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9th International Conference on Future Internet of Things and Cloud, FiCloud 2022

作者： Sheibani, Morteza Konur, Savas Awan, Irfan University of Bradford Faculty of Engineering and Informatics Department of Computer Science Bradford United Kingdom

ISBN: (数字)9781665493505

ISBN: (纸本)9781665493505

The increased risk of Distributed Denial-of Service (DDoS) attacks is one of the crucial security issues for 5G networks. Software defined networking (SDN) is one of the key solutions for the centralised control of network behavior for detection and mitigation of network attacks. Single centralised controllers for the SDN-based 5G networks lead to many problems concerning scalability, reliability and other system-related performance. In this respect, employing dispersed controllers will provide a vital solution. However, there are still some issues such as how to equalise the processing burden among these controllers and how to employ multi-controllers to improve the security aspects of the SDN-based 5G networks. In this paper, we propose a new method to equalise the processing burden among the dispersed controllers in SDN-based 5G networks in a dynamic manner. The proposed system employs two levels of controllers: dispersed controllers and a master controller. The master controller manages and performs the grouping of the dispersed controllers. The decision of the groups' reconstruction is the responsibility of the master controller and it is based on the processing burden of the dispersed controllers. The proposed dispersed algorithm is employed in 5G networks to detect and mitigate the DDoS attacks. The simulation results prove that the proposed system performs well in equalising the processing burden among controllers and detection and mitigation of DDoS attacks. © 2022 IEEE.

关键词： Controllers

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Implementation of Convolutional Neural Network for COVID19 Screening using X-Rays Images

Implementation of Convolutional Neural Network for COVID19 S...

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2023 International Conference on computer Science, Information Technology and engineering, ICCoSITE 2023

作者： Delimayanti, Mera Kartika Mardiyono, Anggi Warsuta, Bambang Puspitaningrum, Eka Suci Naryanto, Rizqi Fitri Naryaningsih, Agustien Politeknik Negeri Jakarta Department of Computer and Informatics Engineering Indonesia Universitas Negeri Semarang Faculty of Engineering Department of Mechanical Engineering Indonesia Fisheries Research Center Indonesia

ISBN: (纸本)9798350320954

Various unwelcome conditions have existed since the introduction of the novel coronavirus illness (COVID-19). COVID-19 can cause fever, muscle soreness, shortness of breath, cough, headache, and other symptoms. Diagnosis at an early stage is a crucial aspect of successful treatment. Therefore, it is necessary to seek out alternate methods for COVID-19 detection. Among the existing imaging resources, X-ray images are generally accessible and inexpensive. Consequently, an alternate diagnostic tool for detecting COVID-19 instances is provided using available resources. In the first stages of COVID-19, X-rays detected the disease before it spread to the lungs and caused more damage. Machine learning models can help clinicians accomplish jobs more quickly and accurately. In addition to chest X-ray pictures and fundus images, deep learning algorithms have been used to diagnose illnesses. This research was conducted to classify the X-ray chest images in COVID-19 and normal cases based on the public datasets which were used. This analysis uses 5600 images from the accessible resources, and a Convolutional Neural Network (CNN) architecture with the VGG16 algorithm was employed to diagnose COVID19. VGG16 is object identification and classification method that can classify with greater precision than most other deep learning algorithms. Transfer learning and fine-tuning were employed to help for improving the performance. The results showed that the VGG16 network had an accuracy of 98.13%. This research has implications for the early detection of COVID-19 by using X-ray images. The experiment and analysis reveal our suggested method's promising and stable performance compared to the current standard. © 2023 IEEE.

关键词： COVID-19

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Mask Detection Using Convolutional Neural Network

Mask Detection Using Convolutional Neural Network

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2022 International Seminar on Application for Technology of Information and Communication, iSemantic 2022

作者： Abdussalam Pramudya, Elkaf Rachmawan Setiono, Oki Muslih, Muslih Dian Nuswantoro University Faculty of Computer Science Department of Informatics Engineering Semarang Indonesia

ISBN: (数字)9781665488396

ISBN: (纸本)9781665488396

COVID-19 virus has hit Indonesia since early March 2020. One of the government's efforts to prevent the spread of COVID-19 is to do physical distancing to require people to wear masks when doing activities outside the home. One way to overcome this problem is by detecting mask users to be more obedient and obedient to the rules, then the identification process is carried out for mask users and those who do not use masks. The process is carried out using the Convolutional Neural Network method. CNN is known to be superior and does not require pre-processing so it saves more time. In terms of algorithmic competence, CNN is considered capable of carrying out the data detection process well. Of the 1376 datasets used, 30 epochs, accuracy = 0.988, recall = 0.990, precision = 0.987, and F1 = 0.988 with the required detection time for each image between 4 to 5 seconds. © 2022 IEEE.

关键词： Convolutional neural networks

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Data Augmentation of Morphological Brain Network Using Multi-variate Normal Distribution 7

Data Augmentation of Morphological Brain Network Using Multi...

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7th International Conference on computer Science and engineering, UBMK 2022

作者： Celik, Mustafa Rekik, Islem Istanbul Technical University Basira Lab Faculty of Computer and Informatics Engineering Istanbul Turkey

ISBN: (数字)9781665470100

ISBN: (纸本)9781665470100

Multi-view Brain Networks (MBNs) have made a significant contribution to the diagnosis of neurological disorders using computers. MBNs can boost the classification accuracy of neurological disorders such as Alzheimer's disease (AD) by capturing diverse characteristics from multiple perspectives of a population. MBNs provide rich data diversity and better generalization for the classifier models, but the classifiers require large-sized dataset to avoid being over-trained. Moreover, it is challenging to collect clinical data because it is scarce and expensive. In this work, we present a method that augments fake (i.e., synthetic) data samples to boost the neurological disorder classification models. It has been shown that the classifiers trained on our synthetic samples achieve better results than those trained on synthetic samples of traditional data augmentation techniques. © 2022 IEEE.

关键词： Classification (of information)

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Face image authentication scheme based on MTCNN and SLT

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Multimedia Tools and Applications 2025年 1-43页

作者： Thabit, Rasha Al-Askari, Mohanad A. Mohammed, Dunya Zeki Anaam, Elham Abdulwahab Mahmood, Zainab H. Jabbar, Dina Jamal Salih, Zahraa Aqeel Information System Department College of Computer Sciences and Information Technology University of Al-Anbar Al-Anbar Iraq Computer Engineering Department College of Engineering Al-Iraqia University Baghdad Iraq Electronic and Communications Engineering Department College of Engineering Gilgamesh University Baghdad Iraq Faculty of Computing and Informatics Multimedia University Selangor Cyberjaya Malaysia Department of Cybersecurity Engineering College of Information Engineering Al-Nahrain University Baghdad Iraq Electrical Engineering Technical Collage Middle Technical University Baghdad Iraq Department of Business Administration Alamaal University College Baghdad Iraq

DeepFakes and face image manipulation methods have been widely distributed in the last few years and several techniques have been presented to check the authenticity of the face image and detect manipulation if exists. Most of the available manipulation detection techniques have been successfully applied to reveal one type of manipulation under specific conditions, however, many limitations and challenges can be encountered in this field. To overcome some limitations and challenges, this paper presents a new face image authentication (FIA) scheme based on Multi-Task Cascaded Conventional Neural Networks (MTCNN) and watermarking in Slantlet transform (SLT) domain. The proposed FIA scheme has three main algorithms that are face detection and selection, embedding, and extraction algorithms. Different block sizes have been used to divide the image into non-overlapping blocks followed by classifying them into two groups that are blocks from face area (FA) and blocks from the remaining area (RA) of the image. In the embedding algorithms, the authentication information is generated from FA blocks and embedded in the RA blocks. In the extraction algorithms, the embedded information is extracted from RA blocks and compared with the calculated data from FA blocks to reveal manipulations and localize the manipulated blocks if exist. Extensive experiments have been conducted to evaluate the performance of the proposed FIA scheme for different face images. The experimental work included tests for payload, capacity, visual quality, time complexity, and localization of manipulations. The results proved the efficiency of the proposed scheme in detecting and localizing different face image manipulations such as attributes attacks, retouching attacks, expression swap, face swap, and morphing attacks. The proposed scheme overcomes many limitations and it is 100% accurate in localizing the tampered blocks which makes it a better candidate for practical applications. © The Author(s), un

关键词： Authentication

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Trimmed Non-Local Means Filtering for the Suppression of Mixed Noise in Color Images

Trimmed Non-Local Means Filtering for the Suppression of Mix...

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2023 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Kusnik, Damian Smolka, Bogdan Smolka, Milena Silesian University of Technology Faculty of Automatics Electronics and Informatics Gliwice Poland Agh University of Science and Technology Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Krakow Poland

ISBN: (纸本)9798350322972

In this paper, a novel approach to the problem of mixed Gaussian and impulsive noise reduction in color images is proposed. The elaborated denoising framework is based on the Non-Local Means technique, which has been shown to effectively suppress Gaussian noise. To overcome the inability of this filter to deal with impulsive distortions, a robust similarity measure between image patches, that is insensitive to the impact of impulsive distortions, was developed. First, the distorted pixels of the image are detected using the concept of a peer group. If the number of neighbors of a pixel at the center of a filtering window, whose Euclidean distance in the RGB color space does not exceed a threshold value, is smaller than a predefined value, then the considered pixel is treated as corrupted. Afterwards, the similarity between image patches are calculated in a standard way omitting the noisy pixels. In this way, only the average of distances between pairs of unaffected pixels are calculated. Thus, the closeness measure of the patches, which serves as the coefficient in weighted averaging of pixels in the processing block, is not affected by impulsive noise and is still able to cope with the Gaussian noise component. The proposed filtering design is capable of reducing even strong mixed noise while preserving image details and edges. Moreover, it is not computationally intensive, so it can be used in real-time image processing scenarios. © 2023 IEEE.

关键词： Pixels

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Adaptive Technique of Impulsive Noise Removal in Color Digital Images 10

Adaptive Technique of Impulsive Noise Removal in Color Digit...

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10th International Conference on Electrical and Electronics engineering, ICEEE 2023

作者： Smolka, Bogdan Mendrela, Monika Kusnik, Damian Smolka, Milena Silesian University of Technology Faculty of Automatics Electronics and Informatics Gliwice Poland Agh University of Science and Technology Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Krakow Poland

ISBN: (纸本)9798350304299

This paper describes a new technique for removing impulsive noise in digital color images. The proposed method uses a measure of the pixel distortion caused by the impulsive noise, expressed as the sum of the Euclidean distances between the central pixel and its closest neighbors in the filtering window. This corruption measure is used to determine a weighted average of the pixels from the local neighborhood that is not affected by the distorted pixels. The filtering output is obtained using a soft switching scheme, where the denoising result is determined as a weighted average of the central pixel of a processing window and its robust estimate. The weighting coefficient is calculated based on the amount of distortion of the central pixel. Thus, if the central pixel is highly distorted, it is replaced by its robust estimate, otherwise it is not significantly changed. The parameters that determine the image enhancement result are adaptively adjusted by using a noise-resistant image contrast measure. The proposed filter design is able to effectively suppress impulsive noise while preserving fine image details and edges. Comparison with existing algorithms shows that the proposed method provides better denoising results than the most efficient filters designed for impulsive noise suppression in color images. Moreover, the new method is very fast and can be used in real-time denoising scenarios. © 2023 IEEE.

关键词： Image enhancement

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Auto-Handover Gateway LoRaWAN based on Autonomous Vehicles 10

Auto-Handover Gateway LoRaWAN based on Autonomous Vehicles

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10th IEEE Information Technology International Seminar, ITIS 2024

作者： Purnama, Iwan Pane, Rahmadani Sooai, Adri Gabriel Edi, Firman Chamid, Ahmad Abdul Adi, Puput Dani Prasetyo Universitas Labuhanbatu Faculty of Science and Technology Department of Information Technology Labuhanbatu Indonesia Universitas Katolik Widya Mandira Faculty of Computer Science Department of Computer Engineering NTT Kupang Indonesia Batam Institute of Technology Faculty of Industrial Technology Department of Engineering Management Batam City Indonesia Muria Kudus University Department of Informatics Engineering Kudus Indonesia Department of Telecommunication Bandung Indonesia

ISBN: (纸本)9798331521295

This article presents a simulation study on the use of LoRaWAN technology in autonomous vehicles. The research focuses on developing an Auto-Handover Gateway for Autonomous Vehicles, utilizing LoRaWAN parameters such as bit rates, spreading factor (SF), packet loss, and received signal strength. These parameters are tested in simulations of autonomous vehicle movements, which include both regular and random motion patterns. The aim is to assess the performance of LoRaWAN in different scenarios and its impact on communication in autonomous vehicle systems. The simulation results show several key trends. As the vehicle moves further, the spreading factor (SF) increases, and bit rates tend to decrease, indicating a drop in communication performance over longer distances. The research also identifies that the gateway with the closest proximity to the vehicle is selected, both in random and non-random movement scenarios. Additionally, packet loss increases as the distance between the vehicle and the gateway grows, highlighting the challenges of maintaining reliable communication over long distances. These findings provide insights into optimizing LoRaWAN parameters for real-time communication in autonomous vehicles, contributing to the development of intelligent vehicle networks. © 2024 IEEE.

关键词： Packet loss

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Deep-Net:Fine-Tuned Deep Neural Network Multi-Features Fusion for Brain Tumor Recognition

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computers, Materials & Continua 2023年第9期76卷 3029-3047页

作者： Muhammad Attique Khan Reham R.Mostafa Yu-Dong Zhang Jamel Baili Majed Alhaisoni Usman Tariq Junaid Ali Khan Ye Jin Kim Jaehyuk Cha Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Informatics University of LeicesterLeicesterUK Information Systems Department Faculty of Computers and Information SciencesMansoura UniversityMansouraEgypt Department of Computer Engineering College of Computer ScienceKing Khalid UniversityAbha61413Saudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Management Information System Department College of Business AdministrationPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

Manual diagnosis of brain tumors usingmagnetic resonance images(MRI)is a hectic process and ***,it always requires an expert person for the ***,many computer-controlled methods for diagnosing and classifying brain tumors have been introduced in the *** paper proposes a novel multimodal brain tumor classification framework based on two-way deep learning feature extraction and a hybrid feature optimization ***-Mobile,a pre-trained deep learning model,has been fine-tuned and twoway trained on original and enhancedMRI *** haze-convolutional neural network(haze-CNN)approach is developed and employed on the original images for contrast ***,transfer learning(TL)is utilized for training two-way fine-tuned models and extracting feature vectors from the global average pooling ***,using a multiset canonical correlation analysis(CCA)method,features of both deep learning models are fused into a single feature matrix—this technique aims to enhance the information in terms of features for better *** the information was increased,computational time also *** issue is resolved using a hybrid feature optimization algorithm that chooses the best classification *** experiments were done on two publicly available datasets—BraTs2018 and BraTs2019—and yielded accuracy rates of 94.8%and 95.7%,*** proposedmethod is comparedwith several recent studies andoutperformed *** addition,we analyze the performance of each middle step of the proposed approach and find the selection technique strengthens the proposed framework.

关键词： Brain tumor haze contrast enhancement deep learning transfer learning features optimization

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Implementation of the SMART Method and Location Based Service Technology in a Sewing Service Search Application 9

Implementation of the SMART Method and Location Based Servic...

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9th IEEE Information Technology International Seminar, ITIS 2023

作者： Mumpuni, Retno Santoso, Bagus Jati Wahanani, Henni Endah Rahajoe, Ani Dijah Maulana, Hendra Maghfiro, Feronika Nur Universitas Pembangunan Nasional 'Veteran' Jawa Timur Faculty of Computer Science Department of Informatics Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia

ISBN: (纸本)9798350306835

The tailoring business involves services where tailors create or repair clothing. Currently, the number of tailors with various skill sets is on the rise. However, customers often struggle to find tailors who meet their specific needs. They have to spend time visiting tailor shops, just to assess whether a tailor is a suitable match or to place an order, even when the distance and location are uncertain. This inefficiency hampers the process. In this rapidly advancing technological era, many businesses leverage technology to promote their services, including the tailoring industry. To address this issue, the author aims to develop an Android-based Tailor Locator Application that utilizes Location Based Services (LBS) and the SMART Method. This application will utilize LBS technology to display the distance and location between customers and tailors, while continuously updating the location information of both customers and tailors. The application will also implement the SMART method to rank tailors based on ratings and provide tailor recommendations. © 2023 IEEE.

关键词： Location based services

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