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Emerging Technologies for Dependable Internet of Things (ICETI), International Conference on

作者： Salah Al-Hagree Khaled M. Alalayah Mohammed Alnahdi Marwan Alhomdy Ala'a Alawdi Khaled Alhamami Faraj Alsaiari Computer Science Department Faculty of Computer and Information Technology Sana’a University Yemen Computer Science Department Faculty of Sciences Ibb University Yemen Computer Science Aljazeera University Yemen Applied College Imam Abdurahman Bin Faisal University Saudi Arabia Department of Computer Science College of Science and Arts Sharurah Najran University Saudi Arabia

ISBN: (数字)9798331533557

ISBN: (纸本)9798331533564

Early diagnosis plays a critical role in preventing severe complications and fatalities associated with kidney disease, especially in regions like Saudi Arabia, where healthcare access may be limited. Prompt identification of the disease can significantly mitigate its severity and enhance patient outcomes. This study introduces an intelligent system for early kidney disease diagnosis utilizing machine learning (ML) models. We conducted a comparative analysis of various models, such as Random Forest (RF), Logistic Regression (LR), Support Vector Classifier (SVC), Gradient Boosting (GB), K-Nearest Neighbor (KNN), and Decision Tree (DT), with the SVC model achieving the highest accuracy of 72.00%. RF and KNN closely followed with 68.00% accuracy each, while the DT model, despite its interpretability benefits, achieved an accuracy of 56.00%. The interpretability of the DT model could be particularly advantageous in healthcare diagnostics where transparency is crucial. RF exhibited the highest precision at 0.71, proving effective in reducing false positives. The proposed system presents a promising solution for improving early kidney disease diagnosis and could be seamlessly integrated into mobile platforms, offering practical real-world applications for healthcare professionals and patients, especially in underserved areas.

关键词： Analytical models Accuracy Static VAr compensators Nearest neighbor methods Medical diagnosis Intelligent systems Kidney Random forests Diseases Classification tree analysis

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Encryption Algorithm for Securing Non-Disclosure Agreements in Outsourcing Offshore Software Maintenance

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computers, Materials & Continua 2022年第11期73卷 3827-3845页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Nadeem Iqbal Nazri Kama Azri Azmi Ahmad Salman Khan Yasir Mahmood Assad Alzayed Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala TerengganuMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia Computer Science and Information Systems Department College of Business Studies Public Authority for Applied Education and Training(PAAET)AdailiyaKuwait

Properly created and securely communicated,non-disclosure agreement(NDA)can resolve most of the common disputes related to outsourcing of offshore software maintenance(OSMO).Occasionally,these NDAs are in the form of *** the work is done offshore,these agreements or images must be shared through the Internet or stored over the *** breach of privacy,on the other hand,is a potential threat for the image owners as both the Internet and cloud servers are not void of *** article proposes a novel algorithm for securing the NDAs in the form of *** an agreement is signed between the two parties,it will be encrypted before sending to the cloud server or travelling through the public network,the *** the image is input to the algorithm,its pixels would be scrambled through the set of randomly generated rectangles for an arbitrary amount of *** confusion effects have been realized through an XOR operation between the confused image,and chaotic ***,5D multi-wing hyperchaotic system has been employed to spawn the chaotic vectors due to good properties of chaoticity it *** machine experimentation and the security analysis through a comprehensive set of validation metric vividly demonstrate the robustness,defiance to the multifarious threats and the prospects for some real-world application of the proposed encryption algorithm for the NDA images.

关键词： Non-disclosure agreement encryption decryption secret key chaoticmap confusion diffusion

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Early Detection of Breast Cancer using An Improved Deep-Learning Model 2

Early Detection of Breast Cancer using An Improved Deep-Lear...

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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Yousif, Muhamamd Alrababah, Hamza Atif, Muhamamd Ahmad, Munir Khan, Muhamamd Asghar School of Computer Science NCBA&E Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan Skyline University College School of Information Technology University City Sharjah Sharjah1797 United Arab Emirates Institute of Business Management Switzerland

ISBN: (纸本)9798350335644

Deep learning algorithms are now being employed to undertake the inquiry, and preliminary results have been assessed as encouraging. Because they rely on comparably small breast pathology imaging datasets and an overoptimistic version of the softmax-cross entropy loss, popular convolutional neural networks (CNNs) and their derivatives are prone to overfitting in their classifiers. This is because their classifiers are prone to overfitting. Furthermore, these CNNs and their variants employ an unduly pessimistic application of the softmax-cross entropy loss. To perform computations, these CNNs use a modified version of the overconfident softmax-cross entropy loss approach. This is the outcome of the union of these two factors. AlexNet, VGGNet, and GoogleNet are a few examples of these networks. This is because both occurrences result in the loss of the overconfident softmax-cross entropy. This is the root of the problem. We present the AlexNet-BC model to alleviate overfitting and improve classification accuracy. It is a cutting-edge diagnostic paradigm for breast illness. This allows us to ensure the accuracy of our forecasts. This allows us to penalize overly pessimistic low-entropy output distributions while also ensuring that our forecasts appropriately represent uniform distributions. It also allows us to ensure the consistency of our output distributions. The proposed method will next be tested using datasets provided by BreaKHis, IDC, and UCSB in comparison to existing methodologies considered state-of-the-art. This will be accomplished by contrasting the proposed method with other strategies considered cutting-edge. The studies' findings show that the proposed approach outperforms what are now considered cutting-edge technologies across a wide range of zoom levels. Because of the high level of consistency and generalizability that it delivers, it has the potential to be a valuable tool in histopathological clinical computer-aided diagnosis systems. The worth o

关键词： Convolutional neural networks

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An Improved Convolutional Neural Network for the Image Enhancement 2

An Improved Convolutional Neural Network for the Image Enhan...

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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Fiaz, Muhammad Al-Khasawneh, Mahmoud Ahmad Irtaza, Ghulam School of Computer Science NCBA&E Lahore Pakistan Applied Science Private University Applied Science Research Center Amman 11937 Jordan Skyline University College School of Information Technology University City Sharjah Sharjah 1797 United Arab Emirates University of Education Lahore Pakistan

Four-dimensional cone-beam computed tomography, or 4D CBCT, is a technique used in image-guided radiation treatment to generate a series of phase-resolved reconstructions of the patient's anatomy. This is done to improve radiation therapy's capacity to target the tumour with more precision. However, both noise and streaking aberrations significantly reduce the quality of 4D CBCT pictures. This technique is known as "very sparse-view CT,"and it gets its name from the fact that it only requires a limited number of under-sampled projections to reconstruct each phase of a phase-resolved picture. The strategy's name derives from the fact that it employs a limited number of predictions to achieve its objectives. Customers can select between two specialized Convolution Neural Network (CNN) models: N-Net and the proposed model. These models were developed to boost our confidence in the quality of the 4D CBCT images we provide. To execute this assignment, it was critical to have access to a large dataset. This is now achievable because of research that resulted in an advancement in 4D CBCT that made it practicable. Each phase-resolved picture produced by the N-Net network, which is presently under development, is expected to outperform the image produced before it in the production chain. This earlier picture was rebuilt using U-Net using the complete projection data. This was done to demonstrate the effectiveness of the N-Net technique. This is done to ensure that the reconstructed picture has the maximum possible level of accuracy. The proposed model network is based on the N-Net foundation and takes into consideration the temporal correlations visible in phase-resolved pictures. After assessing the validity of the suggested CNNs using XCAT simulation data, further testing was performed utilizing real patient 4D CBCT datasets to corroborate the findings. We went through all of this difficulty in order to assist CNN. Each of these networks exceeds the most powerful CNN mode

关键词： Image enhancement

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Backhoe Detection in Surveillance Area from Aerial Imagery 23

Backhoe Detection in Surveillance Area from Aerial Imagery

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Proceedings of the 2023 International Joint Conference on Robotics and Artificial Intelligence

作者： Khantharat Anekboon Nattapat Pratchayatiwat Department of Computer and Information Science Faculty of Applied Science King Mongkut's University of Technology North Bangkok Thailand

ISBN: (纸本)9798400707704

The rapid expansion of urban infrastructure and construction projects require efficient monitoring and security measures in surveillance areas. Traditional methods of ground-level surveillance have limitations such as time-consuming, small exploring areas, and requiring a lot of people or ground-level surveillance devices. In response to this challenge, aerial images are valuable and widely used for various reasons in different fields. They can be used to monitor various areas from the top view. This study provides a method for the detection of backhoes in the surveillance area using aerial images with modified Faster-RCNN. Initially, aerial images were sent to the Resnet-101 structured convolution network to automatically extract features from the input image. The outcome will be the input of two sub-networks, a region proposal network and a Fast-RCNN. This proposed method modified the anchor box sizes in the region proposal network to be suitable to detect very small backhoes from aerial images. Additionally, RoI pooling and RoI align were evaluated and compared a result of a backhoe detection. To evaluate the proposed approach, five iterations of hold-out method with RoI pooling and RoI align technique with different anchor box sizes were experimented. The experimental results demonstrate the efficacy of the proposed system, achieving high precision and recall rates in backhoe detection tasks. This study found that RoI align gives the best backhoe detection result, 86.8% of the mean average precision value while the RoI pooling, the original pooling technique from Faster R-RCNN give mean average precision only 76.7%.

关键词： Aerial Images Backhoe Detection Faster-RCNN RoI Align

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Adaptive energy-preserving algorithms for guiding center system

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Plasma science and technology 2023年第4期25卷 11-22页

作者：朱贝贝刘健张嘉炜祝爱卿唐贻发 Department of Applied Mathematics School of Mathematics and PhysicsUniversity of Science and Technology BeijingBeijing 100083Peopleʼs Republic of China School of Nuclear Science and Technology University of Science and Technology of ChinaHefei 230026Peopleʼs Republic of China Advanced algorithm Joint Lab Shandong Computer Science CenterQilu University of TechnologyJinan 250014Peopleʼs Republic of China The State Key Laboratory of Scientific and Engineering Computing The Institute of Computational Mathematics and Scientific/Engineering ComputingAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190Peopleʼs Republic of China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049Peopleʼs Republic of China

We develop two types of adaptive energy preserving algorithms based on the averaged vector field for the guiding center dynamics,which plays a key role in magnetized *** adaptive scheme is applied to the Gauss Legendre’s quadrature rules and time stepsize respectively to overcome the energy drift problem in traditional energy-preserving *** new adaptive algorithms are second order,and their algebraic order is carefully *** results show that the global energy errors are bounded to the machine precision over long time using these adaptive algorithms without massive extra computation cost.

关键词： guiding center system energy-preserving methods adaptive energy conservation

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Enhancing IoT-Edge Computation with Data Forwarding based Decentralized Deep Neural Networks 7

Enhancing IoT-Edge Computation with Data Forwarding based De...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Prasad, B.M.G. Jopate, Rachappa Savita, Pankaj Basi Reddy, A. Shankar, B. Prabu Arunkumar, M.S. Department of Cse Andhra Pradesh Kurnool518452 India University of Technology And Applied Sciences - Al Mussanah Department of Information Technology Al Muladha Muscat Oman Sagar Institute of Science & Technology Bhopal Organization-Sagar Institute of Science & Technology Department of Cse Madhya Pradesh Bhopal India Mohan Babu University School of Computing Department of Computer Science And Engineering Andhra Pradesh Tirupati India Alliance University Alliance College of Engineering And Design Department of Computer Science And Engineering Karnataka Banglore India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science And Technology Department of Computer Science And Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350371406

The incorporation of edge computing has become essential in the big data and Internet of Things age to tackle issues related to latency, real-time processing, and data scale. This paper delves into the imperative need for edge computing within the context of big data environments. Distributed deep learning methodologies are explored, encompassing a diverse array of deep learning models. Particularly, a novel paradigm named Data Forwarding based Decentralized Deep Neural Network (DF-DDNN) is introduced to achieve low latency IoT-Edge computation. In order to reduce latency in Io T ecosystems, the DF-DDNN model makes use of edge devices' processing capabilities. By strategically distributing data processing and computation at the edge, this model seeks to overcome inherent limitations of conventional Io T architectures, with a keen focus on network, processing, and overall latencies. The research provides a thorough performance evaluation that compares the DF-DDNN model's performance versus conventional IoT models in a number of areas. The outcomes demonstrate how much better the suggested DF-DDNN model is than traditional IoT systems. Notably, improvements are evident in terms of reduced network latency, accelerated processing speeds, and overall latency mitigation. This research underscores the critical importance of embracing edge computing in the context of big data and deep learning applications. The DF-DDNN model emerges as a promising avenue for advancing IoT-Edge computation, effectively addressing latency concerns and augmenting the performance of IoT-enabled systems. © 2023 IEEE.

关键词： Edge computing

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Noise Pollution Detection And Notification Using Flet And Arduino

Noise Pollution Detection And Notification Using Flet And Ar...

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Smart Systems for Electrical, Electronics, Communication and computer Engineering (ICSSEECC), International Conference on

作者： I. Kala Swathy S Vishnu Priya V Harshavarthan P K Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore Tamil Nadu India

ISBN: (数字)9798350378177

ISBN: (纸本)9798350378184

This project seeks to identify urban, traffic, and industrial noise hazards. This is accomplished by Arduino, which uses sound sensors to recognize and differentiate between light and loud noise. With the help of the ThingSpeak server, the noise level data for different time is also tracked. The proj ect extends its benefits to the users by notifying users via mail if they are in an area with high noise levels. The Flet User Interface framework was used in the development of this web application.

关键词： Pollution Environmentally friendly manufacturing techniques Working environment noise Noise Pollution measurement Frequency measurement Noise measurement

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COVID19 detection in chest x-ray using vision-transformer with different patch dimensions 2

COVID19 detection in chest x-ray using vision-transformer wi...

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2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Kadry, Seifedine Abualigah, Laith González Crespo, Rubén Verdú, Elena Damasevicius, Robertas Singh, Vijendra Rajinikanth, Venkatesan Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Higher School of Engineering and Technology Universidad Internacional de la Rioja Logroño26006 Spain Department of Applied Informatics Vytautas Magnus University Kaunas53361 Lithuania School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India

computerized disease detection systems (CDDs) have proven effective for automatic screening in recent years. Among the standard procedures in hospitals for faster and more accurate diagnosis is medical imaging-based disease screening. We aim to develop a CDD that detects COVID-19 using chest X-rays pre-trained vision transformers (PVTs). This scheme includes the following steps: (1) collecting images and resizing them, (2) implementing PVT for feature extraction, and (3) binary classifying the results and validating the proposed schemes. To prove the merit of the developed scheme, 4800 images (2400 normal and 2400 COVID-19) are analyzed. MLP classifiers verify the PVT performance using patch sizes of 6, 12, and 24. A patch size 24 results in 97.5% accuracy for the proposed CDD system. When patch sizes are increased to 12, accuracy increases to over 98%. For this specific task, smaller patch sizes are more effective. These high-accuracy results demonstrate the effectiveness of the developed scheme for detecting COVID-19 in chest X-rays. © 2024 Elsevier B.V.. All rights reserved.

关键词： Diagnosis

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Autism Spectrum Disorder Detection Via Eye Gaze Analysis

Autism Spectrum Disorder Detection Via Eye Gaze Analysis

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Networking and Communications (ICNWC), International Conference on

作者： Sujay Das Akshat Chotia K. M. Uma Maheswari Sadiq A K Department Of Computer Science & Engineering SRM Institute Of Science & Technology Chennai India College of Computing and Information Science University of Technology and Applied Sciences Oman

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

Autism is a complex neuro - developmental condition characterized by challenges in social interaction and communication. Early diagnosis and intervention are crucial for effective support. This paper presents a novel approach for autism detection leveraging Convolutional Neural Networks (CNNs) to analyze eye gaze patterns. The pro0posed method aims to provide an objective and efficient tool for the early screening of Autism. We begin by collecting eye gaze data from a diverse sample of individuals, including both Autism -diagnosed and non Autism individuals. The dataset is preprocessed to extract relevant features from the eye gaze sequences, capturing subtle but significant differences in gaze patterns between the two groups. A CNN architecture is designed and trained on this preprocessed dataset. The network is optimized to automatically learn discriminative features that distinguish between Autism and non Autism gaze patterns. Transfer learning techniques mobilenetv2 are employed to further enhance model performance. The trained CNN demonstrates promising results in accurately classifying individuals into Autism and neurotypical categories. Comprehensive evaluation metrics, including accuracy and loss, attest to the model's effectiveness.

关键词： Measurement Autism Adaptation models Network topology Transfer learning Neural networks Feature extraction

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