arXiv subjects

Zahra Yazdanparast

Publications and source records attributed to Zahra Yazdanparast.

7 recordsLinked to original sources

arXiv2024

A Survey on Self-healing Software System

With the increasing complexity of software systems, it becomes very difficult to install, configure, adjust, and maintain them. As systems become more interconnected and diverse, system architects are less able to predict and design the interaction between components, deferring the handling of these issues to runtime. One of the important problems that occur during execution is system failures, which increase the need for self-healing systems. The main purpose of self-healing is to have an automatic system that can heal itself without human intervention. This system has predefined actions and procedures that are suitable for recovering the system from different failure modes. In this study, different self-healing methods are categorized and a summary of them is presented.

cs.SE↗

arXiv2024

Using rule engine in self-healing systems and MAPE model

Software malfunction presents a significant hurdle within the computing domain, carrying substantial risks for systems, enterprises, and users universally. To produce software with high reliability and quality, effective debugging is essential. Program debugging is an activity to reduce software maintenance costs. In this study, a failure repair method that uses a rule engine is presented. The simulation on mRUBIS showed that the proposed method could be efficient in the operational environment. Through a thorough grasp of software failure and the adoption of efficient mitigation strategies, stakeholders can bolster the dependability, security, and adaptability of software systems. This, in turn, reduces the repercussions of failures and cultivates increased confidence in digital technologies.

cs.SE↗

arXiv2024

Analysing software failure using runtime verification and LTL

A self-healing software system is an advanced computer program or system designed to detect, diagnose, and automatically recover from faults or errors without human intervention. These systems are typically employed in mission-critical applications where downtime can have significant financial or operational consequences. Failure detection is one of the important steps in the self-healing system. In this research, a method using runtime verification is proposed to diagnose four types of errors at the component level. The simulation on mRUBIS shows that the suggested method has the necessary efficiency in detecting the occurrence of failures.

cs.SE↗

arXiv2023

A Survey From Distributed Machine Learning to Distributed Deep Learning

Artificial intelligence has made remarkable progress in handling complex tasks, thanks to advances in hardware acceleration and machine learning algorithms. However, to acquire more accurate outcomes and solve more complex issues, algorithms should be trained with more data. Processing this huge amount of data could be time-consuming and require a great deal of computation. To address these issues, distributed machine learning has been proposed, which involves distributing the data and algorithm across several machines. There has been considerable effort put into developing distributed machine learning algorithms, and different methods have been proposed so far. We divide these algorithms in classification and clustering (traditional machine learning), deep learning and deep reinforcement learning groups. Distributed deep learning has gained more attention in recent years and most of the studies have focused on this approach. Therefore, we mostly concentrate on this category. Based on the investigation of the mentioned algorithms, we highlighted the limitations that should be addressed in future research.

cs.LG↗

arXiv2023

Discovering the Symptom Patterns of COVID-19 from Recovered and Deceased Patients Using Apriori Association Rule Mining

The COVID-19 pandemic has a devastating impact globally, claiming millions of lives and causing significant social and economic disruptions. In order to optimize decision-making and allocate limited resources, it is essential to identify COVID-19 symptoms and determine the severity of each case. Machine learning algorithms offer a potent tool in the medical field, particularly in mining clinical datasets for useful information and guiding scientific decisions. Association rule mining is a machine learning technique for extracting hidden patterns from data. This paper presents an application of association rule mining based Apriori algorithm to discover symptom patterns from COVID-19 patients. The study, using 2875 patient's records, identified the most common signs and symptoms as apnea (72%), cough (64%), fever (59%), weakness (18%), myalgia (14.5%), and sore throat (12%). The proposed method provides clinicians with valuable insight into disease that can assist them in managing and treating it effectively.

cs.LG↗

arXiv2023

Political Sentiment Analysis of Persian Tweets Using CNN-LSTM Model

Sentiment analysis is the process of identifying and categorizing people's emotions or opinions regarding various topics. The analysis of Twitter sentiment has become an increasingly popular topic in recent years. In this paper, we present several machine learning and a deep learning model to analysis sentiment of Persian political tweets. Our analysis was conducted using Bag of Words and ParsBERT for word representation. We applied Gaussian Naive Bayes, Gradient Boosting, Logistic Regression, Decision Trees, Random Forests, as well as a combination of CNN and LSTM to classify the polarities of tweets. The results of this study indicate that deep learning with ParsBERT embedding performs better than machine learning. The CNN-LSTM model had the highest classification accuracy with 89 percent on the first dataset and 71 percent on the second dataset. Due to the complexity of Persian, it was a difficult task to achieve this level of efficiency. The main objective of our research was to reduce the training time while maintaining the model's performance. As a result, several adjustments were made to the model architecture and parameters. In addition to achieving the objective, the performance was slightly improved as well.

cs.CL↗

arXiv2023

BioBERT Based SNP-traits Associations Extraction from Biomedical Literature

Scientific literature contains a considerable amount of information that provides an excellent opportunity for developing text mining methods to extract biomedical relationships. An important type of information is the relationship between singular nucleotide polymorphisms (SNP) and traits. In this paper, we present a BioBERT-GRU method to identify SNP- traits associations. Based on the evaluation of our method on the SNPPhenA dataset, it is concluded that this new method performs better than previous machine learning and deep learning based methods. BioBERT-GRU achieved the result a precision of 0.883, recall of 0.882 and F1-score of 0.881.

cs.CL↗