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7th IET Smart Cities Symposium, SCS 2023

作者： Aljanabi, Mohammad Omran, Alaa Hamza Mijwil, Maad M. Abotaleb, Mostafa El-Kenawy, El-Sayed M. Mohammed, Sahar Yousif Ibrahim, Abdelhameed Department of computer College of Education Aliarqai university Baghdad Iraq University of information technology and communications Baghdad Iraq Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Science Department Computer Science &Information Technology College Anbar University Anbar Iraq Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

Data poisoning attacks, where adversaries manipulate training data to degrade model performance, are an emerging threat as machine learning becomes widely deployed in sensitive applications. This paper provides a comprehensive overview of data poisoning including attack techniques, adversary incentives, impacts on security and reliability, detection methods, defenses, and key research gaps. We examine label flipping, instance injection, backdoors, and other attack categories that enable malicious outcomes ranging from IP theft to accidents in autonomous systems. Promising detection approaches include statistical tests, robust learning, and forensics. However, significant challenges remain in translating academic defenses like adversarial training and sanitization into practical tools ready for operational use. With safety and trustworthiness at stake, more research on benchmarking evaluations, adaptive attacks, fundamental tradeoffs, and real-world deployment of defenses is urgently needed. Understanding vulnerabilities and developing resilient machine learning pipelines will only grow in importance as data integrity is fundamental to developing safe artificial intelligence. © The Institution of engineering & Technology 2023.

关键词： Machine learning

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Multi-Representation Convolutional Neural Network to Recognize Human Emotion 8

Multi-Representation Convolutional Neural Network to Recogni...

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8th International Conference on Information Technology and Digital Applications, ICITDA 2023

作者： Prabowo, Dwi Wahyu Setiawan, Noor Akhmad Debayle, Johan Nugroho, Hanung Adi Universitas Gadjah Mada Faculty of Engineering Department of Electrical and Information Engineering Yogyakarta Indonesia Universitas Darwan Ali Faculty of Computer Science Department of Information System Sampit Indonesia Centre Spin Mines Saint-Etienne Cnrs Umr 5307 Lgf Saint-Etienne France

ISBN: (纸本)9798350344691

Emotion recognition using brain-computer interfaces (BCIs) is an emerging field with the ongoing challenge of developing robust and efficient classification methods. This study introduces an original approach, the multi-representation convolutional neural network (MrCNN), designed to exploit the complementary features of recurrence plots (RP) and spectrograms. The efficacy of this new method was empirically evaluated and compared with the standalone RP and spectrogram methods in emotion classification tasks using electroencephalogram (EEG) signals. Two widely used datasets, DEAP and SEED, were used as the experimental platforms for this study. Employing a multifaceted analytical approach involving descriptive statistics, data visualization, and hypothesis tests, this study revealed that MrCNN significantly outperforms the standalone RP and spectrogram methods in terms of key performance metrics. Specifically, MrCNN achieved an accuracy of 0.9663, sensitivity of 0.9848, specificity of 0.9825, kappa score of 0.9511, and F1 score of 0.9656 for the DEAP dataset. For the SEED dataset, the accuracy was 0.9695, with sensitivity, specificity, kappa scores, and F1 score of 0.9844, 0.9842, 0.9543, and 0.9695, respectively. These results were statistically validated, and a comparative analysis with existing literature substantiated the robustness and superiority of the MrCNN method. This study sets new performance benchmarks and offers a reliable and effective approach for emotion classification using EEG signals. © 2023 IEEE.

关键词： Emotion Recognition

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Compressed VGGNet for Automatic COVID-19 Disease Detection from CT Scan Images 8th

Compressed VGGNet for Automatic COVID-19 Disease Detection f...

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8th IFIP TC 12 International Conference on computer, communication and Signal Processing, ICCCSP 2024

作者： Vinothini, R. Niranjana, G. Dhamanti, Inge Lailiyah, Syifa’ul Yakub, Fitri Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur 603203 India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur 603203 India Department of Health Policy and Administration Faculty of Public Health Universitas Airlangga Surabaya Indonesia Faculty of Health Medicine and Life Sciences Universitas Airlangga East Java Indonesia Electronic System Engineering Department Malaysia-Japan International Institute of Technology Kuala Lumpur Malaysia

ISBN: (纸本)9783031736162

Millions of people across the globe were affected by this rapidly spreading disease by the year 2020. Contamination of the respiratory system results from contracting COVID-19. Nonetheless, establishing a conclusive diagnosis of COVID-19 may prove difficult due to the subtle differences it shares with conventional pneumonia and the complexities involved in identifying areas of infection. A growing number of approaches based on deep learning (DL) are being proposed for the automated detection of COVID-19 from CT scan images. By employing the data pretreatment methodology, unprocessed image data becomes prepared for further analysis. The proposed framework utilised transfer learning to construct VGGNet, which is capable of discerning the Covid-19 disease. The model was subsequently assessed in comparison to the most sophisticated models, VGG16 and VGG19, using the SARS-COV2 CT scan dataset. This dataset contains 1230 CT scans of non-infected images and 1252 CT scans of Covid-19-infected images. The model has achieved 99% accuracy, 98% precision, 97% recall, and 98% F1-score, among other performance metrics. © IFIP International Federation for Information Processing 2025.

关键词： COVID-19

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RDDM: A Rate-Distortion Guided Diffusion Model for Leaned Image Compression Enhancement

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IEEE Journal on Emerging and Selected Topics in Circuits and systems 2025年

作者： Jiang, Sanxin Katto, Jiro Sun, Heming Shanghai University of Electric Power Department of Information Engineering Shanghai China Waseda University Department of Computer Science and Communication Engineering Tokyo Japan Yokohama National University Faculty of Engineering Yokohama Japan

Currently, denoising diffusion probability models (DDPM) have achieved significant success in various image generation tasks, but their application in image compression, especially in the context of learned image compression (LIC), is quite limited. In this study, we introduce a rate-distortion (RD) guided diffusion model, referred to as RDDM, to enhance the performance of LIC. In RDDM, LIC is treated as a lossy codec function constrained by RD, dividing the input image into two parts through encoding and decoding operations: the reconstructed image and the residual image. The construction of RDDM is primarily based on two points. First, RDDM treats diffusion models as repositories of image structures and textures, built using extensive real-world datasets. Under the guidance of RD constraints, it extracts and utilizes the necessary structural and textural priors from these repositories to restore the input image. Second, RDDM employs a Bayesian network to progressively infer the input image based on the reconstructed image and its codec function. Additionally, our research reveals that RDDM’s performance declines when its codec function does not match the reconstructed image. However, using the highest bitrate codec function minimizes this performance drop. The resulting model is referred to as RDDM*. The experimental results indicate that both RDDM and RDDM* can be applied to various architectures of LICs, such as CNN, Transformer, and their hybrid. They can significantly improve the fidelity of these codecs while maintaining or even enhancing perceptual quality to some extent. © 2011 IEEE.

关键词： Image denoising

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Efficient Joint Key Authentication Model in E-Healthcare

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computers, Materials & Continua 2022年第5期71卷 2739-2753页

作者： Muhammad Sajjad Tauqeer Safdar Malik Shahzada Khurram Akber Abid Gardezi Fawaz Alassery Habib Hamam Omar Cheikhrouhou Muhammad Shafiq Department of Computer Science Air University Multan CampusMultan60000Pakistan Faculty of Computing The Islamia University of BahawalpurBahawalpur 63100Pakistan Department of Computer Science COMSATS University IslamabadIslamabadPakistan Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia Faculty of Engineering Moncton UniversityNBE1A3E9Canada CES Laboratory National School of Engineers of SfaxUniversity of SfaxSfax3038Tunisia Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Many patients have begun to use mobile applications to handle different health needs because they can better access high-speed Internet and *** devices and mobile applications are now increasingly used and integrated through the medical Internet of Things(mIoT).mIoT is an important part of the digital transformation of healthcare,because it can introduce new business models and allow efficiency improvements,cost control and improve patient *** the mIoT system,when migrating from traditional medical services to electronic medical services,patient protection and privacy are the priorities of each ***,it is recommended to use different user authentication and authorization methods to improve security and *** this paper,our prosed model involves a shared identity verification process with different situations in the e-health *** aim to reduce the strict and formal specification of the joint key authentication *** use the AVISPA tool to verify through the wellknown HLPSL specification language to develop user authentication and smart card use cases in a user-friendly *** model has economic and strategic advantages for healthcare organizations and healthcare *** medical staff can increase their knowledge and ability to analyze medical data more *** model can continuously track health indicators to automatically manage treatments and monitor health data in real ***,it can help customers prevent chronic diseases with the enhanced cognitive functions *** necessity for efficient identity verification in e-health care is even more crucial for cognitive mitigation because we increasingly rely on mIoT systems.

关键词： E-health systems joint key authentication mutual authentication

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A Mobile Visitor Management system Using a QR Code and PIN for Access Control 2nd

A Mobile Visitor Management System Using a QR Code and PIN f...

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2nd International Conference on Connected Objects and Artificial Intelligence, COCIA 2024

作者： Awotunde, Joseph Bamidele Adeniyi, Abidemi Emmanuel Imoize, Agbotiname Lucky Mejdoub, Youssef Abdualazizu, Zakariyya Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Ilorin240003 Nigeria Department of Computer Science Bowen University Osun State Iwo Nigeria Department of Electrical and Electronics Engineering University of Lagos Akoka Nigeria RITM Laboratory Higher School of Technology ESTC Hassan II University Casablanca Morocco

ISBN: (纸本)9783031704109

Visitor management is a common activity that happens every day all around the world. Accepting visitors at home or in the office, going to the bank, or visiting a private residential area;some sort of visitor management goes on in all those activities to ensure access control and security. Insecurity is a big challenge that comes with receiving visitors and that is why visitor management is important. Traditionally, visitor management is handled physically by recording visitors’ information into a log book and asking visitors to contact their hosts before gaining access to a secured environment but this process is time-consuming and the visitor’s record is exposed to exploitation. Automated Visitor Management systems (VMS) overcome the shortcomings of the traditional method of visitor management. VMS automates the process of recording visitors’ information, keeping a log of all visits, and ensuring some sort of access control or visitor authentication just in case of an imposter or if anything ever goes wrong. This study aims to design and model a flexible Mobile Visitor Management system that can be used to record visitors’ information and authenticate visitors for access control purposes. The study was achieved using Figma software to design and prototype the system and then using React Native and SQLite for the development of the system. The system has proven to be more convenient than the common traditional way of recording visitors’ information into a physical log book. The system protects its users from loss of information, waste of time, and insecurity. The flexible and inexpensive nature of the system makes it adaptable to almost any organization or property owner, this gives the general public a great opportunity to try out Visitor Management Technology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Authentication

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Condition Monitoring and Maintenance Management with Grid-Connected Renewable Energy systems

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computers, Materials & Continua 2022年第8期72卷 3999-4017页

作者： Md.Mottahir Alam Ahteshamul Haque Mohammed Ali Khan Nebras M.Sobahi Ibrahim Mustafa Mehedi Asif Irshad Khan Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah 21589Saudi Arabia Advance Power Electronics Research Lab Department of Electrical EngineeringJamia Millia IslamiaNew Delhi 110025India Department of Electrical Power Engineering Faculty of Electrical Engineering and CommunicationBrno University of TechnologyBrno 61600Czech Republic Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddah 21589Saudi Arabia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia

The shift towards the renewable energy market for carbon-neutral power generation has encouraged different governments to come up with a plan of *** with the endorsement of renewable energy for harsh environmental conditions like sand dust and snow,monitoring and maintenance are a few of the prime *** problems were addressed widely in the literature,but most of the research has drawbacks due to long detection time,and high misclassification *** to overcome these drawbacks,and to develop an accurate monitoring approach,this paper is motivated toward the understanding of primary failure concerning a grid-connected photovoltaic(PV)system and highlighted along with a brief overview on existing fault detection *** on the drawback a data-driven machine learning approach has been used for the identification of fault and indicating the maintenance unit regarding the operation and maintenance ***,the system was tested with a 4 kWp grid-connected PV system,and a decision tree-based algorithm was developed for the identification of a *** results identified 94.7%training accuracy and 14000 observations/sec prediction speed for the trained classifier and improved the reliability of fault detection nature of the grid-connected PV operation.

关键词： Fault detection machine learning fault ride through

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Breast Cancer Detection and Classification from Mammogram Images Using Improved Convolutional Neural Network Model 2nd

Breast Cancer Detection and Classification from Mammogram Im...

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2nd International Conference on Connected Objects and Artificial Intelligence, COCIA 2024

作者： Olanloye, Odunayo Dauda Adeniyi, Abidemi Emmanuel Aworinde, Halleluyah Oluwatobi Awotunde, Joseph Bamidele Imoize, Agbotiname Lucky Mejdoub, Youssef Department of Computer Science Bowen University Osun Iwo Nigeria Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Ilorin240003 Nigeria Department of Electrical and Electronics Engineering University of Lagos Akoka Lagos Nigeria RITM Laboratory Higher School of Technology ESTC Hassan II University Casablanca Morocco

ISBN: (纸本)9783031704109

A prominent application in healthcare is the classification and detection of breast cancer using convolutional neural networks (CNNs). It entails creating a system that can precisely pinpoint malignant spots or gauge the tendency of breast cancer through the examination of diagnostic pictures like mammograms. This study focuses on the use of Convolutional Neural Networks for the detection and classification of breast cancer. The proposed model analyzes mammogram images to distinguish cancerous and non-cancerous instances automatically. Preparing the dataset developing the CNNs model’s architecture, training the model, assessing its performance, and producing predictions based on hypothetical data are all the steps in the process. The CNN model learns to extract relevant features and patterns from a large dataset, increasing classification accuracy. Tests show that the CNN model is effective at correctly identifying breast cancer. This method will help medical professionals diagnose patients quickly and correctly, improving patient outcomes in the fight against breast cancer. The prediction can be improved using post-processing techniques and the trained model can be used in practical applications. The model accuracy was 0.98 and the model loss was 0.14. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks

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A Novel Cyber Threat Intelligence (CTI) Framework for Detecting Android Malware: Work on Progress

A Novel Cyber Threat Intelligence (CTI) Framework for Detect...

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2024 International Conference on Information Technology systems and Innovation, ICITSI 2024

作者： Wijaya, Andri Stiawan, Deris Abdiansah Suprapto, Bhakti Yudho Saputra, Iwan Pahendra Anto Rini, Dian Palupi Sayfudin Idris, Mohd.Yazid Budiarto, Rahmat Universitas Katolik Musi Charitas Faculty of Engineering Universitas Sriwijaya Department of Information System Indonesia Universitas Sriwijaya Faculty of Computer Science Indonesia Sriwijaya University Faculty of Engineering Electrical Engineering Dept. Palembang Indonesia Sriwijaya University Faculty of Comp.Science Informatic Engineering Dept. Palembang Indonesia Universitas Muhammadiyah Palembang Indonesia Universiti Teknologi Malaysia School of Computing Faculty of Computing Johor81310 Malaysia Al-Baha University College of Computing and Information Al Aqiq Saudi Arabia

ISBN: (纸本)9798331511470

The latest approach in cyber threat intelligence (CTI) is becoming increasingly crucial in detecting evolving cyber threats, including malware on Android devices. This problem is getting deeper when the number of Android users in Indonesia is increasing compared to other platforms. Therefore, it is necessary to develop a new method that integrates the CTI framework with the use of machine learning to improve the accuracy and speed in detecting Android malware. By utilizing intelligence data from structured threat reports, this method aims to identify suspicious patterns and abnormal behavior that may not be detected by conventional security systems so that with this updated security system it is proactive towards the development of new types of malware threats, with the hope of being able to make a significant contribution to strengthening especially mobile devices with the Android platform and being able to provide more effective solutions in reducing the risk of malware attacks on Android. © 2024 IEEE.

关键词： Cyber attacks

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An Implementation of Coding Rule Checking Function for C Programming Study 4

An Implementation of Coding Rule Checking Function for C Pro...

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4th IEEE Global Conference on Life sciences and Technologies, LifeTech 2022

作者： Kyaw, Htoo Htoo Sandi Kaneko, Keiichi Funabiki, Nobuo Ueda, Kiyoshi Tokyo University of Agriculture and Technology Division of Advanced Information Technology and Computer Science Institute of Engineering Tokyo Japan Okayama University Faculty of Engineering Department of Electrical and Communication Engineering Okayama Japan Nihon University College of Engineering Department of Computer Science Fukushima Japan

ISBN: (纸本)9781665419048

Presently, C programming is essential for information technology (IT) students to study programming languages, algorithms, and computer architecture. It has been taught as a basic programming language in many universities. Due to the limited lecture time, coding rules to make readable codes are rarely educated, although they are essential in improving programming abilities of students. In this paper, we implement a coding rule checking function for C programming study of novice students. It helps a teacher to check the correctness of the names of variables and functions and the coding styles in the source codes submitted by the students and to give feedback to them. As preliminary evaluation, we verify the correctness of the implemented function through testing 109 source codes from one student. © 2022 IEEE.

关键词： Students

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