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Algorithmic Fairness in Applied machine learning Contexts 2021

Algorithmic Fairness in Applied Machine Learning Contexts

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5th international conference on Compute and Data Analysis, ICCDA 2021

作者： Russell, Jesse United States

ISBN: (纸本)9781450389112

Fairness is a common standard in machine learning principles, ethics declarations, and best practices statements. Fairness, though, does not have a singular definition in machine learning. One common theme among fairness concepts is equal treatment by a machine learning model across groups. In some cases, it might be more fair for the machine learning model to produce different predictions or classifications for each group in concordance with differences in their outcome rates. Equal treatment might be an appropriate standard of fairness in some circumstances but not in others. Codes of ethics and standards for machine learning offer many different suggestions about how machine learning ought to be fair. Not only is there a diversity of fairness concepts, but standards also often offer little or no guidance on how these fairness axioms should guide the real-world practice of developing and deploying machine learning models in applied settings. the context in which machine learning is applied may determine which aspects of fairness are expected or upheld. machine learning to shape, for example, (a) consumer loan approval or rates, (b) job recommendations, (c) text translations, (d) credit decisions, and (e) justice decisions might all impel different conceptions of machine learning fairness. Going beyond an expectation of "equal treatment", machine learning in each of these areas might think about fairness differently. © 2021 ACM.

关键词： human computer interaction

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machine learning for Drug Discovery: Predicting Drug-Protein Binding Affinities using Graph Convolutional Networks

Machine Learning for Drug Discovery: Predicting Drug-Protein...

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Recent Trends in computer Science and Technology (ICRTCST), international conference on

作者： S. RadhaMahendran Akriti Dogra Dinesh Mendhe S. B G Tilak Babu Shriniket Dixit Surya Pratap Singh Department of Bioinformatics School of Life Sciences Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai India Department of Oral & Maxillofacial Surgery Manav Rachna Dental College Faridabad Haryana Computer and Information Research Scientist Office of Research Computing Rutgers State University of New Jersey New Brunswick New Jersey Department of ECE Aditya Engineering College Surampalem School of Computer Science Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of Multi-Disciplinary Research Unit UPUMS Etawah UP

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

Determining the potential interactions between ligands and proteins is a crucial first step in developing new drugs. Graph BAR, our novel prediction model, makes accurate guesses about the strength of ligand-protein bindings by means of a graph convolutional neural network. When compared to graph convolutional neural networks, standard convolutional neural network models are more resource and time intensive to manage. the interaction between proteins and ligands is shown via a network consisting of many adjacency meshes. the atomic distances dictate the alterations to these matrices. Additionally, the atomic properties can be shown in a feature matrix. Using the PDBbind datasets, we verified the functionality of graph convolution and evaluated Graph BAR's ability to forecast the binding strengths of proteins and ligands. In order to maximize the model's performance was improved by utilizing ways for adding additional input, which took advantage of the processing capability of graph convolutional neural networks. Graph BAR's predictions appear to have potential for use in drug creation due to its great accuracy and quickness. the results of our research suggest that Graph Convolutional Networks have the potential to expedite the discovery of new medications by properly predicting the interactions between pharmaceuticals and proteins.

关键词： Proteins Drugs computer science Accuracy Convolution machine learning Predictive models

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5th international conference on Big Data and Security, ICBDS 2023

5th International Conference on Big Data and Security, ICBDS...

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5th international conference on Big Data and Security, ICBDS 2023

ISBN: (纸本)9789819743865

the proceedings contain 18 papers. the special focus in this conference is on Big Data and Security. the topics include: Development of U-net Neural Network for Biomedical Images with Big Data;Research on Implementation of BADGE Active learning to Increase Defect Detection Effectiveness;stationary Multi-scale Hierarchical Dilated Graph Convolution for Multivariate Time Series Anomaly Detection;design of human Resource Optimal Allocation Model for Electric Power Construction Enterprises Based on machine learning;research on the Construction of Digital Education Cloud Resource Database Based on Ant Colony Algorithm;simulation of Short-Term Prediction Model of Real Estate Price Index Based on Neural Network Algorithm;PLMR: An Efficient Pre-trained Model for Aircraft Maneuver Recognition;blockchain-Based WeChat Small Program Platform: A New Type of Data Security and Management Solution;a Survey of Edge Computing Resource Allocation and Task Scheduling Optimization;an Algorithm for Analyzing Psychological Characteristics in Music Education Based on Markov Model;enhancing Privacy Strategy in New Power Systems: Problem Assessment, Comparative Analysis and Suggestions;internet Public Opinion Evolution Model and Simulation Based on Big Data and Grey Clustering Algorithm Under New Media Environment;simulation of Regional Financial and Economic Development Decision Model Based on Decision Tree Algorithm;simulation Research on Pre-alarm Model of Enterprise Shared Financial Crisis Based on Genetic Algorithm;research on Accurate Poverty Alleviation Algorithm of Rural Industry Based on Association Rules Mining;preface.

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5th international conference on Big Data and Security, ICBDS 2023

5th International Conference on Big Data and Security, ICBDS...

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5th international conference on Big Data and Security, ICBDS 2023

ISBN: (纸本)9789819743896

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Attention Assigning: Stacked Dual Network-Based Reinforcement learning for Solving Assignment Problems

Attention Assigning: Stacked Dual Network-Based Reinforcemen...

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

作者： Hongxiang Shao Junbo Ge Guo Wang Marine Design & Research Institute of China Shanghai China

ISBN: (数字)9798350386943

ISBN: (纸本)9798350386950

thereare many processes in the semiconductor manufacturing process. When a shipment completes a certain process, it is very important to assign which vehicle to transport the semiconductor or which machine to send to for the next process. If randomly assigned vehicles lead to machine idle time being too long will cause human ridge source and time wave. therefore, the purpose of this paper is to use the trained dispatching strategy to assign vehicles to transport goods, beyond the traditional dispatching strategy used by most people in the past. this paper proposes a Stacked Dual Network, an architecture similar to Recurrent Neural Networks (RNN) that passes through multiple layers of the same neural network to get the output. It passes through the Dual Attentive Unit in each layer during the forward pass by combining the current state with cell units using the attention mechanism to find correlations and get an attention score. Multiple iterations of the shared attention unit make the information more accurate. then, it translates into reinforcement learning, in which the factory environment is modeled as a state. the agent is a central dispatcher system that is used to assign vehicles to deliver semiconductors. the Advantage Actor-Critic method is used to train the agent so that the agent can deliver goods with the best dispatcher strategy to achieve the goal of maximizing capacity. Extensive experimental results show that our proposed method helps reinforcement learning outperform traditional assignment methods and similar reinforcement learning methods in assigning problems.

关键词： Training Semiconductor device modeling Recurrent neural networks Microprocessors computer architecture Reinforcement learning Semiconductor device manufacture

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StrideSense: Faster RCNN for Accurate human Pathways Gait Recognition

StrideSense: Faster RCNN for Accurate Human Pathways Gait Re...

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Global conference for Advancement in Technology (GCAT)

作者： Ishaan Simran Tuli Deepak Kumar Sanjay Bhatnagar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

human gait analysis is quite a multidimensional field, being at the intersection of biomechanics, kinesiology, physiology, computer science, and machine learning, which studies gait (walking) patterns that depend on the interaction of many physiological, social, psychological, and health factors. this is an article on the thorough research about the human gait analysis using the Fast Region-based Convolutional Network (Fast R-CNN) models. this study considers the evolution of a gait analysis from a descriptive to highly advanced tool that has shown its potential for sports, biometrics, security, and healthcare. In this aspect, the research applies deep learning techniques like Convolutional Neural Networks (CNN) to construct the Fast R-CNN gait recognition method that optimally parameters for accuracy and speed. the methodology is collecting data-sets from various sources, preprocessing of the gait images, and then finally training the fine-tuned Fast R-CNN model with the help of transfer learning. Evaluation metrics such as accuracy, recall, and F-score are used to further examine the performance across different gait types, lighting conditions, and background environments. Experimental outcomes show the accuracy, precision, recall, and F1-score at a high level which is a proof that Fast R-CNN algorithm has been successfully used for human gait analysis recognition. the model is capable of achieving a recall rate of 97%, 94% accuracy, 93% precision, and 95% F1-score. these results demonstrate the model can correctly classify broad and diverse human gait variations and report accuracy of over 95%. On top of that, the paper explores where the project heading by incorporating attention mechanisms and recurrent neural networks to improve the model overall and for some real-time deployment.

关键词： Deep learning Training Accuracy Transfer learning Physiology Convolutional neural networks Internet of things Security Gait recognition Sports

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Detection of Mental Stress using EEG signals - Alpha, Beta, theta, and Gamma Bands

Detection of Mental Stress using EEG signals - Alpha, Beta, ...

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Emerging Technology (INCET), international conference for

作者： Savita Bakare Shruti Kuge Siri Sugandhi Shashank Warad Vinay Panguddi Computer Science & Engineering Department Karnatak Law Society's Gogte Institute of Technology Belagavi India Computer Science and Engineering Department KLE Dr. M. S. Sheshgiri College of Engineering and Technology Belagavi India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

Mental stress, or psychological stress, arises when individuals perceive emotional or psychological strain beyond their coping abilities. Various factors such as personal relationships, work pressure, financial problems, or major life changes, impact both emotional and physical well-being. Electroencephalography (EEG) signals serve as insightful indicators of brain activity, resembling tiny electrical whispers exchanged between brain cells. these brain cells are characterized by EEG patterns such as alpha, beta, theta, and gamma frequency band waves, and offer valuable insights such as frequency analysis, categorization of valence and arousal, and machine learning techniques, into distinct mental states and neural activity. the exploratory data analytics (EDA) techniques using ML methods (KNN, SVM, and RF) on EEG dataset is being performed to analyze mental stress detection. Furthermore, Mean Absolute Error (MAE) scores of the KNN, SVM, and RF models are compared based on the number of folds, which shows that KNN has the lowest MAE of 0.62, compared to SVM scores of 0.83 and RF score of 1.47 respectively. As a result, the KNN model is selected for stress detection due to its superior performance in reducing MAE in both iterations and training data sizes. Furthermore, the study concisely also reviews an existing literature on mental stress detection using EEG signals, highlighting prevalent challenges and research gaps.

关键词： Support vector machines Radio frequency Reviews Psychology human factors machine learning Nearest neighbor methods

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Enhanced human Face Recall by Reconstruction of Real and Synthetic Occlusions

Enhanced Human Face Recall by Reconstruction of Real and Syn...

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Data Intelligence and Cognitive Informatics (ICDICI), international conference on

作者： Dane Brown Iffat Mollah Computer Science Rhodes University Grahamstown South Africa

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

this study investigates occluded face recognition using a Complete Face Recovery Generative Adversarial Network (CFR-GAN). It directly addresses the complexities of identifying faces obscured by real-world occlusions, while underlining the critical role of high-quality input data. the CFR-GAN is trained on both occluded and rotated face datasets, with synthetic occlusions applied for data augmentation. In Experiment 1, the model achieved a poor result of 15.80% accuracy for reconstructing images with sunglasses and 24.11% for masked faces, but produced a consistent recall rate of 100%. Experiment 2 utilised the Labelled Faces in the Wild (LFW) dataset, where the CFR-GAN yielded an accuracy of 52.27%. Despite improvements with higher-quality inputs, the model introduced artefacts and misaligned features, underscoring the ongoing difficulties in occluded face recognition. the results underscored the importance of data quality, advanced training techniques, and refined evaluation metrics in addressing these real-world challenges. While this work demonstrates promise, further study is required to translate these findings into practical applications in biometric security and related areas.

关键词： Training Measurement Accuracy Image resolution Image recognition Face recognition Lighting machine learning Security Image reconstruction

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Numerical Simulation and Design of Sentiment Analysis of Twitter Posts Using Hybrid machine learning Approach 23

Numerical Simulation and Design of Sentiment Analysis of Twi...

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Proceedings of the 5th international conference on Information Management & machine Intelligence

作者： Anand Geet Rajeev Yadav Satish Kumar Alaria Sunil Kumar Gupta Javed Khan Bhutto Department of Computer Science and Engineering Arya Institute of Engineering and Technology India Computer Science and Engineering Arya College of Engineering Jaipur India Education Department Government of Rajasthan India Department of Electrical and Electronics Engineering Poornima University India Department of Electrical Engineering King Khalid University Saudi Arabia

ISBN: (纸本)9798400709418

In today's digital age, social media platforms like Twitter have become a prolific source of real-time information and public sentiment. Analyzing the sentiment expressed in Twitter posts is crucial for understanding public opinion and making data-driven decisions in various domains, including business, politics, and social trends. this paper presents a comprehensive study on the design and analysis of sentiment analysis for Twitter posts, employing a novel hybrid machine learning approach. Traditional sentiment analysis techniques often struggle to capture the nuances and complexities of human language, particularly in the context of short and informal social media posts. To address this challenge, we propose a hybrid machine learning approach that combines the strengths of both deep learning and traditional machine learning algorithms. Our approach leverages deep neural networks, such as Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), to automatically learn features from the raw text data. Additionally, it integrates traditional machine learning algorithms, such as Support Vector machines (SVMs) and Random Forests, to enhance the model's interpretability and generalizability. the key components of our approach include data preprocessing, feature extraction, model training, and evaluation. We preprocess Twitter posts by removing noise, handling emoticons, and tokenizing the text. Feature extraction involves using word embedding's and other linguistic features to represent the text data. the hybrid model is trained on labeled datasets, encompassing a wide range of sentiments, from positive to negative. To evaluate the model's performance, we employ various metrics such as accuracy, precision, recall, and F1-score. Our experimental results on a large dataset of Twitter posts demonstrate that the hybrid machine learning approach outperforms traditional sentiment analysis methods and standalone deep learning models. the hybrid approach effect

关键词： Deep learning

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A Comparative Study of machine learning Models for Identifying Nigerian Languages from Audio Clips

A Comparative Study of Machine Learning Models for Identifyi...

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IEEE international conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Oluwadunsin Emmanuel Fajemila Ayodeji Olalekan Salau Pipelolu Samuel Olayiwola Department of Electronics and Electrical Engineering Obafemi Awolowo University Osun State Nigeria Department of Electrical/Electronics and Computer Engineering Afe Babalola University Ado-Ekiti Nigeria Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Language is a vital aspect of human culture and communication, with over 7,000 languages spoken worldwide. Despite this diversity, many languages, particularly those spoken in Africa, lack extensive research and support for speech processing technologies. this study focuses on identifying five Nigerian languages, which are Yoruba, Nigerian Pidgin, Nigerian English, Hausa, and Igbo using machine learning techniques. We utilized Mel-Frequency Cepstral Coefficients (MFCCs) to extract acoustic features from audio clips and trained several models, including 1-Dimensional Convolutional Neural Network (1D CNN), 2-Dimensional Convolutional Neural Network (2D CNN), Deep Neural Network (DNN), Long Short-Term Memory (LSTM), Random Forest, and Support Vector machine (SVM), to evaluate their effectiveness in language identification. the dataset comprised audio from OpenSLR, Common Voice, local recordings, and NWU, totaling 37.8 hours across all languages. Results indicated that the LSTM model achieved the highest accuracy (0.830) and precision (0.856), while the DNN model showed the best F1 score (0.797) and recall (0.852). Neural network-based models consistently outperformed traditional machine learning approaches. We observed varying levels of classification difficulty among the languages, with some presenting more challenges than others. the dataset's uneven distribution across languages posed additional complexities in model training and evaluation. this research underscores the importance of diverse data sources and advanced neural network architectures in improving language classification for underrepresented African languages.

关键词： Support vector machines Training Accuracy Confusion matrices Transfer learning Feature extraction Multilingual Convolutional neural networks Speech processing Long short term memory

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