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Smart architectures: computerized classification of brain tumors from MRI images utilizing deep learning approaches

Multimedia Tools and Applications 2025年第5期84卷 2261-2292页

作者： Mijwil, Maad M. Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq

Brain tumors are complex medical conditions that appear due to the abnormal proliferation of cells. They are tumors (benign or malignant) that grow abnormally or are masses of tissue that develop in a dreadful manner in the human brain. In most cases, there is no specific or known cause for the occurrence of these tumors, and late diagnosis leads to a disproportionate progression of cancer cells or the growth of malignant masses in the brain, which leads to difficulty in performing treatment directly. Deep learning methods, most notably convolutional neural networks (CNNs), are employed to accurately diagnose and classify brain tumor images due to their ability to process large sets of complex medical images. In this paper, the role of deep learning approaches will be highlighted by utilizing different architectures of the CNNs (DenseNet-201, Inception-V1, AlexNet, and MobileNetV2), comparing their performance, and finding the most suitable solution for identifying and classifying brain tumors and solving the problem of late or incorrect diagnosis with high efficiency and accuracy. The database of this paper includes more than 3000 MRI images taken from Kaggle. During implementation, it became clear that the most suitable performance was the MobileNetV2, as it earned an accuracy of 96.5%, a sensitivity of 96.6%, a precision of 98.2%, a specificity of 96.3% and an F1-score of 97.4%. These effects demonstrate that MobileNetV2 is the best architecture among others, as it is highly capable of classifying aberrant and normal tumors from brain MRI images. This paper emphasizes that deep learning is crucial in supporting hospitals and experts in interpreting MRI images. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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CSRWA:Covert and Severe Attacks Resistant Watermarking Algorithm

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computers, Materials & Continua 2025年第1期82卷 1027-1047页

作者： Balsam Dhyia Majeed Amir Hossein Taherinia Hadi Sadoghi Yazdi Ahad Harati Faculty of Engineering Ferdowsi University of MashhadMashhad***Iran Department of Computer Techniques Engineering Imam Al-Kadhum CollegeBaghdad10087Iraq

Watermarking is embedding visible or invisible data within media to verify its authenticity or protect *** watermark is embedded in significant spatial or frequency features of the media to make it more resistant to intentional or unintentional *** of these features are important perceptual features according to the human visual system(HVS),which means that the embedded watermark should be imperceptible in these ***,both the designers of watermarking algorithms and potential attackers must consider these perceptual features when carrying out their *** two roles will be considered in this paper when designing a robust watermarking algorithm against the most harmful attacks,like volumetric scaling,histogram equalization,and non-conventional watermarking attacks like the Denoising Convolution Neural Network(DnCNN),which must be considered in watermarking algorithm design due to its rising role in the state-of-the-art *** DnCNN is initialized and trained using watermarked image samples created by our proposed Covert and Severe Attacks Resistant Watermarking Algorithm(CSRWA)to prove its *** this algorithm to satisfy the robustness and imperceptibility tradeoff,implementing the Dither Modulation(DM)algorithm is boosted by utilizing the Just Noticeable Distortion(JND)principle to get an improved performance in this ***,luminance,inter and intra-block contrast are used to adjust the JND values.

关键词： Covert attack digital watermarking DnCNN JND perceptual model robustness

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Research on Biomedical engineering 2025年第2期41卷 1-19页

作者： A, Padmashree Sankar, P. Alkhayyat, Ahmad Muniyandy, Elangovan Department of Computer Science and Business Systems Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam India Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Applied Science Research Center Applied Science Private University Amman Jordan

Purpose: The primary objective of this research is to develop a comprehensive framework for the analysis of brain tumor images, addressing the complexities of detection, segmentation, and classification. Given the intricate nature of brain anatomy and tumor variability, the study aims to enhance the accuracy and efficiency of brain tumor diagnosis using advanced imaging and processing techniques. Methods: The framework utilizes Brain Tumor Segmentation (BraTS) 2019 and 2020 datasets, applying an AMW-PM filter to enhance image quality. Tumor segmentation employs a slimmable transformer with hybrid axial attention for precision. Classification uses similarity navigated graph neural networks (SNGNNs), optimized with the crayfish algorithm (CFO) to minimize loss and enhance performance. Results: The framework’s effectiveness was validated using key performance metrics, showing significant improvements in tumor detection and classification. The combination of advanced segmentation techniques and the SNGNN classifier, optimized by the CFO, led to a fivefold increase in diagnostic accuracy. These improvements demonstrated a substantial enhancement in the model's ability to correctly identify tumor types and provide reliable outputs for clinical use. Conclusion: The proposed framework delivers a robust solution for brain tumor analysis, integrating cutting-edge techniques for image processing, segmentation, and classification. The results not only highlight improvements in diagnostic accuracy but also contribute to better clinical decision-making in managing brain tumors. This framework sets the foundation for future advancements in brain tumor diagnostics. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： Graph neural networks

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Topology-adaptive Bayesian optimization for deep ring echo state networks in speech emotion recognition

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Neural Computing and Applications 2025年第1期37卷 399-416页

作者： Soltani, Rebh Benmohamed, Emna Ltifi, Hela University of Sfax BP 1173 Sfax3038 Tunisia Department of computer science Onaizah college of Engineering and Information Technology Oniazah colleges Saudi Arabia Computer Science and Mathematics Department Faculty of Sciences and Techniques of Sidi Bouzid University of Kairouan Kairouan Tunisia

Speech emotion recognition is a difficult task that is gaining attention in a variety of domains, including psychology, human–computer interaction, and speech processing. To recognize speech emotions, machine learning techniques such as convolutional neural networks and long short-term memory networks have been successfully used. However, these models often fail to capture the long-term temporal dynamics of emotional speech. In this work, data augmentation was performed on all datasets. The generated result was used to extract the discriminant features. Then we apply a deep echo state network with a ring topology. The Bayesian optimization (BO) algorithm was applied to the spectral radius of the reservoir matrix. Our approach reveals term-weighted and unweighted accuracies of (75.45%, 74.79%), (86.89%, 86.14%), (98.99%, 97.62%), and (62%, 61.24%) on EMO-DB, SAVEE, RAVDESS, and TESS datasets. The findings demonstrate that by using BO, we are able to determine an optimal spectral radius. Therefore, this approach balances the minimal complexity of reservoir design and high emotion recognition accuracy. Notably, we demonstrate that a simple, deterministically constructed cycle reservoir within DRESN is comparable to other methodologies. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Bayesian networks

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Deep Learning Based Side-Channel Attack Detection for Mobile Devices Security in 5G Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1012-1026页

作者： Amjed A.Ahmed Mohammad Kamrul Hasan Ali Alqahtani Shayla Islam Bishwajeet Pandey Leila Rzayeva Huda Saleh Abbas Azana Hafizah Mohd Aman Nayef Alqahtani Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Imam Alkadhim University College Department of Computer Techniques EngineeringBaghdad 10066Iraq Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran 61441Saudi Arabia Institute of Computer Science and Digital Innovation UCSI UniversityKuala Lumpur 56000Malaysia Department of Intelligent Systems and Cyber Security Astana IT UniversityAstana 20000Kazakstan Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah 42353Saudi Arabia Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Hofuf 31982Saudi Arabia

Mobile devices within Fifth Generation(5G)networks,typically equipped with Android systems,serve as a bridge to connect digital gadgets such as global positioning system,mobile devices,and wireless routers,which are vital in facilitating end-user communication ***,the security of Android systems has been challenged by the sensitive data involved,leading to vulnerabilities in mobile devices used in 5G *** vulnerabilities expose mobile devices to cyber-attacks,primarily resulting from security ***-permission apps in Android can exploit these channels to access sensitive information,including user identities,login credentials,and geolocation *** such attack leverages"zero-permission"sensors like accelerometers and gyroscopes,enabling attackers to gather information about the smartphone's *** underscores the importance of fortifying mobile devices against potential future *** research focuses on a new recurrent neural network prediction model,which has proved highly effective for detecting side-channel attacks in mobile devices in 5G *** conducted state-of-the-art comparative studies to validate our experimental *** results demonstrate that even a small amount of training data can accurately recognize 37.5％of previously unseen user-typed ***,our tap detection mechanism achieves a 92％accuracy rate,a crucial factor for text *** findings have significant practical implications,as they reinforce mobile device security in 5G networks,enhancing user privacy,and data protection.

关键词： Fifth Generation(5G)networks smartphone information leakage Side-Channel Attack(SCA) deep learning

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EEG-Based Emotion Detection Using Roberts Similarity and PSO Feature Selection

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IEEE Access 2025年 13卷 79353-79366页

作者： Hussein Mohammed, Mustafa Noaman Kadhim, Mustafa Al-Shammary, Dhiah Ibaida, Ayman Al-Mustaqbal University Computer Techniques Engineering Department Babylon51001 Iraq University of Al-Qadisiyah College of Computer Science and Information Technology Al Diwaniyah58002 Iraq Victoria University Intelligent Technology Innovation Laboratory MelbourneVIC3011 Australia

In this paper, a novel classifier based on Robert’s similarity measure is introduced for emotion detection using electroencephalogram (EEG) signals. Traditional machine learning classifiers machine learning such as k-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), and Random Forest (RF), often struggle to accurately capture both linear and nonlinear patterns in EEG signals and face limitations in handling high-dimensional datasets. The proposed classifier addresses these challenges by segmenting EEG signals into block sizes categorized as small (1 to 10 samples), medium (20 to 100 samples), and large (200 to 1,000 samples), demonstrating particularly strong performance with medium and large block sizes to capture essential features. Integration of Particle Swarm Optimization (PSO) for feature selection, with Robert’s similarity as the fitness function, effectively refines the feature set, boosting classification accuracy and computational efficiency. Evaluation on an EEG brainwave dataset demonstrated that the method achieved an accuracy of 98.75% with feature selection, compared to 94.04% without it in emotional state detection. The results demonstrate that the proposed classifier is a valuable tool for diverse fields, including healthcare by detecting patient stress, education by assessing student engagement, customer service by monitoring satisfaction, and smart environments by enabling adaptive responses. Furthermore, the classifier has potential for broader industrial applications, such as improving workplace productivity by monitoring employee stress and enhancing safety in autonomous vehicle systems, making it a versatile solution for emotionally-aware systems across multiple domains. © 2013 IEEE.

关键词： Emotion Recognition

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Enhancing Searchable Symmetric Encryption Performance through Optimal Locality

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第7期49卷 165-174页

作者： Alyousif, Aya A. Yassin, Ali A. Mohammed, Hussein M. Department of Medical Instrumentation Engineering Techniques Shatt Al-Arab University College Basra Iraq Department of Computer Science Education College for Pure Sciences University of Basrah Basrah Iraq Directorate General of Education Basrah Ministry of Education Basrah Iraq

Both individuals and institutions place great importance on maintaining the security and privacy of their data, when stored in the cloud server. To achieve this, they often turn to searchable symmetric encryption (SSE), which is considered a crucial technology for safeguarding user data. However, SSE has encountered some challenges, particularly in the case of large databases. One such issue is poor performance, which can be attributed to poor locality. This means that the cloud server must visit a large number of locations during the search process, resulting in slow retrieval times. The main problem however, is not just poor locality. In many cases, optimization methods intended to improve performance can actually lead to increased storage requirements for the encrypted index stored on the cloud server or reduced efficiency when reading data. These issues must be addressed in order for SSE to continue to be an effective tool for protecting sensitive information. In this paper, we introduce a secure and searchable scheme that effectively addresses the issues mentioned above, while also enhancing the performance of information retrieval through an improved encrypted inverted index storage mechanism. Our scheme achieves optimal locality at 0(1), and read efficiency at 0(1), thereby significantly increasing the speed of retrieval. Through experimentation with real-world data, we have demonstrated the practicality, accuracy, and security of our approach, making it a reliable solution for secure and efficient information retrieval. © 2025 Slovene Society Informatika. All rights reserved.

关键词： cloud server information retrieval inverted index locality searchable symmetric encryption

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Enhancing Peer-to-Peer Network Security using 3D Convolutional Autoencoder based Anomaly Detection 1

Enhancing Peer-to-Peer Network Security using 3D Convolution...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Rana Veer Samara Sihman Bharattej, R. Ajznblasm, Zaid Satish, E.G. Adlin Sylvia, A. Devi, M. Saint Leo University Department of Computer Science FL United States The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798331529246

In this era, Peer-to-Peer (P2P) networks have developed as a revolutionized network for data distribution and alternate to traditional client-server network. The features of P2P networks allow a distributed group of users to stake resources in the form of digital content or information. However, P2P networks lack in controlling access and it has limited security. In this research, Three Dimensional-Convolutional Autoencoder (3D-CA) is proposed to improve the security in P2P network. Initially, the data is obtained from University of New South Wales Network Behavior 2015 (UNSW-NB15) dataset and then preprocessed with data cleaning to ensure data quality. After that, the P2P autoencoder architecture is employed to learn the normal patterns which employs the detection in P2P network. Finally, the proposed 3D-CA is trained to detect the anomalies based on reconstruction error between input node and reconstructed pattern. From the results, the proposed 3D-CA model attained better outcomes compared to existing K-means algorithm in terms of accuracy, precision as well as F1-score by achieving 0.764, 0.90 and 0.80 respectively. © 2025 IEEE.

关键词： Peer to peer networks

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Fine-Tuned Xception Model for Accurate Brain Tumor Classification: A Deep Learning Approach

Fine-Tuned Xception Model for Accurate Brain Tumor Classific...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Ankushavali, Md Tuteja, Gaurav Nasra, Parul Salami, Zaeid Ajsan School of Computer Science and Artificial Intelligence Sr University Telangana Warangal506371 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India College of technical engineering The Islamic University Department of computers Techniques engineering Diwaniyah Iraq College of technical engineering The Islamic University of Al Diwaniyah Department of computers Techniques engineering Najaf Iraq

ISBN: (纸本)9798331527495

The conditions are fatal and therefore the need for early diagnosis and accurate results to enhance on proper treatment. The modern advances in the field of Deep learning show great promises in automating the analysis of medical images and bringing accuracy to diagnostic levels. In this work, the performance effectiveness of the fine-tuned Xception model for classifying brain tumors based on MRI images is investigated. Using glioma, meningioma, pituitary tumor, the Xception model was fine-tuned as a transfer learning strategy to identify appropriate higher-level features that increases classification quality. To make the model less sensitive to both the image size and its orientation additional preprocessing steps were used: resizing, normalization and data augmentation. The proposed model provided approximately 96% classification accuracy, and it was faster than the standard CNN architectures. While precision, recall, and accuracy, as well as the F1-score, provided better evidence across all classes of the model's accuracy. The approach used in the current work demonstrates the effectiveness of using the Xception model in the diagnosis of brain tumors and the need to reduce the manual interpretation process in clinics. Future work also plans to use the framework in real-time diagnostic systems where a broader application of better medical imaging will be more available to the public. © 2025 IEEE.

关键词： Deep learning

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Optimal coordination of Production, Logistics, and Maintenance 4.0: A framework for Multi-Site Stamping Plants 6

Optimal coordination of Production, Logistics, and Maintenan...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Zerrad, Raoua Diaz, Kamar Mechkouri, Meriem Hayani Sefiani, Naoufal Department of Mechanical Engineering Research Laboratory in Engineering Innovation and Management of Industrial Systems Faculty of Sciences and Techniques Abdelmalek Essaadi University Tangier Morocco Department of Industrial Engineering and Maintenance Computer Engineering Production and Maintenance Laboratory University of Lorraine Metz France

This paper presents a comprehensive framework for optimal coordination of production, logistics, and maintenance in multi-site stamping plants within the context of Industry 4.0. By integrating key technologies such as the Internet of Things, cloud computing and big data analytics, we developed a system that would enable real-time data sharing, predictive analytics, and intelligent decision-making. This framework aims to enhance operational efficiency, reduce downtime, and improve service rate. Additionally, our approach aims to optimize spare parts inventory management across different sites, minimizing inventory costs and lead times by ensuring timely availability and efficient sourcing of spare parts. © 2024 The Authors. Published by Elsevier B.V.

关键词： Inventory control

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