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Advances in computing, Communication and Networking (ICAC2N), International Conference on

作者： Rahul Singh Avadhesh Kumar Shraddha Sagar Vishnu Sharma School of Computing Science & Engineering Galgotias University Greater Noida India Department of Computer Science Engineering ITS Engineering College Greater Noida India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Affective computing (AC), also known as emotion AI, is a concept that applies modern technology to help systems to recognize and process human emotions and their feelings. Several uses of affective computing have been demonstrated, including marketing, healthcare, driving assistance, and education. Linear Support vector machines (SVM), k-nearest neighbor (k-NN), Hidden Markov models (HMMs), Gaussian mixture models (GMM), discriminate classifiers (LDC), decision tree algorithms and artificial neural networks (ANN), are the classifiers that are most frequently used in research on identifying and categorizing human emotions. Selecting the right classifier can significantly increase the system's overall efficiency, as numerous studies have conclusively demonstrated. Through use of cameras, microphones, skin sensors, or other devices, affective computing gathers signals from human users, gathering data on their facial expressions, voice tones, gestures, and other characteristics that can hint at their emotional states. The system deduces the user's emotional state from those data points. This study emphasizes on the fundamentals of computing emotions.

关键词： Support vector machines computers Affective computing Emotion recognition Hidden Markov models Medical services Artificial neural networks Vectors Skin Strain

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Mandatory Automated Safety Kernel (MASK): Face Mask Recognition Using Deep Learning computer Vision 2

Mandatory Automated Safety Kernel (MASK): Face Mask Recognit...

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Dinesh, G. Singh, Amarjeet Shinz, Nehar Sravanya, Mogalluru Venkata Agrawal, Gaurangi Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Kattankulathur Chennai India New Horizon College of Engineering Department of Computer Science Engineering Bangalore India

ISBN: (纸本)9781665400527

This paper involves utilizing modules to create an efficient and accurate machine learning face mask detection model that can give an accuracy close to 100 percent and a value loss of less than 5%. This paper sees up to the fact that it is the local clusters that need to take care of themselves and this is what will eventually help the society on the larger scale as a whole and aims to aid the concerning authorities in keeping their crowd wearing masks at their respective premises. This is done by programming a system that can recognize the visuals from a camera at the entrance of such premises flagging offenders who do not wear a face mask and notifying authorities as it happens. This can be implemented in private organizations like educational institutions or company offices making it possible to combine it with a facial recognition model thereby notifying the offenders themselves via their cell phones by fetching their information from database resources that would be already existing in the case of these private organizations. © 2022 IEEE.

关键词： Face recognition

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TSEML: A task-specific embedding-based method for few-shot classification of cancer molecular subtypes

TSEML: A task-specific embedding-based method for few-shot c...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Su, Ran Shi, Rui Cui, Hui Xuan, Ping Fang, Chengyan Feng, Xikang Jin, Qiangguo Tianjin University School of Computer Software College of Intelligence and Computing Tianjin China La Trobe University Department of Computer Science and Information Technology Melbourne Australia Shantou University School of Engineering Department of Computer Science Guangdong China Northwestern Polytechnical University School of Software Shaanxi China Yangtze River Delta Research Institute Northwestern Polytechnical University Taicang China

ISBN: (纸本)9798350386226

Molecular subtyping of cancer is recognized as a critical and challenging upstream task for personalized therapy. Existing deep learning methods have achieved significant performance in this domain when abundant data samples are available. However, the acquisition of densely labeled samples for cancer molecular subtypes remains a significant challenge for conventional data-intensive deep learning approaches. In this work, we focus on the few-shot molecular subtype prediction problem in heterogeneous and small cancer datasets, aiming to enhance precise diagnosis and personalized treatment. We first construct a new few-shot dataset for cancer molecular subtype classification and auxiliary cancer classification, named TCGA Few-Shot, from existing publicly available datasets. To effectively leverage the relevant knowledge from both tasks, we introduce a task-specific embedding-based meta-learning framework (TSEML). TSEML leverages the synergistic strengths of a model-agnostic meta-learning (MAML) approach and a prototypical network (ProtoNet) to capture diverse and fine-grained features. Comparative experiments conducted on the TCGA FewShot dataset demonstrate that our TSEML framework achieves superior performance in addressing the problem of few-shot molecular subtype classification. © 2024 IEEE.

关键词： Zero-shot learning

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Body Fat Prediction using Various Regression Techniques 2

Body Fat Prediction using Various Regression Techniques

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2nd IEEE International Conference on Advances in computing, Communication and Applied Informatics, ACCAI 2023

作者： Mahesh, Nikhil Pati, Peeta Basa Deepa, K. Yanan, Suresh Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electrical and Electronics Engineering Bengaluru India University of Technology and Applied Sciences-Al Musannah Sultanate of Oman Department of Engineering Oman

ISBN: (纸本)9798350315905

Predicting body fat percentage is essential for addressing the obesity problem. This paper compares the performance of several machine learning models based on Regression, to predict the body fat percentage. Using a dataset of 252 participants with information on age, weight, height, and fat percentage, the models were assessed based on multiple performance criteria, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Squared Error(MSE). The results demonstrates that Random Forest Regressor surpass other models with a lower RMSE of 0.276. These findings suggest that machine learning models can be a valuable tool for precise BFP, the use of machine learning provides a faster and more precise method for predicting body fat percentage. Overall, the study's results suggest that machine learning models can be valuable tool for accurate body fat percentage prediction. © 2023 IEEE.

关键词： Forecasting

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Estimated Posture for Human Elbow Flexion in Acoustic Environments 4

Estimated Posture for Human Elbow Flexion in Acoustic Enviro...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Selvi, P. Pandi Sangeetha, V. Sathish Kumar, L. Saravanan, D. Manikandan, J. Mangayarkarasi of Organization Department of Computer Science Tamil Nadu Paravai India Karpagam Academy of Higher Education TamilNadu Coimbatore India School of Computing Science and Engineering Vit Bhopal University MP Kotrikalan India

ISBN: (纸本)9798350300888

The present study proposes a novel approach to address the challenge of identifying smoking and phone usage behaviours among individuals amidst the intricate surroundings of construction sites. Specifically, the method involves the recognition of elbow flexor behaviours through posture estimation. The localization and detection of key points pertaining to the human upper body have been achieved through the application of AlphaPose. Subsequently, the present study introduces the HEFBD model, which is a mathematical framework that discriminates human elbow flexion behaviour. The model is constructed using human key points and is designed to identify the area that is relevant for recognising tiny objects while mitigating the impact on the complicated background. A method for image reconstruction with super-resolution is employed as a pre-processing technique for images that exhibit blurring. Furthermore, the performance of You Only Look Once (YOLO) has been enhanced through the incorporation of a tiny object identification layer and the integration of a convolutional inhibitory attention model, thereby improving its detection capabilities. The method has demonstrated an improvement in detection precision of 6.7% and a reduction in false identification rates due to complex backgrounds of 12%. These outcomes outperform existing detect approaches and meet the requirements for immediate results. © 2023 IEEE.

关键词： Image reconstruction

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Multiscale Bilateral Attention Fusion Network for Pansharpening

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5828-5843页

作者： Guo, Zhongyuan Li, Jiawei Lei, Jia Liu, Jinyuan Zhou, Shihua Wang, Bin Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of the Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1061 New Zealand University of Ulster Intelligent Systems Research Center LondonderryBT48 7JL United Kingdom Bulgarian Academy of Sciences IICT Sofia1000 Bulgaria

High-resolution multispectral (HRMS) images combine spatial and spectral information originating from panchromatic (PAN) and reduced-resolution multispectral (LRMS) images. Pansharpening performs well and is widely used to obtain HRMS images. However, most pansharpening approaches determine the ratio of PAN and LRMS images through direct interpolation, which may introduce artifacts and distort the color of the fused results. To address this issue, an unsupervised progressive pansharpening framework, MSBANet, is proposed, which adopts a multistage fusion strategy. Each stage contains an attention interactive extraction module (AIEM) and a multiscale bilateral fusion module (MBFM). The AIEM extracts spatial and spectral features from input images and captures the correlations between features. The MBFM can efficiently integrate information from the AIEM and improve MSBANet context awareness. We design a hybrid loss function that enhances the ability of the fusion network to store spectral and texture details. In qualitative and quantitative experimental studies on four datasets, MSBANet outperformed state-of-the-art pansharpening techniques. The code will be released. © 2024 IEEE.

关键词： Data mining

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An Autonomous Emotion Recognition Strategy Employing Deep Learning for Self-Learning 3

An Autonomous Emotion Recognition Strategy Employing Deep Le...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Kumar, Santosh Yadav, Satya Prakash School of Computing Graphic Era Hill University Dehradun India Gla University Department of Computer Engineering & Applications UP Mathura India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Jaipuria Institute of Management Department of Management Jaipur302033 India Glbitm Department of Computer Science and Engineering UP Greater Noida India

ISBN: (纸本)9798350342338

Because of their individuality and diversity, facial expressions are crucial to deciphering human communication and humour. Predicting a person's facial expression accurately not only helps you understand their emotional state but also gives you greater control over how you engage with them. Since personalised interactions are shown to enhance development and rapport., this ability to autonomously recognise human facial emotions has tremendous promise in fields like finance and education. In this study., we provide a new method for identifying human emotions based on Convolutional Neural Networks (CNNs). This method departs from conventional approaches by harnessing the potential of modern machine learning methods that make use of face datasets. When implemented in the TensorFlow framework., this method might significantly alter the way grammatical constructions are understood. Convolutional Neural Networks are at the core of our technique since they are a complicated design that can easily deal with complex picture data. We overcome the limits of previous methods by using CNNs to extract complex characteristics from face photos, which we access by abandoning traditional paradigms. To ensure the efficacy of our approach., we ran trials using the Facial Expression Recognition Challenge (FERC-2013) Dataset., a collection of 32 thousand different human faces. Surprisingly., our method achieved classification rates over 65%., demonstrating its efficiency in deciphering human emotions across a wide range of photos. Our approach is built on the incorporation of the AlexNet Architecture., a powerful algorithm that acts as the process's conductor. One interesting feature is that it can be trained in batches., which speeds up the learning process and requires less resources. This effectiveness not only aids in faster learning., but also makes the method more widely applicable. Our work essentially advances facial emotion identification into the cutting edge of machine learni

关键词： Affective computing Deep learning Emotion recognition Facial expression analysis Machine learning Neural networks Self-learning

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Enhancing Human-computer Interaction with Attention-Based CNNs in Speech Emotion Recognition

Enhancing Human-Computer Interaction with Attention-Based CN...

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2024 International Conference on Communication, computing and Energy Efficient Technologies, I3CEET 2024

作者： Singh, Rahul Sagar, Shraddha Kumar, Avadhesh Sharma, Vishnu Galgotias University School of Computing Science & Engineering Greater Noida India Its Engineering College Department of Computer Science Engineering Greater Noida India

ISBN: (纸本)9798331541583

Emotions are important in human interaction and greatly affect user satisfaction and customer opinion. Speech emotion recognition (SER) algorithms have played an important role in improving human-computer interaction (HCI) applications, and many SER algorithms have been developed in the past decades Attention-based complex neural networks (CNNs) from multimedia content temporarily It has proven effective in removing disinformation, In view of recent advances in incorporating focusing techniques into CNN systems to increase attention to emotionally important information. This paper reviews recent developments in SER, examines the impact of different conceptual approaches on SER performance, and provides a detailed comparison of system accuracy using the widely accepted IEMOCAP benchmark database. In addition, this paper explores the basics and applications of cognitive computing - an emerging interdisciplinary field that enables computers to recognize, process, and simulate human emotions - that arise emphasizing importance in various industries. © 2024 IEEE.

关键词： Human computer interaction

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Humanoid Robots that Move on Two Feet and Are Led by Vision Systems 3

Humanoid Robots that Move on Two Feet and Are Led by Vision ...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

ISBN: (纸本)9798350342338

At the convergence of robotics and computer vision lies a crucial research field that focuses on the development of vision-guided walking for humanoid robots. The ability of humanoid robots to walk independently while simultaneously seeing and comprehending their surroundings via the use of visual inputs offers tremendous potential. Humanoid robots are developed to reproduce human-like qualities and skills. This abstract discusses current developments in the area of vision-guided walking for humanoid robots as well as the approaches that have been used. Through the use of computer vision methods in conjunction with walking algorithms, humanoid robots are given the capacity to traverse situations that are both complex and dynamic while exhibiting increased flexibility. For this, real-time sensing of the environment, object identification and avoidance of obstacles, as well as analysis of the terrain, are required in order to arrive at intelligent judgements while moving. Vision-guided walking not only makes robotic locomotion more secure and effective, but it also paves the way for a wide range of potential applications, including exploration, human-robot interaction, search and rescue, and many more. Visual sensing systems, sensor fusion procedures, gait generation methods, motion planning, and control strategies. These topics are discussed in relation to vision-guided walking. Different types of sensors, such as cameras, depth sensors, and LiDAR, are being investigated to see how they can best provide a holistic understanding of the surrounding environment. In addition, the abstract draws attention to the difficulties caused by sensor noise, occlusions, fluctuations in illumination, and the need for real-time processing. In places an emphasis on the role that methods of machine learning play in the improvement of vision-guided walking. The use of deep learning algorithms gives robots the ability to learn from data, hence enhancing their capacity to understand and r

关键词： Head movement in robot Humanoid robots Planning and control Proprioceptive sensors Sensors Vision guided

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POINE2: Improving Poincaré Embeddings for Hierarchy-Aware Complex Query Reasoning over Knowledge Graphs 26

POINE2: Improving Poincaré Embeddings for Hierarchy-Aware C...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Liu, Junnan Mao, Qianren Li, Jianxin Fu, Xingcheng Wang, Zheng School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China Zhongguancun Laboratory Beijing China The School of Computing University of Leeds United Kingdom

ISBN: (纸本)9781643684369

Reasoning complex logical queries on incomplete and massive knowledge graphs (KGs) remains a significant challenge. The prevailing method for this problem is query embedding, which embeds KG units (i.e., entities and relations) and complex queries into low-dimensional space. Recent developments in the field show that embedding queries as geometric shapes is a viable means for modeling entity set and logical relationships between them. Despite being promising, current geometric-based methods face challenges in capturing hierarchical structures of complex queries, which leaves considerable room for improvement. This paper presents POINE2, a geometric-based query embedding framework based on hyperbolic geometry to handle complex queries on knowledge graphs. POINE2 maps entities and queries as geometric shapes on a Cartesian product space of Poincaré ball spaces. To capture the hierarchical structures of complex queries, we use the Poincaré radius to represent the different levels of the hierarchy, and we use the aperture of the shape to indicate semantic differences at the same level of the hierarchy. Additionally, POINE2 offers a flexible and expressive definition of logical operations. Experimental results show that POINE2 outperforms existing salient geometric-based embedding methods and significantly improves these methods on evaluation datasets. © 2023 The Authors.

关键词： Knowledge graph

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