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Correction to: Advances in database systems education: Methods, tools, curricula, and way forward

Education and Information Technologies 2025年 1-2页

作者： Ishaq, Muhammad Abid, Adnan Farooq, Muhammad Shoaib Manzoor, Muhammad Faraz Farooq, Uzma Abid, Kamran Helou, Mamoun Abu Department of Computer Science National University of Computer and Emerging Sciences Lahore Pakistan Department of Computer Science University of Management and Technology Lahore Pakistan Department of Computer Science Virtual University of Pakistan Lahore Pakistan Department of Computer Science Lahore Garrison University Lahore Pakistan Department of Electrical Engineering University of the Punjab Lahore Pakistan Faculty of Information Technology Al Istiqlal University Jericho Palestine

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A Comprehensive Analysis on Machine Learning based Methods for Lung Cancer Level Classification

arXiv

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arXiv 2025年

作者： Farshchiha, Shayli Asoudeh, Salman Kuhshuri, Maryam Shavali Eisaei, Mehrshad Azadi, Mohamadreza Hesaraki, Saba Isfahan University of Technology Department of Electrical and Computer Engineering Faculty of Engineering Isfahan Iran Faculty of Computer College of Technical and Engineering University of Velayat Iran University of Isfahan Department of Electrical Engineering Faculty of Engineering University of Isfahan Isfahan Iran University of Mazandaran Mazandaran Iran Department of Computer Science Shamsipour technical college Tehran Iran Islamic Azad University Science and Research Branch Faculty of Mechanics Electrical Power and Computer Iran

Lung cancer is a major issue in worldwide public health, requiring early diagnosis using stable techniques. This work begins a thorough investigation of the use of machine learning (ML) methods for precise classification of lung cancer stages. A cautious analysis is performed to overcome overfitting issues in model performance, taking into account minimum child weight and learning rate. A set of machine learning (ML) models including XGBoost (XGB), LGBM, Adaboost, Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), CatBoost, and k-Nearest Neighbor (k-NN) are run methodically and contrasted. Furthermore, the correlation between features and targets is examined using the deep neural network (DNN) model and thus their capability in detecting complex patterns is established. It is argued that several ML models can be capable of classifying lung cancer stages with great accuracy. In spite of the complexity of DNN architectures, traditional ML models like XGBoost, LGBM, and Logistic Regression excel with superior performance. The models perform better than the others in lung cancer prediction on the complete set of comparative metrics like accuracy, precision, recall, and F-1 score. © 2025, CC BY-SA.

关键词： Lung cancer

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Analysis and modelling of a ML-based NIDS for IoT networks

Analysis and modelling of a ML-based NIDS for IoT networks

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2022 International Conference on Industry sciences and computer science Innovation, iSCSi 2022

作者： Karanfilovska, Martina Kochovska, Teodora Todorov, Zdravko Cholakoska, Ana Jakimovski, Goran Efnusheva, Danijela Computer Science and Engineering Department Faculty of Electrical Engineering and Information Technologies Ss. Cyril and Methodius University Skopje Macedonia

As cyber-attacks become increasingly sophisticated, cybersecurity threats continue to increase. Therefore, it becomes a great challenge to detect, identify and prevent adversary attacks. The inability to detect and avert such attacks can break down the credibility of security services, e.g. data confidentiality, availability, and integrity. An intrusion detection system aims to detect, identify and respond to malicious activities involving computing and network resources. This is even more difficult when the network contains Internet of Things (IoT) devices. The process of developing an intrusion detection system is a tremendous challenge due to false alarm rates, low detection rates, unbalanced datasets and response time. This paper gives a comprehensive model of a network intrusion detection system (NIDS) by applying supervised and unsupervised machine learning (ML) over the NF-ToN-IoT-v2 dataset. Using supervised learning, implemented by Azure automated ML (AML), it is shown that the best results are achieved by algorithm XGBoostClassifier, obtaining an F-Score of 98.8%. When a custom made automated ML (AE2EML) is implemented, the best results are achieved by Random Forest Classifier, obtaining an F-Score of 98.6%. Using clustering with PCA (Principal Component Analysis), implemented by PyCaret automated ML, the proposed ML-based NIDS achieves a Silhouette score of 0.553, Calinski-Harabasz index of 1533106 and Davies-Bouldin index of 0.631. © 2022 Elsevier B.V.. All rights reserved.

关键词： Classification (of information)

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Light field super-resolution using complementary-view feature attention

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Computational Visual Media 2023年第4期9卷 843-858页

作者： Wei Zhang Wei Ke Da Yang Hao Sheng Zhang Xiong Faculty of Applied Sciences Macao Polytechnic UniversityMacao SAR 999078China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191Chinaand Beihang Hangzhou Innovation Institute YuhangXixi Octagon CityYuhang DistrictHangzhou 310023China

Light field(LF)cameras record multiple perspectives by a sparse sampling of real scenes,and these perspectives provide complementary *** information is beneficial to LF super-resolution(LFSR).Compared with traditional single-image super-resolution,LF can exploit parallax structure and perspective correlation among different LF ***,the performance of existing methods are limited as they fail to deeply explore the complementary information across LF *** this paper,we propose a novel network,called the light field complementary-view feature attention network(LF-CFANet),to improve LFSR by dynamically learning the complementary information in LF ***,we design a residual complementary-view spatial and channel attention module(RCSCAM)to effectively interact with complementary information between complementary ***,RCSCAM captures the relationships between different channels,and it is able to generate informative features for reconstructing LF images while ignoring redundant ***,a maximum-difference information supplementary branch(MDISB)is used to supplement information from the maximum-difference angular positions based on the geometric structure of LF *** branch also can guide the process of *** results on both synthetic and real-world datasets demonstrate the superiority of our *** proposed LF-CFANet has a more advanced reconstruction performance that displays faithful details with higher SR accuracy than state-of-the-art methods.

关键词： light field(LF) super-resolution(SR) attention

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Heart-Net: AMulti-Modal Deep Learning Approach for Diagnosing Cardiovascular Diseases

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computers, Materials & Continua 2024年第9期80卷 3967-3990页

作者： DeemaMohammed Alsekait Ahmed Younes Shdefat AymanNabil Asif Nawaz Muhammad Rizwan Rashid Rana Zohair Ahmed Hanaa Fathi Diaa Salama Abd Elminaam Department of Computer Science and Information Technology Applied CollegePrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia College of Engineering and Technology American University of the Middle EastEgaila15453Kuwait Faculty of Computers Science Misr International UniversityCairo11800Egypt University Institute of Information Technology—PMAS Arid Agriculture University Rawalpjndi46000Pakistan Department of Computer Science International Islamic UniversityIslamabad04436Pakistan Jadara Research Center Jadara UniversityIrbid21110Jordan Information System Department Faculty of Computers and Artificial IntelligenceBenha UniversityBanha13511Egypt MEU Research Unit Middle East UniversityAmman11831Jordan

Heart disease remains a leading cause of morbidity and mortality worldwide,highlighting the need for improved diagnostic *** diagnostics face limitations such as reliance on single-modality data and vulnerability to apparatus faults,which can reduce accuracy,especially with poor-quality ***,these methods often require significant time and expertise,making them less accessible in resource-limited *** technologies like artificial intelligence and machine learning offer promising solutions by integrating multi-modality data and enhancing diagnostic precision,ultimately improving patient outcomes and reducing healthcare *** study introduces Heart-Net,a multi-modal deep learning framework designed to enhance heart disease diagnosis by integrating data from Cardiac Magnetic Resonance Imaging(MRI)and Electrocardiogram(ECG).Heart-Net uses a 3D U-Net for MRI analysis and a Temporal Convolutional Graph Neural Network(TCGN)for ECG feature extraction,combining these through an attention mechanism to emphasize relevant *** is performed using Optimized *** approach improves early detection,reduces diagnostic errors,and supports personalized risk assessments and continuous health *** proposed approach results show that Heart-Net significantly outperforms traditional single-modality models,achieving accuracies of 92.56%forHeartnetDataset Ⅰ(HNET-DSⅠ),93.45%forHeartnetDataset Ⅱ(HNET-DSⅡ),and 91.89%for Heartnet Dataset Ⅲ(HNET-DSⅢ),mitigating the impact of apparatus faults and image quality *** findings underscore the potential of Heart-Net to revolutionize heart disease diagnostics and improve clinical outcomes.

关键词： Heart diseases magnetic resonance imaging electrocardiogram deep learning classification

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A Triple-Channel Encrypted Hybrid Fusion Technique to Improve Security of Medical Images

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computers, Materials & Continua 2021年第7期68卷 431-446页

作者： Ahmed S.Salama Mohamed Amr Mokhtar Mazhar B.Tayel Esraa Eldesouky Ahmed Ali Future University in Egypt CairoEgypt Cairo Higher Institute for Engineering Computer Science and ManagementCairoEgypt Department of Electrical Engineering Faculty of EngineeringAlexandria UniversityAlexandria21544Egypt Department of Computer Science Faculty of Computers and InformaticsSuez Canal UniversityIsmailia CityEgypt Higher Future Institute for Specialized Technological Studies CairoEgypt Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia

Assuring medical images protection and robustness is a compulsory necessity *** this paper,a novel technique is proposed that fuses the wavelet-induced multi-resolution decomposition of the Discrete Wavelet Transform(DWT)with the energy compaction of the Discrete Wavelet Transform(DCT).The multi-level Encryption-based Hybrid Fusion Technique(EbhFT)aims to achieve great advances in terms of imperceptibility and security of medical images.A DWT disintegrated sub-band of a cover image is reformed simultaneously using the DCT ***,a 64-bit hex key is employed to encrypt the host image as well as participate in the second key creation process to encode the ***,a PN-sequence key is formed along with a supplementary key in the third layer of the ***,the watermarked image is generated by enclosing both keys into DWT and DCT *** fusions ability of the proposed EbHFT technique makes the best use of the distinct privileges of using both DWT and DCT *** order to validate the proposed technique,a standard dataset of medical images is *** results show higher performance of the visual quality(i.e.,57.65)for the watermarked forms of all types of medical *** addition,EbHFT robustness outperforms an existing scheme tested for the same dataset in terms of Normalized Correlation(NC).Finally,extra protection for digital images from against illegal replicating and unapproved tampering using the proposed technique.

关键词： Medical image processing digital image watermarking discrete wavelet transforms discrete cosine transform encryption image fusion hybrid fusion technique

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UWB at WASSA-2024 Shared Task 2: Cross-lingual Emotion Detection 14

UWB at WASSA-2024 Shared Task 2: Cross-lingual Emotion Detec...

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14th Workshop on Computational Approaches to Subjectivity, Sentiment, and Social Media Analysis, WASSA 2024

作者： Šmíd, Jakub Přibáň, Pavel Král, Pavel Department of Computer Science and Engineering University of West Bohemia Faculty of Applied Sciences Czech Republic NTIS – New Technologies for the Information Society Univerzitní 2732/8 Pilsen301 00 Czech Republic

ISBN: (纸本)9798891761568

This paper presents our system built for the WASSA-2024 Cross-lingual Emotion Detection Shared Task. The task consists of two subtasks: first, to assess an emotion label from six possible classes for a given tweet in one of five languages, and second, to predict words triggering the detected emotions in binary and numerical formats. Our proposed approach revolves around fine-tuning quantized large language models, specifically Orca 2, with low-rank adapters (LoRA) and multilingual Transformer-based models, such as XLM-R and mT5. We enhance performance through machine translation for both subtasks and trigger word switching for the second subtask. The system achieves excellent performance, ranking 1st in numerical trigger words detection, 3rd in binary trigger words detection, and 7th in emotion detection. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Integrating Advanced Healthcare AI into Higher Education of Smart Cities: Skin Cancer Classification with Custom Vision Transformers 2nd

Integrating Advanced Healthcare AI into Higher Education of ...

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2nd International Conference on Sustainability: Developments and Innovations, ICSDI 2024

作者： Usman, Syed Muhammad Shah, Syed Nehal Hassan Dicheva, Nevena Ur Rehman, Ikram Zaib, Samia Department of Computer Science Bahria School of Engineering and Applied Sciences Bahria University Islamabad44000 Pakistan Faculty of Computing and Artificial Intelligence Air University Islamabad44000 Pakistan School of Computing and Engineering University of West London London United Kingdom

ISBN: (纸本)9789819783441

In today’s smart cities, it’s essential to combine advanced healthcare with education. Our research introduces a groundbreaking method for detecting skin cancer, using a new type of artificial intelligence called a Vision Transformer. This model analyzes a wide range of skin images from different people and accurately identifies skin cancer, especially melanoma and benign *** beyond traditional techniques like convolutional neural networks, our approach uses advanced attentionmechanisms to improve accuracy by focusing on key areas in the skin images. The model has been thoroughly tested with specific criteria to ensure it can reliably detect skin cancer while reducing errors. This research is particularly important as skin cancer rates are increasing worldwide, and early, accurate detection is vital for saving lives. Our study focuses on providing assistance to healthcare practitioners for skin disease diagnostics as well as to assist in providing training and education to healthcare researchers. The proposed method outperforms in terms of ROC curve analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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Integrating Security in Software Defined Network (SDN) with IoT Enabled Fog Computing: A Paradigm Shift in Edge Computing 1

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7th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Karale, Ankita Solanki, Ram Kumar Rajawat, Anand Singh Goyal, S.B. Peiyu, Yao Chee, Weiming Sandip Institute of Technology and Research Centre Nashik India School of Computer Science and Engineering Sandip University Nashik422213 India Faculty of Information Technology City University Petaling Jaya46100 Malaysia

ISBN: (数字)9783031733185

ISBN: (纸本)9783031733178

In recent times, there has been a significant increase in the number of Internet of Things (IoT) devices, resulting in an unprecedented amount of data being generated at the periphery of the network. The conventional approach of centralized cloud computing has proven to be ineffective in efficiently processing this data due to issues related to delay, bandwidth limitations, and concerns regarding the privacy of data. As a solution to this problem, edge computing, specifically Fog computing, has emerged as a feasible option by processing data in close proximity to its source. However, the complex and decentralized nature of Fog environments brings forth intricate security challenges. This research paper investigates a revolutionary shift in edge computing by integrating Security Software Defined Network (SDN) with IoT-enabled Fog computing. With its centralized control plane and programmable characteristics, SDN offers a flexible and robust security mechanism to combat threats in a highly dispersed Fog environment. By capitalizing on the inherent benefits of SDN, such as easy enforcement of policies, network visibility, and dynamic flow management, we propose a comprehensive framework that reinforces Fog nodes, safeguards data during transmission, and ensures secure device connection. Empirical findings demonstrate improved security measures, reduced latency, and enhanced data through-put, thus validating the proposition that the integration of SDN and IoT-enabled Fog computing is crucial for the advancement of secure edge computing landscapes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Fog computing

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GastroFuse-Net: an ensemble deep learning framework designed for gastrointestinal abnormality detection in endoscopic images

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Mathematical Biosciences and engineering 2024年第8期21卷 6847-6869页

作者： Aggarwal, Sonam Gupta, Isha Kumar, Ashok Kautish, Sandeep Almazyad, Abdulaziz S. Mohamed, Ali Wagdy Werner, Frank Shokouhifar, Mohammad Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Model Institute of Engineering and Technology J&K Jammu India Chandigarh University Punjab Mohali140413 India Department of Computer Engineering College of Computer and Information Sciences King Saud University P.O. Box 51178 Riyadh11543 Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical Research Cairo University Giza12613 Egypt Applied Science Research Center Applied Science Private University Amman11931 Jordan Faculty of Mathematics Otto-von-Guericke University Magdeburg39016 Germany Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam

Convolutional Neural Networks (CNNs) have received substantial attention as a highly effective tool for analyzing medical images, notably in interpreting endoscopic images, due to their capacity to provide results equivalent to or exceeding those of medical specialists. This capability is particularly crucial in the realm of gastrointestinal disorders, where even experienced gastroenterologists find the automatic diagnosis of such conditions using endoscopic pictures to be a challenging endeavor. Currently, gastrointestinal findings in medical diagnosis are primarily determined by manual inspection by competent gastrointestinal endoscopists. This evaluation procedure is labor-intensive, time-consuming, and frequently results in high variability between laboratories. To address these challenges, we introduced a specialized CNN-based architecture called GastroFuse-Net, designed to recognize human gastrointestinal diseases from endoscopic images. GastroFuse-Net was developed by combining features extracted from two different CNN models with different numbers of layers, integrating shallow and deep representations to capture diverse aspects of the abnormalities. The Kvasir dataset was used to thoroughly test the proposed deep learning model. This dataset contained images that were classified according to structures (cecum, z-line, pylorus), diseases (ulcerative colitis, esophagitis, polyps), or surgical operations (dyed resection margins, dyed lifted polyps). The proposed model was evaluated using various measures, including specificity, recall, precision, F1-score, Mathew’s Correlation Coefficient (MCC), and accuracy. The proposed model GastroFuse-Net exhibited exceptional performance, achieving a precision of 0.985, recall of 0.985, specificity of 0.984, F1-score of 0.997, MCC of 0.982, and an accuracy of 98.5%. ©2024 the Author(s), licensee AIMS Press.

关键词： Endoscopy

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