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Engagement estimation is crucial for advancing natural human-computer interaction, allowing artificial agents to dynamically adjust their responses based on user engagement levels and creating more intuitive and immersive experiences. Despite ...

Nowadays, web robots are predominantly used for auto-accessing web content, sharing almost one-third of the total web traffic and often posing threats to various web applications’ security, privacy, and performance. Detecting these robots is ...

Recent research has found that the activation function (AF) plays a significant role in introducing non-linearity to enhance the performance of deep learning networks. Researchers recently started developing activation functions that can be ...

Web robots are automated computer programs that can be exploited for benign and malicious activities such as website indexing, monitoring, or unauthorized content scraping and scalping. Several methods are available to detect automated web robots ...

The selection of word embedding and deep learning models for better outcomes is vital. Word embeddings are an n-dimensional distributed representation of a text that attempts to capture the meanings of the words. Deep learning models utilize ...

In India, a significant portion of the annual crop is lost due to plant diseases. It is required to monitor agriculture’s growth and health status of plants to help farmers take prompt actions regarding any plant disease. An automatic plant ...

Text summarization involves selecting the most important phrases from a given paragraph to produce a concise and pertinent summary. There are two primary approaches to text summarization. While the second is abstractive, the first is extractive. ...

In software development, defects influence the quality and cost in an undesirable way. Software defect prediction (SDP) is one of the techniques which improves the software quality and testing efficiency by early identification of defects(bug/...

Detecting automated users’ agent activities at any web application through users’ web access logs is a challenging issue. Many machines learning based automated solutions exist to address this issue. However, the existing supervised learning ...

Automated classification of breast cancer can often save lives, as manual detection is usually time-consuming & expensive. Since the last decade, deep learning techniques have been most widely used for the automatic classification of breast cancer ...

• Examine effect of hyperparameters on regression techniques for software defect count prediction.

Prior identification of defects in software modules can help testers to allocate limited resources efficiently. Defect prediction techniques are helpful for this situation because they allow testers to identify and focus on defect ...

With the increasing complexity and volume of the software, the number of defects in software modules is also increasing consistently, which affects the quality and delivery of software in time and budget. To improve the software quality and timely ...

The post-COVID epidemic world has increased dependence on online businesses for day-to-day life transactions over the Internet, especially using the smartphone or handheld devices. This increased dependence has led to new attack surfaces which need to be ...

Due to the unavailability of adequate patients and expensive labeling cost, many real-world biomedical cases have scarcity in the annotated data. This holds very true for HIV-1 protease specificity problem where only a few experimentally verified ...

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• Multitask learning framework for HIV-1 protease cleavage site prediction.
• Use ...

Inadequate patient samples and costly annotated data generations result into the smaller dataset in the biomedical domain. Due to which the predictions with a trained model that usually reveal a single small dataset association are ...

The comprehensive models can be used for software quality modelling which involves prediction of low-quality modules using interpretable rules. Such comprehensive model can guide the design and testing team to focus on the poor quality modules, ...

Gene expression dataset contains a small number of tissues and thousands or tens of thousands of noisy and redundant genes. This can lead to possibly overfitting and curse of dimensionality or even complete failure in the analysis of ...

• Multitask learning framework for virulent protein prediction to handle data scarcity.

The ability of multitask learning promulgated its sovereignty in the machine learning field with the diversified application including but not limited to bioinformatics and pattern recognition. Bioinformatics provides a wide range of ...

• Classification of datasets on diabetes and its complications are considered.
• ...

Diabetes mellitus is a major non-communicable disease ever rising as an epidemic and a public health crisis worldwide. One of the several life-threatening complications of diabetes is hypertension or high blood pressure which mostly ...

The role of human immunodeficiency virus (HIV) protease in viral maturation is indispensable as the drug therapy primarily targets the HIV protease for the treatment of human immunodeficiency virus infection. Protease inhibitors are designed to block ...