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International Conference on Information and Communication technology Convergence (ICTC)

作者： Mohammad Afif Kasno Norhidayah Mohamad Yatim Tae-Won Kang Jo Min Hee JunWoo Nam Jin-Woo Jung dept. of Computer Science and Egineering Dongguk University Seoul South Korea Faculty of Electronics and Computer Technology and Engineering Universiti Teknikal Malaysia Melaka (UTeM) Malacca Malaysia dept. of Artificial Intelligence Dongguk University Seoul South Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

This study presents a unique approach for operating the TurtleBot3 Waffle-Pi robot with voice instructions using the ROS Noetic framework. The system uses the SpeechRecognition library to translate voice inputs into movement commands, with the goal of improving user engagement and providing an intuitive control mechanism that can be applied to a variety of domains including assistive robotics. The system naturally interfaces with ROS Noetic, which uses Python for scripting and control logic. The method entails real-time recording audio with a microphone, filtering it to remove noise, then interpreting commands with Google's speech recognition API. For the TurtleBot3, commands like “forward,” “back,” “left,” “right,” and “stop” are assigned to matching movements. Noise filtering and command disambiguation problems are solved by modifying the recognizer's energy threshold and using a dynamic energy threshold. A complete test was carried out in controlled situations to determine command recognition accuracy, response time, and navigation precision. The results showed good reliability and effectiveness, with accurate command recognition and minimum reaction delay.

关键词： Accuracy Navigation Filtering Noise Speech recognition Real-time systems Libraries Internet Time factors Robots

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Predicting Hand Injury Severity Using Bayesian Networks in Cloud-Based Emergency Medicine

Predicting Hand Injury Severity Using Bayesian Networks in C...

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International Conference on Communications and Signal Processing

作者： S Yuvaraj K. Thinakaran S Porandla Srinivas S. Sivakumar M.V Ishwarya P. Thirumaraiselvan Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Information Technology Malla Reddy Institute of Engineering and Technology Hyderabad Telangana India Department of Computer Science Thanthai Hans Roever College (Autonomous) Perambalur Tamil Nadu India Artificial Intelligence and Data science Department Agni College of Technology Thazhambur Tamil Nadu India Department of Electronics and Communication Engineering Adhiparasakthi Engineering College Melmaruvathur Tamil Nadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

Predicting the severity of hand injuries in emergency care is the focus of this study, which suggests using cloud-based Bayesian networks. It is crucial to have accurate severity prediction in order to treat emergency hand injuries promptly since they are prevalent. By using the scalability and flexibility of cloud computing, this method constructs a Bayesian severity assessment network with patient data, injury characteristics, and historical medical records. The model’s training data comes from a massive database of hand injury cases with different results. Emergency medical professionals may use real-time parameter modifications to strengthen and adapt the Bayesian network. Through the use of the cloud, healthcare providers may simply exchange data and reach consensus. It demonstrates that, compared to other well-established methods of evaluating hand injury severity, the model performs better in making predictions. The suggested method improves severity forecasts and advances cloud-based emergency care. The method for emergency hand injury care uses Bayesian networks in the cloud to improve scalability and accessibility. The findings show that the data-driven and efficient hand injury severity prediction method might transform emergency care by improving patient outcomes.

关键词： Cloud computing Adaptation models Scalability Training data Medical services Transforms Predictive models

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Evaluation and Measures for Improving the Energy Efficiency of Medium-Sized Enterprises

Evaluation and Measures for Improving the Energy Efficiency ...

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Junior Conference on Lighting (Lighting)

作者： Plamen Stanchev Gergana Vacheva Nikolay Hinov Departement of Information Technology in industry Faculty of Computer Systems and Technologies Technical University of Sofia Sofia Bulgaria Departement of Power Electronics Faculty of Electronic Engineering and Technologies Technical University of Sofia Sofia Bulgaria

In the presented article, an assessment and measurements were realized to determine the energy efficiency of a medium-sized industrial enterprise. Real data from a three-shift plant was used. An assessment of the current electricity consumption was made and an energy audit was carried out to reduce electricity costs. The main goal of the current research is to ensure the basic functionality of the buildings, as well as the production-technological process, ensuring minimum electricity costs and maximum efficiency.

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Compressed Data Aggregation Using Regulated Duty Cycle for WSN-IOT Networks 1

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International Conference on Advances in Communication technology and computer engineering, ICACTCE 2023

作者： Gupta, Anuradha Gulati, Tarun Bindal, Amit Kumar Kumar, Karan Ramachandra, A.C. Seleck, Július ECE Department M. M. Engineering College M. M. Deemed to be University Ambala Mullana India Department of Computer Science and Engineering M. M. Engineering College M. M. Deemed to be University Ambala Mullana India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengalore India Faculty of Management Comenius University in Bratislava Odbojárov 10 Bratislava 2582005 Slovakia

ISBN: (数字)9783031371646

ISBN: (纸本)9783031371639

IoT sensors can send the data using different modes i.e. periodically/one time only/continues streaming using different application-types i.e. periodic/single shot/streaming etc. and resource consumption may also vary as per requirement. In order to utilize resources in optimal way, for each application-type, there is need to regulate the data aggregation. It may be affected by other parameters also i.e. transmission range, Signal-to-Noise-Ratio (SNR) etc. This paper introduces a compressed data aggregation approach (using Zlib library) that takes care of application behavior by enforcing a transmission duty cycle and its performance will be analyzed using different parameters (SNR/Throughput/Packet delivery ratio/Residual Energy etc.). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Signal to noise ratio

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Inculcation of Sensors in Enhancing Smart Cities: A Review

Inculcation of Sensors in Enhancing Smart Cities: A Review

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Trupti V Nandikolmath Sandeep Kumar Jain Savita Yadav Department of Electrical and Electronics Engineering Faculty of Engineering and Technology JAIN (Deemed to be University) Bangalore India Department of Electrical Engineering Vivekananda Global University Jaipur India Department of Computer Science and Engineering (lOT) Noida Institute of Engineering & Technology Greater Noida Uttar Pradesh India

By 2050, experts predict that 75% of people on Earth will live in cities. As such, cities need to be prepared to fulfill the needs of their citizens and provide the best possible services. The idea of a “smart city,” which is an improved system that effectively manages its resources and services via monitoring and communication technology, is often central to the notion of an advanced city. Thus, turning cities into smart entities is a critical step toward global urban sustainability. Beyond creating smart buildings in cities, the main goal is to improve people's quality of life by using newly developed digital technologies and sensing capabilities. The key to smarter communities is their capacity to invest in these technologies, adapt, and socialize in a transparent and inclusive manner while taking into account the needs and values of the local and regional communities. Despite these developments, privacy issues and interruptions in cyber security remain serious weaknesses that need addressed.

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CWDM communication system based inline erbium-doped fiber amplifiers with the linear geometrical polarization model

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Journal of Optical Communications 2024年 45卷 s431-s443页

作者： Eid, Mahmoud M. A. Sorathiya, Vishal Lavadiya, Sunil Parmar, Juveriya Patel, Shobhit K. Ali, Shaimaa Ahmed Rashed, Ahmed Nabih Zaki Department of Electrical Engineering College of Engineering Taif University Taif Saudi Arabia Department of Information and Communication Technology Marwadi University Rajkot India Department of Physics Marwadi University Rajkot360003 India Department of Electronics and Communication Engineering Marwadi University Rajkot360003 India Department of Computer Engineering Marwadi University Rajkot360003 India Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32951 Egypt

Optical fiber coarse wavelength division multiplexing (CWDM) with erbium-doped fiber inline-amplifiers for the linear polarization geometrical model is studied in this paper for fiber system optimization by using the Optisystem simulation software. Signal gain (SG), noise figure (NF), and signal/noise are measured against CWDM optical fiber variations. The erbium-doped fiber amplifier (EDFA) is based on the Giles model which solves the steady-state rate equations for SG and signal absorption parameters with the cross-section area of the amplifier itself. The signal is optimized at EDFA length of 6 m, 10 Gb/s data rates (DRs) transmission and 10 km CWDM fiber optic length (FOL). Max. Q factor is degraded and min. BER is upgraded with both CWDM optical FLs and higher DRs transmission increase. © 2021 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Erbium doped fiber amplifiers

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Machine Learning based Comparative Analysis of Cervical Cancer Risk Classifications Algorithms

Machine Learning based Comparative Analysis of Cervical Canc...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Tania Ganguly Peeta Basa Pati K. Deepa Tripty Singh Tolga Özer Department of Computer Science and Engineering Amrita School of Computing Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Bengaluru India Technology Faculty Electric Electronic Engineering Afyon Kocatepe University Afyonkarahisar Turkey

Cervical cancer is a major health concern for women worldwide, and early detection is essential for successful treatment. Since symptoms often do not appear until later stages, early screening is necessary. Machine learning can help classify cervical cancer risk by analyzing patient datasets and identifying the important factors that predict the likelihood of emerging cervical cancer. This paper evaluates six different machine-learning approaches for analyzing risk factors associated with cervical cancer using a dataset of 838 instances with 36 features. Results show that the SVM classifier performs the best, with an accuracy of 99.60% which emphasize the possibility of utilizing machine learning to enhance the precision of cervical cancer risk assessment. This can result in the development of better screening and prevention techniques for cervical cancer, which can be more effective in identifying and managing this disease.

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Pseudonym Mutable Based Privacy for 5G User Identity

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computer Systems Science & engineering 2021年第10期39卷 1-14页

作者： Rashid A.Saeed Mamoon M.Saeed Rania A.Mokhtar Hesham Alhumyani S.Abdel-Khalek Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Electrical Engineering Faculty of EngineeringAlzaiem Alazhari UniversityKhartoum11111Sudan Department of Communications and Electronics Engineering Faculty of EngineeringUniversity of Modern Sciences(UMS)Sana'a16784Yemen Department of Mathematics College of SciencesTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

Privacy,identity preserving and integrity have become key problems for telecommunication *** privacy threats are expected in 5G networks considering the large number of devices that will be *** Internet of Things(IoT)and long-term evolution for machine type(LTE-m)are growing very fast with massive data traffic the risk of privacy attacks will be greatly *** all the above issues standards’bodies should ensure users’identity and privacy in order to gain the trust of service providers and *** such threats,5G specifications require a rigid and robust privacy *** research studies have addressed user privacy in 5G *** paper proposes a method to enhance user identity privacy in 5G systems through a scheme to protect the international mobile subscriber identity(IMSI)using a mutable mobile subscriber identity(MMSI)that changes randomly and avoids the exchange of *** maintains authentication and key agreement(AKA)structure compatibility with previous mobile generations and improves user equipment(UE)synchronization with home *** proposed algorithm adds no computation overhead to UE or the network except a small amount in the home subscriber server(HSS).The proposed pseudonym mutable uses the XOR function to send the MMSI from the HSS to the UE which is reducing the encryption overhead *** proposed solution was verified by ProVerif.

关键词： 5G MMSI IMSI AKA privacy user identity

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AI-based Flexible Online Laboratory Learning System for Post-COVID-19 Era: Requirements and Design

AI-based Flexible Online Laboratory Learning System for Post...

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2021 International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2021

作者： Elmesalawy, Mahmoud M. Atia, Ayman Yousef, Ahmed Mohamed Fahmy El-Haleem, Ahmed M. Abd Anany, Mohamed G. Elmosilhy, Noha A. Salama, Abdullah I. Hamdy, Alaa Zoghby, Helmy M. El Din, Eman Serag El Helwan University Faculty of Engineering Electronics and Communications Engineering Department Cairo Egypt Helwan University HCI-LAB Faculty of Computers and Artificial Intelligent Computer Science Department Cairo Egypt Giza Egypt Fayoum University Faculty of Specific Education Education Technology Department Fayoum Egypt Electronics and Communications Engineering Department Cairo Egypt Helwan University Faculty of Engineering Computer and Systems Engineering Department Cairo Egypt Misr International University Faculty of Computer Science Cairo Egypt Helwan University Faculty of Engineering Electrical Power and Machines Engineering Department Cairo Egypt Network Planning Department Cairo Egypt

ISBN: (纸本)9781665412438

The worldwide outbreak due to COVID-19 pandemic has led to a great interest in e-learning. However, the lack of suitable online laboratory management systems has posed a particular challenge for sectors that need laboratory activities such as engineering, science and technology. In this paper, the requirements and design for a flexible AI-based laboratory learning system (LLS) that can support online laboratory experimentations are presented. The elicitation of the LLS design requirements is decided based on a conducted survey for a set of LLS features. The LLS is designed with the flexibility to support various types of online experimentations such as virtual or remote controlled experiments using either desktop or web applications. The virtualization technique is used to manage the laboratory resources and allow multiple users to access the LLS. Moreover, the proposed LLS introduces the use of AI techniques to provide efficient virtual lab assistant and adaptive assessment process. © 2021 IEEE.

关键词： COVID-19

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A Supervised Machine Learning Approach for Short-Term Forecasting and Demand Response Optimization for Enhancing Energy Flexibility in Smart home

A Supervised Machine Learning Approach for Short-Term Foreca...

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Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： P Glaret Subin V Gomathy Krishnakumar Ramasamy R Dhivya U Arun Kumar S Shargunam Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Vadapalani Campus Chennai Tamilnadu India Department of EEE Kathir College of Engineering Coimbatore Tamilnadu India Department of Electrical and Electronics Engineering Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India Information Technology M.Kumarasamy College of Engineering Karur Tamilnadu India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar Srivilliputhur Tamilnadu India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

This research investigates supervised machine learning algorithms for short-term energy forecast in smart homes. A dataset involving energy consumption in the house was used and it was analyzed across 10-times using the Support Vector Machine, k-Nearest Neighbors, Gradient Boosting Machines, and Ensemble Methods. Results show that the performance of the Support Vector Machine was significantly higher with a mean, standard deviation, and maximum accuracy of 0.84, 0.02, and 0.88. Notably, the SVM had the minimum mean absolute error of 0.035 and root mean square error at 0.045. The coefficient of determination was also high, between 0.72 and 0.76, across 10-times of iteration. Thus, the SVM model is more accurate and reliable for forecasting energy consumption in the house. Although k-NN, GBM, and Ensemble Methods also showed considerable success, the abovementioned information proves that SVM is the most beneficial algorithm for smart home energy forecasting for the short-term. These outcomes can help design efficient energy management systems as they inform practitioners about the effectiveness of this method for energy consumption optimization to decrease costs and increase user comfort. Knowing which algorithms are capable of producing the high-quality results helps to design highly efficient short-term and long-term energy management solutions that satisfy the needs and preferences of smart home users, making the energy as sustainable and efficient as possible.

关键词： Support vector machines Energy consumption Machine learning algorithms Accuracy Smart homes Predictive models Ensemble learning

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