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Music Genre Classification Using Convolutional Neural Network Method

Music Genre Classification Using Convolutional Neural Networ...

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2024 International Conference on Information Technology Systems and Innovation, ICITSI 2024

作者： Yulita, Intan Nurma Mutyara, Muhammad Ariq Farhansyah Suryana, Ino Ardiansyah, Firman Ramdhani, Muhammad Rasyid Wicaksono, Mokhamad Arfan Universitas Padjadjaran Department of Computer Science Sumedang Indonesia Akademi Digital Bandung Applied Bachelor in Digital Business Bandung Indonesia Universitas Padjadjaran Department of Electrical Engineering Sumedang Indonesia Akademi Digital Bandung Bachelor of Software Engineering Bandung Indonesia

ISBN: (纸本)9798331511470

Music genre classification is a prominent challenge in music information retrieval research. Most techniques for music genre classification employ machine learning algorithms to classify feature vectors extracted from sound recording segments. Several datasets have been utilized in experiments to evaluate classification accuracy in this domain. However, the datasets commonly used are relatively small, and much of the research on music genre classification continues to focus on these limited datasets. This research utilizes data from three main sources: (1) metadata derived from the Million Song Dataset, (2) the CD2C tagtraum genre annotations dataset as ground truth for genre labels, and (3) the Million Song Dataset mapping archive, which provides metadata mapping to SpotifyAPI, along with (4) acoustic features extracted via SpotifyAPI. The resulting dataset comprises 64,088 music tracks spanning five popular genres: Rock, Electronic, Jazz, Pop, and Rap. The objective of this research is to develop a model capable of predicting music genres based on acoustic features extracted through SpotifyAPI. To achieve this, a Convolutional Neural Network (CNN) architecture was implemented, with a focus on hyperparameter optimization, particularly in terms of the number of layers and the type of weight initialization. The optimal hyperparameters were identified as a two-layer CNN architecture utilizing Xavier Normal Initialization for weight initialization. The resulting model achieved a weighted F1-score of 0.7030 during testing. © 2024 IEEE.

关键词： Convolutional neural networks

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Task Consistent Prototype Learning for Incremental Few-Shot Semantic Segmentation 27th

Task Consistent Prototype Learning for Incremental Few-Shot...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Xu, Wenbo Wu, Yanan Jiang, Haoran Wang, Yang Wu, Qiang Zhang, Jian Faculty of Engineering and Information Technology University of Technology Sydney Sydney2007 Australia School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Department of Computer Science and Software Engineering Concordia University MontrealH3G2J1 Canada

ISBN: (纸本)9783031783463

Incremental Few-Shot Semantic Segmentation (iFSS) tackles a task that requires a model to continually expand its segmentation capability on novel classes using only a few annotated examples. Typical incremental approaches encounter a challenge that the objective of the base training phase (fitting base classes with sufficient instances) does not align with the incremental learning phase (rapidly adapting to new classes with less forgetting). This disconnect can result in suboptimal performance in the incremental setting. This study introduces a meta-learning-based prototype approach that encourages the model to learn how to adapt quickly while preserving previous knowledge. Concretely, we mimic the incremental evaluation protocol during the base training session by sampling a sequence of pseudo-incremental tasks. Each task in the simulated sequence is trained using a meta-objective to enable rapid adaptation without forgetting. To enhance discrimination among class prototypes, we introduce prototype space redistribution learning, which dynamically updates class prototypes to establish optimal inter-prototype boundaries within the prototype space. Extensive experiments on iFSS datasets built upon PASCAL and COCO benchmarks show the advanced performance of the proposed approach, offering valuable insights for addressing iFSS challenges. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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PolyLite: A Lightweight Transformer With Multi-Scale Attention for Colorectal Polyp Segmentation 15

PolyLite: A Lightweight Transformer With Multi-Scale Attenti...

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15th International Conference on software, Knowledge, Information Management and Applications, SKIMA 2023

作者： Elkarazle, Khaled Raman, Valliappan Chua, Caslon Then, Patrick Faculty of Engineering Sarawak Malaysia Coimbatore Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu India University of Technology Department of Computer Science and Software Engineering Swinburne Melbourne Australia

ISBN: (纸本)9798350316551

The urgent need for early detection and intervention in colorectal cancer highlights the importance of automating colorectal polyp detection. Despite the use of deep learning techniques for polyp detection from medical images, these methods often struggle with issues such as skewed data distribution and class imbalances. In this study, we introduce PolyLite, a lightweight transformer-based model designed to balance performance and efficiency. PolyLite features a custom SegFormer encoder that takes advantage of its lightweight design, and a decoder that uses a multi-scale attention network (MA-NET) to achieve an optimal balance between accuracy and efficiency. To improve accuracy and reduce false negatives, we incorporate a reverse attention layer that effectively counteracts input mask attention during training. This refocuses the model's efforts to better learn non-polyp regions. PolyLite undergoes a two-stage training process, beginning with pre-training on the ImageNet dataset and then fine-tuning on the Kvasir-SEG dataset. We conduct rigorous evaluations on three complex datasets: CVC-ClinicDB, CVC-ColonDB, and ETIS-LaribPolypDB. Empirical results demonstrate PolyLite's superiority over existing techniques, as evidenced by its performance in terms of Intersection over Union and Dice scores. This validates its effectiveness and scalability in polyp segmentation, making complex and computationally intensive architectures unnecessary. © 2023 IEEE.

关键词： Efficiency

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Enhancing Content-Based Histopathology Image Retrieval Using QR Code Representation

Enhancing Content-Based Histopathology Image Retrieval Using...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Rouzegar, Hamidreza Rahnamayan, Shahryar Bidgoli, Azam Asilian Makrehchi, Masoud Ontario Tech University Department of Electrical Computer and Software Engineering OshawaON Canada Brock University Engineering Department St. CatharinesON Canada Laurier University Faculty of Science Wilfrid WaterlooON Canada

ISBN: (纸本)9781665430654

The growing field of Content-Based Medical Image Retrieval (CBMIR) plays an integral role in the diagnosis and treatment plan of numerous diseases, including cancer. However, the effective representation of gigapixel medical images via a large number of extracted features remains a challenging task, crucially influencing other processes in a digital workflow. In this paper, we propose a novel QR code representation strategy to enhance retrieval performance. Unlike the traditional one-dimensional binary representation methods (i.e., barcodes), this 2D approach captures more intricate and informative patterns from the features by differentiating each pair of features resulting in a 2D binary vector. We delve into three distinct QR code generation strategies, namely, the Thresholding QR, the MinMax QR, and the Hybrid QR, each offering unique strengths. Our experiments on representing whole slide images of the Cancer Genome Atlas (TCGA) dataset reveal that while the Hybrid QR tends to provide balanced performance, there are instances where the other two methods outshine. Even though this approach requires more memory usage, the considerable enhancement in accuracy justifies this trade-off;obviously, in medical applications, accuracy holds the highest priority. Hence, the findings indicate that QR codes can effectively improve the performance of CBMIR systems by not only accelerating the retrieval process but also increasing the accuracy of image retrieval, leading to potentially more accurate diagnoses and treatment planning. © 2023 IEEE.

关键词： Barcode Content-Based Image Retrieval Histopathology Images Medical Image Analysis QR Code

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An Ontology-based Approach to Facilitate Semantic Interoperability of Context-Aware Systems 28

An Ontology-based Approach to Facilitate Semantic Interopera...

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28th International computer Conference, computer Society of Iran, CSICC 2023

作者： Barangi, Hamed Rahimi, Shekoufeh Kolahdouz Zamani, Bahman Moradi, Hossein University of Isfahan Mdse Research Group Department of Software Engineering Isfahan Iran Sochool of Arts University of Roehampton London United Kingdom Birjand University of Technology Department of Computer Engineering Birjand Iran

ISBN: (纸本)9798350338195

Semantic interoperability is one of the most critical challenges for software developers while integrating two or more context-aware systems. In such circumstances, it is essential to understand the meaning and interpretation of various contexts in different business domains and to align context ontologies together. Our investigations reveal that existing works poorly address these requirements. To fill this gap, this paper proposes the Ontology as a Service (OaaS) approach to facilitate the semantic interoperability of context-aware systems. In the proposed solution, the complexities of semantic interoperability are resolved and handled by a standalone ontology service, which can be easily reused and consumed by different ontology service consumers and brokers. The proposed ontology service includes several ontology repositories, a web service that positions a context concept in the existing ontologies and another web service that maps the relationship between existing context concepts. We evaluated our approach with a case study that resolves three semantic differences between two IoT applications originating from heterogeneous domains of smart home and health environments. © 2023 IEEE.

关键词： Ontology

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Automated Programming Assignment Marking Tool 7

Automated Programming Assignment Marking Tool

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7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Vimalaraj, Hareeni Thenuwara, T.B.K.P. Wijekoon, Vinuri Udara Sathurjan, Thavaraja Reyal, Shyam Kuruppu, Thilmi Anuththara Tharmaseelan, Janani Department of Computer Science and Software Engineering Malabe Sri Lanka Department of It Malabe Sri Lanka

ISBN: (纸本)9781665421683

Due to the enrolment of a very high number of students to programming modules, marking of programming modules is becoming a very tedious and time-consuming process. Programming assignments mainly test for the student's ability to think logically and approach a solution to the problem. In that case, just running the script and checking the output will not be sufficient enough to award a grade to the student. Marking criteria of programming modules provide certain marks for programs which are not syntactically correct but still have a good approach. Therefore, the code has to be read line by line and the implementation should be checked carefully to provide marks. Source code analysis has become mandatory in the current scenario. This leads to immense pressure and heavy workload on the staff who mark these programs. Considering all these aspects manual marking can lead to inconsistency, biasness, waste of time and less accuracy. Therefore, the main objective of this research is to minimize these problems by implementing an automated programming module marking tool by converting source codes to parse trees, extracting features, generating feature vectors, comparing them and generating a mark along with a feedback and plagiarism report. The solution focuses on automation marking by source code analysis and plagiarism checking. © 2022 IEEE.

关键词： Students

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Compact NSGA-II for Multi-objective Feature Selection

Compact NSGA-II for Multi-objective Feature Selection

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Miyandoab, Sevil Zanjani Rahnamayan, Shahryar Bidgoli, Azam Asilian Ontario Tech University Department of Electrical Computer and Software Engineering OshawaON Canada Brock University Department of Engineering St. CatharinesON Canada Wilfrid Laurier University Faculty of Science WaterlooON Canada

ISBN: (纸本)9798350337020

Feature selection is an expensive challenging task in machine learning and data mining aimed at removing irrelevant and redundant features. This contributes to an improvement in classification accuracy, as well as the budget and memory requirements for classification, or any other post-processing task conducted after feature selection. In this regard, we define feature selection as a multi-objective binary optimization task with the objectives of maximizing classification accuracy and minimizing the number of selected features. In order to select optimal features, we have proposed a binary Compact NSGA-II (CNSGA-II) algorithm. Compactness represents the population as a probability distribution to enhance evolutionary algorithms not only to be more memory-efficient but also to reduce the number of fitness evaluations. Instead of holding two populations during the optimization process, our proposed method uses several Probability Vectors (PVs) to generate new individuals. Each PV efficiently explores a region of the search space to find non-dominated solutions instead of generating candidate solutions from a small population as is the common approach in most evolutionary algorithms. To the best of our knowledge, this is the first compact multi-objective algorithm proposed for feature selection. The reported results for expensive optimization cases with a limited budget on five datasets show that the CNSGA-II performs more efficiently than the well-known NSGA-II method in terms of the hypervolume (HV) performance metric requiring less memory. The proposed method and experimental results are explained and analyzed in detail. © 2023 IEEE.

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Normal and Resilient Mode FPGA-based Access Gateway Function Through P4-generated RTL

Normal and Resilient Mode FPGA-based Access Gateway Function...

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International Workshop on Design of Reliable Communication Networks (DRCN)

作者： Mostafa Abbasmollaei Tarek Ould-Bachir Yvon Savaria Department of Computer and Software Engineering Department of Electrical Engineering Polytechnique Montréal Montreal Canada

ISBN: (数字)9798350348972

ISBN: (纸本)9798350348989

Customizing packet processing is crucial in the evolving network landscape, especially with the rise of 5G telecommunications and beyond. software-Defined Networking and programmable data planes, powered by the P4 language and FPGA-based platforms, offer dynamic network customization that can be used to implement resilient networks. With their high performance and programmability, FPGAs present cost-effective alternatives for diverse network applications, including offloading packet processing from servers. This paper introduces a configurable FPGA-based data plane implementing the Access Gateway Function (AGF). It offers a resilient operating mode to enhance network reliability and availability. The paper leverages the P4 language and the VitisNetP4 Intellectual Property to create RTL streams, enabling AGF on a pure FPGA target. The reported experimental results demonstrate that the proposed architecture can support 50K user flows with a resource utilization lower than 15% of that available in an Ultrascale+ FPGA (xcu280-fsvh2892-21-e). This leaves massive logic resources available to incorporate fault mitigation techniques and spare streams needed to enhance resiliency. Moreover, the presented workflow maintains an average latency of approximately 9 microseconds for each downstream or upstream packet.

关键词： 5G mobile communication Logic gates Telecommunications Telecommunication network reliability Servers Resource management software defined networking

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Malware Detection and Classification for Internet of Things Using Self-Attention Based Long Short-Term Memory 3

Malware Detection and Classification for Internet of Things ...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Sudhakar, K. Husain, Saif O. Babu, D. Raja Kumar, M. Ashok Dineshkumar, R. Nitte Meenakshi Institute Of Technology Dept. Of Ai & Ds Bengaluru India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Sr University Department Of Electrical And Electronics Engineering Warangal India Skyline University Nigeria Department Of Computer Science Software Engineering And Cyber Security Kano State Nigeria Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Electronics And Communication Engineering Chennai India

ISBN: (纸本)9798350318609

The Internet of Things (IoT) connects numerous intelligent devices providing security features that interact with default settings accessed through applications. Additionally, Deep Learning (DL)-based mechanisms was involved for malware detection to identify and mitigate attacks. The Adaptive Synthetic Sampling (ADASYN) technique addresses class imbalances, especially when splitter class instances are distributed data in complex patterns. The proposed Self-Attention based Long Short-Term Memory (LSTM) classification method facilitates the handling of long-range dependencies in transferred data sequences offering high flexibility and adaptability to varying input lengths. For feature selection Particle Swarm Optimization (PSO) is utilized iteratively optimizes a solution in a high-dimensional space although it may have a low convergence rate due to its iterative process. The obtained results demonstrate the proposed Self-Attention based LSTM model achieves better accuracy reaching 98.05% on the NSL-KDD dataset and 99.01 % on the UNSW-NB15 dataset. These results ensure accurate classification compared to other existing methods, such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). © 2024 IEEE.

关键词： Internet of things

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Design and Implementation of Sorting System based on Machine Vision 7

Design and Implementation of Sorting System based on Machine...

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7th International Conference on Big Data Analytics, ICBDA 2022

作者： Mao, Yanghong Chen, Chuixin Jiang, Huiyong Guangzhou Institute of Science and Technology Department of Computer Science and Engineering Guangzhou China Guangzhou Institute of Science and Technology Department of Intelligent Manufacturing and Electrical Engineering Guangzhou China Software Engineering Institute of Guangzhou Department of Software Engineering Guangzhou China

ISBN: (纸本)9781665479387

In order to achieve the goal that the manipulator can automatically obtain any position within its working range, and can adjust the end actuator for grasping, a machine vision sorting system is designed. The system passes through image processing, target recognition and positioning, and finally realizes the sorting effect through end control. The collected image information is processed by MATLAB. The image preprocessing adopts median filtering, image enhancement, binarization and edge detection. For the detection of workpiece surface defects, the improved segmentation network method of convolution neural network is adopted. In the algorithm of identifying workpiece type, the template matching method of log polar coordinate and Fourier transform is adopted. In the algorithm of workpiece position, the centroid of the workpiece is extracted as the positioning reference coordinate, combined with the three degree manipulator, the kinematics model of the manipulator is established. In the algorithm of workpiece grasping, the joint angle of each manipulator is obtained by inverse operation according to the position coordinates of the workpiece, and sent to STM32 through the serial port. Then the STM32 completes the control command to grasp and place the workpiece and complete the sorting task. The experimental results show that the system meets the design requirements. © 2022 IEEE.

关键词： Template matching

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