Coverage Path Planning and Point-of-Interest Detection Using Autonomous Drone Swarms.

Unmanned Aerial Vehicles (UAVs) or drones presently are enhanced with miniature sensors that can provide information relative to their environment. As such, they can detect changes in temperature, orientation, altitude, geographical location, electromagnetic fluctuations, lighting conditions, and more. Combining this information properly can help produce advanced environmental awareness; thus, the drone can navigate its environment autonomously. Wireless communications can also aid in the creation of drone swarms that, combined with the proper algorithm, can be coordinated towards area coverage for various missions, such as search and rescue. Coverage Path Planning (CPP) is the field that studies how drones, independently or in swarms, can cover an area of interest efficiently. In the current work, a CPP algorithm is proposed for a swarm of drones to detect points of interest and collect information from them. The algorithm's effectiveness is evaluated under simulation results. A set of characteristics is defined to describe the coverage radius of each drone, the speed of the swarm, and the coverage path followed by it. The results show that, for larger swarm sizes, the missions require less time while more points of interest can be detected within the area. Two coverage paths are examined here-parallel lines and spiral coverage. The results depict that the parallel lines coverage is more time-efficient since the spiral increases the required time by an average of 5% in all cases for the same number of detected points of interest.

Decision Support System for Breast Cancer Detection Using Biomarker Indicators.

Breast cancer is the second most common type of cancer among women in the USA, and it is very common to appear in its invasive form. Detecting its presence in the early stages can potentially aid in the mortality rate depletion since at that point large tumours are highly unlikely to have developed. Technological advances of the last decades have provided advanced tools that employ machine learning for early detection. Common techniques include tumour imaging using special equipment that in most cases is not widely accessible. In order to overcome this limitation, new techniques that employ blood-based biomarkers are being explored. In the current work machine learning algorithms are exploited for the development of a decision support system for breast cancer using easily obtainable user information, age, body mass index, glucose and resistin. The explored algorithms include Logistic Regression, Naive Bayes, Support Vector Machine and Gradient Boosting Classification, all of which are used for the classification of new patients based on a dataset that includes information from previous breast cancer incidents. The results depict that the optimal algorithm based on the current methodology and implementation is the Gradient Boosting Classification which exhibits the highest prediction scores. In order to ensure wide accessibility, a mobile application is developed. The user can easily provide the required information for the prediction to the application and obtain the results rapidly.

Mobile Application for Monitoring and Preventing Cognitive Decline Through Lifestyle Intervention.

Diagnosing and preventing Alzheimer's disease is a complex task, partly due to being characterized by a lengthy asymptomatic stage. In order to tackle this, most preclinical studies are multidimensional in nature and largely focus on prevention through lifestyle interventions, such as improving nutrition and introducing physical as well as cognitive exercise. With the widespread use of mobile smart devices today, mobile health applications can help inform high-risk individuals at a low cost, while also aiding in the prevention of cognitive decline through constant virtual coaching services that contribute to lifestyle interventions. Under this light, a mobile application is developed in the context of this paper that provides risk assessment of individuals, daily monitoring of factors that have been found to help prevent cognitive impairment, and individually tailored guidance based on the individual's progress. The developed application is also capable of reassessing users' risk to track their progress, while also providing these services in an intuitive and user-friendly manner, which could enable the future development of more accurate models through the collected data.