A Software Framework for Intelligent Security Measures Regarding Sensor Data in the Context of Ambient Assisted Technology.

Ambient assisted technology (AAT), which has the potential to enhance patient care and productivity and save costs, has emerged as a strategic goal for developing e-healthcare in the future. However, since the healthcare sensor must be interconnected with other systems at different network tiers, distant enemies have additional options to attack. Data and resources integrated into the AAT are vulnerable to security risks that might compromise privacy, integrity, and availability.

Autonomous schema markups based on intelligent computing for search engine optimization.

With advances in artificial intelligence and semantic technology, search engines are integrating semantics to address complex search queries to improve the results. This requires identification of well-known concepts or entities and their relationship from web page contents. But the increase in complex unstructured data on web pages has made the task of concept identification overly complex.

AAL and Internet of Medical Things for Monitoring Type-2 Diabetic Patients.

Due to an aging population, assisted-care options are required so that senior citizens may maintain their independence at home for a longer time and rely less on caretakers. Ambient Assisted Living (AAL) encourages the creation of solutions that can help to optimize the environment for senior citizens with assistance while greatly reducing their challenges. A framework based on the Internet of Medical Things (IoMT) is used in the current study for the implementation of AAL technology to help patients with Type-2 diabetes.

Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States.

Machine Learning methods can play a key role in predicting the spread of respiratory infection with the help of predictive analytics. Machine Learning techniques help mine data to better estimate and predict the COVID-19 infection status. A Fine-tuned Ensemble Classification approach for predicting the death and cure rates of patients from infection using Machine Learning techniques has been proposed for different states of India.

Software Fault Localization through Aggregation-Based Neural Ranking for Static and Dynamic Features Selection.

The automatic localization of software faults plays a critical role in assisting software professionals in fixing problems quickly. Despite various existing models for fault tolerance based on static features, localization is still challenging. By considering the dynamic features, the capabilities of the fault recognition models will be significantly enhanced.

Convolutional Neural Network for Automatic Identification of Plant Diseases with Limited Data.

Automated identification of plant diseases is very important for crop protection. Most automated approaches aim to build classification models based on leaf or fruit images. These approaches usually require the collection and annotation of many images, which is difficult and costly process especially in the case of new or rare diseases.

Detection and Mitigation of RPL Rank and Version Number Attacks in the Internet of Things: SRPL-RP.

The rapid growth of the Internet of Things (IoT) and the massive propagation of wireless technologies has revealed recent opportunities for development in various domains of real life, such as smart cities and E-Health applications. A slight defense against different forms of attack is offered for the current secure and lightweight Routing Protocol for Low Power and Lossy Networks (RPL) of IoT resource-constrained devices. Data packets are highly likely to be exposed in transmission during data packet routing.