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Emotion-driven Activity Detection: Detecting Activities Perception through Facial Feature Analysis in Educational Environments 18

Emotion-driven Activity Detection: Detecting Activities Perc...

引用

18th INDIAcom;11th international conference on computing for Sustainable Global Development, INDIACom 2024

作者： Balasubramanian, K. Mariadass, Emmanuel Peo Akalya, L. Cinthuja, K. Murali, N. Rajangam, Balamurugan E. G. S. Pillay Engineering College Department of Computer Science and Engineering Nagapattinam India Panimalar Engineering College Department of Computer Science and Engineering Chennai India K. Ramakrishnan College of Engineering Department of Electronics and Communication Engineering Trichy India

ISBN: (纸本)9789380544519

Understanding and categorizing student activities within educational settings is a critical component of enhancing learning outcomes and well-being. This study introduces an innovative approach to student activity classification by leveraging facial emotion recognition technology. The system is designed to capture and analyze students' facial expressions, interpret their emotional states, and subsequently categorize their activities. By deploying a dataset encompassing a wide range of academic and non - academic activities, this research explores the purpose of deep learning models for recognition of facial emotion. The system is capable of identifying emotions such as sadness, surprise, happiness and anger. Extracted emotion features are then utilized to classify student activities, shedding light on whether a student is engaged, attentive, confused, or disinterested, among other states. This approach holds the potential to enhance educational environments by providing real-time insights into student behaviour and facilitating timely interventions to improve learning experiences and outcomes. Additionally, it opens up avenues for personalized educational support and the development of intelligent learning systems. In this paper we could implement the system to extract the facial features using Grassmann algorithm. And identify the emotions of student at predefined time intervals. Based on emotion classification, predict the active status and send reports to admin. Furthermore, this technology holds promise for the development of intelligent learning systems that adapt to students' emotional states, providing additional support or challenges when needed. For instance, a virtual tutor could adjust the difficulty of exercises based on a student's emotional responses, creating a dynamic and responsive learning experience. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Deep learning Face recognition system Features based matching Slot allocation Smart parking system

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Predicting the Severity of Chronic Kidney Disease with J48 4

Predicting the Severity of Chronic Kidney Disease with J48

引用

4th IEEE international conference on computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Jaya Lakshmi, A. Joe Prathap, P.M. Lokesh, K. Dev Anand, D. Jagathieshwaran, J. Sneha, M. Gojan School of Business and Technology Department of Computer Science and Engineering Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350306118

Chronic kidney disease (CKD) is an intensifying and immutable condition in which the kidneys slowly reduces is actual function. CKD can be effected and induced because of various aspects like high cholesterol, blood pressure, genetic disorders, kidney inflammations and infections. This health issue is affecting millions of people across the globe. The primary objective considered is to envision disease at an early stage by implementing different algorithms on the medical records of the patients. Since early stage of diagnosis may be helpful in reducing the severity of the disease. This study has a significant concern in deciding the suitable Machine learning (ML) algorithm that suits best for analyzing CKD based on the accuracy rate. The demonstration work is performed with the commonly utilized machine learning classifiers. As a result of the demonstration work, it is revealed that J48 algorithm has higher rate of accuracy than other conventional machine learning techniques. © 2023 IEEE.

关键词： Diagnosis

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Cutting-Edge Image Recognition Leveraging Deep learning and Machine learning for Enhanced Accuracy

Cutting-Edge Image Recognition Leveraging Deep Learning and ...

引用

2024 international conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Shrivastava, Abhishek Kumar, Vinesh Maurya, Jay Prakash School of Mechanical Engineering VIT Bhopal University Sehore India School of Computing Science Engineering and Artificial Intelligence VIT Bhopal University Sehore India School of Computing Science & Engineering VIT Bhopal University Sehore India

ISBN: (纸本)9798331517953

This paper investigates advanced techniques in image recognition and classification by integrating deep learning and machine learning approaches to achieve higher accuracy. Through the implementation of sophisticated training algorithms, the study demonstrates enhanced performance in recognizing and categorizing images across various data models. A major turning point in the development of image identification technology came in 2012 when deep neural networks were introduced. These networks surpassed earlier cutting-edge algorithms and completely changed the computer vision industry. This progress has brought us closer to achieving human-level accuracy in tasks such as identity verification. The role of large datasets like ImageNet is crucial, as they provide the foundation for the success of deep learning. With continuous research pushing the limits of picture identification and producing major advances in human knowledge, deep learning has a huge influence on business, society, and technology. Additional research in this area might lead to creative uses that revolutionize our relationship with our surroundings. Key topics discussed include data pre-processing, post-processing, model optimization, and accuracy enhancement. The findings highlight the potential of cutting-edge technologies to advance image classification and recognition in various sectors, such as medical imaging and visual analysis. The approach emphasizes scalability and adaptability, ensuring that models can be effectively applied to real-world scenarios. Future research will focus on refining these models to handle even more complex image datasets, further enhancing their practical utility and reliability. © 2024 IEEE.

关键词： Convolutional neural networks

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Parameter Inversion and Deformation Prediction of Cavern Groups via Machine learning 24

Parameter Inversion and Deformation Prediction of Cavern Gro...

引用

2024 international Academic conference on Edge computing, Parallel and Distributed computing, ECPDC 2024

作者： Chen, Zhanghua Wang, Wenjun Zhu, Qizhi Zhou, Shuguang Su, Chao Chongqing Panlong Pumped Storage Power Plant Co. Ltd. Chongqing400000 China College of Water Conservancy and Hydropower Engineering Hohai University Nanjing210024 China

ISBN: (纸本)9798400718168

This study, based on an example of an underground pumped storage power station, analyzed and processed displacement monitoring data around the main plant room using the existing displacement monitoring data from the excavation of the three-layer caverns. With the assistance of the ABAQUS finite element software, a three-dimensional dynamic full-cycle construction simulation model was established. An extended elastic-viscoplastic constitutive model was chosen, and a machine learning approach was employed to inversely estimate the relevant parameters of the surrounding rock in the plant area. The inverse results were then compared with the actual measurement data, showing good agreement in terms of trends and numerical values, with errors within 10%. This approach can serve as a reference for similar efforts in the field of cavern rock inversion. © 2024 ACM.

关键词： Caves

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Sentiment Analysis with RNNs: Insights from LIME Interpretability 1

Sentiment Analysis with RNNs: Insights from LIME Interpretab...

引用

1st international conference on Advanced computing and Emerging Technologies, ACET 2024

作者： Srivastava, Devesh Bala, Saroj Saxena, Arpna Ajay Kumar Garg Engineering College Department of Mca Ghaziabad India

ISBN: (纸本)9798350367720

This research article explores different facets of sentiment analysis like data pre-processing, extracting useful features, model designs & evaluation criteria. We explore recurrent neural network (RNN), having two layers of Bidirectional LSTM, to classify user language into positive, and negative classes. Our experiment involves training these models on large-scale sentiments-based datasets and evaluating their performance by extending more features and techniques of deep learning. We compare and visualize accuracy with respect to accuracy score, confusion matrix, precision-recall curve as well as ROC curve. The results demonstrate that the BiLSTM model achieves competitive sentiment classification accuracy. The classification trust concern is addressed using LIME, an interpretable machine learning model. The major contribution of this research is towards the field of sentiment analysis by evaluating the model and emphasizing importance of explainability. © 2024 IEEE.

关键词： Long short-term memory

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Generative Machine learning for Energy Predictions of Existing Buildings 30

Generative Machine Learning for Energy Predictions of Existi...

引用

30th international conference on Intelligent computing in engineering 2023, EG-ICE 2023

作者： Singh, Manav Mahan Santer, Klara Smith, Ian F.C. Georg Nemetschek Institute Artificial Intelligence for the Built World Technical University Munich Germany

Buildings consume a substantial amount of energy, with a significant portion dedicated to maintaining thermal comfort. However, predicting energy requirements is challenging due to the influence of numerous uncertain and difficult-to-measure building characteristics. This uncertainty hampers the analysis of potential energy improvement opportunities. We propose a novel approach based on generative machine learning (ML) to estimate values of energy related characteristics such as heat transfer coefficients, permeability, and occupants. Training the generative ML network requires geometrical information, ranges for uncertain values of building characteristics, and energy consumption data. The ML network generates sets of possible values for the characteristics. We tested our approach on an office building to predict its energy characteristics. Our approach utilizing generative ML provides a promising solution for estimating relevant building characteristics. By reducing uncertainty in energy predictions, this approach can inform decision-making processes and facilitate the development of more effective strategies to achieve energy efficiency. © 2023 30th EG-ICE: international conference on Intelligent computing in engineering 2023. All rights reserved.

关键词： Adversarial machine learning

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Real Time Sign Language Recognition using Knowledge Assisted Method 15

Real Time Sign Language Recognition using Knowledge Assisted...

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15th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Srivastava, Vivek Pratap, Jay Sinha, Shobhit Kumar, Sunil ABES Engineering College Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798331300579

As we are aware that verbal communication can be hampered by speech impairment, and sign language is one of the best systems for resolving this problem. The goal of our paper is to create a system or application that can recognize sign language motions in order to reduce the gap between persons with disabilities and others, such as deaf and mute people. The suggested system uses machine learning and image processing to create a real-time system. Preprocessing photos and removing hand motions from the backdrop are both done via image processing. The 24 English alphabets are then included in a dataset created using these photos. Both a specially created dataset and live hand gestures produced by people of different ages are used to evaluate the Faster(R-CNN) that we proposed in our research. The accuracy comes near to 95%. © Grenze Scientific Society, 2024.

关键词： Deep learning

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Emotion Speech Recognition in the Cloud 2

Emotion Speech Recognition in the Cloud

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2nd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Ganesh, Devalla Bhaskar Prasanth, Balisetty Chowdary, Manne Teena Avala, Varsha Sree Gangashetty, Suryakanth V Koneru Lakshmaiah Education Foundation Computer Science and Engineering Vaddeswaram India

ISBN: (纸本)9798350327533

Emotion speech recognition in the cloud has emerged as a transformative technology with the potential to revolutionize various sectors, induding customer service, mental health, and human-computer interaction. This abstract provides an overview of the key concepts and approaches involved in the development and deployment of emotion speech recognition systems in doud-based environments. Emotion speech recognition leverages advances in machine learning, natural language processing, and cloud computing to detect and analyze emotional cues in spoken language. By identifying emotional states, such as happiness, sadness, anger, and more, these systems offer valuable insights for businesses and healthcare providers, enabling them to improve customer experiences and mental health services. © 2024 IEEE.

关键词： Cloud computing Deep learning models Emotion recognition Natural language processing Speech analysis

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Path planning for mobile robots based on improving Dijkstra algorithm

Path planning for mobile robots based on improving Dijkstra ...

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2023 international conference on Cloud computing, Performance computing, and Deep learning, CCPCDL 2023

作者： Ma, Qiaoling Yang, Ruiqi Lian, Tianjiao Wang, Gan Mu, Son College of Computer Engineering Guangzhou City University of Technology Guang Dong Guang Zhou510800 China College of Mechanical Engineering Guangzhou City University of Technology Guang Dong Guang Zhou510800 China College of Electrical Engineering Guangzhou City University of Technology Guang Dong Guang Zhou510800 China

ISBN: (纸本)9781510666474

In response to the traditional Dijkstra algorithm, the long search time of pheromone and the occasional redundant inflection points When the final path is obtained can lead to the inefficiency of the algorithm due to its many traversed nodes. Therefore, this paper proposes the use of pheromone calculation method to improve Dijkstra's algorithm. The experimental results show that the optimized algorithm can largely reduce the inflection points generated during path planning and reduce the movement cost of the mobile robot path finding. © 2023 SPIE.

关键词： Motion planning

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Farming Futures: Machine learning Models for Crop Yield Prediction 3

Farming Futures: Machine Learning Models for Crop Yield Pred...

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3rd IEEE international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Kushalatha, M.R. Sangamesh Shashank, B.C. Kerudi, Siddarth M. Mamatha, A.S. Nitte Meenakshi Institute Of Technology Department Of Electronics And Communication Engineering Bengaluru India Nmam Institute Of Technology Department Of Electronics And Communication Engineering Bengaluru Nitte India

ISBN: (纸本)9798350318609

India's economy depends heavily on agriculture, and choosing the right crops is essential to reducing losses. Crop yield is influenced by factors such as soil conditions and weather patterns. A rational assessment of many aspects is included in data analysis, which is essential for researching agricultural output. Pre-season crop mapping, which is crucial for early warningson agricultural supply networks and output, is one way that machine learning algorithms support agricultural surveillance and the food industry. gathered and examined datasets that combined meteorological and soil data. Pre-season crop-type maps were analyzed and recommended using machine learning techniques, such as XG Boost, Decision Tree, Linear Regression, Bagging Regressor, and Random Forest, based on historical data. Crop forecast was based on the same parameters that were taken into account during computation. Among the evaluated machine learning algorithms, Random Forest achieved the highest accuracy of 0.986572, closely followed by Bagging Regressor with an accuracy of 0.986569. Machine learning-based pre-season crop mapping reduces trade tensions and dangers by providing early warnings on agricultural supply networks. This approach improves agricultural surveillance, assisting farmers and other stakeholders in the food industry in making well-informed decisions. © 2024 IEEE.

关键词： Decision trees

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