arXiv subjects

Seyed Mostafa Fakhrahmad

Publications and source records attributed to Seyed Mostafa Fakhrahmad.

7 recordsLinked to original sources

arXiv2026

A Novel Gravity-Quasi-Laplacian Approach to Identifying Influential Nodes in Complex Networks

Identifying influential nodes in complex networks is a fundamental challenge with broad applications in areas such as social network analysis, communication infrastructure, transportation systems, and information networks. Existing ranking methods typically rely on combinations of structural features-such as degree, k-shell index, and neighborhood connectivity-to estimate a node's importance. However, many of these approaches suffer from key limitations, including insufficient accuracy, low resolution in distinguishing nodes with similar influence, dependence on tunable parameters, and high computational complexity, which restrict their practicality in large-scale or real-world networks. This study introduces a new ranking framework that integrates a quasi-Laplacian structural measure with a gravity-inspired aggregation process. The core idea is to construct a strengthened representation of each node's structural role using only simple yet informative attributes-namely degree and k-shell index-and then evaluate its local influence through a short-range interaction mechanism. The proposed approach is designed to be free of tunable parameters, interpretable, and computationally efficient, requiring only a small fixed gravity radius (R=3), which makes it suitable for large and diverse networks. Experiments conducted on nine real-world networks and compared against eight state-of-the-art methods demonstrate that the proposed framework consistently outperforms existing techniques in terms of accuracy, resolution, and computational simplicity. These results highlight the effectiveness of the gravity-quasi-Laplacian paradigm as a reliable and scalable tool for identifying influential nodes in complex networks.

cs.SI↗

arXiv2026

Uncertainty-Aware Fuzzy Centrality Measures for Influential Node Identification: A Structural Modeling Approach Toward E-Commerce Applications

In recent years, e-commerce platforms have become one of the most prominent examples of large-scale interaction networks, where understanding influence dynamics among users, products, and digital entities is essential for applications such as online marketing, recommendation systems, and customer behavior analysis. A key challenge in these platforms is that interactions are often uncertain, noisy, and inferred from implicit signals rather than explicitly defined relationships. This uncertainty cannot be effectively captured using deterministic network models...

cs.SI↗

arXiv2025

MultiMend: Multilingual Program Repair with Context Augmentation and Multi-Hunk Patch Generation

Debugging software remains a labor-intensive and time-consuming process despite advances in testing and verification. Learning-based automated program repair (APR) has shown promise in reducing the effort of manually fixing bugs. However, existing techniques face several challenges, including language-dependent strategies, limited bug context utilization, and difficulties in handling bugs that span multiple locations in the code. This paper presents MultiMend, a multilingual learning-based APR approach designed to improve repair performance through language-independent context augmentation and multi-hunk patch generation. MultiMend fine-tunes a pre-trained code language model to generate bug-fixing patches. It embeds source code lines and applies retrieval-augmented generation to augment the usual function-based buggy context with relevant lines during patch generation. The approach also systematically constructs patches for multi-hunk bugs to extend the capabilities of single-hunk models and reduce the needed patch validations. We evaluate MultiMend on six benchmarks with 5,501 bugs covering four programming languages and compare it with state-of-the-art methods. Results show that MultiMend achieves competitive effectiveness and efficiency, fixing 2,227 bugs, of which 1,545 are identical to the developer's patch, and 121 are for multi-hunk bugs. Both context augmentation and multi-hunk patch generation contribute positively to these results. Overall, MultiMend's contributions are promising and offer practical and effective techniques to enhance APR performance for real-world software maintenance.

cs.SE↗

arXiv2024

Mining Influential Spreaders in Complex Networks by an Effective Combination of the Degree and K-Shell

Graph mining is an important technique that used in many applications such as predicting and understanding behaviors and information dissemination within networks. One crucial aspect of graph mining is the identification and ranking of influential nodes, which has applications in various fields including marketing, social communications, and disease control. However, existing models and methods come with high computational complexity and may not accurately distinguish and identify influential nodes. This paper develops a method based on the k-shell index and degree centrality of nodes and their neighbors. Comparisons to previous works, such as Degree and Neighborhood information Centrality (DNC) and Neighborhood and Path Information Centrality (NPIC), are conducted. The evaluations, which include the correctness with Kendall's Tau, resolution with monotonicity index, correlation plots, and time complexity, demonstrate its superior results.

cs.SI↗

arXiv2024

T5APR: Empowering Automated Program Repair across Languages through Checkpoint Ensemble

Automated program repair (APR) using deep learning techniques has become an important area of research in recent years, aiming to automatically generate bug-fixing patches that can improve software reliability and maintainability. However, most existing methods either target a single language or require high computational resources to train multilingual models. In this paper, we propose T5APR, a novel neural program repair approach that provides a unified solution for bug fixing across multiple programming languages. T5APR leverages CodeT5, a powerful pre-trained text-to-text transformer model, and adopts a checkpoint ensemble strategy to improve patch recommendation. We conduct comprehensive evaluations on six well-known benchmarks in four programming languages (Java, Python, C, JavaScript), demonstrating T5APR's competitiveness against state-of-the-art techniques. T5APR correctly fixes 1,985 bugs, including 1,442 bugs that none of the compared techniques has fixed. We further support the effectiveness of our approach by conducting detailed analyses, such as comparing the correct patch ranking among different techniques. The findings of this study demonstrate the potential of T5APR for use in real-world applications and highlight the importance of multilingual approaches in the field of APR.

cs.SE↗

arXiv2024

Towards Efficient Patient Recruitment for Clinical Trials: Application of a Prompt-Based Learning Model

Objective: Clinical trials are essential for advancing pharmaceutical interventions, but they face a bottleneck in selecting eligible participants. Although leveraging electronic health records (EHR) for recruitment has gained popularity, the complex nature of unstructured medical texts presents challenges in efficiently identifying participants. Natural Language Processing (NLP) techniques have emerged as a solution with a recent focus on transformer models. In this study, we aimed to evaluate the performance of a prompt-based large language model for the cohort selection task from unstructured medical notes collected in the EHR. Methods: To process the medical records, we selected the most related sentences of the records to the eligibility criteria needed for the trial. The SNOMED CT concepts related to each eligibility criterion were collected. Medical records were also annotated with MedCAT based on the SNOMED CT ontology. Annotated sentences including concepts matched with the criteria-relevant terms were extracted. A prompt-based large language model (Generative Pre-trained Transformer (GPT) in this study) was then used with the extracted sentences as the training set. To assess its effectiveness, we evaluated the model's performance using the dataset from the 2018 n2c2 challenge, which aimed to classify medical records of 311 patients based on 13 eligibility criteria through NLP techniques. Results: Our proposed model showed the overall micro and macro F measures of 0.9061 and 0.8060 which were among the highest scores achieved by the experiments performed with this dataset. Conclusion: The application of a prompt-based large language model in this study to classify patients based on eligibility criteria received promising scores. Besides, we proposed a method of extractive summarization with the aid of SNOMED CT ontology that can be also applied to other medical texts.

cs.CL↗

arXiv2024

Estimating the severity of dental and oral problems via sentiment classification over clinical reports

Analyzing authors' sentiments in texts as a technique for identifying text polarity can be practical and useful in various fields, including medicine and dentistry. Currently, due to factors such as patients' limited knowledge about their condition, difficulties in accessing specialist doctors, or fear of illness, particularly in pandemic conditions, there might be a delay between receiving a radiology report and consulting a doctor. In some cases, this delay can pose significant risks to the patient, making timely decision-making crucial. Having an automatic system that can inform patients about the deterioration of their condition by analyzing the text of radiology reports could greatly impact timely decision-making. In this study, a dataset comprising 1,134 cone-beam computed tomography (CBCT) photo reports was collected from the Shiraz University of Medical Sciences. Each case was examined, and an expert labeled a severity level for the patient's condition on each document. After preprocessing all the text data, a deep learning model based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network architecture, known as CNN-LSTM, was developed to detect the severity level of the patient's problem based on sentiment analysis in the radiologist's report. The model's performance was evaluated on two datasets, each with two and four classes, in both imbalanced and balanced scenarios. Finally, to demonstrate the effectiveness of our model, we compared its performance with that of other classification models. The results, along with one-way ANOVA and Tukey's test, indicated that our proposed model (CNN-LSTM) performed the best according to precision, recall, and f-measure criteria. This suggests that it can be a reliable model for estimating the severity of oral and dental diseases, thereby assisting patients.

cs.CL↗