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arXiv 2023年

作者： Sharif, Omar Basak, Madhusudan Parvin, Tanzia Scharfstein, Ava Bradham, Alphonso Borodovsky, Jacob T. Lord, Sarah E. Preum, Sarah M. Department of Computer Science Dartmouth College United States Department of Computer Science and Engineering CUET Bangladesh Center for Technology and Behavioral Health Geisel School of Medicine Dartmouth College United States Department of Biomedical Data Science Geisel School of Medicine Dartmouth College United States Department of Psychiatry Dartmouth Health United States

Social media sites have become a popular platform for individuals to seek and share health information. Despite the progress in natural language processing for social media mining, a gap remains in analyzing health-related texts on social discourse in the context of events. Event-driven analysis can offer insights into different facets of healthcare at an individual and collective level, including treatment options, misconceptions, knowledge gaps, etc. This paper presents a paradigm to characterize health-related information-seeking in social discourse through the lens of events. Events here are board categories defined with domain experts that capture the trajectory of the treatment/medication. To illustrate the value of this approach, we analyze Reddit posts regarding medications for OPIOID USE DISORDER (OUD), a critical global health concern. To the best of our knowledge, this is the first attempt to define event categories for characterizing information-seeking in OUD social discourse. Guided by domain experts, we develop TREAT-ISE, a novel multilabel treatment information-seeking event dataset to analyze online discourse on an event-based framework. This dataset contains Reddit posts on information-seeking events related to recovery from OUD, where each post is annotated based on the type of events. We also establish a strong performance benchmark (77.4% F1 score) for the task by employing several machine learning and deep learning classifiers. Finally, we thoroughly investigate the performance and errors of ChatGPT on this task, providing valuable insights into the LLM’s capabilities and ongoing characterization efforts. © 2023, CC BY-NC-SA.

关键词： Benchmarking

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LSTM based Soft-Sensor for Estimating Nitrate Concentration in Aquaponics pond

LSTM based Soft-Sensor for Estimating Nitrate Concentration ...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： A. Dharshan Purushottam Kumar S. Ravimaran U Srinivasulu Reddy Department of Artificial Intelligence and Data Science Saranathan College of Engineering Trichy India CoE in Artificial Intelligence Machine Learning & Data Analytics Lab National Institute of Technology Trichy India Department of Computer Applications Machine Learning & Data Analytics Lab National Institute of Technology Trichy India

In the field of aquaponics, where fish and plants coexist in a symbiotic environment, closely monitoring nitrate levels in the water is crucial due to their profound impact on aquatic and plant well-being. Traditional nitrate measurement methods are often time-consuming and costly. Various approaches, including first principles, IoT-based sensors, and machine learning-based soft sensors, have been attempted to address this challenge. However, these efforts face challenges such as expensive sensors, infrequent data collection, multistage data processing using limited sensor types, and the need for regular maintenance like cleaning and calibration. Additionally, varied environmental conditions affect sensor suitability for different water environments, and even some machine learning-based soft sensors have proven inaccurate. In response, soft sensors, especially deep learning-based ones, have gained prominence in industrial applications for their adaptability and accuracy. These sensors provide real-time insights into complex processes without requiring expensive hardware. In this study, an innovative solution was introduced using Long Short-Term Memory (LSTM) technology, a neural network architecture in deep learning known for capturing complex temporal patterns. LSTM is well-suited for modeling and predicting nitrate concentration changes in aquaponics, trained with extensive data collected from various aquaponic ponds. Through rigorous evaluation, a remarkable MSE value of 0.00074 and an impressive R-squared score of 0.98 were achieved, holding potential for scaling up to commercial applications, benefiting aquaponics operations, supporting researchers, and enhancing sustainability and productivity in aquaponic systems.

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Uncovering Android Zero-Day Threats: The Zero-Vuln Approach with Deep and Zero-Shot Learning

Uncovering Android Zero-Day Threats: The Zero-Vuln Approach ...

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Computing and data Analytics (ICCDA), International Conference on

作者： Suresh Palarimath Pyingkodi Maran Wilfred Blessing N. R S. J Kavitha Cibi. A Subitha G. Sutherlin College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman Department of Computer Applications Kongu Engineering College Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies (VISTAS) Pallavaram Chennai India Data & Analytics / Visualization - Delivery Virtusa Consulting Services Private Limited Chennai India Department of Computer Science and Engineering Stella Mary's College of Engineering Kanyakumari India

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

The growing occurrence of cybersecurity hazards, such as Android zero-day vulnerabilities, poses substantial difficulties because they are unattended and imperceptible. With the increasing popularity of Android smartphones, hackers are taking advantage of these weaknesses to quickly spread advanced malware. Conventional detection approaches frequently prove inadequate due to the absence of shown criteria for emergent threats. In order to address this crucial problem, this article presents the Zero-Vuln method, an advanced technique specifically developed to identify and categorize previously unidentified malware on Android platforms. Zero-Vuln combines sophisticated supervised deep learning methods with zero-shot learning algorithms to accurately detect zero-day threats. Through the utilization of comprehensive datasets, the method attains an average classification metric score of 84.22%, showcasing exceptional accuracy, precision, and recall. This advanced solution significantly improves the ability to identify and handle Android zero-day threats, representing a major progress in cybersecurity research and implementation. The Zero-Vuln strategy provides a strong and adaptable solution for the constantly changing mobile security risks, representing a notable advancement in the sector.

关键词： Measurement Instruments Zero shot learning Malware Mobile security Hazards Reliability Protection Smart phones Research and development

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End-to-End Congestion Avoidance Scheme for Underwater High Speed Communication Networks

End-to-End Congestion Avoidance Scheme for Underwater High S...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Shivank Romil Jain Reena R Prema S. Kadam Sunanda Das Swati Singh Department of Computer Science & Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune INDIA Computer Science and Engineering Faculty of Engineering and Technology Bangalore Karnataka India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

The cease-to-give up Congestion Avoidance Scheme for Underwater high pace verbal exchange Networks, commonly known as E2E-UCAN, is a critical technique for lowering plausible throughput by using efficiently warding off congestion inside a community. E2E-UCAN is a disbursed congestion control mechanism which uses statistics obtained from cease-to-end feedbacks to regulate sending prices on the give up systems. By means of monitoring the differences among expected network overall performance and actual performance, E2E-UCAN is able to pick out congestion stages inside the device, and is therefore able to regulate to prevent congestion before it happens. This enables to improve the stableness of the community and to increase the throughput of a network. Moreover, E2E-UCAN is likewise capable of efficaciously coping with queuing, avoiding packet loss in non-uniform networks, and in preferred, permitting applications to maximize performance.

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Enhanced Framework for MRI Brain Tumor Recognition with Residual Learning and Intuitive Heatmap Visualization

Enhanced Framework for MRI Brain Tumor Recognition with Resi...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： L. Velmurugan R. K Maheswari K. Janani K. Agalya M. Poornima K.C. Gayathri School of Computing Science and Engineering (SCOPE VIT Bhopal University Sehore Madhya Pradesh India Artificial Intelligence and Data Science M.A.M College of Enginnering and Technology Trichy Tamilnadu India Department of Data science and computer applications Manipal institute of Technology Manipal Academy of Higher Education Manipal India Department of Computer Science Engineering Sri Eshwar College of Engineering Coimbatore India Department of Information Technology OASYS Institute of Technology Trichy Tamil Nadu India Chettinad School of physiotherapy (CSP Chettinad Hospital and Research Institute (CHRI) Chettinad Academy of Research and Education (CARE) Chennai India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Early detection of brain tumors is crucial for effective treatment, yet it poses a significant challenge due to the rapid nature of the disease's progression. This study explores the application of ResNet, aimed at improving the accuracy of brain tumor diagnosis in magnetic resonance imaging (MRI) scans. Our model was trained on a dataset comprising 253 MRI images, which included 155 confirmed cases of tumors. The residual learning framework of ResNet effectively captures intricate features and mitigates issues such as vanishing gradients. The results indicate that the ResNet model achieved an impressive accuracy rate of 97%, surpassing traditional detection methods. We evaluated the model's performance using metrics like accuracy and F1-score, demonstrating its robustness. To improve the interpretability of our model's predictions, we utilized Grad-CAM (Gradient-weighted Class Activation Mapping), which highlighted critical areas in the MRI scans that influenced the model's decisions. Our findings confirm ResNet’ s effectiveness in medical imaging and stress the importance of model interpretability in healthcare, allowing radiologists to make informed diagnostic choices.

关键词： Deep learning Accuracy Magnetic resonance imaging Brain tumors Medical services Predictive models Brain modeling Robustness Stress Biomedical imaging

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Optimization techniques for preserving privacy in data mining

Optimization techniques for preserving privacy in data minin...

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International Conference on Electronics, Communication and Computing Technologies (ICECCT)

作者： K.Renuka Devi K. Balasamy M. Prathyusha R. Jeevitha P. Balasubramanie Malathi Eswaran Department of Information Technology Dr. Mahalingam college of Engineering and Technology Pollachi Department of Artificial Intelligence and Data Science Bannari amman Institute of Technology Sathyamangalam Department of IT MVSR engineering College Hyderabad Department of CSE KPR Institute of Engineering and Technology Coimbatore Department of Computer science and engineering Kongu Engineering College Perundurai Erode

data mining is one of the significant area where it plays a predominant role in extracting important factors and trends from large volume of data. This covers various areas such as healthcare, education, entertainment, finance, e-commerce applications etc., The data mining domain has used a variety of algorithms, including supervised, unsupervised, semi-supervised, and reinforcement learning techniques. Under healthcare arena, it deals with huge amount of sensitive data such as patients' data such as their name, age, health records. Those sensitive data have been utilized by the intruders for extracting the original data and also became a prey for the authorized access. Hence, the privacy is one of the serious concern that should be addressed. Various privacy preserving in data mining (PPDM) techniques such as anonymization, perturbation, condensation and cryptographic methods are available to protect those data. In this paper, the optimization techniques such as Genetic algorithm(GA) under evolutionary method and Particle swarm optimization(PSO) under meta heuristic method have been discussed and how it plays an important part in providing more optimal results by securing those sensitive and important information from the unauthorized access.

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Traffic forecasting with meta attentive graph convolutional recurrent network

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Expert Systems with Applications 2025年 287卷

作者： Zeb, Adnan Zheng, Jianying Ye, Yongchao Chen, Junde Zhang, Shiyao Wei, Xuetao Jianqiao Yu, James School of Intelligent Manufacturing and Smart Transportation Suzhou City University Suzhou215000 China Department of Data Science City University of Hong Kong Hong Kong Dale E. and Sarah Ann Fowler School of Engineering Chapman University United States Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Shenzhen518055 China

Traffic forecasting is essential for the development of intelligent transportation systems. However, existing forecasting models often struggle to effectively capture the complex spatial-temporal dependencies inherent in traffic data. Many current approaches are limited in their ability to model node-specific patterns and to simultaneously capture both short- and long-range dependencies. In this paper, we propose a novel traffic forecasting model, the Meta Attentive Graph Convolutional Recurrent Network (MAGCRN), which addresses these limitations through two key modules: (1) Node-Specific Meta Pattern Learning (NMPL) and (2) Node Attention Weight Generation (NAWG). The NMPL module captures the unique characteristics of each node in the traffic network by dynamically generating node-specific convolutional filters. The NAWG module enhances the model's ability to capture both short- and long-range temporal dependencies by generating attention weights that connect node-specific features with those across the entire temporal dimension. Comprehensive experiments on six real-world traffic datasets demonstrate that MAGCRN consistently outperforms state-of-the-art baselines in both traffic flow and speed prediction tasks. The code is available at https://***/Aazeb/MAGCRN. © 2025 Elsevier Ltd

关键词： Street traffic control

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Low-Resource Named Entity Recognition: Can One-vs-All AUC Maximization Help?

Low-Resource Named Entity Recognition: Can One-vs-All AUC Ma...

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IEEE International Conference on data Mining (ICDM)

作者： Ngoc Dang Nguyen Wei Tan Lan Du Wray Buntine Richard Beare Changyou Chen Department of Data Science and AI Monash University Melbourne Australia College of Engineering and Computer Science VinUniversity Hanoi Vietnam Faculty of Medicine Peninsula Clinical School Melbourne Australia Department of CS and Engineering University at Buffalo New York USA

Named entity recognition (NER), a task that identifies and categorizes named entities such as persons or organizations from text, is traditionally framed as a multi-class classification problem. However, this approach often overlooks the issues of imbalanced label distributions, particularly in low-resource settings, which is common in certain NER contexts, like biomedical NER (bioNER). To address these issues, we propose an innovative reformulation of the multi-class problem as a one-vs-all (OVA) learning problem and introduce a loss function based on the area under the receiver operating characteristic curve (AUC). To enhance the efficiency of our OVA-based approach, we propose two training strategies: one groups labels with similar linguistic characteristics, and another employs meta-learning. The superiority of our approach is confirmed by its performance, which surpasses traditional NER learning in varying NER settings.

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Tutorial on Using Machine Learning and Deep Learning Models for Mental Illness Detection

arXiv

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arXiv 2025年

作者： Zhang, Yeyubei Wang, Zhongyan Ding, Zhanyi Tian, Yexin Dai, Jianglai Shen, Xiaorui Liu, Yunchong Cao, Yuchen University of Pennsylvania School of Engineering and Applied Science United States New York University Center for Data Science United States Georgia Institute of Technology College of Computing United States University of California Department of EECS Berkeley United States Northeastern University Khoury College of Computer Science United States

Social media has become an important source for understanding mental health, providing researchers a way to detect conditions like depression from user-generated posts. This tutorial provides practical guidance to address common challenges in applying machine learning and deep learning methods for mental health detection on these platforms. It focuses on strategies for working with diverse datasets, improving text preprocessing, and addressing issues such as imbalanced data and model evaluation. Real-world examples and step-by-step instructions demonstrate how to apply these techniques effectively, with an emphasis on transparency, reproducibility, and ethical considerations. By sharing these approaches, this tutorial aims to help researchers build more reliable and widely applicable models for mental health research, contributing to better tools for early detection and intervention. © 2025, CC BY.

关键词： Adversarial machine learning

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Efficient or Powerful? Trade-offs Between Machine Learning and Deep Learning for Mental Illness Detection on Social Media

arXiv

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arXiv 2025年

作者： Ding, Zhanyi Wang, Zhongyan Zhang, Yeyubei Cao, Yuchen Liu, Yunchong Shen, Xiaorui Tian, Yexin Dai, Jianglai University of Pennsylvania School of Engineering and Applied Science United States New York University Center for Data Science United States Georgia Institute of Technology College of Computing United States University of California Berkeley Department of EECS United States Northeastern University Khoury College of Computer Science United States

Social media platforms provide valuable insights into mental health trends by capturing user-generated discussions on conditions such as depression, anxiety, and suicidal ideation. Machine learning (ML) and deep learning (DL) models have been increasingly applied to classify mental health conditions from textual data, but selecting the most effective model involves trade-offs in accuracy, interpretability, and computational efficiency. This study evaluates multiple ML models, including logistic regression, random forest, and LightGBM, alongside deep learning architectures such as ALBERT and Gated Recurrent Units (GRUs), for both binary and multi-class classification of mental health conditions. Our findings indicate that ML and DL models achieve comparable classification performance on medium-sized datasets, with ML models offering greater interpretability through variable importance scores, while DL models are more robust to complex linguistic patterns. Additionally, ML models require explicit feature engineering, whereas DL models learn hierarchical representations directly from text. Logistic regression provides the advantage of capturing both positive and negative associations between features and mental health conditions, whereas tree-based models prioritize decision-making power through split-based feature selection. This study offers empirical insights into the advantages and limitations of different modeling approaches and provides recommendations for selecting appropriate methods based on dataset size, interpretability needs, and computational constraints. © 2025, CC BY.

关键词： Contrastive Learning

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