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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Das, Supantha Hazra, Arnab Pati, Soumen Kumar Ghosh, Soumadip Mallik, Saurav Banerjee, Suharta Mukherji, Ayan Li, Aimin Zhao, Zhongming Academy of Technology Hooghly Department of Information Technology West Bengal India Future Institute of Technology Kolkata Department of Computer Science and Engineering West Bengal India Maulana Abul Kalam Azad University of Technology West Bengal Haringhata Department of Bioinformatics West Bengal Nadia India Sister Nivedita University Department of Computer Science and Engineering West BengalNew Town Kolkata India Harvard T H Chan School of Public Health Department of Environmental Health BostonMA United States Meghnad Saha of Institute of Technology Department of Computer Science and Engineering West Bengal Kolkata India Pailan College of Management & Technology Department of Computer Science and Engineering West Bengal Kolkata India Xi'an University of Technology School of Computer Science & Engineering Shaanxi Xi'an710048 China The University of Texas Health Science Center Center for Precision Health School of Biomedical Informatics HoustonTX77030 United States

ISBN: (纸本)9781665468190

Chronic kidney disease (CKD) is as severe as cancer in today s world. It may even lead to the permanent failure of kidney. The initial detection of this disease is needed for timely cure. Our work presents a classifier (named ANFIS) in accordance with the notion of neuro-fuzzy in order to detect the existence of chronic kidney disease. We use blood test results of several patients for our research study. We compare our proposed classifier with some conventional classifiers such as Multi-layer Perceptron, Support Vector Machine, Logistic Regression and Decision Tree. Experimental results indicates that our proposed neuro-fuzzy rule-based classifier performs better than the other classifiers used here. ANFIS has given 3% to 4% better accuracy compared to the other classifiers. © 2022 IEEE.

关键词： Decision trees

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The Calibration of Single Beam Distance Sensors based on Machine Learning Methods

The Calibration of Single Beam Distance Sensors based on Mac...

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IEEE International Conference on Big Data

作者： Piotr Biernacki Adam Ziebinski Jia-Hao Syu Jerry Chun-Wei Lin Institute of Theoretical and Applied Informatics Polish Academy of Sciences Institute of Informatics Silesian University of Technology Gliwice Poland Institute of Informatics Silesian University of Technology Gliwice Poland Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Smart cities require the use of many different types of sensors to make the communication, and distance sensors are one of the most commonly used elements in transportation systems and related infrastructures. The introduction of increasingly advanced autonomous systems in many areas of smart cities requires high measurement precision of the sensors used. High precision is essential for proper operation, long-term use, and safety in machine-to-machine or machine-to-human interactions. This paper presents a comparison of the accuracy of distance measurements for two commercially available single-beam LiDARs and two ultrasonic sensors. The aim of the research was to develop a calibration method in order to improve the accuracy of distance sensors. Based on the collected distance measurements, the sensors were calibrated using selected machine learning algorithms. The results of the experiments show the effectiveness of the proposed calibration methods, which yield an average mean absolute error (MAE) of 1.76 E-05 meters (m) and a root mean square error (RMSE) of 1.33 E-04 m for the tested sensors.

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Online Affinity of Instructing Methods for Using Personal-Efficacy and Reading Capacious During Covid-19 5th

Online Affinity of Instructing Methods for Using Personal-Ef...

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5th International Conference on Communication, Device and Networking, ICCDN 2021

作者： PremaLatha, V. Sreedevi, E. Sivakumar, S. Nayak, Soumya Ranjan Bhoi, Akash Kumar Ghosh, Uttam Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation K L University Guntur Vaddeswaram522502 India Department of Computer Applications Faculty of Science and Humanities SRM Institute of Science and Technology Kattankulathur603203 India Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India KIET Group of Institutions Delhi-NCR Ghaziabad201206 India Directorate of Research Sikkim Manipal University Sikkim Gangtok737102 India Department of Computer Science and Data Science Meharry School of Computational Sciences Nashville NashvilleTN United States

ISBN: (纸本)9789811920035

Now a days in EFL procedure of education the ability of reading became as significant belief and personal-efficacy reading as a basic understanding for students. By monitoring the acknowledged participates under the ballpark figure of large studying and methods of understanding, the impact of their observation is premeditated on reading of each one’s personal-efficacy. On a daily routine all these things are comparatively considered which are put into effect by teachers of handful in number. Approach towards exhibiting Extensive reading (ER) is inspected to be "more expensive, difficult, and time-consuming". Method of recognition of elements in a various way for effective impact in putting its efforts to utilize for its empowerment. Paper has been segregated into two contexts: Association with attitude is considered as primary one and attitude is considered as secondary one. Whether knowledge work is understood by student or not is considered as the impact of ER by the first review. Procedure which are convenient is taken as the observations of student and is analysed as second one. The examinations are quantifiable to utilize the observations as information in terms of subjective way taken from students who belong to first academic year of reading course in a systematic way and 603 details of undergraduate students from KLEF of Guntur were chosen as participants for extant examination. In ER programme of includes and excludes "comprehension reading work" is treated as fundamental in the proposal of disclosures. In case of any, "the programme appeared to positively affect contributing students". Techniques of classification like "decision tree and Mixed Model Database Miner (MMDBM)" are employed in this paper which leads to improvements of post-test to pre-test in ER group. Observations of students in ER results as optimistic and algorithm of MMDBM which leads to accuracy in higher rate in pre-test and post-test detection. © 2023, The Author(s), under exclusive lice

关键词： Students

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Exploration of the Role of Cryptoprocessors in Advancing IoT Security

Exploration of the Role of Cryptoprocessors in Advancing IoT...

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International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Elena Kakoulli Eleftherios Zacharioudakis Department of Computer Science Intelligent Systems Laboratory Neapolis University Pafos Pafos Cyprus Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Igor Sikorsky Kyiv Polytechnic Institute National Technical University of Ukraine Kyiv Ukraine

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

This paper presents a thorough exploration of Cryptoprocessors in the context of the Internet of Things (IoT). The focus is on hardware support, cryptographic processors, and energy-efficient designs to fortify the security of IoT ecosystems. Leveraging insights from recent research papers, the survey examines various aspects of Cryptoprocessor implementations, ranging from lightweight countermeasures to advanced lattice-based post-quantum cryptography. The proposed paper amalgamates findings from studies on hardware architectures, energy-efficient designs, and innovative cryptographic algorithms to provide a holistic overview of state-of-the-art advancements in IoT security. The synthesis of this survey’s exploration aims to contribute to the ongoing discourse on securing IoT devices through robust hardware-backed cryptographic solutions.

关键词： Surveys Industries Technological innovation Program processors Ecosystems Hardware Energy efficiency

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Sensor Based Smart Wearable Devices for Women: A Review

Sensor Based Smart Wearable Devices for Women: A Review

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Chinmaya Khadse Shital Hajare Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Natural organic menstrual cycles stay in large part taboo during many regions of India mainly rural areas hence threatening teenagers with useless menstrual hygiene requirements mixed with primary sanitation issues and network disapproval. The fitness problems and psychological misery get up from these subjects. The World Health Organization (WHO) establishes that inadequate menstrual hygiene ends in urinary tract infections and infections of the reproductive device. The importance of India's menstrual population now stands at 355 million girls and ladies which needs immediately reaction through powerful answers. This observe investigates how IA and IoT-driven smart wearable generation can increase menstrual health control (MHM) for teenage Indian women. A proposed low cost wearable tool collects basal body temperature (BBT) and heart charge variability (HRV) information and bodily activity information while retaining cultural appropriateness. AI chatbots and academic guide and actual-time comments capabilities exist inside this device. The system features complete menstrual health tracking competencies with the aid of using cycle prediction competencies and implements in-tool heating structures for ache discount together with strain reduction software for emotional aid. The system uses techniques that start from person desires even as imposing LSTM networks together with Random Forest Regression machines for forecasting menstrual cycles and symptom reviews. Validations run on the tool arise in practical conditions which observe both its performance accuracy along person pleasure quotes and reliability through the years. The observe shows wearable era has the capacity to revolutionize menstruation take care of Indian teens via presenting them with a powerful approach to address their public health needs.

关键词： Temperature measurement Heating systems Reviews Internet of Things Biomedical monitoring Wearable devices Heart rate variability Random forests Long short term memory Faces

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Implementation of Machine Learning and Ensemble Learning Techniques to Proactively Predict Students' academic Success in the Realm of Higher Education

Implementation of Machine Learning and Ensemble Learning Tec...

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Technological Advancements in Computational Sciences (ICTACS), International Conference on

作者： Taneja Sanjay Devkishan Sanjay Kumar Singh Ajay Kumar Bharti Amity Institute of Information Technology Amity University Uttar Pradesh Lucknow India Department of Computer Science and Engineering United College of Engineering and Research Prayagraj India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Predicting and analyzing student performance is a huge boon for both students and teachers. A number of analytical methodologies have been utilized in recent studies to forecast students' success; the CGPA is the end product of these analyses, which have been conducted for both the internal assessment as well as for the external assessment. Few well known machine learning algorithms have been suggested. Ensemble Learning along with Decision Trees, Support Vector Machine and Random Forest -obtained perfect 100s for Precision Value, Recall Value, as well as in F1 Scores in the evaluation of performances. Naive Bayes has worse performance in comparison, with F1 Scores of 66, Recall Value of 65, and Precision of 68. When contrasted with decision tree models, Support Vector Machines, Multi-Layer Perceptron, Ensemble Enabled Learning, and Random Forest, Naive Bayes, AdaBoost, and K-Nearest Neighbors all perform worse in terms of accuracy. The Naive Bayes classifier models the input distributions across classes and is very good in text categorization because it is a generative method. Since it fails to identify critical features for class differentiation, it cannot be distinguished from discriminative classifiers.

关键词： Support vector machines Accuracy Text categorization Supervised learning Predictive models Nearest neighbor methods Nonhomogeneous media Decision trees Ensemble learning Random forests

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Integrating Simulation and Optimization: A Case Study in Pamplona for Self-Collection Delivery Points Network Design 35

Integrating Simulation and Optimization: A Case Study in Pam...

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35th European Modeling and Simulation Symposium, EMSS 2023

作者： Izco, Irene Serrano-Hernandez, Adrian Sawik, Bartosz Faulin, Javier GILT-OR Group Institute of Smart Cities Dept. Statitistics Computer Science and Mathematics Public University of Navarra Campus Arrosadia s/n Pamplona31006 Spain Department of Business Informatics and Engineering Management AGH University of Science and Technology Mickiewicza 30 Kraków30059 Poland

ISBN: (纸本)9788885741881

The disruptions experienced by the processes in the last mile delivery during the SARS-CoV-2 pandemic raised the dilemma of up-to-date last mile approaches for Urban Logistics (UL) issues. Self-Collection Delivery Systems (SCDS) have been proved to be an improvement for all the players of the SC, providing flexibility of time-windows and reducing overall mileage, delivery time and, consequently, gas emissions. Differing from previous works involving hybrid modeling for automated parcel lockers (APL) network design, this paper brings a System Dynamics Simulation Model (SDSM) to forecast online shopping demand in the Spanish city of Pamplona. A bi-criteria Facility Location Problem (FLP) is solved by means of an e-constraint method, where e is defined as the level of coverage of the total demand. The experiment run considers 90% of demand coverage, in order to obtain the most complex network possible. The simulation and demand forecast was carried out using Anylogic simulation software and the optimization procedure makes use of the Java-based CPLEX API solver. © 2023 The Authors.

关键词： Complex networks

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Access Control Based on Self-Sovereign Identity 16

Access Control Based on Self-Sovereign Identity

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16th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2024

作者： Nita, Stefania Loredana Mihailescu, Marius Iulian Marascu, Valentina Rogobete, Marius Faculty of Information Systems and Cyber Security Military Technical Academy 'Ferdinand i' Department of Computers and Cybersecurity Bucharest Romania 'Spiru Haret' University Scientific Research Center in Mathematics and Informatics Faculty of Engineering and Computer Science Bucharest Romania National Institute for Laser Plasma and Radiation Physics Low Temperature Plasma Department Bucharest Romania Telematics Harman International Bucharest Romania

ISBN: (纸本)9798350371154

In the rapidly growing interest in digital identity management, there is an important shift toward adopting verifiable credentials, often based on blockchain technology. This transition highlights a growing interest in the concept of Self-Sovereign Identity (SSI), a paradigm that empowers individuals with control over their own digital identities through the use of verifiable credentials. This paper introduces a novel access control framework that leverages the principles of SelfSovereign Identity to significantly enhance the security of system access based on the users' credentials. By integrating the decentralized and user-centric nature of SSI, our proposed framework aims to address existing vulnerabilities in traditional access control systems. It provides a robust, scalable, and controlled mechanism that ensures higher levels of trust and security in digital interactions. This approach aligns with the broader digital identity trends favoring transparency, user autonomy, and trust. © 2024 IEEE.

关键词： Electronic document identification systems

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Contactless Respiratory Rate Monitoring Using FMCW Radar

Contactless Respiratory Rate Monitoring Using FMCW Radar

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2022 International Symposium on Electronics and Smart Devices, ISESD 2022

作者： Widiyasari, Diyah Ahmadi, Nur Adiono, Trio Purwarianti, Ayu Purnomo, Ariana Tulus Lin, Ding-Bing Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung40132 Indonesia Bandung40132 Indonesia National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei Taiwan Sampoerna University Departement of Information System Jakarta Selatan12780 Indonesia

ISBN: (数字)9798350396607

ISBN: (纸本)9798350396607

Vital sign monitoring plays a very important role in the early diagnosis and prognosis of a disease, as well as for patient health management. Among other vital signs, respiratory rate (RR) is rarely recorded and often forgotten despite the fact that it is an essential predictor of serious events such as cardiac arrest and deteriorating clinical condition. In this paper, we propose a frequency-modulated continuous-wave (FMCW) radar for contactless RR monitoring. We employ FFT to calculate RR in frequency-domain and combine it with Kalman filter to smooth out the estimated RR. We use a public dataset measuring RR from 50 children under 13 years of age recorded with 60 GHz FMCW radar. The experimental results show that the proposed method yields MAE and RMSE of 3.20 and 4.10, respectively, with respect to the reference device. Incorporating Kalman filter reduces the percentage of error by 14.83% and 13.36% compared to without it. The overall results suggest that radar-based technology can be potentially used for contactless RR monitoring which is suitable for those who cannot use contact-based monitoring devices. © 2022 IEEE.

关键词： Kalman filters

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Urgent Edge Computing

arXiv

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arXiv 2024年

作者： Dazzi, Patrizio Ferrucci, Luca Danelutto, Marco Tserpes, Konstantinos Makris, Antonis Theodoropoulos, Theodoros Massa, Jacopo Carlini, Emanuele Mordacchini, Matteo Department of Computer Science University of Pisa PI Pisa Italy Department of Informatics and Telematics Harokopio University of Athens School of Electrical and Computer Engineering National Technical University of Athens Attika Athens Greece Department of Computer Science University of Pisa Institute of Information Science and Technologies National Research Council of Italy PI Pisa Italy Institute of Information Science and Technologies National Research Council of Italy PI Pisa Italy Institute of Informatics and Telematics National Research Council of Italy PI Pisa Italy

This position paper introduces Urgent Edge Computing (UEC) as a paradigm shift addressing the evolving demands of time-sensitive applications in distributed edge environments, in time-critical scenarios. With a focus on ultra-low latency, availability, resource management, decentralization, self-organization, and robust security, UEC aims to facilitate operations in critical scenarios such as disaster response, environmental monitoring, and smart city management. This paper outlines and discusses the key requirements, challenges, and enablers along with a conceptual architecture. The paper also outlines the potential applications of Urgent Edge Computing. © 2024, CC BY-NC-ND.

关键词： Edge computing

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