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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Qayyum, Tariq Trabelsi, Zouheir Tariq, Assadullah Belkacem, Abdelkader Nasreddine Serhani, Mohamed Adel United Arab Emirates University College of Information and Technology Al Ain United Arab Emirates United Arab Emirates University Department of Computer and Network Engineering Al Ain United Arab Emirates

ISBN: (纸本)9798350337488

Early treatment is required to control the symptoms and serious complications caused by schizophrenia (SZ). People suffering from SZ require lifelong treatment. The use of machine learning (ML) models to detect various health problems such as SZ has received considerable attention from researchers in recent years. This study investigated the effectiveness of various ML models to detect and predict SZ using electroencephalogram data. A dataset of 14 healthy schizophrenic patients was used, and 12 features were extracted after applying independent component analysis. Three traditional ML models (logistic regression, support vector machine, and K-nearest neighbors) and a convolutional neural network (CNN) were trained, and their performance was compared. Results demonstrated that the CNN model outperformed the other three models with the highest accuracy score of 95% on validation data. Our results highlight the potential of using ML in the early detection and prediction of SZ, which can help in timely and effective treatment. © 2023 IEEE.

关键词： CNN EEG KNN logistic Regression Schizophrenia SVM

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A decouple-decomposition noise analysis model for closed-loop mode-localized tilt sensors

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Microsystems & Nanoengineering 2023年第6期9卷 297-307页

作者： Kunfeng Wang XingYin Xiong Zheng Wang Liangbo Ma BoWen Wang WuHao Yang Xiaorui Bie ZhiTian Li XuDong Zou The State Key Laboratory of Transducer Technology Aerospace Information Research InstituteChinese Academy of SciencesBeijingChina School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijingChina Ming Hsieh Department of Electrical and Computer Engineering University of Southern CaliforniaLos AngelesCAUSA Shangdong Key Laboratory of Low-altitude Airspace Surveillance Network Technology QiLu Aerospace Information Research InstituteJinanChina

The development of mode-localized sensors based on amplitude output metrics has attracted increasing attention in recent years due to the potential of such sensors for high sensitivity and ***-localization phenomena leverage the interaction between multiple coupled resonant modes to achieve enhanced performance,providing a promising solution to overcome the limitations of traditional sensing *** noise plays a key role in determining the resolution of mode-localized sensors,as the output metric is derived from the measured AR(amplitude ratio)within the weakly coupled resonator ***,the amplitude noise originating from the weakly coupled resonator's closed-loop circuit has not yet been fully *** paper presents a decouple-decomposition(DD)noise analysis model,which is applied to achieve high resolution in a mode-localized tilt sensor based on a weakly coupled resonator closed-loop *** DD noise model separates the weakly coupled resonators using the decoupling method considering the nonlinearity of the *** integrating the decoupled weakly coupled resonators,the model decomposes the weakly coupled resonator's closed-loop circuit into distinct paths for amplitude and phase noise *** DD noise model reveals noise effects at various circuit nodes and models the system noise in the closed-loop circuit of the weakly coupled ***/Simulink simulations verify the model's accuracy when compared to theoretical *** the optimal operating point,the mode-localized tilt sensor achieves an input-referred instability of 3.91×10^(-4°)and an input-referred AR of PSD of 2.01×10^(-4°)⁄√Hz using the closed-loop noise *** model is also applicable to other varieties of mode-localized sensors.

关键词： weakly couple overcome

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Internet of Things-Based Air Pollution Monitoring System for the Indoor Environment Utilizing Light Fidelity Technology

Internet of Things-Based Air Pollution Monitoring System for...

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International Conference on Optimization and Applications (ICOA)

作者： Satea Hikmat Alnajjar Haneen Mohammed Mahmoud Department of Network Engineering AL-iraqia University Baghdad Iraq Department of Computer Engineering AL-iraqia University Baghdad Iraq

Indoor air pollution is a major environmental health problem. Exposure to air pollutants can cause many negative health effects, including respiratory problems and heart diseases. To control indoor air pollution, continuous monitoring is necessary. This paper presents an air pollution monitoring system based on the Internet of Things (IoT), which refers to the network of physical objects embedded with sensors, software, and connectivity to enable them to exchange data with other connected devices and systems over the communication network. It is suggested to use optical wireless communication (OWC) via light fidelity (Li-Fi) that utilizes visible LED lighting for data transmission purposes. Visible light communication (VLC) was selected due to the current congestion of the radio frequency band, which is extensively employed for data transmission and unable to satisfy the requirements of the forthcoming 6 th generation networks. In this paper, the air pollution monitoring system consists of a MQ135 sensor and a dust module sensor to measure polluted data in an indoor environment; the sensors are connected to the Arduino microcontroller for digital processing; and the measured data are transmitted to the ThingSpeak cloud by utilizing an ESP-32 module. The user can access the ThingSpeak web application to monitor air pollution levels through the VLC system. Optisystem tool V18 simulates the VLC system connected with fiber optic channel (FOC) and free-space optic (FSO) channels to transmit the link of the web application to the end user. This paper intends to verify the performance of the VLC system. The proposed system’s performance was evaluated based on the quality factor and the bit error rate (BER) at varying distances in dusty weather. The proposed system demonstrated a satisfactory performance up to an estimated distance of 1300 m in dusty conditions. Despite the challenging environmental conditions encountered by the transmission network, VLC has shown its capa

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Control of Magnetization Precession Frequency Using Current Density of Spin-Orbit Torque Applied to Local Area

Control of Magnetization Precession Frequency Using Current ...

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2023 IEEE International Magnetic Conference - Short Papers, INTERMAG Short Papers 2023

作者： Koda, Tetsunori Nakagawa, Ao Muroga, Sho Endo, Yasushi Oshima College National Institute of Technology Department of Electronic Mechanical Engineering Suo-Oshima742-2193 Japan Akita University Graduate School of Engineering Science Department of Mathematical Science and Electrical-Electronic-Computer Engineering Akita010-8502 Japan Department of Electrical Engineering Graduate School of Engineering Tohoku University Sendai980-8579 Japan Center for Spintronics Research Network Tohoku University Sendai980-8577 Japan Organization for Advanced Studies Tohoku University Sendai980-8577 Japan

ISBN: (纸本)9798350338362

We numerically study current density dependence of magnetization dynamics in a local area induced by the spin-orbit torque as a source of spin waves. The precession frequency of magnetization dynamics shows large change at a critical current density, which controls the wave length of spin waves. The origin of the change is due to the increase of exchange field caused by the magnetization torsion at the interface between SOT-applied and non-applied regions. Our new finding gives a way to manipulate spin wave's properties by controlling the magnetization direction in a local area. © 2023 IEEE.

关键词： Spin waves

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Integrating Pepper Robot and GPT for Neuromyth Educational Conversation

Integrating Pepper Robot and GPT for Neuromyth Educational C...

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IEEE Education engineering (EDUCON)

作者： Abdelhadi Hireche Abdelkader Nasreddine Belkacem Department of Computer and Network Engineering College of Information Technology United Arab Emirates University United Arab Emirates

ISBN: (数字)9798350394023

ISBN: (纸本)9798350394030

The emergence of neuromyths, or false beliefs about brain function and learning, has been a significant challenge in the field of education. These myths often hinders the learning process. Our study delves into this challenge by combining the cutting-edge large language models (LLMs) and humanoid robotics, aiming to dispel neuromyths in an educational setting. By integrating OpenAI's advanced Generative Pre-trained Transformers (GPT) model with a humanoid robot. This study focuses on neuromyth dispelling to investigate the integration of advanced large language models with humanoid robotics in education. We developed a system combining a Pepper robot with OpenAI's GPT model to educate users about common neuromyths. The system was evaluated through interactive sessions, where our queries were assessed on relevance, context, accuracy, response time, and speech recognition fluency. The findings reveal that the system effectively delivers relevant, contextually appropriate, and accurate information. Although response time was moderately rated, it did not significantly impact the overall user experience. The speech recognition system showed high efficiency, ensuring smooth interactions. These results demonstrate the potential of AI-humanoid robot integration as a valuable educational tool, especially for neuromyths. Our research contributes to educational technology by highlighting the effectiveness of such integrations in enriching learning experiences.

关键词： Accuracy Large language models Humanoid robots Speech recognition Oral communication Transformers User experience

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Research on Security Data of Federated Learning Privacy LSTM Model Based on Generative Adversarial network

Research on Security Data of Federated Learning Privacy LSTM...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Wang Xu Liang Zhuoming Department of Network Engineering School of Computer Neusoft Institute Guangdong Network Center South china Nomal University

As a new wireless transmission architecture technology, cSO can effectively reduce the influence of fading channels and provide cSO links for low channel quality. Secondly, in the ultra-large traffic cSO, The fitting of data and the constraint on the model must be considered at the same time. Generally speaking, regularization is the most common method, because in the process of inversion, it is far from enough to only use data fitting terms as constraints. In order to make the inversion results stable, model constraint terms should be added to the objective function on the basis of data fitting terms, and there are usually different model constraint methods for different practical problems. When the physical properties of anomalous body are different from those of surrounding rock, the minimum model constraint can be used. When inversion of the moderocess of EMM-connected state was consistent with the determination of the coverage enhancement level.

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Analysis Ethical and Social Implications of Artificial Intelligence (AI)

Analysis Ethical and Social Implications of Artificial Intel...

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Cognitive Computing in engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Faraz Khan P. K. Bharti Md Heshamuddin Muhammad Farhan Rizwan A. Farade Adeel Ahmad Khalique Ahmad Department of Computer Science & Engineering Glocal School of Science & Technology Glocal University Saharanpur Uttar Pradesh India Department of Mechanical Engineering School of Science & Technology Glocal University Saharanpur Uttar Pradesh India Department of Electrical and Electronics Engineering Faculty of Engineering Advanced Lightning Power and Energy Research (ALPER) University Putra Malaysia Serdang Malaysia Department of computer and network Engineering college engineering and computer science Jazan university Saudia Arabia

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

Artificial Intelligence (AI) has progressed significantly in recent years, revolutionizing various industries and influencing numerous aspects of everyday life. This research paper aims to explore unique insights into AI, focusing on its advancements, challenges, and future implications. Through an in-depth analysis of the existing literature and cutting-edge developments, this study presents an original perspective on AI and its role in shaping the world. The increasing use of artificial intelligence (AI) tools in manipulating images, videos, and voices has led to the rise of AI-based voice scams. This research paper aims to shed light on the alarming rate at which Indian individuals are falling prey to such fraudulent activities and the lack of awareness and information that contributes to their victimization. Through a comprehensive analysis of a recent study by McAfee, this paper emphasizes the urgent need to educate and inform individuals about the risks associated with AI-generated voices and provides recommendations for prevention.

关键词： Ethics Privacy Prevention and mitigation Collaboration Psychology Machine learning Regulation Security Artificial intelligence Videos

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Optimization of Photovoltaic Power Forecasting: A Comparative Study of Deep Learning Architectures, Optimization Techniques, and Evaluation Metrics

Optimization of Photovoltaic Power Forecasting: A Comparativ...

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Learning and Technology Conference (L&T)

作者： Husein Mauladdawilah Mohammed Balfaqih Zain Balfagih E.J. Gago M.C. Pegalajar School of Civil Engineering University of Granada Granada Spain Department of Computer and Network Engineering University of Jeddah Jeddah Saudi Arabia Computer Science Department Effat University Jeddah Saudi Arabia Department of Computer Science and Artificial Intelligence University of Granada Granada Spain

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

This study presents a systematic evaluation of deep learning architectures for photovoltaic (PV) power forecasting, comparing nine model configurations across three architectures (MLP, LSTM, CNN) and three optimizers (Adam, RMSprop, Adagrad). Using six years of hourly data from a 350kWp grid-connected PV system in Scotland, we demonstrate that architectural choice and optimizer selection significantly impact forecasting accuracy. The LSTM-RMSprop configuration achieved superior performance with RMSE of 2.651 kWh and MAE of 1.197 kWh, showing a 90.29% coefficient of determination (R 2 ). This outperforms both CNN (RMSE: 2.767-2.902 kWh) and MLP architectures (RMSE: 3.104-3.115 kWh) across all optimizers. Our main contributions include: (1) comprehensive optimization of model architectures through hyperparameter evaluation, revealing optimal configurations for each model type; (2) systematic evaluation of optimizer impact, demonstrating RMSprop's superiority for LSTM with improved accuracy across architectures; (3) detailed error analysis showing model stability across different conditions, with NRMSE ranging from 31.15% to 39.07%; and (4) practical insights into computational requirements, where CNN architectures achieve fastest training times (2.3-3.8 minutes/epoch) compared to LSTM (3.9-5.9 minutes/epoch) and MLP (36.54-46.72 minutes/epoch). Results demonstrate that LSTM architectures with appropriate optimization can outperform simpler models in PV power forecasting, providing valuable guidance for practical implementations.

关键词： Photovoltaic systems Training Analytical models Accuracy Computational modeling computer architecture Predictive models Forecasting Optimization Long short term memory

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The Terms Estimation of S Robotics using MTRS as well as FEM 4

The Terms Estimation of S Robotics using MTRS as well as FEM

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Gopal, R. Mudhafar, Mustafa Hashem, Amjad Ramalingam, Venkadeshan Bordoloi, Dibyahash Khan, Muhammad Numan Ali Adnan, Myasar Mundher Abdulhussain, Zahraa N. University of Buraimi Information and Communication Engineering College of Engineering Oman University of Kerbala Faculty of Medical Applied Sciences Department of Medical Physics Karbala56001 Iraq Al-kunooze University College Computer Engineer Department Basra Iraq University of Technology and Applied Sciences Network Computing College of Computing and Information Sciences Shinas Oman Dept of Cse Graphic Era Hill University Uttarakhand Dehradun India Graphic Era Deemed to Be University Uttarakhand Dehradun India University of Technology and Applied Sciences Network Computing College of Computing and Information Sciences Shinas Oman The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq National University of Science and Technology College of Technical Engineering Dhi Qar64001 Iraq

ISBN: (纸本)9798350360165

This look at addresses the critical project of reaching dependable and precise proprioception in soft robots, it really is essential for his or her effective deployment in complicated environments. By integrating multi-tap resistive sensors in the robot's form, our technique lets in for the distinct sensing of nearby deformations at various factors throughout the robotics' frame, moving beyond traditional techniques that anticipate steady curvature. We beautify the decision of these deformations and couple the sensory facts with a strong finite detail technique (FEM) model. This included framework allows the correct estimation of the smooth robot's form and the significance of outside forces applied to it, attaining a mean relative inaccuracy of about 4%. Additionally, our technique proves its software program in tender manipulation structures, in which it achieves thirteen% relative mistakes in external force estimation inside diverse zero to five N. The approach gives a promising direction for enhancing robotic interactions in sensitive and dynamic responsibilities, providing massive enhancements in robotic proprioception without reliance on outdoor monitoring generation. © 2024 IEEE.

关键词： Microrobots

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Wearable Devices for Early Warning of Acute Exacerbation in Chronic Obstructive Pulmonary Disease Patients

Wearable Devices for Early Warning of Acute Exacerbation in ...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Hsiao, Chun-Chieh Chu, Cai-Ying Lee, Ren-Guey Chang, Jer-Hwa Tseng, Chwan-Lu Lunghwa University of Science and Technology Department of Computer Information and Network Engineering Taoyuan Taiwan National Taipei University of Technology Department of Electronic Engineering Taipei Taiwan School of Respiratory Therapy College of Medicine Taipei Medical University Taiwan Taipei Medical University Wan Fang Hospital Department of Internal Medicine Taipei Taiwan

ISBN: (纸本)9798350337020

Chronic Obstructive Pulmonary Disease (COPD) is one of the leading causes of chronic diseases and deaths worldwide. When acute exacerbation of COPD (AECOPD) occurs, the frequency and severity of malignant attacks are highly correlated with the mortality rate. The purpose of this study is to use wearable devices to collect the physiological parameters of patients for early warning and prevention of complications of possible AECOPD attacks in the future. The subjects used wearable devices to measure Heart Rate Variability (HRV) at home. Physiological data and health assessment scales of 13 COPD patients were collected during the 6-month study period. According to the scale responses, the severity of the condition was classified into mild AE and no AE. If the subject needed emergency medical treatment due to COPD, it was classified as AE. With the scale classification method, a machine-learning Random Forest (RF) algorithm is used to predict the occurrence of AECOPD in the next 7 days, so as to prevent the deterioration of the disease in advance. The results of the study show that the accuracy of the model is more than 92% according to different classification methods, and using the mixed-parameter model as a feature for the prediction can improve the sensitivity of the original warning mechanism. In order to provide predictive results to the nursing staff at any time, the user interface of our system would transmit a warning message to remind the nursing staff to ensure early medical intervention for patients to avoid the occurrence of AECOPD. © 2023 IEEE.

关键词： COPD Heart Rate Variability Machine Learning Telecare Wearable Devices

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