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Predicting Box Office Success: A Transformer-Based Multi-Modal Approach

Predicting Box Office Success: A Transformer-Based Multi-Mod...

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Sentiment analysis and Deep Learning (ICSADL), International conference on

作者： R. Arul Jose R. Santhana Krishnan K. Penyameen J. Relin Francis Raj v. vinoth Kumar M. Priya Department of Electrical and Electronics Engineering Rajarajeswari College of Engineering Bengaluru India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Information Technology Velammal College of Engineering and Technology Madurai India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology PSNA college of Engineering and Technology Dindigul India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

This research presents a Transformer-based multi-modal architecture for predicting box office revenue by integrating diverse data sources: text, visuals, and numerical features. The proposed framework leverages RoBERTa for textual data such as movie descriptions, reviews, and scripts, vision Transformers (viT) for visual data including posters and trailers, and MLPs for numerical data like budget, genre, and actor popularity. A cross-attention mechanism is employed to fuse multi-modal embeddings, enabling comprehensive analysis and accurate predictions. The model is trained using PyTorch, optimized with AdamW, and enhanced by data augmentation and TPUs for efficiency. Performance is evaluated using metrics like MAE, RMSE, and R 2 . Deployment on AWS via FastAPI ensures scalable access, with SHAP explainability providing insights into model decisions. This system offers a robust and interpretable solution for predicting box office performance, catering to the evolving needs of the entertainment industry.

关键词： Deep learning Analytical models Adaptation models visualization Computer vision Predictive models Transformers data augmentation data models Numerical models

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Performance Comparison of Overlapping Sound Event Detection Methods

Performance Comparison of Overlapping Sound Event Detection ...

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International conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Akshay Tyagi Kartikeya Singh P v S Jaswanth Kumar Prof. Rakesh Garg Mohammed Edres Yousef Abdullah Dept. of Computer Science and Engineering Amity University Uttar Pradesh India

Sound event detection is pivotal in various applications, including environmental monitoring and surveillance systems, enhancing situational awareness and response strategies. This paper investigates the intricacies of overlapping sound events, where multiple events occur concurrently, creating complexities in accurate detection. Traditional methods falter when confronted with this challenge, necessitating advanced approaches. This research pursues two primary objectives: comprehensive review of existing overlapping sound event detection methodologies and exploration of innovative solutions to enhance accuracy and robustness. Through meticulous analysis of prior research, this paper offers insights into field progress and identifies unexplored areas. Subsequent sections dissect existing methodologies, evaluating their strengths and limitations, and addressing challenges in common evaluation metrics and datasets. This paper contributes to the advancement of sound event detection technology, particularly in complex audio environments, promoting better solutions for real-world applications.

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The status quo of the development of multidisciplinary and "double innovation" talent training models in the field ofeducation in China 2

The status quo of the development of multidisciplinary and "...

引用

2nd International conference on Information Science and Education, ICISE-IE 2021

作者： Yang, Weimin Shi, Siqi Zhan, Jin Department of Mechanical and Electrical Engineering Beijing University Of Chemical Technology Beijing China

ISBN: (纸本)9781665438292

Based on the data from China HowNet, this paper uses citespace visual analysis software to draw a map of scientific knowledge in this research field. Through literature clustering and data analysis, the development status of the multidisciplinary "double innovation" talent training model in my country's education field is sorted out, and the new talent training model is sorted and summarized from the three perspectives of the country, enterprises, and universities. © 2021 IEEE

关键词： Information analysis

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CoMT: A Novel Benchmark for Chain of Multi-modal Thought on Large vision-Language Models 39

CoMT: A Novel Benchmark for Chain of Multi-modal Thought on ...

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39th Annual AAAI conference on Artificial Intelligence, AAAI 2025

作者： Cheng, Zihui Chen, Qiguang Zhang, Jin Fei, Hao Feng, Xiaocheng Che, Wanxiang Li, Min Qin, Libo School of Computer Science and Engineering Central South University China Key Laboratory of Data Intelligence and Advanced Computing in Provincial Universities Soochow University China Research Center for SCIR Harbin Institute of Technology Harbin China National University of Singapore Singapore

ISBN: (纸本)157735897X

Large vision-Language Models (LvLMs) have recently demonstrated amazing success in multi-modal tasks, including advancements in Multi-modal Chain-of-Thought (MCoT) reasoning. Despite these successes, current benchmarks still follow a traditional paradigm with multi-modal input and text-modal output, which leads to significant drawbacks such as missing visual operations and vague expressions. Motivated by this, we introduce a novel Chain of Multi-modal Thought (CoMT) benchmark to address these limitations. Different from the traditional MCoT benchmark, CoMT requires both multi-modal input and multi-modal reasoning output, aiming to mimic human-like reasoning that inherently integrates visual operations. Specifically, CoMT consists of four categories: (1) visual Creation, (2) visual Deletion, (3) visual Update, and (4) visual Selection to comprehensively explore complex visual operations and concise expression in real scenarios. We evaluate various LvLMs and strategies on CoMT, revealing some key insights into the capabilities and limitations of the current approaches. We hope that CoMT can inspire more breakthroughs on introducing multi-modal generation into the reasoning process. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Modal analysis

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Optimizing Fenugreek Leaf Disease Detection: An exploration of Federated Learning with CNN 14

Optimizing Fenugreek Leaf Disease Detection: An Exploration ...

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14th International conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Mehta, Shiva Kukreja, vinay Sharma, vikrant Manwal, Manika Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350335095

Fenugreek leaf diseases severely hamper agriculture production;thus, effective detection and classification techniques are required. This study describes a novel method for identifying six kinds of fenugreek leaf diseases using federated learning and convolutional neural networks (CNNs) across several client datasets and bypassing the need for raw data exchange to retain the Federated Learning principle of privacy preservation while ensuring accurate illness categorization. Precision, recall, F1-score, and accuracy were the four main metrics to assess the model's robustness. The model consistently performed well in individual client evaluations (part Iv), with F1-scores for all classes reaching 88% in Client A, 92% in Client B, 93% in Client C, and 95% in Client D. On the globally aggregated dataset (part v), the model continued to perform well, with overall F1-scores of 91.78% for Client A, 95.67% for Client B, 93.52% for Client C, and 94.93% for Client D. Three averaging techniques - macro average, weighted average, and micro average - were used for a deeper analysis in Section vI. These averaging methods demonstrated consistently good performance across all customers, with micro and weighted averages falling between 91.98% and 95.71% and macro averages ranging from 91.80% to 95.67%. These findings highlight the model's strong performance against class imbalances across several illness classes. This study highlights the potential of CNNs and federated learning for identifying and categorising agricultural diseases. The method provides a scalable, effective, and privacy-preserving alternative with a lot of promise for increasing agricultural production, particularly for the cultivation of fenugreek. © 2023 IEEE.

关键词： Convolution

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A Bibliometric analysis of visualizing Knowledge Mapping for Learning Management System

A Bibliometric Analysis of Visualizing Knowledge Mapping for...

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International conference on Educational and Information Technology (ICEIT)

作者： Xiquan Ye Jiaming Wang Danya Liu Qinggui Qin Guangdong Business and Technology University Zhaoqing China GXNU School of Design Guangxi Normal University Guilin China Foreign Language School Guangzhou Institute of Science and Technology Guangzhou China School of Art and Design Guangzhou Institute of Science and Technology Guangzhou China

ISBN: (数字)9798331540883

ISBN: (纸本)9798331540890

With the advancement of technology and methodologies, Learning Management System (LMS) has seen a profound and extensive application in the field of education, thereby rendering scientific and systematic research on it particularly significant. The primary objective of this study is to comprehensively explore the current application status and developmental trajectory of LMS in educational practice. To this end, the authors screened approximately 1,347 high-quality related articles from the Web of Science (WOS) and conducted a visual analysis using the CiteSpace. By analyzing countries, institutions, journals, and keywords, this study reveals the research hotspots and evolving trends of LMS over the past two decades. The results show that the research focus of LMS has gradually expanded from the initial exploration of basic theories on system design to a wide range of applications in educational practice. Furthermore, it has increasingly centered on personalized learning experiences, the improvement of learning efficiency, and the enhancement of learning outcomes. Additionally, the analysis reveals that different countries and regions exhibit diversity and variability in their research emphasis and developmental trends regarding LMS. The conclusions and findings of this study provide some references and insights for future research endeavors and practical implementations of LMS in the field of education. These contributions have the potential to promote ongoing innovation and facilitate the development of LMS in education.

关键词： Learning management systems visualization Systematics databases Soft sensors Education Bibliometrics data visualization Market research Trajectory

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ml4xcube: Machine Learning Toolkits for Earth System data Cubes 39

ml4xcube: Machine Learning Toolkits for Earth System Data Cu...

引用

39th Annual AAAI conference on Artificial Intelligence, AAAI 2025

作者： Peters, Julia Neumann, Anja Jaeger, Marco Gienapp, Lukas Umlauft, Josefine Leipzig University Leipzig Germany Leipzig University Germany

ISBN: (纸本)157735897X

Rapidly changing climate conditions and the increase in extreme events are posing severe challenges to human life and infrastructure, requiring sophisticated analytical capabilities for hazard prediction and disaster risk management. Earth System data Cubes (ESDCs) have become an essential tool in Earth System Sciences (ESS) by organizing large-scale, multivariate environmental datasets into a structured, scalable and analysis-ready format. However, modern machine learning techniques are not yet being utilized to their full potential on ESDCs. This is due to the lack of proper tooling, domain-specific challenges, and high barriers of entry for practitioners. We introduce ml4xcube, an open-source Python framework designed to assist ESS domain experts in applying ML techniques on ESDCs for advanced analysis and prediction of environmental variables and impacts. Through a comprehensive suite of tools, it addresses specific challenges associated with the nature of ESS data, such as the non-uniform data distribution due to dynamic gaps, or spatio-temporal autocorrelation of environmental variables. Due to its modular architecture, it covers the complete analysis process, from data exploration, and preparation, to model development, result interpretation and evaluation. With support for distributed computing, it handles large ESDC datasets efficiently. In order to ease the adoption it includes extensive documentation and tutorial notebooks. We demonstrate ml4xcube’s capabilities through three examples, showcasing its potential and capabilities for integrating machine learning with ESDC data. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Cluster computing

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A Systematic Mapping Study on the Role of Software Engineering in Enabling Society 5.0 9

A Systematic Mapping Study on the Role of Software Engineeri...

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9th IEEE International Smart Cities conference, ISC2 2023

作者： Celebic, vladana Bucaioni, Alessio Ericsson Podgorica Montenegro Malardalen University Västerås Sweden

ISBN: (纸本)9798350397758

Society 5.0 is a visionary project aimed at creating sustainable smart societies, which relies on advanced computer science and software engineering techniques. Hence, software engineering plays a critical role in enabling Society 5.0 by providing the necessary technological advances and practices to address societal issues. This paper presents a systematic mapping study that focuses on identifying the key software engineering technologies and challenges that are critical for achieving the goals of Society 5.0. The study begins with an initial set of 1646 peer-reviewed publications, from which a final set of 29 primary studies was selected through a rigorous selection process. The selected studies were subjected to meticulous data extraction, analysis, and synthesis. The primary studies identified 13 software engineering technologies that act as enablers of Society 5.0. However, the application of these technologies also comes with technical challenges. The selected primary studies identified ten software engineering open challenges that hamper the realisation of Society 5.0. The findings of this study have important implications for software engineering researchers and practitioners involved in developing sustainable smart societies. The identification of the key technologies and challenges in Society 5.0 can help guide future research and development efforts in this field. © 2023 IEEE.

关键词： Application programs

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Manufacturing Process Improvement through FMEA analysis and Fuzzy Logic 30

Manufacturing Process Improvement through FMEA Analysis and ...

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30th International Scientific conference Electronics, ET 2021

作者： Ivanova, Malinka Tsenev, valentin Dimitrov, Borislav Technical University of Sofia Sofia1000 Bulgaria

ISBN: (纸本)9781665445184

The paper presents a method for improvement a robotized manufacturing process of electronic devices. It includes statistical Failure Mode and Effects analysis (FMEA), fuzzy logic and visual technique for results interpretation. Collected data from experts are processed according to FMEA and used for development of intelligent fuzzy inference system, which result is visualized through heatmap. Such approach can support engineers for fast and correct decision making at manufacturing process with dynamic changes. © 2021 IEEE.

关键词： Decision making

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Artificial Intelligence Techniques within a Technological Process of Manufacturing Automated Control Systems

Artificial Intelligence Techniques within a Technological Pr...

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2022 International conference "Quality Management, Transport and Information Security, Information Technologies", IT and QM and IS 2022

作者： Chernoded, Andrei I. vasiliev, victor A. Tsyrkov, Alexander v. Aleksandrova, Svetlana v. Edel, Martin F. Moscow Aviation University Chair of Quality Management and Certification Moscow Russia Federal State Unitary Enterprise Npo Tekhnomash Moscow Russia

ISBN: (数字)9781665488709

ISBN: (纸本)9781665488709

The solution of quality control problems in production is in many ways like the optimization of automated control systems of technological processes. These issues are decided using mathematical methods of operations exploration, numerical minimization methods and *** of the new and effective methods of analyzing the state of the production system in industry 4.0 is neural network data analysis. The ongoing research on the use of neural networks is aimed at their use in solving production management *** is proposed to use artificial intelligence apparatus to optimize separate multi-nomenclature production. Each new task of optimal choice of parameters for the structure of production should be based on previously revealed decisions. Thus, it is proposed to create a learning system that provides a solution based on the accumulated "knowledge". Neural networks appertain to such systems [7]-[12].Neural networks will solve 2 tasks:• will be an "add-on"over the existing Product Lifecycle Management and Enterprise Resource Planning functionality at enterprises;• will take into account factors affecting *** proposed approach should expand the enterprise's mobility capabilities in making managerial decisions at all levels of management, throughout the life cycle. The models being developed should become a functional extension of digital production processes. © 2022 IEEE.

关键词： Industry 4.0

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