7 research outputs found

    Deep Learning based Densenet Convolution Neural Network for Community Detection in Online Social Networks

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    Online Social Networks (OSNs) have become increasingly popular, with hundreds of millions of users in recent years. A community in a social network is a virtual group with shared interests and activities that they want to communicate. OSN and the growing number of users have also increased the need for communities. Community structure is an important topological property of OSN and plays an essential role in various dynamic processes, including the diffusion of information within the network. All networks have a community format, and one of the most continually addressed research issues is the finding of communities. However, traditional techniques didn't do a better community of discovering user interests. As a result, these methods cannot detect active communities.  To tackle this issues, in this paper presents Densenet Convolution Neural Network (DnetCNN) approach for community detection. Initially, we gather dataset from Kaggle repository. Then preprocessing the dataset to remove inconsistent and missing values. In addition to User Behavior Impact Rate (UBIR) technique to identify the user URL access, key term and page access. After that, Web Crawling Prone Factor Rate (WCPFR) technique is used find the malicious activity random forest and decision method. Furthermore, Spider Web Cluster Community based Feature Selection (SWC2FS) algorithm is used to choose finest attributes in the dataset. Based on the attributes, to find the community group using Densenet Convolution Neural Network (DnetCNN) approach. Thus, the experimental result produce better performance than other methods

    Evolutionary and biogeographic studies in the genus Kniphofia moench (Asphodelaceae)

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    Kniphofia, a genus of approximately 71 species, is almost entirely African with two species occurring in Madagascar and one in Yemen. Commonly known as ‘red hot pokers’ they are popular among horticulturists. The genus is also well known for its complex alpha taxonomy. To date, no studies have examined the phylogenetic relationships among species or the evolutionary history of the genus, and little work has been done on their biogeography. The main focus of this study was (i) to review the alpha taxonomy, (ii) to assess diversity and endemism in Kniphofia, (iii) to use DNA sequence data to reconstruct a specieslevel phylogeny to understand intra-generic species relationships and evolutionary processes (iv) to use phylogeographic approaches to study the biogeography and evaluate biogeographical patterns, and (v) to assess anatomical variation and determine if anatomical characters are useful for species delimitation. It was found that the genus has six centres of diversity, five of which are centres of endemism. The South African Centre is the most speciose and is also the largest centre of endemism. Kniphofia shows a strong Afromontane grassland affinity in Tropical and East Africa. In South Africa, it is found from high altitudes to coastal habitats, with the most speciose regions being Afromontane grasslands. It is thus not considered to be an Afromontane element, but rather an Afromontane associate. Five major evolutionary lineages were identified using cpDNA sequence data (trnT-L spacer), four of which are southern African. The fifth lineage is represented by material from Madagascar, East and Tropical Africa. The nuclear ITS region failed to provide resolution, as many sequences were identical. The five lineages recovered using cpDNA showed some congruence with geographic origin rather than the taxonomic arrangement based on morphology. All of the species with multiple samples were non-monophyletic. This could be due to hybridisation and/or incomplete lineage sorting. The nested clade analysis, although preliminary, did not completely agree with the phylogenetic analyses. One of the three third level nested clades appears to show fragmentation between the Cape Region, KwaZulu-Natal and northern parts of southern Africa. Furthermore, another nested clades recovered suggest a range expansion and radiation from the Drakensberg into the adjacent Drakensberg-Maputoland-Pondoland transition. Morphological species of Kniphofia exhibited substantial leaf anatomical variation and anatomical characters do not cluster samples into their morphological species. The anatomical results do not fit any geographic pattern, nor do they correspond to the lineages recovered using molecular markers or the nested clades. Leaf anatomical variation does not appear to be influenced by geographical or environmental factors. However, hybridisation may play a role but was not tested in this study. In light of the above findings it is proposed that the evolutionary and biogeographic history of Kniphofia is strongly linked to tectonic events, and Quaternary climatic cycles and vegetation changes. Tectonic events (viz. uplifts) may have resulted in vicariance events that may account for the five cpDNA lineages recovered in phylogenetic analyses, while Quaternary climatic cycles and vegetation changes may have had a more recent impact on evolution and biogeography. It is hypothesised that the ancestral area for Kniphofia was much more widespread when Afromontane grasslands were more extensive during cooler and drier glacial episodes. Kniphofia on the high mountains of Tropical and East Africa would have tracked Afromontane grasslands as they expanded their ranges in cooler periods. While during wetter and warmer interglacial periods Kniphofia would have retreated into refugia on the mountains of Tropical and East Africa, with no gene flow possible between these refugia. In South Africa, where latitude compensates for altitude, Kniphofia may have maintained a distribution that extended into the lowlands even during interglacials. A cyclic climate change hypothesis implies that populations of Kniphofia (at different phases of the climatic cycle) would have experienced periods of contractions and fragmentation followed by periods of range expansion and coalescence or secondary contact. Altitudinal shifting is proposed to be the most likely mechanism for fragmentation and range expansion, and would would possibly promoted hybridisation. Within the five lineages there is evidence for recent differentiation as the branch lengths are short, there are numerous nonmonophyletic species and numerous identical haplotypes (cpDNA and ITS) which collectively indicate a recent radiation in southern Africa. A recent radiation would also account for the taxonomic confusion and difficulty in differentiating morpho-species. These climatic events may also account for the substantial anatomical variation in southern African Kniphofia species

    Multi-scale Optimization-based Energy Transition Strategies for Modeling, Design, and Operation of Process Systems

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    21st century energy production, conversion, and delivery systems are expected to succeed in multiple goals such as meeting the increasing energy demand, being economically feasible, being less carbon-intensive, increasing resource utilization efficiency. This requires a transition in technologies, operation strategies, and use of energy in our everyday life. Such a transition necessitates a better understanding and analysis of both the existing and futuristic technologies, pathways, and scenarios. The aim of my dissertation is to use process systems engineering methods to develop generic frameworks to arrive at realistic integrated solutions to complex energy and environmental problems. Mathematical optimization is at the heart of these systematic and quantitative analysis methods. The systems under investigation range from mesoscale to megascale levels over time horizons from hours to days or years handling chemical engineering problems like modeling, design, planning, and scheduling. The common vision throughout every study is to gain insight on the challenges awaiting the energy transition and provide promising solutions. This dissertation comprises various studies focusing on both improving the current practices like in the petroleum industry operations or chemical process design and analyzing feasibility of long-range energy transition scenarios that put an emphasis on integrating renewables like solar and wind in power, fuels, and chemicals production. The studies include (i) development of an integrated data-driven modeling and global optimization framework for improving short-term production planning operations in petroleum refineries, (ii) use of a process synthesis and global optimization approach to design optimal ammonia production processes from various pathways including natural gas reforming, biomass gasification, and renewable-powered electrolysis, (iii) development of a novel simultaneous design, scheduling, and supply chain strategy to optimize renewable power generation, storage, and transportation systems, and (iv) an extension of this latter strategy to integrate renewable energy systems with fossil energy systems for multi-product process networks to produce power, fuels, and chemicals in integrated facilities

    The 'Race Riot' Within and Without 'The Grrrl One'; Ethnoracial Grrrl Zines' Tactical Construction of Space

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    Honors (Bachelor's)EnglishUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/96647/1/addiecs.pd

    Video Summarization Using Unsupervised Deep Learning

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    In this thesis, we address the task of video summarization using unsupervised deep-learning architectures. Video summarization aims to generate a short summary by selecting the most informative and important frames (key-frames) or fragments (key-fragments) of the full-length video, and presenting them in temporally-ordered fashion. Our objective is to overcome observed weaknesses of existing video summarization approaches that utilize RNNs for modeling the temporal dependence of frames, related to: i) the small influence of the estimated frame-level importance scores in the created video summary, ii) the insufficiency of RNNs to model long-range frames' dependence, and iii) the small amount of parallelizable operations during the training of RNNs. To address the first weakness, we propose a new unsupervised network architecture, called AC-SUM-GAN, which formulates the selection of important video fragments as a sequence generation task and learns this task by embedding an Actor-Critic model in a Generative Adversarial Network. The feedback of a trainable Discriminator is used as a reward by the Actor-Critic model in order to explore a space of actions and learn a value function (Critic) and a policy (Actor) for video fragment selection. To tackle the remaining weaknesses, we investigate the use of attention mechanisms for video summarization and propose a new supervised network architecture, called PGL-SUM, that combines global and local multi-head attention mechanisms which take into account the temporal position of the video frames, in order to discover different modelings of the frames' dependencies at different levels of granularity. Based on the acquired experience, we then propose a new unsupervised network architecture, called CA-SUM, which estimates the frames' importance using a novel concentrated attention mechanism that focuses on non-overlapping blocks in the main diagonal of the attention matrix and takes into account the attentive uniqueness and diversity of the associated frames of the video. All the proposed architectures have been extensively evaluated on the most commonly-used benchmark datasets, demonstrating their competitiveness against other approaches and documenting the contribution of our proposals on advancing the current state-of-the-art on video summarization. Finally, we make a first attempt on producing explanations for the video summarization results. Inspired by relevant works in the Natural Language Processing domain, we propose an attention-based method for explainable video summarization and we evaluate the performance of various explanation signals using our CA-SUM architecture and two benchmark datasets for video summarization. The experimental results indicate the advanced performance of explanation signals formed using the inherent attention weights, and demonstrate the ability of the proposed method to explain the video summarization results using clues about the focus of the attention mechanism
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