Cystic echinococcosis (CE) is a chronic parasitic disease characterized by slow progression and non-specific clinical symptoms, often leading to delayed diagnosis and treatment. Early and precise diagnosis is crucial for effective treatment, particularly considering the five stages of CE as outlined by the World Health Organization (WHO). This study explores the development of an advanced system that leverages artificial intelligence (AI) and machine learning (ML) techniques to classify CE cysts into stages using various imaging modalities, including computed tomography (CT), ultrasound (US), and magnetic resonance imaging (MRI). A total of ten ML algorithms were evaluated across these datasets, using performance metrics such as accuracy, precision, recall (sensitivity), specificity, and F1 score. These metrics offer diverse criteria for assessing model performance. To address this, we propose a normalization and scoring technique that consolidates all metrics into a final score, allowing for the identification of the best model that meets the desired criteria for CE cyst classification. The experimental results demonstrate that hybrid models, such as CNN+ResNet and Inception+ResNet, consistently outperformed other models across all three datasets. Specifically, CNN+ResNet, selected as the best model, achieved 97.55% accuracy on CT images, 93.99% accuracy on US images, and 100% accuracy on MRI images. This research underscores the potential of hybrid and pre-trained models in advancing medical image classification, providing a promising approach to improving the differential diagnosis of CE disease.

The amount of human action video data is increasing rapidly due to the growth of multimedia data, which increases the problem of how to process the large number of human action videos efficiently. Therefore, we devise a novel approach for human action similarity estimation in the distributed environment. The efficiency of human action similarity estimation depends on feature descriptors. Existing feature descriptors such as Local Binary Pattern and Local Ternary Pattern can only extract texture information but cannot obtain the object shape information. To resolve this, we introduce a new feature descriptor, namely Edge based Local Pattern descriptor (ELP). ELP can extract object shape information besides texture information and ELP can also deal with intensity fluctuations. Moreover, we explore Apache Spark to perform feature extraction in the distributed environment. Finally, we present an empirical scalability evaluation of the task of extracting features from video datasets.

Normally, individuals use smartphones for a variety of purposes like photography, schedule planning, playing games, and so on, apart from benefiting from the core tasks of call-making and short messaging. These services are sources of personal data generation. Therefore, any application that utilises personal data of a user from his/her smartphone is truly a great witness of his/her interests and this information can be used for various personalised services. In this paper, we present Lifestyle Pattern MIning (LPaMI), which is a personalised application for mining the lifestyle patterns of a smartphone user. LPaMI uses the personal photograph collections of a user, which reflect the day-to-day photos taken by a smartphone, to recognise scenes (called objects of interest in our work). These are then mined to discover lifestyle patterns. The uniqueness of LPaMI lies in our graph-based approach to mining the patterns of interest. Modelling of data in the form of graphs is effective in preserving the lifestyle behaviour maintained over the passage of time. Graph-modelled lifestyle data enables us to apply variety of graph mining techniques for pattern discovery. To demonstrate the effectiveness of our proposal, we have developed a prototype system for LPaMI to implement its end-to-end pipeline. We have also conducted an extensive evaluation for various phases of LPaMI using different real-world datasets. We understand that the output of LPaMI can be utilised for variety of pattern discovery application areas like trip and food recommendations, shopping, and so on.