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Electronics, Communications, and Computations (JAC-ECC), International Japan-Africa Conference on

作者： Hend A. Bakr Ahmed M. Salama Ahmed Fares Ahmed B. Zaky Biomedical Engineering Program Faculty of Engineering Cairo University Giza Egypt Computer Systems Engineering Program Faculty of Engineering at Shoubra Benha University Banha Egypt Computer science and information technology Programs Egypt-Japan University of Science and Technology (E-JUST) Alexandria Egypt On leave from Computer Systems Engineering Program Faculty of Engineering at Shoubra Benha University

Physician scheduling is a critical task that impacts the quality of patient care, staff satisfaction, and operational efficiency in healthcare institutions. The traditional approach to physician scheduling is manual and time-consuming, which can result in errors, staff burnout, and suboptimal schedules. To address these challenges, researchers have turned to optimization techniques like CSP, which has shown promise in solving physician scheduling problems. This paper reviews the existing literature on CSP for physician scheduling and highlights the benefits and limitations of this approach. CSP's benefits include generating schedules quickly and efficiently, incorporating complex constraints and preferences, and handling changes and disruptions in real time. However, CSP also has some limitations, such as the need for a formalized model and the fact that it may not always generate the most intuitive schedules. Overall, the findings suggest that CSP is a promising approach to physician scheduling that can produce high-quality schedules while minimizing staff burnout and improving operational efficiency.

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PHORTEX: Physically-Informed Operational Robotic Trajectories for Scientific Expeditions

Field Robotics

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Field Robotics 2025年 4卷 138-181页

作者： Victoria L. Preston Genevieve E. Flaspohler John W. Fisher Anna P. M. Michel Nicholas Roy Applied Ocean Physics and Engineering Massachusetts Institute of Technology and Woods Hole Oceanographic Institution Joint Program Cambridge MA 02139 Electrical Engineering and Computer Science Massachusetts Institute of Technology and Woods Hole Oceanographic Institution Joint Program Cambridge MA 02139 Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA 02139 Applied Ocean Physics and Engineering Woods Hole Oceanographic Institution Woods Hole MA 02543

Mobile robots are increasingly used to collect valuable in situ samples during scientific expeditions. However, many phenomena of scientific interest—deep-sea hydrothermal plumes, algal blooms, warm-core eddies, and lava flows—are spatiotemporal distributions that evolve on spatial and temporal scales that complicate sample collection. Here, we consider the problem of charting the space-time dynamics of deep-sea hydrothermal plumes with the state-of-the-art autonomous underwater vehicle (AUV) Sentry. In the hydrothermal plume charting problem, the plume state is driven by complicated and unobserved dynamics in the deep sea. To effectively sample the moving plume, an autonomy system must infer plume dynamics from sparse, point observations, while respecting operational constraints of AUV Sentry that restrict the set of possible trajectories to nonadaptive, uniform-coverage patterns. We frame the plume charting problem as a sequential decision-making problem and formulate a mission planner PHORTEX (PHysically-informed Operational Robotic Trajectories for Expeditions) that strategically designs full mission trajectories for Sentry, where each mission plan is informed by the observations of the last. PHORTEX is composed of a trajectory optimizer, which maximizes expected samples collected within a moving plume, and PHUMES (PHysically-informed Uncertainty Models for Environment Spatiotemporality), a modeling framework that leverages an embedded simulator of idealized plume physics as an inductive bias to enable dynamics learning from extreme partial observations and a few Sentry deployments. In both simulation and in field trials at a hydrothermal site in the Gulf of California, we demonstrate that Sentry using PHORTEX learns to track a moving hydrothermal plume and gather samples that significantly improve upon baseline spatial and temporal diversity for use in downstream science tasks.

关键词： Trajectory Sensors Oceans Vehicle dynamics Robot sensing systems Adaptation models Spatiotemporal phenomena Decision making Marine robots Environmental monitoring Mobile robots

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Cardiovascular Disease Analysis Using Correlational Analysis and Association Rules Mining for In-depth Analysis to Identify Predominant Variables

Cardiovascular Disease Analysis Using Correlational Analysis...

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

作者： Boby Siswanto Haryono Soeparno Nesti F. Sianipar Widodo Budiharto Computer Science Department BINUS Graduate Program- Computer Science Bina Nusantara University Jakarta Indonesia Food Technology Department Engineering Faculty Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Cardiovascular disease is one of the dangerous non-communicable disorders or diseases that has become one of the causes of death worldwide. Various studies have been conducted to prevent cardiovascular disease in the world. This study analyzed cardiovascular disease medical record data from the Kaggle public dataset by implementing correlational analysis combined with association rule mining to identify variables that are the predominant cause of cardiovascular disease. Correlational analysis can analyze the interrelationships between variables in a dataset, but not in depth. Association rule mining can identify the interrelationships of variables in the form of frequent item sets, which can be calculated for their support and confidence values. The result of this study is a combination of correlation analysis with association rule mining that can identify predominant variables to cause cardiovascular disease. Found that the variable gender=woman, height=short (<165 cm), and age=middle (45-60 years) are more likely to be affected by cardiovascular disease. The variable gender=woman with height=short indicates a 76.07% probability of developing cardiovascular disease.

关键词： computer science Correlation Itemsets Cardiovascular diseases Data mining Information technology

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PM2: A New Prompting Multi-modal Model Paradigm for Few-shot Medical Image Classification

PM2: A New Prompting Multi-modal Model Paradigm for Few-shot...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhenwei Wang Qiule Sun Bingbing Zhang Weijian Su Pengfei Wang Jianxin Zhang Qiang Zhang Key Laboratory of Social Computing and Cognitive Intelligence (Ministry of Education) Dalian University of Technology China School of Computer Science and Technology Dalian University of Technology China School of Computer Science and Engineering Dalian Minzu University China School of Information and Communication Engineering Dalian University of Technology China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Few-shot learning has become a key technical solution for addressing the challenges of limited data and difficult annotation acquisition in medical image classification. However, relying solely on a single image modality proves inadequate for capture conceptual categories. This paper proposes a novel medical image classification paradigm based on a multi-modal foundation model, called PM 2 . In addition to the image modality, PM 2 introduces supplementary text input (prompt) to further describe images or conceptual categories and facilitate cross-modal few-shot learning. We empirically studied five different prompting schemes under this new paradigm. Furthermore, linear probing in multi-modal models only takes class token as input, ignoring the rich statistical data contained in high-level visual tokens. Therefore, we alternately perform linear classification on the feature distributions of visual tokens and class token. To effectively extract statistical information, we use global covariance pool with efficient matrix power normalization to aggregate the visual tokens. We then combine two classification heads: one for handling image class token and prompt representations encoded by the text encoder, and the other for classifying the feature distributions of visual tokens. Experiments on two medical datasets demonstrate that regardless of the prompting scheme, our method PM 2 outperforms its counterparts, achieving state-of-the-art performance.

关键词： Visualization Head Foundation models Feature extraction Data models Data mining Few shot learning Covariance matrices Medical diagnostic imaging Image classification

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Polarity Classification of Twitter Data Using Machine Learning Approach

Polarity Classification of Twitter Data Using Machine Learni...

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Human-Centered cognitive Systems (HCCS)

作者： Waqas Haider Bangyal Muddesar Iqbal Ahsan Bashir George Ubakanma Dept. of Computer Science Kohsar Univeristy Murree Muree Pakistan Cognitive Systems Research Centre School of Engineering School of Engineering London South Bank University Dept. of Computer Science Abasyn University Islamabad Paksitan

Sentiment analysis has become very important nowadays because there are lots of social media platforms peoples are using to express their opinion. Twitter is one of the most popular social media platforms which is used for microblogs. People use to express their opinion on current affairs, and there is a challenge for researchers to classify the sentiment accurately. In this research study, we proposed a greatly efficient technique for the detection of fake news on covid-19. The data set of fake news is taken from the corpus and executes the NLP cycle. In this research, we applied five machine learning to predict the sentiment of fake or real news. Support Vector Machine, Logistic Regression, KNN, Decision Trees, and Random Forest are machine learning classifiers used in this research, and results are compared.

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Prior-Guided Parallel Residual Bi-Fusion Network in USV Obstacle Detection

Prior-Guided Parallel Residual Bi-Fusion Network in USV Obst...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Chih-Chung Hsu Sophia Yang Xiu-Yu Hou Yu-An Jhang Institute of Data Science National Cheng Kung University Interdisciplinary Program of Electrical Engineering and Computer Science National Tsing Hua University

In this paper, we propose a novel Prior-Guided Parallel Residual Bi-Fusion Feature Pyramid Network (PPRB-FPN) for accurate obstacle detection in unmanned surface vehicle (USV) sailing. Our method tackles the challenge of detecting small objects, which are prone to information vanishing. To the end, we leverage the PRB-FPN for small object detection and YOLOv7 as a single-stage object detector to effectively identify obstacles. Our experimental results on the Obstacle Detection Challenge dataset at the 1st Workshop on Maritime computer Vision (MaCVi) demonstrate that our method outperforms both Mask R-CNN (mrcnn) and YOLOv7, achieving an F_avg score of 0.514.

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RSCNet: Dynamic CSI Compression for Cloud-Based WiFi Sensing

RSCNet: Dynamic CSI Compression for Cloud-Based WiFi Sensing

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IEEE International Conference on Communications (ICC)

作者： Borna Barahimi Hakam Singh Hina Tabassum Omer Waqar Mohammad Omer Electrical Engineering and Computer Science Department York University Canada School of Computing University of the Fraser Valley BC Canada Cognitive Systems Corp. Waterloo Canada

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

WiFi-enabled Internet-of- Things (IoT) devices are evolving from mere communication devices to sensing instru-ments, leveraging Channel State Information (CSI) extraction capabilities. Nevertheless, resource-constrained IoT devices and the intricacies of deep neural networks necessitate transmitting CSI to cloud servers for sensing. Although feasible, this leads to considerable communication overhead. In this context, this paper develops a novel Real-time Sensing and Compression Network (RSCNet) which enables sensing with compressed CSI; thereby reducing the communication overheads. RSCNet facilitates op-timization across CSI windows composed of a few CSI frames. Once transmitted to cloud servers, it employs Long Short-Term Memory (LSTM) units to harness data from prior windows, thus bolstering both the sensing accuracy and CSI reconstruction. RSCNet adeptly balances the trade-off between CSI compression and sensing precision, thus streamlining real-time cloud-based WiFi sensing with reduced communication costs. Numerical findings demonstrate the gains of RSCNet over the existing benchmarks like SenseFi, showcasing a sensing accuracy of 97.4 % with minimal CSI reconstruction error. Numerical results also show a computational analysis of the proposed RSCNet as a function of the number of CSI frames.

关键词： Accuracy Costs Real-time systems Sensors Servers Internet of Things Data mining

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Determination of Giving 'Tips' Using Fuzzy Logic to Determine Restaurant Customer Service Satisfaction 3

Determination of Giving 'Tips' Using Fuzzy Logic to Determin...

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3rd International Conference on Computational science and Information Management, ICoCSIM 2019

作者： Priyanto, Dadang Zarlis, Muhammad Anggrawan, Anthony Graduate Program of Computer Science Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Informatics Engineering Universitas Bumigora Mataram Indonesia

Fuzzy logic can be used to represent obscurity based on terms such as high, old, hot, cold, and so on. Fuzzy logic can extend the range of truth values to all real numbers in intervals between 0 and 1. Numbers in this interval represent the possibility that the statement given is "true"or "wrong". The use of fuzzy logic can be used in everyday life, such as in the fields of medicine, engineering, agriculture, transportation, environmental science and also in economic and business applications. As in this study, it was used to calculate the ideal amount of 'tips' for restaurant waiters based on the service provided by restaurant waiters. The method used in this study is to use Mamdani fuzzy logic with the process of defuzzyfication using the Centroid of area (COA). The results of this study are that it can be determined the value crisp of giving tips to restaurant waiters is 45 thousand rupiah provided that the service provided for service = 7 (Excellent) and food service = 8 (Delicious).. © Published under licence by IOP Publishing Ltd.

关键词： Fuzzy logic

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Studying Cardiac Electrophysiology via EPSSIM 20

Studying Cardiac Electrophysiology via EPSSIM

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20th International Joint Conference on computer science and Software engineering, JCSSE 2023

作者： Katekarn, Sujitra Sisuk, Noppadon Nuamchit, Teonchit Jittiporn, Kanjana Payakpate, Janjira Naresuan University Faculty of Allied Health Sciences Department of Cardio-thoracic Technology Phitsanulok Thailand Phibunsongkhram Rajabhat University Program in Computer Engineering Faculty of Industrial Technology Phitsanulok Thailand Naresuan University Faculty of Sciences Department of Computer Science and Information Technology Phitsanulok Thailand

ISBN: (纸本)9798350300505

For Cardio-thoracic technology education program, the focus is on teaching pathophysiology, as well as the interpretation of electrocardiograms (ECG), through case studies. The curriculum includes instruction on treatment options and the use of electrophysiology studies (EP studies) for tachycardia and cardiac ablation. A portion of e-learning resources for ECGs and EP studies are delivered via web-based platforms. However, to date, there have been no e-learning platforms that combine instruction on pathological ECGs, EP study, cardiac ablation, or simulations of EP which would provide a more comprehensive learning experience for students. Therefore, the purpose of this study was to create the electrophysiology study simulator program (EPSSIM), which serves as an e-learning platform for EP studies and ECG. The development started with gathering information about ECG from the medical records of patients at a public hospital. MATLAB was used to generate synthetic heart signals. Microsoft Visual Studio 2019 was used to construct the EPSSIM program. A total of 47 cardio-thoracic technology students who used the program completed a pre-test and post-test and questionnaires. The effectiveness and satisfaction of the EPSSIM program were evaluated. The results showed that participants' scores improved, but the improvement was restricted due to the short time that the students were able to use the system. The participants who completed the satisfaction survey generally indicated satisfaction with the effectiveness of EPSSIM. Overall, the use of the application was beneficial and the application could be successfully used in future to enhance users knowledge. © 2023 IEEE.

关键词： Electrocardiograms

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UC3M-PUCPR at SemEval-2022 Task 11: An Ensemble Method of Transformer-based Models for Complex Named Entity Recognition 16

UC3M-PUCPR at SemEval-2022 Task 11: An Ensemble Method of Tr...

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16th International Workshop on Semantic Evaluation, SemEval 2022

作者： Schneider, Elisa Terumi Rubel Rivera-Zavala, Renzo M. Martinez, Paloma Moro, Claudia Paraiso, Emerson Cabrera Graduate Program on Informatics Pontifícia Universidade Catolica do Paraná Brazil Computer Science and Engineering Department Universidad Carlos III de Madrid Spain

ISBN: (纸本)9781955917803

This study introduces the system submitted to the SemEval 2022 Task 11: MultiCoNER (Multilingual Complex Named Entity Recognition) by the UC3M-PUCPR team. We proposed an ensemble of transformer-based models for entity recognition in cross-domain texts. Our deep learning method benefits from the transformer architecture, which adopts the attention mechanism to handle the long-range dependencies of the input text. Also, the ensemble approach for named entity recognition (NER) improved the results over baselines based on individual models on two of the three tracks we participated in. The ensemble model for the code-mixed task achieves an overall performance of 76.36% F1-score, a 2.85 percentage point increase upon our individually best model for this task, XLM-RoBERTa-large (73.51%), outperforming the baseline provided for the shared task by 18.26 points. Our preliminary results suggest that contextualized language models ensembles can, even if modestly, improve the results in extracting information from unstructured data. © 2022 Association for Computational Linguistics.

关键词： Deep learning

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