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arXiv 2023年

作者： Tolotti, Emiliano Zardini, Enrico Blanzieri, Enrico Pastorello, Davide Department of Information Engineering and Computer Science University of Trento via Sommarive 9 Povo Trento38123 Italy Trento Institute for Fundamental Physics and Applications via Sommarive 14 Povo Trento38123 Italy Alma Mater Studiorum - Università di Bologna Piazza Porta San Donato 5 Bologna40126 Italy

In the current era, known as Noisy Intermediate-Scale Quantum (NISQ), encoding large amounts of data in the quantum devices is challenging and the impact of noise significantly affects the quality of the obtained results. A viable approach for the execution of quantum classification algorithms is the introduction of a well-known machine learning paradigm, namely, the ensemble methods. Indeed, the ensembles combine multiple internal classifiers, which are characterized by compact sizes due to the smaller data subsets used for training, to achieve more accurate and robust prediction performance. In this way, it is possible to reduce the qubits requirements with respect to a single larger classifier while achieving comparable or improved performance. In this work, we present an implementation and an extensive empirical evaluation of ensembles of quantum classifiers for binary classification, with the purpose of providing insights into their effectiveness, limitations, and potential for enhancing the performance of basic quantum models. In particular, three classical ensemble methods and three quantum classifiers have been taken into account here. Hence, the scheme that has been implemented (in Python) has a hybrid nature. The results (obtained on real-world datasets) have shown an accuracy advantage for the ensemble techniques with respect to the single quantum classifiers, and also an improvement in robustness. In fact, the ensembles have turned out to be able to mitigate both unsuitable data normalizations and repeated measurement inaccuracies, making quantum classifiers more stable. © 2023, CC BY.

关键词： Machine learning

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Expression of Concern for: A Formal Assessment of Spotting Leaf Syndromes with ConvNets

Expression of Concern for: A Formal Assessment of Spotting L...

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Emerging Technology (INCET), International Conference for

作者： Rehna Baby Joseph M.B Lakshmi Salini Suresh V Suneetha R Sunder M Rajeswari Department of CSE Sahrdaya College of Engineering and Technology Kerala India Dept. of Computer Applications Dayanandasagar College of Arts Science and Commerce Bangalore India

ISBN: (数字)9781728170299

ISBN: (纸本)9781728170305

Leaves are part of plants that interface with the climate and deal with their fundamental requirements. In our lives, healthy plants are important and used for many purposes throughout life. Plant diseases represent a significant environmental concern. A big hurdle is the detection of leaf diseases in large fields. Farmers face great problems in handling different types of leaf diseases. Plant biologist assist cultivators to recognize leaf diseases through agricultural labs or by observing visual features. These approaches cannot be suitable for all cultivators due to the cost of experts and the lack of availability of labs. High performance rate of Convolutional neural networks plays a significant role in detecting leaf diseases. This survey demonstrates various Convnets to assorts leaf syndromes. It also performs an important role in information collection to improve accuracy.

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ARIMA and SPSS statistics based assessment of landslide occurrence in western Himalayas

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Environmental Challenges 2022年 9卷

作者： Fayaz, Mohsin Meraj, Gowhar Khader, Sheik Abdul Farooq, Majid Department of Computer Applications BS. Abdur Rahman Crescent Institute of Science and Technology Vandalur Chennai 600048 India Department of Ecology Environment & Remote Sensing Government of Jammu & Kashmir SDA Colony Bemina Srinagar India

The Jammu-Srinagar National Highway is the critical road connection between Kashmir valley and the rest of India. It passes through extremely steep slopes and high mountains prone to mass movements, particularly landslides and rockslides. Most mountainous roads are constructed on fragile and rocky slopes, and any natural (e.g., precipitation) or human-triggered disturbance (e.g., heavy traffic) can cause a fatal and devastating landslide under the influence of gravity. Many landslide-prone sites along the Highway are responsible for the continuous blockade almost throughout the year but peaking particularly during winters. As a result, it has a high toll on the state's economy and is responsible for many human casualties yearly. The present study aims to characterize various factors and their threshold values responsible for triggering a landslide. Through extensive field surveys, we evaluated different geotechnical parameters of soils at the most landslide-prone site along the Highway and augmented it with the satellite remote sensing datasets to determine the threshold values that trigger a landslide and assess the probability of occurrence of landslide events in the future using Autoregressive Moving Average (ARIMA) model and IBM SPSS Forecasting Model. This work shall help devise countermeasures for managing the landslides in the study area locally and shall serve as the guiding framework for using artificial intelligence and machine learning techniques for hazard management in the Himalayas. © 2022 The Authors

关键词： Disasters Landslide Remote sensing Statistical analysis

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Correction to: Next-generation cyber attack prediction for IoT systems: leveraging multi-class SVM and optimized CHAID decision tree

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Journal of Cloud Computing 2023年第1期12卷 1-1页

作者： Dalal, Surjeet Lilhore, Umesh Kumar Faujdar, Neetu Simaiya, Sarita Ayadi, Manel Almujally, Nouf A. Ksibi, Amel Department of Computer Science and Engineering Amity University Haryana Gurugram India Department of Computer Science and Engineering Chandigarh University Mohali India Department of Computer Engineering and Applications GLA University Mathura India Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia

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Research on key molecules of cold and hot properties chinese herbal medicine based on improved atomic association rules algorithm and hierarchical clustering algorithm 2020

Research on key molecules of cold and hot properties chinese...

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3rd International Conference on Data science and Information Technology, DSIT 2020

作者： Xie, Fenfen Wang, Lu Zhang, Shoude Zhang, Lei Wang, Xiaoying Department of Computer Technology and Applications Qinghai University Xining Qinghai China State Key Laboratory of Plateau Ecology and Agriculture Qinghai University Xining Qinghai China College of Computer Science Information Management Center Sichuan University Chengdu Sichuan China

ISBN: (纸本)9781450376044

The theory of Chinese Herbal Medicine (CHM) properties is the basis to guide the use of CHM. However, this theory is based on the long-term practice experience of Chinese ancient healers, which is constantly challenged due to the lack of support from modern scientific theories. This study used data mining technology to analyze the molecular characteristics of cold and hot properties based on the molecular data set of CHM. Firstly, we put forward an improved atomic association rules algorithm to screen key molecules related to cold and hot properties. Secondly, the hierarchical clustering algorithm was used to analyze the key molecules. The experimental results showed that the key molecules related to the cold property of CHM were mainly polycyclic compounds, and the key molecules related to the hot property of CHM were mainly monocyclic or acyclic compounds. © 2020 ACM.

关键词： Data mining

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Cutting-Edge Machine Learning Algorithms for In-Depth Characterization and Efficiency Improvement of Phase Change Heat Transfer Mechanisms

Cutting-Edge Machine Learning Algorithms for In-Depth Charac...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Geetanjli Khambra K Praveena Manjunatha Amit Dutt Irfan Khan Ali Ashoor Issa Department of Computer Applications The Bhopal School of Social Sciences (BSSS) College Bhopal M.P India Institute of Aeronautical Engineering Dundigal Hyderabad Department of Mechanical Engineering New Horizon College of Engineering Bangalore Lovely Professional University Phagwara Knowledge Park II Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh College of Technical Engineering National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

This study introduces a sophisticated and holistic methodology for the enhanced understanding and optimization of phase change heat transfer mechanisms. The proposed framework integrates Deep Learning Neural Networks (DLNN), Random Forest (RF), Long Short-Term Memory (LSTM) Networks, Gaussian Processes (GP), and Reinforcement Learning (RL) in a synergistic manner, emphasizing the iterative and adaptive nature of each algorithm. A detailed flowchart for each algorithm delineates the process, showcasing their roles in refining predictions and optimizing models. An ablation study underscores the crucial contribution of each component, revealing their collective efficacy. Comparative analyses against existing algorithms highlight the proposed method’s consistent superiority in accuracy, precision, recall, mean squared error, and Rsquared. Resource efficiency metrics further affirm its computational effectiveness. Visualizations provide a comprehensive and intuitive representation of the methodology’s strengths. The proposed framework offers a promising avenue for characterizing and optimizing phase change heat transfer, contributing to advancements in thermal sciences and real-world applications.

关键词： Radio frequency Visualization Thermal expansion Machine learning algorithms Accuracy Neural networks Prediction algorithms

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Developing a Robotic Solar-Powered Fire Detection and Avoidance System With Machine Learning-Based Sensing

Developing a Robotic Solar-Powered Fire Detection and Avoida...

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Communication, Security and Artificial Intelligence (ICCSAI), International Conference on

作者： Preeti Verma Arun Gangwar Bharat Singh Shubhendra Pratap Singh Pooja Vats Reddy Ganesh School of Electronic and Electrical Engineering Dev Bhoomi Uttarakhand University Uttarakhand Department of Electrical Engineering Invertis University Bareilly Uttar Pradesh School of COMPUTER SCIENCE AND APPLICATIONS IIMT University Meerut Department of EE TMU Moradabad India Department of Mathematics School of Basic & Applied Sciences K. R. Mangalam University Gurugram Haryana India Engineering Department UTAS-IBRI Sultanate of Oman

This research addresses the challenge of ensuring a consistent energy supply for forest fire surveillance systems, which is critical in the field of computer science, AI, ML, Data Mining, NLP, computer Vision, and Robotics. Traditional solar-powered solutions rely on fixed-mounted rooftop panels, which are often inadequate due to their limited electricity production capacity, especially under variable weather conditions. This study introduces an innovative, all-weather, autonomous solar tracking system that adapts to the changing position of the sun throughout the seasons, ensuring optimal energy collection for the surveillance equipment. To properly navigate the sun motion, the cautioned gadget merges sun's trajectory monitoring modalities with pv sensing. It begins via determining the season with a sensor that measures the volume of sunshine. The sun function detector & a customized PV itinerary algorithms then estimate the solar's course. The gadget makes use of specific tracking algorithms for sunny, gloomy, and moist instances, using artificial intelligence algorithms to forecast versions in the weather & adjust the panels as it should be. The approach has been proved in simulated experiments to be able to mitigating the influence of versions in the climate at the same time as keeping a steady and specific alignment facing the sun in all climates. The technique will increase the ability of photovoltaic cells utilized for wooded area fire tracking to produce electricity, improving the dependability and effectiveness of such vital device. This precise use of AI and gadget getting to know in environmental surveillance represents a step towards stronger and longer-lasting fire caution systems, which might be important for woodland preservation and management.

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Toeplitz Determinant whose Its Entries are the Coefficients for Class of Non-Bazilevic Functions 1

Toeplitz Determinant whose Its Entries are the Coefficients ...

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1st International Conference on Pure science, ISCPS 2020

作者： Al-Khafaji, Saba N. Al-Fayadh, Ali Hussain, Ahmed Hadi Abbas, Sameer Annon Department of Mathematics Faculty of Computer Science and Mathematics University of Kufa Iraq Department of Mathematics and Computer Applications Al-Nahrain University-Iraq Iraq University of Babylon College of Engineering Al-musayab Department of Energy Engineering Iraq Iraqi Ministry of Education Directorate of Education of the Governorate Babylon Iraq

The famous Toeplitz matrix is a matrix in which each descending diagonals form left to right is constant, this mean Mathematician, engineers, and physicists are interested into this matrix for their computational properties and appearances in various areas: C ∗-dynamical systems [1], dynamical systems [6], operator algebra [2], Pseudospectrum and signal processing [10]. The object of this research is to define a new class Non-Bazilevic functions N δ in unit disk related to exponential function. As well as, we obtained coefficient estimates and an upper bound for the second and third determinant of the Toeplitz matrix such that the entries these matrix are belong to this class. © Published under licence by IOP Publishing Ltd.

关键词： Exponential functions

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Patient health monitoring system using smart IoT devices for medical emergency services

Patient health monitoring system using smart IoT devices for...

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2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Sreelakshmy, R. Sruthy, R. Rajeshwari, R. Thyla, B. Suresh Kumar, M. Veltech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai Avadi600062 India NSS Polytechnic College Department of Electronics & Communication Engineering PandalamPandalamPandalam689501 India S.A. Engineering College Department of Master of Computer Applications Thiruverkadu Chennai600077 India KCG College of Technology Department of Electronics and Communication Engineering KCG College Rd Tamil Nadu Karapakkam Chennai600097 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai Thandalam602105 India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

When devices, applications, monitors, and network services are connected together, the Internet of Things (IoT) is created, which allows these organizations to collect and share data more efficiently. The aware of the status of a person via the testing of numerous metrics, as well as the inference of a positive outcome from the past of this kind of continuous supervision, distinguishes the Internet of Things inside the medical system. A difficult endeavor, the prognosis of cardiovascular disease survival is important in assisting medical practitioners in making the best judgments possible for their individuals. Patients with heart failure need the knowledge and skill of health doctors to be properly cared for. The use of machine learning techniques may aid in the comprehension of the signs of cardiovascular disease. Hand - crafted feature development, on the other hand, is difficult and necessitates the use of specialized knowledge to determine the most suited approach. This research presents a smart health monitoring structure that makes use of the Internet of Things and cloud mechanism to enhance the mortality forecast of patients with chronic heart failure with no need for human classifier, as previously done. In addition, the smart IoT-based infrastructure analyzes individuals just on based entirely information and offers cardiac rehabilitation with fast, efficient, and high-quality medical care. Additionally, the suggested model analyses whether deep learning models are effective in distinguishing between heart failure patients who are alive and those who are died. The framework makes use of Internet of Things-based sensors to collect signals and transmit them to a cloud web application for analysis. Learning algorithms are used to further analyse these data in order to identify the condition of the patients. Health information and process monitoring are communicated with a medical expert who will respond to the patient in the event that emergency assistance is

关键词： Internet of things

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The System Adoption Evaluation of RFID Safety Management System on Campus

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International Journal of Network Security 2022年第1期24卷 176-180页

作者： Hsieh, Yu-Ying Chang, Liang-Hsu Liao, Anthony Y. H. Yang, Cheng-Ying Hwang, Min-Shiang Department of Computer Science and Information Engineering Asia University No. 500 Lioufeng Rd. Taichung41354 Taiwan Department of Management Information Systems National Chung Hsing University Taichung403 Taiwan Department of M-Commerce and Multimedia Applications Asia University Taipei10048 Taiwan Department of Computer Science University of Taipei Taipei10048 Taiwan Department of Medical Research China Medical University Hospital China Medical University No. 91 Xueshi Rd. Taichung40402 Taiwan

In this article, we propose a campus safety management system based on radio frequency identification (RFID) technology. In this work, the evaluation based on the system adoption was used to establish a campus-safety management system model and related implementation issues. With the implementation of the RFID system for campus safety management, the system could save the administrators’ time and improve the efficiency of service quality. In addition, it helps to prevent the occurrence of security incidents on campus. The critical success factors of the system include the participation of students and parents. Finally, the evaluation on the system adoption is made by questioners. The results show the first three factors affecting the administrators in the school to decide whether the system is used the convenience of use, agreement of students’ parents, and the relative advantage. However, school competition and school reorganization could not be the preferred factors to adopt the system. © 2022. All Rights Reserved.

关键词： Radio frequency identification (RFID)

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