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Exploration of the generalization and hallucination in deep learning: a study based on imaging through scattering medium 17

Exploration of the generalization and hallucination in deep ...

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17th International Conference on Photonics and Imaging in Biology and Medicine, PIBM 2024

作者： Liu, Honglin Zhang, Xuyu Xie, Gengchen Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai201800 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Science Beijing100049 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (数字)9781510688070

ISBN: (纸本)9781510688063

With fast developments of computational power and algorithms, deep learning has made breakthroughs and been applied in many fields. However, generalization remains to be a major challenge, and the limited generalization capability severely constrains applications of deep learning in practice. The hallucinations issue is another unresolved conundrum faced by deep learning and large models. By leveraging a physical model of imaging through scattering media, we studied the lack of generalization to datasets and system response functions in deep learning respectively, identified their causes, and proposed universal solutions. The research also provides an explanation for hallucinations. In general, it enhances the interpretability of deep learning from a physics-based perspective. It will pave a way for direct interaction between deep learning and the physical world, facilitating the transition of deep learning from a demo model to a practical tool. © 2025 SPIE.

关键词： Education and training Deep learning Image restoration Imaging systems Associative arrays Image quality Scattering Scattering media Artificial intelligence Light sources and illumination

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Emotion Recognition technology: Decoding Facial Expressions 2

Emotion Recognition Technology: Decoding Facial Expressions

引用

2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Rao, Manjula Gururaj Priyanka, H. Ahamed Shafeeq, B.M. Bhat, Sushma K Misra, Sohini NMAM Institute of Technology Nitte Karkala India PESU Computer Science & Engineering Bangaluru India Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9798350366570

Human emotions are pivotal in communication, and their recognition holds significance across various domains like human-computer interaction and mental health monitoring. This paper presents an image-based approach to emotion recognition, particularly focusing on discerning genuine from insincere emotions. The system uses a combination of machine learning (ML) and deep learning (DL) approaches to improve the accuracy and robustness of emotion recognition by utilizing data from several modalities. Utilizing algorithms such as CNN for facial expression recognition enables the system to extract quantifiable features, thereby improving its capability to distinguish between genuine and fake emotions. Evaluation involves testing with diverse datasets and performance metrics, particularly emphasizing the system's efficacy in detecting fake emotions. The proposed system is able to identify overall 80% of the accuracy over the Decision Tree, SVM and CNN models. © 2024 IEEE.

关键词： Human computer interaction

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PNRITR:Research on Positive and Negative Entity Region Perception Methods for Image-Text Retrieval 36

PNRITR:Research on Positive and Negative Entity Region Perce...

引用

36th International Conference on Software engineering and Knowledge engineering, SEKE 2024

作者： Zhang, Zhiping Sun, Tao Zheng, Hongyan Liu, Hao Liu, Gengchen Yang, Zhi Wang, Xiaoyu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)1891706594

The aim of cross-modal image-text retrieval is to heighten comprehension and to create robust associations between visual and textual content. This process entails a mutual querying and synchronization across various data formats. While conventional techniques have seen enhancements via attention-based approaches, they frequently miss the dynamics between corresponding and non-corresponding entity areas within image-text pairs. These techniques, often fixated on aligning image entities with textual terms, can overlook the congruence of entity characteristics, leading to less accurate localization. This research introduces PNRITR, an innovative strategy for cross-modal retrieval that leverages label learning to discern attributes absent from images, broadening the scope beyond just positive entity areas. PNRITR has delivered notable advancements on the Flickr30K and MS-COCO datasets. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Knowledge based systems

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Mathematical Modelling of Magnetocardiography Signals from Cardiac Sources 15

Mathematical Modelling of Magnetocardiography Signals from C...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Reshma, H. Anu, K.V. Bhat, Vikas R. Anitha, H. Manipal Institute of Technology Manipal Academy of Higher Education Department of Electronics and Communication Engineering Manipal576104 India Manipal Institute of Technology Manipal Academy of Higher Education Department of Biomedical Engineering Manipal576104 India Manipal Institute of Technology Manipal Academy of Higher Education Department of Computer Science and Engineering Manipal576104 India

ISBN: (纸本)9798350370249

A non-invasive technique, magnetocardiography measures the weak magnetic fields generated by the heart's electrical currents. This non-contact method, employing SQUIDs in a shielded environment, is highly expensive and sophisticated. At the same time, in non-shielded scenarios, it results in heavy, bulky, and cumbersome. As a development in cardiac magnetic field detection, a tiny array of coils has been used to passively monitor the heart's magnetic field in an unshielded environment. This paper focuses on illustrating the forward problem of magnetocardiography through the miniature multi-coil practical experiment. The copper coils presumed to be cardiac sources were provided with normal and abnormal ventricular transmembrane potentials generated using Maeda's cardiac cell model. The study's primary goal is to demonstrate the practical experimental setup in an unshielded environment to detect the cardiac magnetic fields. The results indicated the presence of both normal and abnormal transmembrane potential signal components, which could be further utilized in reconstructing the electrical activity in the inverse problem of the heart. © 2024 IEEE.

关键词： bio-electromagnetism cardiac cell model forward problem magnetocardiography transmembrane potential

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L1Norm Minimization in ISAR Imaging 23

L1Norm Minimization in ISAR Imaging

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23rd International Symposium on Electrical Apparatus and Technologies, SIELA 2024

作者： Lazarov, Andon Minchev, Dimitar Nikola Vaptzarov Naval Academy Information Technology Department Varna Bulgaria Burgas Free University Computer Science and Engineering Faculty Burgas Bulgaria

ISBN: (纸本)9798350382365

This work deals with an ISAR (Inverse Synthetic Aperture Radar) image enhancement algorithm using target's sparse image and image's l1 norm minimization. As calculation of l1 norm of two-dimensional (2D) image matrix is too complicated, target's image matrix is resized to one-dimensional (1D) one. According to ISAR theory, the ISAR signal matrix is a Fourier transform of the target's image matrix. Thus, the ISAR signal is obtained one dimensional one while modelling by Fourier transform of a certain target's image column matrix. In the image reconstruction algorithm, 12 norm constrained function and l12 objective function are defined. Simulation experimental results prove functionality of the developed algorithm. © 2024 IEEE.

关键词： Radar imaging

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Hybrid Adam Coati Optimization with DLSTM for Biomedical Parameter Prediction Using Sensor 3

Hybrid Adam Coati Optimization with DLSTM for Biomedical Par...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Rajesh, L. Mohan, H.S. Bindiya, M.K. Visvesvaraya Technological University Dayananda Sagar Academy Of Technology And Management Department Of Information Science And Engineering Karnataka Bengaluru India Karnataka Bengaluru India Sjb Institute Of Technology Department Of Computer Science And Engineering Karnataka Bengaluru India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318609

A real-time patient monitoring system procesfses a wide range of biomedical parameters and can store and safely capture biomedical parameters. It lowers the diagnosis time of the several diseases that are prevalent in healthcare services. Accurate prediction of biomedical parameters can significantly reduce healthcare costs and assist in effective decision-making. This research proposes a deep learning approach with hybrid optimization for biomedical parameter prediction by using sensors. At first, a healthcare centre with many patients having various disorders is considered. Several biomedical sensors are placed on the patients and these sensors include a respiratory Rate (RR) sensor, Heart rate (HR) sensor, Pulse Rate (PR) sensor, Ambulatory Blood Pressure (ABP)-sensor, and Saturation of Peripheral Oxygen (SpO2). Based on these sensed parameters, biomedical parameter prediction by using sensor based Deep Long Short-Term Memory (DLSTM)is executed. The DLSTM is trained using the proposed Hybrid Adam Coati Optimization(HAdCO). Here, the HAdCO is developed by incorporating of Coati Optimization Algorithm (COA) with Adam Optimizer. Furthermore, the HAdCO-DLSTM is evaluated by utilising metrics, like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Squared Error (MSE). The HAdCO-DLSTM had a minimal value of MAE at 0.0063, MSE at 0.0062, and RMSE at 0.0078. © 2024 IEEE.

关键词： Long short-term memory

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Hybrid technology for Multilevel Feature Extraction in CBMIR 7

Hybrid Technology for Multilevel Feature Extraction in CBMIR

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7th IEEE International Conference on Computational Systems and Information technology for Sustainable Solutions, CSITSS 2023

作者： Rao, Manjula Gururaj Priyanka, H. Vatsala, G.A. Reddy, K Hemant Kumar Nmam Institute of Technology Karkala Nitte India Computer Science & Engineering Pesu Bangaluru India Dayanada Sagar Academy of Technology & Management Bangaluru India Vit Ap University Department of Computer Science and Engineering India

ISBN: (纸本)9798350343144

A new technique in the healthcare industry called Content-Based Medical Image Retrieval (CBMIR) gives medical personnel an effective way to search for and access pertinent medical images from huge databases. The successful representation and analysis of medical images is made possible by feature extraction techniques, such as the Discrete Wavelet Transform (DWT), Local Binary Pattern (LBP) which are essential to CBMIR systems. As a result, rather than comparing all of the images directly pixel-wise, the retrieval method concentrates on comparing extracted features that capture the most important image attributes. The proposed system is an hybrid approach. A hybrid feature extraction technique combines LBP and DWT and normalizing the features. Next the reduction of the features is done using principal component analysis (PCA). The representation of the images is done using grey-level cooccurrence matrix (GLCM). The study uses a dataset of wrist images to extract the images, next is used to identify the fractures. © 2023 IEEE.

关键词： Discrete wavelet transforms

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PREDICTION OF OVARIAN SYNDROME USING DEEP FEATURE REPRESENTATION AND LEARNING APPROACHES

PREDICTION OF OVARIAN SYNDROME USING DEEP FEATURE REPRESENTA...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Sherubha, P. Madhumitha, R. Sasi Rekha, S.P. Arivazhagi Department of Information Technology Karpagam College of Engineering Coimbatore India Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore India Department of Computer Science and Engineering University College of Engineering Ariyalur India

ISBN: (纸本)9798350305708

The high incidence of the polycystic ovarian syndrome (PCOS) among fertile women receives more and more attention. Women's physical and mental health may be at risk due to the common disorder PCOS, whose identification is an expanding issue in worldwide public health. In this work, we looked into the potential significance of abnormalities in the identification of PCOS and provided information-based feature representation and learning techniques (IFRL) for PCOS auxiliary identification. Here, a real-time dataset is acquired from the Coimbatore region by examining 74 individuals, and these samples are used in the algorithm. The suggested method performs pre-processing, feature learning and classification using the proposed IFRL. To achieve categorization, a multi-instance model was finally built. Several performance indices were used to evaluate our method's effectiveness, including AUC, precision, recall, accuracy, and F1-score for classification. © 2023 IEEE.

关键词： Classification (of information)

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Design and Stress Analysis of the Frame for an Electric Bike 2nd

Design and Stress Analysis of the Frame for an Electric Bike

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2nd Congress on Control, Robotics, and Mechatronics, CRM 2024

作者： Niranjana, S.J. Shivakumar, S. Raghavendra, Srinivasaiah Ravikumar, H.C. Bangalore560074 India Bangalore560074 India Department of Computer Science Engineering Dayananda Sagar Academy of Technology and Management Bangalore560082 India

ISBN: (纸本)9789819770939

Global emissions have been on the rise since the industrial era because of the increased energy-intensive human activities, which is a direct cause of global warming and climate change. Of the total emissions, around 17% is from the transportation sector, which significantly contributes to the emissions. One of the easiest ways to be more sustainable is to choose electric vehicles instead of Internal combustion engines. Almost 75% of the vehicles registered in India are two-wheelers, but there are no affordable and reliable electric two-wheelers. This research works to optimize and analyze the design of a step-through frame design for an electric bicycle. The frame design is analyzed by providing boundary and loading conditions with two different materials (Steel-AISI4130 and Aluminum AL6061). The numerical analysis is carried out using ANSYS APDL. The result of von Mises’ stress is 166 MPa and 160.4 MPa for steel and aluminum, respectively. The result of stress and displacement is within the acceptable limit. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bicycles

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Advancing Network Anomaly Detection: Comparative Analysis of Machine Learning Models

Advancing Network Anomaly Detection: Comparative Analysis of...

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International Conference on. Cryptology and Network Security with Machine Learning, ICCNSML 2023

作者： Pandey, Rashmikiran Pandey, Mrinal Nazarov, Alexey Department of Radio Engineering and Cybernetics Moscow Institute of Physics and Technology Moscow Russia Federal Research Center Computer Science and Control Russian Academy of Science Moscow Russia

ISBN: (纸本)9789819706402

In the rapidly evolving realm of cyber-security, the detection of network anomalies serves as a pivotal line of defense against a myriad of malicious activities and cyberthreats. This research undertakes the task of enhancing the accuracy and efficacy of network anomaly detection by employing a comparative analysis of various individual machine learning models. The study delves into the performance of distinct models, including Random Forest, Gradient Boosting, AdaBoost, neural networks, and SVM, meticulously scrutinizing their capabilities in detecting network anomalies. The crux of this study lies in its meticulous evaluation of each individual model on the revered NSL-KDD dataset—an established benchmark within the field of network intrusion detection. Through a systematic blend of rigorous mathematical frameworks, precise model implementations, and comprehensive experimental assessments, this research offers a deep understanding of the inner workings of each algorithm. The pivotal aspect of this study revolves around the comprehensive comparative analysis of these standalone models. Going beyond the mere quantifica-tion of accuracy, the exploration delves into aspects of precision, recall, F1-score, and more, shedding light on their diverse facets of performance. With achieved accuracies of 99.2419% for Random Forest, 99.5197% for Gradient Boosting, 86.6044% for AdaBoost, 84.00% for neural networks, and 87.00% for SVM, this research underlines the distinctive attributes and potential of each model in the context of network anomaly detection. As the study unravels the distinct strengths and limitations of each model, it contributes to the broader landscape of cyber-security by providing insights into the efficacy of individual machine learning approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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