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International Conference on Innovations in science, engineering and Technology (ICISET)

作者： Sachitra Halder M Akhtaruzzaman Jamal Uddin Tanvin Md Rifat Islam Md Tausiful Haque Department of Computer Science and Engineering Military Institute of Science and Technology (MIST) Dhaka Bangladesh Quantum Robotics and Automation Research Group (QRARG) Dhaka Bangladesh

ISBN: (数字)9798350355499

ISBN: (纸本)9798350355505

Diabetes is a prevalent issue in Bangladesh, presenting significant challenges for healthcare systems. This project aims to develop an integrated health prescreening solution that combines an interactive chatbot with sensor modules for vital health data logging. Effective diabetes treatment requires comprehensive information from patients, including lifestyle, dietary habits, medications, and comorbidities, which typically consumes a lot of time during medical consultations. The proposed system streamlines this process by using dedicated sensors to collect essential health data, such as temperature, heart rate, and oxygen levels. Patients interact with a conversational chatbot to provide additional information, including symptoms and medical history. This data is then displayed on a screen at the end of the conversation for easy reference by healthcare professionals. The project progresses through four main stages: requirement analysis, system design, chatbot development, and sensor integration. While the sensor output may show slight fluctuations compared to medical-grade equipment, the chatbot achieves high accuracy ($\gt \mathbf{9 0 \%}$) in conversation and information extraction. By integrating vital sign monitoring with an interactive chatbot, this project aims to reduce waiting times, enhance efficiency of medical checkups, and improve doctor-patient communication in a modern healthcare setting.

关键词： Temperature sensors Temperature measurement Deep learning Temperature distribution Accuracy Medical services Oral communication Chatbots Diabetes System analysis and design

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Real-time Prediction of Fluid Resuscitation Rate for Burn Patients: A Web Based Fuzzy Logic Approach

Real-time Prediction of Fluid Resuscitation Rate for Burn Pa...

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International Conference on computer and Information Technology (ICCIT)

作者： Aminul Islam M. Akhtaruzzaman Department of Computer Science and Engineering Military Institute of Science and Technology (MIST) Dhaka Bangladesh Quantum Robotics and Automation Research Group (QRARG) Dhaka Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Effective management of burn patients in the early stages of treatment is crucial for ensuring survival and optimal recovery. A key element of this process is proper fluid resuscitation, which is essential for maintaining organ function and preventing potentially fatal complications such as shock. Accurate and timely assessment of fluid needs, depending on the Total Body Surface Area (TBSA) affected and the patient’s Hourly Urine Output (HUO), is therefore critical. To address these challenges in burn care, this research introduces a modern web application specifically designed for healthcare providers. The application modern technologies, including Spring Boot, React, ***, MySQL Database and a Java fuzzy logic library, to implement fuzzy logic design and modeling based on the Rule of Nines. This application facilitates rapid analysis of burn extent and provides continuous 24-hour monitoring. By offering accurate, hourly recommendations for fluid resuscitation rates, the tool significantly enhances decision-making in critical care settings. The application includes secure doctor login functionality, allowing healthcare professionals to manage patient data efficiently. Patient information is stored in a database for real-time access and ongoing monitoring. The user-friendly interface allows for seamless data entry and quick interpretation of results, making it highly practical in high-pressure emergency situations.

关键词： Fuzzy logic Java Fluids Accuracy Databases Medical services Real-time systems Libraries Monitoring Surface treatment

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Enhanced Necrosis Visualization in Liver Ablation Monitoring with Fiber Artifact Removal: A Cadaver Study

Enhanced Necrosis Visualization in Liver Ablation Monitoring...

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Medical Imaging 2025: Image-Guided Procedures, Robotic Interventions, and Modeling

作者： Hua, Yanbo Gao, Shang Ma, Xihan 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)9781510685949

Surgical resection is a common treatment for liver cancers. When tumors are unresectable, minimally invasive percutaneous radiofrequency ablation (RFA) is an alternative treatment option. Yet, due to challenges in monitoring ablation boundaries, RFA treatment often has a higher recurrence rate. Medical ultrasound (US) imaging is a frequently used tool to oversee various surgical and interventional procedures. However, US imaging has insufficient contrast to visualize ablation-induced necrosis in RFA procedures. This study explores the use of spectroscopic photoacoustic (sPA) imaging techniques leveraging a customized diffusing fiber to enhance the visualization of ablation-induced lesions. While the diffusing fiber enables wide-area illumination, artifacts caused by nonuniform illumination originating from sidelobe interference pose a challenge. Therefore, we implemented the standard Hough Transform (SHT) method to suppress these artifacts, improving the accuracy of necrotic mapping. Using a swine cadaver model, the sPA imaging system demonstrated its capability to identify ablation-induced necrosis, with results confirmed by gross pathology. The detected ablation-induced lesion was measured at 7.81 mm by 6.31 mm in PA imaging, closely aligning with the 7.93 mm average diameter based on pathology. These findings indicate that our approach effectively suppresses artifacts and accurately identifies ablated regions underneath the skin surface. This study concludes that PA-guided ablation could be a viable tool in clinical practice to improve the precision and hence patient outcome of liver RFA procedures. © 2025 SPIE

关键词： Photoacoustic microscopy

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Versatile Tasks on Integrated Aerial Platforms Using Only Onboard Sensors: Control, Estimation, and Validation

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IEEE Transactions on robotics 2025年 41卷 3518-3538页

作者： Wang, Kaidi Lai, Ganghua Yu, Yushu Du, Jianrui Sun, Jiali Xu, Bin Franchi, Antonio Sun, Fuchun Beijing Institute of Technology School of Mechatronical Engineering Beijing100081 China Beijing Institute of Technology School of Mechanical Engineering Beijing100081 China University of Twente Robotics and Mechatronics Lab Faculty of Electrical Engineering Mathematics and Computer Science Enschede7500 AE Netherlands Tsinghua University Department of Computer Science and Technology China

Connecting multiple aerial vehicles to a rigid central platform through passive spherical joints holds the potential to construct a fully-actuated aerial platform. The integration of multiple vehicles enhances efficiency in tasks like mapping and object reconnaissance. This paper proposes a control and state estimation framework for the Integrated Aerial Platform (IAP), enabling it to perform versatile tasks like object reconnaissance and physical interactive tasks with only onboard sensors. In the framework, the 6D motion control serves as the low-level controller, while the high-level controller comprises a 6D admittance filter and a perception-aware attitude correction module. The 6D admittance filter, serving as the interaction controller, is adaptable for aerial interaction tasks. The perception-aware attitude correction algorithm is carefully designed by adopting a geometric Model Predictive Controller (MPC). This algorithm, incorporating both offline and online calculations, proves to be well-suited for the intricate dynamics of an IAP. A 6D direct wrench controller is also developed for the IAP. Notably, both the interaction controller and the direct wrench controller operate without reliance on force/torque sensors. Instead, a wrench observer algorithm is devised, considering external disturbances. Additionally, based on the kinematics constraints of the multiple aerials in the platform, a fusion algorithm for multiple Visual-Inertial Odometry (VIO) and kinematics constraints is developed, providing more accurate localization. A prototype of the IAP is constructed, and its capabilities are demonstrated through experiments including perception-aware object reconnaissance, aerial mapping, aerial peg-in-hole task, and 6D contact wrench generation. All experiments are conducted exclusively with onboard sensors. These tasks exemplify the merits of the proposed IAP and validate the effectiveness of the proposed control framework and fusion algorithm. © 2004-2012

关键词： State estimation

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Fuzzy-Based Automatic Epileptic Seizure Detection Framework

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computers, Materials & Continua 2022年第3期70卷 5601-5630页

作者： Aayesha Muhammad Bilal Qureshi Muhammad Afzaal Muhammad Shuaib Qureshi Jeonghwan Gwak Shaheed Zulfikar Ali Bhutto Institute of Science and Technology IslamabadPakistan Department of Computer Science&IT University of Lakki MarwatKPKPakistan Stockholm University StockholmSweden Department of Computer Science School of Arts and SciencesUniversity of Central AsiaKyrgyz Republic Department of Software Korea National University of TransportationChungju27469Korea Department of Biomedical Engineering Korea National University of TransportationChungju27469Korea Department of AI Robotics Engineering Korea National University of TransportationChungju27469Korea Department of IT&Energy Convergence(BK21 FOUR) Korea National University of TransportationChungju27469Korea

Detection of epileptic seizures on the basis of Electroencephalogram(EEG)recordings is a challenging task due to the complex,non-stationary and non-linear nature of these biomedical *** the existing literature,a number of automatic epileptic seizure detection methods have been proposed that extract useful features from EEG segments and classify them using machine learning *** characterizing features of epileptic and non-epileptic EEG signals overlap;therefore,it requires that analysis of signals must be performed from diverse *** studies analyzed these signals in diverse domains to identify distinguishing characteristics of epileptic EEG *** pose the challenge mentioned above,in this paper,a fuzzy-based epileptic seizure detection model is proposed that incorporates a novel feature extraction and selection method along with fuzzy *** proposed work extracts pattern features along with time-domain,frequencydomain,and non-linear analysis of *** applies a feature selection strategy on extracted features to get more discriminating features that build fuzzy machine learning classifiers for the detection of epileptic *** empirical evaluation of the proposed model was conducted on the benchmark Bonn EEG *** shows significant accuracy of 98%to 100%for normal *** classification cases while for three class classification of normal ***-ictal *** accuracy reaches to above 97.5%.The obtained results for ten classification cases(including normal,seizure or ictal,and seizure-free or inter-ictal classes)prove the superior performance of proposed work as compared to other state-of-the-art counterparts.

关键词： Medical image processing electroencephalography machine learning fuzzy system models seizure detection epileptic seizure virtualization

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Stress Detection Using CNN on the WESAD Dataset

Stress Detection Using CNN on the WESAD Dataset

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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： D. Shirley Benita A. Shamila Ebenezer L Susmitha M.S.P. Subathra S. Jeba Priya Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Departnemt of Robotics Engineering Karunya Institute of Technology and Sciences Coimbatore India

Stress, a pervasive psychological state, profoundly impacts individuals’ well-being and may lead to various health issues. In this paper, the WESAD (Wearable Stress and Affect Detection) dataset serves as the primary data source, from which HRV (Heart Rate Variability) data extracted using PPG (Photoplethysmography) sensor is utilized. This data can be obtained in a non-invasive method and is instrumental in this analysis. The dataset comprises HRV data from 15 individual test subjects obtained from PPG sensors, providing a crucial foundation for the detection and classification of individuals into “stressed” or “not stressed” categories, thus offering a valuable tool for stress monitoring. This paper is a focused exploration into the CNN (Convolutional Neural Network) Deep Learning model, in which the feature selection process is carried out using Random Forest resulting in the selection of HR (Heart Rate), LF_BOXCOX (Low-Frequency Component - Box-Cox Transformed), MEDIAN_RR (Median of RR Intervals), SDI_BOXCOX (Standard Deviation 1 - Box-Cox Transformed), and SDRR_REL_RR (Standard Deviation of Relative RR Intervals) to optimize the model’s performance. The model aims at advancing and refining stress detection methodologies to enhance overall well-being and achieves an accuracy of 98%.

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Distinguishing pianos: The difference in similarity 7

Distinguishing pianos: The difference in similarity

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7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Mukherjee, Himadri Marciano, Matteo Dhar, Ankita Obaidullah, Sk. Md. Roy, Kaushik West Bengal State University Department of Computer Science India Music Program New York University Department of Arts and Humanities Abu Dhabi United Arab Emirates Aliah University Department of Computer Science and Engineering India

ISBN: (纸本)9781665421683

Notating a music piece is not a trivial task. It requires training and experience. This is challenging for new and inexperienced musicians. Automated transcription systems can be very useful in such cases. A piece generally consists of multiple instruments and it is essential to separate them prior to transcription. The challenge aggravates even more when multiple modulations or varieties of the same instrument are present. This is common nowadays and hence, it is essential to distinguish different varieties of the same instrument prior to transcription. In this paper, a line spectral frequency-based technique is used. A highest accuracy of 97.06% was obtained for a dataset composed of 6 different pianos. © 2022 IEEE.

关键词： Music

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Detection of Various Dental Conditions on Dental Panoramic Radiography Using Faster R-CNN

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IEEE Access 2023年 11卷 127388-127401页

作者： Chen, Shih-Lun Chen, Tsung-Yi Mao, Yi-Cheng Lin, Szu-Yin Huang, Ya-Yun Chen, Chiung-An Lin, Yuan-Jin Chuang, Mian-Heng Abu, Patricia Angela R. Chung Yuan Christian University Department of Electronic Engineering Taoyuan320314 Taiwan Feng Chia University Department of Electronic Engineering Taichung407102 Taiwan Chang Gung Memorial Hospital Department of General Dentistry Taoyuan61363 Taiwan National Ilan University Department of Computer Science and Information Engineering Yilan26047 Taiwan Academy of Innovative Semiconductor and Sustainable Manufacturing Department of Program on Semiconductor Manufacturing Technology National Cheng Kung University Tainan717005 Taiwan Ming Chi University of Technology Department of Electrical Engineering New Taipei243303 Taiwan Ateneo de Manila University Department of Information Systems and Computer Science Quezon1108 Philippines

The dental panoramic radiograph (DPR) is a pivotal diagnostic tool in dentistry. However, despite the growing prevalence of artificial intelligence (AI) across various medical domains, manual methods remain the prevailing means of interpreting DPR images. This study aims to introduce an advanced identification system for detecting seven dental conditions in DPR images by utilizing Faster R-CNN. The primary objectives are to enhance dentists' efficiency and evaluate the performance of various CNN models as foundational training networks. This study contributes significantly to the field in several notable ways. Firstly, including a Butterworth filter in the training process yielded an approximately 7% enhancement in judgment accuracy. Secondly, the proposed enhancement technology tailored to different dental symptoms effectively bolstered the training model's accuracy. Consequently, all dental conditions attained an accuracy rate exceeding 95% in CNN analysis. These accuracy enhancements ranged from 1.34% to 13.24% compared to existing recognition technologies. Thirdly, this study pioneers the application of Faster R-CNN for identifying dental conditions, achieving an impressive accuracy rate of 94.18%. The outcomes of this study mark a substantial advancement compared to prior research and offer dentists a more efficient and convenient means of pre-diagnosing dental conditions. © 2013 IEEE.

关键词： Image segmentation

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A Novel Resource Allocation in Software-Defined Networks for IoT Application 31

A Novel Resource Allocation in Software-Defined Networks for...

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31st International Conference on Software, Telecommunications and computer Networks, SoftCOM 2023

作者： De Sousa, Alexandre Ladeira Okey, Ogobuchi Daniel Rosa, Renata Lopes Saadi, Muhammad Rodriguez, Demostenes Zegarra Federal University of Lavras Department of Computer Science Minas Gerais Lavras Brazil Federal University of ABC Santo Andre Graduate Program in Information Engineering Sao Paulo Brazil University of Central Punjab Department of Electrical Enginnering Punjab Pakistan Federal University of Lavras Computer Science Department Minas Gerais Lavras Brazil

ISBN: (纸本)9798350301076

The rapid proliferation of Internet of Things (IoT) devices has posed significant challenges for network resource allocation and management. In this paper, we propose a novel methodology for efficient resource allocation in Software-Defined Networks (SDNs) to meet the unique requirements of IoT applications. Our approach leverages the flexibility and programmability of SDN to dynamically allocate network resources based on the specific needs of IoT devices. Our proposal combines reinforcement learning with dynamic resource allocation, demonstrating its effectiveness in optimizing resource allocation and enhancing network performance. The results highlight the potential of our approach to addressing the resource challenges in SDN-based IoT environments. The proposed method obtains results superior to 30% of throughput in comparison to a traditional resource allocation in SDN to IoT applications. © 2023 University of Split, FESB.

关键词： Internet of things

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Unified Approach to Video-Based AI Inference Tasks in Augmented Reality Systems Assisted by Mobile Edge Computing 31

Unified Approach to Video-Based AI Inference Tasks in Augmen...

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31st International Conference on Software, Telecommunications and computer Networks, SoftCOM 2023

作者： Sousa, Alexandre Ladeira De Okey, Ogobuchi Daniel Rosa, Renata Lopes Saadi, Muhammad Rodriguez, Demostenes Zegarra Federal University of Lavras Department of Computer Science Minas Gerais Lavras Brazil Federal University of ABC Graduate Program in Information Engineering Sao Paulo Santo Andre Brazil University of Central Punjab Department of Electrical Enginnering Punjab Pakistan Federal University of Lavras Computer Science Department Minas Gerais Lavras Brazil

ISBN: (纸本)9798350301076

In the rapidly evolving field of Augmented Reality (AR), delivering real-time, immersive experiences places a significant demand on computational resources, particularly in the context of video-based Artificial Intelligence (AI) inference tasks. Some solutions explorate optimize the tasks using the potential of Mobile Edge Computing (MEC), in which MEC brings the advantages of reduced latency and network congestion, which are critical for AR systems. In this work, we designed a novel algorithm named AR-MEC Optimization for the distribution of AI inference tasks among edge servers while considering several factors including task complexity, server capacity, and network conditions. Our approach not only ensures low-latency responses but also improves overall system efficiency and the user's Quality of Experience (QoE). Experimental results demonstrate that our unified approach significantly outperforms traditional cloud-based methods in terms of response time and computational efficiency, thereby highlighting the potential of MEC-assisted AR systems for real-world applications. © 2023 University of Split, FESB.

关键词： Augmented reality

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