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Innovative Approaches to Dental Age Estimation: A Comprehensive Review of CNN Technologies and Ethical Issue

Innovative Approaches to Dental Age Estimation: A Comprehens...

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Data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： I. Domilin Shyni Sheeja Herobin Rani C I. Rexiline Sheeba Mahizha S I Dept. Of Computer Science and Engineering DMI Engineering College Aralvoimozhi India Dept. Of Electronics and Communication Engineering St.Xavier’s Catholic College of Engineering Nagercoil India Dept. Of Electronics and Communication Engineering Sathyabama Institute of Technology and Science Chennai India Dept. Of Information Technology St.Xavier’s Catholic College of Engineering Nagercoil India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

Estimating dental age is crucial in multiple disciplines, such as forensic science, orthodontics, and pediatric dentistry. Traditional methods, while effective, often involve manual processes and subjective judgment. Recent advancements in Convolutional Neural Networks (CNNs) have introduced innovative approaches that promise greater accuracy and efficiency. This comprehensive review examines the latest CNN technologies applied to dental age estimation, highlighting their methodologies, performance, and advancements. Key aspects covered include the development and optimization of CNN architectures, the role of data preprocessing and augmentation, and the impact of these technologies on predictive accuracy. Additionally, the review addresses critical ethical issues associated with the implementation of CNNs in dental age estimation, such as data privacy, potential biases, and the implications of automated decision-making in sensitive contexts. By integrating insights from current research and practical applications, this review provides a thorough understanding of both the technological innovations and ethical considerations driving progress in determining dental age .

关键词： Ethics Technological innovation Accuracy Reviews Neural networks Estimation Manuals Dentistry Convolutional neural networks Optimization

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Evaluation of RF-EMF Exposure for sub-6GHz 5G NR Massive MIMO Base Station 6

Evaluation of RF-EMF Exposure for sub-6GHz 5G NR Massive MIM...

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6th IEEE International Symposium on Telecommunication Technologies, ISTT 2022

作者： Sali, Aduwati Wali, Sangin Qahtan Osman, Anwar Faizd Universiti Putra Malaysia Systems Engineering Faculty of Engineering WiPNET Research Centre Dept. of Computer and Communication Selangor Malaysia Pat Square Rohde & Schwarz Selangor Malaysia

ISBN: (数字)9781665489423

ISBN: (纸本)9781665489423

The 5G network is intended to accommodate a significant quantity of mobile data traffic and a great number of wireless connections. It improves cost and power consumption;it offers ultra-low latency and ultra-high dependability to enable new services in various sectors. However, the general public is concerned about the possible health dangers linked with 5G equipment's Radio Frequency (RF) radiation, and numerous localities are actively lobbying to prevent 5G implementation. This research measures the maximum exposure emitted by a 5G base station operating on 3.5 GHz. Considering the transmitted power changes over time with data traffic, the analysis is based on a code selective method. Measurements were conducted at two different distances and three different times to investigate the impacts of distance and time on the level of RF-EMF radiation. The maximum radiation from the base station is 11.69V/m, far less than the accepted limit by the ICNIRP standard. © 2022 IEEE.

关键词： Base stations

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SYNERGY OF ROBOTICS AND IOT MONITORING IN INDUSTRIES USING DEEP RESNETS

SYNERGY OF ROBOTICS AND IOT MONITORING IN INDUSTRIES USING D...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Gobinath Kumarasamy, M. Kongu Kumar, M. Anbarasu, L. Padmavathi, N. Kumar, S. Sathish Ramya, P. Dept. of Computer Science and Engineering Chettinad College of Engineering and Technology Puliyur Karur Tamilnadu India College of Engineering Tamilnadu Karur India Engineering College Tamilnadu Perundurai India Department of Electronics and Communication Engineering Chettinad College of Engineering and Technology Puliyur Karur Tamilnadu India Department of EEE Erode Sengunthar Engineering College Tamilnadu Perundurai India General Engineering- Electrical RMD Engineering College Chennai India Department of EEE M. Kumarasamy College of Engineering Karur Tamilnadu India Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350305708

This paper explores the synergistic integration of robotics and Internet of Things (IoT) monitoring within industrial settings, employing Deep Residual Networks (Deep ResNets). The convergence of robotics and IoT has the potential to revolutionize various industries by enabling efficient data collection, real-time analysis, and informed decision-making. Deep ResNets, known for their exceptional feature extraction capabilities, further enhance the fusion of these technologies. In this study, we delve into the collaborative framework of robotics and IoT, elucidating how Deep ResNets bolster the process. We discuss the benefits of this integration, including optimized resource allocation, predictive maintenance, and adaptable production systems. Through empirical analysis, we demonstrate the efficacy of Deep ResNets in handling complex data from integrated robotics and IoT sources. Ultimately, this research sheds light on the transformative impact of amalgamating robotics, IoT monitoring, and Deep ResNets, paving the way for intelligent and responsive industries. © 2023 IEEE.

关键词： Robotics

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Enhancing Brain Tumor Detection in MRI Images Through Fine-Tuning of VGG 16 Networks: A Precision Diagnostic Approach

Enhancing Brain Tumor Detection in MRI Images Through Fine-T...

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communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Deepak Upadhyay Kunj Bihari Sharma Mridul Gupta Abhay Upadhyay Satya Prakash Yadav Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era Deemed to be University Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

This paper explores the impact of fine-tuning on brain tumour diagnosis using MRI data and a pre-trained VGG 16 model on ImageNet. Our objectives in fine-tuning the model were to improve its generalisation ability and diagnostic accuracy. Our results demonstrate a considerable improvement in the model's performance, with decreased validation loss and increased validation accuracy. The findings show that, despite the possibility of overfitting, fine-tuning is a helpful technique for deep learning model optimisation in medical image analysis. By enhancing diagnostic skills in healthcare, particularly in the early detection of brain tumours, this tactic may enhance patient outcomes.

关键词： Training Accuracy Magnetic resonance imaging Brain modeling Data models Telecommunication computing Tuning Optimization Monitoring Overfitting

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An Efficient Analysis of Various Deep learning Model in Brain tumor Detection with MRI

An Efficient Analysis of Various Deep learning Model in Brai...

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communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

This paper compares three of the popular deep learning models being VGG-16, VGG-19 and ResNet50 with respect to how they perform in the classification task. We investigate how much each model learns, the reduction of errors and inferences from data over epochs, through an in-dept. analysis of training and testing phases. The authors concluded that all the models perform efficiently; however, from the study results of performance and accuracy, VGG-19 and ResNet50 have a high-performance scale as well as super accuracy in their deep learning model which encourages business for application towards complex pattern recognition or predictive modelling. These results provide important intuitions on what deep learning architectures can do, and pave the path for further works in this area, b

关键词： Deep learning Training Analytical models Accuracy Computational modeling Predictive models Brain modeling Data models Residual neural networks Testing

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Optimization of New Radio Physical Uplink Shared Channel Resources Allocation with Efficient Demodulated Referenced Signals and PT-RE Deployment

Optimization of New Radio Physical Uplink Shared Channel Res...

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Deepak Upadhyay Mridul Gupta Kunj Bihari Sharma Abhay Upadhyay Satya Prakash Yadav Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era Deemed to be University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

This paper explores the resource allocation optimisation of New Radio Physical Uplink Shared Channels with an emphasis on the effective implementation of Phase Tracking Reference Signals and Demodulation Reference Signals in 5G networks. Understanding how important these reference signals are to dependable communication, we created a Python framework to model and display their distribution on a time-frequency grid. By carefully modifying important factors including mapping type, PRB allocation, and symbol allocation, our method greatly increased resource efficiency, decreased overhead, and improved signal quality. The results show how sophisticated resource allocation techniques may be used to maximise system performance, providing important information for the creation of more reliable and effective 5G NR communication systems

关键词： Wireless communication Time-frequency analysis system performance Symbols New Radio Resource management Reliability Uplink Optimization Demodulation

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The Symphony of Style: An Extensive Examination of Determination Mechanisms in Deep Learning-Inspired Outfit Suggestions 2

The Symphony of Style: An Extensive Examination of Determina...

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2nd International Conference on Computational Intelligence, communication Technology and Networking, CICTN 2025

作者： Guru Prasad, M.S. Bhosagi, Ravatappa A. Madhusudhana, K. Prithviraj Chandrappa, S. Kumar, Vikash Dehradun India School of Computing and Information Technology Reva University Bengaluru India Dept. of Electronics & Communication Engineering SDM Institute of Technology Ujire India NMAMIT Mangalore India Bengaluru India Department of Computer Application Graphic Era Deemed University Dehradun India

ISBN: (纸本)9798331530389

In the field of the fashion industry, the introduction of deep learning technology has taken an innovative revolution by providing assistance in the construction of intellectual systems that are intelligent to deliver personalized ensemble recommendations. In this research work, an innovative ensemble recommendation structure called Deep Learning VGG16 (Visual Geometry Group 16) recommender is presented. The proposed method allows a deep learning model VGG16 to understand complex patterns and developments resulting from a vast group of fashion images. In order to increase the user experience and greater participation in the well-established fashion industry, the system offers personalized ensemble ideas. The important, core and fundamental component of the system is VGG16, consisting of 13 convolutional layers and 3 fully connected layers, which is used to support the analysis of fashion images. A cutting-edge convolutional neural network (CNN) model is improved over a large and diverse collection of fashion images. Model will extract advanced high-level information and statistics with the capability to distinguish particulars in colour patterns, clothing combinations, and stylistic elements. Using this process of development, the system becomes better at identifying user preferences and fashion trends. © 2025 IEEE.

关键词： Convolutional neural networks

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IoT Based Framework for Monitoring and Controlling Movable Harbor Cranes

IoT Based Framework for Monitoring and Controlling Movable H...

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

作者： Awad, A.H. Saraya, M.S. Elksasy, M.S.M. Ali-Eldin, Amr M. T. Mansoura University Faculty of Engineering Computer Engineering and Control System Dept. Mansoura Egypt

ISBN: (纸本)9781665412438

The Internet of Things refers to the type of network that connects anything to the Internet on the basis of the protocols stipulated by the information sensing equipment for the exchange of information and communication in order to achieve smart recognition, positioning, tracing, monitoring and administration. Movable harbor crane (MHC) is a major part of logistics services. Logistics is the management of the flow of goods between the point of origin and the point of consumption in order to meet the requirements of customers or corporations. MHC is mainly used for the loading and unloading of ships. This paper presents recommendations and practices to be used in the future of the smart industry internet (IoSI) and explores the different applications of smart sensor networks in the movable harbor crane. The main objective of the current paper is to develop a conceptual framework for monitoring and controlling harbor movable crane using a cloud on the IoT-PLC control system. Experimental results show that the proposed system is applicable and effective. © 2021 IEEE.

关键词： Internet of things

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Blockchain Based Secure and Decentralized Smart Licensing of Charging Vehicles for Rajshahi City Corporation

Blockchain Based Secure and Decentralized Smart Licensing of...

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Information and communication Technology for Sustainable Development (ICICT4SD), International Conference on

作者： Momenul Haque Subrata Kumer Paul Kamrul Islam Mursheda Nusrat Della Rakhi Rani Paul Sultan Fahim Dept. of Computer Science and Engineering Bangladesh Army University of Engineering & Technology Bangladesh Dept. of Computer Science and Engineering University of Rajshahi Rajshahi Bangladesh Dept. of Computer Science and Engineering Rabindra Maitree University Kusbtia Bangladesh Dept. of Information and Communication Engineering Bangladesh Army University of Engineering & Technology Bangladesh

The adoption of charging vehicles in Rajshahi has emerged as a significant step towards reducing air pollution and promoting a healthy, green city. However, the current centralized licensing system poses challenges such as time-consuming processes and the potential for fraudulent activities. To address these issues, this paper proposes the implementation of a secure and decentralized smart licensing system for charging vehicles in Rajshahi City Corporation, leveraging the smart capabilities of blockchain technology. By utilizing Hyperledger Fabric, the implementation generates blockchain based smart contracts that automate and streamline the licensing process smartly and efficiently. This approach ensures the immutability, transparency, and integrity of license records, minimizing the risk of fraud and enhancing overall efficiency. Additionally, the system incorporates the use of QR codes and an inquiry system to enhance the credibility of paper-based licenses. Compared to other digital licenses, this blockchain-based method significantly reduces risks and contributes to the development of a greener and more sustainable Rajshahi city environment. Furthermore, the introduction of a sustainable business policy, utilizing the G2C model, facilitates revenue generation and distribution fairly and equitably for all stakeholders involved in the charging vehicle ecosystem. This implementation not only offers cost benefits and efficiency improvements for drivers but also promotes a sustainable and environmentally friendly city.

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Beamforming and Phased Arrays: Elevating MIMO-OFDM systems for Next-Generation Wireless Networks

Beamforming and Phased Arrays: Elevating MIMO-OFDM Systems f...

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

This study explores the optimisation of Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems through the use of phased arrays and advanced beamforming methods. With a focus on overcoming the challenges that come with 5G wireless communication, like high data rate needs, interference resistance, and efficient spectrum usage, this work presents a novel approach that combines dynamic beam steering and adaptive precoding to significantly enhance system performance. Through thorough modelling, we demonstrate considerable improvements in bit error rates (BER), throughput, and spectrum efficiency compared to traditional MIMO-OFDM configurations. The results demonstrate a 25% improvement in throughput and a 40% reduction in BER in densely populated metropolitan environments, underscoring the potential of phased arrays and beamforming to further improve 5G network performance. This work not only validates the efficacy of the proposed enhancements but also establishes a foundation for future advancements in wireless communication systems by highlighting the critical roles that phased arrays and beamforming will play in achieving the maximum potential of 5G technology.

关键词： Phased arrays Beam steering Adaptive systems Array signal processing 5G mobile communication Precoding Wireless networks Bit error rate Interference Throughput

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