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检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

共 6952 条记录，以下是4861-4870 订阅

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Classification of Musical Genres Using Audio Spectrograms

Classification of Musical Genres Using Audio Spectrograms

引用

Advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Mohammad Kazim Abbas Kunal Gupta Mohammad Mudassir Rishabh Jain Dept. of Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida India Dept. of Computer Science KIET Group of Institutions Delhi-NCR Ghaziabad India

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330878

The classification of music into distinct genres is a valuable undertaking in the realm of multimedia study, as it enables artists, albums, and songs to be categorized based on shared musical characteristics. This study aims to contribute to the field of music genre categorization by proposing an approach that can more accurately classify musical genres compared to existing methodologies. Our approach leverages the attributes extracted from the MK2 audio dataset, which is a compilation of musical genre data that has been collected and processed to facilitate the prediction of song genres. The proposed method employs convolutional neural networks (CNN) and utilizes audio spectrograms as input features. Additionally, this study presents a comparative analysis of our proposed approach with previous research papers, providing insights into the effectiveness of our proposed model.

关键词： Machine learning algorithms Music Digital signal processing Feature extraction data models Acoustics Multiple signal classification

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SAPMS: A Semantic-Aware Privacy-Preserving Multi-keyword Search Scheme in Cloud 6th

SAPMS: A Semantic-Aware Privacy-Preserving Multi-keyword Sea...

引用

6th Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint international conference on Web and Big data (APWeb-WAIM)

作者： Zhou, Qian Dai, Hua Hu, Zheng Liu, Yuanlong Yang, Geng Nanjing Univ Posts & Telecommun Nanjing 210023 Peoples R China Jiangsu Secur & Intelligent Proc Lab Big Data Nanjing 210023 Peoples R China

ISBN: (纸本)9783031251573;9783031251580

Most of the traditional privacy-preserving search schemes adopt TF-IDF model which is on the basis of keyword frequency statistics. The embedding semantic association between keywords and documents are not considered. To solve this problem, we propose an efficient semantic-aware privacy-preserving multi-keyword search scheme over encrypted cloud data. The LDA topic model is adopted to generate the topic information-embedded vectors for documents and queried keywords. The homomorphic encryption on vectors is used to perform privacy-preserving semantic relevance score computation between queried keywords and documents. To achieve efficient search processing, a novel tree-based index is designed, which is constructed following the divisive hierarchical clustering algorithm. By using the tree-based index, a depth-first privacy-preserving multi-keyword search algorithm is proposed. The experimental results show that the proposed scheme outperforms the existing schemes in terms of the semantic precision of search results and the search time cost.

关键词： Cloud computing Searchable encryption Multi-keywords search Semantic-aware Privacy-preserving

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Using Edge-based Deep Learning Model for Early Detection of Cancer

Using Edge-based Deep Learning Model for Early Detection of ...

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Euromicro conference on Parallel, Distributed and Network-Based processing

作者： Luca Barillaro Giuseppe Agapito Mario Cannataro Dept. of Surgical and Medical Sciences and Data Analytics Research Center University “Magna Graecia” of Catanzaro Catanzaro Italy Dept. of Law Economics and Social Sciences and Data Analytics Research Center University “Magna Graecia” of Catanzaro Catanzaro Italy

Cancer is one of the most frequent causes of death in the world. Usually, cancer can be easily diagnosed if characteristic symptoms occur. However, many people who are suffering from cancer have no symptoms. Early diagnosis of tumors is essential to contrast their progression, helping to define more effective treatments to provide long-term survival. Early cancer detection is effective if sensible data can be investigated through high-performance technologies like edge computing. Edge computing is a new paradigm for analyzing data as close to the source as possible, avoiding exporting them outside. Hence, edge-based deep learning models can be applied to improve early cancer detection. This paper provides an use case of a classification task on tumor-related data based on the famous UCI machine learning data sets repository using a deep learning approach based on edge computing. In addition, the manuscript provides an overview of the edge computing paradigm, highlighting its advantages and usability. We also described a small experiment with real tumor data to characterize performance considerations. Moreover, the presented model can be used with different data types, such as images, EGC, and ECC signals.

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Determine the Blood Group by using Image processing and Machine Learning

Determine the Blood Group by using Image Processing and Mach...

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Sustainable computing and data Communication Systems (ICSCDS), international conference on

作者： K.S. Chakradhar B.Anjanee Kumar Anantha Rao Gottimukkala G.N.R. Prasad Electronics and Communication Engineering Mohan Babu University Tirupathi India Electronics and Communication Engineering Dadi institute of engineering and technology Anakapalle Visakhapatnam India Department of Computer Science and Engineering Koneru Lakshmaiah Educationa Foundation Vaddeswaram Guntur India Department of MCA Chaitanya Bharathi Institute of Technology (A) Gandipet Hyderabad India

In a life-or-death scenario, knowing someone’s blood type is crucial. Blood typing refers to identifying a person’s unique blood type. Identifying one’s blood type is a requirement for entry into some professions. Identifying a person’s blood type is crucial in several life-or-death circumstances, including blood transfusions, blood donations, roadside disasters, and other similar emergencies. In recent years, it has become more common to do these tests by hand in the lab. However, this can be time-consuming and lead to mistakes when dealing with large samples. The system’s primary goal is to provide results to multiple users quickly and precisely simultaneously. Parallel image processing system evaluates. Thus, this approach is beneficial for detecting blood type in an emergency case without any mistakes.

关键词： Image processing Machine learning data communication Blood

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NCL: Textual Backdoor Defense Using Noise-Augmented Contrastive Learning

NCL: Textual Backdoor Defense Using Noise-Augmented Contrast...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Shengfang Zhai Qingni Shen Xiaoyi Chen Weilong Wang Cong Li Yuejian Fang Zhonghai Wu School of Software and Microelectronics & National Engineering Research Center for Software Engineering & PKU-OCTA Laboratory for Blockchain and Privacy Computing Peking University

At present, backdoor attacks attract attention as they do great harm to deep learning models. By poisoning the training data, the adversary makes the model trained based on this dataset being injected with a backdoor. In the field of text, however, existing works do not provide sufficient defense against backdoor attacks. In this paper, we propose a Noise-augmented Contrastive Learning (NCL) framework to defend against textual backdoor attacks when training models with untrustworthy data. With the aim of mitigating the mapping between triggers and the target label, we add appropriate noise perturbing possible backdoor triggers, augment the training dataset, and then pull homology samples in the feature space utilizing contrastive learning objective. Experiments demonstrate the effectiveness of our method in defending three types of textual backdoor attacks, outperforming the prior works.

关键词： Training Deep learning Training data Signal processing data models Acoustics Speech processing

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IoT Based Hydroponic System

IoT Based Hydroponic System

引用

international conference on Advanced computing and Communication Systems (ICACCS)

作者： Pola Anirudh G.A.E Satish Kumar R Phani Vidyadhar Gadwal Pranav Bathula Anil Kumar Dept. of Electronics and Communication Engineering Vardhaman College of Engineering Hyderabad India

In 21st century people don’t have time for traditional method of farming it is a time consuming work and requires lot of man power. In counties like India there is a shortage of food production which turn into worst situation in future, to get rid of such situations we should encourage indoor agriculture which can be automated. In our proposed model we come across an IoT based hydroponic system to automate the agriculture process and reduce the work load in it. In this proposed method we use an IoT to connect the sensor data to cloud and provide user interface to monitor the plant growth, we gather the sensor data and use the data to get insights and proceed with further steps providing the essentials to plant like sunlight and water. In this proposed model we interface different types of sensors to cloud in order to automate the indoor agriculture process. Our model observes the requirement of water to the plants and send the required amount of water directly to the roots of the plants.

关键词： Cloud computing Machine learning algorithms Hydroponics Production User interfaces Agriculture data models

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Resource-Limited Localized Adaptive Adversarial Training for Machine Learning Model

Resource-Limited Localized Adaptive Adversarial Training for...

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IEEE international conference on Big data and Cloud computing (BdCloud)

作者： Mohammed Rajhi Niki Pissinou School of Computing and Information Sciences Florida International University Miami FL USA Faculty of Computer Science & Information Technology Jazan University Jazan Saudi Arabia

Balancing robustness and computational efficiency in machine learning models is challenging, especially in settings with limited resources like mobile and IoT devices. This study introduces Adaptive and Localized Adversarial Training (ALAT), an optimization approach that balances these competing needs. ALAT combines generalized models with localized adversarial perturbations and adaptive data augmentation. As a result, the model strengthens its weak points without needing to explore all possible adversarial threats, saving computational effort Our data shows that ALAT-trained models perform robustly with less computational cost compared to traditional adversarial training methods. This adaptability makes ALAT suitable for various machine learning architectures and particularly valuable in resource-constrained settings requiring resilience to adversarial threats.

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1st international conference on Intelligent Systems in computing and Communications, ISCComm 2023

1st International Conference on Intelligent Systems in Compu...

引用

1st international conference on Intelligent Systems in computing and Communications, ISCComm 2023

ISBN: (纸本)9783031756078

The proceedings contain 53 papers. The special focus in this conference is on Intelligent Systems in computing and Communications. The topics include: Towards Hands-Free computing: AI Virtual Mouse Interface Powered by Gestures;smartAgro: Precision Yield Prediction, Crop Insights, and Real-Time Dashboard;Artificial Intelligence with MRI-Guided Radiation Therapy for Cancer Treatment;Software Requirements to UML Class Diagrams Using Machine Learning and Rule-Based Approach;CFALEA_LSTM: Adaptive Lotus Effect Algorithm Enabled Long Short-Term Memory for Rainfall Prediction Using Time Series data;a Deep Learning Survey on Diseases Prediction and Detection in Health Care;fault Diagnosis in Belts Using Signal processing Techniques and Machine Learning;machine Learning Techniques Based Chronic Kidney Disease Detection with Performance Analysis of Fuzzy Rough Set and Correlation Attribute Selection;segmentation and Classification of Unharvested Arecanut Bunches Using Deep Learning;enhanced Satellite Image Fusion Using Deep Learning and Feature Extraction Techniques: A Survey;intelligent Aircraft Antiskid Braking Systems – A Review;artificial Neural Networks Applied in the Detection of Breast Cancer;supervised and Unsupervised Learning Techniques for Malware Classification Based on Opcode Frequency Features;predictive Analytics for Diagnosing Alzheimer’s Disease Using Artificial Intelligence and Machine Learning algorithms;A Comprehensive Study on Artificial Intelligence (AI) Driven Internet of Healthcare Things (IOHT);predictive Models for the Early Diagnosis and Prognosis of Knee Osteoarthritis Using Deep Learning Techniques;predicting Salinity Resistance of Rice at the Seedling Stage: An Evaluation of Transfer Learning Methods;Multi-camera HD Pedestrian dataset for Person Detection and Re-identification;Fire Detection System Using Deep CNN.

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An Online Algorithm for Contrastive Principal Component Analysis

An Online Algorithm for Contrastive Principal Component Anal...

引用

international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Siavash Golkar David Lipshutz Tiberiu Tesileanu Dmitri B. Chklovskii Center for Computational Neuroscience Flatiron Institute NYU Langone Medical School Neuroscience Institute

Finding informative low-dimensional representations that can be computed efficiently in large datasets is an important problem in data analysis. Recently, contrastive Principal Component Analysis (cPCA) was proposed as a more informative generalization of PCA that takes advantage of contrastive learning. However, the performance of cPCA is sensitive to hyper-parameter choice and there is currently no online algorithm for implementing cPCA. Here, we introduce a modified cPCA method, which we denote cPCA ∗ , that is more interpretable and less sensitive to the choice of hyper-parameter. We derive an online algorithm for cPCA ∗ and show that it maps onto a neural network with local learning rules, so it can potentially be implemented in energy efficient neuromorphic hardware. We evaluate the performance of our online algorithm on real datasets and highlight the differences and similarities with the original formulation.

关键词： data analysis Neuromorphics Neural networks Signal processing algorithms Signal processing Hardware Energy efficiency

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Fusion of topological features by graph convolutional network for the classification of single-cell RNA sequencing data

Fusion of topological features by graph convolutional networ...

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6th international conference on Intelligent computing and Signal processing (ICSP)

作者： Julong Li Deguang Chen Zheyu Zheng Jianrui Chen School of Computer Science Shaanxi Normal University Xi’an China

Cells are the most basic unit of life. Organisms have trillions of cells, and these cells perform all their life activities. Although the structures in cells are all broadly similar, they are often not identical in type. Therefore, the correct classification of cells is a very meaningful task. Single-cell RNA sequencing technology is available to facilitate our study of cell classification. single-cell RNA sequencing data contains detailed cell expression information. However, these data are not well utilized in previous methods. In this paper, we fuse topological features through graph convolutional network (FTGCN) for the classification of single-cell RNA sequencing data. First, we construct the k-nearest neighbor (KNN) graph based on the gene expression levels between cells to obtain the similarity and construct connections between cells. Then, FTGCN learns the topology and the features between cells, and the finally fused topological features are used for the classification of cells. In the experiment, the comparison results with the baselines proved the effectiveness of FTGCN.

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