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IEEE International Conference on Electro-Information Technology

作者： Rabia Al Mamlook Sujeet Shresth Tasnim Gharaibeh Ali Saeed Almuflih Wassnaa Al-Mawee Hanin Bzizi Dept. Industrial Engineering University of Al-Zawiya Al-Zawiya Libya Trine University Indian USA Dept. Industrial Engineering Wayne Stata University MI USA Dept. of computer science Kalamazoo College Kalamazoo USA Dept. Industrial Engineering King Khalid University Guraiger Saudi Arabia Dept. of Computer Science Department of Computer Science Western Michigan University Kalamazoo MI Department of Biomedical Science Western Michigan University Kalamazoo MI

Breast cancer is a leading cause of death among women worldwide. Early detection and diagnosis are crucial to improving the chances of survival. This paper presents a study on the diagnosis of breast cancer using various machine-learning approaches. The study includes the performance evaluation of nine different techniques using confusion matrix accuracy for Sensitivity, Specificity, Precision, PME, PPV, NPV, and Model Accuracy. AdaBoost is found to have the highest Sensitivity and PME, while Random Forest and MLP gave the best Specificity and Precision. Logistic Regression is found to be the best model for accuracy with 97.8%, followed by SVM with 96.49%, Random Forest with 95.61%, and KNN & Decision Forest with 94.73%. The proposed approach is found to have the highest accuracy of 97.80 % compared to other approaches studied. Our results indicate that the proposed approach using discretization can significantly improve the signal-to-noise ratio in the diagnosis of breast cancer. This approach can accurately predict and diagnose breast cancer using a subset of features.

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Quality Grading of Beef using Deep Convolutional Neural Network 9

Quality Grading of Beef using Deep Convolutional Neural Netw...

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9th International Conference on Information Technology Trends, ITT 2023

作者： Sharma, Aarav Choudhury, Tanupriya Sarkar, Tanmay Mohanty, Madhusmita Bansal, Nikunj Mohanty, Sachi Nandan Informatics Cluster School of Computer Science Uttarakhand Dehradun2480007 India Cse Dept. Uttarakhand Dehradun India Graphic Era Hill University Cse Dept. Uttarakhand Dehradun248002 India Malda Polytechnic West Bengal State Council of Technical Education Govt. of West Bengal Department of Food Processing Technology Malda732102 India Andhra Pradesh Amaravati India VIT-AP University School of Computer Science & Engineering Andhra Pradesh Amaravati5222237 India

ISBN: (纸本)9798350327502

Beef is one of the most widely consumed meat, being an organic substance it is prone to degradation over time. In our paper we have proposed a Convolutional Neural Network model to grade a given sample of Beef and predict its freshness as good or bad. We have proposed a CNN model architecture along with a data (image) pre-processing pipeline that can be used to classify a given Beef sample. We have done comparative analysis between different pre-processing pipelines as well as other CNN based classification models and after training have achieved 99.63% test accuracy. © 2023 IEEE.

关键词： computer vision

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PLPP: A Pseudo Labeling Post-Processing Strategy for Unsupervised Domain Adaptation

PLPP: A Pseudo Labeling Post-Processing Strategy for Unsuper...

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IEEE International Symposium on biomedical Imaging

作者： Tomer Bar Natan Hayit Greenspan Jacob Goldberger Computer Science Dept. Tel-Aviv University Israel Department of Biomedical Engineering Tel Aviv University Israel Faculty of Engineering Bar-Ilan University Israel

A well known problem in medical imaging is the ability to use an existing model learned on source data, in a new site. This is known as the domain shift problem. We propose a pseudo labels procedure, which was originally introduced for semi-supervised learning, that is suitable for unsupervised domain adaptation (UDA). We iteratively improve the pseudo labels of the target domain data only using the current pseudo labels without involving the labeled source domain data. We applied our method to several medical MRI image segmentation tasks. We show that, by combining our approach as a post-processing step in standard UDA algorithms, we consistently and significantly improve the segmentation results on test images from the target site.

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End-to-End n-ary Relation Extraction for Combination Drug Therapies

arXiv

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arXiv 2023年

作者： Jiang, Yuhang Kavuluru, Ramakanth Department of Computer Science University of Kentucky LexingtonKY United States Division of Biomedical Informatics Dept. of Internal Medicine Univ. of Kentucky LexingtonKY United States

Combination drug therapies are treatment regimens that involve two or more drugs, administered more commonly for patients with cancer, HIV, malaria, or tuberculosis. Currently there are over 350K articles in PubMed that use the combination drug therapy MeSH heading with at least 10K articles published per year over the past two decades. Extracting combination therapies from scientific literature inherently constitutes an n-ary relation extraction problem. Unlike in the general n-ary setting where n is fixed (e.g., drug-gene-mutation relations where n = 3), extracting combination therapies is a special setting where n ≥ 2 is dynamic, depending on each instance. Recently, Tiktinsky et al. (NAACL 2022) introduced a first of its kind dataset, CombDrugExt, for extracting such therapies from literature. Here, we use a sequence-to-sequence style end-to-end extraction method to achieve an F1-Score of 66.7% on the CombDrugExt test set for positive (or effective) combinations. This is an absolute ≈ 5% F1-score improvement even over the prior best relation classification score with spotted drug entities (hence, not end-to-end). Thus our effort introduces a state-of-the-art first model for end-to-end extraction that is already superior to the best prior non end-to-end model for this task. Our model seamlessly extracts all drug entities and relations in a single pass and is highly suitable for dynamic n-ary extraction scenarios. © 2023, CC BY.

关键词： Extraction

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Feasibility of a Digital Health Puzzle Game for Detecting computer Mouse Behavioral Patterns in ADHD

Feasibility of a Digital Health Puzzle Game for Detecting Co...

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International Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Shehjar Sadhu Elijah Castillo Lisa Weyandt Dhaval Solanki Kunal Mankodiya Dept. of Electrical Computer and Biomedical Engineering University of Rhode Island Kingston RI USA Department of Computer Science & Statistics University of Rhode Island Kingston RI USA Department of Psychology University of Rhode Island Kingston RI USA

ISBN: (数字)9798331530143

ISBN: (纸本)9798331530150

Attention Deficit Hyperactive Disorder (ADHD) is a neurodevelopmental condition. Globally, more than 366 million individuals are diagnosed with ADHD. ADHD is a chronic condition and has no known cure. However, treatments do exist in the form of behavior therapy and medication. Currently, management of ADHD symptoms during treatment is done through standardized questionnaire assessments. While valuable, these assessments are subjective. Researchers have proposed using computer-based games for objective monitoring of ADHD symptoms. In this work, we present MindGame, a puzzle game designed to monitor ADHD symptoms using computer mouse movement. To evaluate the feasibility of this application, we conducted an in-home study (N=4; 2 Neurotypical and 2 ADHD) participants who were asked to play the game 8–10 times in one- week at-home. computer mouse data from 128 individual puzzle levels were analyzed. The game has three levels built-in with increasing level of difficulty. A significant difference (p-value=0.01786) between ADHD and Neurotypical participants in the Euclidean distance of the mouse cursor movement. All participants completed the study at-home 8–10 times except for P2 who was found dropped out of the study due to personal reasons.

关键词： Body sensor networks Medical treatment Games Euclidean distance Electronic healthcare biomedical monitoring Monitoring Medical diagnostic imaging

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MRIGAT: Many-to-many Recommendation using Interaction based Knowledge Graph Attention 22

MRIGAT: Many-to-many Recommendation using Interaction based ...

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3rd International Symposium on Artificial Intelligence for Medical sciences, ISAIMS 2022

作者： Gao, Feng Yuan, Kai Gu, Jinguang Liu, Yun Department of Computer Science and Technology Wuhan University of Science and Technology China The Key Laboratory of Rich-Media Knowledge Organization Service of Digital Publishing Content Institute of Scientific and Technical Information of China China Guangxi Eco-engineering Vocational & Technical College China 1st Affiliated Hospital Dept. of Medical Informatics School of Biomedical Engineering & Informatic Nanjing Medical University China

ISBN: (纸本)9781450398442

Recommendation systems combining Graph Neural Networks and Knowledge Graphs have been successfully applied in various domains. However, most existing approaches only consider one-to-one or one-to-many user-item interactions and cannot cater to many-to-many recommendation scenarios, such as providing prescriptions based on clinical diagnosis and medical history. Providing such a recommendation requires considering the implicit interaction within input user features as well as output candidates. In this paper, we propose a two-stage knowledge graph attention aggregation mechanism that helps recommend drug combinations for a patient based on his conditions. First, a disease-drug interaction graph is constructed and integrated with the medical domain knowledge, forming a collaborative knowledge graph. Secondly, an intra-feature attention aggregation is performed to obtain the representations of diseases and drugs based on drug-drug and disease-disease interactions, respectively. Thirdly, an inter-feature attention aggregation is performed using the disease-drug interaction to better represent a user's condition. Finally, the user's condition representation is concatenated with other user features to generate the final user representation for the prescription recommendation. Experiments with realistic datasets show that our approach can outperform existing recommendation systems by 4.3% and 6.1% in precision and recall, respectively. © 2022 ACM.

关键词： Knowledge graph

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Coach, Data Analyst, and Protector: Exploring Data Practices of Collegiate Coaching Staff 25

Coach, Data Analyst, and Protector: Exploring Data Practices...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Brewer, Mollie Childs, Kevin Thomas, Spencer Wilkins, Celeste Boyer, Kristy Elizabeth Nichols, Jennifer A. Butler, Kevin R.B. Beatty, Garrett F. Ferris, Daniel P. Department of Computer & Information Science & Engineering University of Florida Gainesville United States University Athletic Association GainesvilleFL United States J. Crayton Pruitt Family Dept. of Biomedical Engineering University of Florida GainesvilleFL United States College of Health & Human Performance University of Florida GainesvilleFL United States

ISBN: (纸本)9798400713941

A rapidly emerging research community at the intersection of sport and human-computer interaction (SportsHCI) explores how technology can support physically active humans, such as athletes. At highly competitive levels, coaching staff play a central role in the athlete experience by using data to enhance performance, reduce injuries, and foster team success. However, little is known about the practices and needs of these coaching staff. We conducted five focus groups with 17 collegiate coaching staff across three women's teams and two men's teams at an elite U.S. university. Our findings show that coaching staff selectively use data with the goal of balancing performance goals, athlete emotional well-being, and privacy. This paper contributes design recommendations to support coaching staff in operating across the data life cycle through gathering, sharing, deciding, acting, and assessing data as they aim to support team success and foster the well-being of student-athletes. © 2025 Copyright held by the owner/author(s).

关键词： SportsHCI

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Implementing a Robust and Scalable Detect-and-Avoid Algorithm for UAV Navigation in ROS

Implementing a Robust and Scalable Detect-and-Avoid Algorith...

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International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： Dimitrios Meimetis Ioannis Daramouskas Vaios Lappas Isidoros Perikos Vaggelis Kapoulas Michael Paraskevas Department of Aerospace Science & Technology NKUA Athens Greece Computer Technology Institute and Press “Diophantus” Patras Greece Applied Mechanics Lab UoP Patras Greece Computer Engineering and Informatics Department UoP Patras Greece Electrical and Computer Engineering Dept. Computer Technology Institute and Press “Diophantus” University of Peloponnese Patras Greece

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

Unmanned aerial vehicles (UAVs) have garnered significant attention from the research community during the last decade, due to their diverse capabilities and potential applications. One of the most critical functions that drones must execute efficiently is navigation in real-world environments. This paper presents a decentralized approach for enabling unmanned aerial vehicles (UAVs) to navigate safely in unknown environments and avoid obstacles. Leveraging the Optimal Reciprocal Collision Avoidance (ORCA) algorithm, implemented in the Robot Operating System (ROS), our method facilitates conflict detection and resolution in 2D environments. Through simulations using ROS, Gazebo, and Iris drones, we validate the effectiveness of our approach in scenarios with initial trajectory conflicts. Our work addresses the pressing need for UAVs to autonomously plan and execute safe flights, laying the groundwork for enhanced UAV capabilities in various real-world applications. The simulation results demonstrate the efficiency and robustness of our approach.

关键词： Simulation Autonomous aerial vehicles Aircraft navigation Sensor systems Robustness Real-time systems Trajectory Safety Sensors Drones

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Eye Tracking System Using Convolutional Neural Network

Eye Tracking System Using Convolutional Neural Network

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International Conference on computer and Applications (ICCA)

作者： Shatha Iskandar Zahraa Jaara Mohamad Abou Ali Fatima Sbeity Abdallah Kasem Lara Hamawy Department of Biomedical Engineering Lebanese International University Lebanon Dept.of Computer Science & Artificial Intelligence University of the Basque Country Spain Dept. of Physics & Electronics Lebanese University Lebanon Dept. of Electrical & Computer Engineering Notre Dame University Lebanon

Several diseases and disabilities, including cerebral palsy (CP), cause patients to lose their ability to communicate easily with the outside world. In this paper we aim to detect the movement of the iris that can be used to move a wheelchair for CP patients, write sentences by simply looking at an interactive screen, or even by moving an artificial arm. Thus, the two-dimensional CNN model and the coding method, we proposed, tracks the eye movement, specifically the movement of the iris, using VGG image annotator software and a dataset of several people’s eyes images A dataset of 500 images from several patients, where only the eyes were cropped and studied, was taken to train the model about eye movement and detection. The proposed model achieved a mean squared error drop of about 49,720 from the original we started with, which was 50,000.

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A Smart Handling of Bio-Medical Waste and its Segregation with Intelligant Machine Learning Model

A Smart Handling of Bio-Medical Waste and its Segregation wi...

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2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Gupta, Brijendra Sreelatha, P. Shanmathi, M. Philip Bhimavarapu, John John Augustine, P. Sathyarajasekaran, K. Siddhant College of Engineering Sudumbre Department of It Maharashtra Pune India Kpr Institute of Engineering and Technology Dept. of Biomedical Engineering Tamil Nadu Arasur India Electronics and Communications Saveetha Engineering College Kanchipuram Tamil Nadu Chennai India Koneru Lakshmaiah Education Foundation Department of Electronics and Communication Engineering Andhra Pradesh Guntur India Sri Eshwar College of Engineering Department of Information Technology Tamil Nadu Coimbatore India Vellore Institute of Technology Chennai Campus School of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350323887

The Bio-Medical waste management system organizes everyday medical waste disposal in hospitals. Daily medical waste from hospitals is delivered through it. A separate system is in place for treating medical supplies, including needles, plastic, glassware, medical clothes, expired medications, and human waste. Based on that, they use the biomedical Waste Management Centre to accept everyday medical waste from their hospitals and appropriately dispose of it. No hospital should ever dispose of medical trash. It is illegal, and the hospital responsible must appropriately separate the medical waste and deliver it to the biomedical waste treatment facility. In this paper, an intelligent machine learning model was proposed to handling the different bio medical wastages and segregate it based on the medical rules. Medical waste disposed of in hospitals is safely transported and incinerated. The proposed model helpful the disposal of such medical waste, which is usually contagious, takes place. © 2023 IEEE.

关键词： Hospitals

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