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Capacitive Power Transfer Modeling of Charging Inner-body Devices 30

Capacitive Power Transfer Modeling of Charging Inner-body De...

引用

30th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2023

作者： Abdelhafiz, Shahenda M. Fouda, M.E. Said, Lobna A. Radwan, A.G. Giza Egypt Cairo University Faculty of Engineering Engineering Mathematics and Physics Dept. Egypt Nile University School of Engineering and Applied Sciences Giza Egypt

ISBN: (纸本)9798350326499

Wireless power transfer (WPT) is highly desirable for applications with battery restrictions, such as biomedical applications. For example, in the case of implantable devices, power is transmitted through the human body, which has dielectric characteristics that must be considered during the design of the WPT system. This paper examines capacitive power transfer through the human body and formulates the complete WPT system, including the human body model. The power delivered to the implantable device is also analyzed. Finally, the system efficiency is discussed under different body and load scenarios to determine the optimal transmission frequency and load impedance. © 2023 IEEE.

关键词： Energy transfer

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Collocation Extraction Method Using Mutual Information Contents 12

Collocation Extraction Method Using Mutual Information Conte...

引用

12th International Congress on Advanced applied Informatics, IIAI-AAI 2022

作者： Fukumura, Iori Shin, Sanggyu Tokai University Dept. of Applied Computer Engineering Kanagawa Japan

ISBN: (数字)9781665497558

ISBN: (纸本)9781665497558

In this paper, we obtained nouns and verbs in dependencies from a corpus and extracted collocations using a statistical measure, mutual information content. We compared the extracted collocations with the NINJAL (Japanese corpus published by the National Institute for Japanese Language and Linguistics). This comparison was based on whether the collocation was a correct idiomatic expression and whether it existed in the NINJAL corpus. As a result, we could estimate collocations with a certain degree of accuracy. © 2022 IEEE.

关键词： Natural language processing systems

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'Do Not Disturb': An Implication Design To Be Alerted But Less Stress

'Do Not Disturb': An Implication Design To Be Alerted But Le...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Jarupreechachan, Weerachaya Kitchat, Kotcharat Surasak, Thattapon Kasetsart University Faculty of Engineering at Kamphaeng Saen Dept. of Computer Engineering Nakhon Pathom Thailand National Central University Dept. of Computer Science and Information Engineering Taoyuan Taiwan King Mongkut's University of Technology North Bangkok Faculty of Applied Science Dept. of Computer and Information Science Bangkok Thailand

ISBN: (纸本)9798350339215

The pervasive utilization of smartphones, while augmenting personal and professional communications, also elicits mental stress and disrupts the work-life balance. Users necessitate staying connected to critical updates without undue disruption, necessitating an optimized, user-friendly smartphone interface. However, current "Do Not Disturb (DND)"features fall short of providing the desired customization options, thereby contributing to heightened anxiety and diminished productivity. Consequently, this study proposes a design modification to the DND mode by incorporating an "Exceptional Apps Notification", feature thereby aiding in alleviating stress and enhancing focus by permitting essential alerts from selected apps or contacts. In other words, the users only need to turn off notifications for specific applications, not every application. This approach addresses the problem of users fearing they will miss some important notifications. Therefore, the design process employed principles of selective attention and color theory, and user experience was assessed through questionnaires and psycho-physiological feedback. Early findings suggest the potential for stress reduction, albeit with opportunities for further optimization, such as including a memory feature for user preferences and allowing selective access for specific contacts. © 2023 IEEE.

关键词： User interfaces

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Exploration of the Impact of Remote work and Online Education on Visual Health during the COVID-19 using Machine Learning Approaches 14

Exploration of the Impact of Remote work and Online Educatio...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Shanmukha Aditya, G. Kruthika, B. Venkata Sneha, G. Gupta, Deepa Smitha, T.V. Manek, Asha S. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India Amrita School of Engineering Amrita Vishwa Vidyapeetham Dept. of Mathematics Bengaluru India T. John Institute of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350335095

The COVID-19 pandemic has been associated with several manifestations that affect an individual's visual health. To address these concerns, we propose a classification model to predict the impact of COVID-19 on vision. The model is built on a dataset corresponding to visual symptoms and other factors. The proposed classification model will classify people into three categories: those with no impact on vision, those with mild-to-moderate impact, and those with severe impact. Some machine learning algorithms, such as logistic regression, decision trees, random forests, etc., were applied by us to build the model to identify the optimal algorithm for the task. The results obtained from the model show that it has high accuracy, precision, and recall. The model can predict the severity of visual symptoms in people due to COVID-19 with an accuracy of over 84%. The study findings are entirely focused on the E-learning or online learning caused due to COVID-19 pandemic and their impact on vision of people from different age groups. © 2023 IEEE.

关键词： COVID-19

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Matched Mode Processing with Phase-Difference Weighting for Underwater Acoustic Source Localization

Matched Mode Processing with Phase-Difference Weighting for ...

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2023 MTS/IEEE U.S. Gulf Coast, OCEANS 2023

作者： Ye, Yun Zheng, Yahong Rosa Gerstoft, Peter LaGuardia Community College at City University of New York Dept. of Mathematics Engineering & Computer Science New York City United States Lehigh University Dept. of Electrical and Computer Engineering Bethlehem United States University of California San Diego Dept. of Electrical and Computer Engineering La Jolla United States

ISBN: (纸本)9798218142186

Matched Field Processing (MFP) and its derivative Matched Mode Processing (MMP) estimates similarity between in-situ measurement and the model-based replica to identify source locations from an array of received signals. The similarity is evaluated in an ambiguity function using magnitude and phase of mode coefficients in the received signal. While phase is vulnerable to noises and interference, we find that the phase-difference between signals from adjacent receivers proves to be more robust. We propose to use phase difference as a weighting factor in MMP to select sensors in the space domain and the snapshots in the time domain. The consistency of phase difference in the replica and the measured data is demonstrated with a real experimental data set. Two MMP methods, the singular-value decomposition method and the smallest regularization method, are compared with and without the proposed phase-difference sensor and snapshot selection. The results demonstrate improved sidelobe effect and range detection using the phase-difference selection. © 2023 The Marine Technology Society (MTS).

关键词： Underwater acoustics

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On Min-Max Graph Balancing with Strict Negative Correlation Constraints 34

On Min-Max Graph Balancing with Strict Negative Correlation ...

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34th International Symposium on Algorithms and Computation, ISAAC 2023

作者： Kuo, Ting-Yu Chen, Yu-Han Frosini, Andrea Hsieh, Sun-Yuan Tsai, Shi-Chun Kao, Mong-Jen Dept. of Computer Science National Yang-Ming Chiao-Tung University Hsinchu Taiwan Dept. of Computer Science and Information Engineering National Cheng-Kung University Tainan Taiwan Dept. of Mathematics and Informatics University of Florence Italy Dept. of Computer Science and Information Engineering National Chi-Nan University Puli Taiwan

ISBN: (纸本)9783959772891

We consider the min-max graph balancing problem with strict negative correlation (SNC) constraints. The graph balancing problem arises as an equivalent formulation of the classic unrelated machine scheduling problem, where we are given a hypergraph G = (V, E) with vertex-dependent edge weight function p: E × V 7→ Z≥0 that represents the processing time of the edges (jobs). The SNC constraints, which are given as edge subsets C1, C2, . . ., Ck, require that the edges in the same subset cannot be assigned to the same vertex at the same time. Under these constraints, the goal is to compute an edge orientation (assignment) that minimizes the maximum workload of the vertices. In this paper, we conduct a general study on the approximability of this problem. First, we show that, in the presence of SNC constraints, the case with maxe∈E |e| = maxi |Ci| = 2 is the only case for which approximation solutions can be obtained. Further generalization on either direction, e.g., maxe∈E |e| or maxi |Ci|, will directly make computing a feasible solution an NP-complete problem to solve. Then, we present a 2-approximation algorithm for the case with maxe∈E |e| = maxi |Ci| = 2, based on a set of structural simplifications and a tailored assignment LP for this problem. We note that our approach is general and can be applied to similar settings, e.g., scheduling with SNC constraints to minimize the weighted completion time, to obtain similar approximation guarantees. Further cases are discussed to describe the landscape of the approximability of this prbolem. For the case with |V | ≤ 2, which is already known to be NP-hard, we present a fully-polynomial time approximation scheme (FPTAS). On the other hand, we show that the problem is at least as hard as vertex cover to approximate when |V | ≥ 3. © Ting-Yu Kuo, Yu-Han Chen, Andrea Frosini, Sun-Yuan Hsieh, Shi-Chun Tsai, and Mong-Jen Kao;licensed under Creative Commons License CC-BY 4.0.

关键词： Computational complexity

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Data Mining Techniques in Financial Fraud Detection 1

Data Mining Techniques in Financial Fraud Detection

引用

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

作者： Rathore, Rachna Sharma, Yash Ambika, P. Upadhyay, Amit Kumar Mahajan, Samriti Kumar, Rupendra Jiwaji University Dept. of Mathematics Gwalior India CSE-APEX Chandigarh India Meenakshi A.H.E.R Dept. of Commerce Chennai India Mangalayatan University Dept. of Computer Engineering & Applications Aligarh India Lingayas Vidyapeeth University Dept. of Commerce & Management Faridabad India JS University Dept. Computer Science Shikohabad India

ISBN: (纸本)9798350356816

In today's world, financial fraud poses a real threat to global economies, necessitating robust detection systems. Utilizing data mining methods in fraud detection has emerged as a new approach, offering powerful tools, especially with large financial transaction datasets. This paper explores various Data Mining techniques applied in Financial Fraud Detection, including decision trees, neural networks, and clustering methods, highlighting their effectiveness in identifying fraud. As decision trees can easily classify & predict transactions based on this historical data, it helps to recognize fraudulent activities. Neural networks can improve the accuracy of predicting fraudulent transactions by detecting intricate patterns, they are capable & adaptable for learning even odd more. Data points that are close together with each other end up in the same cluster - revealing outliers such as fraud;which would appear to be a point very distant from its neighbours. The adoption of these methodologies within financial systems prevents fraud before a crime has been committed, allowing for the resources & time spent on manual investigation to be reallocated. In addition, the development of machine-learning solutions & further enhancements in artificial intelligence allows for specialized fraud prevention methods to evolve. This abstract emphasises the importance of data mining for financial security which also points at some on-going research in this area. © 2024 IEEE.

关键词： Support vector machines

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Early Cancer Detection and Diagnosis using Deep Learning Models in Medical Imaging 4

Early Cancer Detection and Diagnosis using Deep Learning Mod...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Sikarwar, Shailendra Singh Rathore, Rachna Kumar, Sharma Sonu Monalisha, Mitali Satapathy, Lopamudra Nisha, M. Jiwaji University Dept. of Computer Science Pgv College Gwalior India Jiwaji University Dept. of Mathematics Gwalior India Mangalayatan University Dept. of Computer Engineering and Applications Aligarh India Usha Martin University Dept. of Paramedical Science Ranchi India Usha Martin University Dept. of Agriculture Ranchi India Dhanalakshmi Srinivasan University Dept. of Cse Coimbatore India

ISBN: (纸本)9798350364729

Breast cancer is a leading cause of mortality among women globally. While its exact cause remains uncertain, no definitive methods exist for its prevention or treatment. However, early detection through mammography plays a crucial role in improving management strategies and increasing the likelihood of recovery. Mammography aids not only in early cancer detection but also in identifying various conditions and distinguishing between cancer types - benign, malignant, or normal. This study explores an advanced machine learning and image processing framework for detecting and classifying breast cancer. The approach incorporates machine learning techniques alongside feature extraction, selection, and image preprocessing to classify breast cancer and related skin diseases effectively. Key advancements include the use of a geometric mean filter to enhance image quality and AlexNet for feature extraction. The relief algorithm is employed for feature selection, and classification is performed using machine learning models such as random forest, K-nearest neighbors (KNN), least square support vector machine (LS-SVM), and Naïve Bayes. This innovative methodology provides an accurate and efficient solution for breast cancer detection through advanced image analysis techniques. © 2024 IEEE.

关键词： Lung cancer

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Evaluation and Comparison of Facial Recognition Technology in Drawn Portraits 13

Evaluation and Comparison of Facial Recognition Technology i...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Chang, Cheng Fanjiang, Yong-Yi Hung, Chi-Huang Chen, Jung-Hua Phung, Kiem-Trinh Trieu, Van-Duc Fu Jen Catholic University Graduate Institute of Applied Science and Engineering New Taipei City Taiwan Fu Jen Catholic University Graduate Institute of Applied Science and Engineering Dept. of Computer Science and Information Engineering New Taipei City Taiwan Fu Jen Catholic University Lee-Ming Institute of Technology Graduate Institute of Applied Science and Engineering Dept. of Information Technology New Taipei Taiwan Fu Jen Catholic University Dept. of Computer Science and Information Engineering New Taipei City Taiwan Lee-Ming Institute of Technology Dept. Electronical Engineering New Taipei City Taiwan

ISBN: (纸本)9798350355079

Numerous studies have proposed various methods and applications for facial recognition. In camera-equipped environments, these methods can easily identify real faces. However, recognizing faces on printed or Portrait Drawing becomes more complex. In other words, real faces are three-dimensional, and while algorithms can sample feature values, identifying portrait drawing face on paper is relatively more challenging. This study evaluates and compares the effectiveness of various facial recognition techniques on paper-based images, including Haar-cascade, MTCNN, and Dlib 68 feature points. © 2024 IEEE.

关键词： Face recognition

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Amortized Variational Peak Fitting For Spectroscopic Data 33

Amortized Variational Peak Fitting For Spectroscopic Data

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33rd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2023

作者： Hansen, David Frich Alstrom, Tommy Sonne Schmidt, Mikkel N. Technical University of Denmark Richard Petersens Dept. of Applied Mathematics and Computer Science Plads 324 Kgs. Lyngby2800 Denmark

ISBN: (纸本)9798350324112

Spectroscopic analysis relies on identifying and understanding the spectral peaks that represent unique characteristics of an analyte. In high-speed real-time settings, current peak fitting techniques, particularly Bayesian methods involving MCMC or variational approximation, can be prohibitively expensive. We propose an unsupervised method using a convolutional neural network to estimate the number of peaks and their parameters along with posterior uncertainty, by amortizing variational inference in a classical parametric peak model. In a simulated data study, our method reliably determines the number of peaks, precisely estimates parameters similar to direct variational inference, and accurately captures uncertainty comparable to MCMC methods. Our novel, fast, and precise method for Bayesian spectral analysis opens new possibilities in real-time spectral data processing for high-speed monitoring and control. © 2023 IEEE.

关键词： Spectrum analysis

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