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共 1333 条记录，以下是151-160 订阅

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Developing Interactive Bible learning Model Based on Mobile for Children 2019

Developing Interactive Bible Learning Model Based on Mobile ...

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2nd international conference on Software Engineering and Information Management (ICSIM) / 2nd international conference on Big Data and Smart Computing (ICBDSC)

作者： Sutopo, Hadi Purnomo, Hindriyanto D. Maria, Silaen Sondang Lee, Swati Lobodally, Altobeli Prasida, Arie Setiawan Inst Teknol & Bisnis Kalbis Jl Pulomas Selatan Kav 22 Jakarta 13210 Indonesia Univ Kristen Satya Wacana Jl Dr O Notohamidjojo Blotongan 50714 Salatiga Indonesia Univ Persada Indonesia YAI Jl Diponegoro 74 Jakarta 10430 Indonesia Dr Lee Brain Acad Grand Slipi Tower Lt-23DJalan S Parman Kav 22-24 Jakarta 11480 Indonesia

ISBN: (纸本)9781450366427

Bible stories have been introduced to children at early ages and continuously learned. The learning process is conventionally conducted in classrooms with books as the main media and lately accompanied by video presentations. However, the current life style in the IT era may shift children learning style. For them, smartphones and other digital media are more interesting than books. This paper is intended to create a mobile-based Bible-learning for children and youth. In developing the application, the researchers conduct qualitative approaches combining Borg and Gall's Research and Development Method with Luther's Multimedia Development Life Cycle. It is the utmost hope that the new IT-based learning model could facilitate easier, more fun and more interesting learning for children and youth. This research is still on going and the researchers would like to present a smartphone-based Bible learning games. Collected data is processed using NVivo, a qualitative software data processing. Research findings show that mobile-based learning including games is the most feasible model.

关键词： mobile multimedia Bible learning game interactive

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Aspect Ratio Estimation for MOS Amplifier using machine learning 2

Aspect Ratio Estimation for MOS Amplifier using Machine Lear...

引用

2nd IEEE international conference on Applied Electromagnetics, signal processing, and Communication, AESPC 2021

作者： Shah, Jeenang Patel, Harshil Gajjar, Ruchi Panchal, Dipesh Patel, Manish I. Institute of Technology Nirma University Department of Electronics and Communication Engineering Ahmedabad India

ISBN: (纸本)9781665442992

Conventional analog circuit design is complicated due to non-linearity. Due to which the design process was more complex in terms of hand calculations and was time-consuming. This paper estimate the aspect ratio for MOSFET based Common Source Amplifier with Diode connected load using machine learning techniques like Neural Network (NN), General Regression Neural Network (GRNN), Support Vector Regression (SVR) and Decision Tree Regression. The aim is to predict the size of the transistors of the circuit according to the corresponding design constraints or design parameters, without the knowledge of SPICE technology parameters. The designed models were trained using 180 nm and 250 nm technology nodes. These models take parameters like Voltage Gain, Power Dissipation, Power Supply, and Input Impedance as input and give the sizes of transistors as output. As the design constraints were applied to these techniques, the Neural Network provided the most accurate size of the transistors as an output with maximum accuracy of 97.49%. © 2021 IEEE.

关键词： Neural networks

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THE 2nd CLARITY PREDICTION CHALLENGE: A machine learning CHALLENGE FOR HEARING AID INTELLIGIBILITY PREDICTION 49

THE 2ND CLARITY PREDICTION CHALLENGE: A MACHINE LEARNING CHA...

引用

49th IEEE international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Barker, Jon Akeroyd, Michael A. Bailey, Will Cox, Trevor J. Culling, John F. Firth, Jennifer Graetzer, Simone Naylor, Graham Univ Sheffield Dept Comp Sci Sheffield S Yorkshire England Univ Nottingham Sch Med Nottingham England Univ Salford Acoust Res Ctr Salford Lancs England Cardiff Univ Sch Psychol Cardiff Wales

ISBN: (纸本)9798350344868;9798350344851

This paper reports on the design and outcomes of the 2nd Clarity Prediction Challenge (CPC2) for predicting the intelligibility of hearing aid processed signals heard by individuals with a hearing impairment. The challenge was designed to promote new approaches for estimating the intelligibility of hearing aid signals that can be used in future hearing aid algorithm development. It extends an earlier round (CPC1, 2022) in a number of critical directions, including a larger dataset coming from new speech intelligibility listening experiments, a greater degree of variability in the test materials, and a design that requires prediction systems to generalise to unseen algorithms and listeners. This paper provides a full description of the new publicly available CPC2 dataset, the CPC2 challenge design, and the baseline systems. The challenge attracted 12 systems from 9 research teams. The systems are reviewed, their performance is analysed and conclusions are presented, with reference to the progress made since the earlier CPC1 challenge. In particular, it is seen how reference-free, non-intrusive systems based on pre-trained large acoustic models can perform well in this context.

关键词： speech-in-noise speech intelligibility hearing aids hearing loss machine learning

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Emotion Recognition using machine learning Models on Electroencephalogram (EEG) Data 2

Emotion Recognition using Machine Learning Models on Electro...

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2nd international conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Asha, S. Roshini, S. Sai Saravanan, S. Vignesh, K. Saveetha Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350325799

Analyzing Electroencephalogram (EEG) data using cutting-edge machine learning techniques to comprehend emotional states. EEGs are non-invasive and have been shown to illuminate the complex neural mechanisms at play during affective experiences in the human brain. Everything is addressed in detail, from data collection and organization to feature extraction for model training and evaluation. It has the potential to increase the accuracy of diagnosing mental health disorders and has additional applications in psychology, human-computer interaction, and healthcare. The proposed machine learning algorithms and refined data processing strategies are being investigated to improve the accuracy and applicability of EEG-based emotion identification. This is the case despite the challenges posed by factors such as the signal's complexity, individual variation, and the need for robust feature extraction. Due to the interdisciplinary nature of the discipline and the ongoing collaboration of its practitioners, EEG analysis has the potential to shed light on underexplored aspects of the human emotional experience. © 2023 IEEE.

关键词： Electroencephalography

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A comprehensive study on machine learning approaches for emotion recognition 2

A comprehensive study on machine learning approaches for emo...

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2nd international conference on Artificial Intelligence and signal processing, AISP 2022

作者： Kumar, Nitin Gupta, Nidhi Department of Mathematics and Scientific Computing National Institute of Technology Himachal Pradesh Hamirpur India

ISBN: (纸本)9781665442909

Emotion recognition is the process to study about the emotions in a human being. This is a research field where one can understand and recognize the feelings of human and ability of expression which varies from each other at great extent. Several methods have been developed to study emotions such as facial expression, speech method, textual method and EEG signal. In this study work, we have reviewed several methods to find an efficiency of emotions up to accurate observations. Several papers on emotion recognition from the year 2007 to 2021 are been explored in this paper to observe the accuracy 95.20% using electroencephalogram (EEG) signal and 95% using EEG signals with statistical features and neural network. The average accuracy ranges in between 63% to 73% using EEG signal and facial expressions, both. © 2022 IEEE.

关键词： Emotion Recognition

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WInd TURBINE PLANETARY GEAR FAULT IDENTIFICATION USING STATISTICAL COndITION IndICATORS And machine learning 38

WIND TURBINE PLANETARY GEAR FAULT IDENTIFICATION USING STATI...

引用

38th ASME international conference on Ocean, Offshore and Arctic Engineering (OMAE 2019)

作者： Peeters, Cedric Verstraeten, Timothy Nowe, Ann Helsen, Jan Vrije Univ Brussel Dept Appl Mech Acoust & Vibrat Res Grp Pl Laan 2 B-1050 Brussels Belgium Vrije Univ Brussel Artificial Intelligence Lab Pl Laan 2 B-1050 Brussels Belgium

ISBN: (纸本)9780791858899

This work describes an automated condition monitoring framework to process and analyze vibration data measured on wind turbine gearboxes. The current state-of-the-art in signal processing often leads to a large quantity in health indicators thanks to the multiple potential pre-processing steps. Such large quantities of indicators become unfeasible to inspect manually when the data volume and the number of monitored turbines increases. Therefore, this paper proposes a hybrid analysis approach that combines advanced signal processing methods with machine learning and anomaly detection. This approach is investigated on an experimental wind turbine gearbox vibration data set. It is found that the combination of physics -based statistical indicators with machine learning is capable of detecting planetary gear stage damage and significantly simplifying the data analysis and inspection in the process.

关键词： Planetary gears Wind turbines machine learning Vibration signal processing Turbines Physics Mechanical drives Inspection Condition monitoring Damage

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A Comparative Analysis of the Evolution of DNA Sequencing Techniques along with the Accuracy Prediction of a Sample DNA Sequence Dataset using machine learning 2

A Comparative Analysis of the Evolution of DNA Sequencing Te...

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2nd international conference on Paradigm Shifts in Communications Embedded Systems, machine learning and signal processing, PCEMS 2023

作者： Mohammed, Khizar Baig Boyapati, Sai Venkat Kandimalla, Manasa Datta Kavati, Madhu Babu Saleti, Sumalatha SRM University AP Department of Computer Science Engineering India

ISBN: (纸本)9798350310719

DNA is widely considered the blueprint of life. The instructions required for all life forms, to evolve, breed, and thrive are found in DNA. Deoxyribonucleic acid, more commonly known as DNA, is among the most essential chemicals in living cells. A biological macro-molecule is DNA, also known as deoxyri-bonucleic acid. Life's blueprint is encoded by it. Sequencing of DNA has exponentially progressed due to the immense increase in data production in today's world. By means of this paper, we intend to evaluate the evolution of DNA Sequencing methods and perform a comparative analysis of modern-day DNA sequencing techniques to the ones of the past. We also illuminate the potential of machine learning in this domain by taking an exploratory and predicting the DNA Sequence using a Multinomial Naive Bayes classifier. © 2023 IEEE.

关键词： DNA

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Analysis on EEG signal with machine learning 2

Analysis on EEG signal with Machine Learning

引用

2nd international conference on Image and Video processing, and Artificial Intelligence (IPVAI)

作者： Cha, Jaehoon Kim, Kyeong Soo Zhang, Haolan Lee, Sanghyuk Xian Jiaotong Liverpool Univ Dept Elect & Elect Engn SIP 111 Renai Rd Suzhou 215123 Jiangsu Peoples R China Zhejiang Univ Ningbo Inst Technol SCDM Ctr Hangzhou Peoples R China

ISBN: (数字)9781510634107

ISBN: (纸本)9781510634107

In this paper, research on electroencephalogram (EEG) is carried out through principal component analysis (PCA) and support vector machine (SVM). PCA is used to collect EEG data characteristics to discriminate the behaviors by SVM methodology. The actual EEG signals are obtained from 18 experimenters who raised hands with meditation and actual movement during the experiments. The 16-channel data from the experiments form one data set. In order to get principal component of EEG signal, 16 features are considered from each channel and normalized. Simulation results demonstrate that two behaviors - i.e., raising hands and meditation - can be clearly classified using SVM, which is also visualized by a 2-dimensional principal component plot. Our research shows that specific human actions and thinking can be efficiently classified based on EEG signals using machine learning techniques like PCA and SVM. The result can apply to make action only with thinking.

关键词： Electroencephalogram (EEG) principal component analysis (PCA) neural network decision making brain computer interface (BCI)

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Measurement of dielectric loss factor based on LabVIEW signal processing module 2

Measurement of dielectric loss factor based on LabVIEW signa...

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2nd international conference on machine learning, Big Data and Business Intelligence, MLBDBI 2020

作者： Hu, Ronghui Zhao, Aiming Yu, Chao Shi, Yawei Zhang, Xing Shanghai Dianji University Shanghai200000 China

ISBN: (纸本)9781728196381

Online monitoring of the dielectric loss of capacitive equipment is an effective method to judge its insulation status. Because the higher harmonics and noise signals in the power grid will bring errors to the measurement of dielectric loss, in order to improve the accuracy of the measurement, this paper designs a FIR digital filter based on LabVIEW to remove the harmonic and noise signals, in order to ensure the measurement For speed and accuracy, an algorithm program of sine wave parameter method (including least square method and trigonometric function integration method) based on LabVIEW is designed, and the dielectric loss angle is obtained. The two algorithms are simulated under different signal-to-noise ratios and multiple complete cycles. The simulation results show the feasibility and effectiveness of the two methods. The two algorithms are simulated under a non-integer period. The simulation results show that the trigonometric function integration rule must be calculated under the complete period, and the least square method does not have this requirement. © 2020 IEEE.

关键词： Dielectric losses

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Speech Recognition and Filipino Sign Language E-Tutor System: An Assistive Multimodal learning Approach 19

Speech Recognition and Filipino Sign Language E-Tutor System...

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2nd international conference on Image and Graphics processing (ICIGP) / 5th international conference on Virtual Reality

作者： Samonte, Mary Jane C. Mapua Univ Makati 1200 Philippines

ISBN: (纸本)9781450360920

Speech recognition technology facilitates student learning. It has potential benefits for students with physical disabilities and the technology has been implemented in the classroom over the years in order to learn in a more efficient way. This study provides deaf students with various methods of studying, learning, and remembering new information. Aside from speech-to-text, the developed system is also provided with speech-to-visual approach, which represents information associated with objects. Also, Filipino Sign Language was used utilize as an alternative way of presenting Statistics lessons included in K-12 curriculum. Practical real world approach in presenting Statistics lessons are used to enhance delivery in face to face class se-up or in self-phasing learning. These multiple learning strategies were combined together to have a balance approach for a greater practice and recall will be more successful, especially for the target users. From the initial results, this research showed a significant advantage of using speech recognition and Filipino Sign Language in learning basic Statistics lessons compared from the traditional method.

关键词： Speech Recognition Filipino Sign Language E-Tutor Multimodal learning Hearing and Speech Impairment

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