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Representing a Model for the Anonymization of Big Data Stream Using In-Memory Processing

Annals of Data science 2025年第1期12卷 223-252页

作者： Shamsinejad, Elham Banirostam, Touraj Pedram, Mir Mohsen Rahmani, Amir Masoud Department of Computer Engineering Central Tehran Branch Islamic Azad University Tehran Iran Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran

In light of the escalating privacy risks in the big data era, this paper introduces an innovative model for the anonymization of big data streams, leveraging in-memory processing within the Spark framework. The approach is founded on the principle of K-anonymity and propels the field forward by critically evaluating various anonymization methods and algorithms, benchmarking their performance with respect to time and space complexities. A distinctive formula for optimized cluster determination in the K-means algorithm is presented, along with a novel tuple expiration time strategy for the efficient purging of clusters. The integration of these components into Spark’s RDD and MLlib modules results in a significant decrease in execution time and data loss rates, even with increasing data volumes. The paper’s notable contributions are its methodological advancements that offer a robust, scalable solution for data anonymization, safeguarding user privacy without sacrificing data utility or processing efficiency. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Big data

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Visual creativity through concept combination using quantum cognitive models

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Scientia iranica 2024年第2期31卷 118-135页

作者： Tabriz, M. Ahrabi Atani, T. Rafiei Ashtiani, M. Jahed-Motlagh, M.R. Laboratory School of Computer Engineering Iran University of Science and Technology Tehran16846-13114 Iran

Computational creativity modeling, including concept combination, enables us to foster deeper abilities of Artificial Intelligence (Al) agents. All hough concept combination lias been addressed in a lot of computational creativity studies, findings show incompatibility amongst empirical data of concept combination and the results of the used methods. In addition, even though recent neuroscientific studies show the crucial impact of retrieving concepts’ relations explicitly stored in episodic memory, it has been underestimated in modeling creative processes. In this paper, a quant urn cognition-based approach is used to consider the context more effectively and resolve logical inconsistencies. Also, episodic memory is leveraged as the basis for the concept combination modeling process based on the created context. The result of the proposed process is a set of meaningful concepts and expressions as a combination of stimuli and related episodes, which are used to depict a visual collage as an image. The significant improvement in the quality of results in comparison with the existing methods suggests that quantum-like modeling can be considered the foundation for developing Al agents capable of creating artistic images or assisting a person during a creative process. © 2024 Sharif university of technology.

关键词： Artificial intelligence

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A Review of Anonymization Algorithms and Methods in Big Data

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Annals of Data science 2025年第1期12卷 253-279页

In the era of big data, with the increase in volume and complexity of data, the main challenge is how to use big data while preserving the privacy of users. This study was conducted with the aim of finding a solution to this challenge. In this study, we examined various data anonymization methods, including differential privacy, advanced encryption, and strong access controls. In addition, the operation, advantages, disadvantages, and use of these methods, the challenges of adapting these methods to big data, and possible solutions for them were also examined. Our results show that traditional data anonymization methods lack scalability, leading to privacy breaches and data loss. When faced with large volumes of data, these methods may not be able to fully process the data. Also, these methods may be ineffective against re-identification attacks, linkage attacks, and inference attacks. We introduced emerging methods that are capable of providing improved privacy with minimal data loss. These methods have scalability for big data. Finally, we examined future research works and raised important questions that can help improve existing algorithms or develop new methods, better manage the complexity and scale of unstructured data. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Big data

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New logarithmic step size for stochastic gradient descent

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Frontiers of computer science 2025年第1期19卷 109-118页

作者： Mahsa Soheil SHAMAEE Sajad Fathi HAFSHEJANI Zeinab SAEIDIAN Department of Computer Science Faculty of Mathematical ScienceUniversity of KashanKashan 87317-53153Iran Department of Applied Mathematics Shiraz University of TechnologyShiraz 13876-71557Iran Department of Mathematical Sciences University of KashanKashan 87317-53153Iran

In this paper, we propose a novel warm restart technique using a new logarithmic step size for the stochastic gradient descent (SGD) approach. For smooth and non-convex functions, we establish an O(1/√T) convergence rate for the SGD. We conduct a comprehensive implementation to demonstrate the efficiency of the newly proposed step size on the FashionMinst, CIFAR10, and CIFAR100 datasets. Moreover, we compare our results with nine other existing approaches and demonstrate that the new logarithmic step size improves test accuracy by 0.9% for the CIFAR100 dataset when we utilize a convolutional neural network (CNN) model.

关键词： stochastic gradient descent logarithmic step size warm restart technique

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Abnormal behaviors recognition in crowd environments based on semi-supervised deep learning and hierarchical approach

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Journal of Ambient Intelligence and Humanized Computing 2024年第12期15卷 3925-3943页

作者： Asl, Vahid Fazel Karasfi, Babak Masoumi, Behrooz Keyvanpour, Mohammad Reza Department of Computer and Information Technology Engineering Qazvin Branch Islamic Azad University Qazvin Iran Department of Computer Engineering Alzahra University Tehran Iran

This paper tackles the complexity of detecting abnormal behavior by focusing on temporal patterns, such as social force and optical flow, rather than spatial patterns. The CycleGAN system trains on normal behaviors, ensuring that abnormal behaviors are not reproduced during testing. Hierarchical supervised learning and transfer learning techniques are applied to recognize abnormal behaviors. Given the scarcity of abnormal patterns, geometric techniques are employed to augment these patterns, followed by training with temporal models. Experimental results on the UCSD and UMN datasets demonstrate that the proposed method significantly outperforms existing approaches, achieving average AUCs of 99.90% on Ped1, 99.89% on Ped2, and 99.82% on UMN. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Generative adversarial networks

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Patient-independent epileptic seizure detection using weighted visibility graph features and wavelet decomposition

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Multimedia Tools and Applications 2025年第6期84卷 3197-3221页

作者： Mohammadpoory, Zeynab Nasrolahzadeh, Mahda Amiri, Sekineh Asadi Department of Biomedical Engineering Shahrood University of Technology Shahrood Iran Department of Electrical Engineering University of Torbat Heydarieh Torbat Heydarieh Iran Department of Computer Engineering University of Mazandaran Babolsar Iran

Epileptic seizure detection has been a complex task due to the chaos and non-stationariness observed in the electroencephalogram (EEG) signals. Most existing EEG-based seizure detection algorithms are patient-dependent and train a detection model for each patient. This study investigates the patient-independent detection of seizure events using the big dataset CHB-MIT Scalp EEG. This paper proposes a new method based on weighted visibility graph (WVG) features to identify seizures from EEG signals. EEG signals from 22 channels and delta, theta, alpha, beta, and gamma sub-bands of EEGs are mapped into the WVG, and then WVG features are calculated from these WVGs. Then more informative features were selected using a combination of some feature selection methods and were given to five classifiers to investigate the performance of these features to classify the brain signals into seizure free (interictal) and during a seizure (ictal) groups. After that, the post-processing step was used to promote the performance of the method and also detect the seizure onset and offset times detection. The proposed method could detect 163 seizures out of 184, considering patient-independent situations for training classifiers and it could obtain an accuracy, sensitivity, and specificity of 94.02%, 92.31%, and 94.12% respectively. It also could obtain an FDR equal to 0.12/h and an ADL of 5.11 Seconds. Then, the proposed method has promising results in detection of seizures and it may use in online seizure detection applications such as closed-loop therapies. This study proposed extracting some WVG-based features from the signal and its sub-band with a feature selection scheme combining some feature selection methods and using some classifiers. Then a post-processing proposed to process the classifiers output and promote the performance of the proposed method. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Electroencephalography

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A mathematical multi-objective optimization model and metaheuristic algorithm for effective advertising in the social internet of things

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Neural Computing and Applications 2025年第6期37卷 4797-4821页

作者： Molaei, Reza Rahsepar Fard, Kheirollah Bouyer, Asgarali Department of Computer Engineering and Information Technology University of Qom Qom Iran Department of Software Engineering Azarbaijan Shahid Madani University Tabriz Iran Department of Software Engineering Faculty of Engineering and Natural Science Istinye University Istanbul Turkey

The integration of social networks with the Internet of Things (IoT) has been explored in recent research, giving rise to the Social Internet of Things (SIoT). One promising application of SIoT is viral marketing, which plays a critical role in modern advertising. However, current solutions proposed for viral marketing are often impractical in real-world scenarios, and there has been limited investigation into viral marketing within the context of SIoT. To address this issue, we present a multi-objective integer linear programming model named ILP-ASoT that facilitates advertising through SIoT. Our approach introduces two realistic parameters: the node sociality rate and the node appropriateness rate as an advertising destination. By balancing the cost of selecting a seed set with the number of users who receive the advertisement, our model offers an appropriate trade-off. Since this is proven as an NP-hard problem, A PSO-based metaheuristic algorithm is proposed to provide near-optimal solutions for advertising in SIoT, called PSO-ASoT. Through simulations, we show that our PSO-ASoT algorithm outperforms state-of-the-art algorithms in several metrics, such as influence spread, advertising cost, and duplicated advertising rate. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Heuristic algorithms

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Skin Lesion Classification via ensemble method on deep learning

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Multimedia Tools and Applications 2025年第18期84卷 19379-19397页

作者： Amiri, Sekineh Asadi Nasrolahzadeh, Mahda Mohammadpoory, Zeynab Kordkheili, Amir Hossein Zare Department of Computer Engineering University of Mazandaran Babolsar Iran Department of Electrical Engineering University of Torbat Heydarieh Torbat Heydarieh Iran Department of Electrical Engineering Shahrood University of Technology Shahrood Iran

Deep neural networks have shown promising results in the classification of skin lesion images, particularly when they focus on the most significant regions of an image. However, the identification of melanoma continues to pose a significant challenge, primarily because of the substantial variability both within and between classes in images of moles. A new framework is proposed in this paper to develop an innovative method for effective melanoma diagnosis, presenting a new approach for Skin Lesion Classification (SLC) that combines a modified Inception ResNet v2 and Efficient Net-B4 in an ensemble. In the modified Inception ResNet v2, we incorporate a Soft-Attention (SA) mechanism. The goal is to enhance the network’s performance by using the SA mechanism to highlight informative features and suppress those that induce noise. Given the diversity of skin lesions in terms of type, texture, color, shape, and distribution, our proposed method extracts crucial features from the images by employing both the SA and merging the features of two layers of Inception ResNet ***, we have modified EfficientNet-B4 by adding two dense layers to improve its classification capabilities. After classifying the images with these two models, we employ soft voting for the final classification. We evaluated the performance of our proposed method on the ISIC-2017 and ISIC-2018 datasets, considering metrics such as Accuracy, Precision, Recall, and F-measures. Experimental results and a comparison with existing techniques demonstrate that our method achieves higher accuracy than current state-of-the-art methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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Performance improvement of the hybrid switch reluctance motor by notching method

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Scientia iranica 2024年第10期31卷 790-797页

作者： Hasanzadeh, Saeed Rezaei, Hossein Taheri, Hadis Department of Electrical and Computer Engineering Qom University of Technology Qom Iran Department of Electrical and Computer Engineering Babol Noshirvani University of Technology Babol Iran Mazandaran Regional Electric Company Sari Iran

Hybrid switch reluctance motors are the family of switch reluctance motors (SRMs) that attenuate the magnetic saturation and increase the air gap magnetic flux by exploiting permanent magnets. The permanent magnet auxiliary air gap flux can affect the average torque and ripple. Commonly, the torque ripples reduction comes with the average torque drop. In this paper, the torque ripple reduction and average torque improvement are achieved for a 6/10 pole hybrid switch reluctance motor by inserting two symmetrical notches on its rotor. Also, the lengths of magnet and rectangular notches are optimized by the finite element method and sensitivity analysis. The comparison of the optimized design with the initial one carried out by finite element proves the efficiency of the proposed model. © 2024 Sharif university of technology. All rights reserved.

关键词： Sensitivity analysis

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Functional Model of Integrated Maintenance (Reliability, Overall Equipment Effectiveness, Safety, and Cost) in Petrochemical Industries

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International Journal of engineering, Transactions B: Applications 2024年第11期37卷 2392-2404页

作者： Hosseini, S.M. Seyed Shahanaghi, K. Shasfand, S. Department of Industrial Engineering Iran University of Science and Technology Tehran Iran Iran University of Science and Technology Tehran Iran Tabriz Petrochemical Company Tabriz Iran

In the petrochemical industry, effective maintenance management is critical to operational efficiency and safety. Key indicators such as overall equipment effectiveness (OEE), reliability, safety incidents, and maintenance costs are commonly used by management to evaluate maintenance system performance. Increasing equipment availability and product quality can boost OEE and lead to fewer safety incidents and improved overall performance. Studies in iran's petrochemical sector have shown that investing in maintenance and training can bring significant benefits. For example, data from 42 petrochemical complexes in iran, covering 2015 to 2021, show an increase in equipment effectiveness and reliability over time. However, there was a slight slowdown in 2021 due to the impact of the Covid-19 pandemic. Improvements in maintenance performance, the use of excellence models along with the necessary training have been associated with increased product realization and productivity growth. Research also shows that increased reliability can positively impact the reduction of safety incidents, environmental issues, and overall operating costs. By prioritizing maintenance excellence and adopting best practices, organizations can strengthen their competitiveness and sustainability in a dynamic business landscape. ©2024 The author(s).

关键词： Reliability

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