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5th International Conference on Medical and Health Informatics, ICMHI 2021

作者： Ghaderzadeh, Mustafa Aria, Mehrad Student Research Committee Department and Faculty of Health Information Technology and Management School of Allied Medical Science Shahid Beheshti University of Medical Sciences Tehran Iran Department of Computer Engineering Faculty of Electrical and Computer Engineering Shiraz University Shiraz Iran

ISBN: (纸本)9781450389846

Successful early detection of Covid-19 disease plays an important role in improving the effectiveness of treatment and managing the pandemic. Various diagnostic methods for the rapid detection of COVID-19 are presented. The first and most important test to detect Covid19 is the PCR test. Studies have shown that PCR testing is time-consuming, expensive, and has a large number of false negatives. As a trend in the scientific community, artificial intelligence has succeeded in Covid19 detection and diagnosis. This article identifies the key achievements reflected in the performance measurement indicators of the application of artificial intelligence algorithms in Covid-19 detection. Besides, this study discusses the finding and future lessons as a roadmap for Corona Pandemic Age. Mean diagnosis of all AI algorithms in the studies performed through Radiology modality had sensitivity with an average higher than 95% and a specificity of higher than 92%, which have a higher diagnostic rate than of traditional radiological methods. Based on relevant research in the field of diagnosis of Covid-19, the Health care managers and Specialists could be able to manage the Pandemic much better and scientifically. Equipping devices and Radiological Center by AI Algorithms and software can cause increases covid19 identification tests and hence can cover more people exposed to Diagnosing Test. This study presents a comprehensive review of Artificial Intelligence techniques and evolving deep learning techniques for Covid-19 Detection. © 2021 ACM.

关键词： Diagnosis

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Real Vulnerabilities in Partial Reconfigurable Design Cycles;Case Study for Implementation of Hardware Security Modules 20

Real Vulnerabilities in Partial Reconfigurable Design Cycles...

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20th International Symposium on computer Architecture and Digital Systems, CADS 2020

作者： Jafarzadeh, Hanieh Jahanian, Ali Shahid Beheshti University Faculty of Computer Science and Engineering Tehran Iran

ISBN: (纸本)9781728181110

In this paper, the security of existing DPR design flows are analyzed, and some practical vulnerabilities are introduced by an attack scenario in which the adversary exploits the remote DPR capability of the FPGA to launch Hardware Trojan Horse (HTH) attacks on FPGA-based Hardware Security Module (HSM). Our analyses show that there are serious potential vulnerabilities in current reconfigurable FPGA design flows, which should address in architecture level instead of the design flow. The result of this analysis can help FPGA vendors or designers to protect the FPGA-based IPs against dynamic Trojan insertion. © 2020 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Cost-Effective and Practical Countermeasure against the Template Side Channel Attack 17

Cost-Effective and Practical Countermeasure against the Temp...

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17th International ISC Conference on Information Security and Cryptology, ISCISC 2020

作者： Kordi, Farshideh Hosseintalaee, Hamed Jahanian, Ali Shahid Beheshti University Faculty of Computer Science and Engineering Tehran Iran

ISBN: (纸本)9781728185699

One of the most efficient attacks for exploiting the secret key is a template attack. Recent researches show that regular countermeasures against these attacks are costly. Randomized shuffling in the time domain is known as a cost-effective countermeasures against side-channel attacks are widely used. In this article, we implemented a real template attack and proposed an efficient countermeasure against it. We practically evaluated security improvement with this method in the AES algorithm on a template attack and reported the analysis results of our countermeasure. According to these results, template attack will be hardened significantly after the proposed protection such that the grade of the key recovery increases from 1 with 350K traces in unprotected design to 2100 with 700K traces in the protected circuit. This security improvement gains in the cost of about 7% delay overhead. © 2020 IEEE.

关键词： Side channel attack

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A neural-based approach to aid early parkinson's disease diagnosis 11

A neural-based approach to aid early parkinson's disease dia...

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11th International Conference on Information and Knowledge Technology, IKT 2020

作者： Salimi-Badr, Armin Hashemi, Mohammad Shahid Beheshti University Faculty of Computer Science and Engineering Tehran Iran

ISBN: (纸本)9781665404419

In this paper, a neural approach based on using Long-Short Term Memory (LSTM) neural networks is proposed to diagnose patients suffering from PD. In this study, it is shown that the temporal patterns of the gait cycle are different for healthy persons and patients. Therefore, by using a recurrent structure like LSTM, able to analyze the dynamic nature of the gait cycle, the proposed method extracts the temporal patterns to diagnose patients from healthy persons. Utilized data to extract the temporal shapes of the gait cycle are based on changing vertical Ground Reaction Force (vGRF), measured by 16 sensors placed in the soles of shoes worn by each subject. To reduce the number of data dimensions, the sequences of corresponding sensors placed in different feet are combined by subtraction. This method analyzes the temporal pattern of time-series collected from different sensors, without extracting special features representing statistics of different parts of the gait cycle. Finally, by recording and presenting data from 10 seconds of subject walking, the proposed approach can diagnose the patient from healthy persons with an average accuracy of 97.66%, and the total F1 score equal to 97.78%. © 2020 IEEE.

关键词： Long short-term memory

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Multi-Attribute Decision Making using Competitive Neural Networks 10

Multi-Attribute Decision Making using Competitive Neural Net...

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10th International Conference on computer and Knowledge engineering, ICCKE 2020

作者： Abdoos, Monireh Shahid Beheshti University Faculty of Computer Science and Engineering Tehran Iran

ISBN: (纸本)9781728185668

Multi Attribute Decision Making (MADM) methods are widely used for making the optimal decision. Different approaches have been presented to solve decision-making problems. The aim of MADM is ranking of feasible alternatives. In this paper, a new approach to solve MADM problems using an artificial neural network has been presented. The competition among alternatives is modeled by a competitive network. The ordered list of the alternatives is achieved in two phases: partial ranking and fine ranking. The results of this approach are compared with Simple Additive Weighting (SAW) and Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). © 2020 IEEE.

关键词： Decision making

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Analysis of Geometrical Parameters for Remote Side-Channel Attacks on Multi-Tenant FPGAs 17

Analysis of Geometrical Parameters for Remote Side-Channel A...

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17th International ISC Conference on Information Security and Cryptology, ISCISC 2020

作者： Salimian, Milad Jahanian, Ali Shahid Beheshti University Faculty of Computer Science and Engineering Tehran Iran

ISBN: (纸本)9781728185699

Considering the dependency between the power consumption of implemented cryptographic algorithms and the data being processed, side-channel analysis methods can reveal the secret information of these systems. It was previously thought that data acquisition of dynamic power needs physical access to these systems, but recent studies show, it is possible to gather information about power consumption from FPGAs without any physical access. High flexibilities of modern FPGAs cause that they are used for cloud accelerator in Platform as a Service (PaaS) system;however, new serious vulnerabilities emerged for these platforms. Although there are some reports about how switching activities from one region of FPGA affect other regions, details of this technique are not analyzed. In this paper, we analyzed the strength of this kind of attack and examined the impact of geometrical and electrical parameters of the victim/attacker modules on the efficiency of this attack. Experimental results and analyses show that the relational location, and the distance of victim/attacker modules, have considerable impacts on the quality of attack. Results of this analysis can help the FPGA manufacturer and IP developers to protect their systems against this serious attack. © 2020 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Business process improvement challenges: A systematic literature review 11

Business process improvement challenges: A systematic litera...

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11th International Conference on Information and Knowledge Technology, IKT 2020

作者： Kashfi, Hanieh Aliee, Fereidoon Shams Shahid Beheshti University Faculty of Computer Science and Engineering Tehran Iran

ISBN: (纸本)9781665404419

Business process is a set of activities that provides outputs to achieve a specific and predefined goal. Accordingly, Business Process Improvement is one of the most important factors in achieving the goals of enterprises. Unfortunately, despite the exorbitant costs in enterprises to improve business processes, activities in this area often fail. The reason for this, is the existence of numerous challenges on the path to improvement, which are often overlooked by researchers and practitioners in the field. Following this issue, this paper tries to identify the main challenges in this direction. While creating a comprehensive set of business process improvement challenges, a high-level abstraction cycle is presented for business process improvement activities. Finally, the identified challenges are categorized according to the steps in the proposed cycle. © 2020 IEEE.

关键词： Life cycle

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Fully Automated Assessment of Cardiac Coverage in Cine Cardiovascular Magnetic Resonance Images using an Explainable Deep Visual Salient Region Detection Model

arXiv

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arXiv 2022年

作者： Nabavi, Shahabedin Hashemi, Mohammad Moghaddam, Mohsen Ebrahimi Abin, Ahmad Ali Frangi, Alejandro F. The Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran School of Computing University of Leeds United Kingdom

Cardiovascular magnetic resonance (CMR) imaging has become a modality with superior power for the diagnosis and prognosis of cardiovascular diseases. One of the essential basic quality controls of CMR images is to investigate the complete cardiac coverage, which is necessary for the volumetric and functional assessment. This study examines the full cardiac coverage using a 3D convolutional model and then reduces the number of false predictions using an innovative salient region detection model. Salient regions are extracted from the short-axis cine CMR stacks using a three-step proposed algorithm. Combining the 3D CNN baseline model with the proposed salient region detection model provides a cascade detector that can reduce the number of false negatives of the baseline model. The results obtained on the images of over 6,200 participants of the UK Biobank population cohort study show the superiority of the proposed model over the previous state-of-the-art studies. The dataset is the largest regarding the number of participants to control the cardiac coverage. The accuracy of the baseline model in identifying the presence/absence of basal/apical slices is 96.25% and 94.51%, respectively, which increases to 96.88% and 95.72% after improving using the proposed salient region detection model. Using the salient region detection model by forcing the baseline model to focus on the most informative areas of the images can help the model correct misclassified samples' predictions. The proposed fully automated model's performance indicates that this model can be used in image quality control in population cohort datasets and also real-time post-imaging quality assessments. Copyright © 2022, The Authors. All rights reserved.

关键词： Quality control

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Multi-Transfer Learning Techniques for Detecting Auditory Brainstem Response

SSRN

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SSRN 2023年

作者： Özyurt, Fatih Majidpour, Jafar Rashid, Tarik A. Majidpour, Amir Koç, Canan Department of Software Engineering Faculty of Engineering Firat University Elazig Turkey Department of Computer Science University of Raparin Rania Iraq Computer Science and Engineering Department University of Kurdistan Hewlêr Erbil Iraq Audiology Department School of Rehabilitation Shahid Beheshti University of Medical Sciences Tehran Iran

Evaluation of peripheral auditory nerve system health in patients with hearing loss by auditory brainstem response (ABR) testing. Audiologists record and evaluate ABRs by manually timing and evaluating the waveforms for quality. Inaccurate assessment of ABR tests may lead to incorrect judgments regarding the integrity of the auditory nerve system;therefore, proper diagnosis and analysis are essential. In order to identify and assess ABR automation while decreasing the possibility of human error, machine learning methods, notably deep learning, may be an appropriate option. To address these issues, this study proposed deep-learning models using the transfer-learning (TL) approach to extract features from ABR testing and diagnose using support vector machines (SVM). Pre-trained convolutional neural network (CNN) architectures like AlexNet, DenseNet, GoogleNet, InceptionResNetV2, InceptionV3, MobileNetV2, NASNetMobile, ResNet18, ResNet50, ResNet101, ShuffleNet, and SqueezeNet are used to extract features from the collected ABR reported images dataset in the proposed model. It has been decided to use six measures accuracy, precision, recall, geometric mean (GM), standard deviation (SD), and area under the ROC curve to measure the effectiveness of the proposed model. According to experimental findings, the ShuffleNet and ResNet50 models' TL is effective for ABR diagnosis using an SVM classifier, with a high accuracy rate of 95% when using the 5-fold cross-validation method. © 2023, The Authors. All rights reserved.

关键词： Audition

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Multi-Transfer Learning Techniques for Detecting Auditory Brainstem Response

arXiv

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arXiv 2023年

The assessment of the well-being of the peripheral auditory nerve system in individuals experiencing hearing impairment is conducted through auditory brainstem response (ABR) testing. Audiologists assess and document the results of the ABR test. They interpret the findings and assign labels to them using reference-based markers like peak latency, waveform morphology, amplitude, and other relevant factors. Inaccurate assessment of ABR tests may lead to incorrect judgments regarding the integrity of the auditory nerve system;therefore, proper Hearing Loss (HL) diagnosis and analysis are essential. To identify and assess ABR automation while decreasing the possibility of human error, machine learning methods, notably deep learning, may be an appropriate option. To address these issues, this study proposed deep-learning models using the transfer-learning (TL) approach to extract features from ABR testing and diagnose HL using support vector machines (SVM). Pre-trained convolutional neural network (CNN) architectures like AlexNet, DenseNet, GoogleNet, InceptionResNetV2, InceptionV3, MobileNetV2, NASNetMobile, ResNet18, ResNet50, ResNet101, ShuffleNet, and SqueezeNet are used to extract features from the collected ABR reported images dataset in the proposed model. It has been decided to use six measures accuracy, precision, recall, geometric mean (GM), standard deviation (SD), and area under the ROC curve to measure the effectiveness of the proposed model. According to experimental findings, the ShuffleNet and ResNet50 models' TL is effective for ABR to diagnose HL using an SVM classifier, with a high accuracy rate of 95% when using the 5-fold cross-validation method. © 2023, CC BY.

关键词： Support vector machines

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