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Multi-constraint Coupling Optimization for Salient Object Detection 5

Multi-constraint Coupling Optimization for Salient Object De...

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5th international conference on natural language processing, ICNLP 2023

作者： Zhu, Zhijie Fang, Jie Wang, Nan Guan, Jiaqiu Xi'an University of Posts and Telecommunications School of Communications and Information Engineering Xi'an China University of Chinese Academy of Sciences Chinese Academy of Sciences Xi'an Institute of Optics and Precision Mechanics Key Laboratory of Spectral Imaging Technology Cas Xi'an China

ISBN: (纸本)9798350302219

In this paper, we propose a lightweight salient object detection framework called Multi-Constraint Coupling optimization Network (MCONet) to address the conflict between model scale and inference ability, which can learn more knowledge with fewer parameters through embedding feature priors. Specifically, we build a lightweight encoder as the backbone network to represent the image, and then use two parallel decoders to infer salient mask features and salient edge features respectively. Besides, we fuse the output features of different decoders by a convolutional block attention module (CBAM) module. In addition, we adopt a multi-constraint coupling optimization strategy to increase the soft constraints in the training phase, and improve the prior guidance of the edge to the inference results. Experimental results on 5 public benchmark datasets show that the proposed MCONet can reach comparable even better performance of state-of-the-art lightweight salient object detection models. © 2023 IEEE.

关键词： Object detection

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Arabic Idioms Detection by Utilizing Deep Learning and Transformer-based Models 6

Arabic Idioms Detection by Utilizing Deep Learning and Trans...

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6th international conference on AI in Computational Linguistics, ACLing 2024

作者： Himdi, Hanen Computer Science and Artificial Intelligence Department College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia

Arabic language resources and natural language processing technologies have seen significant advancements in recent years. The detection of idiomatic expressions is a crucial problem in Arabic natural language processing. Unfortunately, there have been few advancements in this field due to the scarcity of datasets and computational models that still struggle to handle Arabic idiomatic phrases. Although idioms have significance in Arabic because of their distinctive relation to history and cultural knowledge, they also offer a language tool for conveying concepts and thoughts that may diverge from their literal interpretation. Hence, to accurately detect their incongruent language, a model requires the ability to interpret the idioms' contextual meaning. To tackle this issue, this study entails comprehensive work on detecting Arabic idiomatic and literal statements. For that, we introduce a large Arabic idiomatic and literal statements. The dataset includes idioms from several Arabic idiomatic platforms balanced with literal statements from titles in news platforms with similar keywords found in the idioms. This study adopts four deep learning models, namely, CNN, LSTM, Bi-LSTM, and GRU trained with word embeddings, FastText and Skip-Gram Word2Vec, and three transformer-based models, BERT, RoBERTa, and DistilBERT were compiled and assessed via empirical evaluations. Our experiments demonstrated that transformer-based models outperformed deep learning models trained by word embedding to detect Arabic idioms and literal statements, reaching 97% in favor of DistilBERT. We also enhanced the accuracy through the ensemble stacking method, boosting it by 0.8 points. Furthermore, we propose a novel Arabic idiom interpreter that offers simple explanations for selected idiomatic contents within the literal text. Our work enriches the challenge of detecting incongruities found in similar figurative genres, specifically in the rich morphological language, Arabic. © 2024 Elsevier

关键词： Embeddings

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An Analysis of Various Classification Algorithms for Fake News Detection 2

An Analysis of Various Classification Algorithms for Fake Ne...

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2nd international conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Khare, Sakshi Singh, Pushpa Kumar, Pankaj Dept. of Computer Science and Engineering G. L. Bajaj Institute of Technology & Management Greater Noida India

ISBN: (纸本)9798331530389

With the expansion of social media and advanced stages, the spread of fake news has ended up a noteworthy societal issue. This paper presents a comprehensive outline of machine learning strategies utilized for the detection of fake news. We talk about different approaches such as natural language processing (NLP), unsupervised learning calculations, and machine learning models utilized within the recognizable proof of fake news. We dive into highlight designing, opinion investigation, and organize examination as key components of fake news detection frameworks. Moreover, we look at datasets, assessment measurements, and challenges related with detection of fake news. Through this analysis, we point to supply bits of knowledge into the current state-of-the-art techniques and headings for future investigate in combating fake news dispersal. This paper mainly used K-Nearest Neighbor, Jaccard similarity, LSTM, Decision Tree, and Logistic regression Classifiers for the classification. Main objective of this paper to verify the accuracy of models by performing the pre-processing the data and handle the unbalanced data more appropriately. The results of this papers show comparison of all classifiers with accuracy. Our results shows that KNN achieves 83%, 84% for Logistic Regression, 84% for Decision Tree, 85% for Jaccard Similarity & Random Forest is 85% and 98% for LSTM in our experiment. © 2025 IEEE.

关键词： Adversarial machine learning

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A Comparative Analysis of Large language Models with Retrieval-Augmented Generation based Question Answering System 8

A Comparative Analysis of Large Language Models with Retriev...

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8th international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Patel, Hetul Niteshbhai Surti, Azara Goel, Parth Patel, Bankim Computer Science & Engineering Department Anand Changa India

ISBN: (纸本)9798350376425

In recent studies, Large language Models (LLMs) have shown remarkable effectiveness in a wide range of natural language processing tasks. However, their knowledge is limited to the data they were trained on, which may not cover context-aware information. This limitation reduces their capability to provide precise and specific information apart from other parameters, especially in specialized fields. Retrieval Augmented Generation (RAG) Systems utilizes to overcome this limitation by enhancing the capabilities of LLMs through the retrieval of relevant information from external sources during the generation process. This research work presents the comparative analysis of the performance of three popular LLMs' - GPT-3.5-turbo from OpenAI, Gemini-Pro from Google, LLama3 by Meta when integrated into RAG system for question answering application. The study contrasts the efficiency of these LLM in generating relevant response with the aid of retrieved information in Q&A task. Along with these three LLMs other embedding models such as Text-embedding-ada-002-v2, embedding-001 and nomic-embed-text embedding model are used. Seven evaluation matrices are used from the RAGAS evaluation framework on self-created dataset to assess the performance of the RAG QA systems. The result showed that the gpt-3.5-turbo Large language model achieved highest performance outperforming Gemini Pro by 5.66% and Llama3 by 8.40%. © 2024 IEEE.

关键词： Question answering

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Automated Detection of Allusions in Uzbek language: A Computational Approach 3

Automated Detection of Allusions in Uzbek Language: A Comput...

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3rd IEEE international conference on Problems of Informatics, Electronics and Radio engineering, PIERE 2024

作者： Mengliev, Davlatyor B. Abdurakhmonova, Nilufar Z. Shirinova, Raima Kh. Saparova, Mohira F. Azimov, Inomjon M. Ibragimov, Bahodir B. Novosibirsk State University Novosibirsk Russia Urgench branch of Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Urgench Uzbekistan National University of Uzbekistan named after Mirzo Ulugbek Department of Computer linguistics Tashkent Uzbekistan National University of Uzbekistan named after Mirzo Ulugbek Tashkent Uzbekistan Mamun University of Khorazm Khiva Uzbekistan Tashkent State University of Uzbek language and literature Tashkent Uzbekistan Urgench State University Urgench Uzbekistan

ISBN: (纸本)9798331516321

Allusions play a significant role in literary and cultural works, serving as a tool for conveying deep meanings embedded by authors. In the Uzbek language, as in many other languages, allusions are commonly used to create more complex narratives, which allows us to connect the work with broader cultural or historical traditions. However, identifying these allusions, and even more so interpreting them, presents significant challenges for readers. In recent years, it might be seen the active development of algorithms and different kind of solutions for various text analysis tasks. Among these tasks, identifying metaphors, idioms, and allusions can be singled out as one of the most difficult tasks, due to the need for a deeper understanding of the text. Moreover, interpreting allusions requires interdisciplinary knowledge in the field of literature, cultural studies, linguistics, and more. This article discusses the development of an algorithm for the automatic detection of allusions in the Uzbek language, aimed at helping researchers and readers fully understand the meaning conveyed by authors. In addition, the proposed solution can help linguists and those who study the Uzbek language. The algorithm uses two dictionaries, where first consist of 10,000 tagged sentences, while second contains over 80 thousand word forms. At the same time, the algorithm was tested and as a result of a number of experiments, the efficiency of the work was revealed, where the accuracy of the algorithm reached range from 82,6% to 100%. © 2024 IEEE.

关键词： natural language processing systems

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Mongolian-Chinese Cross-Lingual Topic Detection Based on knowledge Distillation and Contrastive Learning Methods 21st

Mongolian-Chinese Cross-Lingual Topic Detection Based on Kn...

引用

21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Wang, Yanli Ji, Yatu Sun, Baolei Wu, Nier Ren, Qing-Dao-Er-Ji Wang, Bailun School of Information Engineering Inner Mongolia University of Technology Hohhot China

ISBN: (纸本)9789819601189

Most topic detection research focuses on cross-language information processing in languages with abundant resources, such as English-Chinese, English-German, and others. There are also some studies targeting low-resource languages such as Vietnamese-Chinese and Tibetan-Chinese cross-language topic detection research. However, there is very little research on Mongolian-Chinese cross-language studies. The primary reasons are the lack of corpora in the Mongolian language and the limitations of conventional topic detection methods in text representation, clustering, and topic representation within the Mongolian language domain. Therefore, this paper proposes a Mongolian-Chinese cross-lingual topic detection method based on knowledge distillation and contrastive learning. Firstly, the pre-trained language model is fine-tuned through knowledge distillation tasks to enable the model to integrate the semantic representation of Mongolian-Chinese cross-lingual texts. Then, the effect of cross-lingual topic detection is improved by combining contrastive learning training between news of different topics. Experimental results show that the proposed method combining knowledge distillation and contrastive learning outperforms other baseline models, achieving at least 2, 0.8, and 3% points higher in F1 score, topic diversity, and topic consistency, respectively. This approach effectively enhances the accuracy of Mongolian-Chinese cross-lingual topic detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Enhancing engineering Education: The Role of Artificial Intelligence in Personalizing Learning and Outcomes 4

Enhancing Engineering Education: The Role of Artificial Inte...

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4th international conference on Big Data engineering and Education, BDEE 2024

作者： Furman, Gabriela Dorfman Shenkar College of Engineering Design and Art Ramat-Gan Department of Electrical & Electronic Engineering Israel

ISBN: (纸本)9798350388268

The emergence of artificial intelligence (AI) in educational contexts presents transformative potential for higher engineering education. This paper conducts a comprehensive literature review to explore the role of AI-driven analytics such as personalized learning pathways and adaptive assessment techniques, in personalizing learning experiences and improving educational outcomes for engineering students. Through a systematic analysis of existing research, we identify how AI technologies - such as machine learning algorithms, data mining, and natural language processing - can be utilized to tailor educational content, improve learning engagement, and optimize curriculum design. A synthesis of the findings highlights the significant benefits of AI in identifying diverse learning styles, predicting academic performance, and providing real-time feedback, thus fostering a more individualized and effective learning environment. In addition, this article discusses the challenges and ethical considerations associated with implementing AI in education, including data privacy, algorithmic bias, and the need for human oversight to ensure ethical AI deployment. Building upon insights gleaned from a thorough review of published literature, we propose a conceptual framework for integrating AI into engineering education, with the aim of equipping the students with the skills and knowledge required to navigate the complexity of the modern engineering landscape. This theoretical exploration underscores the transformative potential of AI in engineering education and sets a foundation for future empirical research to further investigate and expand upon these benefits and strategies. © 2024 IEEE.

关键词： Adversarial machine learning

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Medibot: Personal Medical Assistant 2

Medibot: Personal Medical Assistant

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2nd IEEE international conference on Networking and Communications, ICNWC 2024

作者： Kumar, K. Antony Rajan, Jerlin Francy Appala, Charan Balurgi, Shreya Balaiahgari, Praveen Royal Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Apollo College of Nursing Department of Medical Surgical Nursing Chennai India

ISBN: (纸本)9798350365269

In many parts of the world, there is a lack of access to healthcare professionals, Medical consultations can be expensive. Using cutting-edge Artificial Intelligence (AI) technology, Medibot, a smart healthcare conversational robot, addresses the issue of long wait times for medical visits. Designed with the healthcare industry in mind, Medibot offers people looking for medical information and assistance an easy- to-use interface. Its proficiency in natural language processing (NLP), Machine Learning (ML), and knowledge representation is enhanced by its ability to precisely comprehend customer inquiries and provide pertinent answers. This live virtual assistant demonstrates its ability to provide individualized medical insights by helping with the identification of certain ailments based on user-reported symptoms, going beyond standard health questions. The unique quality of Medibot is its capacity to negotiate healthcare-related interactions effectively. This makes it an invaluable tool for resolving challenges related to responsiveness and accessibility that are frequently tied to the availability of healthcare practitioners. The capacity of Medibot to help diagnose particular ailments based on user-reported symptoms makes it stand out. © 2024 IEEE.

关键词： knowledge representation

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Predictive Lung Disease Identification Using Artificial Intelligence and Machine Learning Algorithm 2

Predictive Lung Disease Identification Using Artificial Inte...

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2nd international conference on Disruptive Technologies, ICDT 2024

作者： Singh, Rajani Das Lodhi, Abhinav Kumar Mishra, Amit Aeron, Anurag Sharma, Vyom Gl Bajaj Institute of Technology & Management Department of Information Technology UP Greater Noida India Raj Kumar Goel Institute of Technology Department of CSE-DS UP Ghaziabad India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun India Miet Computer Science & Engineering UP Meerut India Shri Ram Group of Colleges Computer Science & Engineering UP. Muzaffarnagar India

ISBN: (纸本)9798350371055

Lung sicknesses, which include continual obstructive pulmonary ailment (COPD), are a big international health difficulty, affecting hundreds of thousands of humans every 12 months. Traditional diagnostic strategies rely on subjective checks with the aid of medical professionals, leading to a high price of misdiagnosis and not on-time remedy. This highlights the need for an extra accurate and efficient lung ailment identification gadget. The Predictive Lung sickness identification (PLDI) gadget makes use of artificial intelligence (AI) and device-gaining knowledge of (ML) algorithms to locate and classify numerous lung diseases, including COPD. It uses techniques, image reputation, natural language processing, and records mining to research clinical snapshots and reviews, identifying patterns and markers of lung illnesses. The PLDI system has shown promising results in accurately detecting and classifying lung diseases, imparting a timely and correct diagnosis for patients. © 2024 IEEE.

关键词： Identification Misdiagnosis Obstructive Pulmonary Reputation Sicknesses

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A Review on Chatbots: Advancements and its Downsides

A Review on Chatbots: Advancements and its Downsides

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2023 international conference on IoT, Communication and Automation Technology, ICICAT 2023

作者： Mishra, Anamika Singh, Awantika Singh, Prashenjeet Singh, Shailesh Pandey, Shrawan Kumar Tripathi, Abhinandan Buddha Institute of Technology Computer Science & Engineering Gorakhpur India

ISBN: (纸本)9798350302820

Nowadays, using chatbots in the corporate and technology worlds is necessary. The recent development of chatbots like ChatGPT has raised various issues for technological titans and academic researchers, particularly with regard to the education sector, the health area, and business enterprises. The world was astonished by the exceptional proficiency of the generative AI tool, ChatGPT in handling remarkably complex tasks. natural language generation (NLG), machine learning (ML), and natural language processing (NLP) are popular technologies utilized in Chabot development. In this paper, we have attempted to explain how chatbots are used in everyday life as well as the rapid advances in their design methodologies. This study includes a survey of numerous works that were based on research on the use of chatbots like ChatGPT and their effects in the real world. These recent articles have been analyzed with a focus on the type of knowledge provided to these systems and the domains for which these systems have been created, among other variables. This analysis aims to understand the most recent developments in the evolution of chatbot systems. © 2023 IEEE.

关键词： Machine learning

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