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8th International Conference on computer Science and engineering, UBMK 2023

作者： Kanburoglu, Ali Bugra Tek, F. Boray Işik University Huawei R&d Center Dept. of Computer Engineering İstanbul Turkey Istanbul Technical University Dept. of Artificial Intelligence and Data Engineering İstanbul Turkey

ISBN: (纸本)9798350340815

The field of converting natural language into corresponding SQL queries using deep learning techniques has attracted significant attention in recent years. While existing Text-to-SQL datasets primarily focus on English and other languages such as Chinese, there is a lack of resources for the Turkish language. In this study, we introduce the first publicly available cross-domain Turkish Text-to-SQL dataset, named TUR2SQL. This dataset consists of 10,809 pairs of natural language statements and their corresponding SQL queries. We conducted experiments using SQLNet and ChatGPT on the TUR2SQL dataset. The experimental results show that SQLNet has limited performance and ChatGPT has superior performance on the dataset. We believe that TUR2SQL provides a foundation for further exploration and advancements in Turkish language-based Text-to-SQL research. © 2023 IEEE.

关键词： Deep learning

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Ontology population using CNN model: Application to COVID-19 diagnosis 6

Ontology population using CNN model: Application to COVID-19...

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6th International Conference on Pattern Analysis and Intelligent Systems, PAIS 2024

作者： Mellal, Nacima Saighi, Asma Institute of Applied Science & Technology University of Oum Research Laboratory of Artificial Intelligence And Autonomous Objects Dept. Networks And Telecommunication El Bouaghi Algeria University of Oum Research Laboratory of Artificial Intelligence And Autonomous Objects Dept. Computer Science El Bouaghi Algeria

ISBN: (纸本)9798350350265

AI models and especially deep learning models have found applications across various medical domains. While many studies focus on using electronic health records (EHRs) data to train AI models, implementing the reverse process poses several challenges. Also, the heterogeneous nature of the used data in EHRs, such as diagnoses, clinical reports, test results, prescriptions, and medical images, makes their interpretation difficult for the practitioners which require a unified understanding. Medical ontologies are useful to improve data interpretability and semantics. In this paper, we aim to create a synergy between ontologies and deep learning techniques to enhance efficiency, data understanding, and provide healthcare quality. We propose a system in which our developed ontology is populated automatically using the predictions of an AI model. We have developed and trained a CNN model to aid in the COVID-19 diagnosis. The initial obtained results are promising with an accuracy of 98%. © 2024 IEEE.

关键词： COVID-19

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Accelerating the Run-Time of Convolutional Neural Networks through Weight Pruning and Quantization 8

Accelerating the Run-Time of Convolutional Neural Networks t...

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8th International engineering Conference on Renewable Energy and Sustainability, ieCRES 2023

作者： Alhimdiat, Rajai Ashour, Wesam Battrawy, Ramy Stricker, Didier Islamic University of Gaza Iug Computer Engineering Dept. Gaza Palestine German Research Centre for Artificial Intelligence Dfki Kaiserslautern Germany

ISBN: (纸本)9798350300758

Accelerating the processing of Convolutional Neural Networks (CNNs) is highly demand in the field of artificial intelligence (AI), particularly in computer vision domains. The efficiency of memory resources is crucial in measuring run-time, and weight pruning and quantization techniques have been studied extensively to optimize this efficiency. In this work, we investigate the contribution of these techniques to accelerate a pre-trained CNN model. We adapt the percentile-based weights pruning with focusing on unstructured pruning by dynamically adjusting the pruning thresholds based on the fine-tuning performance of the model. In the same context, we perform uniform quantization for presenting the weights values of the model's parameters with a fixed number of bits. We implement different levels of post-training and aware-training -fine-tuning the model with the same learning rate and number of epochs as the original. We then refine-tune the model with a lower learning rate and a factor of 10x for both techniques. Finally, we combine the best levels of pruning and quantization and refine-tune the model to explore the best-pruned and quantized pre-trained model. We evaluate each level of the techniques and analyze their trade-offs. Our results demonstrate the effectiveness of our strategy in accelerating the CNN and improving its efficiency, and provide insights into the best combination of techniques to accelerate its inference time. © 2023 IEEE.

关键词： Neural network models

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Lung Cancer Image Identify and Categorization using artificial Neural Networks

Lung Cancer Image Identify and Categorization using Artifici...

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2024 International Conference on Computational intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Lingappa, Ediga Begum, D Jareena Pachouri, Priya Rashmi, Kuderu Varalakshmi, Thokala Tharuna Kumar, Anoop Mallareddy College of Engineering Maisammaguda Dept of Computer Science and Engineering TS Secunderabad500100 India CMR Institute of Technology Dept. of Artificial Intelligence and Data Science Bengaluru560037 India Malla Reddy University Dept of Cyber Security Hyderabad India

ISBN: (纸本)9798350381887

Lung cancer that is made up of small cells is called small cell lung cancer (SCLC). Smoking tobacco in any form-cigarettes, cigars, or pipes-is by far the most significant risk factor for developing lung cancer, though it is only one of many. Cancer of the lungs is known as lung cancer. When cells in the body multiply uncontrollably, cancer develops. If cancer cells enter the lymphatic system or the bloodstream, they can travel to other organs and spread the disease. Cheap and fast cancer diagnostics could save millions. Latestage cancer treatment is hard. Lung cancer is diagnosed non-invasively. Biopsy requires careful ANN architecture selection. This study proposes a diagnosis ANN model. ANN research is 'hot' in cardiology, cancer, and radiology. Medical research uses ANNs. Lung cancer diagnosis was tested. MR images assess lung health for the neural network. MATLAB created ANNs and processed images. Training data was extracted from MR images using Harlick texture features. MATLAB made ANNs. Elman Network exceeds ANNs with 90.30 %. Lung cancer can develop. MRIs detect lung cancer. MRIs show lung development. Many lung tumour detection research use machine learning and deep learning. MRI algorithms swiftly detect lung malignancies, improving patient therapy. Radiologists use these forecasts. The study evaluates a self-defined ANN for lung tumour identification. The radiologist needs time to find and remove the cancer from the MR. Technology improves performance and segmentation. Machines always predict and detect. Neural networks outperformed SVM, Random forest, regression, and others. © 2024 IEEE.

关键词： Lung cancer

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Dynamic Multi-Behavior Sequence Modeling for Next Item Recommendation 37

Dynamic Multi-Behavior Sequence Modeling for Next Item Recom...

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37th AAAI Conference on artificial intelligence, AAAI 2023

作者： Cho, Junsu Hyun, Dongmin won Lim, Dong jae Cheon, Hyeon Park, Hyoung-Iel Yu, Hwanjo Dept. of Computer Science and Engineering POSTECH Korea Republic of Institute of Artificial Intelligence POSTECH Korea Republic of GS Retail Korea Republic of

ISBN: (纸本)9781577358800

Sequential Recommender Systems (SRSs) aim to predict the next item that users will consume, by modeling the user interests within their item sequences. While most existing SRSs focus on a single type of user behavior, only a few pay attention to multi-behavior sequences, although they are very common in real-world scenarios. It is challenging to effectively capture the user interests within multi-behavior sequences, because the information about user interests is entangled throughout the sequences in complex relationships. To this end, we first address the characteristics of multi-behavior sequences that should be considered in SRSs, and then propose novel methods for Dynamic Multi-behavior Sequence modeling named DyMuS, which is a light version, and DyMuS+, which is an improved version, considering the characteristics. DyMuS first encodes each behavior sequence independently, and then combines the encoded sequences using dynamic routing, which dynamically integrates information required in the final result from among many candidates, based on correlations between the sequences. DyMuS+, furthermore, applies the dynamic routing even to encoding each behavior sequence to further capture the correlations at item-level. Moreover, we release a new, large and up-to-date dataset for multi-behavior recommendation. Our experiments on DyMuS and DyMuS+ show their superiority and the significance of capturing the characteristics of multi-behavior sequences. Copyright © 2023, Association for the Advancement of artificial intelligence (***). All rights reserved.

关键词： Encoding (symbols)

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Deep Learning based Chatbot Architecture for Medical Diagnosis and Treatment Recommendation 3

Deep Learning based Chatbot Architecture for Medical Diagnos...

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3rd International Conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Rajani, Girish Ruparel, Khushi Thadomal Shahani Engineering College Dept. of Artificial Intelligence and Data Science Mumbai India

ISBN: (纸本)9798350348347

With the exponential growth and rapid development in the fields of deep learning and neural networks, chatbots have gained a lot of popularity and have become a proven, and efficient tool to interact and provide service to users. Healthcare is one of the most promising fields where chatbots can be used more efficiently. This has become important, especially in the current medical landscape, where there is a shortage of doctors, and patients often have to wait long periods before getting any medical guidance. By using the power of transformer models and machine learning algorithms chatbots can help patients with personalized diagnoses and treatment recommendations, efficiently at ease and convenience. This helps the patients to access medical services anywhere, at any point in time. This paper proposes a well-planned systematic architecture for a medical chatbot that utilizes the potential of transformers, classification algorithms and machine-learning models. The architecture includes three main components: a Naïve Bayes Classifier, a Binary Tree classifier along with a Support Vector Classifier, and a sequence-to-sequence model. These algorithms are used to classify symptoms and determine the severity of a medical condition to provide patients with accurate medical diagnoses and treatment recommendations. Overall, the proposed architecture is built and designed to bridge the gap between doctors and patients by providing immediate access to medical advice, making it a promising tool for improving the quality and accessibility of healthcare services. © 2023 IEEE.

关键词： Diagnosis

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ABC-DE-WOA: A New Hybrid Algorithm for Optimization Problems 4

ABC-DE-WOA: A New Hybrid Algorithm for Optimization Problems

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4th International Conference on Circuits, Control, Communication and Computing, I4C 2022

作者： Pradeep Kumar, D. Sowmya, B.J. Kanavalli, Anita Vincent, Annvin Dept. of Computer Science and Engineering Bangalore India Dept. of Artificial Intelligence and Data Science Bangalore India

ISBN: (纸本)9798350397475

One of the most effective swarm intelligence-based algorithms for many global optimization issues is the artificial Bee Colony (ABC) strategy. Despite the fact that there are many artificial Bee Colony (ABC) variants, the algorithm normally has a low convergence rate. Therefore, it is still essential to moderate an algorithm's intensity and diversity. In this instance, the standard artificial Bee Colony (ABC) algorithm has been combined with the Whale Optimization Algorithm (WOA) and Differential Evolution Algorithm (DE) to generate a novel Hybrid artificial Bee Colony algorithm (ABC), artificial Bee Colony-Differential Evolution-Whale Optimization Algorithm (ABC-DE-WOA). For simple benchmark problems with up to 100 dimensions, 50 dimensions, 30 dimensions, and 10 dimensions, the proposed hybrid technique is compared with artificial Bee Colony (ABC) variants like artificial Bee Colony-Whale Optimization Algorithm (ABC-WOA), artificial Bee Colony-Differential Evolution Algorithm (ABC-DE), and original artificial Bee Colony Algorithm (ABC). The results show that the proposed technique performs better than its competitors in terms of convergence speed. © 2022 IEEE.

关键词： Global optimization

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Survey on Deep Learning-based Speech Technologies in Voice Chatbot Systems

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KSII Transactions on Internet and Information Systems 2025年第5期19卷 1406-1440页

作者： Ma, Seunghee Oh, Junseok Kim, Minseo Kim, Ji-Hwan Dept. of Artificial Intelligence Sogang University Seoul Korea Republic of Dept. of Computer Science and Engineering Sogang University Seoul Korea Republic of Dept. of Artificial Intelligence Technology LOTTE INNOVATE Company Seoul Korea Republic of

Recent advancements in large language models (LLMs) such as ChatGPT have contributed to the development of chatbot systems. Specifically, speech has been recognized as the optimal tool for interactive dialogue, leading to increased interest in voice chatbots. Voice chatbots offer information and services through voice interactions, enhancing user experience and improving service accessibility. This survey paper introduces the latest developments in the core technologies of voice chatbot systems, including deep learning-based automatic speech recognition, speech synthesis, and speech emotion recognition. It focuses on advanced research to enhance speed and performance, which is crucial for applying speech technologies in voice chatbots. In automatic speech recognition, we introduce methodologies such as Connectionist Temporal Classification (CTC), Attention based Encoder-Decoder (AED), and Recurrent Neural Network Transducer (RNN-T), along with studies optimizing Transformer-based models for real-time automatic speech recognition. In speech emotion recognition, we explore the use of pre-trained models and the latest techniques for accurate emotion prediction. For speech synthesis, the focus extends to two-stage and End-to-End (E2E) approaches, with additional research on integrating emotional information to generate natural speech. Copyright © 2025 KSII.

关键词： Chatbots

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Human Everyday Experience Metric Recognition Based on Classifier Chains Using Heart Rate 15

Human Everyday Experience Metric Recognition Based on Classi...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Kim, Seoyeon Ahn, Seong-Ho Kim, Min-Seo Lee, Minji Jeong, Dong-Hwa The Catholic University of Korea Dept. Artificial Intelligence Bucheon Korea Republic of The Catholic University of Korea Dept. Biomedical Software Engineering Bucheon Korea Republic of

ISBN: (纸本)9798350364637

This study examines the potential of heart rate data from wearable devices to assess sleep quality, emotional responses, and stress levels, among other sleep-related factors. There is a growing need for a nuanced understanding of emotional and physical health, but conventional assessment methods often lack real-time analysis and objectivity. Utilizing data from smartwatches, this study employs a multifaceted analytical technique that integrates feature extraction, feature selection, and machine learning algorithms to explore the interconnectedness of various health metrics. The examination of the relationships among multiple labels has shown that many features, except for sleep efficiency (S2) and wake after sleep onset (S4), can serve as statistically significant biomarkers. The findings illustrate the efficacy of classifier chains, enhanced by effective feature selection, in uncovering hierarchical relationships among seven indicators, indicating that it achieved a high macro F1-score in binary classification. These outcomes signify a significant advancement in predictive accuracy and contribute to the evolution of health monitoring by providing reliable, data-driven insights to support personalized interventions. This study bridges the divide between clinical assessments and daily monitoring, allowing future research to refine methodologies and develop enhanced health and wellness solutions. © 2024 IEEE.

关键词： Sleep research

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Comment Compass: An Approach to Analyze YouTube Comments through Web Scraping, Sentiment Analysis, and Generative AI for Actionable Insights 1

Comment Compass: An Approach to Analyze YouTube Comments thr...

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1st International Conference on Pioneering Developments in computer Science and Digital Technologies, IC2SDT 2024

作者： Rathod, Manav Yadav, Ankita Phutane, Soham Bhise, Suvarna Vasantdada Patil Pratishthan's College of Engineering & Visual Arts Dept. of Artificial Intelligence & Data Science Mumbai India

ISBN: (纸本)9798350365016

In this paper, we introduce 'Comment Compass,' a novel system designed to revolutionize the analysis of YouTube video comments by content creators. The hypothesis driving this research is that integrating web scraping, sentiment analysis, and generative AI can provide content creators with deeper insights into their audience's sentiments and perspectives, thereby enhancing content strategy and engagement. Methodologically, 'Comment Compass' employs Python-based web scraping to extract comments from YouTube videos. Sentiment analysis is conducted using the SentimentIntensityAnalyzer from the Natural Language Toolkit (NLTK) library, which produces a pie chart depicting the distribution of positive, negative, and neutral comments. Additionally, the system leverages Generative models to generate further insights from the comments, such as future content ideas, criticisms, and inquiries. Our results indicate that 'Comment Compass' offers content creators actionable insights that can significantly optimize content strategies for better audience engagement and impact. This work demonstrates the substantial potential of combining web scraping, sentiment analysis, and generative AI, contributing valuable knowledge to these fields. The findings are contextualized within existing literature, underscoring the innovative contributions of this research. © 2024 IEEE.

关键词： Video analysis

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