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4th Annual International Conference on Multimedia Retrieval (ICMR)

作者： Kastner, Marc A. Cheema, Gullal S. Hakimov, Sherzod Garcia, Noa Kyoto Univ Kyoto Japan Leibniz Univ Hannover L3S Res Ctr Hannover Germany Univ Potsdam Potsdam Germany Osaka Univ Osaka Japan

ISBN: (纸本)9798400706028

Multimodal human understanding and analysis are emerging research areas that cut through several disciplines like Computer vision (CV), Natural Language processing (NLP), Speech processing, Human-Computer Interaction (HCI), and Multimedia. Several multimodal learning techniques have recently shown the benefit of combining multiple modalities in image-text, audio-visual and video representation learning and various downstream multimodal tasks. At the core, these methods focus on modelling the modalities and their complex interactions by using large amounts of data, different loss functions and deep neural network architectures. However, for many Web and Social media applications, there is the need to model the human, including the understanding of human behaviour and perception. For this, it becomes important to consider interdisciplinary approaches, including social sciences and psychology. The core is understanding various cross-modal relations, quantifying bias such as social biases, and the applicability of models to real-world problems. Interdisciplinary theories such as semiotics or gestalt psychology can provide additional insights on perceptual understanding through signs and symbols across multiple modalities. In general, these theories provide a compelling view of multimodality and perception that can further expand computational research and multimedia applications on the Web and Social media. The theme of the MUWS workshop, multimodal human understanding, includes various interdisciplinary challenges related to social bias analyses, multimodal representation learning, detection of human impressions or sentiment, hate speech, sarcasm in multimodal data, multimodal rhetoric and semantics, and related topics. The MUWS workshop is an interactive event and includes keynotes by relevant experts, a poster session, research presentations and discussion.

关键词： multimodality machine learning image-text relations social media web human understanding semiotics

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The partitioning ensemble control chart for on-line monitoring of high-dimensional image-based quality characteristics

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ENGINEERING applications OF ARTIFICIAL INTELLIGENCE 2024年第PartB期127卷

作者： Yeganeh, Ali Johannssen, Arne Chukhrova, Nataliya Univ Hamburg Fac Business Adm D-20146 Hamburg Germany

The widespread implementation of computer technology has led to an increased use of machine vision Systems (MVS) for quality control in advanced manufacturing industries. The application of Statistical Process Control (SPC) techniques, especially control charts, is one of the most important and effective ways for fault detection in this field. Several statistical control charts have been extended to monitor images in the framework of MVS where the major aim is to improve the on-line detection ability, which is equivalent to avoid production of nonconforming products. In various industrial and non-industrial applications, control charts have been shown to be better able to detect anomalies in the underlying process(es) by integrating machine and ensemble learning techniques. However, in the field of image monitoring, approaches based on intelligent techniques have rarely been proposed. To bridge this gap and to improve the detection ability of control charts in monitoring imagebased quality characteristics, this paper presents a novel control chart that combines machine learning, ensemble learning and image partitioning. In order to reach the maximum performance in Phase ii SPC applications, specific designing and parameter tuning procedures are provided. The superiority of the proposed Partitioning Ensemble Control Chart (PECC) is verified by comparative analysis including conventional monitoring schemes by means of extensive simulations, in which different fault sizes and fault locations are applied to the images. Finally, a real MVS for quality control of O-rings is discussed to illustrate the practical application of the PECC.

关键词： Control charts Ensemble learning image quality control machine learning machine vision system Statistical process control

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image processing Technique for Tracking and Counting of Vehicles Using ROI

Image Processing Technique for Tracking and Counting of Vehi...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Kumar, Ankit Chandel, Ritika Singh, Monika Apex Institute of Technology Chandigarh University Mohali India

ISBN: (纸本)9798350357769

This research paper presents a novel approach for vehicle tracking and counting utilizing the advanced object detection model YOLOv8 in the field of image processing. The accurate monitoring of vehicular traffic is crucial for various applications, including traffic management, urban planning, and safety analysis. Existing methods often struggle with real-time tracking and counting of vehicles due to challenges such as occlusions, varying lighting conditions, and complex traffic scenarios. In our proposed methodology, we leverage the power of YOLOv8, a deep learning-based object detection model, uses real-time detection to identify and monitor vehicles, ensuring high accuracy and robustness in various traffic environments. The proposed approach provides a valuable tool for traffic management authorities and urban planners, offering precise data for traffic flow analysis and infrastructure development. © 2023 IEEE.

关键词： Deep Learning image processing machine vision Object Detection ROI (Region of Interest) Vehicle Detection YOLO (You Only Look Once)

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Design of inception with deep convolutional neural network based fall detection and classification model

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MULTIMEDIA TOOLS AND applications 2023年第8期83卷 23799-23817页

作者： Bhavani, K. Durga Ukrit, M. Ferni SRM Inst Sci & Technol Dept Computat Intelligence Chennai 603203 India

Falling is the most serious health problem for elderly population resulting in serious injuries, if not treated quickly. As the world population gets increased, the number of serious falls and succeeding financial burdens rise accordingly. It is important to detect falls timely to initiate appropriate medical responses to decrease considerable physical, social, and financial losses. Earlier detection of fall events can helps to provide timely medical services and reduce severe injuries. Different techniques have been developed for fall detection process among elderly population. Recently, the development of Internet of Things (IoT) and artificial intelligence (AI) technologies involving deep learning (DL) and machine learning (ML) approaches can be employed in the field of healthcare for automating the diagnosis procedure of diseased and abnormal cases. This study designs a new Inception with deep convolutional neural network-based fall detection and classification (INDCNN-FDC) model. The presented INDCNN-FDC model intends to categorize the events into two class labels namely fall and not fall. To accomplish this, the INDCNN-FDC model carries out two stages of data pre-processing: Gaussian filter (GF) based image sharpening and Guided Filter (GIF) based image smoothing. In addition, the presented INDCNN-FDC model applies deep transfer learning-based Inception v3 model for generating a helpful group of feature vectors. Finally, DCNN approach receives the feature vectors as input and performs fall detection process. The experimental validation of the INDCNN-FDC approach is performed on benchmark dataset. The comparative study reported the supremacy of the INDCNN-FDC model over state-of-the-art.

关键词： Fall detection Computer vision Transfer learning Convolution neural network Inception v3

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A systematic review and Meta-data analysis on the applications of Deep Learning in Electrocardiogram

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 9677-9750页

作者： Musa, Nehemiah Gital, Abdulsalam Ya’u Aljojo, Nahla Chiroma, Haruna Adewole, Kayode S. Mojeed, Hammed A. Faruk, Nasir Abdulkarim, Abubakar Emmanuel, Ifada Folawiyo, Yusuf Y. Ogunmodede, James A. Oloyede, Abdukareem A. Olawoyin, Lukman A. Sikiru, Ismaeel A. Katb, Ibrahim Department of Mathematical Sciences Abubakar Tafawa Balewa University Bauchi Nigeria University of Jeddah Jeddah Saudi Arabia Computer Science and Engineering University of Hafr Al-Batin Hafr Saudi Arabia Computer Science and Engineering University of Hafr Al-Batin Hafr Al-Batin Saudi Arabia Department of Computer Science University of Ilorin Ilorin Nigeria Department of Physics Sule Lamido University Kafin Hausa Nigeria Department of Electrical Engineering Ahmadu Bello University Zaria Zaria Nigeria Department of Medicine University of Ilorin Ilorin Nigeria

The success of deep learning over the traditional machine learning techniques in handling artificial intelligence application tasks such as image processing, computer vision, object detection, speech recognition, medical imaging and so on, has made deep learning the buzz word that dominates Artificial Intelligence applications. From the last decade, the applications of deep learning in physiological signals such as electrocardiogram (ECG) have attracted a good number of research. However, previous surveys have not been able to provide a systematic comprehensive review including biometric ECG based systems of the applications of deep learning in ECG with respect to domain of applications. To address this gap, we conducted a systematic literature review on the applications of deep learning in ECG including biometric ECG based systems. The study analyzed systematically, 150 primary studies with evidence of the application of deep learning in ECG. The study shows that the applications of deep learning in ECG have been applied in different domains. We presented a new taxonomy of the domains of application of the deep learning in ECG. The paper also presented discussions on biometric ECG based systems and meta-data analysis of the studies based on the domain, area, task, deep learning models, dataset sources and preprocessing methods. Challenges and potential research opportunities were highlighted to enable novel research. We believe that this study will be useful to both new researchers and expert researchers who are seeking to add knowledge to the already existing body of knowledge in ECG signal processing using deep learning algorithm. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Electrocardiograms

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SAR image Fusion Classification Based on Improved D-S Evidence Theory 8

SAR Image Fusion Classification Based on Improved D-S Eviden...

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2024 8th International Conference on machine vision and Information Technology, CMVIT 2024

作者： Liang, Jialu Fang, Hui Zhang, Xinggan School of Electronic Science and Engineering Nanjing University Nanjing China

applications for classifying Synthetic Aperture Radar (SAR) images are critical to environmental monitoring, urban planning, and land resource surveying. Fusion approaches work well for increasing SAR image categorization accuracy. However, effective processing of uncertainty information is frequently overlooked by conventional fusion classification systems. This paper presented a fusion classification approach for SAR images based on improved Dempster-Shafer (D-S) evidence theory, taking into account the uncertainty and possible conflicts in the classifiers' output. First, after adaptively identifying the optimal hyperparameters of models, the SAR data is identified using Gradient Boosting machine (GBM), Multilayer Perceptron (MLP), and Random Forest (RF). Different weights are then allocated in accordance with the variations in classifier performance. Ultimately, enhanced D-S evidence theory is used to integrate the output of these classifiers. In addition, uncertainty in classification results is also modeled and visualized. Experiments with Flevoland and Oberpfaffenhofen datasets show that the accuracy of this method is 85.21% and 91.88% respectively, outperforming both the soft voting ensemble classifier (SVE) and D-S evidence theory fusion method. This approach enhances the comprehension and capacity to manage uncertainty in the model output in addition to increasing the accuracy and reliability of the classification of SAR data. © Published under licence by IOP Publishing Ltd.

关键词： Synthetic aperture radar

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Automated waste-sorting and recycling classification using artificial neural network and features fusion: a digital-enabled circular economy vision for smart cities

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MULTIMEDIA TOOLS AND applications 2023年第25期82卷 39617-39632页

作者： Mohammed, Mazin Abed Abdulhasan, Mahmood Jamal Kumar, Nallapaneni Manoj Abdulkareem, Karrar Hameed Mostafa, Salama A. Maashi, Mashael S. Khalid, Layth Salman Abdulaali, Hayder Saadoon Chopra, Shauhrat S. Univ Anbar Coll Comp Sci & Informat Technol 11 Ramadi Anbar Iraq Al Ayen Univ Sci Res Ctr Environm Res Grp Thi Qar Iraq City Univ Hong Kong Sch Energy & Environm Kowloon Hong Kong Peoples R China HICCER Hariterde Int Council Circular Econ Res Ctr Digital Circular Econ Palakkad 678631 Kerala India Al Muthanna Univ Coll Agr Samawah 66001 Iraq Univ Warith Al Anbiyaa Coll Engn Karbala Iraq Univ Tun Hussein Onn Malaysia Fac Comp Sci & Informat Technol Batu Pahat 86400 Johor Malaysia King Saud Univ Coll Comp & Informat Sci Software Engn Dept Riyadh 11451 Saudi Arabia Univ Tun Hussein Onn Malaysia Fac Civil & Environm Engn Batu Pahat 86400 Johor Malaysia Univ Kebangsaan Malaysia Fac Engn & Built Environm Dept Architecture Bangi Selangor Malaysia

Waste generation in smart cities is a critical issue, and the interim steps towards its management were not that effective. But at present, the challenge of meeting recycling requirements due to the practical difficulty involved in waste sorting decelerates smart city CE vision. In this paper, a digital model that automatically sorts the generated waste and classifies the type of waste as per the recycling requirements based on an artificial neural network (ANN) and features fusion techniques is proposed. In the proposed model, various features extracted using image processing are combined to develop a sophisticated classifier. Based on the different features, different models are built, and each model produces a single decision. Besides, the kind of class is determined using machine learning. The model is validated by extracting relevant information from the dataset containing 2400 images of possible waste types recycled across three categories. Based on the analysis, it is observed that the proposed model achieved an accuracy of 91.7%, proving its ability to sort and classify the waste as per the recycling requirements automatically. Overall, this analysis suggests that a digital-enabled CE vision could improve the waste sorting services and recycling decisions across the value chain in smart cities.

关键词： Waste management in smart cities Waste images Automated sorting approach Trash recycling classification AI for waste management Circular economy in smart cities

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Survey of Deep Learning Models for image-Based Disease Detection in Plants 7th

Survey of Deep Learning Models for Image-Based Disease Detec...

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7th International Conference on Soft Computing: Theories and applications, SoCTA 2023

作者： Mathur, Abhishek Shrivastava, Shailendra Ku. Department of Information Technology Samrat Ashok Technological Institute Vidisha India Samrat Ashok Technological Institute Vidisha India

ISBN: (纸本)9789819720880

Crop diseases pose a severe risk to global food supplies, but accurate and timely diagnosis is hampered by a lack of resources in many regions. As the number of people with access to smartphones continues to rise throughout the world, and as recent advancements in computer vision allowed by deep learning pave the way, we may soon be able to use them to aid in the detection of diseases. Acquiring pictures, preprocessing, segmenting, and extracting features are all part of the image processing pipeline for disease detection in plants. In this review study, we take a look back at some of the most popular methods for classifying plant diseases: "Convolutional Neural Network (CNN)", "Support Vector machine (SVM)", "K-Nearest Neighbors", and "Artificial Neural Network (ANN)". The results of the study show that the convolutional neural network method outperforms the more conventional techniques in terms of accuracy. This review study discusses the use of DL models to depict different plant diseases and gives a detailed description of their operation. Furthermore, several research gaps are noted from which better clarity may be obtained in regard with early detection of plant diseases. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Artificial neural network Convolution neural network Deep learning image processing Plant disease detection Recurrent neural network

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A machine Learning based Facial Expression and Emotion Recognition for Human Computer Interaction through Fuzzy Logic System 6

A Machine Learning based Facial Expression and Emotion Recog...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Vinutha, K. Niranjan, Manoj Kumar Makhijani, Jagdish Natarajan, B. Nirmala, V. Vijaya Lakshmi, T.R. Department of Information Science and Engineering BMS Institute of Technology and Management Karnataka Bengaluru India Department of Computer Applications Rustamji Institute of Technology Madhya Pradesh Gwalior India Department of Computer Science and Engineering Rustamji Institute of Technology Madhya Pradesh Gwalior India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Tamil Nadu Chennai India Department of Science and Humanities Faculty of Engineering Karpagam Academy of Higher Education Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Mahatma Gandhi Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9798350398496

Facial recognition is in use for the past decade there are many applications that needs facial expression to learn the human behaviour and emotions for certain activities. Facial recognition is in a development phase where many service providers use this feature to find the expression of the people on using their BlogSpot or website or reading any news article. This recognition of facial expression is highly possible with the help of machine learning technology. This research study has developed a facial expression recognizing algorithm using Python programming language with the help of Keras software package. This algorithm is purely based on machine learning approach that enables the programmer to process the facial image and convert it into data that is helpful in prediction of facial expression using the fuzzy logic technique. The fuzzy logic technique is a prediction method that helps programmer to predict the intermediate data by providing the initial and ending conditions. For enabling the facial recognition to process any system or a mobile device the algorithm needs permission to access the camera, once the onto the access is permitted the algorithm retrieves the image from the vision sensor and with the help of image processing technology of the machine learning algorithm the program the program converts the data from the vision sensor into required facial expression and emotional content. © 2023 IEEE.

关键词： Emotion Recognition

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Deepfake image and Video Detection Using Deep Learning Algorithms

Deepfake Image and Video Detection Using Deep Learning Algor...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Srinivasan, Srivanth Nischitha, P. Chavan, Akshita Mohana Rv College of Engineering® Bengaluru India Rv College of Engineering® Bengaluru India

ISBN: (纸本)9798331509828

Deep learning (DL) algorithms are swiftly finding applications in computer vision and natural language processing. Nonetheless, they can also be employed for creating convincing deepfakes, which are challenging to distinguish from reality. The advancements in image and video technology and tools, especially on social media platforms, potentially lead to misuse for malicious purposes like blackmail or defamation. To tackle this issue, several group of researchers tried upon spreading or creating awareness on real or fake data. The proposed approach involves combining Deepfake generation using GANs and Autoencoders with a Deepfake detection method. The aim of this initiative is exclusively to combat disinformation and online fraud for the welfare of the general population. Deepfakes, products of AI, have become increasingly realistic, rendering it nearly difficult to distinguish the content. Auto-encoders with sufficient time can achieve about 92 % accuracy. As the generator improves, the discriminator performance worsens as it struggles to differentiate real or fake data. A perfect generator results in 50% accuracy. With advancements in computational capacity and data availability, the proposed DDM (Deepfake Detection Model) has achieved greater accuracy rate of up to 92.3%. © 2025 IEEE.

关键词： Adversarial machine learning

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