Hamideh Hajiabadi

• Researcher at Karlsruhe Institute of Technology

Topic modeling is widely recognized as one of the most effective and significant methods of unsupervised text analysis. This method facilitates identifying and extracting topics in document sets associated with various entities (e.g., countries, websites, journals, etc.). Nonetheless, the method's output lacks high-level information per entity. App...

With the flare-up of the COVID-19 infection since 2020, COVID-19 has been one of the hottest topics on Twitter. Topic modeling is one of the most popular analyses, which extracts the topics from the text. This paper proposes a method to extract the most-discussed topics for 32 countries of the world. In this regard, more than five million related t...

The outbreak of the COVID-19 in 2020 and lack of an effective cure caused psychological problems among humans. This has been reflected widely on social media. Analyzing a large number of English tweets posted in the early stages of the pandemic, this paper addresses three psychological parameters: fear, hope, and depression. The main issue is the e...

Fluorescence microscopy, a central tool of biological research, is subject to inherent trade-offs in experiment design. For instance, image acquisition speed can only be increased in exchange for a lowered signal quality, or for an increased rate of photo-damage to the specimen. Computational denoising can recover some loss of signal, extending the...

In 2020, COVID-19 became one of the most critical concerns in the world. This topic is even still widely discussed on all social networks. Each day, many users publish millions of tweets and comments around this subject, implicitly showing the public’s ideas and points of view regarding this subject. In this regard, to extract the public’s point of...

The Covid-19 virus has been one of the most discussed topics on social networks in 2020 and 2021 and has affected the classic educational paradigm, worldwide. In this research, many tweets related to the Covid-19 virus and education are considered and geo-tagged with the help of the GeoNames geographic database, which contains a large number of pla...

Flood is a natural phenomenon that causes severe environmental damage and destruction in smart cities. After a flood, topographic, geological, and living conditions change. As a result, the previous information regarding the environment is no more valid. Rescue and relief organizations that intend to help the affected people need to obtain new and...

Fluorescence microscopy, a central tool of biological research, is subject to inherent trade-offs in experiment design. For instance, image acquisition speed can only be increased in exchange for a lowered signal quality, or for an increased rate of photo-damage to the specimen. Computational denoising can recover some loss of signal, extending the...

With the outbreak of the Covid-19 virus, the activity of users on Twitter has significantly increased. Some studies have investigated the hot topics of tweets in this period; however, little attention has been paid to presenting and analyzing the spatial and temporal trends of Covid-19 topics. In this study, we use the topic modeling method to extr...

In 2020, COVID-19 became the chief concern of the world and is still reflected widely in all social networks. Each day, users post millions of tweets and comments on this subject, which contain significant implicit information about the public opinion. In this regard, a dataset of COVID-related tweets in English language is collected, which consist...

One of the most important incidents in the world in 2020 is the outbreak of the Coronavirus. Users on social networks publish a large number of comments about this event. These comments contain important hidden information of public opinion regarding this pandemic. In this research, a large number of Coronavirus-related tweets are considered and an...

With the outbreak of the Covid-19 virus, the activity of users on Twitter has significantly increased. Some studies have investigated the hot topics of tweets in this period; however, little attention has been paid to presenting and analyzing the spatial and temporal trends of Covid-19 topics. In this study, we use the topic modeling method to extr...

Science is a key pillar for human progress. Especially Software Engineering (SE)-as a research subdomain of Computer Science-is an intriguing field of research that has a high economic relevance nowadays and serves as a significant enabler for innovations in other research disciplines as well. However, conducting research in SE comes with several c...

Every day, women face plenty of challenges regarding their family, taking care of the seniors, equality, and appreciation at the workplace, etc.-usually with little outside support. While these challenges are not new, the awareness level towards these challenges is still low. This research aims to create awareness towards women's difficulties by st...

With rapid advances in experimental instruments and protocols, imaging and sequencing data are being generated at an unprecedented rate contributing significantly to the current and coming big biomedical data. Meanwhile, unprecedented advances in computational infrastructure and analysis algorithms are realizing image-based digital diagnosis not on...

Distance/Similarity learning is a fundamental problem in machine learning. For example, kNN classifier or clustering methods are based on a distance/similarity measure. Metric learning algorithms enhance the efficiency of these methods by learning an optimal distance function from data. Most metric learning methods need training information in the...

Cancer detection can be formulated as a binary classification in a machine learning paradigm. Loss functions are a critical part of almost every machine learning algorithm. While each loss function comes up with its own advantages and disadvantages, in this paper, inspired by ensemble methods, we propose a novel objective function that is a linear...

Ensemble methods have shown to improve the results of statistical classifiers by combining multiple single learners into a strong one. In this paper, we explore the use of ensemble methods at the level of the objective function of a deep neural network. We propose a novel objective function that is a linear combination of single losses and integrat...

Through Metric learning techniques, a metric function is learned, which shows how similar/dissimilar two samples are. From the perspective of feature selection, metric learning can be represented as a transform function mapping each sample into a new point in the new feature space. Geometric Mean Metric Learning (GMML) is one of promising methods w...

Ensemble techniques are powerful approaches that combine several weak learners to build a stronger one. As a meta-learning framework, ensemble techniques can easily be applied to many machine learning methods. Inspired by ensemble techniques, in this paper we propose an ensemble loss functions applied to a simple regressor. We then propose a half-q...

Ensemble techniques are powerful approaches that combine several weak learners to build a stronger one. As a meta-learning framework, ensemble techniques can easily be applied to many machine learning methods. Inspired by ensemble techniques, in this paper we propose an ensemble loss functions applied to a simple regressor. We then propose a half-q...

Ensemble techniques are powerful approaches that combine several weak learners to build a stronger one. As a meta learning framework, ensemble techniques can easily be applied to many machine learning techniques. In this paper we propose a neural network extended with an ensemble loss function for text classification. The weight of each weak loss f...

This paper published on eprint.

Internet which is included plenty of huge data source is now rapidly increasing in all domains. It is considered as valuable data sources if the data can be processed that results in information. Data mining techniques are widely utilized in web documents in order to extract information. In this paper a data mining approach based on Ontology is pro...