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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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A Novel Method to Detect Oral Carcinoma Using Box Annotation Based on YOLO Model

A Novel Method to Detect Oral Carcinoma Using Box Annotation...

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2024 International Conference on Advances in Computing, Communication, electrical, and Smart Systems, iCACCESS 2024

作者： Laila, Shahara Ema, Romana Rahman Galib, Syed Md. Jamil, Abu Rafe Md Uddin, A. F. M. Shahab Jashore University of Science and Technology Dept. of Computer Science and Engineering Jashore Bangladesh

ISBN: (纸本)9798350350289

Oral carcinoma affects millions of individuals globally and is a major public health problem due to its high fatality rate. Treatment effectiveness depends on early detection and improves survival rates. An effective method for automated oral cancer screening is deep learning. The paper's primary goal is to use deep learning techniques to advance the state of oral cancer diagnosis, specifically YOLOv5 and YOLOv7. We aimed to achieve accurate detection by leveraging bounding box annotations for both two distinct types of data: collected clinical data and benchmark data of oral carcinoma. The proposed methodology encompasses data collection, preprocessing, expert-driven bounding box annotation and the use of cutting-edge deep learning models for lesion identification, YOLOv5 and YOLOv7. Bounding box serving as expert annotations are meticulously added to pinpoint cancerous regions within the images. Subsequently, a comprehensive evaluation is conducted, measuring the performance of both models based on precision, recall rate (sensitivity) and mean average precision (mAP). Our findings reveal that YOLOv5 outperforms YOLOv7 in terms of oral cancer detection, demonstrating superior precision and recall rates. In the evaluation of this research, the YOLOv5 model demonstrated strong performance metrics on both clinical and lip and tongue cancer datasets. For the lip and tongue cancer dataset, the YOLOv5 model achieved a precision of 97.2%, a mAP (mean Average Precision) of 97.7% and a recall of 96.3%. © 2024 IEEE.

关键词： Clinical research

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Optical Mark Recognition with Object Detection and Clustering 6

Optical Mark Recognition with Object Detection and Clusterin...

引用

6th International Conference on electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Tabassum, Kaifa Rahman, Zhalok Shahjalal University of Science and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350385779

OMR, or optical mark recognition, is the automated analysis of human-marked documents. Presently, many competitive examinations rely on multiple-choice questions, and the answers to these questions are recorded in OMR sheets. In this study, we present an automated answer detection system from an OMR sheet image using computer vision techniques. Typically, evaluating an OMR sheet from an image involves scanning, extracting data, and interpreting the results through various image processing techniques. However, traditional approaches have limitations. They cannot detect empty responses or recognize multiple choices properly without a specified layout or total number of questions stated. We took a unique approach by employing an object detection model, YOLOv8n, to detect and classify OMR marks. To address overlapping bounding boxes, we implemented the DBSCAN clustering algorithm to effectively group the columns and determine question order. Our system is designed to work with multiple layouts, as it can detect columns through clustering. Furthermore, it can identify unanswered questions as well as multiple marked options. To train and evaluate our model, we created a custom dataset with five different OMR sheet layouts. We achieved a remarkable precision of 96.5%, a recall of 99.8%, and an mAP (mean average precision) of 97.3%. These results demonstrate the efficacy of our approach to OMR sheet analysis. © 2024 IEEE.

关键词： Object detection

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Advanced Forecasting and Analysis Techniques for Optimizing Website Performance: A Comparative Study of Time Series Models 2

Advanced Forecasting and Analysis Techniques for Optimizing ...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Raikar, Gayathri Harshith, D. Guruprasad, N. Global Academy of Technology Dept. of Computer Science and Engineering Bangalore India Buddha Institute of Technology Computer Science and Engineering Gorakhpur India

ISBN: (纸本)9798350368109

Analyzing website performance is crucial for optimizing a site's functionality, enhancing user engagement, and aligning with business goals. This evaluation focuses on essential metrics such as average engagement time per session, engagement rate, and events per session, which significantly impact user experience and overall business success. Through effective performance analysis, organizations can improve user satisfaction, boost conversion rates, and enhance their profitability and reputation. This paper reviews different approaches for analyzing these key metrics, explores their effects on user behavior and business performance, and offers best practices for optimizing website performance. By employing detailed performance analysis, businesses can make informed decisions and realize their strategic goals in the digital environment. © 2024 IEEE.

关键词： Channel Performance Data Analysis Time Series Forecasting User Engagement Website Performance

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Interference Mitigation and Resource Allocation in Integrated TN-NTN Systems via Deep Q-Learning 15

Interference Mitigation and Resource Allocation in Integrate...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Park, Seoyeong Jung, Soyi Ajou University Dept. Electrical and Computer Engineering Suwon16499 Korea Republic of

ISBN: (纸本)9798350364637

As the demand for spectrum increases, the coexistence of terrestrial networks (TNs) and non-terrestrial networks (NTNs) is becoming a critical aspect of 6th generation (6G) communication scenarios. Integrated TN-NTN systems share the same frequency bands, leading to significant performance degradation due to co-channel interference. This paper proposes a deep Q-network (DQN) based deep reinforcement learning (DRL) algorithm to efficiently allocate resource blocks within shared frequency bands to each user equipment (UE) in integrated TN-NTN systems. The proposed algorithm continuously interacts with the network environment to learn optimal resource allocation policies. The proposed algorithm continuously interacts with the network environment to learn optimal resource allocation policies, thereby minimizing interference and maximizing system data throughput, promoting efficient use of spectrum resources. © 2024 IEEE.

关键词： Deep reinforcement learning

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Effective Polyp Segmentation in Medical Imaging: A Semi-Supervised Approach with Progressive Supervision 3

Effective Polyp Segmentation in Medical Imaging: A Semi-Supe...

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3rd International Conference on Big Data, Information and computer Network, BDICN 2024

作者： Ye, Shunlong Ji, Zexuan Nanjing University of Science and Technology Dept. of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350363821

Medical imaging plays a pivotal role in diagnosing and monitoring various diseases, and accurate polyp segmentation is essential for early disease detection and treatment planning. However, the scarcity of well-Annotated medical imaging data hampers the development of effective segmentation models. Semi-supervised learning stands out as a critical approach to address this issue. Traditional semi-supervised medical image segmentation models leverage strong-weak perturbation consistency losses to provide supervision signals. While these losses are effective in promoting consistency, they often exhibit a drawback, that is the dominance of strong perturbations during early training stages. This dominance hinders the model's ability to extract meaningful features, ultimately impacting its overall performance. In response to these challenges, we introduce a novel training method applied to polyp segmentation. We employ a progressive approach, gradually introducing strong-weak perturbation supervision signals. This gradual guidance empowers the model to focus on learning relevant features from the beginning of training, mitigating the issue of early dominance and enhancing its performance. Our method is evaluated on two widely-used polyp segmentation datasets and surpasses state-of-The-Art methods, demonstrating substantial improvements in segmentation accuracy. © 2024 IEEE.

关键词： Self-supervised learning

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Optical Character Recognition of Persian Epigraphs by Using Artificial Neural Networks in MATLAB

Optical Character Recognition of Persian Epigraphs by Using ...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Khan, Sumaiyya Bardekar, Aashish Sipna College of Engineering & Technology Dept. of Computer Science and Engineering Amravati India

ISBN: (纸本)9798331517953

Persian, also known as Farsi, was once a dominant language and cultural influence worldwide. Today, it is nearly extinct in the Indian subcontinent. However, for centuries, Farsi played a major role in high culture and administration across the subcontinent. Persian characters are complex, with various features such as ascenders, descenders, vertical lines, and curves. Automatic translation of Persian characters from inscriptions into English is currently unavailable. The process of deciphering and translating inscribed text in epigraphy can be time-consuming. This paper specifically focuses on identifying Persian epigraphs. First, the inscription image is cleaned up, and images containing Persian characters are obtained. Using preprocessing techniques, the images are then prepared for input to artificial neural networks. The artificial neural networks are trained to effectively recognize various patterns of Persian characters, as they can learn complex patterns and adapt to changes in images, resulting in high accuracy in Persian character recognition. Finally, the proposed method's performance is evaluated using accuracy, precision, and speed metrics. The results demonstrate that the method accurately and rapidly detects Persian characters in various images. This research has the potential to advance Persian character recognition and image processing, enabling broader applications such as converting image documents into text, character recognition in documents, and text translation with Persian image content. © 2024 IEEE.

关键词： MATLAB

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Wearable Device With Speech and Voice Recognition for Hearing-Impaired People 21

Wearable Device With Speech and Voice Recognition for Hearin...

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21st International Learning and Technology Conference, L and T 2024

作者： Tharwat, Marwa Wardak, Yasmin Balbaid, Shumokh Radin, Ejlal King Abdulaziz University Dept. of Electrical and Computer Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

Hearing impairment is a widespread health condition that affects millions of people worldwide. It refers to the lack of or low functionality of the hearing organs, resulting in difficulties in effective communication and participation in everyday conversations. This limitation can negatively impact various aspects of the social and personal lives of the hearing-impaired individuals. Moreover, the high cost and limited availability of assistive devices further restrict accessibility for those with limited financial resources. To address these challenges, this study develops an affordable wearable device equipped with speech and voice recognition technologies to enhance the accessibility of spoken language for individuals with hearing impairment. OpenAI Whisper is employed for real-time conversion of spoken words into written text. Voice recognition, utilizing Mel Frequency Cepstral Coefficients (MFCCs) and Random Forest (RF) classifier, identifies and distinguishes speakers within the user's family and friends zone. The recognized spoken words from the speech recognition process and the speaker's identity from the voice recognition process are combined and displayed on a screen to the hearing impaired. By combining speech and voice recognition technologies, the proposed device breaks communication barriers for individuals with hearing impairment and enhance their overall communication experience. © 2024 IEEE.

关键词： Speech recognition

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Cat-CNN: Human Eye Cataract Detection from Color Fundus Photograph with Deep CNN with Optimized Cascaded Network

Cat-CNN: Human Eye Cataract Detection from Color Fundus Phot...

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2024 IEEE International Conference on Signal Processing, Information, Communication and Systems, SPICSCON 2024

作者： Islam, Md Sariful Bappy, Md Tusher Ahmad Shawon, Jubayer Ahmed Hasan, Mehedi Rahman, Wahidur Akter, Lija Azad, Mir Mohammad Dept. of Computer Science and Engineering Uttara University Uttara Dhaka Bangladesh Dept. of ICE Bangladesh Army University of Engineering and Technology Natore Bangladesh Dept. of Computer Science and Engineering Inpendent Researcher Dhaka Bangladesh

ISBN: (纸本)9798331510213

Cataracts are clouding of the lens in the eye, leading to loss of vision that can progress to blindness if not treated. This paper proposed a new method for automatic cataract detection using color fundus images and deep learning methods. A dataset of 1,105 color fundus images labeled by expert ophthalmologists was used in this process. We used seven pre-trained CNNs (DenseNet121, EfficientNetB0, MobileNetV2, InceptionV3, Xception, ResNet50, VGG16, and VGG19) for feature extraction before reducing the extracted features using PCA. We used the following combination of machine learning classifiers: SVC, RF, Decision Tree, Gaussian Naive Bayes, XGBoost, K-Nearest Neighbors, and Logistic Regression. For evaluating the models’ performance, we used accuracy, precision, recall, F1-score, and computational efficiency. For all metrics, MobileNetV2 with Random Forest achieved perfect scores: 100% accuracy, precision, recall, and F1-score, with an average processing time of 669 ms ± 28.8 ms. Thus, it can be applied in real-time applications. EfficientNetB0 with SVC gave an average accuracy of 87.33%, with the rest of the precision and recall metrics above 86%. Then, ResNet50, VGG16, and VGG19 followed with high accuracies between the range of 89.64% to 90.50%. It systemizes the proper choice of architectures of CNNs and classifiers, making the system both accurate and computationally efficient. Future work will include augmentation of the dataset, real-time support in the clinical setting, and advanced techniques for image preprocessing, generative adversarial networks. In addition, the development of an automated annotation tool, improvement of explainable AI, will further improve the deployment of robust AI systems in early diagnosis of cataracts, enhancing the outcome for patients. © 2024 IEEE.

关键词： Cataract detection CNN color fundus photography deep learning feature extraction image classification machine learning ophthalmology PCA

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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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