carried out using miniNDN by varying the length of the prefix and the number of interests sent. From the simulation and results of the analysis that has been carried out, it is known that the correlation between the length of the prefix and RTT is directly proportional. The longer the prefix of an interest packet, the higher the RTT value. Apart from the simulation results, the number of interest packets sent is the same as those received at each node. © 2023 IEEE.

关键词： Topology

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Detection and Classification of Cardiac Arrhythmia from Photoplethysmography Signal

Detection and Classification of Cardiac Arrhythmia from Phot...

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IEEE International Conference on Internet of Things and Intelligence System (IOTAIS)

作者： Bima Sena Bayu Dewantara Dewi Mutiara Sari Ahmad Nur Fahmi Dept. of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia

This paper presents the development of an application for classifying cardiac arrhythmia by utilizing photoplethys-mography sensor signals. The Photoplethysmography (PPG) sensor is used as a reader for the cardiovascular system by measuring changes in blood volume in the skin tissue. The PPG sensor captures electrical signals originating from the reflected light source due to changes in blood flow during heart activity. After going through the preprocessing stage, the signals are then extracted into several features that will be classified using the Random Forest algorithm. The results from the Random Forest algorithm will provide a diagnosis based on the detection of arrhythmia. Based on the experimental results, the accuracy achieved using the method and dataset is 72%. The accuracy of the system testing using the smartwatch under normal heart conditions is 88.88%.

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An Efficient Authentication System to Access Electric Vehicle Data in The Cloud Based on Identity Role-based Access Control

An Efficient Authentication System to Access Electric Vehicl...

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International Electronics Symposium (IES)

作者： Amang Sudarsono Rahardhita Widyatra Sudibyo Idris Winarno Mike Yuliana Dept. of Electrical Engineering and Dept. of Informatics & Computer Engineering Politeknik Elektronika Negeri Surabaya (PENS) Electronics Engineering Polytechnic Institute of Surabaya (EEPIS) Surabaya Indonesia

ISBN: (数字)9798350391992

ISBN: (纸本)9798350392005

Security is one of the main considerations in choosing file storage media. Cloud server is one of storage mediums that provides virtual resources to users through third party and it can be accessed through the Internet connection. Cloud computing has several advantages, such as ease to access, ease to control, user-friendly to operate, and flexible capacity. However, these advantages cause potential security problems such as data breach attack. Therefore, a strong security system becomes important to overcome the losses caused by these security problems. A solution to increase the security level is utilizing cryptography algorithms. Due to the discovery of several loopholes in the traditional cryptosystems, the concept of using Computational Deoxyribonucleic Acid (DNA) cryptosystem was identified as a possible more complex cryptosystem to solve. Meanwhile, Role-based Access Control (RBAC) provides efficient and strong ways to manage authorization of the system resources based on user authentication. By cooperating with Identity-based Cryptography (IBC) and DNA cryptosystem, we propose an efficient authentication system to securely access electric vehicle (EV) data stored in the cloud server based on identity-based RBAC.

关键词： Cloud computing System performance DNA Authentication Public key Media Electric vehicles

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Navigation System Development on ATRACBOT using F-SFM

Navigation System Development on ATRACBOT using F-SFM

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International Electronics Symposium (IES)

作者： Mochamad Farhan Eka Widarto Bayu Sandi Marta Bima Sena Bayu Dewantara Setiawardhana Dept. of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia

Navigation systems are very important for social robots, especially autonomous robots, because they are required to be adaptive to their surrounding environment. This paper proposes a new navigation system for the Autonomous Trash Can Robot (ATRACBOT) using the Fuzzy-based Social Force Model (F-SFM). The navigation system employed in earlier stages of development depended on the Braitenberg approach, leading to the robot’s motion appearing rigid. On the other hand, the Social Force Model (SFM) employs the forces surrounding objects to engage with and evade other entities, presenting a different approach. The magnitude of the force to avoid objects is determined by the SFM parameters and the stimulus received by the robot. The Fuzzy Inference System (FIS) is used to obtain the optimal value of the gain factor, k, which is one of the SFM parameters, based on the relative distance, d, and relative angle, $\gamma$, between the obstacle and robot. The FIS will process both inputs and produce a suitable gain factor, k, so that the stimulus received can match the robot’s reactivity and responsiveness. Based on the results from a realistic 3D simulator V-Rep, the implementation of the F-SFM method can deliver the robot to the goal point without hitting obstacles and make the robot’s movements more natural compared to the Braitenberg method.

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Optimizing Resource Allocation in 5G Networks Through Downlink and Uplink Decoupling

Optimizing Resource Allocation in 5G Networks Through Downli...

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International Conference on Ultra Modern Telecommunications and Workshops (ICUMT)

作者： Christos Bouras Charalampos Chatzigeorgiou Vasileios Kokkinos Apostolos Gkamas Philippos Pouyioutas Computer Engineering & Informatics Dept University of Patras Greece Computer Science Dept University Ecclisiastical Academy of Vella Greece Computer Science Dept University of Nicosia

This research paper sets out to explore the multi-faceted and complex topic of decoupling downlink and uplink in 5G networks, with a view to optimizing resource allocation. Drawing on an extensive range of current literature and industry trends, the authors conduct a comprehensive and in-depth analysis of the potential benefits and drawbacks of decoupling, taking into account the impact on network capacity, flexibility, and user experience. Additionally, the article offers valuable insights into the practical implementation of decoupling in real-world 5G networks, including an exploration of the challenges and considerations that may arise, and potential future developments in this field. Through a rigorous and meticulous examination of the topic, the authors aim to provide an understanding of the subject, offering valuable guidance and insights to future researchers and developers working in this area. With a focus on depth and detail, this paper represents a vital contribution to the ongoing dialogue surrounding decoupling in 5G networks and has the potential to drive significant advancements in the field in the years to come.

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Early Prediction of Depressive Episodes in Mood Disorders Using Circadian Rhythm Indicators and Deep Learning

Early Prediction of Depressive Episodes in Mood Disorders Us...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Kim, Byeongsu Chae, Minsu Kim, Yihyun Kong, Seokjin Kim, Yeongmin Jung, Taewon Jeong, Jaegwon Park, Soohyun Cho, Chul-Hyun Yeom, Ji Won Lee, Taek Lee, Heon-Jeong Lee, Hwamin Korea University College of Medicine Dept. of Biomedical Informatics Seoul Korea Republic of Korea University College of Medicine Dept. of Psychiatry Seoul Korea Republic of Korea University Chronobiology Institute Seoul Korea Republic of SunMoon University Dept. of Computer Science and Engineering Asan Korea Republic of

ISBN: (纸本)9798350386226

The early prediction of depressive mood episodes is crucial for effective intervention in patients with Major Depressive Disorder (MDD) and Bipolar Disorder (BD). This study explores a predictive framework leveraging digital phenotypic data collected from smartphones and smartwatches, with a focus on circadian rhythm indicators such as Dim Light Melatonin Onset (DLMO). Using data from 164 participants within the Mood Disorder Cohort Research Consortium in Korea, time-series features related to sleep, heart rate, activity levels, and light exposure were processed to predict mood episodes seven days in advance. Deep learning models, including LSTM, GRU, and an LSTM-GRU hybrid, were applied to analyze this data, with the GRU model achieving the highest recall (0.767) and the LSTM model displaying superior robustness across metrics. SHAP value analysis of DLMO-related variables further underscored the association between circadian rhythm disruptions and depressive episodes, with delayed wake-up times relative to ideal schedules linked to increased depressive symptoms. Our findings demonstrate the feasibility of using digital phenotypes for early detection of mood episodes. These results highlight the potential of automated monitoring systems in clinical practice, which enable proactive intervention strategies through continuous, objective monitoring of patient conditions. © 2024 IEEE.

关键词： Sleep research

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Optimizing Network Performance in 5G Systems with Downlink and Uplink Decoupling

Optimizing Network Performance in 5G Systems with Downlink a...

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International Conference on Advanced Communication Technologies and Networking (CommNet)

This research paper presents a comprehensive investigation into the optimization of resource allocation in 5G networks through the technique of Downlink and Uplink Decoupling (DUDe). With the growing need for accurate modeling and scenario planning in 5G systems, DUDe allows for the configuration of an additional, lower frequency signal on the uplink to complement the existing uplink signal, rebalancing the uplink/downlink difference at the cell edge and enhancing coverage and network capacity. Drawing on extensive literature and industry trends, this study explores the benefits and challenges of DUDe, considering its impact on network performance, user experience, and future developments. The paper also introduces a simulation-based methodology and experiments to evaluate the effectiveness of DUDe in improving coverage, capacity, latency, and energy efficiency. The findings contribute to the understanding of DUDe’s potential in optimizing 5G networks, providing valuable insights for researchers and network operators in designing and deploying efficient resource allocation strategies for enhanced network performance in various scenarios.

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