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Machine learning in health condition check-up: An approach using Breiman's random forest algorithm

Measurement: Sensors 2022年 23卷

作者： Abd Algani, Yousef Methkal Ritonga, Mahyudin Kiran Bala, B. Al Ansari, Mohammed Saleh Badr, Malek Taloba, Ahmed I. Department of Mathematics Sakhnin College Israel Department of Mathematics The Arab Academic College for Education in Israel-Haifa Israel Muhammadiyah University of West Sumatra Indonesia Samayapuram 621112 Trichy India College of Engineering Department of Chemical Engineering University of Bahrain Bahrain The University of Mashreq Research Center Baghdad Iraq Department of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad10021 Iraq Department of Computer Science College of Science and Arts in Qurayyat Jouf University Saudi Arabia Information System Department Faculty of Computers and Information Assiut University Assiut Egypt

Nowadays majority of the college students' physical condition is worrying. They are not physically and also mentally healthy. If so, why? Their selection of foods is not consistent. Thus, they are more likely to suffer from chronic illnesses such as diabetes, hypertension, stress, etc. in the future. Awareness should be created to prevent such diseases before they occur. Physiological parameters measured included Systolic (SBP) and Diastolic (DBP) Blood Pressure, Body mass Index (BMI), Blood Serum Cholesterol (BSC), and percentage of Body Fat (%BF). These parameters are retrieved and classified to check the physical health or predict if any abnormalities are found in the health condition of college students. Therefore, to predict and classify their health status using Breiman's Random Forest (RF) Algorithm is proposed in this paper. Of all the classification methods available, random forests offer the greatest accuracy. Random forest method also handles large data with thousands of variables. When a class is more sparse than further classes in the data it can spontaneously balance the data sets. The outcome shows that the proposed Random Forest algorithm is accurate in predicting and checking the health condition of students. Students' physical condition should be diagnosed through this method. By knowing the healthy body parameters of the students, a physician can know whether they are healthy or not. © 2022

关键词： Machine learning

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Pre-Chirper Free Nonlinear Fiber Amplifier Generating Tunable Picosecond Pulses for Coherent Anti-Stokes Raman Imaging

Pre-Chirper Free Nonlinear Fiber Amplifier Generating Tunabl...

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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Jiaying Li Jiamei Wu Kangwen Yang Qiang Hao Minbiao Ji Ming Yan Kun Huang Heping Zeng Shanghai Key Laboratory of Modern Optical System and Engineering Research Center of Optical Instrument and System Ministry of Education School of Optical Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China State Key Laboratory of Surface Physics and Department of Physics Human Phenome Institute Multiscale Research Institute of Complex Systems Academy for Engineering and Technology Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education) Fudan University Shanghai China State Key Laboratory of Precision Spectroscopy East China Normal University Shanghai China Chongqing Institute of East China Normal University Chongqing China Jinan Institute of Quantum Technology Jinan China

A pre-chirper free, core-pumped nonlinear fiber amplifier was demonstrated to generate parabolic pulse with spectral bandwidth of 53 nm, these pulses were filtered and passively synchronized for conducting coherent an...

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A Multi-Stage Stochastic Dispatching Method for Electricity-Hydrogen Integrated Energy systems Driven by Model and Data

SSRN

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SSRN 2023年

作者： Yang, Zhixue Ren, Zhouyang Li, Hui Sun, Zhiyuan Feng, Jianbing The State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University Chongqing400044 China The State Key Laboratory of Internet of Things for Smart City Department of Electrical and Computer Engineering University of Macau 999078 China The State Key Laboratory of Internet of Things for Smart City University of Macau 999078 China Electric Power Research Institute of Guangxi Power Grid Co. Ltd Guangxi Nanning530000 China

To balance the contradiction between economy, security, and computational burden caused by the uncertainty of the electricity-hydrogen integrated energy systems (EH-IESs), a multi-stage coordinated dispatching framework of "day-ahead deterministic dispatching - online security monitoring - intra-day flexible correction" is proposed. The flexibility of the hydrogen energy system at the source-side and load-side is fully exploited and incorporated into the day-ahead dispatching model. To online monitor the future security of the EH-IESs operation in an uncertain environment, a security monitoring method is proposed by combining deep learning and Monte Carlo simulation. The predetermined dispatching scheme may not ensure the security of system operation due to the renewable energy uncertainty. Thus, an intra-day correction method based on chance-constrained model and multi-agent deep reinforcement learning is established to determine the correction scheme. Finally, the effectiveness and adaptability of the proposed method are demonstrated using the data of two EH-IESs based on the standard IEEE 57-node and IEEE 118-node test systems. © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Microwave-multiplexed qubit controller using adiabatic superconductor logic

arXiv

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arXiv 2023年

作者： Takeuchi, Naoki Yamae, Taiki Yamashita, Taro Yamamoto, Tsuyoshi Yoshikawa, Nobuyuki Ibaraki Tsukuba305-8568 Japan Ibaraki Tsukuba305-8568 Japan Institute of Advanced Sciences Yokohama National University Kanagawa Yokohama240-8501 Japan Department of Applied Physics Tohoku University Miyagi Sendai980-8579 Japan Secure System Platform Research Laboratories NEC Corporation Kanagawa Kawasaki211-0011 Japan Department of Electrical and Computer Engineering Yokohama National University Kanagawa Yokohama240-8501 Japan

Cryogenic qubit controllers (QCs) are the key to build large-scale superconducting quantum processors. However, developing scalable QCs is challenging because the cooling power of a dilution refrigerator is too small (~10 μW at ~10 mK) to operate conventional logic families, such as complementary metal-oxide-semiconductor logic and superconducting single-flux-quantum logic, near qubits. Here we report on a scalable QC using an ultra-low-power superconductor logic family, namely adiabatic quantum-flux-parametron (AQFP) logic. The AQFP-based QC, referred to as the AQFP-multiplexed QC (AQFP-mux QC), produces multi-tone microwave signals for qubit control with an extremely small power dissipation of 81.8 pW per qubit. Furthermore, the AQFP-mux QC adopts microwave multiplexing to reduce the number of coaxial cables for operating the entire system. As a proof of concept, we demonstrate an AQFP-mux QC chip that produces microwave signals at two output ports through microwave multiplexing and demultiplexing. Experimental results show an output power of approximately −80 dBm and on/off ratio of ~40 dB at each output port. Basic mixing operation is also demonstrated by observing sideband signals. Copyright © 2023, The Authors. All rights reserved.

关键词： Oxide semiconductors

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Hard Sample Matters a Lot in Zero-Shot Quantization

arXiv

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arXiv 2023年

作者： Li, Huantong Wu, Xiangmiao Lv, Fanbing Liao, Daihai Li, Thomas H. Zhang, Yonggang Han, Bo Tan, Mingkui South China University of Technology China Changsha Hisense Intelligent System Research Institute Co. Ltd China Hong Kong Baptist University Hong Kong Key Laboratory of Big Data and Intelligent Robot Ministry of Education China PengCheng Laboratory China Information Technology R&D Innovation Center of Peking University China School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen China

Zero-shot quantization (ZSQ) is promising for compressing and accelerating deep neural networks when the data for training full-precision models are inaccessible. In ZSQ, network quantization is performed using synthetic samples, thus, the performance of quantized models depends heavily on the quality of synthetic samples. Nonetheless, we find that the synthetic samples constructed in existing ZSQ methods can be easily fitted by models. Accordingly, quantized models obtained by these methods suffer from significant performance degradation on hard samples. To address this issue, we propose HArd sample Synthesizing and Training (HAST). Specifically, HAST pays more attention to hard samples when synthesizing samples and makes synthetic samples hard to fit when training quantized models. HAST aligns features extracted by full-precision and quantized models to ensure the similarity between features extracted by these two models. Extensive experiments show that HAST significantly outperforms existing ZSQ methods, achieving performance comparable to models that are quantized with real data. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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HI-Net: Hyperdense inception 3D UNet for brain tumor segmentation

arXiv

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arXiv 2020年

作者： Qamar, Saqib Ahmad, Parvez Shen, Linlin Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

The brain tumor segmentation task aims to classify tissue into the whole tumor (WT), tumor core (TC) and enhancing tumor (ET) classes using multimodel MRI images. Quantitative analysis of brain tumors is critical for clinical decision making. While manual segmentation is tedious, time-consuming, and subjective, this task is at the same time very challenging to automatic segmentation methods. Thanks to the powerful learning ability, convolutional neural networks (CNNs), mainly fully convolutional networks, have shown promising brain tumor segmentation. This paper further boosts the performance of brain tumor segmentation by proposing hyperdense inception 3D UNet (HI-Net), which captures multi-scale information by stacking factorization of 3D weighted convolutional layers in the residual inception block. We use hyper dense connections among factorized convolutional layers to extract more contexual information, with the help of features reusability. We use a dice loss function to cope with class imbalances. We validate the proposed architecture on the multi-modal brain tumor segmentation challenges (BRATS) 2020 testing dataset. Preliminary results on the BRATS 2020 testing set show that achieved by our proposed approach, the dice (DSC) scores of ET, WT, and TC are 0.79457, 0.87494, and 0.83712, respectively. © 2020, CC BY.

关键词： Convolution

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Policies Selection for Pedagogical Agent Based on the Roulette Wheel Algorithm

Policies Selection for Pedagogical Agent Based on the Roulet...

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Information and Education Technology (ICIET), International Conference on

作者： Fabiola M. Talavera-Mendoza Carlos E. Atencio-Torres David A. Deza Veliz Juan M. Llano-Barsaya Universidad Nacional de San Agustin Arequipa Peru Computer Science School Universidad Nacional de San Agustin Arequipa Peru System Engineering School Universidad Nacional de San Agustín Arequipa Peru

ISBN: (纸本)9781665430159

Pedagogical agents are computational entities that interact with users and facilitate learning opportunities. They usually need to be programmed to follow a set of commands for an effective personalized exchange of knowledge and tasks. In this study, we evaluate the effectiveness of a policy-based model and its level of satisfaction about the interaction without and with the pedagogical agent using the bio-inspired roulette selection algorithm. The approach is quantitative, with an exploratory and descriptive study. The results revealed that our agent achieved great acceptance among the users who rated it as intelligent, friendly, and reliable. It is evidenced that the agent can influence the attitude, perception, and behavior of the user to reach better self-regulated learning.

关键词： Computational modeling Biological system modeling Education Wheels Reliability Task analysis

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A quantitative framework for network resilience evaluation using dynamic bayesian network

arXiv

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arXiv 2021年

作者： Jiang, Shanqing Yang, Lin Cheng, Guang Gao, Xianming Feng, Tao Zhou, Yuyang School of Cyber Science and Engineering Southeast University Nanjing China National Key Laboratory of Science and Technology on Information System Security Beijing China Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing China

Measuring and evaluating network resilience has become an important aspect since the network is vulnerable to both uncertain disturbances and malicious attacks. Networked systems are often composed of many dynamic components and change over time, which makes it difficult for existing methods to access the changeable situation of network resilience. This paper establishes a novel quantitative framework for evaluating network resilience using the Dynamic Bayesian Network. The proposed framework can be used to evaluate the network's multistage resilience processes when suffering various attacks and recoveries. First, we define the dynamic capacities of network components and establish the network's five core resilience capabilities to describe the resilient networking stages including preparation, resistance, adaptation, recovery, and evolution;the five core resilience capabilities consist of rapid response capability, sustained resistance capability, continuous running capability, rapid convergence capability, and dynamic evolution capability. Then, we employ a two-time slices approach based on the Dynamic Bayesian Network to quantify five crucial performances of network resilience based on core capabilities proposed above. The proposed approach can ensure the time continuity of resilience evaluation in time-varying networks. Finally, our proposed evaluation framework is applied to different attacks and recovery conditions in typical simulations and real-world network topology. Results and comparisons with extant studies indicate that the proposed method can achieve more accurate and comprehensive evaluation and can be applied to network scenarios under various attack and recovery intensity. Copyright © 2021, The Authors. All rights reserved.

关键词： Recovery

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LCCG: A Locality-Centric Hardware Accelerator for High Throughput of Concurrent Graph Processing

LCCG: A Locality-Centric Hardware Accelerator for High Throu...

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Supercomputing Conference

作者： Jin Zhao Yu Zhang Xiaofei Liao Liaang He Binzsheng He Haikun Liu Hai Jin Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China University of Warwick United Kingdom National University of Singapore Singapore

ISBN: (数字)9781450384421

ISBN: (纸本)9781665483902

In modern data centers, massive concurrent graph processing jobs are being processed on large graphs. However, existing hardware/-software solutions suffer from irregular graph traversal and intense resource contention. In this paper, we propose LCCG, a Locality-entric programmable accelerator that augments the many-core processor for achieving higher throughput of Concurrent Graph processing jobs. Specifically, we develop a novel topology-aware execution approach into the accelerator design to regularize the graph traversals for multiple jobs on-the-fly according to the graph topology, which is able to fully consolidate the graph data accesses from concurrent jobs. By reusing the same graph data among more jobs and coalescing the accesses of the vertices' states for these jobs, LCCG can improve the core utilization. We conduct extensive experiments on a simulated 64-core processor. The results show that LCCG improves the throughput of the cutting-edge software system by 11.3~23.9 times with only 0.5% additional area cost. More-over, LCCG gains the speedups of 4.7~10.3, 5.5~13.2, and 3.8~8.4 times over state-of-the-art hardware graph processing accelerators (namely, HATS, Minnow, and PHI, respectively).

关键词： Data centers Costs High performance computing Throughput Software systems Topology Hardware acceleration

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Multi-stage network embedding for exploring heterogeneous edges

arXiv

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arXiv 2021年

作者： Huang, Hong Song, Yu Ye, Fanghua Xie, Xing Shi, Xuanhua Jin, Hai The National Engineering Research Center for Big Data Technology Service Computing Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China The Department of Computer Science University College London London United Kingdom Microsoft Research Asia Beijing China

The relationships between objects in a network are typically diverse and complex, leading to the heterogeneous edges with different semantic information. In this paper, we focus on exploring the heterogeneous edges for network representation learning. By considering each relationship as a view that depicts a specific type of proximity between nodes, we propose a multi-stage non-negative matrix factorization (MNMF) model, committed to utilizing abundant information in multiple views to learn robust network representations. In fact, most existing network embedding methods are closely related to implicitly factorizing the complex proximity matrix. However, the approximation error is usually quite large, since a single low-rank matrix is insufficient to capture the original information. Through a multi-stage matrix factorization process motivated by gradient boosting, our MNMF model achieves lower approximation error. Meanwhile, the multi-stage structure of MNMF gives the feasibility of designing two kinds of non-negative matrix factorization (NMF) manners to preserve network information better. The united NMF aims to preserve the consensus information between different views, and the independent NMF aims to preserve unique information of each view. Concrete experimental results on realistic datasets indicate that our model outperforms three types of baselines in practical applications. © 2021, CC BY.

关键词： Non-negative matrix factorization

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