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Latest Deep Learning Techniques for Fall Detection in Monitoring Real-Time Video Data 4th

Latest Deep Learning Techniques for Fall Detection in Monito...

引用

4th International Conference on Data, engineering, and Applications, IDEA 2022

作者： Agrawal, Madhuri Agrawal, Shikha Department of Computer Science and Engineering University Institute of Technology RGPV Bhopal India

ISBN: (纸本)9789819700363

Escorted by the latest improvements in technology, real-time monitoring of video data plays a rising supreme role in our lives. It has already had a significant influence on various areas of indoor and outdoor activities. To correctly classify the suspicious event as fall detection in monitoring real-time video data, latest deep learning techniques are in the demand of computer vision. Detecting correct suspicious events is highly famous and demanding in monitoring real-time video. In the paper, real-time events are considered for monitoring a person living in an indoor environment and performing their own daily activity in a normal manner. Suspicious events are considered as detecting the falling of a person. As the falling of a person is not a normal event, it is considered a suspicious event in real time. Although different techniques have been developed to efficiently classify suspicious events of fall detection, all deep learning techniques are the most famous. A brief overview of various machine learning and deep learning techniques applied by various researchers for detecting suspicious events, such as fall detection in the monitoring of real-time video data, has been considered. The paper also exposes different affairs of machine learning and deep learning techniques that may encourage intended researchers to work in the domain of monitoring video data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Video analysis

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Enhanced Agricultural Decision-Making: Merging Climatic Data with Machine Learning Algorithms 5

Enhanced Agricultural Decision-Making: Merging Climatic Data...

引用

5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Rathi, Snehal Wamanacharya, Shubham Shah, Samkit Khedkar, Vijayshri BTACT'S Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Symbiosis Institute of Technology Department of Computer Science & Engineering Pune India

ISBN: (纸本)9798350389609

Climate change still implies risks for economic security, such as for agriculture and its productivity and, therefore, national economic potential. Although similar approaches have been used in publications concerning individual crops or soil types, the application of climatology parameters in agriculture and it limited indices have not been optimal. Owing to the recognized limitations of traditional linear productivity models, this thesis subjects' agricultural productivity to a Cobb-Douglas multi-factorial modeling approach through intensive levels of data decomposition. The proposed approach enabled the researchers to adjust the impact of climate parameters during students' evaluations in the secondary education curricula. Additionally, frameworks which relate temperature variations and crop growth do exist, and therefore the temperature and agriculture performance model effects determinants were determined using complex analysis of temperature relations and crop outputs. This highlights the various factors affecting production, its structure, and the relative importance of some conditions in agriculture and explains their dynamics depending on the changes in growth factors. Hence, a set of factors employed in the modeling resulted mainly from economic conditions, production system structures and advanced agricultural technology applications in the economy as prediction © 2024 IEEE.

关键词： Agricultural Data Analysis Climate Impact on Agriculture Crop Growth Days Growth Degree Days (GDD) R Programming

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Detection of Cyber Attacks using AI 10

Detection of Cyber Attacks using AI

引用

10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Saravanan, Gurunishal Dhas, Julia Punitha Malar Devapriya, I. Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350353068

Several cyber attacks happen globally on several commercial and industrial technological devices. If these attacks are detected early, the damages can be reverted or even prevented. Models such as Gaussian Naive Bayes, Decision Tree, SVM, Random Forest, and Logistic Regression are used to detect these attacks. After these algorithms are trained, a custom model is designed using ensemble learning methods. The ensemble learning method used is known as Stacking, which integrates many algorithms to provide a better accuracy value. The model's performance is assessed and compared to consider the best model to detect cyber attacks. The accuracy score is used to evaluate the models. Based on these results, the most suitable model is chosen. © 2024 IEEE.

关键词： Decision trees

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Unsupervised Sentiment Analysis of Amazon Fine Food Reviews Using Fuzzy Logic 2nd

Unsupervised Sentiment Analysis of Amazon Fine Food Reviews ...

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2nd International Conference on Computing, Communication, and Learning, CoCoLe 2023

作者： Sharaff, Aakanksha Rajput, Nandini Papatla, Sai Rohith Department of Computer Science and Engineering National Institute of Technology Raipur Raipur India

ISBN: (纸本)9783031569975

Sentiment analysis has been increased popularity among the social media. The sentiment classification with machine learning identifies the positive and negative sentiment but the problem is that it doesn’t identify the duplication of reviews by the same individual. In this study, the sentiment of food reviews is assessed by employing a set of fuzzy rules and this suggested fuzzy system uses NLP (Natural Language Processing) techniques accompanied by an experimental unsupervised fuzzy rule-based system consisting of nine rules to classify the reviews into positive and negative. Unsupervised fuzzy rule-based systems are particularly advantageous in handling intricate and disorganized data, where the interrelationships among variables may not be precisely defined or readily apparent. This research took the dataset from Amazon fine food reviews and explores the more insight of food reviews. The proposed method considers the unlabeled text that utilizes the VADER tool with Fuzzy based system that outperforms among the other state-of-art algorithms with 86.3% of accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Sentiment analysis

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Comprehensive Influence-Based Social User Hiding Scheme in Overlapping Community Networks 6

Comprehensive Influence-Based Social User Hiding Scheme in O...

引用

6th International Conference on Next Generation Data-Driven Networks, NGDN 2024

作者： Liu, Jian Gu, Ke Deng, Yang Lei, Jiaqi Changsha University of Science & Technology School of Computer and Communication Engineering ChangSha China

ISBN: (纸本)9798350388374

As overlapping community detection algorithms continue to mature, the issue of information being over-mined has also become apparent. Existing overlapping community hiding algorithms are inadequate. Therefore, in this paper, we propose a user hiding and community hiding scheme based on node comprehensive influence, which achieves user hiding and community hiding by adjusting user link relations. First, we propose a node comprehensive influence calculation method based on K-Shell decomposition and improved particle competition mechanism. On the basis of comprehensive influence of node, we construct a scheme to adjust link relations. Finally, relevant experiments prove that our scheme can effectively achieve user hiding and user group hiding. © 2024 IEEE.

关键词： adjust links community hiding comprehensive influence

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The Evolution of Large Language Model: Models, Applications and Challenges 12

The Evolution of Large Language Model: Models, Applications ...

引用

12th International Conference on Current Trends in Advanced Computing, ICCTAC 2024

作者： Sindhu, B. Prathamesh, R.P. Sameera, M.B. KumaraSwamy, S. Global Academy of Technology Department of Computer Science and Engineering Karnataka Bangalore India

ISBN: (数字)9798350395808

ISBN: (纸本)9798350395808

Large Language Models (LLMs) have attracted a lot of attention due to their success in natural language processing tasks. This paper provides a thorough overview by examining the architecture, applications, problems, assessment techniques, and future directions of LLM. With the constantly growing body of literature, a succinct yet comprehensive overview of recent developments is essential. Following the development of NLP, it highlights the move from rule-based systems to sophisticated transformer structures like as BERT and GPT. Important LLMs for text creation, translation, and summarization are mentioned, including T5, BART, and BioGPT. LLM performance is evaluated using metrics including as accuracy, perplexity, BLEU score, and ROUGE score. Research is still being done because of issues with bias, overfitting, and real-Time processing. Future directions include managing longer contexts, lowering bias, and increasing efficiency through methods like federated learning. Continuous learning and multimodal LLMs are promising fields, as well as interpretive AI. In conclusion, LLMs have transformed natural language processing (NLP) and brought up both technical and ethical issues about the future of AI. © 2024 IEEE.

关键词： Natural language processing systems

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Facial Recognition Using Retina 1

Facial Recognition Using Retina

引用

1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Jaishwal, Abhishek Pandey, Abhishek Shukla, Abhay Rai, Pranjal Singh, Arun Kumar Greater Noida Institute of Technology Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350356816

With common face databases, this study's investigation into the use of ocular traits for face detection and identification yielded encouraging findings. The principal contributions include face detection, eye detection, and person identification techniques that target anatomical features such as the mouth, nose, eyes, and retina. The centers of the eyes, the distance between the eyes, and the brows are important characteristics for eye recognition. For a comprehensive recognition pipeline, the study makes use of the Python deep face library and the Arc Face module from the insight face project. Applications include crowd identification, missing person searches, area security, and high-security monitoring in places like parliaments and bank vaults. [1] © 2024 IEEE.

关键词： Face recognition

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Anomaly Detection in Medical Imaging: Bridging the Gap between 2D Processing and Interactive 3D Visualization

Anomaly Detection in Medical Imaging: Bridging the Gap betwe...

引用

2024 IEEE International Conference on Recent Advances in science and engineering technology, ICRASET 2024

作者： Abhisri, M. Sasidhar, Manikrishneshwar Subramanian, Aditya Sreeraghavan, Mirdhula Sivakumar, Surindernath Prabakaran, N. Vellore Institute of Technology School of Computer Science and Engineering Vellore632014 India

ISBN: (纸本)9798350388602

This paper mainly aims to present the use of transform a 2D image into a 3D interactive model which will help us visualize the anomaly, allowing the medical sector a better understanding of anomalies in medical images, which may even unearth new findings. Using stable diffusion algorithms, the generative model and computer vision converts 2D images into interactive 3D models. Existing structures usually struggle with complex 2D images when the anomaly is detected, hence focus on building a 3D model for deeper understanding is necessary. In this model, the challenges and roadblocks in normal 2D images for anomaly detection and why a 3D model will provide a better insight into the anomaly from medical images is discussed. Next, it explores the technological benefits of having an advanced 3D model and how the 3D model will help in interacting with the anomaly by employing 3D models to show the expected progression in the anomaly extraction process. Finally outlining the previously implemented models to generate 3D interactive objects such as, generative adversarial networks (GANs) and convolutional neural networks (CNNs), as they have exhibited promising results in extracting meaningful features and detecting anomalies in medical images. It emphasizes the importance of bridging the gap between 2D image processing and interactive 3D visualization in anomaly by incorporating advanced techniques, leveraging 3D information, and enabling interactive exploration, that enhance the precision and efficacy of anomaly detection, aiding in achieving quicker diagnosis and treatment as outcomes in healthcare. © 2024 IEEE.

关键词： Generative adversarial networks

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DAT: Dialogue-Aware Transformer with Modality-Group Fusion for Human Engagement Estimation 24

DAT: Dialogue-Aware Transformer with Modality-Group Fusion f...

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： Li, Jia Yu, Yangchen Chen, Yin Zhang, Yu Jia, Peng Xu, Yunbo Li, Ziqiang Wang, Meng Hong, Richang School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9798400706868

Engagement estimation plays a crucial role in understanding human social behaviors, attracting increasing research interests in fields such as affective computing and human-computer interaction. In this paper, we propose a Dialogue-Aware Transformer framework (DAT) with Modality-Group Fusion (MGF), which relies solely on audio-visual input and is language-independent, for estimating human engagement in conversations. Specifically, our method employs a modality-group fusion strategy that independently fuses audio and visual features within each modality for each person before inferring the entire audio-visual content. This strategy significantly enhances the model's performance and robustness. Additionally, to better estimate the target participant's engagement levels, the introduced Dialogue-Aware Transformer considers both the participant's behavior and cues from their conversational partners. Our method was rigorously tested in the Multi-Domain Engagement Estimation Challenge held by MultiMediate'24, demonstrating notable improvements in engagement-level regression precision over the baseline model. Notably, our approach achieves a CCC score of 0.76 on the NoXi Base test set and an average CCC of 0.64 across the NoXi Base, NoXi-Add, and MPIIGI test sets. The source code will be available at https://***/MSA-LMC/DAT. © 2024 ACM.

关键词： engagement estimation

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Alzheimer's Disease Prediction Using Deep Learning Techniques 3

Alzheimer's Disease Prediction Using Deep Learning Technique...

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Lavanya, V. Deepalakshmi, R. Aartheeswari, K.S. Nithika Sri, M. Velammal College of Enginnering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350381931

The brain anatomy is studied by Magnetic resonance imaging (MRI). In order to take preventive measures that are necessary, early diagnosis of patients affected by Alzheimer's disease(AD) is more essential. The most prevalent illness affecting the old people is AD. For the diagnosis of AD, numerous, intricate segmentation methods have been presented. Deep Learning (DL) techniques have attracted interest for the technique of segmenting the anatomy of the brain and categorizing AD because they can produce efficient results over a vast data collection. How the knowledge of brain anatomy can be used to identify AD using concepts from convolutional neural networks are explained. New methods, their outcomes on public datasets, and the advantages of MRI segmentation of brain for classification of AD is described. This work investigates the potential use of DL techniques to enhance and strengthen early diagnosis, predict AD phases as mild, moderate, low, or medium, and investigate strategies for strengthening overall performance analysis. © 2024 IEEE.

关键词： Magnetic resonance imaging

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