Digital accessibility revolves around the design and production of digital content and electronic devices easy to use and adapt to, meeting all audience regardless of physical or mental ability. While Digital accessibility is very important for ensuring that individuals with disabilities can fully participate in the digital space with adequate web adjustments, Artificial Intelligence (AI) has shown a huge potential in the advancement of digital accessibility. These two fields together have created an opportunity to solve the needs of individuals lacking equal access. AI has leveraged on non-visual modalities such as haptic, auditory and tactile feedback to enable people with visual impairments perform everyday tasks independently. However, a number of issues such as inclusivity concerns, data limitations and technical complexity poses limitations to AI. This article provides an analysis review of AI-driven non-visual accessibility methodologies and solutions for the visually impaired and also explored key challenges faced, and future prospects. By throwing more light on the potential and limitations of non-visual modalities, we aims to guide further research and development in this field.

The adoption of big data techniques in the capturing, processing and storage of health data was necessitated by the need to improve healthcare quality and patient outcomes.Big Data applications have huge potentials, especially with the availability of huge and growing healthcare data to transform and improve patient monitoring operations, which in turn can help in early detection of diseases, mitigation and the cure of diseases based on historic data. This could also help identify the best treatment for a patient’s specific circumstance, thus improving personalized treatment in patients.Designing effective Big Data applications in healthcare presents some challenges such as real-time data analysis, data privacy, data integration and system interoperability. This paper examines the challenges and opportunities associated with designing Big Data applications for healthcare monitoring, providing insights into the requirements for data architecture, interoperability, security, and real-time analytics.

Predicting the occurrence of solar activities have been a major concern over time. Solar flare which is a predominant solar activity affects communication and power systems here on earth. In this research paper, various machine learning and visualization techniques were deployed to discover hidden pattern within dataset and develop machine learning models for predicting the class of solar flares. The used datasets were provided by NGDC, SILSO and SWPC space bodies. These datasets contain Solar flare datasets, sunspot datasets and Active region magnetic properties data. These datasets were associated using python programming language with Active region number and occurrence data been the important features for association of flares, sunspots and Active region magnetic properties. A robust single dataset was generated. Visualization was then performed on the dataset using various visualization techniques such as bar plots, scatter plots, box plots and bubble plots. For model development, 4 machine learning models were deployed which are Random forest, Decision Tree, Bagging Classifier and Support Vector Machine (SVM). Random forest outperformed other models on predicting the class of an associated flare by producing an accuracy of 79% while SVM performed poorly on the used dataset.