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7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Thakur, Narina Jain, Rachna Mahajan, Ashee Islam, Sardar M. N. Bhagwan Parshuram Institute of Technology Department of Computer Science and Engineering New Delhi India Bhagwan Parshuram Institute of Technology Department of Information Technology New Delhi India Bharati Viydapeeth's College of Engineering Department of Electronics and Communication Engineering New Delhi India Victoria University Footscray Australia

ISBN: (纸本)9781665421683

Airbnb is an online platform that has a remarkable performance of helping rent a local home to tourists for a short period of time. While the ever-changing nature of the tourism industry necessitates an interminable upgradation of the state-of-the-art algorithms such as XGBoost, Support Vector Regression and Regression Tree used to predict rental pricing for Airbnb listings, a need to change and upgrade persists. The homeowners are still in the dilemma of the factors that help in pricing their rental home to attract consumers. Even the customers face a challenge to properly access a rental property's worth. With the highly correlated, complex, and incomplete data that the past researchers have worked on, understanding the limitations and overcoming them has been a difficult endeavor. The objective of this paper is to conquer the challenges by extensively understanding the factors that affect the nightly charges while presenting a solution to price property with a three-layered deep neural network with two outcomes that has an accuracy of 74.43%. These outputs represent the minimum and the maximum amount that a renter could charge based on the information he provided about his property. The advantage of such a system is the ability of the user to dynamically change price based on various considerations like increase in demand, peak season, and improvement of locality and the house itself. This research also presents the top factors which one must include in their renting place to increase their profits. © 2022 IEEE.

关键词： Deep neural networks

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Factory Floor Transformation for Achieving Operational Excellence with IoT and Machine Learning

Factory Floor Transformation for Achieving Operational Excel...

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Advances in Modern Age Technologies for Health and engineering Science (AMATHE), International Conference on

作者： T. J. Nagalakshmi P. Santhuja J. Rangarajan S. Muthukumarasamy N. Mohankumar S. Velmurugan Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science and Engineering CMR Technical Campus Hyderabad Telangana India Department oj Etectronics and Communication Engineering Muthayammal Engineering College (Autonomous) Rasipuram Tamil Nadu India Department of Computer Science and Engineering S.A.Engineering College Chennai Tamil Nadu India Symbiosis Institute of Technology Symbiosis International (Deemed University) Nagpur Maharashtra India Department of Electronics and Communication Engineering T.J.S. Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

In today's competitive industry, manufacturers seek novel approaches to improve operations and enhance efficiency. To achieve Operational Excellence, this abstract provides a Factory Floor Transformation plan that uses the Internet of Things (IoT), Machine Learning, and the Random Forest algorithm. The Factory Floor Transformation employs IoT sensors and devices throughout production. These sensors monitor machine operation, product quality, and energy usage in real-time. Using data preprocessing, feature engineering, and predictive modeling, a strong Machine learning framework is created. The Random Forest algorithm, which handles complicated, multi-dimensional data, powers Machine Learning. Random Forest is appropriate for manufacturing complex problems because its ensemble learning technique yields reliable and accurate forecasts. Predictive maintenance, quality control, and resource optimization are enabled by the model's previous training and real-time updates. Factory Floor Transformation provides enterprises with a complete Operational Excellence solution. Companies may minimize production bottlenecks, downtime, defects, and resource allocation by using IoT data and Machine Learning. This abstract illustrates how this method may transform production and make it more efficient and competitive.

关键词： Industries Machine learning algorithms Machine learning Quality control Prediction algorithms Production facilities Sensors

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Computation Offloading in Air-Ground Integrated Vehicular Edge Computing Networks

Computation Offloading in Air-Ground Integrated Vehicular Ed...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Li, Shichao Chen, Hongbin Tan, Fangqing Zhang, Ning Lin, Siyu S. Quek, Tony Q. School of Information and Communication Guilin University of Electronic Technology Guilin541004 China University of Windsor Department of Electrical and Computer Engineering WindsorONN9B3P4 Canada School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore

ISBN: (纸本)9798350370218

The integration of vehicle edge computing (VEC) and air-ground integrated network is considered as a key technology to achieve autonomous driving, where the tasks can be offloaded to the high altitude platform (HAP) through an unmanned aerial vehicle (UAV) as a relay. In this paper, we investigate a joint access equipment selection and resource allocation problem, with the objective of minimizing the total task offloading delay in the air-ground integrated VEC network. Considering the formulated problem is a non-convex problem, we decompose it into three subproblems: access equipment selection, joint bandwidth and computation resource allocation, and UAV trajectory design. Considering the first subproblem is an integer programming problem, the many-to-one matching method is utilized to solve it. Meanwhile, we utilize the CVX package and successive convex approximation (SCA) method to solve the last two subproblems, respectively. Considering the matching externality, we utilize coalition game method to deal with it. Based on the solutions of the three subproblems, the joint computation offloading and resource allocation (JCORA) algorithm has been proposed. Simulation results show that the proposed JCORA algorithm can reduce the total task offloading delay compared with other algorithms. © 2023 IEEE.

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Novel fabrication techniques for ultra-thin silicon based flexible electronics

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International Journal of Extreme Manufacturing 2024年第4期6卷 116-149页

作者： Ju Young Lee Jeong Eun Ju Chanwoo Lee Sang Min Won Ki Jun Yu School of Electrical and Electronic Engineering Yonsei University50 Yonsei-roSeodaemunguSeoul 03722Republic of Korea Department of Electrical and Computer Engineering Sungkyunkwan UniversitySuwon 16419Republic of Korea YU-Korea Institute of Science and Technology(KIST)Institute Yonsei University50Yonsei-roSeodaemun-guSeoul 03722Republic of Korea

Flexible electronics offer a multitude of advantages,such as flexibility,lightweight property,portability,and high *** unique properties allow for seamless applications to curved and soft surfaces,leading to extensive utilization across a wide range of fields in consumer *** applications,for example,span integrated circuits,solar cells,batteries,wearable devices,bio-implants,soft robotics,and biomimetic ***,flexible electronic devices have been developed using a variety of materials such as organic,carbon-based,and inorganic semiconducting ***(Si)owing to its mature fabrication process,excellent electrical,optical,thermal properties,and cost efficiency,remains a compelling material choice for flexible ***,the research on ultra-thin Si in the context of flexible electronics is studied rigorously *** thinning of Si is crucially important for flexible electronics as it reduces its bending stiffness and the resultant bending strain,thereby enhancing flexibility while preserving its exceptional *** review provides a comprehensive overview of the recent efforts in the fabrication techniques for forming ultra-thin Si using top-down and bottom-up approaches and explores their utilization in flexible electronics and their applications.

关键词： flexible electronics silicon fabrication technique top-down approach bottom-up approach

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Dynamic Energy Efficient Resource Allocation for Massive MIMO Networks Using Randomized Ensembled Double Q-learning Algorithm

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IEEE Transactions on Cognitive communications and Networking 2024年

作者： Liu, Zhikai Garg, Navneet Ratnarajah, Tharmalingam University of Edinburgh Institute for Data Image and Communications School of Engineering United Kingdom Lnm Institute of Information Technology Department of Electronics and Communication Engineering Jaipur302004 India San Diego State University Electrical and Computer Engineering College of Engineering San DiegoCA United States

This paper tackles the challenge of power consumption in the massive multiple-input multiple-output (mMIMO) base station (BS), where continuous operation of all antennas generates significant heat within a limited physical area. We propose a strategic power control scheme to enhance energy efficiency and mitigate thermal impact. Our approach introduces a time-slotted model incorporating dynamic, time-varying user quality of service (QoS) requirements. We examine energy efficiency under various conditions, presenting discrete and analog power allocation methods for both hybrid and fully digital precoding, with consideration of hardware impairments (HWI).We frame the energy efficiency optimization as dynamic Markov decision process (MDP) problems, constrained by total power, per-antenna power, and dynamic QoS requirements. The randomized ensembled double Q-learning (REDQ) algorithm is utilized with an action coding scheme to reduce computational complexity. By comparing existing reinforcement learning algorithms and evaluating our proposed power allocation schemes across diverse scenarios, simulations demonstrate that our approach improves energy efficiency effectively under varying operational conditions, showcasing its potential as a robust solution for adaptive resource allocation in mMIMO systems. © 2015 IEEE.

关键词： Markov processes

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Critical review on water quality analysis using IoT and machine learning models

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International Journal of Information Management Data Insights 2024年第1期4卷

作者： Jayaraman, Poornima Nagarajan, Kothalam Krishnan Partheeban, Pachaivannan Krishnamurthy, Vallidevi Department of Electronics and Communication Engineering Chennai Institute of Technology India Department of Electrical and Electronics Engineering SSN College of Engineering India Department of Civil Engineering Chennai Institute of Technology India Department of Computer Science and Engineering SSN College of Engineering India

Water quality and its management are the most precise concerns confronting humanity globally. This article evaluates the various sensors used for water quality monitoring and focuses on the water quality index considering the multiple physical, chemical, and biological parameters. A Review of Internet of Things (IoT) research for water quality monitoring and analysis, sensors used for water quality can help remote monitoring of the water quality parameters using various IoT-based sensors that convey the assembled estimations utilizing Low-Power Wide Area Network innovations. Overall, the IoT system was 95 % accurate in measuring pH, Turbidity, TDS, and Temperature, while the traditional method was only 85 % accurate. Also, this study reviewed the different A.I. techniques used to assess water quality, including conventional machine learning techniques, Support Vector Machines, Deep Neural Networks, and K-nearest neighbors. Compared to traditional methods, machine learning and deep learning can significantly increase the accuracy of measurements of groundwater quality. However, various variables, such as the caliber of the training data, the water quality metrics' complexity, and the monitoring frequency, will affect the accuracy. The geographical information system (GIS) is used for spatial data analysis and managing water resources. The quality of its data is also reviewed in the paper. Based on these analyses, the study has forecasted the future sensors, Geospatial Technology, and machine learning techniques for water quality monitoring and analysis. © 2023

关键词： and Integrated Technology GIS Internet of Things Sensor technology Water quality indices

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Enhanced security and efficiency in flying ad-hoc networks through blockchain-enabled clustering routing and optimized rotation-invariant coordinate convolutional neural networks

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Expert Systems with Applications 2025年 287卷

作者： Fareena, N. Banu, Ezas Afreen Sivakumar, Jothilingam Chaudhary, Sumit Department of Computer Science and Engineering Anna University Regional Campus Tamil Nadu Coimbatore India Department of Computer Engineering Shah & Anchor Kutchhi Engineering College Maharashtra Mumbai400088 India Department of Electronics and Communication Engineering St. Joseph's College of Engineering Chennai Tamil Nadu India Department of Computer Science & Engineering Uttaranchal Institute of Technology Uttaranchal University Uttarakhand Dehradun India

Flying Ad hoc Network (FANET) has attracted major attention owing to its fast development and prevalent use in presentations. Nevertheless, the features of FANET, such as extreme mobility, limited resources, scattered nature have created new problem in developing a safe and effectual routeing system for FANET. To address these issues, an Enhanced Security and Efficiency in Flying Ad-Hoc Networks through Blockchain-Enabled Clustering Routing and Optimized Rotation-Invariant Coordinate Convolutional Neural Networks (RICCNN-BC-SCR-FANET) is proposed in this paper. Here, the data is taken from UNSW-NB15 dataset. First, the input data is cluster head selection using the Portia Spider Optimization Algorithm (PSOA).After blockchain method Green-PoW Consensus Mechanism (GPoW) is used for secure the data. Rotation-Invariant Coordinate Convolutional Neural Network (RICCNN) is used for classifying attacks, like Fuzzers, Analysis, Backdoor, Exploits and DoS. Generally, RICCNN does not adopt any optimization technique to determine the optimum parameter to ensure the detection and classification task. Hence, Enhanced Dung Beetle Optimization Algorithm (EDBOA) is used to optimize the RICCNN. The proposed RICCNN-BC-SCR-FANET is simulated in MATLAB. The metrics, like throughput, network life time, packet delivery ratio, end-to-end delay, packet loss ratio accuracy, precision, recall, RoC is examined. The performance of the proposed RICCNN-BC-SCR-FANET achieves 29.24 %, 33.45 %, and 28.73 % better accuracy;30.53 %, 27.64 %, and 26.25 % better precision;16.81 %, 24.25 % and 30.25 % lower end-to-end delay, 30.53 %, 27.64 %, and 26.25 % lower packet loss ratio compared with existing methods: stochastic gradient descent intrusion detection for wireless sensor network attack finding scheme utilizing machine learning: a new improved artificial bee colony along blockchain-dependent safe clustering routing approach for FANET (IABC-BC-SCR-FANET), cluster-dependent routing protocols for FANETs

关键词： Convolutional neural networks

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Predictive Analytics for Improving Physician Insights in Health Informatics 1

Predictive Analytics for Improving Physician Insights in Hea...

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1st International Conference on Smart Technologies communication and Robotics, STCR 2021

作者： Mythili, S. Nithya, K. Kalamani, M. Krishnamoorthi, M. Bannari Amman Institute of Technology Department of Electronics and Communication Engineering Tamilnadu India Kongu Engineering College Department of Computer Science and Engineering Tamilnadu India KPR Institute of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu India Dr. N G P Institute of Technology Department of Computer Science and Engineering Tamilnadu India

ISBN: (纸本)9781665418065

In this modern world everything is expected to be handy without taking risks and spending much time. The thought brought the invention of the patient monitoring system. The on-body sensors such as temperature sensor, pressure sensor, glucose monitoring sensor, ECG sensor, and SPO2 sensor are used to observe the physical conditions of the patient. The data collected through the sensors will be directed to the doctor's side. The ability of delivering this kind of sensitive information to the doctor's database has to be accurate enough to make the decision clearly by the doctor. Currently the research work on the body area network is mostly based on machine learning techniques for identifying the diseases. The proposed work uses a deep Convolutional Neural Network algorithm for health informatics to classify the data and predict the disease with high accuracy of 86.6%. By adding the layers in CNN, the accuracy is quite improved when compared to the SVM, NB, DT, RF algorithms for health informatics. © 2021 IEEE.

关键词： Medical informatics

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Artificial Intelligence Assisted Student Learning and Performance Analysis using Instructor Evaluation Model 3

Artificial Intelligence Assisted Student Learning and Perfor...

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3rd International Conference on electronics and Sustainable communication Systems, ICESC 2022

作者： Nithiyanandam, N. Dhanasekaran, S. Kumar, A. Sivanesh Gobinath, D. Vijayakarthik, P. Rajkumar, G. Vinoth Muthuraman, U. Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India Kalasalingam University Department of Information Technology Tamilnadu Srivilliputtur India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Sri Manakulavinayagar Engineering College Department of Information Technology Puducherry India Sir M Visvesvaraya Institute of Technology Department of Information Science and Engineering Bengaluru India J P College of Engineering Department of Electronics and Communication Engineering Tamilnadu Tenkasi627852 India Francis Xavier Engineering College Dept of Eee Tirunelveli India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Student's proficiency in a subject may be gauged through the use of Learning Management Systems (LMS) (LMSs). Using a scoring algorithm to calculate the percentage of each student's attentiveness in the specific class, this system aims to increase the use of artificial intelligence approaches. The graph provided by this tool may be used to measure how well a student understands the material being taught. Data mining tools are increasingly being used in higher education to help students and administrators better understand and resolve educational and administrative difficulties. The majority of educational mining research focuses on replicating students' performance rather than instructors' performance. The course assessment questionnaire is a standard instrument for evaluating teachers' performance based on students' perceptions. There are a number of different ways for creating classification techniques in this system, from decision trees to support vector machines to machine learning techniques, including differential evolution. Comparing their answers to an actual course assessment question based on its accuracy, retention and sensitivity. Students' online conduct has become more accessible because to the use of Learning Management Systems (LMSs) in educational institutions. These statistics have been utilized by several studies to predict student outcomes. A wide range of subjects and predictive characteristics collected from the LMS make it difficult to draw generalized statements about the processes that control student performance. To get started let's take a look at some of the most recent research' theoretical foundations and common predictions in the field of learning analytics. A total of 4,989 students took 17 blended courses with Moodle LMS at the same school, and both multi-level and conventional regressions are utilized to examine the impact of LMS predictor factors and between-course evaluation grades on student performance. Despite the fact that the

关键词： Students

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Assessment of Enterobacteriaceae Bacteria and Antibiotic Resistance in Spring Water of Sikkim Using OPTICS Clustering Algorithm

Assessment of Enterobacteriaceae Bacteria and Antibiotic Res...

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Next Generation communication & Information Processing (INCIP), International Conference on

作者： Shobha Prabha Niranjan Dept. of Computer Science and Engineering Nitte Meenakshi Institute of Technology Benagaluru India Department of Electronics and Communication Engineering NMAM Institute of Technology Nitte (Deemed to be University) Nitte Karnataka India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

Waterborne diseases pose meaningful public health risks, especially in regions reliant on natural spring water like Sikkim, India. This study aims to evaluate water quality in four districts of Sikkim, focusing on Enterobacteriaceae bacteria, indicators of fecal contamination and potential pathogens. Using selective culture techniques, Enterobacteriaceae were isolated and taxonomically characterized. Multiple species were identified, indicating a risk of waterborne diseases. Antibiotic exposure testing proven that Escherichia coli isolates were resistant to commonly used antibiotics and carried both virulence and resistance genes. The proposed study introduces OPTICS (Ordering Points to Identify the Clustering Structure) for bacterial strain clustering analysis The results highlight the critical need for robust water quality management strategies in Sikkim to reduce health risks linked to antibiotic-resistant bacteria and fecal contamination. This proposed study specifies critical insights into community health risks and offers a novel approach for clustering bacterial strains in water quality assessment. The escalating issue of antibiotic resistance in bacterial infections, a critical public health concern, is discussed in the proposed work. The study's primary objective is to analyze the correlation between antibiotic usage patterns and the development of resistance across various bacterial strains. By employing a combination of data analysis, laboratory experiments, and statistical modeling, the research seeks to identify key factors that drive the emergence of antibiotic-resistant bacteria. The findings reveal a strong association between the misuse and overuse of antibiotics and the rapid evolution of resistance in several common bacterial pathogens. The study highlights the role of specific antibiotics that contribute disproportionately to resistance, emphasizing the need for stricter regulation and more judicious use of these drugs. Moreover, the research ide

关键词： Antibiotics Water quality Optics Water pollution Public healthcare Springs Contamination Water resources Immune system Strain

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