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5th International Conference on engineering Technology and its Applications, IICETA 2022

作者： Mustafa, Wan Azani Razak, Khairul Nizam Abdul Jamlos, Mohd Aminudin Alquran, Hiam Ismail, Shahrina Almaameri, Ihab Mahdi Faculty of Electrical Engineering Technology Perlis Arau Malaysia Malaysia Yarmouk University Biomedical Systems and Medical Informatics Engineering Irbid Jordan Faculty of Science and Technology Bandar Baru Nilai Negeri Sembilan 71800 Malaysia The Islamic University Faculty of Engineering Najaf Iraq

ISBN: (纸本)9781665472159

Uterine cancer, also known as endometrial cancer, is a form of cancer that affects the female reproductive system. Women over the age of 50 are more likely to get uterine cancer. At this current time, there are two methods that the physician or health care provider tends to use to diagnose cancer, which are ultrasound technique and endometrial biopsy. If the patient is suspected of having cancer, ultrasound is first used to measure the length of the endometrial lining. Then, if the lining is greater than 4mm, an endometrial biopsy is performed. At this stage, small tubular instruments are used to extract the cells from the human body. The cells are then sent to a pathologist to determine the cell type. Using the pathological image analysis system, time is taken, where human factors are the main issues in using the system. In cancer treatment, both aspects of time and human error are important because both issues can lead to death. Also, the cell nucleus is key since the damaged cells do not tend to produce the circular cell nucleus. Therefore, the pathologist usually requires more time to identify whether it is a Low-Grade Squamous Intraepithelial Lesion (LSIL) or a High-Grade Squamous Intraepithelial Lesion (HSIL). Based on the limitations that occur, the paper aims to automate classification techniques based on the nucleus roundness value. Image processing via machine learning is an alternative method that can provide high accuracy with a short processing time. The system used multiple image algorithms to determine the circularity of the cell nucleus and provide additional data to determine the cell type. Based on calculations, detection accuracy was achieved up to 100%. © 2022 IEEE.

关键词： Biopsy

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ECG Signal-Based Feature Identification of Obstructive Sleep Apnea Using Dual-Tree Complex Wavelet Transform and Adaptive Neuro-Fuzzy Inference System

ECG Signal-Based Feature Identification of Obstructive Sleep...

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biomedical engineering Conference (JIBEC), Jordanian International

作者： Al-Naami Bassam Awad Al-Zaben Paolo Visconti Roberto De Fazio Yahia Rawash Biomedical Engineering Department Engineering Faculty The Hashemite University Zarqa Jordan Biomedical Systems and Medical Informatics Department Hijjawi Faculty For Engineering Technology Yarmouk University Jordan Dept. of Innovation Engineering Center for Biomolecular Nanotech. IIT University of Salento Lecce Italy Arnesano Italy Dept. of Innovation Engineering University of Salento Lecce Italy Biomedical Engineering Department Engineering Faculty Hashimaite University Jordan

ISBN: (数字)9798331539542

ISBN: (纸本)9798331539559

Obstructive sleep apnea (OSA) is a common disease that affects a large number of people around the world. It could be prevented if diagnosed early, but many cases go undetected due to the cost and limitations of home overnight polysomnography (PSG) systems. In this research, we propose a new diagnostic method for detecting the OSA disease using a feature extraction and classification approach from the electrocardiograph (ECG) signal only. The ECG can be easily extracted at home in a comfortable and minimally invasive way by anyone; then, the Dual-Tree Complex Wavelet Transform (DTCWT) is used to denoise the ECG signal. Five features were extracted, and the classification process was performed using the Adaptive Neuro Fuzzy Inference System (ANFIS).

关键词： Wavelet transforms Fuzzy logic Accuracy Adaptive systems Minimally invasive surgery Filtration Electrocardiography Feature extraction Sleep apnea Diseases

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DECODE: A New Cloud-Based Framework for Advanced Visualization, Simulation, and Optimization Treatment of Peripheral Artery Disease 23

DECODE: A New Cloud-Based Framework for Advanced Visualizati...

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23rd IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2023

作者： AboArab, Mohammed A. Potsika, Vassiliki T. Gkois, George Fotiadis, Dimitrios I. Unit of Medical Technology and Intelligent Information Systems University of Ioannina IoanninaGR 45110 Greece Electronics and Electrical Communication Engineering Department Faculty of Engineering Tanta Egypt Biomedical Research Institute Foundation for Research and Technology-Hellas IoanninaGR 45110 Greece

ISBN: (纸本)9798350393118

Recent research indicates a worrisome surge in the prevalence of Peripheral Artery Disease (PAD). This alarming rise underscores the pressing need for new approaches to PAD treatment. Consequently, this paper introduces the DECODE platform, a cloud-based solution aimed at revolutionizing the treatment of PAD using Drug-Coated Balloons (DCBs). DECODE's main contribution lies in its integration of cutting-edge technologies, including 3D-enabled visualization, augmented and virtual reality, investigation of drug release mechanisms, in-vitro and in-silico simulations, computational modeling, multiscale analysis, and data analysis, into a cohesive system. This integration facilitates collaboration among researchers, clinicians, and medical professionals and provides a holistic approach to PAD treatment, encompassing visualization, simulation, optimization, and outcome analysis. DECODE's impressive performance metrics, assessed by Google's Lighthouse tool, further emphasize its efficiency: a performance score of 91%, accessibility at 90%, adherence to best practices at 83%, SEO-friendliness with a score of 100%, and optimization as a progressive web app. These metrics highlight its strengths as a comprehensive and effective solution for PAD treatment. © 2023 IEEE.

关键词： Data visualization

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Improved Processing Circuit for MEMS Accelerometer as a Respiration Sensor

Improved Processing Circuit for MEMS Accelerometer as a Resp...

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International Conference on Advances in biomedical engineering (ICABME)

作者： Awad Al-Zaben Bassam Al-Naami Ghadeer Al-Omari Biomedical Systems and Medical Informatics Department Hijjawi Faculty For Engineering Technology Yarmouk University Jordan Biomedical Engineering Department Engineering College Hashemaite University Jordan

Capturing respiration signal using a microelectromechanical (MEMS) accelerometer is a challenging task. The design of the interface circuit of the sensor must consider the characteristics of the respiration signal resulting from abdominal movement. It is mainly characterized by very low magnitude and low-frequency contents. Hence, any DC offset or drift in the sensor's response is a key design factor of the circuit. In addition, the OnChip processing circuit of the sensor itself may introduce DC offset and limit the lower range of the frequency response. This paper presents a design and implementation of a signal processing interface circuit that can be used to process MEMS accelerometer output to capture respiration signals. The circuit consists of a low pass filter that captures the DC component of the signal, then a differential amplifier that subtracts the estimated DC component from the signal followed by amplification. The circuit is self-calibrated by the position of the sensor. The circuit diagram, evaluation results, and validation of the circuit are presented in this paper.

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ITNR: Inversion Transformer-based Neural Ranking for cancer drug recommendations

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Computers in Biology and Medicine 2024年 172卷 108312-108312页

作者： Sotudian, Shahabeddin Paschalidis, Ioannis Ch. Department of Electrical and Computer Engineering Division of Systems Engineering Boston University BostonMA United States Department of Biomedical Engineering and Faculty of Computing and Data Sciences Boston University BostonMA United States

Personalized drug response prediction is an approach for tailoring effective therapeutic strategies for patients based on their tumors’ genomic characterization. While machine learning methods are widely employed in the literature, they often struggle to capture drug-cell line relations across various cell lines. In addressing this challenge, our study introduces a novel listwise Learning-to-Rank (LTR) model named Inversion Transformer-based Neural Ranking (ITNR). ITNR utilizes genomic features and a transformer architecture to decipher functional relationships and construct models that can predict patient-specific drug responses. Our experiments were conducted on three major drug response data sets, showing that ITNR reliably and consistently outperforms state-of-the-art LTR models. © 2024 Elsevier Ltd

关键词： Cell culture

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PPG Segmentation Using Deep Learning 3

PPG Segmentation Using Deep Learning

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Tabbakha, Mohammad Masri, Hassan Al Hammad, Abdullatif Alsulatie, Mohammed Alquran, Hiam Mustafa, Wan Azani Almusawi, Muntather Hussein, Abbas Hameed Abdul Yarmouk University Department of Biomedical Systems and Informatics Engineering Irbid Jordan The Institute of Biomedical Technology King Hussein Medical Center Royal Jordanian Medical Service Amman Jordan Centre of Excellence Perlis Arau Malaysia The Islamic University Faculty of Engineering Najaf Iraq College of MLT Ahl Al Bayt University Karbala Iraq

ISBN: (纸本)9798350317022

As an indicator regarding cardiovascular health, vascular dynamics, and more, Photoplethysmography (PPG) consists of a baseline with two pulsatile peaks that reflect the blood flow variations. In the absence of critical studies and a precise understanding of PPG signal this study proposes a deep learning approach to predict and define each phase of PPG waveform utilizing Gated recurrent unit (GRU) due to its optimal performance in the short-term dependencies in sequences, the model is composed mainly of GRU layer with 20 hidden units applying tanh and sigmoid activation function, followed by dropout layer and a fully connected layer, afterward a softmax layer was added, we conducted our study on a dataset combined of an accessible Bed-Based Ballistocardiography Dataset (BBB), after that, the data was manually labeled. Following up, hyperparameters such as learning rate, batch size, and other parameters were adjusted after numerous trials, which resulted in test accuracy, precision, and recall of 0.875, 0.892, and 0.854, respectively in the used dataset. These promising results might be the first step of more future investigations concerning PPG. © 2023 IEEE.

关键词： Deep learning Gated recurrent unit PPG Segmentation

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Machine Learning_Based Paradigm for Diagnosis of Gestational Diabetes

Machine Learning_Based Paradigm for Diagnosis of Gestational...

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Electronic engineering (ICEEM), International Conference on

作者： Neven Saleh Kerolous M. Yacoub Ahmed M. Salaheldin Systems and Biomedical Engineering Department Higher Institute of Engineering El Shorouk Academy Electrical Communication and Electronic Systems Engineering Department Engineering Faculty October University for Modern Sciences and Arts Systems and Biomedical Engineering Department Higher Institute of Engineering El Shorouk Academy Cairo Egypt

One of the issues related to pregnancy is gestational diabetes mellitus (GDM). The risk of exploration in the GDM is lowered with early detection. The study aims to develop a machine learning-based paradigm for the detection of the GDM. Four distinct classifiers-logistic regression, decision tree, gradient boosting, and support vector machine were used. Nine factors of the clinical setting are distinguished in each case. A data set called PIMA for diabetes is used as the data source. The challenge of the existence of missing values is solved by substituting the mean and median values alternatively. Therefore, three scenarios have been applied to the dataset. The performance of the classifiers was assessed by measuring accuracy, sensitivity, specificity, and precision. Results yielded reliable outcomes for the proposed paradigm. In specific, the best results were obtained for the gradient boosting classifier by substituting the missing value by the median value. Results demonstrate the effectiveness of the paradigm.

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Knowledge Leaks in Data-Driven Business Models? Exploring Different Types of Knowledge Risks and Protection Measures

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Schmalenbach Journal of Business Research 2024年第3期76卷 357-396页

作者： Fruhwirth, Michael Pammer-Schindler, Viktoria Thalmann, Stefan Know-Center GmbH Institute for Interactive Systems and Data Science Faculty of Computer Science and Biomedical Engineering Graz University of Technology Silicon Austria Labs GmbH Business Analytics and Data Science-Center (BANDAS-Center) School of Business Economics and Social Sciences University of Graz

Data-driven business models imply the inter-organisational exchange of data or similar value objects. Data science methods enable organisations to discover patterns and eventually knowledge from data. Further, by training machine learning models, knowledge is materialised in those models. Thus, organisations might risk the exposure of competitive knowledge by sharing data-related value objects, such as data, models or predictions. Although knowledge risks have been studied in traditional business models, little research has been conducted in the direction of data-driven business models. In this explorative qualitative study, we conducted 28 expert interviews in three rounds (two exploratory and one evaluatory) and identified five types of risks along the three basic types of value objects: data, models and predictions. These risks depend on the context, i.e., when competitive knowledge could be discovered from shared value objects. We found that those risks can be mitigated by technology, contractual regulations, trusted relationships, and adjusting the business model design. In this study, we show that the risk of knowledge leakage is a relevant risk factor in data-driven business models. Overall, knowledge risks should be considered already during business model design, and their management requires an interdisciplinary approach via a balanced assessment. The level of knowledge protection from a technology perspective highly depends on computer science innovations and thus is a moving target. As an outlook, we suggest that knowledge risk will become even more relevant with the extensive usage of machine learning and artificial intelligence in data-driven business models. © The Author(s) 2024.

关键词： Business model innovation Data analytics Data-driven business models Knowledge risks Risk management Value objects

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Intelligent vehicle routing for stochastic service times: a grouping evolution strategy approach

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International Journal of Transportation Science and Technology 2025年

作者： Rezvanian, Sepinoud Husseinzadeh Kashan, Ali Rezvanian, Alireza Sabzevari, Alireza Faculty of Industrial and Systems Engineering Tarbiat Modares University Tehran Iraq Department of Computer Engineering University of Science and Culture Tehran Iraq Department of Biomedical Engineering Meybod University Meybod Iraq Department of Chemical Engineering Hamedan University of Technology Hamadan Iraq

After-sales service companies often need to transport and distribute their products, as well as provide services like installation and setup. In this paper, we study a vehicle routing problem (VRP) that considers the possibility of on-site servicing by installers, with stochastic service times. We formulate the distribution of products to customers as a VRP and extract the route plan for installers by solving a traveling salesman-type problem. To model the stochastic service times, we use a chance constraint while minimizing the total cost for both vehicles and installers. We develop a Grouping Evolution Strategy (GES) algorithm equipped with various heuristics to generate efficient solutions for large-scale problems. Numerical results are reported from a case study using existing data for the problem. The computational experiments demonstrate that GES can produce near-optimal solutions for operational use within a reasonable amount of time. In summary, our study shows the effectiveness of GES in solving the VRP with on-site stochastic service times, which is relevant to many after-sales service companies. © 2025 Tongji University and Tongji University Press

关键词： Traveling salesman problem

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Numerical approach to design of low frequency magnetic field irradiation system for lab on chip experiments 14

Numerical approach to design of low frequency magnetic field...

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14th International Conference ELEKTRO, ELEKTRO 2022

作者： Radil, Roman Bajtos, Marek Carnecka, Lucia Hargasova, Klaudia Kamencay, Patrik University of Zilina Department of Electromagnetic and Biomedical Engineering Zilina Slovakia Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Systems Biology and Engineering Gliwice Poland

ISBN: (纸本)9781665467261

The article is focused on design of specific electromagnetic coil system using numerical modelling and simulation methods. The proposed solution would be capable of delivering magnetic field of desired strength/ flux density and homogeneity within the specific area to affect the cultivation process of single cells in lab on chip experiments. © 2022 IEEE.

关键词： Numerical models

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