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International Conference on Soft computing and Machine Intelligence (ISCMI)

作者： Anand Matheven Burra Venkata Durga Kumar School of Computing & Data Science Xiamen University Malaysia Sepang Selangor Darul Ehsan Malaysia

The rise of social media has brought the rise of fake news and this fake news comes with negative consequences. With fake news being such a huge issue, efforts should be made to identify any forms of fake news however it is not so simple. Manually identifying fake news can be extremely subjective as determining the accuracy of the information in a story is complex and difficult to perform, even for experts. On the other hand, an automated solution would require a good understanding of NLP which is also complex and may have difficulties producing an accurate output. Therefore, the main problem focused on this project is the viability of developing a system that can effectively and accurately detect and identify fake news. Finding a solution would be a significant benefit to the media industry, particularly the social media industry as this is where a large proportion of fake news is published and spread. In order to find a solution to this problem, this project proposed the development of a fake news identification system using deep learning and natural language processing. The system was developed using a Word2vec model combined with a Long Short-Term Memory model in order to showcase the compatibility of the two models in a whole system. This system was trained and tested using two different dataset collections that each consisted of one real news dataset and one fake news dataset. Furthermore, three independent variables were chosen which were the number of training cycles, data diversity and vector size to analyze the relationship between these variables and the accuracy levels of the system. It was found that these three variables did have a significant effect on the accuracy of the system. From this, the system was then trained and tested with the optimal variables and was able to achieve the minimum expected accuracy level of 90%. The achieving of this accuracy levels confirms the compatibility of the LSTM and Word2vec model and their capability to be synergiz

关键词： Industries Deep learning Training Social networking (online) Natural language processing Fake news Machine intelligence

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Evaluation of Optimizers in DNN-Based Classification Model for Quadrature Rule in Isogeometric Analysis 6th

Evaluation of Optimizers in DNN-Based Classification Model f...

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6th National Conference on Advances in Multidisciplinary Design, Analysis and Optimization, NCMDAO 2023

作者： Nath, Dipjyoti Ankit Neog, Debanga Raj Gautam, Sachin Singh Department of Mechanical Engineering Indian Institute of Technology Guwahati Guwahati India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Guwahati Guwahati India

ISBN: (纸本)9789819611577

In the training process of an artificial neural network (ANN) model, optimizers play a pivotal role by determining how the ANN model’s parameters are adjusted during each iterative step. Within the machine learning (ML) literature, a range of gradient-based optimizers are commonly employed, including stochastic gradient descent (SGD), momentum-based methods, mini-batch gradient descent, as well as more advanced techniques such as AdaGrad, AdaDelta, RMSProp, and Adam. In this work, the performance of these optimizers is studied for a deep neural network (DNN) model. The DNN model is used to predict the number of Gauss quadrature points to compute the element stiffness matrix in isogeometric analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep neural networks

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Intensity Confusion Matters: An Intensity-Distance Guided Loss for Bronchus Segmentation

arXiv

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arXiv 2024年

作者： Gong, Haifan Huang, Wenhao Zhang, Huan Wang, Yu Wan, Xiang Shen, Hong Li, Guanbin Li, Haofeng Shenzhen Research Institute of Big Data China InferVision China School of Computer Science and Engineering Sun Yat-sen University China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China

Automatic segmentation of the bronchial tree from CT imaging is important, as it provides structural information for disease diagnosis. Despite the merits of previous automatic bronchus segmentation methods, they have paied less attention to the issue we term as Intensity Confusion, wherein the intensity values of certain background voxels approach those of the foreground voxels within bronchi. Conversely, the intensity values of some foreground voxels are nearly identical to those of background voxels. This proximity in intensity values introduces significant challenges to neural network methodologies. To address the issue, we introduce a novel Intensity-Distance Guided loss function, which assigns adaptive weights to different image voxels for mining hard samples that cause the intensity confusion. The proposed loss estimates the voxel-level hardness of samples, on the basis of the following intensity and distance priors. We regard a voxel as a hard sample if it is in: (1) the background and has an intensity value close to the bronchus region;(2) the bronchus region and is of higher intensity than most voxels inside the bronchus;(3) the background region and at a short distance from the bronchus. Extensive experiments not only show the superiority of our method compared with the state-of-the-art methods, but also verify that tackling the intensity confusion issue helps to significantly improve bronchus segmentation. Project page: https://***/lhaof/ICM. Copyright © 2024, The Authors. All rights reserved.

关键词： Computerized tomography

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SkinGEN: an Explainable Dermatology Diagnosis-to-Generation Framework with Interactive Vision-Language Models 25

SkinGEN: an Explainable Dermatology Diagnosis-to-Generation ...

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30th International Conference on Intelligent User Interfaces, IUI 2025

作者： Lin, Bo Xu, Yingjing Bao, Xuanwen Zhao, Zhou Wang, Zhouyang Yin, Jianwei Innovation Centre for Information Binjiang Institute of Zhejiang University Hangzhou China School of Software Technology Zhejiang University Ningbo China Zhejiang University School of Software Ningbo China The First Affiliated Hospital of Zhejiang University School of Medicine Zhejiang Province Hangzhou China Zhejiang University Hangzhou China School of Design Hunan University Changsha China Center for Data Science Zhejiang University Hangzhou China

ISBN: (纸本)9798400713064

With the continuous advancement of vision language models (VLMs) technology, remarkable research achievements have emerged in the dermatology field, the fourth most prevalent human disease category. However, despite these advancements, VLM still faces explainable problems to user in diagnosis due to the inherent complexity of dermatological conditions, existing tools offer relatively limited support for user comprehension. We propose SkinGEN, a diagnosis-to-generation framework that leverages the stable diffusion(SD) model to generate reference demonstrations from diagnosis results provided by VLM, thereby enhancing the visual explainability for users. Through extensive experiments with Low-Rank Adaptation (LoRA), we identify optimal strategies for skin condition image generation. We conduct a user study with 32 participants evaluating both the system performance and explainability. Results demonstrate that SkinGEN significantly improves users' comprehension of VLM predictions and fosters increased trust in the diagnostic process. This work paves the way for more transparent and user-centric VLM applications in dermatology and beyond. © 2025 Copyright held by the owner/author(s).

关键词： Visual languages

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The unequal effects of the health–economy trade-off during the COVID-19 pandemic

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Nature Human Behaviour 2024年第2期8卷 264-275页

作者： Pangallo, Marco Aleta, Alberto del Rio-Chanona, R. Maria Pichler, Anton Martín-Corral, David Chinazzi, Matteo Lafond, François Ajelli, Marco Moro, Esteban Moreno, Yamir Vespignani, Alessandro Farmer, J. Doyne CENTAI Institute Turin Italy Institute for Biocomputation and Physics of Complex Systems and Department of Theoretical Physics University of Zaragoza Zaragoza Spain Complexity Science Hub Vienna Austria Department of Mathematics and GISC Universidad Carlos III de Madrid Leganes Spain MOBS Lab Northeastern University Boston MA United States The Roux Institute Northeastern University Portland ME United States Institute for New Economic Thinking at the Oxford Martin School and Smith School of Enterprise and the Environment University of Oxford Oxford United Kingdom Laboratory for Computational Epidemiology and Public Health Department of Epidemiology and Biostatistics Indiana University School of Public Health Bloomington IN United States Connection Science Institute for Data Science and Society MIT Cambridge MA United States Santa Fe Institute Santa Fe NM United States

Despite the global impact of the coronavirus disease 2019 pandemic, the question of whether mandated interventions have similar economic and public health effects as spontaneous behavioural change remains unresolved. Addressing this question, and understanding differential effects across socioeconomic groups, requires building quantitative and fine-grained mechanistic models. Here we introduce a data-driven, granular, agent-based model that simulates epidemic and economic outcomes across industries, occupations and income levels. We validate the model by reproducing key outcomes of the first wave of coronavirus disease 2019 in the New York metropolitan area. The key mechanism coupling the epidemic and economic modules is the reduction in consumption due to fear of infection. In counterfactual experiments, we show that a similar trade-off between epidemic and economic outcomes exists both when individuals change their behaviour due to fear of infection and when non-pharmaceutical interventions are imposed. Low-income workers, who perform in-person occupations in customer-facing industries, face the strongest trade-off. © 2023, The Author(s).

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Intelligent traffic prediction system using hybrid convolutional neural networks for smart cities

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Multimedia Tools and Applications 2024年 1-19页

作者： J, Jeba Sonia G, Arun Kumar kumar, ERajesh Raju, K. Narasimha Sudha, V. Kshirsagar, Pravin R. Tirth, Vineet Rajaram, A. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Chennai603203 India Department of Electronics and Communication Engineering JSS Academy of Technical Education Uttar Pradesh Noida201301 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Andhra Pradesh Visakhapatnam530048 India Department of Electronics and Communication Engineering Sona College of Technology Tamil Nadu Salem636005 India Department of Electronics & amp Telecommunication Engineering J D College of Engineering & ampManagement Maharashtra Nagpur India Mechanical Engineering Department Centre for Engineering and Technology Innovations College of Engineering King Khalid University Asir Abha61421 Saudi Arabia Department of Electronics and Communication Engineering EGS Pillay Engineering College Nagapattinam India

Intelligent Traffic Management Systems (ITMS) play a crucial role in advancing smart city applications by addressing the rapid increase in road traffic due to the growing number of vehicles. Effective real-time traffic prediction is essential to avoid congestion, yet existing systems often struggle with high delays and insufficient accuracy. This paper introduces an innovative ITMS based on a Hybrid Convolutional Neural Network (HCNN) to overcome these challenges. The proposed system integrates numerous smart devices, including smart vehicles, with an edge computing framework where the HCNN model is deployed. The HCNN combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to enhance prediction accuracy and reduce latency. Experimental results demonstrate that the HCNN model achieves up to 95% prediction accuracy while reducing delays by up to 50% compared to conventional methods. These improvements are significant for smart city applications, ensuring timely and precise traffic management. The study validates the effectiveness of the HCNN model in improving traffic prediction and proposes its potential for broader smart city implementation. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Sentiment Analysis of Movie Reviews: Optimizing Predictions Through TF-IDF Vectorization

Sentiment Analysis of Movie Reviews: Optimizing Predictions ...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Mohd Irfan Anne Sreyas Venkatesam Hariharan Shanmugasundaram Mahendra Kumar Gourisaria Subrata Chowdhury Sudhansu Shekhar Patra Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad Telangana India Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India School of Computer Engineering Kalinga Institute of Industrial Technology (KIIT) Deemed to be University Bhubaneswar India Dept. of Computer Science and Engineering Sreenivasa Institute of Technology and Management Studies(A) Chittoor Andhra Pradesh India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Sentiment analysis model is aimed to accurately interpret movie reviews, addressing challenges like sarcasm and noisy data. By integrating multiple machine learning classifiers Naive Bayes, Random Forest, K-Nearest Neighbors, Logistic Regression, Decision Tree, and XGBoost—the model enhances sentiment prediction accuracy. The Term Frequency-Inverse Document Frequency (TF-IDF) method is used to extract features from a dataset of 50,000 movie reviews. Performance is evaluated using accuracy, precision, recall, and F1-score, with Logistic Regression achieving the best results, including an highest accuracy of 84.33 % and an F1-score of 0.847. In comparison to traditional methods, this multi-classifier approach offers a deeper understanding of movie reviews and its influence on audience decisions. The study demonstrates the potential of using these machine learning techniques to provide real-time, scalable sentiment analysis, which is crucial for filmmakers and marketers seeking insights into audience satisfaction and box office performance.

关键词： Sentiment analysis Logistic regression Accuracy Reviews Predictive models Motion pictures Feature extraction Real-time systems Noise measurement Random forests

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An Even Analysis: The Nickel Short Squeeze by Tsingshan and Lessons for Enterprises

An Even Analysis: The Nickel Short Squeeze by Tsingshan and ...

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作者： Jiayan Qi Tianyi Wu Zijian Xu Ziyan Li SWUFE-UD Institute of Data Science Southwestern University of Finance and Economics Department of Art and Science Ohio State University Department of Business School University of Shanghai for Science and Technology Department of Business Administration Shanghai University

The short squeeze by Tsingshan has arisen a heated discussion in financial markets. Market participants have learned again the risk of shorting. Here are details of the case. After nickel prices went up in 2022, Tsingshan expected that the nickel price would decrease in the future. In order to lock into the profits, Tsingshan sold 200,000 tons of electroless nickel futures at about 20,000 dollars/ton. Prices went up even more and Tsingshan got a huge margin bill that endangered the firm's existence. With the help of some government measures, Tsingshan managed to minimize its loss and survive the unprecendented crisis. The incident has come to an end. Tsingshan's debacle provides us with some important lessons for enterprises' risk management. This paper first introduces the process of the whole event. After that, it analyzes the incident that happened to Tsingshan. Finally, it reflects on the risks in the process of short squeeze by Tsingshan.

关键词： Tsingshan nickel financial markets

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Fake Profile Identification Using Machine Learning

Fake Profile Identification Using Machine Learning

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2022 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering, WIECON-ECE 2022

作者： Sudhakar, T. Gogineni, Bhuvana Chendrica Vijaya, J. VIT-AP University School of Computer Science and Engineering Andrapradesh Vijayawada India Ira A. Fulton Schools of Engineering Arizona State University School of Computer Science and Engineering United States International Institute of Information Technology Department Data Science and Artificial Intelligence Chhattisgarh Naya Raipur India

ISBN: (纸本)9798350311563

Online social networks have permeated our social lives in the current generation. These sites have allowed us to see our social lives differently than they did in the past. Nowadays we can connect with new friends and maintain relationships with them via social and personal activities become quite easy. Online Social Networks (OSN) are contributed in all areas such as Research in all domains, Job-related areas, Technology oriented areas, Health care, and business-oriented areas, Information gathering and data collection, and so on. One of the biggest problems on these social media platforms is fake profiles. Impersonating to be someone else and causing harm and defamation to the real person or advertising or popularizing removed propaganda on someone's name to get more benefit is the motto of such profile creators. There have been many studies regarding these fake accounts and how can they be mitigated. Many approaches such as graph-level activities or feature analysis have been taken into consideration to identify fake profiles. These methods are outdated when compared to arising issues of these days. In this paper, we proposed a technique using machine learning for fake profile detection which is efficient.. The benchmark data set is collected and mixed with manual data first furthermore;a data cleaning technique is used to present the data more feasibly. Then the preprocessed data is used for model building with sufficient information such as profile name, profile ID name, number of followers, and so on. We added Cross validation process where many training algorithms are implemented on the given data and are then tested on the same data. Based on the experiments the RF classifier performed better than the other classification methods. The Random Forest classifier is used to forecast the profile whether is fake or genuine in an efficient way. © 2022 IEEE.

关键词： Social networking (online)

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Prediction of Loan Approval in Banks Using Machine Learning Algorithm

Prediction of Loan Approval in Banks Using Machine Learning ...

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Smart Technologies for Sustainable Development Goals (ICSTSDG), International Conference on

作者： Mathan Kumar Mounagurusamy P A Manoharan J. Anvar Shathik A. Pandiaraj N. Ramshankar Naveen G Department of Computing Technologies SRM Institute of Science and Technology Chennai Chennai Institute of Technology Chennai AIML and Data science Anjuman Institute of Technology and Management Karnataka India Department of Computer Science and Engineering R.M. D. Engineering College Department of Electronics and Communication Madha Institute of Engineering and Technology

ISBN: (数字)9798331516284

ISBN: (纸本)9798331516291

Normally, the banking sector gains profit from different kinds of sources, but the main factor in their profitable growth is giving loans to their customers. A bank's profit or loss is determined by the quantity of the loan. A huge number of people are taking loans from banks and not clearing their dues. So there is a tedious process for banks to identify them and collect them. This in turn leads to following up on a complex verification process and issuing loans to their customers. Customer satisfaction is mandatory in every field, but in the banking sector, it is essential to identify genuine applicants and satisfy their needs, as well as to keep bankers free from non-payable customers. The manual method of identifying the right person and providing a loan to them may cause misconceptions. Several studies in this field suggest various kinds of approaches to identifying applicant status. Therefore, the use of machine learning (ML) approaches in the banking sector to resolve difficulties faced by bankers is becoming more popular. This reduces the problems faced by bankers and makes their applicants satisfactory. The time taken for this process is too long. It is essential to select the best ML approach from the different kinds of classifier algorithms available. The attributes chosen for this study include the applicant's education, type of work, applicant income, loan amount, co-applicant income, credit history, and property area. The proposed study reviews ML approaches such as K-nearest Neighboring (KNN), Logistic Regression, Decision Tree and Support vector Mechanism (SVM). Among those, KNN outperforms the other three algorithms, and the efficiency obtained by KNN is 87.50%, Logistic Regression is 83.512%, Decision Tree is 77.09%, and Random forest (RF) is 74.49%.

关键词： Support vector machines Logistic regression Machine learning algorithms Reviews Banking Nearest neighbor methods Vectors Sustainable development Recommender systems Testing

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