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Deep learning for sleep disorder diagnosis: Enhancing EEG Analysis with Machine Learning Techniques

Deep learning for sleep disorder diagnosis: Enhancing EEG An...

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Learning and Technology Conference (L&T)

作者： Baher Mohamed Enfel Barkat Dept. of Electrical & Computer Engineering Effat University Jeddah Saudi Arabia

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

The rising prevalence of sleep disorders highlights their disastrous effects on health, well-being, cognitive performance, memory, and learning outcomes. Sleep disorders such as insomnia and sleep apnea normally require extensive instrumentation and expertise for diagnosis, which is not always possible or even available in most cases. This work, therefore, proposes a state-of-the-art use of Machine Learning (ML) to develop a model for the classification and detection of sleep patterns from electroencephalogram (EEG) signals. The model is trained on a synthetic dataset, developed using mathematical simulations of real-life EEG data. This is a novelty in this area, which allows this model to be precise yet robust without relying too much on real-world *** application bridges advanced AI techniques with real-world applications and smooths out the experience for healthcare professionals and researchers alike. This research highlights how ML might transform sleep medicine through efficient, extended solutions for EEG data analysis and sleep disorder detection. We provide a realistic EEG simulation combined with an intuitive application, a powerful platform that makes advanced sleep diagnostics more accessible and impactful, which supports the development of educational strategies or technologies.

关键词： Deep learning Training Sleep Transforms Brain modeling Electroencephalography Mathematical models Data models Medical diagnostic imaging Synthetic data

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A Study of Resonance Modes for Microwave Imaging in Biomedical Applications

A Study of Resonance Modes for Microwave Imaging in Biomedic...

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International Conference on Electromagnetics in Advanced Applications (ICEAA)

作者： Amir Attar Joe LoVetri Dept. of Electrical and Computer Engineering University of Manitoba Winnipeg Canada

ISBN: (数字)9798350360974

ISBN: (纸本)9798350360981

The present numerical study explores the resonant modes generated within a microwave imaging chamber having perfectly conducting walls and loaded by a breast phantom. The objective of this preliminary work is to investigate whether such a resonant set-up, and an understanding of the modal field distribution, can be used to improve microwave imaging. As such the perturbation of the resonant frequencies and associated modes are identified utilizing the eigenmode projection technique. A simplified 2D model is utilized to assess the sensitivity of the resonant modes and frequencies to various tissues, such as different sizes, shapes, and locations of tumour and fibroglandular tissue regions, located within the breast model. It is found that there is a considerable variation in the sensitivity of the resulting modes to tissue anomalies located within the breast.

关键词： Microwave integrated circuits Sensitivity Biological system modeling Resonant frequency Breast Microwave theory and techniques Resonance Microwave FET integrated circuits Microwave imaging Load modeling

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First-principles density functional calculations for non-lead perovskites 12

First-principles density functional calculations for non-lea...

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Physics, Simulation, and Photonic engineering of Photovoltaic Devices XII 2023

作者： Esther, Emily Zhu, Peifen Henshaw, John Dept. of Mechanical Engineering Univ. of Tulsa 800 South Tucker Drive TulsaOK74104 United States Dept. of Electrical Engineering and Computer Science Univ. of Missouri 230 Jesse Hall ColumbiaMO65211 United States

ISBN: (纸本)9781510659377

The possibility of replacing lead in perovskites with other elements in 2D and 3D perovskites is explored. The most highly efficient perovskites are made with an inorganic-organic compound with lead. The influence on band structure, the density of states, and absorption coefficient are explored using first-principals density functional calculations. The advantages and disadvantages of 2D vs. 3D perovskites and their efficiency for energy conversion are also discussed. Ab initio DFT methods were used and implemented using a VASP software package. This research is important because perovskites solar cells are the most efficient solar cells currently studied by researchers, but they are prevented from being introduced to the market due to their instability. Perovskites are unstable and break down when they encounter things that they would commonly encounter during their application including moisture, high UV light, and excessive heat. When the perovskite structure starts to break down, lead is released into the environment. © 2023 SPIE.

关键词： Perovskite

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Mental Health Tracker Using Machine Learning Approaches 4

Mental Health Tracker Using Machine Learning Approaches

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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Thotad, Puneeth N. Kallur, Shanta Mundaragi, Laxmi Kadam, Swati H. Dept. of Master of Computer Applications K.L.E. Institute of Technology Hubballi580 027 India Dept. of Computer Science and Engineering K.L.E. Institute of Technology Hubballi580 027 India

ISBN: (纸本)9798350305258

Mental illness is a common condition in the 21st century. Despite the significant number of people who experience mental illness, many continue to experience stress without comprehending its damaging effects on their brain. Python and machine learning tools are adopted to create the mental health tracker system. According to the experimentation, the work focuses on developing a psychological well-being tracker. The prediction uses various models such as logistic regression, random forest, decision tree, and k-nearest neighbor. In the proposed system, the patient must answer the questions to predict their mental illness condition. Based on the responses given by the patient, the system will predict whether they need treatment or not. The system will attempt to gain an understanding of a user's mental state, determine whether they are suffering, and then suggest steps they can take to improve their mental health situation. Mental health prediction might benefit psychological departments looking to enhance their counseling and diagnosis abilities. The model was developed by collecting the question-answer set as options related to mental health by consulting specialized doctors in the hospital. The model is trained using different ML algorithms, finding their accuracy and predicting the results. The analysis was made by considering the raw data and normalized data. Models performance was improved with normalization with the following accuracies. 71.4%, 73.5%, 80.6%, and 81.2% accuracies are obtained for Logistic regression, K-Nearest Neighbor, Decision Tree, and Random Forest, respectively. © 2023 IEEE.

关键词： Decision Tree Digital Mental Health K-Nearest Neighbor Logistic Regression Machine Learning Random Forests

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Optimizing Atmospheric Water Generation Through Advanced Weather Prediction Using Solar Energy Data 9

Optimizing Atmospheric Water Generation Through Advanced Wea...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Dar, Eman Rana, Khadija Hussain, Muhammad Zunnurain Zulkifl Hasan, Muhammad Khalid, Aqsa Javaid, Arslan Altaf, Jawad Sattar, Basit Chuhan, Saad Hussain Atif Yaqub, Muhammad Qureshi, Ali Moiz Mubarak, Zaima Information Technology University Computer Engineering Department Lahore Pakistan Bahria University Lahore Campus Dept. of Computer Science Lahore Pakistan University of Central Punjab Faculty of Information Technology Lahore Pakistan National College of Business Adm. & Economics Department of Computer Science Lahore Pakistan National College of Ireland Nci Department of Computer Science Ireland Umt Department of Ai Lahore Pakistan

ISBN: (纸本)9798350394474

This research focuses on enhancing the efficiency of Atmospheric Water Generation (AWG) systems through the development of an accurate weather prediction model. Leveraging solar energy data, the project aims to forecast key weather parameters such as solar radiation, temperature, and humidity. The objective is to optimize the performance of the AWG device across diverse weather conditions. By integrating advanced weather prediction, the study endeavors to enable proactive adjustments in the AWG operation, ensuring optimal water production. This research contributes to the sustainable utilization of solar energy for atmospheric water generation, addressing water scarcity challenges with a forward-thinking and adaptive approach. © 2024 IEEE.

关键词： Solar energy

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Integrating SHAP, LIME and Grad-CAM with CNNs for Transparent Kidney Condition Classification

Integrating SHAP, LIME and Grad-CAM with CNNs for Transparen...

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International Conference on computer and Information Technology (ICCIT)

作者： Md. Ismiel Hossen Abir Hasibul Islam Peyal Sumya Rashid Md Jahid Syed Mustafizur Rahman Chowdhury Md. Nazrul Islam Mondal Dept. of Computer Science & Engineering International Standard University Dhaka Bangladesh Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

This research introduces a method for diagnosing kidney conditions by combining Convolutional Neural Networks (CNNs) with cutting-edge explainable AI techniques. The study aims to classify CT kidney images into four categories: Normal, Cyst, Tumor, and Stone. The proposed lightweight CNN model demonstrates high performance with validation and test accuracy of 92.71% and 92.12%, respectively, and a training accuracy of 97.92%. Precision, recall, and F1-score are 0.92%, 0.86%, and 0.88%, respectively, with an outstanding AUC of 0.99%. To improve the clarity of the model's decision-making process, SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and Grad-CAM (Gradient-weighted Class Activation Mapping) were used. LIME provided insights into the most influential image regions, SHAP values explained feature contributions to predictions, and Grad-CAM visualizations highlighted critical areas of images impacting classification. The integration of these explainable AI methods not only improves diagnostic Kidney condition accuracy but also fosters trust in the model prediction and makes medical image analysis.

关键词： Training Visualization Accuracy Explainable AI Decision making Predictive models Convolutional neural networks Kidney Medical diagnostic imaging Tumors

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Vacuum-Driven Origami Based Soft Robotic Manipulator

Vacuum-Driven Origami Based Soft Robotic Manipulator

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Roufun Pronoy Karima Kamal Shahreen Sanjida Shahnewaz Siddique Dept. of Electrical & Computer Engineering North South University Dhaka Bangladesh

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This paper presents the development and analysis of a vacuum-driven origami-based soft robotic gripper named the Pneumosoft Gripper. The designed gripper has four fingers. Each finger consists of an origami structure enclosed in a flexible material. When vacuum-actuated, these fingers bend and conform to grip objects of various shapes and sizes. The design process involved repetitive testing with different materials and vacuum pump specifications to optimize performance. Sufficient bending and gripping force to handle objects of up to 250 grams was achieved using latex as the outer cover material for the fingers. The improved lifting capability at reduced pressure, compared to other soft robotic grippers, can be attributed to the origami-inspired structure for enhancing deformation efficiency. An analysis of the gripper performance is provided based on bending angle and maximum mass lifted at different voltage levels.

关键词： Vacuum systems Deformation Force Soft robotics Bending Manipulators Valves Grippers Solenoids Testing

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Quantitative Analysis of Error Propagation in Hindi–English Cascaded Speech-to-Speech Translation Models 3

Quantitative Analysis of Error Propagation in Hindi–English...

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3rd International Conference on Advancement in Computation and computer Technologies, InCACCT 2025

作者： Gupta, Mahendra Dutta, Maitreyee Maurya, Chandresh Kumar Dept. of Computer Science and Engineering NITTTR Chandigarh Chandigarh India Dept. of IMEE NITTTR Chandigarh Chandigarh India Dept. of Computer Science and Engineering IIT Indore Indore India

ISBN: (纸本)9798331543891

Speech-to-Speech Translation (S2ST) plays a crucial role in reducing language barriers and enabling seamless communication between people from different linguistic backgrounds. Traditional S2ST systems typically rely on a cascaded design, where components like Automatic Speech Recognition (ASR), Machine Translation (MT), and Text-to-Speech (TTS) work together in sequence to complete the translation process. Cascaded models are widely used S2ST models and are prone to error propagation (EP) across the pipeline, significantly impacting translation quality. EP is discussed in various literature. However, a comprehensive quantitative analysis is not available, particularly for low-resource languages like Hindi and English. This work presents a detailed quantitative study of EP in Hindi–English cascaded S2ST models, bridging this critical research gap. This study utilizes both text-based and textless evaluation metrics such as BLEU, Translation Edit Rate (TER), and BLASER score for translation accuracy to quantify the impact of EP at various stages of the pipeline on the FLEURES dataset. The result analysis shows that due to EP, the translation quality of the S2ST model decreases in BLEU score of 11.55 for English→Hindi and BLEU score of 12.16 for Hindi→English. Similarly, reference-based BLASER decreases by 0.61 and 0.45 for English→Hindi and Hindi→English, respectively. © 2025 IEEE.

关键词： Machine translation

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Observation-Transformed Distributed Detection in Sensor Networks

Observation-Transformed Distributed Detection in Sensor Netw...

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Annual Conference on Information sciences and Systems (CISS)

作者： Lei Cao Ramanarayanan Viswanathan Dept. of Electrical and Computer Engineering University of Mississippi Oxford MS USA

ISBN: (数字)9798331513269

ISBN: (纸本)9798331513276

Distributed detection (DD) plays a crucial role in sensor networks, where sensors gather data from a region of interest and report their observations to a fusion center (FC). The FC then makes a decision regarding a specific task, such as determining the presence or absence of a target. Given the constraints imposed by the transmission power, bandwidth, and noise impact in both sensing and data reporting channels, what should be reported from the sensors significantly affects the final detection accuracy. Existing methods include observation-untransformed DD (UDD), codewords-based DD (CDD), and quantized-but-uncoded DD (QDD). In this paper, we attempt to find the optimal sensor decision rule, i.e., the best transform for the observed data to be reported, by formulating the task as a functional optimization problem. To bypass the difficulty of theoretically solving the problem, we present two methods: an approximation function and a numerical solution for the Gaussian noise case. Our simulation results demonstrate that a variation of the hyperbolic tangent function can be an effective approximation of the optimal transform. This work could lead to further studies on the optimal design for distributed detection.

关键词： Accuracy Simulation Gaussian noise Computational modeling Distributed databases Transforms Bandwidth Sensors Optimization

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Efficient Polynomial Arithmetic and Hash Modules for ML-DSA and ML-KEM Standards

Efficient Polynomial Arithmetic and Hash Modules for ML-DSA ...

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IEEE Asia-Pacific Conference on Circuits and Systems

作者： Quang Dang Truong Hanho Lee Dept. of Electrical and Computer Engineering Inha University Incheon Korea

ISBN: (数字)9798350378771

ISBN: (纸本)9798350378788

In light of the emerging threat posed by quantum computers to current cryptographic standards, the National Institute of Standards and Technology has introduced the Module-Lattice-Based Key-Encapsulation Mechanism (ML-KEM) and Digital Signature Standards (ML-DSA). These standards are derived from the lattice-based cryptosystems, CRYSTALS-Kyber and Dilithium, which rely on the Module Learning with Errors assumption to enhance security. Therefore, the efficient hash and arithmetic modules are essential for implementing these schemes, as they are the most time-consuming phases. This paper presents an efficient hardware architecture that optimizes and unifies hash and arithmetic modules, specifically tailored to accelerate FPGA-based implementations of ML-KEM and ML-DSA. Our polynomial arithmetic module demonstrates an improvement in terms of latency while consuming comparable hardware resources compared to state-of-the-art studies. Additionally, we detail a Keccak-based hash module with four distinct modes, efficiently supporting both cryptographic schemes.

关键词： Quantum computing computer architecture NIST Throughput Hardware Polynomials Cryptography Standards Optimization Arithmetic

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