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A Deep learning Analysis on Facial Skin Using CNN 3

A Deep Learning Analysis on Facial Skin Using CNN

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3rd IEEE international conference on intelligent Systems, Smart and Green Technologies, ICISSGT 2024

作者： Chandana, Kavuri Swathi, Nerusu Vijay, Vemuri Kumar, M. Ashok Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Information Technology Kanuru India

ISBN: (纸本)9798350304329

Skin Diseases can have cerebral goods in addition to medical bones, especially for those who have disfigurement or facial trauma. Since smart widgets are so extensively used, utmost people may readily take clinical filmland of their skin issues on their faces. In image processing, convolutional neural networks (CNNs) have demonstrated amazing performance, constantly outperforming mortal bents. Using clinical images, this study investigates different CNN algorithms for the bracket of six current skin conditions on the face rosacea, actinic keratosis, seborrheic keratosis, lupus erythematosus, rudimentary cell melanoma, and scaled cell melanoma. Xiangya- Derm was used to produce a dataset of 2656 facial prints that represented the forenamed skin conditions. In China, this is the largest clinical imaging dataset for skin conditions. Five extensively used network approaches for these complaint groups were compared. An independent dataset with the same conditions from several body locales was used to apply transfer literacy. Transfer literacy models demonstrated better average recall and perfection. In a test dataset conforming of 388 facial filmland, the bestmodel attained recalls of 92.9,89.2, and 84.3 for lupus erythematosus, rudimentary cell melanoma, and seborrheic keratosis, independently. Recall and perfection were on average 77.0 and 70.8, independently. © 2024 IEEE.

关键词： Convolutional neural networks

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Comparative Analysis of Deep learning-Based Brain Tumor Prediction Models Using MRI Scan 3

Comparative Analysis of Deep Learning-Based Brain Tumor Pred...

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3rd international conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Gangadharan, S.M.P. Dharani, M. Thapliyal, Nitin Yamsani, Nagendar Singh, Jagendra Singh, Prabhishek Liverpool John Moores University Liverpool United Kingdom Electronics and Communication Engineering K S R Institute for Engineering and Technology Tiruchengode India Graphic Era Hill University Department of Computer Science Engineering Dehradun India School of Computer Science and Artificial Intelligence SR University Warangal India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350303360

This research examines using MRI images for brain tumour identification in deep learning-based tumour prediction models. Four well-known deep learning architectures' accuracy and generalization capacities, including VGG16, Inception V3, ResNet-152, and ResNet-50, were assessed. A large dataset of MRI images that included both tumour and non-tumor patients was used to train and test the models. The comparison study' findings revealed encouraging performance across all models. With exceptional training accuracy of 99.97% and testing accuracy of 96.55%, VGG16 emerged as the best performance. It had exceptional recall (95.25%), accuracy (99.95%), and a shallow false positive rate. While retaining respectable precision and recall levels, ResNet-152 and Inception V3 demonstrated competitive accuracy rates of 93.34% and 94.23%, respectively. The overall performance study showed that all models performed admirably in detecting brain cancers, with high true positive rates and low false positive rates. The results highlight the potential for these models to serve as useful instruments for early tumour identification, assisting medical practitioners in providing prompt and precise diagnosis. The research also emphasizes the clinical importance of the models' generalization ability, confirming their suitability for processing as-yet-untested MRI data. The study also recommends topics for further investigation, such as improving model architectures, adding multimodal imaging data, and carrying out rigorous clinical validation. The robustness and practical application of the models might be further improved by working with radiologists and other healthcare professionals. These deep learning-based tumour prediction models ultimately represent a promising development in medical imaging, opening the door for enhanced patient care and brain tumour detection. © 2023 IEEE.

关键词： Tumors

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An Ephemeris and Handover Based Live Migration Scheme for Seamless Service Transfer in LEO Communications System 3

An Ephemeris and Handover Based Live Migration Scheme for Se...

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3rd international conference on intelligent Communications and Computing, ICC 2023

作者： Li, Jinze Chen, Xi Wang, Shengfeng Zhou, Changsheng Mei, Chengli Du, Ping Qin, Hao Zhao, Xinsheng School of Information Science and Engineering Southeast University National Mobile Communications Research Laboratory Nanjing China Shanghai Satellite Network Research Institute Co. Ltd. Shanghai Key Laboratory of Satellite Network Shanghai China

ISBN: (纸本)9798350308327

Facing the problem of rapid mobility of low Earth orbit (LEO) satellite, frequent handover process will occur while UE User Equipment (UE) is receiving service from satellite-based core network. The Quality of Service (QoS) for UE accessing the satellite-based core network cannot be ensured due to service interruptions and signaling storm caused by frequent handover. To solve this problem, we proposed a live migration scheme based on cooperation of ephemeris and UE handover request. In this scheme, we introduced a live migration method to transfer user and network function contexts in advance before initiating the handover process. Then, we built a simulation platform for the core network based on OAI (Open Air Interface) and validated the proposed network function live migration scheme through simulations. The simulation results indicate that our proposed live migration scheme can support on-demand, automatic deployment of core network functions in LEO satellites. The migration latency can be controlled within seconds, and the volume of migrated data is within the range supported by inter-satellite links. © 2023 IEEE.

关键词： Satellites

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Future Perspectives on automated Machine learning in Biomedical Signal Processing 4th

Future Perspectives on Automated Machine Learning in Biomedi...

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4th international conference on intelligent Technologies and Applications (INTAP)

作者： Lopez-Ramos, Luis Miguel Univ Agder SFI Offshore Mech Ctr Grimstad Norway Univ Agder Dept ICT Grimstad Norway

ISBN: (纸本)9783031105258;9783031105241

Recent developemnts in Machine learning (ML) and Signal Processing (SP) are of paramount importance for the advancement of artificial intelligence in the health sector. The implications of these enabling technologies encompass several broad fields from mathematics to product development in connection with the practitioners and patients. This paper aims at guiding researchers and entrepreneurs in the journey from basic research to application of data-driven technology for the health sector. Two key topics related to automatization of data processing, namely sparsity-based processing, and automated ML, are introduced and discussed in relation to biomedical SP. The discussed topics are exemplified in the context of cardiac signal processing.

关键词： Signal processing Biomedical engineering Interpretability Machine learning automated machine learning Autonomous information management

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Overview of the shared task on Fake News Detection from Social Media Text 3

Overview of the shared task on Fake News Detection from Soci...

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3rd Workshop on Speech and Language Technologies for Dravidian Languages, DravidianLangTech 2023

作者： Subramanian, Malliga Chakravarthi, Bharathi Raja Shanmugavadivel, Kogilavani Pandiyan, Santhiya Kumaresan, Prasanna Kumar Palani, Balasubramanian Singh, Muskaan Raja, Sandhiya Vanaja Mithunajha, S. Kongu Engineering College Tamil Nadu India Insight SFI Research Centre for Data Analytics School of Computer Science University of Galway Ireland Indian Institute of Information Technology Kottayam India Ulster University United Kingdom

ISBN: (纸本)9789544520854

The rapid proliferation of fake news has emerged as a significant challenge to the credibility of online information, largely due to the swift dissemination of content on social media platforms. This article provides a concise summary of the findings of the shared task on"Fake News Detection in Dravidian Languages1 - DravidianLangTech@RANLP 2023". The aim of this shared task is to categorize social media posts as either fake or original, specifically focusing on content in Malayalam. The shared task garnered participation from 8 teams who presented their systems. These systems encompassed a spectrum of methodologies including machine learning techniques and transformer-based models like MuRIL, XLMRoBERTa, and Indic BERT. Notably, the XLMRoBERTa-based model demonstrated exceptional performance, achieving a macro F1-score of 0.90. © DravidianLangTech 2023 - 3rd Workshop on Speech and Language Technologies for Dravidian Languages, associated with 14th international conference on Recent Advances in Natural Language Processing, RANLP 2023 - Proceedings.

关键词： learning systems

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INFINIMIND AI - Supercharging User Expediency using AI and RPA 3

INFINIMIND AI - Supercharging User Expediency using AI and R...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Karthick Raja, M. Singh, Ashish Balajiram, N. Hemraj Kumar, V.S. Karthik, V. Paithankar, Sumit Sri Eshwar College of Engineering Department of Computer Science Tamil Nadu India Huron Eurasia Ltd Bangalore India

ISBN: (纸本)9798350343632

This paper introduces INFINIMIND AI, an innovative system seamlessly integrating advanced AI technologies, especially Natural Language Processing (NLP) and ensemble learning, to reshape user interactions. INFINIMIND AI boasts a user-friendly website interface, powered by FastAPI and Spring Boot for efficient API calls. The backend relies on the robust MongoDB and MySQL for precise database management. A standout feature is INFINIMIND AI's high level of proficiency in NLP-driven input classification, coupled with sentiment analysis to gauge emotional context. SMTP communication facilitates data exchange, while UiPath automation consolidates and refines results into various user-friendly formats. INFINIMIND AI represents a novel and expedient approach to AI interactions, enhancing user experiences through simplified input processing and sentiment analysis. This paper details the innovative design and methodologies that underpin this AI-driven system, promising a user-centric future in AI interactions. © 2023 IEEE.

关键词： AI integration Ensemble learning FastAPI MongoDB MySQL NLP Sentiment analysis SMTP SpringBoot UiPath User experience Web development

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intelligent Recognition of Traffic Safety Facilities Using DINO Algorithm in Deep learning

Intelligent Recognition of Traffic Safety Facilities Using D...

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Electronic Information engineering and Computer Technology (EIECT), international conference on

作者： Lingxin Lu Zhonglin Xiao Lu Zhou College of Artificial Intelligence Southwest University Chongqing China

In an era of rapid technological advancement, the deep integration of computer technology with various fields, including traffic engineering, has become increasingly profound. Traditional methods of facility inspection and management suffer from inefficiencies and incomplete information, challenges that are effectively addressed by the intelligent and automated capabilities of computer technology. This pioneering study employs the DINO deep learning algorithm, focusing on comprehensive facility inspection and proposing an efficient intelligent recognition model. The model achieves an outstanding mean Average Precision (mAP) of 80.6%. Simultaneously, the article compares the training results of DINO with its predecessors, DN-DETR and Deformable DETR, highlighting the significant performance advantage of DINO. In comparison to previous research, this study places greater emphasis on the identification of various types of traffic safety facilities, further significantly enhancing the efficiency of traffic facility management and road safety.

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Seamless Machine learning Models to Detect Faulty Solar Panels 3

Seamless Machine Learning Models to Detect Faulty Solar Pane...

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3rd IEEE international conference on Electronic engineering, ICEEM 2023

作者： Elgamal, Muhammad Abdelmaksoud, Ahmed Ismail, Yehea The American University in Cairo Center of Nanoelectronics and Devices Cairo Egypt The American University in Cairo Department of Electronics and Communication Engineering Cairo Egypt

ISBN: (纸本)9798350323511

Photovoltaic energy is a renewable source that provides good opportunities for investments by manufacturing and deployment companies. Several management paradigms exist to monitor solar stations, most importantly industry 4.0 technology, which incorporates the internet of things (IoT) and machine learning (ML), among others, to provide commercial systems for fault detection and localization. We compare various ML models based on their capability of classifying faulty and functional solar panels from their measured current-voltage (IV) curves. Regularly, one needs to compare the measurements taken in the field to the values projected from the datasheets of the solar panel. This method is sensitive to the accuracy of measuring devices (i.e., characterizers) attached to each panel in our photovoltaic 4.0 monitoring technology. However, we depend solely on the historical measurements of each panel when it is faulty or functional (possibly by imitating operational faults), and we do not care about the quality of characterizers (an issue raised when we depend on datasheet performance), and then we can successfully detect operation faults. By depending on many features extracted from IV curves, we can use several ML algorithms. However, we can depend on lesser features and an even smaller training dataset. Among our tried models, we found random forest to be the most successful ML model for classification. Additionally, it is insensitive to the reduction of the training dataset and suitable for data augmentation techniques like SMOTE (Synthetic Minority Oversampling Technique). © 2023 IEEE.

关键词： Support vector machines

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Transfer learning for Hand Arthritis Prediction from X-Ray Images 3

Transfer Learning for Hand Arthritis Prediction from X-Ray I...

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3rd international conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Raman, Ramakrishnan Inbamalar, T.M. Pushpalatha, N. Meenakshi, S. Kumar, Ashok Razia, Shaik Gopinath, N. Symbiosis Institute of Business Management Maharashtra Pune India Symbiosis International Maharashtra Pune India R.M.K. College of Engineering Department of Electronics &communication Tamil Nadu Thiruvallur India Marri Laxman Reddy Institute of Technology Department of Computer Science Telangana Hyderabad India R.M.K. Engineering College Department of Science and Humanities Tamil Nadu Kavarapettai India Banasthali Vidyapith Department of Computer Science Rajasthan 304022 India Koneru Lakshmaiah Education Foundation Department of Computer Science Andhra Pradesh Vaddeswaram India Sri Sairam Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350336238

Arthritis is a bone disorder that includes swelling and pain in one or more joints. Everyone can develop osteoarthritis, but it grows more common as individuals get older. When arthritis deteriorates over time, it can lead to persistent pain, making it challenging to do daily tasks, and making activities like walking and climbing stairs painful and difficult. If arthritis is correctly identified and treated in its early stages, these consequences can be avoided. The goal of this project is to create two transfer learning models that, by spotting arthritis in its earliest stages, can lower the likelihood of acquiring chronic arthritis. For this purpose, Google served as the source of the images used in this study. After being purchased from Google, the data collection is preprocessed using three different methods. Image scaling, noise reduction, and image enhancement are a few of the pre-processing approaches. The transfer learning models are trained and assessed using this preprocessed dataset. In this work, two distinct transfer learning models are established. The models include SegNet and ENet. On a graph, the outcomes for the performances of both models are displayed. The training data from the first few epochs of the ENet model and SegNet model are also used in the analysis. The models' final accuracy and loss values are then assessed. In the end, it was discovered that the SegNet model had a lower loss value and more accuracy than the other. The model created in this study can be utilised as a preliminary test for arthritis when a person exhibits moderate arthritis symptoms because the final accuracy of the model is higher than or equal to 95%. © 2023 IEEE.

关键词： Image enhancement

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data Urbanity: Smart City Evolution Through IoT and data Science 3

Data Urbanity: Smart City Evolution Through IoT and Data Sci...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Singh, Ajay Kumar, Manni Chandigarh University Department.of Computer Science and Engineering Punjab Mohali India

ISBN: (纸本)9798350343632

The rapid increase in population density in urban cities has created a demand for suitable infrastructure and services to meet the needs of urban residents. To address this demand, urban planners and developers are turning to data science for assistance in creating smart city solutions. The Internet of Things (IoT) has played a significant role in the application of data science in urban planning, as IoT devices such as sensors and smart devices provide vast amounts of data that can be used to gain insights into the operations of the city and make data-driven decisions to enhance its productivity, efficiency, and overall quality of life. This paper provides a general overview of data science in urban planning for smart city development. It highlights the importance of data science in urban planning and the role it plays in creating smarter, more effective cities. By applying data analytics, machine learning, and other data science approaches, urban planners can gain insights into various aspects of urban life, including traffic patterns, energy usage, and public safety. These insights can then be used to develop strategies that improve traffic flow, reduce energy waste, and enhance overall quality of life in cities. The study aims to describe the function of data science in urban planning, examine case studies and best practices, discuss the challenges and limitations associated with applying data science in urban planning, and provide suggestions for further research and advancements in the field. By investigating how data science can be effectively used in urban planning for smart city development, this study contributes to the growing body of knowledge on creating more sustainable and livable cities. © 2023 IEEE.

关键词： Artificial intelligence Biomarkers cardiovascular disease Computed Tomography (CT) data analysis Deep learning Diagnostic methods Early detection Electrocardiogram (ECG/EKG) Health informatics heart disease Machine learning Magnetic Resonance Imaging (MRI) Medical imaging Predictive modeling Remote monitoring Risk assessment Signal processing Ultrasound Wearable devices

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