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Detecting Plagiarism in Music Clips Using Audio Signature Matching

Detecting Plagiarism in Music Clips Using Audio Signature Ma...

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International Conference on Knowledge and Smart Technology (KST)

作者： Nikitha V Sudhanva Vasanth Samaga Shrey Yashwanth Rao M P Charu Kathuria Dept. of Computer Science and Engineering PES University Bengaluru India

ISBN: (数字)9798331520403

ISBN: (纸本)9798331520410

With millions of music clips released online, plagiarism detection is essential to safeguarding creativity and protecting artists. Our model aims to address two types of plagiarism: (i) accidental plagiarism, in which an artist unknowingly reproduces elements of existing works, and (ii) intentional plagiarism, where an existing song is altered or significant portion of the song is reproduced without crediting the original creator. To solve this issue our model performs spectral analysis to extract Mel-frequency Cepstral Coefficients (MFCCs) and Chroma features from MP3 clips in the MagnaTagATune (MTT) dataset. These features are processed and compressed into compact representations called signatures using a Convolutional Autoencoder (CAE). Each signature serves as a unique representation of a song and is stored in a database. When a user uploads a clip, its signature is generated and compared with existing signatures in the database using Euclidean distance. The system retrieves the top 10 most similar signatures and flags the clip as plagiarized if the similarity exceeds a predefined threshold. Our model achieved an accuracy of 91 % and a loss of 0.032. The solution provides a powerful tool backed by a novel approach in music plagiarism detection to empower users to proactively check their work for accidental plagiarism, preventing future legal disputes. Simultaneously, it enables creators to identify unauthorized use of their work and provides them with relevant metadata about the plagiarizers to quickly identify them and pursue legal action.

关键词： Training Analytical models Accuracy Databases Law Plagiarism Autoencoders Metadata Feature extraction Digital audio players

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Facial Attribute Analysis for Emotion Recognition using Deep Learning 6

Facial Attribute Analysis for Emotion Recognition using Deep...

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6th International Conference on Intelligent Computing and Control Systems, ICICCS 2022

作者： Arun, D.N. Joshua, S. Daniel, Esther Sujith Mithun, M. Galvin Richardson, V. Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665410359

computerized test highlights are involved every day in an assortment of ways of understanding biomedical examination, picture control, between PC connections, electronic gadgets and other business exercises. The principal motivation behind the venture is to work on the workplace. Orientation uniformity, skin tone, and feeling are assumed a key part in deciding the course of a PC. This task requires the development of a Convolutional Neural Network (CNN) for age and orientation. The structure is intended to highlight a pay of around 8 years and 2 names of orientation. As well as counting the years, looks a significant part in PC vision. It is marked as 6 passionate and 6 racial characteristics. Non-verbal relational abilities, like looks, looks, and signals, are utilized in numerous human PC capacities. Utilizing the Viola-Jones calculation, pictures are eliminated from CNN. With the right utilization of the client, the normal outcomes will be apparent. Knowing individuals' feelings, the directing framework can be further developed. Assuming that the underlying face is impartial or quiet, present day strategies work best as a worldwide picture, and there is an absence old enough forecast when the facial highlights change. The information is taken from IMDb-WIKI by age and orientation, while Fer2013 and Kaggle depend on feeling and racial character. The undertaking has two models, one for age and one for foreseeing orientation correspondence through the Resnet vault, and the other for passionate mindfulness. Another closure such as old enough, feeling, skin tone and orientation, the presence of the world, spoiling and arranging resources are offered. Specifically, the huge changes in reality that are not sifted by the picture handling calculation that plans and cycles those pictures are considered before they are communicated on CNN. © 2022 IEEE.

关键词： Convolutional neural networks

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Estimation of Convolutional Neural Network for sign Language Recognition on Twitter Data 2

Estimation of Convolutional Neural Network for sign Language...

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Preethi, D. Prasath, B. Priyanka, G. Srikanteswara, Ramya Dr.N.G.P Institute of Technology Dept of Ece India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Sri Eshwar College of Engineering Computer Science and Business Systems India Nitte Meenakshi Institute of Technology Department of Computer Science & Engineering Bengaluru India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

As technology has increased these social media has became vast plat for people to share their views, thought , and twitter has become one the best social media plat form where people are sharing their thoughts ,views and their emotions publically , that's why twitter has become one of the best platform where group of people will share their thought about leader , or a trending news or about politics and religion, education etc the fast in the number of web user, In today's world has an huge measure of data produce by the inhabitants. This information plays vital role when it comes for selling a product, country's election, differentiating fake news and real news through twitter. Bias detection where we identify the statement To analysis these kind of data, deep estimation analysis is used. The aim is to find the opinion of the people by taking the data and classify it according to its divergence, i.e. the tweets will get divided into positive negative and neutral, it will help to know the opinion of the people ,and mentality of the people. © 2022 IEEE.

关键词： Convolutional neural networks

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Advancing Agricultural Diagnostics with a Hybrid Deep Learning Model for Sugarcane Leaf Disease Classification

Advancing Agricultural Diagnostics with a Hybrid Deep Learni...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Md. Asraful Sharker Nirob Ikteshad Binte Kalam Deeya Lamia Rukhsara Tapasy Rabeya Israt Jahan Dept of Computer Science and Engineering Daffodil International University Bangladesh

ISBN: (数字)9798331531973

ISBN: (纸本)9798331531980

Sugarcane, an essential crop for sugar production and biofuel, is susceptible to diseases that greatly affect its yield and quality. To address this, we propose a hybrid deep learning model combining ResNet152 and MobileNetV3Large to accurately classify sugarcane leaf diseases. Our method involves careful dataset preparation and data augmentation to enhance model robustness. The hybrid model leverages the strengths of fine-tuned pre-trained networks with custom layers, achieving an impressive accuracy of 98.19%, outperforming other models such as DenseNet121 (76.09%) and NASNetLarge (82.65%). The dataset includes images from ten sugarcane leaf disease classes, including healthy leaves. Thorough metrics, such as recall, precision, and F1-score, along with visualizations of training and validation results, emphasize the model's effectiveness. This hybrid model shows significant potential for agricultural diagnostics. Future work will focus on fine-tuning and exploring ensemble methods to improve accuracy and generalization across various agricultural environments.

关键词： Deep learning Training Accuracy Biological system modeling Scalability Crops Ensemble learning Sustainable development Sugar Diseases

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Hate Speech Detection:A Survey 4

Hate Speech Detection:A Survey

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4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Kumar, Sanjay Nagar, Abhishek Kumar, Akash Singh, Amar Delhi Technological University Dept. of Computer Science & Engineering New Delhi India

ISBN: (纸本)9781665474368

Social media has been continuously acting as a medium for people to share and convey their emotions and information. At the same time, it has been observed that people utilize social media platforms like Facebook, Twitter, etc. to post offensive and hateful content. This happens on a large scale which makes it extremely challenging to monitor them manually, paving way for automation in hate speech. This survey paper explores, summarises, and compares various methods that target hate speech detection, how they are being utilized at solving past challenges, and dive into the current and future prospects for hate speech detection. © 2022 IEEE.

关键词： Classification (of information)

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FEZ: Flexible and Efficient Zoom-In for Ultra-Large Image Classification

FEZ: Flexible and Efficient Zoom-In for Ultra-Large Image Cl...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Lu, Cheng-Han Hung, Shih-Hao National Taiwan University Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9781665480451

Mainstream deep learning algorithms are designed for typical images sizing from 224 × 224 to 600 × 600 pixels, which are relatively small compared to the whole-slide images (WSI) with billions of pixels, causing existing methods to be inefficient due to computational and memory constraints. Furthermore, most WSIs are annotated with slide-level labels, as patch-level annotations are time-consuming and have to be conducted by experienced pathologists. At the same time, the regions of interest (ROIs), e.g., where cancer, tumor, and bacteria reside, would occupy relatively small areas. Thus, we propose Flexible and Efficient Zoom-In (FEZ) as a method to precisely locate ROIs to accelerate ultra-large image classification. FEZ initially examines a low-resolution WSI with a policy network (PN) to select the potentially critical ROIs and zooms in the selected regions using medium-resolution images to select finer-grain ROIs with another PN. Eventually, FEZ loads the high-resolution patches and classifies them with a value network (VN) to make a prediction. Since FEZ only loads a few selected patches instead of the high-resolution WSI, it dramatically reduces the image loading time and the computation time needed for analyzing the patches. Experimental results show that FEZ is capable of delivering similar accuracy with higher speed than a recently proposed whole-slide training method (WST) and existing sampling methods such as Multiple Instance Learning (MIL) and CLAM. FEZ is 42× and 22× faster than MIL on the Camelyon16 and TCGA Lung Cancer datasets, respectively. With FEZ, we can reduce the classification time for ultra-large images from minutes to seconds to support interactive applications. © 2022 IEEE.

关键词： Image classification

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ADNet: An Advanced Artificial Neural Network for Early Diagnosis of Alzheimer's Disease

ADNet: An Advanced Artificial Neural Network for Early Diagn...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Md. Arif Hasan Rifat Nusrat Jahan Tanha Taranum Muhammad Nazrul Islam Faiz Al Faisal Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Dept. of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh

ISBN: (数字)9798331531973

ISBN: (纸本)9798331531980

Alzheimer's disease is a progressive neurological disorder, often referred to as dementia, that requires early and accurate diagnosis for effective treatment and management. It's a complex disease type that originates in the brain and only gets worse with timestamps, which is why it should be diagnosed early and accurately. Traditional diagnostic methods often fall short, posing challenges to healthcare professionals. This paper introduces a novel deep learning technique, ADNet, a specialized convolutional neural network (CNN) developed to classify Alzheimer's disease into four stages. For MRI image processing, we applied preprocessing techniques, including Green Fire Blue conversion and contrast enhancement using CLAHE, to improve image clarity. To address data imbalance, various data augmentation methods were implemented. During experimentation, pre-trained models like VGG16, MobileNet-V2, InceptionV3, and ResNet-50 achieved accuracies of 85%, 85%, 79 %, and 67 %, respectively, but fell short of our goals. Our ADNet model, however, attained an impressive 97.26% accuracy. This work offers significant contributions to Alzheimer's research and advances deep learning applications in medical imaging and neurological disease diagnosis.

关键词： Neurological diseases Deep learning Training Technological innovation Accuracy Magnetic resonance imaging Computational modeling Brain modeling Convolutional neural networks Alzheimer's disease

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Prediction of Suicidal Behaviour among the users on Social Media using NLP and ML

Prediction of Suicidal Behaviour among the users on Social M...

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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： Tapas Kumar Mishra G. Sucharitha Narala Siddhartha Bommena Raju Sachi Nandan Mohanty Dept of Computer Science and Engineering SRM University-Ap Andhra Pradesh India Dept of Electronics and Communication Engineering Institute of Aeronautical Engineering Telangana India Dept of Computer Science and Engineering(Data Science) Institute of Aeronautical Engineering Telangana India School of Computer Science and Engineering VIT-AP University Amaravati Andhra Pradesh India

suicide attempts. Our findings contribute to the growing field of suicide prevention through cutting-edge machine learning techniques applied to natural language data. According to our experimental results, the PM model outperformed other machine learning algorithms with the highest classification score. It achieved an impressive accuracy of 92% and an F1 score of 85%. These findings highlight the effectiveness and potential of the PM model for the task at hand. Suicides are happening more often these days. Social media messages and chats are the preferred means of expression for those who are committing suicide. Several studies have shown that it is possible to spot suicide suspects from their online conversations and posts on social media. Consequently, it is imperative to create a machine learning system for automatic early identification of suicide ideation or any abrupt changes in a user’s behaviour by examining his or her posts and chats on social media such as Twitter, Instagram, WhatsApp, and Facebook. Our research is focused on significantly improving the performance of our model to accurately recognize early indications of suicide attempts with high precision, thus contributing to the prevention of such tragic events. To achieve this, we utilized advanced text pre-processing techniques and feature extraction approaches, including CountVectorizer and word embedding. Our study involved training both XGBoost and NLP models, which were rigorously evaluated using a substantial dataset of 34,100 samples. Furthermore, to assess real-world applicability, we conducted live tests of our model using tweets collected via the Stream lit python-based web interface tool. The results of our experiments are promising, demonstrating the potential of our approach in detecting early signs of distress and aiding in the timely intervention to prevent.

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Implementation of a Camera Sensor for Helmet Detection in Two-Wheelers and Its Integration with the Electronic Control Unit (ECU)

Implementation of a Camera Sensor for Helmet Detection in Tw...

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IEEE Industrial Electronics and Applications Conference (IEACon)

作者： Akul P Abhijna MB Dhruvi T Avlani Dept. of Computer Science and Engineering Global Academy of Technology

ISBN: (数字)9798350382976

ISBN: (纸本)9798350382983

The governing bodies set across different regions have several rules and regulations for the safety of motorcyclists, which they must adhere to. Accidents are undesirable events that result in injuries, or sometimes even worse, deaths. These accidents could be averted by wearing helmets while riding. The idea of integrating the output of the image processing algorithms with the ignition system stems from the increased fatality rate of motorcyclists on the road. Therefore, this project aims to reduce such accidents by using a camera tracker as a sensor, where it is further implemented with image processing hardware, such as the Raspberry Pi Camera Module, which runs image processing algorithm to determine if helmet is being worn by the rider or not. Furthermore, Raspberry Pi Camera Module is connected to the ECU using appropriate communication protocols, and the ECU sends outputs that control the ignition system. For the helmet detection, control logics on microcontroller are developed in a way to send signals to the ECU regarding helmet detection status. This project is focused on improving safety and reducing accidents.

关键词： Visualization Accuracy Image processing Motorcycles Ignition Helmets Cameras Turning Real-time systems Accidents

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Public Transport Guiding System for Visually Impaired Users Easy to Deploy, Maintain and Extend 8

Public Transport Guiding System for Visually Impaired Users ...

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8th IEEE International Smart Cities Conference, ISC2 2022

作者： Lopez, Alberto Ferrero Ohmura, Ren Toyohashi University of Technology Dept. of Computer Science and Engineering Aichi Toyohashi Japan

ISBN: (数字)9781665485616

ISBN: (纸本)9781665485616

Public transport services are essential for any citizen but critical for those with visual disorders. Many projects have been created to help blind people overpass public transport services' inherent barriers and ensure their independence. However, the most powerful ones are usually based on complex design, making them hard to deploy, maintain, or extend, creating new problems for users in the future. This paper discusses an easy to deploy, maintain and extend tool for guiding visually impaired users on a bus journey, including IoT features for tracking vehicles and virtually requesting stops. The presented tool is based on an application server centralised design, which implements popular open data schemas and communication protocols for ensuring it is easy to be deployed, maintained and extended. The proposed design has been checked and tested by the construction of a prototype, measuring the accuracy and response times in a real public transport network, showing that this tool use is possible with a very low investment. © 2022 IEEE.

关键词： Open Data

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