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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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Performance Assessment Of P&O-Based And InC-Based Double Integral Sliding Mode Control MPPT For A Solar Photovoltaic System And DC Voltage Enhancement Using Synchronous Boost Converter

Performance Assessment Of P&O-Based And InC-Based Double Int...

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Futuristic Technologies in Control Systems & Renewable Energy (ICFCR), International Conference on

作者： Anjumol C S Preethi Thekkath Dept. of Electrical and Computer Engineering Toc H Institute of Science and Technology APJ Abdul Kalam Technological University Trivandrum Kerala India Dept. of Electrical and Electronics Engineering Toc H Institute of Science and Technology APJ Abdul Kalam Technological University Trivandrum Kerala India

ISBN: (数字)9798331504601

ISBN: (纸本)9798331504618

The depletion of fossil fuels has made it more important to look for alternate power sources. Power electronics have made it feasible to effectively and efficiently harvest power from renewable energy sources based on the demands of electrical loads. A Solar Photo Voltaic (SPV) system's power output is dependent on the temperature of the cells and the amount of irradiation. Thus, a suitable Maximum Power Point Tracking (MPPT) technique is required to track the maximum power from the SPV. Conventional MPPT methods may cause the system to operate at a Local Maximum Power Point (LMPP) rather than a Global Maximum Power Point (GMPP) when subjected to Partial Shading Conditions (PSC). In this article, Perturb and Observation (P&O) algorithm and Incremental Conductance (InC) algorithms are considered as conventional MPPT algorithms to track reference voltage, and the error for reaching reference voltage is reduced by the Double Integral Sliding Mode Control (DISMC) technique to form a hybrid MPPT. The main focus is to compare both hybrid MPPT techniques under different operating conditions and implement a synchronous boost converter in efficient MPPT. This work is a part of the review of research findings to select a suitable and efficient hybrid MPPT technique to track maximum power and DC voltage under PSC to implement in a solar charging of Electric Vehicle (EV) with a grid-integrated system. MPPT charge controller for a 426W panel is designed and simulated and the performance is evaluated. By swapping out the converter's diode with a MOSFET (also known as a “Synchronous Boost Converter”), the efficiency of the boost converter can be raised.

关键词： Maximum power point trackers Renewable energy sources Temperature dependence System performance Solar energy Hybrid power systems Electric vehicle charging Time factors Voltage control Sliding mode control

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Hair Disease Detection with Deep Learning: A VGG16-Driven Model for Precise Classification of Hair and Scalp Conditions

Hair Disease Detection with Deep Learning: A VGG16-Driven Mo...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Baresary, Devdutt Jambu, Mohit Bansal, Ankit Singh Brar, Tejinder Pal Kaushik, Pratham Mehta, Shiva Lovely Professional University Depatment of Computer Science and Engineering Punjab Phagwara India Gulzar Group of Institutions Dept of Core Engineering Punjab Ludhiana India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Mullana Haryana Ambala133 207 India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India

ISBN: (纸本)9798350369106

This study enhances diagnostic precision by employing deep learning techniques to classify ten common hair and scalp disorders. A balanced dataset of 12,000 images from Kaggle was used with a 12,000 images total amount of 1200 images per class, and VGG16 convolutional neural network architecture was employed alongside transfer learning to learn intricate patterns present in medical images. Equal representation of all classes in training, validation, and test sets was ensured by partitioning the dataset into 80- 10 - 10 ratio of training, validation and test sets, respectively. The model was trained and evaluated on the Adam optimizer based on metrics such as accuracy, recall, precision, and F1 score. The model was able to achieve 98.24 % accuracy for the ten hair and scalp diseases and also traded above 97% precision and recall for all classes. This indicates that training and validation losses have a minimal difference between them, meaning that we have learned to generalize well without overfitting. By studying the disease confusion matrix, it turned out that the model could have a high true positive rate and relatively a lot of correct classification. The work presented here makes significant contribution in hair disease detection bringing deep learning models closer to seeing improved diagnostic accuracy in dermatology. Such models can not only help in early and precise diagnosis but can support telemedicine initiatives (especially critical during time where there is limited physical consultations). Other work includes expanding the dataset and exploring more advanced architectures to achieve even better model robustness and applicability to clinical settings. © 2024 IEEE.

关键词： Convolutional neural networks

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Genetic Algorithm-Based Feature Selection for Accurate Breast Cancer Classification

Genetic Algorithm-Based Feature Selection for Accurate Breas...

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2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Shabbir, Attia Ali, Raja Hashim Shabbir, Muhammad Zeeshan Abideen, Zain Ul Khan, Talha Ali Ijaz, Ali Zeeshan Ali, Nisar Imad, Muhammad Bakar, Muhammad Abu Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Department of Electrical Engineering Lahore Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

Breast cancer is a significant global healthcare challenge, particularly in developing and underdeveloped countries, with profound physical, emotional, and psychological consequences, including mortality. Timely diagnosis and accurate treatment are crucial in addressing this issue. We propose the utilization of a feature selection technique to identify the most relevant features from among all features for breast cancer diagnosis, and show that Genetic Algorithms are impressive for this task. The study compares the results of GA with no selection and an alternative method, Principle Component Analysis (PCA). Three machine learning models, all based on supervised learning with data split into training and test data, are employed for binary classification using the selected feature subset. The evaluation metrics employed encompass accuracy, precision, recall, and F1-score. Among the selected models, Random Forest demonstrates the most favorable outcomes, achieving an accuracy score of 0.96, precision score of 0.96, recall value of 0.98, and an F1-score of 0.97. These results underscore the effectiveness of GA in feature selection for breast cancer diagnosis. Consequently, the integration of Genetic Algorithms (GA) with Random Forest showcases the superior performance among the evaluated models. © 2023 IEEE.

关键词： Genetic algorithms

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Multichannel Polarization Fiber Optic Sensors Using Wavelength Multiplexing

Multichannel Polarization Fiber Optic Sensors Using Waveleng...

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2024 Latin America Optics and Photonics Conference, LAOP 2024

作者： Kyselak, Martin Vylezich, Zdenek Vavra, Jiri Grenar, David Slavicek, Karel Department of Electrical Engineering Faculty of Military Technology University of Defense Brno Czech Republic IT Infrastructure Division Institute of Computer Science Masaryk University Zerotinovo nam. 617/9 Brno Czech Republic Dept. of Telecommunications Faculty of Electrical Eng. and Comm. Brno University of Technology Antoninska 548/1 Brno Czech Republic

A novel approach for multichannel polarization fiber optic sensors using wavelength multiplexing. By exploiting the unique properties of polarization and multiplexing techniques, our system achieves enhanced sensitivi... 详细信息

关键词： Fiber optic sensors

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Empirical Evaluation of Machine Learning Techniques for Autism Spectrum Disorder

Empirical Evaluation of Machine Learning Techniques for Auti...

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International Electrical engineering Congress (iEECON)

作者： Akshit Sethi Kainat Khan Rahul Katarya Thaweesak Yingthawornsuk Dept. of Computer Science & Engineering Delhi Technological University New Delhi India Dept. of Media Technology King Mongkut's University of Technology Thonburi Bangkok Thailand

ISBN: (数字)9798350383591

ISBN: (纸本)9798350383607

Autism spectrum disorder (ASD) is a complicated neurological system disorder that has severe effects on a person's spoken communication, logical thinking, and person-to-person interaction. The early symptoms start looking visible at the age of two to four years and the main ground of these symptoms could be hereditary or ecological. Presently, the standardized approach for detecting ASDs is usually very time-consuming and the cost of medical care is extravagant. Therefore, early detection of ASD would be helpful. Machine Learning (ML) algorithms have the required potential to predict the chances of having autism or not, based on the type of dataset provided to it and the kind of algorithms that are effectively applied to it. The focus of this study is on screening data sets and the use of machine learning algorithms, including XGBoost (XGB) (Ensemble Technique), Random Forest Classifier (RFC), Support Vector Classifier (SVC), Logistic Regression (LR), and Artificial Neural Network (ANN). Our aim for this research was to develop a model that shortens the process of identification of ASDs at an early stage. The accuracy and precision scores of 92.2% and 0.88, respectively, indicate that our model has done well. Our research could be extended in developing a large-scale model that contains a large dataset having a variety of attributes and the integration of brain and facial MRI scans could be helpful in identifying ASD.

关键词： Autism Machine learning algorithms Biological system modeling Static VAr compensators Artificial neural networks Medical services Prediction algorithms

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LipoPose: Adapting Cellpose to Lipid Nanoparticle Segmentation

LipoPose: Adapting Cellpose to Lipid Nanoparticle Segmentati...

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16th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2023

作者： Basu, Semanti Bajcsy, Peter Cleveland, Thomas Carrasco, Manuel J. Bahar, R. Iris Dept. of Computer Science Brown University United States National Institute of Standards and Technology United States George Mason University United States Colorado School of Mines United States

The goal of this study is to precisely localize lipid nanoparticles (LNPs) from cryogenic electron microscopy (cryoEM) images. LNPs found in cryoEM images are characterized by nonuniform shapes with varying sizes and textures. Moreover, there is no publicly available training dataset for LNP segmentation/detection. Thus, accurate supervised localization must overcome the challenges posed by heterogeneity of LNPs and nonexistent large training datasets. We evaluate benchmarks in closely related areas such as particle-picking and cell-segmentation in the context of LNP localization. Our experimental results demonstrte that, of the benchmarks tested, Cellpose is the best suited to LNP localization. We further adapt Cellpose to segmentation of heterogenous particles of unknown size distribution by introducing a novel optimization pipeline to remove uncertainty in Cellpose’s inference diameter parameter selection. The overall workflow speeds up the process of manually annotating LNPs by approximately 5X. © 2023 by SCITEPRESS - science and technology Publications, Lda.

关键词： Nanoclay

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Better Fine-Tuning with Extracted Important Sentences for Abstractive Summarization

Better Fine-Tuning with Extracted Important Sentences for Ab...

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2021 International Conference on Communication, Control and Information sciences, ICCISc 2021

作者： Sahu, Ankit Sanjeevi, Sriram G. National Institute of Technology Dept. of Computer Science and Enginnering WarangalTelangana India

ISBN: (纸本)9781665412797

Recent work in abstractive text summarization using pre-Trained transformers has achieved great results. Much of the work has been done on the model architectures and designing pre-Training objectives. Models used for NLP tasks have grown bigger and increasing the model's size doesn't always improve the performance of the task. In this work we improve the results of an existing model by incorporating a new fine-Tuning strategy which closely resembles how a human would summarize by a piece of text i.e. by focusing on important fragments of the text. We used PEGASUS model to work on our fine-Tuning strategy. We fine-Tune the model with varying ratios of important sentences in a phased manner and achieve improved ROUGE scores with an additional few thousand steps of fine-Tuning. We fine-Tune the model on AESLC and CNN/DailyMail dataset and achieve better ROUGE scores on both the datasets. With our proposed fine-Tuning method, we get state-of-The-Art results on the AESLC dataset. We also achieve better performance when fine-Tuning with a limited number of examples with our fine-Tuning method on AESLC dataset. © 2021 IEEE.

关键词： Focusing Data mining Task analysis

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Comparative Analysis of Robotic Arm Efficiency: Evaluating PID and Fuzzy-PID Control using Printed Mechanical Structure and ESP8266 Integration 6

Comparative Analysis of Robotic Arm Efficiency: Evaluating P...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Crisnapati, Padma Nyoman Susila, I. Made Darma Aryanto, I. Komang Agus Ady Krohkaew, Jaturapith Diaz, Ricky Aurelius Nurtanto Pande, I. Made Suandana Astika Department of Information Systems Faculty of Informatics and Computers Institute Technology and Business STIKOM Bali Denpasar Indonesia Department of Computer Systems Faculty of Informatics and Computers Institute Technology and Business STIKOM Bali Denpasar Indonesia Department of Information Technology Faculty of Informatics and Computers Institute Technology and Business STIKOM Bali Denpasar Indonesia Dept. of Big Data Management and Analytics Faculty of Science and Technology Rajamangala University of Technology Thanyaburi Thanyaburi Thailand

ISBN: (纸本)9798331539603

This study investigates the comparative effectiveness of manual PID tuning versus Fuzzy-PID control in enhancing the performance of a robot arm with two degrees of freedom (2-DOF). The experimental approach involves conducting two scenarios: manual tuning of PID parameters in Scenario 1 and utilizing a fuzzy algorithm for PID parameter tuning in Scenario 2. The novelty of this research lies in the new 3D mechanical design of the 2-DOF robot arm and the utilization of the ESP8266 microcontroller for implementing Fuzzy-PID control. Performance evaluation is conducted using Root Mean Square Error (RMSE) and Root Mean Square Percentage Error (RMSPE) calculations. The results reveal that Fuzzy-PID control significantly reduces errors compared to manual PID tuning, with lower RMSE and RMSPE values indicating more precise and stable control. These findings underscore the potential of integrating fuzzy logic with PID control to enhance system adaptability and flexibility. Additionally, the ESP8266 is capable of handling the computations required for fuzzy-PID control while being cost-effective. © 2024 IEEE.

关键词： Robotic arms

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SoftHealth: Softwarized 5G-Driven Network Slicing for Real-time e-Healthcare Applications using ML 14

SoftHealth: Softwarized 5G-Driven Network Slicing for Real-t...

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14th International Conference on COMmunication Systems and NETworkS, COMSNETS 2022

作者： Basu, Deborsi Krishnakumar, Vikram Ghosh, Uttam Datta, Raja Indian Institute of Technology G.S. Sanyal School of Telecommunications Kharagpur India SASTRA Deemed to Be University Tamil Nadu Thanjavur India Vanderbilt University Dept. of Electrical Engineering and Computer Science NashvilleTN United States Indian Institute of Technology Dept. of Electronics and Electrical Communication Engineering Kharagpur India

ISBN: (纸本)9781665421041

The recent years have whiteness the substandard situations of the modern healthcare system due to a fatal pandemic called COVID19. The rapid advancements of modern technology have disseminated the superficial benefits of medical infrastructure, but significant improvements are still extremely necessary over the massive e-healthcare system (mHS). Considering the fact of limited resources and unlimited demands, a highly stable end-to-end optimization model is required. Healthcare also struggles with real-time communication. The next-generation communication networks (e.g 5G and beyond) proficiently influence the network resource distribution for URLLC. In this work, we have envisioned a novel on-demand e-Healthcare dynamic network slice architecture that uses the ML algorithms at the edge server for real-time classification and access of the offloaded data from the central controller (vSDN-Control layer to Data plane layer). The comparative analysis over the datasets of patients consisting of special index parameters shows that our proposed model allows the end-user more efficient data accessibility over the conventional approaches. We have studied the model over the multi-classification ML models (kNN, DT and RF) and we have found an average improvement of 10% to 15% of average data offloading time efficiency from the local machines from the edge servers. This approach can be further extended as the QoS improvement of the healthcare data traffic over the dynamic network slice instances. We have kept the model simple but standard in nature. © 2022 IEEE.

关键词： Quality of service

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