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Photoplethysmography Signal Quality Assessment Using Neighbour Edge Restricted Horizontal Visibility Graph and Machine Learning Classifiers 16

Photoplethysmography Signal Quality Assessment Using Neighbo...

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16th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2024

作者： Khan, Zahir Manikandan, M. Sabarimalai Mahmud, Mufti Indian Institute of Technology Palakkad Dept. of Computer Science and Engineering Kerala Palakkad678623 India Indian Institute of Technology Palakkad Dept. of Electrical Engineering Kerala Palakkad678623 India Nottingham Trent University Dept. of Computer Science Nottingham Clifton Lane United Kingdom

ISBN: (纸本)9798350371154

Photoplethysmography (PPG) signals are vital for monitoring pulse rate, blood pressure, and more, but they are prone to motion artefacts and noise, leading to unreliable data. Assessing PPG signal quality is crucial for reliable healthcare and accurate medical diagnoses. By transferring the PPG time domain signal to a Horizontal Visibility Graph (HVG) network and combining extracted features from HVG with machine learning algorithms, we can classify the PPG signal into clean (or high quality) and noisy. We have proposed a new version of HVG called Neighbour Edge Restricted Horizontal Visibility Graph (NERHVG) by invoking some extra conditions for joining edges in HVG for PPG signal quality assessment (SQA). We have used the average degree (AD) of graphs extracted from HVG, and NERHVG algorithms as features in 3 different machine learning classifiers such as Random Forest (RF), Gaussian Naive Bayes (GNB), Decision Tree (DT) to classify 4 standard untrained PPG datasets (DS). The classifier models DT, RF, GNB associated with graph feature AD of HVG, NERHVG algorithms are named as: DT-HVG, DT-NERHVG, RF-HVG, RF-HVG, RF-NERHVG, GNB-HVG and GNB-NERHVG. After all the performance of HVG and NERHVG algorithms using AD feature are compared over the mentioned classifier models. It is observed that the NERHVG algorithm outperformed the HVG algorithm with the AD feature in all 4 datasets with a maximum accuracy of: 99.09%, 95.03%, 96.56 % and 84.63% using the GNB classifier. © 2024 IEEE.

关键词： Decision trees

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Comparing Traditional computer Vision Algorithms and Deep Convolutional Neural Networks as Self Driving Algorithms for Use in Dynamic Conditions

Comparing Traditional Computer Vision Algorithms and Deep Co...

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2023 IEEE MIT Undergraduate Research Technology Conference, URTC 2023

作者： Shah, Shilpi Franz, Brendan Forgach, Travis Jostes, Milan Siegel, Joshua Chung, Chan-Jin Rutgers University Dept. of Computer Science New BrunswickNJ United States Harvard University Dept. of Computer Science CambridgeMA United States University of Michigan Dept. of Computer Science and Engineering Ann ArborMI United States Lawrence Technological University Dept. of Math and Computer Science SouthfieldMI United States Michigan State University Dept. of Computer Science and Engineering East LansingMI United States

ISBN: (纸本)9798350308600

This research develops, compares, and analyzes both a traditional algorithm using computer vision and a deep learning model to deal with dynamic road conditions. In the final testing, the deep learning model completed the target of five laps for both the inner and outer lane, whereas the computer vision algorithm only completed almost three laps for the inner lane and slightly over four laps for the outer. After conducting statistical analysis on the results of our deep learning model by finding the p-value between the absolute error and squared error of the self-driving algorithm in the outer lane and inner lane, we find that our results are statistically significant based on a two-Tailed T test with unequal variances where the p-value for absolute error is 0.009, and 0.001 for squared error. Self-driving vehicles are not only complex, but they are growing in necessity-therefore, finding an optimal solution for lane detection in dynamic conditions is crucial to continue innovation. © 2023 IEEE.

关键词： Convolutional neural networks

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Accurate and Optimized Labelling of Fashion Products Through Attention Based Spiking Neural Networks 10

Accurate and Optimized Labelling of Fashion Products Through...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Vithyatharshana, N. Thrinayani, Yedhoti Kulkarni, Yogeshwar Nitin Singh, Rimjhim Padam Kanchan, Sneha Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Universiti Tunku Abdul Rahman Department of Internet Engineering and Computer science Malaysia

ISBN: (纸本)9798350384369

Automation of garment classification has become the backbone of e-commerce, influencing product search, personalization, and customer experience. This paper uses the Fashion MNIST dataset, a rich collection of clothing images to investigate the potential of Spiking Neural Networks (SNNs) for accurate and efficient classification. SNNs are an excellent choice for widespread usage and real-world integration due to their low power consumption and biomimicry. With the intention of revolutionizing product search, personalization, and customer experience, SNNs are made to address style differences, perspectives, and illumination. In order to fully utilize Fashion MNIST, this paper suggests strategic improvements to the SNN architecture. By addressing key challenges and introducing novel optimizations, our research contributes to a more robust and intelligent future for automated fashion product labelling. An SNN model with 93.6% accuracy has been developed as the base model for all the applications for the same. This data-driven approach leads the way to flawless product searches, highly customized recommendations, and a more intelligent fashion technology in future. © 2024 IEEE.

关键词： Deep learning

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How to Spatial Audio with the WebXR API: a comparison of the tools and techniques for creating immersive sonic experiences on the browser

How to Spatial Audio with the WebXR API: a comparison of the...

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2023 Immersive and 3D Audio: from Architecture to Automotive, I3DA 2023

作者： Tomasetti, Matteo Boem, Alberto Turchet, Luca University of Trento Dept. of Information Engineering and Computer Science Trento Italy

ISBN: (纸本)9798350311044

The WebXR Device API provides a powerful set of functionalities that can be used for creating immersive experiences that can be accessed directly from a web browser. However, while creating a compelling visual experience is easy to implement using the WebXR API and related tools, creating the audio part of the experience is less clear and with more open questions and possibilities. This paper presents a comparison of six tools oriented to practitioners and used for implementing and delivering interactive spatial audio experiences in XR applications running on a browser. These tools are selected and evaluated based on their compatibility with the WebXR Device API, ease of use, provided support and documentation, and specific features like reverberation, head-related transfer functions (HRTFs), sound scene rotation, and real-time microphone input. The paper then discusses the integration of these spatial audio tools into WebXR applications, highlighting their potential and limitations. It also addresses the absence of standardized spatial audio frameworks for the web. The challenges of choosing the right tool regarding resource allocation, learning curve, and compatibility with XR systems are acknowledged. This overview aims to provide insights into the current state of spatial audio tools for the web, making them more accessible to developers, musicians, and researchers seeking guidance on selecting suitable 3D spatial audio tools for experiences based on WebXR. © 2023 IEEE.

关键词： Audio acoustics

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Enhancing Early Detection of Pancreatic Cancer: A Machine Learning Approach with Explainable AI Insights

Enhancing Early Detection of Pancreatic Cancer: A Machine Le...

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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Pattanashetti, Neeraj V Mayur, R. Prakash, Adarsh Adiga, Tryambaka B Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Dept. of Computer Science & Engineering Karnataka Bangalore India Global Academy of Technology Dept. of Information Science & Engineering Karnataka Bangalore India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bangalore India

ISBN: (纸本)9798350376876

Among the most deadly and complex illnesses in the world, pancreatic cancer is typified by its early asymptomatic stage and subsequent discovery of the disease when it has progressed to an advanced stage. Getting the patients in a correct treatment path at the earliest point is important for suitable intervention and patient outcomes in the long run. We utilized advanced algorithms and model prediction techniques includes, feature extraction, hyperparameter tuning and explainability methods which aims to enhance both the comprehension and predictive accuracy of models employed. The goal is to assess patient health status and diagnose pancreatic diseases (cancer vs. non-cancer) by developing a learning algorithm. We obtained a dataset containing demographic information and urine biomarkers to start the investigation. First stage of our analysis employs a single classifier and later we apply bagging and boosting algorithms to achieve ensemble models. Hyperparameter fine-tuning has got a positive outcome, which has Gradient Boosting and Cat Boosting as its top performing models. The ensemble model produced excellent results (92.02 %), demonstrating the usefulness of the suggested approach. LIME and SHAP techniques play the key role in model outcome interpretability providing an insight of algorithmic decision-making process. Such a wide range of combinations of biomarkers have a potential of pancreatic cancer detection in early stages and thus will help shape diagnostics strategies in near future. © 2024 IEEE.

关键词： Diseases

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Early Warning System from Threat of Wild Animals using Digital Image Processing 3

Early Warning System from Threat of Wild Animals using Digit...

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3rd International Conference on Smart Data Intelligence, ICSMDI 2023

作者： Raji, C.G. Safa, Fathima Jishana, P. Adhil, Mohammed Assumption Autonomous College Dept. of Computer Science Changanassery Kerala India Mea Engineering College Dept. of Computer Science & Engg Kerala Perinthalmanna India

ISBN: (纸本)9781665464871

Wildlife infiltration in places with high human mobility has been proven to be dangerous for both humans and animals. If people fail to recognize an approaching wild animal, it may result in a direct attack. Due to their size and style of movement, monitoring and surveillance of wild animals is challenging. Additionally, it is a significant task to identify the species that were photographed. Elephants, tigers, and monkeys pose a serious threat to humans, and it will take a very long time for them to recover. Because interactions between humans and animals can be harmful to both species, successive frame differencing makes it possible to identify moving objects in videos. By utilizing the traits, the moving objects can be identified. Interactions between humans and animals can be hazardous. The proposed system is based on digital image processing, convolutional neural network and background subtraction method. © 2023 IEEE.

关键词： Animals

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Sparse Channel Estimation in Massive MIMO Systems Using Compressed Sensing and Neural Networks

Sparse Channel Estimation in Massive MIMO Systems Using Comp...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India ABV Indian Institute of Information Technology and Management Data Science and Quantum Computing Gwalior India

ISBN: (纸本)9798350379525

In this paper, based on compressed sensing and neural networks, we present an intelligent sparse channel estimation technique for Massive MIMO systems. We show that our approach improves considerably the estimation accuracy in terms of Mean Squared Error (MSE) and Normalized Mean Squared Error (NMSE) with respect to classical methods. We demonstrate [Figure: ] that our approach is computationally more efficient, requires less time and resources (memory/disk) compared to the existing literature but for a robustness of performance across distal or low Signal-to-Noise Ratios (SNRs). We also confirm our method exhibits fast convergence and efficiently accommodates a variety of sparsity levels. By visualizing it using heatmaps and ROC curves we show that this method is able to effectively differentiate between signal and noise, asserting our approach as a powerful tool for the contemporary set of problems present in wireless communications. © 2024 IEEE.

关键词： Signal to noise ratio

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Secrecy Capacity Optimization in RF/FSO Systems: Impact of Mixed Rayleigh and Log-Normal Fading with Atmospheric Turbulence

Secrecy Capacity Optimization in RF/FSO Systems: Impact of M...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India ABV Indian Institute of Information Technology and Management Data Science and Quantum Computing Gwalior India

ISBN: (纸本)9798350379525

This paper investigates the secrecy capacity optimization in RF/FSO systems with mixed Rayleigh and log-normal fading, while masking eavesdroppers channel state information () as well as suffering from atmospheric turbulence. We explore the impact of several parameters including beamforming angles, modulation schemes and hybrid RF/FSO link configurations through a combination of simulations as well as analytical models. Secrecy capacity is enhanced for an improved SNR and vice versa under higher turbulence conditions and cooler influents eavesdropped scenarios. Performance of the hybrid RF/FSO systems dominates considerably over single-link solutions, especially at longer distances. All these mechanisms appreciably increase the secrecy capacity, while beamforming and advanced modulation techniques offer most of the improvement. The study also demonstrates the interdependencies of system parameters, providing guidelines for designing secure communication systems under different environmental and operational conditions. © 2024 IEEE.

关键词： Beamforming

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A Sustainable Smart Irrigation and Crop Protection System for Bangladesh 8

A Sustainable Smart Irrigation and Crop Protection System fo...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Ahmed, Khandakar Rabbi Salam, Towfika Miraz, Shafiun Jahan, Israt Hoque, Aminul Al Noman, Abdullah Miyan Research Institute International University of Business Agriculture and Technology Uttara Model Town Dhaka Bangladesh Dept. of Mechatronics Engineering Kazla Rajshahi6204 Bangladesh Dept. of Computer Science and Engineering Rajshahi Bangladesh International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Uttara Model Town Dhaka Bangladesh Asian University of Bangladesh Dept. of Computer Science and Engineering Bangabandhu Road Tongabari Ashulia Dhaka Bangladesh Jahangirnagar University Dept. of Institute of Information Technology Savar Dhaka Bangladesh

ISBN: (纸本)9798331505462

Bangladesh's most crucial sector for development is agriculture. Rainfall has been irregular in recent years due to climate change. In our country, the untimely rain and hailstorms harm crops and vegetables, especially winter vegetables. Surface water is mainly used for agriculture, but its availability constantly declines. Our current key challenge is to save surface water while protecting crops from rain and hail. IoT is cutting-edge technology in many applications, such as smart homes, smart cities, and intelligent traffic management. The IoT may be implemented in so many different industries and sectors. This research work is about smart irrigation with crop protection systems in agriculture. The system's primary parts are the sensors, water pump, servo motors, embedded systems, and internet connectivity. IoT sensors like soil moisture sensors and rain sensors are used in the suggested methodology. The fields of crops have received water from a pump. A motor has covered the crops. The proposed solution is a straightforward Internet of Things setup that sends data to the Arduino after detecting rainfall and soil moisture levels. Depending on the sensor data, the Arduino sends signals to the motor and pump to irrigate the crops and cover crops as necessary, revealing the crops after rain. The system is constructed economically using bamboo, and poly and plastic nets have been utilized to protect the crops from rain and fog. The system's primary effects are increased productivity, decreased crop damage rate, and saved surface water. © 2024 IEEE.

关键词： Soil moisture

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CELL SEGMENTATION IN FLUORESCENCE MICROSCOPY IMAGES

CELL SEGMENTATION IN FLUORESCENCE MICROSCOPY IMAGES

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2024 IET International Conference on engineering Technologies and applications, ICETA 2024

作者： Yin, Shao-Yun Hsu, Wei-Wen Hsia, Chih-Hsien Master Program of Artificial Intelligence National Ilan University Ilan Taiwan Dept. of Computer Science and Information Engineering National Taitung University Taitung Taiwan Dept. of Computer Science and Information Engineering National Ilan University Ilan Taiwan

ISBN: (纸本)9781837242672

For microscopic cells images, cells’ morphologies are widely varied among different imaging modalities and staining methods. For fluorescence microscopy images, Poisson noises present in images due to the lightening, causing photobleaching, which makes it challenging in analysis with the deep learning models. Traditionally, median filtering can be applied to the original image for denoising, however, it also results in a blurry image, which is harmful to segmentation. In this study, the Haar Wavelet Transform is used to alleviate the photobleaching problem for cell segmentation, integrated to the framework without interfering with feature extraction by the backbone model. For our experiments, instance segmentation for fluorescence-stained cells was performed based on U-Net architecture with the method of gradient flow tracking proposed by Cellpose. And the Haar wavelet downsampling is embedded in the skip connections to verify if it can improve the segmentation performance for fluorescence images. The experimental results show that the approach of Haar wavelet downsampling embedded in the skip connections achieve superior performance among the other settings. Compared with the performance of the baseline model, the performance of the proposed model reached 0.732 in F1-score, which surpassed the one of baseline model (0.6341) by almost 0.1. © The Institution of engineering & Technology 2024.

关键词： Fluorescence microscopy

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