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10th International Conference on Optimization and Applications, ICOA 2024

作者： El Majdouby, Karima El Mohadab, Mohamed Daoud, Rachid Ait Abouelmehdi, Karim University of Chouaib Doukkali Elites laboratory Faculty of Sciences Computer Science department Eljadida Morocco Sultan Moulay Slimane University Systems Engineering Laboratory Computer Science department Beni Mellal Morocco

ISBN: (纸本)9798350387353

Agriculture is evolving towards more sustainable practices thanks to the integration of the machine learning and Internet of Things, which addresses many of the issues related to agricultural production and leads to increased yields with reduced input and labor costs. Smart farming has developed solutions to many conventional agricultural problems to automatically manage and monitor farmland. These technologies reduce the risk of field management in the event of bad weather, and also provide benefits to farmers in the event of labor shortages. The Internet of Things ensures the connection of physical objects that are equipped with sensors to the internet, enabling them to collect and exchange data and communicate with each other and with computer systems to gather information, perform analysis and take actions in real time that require rapid data analysis, and this task can be accomplished through machine learning algorithms with greater efficiency and high-quality decision-making. In addition, we carry out an in-depth state-of-the-art survey to identify very recent work that has implemented the Internet of Things and machine learning in smart agriculture, analyzing the development of practical solutions and discussing recent research trends and future prospects. © 2024 IEEE.

关键词： Internet of things Machine learning Smart Agriculture

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Comprehensive Approach for Image Noise Analysis: Detection, Classification, Estimation, and Denoising 7th

Comprehensive Approach for Image Noise Analysis: Detection, ...

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7th International Conference on Microelectronics and Telecommunication engineering, ICMETE 2023

作者： Al Mudhafar, Rusul A. El Abbadi, Nidhal K. Computer Science Department Faculty of Computer Science and Mathematics University of Kufa Najaf Iraq Computer Techniques Engineering Department Al-Mustaqbal University Babylon Iraq

ISBN: (纸本)9789819995615

Image noise is undesirable that can negatively affect the quality of digital images. It reduces the image quality and increases the processing failure ratio. It is highly recommended to remove the noise, and before removing the noise, we have to know the type of noise and estimate the parameters of noise for developing effective noise reduction techniques. This study introduces a method to effectively detect, recognize, and estimate image noise of various types (Gaussian, lognormal, Rayleigh, salt and pepper, and speckle). The proposed model consists of four stages: the first stage is detecting the noise in an image using a convolutional neural network. The second stage classifies the noisy images into one of five types of noise using a new method based on a combination of deep wavelets and support vector machines (SVM) classifier. The third stage involves estimating the parameters of the noise using maximum likelihood estimation (MLE). Finally, choosing the most suitable noise reduction technique for each type using linear and nonlinear filters and showing the capability of the suggested technique in estimating multiple noises commonly present in digital images. The proposed method utilizes a likelihood function derived from the MLE model for each noise type to estimate the noise parameters. Then used to select the most suitable noise reduction technique for each type. The quality of the denoised images is calculated utilizing the peak signal-to-noise ratio (PSNR) as the evaluation metric. The results show that the combination of wavelets with machine learning, specifically SVM, can highly enhance the results, where the accuracy was 93.043% through many experiments conducted to build a sturdy classification model. The MLE-based noise estimation method is also a reliable and accurate method for image noise estimation, especially for Gaussian, salt and pepper, lognormal, and Rayleigh noise. However, for highly noisy types such as speckle noise, further research is re

关键词： Maximum likelihood estimation

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Assessment of Spring Water Security for Small Community Using Internet of Things (IoT) 7th

Assessment of Spring Water Security for Small Community Usin...

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7th International Conference on Architecture and Civil engineering, ICACE 2023

作者： Nadzri, Mohamad Nazrul Hafiz Mohd Othman, Maidiana Razali, Noor Afiza Mat Nordin, Norzaima Department of Civil Engineering Faculty of Engineering Universiti Pertahanan Nasional Malaysia Kuala Lumpur Malaysia Department of Computer Science Faculty of Defence Science and Technology Universiti Pertahanan Nasional Malaysia Kuala Lumpur Malaysia Department of Aeronautic and Aviation Engineering of Computer Science Faculty of Defence Science and Technology Universiti Pertahanan Nasional Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9789819707508

The securing of clean and sustainable water sources is a fundamental entitlement of individuals and an essential factor in the sustained advancement of societies. Alternative water resources, springs can serve as a primary water source for small, remote communities. The preservation of the security and quality of spring water is of the highest priority. This study provides an assessment of the security of spring water resources within the community, facilitated through the utilization of an Internet of Things (IoT) monitoring system. The study aimed to assess the variations in temperature, turbidity, pH, TDS, and electrical conductivity of spring water on the NDUM campus over the course of a day, specifically during the morning, afternoon, and evening periods. The monitoring was conducted over a span of one year to capture potential patterns and changes. The obtained data is sent to the cloud by using an IoT-based ThingSpeak application to monitor the quality of water. As a result of the collected data, the system provides an efficient water quality parameter monitoring system, and early warning can be taken to reduce any harmful effects to the environment. The outcome of this study may contribute to the proposal of a new way of monitoring water quality on campus by using a real-time system. In addition, the outcome also provides baseline information about water quality for the welfare of society and supports the green campus campaign, which may also help in future water security and sustainability planning for the NDUM campus area. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Water quality

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ResMHA-Net:Enhancing Glioma Segmentation and Survival Prediction Using a Novel Deep Learning Framework

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computers, Materials & Continua 2024年第10期81卷 885-909页

作者： Novsheena Rasool Javaid Iqbal Bhat Najib Ben Aoun Abdullah Alharthi Niyaz Ahmad Wani Vikram Chopra Muhammad Shahid Anwar Department of Computer Science School of Engineering and TechnologyIslamic University of Science and TechnologyAwantipora192122India Faculty of Computing and Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia Research Groups in Intelligent Machines National School of Engineers of SfaxUniversity of SfaxSfax3038Tunisia Department of Electrical Engineering College of EngineeringKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatiala147004Punjab Department of Electrical and Instrumentation Engineering Thapar Institute of Engineering and TechnologyPatiala147004Punjab Department of AI and Software Gachon UniversitySeongnam-si13120Republic of Korea

Gliomas are aggressive brain tumors known for their heterogeneity,unclear borders,and diverse locations on Magnetic Resonance Imaging(MRI)*** factors present significant challenges for MRI-based segmentation,a crucial step for effective treatment planning and monitoring of glioma *** study proposes a novel deep learning framework,ResNet Multi-Head Attention U-Net(ResMHA-Net),to address these challenges and enhance glioma segmentation ***-Net leverages the strengths of both residual blocks from the ResNet architecture and multi-head attention *** powerful combination empowers the network to prioritize informative regions within the 3D MRI data and capture long-range *** doing so,ResMHANet effectively segments intricate glioma sub-regions and reduces the impact of uncertain tumor *** rigorously trained and validated ResMHA-Net on the BraTS 2018,2019,2020 and 2021 ***,ResMHA-Net achieved superior segmentation accuracy on the BraTS 2021 dataset compared to the previous years,demonstrating its remarkable adaptability and robustness across diverse ***,we collected the predicted masks obtained from three datasets to enhance survival prediction,effectively augmenting the dataset *** features were then extracted from these predicted masks and,along with clinical data,were used to train a novel ensemble learning-based machine learning model for survival *** model employs a voting mechanism aggregating predictions from multiple models,leading to significant improvements over existing *** ensemble approach capitalizes on the strengths of various models,resulting in more accurate and reliable predictions for patient ***,we achieved an impressive accuracy of 73%for overall survival(OS)prediction.

关键词： Glioma MRI segmentation multihead attention survival prediction deep learning

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Design of an Efficient and Provable Secure Key Exchange Protocol for HTTP Cookies

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computers, Materials & Continua 2024年第7期80卷 263-280页

作者： Waseem Akram Khalid Mahmood Hafiz Burhan ul Haq Muhammad Asif Shehzad Ashraf Chaudhry Taeshik Shon Graduate School of Engineering Science and Technology National Yunlin University of Science and TechnologyYunlin64002TaiwanChina Future Technology Research Center National Yunlin University of Science and TechnologyYunlin64002TaiwanChina Department of Computer Science Lahore Garrison UniversityLahore54920Pakistan Department of Computer Science and Information Technology College of EngineeringAbu Dhabi UniversityAbu Dhabi69911United Arab Emirates Department of Software Engineering Faculty of Engineering and ArchitectureNisantasi UniversityIstanbul34398Turkey Department of Cybersecurity Ajou UniversitySuwon16499Republic of Korea

Cookies are considered a fundamental means of web application services for authenticating various Hypertext Transfer Protocol(HTTP)requests andmaintains the states of clients’information over the *** cookies are exploited to carry client patterns observed by a *** client patterns facilitate the particular client’s future visit to the corresponding ***,security and privacy are the primary concerns owing to the value of information over public channels and the storage of client information on the *** protocols have been introduced that maintain HTTP cookies,but many of those fail to achieve the required security,or require a lot of resource *** this article,we have introduced a lightweight Elliptic Curve Cryptographic(ECC)based protocol for authenticating client and server transactions to maintain the privacy and security of HTTP *** proposed protocol uses a secret key embedded within a *** proposed protocol ismore efficient and lightweight than related protocols because of its reduced computation,storage,and communication ***,the analysis presented in this paper confirms that proposed protocol resists various known attacks.

关键词： Cookies authentication protocol impersonation attack ECC

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Segmentation Of Intracerebral Haemorrhage CT-Scan Images Using N-Net Architecture 3

Segmentation Of Intracerebral Haemorrhage CT-Scan Images Usi...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Santhi, P. Maheswari, S. Deepa, K. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai Avadi India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Tiruchirapalli Avadi India

ISBN: (纸本)9798350369083

In recent years, image segmentation has emerged as a critical task in computer vision and various applications such as medical image processing, autonomous driving, and satellite imagery analysis. Deep learning techniques, particularly convolutional neural networks (CNNs), have played a pivotal role in revolutionizing image segmentation by offering unprecedented accuracy and efficiency. Many algorithms have used for the segmentation such as U-Net, V-Mesh, U-Net++ and etc. But, U-Net having the challenges in parameter transfer, model size and in speed. To overcome these issues, this paper explores the evolution of image segmentation techniques into ICH (Intracerebral Haemorrhage) for better diagnosis. This model is tested using ICH dataset available in Kaggle. In which, N-Net have produced the better performance in terms of precision, Dice, Recall and F1 Score. © 2024 IEEE.

关键词： Mesh generation

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A DL-DeNoiseNet Framework for Robust Image Denoising and Artifact Reduction in Medical Image Diagnostics 2

A DL-DeNoiseNet Framework for Robust Image Denoising and Art...

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2nd IEEE International Conference on Integrated Intelligence and Communication systems, ICIICS 2024

作者： Nanthini, S. Maindola, Meenakshi Madhu, R. Ravishankar, M.D. Gandhi, Jay Patil, Harshal Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Mechanical Engineering The National Institute of Engineering Mysore India Faculty of Engineering and Technology Parul institute of Engineering and Technology Vadodara India School of Computer Science and Engineering IILM University Greater Noida India

ISBN: (纸本)9798331504960

For precise illness diagnosis and therapy planning, medical imaging diagnostics are necessary. Nevertheless, diagnostic errors can occur due to noise and aberrations that are inherent to imaging modalities including CT, MRI, and ultrasound, which can hide important features. In order to overcome this obstacle, we present a medical diagnostics-specific framework for deep learning-based picture denoising and artifact reduction. Our method improves feature extraction while maintaining picture integrity by combining a Convolutional Neural Network (CNN) with a Residual Dense Block (RDB). To guarantee optimal performance across varying degrees of noise and image types, the framework is trained on a broad dataset of medical photos, which includes images that are artifact-laden and noisy. A combination of supervised and adversarial learning techniques is used for this purpose. Adaptive filtering for preprocessing, the CNN-RDB network for denoising, and a refinement module that uses perceptual loss to keep things clinically relevant for postprocessing are the three primary steps in the methodology. The model outperforms traditional denoising methods when tested on various medical imaging metrics, including a PSNR of 40.3 dB and an SSIM of 0.97. Furthermore, the suggested architecture successfully mitigates artifacts by an average of 35% as assessed by the Normalized Root Mean Squared Error (NRMSE), guaranteeing that essential diagnostic characteristics are preserved. The results show that the model can improve image clarity, which means that diagnoses can be made more accurately and fewer scans are needed. Better healthcare outcomes are a direct result of the substantial enhancements to picture quality that this framework provides to physicians and radiologists. © 2024 IEEE.

关键词： Adaptive filtering

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Aerosol jet printing of surface acoustic wave microfluidic devices

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Microsystems & Nanoengineering 2024年第1期10卷 231-242页

作者： Joseph Rich Brian Cole Teng Li Brandon Lu Hanyu Fu Brittany N.Smith Jianping Xia Shujie Yang Ruoyu Zhong James L.Doherty Kanji Kaneko Hiroaki Suzuki Zhenhua Tian Aaron D.Franklin Tony Jun Huang Department of Biomedical Engineering Duke UniversityDurhamNC 27708USA Department of Electrical and Computer Engineering Duke UniversityDurhamNC 27708USA Department of Mechanical Engineering Virginia Polytechnic Institute and State UniversityBlacksburgVA 24061USA Thomas Lord Department of Mechanical Engineering and Materials Science Duke UniversityDurhamNC 27708USA Deptartment of Precision Mechanics Faculty of Science and EngineeringChuo UniversityTokyo 112-8551Japan Department of Chemistry Duke UniversityDurhamNC 27708USA

The addition of surface acoustic wave(SAW)technologies to microfluidics has greatly advanced lab-on-a-chip applications due to their unique and powerful attributes,including high-precision manipulation,versatility,integrability,biocompatibility,contactless nature,and rapid ***,the development of SAW microfluidic devices is limited by complex and time-consuming micro/nanofabrication techniques and access to cleanroom facilities for multistep photolithography and vacuum-based *** simplify the fabrication of SAW microfluidic devices with customizable dimensions and functions,we utilized the additive manufacturing technique of aerosol jet *** successfully fabricated customized SAW microfluidic devices of varying materials,including silver nanowires,graphene,and poly(3,4-ethylenedioxythiophene)polystyrene sulfonate(PEDOT:PSS).To characterize and compare the acoustic actuation performance of these aerosol jet printed SAW microfluidic devices with their cleanroom-fabricated counterparts,the wave displacements and resonant frequencies of the different fabricated devices were directly measured through scanning laser Doppler ***,to exhibit the capability of the aerosol jet printed devices for lab-on-a-chip applications,we successfully conducted acoustic streaming and particle concentration ***,we demonstrated a novel solution-based,direct-write,single-step,cleanroom-free additive manufacturing technique to rapidly develop SAW microfluidic devices that shows viability for applications in the fields of biology,chemistry,engineering,and medicine.

关键词： printing wave fluid

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The Optimum Switching Bilateral of Various Mix Noise Filters to reduce Medical X-Ray Images 12

The Optimum Switching Bilateral of Various Mix Noise Filters...

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12th International Electrical engineering Congress, iEECON 2024

作者： Langampol, Kriengkri Prayote, Akara Lee, Wilaiporn Srisomboon, Kanabadee Rajamangala University Of Technology Phra Nakhon Faculty Of Engineering Department Of Computer Engineering Thailand King Mongkut's University Of Technology North Bangkok Faculty Of Applied Science Department Of Computer And Information Science Thailand King Mongkut's University Of Technology North Bangkok Faculty Of Engineering Department Of Electrical And Computer Engineering Thailand

ISBN: (纸本)9798350383591

In the past few years, image processing has been widely adopted for symptom diagnosis of medical application. To achieve accurate analysis, the medical applications require high quality image for applying to the symptom diagnosis applications. However, the medical image quality may drop due to the imperfection of devices and the environment. Noise is one of the most critical factors that reduces the image quality where the noise may form by combining two types of noises. In this paper, we considered four well-known mixed noise filters including TF, BM3D, NCSR and OSBF filters. Since the OSBF filter is the alternative version of SBF filter that requires to determine the optimal weights, therefore we first determine the optimal weights for the medical image under the combination of Gaussian noise and salt-and-pepper noise. Then, we evaluate the filtering performance under three metrics including PSNR, SSIM and filtering time. As a result, OSBF filter achieves the best filtering performance since it can reduce the noise effect, preserve image details and consumes the short filtering time. On the other hand, the filtering performance of the TF filter suffers from the combining of salt-and-pepper noise. © 2024 IEEE.

关键词： Salt and pepper noise

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Absorptive Microwave Filters: From Narrowband to Wideband Operation

Absorptive Microwave Filters: From Narrowband to Wideband Op...

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2024 International Applied Computational Electromagnetics Society Symposium, ACES-China 2024

作者： Cui, Jiaqian Zhu, Lei University of Macau Faculty of Science and Technology Department of Electrical and Computer Engineering Macau China

ISBN: (纸本)9798350355581

This paper presents a method to control the out-ofband performance of absorptive filters in both narrowband and wideband cases. To verify the method, a narrowband absorptive filter is designed with wideband matching, and a wideband one is proposed to achieve an infinite low-reflection range. Finally, the narrowband filter is implemented in microstrip-line platform, not only attaining symmetrical geometry but also improving impedance-matching performance. © 2024 IEEE.

关键词： Wiener filtering

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