Cristina Bianca Pop

Bio: Cristina Bianca Pop is an academic researcher from Technical University of Cluj-Napoca. The author has contributed to research in topics: Semantic similarity & Cuckoo search. The author has an hindex of 12, co-authored 60 publications receiving 436 citations.

Optimizing the Semantic Web Service Composition Process Using Cuckoo Search

Abstract: The behavior of biological individuals which efficiently deal with complex life problems represents an inspiration source in the design of meta-heuristics for solving optimization problems. The Cuckoo Search is such a meta-heuristic inspired by the behavior of cuckoos in search for the appropriate nest where to lay eggs. This paper investigates how the Cuckoo Search meta-heuristic can be adapted and enhanced to solve the problem of selecting the optimal solution in semantic Web service composition. To improve the performance of the cuckoo-inspired algorithm we define a 1-OPT heuristic which expands the search space in a controlled way so as to avoid the stagnation on local optimal solutions. The search space is modeled as an Enhanced Planning Graph, dynamically built for each user request. To identify the optimal solution encoded in the graph we define a fitness function which uses the QoS attributes and the semantic quality as selection criteria. The cuckoo-inspired method has been evaluated on a set of scenarios from the trip planning domain.

Medical data processing and analysis for remote health and activities monitoring

Abstract: Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human’s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions.

Smart Environments and Social Robots for Age-Friendly Integrated Care Services.

Abstract: The world is facing major societal challenges because of an aging population that is putting increasing pressure on the sustainability of care. While demand for care and social services is steadily increasing, the supply is constrained by the decreasing workforce. The development of smart, physical, social and age-friendly environments is identified by World Health Organization (WHO) as a key intervention point for enabling older adults, enabling them to remain as much possible in their residences, delay institutionalization, and ultimately, improve quality of life. In this study, we survey smart environments, machine learning and robot assistive technologies that can offer support for the independent living of older adults and provide age-friendly care services. We describe two examples of integrated care services that are using assistive technologies in innovative ways to assess and deliver of timely interventions for polypharmacy management and for social and cognitive activity support in older adults. We describe the architectural views of these services, focusing on details about technology usage, end-user interaction flows and data models that are developed or enhanced to achieve the envisioned objective of healthier, safer, more independent and socially connected older people.

Ant-Inspired Technique for Automatic Web Service Composition and Selection

Abstract: This paper presents a technique for semantic Web service composition inspired by the behavior of ants. The proposed technique combines a service composition graph model with the ant colony optimization met heuristic to select the optimal composition solution. In our approach, we have considered as selection criteria the QoS attributes of the services and the semantic quality of the connections between the services involved in a composition solution.

A Hybrid Firefly-inspired Approach for Optimal Semantic Web Service Composition

Abstract: Inspired from biology, in this paper we propose a hybrid firefly method for selecting the optimal solution in semantic Web service composition. In our approach, the search space of the selection method is represented by an Enhanced Planning Graph structure which encodes all the Web service composition solutions for a given user request. As selection criteria we have considered the QoS attributes of the services involved in the composition as well as the semantic similarity between them. For the evaluation of the proposed selection method we have implemented an experimental prototype and carried out experiments on a scenario from the trip planning domain.

A comprehensive review of firefly algorithms

Abstract: The firefly algorithm has become an increasingly important tool of Swarm Intelligence that has been applied in almost all areas of optimization, as well as engineering practice. Many problems from various areas have been successfully solved using the firefly algorithm and its variants. In order to use the algorithm to solve diverse problems, the original firefly algorithm needs to be modified or hybridized. This paper carries out a comprehensive review of this living and evolving discipline of Swarm Intelligence, in order to show that the firefly algorithm could be applied to every problem arising in practice. On the other hand, it encourages new researchers and algorithm developers to use this simple and yet very efficient algorithm for problem solving. It often guarantees that the obtained results will meet the expectations.

Nature-Inspired Optimization Algorithms

Abstract: Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference.Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literatureProvides a theoretical understanding as well as practical implementation hintsProvides a step-by-step introduction to each algorithm

Cuckoo Search: Recent Advances and Applications

Abstract: Cuckoo search (CS) is a relatively new algorithm, developed by Yang and Deb in 2009, and CS is efficient in solving global optimization problems. In this paper, we review the fundamental ideas of cuckoo search and the latest developments as well as its applications. We analyze the algorithm and gain insight into its search mechanisms and find out why it is efficient. We also discuss the essence of algorithms and its link to self-organizing systems, and finally we propose some important topics for further research.

Data Center Energy Consumption Modeling: A Survey

Abstract: Data centers are critical, energy-hungry infrastructures that run large-scale Internet-based services. Energy consumption models are pivotal in designing and optimizing energy-efficient operations to curb excessive energy consumption in data centers. In this paper, we survey the state-of-the-art techniques used for energy consumption modeling and prediction for data centers and their components. We conduct an in-depth study of the existing literature on data center power modeling, covering more than 200 models. We organize these models in a hierarchical structure with two main branches focusing on hardware-centric and software-centric power models. Under hardware-centric approaches we start from the digital circuit level and move on to describe higher-level energy consumption models at the hardware component level, server level, data center level, and finally systems of systems level. Under the software-centric approaches we investigate power models developed for operating systems, virtual machines and software applications. This systematic approach allows us to identify multiple issues prevalent in power modeling of different levels of data center systems, including: i) few modeling efforts targeted at power consumption of the entire data center ii) many state-of-the-art power models are based on a few CPU or server metrics, and iii) the effectiveness and accuracy of these power models remain open questions. Based on these observations, we conclude the survey by describing key challenges for future research on constructing effective and accurate data center power models.

Cuckoo search: recent advances and applications

Abstract: Cuckoo search (CS) is a relatively new algorithm, developed by Yang and Deb in 2009, and the same has been found to be efficient in solving global optimization problems. In this paper, we review the fundamental ideas of cuckoo search and the latest developments as well as its applications. We analyze the algorithm and gain insight into its search mechanisms and find out why it is efficient. We also discuss the essence of algorithms and its link to self-organizing systems, and finally, we propose some important topics for further research.