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检索条件"任意字段=5th International Conference on Signal Processing and Machine Learning, SPML 2022"

共 783 条记录，以下是411-420 订阅

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Comparison of Feature Extraction and Classification Techniques of PE Malware

Comparison of Feature Extraction and Classification Techniqu...

引用

international conference on signal processing and Information Security (ICSPIS)

作者： Leena El Neel Abigail Copiaco Walid Obaid Husameldin Mukhtar University of Dubai UAE Dubai UAE

ISBN: (纸本)9781665492669

Static and dynamic analysis are widely used in malware research. Although both approaches present considerable advantages according to their specific applications, they also present some challenges in terms of the time and resources constraints when extracting features. To alleviate those constraints, recent research works explore the use of techniques such as computer vision, in detecting malicious samples and their corresponding malware family. In this research, we aim to conduct a thorough comparison between a combination set of different feature extraction and classification techniques in order to identify the optimal approach that can be used in a real-time malware detection application portable executable (PE). Multiple machine learning (ML) and Deep learning (DL) algorithms were used with image-based features and non image-based features. the results obtained show high performance when using static PE header features with K-Nearest Neighbor (KNN) and Bagged Trees algorithms for bi-level classification and Image-based Fast Fourier Transform (FFT) features with AlexNet for multi-level classification.

关键词： machine learning algorithms Transfer learning signal processing algorithms Static analysis signal processing Feature extraction Malware

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Automatic Calculation of Loss Rate of Cotton Pickers Based on Deep Neural Network and Image processing 5

Automatic Calculation of Loss Rate of Cotton Pickers Based o...

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5th international conference on machine learning and machine Intelligence, MLMI 2022

作者： Tian, Jialue Qian, Zhongmin Xie, Qing Sun, Yongfei Shi, Lei College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China Fundamental Experiment Teaching Department Nanjing University of Aeronautics and Astronautics China Key Laboratory of Modern Agricultural Equipment Ministry of Agriculture and Rural Affairs China Nanjing Institute of Agricultural Mechanization Ministry of Agriculture and Rural Affairs China

ISBN: (纸本)9781450397551

the loss rate of cotton pickers is an important indicator to measure the quality of cotton pickers, which is directly the vital interests of cotton farmers. To obtain the loss rate of cotton pickers in real-time, this paper takes the cotton field images after a cotton picker harvesting as the research object, combines the deep learning method and image processing technology, and proposes a method for calculating the loss rate of cotton pickers. First, nearly 5,000 ha-rvested cotton field images were collected manually to establish a data set. then a lightweight Yolov5s neural network model was constructed to detect the cotton areas in the image and obtain the regions of interest (ROI). After that, each ROI was processed by binarization, dilation, erosion, and masking to obtain a more accurate cotton area, which excludes cotton husks, branches, and leaves in the entire image. Finally, a convolutional neural network was used to build a regression model to calculate the cotton loss rate for the whole image. the experimental results show that: 1) the constructed lightweight Yolov5s model can detect cotton areas at a speed of 43 frames per second and an average accuracy of 84.8%, which meets the needs of cotton pickers. 2) For the calculation of the image cotton loss rate, the difference between the calculated loss rate and the actual loss rate is less than 1% for 90% of the images;only 1.1% of the images have a difference greater than 2%, and the maximum difference does not exceed 3%. therefore, the method proposed in this paper can realize the real-time calculation of the loss rate of cotton pickers. © 2022 ACM.

关键词： Cotton

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Using a Technique Based on Moment of Inertia About an Axis for the Recognition of Handwritten Digit in Devanagri Script 5th

Using a Technique Based on Moment of Inertia About an Axis f...

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5th international conference on Emerging Technologies in Computer Engineering: Cognitive Computing and Intelligent IoT, ICETCE 2022

作者： Sharma, Diwakar Sood, Manu Department of Computer Science Himachal Pradesh University Shimla India

ISBN: (纸本)9783031070112

A neural network for handwritten digit recognition is dependent on the dataset used to train it. Most of the techniques available in the literature recognize the handwritten characters of any script based upon the moment of inertia of the character uses a central point to it and suffers from certain drawbacks. In this paper, we develop methods that can be applied to any neural network, to make the dataset insensitive to brush size and insensitive to rotation applied to the digit based on the moment of inertia that the digit produces about an axis. All images in the dataset undergo pre-processing. First, the image is made independent of brush size. then, the image is rotated to negate the rotation that is applied to the image according to the axis of the least moment of inertia. this pre-processing makes the dataset independent of brush size and rotation. the results show that when this preprocessing technique is applied to neural a network, it does makes it insensitive to rotation and brush size. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： machine learning

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DCNN-Based Vegetable Image Classification Using Transfer learning: A Comparative Study 5

DCNN-Based Vegetable Image Classification Using Transfer Lea...

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5th international conference on Computer, Communication, and signal processing, ICCCSP 2021

作者： Ahmed, M. Israk Mahmud Mamun, Shahriyar Zaman Asif, Asif Uz Daffodil International University Dept. of CSE Dhaka Bangladesh

ISBN: (纸本)9781665432771

In this paper, an attempt is addressed towards accurate vegetable image classification. A dataset consisting of 21,000 images of 15 classes is used for this classification. Convolutional neural network, a deep learning algorithm is the most efficient tool in the machine learning field for classification problems. But CNN requires large datasets so that it performs well in natural image classification problems. Here, we conduct an experiment on the performance of CNN for vegetable image classification by developing a CNN model from the ground. Additionally, several pre-trained CNN architectures using transfer learning are employed to compare the accuracy with the typical CNN. this work proposes the study between such typical CNN and its architectures(VGG16, MobileNet, InceptionV3, ResNet etc.) to build up which technique would work best regarding accuracy and effectiveness with new image datasets. Experimental results are presented for all the proposed architectures of CNN. Besides, a comparative study is done between developed CNN models and pre-trained CNN architectures. And the study shows that by utilizing previous information gained from related large-scale work, the transfer learning technique can achieve better classification results over traditional CNN with a small dataset. And one more enrichment in this paper is that we build up a vegetable images dataset of 15 categories consisting of a total of 21,000 images. © 2021 IEEE.

关键词： Image classification

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Sleep Quality Analysis Based on machine learning 11th

Sleep Quality Analysis Based on Machine Learning

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11th international conference on Communications, signal processing, and Systems, CSPS 2022

作者： Tian, Lifeng Honor Device Co. Ltd Shenzhen518000 China

ISBN: (纸本)9789819923618

One third of a person’s life is spent in sleep, sleep is a need of life, and the quality of sleep plays a vital role in human health. With the accelerated pace of life, the average sleep duration of human beings has continued to decline over the past time. Poor sleep quality has led to serious social problems. According to some studies, short-term lack of sleep can lead to distraction and lack of energy, while long-term lack of sleep can lead to an increased risk of many fatal diseases. However, today’s monitoring of sleep quality is far from adequate. For example, sleep diaries are subjective and the polysomnography monitoring process is too cumbersome. Based on this, this paper proposes a convenient, fast and low-cost sleep quality detection system based on the Internet of things. this paper investigates the relationship between cardiopulmonary coupling (CPC) signals and sleep stages and various sleep disorders. the algorithm is based on MATLAB programs such as constitutional neural networks and disease index prediction models. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： MATLAB

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Research on Methods of Truck Plate Detection in Expressway Scene Based on machine learning 7

Research on Methods of Truck Plate Detection in Expressway S...

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7th international conference on signal and Image processing, ICSIP 2022

作者： Ma, Teng Wang, Xinliang Wu, Jialun Zhao, Chihang Zhang, Jing Qin, Xiaoming Su, Zijun Wu, Yuhang Shandong Hi-Speed Xinwei Information Technology Co. Ltd. Jinan China Southeast University School of Transportation Nanjing China

ISBN: (数字)9781665495639

ISBN: (纸本)9781665495639

Due to the long shooting time span of truck images and the drastic changes in lighting conditions during daytime and night time, phenomena such as large differences in image brightness, overexposure, blurred truck plate writing and edges of characters will occur. therefore, in order to accurately locate the position of the truck plate in the image, this paper studies the truck plate location method in the expressway scene based on machine learning, and compares and analyzes six different truck plate location methods such as deformable part model, edge-information-based model, color-information-based model, template matching model, character-feature-based model, and HOG-feature-based support vector machine model. then, six different experiments based on different methods by actual truck image dataset were implemented. the experimental results show that the truck plate location method based on deformable part model is better than the other five truck plate location methods, and its location accuracy reaches 99.34%. © 2022 IEEE.

关键词： Support vector machines

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A machine learning Based Design of mmWave Compact Array Antenna for 5G Communications

A Machine Learning Based Design of mmWave Compact Array Ante...

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international conference on Communications, signal processing, and their Applications (ICCSPA)

作者： Nazih K. Mallat Alireza Jafarieh Mahdi Nouri Hamid Behroozi College of Engineering Al Ain University (AAU) Al Ain UAE Dept. of Elect. Eng. Sharif Unversity of Technology Tehran Iran

ISBN: (纸本)9781665482387

Wider impedance bandwidth (IBW), and lower latency rate than older mobile communication systems possess are required for fifth-generation (5G) mobile communication systems. Furthermore, with respect to the high operation frequency of 5G systems, a high released gain is necessary to compensate for the high path loss on these frequencies. With respect to the requirements mentioned above, millimeter-wave (MMW) antennas seem to be a good solution for 5G applications. the low wavelength of MMW frequency bands, makes it practical to use large array antennas for massive multi input multi-output (MIMO) 5G systems with high gain. the high number of design variables of antennas makes an optimum antenna harder to design. Using machine learning (ML) approaches, however, alleviates this challenge. However, most ML approaches entail high computational complexity. therefore, surrogate-based optimization (SBO) approaches must be used to handle the high computational complexity of ML approaches.

关键词： 5G mobile communication Array signal processing machine learning Bandwidth Impedance Millimeter wave communication Computational complexity

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Feature selection methods for high-dimensional biomedical time-to-event data: a review 22

Feature selection methods for high-dimensional biomedical ti...

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5th international conference on Big Data Technologies, ICBDT 2022

作者： Tan, Huaning Deng, Chutong Chen, Shaobo Luo, Qianlin Hu, Guoqiang Quan, Yujuan International School Jinan University China College of Information Science and Technology Jinan University China

ISBN: (纸本)9781450396875

In digital era, time-to-event data collected from biomedical studies and healthcare are often of high dimensionality, presenting computational challenges for traditional survival models. To make full use of these data, feature selection (FS), a data processing technique for dimensionality reduction, shows great significance. this work introduces statistical, machine learning, and deep learning FS methods for time-to-event data, mainly focusing on lasso, elastic net, adaptive lasso, adaptive elastic net, random survival forest, and XGBoost. We also describe three state-of-art FS methods - BASIL, FilterDeepHit+, and SparseDeepHit+. then, we compare C-Index of 4 basic FS methods in experiment. Finally, we discuss future challenges and draw a conclusion. © 2022 ACM.

关键词： Feature Selection

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machine learning Based Obfuscated Malware Detection in the Cloud Environment with Nature-Inspired Feature Selection

Machine Learning Based Obfuscated Malware Detection in the C...

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international conference on Multimedia, signal processing and Communication Technologies, (IMPACT)

作者： Mohd. Rehan Ghazi N. S. Raghava Dept. of Electronics and Comm. Engineering Delhi Technological University Delhi India

ISBN: (纸本)9781665476485

One of the most significant and widely used IT breakthroughs nowadays is cloud computing. Today, the majority of enterprises use private or public cloud computing services for their computing infrastructure. Cyber-attackers regularly target Cloud resources by inserting malicious code or obfuscated malware onto the server. these malware programmes that are obfuscated are so clever that they often manage to evade the detection technology that is in place. Unfortunately, they are discovered long after they have done significant harm to the server. machine learning (ML) techniques have shown to be effective at finding malware in a wide range of fields. To address feature selection (FS) challenges, this study uses the wrapper-based Binary Bat Algorithm (BBA), Cuckoo Search Algorithm (CSA), Mayfly Algorithm (MA), and Particle Swarm Optimization (PSO), and then k-Nearest Neighbor (kNN), Random Forest (RF), and Support Vector machine (SVM) are used to classify the benign and malicious records to measure the performance in terms of various metrics. CIC-MalMem-2022, the most recent malware memory dataset, is used to evaluate and test the proposed approach and it is found that the proposed system is an acceptable solution to detect malware.

关键词： Support vector machines Radio frequency Cloud computing machine learning algorithms signal processing algorithms Feature extraction Malware

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Lightweight Single-Channel Speech Enhancement Based on Multi-Frame MVDR Filters with Learnable Parameters

Lightweight Single-Channel Speech Enhancement Based on Multi...

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Electronic Communication and Artificial Intelligence (IWECAI), international Workshop on

作者： Huajian Li Zhanheng Zheng School of Information and Communication Guilin University of Electronic Technology Guilin China Ministry of Education Key Lab. of Cognitive Radio and Information Processing (Guilin University of Electronic Technology) School of Information and Communication Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798350386943

ISBN: (纸本)9798350386950

In daily life, noise may interfere with speech signals, thus seriously affecting speech quality and intelligibility. To solve this problem, many traditional methods based on statistical modeling and methods based on machine learning have been proposed. Nevertheless, most of these algorithms inevitably suffer from a drawback: the attenuation of the noise component may lead to some distortion of the speech component in the enhanced signal. In this paper, we estimate the noise covariance matrix and speech interframe correlation vectors by employing DNN networks, and use them to construct a learnable parametric multi-frame MVDR filter as a way to fully utilize the short-term correlation within the speech signal and apply it to single-channel speech enhancement; we also evaluate the method with baseline model, and objectively demonstrate it's excellent performance and the less number of model parameters and computation compared to mainstream models.

关键词： Correlation machine learning algorithms Computational modeling Noise machine learning Speech enhancement Filtering algorithms

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