Distributed Online Machine Learning for Mobile Care Systems

Appendix D: Wavecomm Tech Docs removed for copyright reasonsTelecare and especially Mobile Care Systems are getting more and more popular. They have two major benefits: first, they drastically improve the living standards and even health outcomes for patients. In addition, they allow significant cost savings for adult care by reducing the needs for medical staff. A common drawback of current Mobile Care Systems is that they are rather stationary in most cases and firmly installed in patients’ houses or flats, which makes them stay very near to or even in their homes. There is also an upcoming second category of Mobile Care Systems which are portable without restricting the moving space of the patients, but with the major drawback that they have either very limited computational abilities and only a rather low classification quality or, which is most frequently, they only have a very short runtime on battery and therefore indirectly restrict the freedom of moving of the patients once again. These drawbacks are inherently caused by the restricted computational resources and mainly the limitations of battery based power supply of mobile computer systems. This research investigates the application of novel Artificial Intelligence (AI) and Machine Learning (ML) techniques to improve the operation of 2 Mobile Care Systems. As a result, based on the Evolving Connectionist Systems (ECoS) paradigm, an innovative approach for a highly efficient and self-optimising distributed online machine learning algorithm called MECoS - Moving ECoS - is presented. It balances the conflicting needs of providing a highly responsive complex and distributed online learning classification algorithm by requiring only limited resources in the form of computational power and energy. This approach overcomes the drawbacks of current mobile systems and combines them with the advantages of powerful stationary approaches. The research concludes that the practical application of the presented MECoS algorithm offers substantial improvements to the problems as highlighted within this thesis

Review of QSAR Models and Software Tools for Predicting Developmental and Reproductive Toxicity

This report provides a state-of-the-art review of available computational models for developmental and reproductive toxicity, including Quantitative Structure-Activity Relationship (QSARs) and related estimation methods such as decision tree approaches and expert systems. At present, there are relatively few models for developmental and reproductive toxicity endpoints, and those available have limited applicability domains. This situation is partly due to the biological complexity of the endpoint, which covers many incompletely understood mechanisms of action, and partly due to the paucity and heterogeneity of high quality data suitable for model development. In contrast, there is an extensive and growing range of software and literature models for predicting endocrine-related activities, in particular models for oestrogen and androgen activity. There is a considerable need to further develop and characterise in silico models for developmental and reproductive toxicity, and to explore their applicability in a regulatory setting.JRC.DG.I.6-Systems toxicolog

An ongoing review of speech emotion recognition

User emotional status recognition is becoming a key feature in advanced Human Computer Interfaces (HCI). A key source of emotional information is the spoken expression, which may be part of the interaction between the human and the machine. Speech emotion recognition (SER) is a very active area of research that involves the application of current machine learning and neural networks tools. This ongoing review covers recent and classical approaches to SER reported in the literature.This work has been carried out with the support of project PID2020-116346GB-I00 funded by the Spanish MICIN

A secure and intelligent framework for vehicle health monitoring exploiting big-data analytics

This is an accepted manuscript of an article published by IEEE in IEEE Transactions on Intelligent Transportation Systems on 04/01/2022. Available online: https://doi.org/10.1109/TITS.2021.3138255 The accepted version of the publication may differ from the final published version.The dependency on vehicles is increasing tremendously due to its excellent transport capacity, fast, efficient, flexible, pleasant journey, minimal physical effort, and substantial economic impact. As a result, the demand for smart and intelligent feature enhancement is growing and becoming a prime concern for maximum productivity based on the current perspective. In this case, the Internet of Everything (IoE) is an emerging concept that can play an essential role in the automotive industry by integrating the stakeholders, process, data, and things via networked connections. But the unavailability of intelligent features leads to negligence about proper maintenance of vehicle vulnerable parts, reckless driving and severe accident, lack of instructive driving, and improper decision, which incurred extra expenses for maintenance besides hindering national economic growth. For this, we proposed a conceptual framework for a central VHMS exploiting IoE-driven Multi-Layer Heterogeneous Networks (HetNet) and a machine learning technique to oversee individual vehicle health conditions, notify the respective owner driver real-timely and store the information for further necessary action. This article transparently portrayed an overview of central VHMS and proposed the taxonomy to achieve such an objective. Subsequently, we unveiled the framework for central VHMS, IoE-driven Multi-tire HetNet, with a secure and trustworthy data collection and analytics system. Finally, anticipating this proposition’s outcome is immense in the automotive sector. It may motivate the researcher to develop a central intelligent and secure vehicular condition diagnostic system to move this sector towards Industry 4.0.The authors would like to thank University Malaysia Pahang for providing the laboratory facilities and financial support under the University FLAGSHIP Research Grants (Project number RDU192203), International Matching Grant (No. RDU192704), and Postgraduate Research Scheme Grant (No. PGRS200325)

Of Fish and Fishermen: Using Human Behavior to Improve Marine Resource Management

People around the world depend on the ocean for their livelihoods and cultural identity. Properly done, marine resource management can help communities balance their extractive needs with the importance of maintaining healthy ecosystems. But, limited data and understanding often inhibits our ability to effectively manage our interactions with the sea, threatening both food security and ecological integrity. My research uses simulation modeling and quantitative methods to demonstrate how integrating data and theories of human behavior with ecological information can improve our understanding and management of marine ecosystems. For my first project, I ask whether we can use satellite data on the behavior of fishermen provided by Global Fishing Watch to predict the abundance of fish. We show that while a reasonably strong predictive model can be made from the effort data, environmental data is a better predictor, and neither is reliable in new times or locations. My next line of research shows that the region-wide conservation and fishery effects of Marine Protected Areas may be smaller, more variable, and harder to detect than we thought, and demonstrate an empirical approach for estimating these regional MPA effects in the Channel Islands National Marine Sanctuary. Lastly, I present a novel approach for using local historic economic information, together with biological data, to improve the ability of communities to estimate the health of their fishery. We show that integration of bio-economic theory, along with data on costs, prices, and profitability, can in many cases improve the ability of our model to provide accurate estimates of fishing mortality rate

Essays in Firm Dynamics, Ownership and Aggregate Effects

Administrative registers maintained by statistical offices on vastly heterogeneous firms have much untapped potential to reveal details on sources of productivity of firms and economies alike. It has been proposed that firm-level shocks can go a long way in explaining aggregate fluctuations. Based on novel monthly frequency data, idiosyncratic shocks are able to explain a sizable share of the Finnish economic fluctuations, providing support to the granular hypothesis. The global financial crisis of 2007-2008 has challenged the field of economic forecasting, and nowcasting has become an active field. This thesis shows that the information content of firm-level sales and truck traffic can be used for nowcasting GDP figures, by using a specific mixture of machine learning algorithms. The agency problem lies at the heart of much of economic theory. Based on a unique dataset linking owners, CEOs and firms, and exploiting plausibly exogenous variations in the separation of ownership and control, agency costs seem to be an important determinant of firm productivity. Furthermore, the effect appear strongest in medium-sized firms. Enterprise group structures might have important implications on the voluminous literature on firm size, as large share of SME employment can be attributed to affiliates of large business groups. Within firm variation suggests that enterprise group affiliation has heterogeneous impacts depending on size, having strong positive impact on productivity of small firms, and negative impact on their growth. In terms of aggregate job creation, it is found that the independent small firms have contributed the most. The results in this thesis underline the benefits of paying attention to samples encompassing the total population of firms. Researchers should continue to explore the potential of rich administrative data sources at statistical offices and strive to strengthen the ties with data producers