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

共 6977 条记录，以下是4331-4340 订阅

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The prediction of Digital Political Commercial Evaluation Using Improved Linear Regression

The prediction of Digital Political Commercial Evaluation Us...

引用

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

作者： Naga Vishnu Vardhan Reddy Saravanan M.S Surendran R Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

Analysis of digital advertisements for politicians with an age factor using linear regression algorithms versus decision trees to determine improved accuracy. The study gathered 116 samples from various internet sources using a 0.05 percent threshold, a confidence level of 95 percent interval, a mean and standard deviation, and an 80 percent G-power. Because the Linear Regression algorithm has already achieved an accuracy of 95.25 percent in forecasting political advertisement analysis, the DT approach must achieve greater accuracy in this research. In this study, the DT algorithm was used to analyzed political ads, yielding a 95.00 percent accuracy with a significant value of 0.007 (p0.05) for two-tailed tests with a confidence level of 95 intervals. The DT approach outperforms the Linear Regression algorithm within the constraints of this experiment.

关键词： Linear regression Media Aging Prediction algorithms data communication Forecasting Standards

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Adaptive Noise Canceller Algorithm with SNR-Based Stepsize and data-Dependent Averaging

Adaptive Noise Canceller Algorithm with SNR-Based Stepsize a...

引用

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

作者： Akihiko Sugiyama Yahoo! Japan Research Yahoo Japan Corporation Tokyo Japan Faculty of System Design Tokyo Metropolitan University Hino Japan

This paper proposes an adaptive noise canceller algorithm with an SNR-based stepsize and data-dependent averaging. A first and a second SNR with different time constants for averaging are used one after the other to control the stepsize in adaptation. The time constants reflect the accuracy of the SNR estimates such that lower accuracy is offset with a larger time constant. The first SNR estimate with lower accuracy guarantees the initial coefficient growth whereas the second SNR estimate provides more accurate adaptation control. Switchover from the first to the second SNR estimate takes place when the coefficient growth is saturated. Evaluations with clean speech and noise recorded at a busy station demonstrate that the coefficient error by the proposed algorithm is as much as 6dB smaller than that by conventional algorithms with a constant averaging. It is a counterintuitive result that averaging a first estimate does not always make it better.

关键词： Signal processing algorithms Switches Signal processing Noise cancellation Acoustics Speech processing Signal to noise ratio

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Malware Detection and Analysis using Machine Learning

Malware Detection and Analysis using Machine Learning

引用

international conference on computing Methodologies and Communication (ICCMC)

作者： Manoj Sirigiri Divya Sirigiri R. Aishwarya R. Yogitha Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Data Scientist Brillio Technologies Bangalore India

Online privacy for people is getting worse every day. Computer malware is tainting the data records of some well-known companies. Hackers can gain access to a network and change data, once inside. This work discusses several types of malware and communication strategies, such as Trojans, keyloggers, port forwarding, source code obfuscation, application format converters, and social engineering techniques. Three different machine learning algorithms are applied in this work to derive meaningful insights from the data. As a result of the work proposed, given malware can be categorized as a malicious or a non-malicious application. This is executed by analyzing the classification report, accuracy and f1 score metrics. The Random Forest is selected as a champion model based on the highest f'1-score for the validation dataset.

关键词： Measurement Privacy Machine learning algorithms Computer hacking Source coding Companies Trojan horses

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data Mining in Healthcare using Machine Learning Techniques

Data Mining in Healthcare using Machine Learning Techniques

引用

Sustainable computing and Smart Systems (ICSCSS), international conference on

作者： Honey Goel Deepak Kumar Amity Institute of Information Technology AIIT Amity University Noida India

Classification techniques have become increasingly important in healthcare due to the need for accurate and efficient disease diagnosis, treatment planning, and patient care. Supervised learning algorithms, such as decision trees, logistic regression, and support vector machines, are used for disease diagnosis, predicting patient outcomes, and identifying potential risk factors. Classification techniques are also used in image recognition and analysis, such as in radiology and pathology. Classification is a supervised learning technique used to predict the class or category of an instance based on the given set of attributes. This research study explores the use of classification techniques in data mining for healthcare applications. The goal of this study is to apply classification algorithms such as Naive Bayes, Logistic Regression and Random Forest to healthcare datasets and evaluate their performance. The datasets used in this study include patient information such as demographics, medical history, and diagnosis. The findings suggest that the classification techniques can be effective in data mining for healthcare applications, enabling healthcare professionals to make more informed decisions based on patient data.

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Three-Way Hybrid Sampling Using Granular Balls for Imbalanced Classification

Three-Way Hybrid Sampling Using Granular Balls for Imbalance...

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international Joint conference on Rough Sets (IJCRS)

作者： Xie, Qin Zhang, Qinghua Luo, Nanfang Wang, Guoyin Chongqing Univ Posts & Telecommun Chongqing Key Lab Computat Intelligence Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Key Lab Big Data Intelligent Comp Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Sch Comp Sci & Technol Chongqing 400065 Peoples R China

ISBN: (纸本)9783031656675;9783031656682

Imbalanced data is prevalent in various fields, including disease diagnosis. Effective imbalanced learning methods are crucial for improving supervised learning algorithms. Existing preprocessing methods of imbalanced learning still suffer from blurred class boundaries, weak robustness, and high time costs. In this paper, a three-way hybrid sampling method using granular balls (TWHGBS) for imbalanced binary classification is proposed. First, an overlap-based granular ball generation (OGBG) method is proposed using k-division. Second, undersampling and oversampling methods are concatenated to give a new hybrid sampling method. Specifically, based on the overlap relation between granular balls (GBs), an undersampling method is proposed to identify borderline samples. An oversampling method is proposed for synthesizing minority samples within GB based on overlap degrees adaptively. Experimental results demonstrate that the TWHGBS-based classifiers exhibit superior effectiveness and robustness in terms of G - mean when compared with the existing GB-based undersampling method (GBU) and two conventional hybrid methods, namely, SMOTE-Tomek Links method (STomek) and SMOTE-ENN method (SENN).

关键词： Imbalanced datasets Hybrid sampling Granular computing Three-way decisions Imbalanced classification

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Importance of Web Scraping as a data Source for Machine Learning algorithms - Review

Importance of Web Scraping as a Data Source for Machine Lear...

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international conference on Industrial and Information Systems, ICIIS

作者： SCM De S Sirisuriya Department of Computer Science Faculty of Computing General Sir John Kotelawala Defence University Sri Lanka

Machine learning is a branch of artificial intelligence (AI) that trains a machine to think as humans. Machine learning algorithms need large amounts of data for high-value predictions which helps to get better decisions in real-time environment with minimal human intervention. Machine Learning inherently requires data, but collecting data is very tedious and time-consuming task. Web Scraping is the technique which aims to address this issue. Web scraping or web data extraction used for extracting unstructured data from websites and transform into structured data and stored in a central local database or spreadsheet for future retrieval or analysis. In this review, we discuss the importance of web scraping as a data source for machine learning algorithms.

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An Automatic Segmentation Fitting Method for Freehand Sketches Based on Greedy Strategy 7th

An Automatic Segmentation Fitting Method for Freehand Sketch...

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7th international conference on computing, Control and Industrial Engineering, CCIE 2023

作者： Zhang, Xiuli Chen, Lei Wan, Zhong College of Information and Intelligence Engineering Zhejiang Wanli University Ningbo China College of Big Data and Software Engineering Zhejiang Wanli University Ningbo China

ISBN: (纸本)9789819927296

In this paper, we propose an automatic segmentation and fitting method for hand-drawn sketches based on greedy strategy. This method first resamples and smoothly filter the input sketch stroke sequence, and then automatically segments the hand-drawn sketches based on the greedy strategy. The maximum Euclidean distance is found as the segmentation point in the sketch strokes before each iteration starts, the proximity error of sub-strokes between adjacent segmentation points is calculated, the parameters of sub-strokes whose proximity error less than the threshold are stored, the sub-strokes whose proximity error great than the threshold are recorded to be repartitioned in next iteration processing until proximity error of all the sub-strokes less than the threshold. The least squares method is used to fit the sub-strokes one by one, and removes the redundant segmentation points by relying on error control to maximize the retention of the original information of the hand-drawn sketches while obtaining the canonical geometric This provides the basis for recognition, semantic understanding and retrieval from sketches to normative design drawings. The experiments show that the method has good segmentation fitting effect for all kinds of figures, and the fitting residuals are controllable. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Errors

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A Controllable Lifestyle Simulator for Use in Deep Reinforcement Learning algorithms

A Controllable Lifestyle Simulator for Use in Deep Reinforce...

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

作者： Libio Gonçalves Braz Allmin Susaiyah UPSSITECH - Paul Sabatier University France and Philips Research Netherlands

Deep learning, especially deep reinforcement learning (DRL), has become one of the most useful tools for solving sequential decision-making problems. This is particularly relevant to recommender systems and drug administration in healthcare. This approach has the main drawback of requiring a lot of training, usually several millions of examples before it can learn an interesting policy. Therefore, in healthcare, it is crucial for training the neural network to use reliable and realistic simulations of users. These simulations can be measurements of the user’s vital signals throughout the day or the occurrence of lifestyle events such as sleeping, eating, etc. In this paper, we present a novel and highly generalizable simulation system based on state machines associated with probabilistic transitions to simulate the user’s lifestyle. Thanks to its random nature, the proposed approach can generate infinite yet realistic data from the simulation. It can also be controlled based on the simulation constraints, which is an important requirement for DRL algorithms. We demonstrate its usefulness and relevance in two simple cases of user behavior simulation using different types of input data. We also show its application for a DRL scenario involving behavior change interventions.

关键词： Deep learning Training Signal processing algorithms Reinforcement learning Medical services Signal processing data models

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High-Capacity Reversible data Hiding in Encrypted Images Based on MSB Bit-Plane Serpentine Rearrangement and Compression

High-Capacity Reversible Data Hiding in Encrypted Images Bas...

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Communication, Image and Signal processing (CCISP), international conference on

作者： Tinghua Hu Sheng Qin Kaiyang Wang Guangxi Key Lab of Brain-inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guangxi Normal University Guilin China International Joint Audit Institute Nanjing Audit University Nanjing China

Reversible data hiding techniques in encrypted images (RDH-EI) have been extensively researched to safeguard the privacy of images. To enhance the embedding capability of RDH-EI algorithms and security, this work leverages the topological coherence within the input plaintext image to create more additional space for embedding confidential information prior to image encryption. Initially, the prediction error of the input image is computed using the pixel prediction technique. Subsequently, the bit-plane serpentine rearrangement mechanism and the bit-stream compression method are employed to further enhance the process. This compression technique efficiently compresses the most significant bit-planes, resulting in a high-capacity space for embedding. Finally, the end-user successfully extracts the data and restores the input image using the possessed keys. The results clearly show that the proposed approach achieves not only attains a superior embedding capacity which achieves the maximum average embedding rate with a value of 3.624 (bpp), but also obtains a commendable reconstructed image quality that is PSNR=+∞ and SSIM=1.

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Research on the Methods of data Mining based on the Edge computing for the IoT

Research on the Methods of Data Mining based on the Edge Com...

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Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： Yan Huang Weimeng Zhang Xi'an Traffic Engineering Institute Shaanxi China Xian Technician College Shaanxi China

With the advent of the internet of everything, the number of network edge devices has increased rapidly, causing the data generated by such devices to increase rapidly. In order to enable the internet of things to provide users with information services and reliable decision-making more intelligently, valuable knowledge can be fully mined from these massive data. At present, the use of cloud computing models for centralized processing of big data generated by the internet of things has begun to show many drawbacks. Compared with cloud computing, edge computing migrates data computing or storage to the “edge” of the network close to users, reducing the delay brought by network transmission. In addition, edge computing mode can better protect users' privacy data and reduce the risk of sensitive data privacy leakage. Edge computing has become a new paradigm to solve the needs of Internet of things and localized computing. The thesis studies the method of image data classification processing of the Internet, the edge agent is established by deploying the shallow part of the neural network on the edge device, and the cloud agent is established by deploying the deep part of the neural network on the cloud server device. The multi-agent is constructed by the way of edge cloud cooperation. The reasoning execution of convolutional neural network can complete the classification and exit on the local terminal for fast local inference; when additional processing is needed, the data can be transmitted to the cloud server for further processing by using the deep neural network in the cloud to improve the performance accuracy of the system.

关键词： Integrated circuits Cloud computing data privacy Image edge detection Neural networks Information services Internet of Things

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