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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Kojima, Iori Goto, Tomio Nagoya Institute of Technology Dept. of Computer Science Nagoya Japan

ISBN: (纸本)9798350355079

While the performance of denoising and deblurring has continued to improve, there is a lack of prior research on methods that address both simultaneously. Therefore, this paper employs a model known as NAFNet, which has been shown to be capable of removing either noise or blur. We verify that it is possible to simultaneously remove both noise and blur using this model. © 2024 IEEE.

关键词： Adversarial machine learning

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Advancing Pediatric Appendicitis Diagnosis: Harnessing CNN and Hybrid Models with Ultrasound Imaging

Advancing Pediatric Appendicitis Diagnosis: Harnessing CNN a...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Kulkarni, Supritha Sharma, Sanchit Manavi, J. Vachana, U. Bhargavi, M.S. Bangalore Institute of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331508685

Pediatric appendicitis, an acute inflammatory disease, arises from the obstruction of the appendix, often due to inflammation or a fecalith. This common abdominal emergency in children presents diagnostic challenges due to its varied clinical manifestations, which can lead to misdiagnosis or higher perforation rates stemming from atypical symptoms. This research investigates the use of deep learning, more especially Convolutional Neural Networks (CNNs), by utilizing ultrasonic imaging data, which is less expensive, non-invasive, and quicker than the other approaches now in use. Several variants are evaluated including a basic CNN and more advanced hybrid models such as CNN-Support Vector Regression, CNN-Transformer, and Hierarchical Deep CNN, to identify the most optimal model to significantly enhance diagnostic accuracy, for young patients facing this condition. The study's main goal is to develop a highly accurate and dependable diagnostic tool for evaluating the severity of pediatric appendicitis by differentiating between simple and complex cases and identifying whether surgery is required to inform treatment plans. Various challenges that need to be tackled are managing significant dataset imbalances, enhancing interpretability and transparency for real-world clinical settings, and ensuring the robustness and adaptability of all these models across various healthcare environments. © 2025 IEEE.

关键词： Pediatrics

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Image Quality Improvement of Denoising Method based on DRUNet for Real Noise 13

Image Quality Improvement of Denoising Method based on DRUNe...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Yasue, Sho Goto, Tomio Nagoya Institute of Technology Dept. of Computer Science Nagoya Japan

Collecting a large amount of real noise images is difficult, and effective learning must occur from a small amount of datasets. In this study, we use DRUNet, a noise eliminator. By following an effective learning proc... 详细信息

ISBN: (纸本)9798350355079

关键词： Adversarial machine learning

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Secured Entry Control: An IoT and Deep Learning based Mask Detection System for COVID-19 Suspected Patient Screening and Access Regulation 3

Secured Entry Control: An IoT and Deep Learning based Mask D...

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3rd International Conference on Advancement in Electrical and Electronic Engineering, ICAEEE 2024

作者： Sultana, Abida Peyal, Hasibul Islam Islam, Md. Injamul Abdal, Md. Nazmul Miraz, Shafiun Kontho, Md. Mahin Rahaman Leion, Zannatul Mawa Mamun, Abdul Al Rajshahi University of Engineering & Technology Dept. of Electrical & Computer Engineering Rajshahi Bangladesh Uttara University Dept. of Computer Science & Engineering Dhaka Bangladesh University of Liberal Arts Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi Bangladesh Tejgaon College Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350388282

The new coronavirus SARS-CoV-2, which triggered the COVID-19 pandemic, has had an unparalleled effect on economies, cultures, and world health. In response to the critical need for strict COVID-19 screening systems in public areas, this study presents a creative Secured Entry Control system. Detecting and controlling possible COVID-19 carriers attempting to enter the country is made possible by this system, which makes use of deep learning algorithms and IoT technology. The mask detection algorithm, MobileNetV2 model, has an exceptional validation accuracy of 98.96\%. The model's reliability is supported by performance evaluations using ROC curves, confusion matrix analysis, and an AUC value of 98.96\%, which is close to the optimal AUC score of 100\%. Because MobileNetV2 is well-suited for low-processing devices, it is easy to deploy it on Raspberry Pi, which helps to create an affordable system. Furthermore, by spotting increased body temperatures, a contactless temperature sensor improves the system's ability to identify possible COVID-19 carriers. The functioning of the system is confirmed by the working prototype that is presented in the Experimental Results section. The main goal of this research is to create an autonomous system that is affordable and selectively allows access to people who are less likely to transmit COVID-19 in public areas. To achieve the overall objective of reducing the spread of COVID-19 in public spaces, this research highlights the successful integration of mask identification algorithms and IoT. © 2024 IEEE.

关键词： COVID-19

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Survey on Deep Learning-based Speech Technologies in Voice Chatbot Systems

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KSII Transactions on Internet and Information Systems 2025年第5期19卷 1406-1440页

作者： Ma, Seunghee Oh, Junseok Kim, Minseo Kim, Ji-Hwan Dept. of Artificial Intelligence Sogang University Seoul Korea Republic of Dept. of Computer Science and Engineering Sogang University Seoul Korea Republic of Dept. of Artificial Intelligence Technology LOTTE INNOVATE Company Seoul Korea Republic of

Recent advancements in large language models (LLMs) such as ChatGPT have contributed to the development of chatbot systems. Specifically, speech has been recognized as the optimal tool for interactive dialogue, leading to increased interest in voice chatbots. Voice chatbots offer information and services through voice interactions, enhancing user experience and improving service accessibility. This survey paper introduces the latest developments in the core technologies of voice chatbot systems, including deep learning-based automatic speech recognition, speech synthesis, and speech emotion recognition. It focuses on advanced research to enhance speed and performance, which is crucial for applying speech technologies in voice chatbots. In automatic speech recognition, we introduce methodologies such as Connectionist Temporal Classification (CTC), Attention based Encoder-Decoder (AED), and Recurrent Neural Network Transducer (RNN-T), along with studies optimizing Transformer-based models for real-time automatic speech recognition. In speech emotion recognition, we explore the use of pre-trained models and the latest techniques for accurate emotion prediction. For speech synthesis, the focus extends to two-stage and End-to-End (E2E) approaches, with additional research on integrating emotional information to generate natural speech. Copyright © 2025 KSII.

关键词： Chatbots

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Intelligent Traffic Management System: An Advanced Solution 7

Intelligent Traffic Management System: An Advanced Solution

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Rushi, Tankala Zamaan, Shaik Mohammad Pravin, A. Sathyabama Institute of Science And Technology Dept. of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350359299

The Intelligent Traffic Management System (ITMS) is a cutting-edge solution designed to address multiple aspects of traffic management and safety. This advanced system incorporates various technologies to tackle issues such ashelmet compliance, traffic signal violations, number plate identification, cell phone usage tracking, and proximity-based police station alerts. By leveraging the power of artificial intelligence and machine learning algorithms, the ITMS can accurately detect and enforce helmet compliance among motorcyclists, ultimately reducing the risk of head injuries in road accidents. Furthermore, the system utilizes advanced video analytics to detect violations of traffic signals, ensuring that motorists adhere to traffic rules and regulations. Additionally, the ITMS employs automatic number plate recognition (ANPR) technology to quickly and efficiently identify vehicles with illegal or expired license plates, enabling law enforcement agencies to take appropriate action. Moreover, the system includes a cell phone usage tracking feature which uses computer vision techniques to identify drivers who are using their phones while driving, contributing toa decrease in distracted driving incidents. Lastly, by introducing proximity-based police station alerts, the system offers a comprehensive approach to address challenges and improve efficiency, the ITMS enables prompt response from law enforcement agencies by automatically notifying the nearest police station in the event of a traffic violation or accident. In summary, the Intelligent Traffic Management System provides a comprehensive and holistic approach to management and safety through itsfeatures of helmet compliance, traffic signal violation detection, number plate identification, cell phone usage tracking, and proximity-based police station alerts. © 2024 IEEE.

关键词： Traffic signals

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State of the art: Security Testing of Machine Learning Development Systems 14

State of the art: Security Testing of Machine Learning Devel...

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2024 IEEE 14th Annual Computing and Communication Workshop and Conference, CCWC 2024

作者： Das, Saikat Krishnamurthy, Bhargavi Das, Raktim Ranjan Shiva, Sajjan G Utah Valley University Dept. of Computer Science Orem United States Siddaganga Institute of Technology Dept. of Computer Science Karnataka Tumakuru India Stamford University Bangladesh Dept. of Computer Science Dhaka Bangladesh University of Memphis Dept. of Computer Science Memphis United States

ISBN: (纸本)9798350360134

In recent days, machine learning (ML) systems have become integral to nearly all mainstream applications. Understanding the underlying logic that contributes to the desired behavior in ML systems can be challenging. Humans play a crucial role in providing the necessary data samples for training ML models to ensure accurate predictions. The complexity of ML testing arises from various factors such as data dependency, dynamic model behavior, the absence of a test oracle, a vast input space, and the lack of a specific testing life cycle tailored for ML. Testing ML systems is not a straightforward process, as it not only involves verifying the code and their corresponding data but also is further complicated by the dynamic nature of these systems. A typical ML model undergoes seven stages, including eliciting business needs/requirements, gathering data, selecting a model, training the model, testing the model, deploying the model, and monitoring the model. Throughout the ML Development Life Cycle (MLDLC), each stage introduces various security risks. This paper explores the current state of the literature on security attacks and defense approaches concerning data, model, and prediction output of ML models. The paper also addresses potential security attacks at each stage of the MLDLC and the corresponding security measures to mitigate those attacks. © 2024 IEEE.

关键词： Life cycle

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Comprehensive Women's Health Risk Assessment Platform 2

Comprehensive Women's Health Risk Assessment Platform

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Navya, S. Saha, Sharmistha Nagarajan, Deepa Sathyabama Institute of Science and Technology Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350349221

The goal of this research is to develop a machine learning-based method for predicting several diseases in women, with an emphasis on polycystic ovarian syndrome (PCOs), diabetes, heart disease, and breast cancer. Timely and accurate prediction of these disorders is essential for early intervention and prevention due to their impact on women's health and prevalence. Predictive models are trained using a wide variety of medical data, including genetic markers, lifestyle factors, demographic data, and clinical test findings. To find patterns and connections between these predictors and the incidence of diseases, machine learning methods are used, such as decision trees, random forests, and support vector machines. The models are able to forecast with accuracy the probability that a woman would develop one or more of these diseases by efficiently obtaining and evaluating data from these datasets. Metrics including accuracy, precision, recall, and F1-score are used to assess the models' performance;the outcomes demonstrate the models' promising levels of predictive power. The creation of a system like this can facilitate targeted screening and preventive measures, assist medical practitioners in identifying high-risk individuals, and eventually lead to better patient outcomes. © 2024 IEEE.

关键词： Diseases

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Augmenting Underwater Plastic Detection: A Study with YOLO-V8m on Enhanced Datasets 3

Augmenting Underwater Plastic Detection: A Study with YOLO-V...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Reddy, Veeramreddy Bharath Kumar Basha, Shaik Sameer Swarnalakshmi, J. Hindustan Institute of Technology and Science Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350375190

Ocean animals and plants are in danger because there is a lot of trash in the water. This old way of keeping an eye on trash made of plastic in the ocean is hard to do, takes a long time, and can't be used to check the whole ocean. In this plan, the new deep learning algorithms, called You Only Look Once version 8 (YOLOv8), are used to find and sort plastics in the ocean right away. There's a new way to handle this subject. Everyone knows YOLOv8 for how fast and well it can find things. It has been changed in this study to deal with the problems that only happen when taking pictures underwater, like changing light, cloudy water, and different kinds of plastic. A unique set of labeled underwater pictures are used to help it find different kinds of plastic very accurately and quickly. They are different sizes and shapes. Together, YOLOv8 and underwater robotic platforms make a useful and adaptable way to always keep an eye on the ocean. This collects useful information that can be used to protect the environment and lower pollution. Better tools for keeping an eye on the environment are made possible by the study. It also shows how cutting edge AI can be used to solve big environmental problems. © 2024 IEEE.

关键词： Object detection

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Beyond Copy Paste: Plagiarism Detection using Machine Learning 7

Beyond Copy Paste: Plagiarism Detection using Machine Learni...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Vasuteja, Akula Reddy, Alavalapalli Vasanth Pravin, A. Sathyabama Institute of Science And Technology Dept. of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350359299

Since plagiarism is a major problem in both academic and professional settings, it is crucial to create effective plagiarism detection methods. Accuracy evaluation for classical models such as SVM, Naive Bayes, and Random Forests usually involves cross-validation, where the dataset is split into training and testing folds, and the accuracy for each fold is then computed. These techniques lack the neural network-like loss functions, but they can still be used to analyze model performance by charting accuracy against the number of folds. But in CNNs and RNNs, training entails updating a loss function over epochs through iterative optimization;for classification problems, this is usually categorical cross-entropy. The values of the loss function are monitored during training, and the convergence of the model is shown by graphing them against the number of epochs. Accuracy metrics are calculated in parallel for the training and validation stages, and the model's classification accuracy throughout successive epochs is displayed in graphs. The plagiarism detector provides users with a complete report that highlights plagiarized portions together with their original sources which results in 97.99% of accuracy in order to aid educators, researchers, and other professionals in preventing plagiarism. © 2024 IEEE.

关键词： Support vector machines

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