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Proceedings of the 4th African Human Computer Interaction conference, AfriCHI 2023

Proceedings of the 4th African Human Computer Interaction Co...

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4th African Human Computer Interaction conference, AfriCHI 2023

ISBN: (纸本)9798400708879

the proceedings contain 56 papers. the topics discussed include: a need finding study with low-resourced language content creators;designing a voice-controlled dialogue system for workplace learning of routine physical workers;an interface design methodology for serving machine learning models;lions out of bounds? reflections on digital technology and matristic design to address human-wildlife conflict;an integration model to enhance information systems administration and data sharing – a case of Namibian lower courts;unmasking trust: examining users’ perspectives of facial recognition systems in Mozambique;and investigating the efficacy of large language models in reflective assessment methods through chain of thoughts prompting.

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Emotion Detection from Textual data Using Supervised machine learning Models 4

Emotion Detection from Textual Data Using Supervised Machine...

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4th international conference for Emerging Technology, INCET 2023

作者： Malagi, Rakshit R Yogith, R. Sai Prashanth, T.K. Kodipalli, Ashwini Rao, Trupthi Rohini, B.R. Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bangalore India

ISBN: (纸本)9798350335750

Emotion detection and recognition from text is a recent field of research that is closely related to Sentiment analysis. Many people express themselves using text, photographs, music, and video. Text communication using web-based networking platforms, however, could be a little overwhelming. Every second, a substantial amount of unstructured data is produced on the Internet as a result of social media sites. this is where sentiment analysis, which recognises polarity in texts, can be useful. It assesses the author's attitude towards a specific object, administration, person, or location and concludes if it is positive, negative, or neutral. In some cases, sentiment analysis is inadequate, necessitating emotion detection, which precisely ascertains a person's mental/emotional state. the development of a text-based emotion detection and prediction model is the primary goal of this work. the development of emotion analysis is confronting several market hurdles, with accuracy being the key one. As a result, the Decision Trees, Naive Bayes, Support Vector machine, Logistic Regression, k-Nearest Neighbors and Random Forest, supervised machine learning classification algorithms were examined. the six main emotions recognized by Ekman are joy, fear, anger, love, surprise, and sadness, these were the foundation through which the model was constructed. strategies for preprocessing data containing stemming, stop-words, numerals, and punctuation marks removal, tokenization, and spelling correction were implemented. this review paper delves into the degrees of several emotion models as well as the technique of emotion detection from text. © 2023 IEEE.

关键词： Decision trees

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Accuracy Improvement Based on Classic Neural Network: Voting, Restarting and Quantization 4

Accuracy Improvement Based on Classic Neural Network: Voting...

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4th international conference on Computing and data Science, CONF-CDS 2022

作者： Zhu, Yunxiang Zhao, Yanpeng Liu, Guanghong Zeng, Lifan School of Mechanical and Electrical Engineering and Automation Shanghai University Shanghai200444 China University of Wisconsin-Madison WI53711 United States School of Electrical Engineering Zhejiang University Zhejiang310027 China Electrical Engineering of the State University of Buffalo buffaloNY14260 United States

Since AlphaGo beat the world Go champion in 2016, which attracted wide attention, the neural network has become more and more popular in recent years, and people's research on it has gradually improved and been applied in different fields. Today, artificial intelligence and machine learning have become an essential part of modern society and intelligent systems. We do image recognition, speech recognition, and visual learning, closely related to machine learning. However, in machine learning, unsatisfactory training results or even training failure is always encountered. therefore, in machine learning, it is imperative to improve the accuracy of neural network training results. In this paper, speech recognition, image processing, MNIST, and other classical neural network models will be used to set the training parameters of the neural network better and improve the accuracy of training through voting, quantization, restart, and other methods. the part of research is aiming to find the relationship between restart numbers on the training process and the total extent of learning improvement. At the same time, several algorithms on utilizing these restart numbers are to be compared and selected. Finally, the conclusion is drawn that the more restart made in the training process with a convolutional neural network, the less profit on accuracy improvement we gain from restarting the process. © Published under licence by IOP Publishing Ltd.

关键词： machine learning

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machine learning Solutions for Predicting Bankruptcy in Indian Firms 27th

Machine Learning Solutions for Predicting Bankruptcy in In...

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27th international conference on pattern recognition, ICPR 2024

作者： Chaithra Sharma, Priyanshu Mohan, Biju R. National Institute of Technology Karnataka Surathkal India

ISBN: (纸本)9783031781216

the growing demand to identify potential bankrupt companies has prompted more research into bankruptcy prediction, assisting stakeholders in determining the worthiness of an investment. the Indian stock market offers investment opportunities, but it also involves risk. As a result, it is critical to invest in fundamentally sound companies for long-term investment. To address this need, we created a machine learning-based model for identifying a healthy and distressed firm in the Indian scenario. We created a dataset consisting of 118 bankrupt and 310 healthy firms. the dataset contains three labels: bankrupt, healthy, and financial distress. the addition of the financial distress category improves our ability to recognize and identify firms that are more likely to declare bankruptcy. Recognizing the shortcomings of limited data in the Indian scenario in previous research, our study aimed to include more data instances for training. the dataset included widely recognized financial ratios and macroeconomic data that recognize the interconnectedness of broader economic trends with the company’s financial health. Advanced machine learning algorithms, namely Support Vector machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting machine (LGBM), Categorical Boosting (CatBoost), Gradient Boost (GB), and K-Nearest Neighbors (KNN) were applied. the XGBoost and LGBM demonstrated the highest level of classification accuracy and also performed well on real-world data, demonstrating their potential use in supporting investors with decision-making processes. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Decision making

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Mining and recognition of English learning patterns of Mobile Users Based on Intelligent Algorithm 4

Mining and Recognition of English Learning Patterns of Mobil...

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4th international conference on Emerging Research in Electronics, Computer Science and Technology, ICERECT 2022

作者： Li, Jingtai School of Foreign Languages Jiaying University Guangdong Meizhou China

ISBN: (纸本)9781665456357

the latest research results show that activity recognition based on frequent pattern mining is the main method. Its advantages are that the activity patterns obtained through learning mining are comprehensive and accurate, and the recognition effect of activities in the corresponding environment is good. Besides, some activities that can only be carried out in a specific environment can be identified. this study attempts to use data mining technology to help foreign language education systems to further transform massive data, so as to mine more valuable content, helping educators to further tap teaching potential, and improving the efficiency of teaching resources utilization. © 2022 IEEE.

关键词： data mining

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Pose Estimation Keypoints in Age recognition of Full Body Image 4

Pose Estimation Keypoints in Age Recognition of Full Body Im...

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4th IEEE international Multidisciplinary conference on Engineering Technology, IMCET 2023

作者： Lakis, Rachad Constantin, Joseph Constantin, Ibtissam Hoang, Vinh Truong Ruichek, Yassine Lebanese University LaRRIS Laboratory Fanar Lebanon Ho Chi Minh City Open University Ho Chi Minh City Viet Nam ESIB Saint Joseph University Beyrouth Lebanon UTBM University CIAD Laboratory Montbéliard France

ISBN: (纸本)9798350313826

Age recognition based on an image has many potential applications, such as developing intelligent human-machine interfaces and improving safety in various fields, such as transportation and security. Most age classification learning models use a person's face to estimate the age category. However, images are usually captured in-the-wild, where often no near-frontal information is available. Also, images are taken under different illumination conditions and different camera viewing angles, providing poor visual quality. To address this problem, we proposed a novel deep learning algorithm for age recognition of adults and children using images in which faces are not well recognized. Our results clearly show that the addition of Pose Estimation Keypoints leads to significant improvement in age classification accuracy, precision, recall, and F1-score using deep learning models with simple architecture for online decision making. It can be seen that Pose Estimation Keypoints can have important implications for a range of applications in areas such as CCTV footage, recommendation of videos and advertisements according to the target audience, granting additional privileges to individuals depending on their age and even on social media where there are age restrictions for viewing or using certain content on the social platforms. © 2023 IEEE.

关键词： learning algorithms

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Research on the application of a hybrid model based on deep learning and cluster analysis in anomaly detection of cloud platform access 4

Research on the application of a hybrid model based on deep ...

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4th international conference on Computer Vision and pattern Analysis, ICCPA 2024

作者： Li, Guohua Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China Guangxi Colleges and Universities Key Laboratory of Intelligent Industry Software Wuzhou University Wuzhou China

ISBN: (纸本)9781510682528

With the rapid development of digitalization, cloud platforms have become a key technology supporting remote networking. However, the accompanying cybersecurity issues, particularly the detection of anomalous access activities, pose significant challenges in maintaining platform stability and user data security. this paper proposes a hybrid model that integrates a one-dimensional convolutional neural network (1D-CNN) with the DBSCAN clustering algorithm, aimed at enhancing the detection accuracy of anomalous access behaviors in cloud platforms. through in-depth analysis and feature extraction of network traffic data, combined with the effective identification of abnormal patterns by the clustering algorithm, this study not only improves detection efficiency but also enhances the model's adaptability to novel anomalous behaviors. Experimental results demonstrate that this model surpasses traditional detection methods in multiple performance metrics such as accuracy, precision, recall, and F1 score, confirming its feasibility and effectiveness in practical application scenarios. © 2024 SPIE.

关键词： Cloud platforms

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Multilevel Credit Card Fraud Detection Using Face recognition and machine learning 1

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4th international conference on Communication and Intelligent Systems, ICCIS 2022

作者： Deshpande, Tushar Datta, Shourjadeep Shah, Rushabh Doshi, Vatsal Dongre, Deepika Department of Computer Engineering D. J. Sanghvi College of Engineering Mumbai India

ISBN: (数字)9789819923229

ISBN: (纸本)9789819923212

Technology in today’s world has wholly modified payment methods from cash to digital or card payments. therefore, in these situations, the authentication system is essential for guaranteeing the legitimacy of a transaction. this is because such payment systems are susceptible to fraud or false transactions. As a result, numerous techniques to authenticate the user’s credentials have been developed and practiced. We use machine learning to predict the fate of the transaction by training our model on past or existing transactional data. Despite the fact that these solutions boost security, they could not be more reliable. these systems may predict that the transaction is legitimate when it’s not. this paper intends to illustrate the idea that helps to increase the credibility of such a machine learning system with the help of integration with a face recognition system. the final decision of whether the transaction is fraudulent or not will be taken based on outputs obtained by the machine learning module and face recognition module. the face recognition module uses the LBPH algorithm. To decide the threshold in the face recognition module and test it, we experimented with 30 users for fraudulent and non-fraudulent face data. For the machine learning module, to select the algorithm to use in our system, we performed a comparative analysis between different algorithms and chose the one that had the best performance on the input dataset. In this way, a multilevel authentication system is proposed to help increase a transaction’s truthfulness. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Face recognition

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Exploring ASR Models in Low-Resource Languages: Use-Case the Macedonian Language 1

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4th international conference on Deep learning theory and Applications (DeLTA)

作者： Bogdanoski, Konstantin Mishev, Kostadin Simjanoska, Monika Trajanov, Dimitar Ss Cyril & Methodius Univ Fac Comp Sci & Engn Rugjer Boshkovikj 16 Skopje North Macedonia iReason LLC 3rd Macedonian Brigade 37 Skopje North Macedonia

ISBN: (数字)9783031390593

ISBN: (纸本)9783031390586;9783031390593

We explore the use of Wav2Vec 2.0, NeMo, and ESPNet models trained on a dataset in Macedonian language for the development of Automatic Speech recognition (ASR) models for low-resource languages. the study aims to evaluate the performance of recent state-of-the-art models for speech recognition in low-resource languages, such as Macedonian, where there are limited resources available for training or fine-tuning. the paper presents a methodology used for data collection and preprocessing, as well as the details of the three architectures used in the study. the study evaluates the performance of each model using WER and CER metrics and provides a comparative analysis of the results. the findings of the research showed that Wav2Vec 2.0 outperformed the other models for the Macedonian language with a WER of 0.21, and CER of 0.09, however, NeMo and ESPNet models are still good candidates for creating ASR tools for low-resource languages such as Macedonian. the research presented provides insights into the effectiveness of different models for ASR in low-resource languages and highlights the potentials for using these models to develop ASR tools for other languages in the future. these findings have significant implications for the development of ASR tools for other low-resource languages in the future, and can potentially improve accessibility to speech recognition technology for individuals and communities who speak these languages.

关键词： machine learning Deep learning Automatic speech recognition Inference metric ASR pipeline Speech-to-text Wav2Vec2 ESPNet NeMo Natural language processing Macedonian language Low-resource languages Word error rate WER Character error rate CER

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Adversarial Augmentation Training Makes Action recognition Models More Robust to Realistic Video Distribution Shifts 4th

Adversarial Augmentation Training Makes Action Recognition M...

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4th international conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

作者： Kim, Kiyoon Gowda, Shreyank N. Eustratiadis, Panagiotis Antoniou, Antreas Fisher, Robert B. University of Edinburgh Edinburgh United Kingdom

ISBN: (纸本)9789819787012

Despite recent advances in video action recognition achieving strong performance on existing benchmarks, these models often lack robustness when faced with natural distribution shifts between training and test data. We propose two novel evaluation methods to assess model resilience to such distribution disparity. One method uses two different datasets collected from different sources and uses one for training and validation, and the other for testing. More precisely, we created dataset splits of HMDB-51 or UCF-101 for training, and Kinetics-400 for testing, using the subset of the classes that are overlapping in both train and test datasets. the other proposed method extracts the feature mean of each class from the target evaluation dataset’s training data (i.e. class prototype), and estimates test video prediction as a cosine similarity score between each sample to the class prototypes of each target class. this procedure does not alter model weights using the target dataset and it does not require aligning overlapping classes of two different datasets, thus it is a very efficient method to test the model robustness to distribution shifts, without prior knowledge of the target distribution. We address the robustness problem by adversarial augmentation training – generating augmented views of videos that are "hard" for the classification model by applying gradient ascent on the augmentation parameters – as well as "curriculum" scheduling the strength of the video augmentations. We experimentally demonstrate the superior performance of the proposed adversarial augmentation approach over baselines across three state-of-the-art action recognition models - TSM, Video Swin Transformer, and Uniformer. Our curated datasets and source code are publicly available (https://***/kiyoon/video-adversarial-augmentation). the presented work provides critical insight into model robustness to distribution shifts and presents effective techniques to enhance video action recogni

关键词： Adversarial machine learning

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