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Disentangling the Components of Ethical Research in machine learning 22

Disentangling the Components of Ethical Research in Machine ...

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5th ACM conference on Fairness, Accountability, and Transparency, FAccT 2022

作者： Ashurst, Carolyn Barocas, Solon Campbell, Rosie Raji, Deborah Alan Turing Institute United Kingdom Cornell / Microsoft Research United States OpenAI United States Uc Berkeley / Mozilla United States

ISBN: (纸本)9781450393522

While practical applications of machine learning have been the target of considerable normative scrutiny over the past decade, there is growing concern with machine learning research as well. Debates are currently unfolding about how the research community should develop its research agendas, conduct its research, evaluate its research contributions, and handle the publication and dissemination of its findings, among other matters. At times, these debates have been quite heated, with different actors adopting different positions on what it means to do machine learning research ethically. In this paper, we show that some of the disagreement owes to a lack of clarity about what ethical issues are at stake in machine learning research, how these issues - in particular, the concerns with research integrity, research process harms, and downstream consequences - relate to (or, more often, differ from) one another. We then explore which mechanisms are most appropriate for dealing with the different types of ethical issues, and highlight which ethical issues require more attention than they are currently receiving. Ultimately, we hope to foster more productive discussions about the responsibilities that the community bears in addressing the ethical challenges tied to machine learning research and how to best fulfil these responsibilities. © 2022 ACM.

关键词： machine learning

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Application of signal Averaging CEEMDAN and Adaptive Wavelet threshold Denoising in signal processing

Application of Signal Averaging CEEMDAN and Adaptive Wavelet...

引用

machine learning and Computer Application (ICMLCA), international conference on

作者： Junbo Liu Jingyao Luan Yufeng Tai Changlong Gao Shouxue Li Lixin Jiao Jilin Electric Power Scientific Research Institute Co. Ltd jilin China Electric Power Research Institute of State Grid Jilin Electric Power Co. Ltd jilin China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

Insulators play a vital role in the power system, and their safety and reliability are crucial for power transmission. However, the insulator ultrasonic detection echo signal contains a lot of noise, and a joint denoising method based on signal averaging, CCEMDAN and adaptive wavelet threshold is proposed to solve this problem. In this method, firstly, the preprocessing of the echo signal is used to mean the signal average, the noise is denoised and decomposed by CCEMDAN, the high-frequency components are denoised by the adaptive wavelet threshold, and finally the denoised signal is obtained through signal reconstruction. the experimental results show that the proposed method has a good denoising effect, effectively retains the real information in the echo signal, improves the signal-to-noise ratio of the ultrasonic echo signal, and reduces the mean square error of the echo signal.

关键词： Noise reduction signal processing algorithms Mean square error methods Insulators Acoustics signal reconstruction Timing Reliability signal analysis signal to noise ratio

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(LISACMT) Language Identification and Sentiment analysis of English-Urdu 'code-mixed' text using LSTM 5

(LISACMT) Language Identification and Sentiment analysis of ...

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5th international conference on Inventive Computation Technologies (ICICT)

作者： Ahmad, Gazi Imtiyaz Singla, Jimmy Lovely Profess Univ Phagwara Punjab India Lovely Profess Univ Dept Comp Sci & Engn Phagwara Punjab India

ISBN: (数字)9781665408370

ISBN: (纸本)9781665408387;9781665408370

Sentiment analysis is the method of identifying and extracting the opinions, attitudes, and polarity indicated in a text. Individuals, organizations and companies make decisions based on the sentiments expressed by users toward products, services, social or cultural issues, and government policies. Advancements in Natural Language processing and Artificial Intelligence makes text analysis easier. However, text classification is more focused on the English language. the increase in social-media content available on a number of platforms is predominantly informal and noisy. Social-media content influenced by the regional languages makes the text analysis more challenging. A new phenomenon called code-mixing which involves mixing of linguistic units of one language into the utterances of another language is nowadays exhibited on social-media textual content. To deduce useful information from this type of text is a challenging task. this paper presents a language identification and sentiment analysis method for English-Urdu code-mixed text using deep learning approaches. For language identification Artificial Neural network along with character based embedding has been used. A baseline Long Short-Term Memory approach using word based embeddings has been employed for sentiment classification. Both language identification and sentiment classification approaches showed promising results.

关键词： code-mixing sentiment analysis machine learning deep learning natural language processing Neural Network Long Short-Term Memory

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Survival Status Prediction for Non-small Cell Lung Cancer Patients using machine learning

Survival Status Prediction for Non-small Cell Lung Cancer Pa...

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15th international conference on Bio-inspired Systems and signal processing (BIOsignalS) held as part of 15th international Joint conference on Biomedical Engineering Systems and Technologies (BIOSTEC)

作者： Mohan, Aishwarya Jeremic, Aleksandar McMaster Univ Dept Elect & Comp Engn Hamilton ON Canada

ISBN: (纸本)9789897585524

Lung cancer is the leading cause among cancer-related deaths worldwide. Clinically, it could be divided into several groups: 1) the non-small cell lung cancer (NSCLC, 83.4%), 2) the small cell lung cancer (SCLC,13.3%), 3) not otherwise specified lung cancer (NOS, 3.1%), 4) aarcoma lung carcinoma (0.2%), and 5) other specified carcinoma (0.1%). According to SEER Cancer Statistics Review, 5-year survival rate of patients with advanced non-small cell lung cancer (NSCLC) who received chemotherapy was less than 5%. Our ability to provide survival status at any time in future is important from at least two standpoints: a) from the clinical standpoint it enables clinicians to provide optimal delivery of healthcare and b) from a personal standpoint, by providing patient's family with opportunities to plan their life ahead and potentially cope with emotional aspect of loss of life. In this paper we propose to utilize machine learning techniques to achieve this goal and evaluate several techniques in order to determine their prediction performance using publicly available dataset.

关键词： Survival Prediction Logistic Regression machine learning

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Research of Scene Text Detection Algorithms 5

Research of Scene Text Detection Algorithms

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5th international conference on Intelligent Robotics and Control Engineering, IRCE 2022

作者： Chen, Mengmeng Ibrayim, Mayire Hamdulla, Askar School of Information Science and Engineering Xinjiang University Xinjiang Key Laboratory of Signal Detection and Processing Xinjiang Urumqi China School of Information Science and Engineering Xinjiang University Xinjiang Key Laboratory of Multilingual Information Technology Xinjiang Urumqi China

ISBN: (数字)9781665469951

ISBN: (纸本)9781665469951

With the development of artificial intelligence, obtaining textual information from natural scenes has become a hot topic. there are still huge challenges for curved text and arbitrary orientation text detection in real-world scenes. In this work, we briefly introduce the development of text detection algorithms in recent years and study a typical lightweight detection algorithm. We have improved the algorithm to different degrees to make it more suitable for detection of the curved text. Finally, we select the three most representative public text datasets for experiments. Experiments conducted on CTW1500, ICDAR2015, and MSRA-TD500 have demonstrated the superiority of this model. © 2022 IEEE.

关键词： Deep learning

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Research on Machining Product Quality Prediction Based on machine learning 5

Research on Machining Product Quality Prediction Based on Ma...

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2022 5th international conference on Advanced Algorithms and Control Engineering, ICAACE 2022

作者： Lan, Chen Fei, Wan School of Electrical and Electronic Engineering Shanghai Institute of Technology Shanghai201499 China

Data empowerment has attracted widespread attention from manufacturing companies. Enterprises control the production process by mining the data generated in the processing process, so as to achieve the purpose of improving product quality. this paper mainly uses the classification algorithm in data mining to model the data in the processing process and predict the product quality. Using the accuracy of the model prediction as the evaluation standard, the ID3, CART, SVM, KNN and Adaboost algorithms are compared. the experimental results show that the Adaboost classifier is significantly better than other algorithms in the evaluation index of accuracy, so the Adaboost classifier is selected as the final classifier. © Published under licence by IOP Publishing Ltd.

关键词： Data mining

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Detection of Fraudulent Credit Card Transactions Using Advanced LightGBM Approach 1

引用

4th international conference on machine Intelligence and signal processing, MISP 2022

作者： Yazna Sai, Kotireddy Venkata Bhavana, Repalle Sudha, N. SASTRA Deemed University Thanjavur India

ISBN: (数字)9789819900855

ISBN: (纸本)9789819900848

Due to the rapid growth of e-commerce businesses, the use of credit cards for online purchases has increased exponentially and so has fraud. In recent years, it has gotten increasingly difficult for banks to detect fraud in credit card systems. Hence, it is important to have effective and reliable methods for identifying fraud in credit card transactions. machine learning is critical for the detection of fraudulent credit card transactions. Banks utilize various machine learning approaches to forecast these transactions;prior data are collected, and new features are applied to improve prediction capability. Classical algorithms such as Logistic Regression (LR) and Random Forest (RF) are proven useful for detection of fraudulent credit card transactions. However, their performance is not satisfactory. Hence, we proposed an approach to improve the detection accuracy. this paper introduces an Advanced Light Gradient Boosting machine technique to detect credit card frauds. In this method, a Bayesian-based Hyperparameter Optimization technique is incorporated to Light Gradient Boosting machine (LightGBM) in modifying its hyperparameters. To validate its efficacy in identifying fraudulent credit card transactions, the proposed Advanced LightGBM technique was evaluated on two credit card transaction datasets that comprised both fraudulent and genuine transactions. the method outperformed current techniques in terms of performance. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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A novel Deep-learning model for Human Activity Recognition based on Continuous Wavelet Transform 5

A novel Deep-Learning model for Human Activity Recognition b...

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5th international conference on Informatics and Data-Driven Medicine (IDDM)

作者： Pavliuk, Olena Mishchuk, Myroslav Silesian Tech Univ Ul Akad 2A PL-44100 Gliwice Poland Lviv Polytech Natl Univ Stepana Bandery St 12 UA-79000 Lvov Ukraine

Human Activity Recognition (HAR) has recently become in the spotlight of scientific research due to the development and proliferation of wearable sensors. HAR has found applications in such areas as digital health, mobile medicine, sports, abnormal activity detection and fall prevention. Neural Networks have recently become a widespread method for dealing with HAR problems due to their ability automatically extract and select features from the raw sensor data. However, this approach requires extensive training datasets to perform sufficiently under diverse circumstances. this study proposes a novel Deep learning - based model, pre-trained on the KU-HAR dataset. the raw, six-channel sensor data was preliminarily processed using the Continuous Wavelet Transform (CWT) for better performance. Nine popular Convolutional Neural Network (CNN) architectures, as well as different wavelets and scale values, were tested to choose the best-performing combination. the proposed model was tested on the whole UCI-HAPT dataset and its subset to assess how it performs on new activities and different amounts of training data. the results show that using the pre-trained model, especially with frozen layers, leads to improved performance, smoother gradient descent and faster training on small datasets. Additionally, the model performed on the KU-HAR dataset with a classification accuracy of 97.48% and F1-score of 97.52%, which is a competitive performance compared to other state-of-the-art HAR models.

关键词： Human activity recognition biomedical signal processing transfer learning continuous wavelet transform convolutional neural network

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Vehicle theft Identification Using machine learning and OCR 4th

Vehicle Theft Identification Using Machine Learning and OCR

引用

4th international conference on machine Intelligence and signal processing, MISP 2022

作者： Rai, Priyam Kothari, Eshan Eshwar Bhushan, Megha Negi, Arun School of Computing DIT University Dehradun India Deloitte USI Gurgaon India

ISBN: (纸本)9789819900848

In today’s world, a vehicle is an important asset which makes our life easier. For the most part in urban cities, the crime rate all around the world these days has become a menacing manner such that vehicles are now used for committing lawbreaking activities more than before. Vehicle theft is a critical issue that has increased drastically, mostly at car parking or at gunpoint. In this regard, there is a necessity for adequate records of stolen, identified, and recovered vehicles that are currently not easily accessible. this paper aims to solve this issue that is tracking stolen vehicles, as currently, it takes months or even years to track and locate the reportedly stolen vehicle. this paper focuses on solving the issue using OpenCV, OCR, and Python Graphic development. In this paper, the image classification and preprocessing techniques using TensorFlow, OpenCV, and Keras have been used;further, OCR is used to extract the text from the image in string format. the preprocessing of the image is done using OpenCV, so the OCR engine receives a modified image with reduced noise and bilateral filter already applied on it. It will reduce the current time for locating and tracking certain vehicles. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Difficulty level estimation of mathematics problems using machine learning 2022

Difficulty level estimation of mathematics problems using ma...

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4th international conference on Image, Video and signal processing, IVSP 2022

作者： theephoowiang, Kittipong Chaowicharat, Ekawat Department of Mathematics Faculty of Science Mahidol University Thailand

ISBN: (纸本)9781450387415

the aim of this research is to design an automatic system that can estimate the difficulty level of mathematics problems in the way that is similar to human judgment. this system helps reduce the teacher workload in the question bank construction and also helps students who want to practice the problems with varieties of difficulty levels for self-learning. Our system started with extracting features from the mathematics problem directly and then using machine-learning algorithms to estimate the difficulty level so that the desired value is consistent with the estimation made by human experts. the designed system extracts feature from the mathematics problems by simulating the human calculation process and counting the number of applying formulas during the optimal path of the problem-solving process, then the features are used for training naive Bayes, neural network, regression, and support vector machine (SVM). the comparative result from the 4 model prediction on the differential calculus dataset shows that the regression model is the best predictor, where the mean absolute error between the machine learning predicted value and the labels from human experts is approximately 0.57 level out of 1 - 5 scales. © 2022 ACM.

关键词： Estimation

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