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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...

引用

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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Real Time Monitoring and Configuration Interface for Smart Home Device Management Using Streamlit and Pygame

Real Time Monitoring and Configuration Interface for Smart H...

引用

2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Mani, G. Suma Krishna, G. Venkata Praveena, M.D. Anto Asha, P. Selvan, Mercy Paul Sathyabama Institute Of Science And Technology Dept. Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

One of the helpful tool for measuring and managing home power consumption is one of the clever and creative tool for tracking the usage of Home Power Management System. The Goal of this system is to lower the power bills, also promote energy efficiency, and provide the users active control over how much energy they use. Smart meters, an intuitive and user-friendly smartphone app, and real-time energy monitoring sensors are some of the system's essential parts and these are the key parts of the system. Installing smart meters makes it possible to accurately track the power usage and provide data to the system. Users can be able to see their energy usage instantly and make smart decisions when real-time energy monitoring equipment is included. Further, the system uses advanced smart technology, analytics and machine learning algorithms to provide clients customized suggestions on how to save energy consumption. Homes may save a lot of money on electricity bills, help the environment, and have more control over how much energy they use by using our Smart Home Power Management System. © 2024 IEEE.

关键词： Smart meters

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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 ...

引用

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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Real-Time Dashboarding using Big Data Tools 7

Real-Time Dashboarding using Big Data Tools

引用

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Sanjay, R. Pulakhandam, Dheeraj Nirmalrani, V. Sathyabama Institute of Science And Technology Dept. Computer Science And Engineering Chennai India

ISBN: (纸本)9798350359299

Real-time dashboarding software empowers organizations to harness the burgeoning IoT data stream, transforming raw sensor readings into actionable insights through a secure and scalable architecture. Data from connected devices seamlessly flows via lightweight protocols like MQTT and is managed by a robust streaming platform like Kafka. Big data tools like Spark then extract meaning from this high-velocity data, preparing it for visualization on dynamic dashboards hosted by cloud platforms. These user-centric dashboards track key metrics, identify trends, and drive informed decision-making, but tackling challenges like scalability, security, and data quality is crucial to unlock the full potential of this transformative technology. © 2024 IEEE.

关键词： Data visualization

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Beyond Copy Paste: Plagiarism Detection using Machine Learning 7

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

引用

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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Sentient Sound waves: Elevating Emotional Communication with AI-Generated Speech Technology 5

Sentient Sound waves: Elevating Emotional Communication with...

引用

5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Roshan, Rahul G. Roshan, Rohit Sohan, M.H. Raj, S.M. Sutharsan Prasad, V.R. Badri Dept. of Computer Science and Engineering PES University Bangalore India

ISBN: (纸本)9798350361155

This research-based project is about a new way to put feelings into computer-generated speech. It has two stages: text emotion detection and emotional speech synthesis. In the former part, labeled text data is used to build models to spot the emotions. This model learns to change pitch, volume, and rhythm based on the given emotion. During the latter phase, the text is changed into neutral speech using text-to-speech (TTS) methods. The model is used for practice to find the emotion in the text, tagging the text with that emotion. Also, a deep-learning model known as a tacotron is employed to make an emotional-sounding speech. The model perfectly uses machine learning strategies to blend feelings into speech. This opens up possibilities for turning plain text into speech with feelings of varied emotions. The proposed system makes a novice and important contribution to natural language processing for emotion identification and deep learning for emotional speech synthesis. © 2024 IEEE.

关键词： Speech synthesis

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Performance Evaluation of Openairinterface's Scheduling Algorithms for 5G Networks 4

Performance Evaluation of Openairinterface's Scheduling Algo...

引用

4th International Conference for Emerging Technology, INCET 2023

作者： Bhavana, D. Chaudhari, Shilpa Dept. of Computer Science and Engineering Bengaluru India Dept. Computer Networks and Engineering Bengaluru India

ISBN: (纸本)9798350335750

The introduction of the fifth generation (5G) of mobile networks (5G) has showed how crucial enhanced scheduling approaches are managing the scarce frequency spectrum whereas meeting 5G transmission needs. Openairinterface (OAI) simulation environment helps the researcher to analyses various components of 4G/5G Networks. In this paper, performance of 5G mobile network is analyzed by comparing the results with 5G scheduler algorithms available in OAI in terms of throughput. offered by openairinterface to check the performance. Simulated OAI environment consists of UE and gNodeB (gNB). The pcap file is generated and analyzed using Wireshark. The results show how schedulers allocated resources to UE from gNodeB (gNB) and throughput is the performance metric used to analyze. Resource allocation using Round Robin at UE gives uniform throughput compare to first in first out based resource allocation. © 2023 IEEE.

关键词： 5G mobile communication systems

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A Deep Learning Approach to Ringworm and Vitiligo Identification in Skin Imagery 5

A Deep Learning Approach to Ringworm and Vitiligo Identifica...

引用

5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Kamasani, Kavya Venna, Deepa Badavathula, Harshitha Ranga, Namratha Velagapudi Ramakrishna Siddhartha Engineering College Dept of Computer Science & Engineering Vijaywada India

ISBN: (纸本)9798331504403

Diagnosing skin illnesses such as vitiligo and ringworm can be difficult because of their similarity to other dermatological conditions and their different looks. The suggested approach combines Local Binary Patterns and Grey Level Co-occurrence Matrix, two potent feature extraction techniques. While Local Binary Patterns depicts the local texture patterns inside an image, Grey Level Co-occurrence Matrix analyses the spatial relationships between pixels to collect textural information. These traits are successfully used to depict the feature extraction with the unique qualities of vitiligo and ringworm lesions with accuracy 98%, having been retrieved from dermatological photographs. However, previous machine learning research produced inaccurate results. Then, for classification we tried different deep learning models like Google Net, Mobile Net, ResNet50, ResNet101, Xception, CNN, and VGG19. Among these, we found that VGG19 provided good results for us with an accuracy of about 99.19%. The pre-trained VGG19 model is improved by utilising deep learning and the extracted characteristics to reliably identify and categorise vitiligo and ringworm lesions. The model's discriminative power is increased by integrating Grey Level Co-occurrence Matrix and Local Binary Patterns features, and it maintains strong classification performance with less training data by utilising VGG19. All things considered, the suggested method presents a User Interface for the automated diagnosis of vitiligo and ringworm, giving medical practitioners a useful instrument for early identification and treatment, eventually enhancing patient outcomes and care quality. © 2024 IEEE.

关键词： Local binary pattern

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Revolutionizing Road Safety for Advanced Vehicular Accident Prediction 2

Revolutionizing Road Safety for Advanced Vehicular Accident ...

引用

2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Deepika, T. Yuvaraj, N. Sri Preethaa, K.R. KPR Institute of Engineering and Technology Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350327533

The detection and tracking of vehicle speed are crucial for ensuring the safety of civilians and preventing accidents. Speed tracking plays a pivotal role in traffic surveillance, where effective management and the well-being of citizens take precedence. The approach leverages computer vision-based models, specifically Multi-scale Detector for object detection, and a fusion of the DeepSORT and Centroid Sort algorithms for vehicle tracking from streaming video. To cater to the requirements of these algorithms, a custom dataset was developed within a controlled setting. Dataset augmentation was employed to prevent overfitting, thereby enhancing the dataset's diversity and the model's ability to generalize. The method demonstrates the capacity to accurately detect and track vehicles, maintaining performance even in high-traffic conditions. Cross-validation at every epoch during the training phase was implemented to increase the model's accuracy. In addition to the technical aspects, an LED display board was integrated to provide real-time data to oncoming vehicles at road curves. This display showcases information such as vehicle types, passage time, and the number of vehicles within the curvy road. The proposed Multi-Scale Detector model, a customized version of the You Only Look Once (YOLO) architecture, achieved a mean average precision (mAP) of 0.954 at a confidence interval of 0.5. This overall research provides an effective and accurate solution for optimizing vehicle tracking, enhancing transportation safety, and enabling a variety of practical applications in traffic management and monitoring. © 2024 IEEE.

关键词： DeepSORT Euclidean Distance and Vehicle Intelligence Multi-scale Detector Object Detection Speed Tacking

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Addressing the Long-Tailed Data Distribution in Bone Marrow Cell Identification through Class Balance Deep Classification Model 2

Addressing the Long-Tailed Data Distribution in Bone Marrow ...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Ghosh, Rakesh Shankar Shill, Pintu Chandra Khulna University of Engineering & Technology Dept. of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350317558

The accurate classification of bone marrow cells has long been a critical component of diagnosing blood diseases. However, traditional methods rely on subjective human interpretation and lack standardized quantitative criteria. To address this challenge, this research proposes a Class Balance Deep Classification Model (CBDCM) designed to classify bone marrow cells using EfficientNet to handle the long-tailed data distribution problem. In order to validate the propose model by applying it to proper dataset and analyzing the experimental outcomes of the propose method. Notably, CBDCM attains impressive precision, sensitivity, and specificity values of 86.14%, 87.53%, and 99.45%, respectively. Simulation results show that CBDCM outperforms, demonstrating superior performance in a detailed comparison with both deep neural networks and traditional machine learning techniques. The findings of this study, with significant implications for addressing class imbalance in datasets, hold promise for standardizing the classification of bone marrow cells, potentially revolutionizing the diagnosis of blood diseases. © 2024 IEEE.

关键词： bone marrow cell CBDCM data imbalance deep learning EfficientNet gaussian smoothing

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