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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Sohal, Harwinder Singh Lovely Professional University Schoool of Computer Science & Engineering Punjab Phagwara144411 India

ISBN: (纸本)9798331508685

Chronic Kidney Disease (CKD) is a major health burden that is becoming more and more of a global concern. Improving patient outcomes requires early identification. The present work focuses on identifying key biomarkers for CKD prediction using machine learning classifiers. Biomarkers such as glomerular filtration rate, hemoglobin, proteinuria and serum creatinine are essential for determining kidney damage and function. A dataset of 400 individuals with 25 variables from the UCI ML repository was analyzed and evaluated using six machine learning classifiers: Decision Tree, Random Forest, KNN, Logistic Regression, XGBoost and AdaBoost. Information gain was used to enhance feature selection and missing values were addressed through data preprocessing. The classifiers were assessed using F1-score, recall, accuracy and precision. Among others, KNN showed exceptional performance with 100% recall and 97.22% accuracy. Random Forest and Decision Tree both achieved 97.22% accuracy, along with 96.30% recall and precision. Hemoglobin vs. serum creatinine, Diabetes vs. Hypertension and Specific Gravity vs. Albumin are examples of relationships between biomarkers that were identified as crucial in diagnosing CKD cases. With all classifiers achieving accuracy rates above 96%, the study concludes that machine learning classifiers provide reliable and effective tools for early CKD diagnosis, facilitating prompt intervention and treatment. © 2025 IEEE.

关键词： Creatinine

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Machine Learning-Based Biomarker Identification for Early Chronic Kidney Disease Diagnosis

Machine Learning-Based Biomarker Identification for Early Ch...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Harwinder Singh Sohal Schoool of Computer Science & Engineering Lovely Professional University Phagwara Punjab

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

关键词： Hypertension Logistic regression Accuracy Biomarkers Nearest neighbor methods Chronic kidney disease Diabetes Decision trees Random forests Gravity

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Optimizing Solar-Energy Harvesting Communication Systems: An energy allocation scheme for maximizing data transmission 4

Optimizing Solar-Energy Harvesting Communication Systems: An...

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4th International Conference on Internet of Things and Smart City, IoTSC 2024

作者： Li, Shuai Yao, Zhonghao Chen, Shengbo Schoool of Software Henan University Henan Kaifeng China School of Computer Science & Technology Beijing Institute of Technology Beijing China School of Computer and Information Engineering Henan University Henan Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Henan Kaifeng China

ISBN: (纸本)9781510681767

The solar energy harvesting communication system is the basic support layer that is associated with the Internet of Things (IoT). However, the inherent stochasticity of solar energy brings about extensive research on energy allocation in the system. In this paper, we propose an energy allocation algorithm for maximizing the data transfer (MDT) of a solar energy harvesting communication system while jointly considering the practical constraints of battery capacity and link capacity restrictions. We can solve it through the convex optimization problem, but the solution is extremely complicated. This complexity is the result of the coupling of energy allocation problems caused by restricted battery capacity and link capacity. To simplify the solution process, we propose a decoupled energy allocation (DEA) algorithm. Simulation experiments are conducted through real energy harvesting data sets, and the results show that DEA outperforms the other two heuristic schemes. © 2024 SPIE.

关键词： Convex optimization

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A hybrid protocol using fuzzy logic and rough set theory for target coverage

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Recent Advances in computer science and Communications 2021年第2期14卷 467-476页

作者： Chaturvedi, Pooja Daniel, Ajai K. Department of Computer Science Schoool of Management Sciences Varanasi India Department of Computer Science and Engineering M. M. M. University of Technology Gorakhpur India

Background: Target coverage is considered a significant problem in the area of wireless sensor networks, which usually aims at monitoring a given set of targets with the required confidence level so that network lifetime can be enhanced while considering the constraints of the re-sources. Objective: To maximize the lifetime of the sensor network and minimize the overhead involved in the scheduling approach, such that the pre specified set of targets is monitored for a longer duration with the required confidence level. Methods: The paper uses a fuzzy logic system based on Mamdani inference in which the node sta-tus to remain in the active state is determined on the basis of coverage probability, trust values and node contribution. The rule set for determining the set cover is optimized by using the rough set theory, which aims to determine the node validity for the trust calculation. Results: The results show that the proposed approach improved the network performance in terms of processing time, throughput and energy conservation by a factor of 50%, 74% and 74%, respec-tively, as compared to the existing approaches. Conclusion: The paper proposes a scheduling strategy of the nodes for target coverage as an en-hancement to the Energy Efficient Coverage Protocol (EECP) on the basis of rough set theory. The rule set for determining the set cover is optimized by using the rough set theory so that the network performance is improved in terms of the processing time, throughput and energy consumption. © 2021 Bentham science Publishers.

关键词： Energy efficiency

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An Optimized GNN-Based Caching Scheme for SDN-Based Information-Centric Networks

An Optimized GNN-Based Caching Scheme for SDN-Based Informat...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Jiacheng Hou Tianhao Tao Haoye Lu Amiya Nayak Schoool of Electrical Engineering and Computer Science University of Ottawa Canada David R. Cheriton School of Computer Science University of Waterloo Canada

Information-Centric Networking (ICN) has recently attracted much attention due to in-network caching and named-based routing. Caching has played a critical role in the era of proliferating network traffic. More importantly, effective caching can improve content delivery and optimize network efficiency. This paper aims to improve cache performance by maximizing cache hit ratio, minimizing content delivery latency, and path stretching in the Software-Defined Networking-ICN (SDN-ICN) context. We first propose a statistical model to generate users' preferences for different contents, which contains many essential ingredients observed in real-world scenarios. Next, we built a Graph Neural Network-Deep Reinforcement Learning (GNN-D RL) agent to generate caching decisions for each node (and at every time step) based on users' content request history. Simulation results show that the proposed caching scheme can reach a 12.3 % higher cache hit ratio, 6.5 % lower average latency time and 6.5 % lower average path stretch compared to the state-of-the-art caching strategy.

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Ftsc: Fault-Tolerant Scheduling and Control Co-Design for Distributed Real-Time System

SSRN

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SSRN 2022年

作者： Zhang, Yuanhai Xu, Zijin Zhang, Yibo Guan, Nan Chen, Gang Huang, Kai Schoool of Computer Science and Engineering Sun Yat-sen University Guang Zhou China Department of Computer Science City University of Hong Kong Hong Kong

Ensuring control stability is essential for safety-critical embedded systems. Task redundancy is a commonly used method to improve the control performance of applications suffering an intermittent fault. The increasing number of replicas will improve control performance while raising difficulty for scheduling and mapping. Existing works aim at improving control performance by controller design, while very few of them consider fault tolerance and scheduling simultaneously. In this paper, we propose a co-design scheme FTSC to provide stable control performance, feasible mapping, and scheduling solutions. We provide a complete formulations to model the mapping, scheduling, fault-tolerant to optimize the control performance in a complete MILP problem. The task redundancy problem can either be solved in a heuristic algorithm in a fast way or be solved in a MILP-based approach with a better solution. A case study demonstrates that our scheme efficiently reduces the system control cost while meeting the real-time, reliability, and schedulability constraints. © 2022, The Authors. All rights reserved.

关键词： Scheduling

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COVID-19 Patient Care: A Content-Based Collaborative Filtering Using Intelligent Recommendation System 6th

COVID-19 Patient Care: A Content-Based Collaborative Filteri...

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6th EAI International Conference on science and Technologies for Smart Cities, SmartCity 2020

作者： Deebak, B.D. Al-Turjman, Fadi Schoool of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India Department of Artificial Intelligence Engineering Research Center for AI and IoT Near East University Nicosia Mersin 10 Turkey

ISBN: (纸本)9783030760625

COVID-19 is a more transferable illness caused by a new novel coronavirus. It is highly emerging with efficient biosensors such as sensitive and selective that afford the diagnostic tools to infer the disease early. It can maintain a personalized healthcare system to evaluate the growth of disease under proper patient care. To discover as a personalized technology, the healthcare system prefers collaborative filtering. It can effectively deal with cold-start and sparse-data to conduct useful extensions. Due to the continuous expansion of scaling data in a medical scenario, content-based, collaborative filtering, and similarity metrics are preferred. It relies on the most similar social users or threats when the information is large. Many neighbors gain importance to obtain a set of users with whom a target user is likely to match. Forming communities reveal vulnerable users and also reduce the challenges of collaborative filtering like data-sparsity and cold-start problems. Thus, this framework proposes content-based collaborative filtering using intelligent recommendation systems (CCF-IRS) based on high correlation and shortest neighbor in the social community. The result is shown that the proposed CCF-IRS achieves better accuracy than the existing algorithms. © 2021, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： COVID-19

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Clustering Dynamic Networks by Discriminating Roles of Vertices and Capturing Temporality with Subsequent Feature Projection

SSRN

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SSRN 2024年

作者： Ma, Yaxiong Gao, Yue Dou, Zengfa Huang, Guohua Ma, Xiaoke School of Computer Science and Technology Xidian University Shaanxi Xi’an710071 China School of Information Engineering Xian University Shaanxi Xi’an710065 China Schoool of Computer and Information Science Qinghai Institute of Technology Qinghai Xining810016 China School of Information Technology and Administration Hunan University of Finance and Economics Hunan Changsha410215 China

Clustering dynamic networks is becoming more and more popular since many complex systems in nature and society are dynamic which cannot be fully characterized by the traditional static ones. It is highly non-trivial in comparison to clustering static network since it requires simultaneously to balance clustering accuracy and clustering drift, where clustering accuracy measures how clustering reflects structure of graph at current time, and clustering drift quantifies how clustering smoothes historical snapshot(s). In this study, we propose an algorithm \underline{\textbf{c}}lustering \underline{\textbf{d}}ynamic \underline{\textbf{n}}etwork by \textbf{\underline{d}}iscriminating \textbf{\underline{r}}oles of vertices and \underline{\textbf{c}}apturing \underline{\textbf{t}}emporality with subsequent feature projection (\textbf{CDN-DRCT}). Specifically, clustering accuracy is achieved by factorizing high-order matrix of slice at current time, and vertices are divided into static and dynamic ones by the reconstruction errors. Finally, the proposed algorithm measures temporality of networks with a projection matrix, which connects subsequent features at the previous and current time, thereby enhancing clustering drift of clusters. In this case, temporality of dynamic networks is characterized from vertex and global level, providing a better way to balance clustering accuracy and clustering drift. Experimental results on 10 typical dynamic networks demonstrate the proposed algorithm is superior to baselines in terms of accuracy as well efficiency. © 2024, The Authors. All rights reserved.

关键词： Non-negative matrix factorization

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A Data-driven RUL Prediction Method Enhanced by Identified Degradation Model for Lithium-ion Battery of EVs

A Data-driven RUL Prediction Method Enhanced by Identified D...

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IEEE Energy Conversion Congress and Exposition

作者： Jun Peng Mingjian Wu Dianzhu Gao Xiaoyong Zhang Yijun Cheng Zhiyong Zheng Bin Chen Fu Jiang Zhiwu Huang School of Computer Science and Engineering Central South University Changsha China Schoool of Automation Central South University Changsha China

A reliable and high fidelity of the battery remaining useful life (RUL) prediction is dependent on a large amount of high qualified labeled data, which is a difficult and tedious work in practical implementation. To ease the training process and improve the prediction accuracy under limited labeled training data, this paper proposes a data-driven RUL prediction method enhanced by the identified mechanism degradation model. With the Frisch Scheme based Bias Compensating Recursive Least Squares (FBCRLS) algorithm, the battery mechanism aging model parameters are identified under limited labeled data. The model is then used to generate labeled data in a full life cycle, which is used to enhance the training of the Support Vector Regression (SVR) model. Test results reveal that the proposed method can effectively reduce the requirement of measured data and ensure high prediction accuracy even under very limited training data.

关键词： Batteries Data models Degradation Aging Training data Computational modeling Predictive models

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Vibration isolation system with a compact damping system for power recycling mirrors of KAGRA

arXiv

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arXiv 2019年

作者： Akiyama, Y. Akutsu, T. Ando, M. Arai, K. Arai, Y. Araki, S. Araya, A. Aritomi, N. Asada, H. Aso, Y. Bae, S. Baiotti, L. Barton, M.A. Cannon, K. Capocasa, E. Chen, C.-S. Chiu, T.-W. Cho, K. Chu, Y.-K. Craig, K. Dattilo, V. Doi, K. Enomoto, Y. Flaminio, R. Fujii, Y. Fujimoto, M.-K. Fukunaga, M. Fukushima, M. Furuhata, T. Haino, S. Hasegawa, K. Hashimoto, Y. Hashino, K. Hayama, K. Hirayama, T. Hirose, E. Hsieh, B.H. Huang, C.-Z. Ikenoue, B. Inoue, Y. Ioka, K. Itoh, Y. Izumi, K. Kaji, T. Kajita, T. Kakizaki, M. Kamiizumi, M. Kanbara, S. Kanda, N. Kanemura, S. Kang, G. Kasuya, J. Kawai, N. Kawasaki, T. Kim, C. Kim, W.S. Kim, J. Kim, J.C. Kimura, N. Kirii, S. Kitaoka, Y. Kitazawa, H. Kojima, Y. Kokeyama, K. Komori, K. Kong, A. Kotake, K. Kozu, R. Kumar, R. Kuo, H.-S. Kuroki, S. Kuroyanagi, S. Lee, H.K. Lee, H.M. Lee, H.W. Leonardi, M. Lin, C.-Y. Lin, F.-L. Liu, G.C. Marchio, M. Matsui, T. Michimura, Y. Mio, N. Miyakawa, O. Miyamoto, A. Miyoki, S. Morii, W. Morisaki, S. Moriwaki, Y. Musha, M. Nagano, S. Nagano, K. Nakamura, K. Nakamura, T. Nakano, H. Nakano, M. Narikawa, T. Quynh, L. Nguyen Ni, W.-T. Nishizawa, A. Obuchi, Y. Oh, J. Oh, S.H. Ohashi, M. Ohishi, N. Ohkawa, M. Okutomi, K. Ono, K. Oohara, K. Ooi, C.P. Pan, S.-S. Paoletti, F. Park, J. Passaquieti, R. Peña Arellano, F.E. Sago, N. Saito, S. Saito, Y. Sakai, K. Sakai, Y. Sasai, M. Sato, S. Sato, T. Sekiguchi, T. Sekiguchi, Y. Shibata, M. Shimoda, T. Shinkai, H. Shishido, T. Shoda, A. Someya, N. Somiya, K. Son, E.J. Suemasa, A. Suzuki, T. Suzuki, T. Tagoshi, H. Tahara, H. Takahashi, H. Takahashi, R. Takeda, H. Tanaka, H. Tanaka, K. Tanaka, T. Tanioka, S. San Martin, E.N. Tapia Tomaru, T. Tomura, T. Travasso, F. Tsubono, K. Tsuchida, S. Uchikata, N. Uchiyama, T. Uehara, T. Ueno, K. Uraguchi, F. Ushiba, T. van Putten, M.H.P.M. Vocca, H. Wakamatsu, T. Watanabe, Y. Xu, W.-R. Yamada, T. Yamamoto, K. Yamamoto, K. Yamamoto, S. Yamamoto, T. Yokogawa, K. Yokoyama, J. Yokozawa, T. Yoshioka, T. Yuzurihara, H. Zeidler, S. Zhu, Z.-H. Graduate School of Science and Engineering Hosei University TokyoKoganei184-8584 Japan National Astronomical Observatory of Japan TokyoMitaka181-8588 Japan Department of Physics University of Tokyo TokyoBunkyo113-0033 Japan University of Tokyo KashiwaChiba277-8582 Japan High Energy Accelerator Research Organization TsukubaIbaraki305-0801 Japan Earthquake Research Institute University of Tokyo TokyoBunkyo113-0032 Japan Department of Physics University of Tokyo TokyoBunkyo113-0033 Japan Department of Mathematics and Physics Hirosaki University AomoriHirosaki036-8561 Japan National Astronomical Observatory of Japan GifuHida506-1205 Japan Korea Institute of Science and Technology Information Yuseong Daejeon34141 Korea Republic of Department of Physics Osaka University ToyonakaOsaka560-0043 Japan University of Tokyo TokyoBunkyo113-0033 Japan Department of Physics National Taiwan Normal University Taipei116 Taiwan Department of Physics Sogang University Seoul121-742 Korea Republic of PisaCascinaI-56021 Italy Department of Physics University of Toyama ToyamaToyama930-8555 Japan Department of Astronomy University of Tokyo TokyoBunkyo113-0032 Japan Advanced Technology Center National Astronomical Observatory of Japan TokyoMitaka181-8588 Japan Institute of Physics Academia Sinica TaipeiNankang11529 Taiwan Department of Applied Physics Fukuoka University FukuokaJonan814-0180 Japan Yukawa Institute for Theoretical Physics Kyoto University KyotoSakyo606-8502 Japan Graduate School of Science Osaka City University OsakaSumiyosi558-8585 Japan Institute of Space and Astronautical Science Japan Aerospace Exploration Agency SagamiharaKanagawa252-5210 Japan University of Tokyo GifuHida506-1205 Japan Graduate Schoool of Science and Technology Tokyo Institute of Technology MeguroTokyo152-8551 Japan Department of Physics Ewha Womans University SeoulSeodaemun-gu03760 Korea Republic of National Institute for

A vibration isolation system called Type-Bp system used for power recycling mirrors has been developed for KAGRA, the interferometric gravitational-wave observatory in Japan. A suspension of the Type-Bp system passively isolates an optic from seismic vibration using three main pendulum stages equipped with two vertical vibration isolation systems. A compact reaction mass around each of the main stages allows for achieving sufficient damping performance with a simple feedback as well as vibration isolation ratio. Three Type-Bp systems were installed in KAGRA, and were proved to satisfy the requirements on the damping performance, and also on estimated residual displacement of the optics. Copyright © 2019, The Authors. All rights reserved.

关键词： Mirrors

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