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Respiratory Estimation During Sleep Using dept. Camera in Real Environment

Respiratory Estimation During Sleep Using Depth Camera in Re...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Daisuke Kushida Ryuga Kodani Dept. Electorical Engineering and Computer Science Faculty of Engineering Tottori University Tottori Japan Dept. Engineering Graduate School of Sustainability Science Tottori University Tottori Japan

ISBN: (数字)9798350355079

ISBN: (纸本)9798350355086

Sleep apnea syndrome (SAS) is common but people are generally unaware of this condition, as it occurs during sleep. A method was previously proposed to estimate respiration during sleep in a non-contact and non-constraint manner using a dept. camera that is suitable for everyday use. The method was validated on subjects who were awake but remains untested under sleeping conditions. In this study, 12 men aged 65 years or older were tested under actual sleeping conditions for eight days. The effectiveness and accuracy of the proposed method were confirmed through a comparison with commercially available products.

关键词： Accuracy Estimation Aging Cameras Sleep apnea Synthetic aperture sonar Consumer electronics

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Identification of Affective States from Malayalam Audios Based on Valence Arousal Prediction

Identification of Affective States from Malayalam Audios Bas...

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Brain computer Interface & Healthcare Technologies (iCon-BCIHT), International Conference on

作者： Talit Sara George Jini George Pournami P N Dept. of Computer Science and Engg. National Institute of Technology Calicut India Dept. of Computer Science and Engg. Providence College of Engineering Chengannur India

ISBN: (数字)9798331540067

ISBN: (纸本)9798331540074

Affective state plays a key role in emotion recognition, influencing various applications from mental health evaluation to human-machine interaction. These states can be described using two primary aspects: valence, which indicates the pleasantness of the speech, and arousal, which relates to the intensity of the emotion. This study aims to automate the prediction of valence and arousal values from speech data, addressing the limitations of manual annotation and human error. The IIITSAINT-EMOMDB dataset, created from Malayalam films, uses the Circumplex Model of Affect to map emotions such as happy, sad, angry, and calm. The approach leverages machine learning models, including Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN), for prediction. Experimental results demonstrate that the traditional models like SVR and ANN are outperformed by the CNN model, which achieved a mean squared error (MSE) of 0.3 for valence and 0.1 for arousal. These findings highlight the power of automated emotion recognition in large-scale audio datasets, offering promising applications in affective computing and psychological analysis.

关键词： Emotion recognition Affective computing Accuracy Annotations Computational modeling Machine learning Predictive models Data models Convolutional neural networks Cultural differences

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Beyond the Blink: IoT's Next-Gen Drowsiness Detection system for Drivers 3

Beyond the Blink: IoT's Next-Gen Drowsiness Detection System...

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3rd IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Routray, Sidheswar Patel, Nachiket Patel, Dhruv Srivastava, Jyoti Dash, Rudra Narayan Pandit Deendayal Energy University Dept. of Computer Science & Engg. Gandhinagar India Indrashil University Dept. of Computer Science & Engg. Mehsana India Ganpat University Dept. of Computer Engineering Mehsana India School of Electrical Engineering KIIT University Bhubaneswar India

ISBN: (纸本)9798331533205

Drowsiness during driving includes high risks for the driver, the co-passengers, and the other people on the road. Advance Driver Assistance systems (ADAS) have been proposed to reduce these risks. This work introduces a new Internet of Things (IoT) seizure detection system to improve driver safety using Raspberry Pi, which is equipped with various sensors and utilizes machine learning for real-time monitoring and analysis. Using sensor fusion technology, including monitoring yawning and eye-closing patterns, the system aims to detect early signs of driver sleepiness. Being a low-cost, versatile, and tiny device, it becomes an ideal core processor for integration into an in-car environment. Additionally, the simplicity enables seamless communication with various sensors and facilitates effective data collection. Extensive tests and simulations were conducted to evaluate the effectiveness and reliability of the proposed system for real-world driving situations. The result outcomes prove the system's accuracy and efficiency in detecting early signs of driver fatigue to enhance driver safety. The integration of Raspberry Pi and sensor fusion technology makes it economical as well as scalable approaches supporting multiple platforms. © 2025 IEEE.

关键词： Advanced driver assistance systems

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Quantum-Enhanced Machine Learning for Precision Breast Cancer Diagnosis

Quantum-Enhanced Machine Learning for Precision Breast Cance...

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International Communication systems and Networks and Workshops, COMSNETS

作者： Agniva Banerjee Dept. Electrical Engineering and Computer Science Indian Institute of Science Education and Research Bhopal Bhopal India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Cancer is the leading cause of premature mortality. Globally, the prevalence of cancer is on the rise, and early-stage diagnosis is crucial for cancer recovery and survival. However, the presence of circulating tumor cells (CTCs) and heterogeneity within the tumor makes it hard to track how cancer changes over time and how resistance to treatment develops. Unfortunately, the cancer diagnosis precision remains inadequate, endangering precious human lives and necessitating costly procedures for accurate treatment. Machine learning (ML) has proven capable of discovering meaningful information and making predictions, particularly with large datasets. Moreover, quantum computers (QCs) inherently possess superposition and parallel computation, features not found in classical computing. Researchers have demonstrated that quantum neural networks (QNNs) achieve a significantly better effective dimension than classical NNs. In this context, this study investigates the application of classical and quantum machine learning (QML) algorithms to classify breast cancer images. Region-based segmentation is used to identify regions of interest and image processing algorithms are performed on segmented breast cancer images to extract meaningful features (e.g., perimeter, area, compactness). An user interface (UI) based software is created to swiftly distinguish benign and malignant instances during image analysis. Although QNNs perform poorly than the classical NNs, there is still a long way to go in developing feature maps and techniques for better performance. In contrast, the classical NNs have gone a long way with well-established algorithms and packages available to us. This research shows that with further development, QML has the potential to enhance cancer diagnosis accuracy and speed, ultimately improving early detection and treatment outcomes.

关键词： Image segmentation Accuracy Machine learning algorithms Software algorithms Artificial neural networks Machine learning Feature extraction Breast cancer Software Tumors

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Geometric Transformations and Tensor Product 20

Geometric Transformations and Tensor Product

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20th IEEE Jubilee World Symposium on Applied Machine Intelligence and Informatics, SAMI 2022

作者： Skala, Vaclav University of West Bohemia Faculty of Applied Sciences Dept. of Computer Science and Engineering Pilsen Czech Republic

ISBN: (纸本)9781665497046

This contribution describes new useful geometric transformations using the tensor product. The geometric transformations are used widely in many applications, especially in CAD/CAM systems, systems for Civil engineering, computer graphics, virtual and augmented reality, etc. The basic geometric transformations, e.g. rotation, translation, scaling, etc., are usually applied on points represented in homogeneous coordinates and described in many relevant books. However, there are also other primitives, e.g. lines, planes, normal 'vector' of a line, plane, triangle, etc., on which some geometric transformations can be applied. © 2022 IEEE.

关键词： Tensors Graphics processing units computer graphics Informatics Machine intelligence Civil engineering Augmented reality

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Performance Analysis of Hybrid Quantum-Classical Convolutional Neural Networks for Audio Classification

Performance Analysis of Hybrid Quantum-Classical Convolution...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Yash Thakar Bhuvi Ghosh Vishma Adeshra Kriti Srivastava Dept. of Computer Science and Engineering (Data Science) Dwarkadas J. Sanghvi College of Engineering Mumbai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Audio signals being high-dimensional and complex pose challenges for classical machine learning techniques in terms of computation and generalization on real-world data. This paper evaluates the use of hybrid quantum-classical convolutional neural networks (QC-CNN) that leverage quantum effects like superposition and entanglement for audio classification using mel-spectrograms obtained from audio data. Evaluated on both small-sized and large-sized datasets, the proposed QC-CNN model gave comparable training accuracy with classical CNN (Convolutional Neural Network) on the smaller dataset but outperformed classical CNN on test accuracy ($\mathbf{95.04 \%}$ vs $\mathbf{92.88 \%}$) for a larger birdsong dataset and reduced overfitting, thus highlighting the potential advantages of QC-CNNs for audio data. The QC-CNN exhibited higher cross-entropy loss in case of the small-sized dataset which was further significantly reduced when evaluated on the large-sized birdsong dataset. The work demonstrates the application of QC-CNN for audio classification.

关键词： Training Accuracy Quantum computing Quantum entanglement Neural networks computer architecture Data models Performance analysis Convolutional neural networks Testing

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Development of an All-In-One Smart Home and Irrigation system Prototype

Development of an All-In-One Smart Home and Irrigation Syste...

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2022 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2022

作者： Hasan, Hadi Madi, Mervat Rahi, Mahmood American University of Beirut Dept. of Electrical and Computer Engineering Beirut Lebanon Dept. of Science Technology and Engineering Higher Colleges of Technology Abu Dhabi United Arab Emirates

ISBN: (数字)9781665456005

ISBN: (纸本)9781665456005

The advancement of automation technologies made lives simpler and easier in all aspects. In today's world, automated systems [1] are replacing manual systems progressively. In this paper, an overview of current and emerging home automation systems is discussed. Then, the developed system is presented that is a one-stop-shop for all the use cases with the novelty of transforming traditional homes into smart homes through merging Artificial Intelligence (AI) [2] and Internet of Things (IoT) [3] thus providing the freedom to connect, integrate, monitor, and control houses remotely, securely, and at a cheaper cost. The use of AI allows monitoring and analyzing data through dashboards. Hence, sending realtime notifications to the user to ensure home management and security remotely. Also, the novelty in introducing IoT in this system is through incorporating different modalities and functions in one system controlled remotely for example the automation and monitoring system [4], the wireless [5] irrigation system, and the security system. All the components are controlled and monitored using a central web and mobile application. On the hardware components side, the system consists of the Raspberry Pi, the RFID Card Reader, the 8-channel relay module, and some sensors. Hence, the presented system is wholesome and scalable, so the user can start small and grow on demand, as this solution is loosely coupled, and tightly integrated. In brief, the solution is a secure, reliable, cheap, and all-inclusive smart home automated system. © 2022 IEEE.

关键词： Irrigation

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Detection of Cataracts from Retinal Fundus Image Using Ensemble Machine Learning Approach

Detection of Cataracts from Retinal Fundus Image Using Ensem...

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Next-Generation Computing, IoT and Machine Learning (NCIM), International Conference on

作者： Md. Nahid Hasan Md. Ali Hossain Dept. of Computer Science and Engineering Rajshahi University of Engineering and Technology

Cataract is a common eye condition that causes clouding of the eye's natural lens, resulting in blurry vision and it is very common in older ages people. However, it can also occur in younger aged people due to various genetical effects, diabetes, smoking, and prolonged exposure to sunlight. Cataracts can be treated through surgery to remove the clouded lens and replaced by an artificial one, which can significantly improve the vision. This problem can be reduced through early detection, regular eye examinations etc. In this research, a Contrast Limited Histogram Equalization (CLAHE) of retinal fundus images is applied for a better presentation of the above cataract effects. These improved images are fed to a machine learning-based model for effective detection. Finally, the ensembling of the proposed machine learning-based classifiers is performed for the accurate detection of cataracts. The proposed model was tested experimentally on a real dataset and achieves excellent performance with accuracy, precision, and recall scores of 99.67%, 100%, and 99.25%, respectively which outperform the baseline methods. The robustness of the proposed ensemble model is evaluated through 5-fold cross-validation and achieved an average accuracy of 99.6%. The implementation code can be accessed from the GitHub repository: https://***/nahid-tech/cataract-detection.

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A Deep Learning Approach for Classification and Segmentation of Leafy Vegetables and Diseases

A Deep Learning Approach for Classification and Segmentation...

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Next-Generation Computing, IoT and Machine Learning (NCIM), International Conference on

作者： Ashraful Islam Syeda Rafiatus Sama Raisa Nahiyan Habib Khan Ariful Islam Rifat Dept. of Computer Science and Engineering Sylhet Engineering College Sylhet Bangladesh Dept. of Computer Science and Engineering American International University-Bangladesh Dhaka Bangladesh

Bangladesh's economy is heavily dependent on agriculture, making the region an exceedingly crucial sector that requires great vigilance. In comparison to other vegetables, leafy greens have yet to garner as much attention as other crops that this study's large-scale analysis comes up with, particularly when categorizing readily available leafy vegetables and identifying the affected area and whether they are affected or not. Plant diseases can cause substantial losses in agricultural productivity, and many researchers are working to address the difficulties farmers encounter in identifying and treating these conditions while diagnosing their diseases. The purpose of this study is to develop a dependable and effective approach for classifying seven varieties of leafy vegetables and detecting seven types of plant diseases precisely in Bangladesh using machine learning and deep learning CNN models. To this end, this paper analyzed and provided solutions using a total of eight models: five classification models (VGG16, VGG19, ResNet50, YOLOv5, and YOLOv8) and three instance segmentation models (YOLOv5, YOLOv7, and YOLOv8). Two distinct datasets, “LeafyVclassify7BD” with 3306 images and “LeafyVdisease7BD” with 4493 images, were developed to classify leaves, segment the diseased area, and classify and detect damaged leaves. The research outputs will help strengthen the region's ability to control plant diseases more effectively while also cutting diagnostic costs and enhancing disease detection accuracy in the country's agricultural surroundings.

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Faulty Classes Prediction in Object-Oriented Programming Using Composed Dagging Technique

Faulty Classes Prediction in Object-Oriented Programming Usi...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Nagib Mahfuz Pintu Chandra Shill Dept. of Computer Science and Engineering North Western University Khulna Bangladesh Dept. of Computer Science and Engineering Khulna University of Engineering and Technology Khulna Bangladesh

Class is one of the fundamental concepts of the object-oriented paradigm and has been scrutinized since the developers moved on from procedural programming design. In software fault prediction, the legalization of software metrics is essential. As a handful of software metrics suites exist, it is a very hard task to predict the defective classes flawlessly using a particular set of metrics suites. However, it is a rational approach to use only the object-oriented metrics that are directly relatable to the class definitions in the code that helps the developers foresee the errors in defining the classes and minimize the errors as much as possible. This paper utilized twelve object-oriented metrics selected from various metrics suites. The dagging ensemble model is merged with three well-known classification algorithms (Naive Bayes, Multilayer Perceptron, J48 Decision Tree) individually and applied to twelve java projects. The study depicts that the proposed ensemble method gives improved outcomes that are statistically significant when merged with Naive Bayes and Multilayer Perceptron. The proposed ensemble method shows improvements up to 12.5% in accuracy and 15% in F-Score.

关键词： Measurement Software metrics Object oriented modeling Multilayer perceptrons Predictive models Software Classification algorithms

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