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M2auth: A multimodal behavioral biometric authentication using feature-level fusion

Neural Computing and Applications 2024年第34期36卷 21781-21799页

作者： Mahfouz, Ahmed Mostafa, Hebatollah Mahmoud, Tarek M. Sharaf Eldin, Ahmed Faculty of Computer Studies Arab Open University Muscat Oman Computer Science Department Minia University Minya Egypt Faculty of Computers and Information Minia University Minya Egypt Faculty of Computers and Artificial Intelligence Sadat City University Sadat City Egypt Faculty of Information Technology and Computer Science Sinai University Arish Egypt Information Systems Department Helwan University Helwan Egypt

Conventional authentication methods, such as passwords and PINs, are vulnerable to multiple threats, from sophisticated hacking attempts to the inherent weaknesses of human memory. This highlights a critical need for a more secure, convenient, and user-friendly approach to authentication. This paper introduces M2auth, a novel multimodal behavioral biometric authentication framework for smartphones. M2auth leverages a combination of multiple authentication modalities, including touch gestures, keystrokes, and accelerometer data, with a focus on capturing high-quality, intervention-free data. To validate the efficacy of M2auth, we conducted a large-scale field study involving 52 participants over two months, collecting data from touch gestures, keystrokes, and smartphone sensors. The resulting dataset, comprising over 5.5 million action points, serves as a valuable resource for behavioral biometric research. Our evaluation involved two fusion scenarios, feature-level fusion and decision-level fusion, that play a pivotal role in elevating authentication performance. These fusion approaches effectively mitigate challenges associated with noise and variability in behavioral data, enhancing the robustness of the system. We found that the decision-level fusion outperforms the feature level, reaching a 99.98% authentication success rate and an EER reduced to 0.84%, highlighting the robustness of M2auth in real-world scenarios. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Smartphones

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Neutrosophic Hybrid MCDM Framework to Evaluate the Risks of Excavation System

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Neutrosophic Sets and Systems 2023年 55卷 173-186页

作者： Abdel-Monem, Ahmed Abdelhafeez, Ahmed Faculty of Computers and Informatics Zagazig University Zagazig11544 Egypt Faculty of Information Systems and Computer Science October 6th University Cairo12585 Egypt

The building of excavations is an extremely dangerous job that incorporates a variety of different variables. It is possible to significantly lower the likelihood of an accident occurring by first accurately identifying high-risk variables and then taking appropriate preventative steps. Single-valued neutrosophic verbal sets (SVNVS) can effectively represent qualitative and vague information when used in the identification process for high-risk variables of excavation systems. In addition, the identification of highrisk elements associated with an excavation system is a multi-criteria decision-making (MCDM) issue. This issue may be resolved by using the multi-attribute border approximation area comparison (MABAC) technique. The MABAC method operates on the presumption that criteria are compensating. However, the identification process for high-risk variables of excavation systems may include characteristics that are not compensatory. Under conditions of single-valued neutrosophic sets, a MABAC approach is developed. The weights of the criterion are calculated using this approach, which uses the mean-squared deviation weight method. In addition to that, an illustrated example is carried out to demonstrate the process that is involved in the MABAC approach © 2023, Neutrosophic Sets and *** Rights Reserved.

关键词： Excavation

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Choice of Suitable Referral Hospital to Improve the Financial Result of Hospital Operations and Quality of Patient Care under a Neutrosophic Environment

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Neutrosophic Sets and Systems 2023年 56卷 351-360页

作者： Abdelhafeez, Ahmed Abdel-Monem, Ahmed Aziz, Alber S Tantawy, Alshaimaa A. Faculty of Information Systems and Computer Science October 6th University Cairo Egypt Faculty of Computers and Informatics Zagazig University Sharqiyah Zagazig44519 Egypt

The referral cooperation hospital choice has been studied to better rationalize the allocation of healthcare assets and enhance the efficacy of resource utilization. Choosing hospitals to work within a collaborative referral arrangement is a crucial step in the patient-referral process. A referral cooperative hospital is chosen after careful consideration of these aspects to guarantee that patients will get the kind of detailed treatment that is appropriate for their condition. The concept of multi-criteria decision-making (MCDM) is used due to various criteria. The VIKOR method is the MCDM method used in this paper to rank the referral cooperation hospitals. The VIKOR method is integrated with the single-valued neutrosophic set to overcome uncertain information. The single-valued neutrosophic VIKOR method is applied to a case study in Egypt. We achieved quality of care as the best criterion from the eleven criteria used. © (2023). All Rights Reserved.

关键词： Hospitals

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Evaluation the Impact Potentials of Materials and Systems with Specific Criteria under Neutrosophic Set

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Neutrosophic Sets and Systems 2023年 57卷 342-355页

作者： Abdel-aziem, Ayman H. Soliman, Tamer H. M. Ali, Ahmed M. Abdelhafeez, Ahmed Faculty of Information Systems and Computer Science October 6th University Cairo Egypt Faculty of Computers and Informatics Zagazig University Sharqiyah Zagazig44519 Egypt

Energy, healthcare, electronics, transportation, ecology, and infrastructure are just a few of the areas that might greatly benefit from the use of new materials and systems. This study delves into the factors that should be taken into account when determining the possible effect of a certain substance or system. This analysis takes into account ecological, monetary, technical, health and safety, regulatory, commercial, and cooperative factors. Stakeholders may make more well-informed decisions and ensure that materials and systems are used to their maximum capacity if they take these into account. This paper used the concept of multi-criteria decision-making (MCDM) to deal with various criteria and factors. The VIKOR method is used as an MCDM method to rank the materials according to various criteria. The VIKOR method is integrated with the single-valued neutrosophic set to overcome uncertain information. This paper used eight criteria and ten materials to select the best one. The results show the cost criteria is the height weight in all criteria. © (2023). All Rights Reserved.

关键词： Ecology

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A Survey on Acute Leukemia Expression Data Classification Using Ensembles

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computer Systems science & Engineering 2023年第11期47卷 1349-1364页

作者： Abdel Nasser H.Zaied Ehab Rushdy Mona Gamal Department of Information Systems Faculty of Computer ScienceMisr International UniversityCairo11828Egypt Department of Information Technology Faculty of Computers and InformaticsZagazig UniversityZagazig44516Egypt Department of Information Systems Faculty of Computers and InformaticsZagazig UniversityZagazig44516Egypt

Acute leukemia is an aggressive disease that has high mortality rates *** error rate can be as high as 40%when classifying acute leukemia into its ***,there is an urgent need to support hematologists during the classification *** than two decades ago,researchers used microarray gene expression data to classify cancer and adopted acute leukemia as a test *** high classification accuracy they achieved confirmed that it is possible to classify cancer subtypes using microarray gene expression *** machine learning is an effective method that combines individual classifiers to classify new *** classifiers are recognized as powerful algorithms with numerous advantages over traditional *** the past few decades,researchers have focused a great deal of attention on ensemble classifiers in a wide variety of fields,including but not limited to disease diagnosis,finance,bioinformatics,healthcare,manufacturing,and *** paper reviews the recent ensemble classifier approaches utilized for acute leukemia gene expression data ***,a framework for classifying acute leukemia gene expression data is *** pairwise correlation gene selection method and the Rotation Forest of Bayesian Networks are both used in this *** outcomes show that the classification accuracy achieved by the acute leukemia ensemble classifiers constructed according to the suggested framework is good compared to the classification accuracy achieved in other studies.

关键词： Leukemia classification ensemble rotation forest pairwise correlation bayesian networks gene expression data microarray gene selection

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Explainable ensemble deep learning-based model for brain tumor detection and classification

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Neural Computing and Applications 2025年第3期37卷 1289-1306页

作者： Hosny, Khalid M. Mohammed, Mahmoud A. Salama, Rania A. Elshewey, Ahmed M. Department of Information Technology Faculty of Computers and Information Zagazig University P.O.BOX 44519 Zagazig Egypt Department of Computer Science Faculty of Computers and Information Suez University P.O.BOX 43221 Suez Egypt Department of Information Technology Faculty of Computers and Information Suez University P.O.BOX 43221 Suez Egypt

Brain tumors are very dangerous as they cause death. A lot of people die every year because of brain tumors. Therefore, accurate classification and detection in the early stages can help in recovery. Various deep learning techniques have achieved good results in brain tumor classification. The traditional deep learning methods and training the neural network from scratch are time-consuming and can last for weeks of training. Therefore, in this work, we proposed an ensemble approach depending on transfer learning that utilizes pre-trained models of DenseNet121 and InceptionV3 to detect three forms of brain tumors: meningioma, glioma, and pituitary. While developing the ensemble model, some changes were made to the architecture of pre-trained models by replacing their classifiers (fully connected and SoftMax layers) with a new classifier to adopt the recent task. In addition, gradient-weighted class activation maps (Grad-CAM) are an explainable model to verify results and achieve high confidence. The suggested model was validated using a publicly available dataset and achieved 99.02% accuracy, 98.75% precision, 98.98% recall, and a 98.86% F1 score. The suggested approach outperformed others in detecting and classifying brain tumor MRI data, and verifying results using the explainable model achieved a high degree of trust. © The Author(s) 2024.

关键词： Transfer learning

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Assessment Software Reliability in Component-Based Software Systems: A Comprehensive Review 6

Assessment Software Reliability in Component-Based Software ...

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6th International Conference on Computing and informatics, ICCI 2024

作者： Tagen, Ibrahim Ayman Wassif, Khaled T. Sayed, Sabah Moawad, Ramadan Department of Computer Science Faculty of Computers and Artificial Intelligence Cairo University Giza Egypt Department of Computer Science Faculty of Computers and Information Technology Future University in Egypt New Cairo Egypt

ISBN: (纸本)9798350373875

The reliability of Component-Based Software Systems (CBSS) is a critical factor in the success and longevity of software applications across various domains. This comprehensive review synthesizes methodologies and findings from thirteen key studies, offering a panoramic view of the landscape of CBSS reliability assessment. We explore a spectrum of approaches, from traditional statistical models to advanced computational intelligence techniques, evaluating their effectiveness, challenges, and contexts of applicability. Our review reveals a trend towards the integration of machine learning and fuzzy logic in reliability estimation, reflecting the complexity and dynamic nature of CBSS. Moreover, we identify a significant emphasis on predictive models that cater to early detection of reliability issues, underscoring the proactive aspect of modern software reliability engineering. The findings suggest a need for more adaptable and context-sensitive methods that can accommodate the evolving nature of software components and their interactions. This review not only charts the current state of CBSS reliability assessment but also highlights future directions for research, including the integration of real-time data and the development of more robust models for uncertainty management. Our study serves as a crucial resource for researchers and practitioners seeking to enhance the reliability of CBSS, ultimately contributing to the development of more dependable software systems. © 2024 IEEE.

关键词： Machine learning

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A secure data hiding approach based on least-significant-bit and nature-inspired optimization techniques

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Journal of Ambient Intelligence and Humanized Computing 2023年第5期14卷 4639-4657页

作者： Hameed, Mohamed Abdel Abdel-Aleem, Omar A. Hassaballah, M. Department of Computer Science Faculty of Computers and Information Luxor University Luxor Egypt College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University AlKharj Saudi Arabia Department of Computer Science Faculty of Computers and Information South Valley University Qena83523 Egypt

With remarkable information technology development, information security has become a major concern in the communication environment, where security must be performed for the multimedia messages exchanged between the sender and the intended recipient. Digital multimedia steganography techniques have been developed to attain a security for covert communication and secure data. This paper proposes an approach for image steganography using the Least Significant Bit Substitution (LSB) and Nature-Inspired Harris Hawks Optimization (HHO) algorithm for efficient concealing of the secret data inside a cover image;thus providing high confidentiality. The HHO based data encoding operation uses the PSNR visual quality metric as an objective function. The objective function is used to determine the ideal encoding vector to convert the secret message to its encoded form. The proposed approach performs better than other state-of-the-art methods in terms of standard measures of visual quality with maintaining high embedding capacity. Comparisons with existing LSB or multi-directional PVD embedding methods demonstrate that the proposed method has more optimized and higher embedding capacity with maintaining visual quality. Besides, the proposed approach achieves high security against statistical StegoExpose analysis, ALASKA2 deep learning steganalysis, and image processing attacks. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Steganography

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COVID19 Outbreak:A Hierarchical Framework for User Sentiment Analysis

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computers, Materials & Continua 2022年第2期70卷 2507-2524页

作者： Ahmed F.Ibrahim M.Hassaballah Abdelmgeid A.Ali Yunyoung Nam Ibrahim A.Ibrahim Department of Computer Science Faculty of Computer ScienceNahda UniversityBanisuefEgypt Department of Computer Science Faculty of Computers and InformationSouth Valley UniversityQenaEgypt Faculty of Computers and Information Minia UniversityAlMiniaEgypt Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Social networking sites in the most modernized world are flooded with large data *** the sentiment polarity of important aspects is necessary;as it helps to determine people’s opinions through what they *** Coronavirus pandemic has invaded the world and been given a mention in the social media on a large *** a very short period of time,tweets indicate unpredicted increase of *** reflect people’s opinions and thoughts with regard to coronavirus and its impact on *** research community has been interested in discovering the hidden relationships from short texts such as Twitter and Weiboa;due to their shortness and *** this paper,a hierarchical twitter sentiment model(HTSM)is proposed to show people’s opinions in short *** proposed HTSM has two main features as follows:constructing a hierarchical tree of important aspects from short texts without a predefined hierarchy depth and width,as well as analyzing the extracted opinions to discover the sentiment polarity on those important aspects by applying a valence aware dictionary for sentiment reasoner(VADER)sentiment *** tweets for each extracted important aspect can be categorized as follows:strongly positive,positive,neutral,strongly negative,or *** quality of the proposed model is validated by applying it to a popular product and a widespread *** results show that the proposed model outperforms the state-of-the-art methods used in analyzing people’s opinions in short text effectively.

关键词： COVID19 COVID data sentiment analysis hierarchical clustering sentiment tree

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Developing a method to detect driver drowsiness based on a single EEG channel and discriminated features

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International Journal of Intelligent Systems Technologies and Applications 2024年第1期22卷 29-40页

作者： Hussein, Raed Mohammed George, Loay E. Miften, Firas Sabar Iraqi Commission for Computers and Informatics Informatics Institute of Postgraduate Studies Baghdad Iraq Computer Sciences Department University of Information Technology and Communication Baghdad Iraq Computer Sciences Department College of Education for Pure Science University of Thi-Qar Thi-Qar Iraq

Driver drowsiness is one of the leading causes of road deaths and transportation industry dangers. Due to its direct evaluation of neurophysiological brain activity, electroencephalography (EEG) has been regarded as one of the most reliable physiological indicators for identifying driver drowsiness. This study proposes a straightforward, cost-effective method for detecting driver drowsiness using a single channel. The contribution of this research is the discovery of drowsiness using discriminated features [moments features (M1, M2, M3, M4), roughness features (R1, R2, R3, R4), zero crossing rate (ZCR), sample entropy (SE) and median absolute deviation (MAD)] from publicly available datasets. A novel model was introduced in this study, which involved the fusion of wavelet transform Daubechies order 4 (WTDB4) and residue decomposition (RD) techniques for feature extraction. Various classification algorithms, including the least-square support vector machine (LSSVM) and ensemble models were compared in terms of their performance metrics. The algorithm that exhibited superior accuracy with reduced computational time was chosen to classify the driver's status into two groups: awake and drowsy. Notably, the proposed model achieved an impressive accuracy of 97.95%. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Electroencephalography

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