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Comprehensive Examination of Depression Detection Through Multimedia Content on Social Media Platforms

Comprehensive Examination of Depression Detection Through Mu...

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Cognitive Computing in engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), international conference on

作者： Chayan Preet Walia Priti Maheshwary Rabindranath Tagore University Bhopal India Department of Computer Science and Engineering Rabindranath Tagore University Bhopal India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

Depression is a mental health disorder that causes persistent feelings of sadness, hopelessness, and a loss of interest or pleasure in activities that one used to enjoy. It can be caused by a combination of genetic, biological, environmental, and psychological factors. Identifying depression causes in social media involves the use of data mining and natural language processing techniques to analyze user-generated content and behavior. Depression analysis is a popular research topic in the natural language processing field, and researchers are increasingly publishing papers on this subject. This review paper aims to explore the use of machine learning techniques for depression analysis using social media text, audio, and video data. The paper provides an overview of the different machine learning techniques used for depression analysis and their effectiveness. The review paper can help researchers understand the current state of knowledge on depression analysis using social media text, images, and videos along with machine learning techniques and identify potential areas for future research.

关键词： Analytical models Social networking (online) Reviews Computational modeling Biological system modeling User-generated content Mental health Depression natural language processing Data models

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QG-SKI: Question Classification and MCQ Question Generation Using Sequential knowledge Induction 22nd

QG-SKI: Question Classification and MCQ Question Generation ...

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22nd international conference on Hybrid Intelligent Systems, HIS 2022 and the 18th international conference on Information Assurance and Security, IAS 2022

作者： Dhanvardini, R. Deepak, Gerard Santhanavijayan, A. Health Care Informatics Domain Optum-United Health Groups Hyderabad India Department of Computer Science Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science Engineering National Institute of Technology Tiruchirappalli India

ISBN: (纸本)9783031274084

E-Learning has emerged as the most effective way of getting information in a range of sectors in the contemporary age. The utilisation of electronic content to provide education and development is referred to as e-learning. While the broadening internet has a plethora of e-learning tools, knowledge acquisition is not just an aspect that adds to an individual's enrichment. Assessment and evaluation are crucial parts of every learning system. Due to more complex assessments and quicker inspection, multiple choice questions are becoming extremely prevalent in current evaluations. However, establishing a diversified pool of MCQs relevant to a certain subject matter presents a hurdle. Manually creating high-quality MCQ exams is a time-consuming and arduous procedure that demands skill. As a result, research has concentrated on the automated construction of well-structured MCQ-based tests. This paper presents a paradigm using natural language processing based on semantic similarity and dynamic ontology. The proposed QG-SKI model uses LOD Cloud and Wiki Data to generate Ontologies dynamically and knowledge reservoir is performed. The dataset is analysed using TF-IDF algorithm and the Semantic Similarity and Semantic Dissimilarity are computed using Shannon’s entropy, Jaccard Similarity and Normalised Google Distance. These algorithms are executed for a multitude of degrees and levels to generate a similar of similar instances. The suggested model has a 98.15% accuracy and outperforms previous baseline models by dampening resilience. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： E-learning

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TOWARD FORMAL QUALITATIVE REASONING TO SUPPORT FUNCTIONAL DECOMPOSITION

TOWARD FORMAL QUALITATIVE REASONING TO SUPPORT FUNCTIONAL DE...

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ASME international Design engineering Technical conferences / 42nd Annual Computers and Information in engineering conference (IDETC-CIE)

作者： Mao, Xiaoyang Sen, Chiradeep Nanjing Univ Sci & Technol Sch Design Art & Media Nanjing Jiangsu Peoples R China Florida Inst Technol Mech & Aerosp Engn Melbourne FL 32901 USA

ISBN: (纸本)9780791886212

Functional decomposition is an important task in early systems engineering and design, where the overall function of the system is resolved into the functions of its components or subassemblies. Conventionally, this task is performed manually, because of multiple possible solution paths and the need for understanding the physics phenomena that could realize the desired effects. This paper presents an approach of developing a formal method for functional decomposition using physics-based qualitative reasoning. The representation includes three parts: (1) a natural language reasoner that detects the changes of physical states of material and energy flows, (2) a set of causation tables that abstract the knowledge of qualitative physics by capturing the causal relations between the various quantities involved in a physical phenomenon or process, and (3) a process-to-subgraph mapping that translate the physical processes into function structure constructs. The algorithm uses the above three representations and some topological reasoning to assemble function models that represent the decomposition of a given black box model. The paper illustrates the potential of this method for functional decomposition using an example of an air-heating device. The paper also discusses the limitations and challenges in maturing this approach into an end-usable design tool.

关键词： formal representation reasoning function-based design qualitative physics natural language processing

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MDT-Net: A Mask Decoder Tuning Strategy for CLIP-Based Zero-Shot 3D Classification 31st

MDT-Net: A Mask Decoder Tuning Strategy for CLIP-Based Zero...

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31st international conference on Multimedia Modeling, MMM 2025

作者： Yan, Hao Bai, Jing The School of Computer Science and Engineering North Minzu University Ningxia Yinchuan750021 China The Key Laboratory of Images and Graphics Intelligent Processing of State Ethnic Affairs Commission Ningxia Yinchuan750021 China

ISBN: (纸本)9789819620609

PointCLIP shows that transferring 2D knowledge to zero-shot 3D shape classification tasks can be effective, even without incorporating 3D-specific knowledge. However, the absence of 3D knowledge limits the model’s performance, leading to efforts focused on improving 3D-to-2D projection algorithms. This work advances previous methods by introducing a learnable fine-tuning strategy, called MDT-Net, for Contrastive language-Image Pre-training (CLIP) on 3D datasets. MDT-Net freezes the CLIP encoder to keep view features aligned with semantic information and incorporates a shape decoder with masked view consistency and attention-guided fusion, enabling more effective fine-tuning of CLIP for 3D tasks. The decoder receives both raw view features and masked view features. In the shallow layers, a mask consistency constraint is utilized between two types of view features to enhance the model’s generalization and robustness. In the deeper layers, attention-guided fusion is introduced to better capture the contextual information between views and compact all view features into one. MDT-Net is evaluated on different benchmarks of 5 datasets for zero-shot classification, achieving a competitive classification accuracy on ModelNet10, ModelNet40, McGill, and ZS3D datasets. These experiment results demonstrate the effectiveness of this approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Zero-shot learning

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AI-Driven Advanced Text Matching for Improved Information Capture and Retrieval 3

AI-Driven Advanced Text Matching for Improved Information Ca...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Agrawal, Trapty Kulhar, Kuldeep Singh Singh, K. Ranjith Indira, G. Shelke, Priya M. Kannagi, A. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Civil Engineering Jaipur India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Department of Computer Science and Information Technology Karnataka Bangalore India

ISBN: (纸本)9798350319125

This paper presents an AI-pushed advanced text matching technique to improve facts capture and retrieval. The proposed approach makes use of deep getting to know and natural language processing algorithms to successfully compare texts and discover similarities and dissimilarities among them. The deep gaining knowledge of algorithm is trained on a textual content corpus that serves as enter statistics for the model. This corpus may be made from diverse styles of textual information depending at the goal of the assessment. To make certain accuracy, the model is outfitted with parameters consisting of word embedding, semantic similarity, and fuzzy string matching. Once skilled, the version is tested to discover false positives and fake negatives. Finally, the model is carried out to a hard and fast of statistics to quantify fits among texts and retrieve useful statistics. Consequences show a discount in false positives and a higher precision of the version in retrieving applicable statistics from given facts. © 2023 IEEE.

关键词： dissimilarities presents similarities statistics version

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Exploring the Impacts of Scenario Variability on Speech Separation Tasks 9

Exploring the Impacts of Scenario Variability on Speech Sepa...

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9th international conference on Communication and Information processing, ICCIP 2023

作者： Yuan, Junming Ablimit, Mijit Hamdulla, Askar College of Information Science and Engineering Xinjiang University Urumqi Xinjiang China

ISBN: (纸本)9798400708909

Speech signal contains mixture of information like channel, speaker, and background noise. knowledge about these background scenarios is very helpful for voice processing tasks like separation and identification. In recent years, speech separation techniques have shown remarkable performance in dealing with complex acoustic scenes with multiple overlapping speakers. Research attention has been shifted towards tackling real-world challenges in this field. However, the domain variability in different situations remains a challenge for the practical application in reality scenarios. The background domain mismatch issue can be attributed mainly to channel and scene two factors. Recently, researchers have conducted plenty of data-driven analyses on speaker or language identification tasks. Nevertheless, there are few studies on addressing the affect of background scenarios and their effects on separation tasks. In this paper, we first created simulation datasets of multiple scenarios to explore the influence of scenario factors on the separation models. The results indicated that the scenario variability seriously affects the overall performance of the separation model. Subsequently, we selected four comprehensive training datasets containing different complex background noises, and the improved speech separation performance proved the importance of scenario information. © 2023 Copyright held by the owner/author(s)

关键词： Source separation

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Ameliorating Semantic Search Through Advanced AI Techniques 3

Ameliorating Semantic Search Through Advanced AI Techniques

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Ravikumar, V. Dhabliya, Dharmesh Mathur, Mala Das, Sidhant Kumar, Ritesh Rao, Srikrishna B Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Vivekananda Global University Department of Civil Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Ramnagar562112 India

ISBN: (纸本)9798350319125

Recent synthetic intelligence (AI) advances have enabled some thrilling semantic search applications. In particular, the development of advanced AI strategies, which include natural language processing (NLP) and deep learning, has enabled the creation of search engines like Google and Yahoo that leverage semantic data to enhance the accuracy of the quest outcomes. No longer simplest does this enable customers to find applicable statistics speedy. However, it additionally facilitates ameliorating any semantic mistakes resulting from typos, incorrect syntax, or wrong interpretations of the question. In this paper, we present a survey of new advances in semantic seek powered by AI and techniques for query expansion, generating question-precise training statistics, and semi-supervised gaining knowledge. We additionally talk about the results for the future of semantic search and gift some case research on the successful implementation of semantic seek with AI structures. Our assessment concludes with a dialogue of the open challenges and potential research avenues. © 2023 IEEE.

关键词： development discussion implementation implications leverage

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Deep-to-Bottom Weights Decay: A Systemic knowledge Review Learning Technique for Transformer Layers in knowledge Distillation 1

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15th international conference on knowledge Science, engineering, and Management (KSEM)

作者： Wang, Ankun Liu, Feng Huang, Zhen Hu, Minghao Li, Dongsheng Chen, Yifan Xie, Xinjia Natl Univ Def Technol Natl Key Lab Parallel & Distributed Proc Changsha Peoples R China Informat Res Ctr Mil Sci Beijing Peoples R China

ISBN: (数字)9783031109867

ISBN: (纸本)9783031109867;9783031109850

There are millions of parameters and huge computational power consumption behind the outstanding performance of pre-trained language models in natural language processing tasks. knowledge distillation is considered as a compression strategy to address this problem. However, previous works have the following shortcomings: (i) distill partial transformer layers of the teacher model, which not only do not make full use of the teacher-side information. But also break the coherence of the information, (ii) neglect the difficulty differences of knowledge from deep to shallow, which corresponds to different level information of teacher model. In this paper, we introduce a deep-to-bottom weights decay review mechanism to knowledge distillation, which could fuse teacher-side information while taking each layer's difficulty level into consideration. To validate our claims, we distill a 12-layer BERT into a 6-layer model and evaluate it on the GLUE dataset. Experimental results show that our review approach is not only able to outperform other existing techniques, but also outperform the original model on partial datasets.

关键词： knowledge distillation Pre-training language model Model compression

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ACORD: ANOVA Context Aware Relation Extraction from Textual Data

ACORD: ANOVA Context Aware Relation Extraction from Textual ...

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international conference on Web Research (ICWR)

作者： Parinaz Soltanzadeh Hossein Rahmani Amirhossein Derakhshan Milad Allahgholi School of Computer Engineering Iran University of Science and Technology Tehran Iran

ISBN: (数字)9798331508913

ISBN: (纸本)9798331508920

Relation extraction in textual data is an important task in natural language processing with diverse applications in QA systems, knowledge graph construction and text analysis. The research conducted in this area is categorized into rule-based methods, machine learning methods and deep learning methods. However, to the best of our knowledge, none of the existing models have considered context-awareness in relation extraction. In this paper, we propose a method called ACORD, which first attempts to identify the context using ANOVA measure and second utilizes identified context to determine the relationship between two entities in a text through an ensemble learning model. The evaluation results demonstrate that ACORD achieves a precision of 88% in determining the correct relationship. Moreover, the non-numerical interpretation of the results allows for the identification of the most discriminative terms for each class.

关键词： Deep learning Context-aware services Text analysis knowledge graphs Context awareness natural language processing Ensemble learning Analysis of variance Context modeling

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AcawebAgent: A Large language Model-Powered Assistant for Early Academic Research

AcawebAgent: A Large Language Model-Powered Assistant for Ea...

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international conference on Computer engineering and Applications (ICCEA)

作者： Yingli Yang Xiaoting Wang Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

The rapid advancement of artificial intelligence (AI) and machine learning (ML) technologies, particularly in large language models (LLMs) like the GPT series, has significantly impacted research and industrial applications. These models excel in various natural language processing (NLP) tasks, including text generation, comprehension, and translation. However, harnessing these capabilities for academic research still presents challenges, particularly for early-career researchers navigating extensive literature. In this paper, we introduce AcawebAgent, an inventive AutoAgent specifically crafted to enhance the abilities of beginner researchers. It leverages the advanced generation and analysis capabilities of large language models (LLMs) to collect open academic knowledge from the web. AcawebAgent offers customized research reports that include indepth overviews, practical applications, the latest developments, and future trajectories of specific research domains, thereby significantly diminishing the time and effort needed for comprehensive literature reviews and trend analyses.

关键词： knowledge engineering Technological innovation Navigation Large language models Bibliographies Refining Market research

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