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12th International Conference on computer and Knowledge engineering, ICCKE 2022

作者： Sadr, Ali Salimi Khojasteh, Mahdi Shahbazi Malek, Hamed Salimi-Badr, Armin Shahid Beheshti University Faculty of Computer Science and Engineering Tehran Iran

ISBN: (数字)9781665476133

ISBN: (纸本)9781665476133

In this paper, a planning method to autonomously navigate a wheeled-robot based on deep reinforcement learning is proposed. The planning algorithm aims at reaching a predetermined target configuration while avoiding obstacles. It is assumed that the robot chassis is differential-drive and it is equipped with multiple distance sensors to perceive the environment and Global Positioning System (GPS) for localization. A Deterministic Actor-Critic model is designed to navigate the robot in an environment including multiple obstacles. Since navigation is a multi-objective optimization problem, a novel reward function considering different contradicted criteria including reaching the target and avoiding the obstacles is proposed in this paper. To improve the performance and convergence of the learning method, a gradual learning method is applied to first learn target reaching and next obstacle avoidance. The effect of the proposed reward function is compared with some other reward functions to show its effectiveness. © 2022 IEEE.

关键词： Multiobjective optimization

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SAT Based Analogy Evaluation Framework for Persian Word Embeddings 12

SAT Based Analogy Evaluation Framework for Persian Word Embe...

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12th International Conference on computer and Knowledge engineering, ICCKE 2022

作者： Mahmoudi, Seyed Ehsan Shamsfard, Mehrnoush Shahid Beheshti University Faculty of Computer Science and Engineering Tehran Iran

ISBN: (数字)9781665476133

ISBN: (纸本)9781665476133

In recent years there has been a special interest in word embeddings as an approach to convert words to vectors. It has been a focal point to understand how much of the semantics of the words has been transferred into embedding vectors. Intrinsic evaluation of word embeddings is cheaper than evaluating them extrinsically and it is usually costly to evaluate the downstream application end-to-end in order to determine the quality of the used embedding model. Generally the word embeddings are evaluated through a number of tests, including analogy test. In this paper we propose a test framework for Persian embedding models. Persian is a low resource language and there is no rich semantic benchmark to evaluate word embedding models for this language. In this paper we introduce an evaluation framework including a hand crafted Persian SAT based analogy dataset, a colliquial test set (specific to Persian) and a benchmark to study the impact of various parameters on the semantic evaluation task. © 2022 IEEE.

关键词： Embeddings

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Automatic extraction of lightweight and efficient neural network architecture of heavy convolutional architectures to predict microsatellite instability from hematoxylin and eosin histology in gastric cancer

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Neural Computing and Applications 2024年第25期36卷 15295-15321页

作者： Rostami, Habib Ashkpour, Maryam Behzadi-Khormouji, Hamed Mokhtari, Maral Khayati, Armin Keshavarz, Ahmad Talatian Azad, Saeed Tabesh, Yahya Computer Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran Artificial Intelligence and Intelligent Healthcare Lab Artificial Intelligence and Data Mining Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Faculty of New Science and Technologies University of Tehran Tehran Iran Department of Computer Science Imec-IDLab University of Antwerp Sint-Pietersvliet 7 Antwerp2000 Belgium Shahid Faghihi Hospital Shiraz University of Medical Sciences Shiraz Iran Department of Computer Science Engineering and IT School of Electrical and Computer Engineering Shiraz University Shiraz Iran Electrical Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran IoT and Signal Processing Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Department of Mathematical Sciences Sharif University of Technology Tehran Iran

Cancers have emerged as a significant concern due to their impact on public health and society. The examination and interpretation of tissue sections stained with Hematoxylin and Eosin (H&E) play a crucial role in disease assessment, particularly in cases like gastric cancer. Microsatellite instability (MSI) is suggested to contribute to the carcinogenesis of specific gastrointestinal tumors. However, due to the nonspecific morphology observed in H&E-stained tissue sections, MSI determination often requires costly evaluations through various molecular studies and immunohistochemistry methods in specialized molecular pathology laboratories. Despite the high cost, international guidelines recommend MSI testing for gastrointestinal cancers. Thus, there is a pressing need for a new diagnostic modality with lower costs and widespread applicability for MSI detection. This study aims to detect MSI directly from H&E histology slides in gastric cancer, providing a cost-effective alternative. The performance of well-known deep convolutional neural networks (DCNNs) and a proposed architecture are compared. Medical image datasets are typically smaller than benchmark datasets like ImageNet, necessitating the use of off-the-shelf DCNN architectures developed for large datasets through techniques such as transfer learning. Designing an architecture proportional to a custom dataset can be tedious and may not yield desirable results. In this work, we propose an automatic method to extract a lightweight and efficient architecture from a given heavy architecture (e.g., well-known off-the-shelf DCNNs) proportional to a specific dataset. To predict MSI instability, we extracted the MicroNet architecture from the Xception network using the proposed method and compared its performance with other well-known architectures. The models were trained using tiles extracted from whole-slide images, and two evaluation strategies, tile-based and whole-slide image (WSI)-based, were employed and comp

关键词： Network architecture

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Robat-e-Beheshti: A Persian Wake Word Detection Dataset for Robotic Purposes 12

Robat-e-Beheshti: A Persian Wake Word Detection Dataset for ...

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12th International Conference on computer and Knowledge engineering, ICCKE 2022

作者： Raji, Parisa Ahmadzadeh Shekofteh, Yasser Shahid Beheshti University Faculty of Computer Science and Engineering Tehran Iran

ISBN: (数字)9781665476133

ISBN: (纸本)9781665476133

In this paper a dataset is explained which was collected for a wake word detection project which works on classifying audio data. The data are audio files in Persian language. The number of 5738 audio files with different formats and a maximum length of 3 seconds were collected. Afterwards the format of audio files and their sample rates were changed to a specific one. There are positive and negative examples for this dataset. Different audio recorders were used for recording the audios and most of the data were gathered from 187 individuals and the other files were collected from the open source ShEMO dataset: a large-scale validated database for Persian speech emotion detection. In this paper the process of collecting data and how they were organized in different files, are explained and the data analysis is also presented. This dataset will be free for academic usage with official request to the authors. © 2022 IEEE.

关键词： Wakes

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Cell-based allometry:an approach for evaluation of complexity in morphogenesis

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Quantitative Biology 2023年第2期11卷 183-203页

作者： Ali Tarihi Mojtaba Tarihi Taki Tiraihi Department of Computer Engineering Faculty of Computer Science and EngineeringShahid Behshti UniversityTehran 19839-69411Iran Department of Computer Engineering Sharif University of TechnologyTehran 11365-11155Iran Department of Anatomical Sciences School of Medical SciencesTarbiat Modares UniversityTehran 14117-13116Iran

Background:Morphogenesis is a complex process in a developing animal at the organ,cellular and molecular *** this investigation,allometry at the cellular level was ***:Geometric information,including the time-lapse Cartesian coordinates of each cell's center,was used for calculating the allometric coefficients.A zero-centroaxial skew-symmetrical matrix(CSSM),was generated and used for constructing another square matrix(basic square matrix:BSM),then the determinant of BSM was calculated(d).The logarithms of absolute d(Lad)of cell group at different stages of development were plotted for all of the cells in a range of development stages;the slope of the regression line was estimated then used as the allometric ***,the lineage growth rate(LGR)was also calculated by plotting the Lad against the logarithm of the *** complexity index at each stage was *** method was tested on a developing Caenorhabditis elegans ***:We explored two out of the four first generated blastomeres in *** *** ABp and EMS lineages show that the allometric coefficient of ABp was higher than that of EMS,which was consistent with the complexityindexas well ***:The conclusion of this study is that the complexity of the differentiating cells in a developing embryo can be evaluated by allometric scaling based on the data derived from the Cartesian coordinates of the cells at different stages of development.

关键词： embryogenesis allometry complexity C.elegans bioinformatics skew matrix morphogenesis

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SincPD: An Explainable Method based on Sinc Filters to Diagnose Parkinson’s Disease Severity by Gait Cycle Analysis

arXiv

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arXiv 2025年

作者： Salimi-Badr, Armin Veisi, Mahan Berangi, Sadra Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran

In this paper, an explainable deep learning-based classifier based on adaptive sinc filters for Parkinson’s Disease diagnosis (PD) along with determining its severity, based on analyzing the gait cycle (SincPD) is presented. Considering the effects of PD on the gait cycle of patients, the proposed method utilizes raw data in the form of vertical Ground Reaction Force (vGRF) measured by wearable sensors placed in soles of subjects’ shoes. The proposed method consists of Sinc layers that model adaptive bandpass filters to extract important frequency-bands in gait cycle of patients along with healthy subjects. Therefore, by considering these frequencies, the reasons behind the classification a person as a patient or healthy can be explained. In this method, after applying some preprocessing processes, a large model equipped with many filters is first trained. Next, to prune the extra units and reach a more explainable and parsimonious structure, the extracted filters are clusters based on their cut-off frequencies using a centroid-based clustering approach. Afterward, the medoids of the extracted clusters are considered as the final filters. Therefore, only 15 bandpass filters for each sensor are derived to classify patients and healthy subjects. Finally, the most effective filters along with the sensors are determined by comparing the energy of each filter encountering patients and healthy subjects. © 2025, CC BY-NC-ND.

关键词： Adaptive filters

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Prognostic Model for Idiopathic Pulmonary Fibrosis Using Context-Aware Sequential-Parallel Hybrid Transformer and Enriched Clinical Information

arXiv

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arXiv 2025年

作者： Dolatabadi, Mahdie Nabavi, Shahabedin Moghaddam, Mohsen Ebrahimi Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran

Idiopathic pulmonary fibrosis (IPF) is a progressive disease that irreversibly transforms lung tissue into rigid fibrotic structures, leading to debilitating symptoms such as shortness of breath and chronic fatigue. The heterogeneity and complexity of this disease, particularly regarding its severity and progression rate, have made predicting its future course a complex and challenging task. Besides, traditional diagnostic methods based on clinical evaluations and imaging have limitations in capturing the disease’s complexity. Using the Kaggle Pulmonary Fibrosis Progression dataset, which includes computed tomography images, and clinical information, the model predicts changes in forced vital capacity (FVC), a key progression indicator. Our method uses a proposed context-aware sequential-parallel hybrid transformer model and clinical information enrichment for its prediction. The proposed method achieved a Laplace Log-Likelihood score of −6.508, outperforming prior methods and demonstrating superior predictive capabilities. These results highlight the potential of advanced deep learning techniques to provide more accurate and timely predictions, offering a transformative approach to the diagnosis and management of IPF, with implications for improved patient outcomes and therapeutic advancements. Code Availability: GitHub Copyright © 2025, The Authors. All rights reserved.

关键词： computerized tomography

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Small Object Detection: A Comprehensive Survey on Challenges, Techniques and Real-World Applications

arXiv

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arXiv 2025年

作者： Nikouei, Mahya Baroutian, Bita Nabavi, Shahabedin Taraghi, Fateme Aghaei, Atefe Sajedi, Ayoob Ebrahimi Moghaddam, Mohsen Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran

Small object detection (SOD) is a critical yet challenging task in computer vision, with applications like spanning surveillance, autonomous systems, medical imaging, and remote sensing. Unlike larger objects, small objects contain limited spatial and contextual information, making accurate detection difficult. Challenges such as low resolution, occlusion, background interference, and class imbalance further complicate the problem. This survey provides a comprehensive review of recent advancements in SOD using deep learning, focusing on articles published in Q1 journals during 2024-2025. We analyzed challenges, state-of-the-art techniques, datasets, evaluation metrics, and real-world applications. Recent advancements in deep learning have introduced innovative solutions, including multi-scale feature extraction, Super-Resolution (SR) techniques, attention mechanisms, and transformer-based architectures. Additionally, improvements in data augmentation, synthetic data generation, and transfer learning have addressed data scarcity and domain adaptation issues. Furthermore, emerging trends such as lightweight neural networks, knowledge distillation (KD), and self-supervised learning offer promising directions for improving detection efficiency, particularly in resource-constrained environments like Unmanned Aerial Vehicles (UAV)-based surveillance and edge computing. We also review widely used datasets, along with standard evaluation metrics such as mean Average Precision (mAP) and size-specific AP scores. The survey highlights real-world applications, including traffic monitoring, maritime surveillance, industrial defect detection, and precision agriculture. Finally, we discuss open research challenges and future directions, emphasizing the need for robust domain adaptation techniques, better feature fusion strategies, and real-time performance optimization. By consolidating recent findings and identifying research gaps, this survey serves as a valuable resource for re

关键词： Unmanned aerial vehicles (UAV)

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MOCBOA:Multi-Objective Chef-Based Optimization Algorithm Using Hybrid Dominance Relations for Solving engineering Design Problems

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computer Modeling in engineering & sciences 2025年第4期143卷 967-1008页

作者： Nour Elhouda Chalabi Abdelouahab Attia Abdulaziz S.Almazyad Ali Wagdy Mohamed Frank Werner Pradeep Jangir Mohammad Shokouhifar Computer Science Department University of M’silaIchebiliaM’sila28000Algeria Computer ScienceDepartment Mohamed El Bachir El IbrahimiUniversity of Bordj BouArreridjBordj BouArreridj34000Algeria Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan Faculty of Mathematics Otto-von-Guericke UniversityMagdeburg39016Germany University Centre for Research and Development Chandigarh UniversityMohali140413India Department of Electrical and Computer Engineering Shahid Beheshti UniversityTehran14399Iran DTU AI and Data Science Hub(DAIDASH) Duy Tan UniversityDa Nang550000Vietnam

Multi-objective optimization is critical for problem-solving in engineering,economics,and *** study introduces the Multi-Objective Chef-Based Optimization Algorithm(MOCBOA),an upgraded version of the Chef-Based Optimization Algorithm(CBOA)that addresses distinct *** approach is unique in systematically examining four dominance relations—Pareto,Epsilon,Cone-epsilon,and Strengthened dominance—to evaluate their influence on sustaining solution variety and driving convergence toward the Pareto *** comparison investigation,which was conducted on fifty test problems from the CEC 2021 benchmark and applied to areas such as chemical engineering,mechanical design,and power systems,reveals that the dominance approach used has a considerable impact on the key optimization measures such as the hypervolume *** paper provides a solid foundation for determining themost effective dominance approach and significant insights for both theoretical research and practical applications in multi-objective optimization.

关键词： Multi-objective optimization chef-based optimization algorithm(CBOA) pareto dominance epsilon dominance cone-epsilon dominance strengthened dominance

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A Framework for Automated Cardiovascular Magnetic Resonance Image Quality Scoring based on EuroCMR Registry Criteria 13

A Framework for Automated Cardiovascular Magnetic Resonance ...

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13th International Conference on computer and Knowledge engineering, ICCKE 2023

作者： Nabavi, Shahabedin Moghaddam, Mohsen Ebrahimi Abin, Ahmad Ali Frangi, Alejandro F. Shahid Beheshti University Faculty of Computer Science and Engineering Tehran Iran The University of Manchester Division of Informatics Imaging and Data Sciences Schools of Computer Science and Health Sciences Manchester United Kingdom

ISBN: (纸本)9798350330151

Cardiovascular magnetic resonance (CMR) imaging is a radiation-free modality widely used for functional and structural evaluation of the cardiovascular system. Achieving an accurate diagnosis requires having good-quality images. Subjective CMR image quality assessment is a tedious, time-consuming and costly process. This paper presents an automated scoring framework for CMR image quality assessment that uses deep learning models to evaluate left ventricular coverage and CMR imaging artefacts. The quality scoring in the proposed framework is an attempt to automate some of the subjective quality control criteria of the EuroCMR registry for the short-axis cine steady-state free precession (SSFP) CMR images. The scores given by a radiologist and a cardiologist with experience in CMR imaging for the images of 50 subjects from the UK Biobank were used to validate the proposed framework. The Pearson correlation coefficient (PCC) and the Spearman rank-order correlation coefficient (SRCC) calculated for the experts' quality scores versus ones obtained from the proposed framework are 0.908 and 0.806 on average. The results show that the quality scoring by the proposed framework is highly correlated with the experts' opinions. The proposed framework can be used for post-imaging quality assessment of short-axis cine SSFP CMR images and quality control of large population studies such as the UK Biobank. © 2023 IEEE.

关键词： Quality control

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