Quadri-dimensional approach for data analytics in mobile networks

The telecommunication market is growing at a very fast pace with the evolution of new technologies to support high speed throughput and the availability of a wide range of services and applications in the mobile networks. This has led to a need for communication service providers (CSPs) to shift their focus from network elements monitoring towards services monitoring and subscribers’ satisfaction by introducing the service quality management (SQM) and the customer experience management (CEM) that require fast responses to reduce the time to find and solve network problems, to ensure efficiency and proactive maintenance, to improve the quality of service (QoS) and the quality of experience (QoE) of the subscribers. While both the SQM and the CEM demand multiple information from different interfaces, managing multiple data sources adds an extra layer of complexity with the collection of data. While several studies and researches have been conducted for data analytics in mobile networks, most of them did not consider analytics based on the four dimensions involved in the mobile networks environment which are the subscriber, the handset, the service and the network element with multiple interface correlation. The main objective of this research was to develop mobile network analytics models applied to the 3G packet-switched domain by analysing data from the radio network with the Iub interface and the core network with the Gn interface to provide a fast root cause analysis (RCA) approach considering the four dimensions involved in the mobile networks. This was achieved by using the latest computer engineering advancements which are Big Data platforms and data mining techniques through machine learning algorithms.Electrical and Mining EngineeringM. Tech. (Electrical Engineering

Feature Space Modeling for Accurate and Efficient Learning From Non-Stationary Data

A non-stationary dataset is one whose statistical properties such as the mean, variance, correlation, probability distribution, etc. change over a specific interval of time. On the contrary, a stationary dataset is one whose statistical properties remain constant over time. Apart from the volatile statistical properties, non-stationary data poses other challenges such as time and memory management due to the limitation of computational resources mostly caused by the recent advancements in data collection technologies which generate a variety of data at an alarming pace and volume. Additionally, when the collected data is complex, managing data complexity, emerging from its dimensionality and heterogeneity, can pose another challenge for effective computational learning. The problem is to enable accurate and efficient learning from non-stationary data in a continuous fashion over time while facing and managing the critical challenges of time, memory, concept change, and complexity simultaneously. Feature space modeling is one of the most effective solutions to address this problem. For non-stationary data, selecting relevant features is even more critical than stationary data due to the reduction of feature dimension which can ensure the best use a computational resource to produce higher accuracy and efficiency by data mining algorithms. In this dissertation, we investigated a variety of feature space modeling techniques to improve the overall performance of data mining algorithms. In particular, we built Relief based feature sub selection method in combination with data complexity iv analysis to improve the classification performance using ovarian cancer image data collected in a non-stationary batch mode. We also collected time series health sensor data in a streaming environment and deployed feature space transformation using Singular Value Decomposition (SVD). This led to reduced dimensionality of feature space resulting in better accuracy and efficiency produced by Density Ration Estimation Method in identifying potential change points in data over time. We have also built an unsupervised feature space modeling using matrix factorization and Lasso Regression which was successfully deployed in conjugate with Relative Density Ratio Estimation to address the botnet attacks in a non-stationary environment. Relief based feature model improved 16% accuracy of Fuzzy Forest classifier. For change detection framework, we observed 9% improvement in accuracy for PCA feature transformation. Due to the unsupervised feature selection model, for 2% and 5% malicious traffic ratio, the proposed botnet detection framework exhibited average 20% better accuracy than One Class Support Vector Machine (OSVM) and average 25% better accuracy than Autoencoder. All these results successfully demonstrate the effectives of these feature space models. The fundamental theme that repeats itself in this dissertation is about modeling efficient feature space to improve both accuracy and efficiency of selected data mining models. Every contribution in this dissertation has been subsequently and successfully employed to capitalize on those advantages to solve real-world problems. Our work bridges the concepts from multiple disciplines ineffective and surprising ways, leading to new insights, new frameworks, and ultimately to a cross-production of diverse fields like mathematics, statistics, and data mining

Genetic and epigenetic mechanisms of adaptation in stony corals

In this dissertation, I used genomic techniques to examine interrelationships between genotype, gene expression, DNA methylation and environmental conditions in the model coral Acropora millepora. I present three major findings: 1) populations in the Great Barrier Reef have the potential for rapid genetic adaptation to climate change 2) patterns of DNA methylation predict gene expression plasticity 3) patterns of DNA methylation can predict fitness under environmental change.Cellular and Molecular Biolog

Impacts of Climate Warming on Nutritional Quality and Soil Bacterial Communities of Wild Blueberries

Under anthropogenic global climate change associated with greenhouse gas emissions, the mean surface temperature of the earth has increased by approximately 1 oC since the industrial age and is projected to rise by 2.6 to 4.6 oC by 2100. Wild blueberry fields in Maine are experiencing unprecedented warming, which may affect the fruit flavor and nutritional quality, soil health and microbial community, and consequently impact yield, human nutrition, and the economic well-being of wild blueberry producers. It is therefore important to assess the impact of climate warming on fruit crop nutritional quality and soil microbial community, and test potential strategies to mitigate the potential negative effects. Additionally, the genetic diversity of wild blueberries has not been well-studied, which will influence their response to climate change. The objectives of this research were to: 1) quantify the impact of warming on the nutritional qualities of wild blueberries, 2) assess the effect of biochar-compost mix and mulching on mitigating the negative impact of warming on fruit nutritional qualities, 3) investigate the impact of warming on the wild blueberry soil bacterial communities, and 4) evaluate the genetic diversity and structure of wild blueberry populations in managed and unmanaged fields using single nucleotide polymorphic markers. First, I examined the nutritional quality (i.e., berry minerals, total soluble protein, total soluble solids, soluble sugars, anthocyanin) of wild blueberries grown under three temperature conditions: (1) ambient conditions, (2) passive open-top heating that elevated average temperatures by 1.2 oC, (3) active open-top heating that elevated average temperatures by 3.3 oC. Our results suggest that total soluble solids, fructose, total soluble sugars, and total soluble protein concentrations decreased as temperature increased. These changes need to be further studied to determine if they would impact consumer preference or human nutrition, and if so, mitigation techniques should be developed and tested. Second, I assessed the potential of two mitigation strategies, biochar-compost mix (BCM) and mulching, for mitigating the detrimental impacts of warming on the nutritional quality of wild blueberries. The negative effect of warming on total soluble protein and total soluble solids, as well as demonstrating strong negative effects on secondary metabolites, antioxidants, organic acids, and phenolic components, was confirmed. The mitigation strategies did not reduce the negative effect of warming on wild blueberry fruit quality except for potassium and magnesium mineral concentrations. The application rate of the biochar-compost mix and mulching currently used may not be sufficient to mitigate the negative effect of warming on berry nutritional quality. Third, I studied how bacterial communities respond to warming during the growing season, using the passive and active open-top chambers to simulate climate warming scenarios in wild blueberry fields. Overall, soil bacteria diversity and richness (June, July, and August data combined) under the warming (passive and active) treatments and ambient controls did not show significant differences after experimental warming for two years. However, significantly higher bacterial evenness and diversity under warming treatments were found in the early growing season (June). The increased bacteria evenness and diversity under warming treatments in June could be related to advanced plant phenology, suggesting a future shift of seasonal dynamics in bacterial activity under global warming. Last but not the least, I evaluated the genetic variation of two wild blueberry species across four fields in Maine using single nucleotide polymorphic markers. Most of the wild blueberry plants were genetically related, regardless of the region. Overall, no distinct genetic differentiation and no difference in genetic diversity was found between managed and unmanaged fields. This study quantified the genetic variation of wild blueberries within and among fields and provided some insights about the impact of warming on wild blueberry nutritional quality and soil microbial communities. Further studies could be done to determine if changes in nutritional quality would impact consumer preference and human nutrition, and if longer-term warming will change soil microbial communities. Techniques to mitigate the negative effects of warming on wild blueberry nutritional quality should be developed and tested

Toward a Molecular Classification of Peripheral T-Cell Lymphomas: The Role of Gene Expression Profiling

Peripheral T-cell lymphomas-not otherwise specified (PTCL/NOS) are the most common T-cell neoplasms. This study sought to reshape the PTCL/NOS sub-classification (including its two main morphological variants, Lennert lymphoma, LL, and Follicular variant, F-PTCL) based on the correspondence between their molecular features and those of different functional T-cell subsets, also assessing the clinical impact of such an approach. We found that PTCLs/NOS could be divided into groups corresponding to T-cell subsets differently reliant on transcription regulators including mTOR and FOXP3, and identified minimal gene sets discriminating among these groups. Notably, by grouping tumors according to their dependency on master regulators of T-lymphocyte fate, we identified three groups (T-cytotoxic, Treg/TFH, and other-T-helper) characterized by specific genetic patterns and significantly different clinical outcomes. Immunohistochemistry partially substituted for the molecular analysis by consistently recognizing only Treg and TFH cases. Finally, targeted inhibition of MTOR in T-helper cases (that were characterized by genetic lesions targeting the pathway) was proved to be effective ex vivo. We conclude that PTCL/NOS can be divided into subgroups corresponding to different cellular counterparts, characterized by different genetic patterns and possibly sensitivity to specific therapeutic approaches. Furthermore, we identified different gene and microRNA signatures for LL capable of differentiating it from other PTCL/NOS and enriched in cytotoxic function. Moreover, PI3K/Akt/mTOR pathway emerged as novel therapeutic targets for LL. Additionally, LL showed some differences with other PTCL/NOS in terms of clinical features, all supporting its recognition as a distinct entity. Besides, we found that F-PTCL has a distinct molecular signature more similar to PTCL/NOS rather than AITL, and therefore cannot be included among AITLs at least based on GEP, although this necessities more genetic studies. Overall, these results may impact on PTCL classification as well as on future studies aimed to define the more appropriate therapeutic strategy for each identified subgroup/entity

Factors Influencing Customer Satisfaction towards E-shopping in Malaysia

Online shopping or e-shopping has changed the world of business and quite a few people have decided to work with these features. What their primary concerns precisely and the responses from the globalisation are the competency of incorporation while doing their businesses. E-shopping has also increased substantially in Malaysia in recent years. The rapid increase in the e-commerce industry in Malaysia has created the demand to emphasize on how to increase customer satisfaction while operating in the e-retailing environment. It is very important that customers are satisfied with the website, or else, they would not return. Therefore, a crucial fact to look into is that companies must ensure that their customers are satisfied with their purchases that are really essential from the ecommerce’s point of view. With is in mind, this study aimed at investigating customer satisfaction towards e-shopping in Malaysia. A total of 400 questionnaires were distributed among students randomly selected from various public and private universities located within Klang valley area. Total 369 questionnaires were returned, out of which 341 questionnaires were found usable for further analysis. Finally, SEM was employed to test the hypotheses. This study found that customer satisfaction towards e-shopping in Malaysia is to a great extent influenced by ease of use, trust, design of the website, online security and e-service quality. Finally, recommendations and future study direction is provided. Keywords: E-shopping, Customer satisfaction, Trust, Online security, E-service quality, Malaysia

IV Міжнародний науковий конгрес "Society of Ambient Intelligence - 2021" (ISCSAI 2021). Кривий Ріг, Україна, 12-16 квітня 2021 року

IV Міжнародний науковий конгрес "Society of Ambient Intelligence - 2021" (ISCSAI 2021). Кривий Ріг, Україна, 12-16 квітня 2021 року - матеріали.IV International Scientific Congress “Society of Ambient Intelligence – 2021” (ISCSAI 2021). Kryvyi Rih, Ukraine, April 12-16, 2021 - proceedings