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15th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2024 / The 7th International Conference on Emerging Data and Industry 4.0, EDI40 2024

作者： Strand, R.D. Kristensen, L.M. Department of Safety Chemistry and Biomedical Laboratory Sciences Western Norway University of Applied Sciences Haugesund Norway Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway

The pervasive presence of IoT sensing devices combined with advances in cloud-based services has made historical- and forecast weather data services widely available for use in smart software systems. The contribution of this paper is the design, performance evaluation, and validation of a software implementation of model-based dynamic fire risk prediction for wooden homes using local weather data. A key feature of the implementation is that the software architecture has been designed to support the use of different underlying cloud-based weather data services, and the integration as a service in third-party smart systems embedding fire risk predictions. The performed evaluation shows that the implementation is efficient, as weather data can be retrieved, preprocessed and fire risk predictions computed within seconds using only in the order of Kb's of memory;and accurate considering our sets of data, as the fire risks computed by the implementation have been validated against a set of in-situ measurements. © 2024 Elsevier B.V.. All rights reserved.

关键词： Fires

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Complex-frequency waves:beat loss and win sensitivity

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Light(science & Applications) 2024年第2期13卷 181-183页

作者： Qingqing Cheng Tao L School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai200093China Department of Respiratory Diseases and Critical Medicine Quzhou Hospital Affiliated to Wenzhou Medical UniversityQuzhou324000China National Laboratory of Solid State Microstructures College of Engineering and Applied SciencesNanjing UniversityNanjing210093China

Recent experiments have demonstrated that synthesized complex-frequency waves can impart a virtual gain to molecule sensing systems,which can effectively restore information lost due to intrinsic molecular *** enhancement notably amplifies the signal of trace molecular vibrational fingerprints,thereby substantially improving the upper limit of sensitivity.

关键词： thereby Complex limit

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Deep Learning Approach for COVID-19 Detection in Computed Tomography Images

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computers, Materials & Continua 2021年第5期67卷 2093-2110页

作者： Mohamad Mahmoud Al Rahhal Yakoub Bazi Rami M.Jomaa Mansour Zuair Naif Al Ajlan Department of Applied Computer Science College of Applied Computer EngineeringKing Saud UniversityRiyadh11362Saudi Arabia Department of Computer Engineering College of Computer and Information ScienceKing Saud UniversityRiyadh11362Saudi Arabia Department of Computer Science College of Computer and Information ScienceKing Saud UniversityRiyadh11362Saudi Arabia

With the rapid spread of the coronavirus disease 2019(COVID-19)worldwide,the establishment of an accurate and fast process to diagnose the disease is *** routine real-time reverse transcription-polymerase chain reaction(rRT-PCR)test that is currently used does not provide such high accuracy or speed in the screening *** the good choices for an accurate and fast test to screen COVID-19 are deep learning *** this study,a new convolutional neural network(CNN)framework for COVID-19 detection using computed tomography(CT)images is *** EfficientNet architecture is applied as the backbone structure of the proposed network,in which feature maps with different scales are extracted from the input CT scan *** addition,atrous convolution at different rates is applied to these multi-scale feature maps to generate denser features,which facilitates in obtaining COVID-19 findings in CT scan *** proposed framework is also evaluated in this study using a public CT dataset containing 2482 CT scan images from patients of both classes(i.e.,COVID-19 and non-COVID-19).To augment the dataset using additional training examples,adversarial examples generation is *** proposed system validates its superiority over the state-of-the-art methods with values exceeding 99.10%in terms of several metrics,such as accuracy,precision,recall,and *** proposed system also exhibits good robustness,when it is trained using a small portion of data(20%),with an accuracy of 96.16%.

关键词： COVID-19 deep learning computed tomography multi-scale features atrous convolution adversarial examples

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Swallowing Assessment using High-Resolution Cervical Auscultations and Transformer-based Neural Networks 46

Swallowing Assessment using High-Resolution Cervical Auscult...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Anwar, Ayman Khalifa, Yassin Lucatorto, Erin Coyle, James L. Sejdic, Ervin University of Toronto Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada University of Pittsburgh Center for Research Computing and Information Technology Analytics PittsburghPA United States Cairo University Faculty of Engineering Systems and Biomedical Engineering Department Giza Egypt University of Pittsburgh School of Health and Rehabilitation Sciences Department of Communication Science and Disorders PittsburghPA United States University of Toronto North York General Hospital Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada

ISBN: (纸本)9798350371499

Swallowing assessment is a crucial task to reveal swallowing abnormalities. There are multiple modalities to analyze swallowing kinematics, such as videofluoroscopic swallow studies (VFSS), which is the gold standard method, and high-resolution cervical auscultation (HRCA), which is a noninvasive technique that uses a triaxial accelerometer attached to the patient's neck. Deep learning models play an essential role in data driven analysis of swallowing landmarks using VFSS and/or HRCA as input data. Most of these models utilize convolutional and recurrent neural networks. Here, we investigate the ability of transformers to analyze swallowing kinematics;specifically upper esophageal sphincter opening and laryngeal vestibule closure using HRCA signals. We tested the model using an independent test dataset to assess the generalizability of the proposed network. The proposed network achieved an average detection accuracy higher than 90% and 85% for both segmentation tasks, which outperform the hybrid neural networks from the literature, and the model obtained high-performance measures for the independent dataset, showing the transformers' ability to generalize on unseen data. © 2024 IEEE.

关键词： Recurrent neural networks

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Data-Driven Analysis of EV Energy Prediction and Planning of EV Charging Infrastructure 9

Data-Driven Analysis of EV Energy Prediction and Planning of...

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9th IEEE International Conference on Big Data Computing Service and Applications, BigDataService 2023

作者： Palaniappan, Arul Bhukya, Purnima Chitti, Sai Kiran Gao, Jerry San Jose State University Department of Applied Data Science San Jose United States San Jose State University Department of Applied Data Science Department of Computer Engineering San Jose United States

ISBN: (纸本)9798350333794

To reduce the current crisis on energy and environmental problems, the adoption of electric vehicles is an effective way. But limited access and availability of charging infrastructure are restricting the EV market development. This paper presents data-driven analysis of current EV charging infrastructure to plan future EV infrastructure in the city. This paper tackles the problem using three approaches. The first approach that is covered in this paper is to predict the number of EV charging stations that need to be implemented in a geographic location. This is done using the SARIMAX model on the California EV charging station dataset that is preprocessed to be split into zip codes. The second approach that is tackled by this paper is the energy consumption prediction by an EV charging station. This information is useful to allow planners to understand how much energy is consumed by an EV station across multiple different time periods. An optimized Meta-Fusion regression model based on a stacked model is proposed for this approach and is applied to the Palo Alto charging station dataset. The third approach utilized the K-means clustering algorithm on the Palo Alto charging station dataset to predict the optimal location for EV charging stations in that city. The SARIMAX mode had an R2 score of 0.97, a MAE of 6.50, and RMSE of 17.43 when predicting EV station counts in the zip code 92101. The stacked model had an R2 score of 0.98, a MAE of 0.91 and an RMSE of 2.26 when predicting the daily energy consumption by an EV station. © 2023 IEEE.

关键词： Forecasting

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Towards Underwater Sustainability with AIoT: Underwater Trash Management System Comprised of YOLOv8 with IoT-applied Segmentation 2

Towards Underwater Sustainability with AIoT: Underwater Tras...

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2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Paneru, Biplov Poudel, Buddhi Sagar Shah, Krishna Bikram Paneru, Bishwash Chhetri, Sanjog Poudel, Yam Krishna Nepal Engineering College Pokhara University Department of Electronics and Communication Bhaktapur Nepal Nepal Engineering College Pokhara University Department of Computer Science and Engineering Bhaktapur Nepal Institute of Engineering Tribhuvan University Department of Applied Science & Chemical Engineering Lalitpur Nepal Nepal Research and Collaboration Center Department of Research and Development Kathmandu Nepal

ISBN: (纸本)9798331517984

Effective techniques for managing rubbish underwater are essential due to growing environmental concerns about contamination under the sea. In this research, we offer a novel method for efficient underwater trash detection and management that makes use of the Internet of Things (IoT) and the YOLOV8 object detection model. Using YOLOV8, the suggested system seeks to identify and categorize underwater garbage in real-time. It then uses IoT devices for segmentation and management. We outline the suggested system's design, talk about how YOLOV8 integrates with Internet of Things gadgets, and provide experimental results to validate the system's efficacy. Our findings suggest that the suggested strategy has great potential to advance efforts to manage undersea waste and manage pollution as well as segment using 6-DOF Robotic Arm. The IoT based system allows remote monitoring of trash density and location along with the raspberry-pi server-based video feed allows the users to suitably monitor aquatic conditions remotely. The fusion of IoT and deep learning on YOLO v8 model opens gateway for water sustainability and enhances further opportunities in employing robotic arm-based trash collection system. © 2024 IEEE.

关键词： Robotic arms

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Optimization of Artificial Neural Network Modelling for Improving Diabetic Diagnosis: A Promising Approach for Early Detection

Optimization of Artificial Neural Network Modelling for Impr...

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2024 International Conference on Brain computer Interface and Healthcare Technologies, iCon-BCIHT 2024

作者： Thumu, Muni Balaji Balajiraja, N. Yousoof, Muhammed Selvaraju, Sivamani PG and Research Department of Computer Science Pudukkottai India Pudukkottai India Dhofar University Salalah Oman College of Engineering and Technology Univarsity of Technology and Applied Sciences-Salalah Engineering Department Salalah Oman

ISBN: (纸本)9798331540067

Diabetes impacts food-to-energy conversion and is chronic. Diabetics store glucose in their blood instead of using it for energy, causing major health issues. The WHO reports that diabetes is rising worldwide, and early detection and treatment are crucial to preventing complications. Artificial intelligence (AI) and machine learning (ML) techniques have the potential to revolutionize the diagnosis of diabetes by making it possible to identify people at risk earlier and diagnose it more accurately. This work introduces a supervised learning approach in the artificial neural network (ANN) to improve the accuracy of diabetes diagnosis. Eight input variables from the National Institute of Diabetes and Digestive and Kidney Diseases dataset were used, including 2000 patients with 65.8% healthy and 34.2% diabetic. Altering the feedforward backpropagation neural network architecture improved the ANN's accuracy. Changes were made to hidden layers (1 to 3) and neurons (20 to 100) using R2 and MSE. The ANN model suited the dataset well with R2 and MSE of 99.1% and 7.38× 10-6 in the optimal architecture. These results imply that the suggested method might quickly detect diabetes. © 2024 IEEE.

关键词： Neural network models

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Analyzing the function of smart security in the context of the internet of things 3

Analyzing the function of smart security in the context of t...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Naidu, K. Venkatagurunatham Pillai, Anchu Krishnan Rajaprabha, C.E. Sharma R, Rajesh Ul Islam, Atowar Khasimbee, Shaik Dr M.G.R Educational and Research Institute Dept. of Computer Science and Engineering Chennai India Xavier Institute of Engineering Applied Science and Humanities Maharashtra Mumbai India Hindusthan Institute of Technology Department of Computer Science and Engineering Coimbatore India Adama Science and Technology University School of Electrical Engineering and Computing Department of Computer Science and Engineering Nazareth1888 Ethiopia University of Science and Technology Meghalaya Department of Computer Science and Electronics Meghalaya Baridua793101 India Aditya College of Engineering Department of Computer Science and Engineering Andhra Pradesh Surampalem533437 India

ISBN: (纸本)9798350399264

Technology is getting better day by day and human beings are using the latest technology possible for their needs and safety. Many devices have been invented that use the internet and with this human safety issues are also taken care of up to a great extent. GPS tracking devices, SHE (Society Harnessing Equipment), AESHS (Advanced Electronic System for Human Safety), ILA Systems are one of the most useful applications and devices that are used for human safety. Human safety and especially the safety of girls have become an emerging and sensitive issue in the present world scenario. This article deals with the role of the Internet of Things in advancing smart security. The issues are taken care of with the help of these upgraded devices that are easily accessible from a remote location with an internet connection. © 2023 IEEE.

关键词： Internet of things

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Optimized Smart Agricultural Production Systems using Enhanced Parallel Green Anaconda Convolutional Neural Network 8

Optimized Smart Agricultural Production Systems using Enhanc...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Meena, S. Gomathi Goyal, Himanshu Rai Lavanya Muthukumar, E. Megha, Mamtaben Kamleshbhai Parikh, Swapnil Peri College of Arts and Science Pg Department of Computer Science and Computer Applications Tamil Nadu Mannivakkam600048 India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun248 002 India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Viruthunagar626126 India Nehru College of Management Department of Mba Coimbatore Tamil Nadu Thirumalayampalayam641105 India Natubhai V. Patel College of Pure and Applied Sciences A Constituent College of Cvm University Department of Computer Science Gujarat Vallabh Vidyanagar388120 India Parul University Parul Institute of Engineering and Technology Department of Computer Science and Engineering Post Limda Gujarat Waghodia391760 India

ISBN: (纸本)9798350376425

Smart agriculture, driven by IoT technologies, is becoming increasingly essential due to global population growth and rising food demands. However, challenges such as energy supply constraints and routing issues often result in data transmission failures, high energy consumption, reduced network lifetime, and delays. To address these issues, this study proposes a novel solution: an enhanced smart agricultural production system based on an Optimized Smart Agricultural Production Systems using Enhanced Parallel Green Anaconda Convolutional Neural Network (EPar-GrAnCNN). This system enhances energy management by incorporating the Emperor Penguin Optimizer (EPO) for the formation and selection of cluster heads, aiming to increase power conservation and extend network lifespan. Additionally, the system leverages deep learning techniques, including the Enhanced Parallel Convolutional Neural Networks (EPar-CNN) and Green Anaconda Optimization (GAO), to analyze agricultural data and extract valuable insights. The EPar-GrAnCNN model accurately predicts crop health, the severity of water deficits, and other factors affecting crop growth, guiding farmers in the optimal utilization of resources. computer experiments conducted on the Python platform demonstrate that the proposed approach outperforms existing methods, achieving higher throughput (98.4%), a superior packet delivery ratio (99.3%), and lower energy consumption (0.30 mJ). These results highlight the efficiency of the approach and its potential for further advancement in smart agriculture. © 2024 IEEE.

关键词： Emperor Penguin Optimizer (EPO) Enhanced Parallel Convolutional Neural Network (EPar-CNN) Green Anaconda Optimization (GAO) Internet of Things (IoT) Smart Agricultural Systems

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Application of Machine Learning for Online Reputation Systems

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International Journal of Automation and computing 2021年第3期18卷 492-502页

作者： Ahmad Alqwadri Mohammad Azzeh Fadi Almasalha Department of Computer Science Applied Science Private UniversityAmman 11931Jordan Department of Software Engineering Applied Science Private UniversityAmman 11931Jordan

Users on the Internet usually require venues to provide better purchasing *** can be provided by a reputation system that processes ratings to provide *** rating aggregation process is a main part of reputation systems to produce global opinions about the product *** methods that are frequently used do not consider consumer profiles in their calculations and cannot discover unfair ratings and trends emerging in new *** sophisticated rating aggregation methods that use a weighted average technique focus on one or a few aspects of consumers′profile *** paper proposes a new reputation system using machine learning to predict reliability of consumers from their *** particular,we construct a new consumer profile dataset by extracting a set of factors that have a great impact on consumer reliability,which serve as an input to machine learning *** predicted weight is then integrated with a weighted average method to compute product reputation *** proposed model has been evaluated over three Movie Lens benchmarking datasets,using 10-folds cross ***,the performance of the proposed model has been compared to previous published rating aggregation *** obtained results were promising which suggest that the proposed approach could be a potential solution for reputation *** results of the comparison demonstrated the accuracy of our ***,the proposed approach can be integrated with online recommendation systems to provide better purchasing recommendations and facilitate user experience on online shopping markets.

关键词： Reputation system rating aggregation machine learning consumer reliability user trust

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