Abnormal behavior detection is challenging and one of the growing research areas in computer *** main aim of this research work is to focus on panic and escape behavior detections that occur during unexpected/uncertai...
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Abnormal behavior detection is challenging and one of the growing research areas in computer *** main aim of this research work is to focus on panic and escape behavior detections that occur during unexpected/uncertain *** this work,Pyramidal Lucas Kanade algorithm is optimized using EME-HOs to achieve the *** stage,OPLKT-EMEHOs algorithm is used to generate the opticalflow from *** stage,the MIIs opticalflow is applied as input to 3 layer CNN for detect the abnormal crowd *** of Minnesota(UMN)dataset is used to evaluate the proposed *** experi-mental result shows that the proposed method provides better classification accu-racy by comparing with the existing *** method provides 95.78%of precision,90.67%of recall,93.09%of f-measure and accuracy with 91.67%.
With the continuous development of informatization, the system operation in today's society is becoming increasingly efficient, which prompts individuals and teams to continuously optimize their workflow design an...
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Sarcasm detection in social media is a challenging task due to its inherent reliance on contextual cues, tone, and cultural nuances. In recent years, multi-model deep learning frameworks have emerged as a powerful app...
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ISBN:
(纸本)9798350355611
Sarcasm detection in social media is a challenging task due to its inherent reliance on contextual cues, tone, and cultural nuances. In recent years, multi-model deep learning frameworks have emerged as a powerful approach for addressing these challenges, particularly in regional social media, where language variations and local idiomatic expressions complicate the detection process. This survey explores the latest developments in multi-model deep learning frameworks for sarcasm detection, focusing on their application in regional social media. The survey begins by reviewing foundational techniques in sarcasm detection, including traditional machine learning approaches that rely on handcrafted features. These methods, although effective in certain contexts, often fail to capture the subtleties of sarcasm in informal, region-specific languages. The advent of deep learning has led to significant advancements, particularly through models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers. These architectures, combined with Natural Language Processing (NLP) techniques, have enhanced the ability to identify sarcasm through text analysis. However, single-modal approaches focusing solely on text fail to fully capture sarcasm's multimodal nature, especially on platforms where users often express themselves through a combination of text, images, emojis, and video. This has led to the development of multi-model frameworks that integrate various data modalities, such as text, image, and user behaviour, to better understand the context of sarcastic expressions. In regional social media, where local language and cultural symbols play a crucial role, these multi-model approaches prove even more valuable. This survey highlights key multi-model frameworks, emphasizing their use in regional settings. By examining datasets, model architectures, and evaluation metrics, the survey underscores the importance of combining textual and non-textual
The study of credit risk is a major concern for financial companies looking to make wise lending decisions and limit potential losses. In this study, the use of R, a potent open-source programming language, is examine...
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ISBN:
(纸本)9789819745395
The study of credit risk is a major concern for financial companies looking to make wise lending decisions and limit potential losses. In this study, the use of R, a potent open-source programming language, is examined in the context of credit risk analysis. This study offers a thorough framework to improve the accuracy and efficiency of credit risk assessment by utilizing the flexible data manipulation, statistical analysis, and machine learning capabilities of R. The importance of credit risk analysis in financial institutions is discussed in the paper’s opening section, along with some of the difficulties it faces. The rich libraries, data processing capability, and data visualization features of R highlight how well-suited it is for this purpose. Data quality and consistency are stressed in the technique portion since it encompasses data collection, preprocessing, and feature engineering. To predict credit risk, a variety of statistical methods and machine learning models are used, which offers details on their benefits and interpretability. The research study also looks at model validation and evaluation, which ensures the stability and dependability of the credit risk models. Model accuracy, precision, and recall are evaluated using methods including exploratory data analysis (EDA), ROC analysis, and model performance indicators. This study concludes by highlighting how the use of R in credit risk analysis might enable financial institutions to make more knowledgeable lending decisions, lowering financial risks and promoting the stability of the financial sector. The purpose of the article is to offer a thorough methodology that financial institutions can use when performing credit risk analysis. This entails data gathering, preprocessing, feature engineering, statistical analysis, choosing a machine learning model, and model assessment. The goal of the paper is to provide practitioners with a detailed manual for implementing R-based data-driven credit risk an
Recommendation engines leverage past user preferences to forecast their future interests. Many deep learning-based recommendation systems aim to explore the intricate dynamics between users and particular items. Typic...
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Innovative technology solutions have been developed in response to the growing need for effective and customized client contact on e-commerce platforms. This work introduces an intelligent chatbot system that uses mac...
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Traditional autonomous navigation methods for mobile robots mainly rely on geometric feature-based LiDAR scan-matching algorithms, but in complex environments, this method is often affected due to the presence of movi...
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These trackers based on the space-time memory network locate the target object in the search image employing contextual information from multiple memory frames and their corresponding foreground-background features. I...
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作者：
Thomas, MerinShreenidhi, H.S.
Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India
Everybody like to listen to music, depending on their mood. But choosing a music manually based on mood is a task that has to be addressed because it takes time and effort. When anticipating a person's emotions an...
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The design and implementation of a few-shot learning-based diabetic retinopathy diagnostic system is the focus of this research project. According to the NEI (National Eye Institute), The United States report 2021, di...
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