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High-Precision Detection of Suicidal Ideation on Social Media Using Bi-LSTM and BERT Models 7th

High-Precision Detection of Suicidal Ideation on Social Medi...

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7th international conference on Cognitive computing (ICCC) Held as Part of the Services conference Federation Meeting (SCF)

作者： Wang, Zhenxi Jin, Mingzhe Lu, Yong Queen Mary Univ London London 15 2ZL England Minzu Univ China Beijing 100190 Peoples R China

ISBN: (纸本)9783031516702;9783031516719

In a poignant moment on social media, a post suggesting suicidal thoughts is rapidly overshadowed by an array of memes, updates, and ads. Such overlooked cries for help underscore the dire need for reliable detection mechanisms. Addressing this urgent issue, our research introduces an innovative approach to detecting suicidal ideation from text. Using TF-IDF and Chi-square tests, we identify crucial keywords and narrow them down for frequency analysis. Our framework leverages Word2Vec embeddings and is further enriched with readability scores, sentiment assessments via TextBlob, and text lengthmetrics. Latent Dirichlet Allocation (LDA) aids in topic modeling, its insights visualized through compellingword clouds. this multifaceted approach enhances predictive accuracy and provides a nuanced understanding of suicidal ideation. We initially employed conventional algorithms like AdaBoost, Random Forest, XGBoost, and Logistic Regression before transitioning to advanced deep learning models. the Bi-LSTM and BERT models emerged as top performers, achieving detection accuracies of 97% and 98%, respectively. While our study is constrained by dataset limitations and potential biases, it marks a significant step forward in the identification of suicidal tendencies in digital spaces. Looking forward, broader dataset integration, refined models, and a continuous recalibration in line with evolving digital discourse are anticipated next steps.

关键词： Suicidal Ideation machine learning Deep learning

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A New End-to-End Image Contrastive Clustering Based on Mining Latent Positive Samples 7

A New End-to-End Image Contrastive Clustering Based on Minin...

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7th international conference on machine learning and Natural Language Processing, MLNLP 2024

作者： Feng, Bo Yan, Xi Yan, Li Luo, Bing Pei, Zheng School of Science Xihua University Chengdu China School of Computer Science and Software Engineering Xihua University Chengdu China

ISBN: (纸本)9798350354973

the integration of the contrastive learning paradigm into deep clustering has led to enhanced performance in image clustering. However, in existing researches, the samples in the class of the target may be still treated as negative samples, this means that more semantic sample pairs cannot be constructed and the intra-class compactness may be compromised. In this paper, we propose a new contrastive clustering method based on mining latent positive samples (CCLPS), which utilizes the nearest neighbor relationship between samples to mine semantic positive samples in the same class. In more detail, by mining latent positive samples in both the feature and cluster spaces, CCLPS can obtain more cluster-friendly feature representation of samples to enhance clustering performance. Samples mined from the feature space fuel the cluster-level contrastive learning task, while samples mined from the cluster space fuel the instance-level task. three image datasets CIFAR-10, CIFAR-100 and Tiny-ImageNet are employed to experimentally demonstrate usefulness and effectiveness of the proposed method in image clustering, and transfer learning based on mining latent positive samples is also discussed and comparisons are made with 19 representative clustering methods on the datasets. Experimental results indicate that the proposed method can serve as a pre-training model for transfer learning and exhibits commendable feature modeling capabilities. © 2024 IEEE.

关键词： Contrastive learning

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Scaling machine learning with a Ring-based Distributed Framework 7

Scaling Machine Learning with a Ring-based Distributed Frame...

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7th international conference on Computer Science and Artificial Intelligence, CSAI 2023

作者： Zhao, Kankan Leng, Youfang Zhang, Hui Shandong Inspur Database Technology Co. Ltd. Shandong Jinan China

ISBN: (纸本)9798400708688

In centralized distributed machine learning systems, communication overhead between servers and computing nodes has always been an important issue affecting the training efficiency. Although existing research has proposed various measures to reduce communication overhead between nodes in parameter server frameworks, the communication pressure and overhead inherited from centralized architectures are still significant. To address the above issue, this paper proposes a ring-based parameter server framework that is distinct from node division and model training mechanism in the standard p/s framework. the ring-based architecture cancels the global model stored on the server side, and each computing node stores a local copy of the model. During model training, computing nodes can asynchronously train local models based on local or remote training data. After all nodes finish learning, the ensemble learning method can predict test data based on all local models. To avoid the negative impact of remote data reading on model training efficiency, a producer-consumer data reading strategy is proposed. this strategy can reduce data reading overhead in a pipeline manner. To make rational use of the input and output bandwidths of all nodes, a circular data scheduling mechanism is proposed. At any given time, this mechanism ensures each node has at most one input stream and one output stream, thereby dispersing communication pressure. the experimental results show that the proposed distributed architecture achieves significantly better performance (1.7%-2.1% RMSE) than the state-of-the-art baselines and also achieves a 2.2x-3.4x speedup when reaching a comparable RMSE performance. © 2023 ACM.

关键词： machine learning

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Automated MOM Generator with Attendance Monitoring 7

Automated MOM Generator with Attendance Monitoring

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Dharmale, Gulbakshee Deoghare, Radha Devkar, Anita Ganguly, Tanaya Deshmukh, Sphurti Pimpri Chinchwad College of Engineering Department of Information Technology Pune India Koneru Lakshmaiah Education Foundation Department Computer Science Engineering Guntur India

ISBN: (纸本)9798350304268

To automate the process of scripting the minutes of meetings and monitoring the attendance of meeting attendees. Minutes of the meeting contains revised information about various decisions taken, discussions done on many agendas, the final abstract of the meeting and the attendance of attendee's present in the meeting. Generating the Meeting minutes document is a manual and time-consuming process. We are proposing a solution to automate the process of scripting minutes of meetings which is currently done manually and keep track of attendees of meetings. this would reduce the effort of generating minutes document manually and distributing it among attendees of meeting. this project is divided into 3 modules the first module would be about converting meeting recorded audio to text, the second module would focus on text summarization which would include objectives, agendas, date and time of the meeting, decisions, and issues discussed in meeting, and the third module would focus on getting the attendance of meetings participants. Our proposed hardware device would be able to capture long range voice as it would be using sound shark mic, after which it would process and convert this recorded meeting audio to text and summary of this text would be generated with inbuilt machine learning model with help of embedded chip. this hardware device would contain a camera to capture faces of meeting attendees which would be passed to face recognition model for monitoring the attendance of meeting attendees. After the meeting MOM document would be generated and sent via mail to admin/host. © 2023 IEEE.

关键词： Automation Deep learning NLTK Speech-to-text Text Summarization VHOSK

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Towards Robust Underwater Image Enhancement 7th

Towards Robust Underwater Image Enhancement

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7th international conference on soft computing in Data Science (SCDS)

作者： Marvi, Jahroo Nabila Rahadianti, Laksmita Univ Indonesia Fac Comp Sci Depok 16424 Indonesia

ISBN: (纸本)9789819904044;9789819904051

Underwater images often suffer from blurring and color distortion due to absorption and scattering in the water. Such effects are undesirable since they may hinder computer vision tasks. Many underwater image enhancement techniques have been explored to address this issue, each to varying degrees of success. the large variety of distortions in underwater images is difficult to handle by any singular method. this study observes four underwater image enhancement methods, i.e., Underwater Light Attenuation Prior (ULAP), statistical Background Light and Transmission Map estimation (BLTM), and Natural-based Underwater Image Color Enhancement (NUCE), and Global-Local Networks (GL-Net). these methods are evaluated on the Underwater Image Enhancement Benchmark (UIEB) dataset using quantitative metrics, e.g., SSIM, PSNR, and CIEDE2000 as the metrics. Additionally, a qualitative analysis of image quality attributes is also performed. the results showthat GL-Net achieves the best enhancement result, but based on the qualitative assessment, this method still has room for improvement. A proper combination between the non-learning-based component and learningbased component should be investigated to further improve the robustness of the method.

关键词： Image enhancement Image restoration Deep learning Underwater images

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Employing the SMOTE Technique, a machine learning Model for Predicting Student Grades 7

Employing the SMOTE Technique, a Machine Learning Model for ...

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7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Vaishnavi, P. Prathima, Ch. Rakesh, Vasa Sujala, P. Nitin, P Anjan Yadav, D Rama Krishna Sree Vidyanikethan Engineering College IT Dept. Andhra Pradesh Tirupati India Mohan Babu University School of Computing Tirupati India

ISBN: (纸本)9798350397253

Predicting a student's academic grade and performance prediction is a complex task in the educational sector especially in India because of the large volumes of datasets. To check and analyze a student's performance and progress, a novel system should be developed to monitor the results based on the given data. No such technology currently exists in the Higher education system in India. Different institutions analyze the progress of a student based on different criteria and also follow their methods. the reason for this was that the people do not have much knowledge about the techniques that existed initially and they do not know how they are going to work effectively and even they cannot analyze the best way is also the main reason. Prediction mainly depends on some factors are the details of the students that related to their academic performance which includes the course details and proper investigation of the factors leading to better analysis of the results. For that reason, we proposed a prediction model for multiclass that helps to remove the problem of overfitting and misclassification of the factors, which gives outcomes developed by an unbalanced multi-classification technique based on oversampling which is SMOTE i.e., Synthetic Minority Oversampling. SMOTE includes feature nomination methods. this proposed model helps to get accurate results and reduces the chance of misleading and misclassification problems and enhances both performances of analyzing the student grades. © 2023 IEEE.

关键词： Students

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Teaching–learning-Based Optimization Algorithm Approach for Water Management of Canal Command Area of Upper Ganga Canal 7th

Teaching–Learning-Based Optimization Algorithm Approach for...

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7th international conference on soft computing: theories and Applications, SoCTA 2023

作者： Nandan, Devaki Tare, Vinod Kapoor, Vishal Suman, Rajiv G. B. Pant University of Agriculture and Technology Pantnagar263145 India Indian Institute of Technology Kanpur Kanpur208016 India

ISBN: (纸本)9789819720309

Commissioned way back in 1854–1855, the Upper Ganga Canal (UGC) with its branches is one of the oldest irrigation canal systems in India irrigates nearly 9000 km2 of fertile agricultural land covering ten districts of Uttarakhand and Uttar Pradesh. High variability in the soil of the UGC command area leads to diversification in the cropping pattern. the poor economic efficiency of the existing system is a major factor for improper utilization of available resources by existing cropping patterns. therefore, in the current article to increase the efficiency of the existing system an algorithm Teaching–learning-Based Optimization (TLBO)-based approach is used which leads to the enhancement of net profit by 1.2% and 7.3% reduction in water consumption under optimal cropping pattern. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Water management

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Interpretable Models For ML-Based Classification of Obesity 23

Interpretable Models For ML-Based Classification of Obesity

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2023 7th international conference on Cloud and Big Data computing, ICCBDC 2023

作者： Khater, Tarek Tawfik, Hissam Sowdagar, Sahar Singh, Balbir Department of Electrical Engineering University of Sharjah United Arab Emirates School of Built Environment Engineering and Computing Leeds Beckett University United Kingdom

ISBN: (纸本)9798400707339

Artificial Intelligence is revolutionizing the healthcare business, thanks to the rising availability of structured and unstructured data and the rapid advancement of analytical methodologies. With artificial intelligence's expanding importance in healthcare, there are growing issues about a lack of transparency and explainability, as well as potential bias in model predictions. the goal of this paper is to use interpretable ML to provide a better understanding of the lifestyle factors that influence the model's predictions of the weight levels, as well as to identify the most critical features for the classification task. We aim to create more accurate and effective predictive models for weight management, which could eventually help individuals make more informed decisions about their diet and lifestyle choices. Our machine learning model got an accuracy of 76% using XG-boost and Random Forest and we tried to interpret the result using a model-agnostic method including, the permutation importance method and partial dependence plot. the results showed that the number of main meals, frequent consumption of vegetables, and time using technology are the most important features. Further explanation was performed using a partial dependence plot which interprets the relationship between these important features and the model behavior. © 2023 ACM.

关键词： machine learning

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Deep learning in Automated Tests for the Automotive Industry 15th

Deep Learning in Automated Tests for the Automotive Indus...

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15th international conference on soft computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Branco, Beatriz C. Barbosa, Ramiro S. Porto Portugal GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development ISEP/IPP Porto Portugal

ISBN: (纸本)9783031810824

the automotive industry is one of the biggest industries nowadays, representing almost 80% of the projects in Capgemini Engineering Portugal, where this work was developed. In this paper, a new approach for automated tests in this sector is presented, based on the application of Deep learning (DL) algorithms. Experimental results show that the new solution reduces the frameworks needed for testing, as well as the human interaction, improving the efficiency in HMI testing for the company. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Automated tests Automotive industry Deep learning

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Water Quality Prediction Using machine learning 7th

Water Quality Prediction Using Machine Learning

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7th international conference on soft computing: theories and Applications, SoCTA 2023

作者： Luthra, Gauransh Kukkar, Srishti Harnal, Shilpi Tiwari, Rajeev Upadhyay, Shuchi Chhabra, Gunjan Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India IILM University Uttar Pradesh Greater Noida India SOHST UPES Uttarakhand Dehradun India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India

ISBN: (纸本)9789819720880

Over 70% of the ground’s surface is below water, which is one of the ultimate detracting possessions for claiming existence. Increasing industrialization and urbanization have led to a disturbing rate of water character depravity, that has resulted in bad ailments. Traditional procedures for judging water status involved valuable and behind mathematical and workshop tests, making the plan of real-opportunity listening trivial contemporary. there must be an active, more practical resolution by way of the urgent belongings of distressing water quality. In order to estimate the water status, this research investigates any of directed machine intelligence methods. this study investigates the performance of machine intelligence algorithms to determine the kind of water. the algorithms used to do so involve of logistic regression, decision tree, random forest, KNN, SVM, Adaboost, BAGGING, and perceptron. To evolve these algorithms different types of kernels and transfers were captured in the report. the effect of the model was accompanying SVM showing the capital veracity accompanying veracity score of 0.7012 trailed by the perceptron and BAGGING accompanying veracity score of 0.6753 and 0.6595 individually. the slightest acting showed by LOGISTIC REGRESSION treasure accompanying veracity score of 0.6051. In order to prove the being of its request in evident-occasion water character discovery systems, the submitted method achieves acceptable veracity accompanying a limited number of limits. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Logistic regression

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