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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： S. Tsumoto T.Y. Lin J.F. Peters Department of Medicine Informatics School of Medicine Shimane Medical University Izumo Shimane Japan Department of Mathematics and Computer Science San Jose State University Foundation San Jose CA USA Electrical & Computer Engineering University of Manitoba Winnipeg MAN Canada

This workshop introduces the foundations of data mining in the context of granular and rough computing. Unlike conventional data mining research, this workshop focuses on deep reflection into the meaning of patterns obtained from data. Since patterns are embedded in sets of real world objects, algebraic or geometric views on sets may play important roles. For this purpose, granular and rough computing are most important methodologies. In other words, granular and rough computing gives one of the best tools to analyze algebraic or geometric characteristics of patterns and data.

关键词： Data mining Rough sets Pattern recognition Databases Data acquisition Biomedical informatics Cities and towns Mathematics computer science Biomedical engineering

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Evaluate underdiagnosis and overdiagnosis bias of deep learning model on primary open-angle glaucoma diagnosis in under-served patient populations

arXiv

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arXiv 2023年

作者： Lin, Mingquan Xiao, Yunyun Hou, Bojian Wanyan, Tingyi Sharma, Mohit Manoj Wang, Zhangyang Wang, Fei Van Tassel, Sarah Peng, Yifan Population Health Sciences Weill Cornell Medicine New YorkNY United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania United States Electrical and Computer Engineering The University of Texas Austin United States

In the United States, primary open-angle glaucoma (POAG) is the leading cause of blindness, especially among African American and Hispanic individuals. Deep learning has been widely used to detect POAG using fundus images as its performance is comparable to or even surpasses diagnosis by clinicians. However, human bias in clinical diagnosis may be reflected and amplified in the widely-used deep learning models, thus impacting their performance. Biases may cause (1) underdiagnosis, increasing the risks of delayed or inadequate treatment, and (2) overdiagnosis, which may increase individuals’ stress, fear, well-being, and unnecessary/costly treatment. In this study, we examined the underdiagnosis and overdiagnosis when applying deep learning in POAG detection based on the Ocular Hypertension Treatment Study (OHTS) from 22 centers across 16 states in the United States. Our results show that the widely-used deep learning model can underdiagnose or overdiagnose under-served populations. The most underdiagnosed group is female younger ( © 2023, CC0.

关键词： Diagnosis

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Robust Trajectory and Offloading for Energy-Efficient UAV Edge Computing in Industrial Internet of Things

arXiv

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arXiv 2023年

作者： Tang, Xiao Zhang, Hongrui Zhang, Ruonan Zhou, Deyun Zhang, Yan Han, Zhu The School of Electronics and Information Northwestern Polytechnical University Xi’an710072 China The Department of Informatics University of Oslo Oslo0316 Norway The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States

Efficient data processing and computation are essential for the industrial Internet of things (IIoT) to empower various applications, which yet can be significantly bottlenecked by the limited energy capacity and computation capability of the IIoT nodes. In this paper, we employ an unmanned aerial vehicle (UAV) as an edge server to assist IIoT data processing, while considering the practical issue of UAV jittering. Specifically, we propose a joint design on trajectory and offloading strategies to minimize energy consumption due to local and edge computation, as well as data transmission. We particularly address the UAV jittering that induces Gaussian-distributed uncertainties associated with flying waypoints, resulting in probabilistic-form flying speed and data offloading constraints. We exploit the Bernstein-type inequality to reformulate the constraints in deterministic forms and decompose the energy minimization to solve for trajectory and offloading separately within an alternating optimization framework. The subproblems are then tackled with the successive convex approximation technique. Simulation results show that our proposal strictly guarantees robustness under uncertainties and effectively reduces energy consumption as compared with the baselines. Copyright © 2023, The Authors. All rights reserved.

关键词： Internet of things

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Implementation of miniaturized telemetry module for biomedical signal transmission at wearable computing

Implementation of miniaturized telemetry module for biomedic...

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3rd IASTED International Conference on Telehealth, Telehealth 2007

作者： Park, H.J. Jin, J.S. Lee, J.H. Woo, S.H. Moon, Y.K. Cho, J.H. Kim, Y.N. Dept. of Medical Informatics School of Medicine Keimyung University Dongsan Medical Center Daegu 700-712 Korea Republic of School of Electrical Engineering and Computer Science Kyungpook National University Daegu 700-712 Korea Republic of Dept. of Internal Medicine School of Medicine Keimyung University Dongsan Medical Center 194 Dongsan-Dong Jung-Ku Daegu 700-712 Korea Republic of

ISBN: (纸本)9780889866676

In order to monitor the variation of body temperature from multi-patient in real-time in the hospital room, a miniaturized wireless telemetry module based on PAN was designed and implemented in this paper. The transmitting part of telemetry system consists of a temperature sensor, a miniaturized UHF transmitter with a transmitting antenna, a microprocessor for data encoding, and a battery. The diameter and thickness of implemented transmitter are 20 mm and 3 mm respectively. The receiving part consists of a receiving antenna, a RF receiver, and a processor for data decoding, and the decoded data was transformed to RS232C format to be transferred to computer. From the experimental result in the simulation environment that resembles a hospital room, the implemented telemetry module was verified to operate well with sufficient reliability.

关键词： Personal communication systems

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Human intention recognition for human aware planning in integrated warehouse systems

arXiv

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arXiv 2020年

作者： Petković, Tomislav Hvězda, Jakub Rybecký, Tomáš Marković, Ivan Kulich, Miroslav Přeučil, Libor Petrović, Ivan University of Zagreb Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering Laboratory for Autonomous Systems and Mobile Robotics Unska 3 ZagrebHR-10000 Croatia Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague160 00 Czech Republic

With the substantial growth of logistics businesses the need for larger and more automated warehouses increases, thus giving rise to fully robotized shop-floors with mobile robots in charge of transporting and distributing goods. However, even in fully automatized warehouse systems the need for human intervention frequently arises, whether because of maintenance or because of fulfilling specific orders, thus bringing mobile robots and humans ever closer in an integrated warehouse environment. In order to ensure smooth and efficient operation of such a warehouse, paths of both robots and humans need to be carefully planned;however, due to the possibility of humans deviating from the assigned path, this becomes an even more challenging task. Given that, the supervising system should be able to recognize human intentions and its alternative paths in real-time. In this paper, we propose a framework for human deviation detection and intention recognition which outputs the most probable paths of the humans workers and the planner that acts accordingly by replanning for robots to move out of the human's path. Experimental results demonstrate that the proposed framework increases total number of deliveries, especially human deliveries, and reduces human-robot encounters. Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile robots

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Dynamics Personalized Learning Path Based on Triple Criteria using Deep Learning and Rule-Based Method

Dynamics Personalized Learning Path Based on Triple Criteria...

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IEEE Region 10 International Conference TENCON

作者： Imamah Umi Laili Yuhana Arif Djunaidy Mauridhi Hery Purnomo Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Computer Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia

Personalized learning paths are designed to optimize learning time and improve student learning performance by providing an appropriate learning sequence based on the unique characteristics of each student. A common method for constructing personalized learning paths is based on the student's knowledge but disregards the student's interest in the subject matter. This research employs a deep learning and rule-based approach to recommend suitable material based on the topic's difficulty, student interest, and knowledge level. The difficulty level of the topic is predicted using deep learning. A questionnaire is used to determine the level of student interest, which is then processed using a rule-based approach to generate a learning path. Modeling a dynamic learning path requires measuring student knowledge in each topic and updating the learning path accordingly. Comparing the learning outcomes of students who utilized conventional e-learning versus those who followed a personalized learning path constitutes the evaluation. The results demonstrated that students scored 29% higher, or 15.06 points, than those who utilized conventional e-learning.

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Informal caregivers' attitudes and compliance towards a connected health platform for home care support: Insights from a long-term exposure

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Gerontechnology 2019年第4期18卷 231-242页

作者： Guisado-Fernández, Estefanía Giunti, Guido Mackey, Laura Silva, Paula Alexandra Blake, Catherine Caulfield, Brian Insight Centre for Data Analytics University College Dublin 3rd floor O'Brien Science Building East Dublin Ireland M3S Research Unit Faculty of Information Technology and Electrical Engineering University of Oulu Oulu Finland School of Public Health Physiotherapy and Sports Science University College Dublin Dublin Ireland Department of Computer Science Center for Informatics and Systems of the University of Coimbra (CISUC) University of Coimbra Coimbra Portugal

Background When designing Connected Health (CH) solutions for home care, it is vital to focus on usability and user experience to ensure that technologies are easy to use and meet users' expectations and needs. Generally, the usability and user experience tests are conducted during short-term exposure, which does not allow a true understanding of how the technology can help with the home caring tasks. Research aim We aim to investigate informal caregivers' feedback on the utility and usability of a CH monitoring platform for People with Dementia (PwD) during a period of extended use in the natural living context, and to understand how this was related to compliance patterns. Methods Informal caregiver's feedback about the CH platform, usability, and the impact of short-term versus long-term exposure were investigated through semi-structured individual interviews at the beginning and end of a 6-month deployment in the home care setting. Informal caregivers' compliance with the CH platform was analysed from their daily platform utilization during the deployment time. Results 11 informal caregivers agreed to participate. There was a change in the participants' opinions about the CH platform between the short-term and the long-term exposure feedback. Their initial impressions about what the CH platform could offer them to improve their delivery of home care for the PwD did not correspond with what they found that the CH platform could provide them following the long-term exposure. If at the beginning they saw the CH platform as a helpful tool to facilitate home care delivery and to improve their self-efficacy, after the deployment they expressed that because of the way the platform was designed it was mainly conceived for dementia research benefit and not to fulfill their caring needs. Compliance with the CH platform was quite low and similar between all participants. Conclusions Most contemporary CH studies are not conducted in real-life settings and without enough dur

关键词： Connected health Dementia Informal caregiver Usability User experience

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Machine Learning Analysis for Temperature Classification using Bioelectric Potential of Plant

Machine Learning Analysis for Temperature Classification usi...

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International Conference on Information Technology, Information Systems and electrical engineering (ICITISEE)

作者： Imam Tahyudin Hasri Akbar Awal Rozaq Hidetaka Nambo Department of Information System Faculty of Computer Science Amikom Purwokerto University Banyumas Indonesia Faculty of Computer Science Graduate School of Informatics Gazi University Yenimahalle Ankara Turkey Division of Electrical Engineering and Computer Science Graduate School of NST Kanazawa University Kakumamachi Kanazawa Ishikawa Japan

Plants can respond to changes in their surroundings, such as temperature, humidity, and human behavior. This ability is due to photosynthesis and respiration in plants, resulting in low electrical signals. We focus on room temperature change by plant bioelectric potential detection. The temperature topic is interesting for the actual research because it is one of important factor in health aspect. This study aims to analysis of temperature of Bioelectric potential of plant using machine learning. This implementation identifies the plants' bioelectric potential in distinguishing three-room temperature categories: A, B, and C temperature. We conducted the experimental in room control. For the transformation process is using wavelet transform. Furthermore, the analysis process, we used the Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighborhood (K-NN), Naïve Bayes, and linear regression. Then, for the evaluation is using confusion Matrix. The result showed that the best level of transformation using Wavelet is 7 th level for reducing the noise. Furthermore, the best accuracy of analysis process is using Decision tree which obtained 75%.

关键词： Wavelet transforms Support vector machines Radio frequency Temperature Linear regression Bioelectric phenomena Decision trees

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High-Speed Optical Cache Memory as Single-Level Shared Cache in Chip-Multiprocessor Architectures

High-Speed Optical Cache Memory as Single-Level Shared Cache...

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Workshop on Exploiting Silicon Photonics for Energy-Efficient High Performance Computing (SiPhotonics)

作者： Pavlos Maniotis Savvas Gitzenis Leandros Tassiulas Nikos Pleros Department of Informatics Center for Research and Technology Hellas Thessaloniki Greece Information Technologies Institute Center for Research and Technology Hellas Thessaloniki Greece Department of Electrical and Computer Engineering Center for Research and Technology Hellas Thessaloniki Greece

We present an optical bus-based Chip Multiprocessor architecture where the processing cores share an optical single-level cache unit. Physically, the optical cache is implemented externally in a separate chip located next to the CPU die. The cache interconnection system is realized through WDM optical interfaces that connect the shared cache module with the processing cores and the Main Memory via spatial-multiplexed optical waveguides; hence, the CPU-DRAM communication completely takes place in the optical domain. To evaluate the shared optical cache approach, we carry out system-level simulations of 6 realistic processor parallel workloads via the Gem5 platform. The optical cache architecture is compared against the conventional electronic Chip Multiprocessor topology that uses dedicated on-chip L1 electronic caches and a shared L2 cache. The results show significant reduction in the L1 miss rate of up to 96% for certain cases; on average, a performance speed-up of up to 20.53% or a reduction of up to 65.8% in cache capacity requirements is attained. Combined with high-bandwidth CPU-DRAM bus solutions based on optical interconnects, the proposed design is a quite promising system architecture that bridges the gap between high-speed optically connected CPU-DRAM schemes and high-speed optical memory technologies.

关键词： Optical waveguides Optical transmitters High-speed optical techniques Adaptive optics Optical receivers Optical buffering Optical interconnections

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Semantic Annotation of Sensor Data Using a Sequential Possibilistic Clustering Methodology

Semantic Annotation of Sensor Data Using a Sequential Possib...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Wenlong Wu James M. Keller Marjorie Skubic Mihail Popescu Kari R. Lane Marilyn Rantz Electrical Engineering and Computer Science Department University of Missouri Columbia MO USA Health Management and Informatics Department University of Missouri Columbia MO USA Sinclair School of Nursing University of Missouri Columbia MO USA

The rapid increase of the older population has gained significant healthcare attention in recent years. Early detection of illness might result in better outcomes and reduced healthcare cost. The use of health monitoring sensors such, motion or bed or depth ones, may be able to detect early sign of illness but they are hard to interpret. To enhance caretakers understanding of health sensor data we propose to link it to signs and symptoms extracted from related electronic health record (EHR). The proposed methodology is based on detecting changes in sensor patterns using an unsupervised method (Sequential Possibilistic Gaussian Mixture Model, SPGMM), and associate health patterns from the person's EHR extracted using the clinical language annotation, modeling, and processing (CLAMP) toolkit. However, the evaluation of these health algorithms requires substantial time and effort by clinicians to examine the related electronic health record (EHR) notes. We validated our approach on three nursing home residents monitored with a collection of motion, bed, and depth sensors over several years. The obtained results show that the agreement of CLAMP-clinician and clinician-clinician evaluation are substantial, and provide strong support for our proposed methodology.

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