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OATH-Frames: Characterizing Online Attitudes Towards Homelessness with LLM Assistants

OATH-Frames: Characterizing Online Attitudes Towards Homeles...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Ranjit, Jaspreet Joshi, Brihi Dorn, Rebecca Petry, Laura Koumoundouros, Olga Bottarini, Jayne Liu, Peichen Rice, Eric Swayamdipta, Swabha Thomas Lord Dept. of Computer Science University of Southern California United States Suzanne-Dwork School of Social Work University of Southern California United States

ISBN: (纸本)9798891761643

Public attitudes towards key societal issues, expressed on online media, are of immense value in policy and reform efforts, yet challenging to understand at scale. We study one such social issue: homelessness in the U.S., by leveraging the remarkable capabilities of large language models to assist social work experts in analyzing millions of posts from Twitter. We introduce a framing typology: Online Attitudes Towards Homelessness (OATH) Frames: nine hierarchical frames capturing critiques, responses and perceptions. We release annotations with varying degrees of assistance from language models, with immense benefits in scaling: 6.5× speedup in annotation time while only incurring a 3 point F1 reduction in performance with respect to the domain experts. Our experiments demonstrate the value of modeling OATH-Frames over existing sentiment and toxicity classifiers. Our large-scale analysis with predicted OATH-Frames on 2.4M posts on homelessness reveal key trends in attitudes across states, time periods and vulnerable populations, enabling new insights on the issue. Our work provides a general framework to understand nuanced public attitudes at scale, on issues beyond homelessness. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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CRISP - DM Process in Smart Building Management 11

CRISP - DM Process in Smart Building Management

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11th International Conference on Electrical and Electronics Engineering, ICEEE 2024

作者： Dursun, Mahir Sabyrzhan, Moldir Gazi University Faculty of Technology Dept. of Electrical and Electronics Engineering Ankara Turkey Graduate School of Informatics Gazi University Department of Computer Science Ankara Turkey

ISBN: (纸本)9798350362541

Smart Buildings are buildings that integrate and combine intelligence, management, control, materials, and construction as a single system to meet all the needs of the building, such as comfort, energy efficiency, and durability. In universities, laboratories are used less frequently than other facilities and classrooms. Courses and students may change in the same classroom, and the period of use of such classrooms is much more extended than in laboratories. However, classrooms may only be used for classes during long breaks or for short periods of non-use between exams. In addition, there is no provision for when the heating system will start to heat up before classes or when it will be turned off after classes. By planning heating and lightning, energy and costs will be saved. This paper presents data acquisition methods for preparing a dataset to create a machine learning (ML) model to save energy loss in university laboratories. The developed model will predict energy consumption in classrooms and laboratories based on weather conditions. The study is being conducted in two stages. The first stage describes how to collect data using electronic devices such as Internet of Things (IoT) controllers of Smart Building Management Systems. The second stage gave possibilities for data analysis and preparation using the phases of the Cross-Industry Standard Process for Data Mining (CRISP-DM). © 2024 IEEE.

关键词： Information management

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Identifying COVID-19 through X-ray and CT scan images using Machine Learning 18

Identifying COVID-19 through X-ray and CT scan images using ...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Shehzadi, Maham Arshad, Usama Abideen, Zainul Ali, Raja Hashim Khan, Ammad Aslam Ijaz, Ali Zeeshan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Virtual University of Pakistan Dept. of Biological Sciences Rawalpindi Pakistan National University of Computer & Emerging Sciences Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350328172

In the current global scenario, marked by the COVID-19 pandemic, it has become imperative for healthcare systems around the globe to swiftly and accurately diagnose the disease. This is where cutting-edge approaches such as machine learning (ML) come into play, specifically, ML-based identification of COVID-19 from chest X-ray and CT-Scan imagery. Our research collective is devotedly working towards devising a robust, accurate ML model capable of deciphering these images to identify COVID-19 effectively. This involves the application of a variety of AI algorithms and vast data resources. Our work is grounded on a large, annotated image dataset that aids in training and refining the model. Our findings indicate that ML methodologies can discern COVID-19 from chest X-ray and CT scan images with a mean accuracy of 90%. Such a tool holds promise in aiding medical practitioners in prompt and precise patient diagnosis, thereby improving patient prognosis. The most notable contribution of our study lies in the creation of an ML model that can accurately detect COVID-19 in chest X-ray and CT-Scan images, potentially aiding in early disease detection and management. © 2023 IEEE.

关键词： COVID-19

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Artificial Intelligence Based System on Enhancing the Capabilities of Transport System: A Systemic Literature Review

Artificial Intelligence Based System on Enhancing the Capabi...

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2023 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2023

作者： Malik, Muhammad Zain Nazir, Shah Khan, Habib Ullah College of Management and Technology Qatar University Dept. of Accounting & Information System Doha Qatar University of Swabi Swabi Pakistan Dept. of Computer Science Swabi Pakistan

ISBN: (纸本)9798350347494

Recent years have seen a considerable increase in the difficulty for our society in ensuring deep-rooted sustainability of traditional technology involve in critical structure. The role of Artificial intelligence (AI) is gaining importance significantly. By delivering ubiquitous real-time applications, It has aided in improving smart environments in many different industries, including smart cities, manufacturing, transportation, health, and the Internet of Things (IoT) by delivering ubiquitous real-time applications. As the technologies like natural language processing, deep learning, machine learning (ML), and others dramatically improve the functionality of communications, digital commerce, content, and apps, AI is poised to fundamentally change the information and communications technology (ICT) industry. Technological advancements brought changes that affect how we live nationally and internationally. The expansion of the economy has been significantly influenced by transportation. It has recently faced several challenges, including the competitiveness of public transportation and an inability to improve its services. Transport systems continue to have operational issues that deter people from using them. These include the fact that drivers frequently travel either deliberately, slowly, or dangerously quickly, making the schedules posted at bus stops untrustworthy. The most effective remedies to these cyber threats are developed using AI, which also offers the best answer. Security hazards are another unexpected effect of these technological improvements. In order to increase the effectiveness of transportation operations and improve public service, this study makes use of cutting-edge artificial intelligence technology. It also clarifies the capabilities of an AI-based transport system to guarantee high dependability and security in the future. The paper also discusses how AI-based systems can play a part in the intelligent transport system. © 2023 IEEE.

关键词： Deep learning

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Explaining Environmental Distribution of Aedes albopictus using Machine Learning

Explaining Environmental Distribution of Aedes albopictus us...

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2024 Symposium on Spatial Information to Empower the Metaverse

作者： Singh, Priyanka Saran, Sameer UPES School of Computer Science Uttarakhand Dehradun248007 India Regional Remote Sensing Center-North ISRO Dept. of Space New Delhi110049 India

The Aedes albopictus mosquito, known for its role in transmitting diseases such as dengue fever, Zika virus, and chikungunya, poses a significant public health threat globally. Understanding its distribution patterns is crucial for effective disease surveillance and control. This study employs machine learning techniques, specifically MaxEnt modeling, to elucidate the relationship between environmental factors and the distribution of Aedes albopictus. Using presence-only data and a suite of environmental variables, we trained MaxEnt models to predict the potential distribution of Aedes albopictus across a geographical region. The models were validated using independent datasets and evaluated for their predictive accuracy and robustness. Our results reveal significant associations between Aedes albopictus presence and environmental factors such as temperature related variables. Furthermore, we employed spatial analysis techniques to identify areas at high risk of Aedes albopictus presence, aiding in targeted vector control strategies and disease prevention efforts. MaxEnt models demonstrated high predictive performance, effectively capturing the complex relationships between environmental variables and mosquito distribution in Nepal, India and Myanmar, along with Spain and Italy. By integrating machine learning algorithms with environmental data, this study provides valuable insights into the ecological drivers of Aedes albopictus distribution, enhancing our ability to mitigate the risk of mosquito-borne diseases in affected regions. © Author(s) 2024.

关键词： Adversarial machine learning

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Prediction of Typhoid Using Machine Learning and ANN Prior to Clinical Test

Prediction of Typhoid Using Machine Learning and ANN Prior t...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Bhuiyan, Md. Atik Rad, Sharaf Shahariare Johora, Fatema Tuj Islam, Abdullah Hossain, Md Ismail Khan, Aliza Ahmed Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh Rhine-Waal University of Applied Sciences Dept. of Communication and Environment Kamp-Lintfort Germany

ISBN: (纸本)9798350348217

One of the most prevalent illnesses, typhoid causes a large number of fatalities each year, primarily in Africa. A quick and accurate diagnosis is essential in the medical sector. Self-medication, delayed diagnosis, a lack of medical expertise, and inadequate healthcare facilities all contribute to the high incidence of typhoid fever mortality. Machine learning as well as deep learning has worked wonders for extrapolative analysis in the health industry, and as a result, more health industries are utilizing machine learning techniques. This is the earliest evaluation where a typhoid fever prediction model is being developed which predicts prior to a clinical trial. In this paper, deep learning and machine learning have been employed to develop the model. Ten algorithms have been utilized here, and the XGBoost classifier is the best performer with 97.87% accuracy. © 2023 IEEE.

关键词： Neural networks

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Sitting posture Analysis using CNN and RCNN 9

Sitting posture Analysis using CNN and RCNN

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9th International Conference on Biosignals, Images, and Instrumentation, ICBSII 2023

作者： Nishitha, R. Sudhakar, T. Bethanney Janney, J. Krishnamoorthy, N.R. Dhanalakshmi, K. Vigneshwaran, S. Sathyabama Institute of Science and Technology Biomedical Engg. Department Chennai India Sathyabama Institute of Science & Technology Dept. of ECE Chennai India Sathyabama Institute of Science and Technology Dept. of Computer Science Engg Chennai India Sathyabama Institute of Science and Technology M.Tech-Medical Instrumentation Chennai India

ISBN: (纸本)9798350338171

Based to statistics, about 80 percent of the population follows incorrect sitting posture. From the year 2020, due to the Covid-19 curfew, the traditional work office of many professionals turned into Work from home and remote office lifestyle. Many of the working professionals has no proper ergonomic workplace setup in their house leading to improper sitting position. Monitoring the sitting posture is needed to avoid chronic complication of MSD, neck, and spine related injuries. Many techniques were emerged for correcting and monitoring the posture. Neural Network based posture detection is being into the field of research as the come with more range of accuracy. This paper discusses about the various Neural Networks used and their accuracies. © 2023 IEEE.

关键词： Convolution

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Comparing Fine Tuned-LMs for Detecting LLM-Generated Text 3

Comparing Fine Tuned-LMs for Detecting LLM-Generated Text

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Shukla, Sanidhya Madhav Magoo, Chandni Garg, Puneet Manav Rachna University Computer Science and Engineering dept. Faridabad India Kiet Group of Institutions Department of CSE-AI U.P. Ghaziabad India

ISBN: (纸本)9798331518592

With the introduction of LLMs (Large Language Models), NLP has demonstrated strong skills in a variety of applications, including question answering, language translation, and text summarization. As a result, their use in our day-to-day activities and at work has gained general acceptability. It is nevertheless imperative to develop cutting-edge procedures or approaches since, despite their enormous capabilities, they are still unable to live up to human expectations. It is crucial to apply the cutting-edge methods that enable LLMs to specialize in specific fields and reduce any potential for abuse. In contrast, our study has concentrated on employing fine-tuned LLM as detectors to determine whether a text was produced by a machine or by a person. Comparing how well various fine-tuned LMs function as content detectors-that is, as indicators of whether or not material is generated by LLMs-is the goal of this research. Three distinct fine-tuned LLMs are used to solve this binary classification problem: Fas'I'Text, also known as Generative Pre- Trained Transformers;DistilBERT;and BERT, or Bidirectional Encoder Representations from Transformers. The models were tested using various performance metrics, including precision, recall, Fl score, MCC, NPV, and FDR. The results showed that the BERT model outperformed the FasTText and DistilBERT models, which displayed overfitting tendencies when fine-tuned under comparable settings. © 2024 IEEE.

关键词： Character recognition

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High-Performance Image Restoration with SpectraNet: A Lightweight CNN Architecture

High-Performance Image Restoration with SpectraNet: A Lightw...

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3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024

作者： Patnaik, Nitesh Raj, Rishi Singh, Piyush Kumar Mahapatra, Shreekara Pani, Debashis Singh, Ravi Shankar Dept. of Computer Science and Engineering Kalinga Institute of Industrial Technology Bhubaneswar751024 India Dept. of Computer Science Central University of South Bihar Bihar 824236 India Dept. of Computer Science and Engineering Indian Institute of Technology BHU Varanasi221005 India

Deep learning-based image restoration faces significant challenges when deployed on resource-constrained platforms due to the computational demands and large number of parameters in existing models. The high computational load and extensive memory requirements make it difficult to implement these models on devices with limited processing power and storage capacity, such as mobile phones and embedded systems. Additionally, maintaining real-time performance while ensuring high-quality image restoration is a critical challenge, as traditional deep learning models often fail to meet the stringent latency and efficiency requirements of such platforms. This paper introduces a new, lightweight convolutional neural network (CNN) architecture tailored for efficient pixelation detection and restoration. Our approach combines a pre-trained MobileNetV2 with finetuning for pixelation detection, and SpectraNet, a architecture incorporating dept.-wise separable convolutions for image restoration. The proposed architecture was trained on the HQ-50k dataset, with 70% of the data used for training and 30% for testing. It achieved a Peak Signal-to-Noise Ratio (PSNR) of 17-23, outperforming 10 state-of-the-art architectures in both efficiency and image quality. This design addresses the dual problems of high computational load and extensive memory usage, prioritizing real-time performance and resource efficiency. The proposed architecture is ideal for deployment on mobile and embedded devices since it maintains high accuracy while significantly reducing the number of trainable parameters. Our findings advance the feasibility of deploying advanced image restoration techniques in real-world, resource-constrained environments. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Convolutional neural networks

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Transformative Insights into Pneumonia Detection with a Novel Artificial Intelligence and Deep Learning Approach 15

Transformative Insights into Pneumonia Detection with a Nove...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Bhanusri, Kodati Reddy, Koti Leela Sai Praneeth Jashwanth, Kandukuri Singh, Tripty Tripathi, Amrita Patra, Payel Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India University of L'Aquila Disim Department L'Aquila Italy

ISBN: (纸本)9798350370249

Pneumonia is a prominent cause of illness and death worldwide. The early and precise diagnosis of the disease remains essential for effective treatment of the patient. Traditional methods of diagnosis, while effective, often involve significant time and expertise, that can pose numerous challenges and difficulties during situations with limited resources and in peak demand times. Many recent advances in deep learning and artificial intelligence have shown positive findings in the processing of medical pictures. Chest radiographs have been extensively studied for different purposes, especially in the diagnosis of diseases like Pneumonia, Tuberculosis, Asthma, Bronchitis, Pneumothorax, and many more. This paper focuses on using many Deep Learning architectures and Artificial intelligence concepts, such as explainable AI, to use patients' chest X-rays to detect pneumonia in patients in its early phase. © 2024 IEEE.

关键词： Artificial Intelligence Deep Learning Explainable AI Medical Imaging for Diagnosis Pneumonia X-Ray

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