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2018 International Conference on Computational science and Computational Intelligence, CSCI 2018

作者： Wladislav, Schulz Johannes, Zenkert Christian, Weber Andre, Klahold Madjid, Fathi Department of Electrical Engineering and Computer Science Institute of Knowledge Based Systems and Knowledge Management University of Siegen Siegen Germany

ISBN: (纸本)9781728113609

Aspect-oriented sentiment analysis is used in particular on textual product reviews to identify positive or negative product characteristics. This information is beneficial not only for customers related to purchasing decisions, but also used by manufacturers to improve their products. In this short paper, we introduce Sentilyzer, a system which performs state-of-the-art aspect-oriented sentiment analysis based on automatically generated category-specific sentiment and aspect dictionaries from Amazon product reviews. © 2018 IEEE.

关键词： Sentiment analysis

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Most preferable combination of explicit drift detection approaches with different classifiers for mining concept drifting data streams

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International Journal of Data science 2019年第3期4卷 196-214页

作者： Srivastava, Ritesh Mittal, Veena Computer Science and Engineering Department Galgotias College of Engineering and Technology Knowledge Park II Uttar Pradesh Greater Noida201310 India Computer Science and Engineering Department Faculty of Engineering and Technology MRIIRS Faridabad India

Sensors in the real-world applications are the major sources of big data streams with varying underlying data distribution. Continuously generated time varying data streams are commonly referred as concept drifting data streams. Many concept drifting data mining algorithms explicitly utilise the drift detection algorithms for ensuring the forgetting of out-dated concepts and learn new concepts upon occurrence of drifts. In concept drifting data streams, the accuracy of the learner depends on the accuracy of the drift detection algorithm and its promptness towards drifts detection. For maintaining the consistent high accuracy in the classification of concept drifting data streams, it is very important to understand the preferable combinations of drift detection algorithms with the classification algorithms. In order to explore such preferable combinations, this work presents an empirical evaluation of some popular drift detection methods with some state-of-art classification algorithms on some standard benchmark datasets of real world. Copyright © 2020 Inderscience Enterprises Ltd.

关键词： Classification (of information)

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ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning

arXiv

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arXiv 2021年

作者： Chatterjee, Soumick Sciarra, Alessandro Dünnwald, Max Mushunuri, Raghava Vinaykanth Podishetti, Ranadheer Rao, Rajatha Nagaraja Gopinath, Geetha Doddapaneni Oeltze-Jafra, Steffen Speck, Oliver Nürnberger, Andreas Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Faculty of Computer Science Otto von Guericke University Magdeburg Germany Department of Biomedical Magnetic Resonance Otto von Guericke University Magdeburg Germany MedDigit Department of Neurology Medical Faculty University Hospital Magdeburg Germany German Centre for Neurodegenerative Diseases Magdeburg Germany Center for Behavioral Brain Sciences Magdeburg Germany Leibniz Institute for Neurobiology Magdeburg Germany

Diffusion-weighted magnetic resonance imaging (DW-MRI) can be used to characterise the microstructure of the nervous tissue, e.g. to delineate brain white matter connections in a non-invasive manner via fibre tracking. Magnetic Resonance Imaging (MRI) in high spatial resolution would play an important role in visualising such fibre tracts in a superior manner. However, obtaining an image of such resolution comes at the expense of longer scan time. Longer scan time can be associated with the increase of motion artefacts, due to the patient’s psychological and physical conditions. Single Image Super-Resolution (SISR), a technique aimed to obtain high-resolution (HR) details from one single low-resolution (LR) input image, achieved with Deep Learning, is the focus of this study. Compared to interpolation techniques or sparse-coding algorithms, deep learning extracts prior knowledge from big datasets and produces superior MRI images from the low-resolution counterparts. In this research, a deep learning based super-resolution technique is proposed and has been applied for DW-MRI. Images from the IXI dataset have been used as the ground-truth and were artificially downsampled to simulate the low-resolution images. The proposed method has shown statistically significant improvement over the baselines and achieved an SSIM of 0.913 ± 0.045. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Correction to: Partially Overlapped Channel Assignment for Cloud-Based Heterogeneous Cellular and Mesh Networks

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Wireless Personal Communications 2021年第3期122卷 2583-2583页

作者： Iqbal, Saleem Qureshi, Kashif Naseer Majeed, Saqib Ghafoor, Kayhan Zrar Jeon, Gwanggil University Institute of Information Technology PMS UAAR Rawalpindi Pakistan Department of Computer Science Bahria University Islamabad Pakistan Department of Computer Science Knowledge University Erbil Iraq Department of Embedded Systems Engineering Incheon National University Incheon South Korea

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Structural Modelling, Design Automation, and a Generalized Routing Technique for Digital Microfluidic Biochips with Hexagonal Electrodes

Structural Modelling, Design Automation, and a Generalized R...

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IEEE Region 10 Symposium

作者： Amartya Dutta Riya Majumder Debasis Dhal Rajat Kumar Pal Department of Computer Science and Engineering B. P. Poddar Institute of Management and Technology Kolkata India Department of Computer Science and Engineering Supreme Knowledge Foundation Group of Institutions West Bengal India Department of Computer Science and Engineering University of Calcutta Kolkata India

Digital microfluidic biochips skilfully manoeuvre a significant assignment in the realm of biochips by supervising droplets of nano-or micro-litre volume. Enhancement of reliable and precise outcomes of digital microfluidic biochips open a new era in environmental as well as biological science. Being concerned about various advancements and considering all design specifications along with the decisive factors like fluidic and pin constraints, we have structured the hexagonal electrode based digital microfluidic biochip system over traditional square electrodes, and also implemented the most significant modular operation, i.e. droplet routing regarding its behavioural natures. Finally, we have devised an improved safe droplet routing algorithm on the hexagonal cell array without any droplet interference and incorporated a comparative study between this new architecture and the existing square one.

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Collective fast delivery by energy-efficient agents 43

Collective fast delivery by energy-efficient agents

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43rd International Symposium on Mathematical Foundations of computer science, MFCS 2018

作者： Bärtschi, Andreas Graf, Daniel Mihalák, Matúš Department of Computer Science ETH Zürich Switzerland Department of Data Science and Knowledge Engineering Maastricht University Netherlands

ISBN: (纸本)9783959770866

We consider k mobile agents initially located at distinct nodes of an undirected graph (on n nodes, with edge lengths). The agents have to deliver a single item from a given source node s to a given target node t. The agents can move along the edges of the graph, starting at time 0, with respect to the following: Each agent i has a weight ωi that defines the rate of energy consumption while travelling a distance in the graph, and a velocity υi with which it can move. We are interested in schedules (operating the k agents) that result in a small delivery time T (time when the item arrives at t), and small total energy consumption E. Concretely, we ask for a schedule that: either (i) Minimizes T , (ii) Minimizes lexicographically (T, E) (prioritizing fast delivery), or (iii) Minimizes · T + (1 − ) · E, for a given ∈ (0, 1). We show that (i) is solvable in polynomial time, and show that (ii) is polynomial-time solvable for uniform velocities and solvable in time O(n + k log k) for arbitrary velocities on paths, but in general is NP-hard even on planar graphs. As a corollary of our hardness result, (iii) is NP-hard, too. We show that there is a 2-approximation algorithm for (iii) using a single agent. © Andreas Bärtschi, Daniel Graf, and Matúš Mihalák.

关键词： Energy utilization

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Learning analogy-preserving sentence embeddings for answer selection

arXiv

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arXiv 2019年

作者： Diallo, Aïssatou Zopf, Markus Fürnkranz, Johannes Research Training Group Aiphes Knowledge Engineering Group Department of Computer Science Technische Universität Darmstadt

Answer selection aims at identifying the correct answer for a given question from a set of potentially correct answers. Contrary to previous works, which typically focus on the semantic similarity between a question and its answer, our hypothesis is that question-answer pairs are often in analogical relation to each other. Using analogical inference as our use case, we propose a framework and a neural network architecture for learning dedicated sentence embeddings that preserve analogical properties in the semantic space. We evaluate the proposed method on benchmark datasets for answer selection and demonstrate that our sentence embeddings indeed capture analogical properties better than conventional embeddings, and that analogy-based question answering outperforms a comparable similaritybased technique. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

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A deep belief network-based method to identify proteomic risk markers for Alzheimer’s disease

arXiv

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arXiv 2020年

作者： An, Ning Jin, Liuqi Ding, Huitong Yang, Jiaoyun Yuan, Jing Key Laboratory of Knowledge Engineering Big Data of Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Neurology Peking Union Medical College Hospital Chinese Academy of Medical Sciences Beijing China

While a large body of research has formally identified apolipoprotein E (APOE) as a major genetic risk marker for Alzheimer’s disease, accumulating evidence supports the notion that other risk markers may exist. The traditional Alzheimer’s-specific signature analysis methods, however, have not been able to make full use of rich protein expression data, especially the interaction between attributes. This paper develops a novel feature selection method to identify pathogenic factors of Alzheimer’s disease using the proteomic and clinical data. This approach has taken the weights of network nodes as the importance order of signaling protein expression values. After generating and evaluating the candidate subset, the method helps to select an optimal subset of proteins that achieved an accuracy greater than 90%, which is superior to traditional machine learning methods for clinical Alzheimer’s disease diagnosis. Besides identifying a proteomic risk marker and further reinforce the link between metabolic risk factors and Alzheimer’s disease, this paper also suggests that apidonectin-linked pathways are a possible therapeutic drug target. Copyright © 2020, The Authors. All rights reserved.

关键词： Feature extraction

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Higgs Boson Discovery using Machine Learning Methods with Pyspark

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Procedia computer science 2020年 170卷 1141-1146页

作者： Mourad Azhari Abdallah Abarda Badia Ettaki Jamal Zerouaoui Mohamed Dakkon Laboratory of Engineering Sciences and Modeling Faculty of Sciences- Ibn Tofail University Campus Universitaire BP 133 Kenitra Morocco Laboratoire de Modélisation Mathématiques et de Calculs Economiques FSJES Université Hassan 1er Settat Morocco Laboratory of Research in Computer Science Data Sciences and Knowledge Engineering Department of Data Content and knowledge Engineering School of Information Sciences Rabat Morocco Département de Statistique et Informatique de Gestion Université Abdelmalek Essaadi Tetouan Morocco

Higgs Boson is an elementary particle that gives the mass to everything in the natural world. The discovery of the Higgs Boson is a major challenge for particle physics. This paper proposes to solve the Higgs Boson Classification Problem with four Machine Learning (ML) Methods, using the Pyspark environment: Logistic Regression (LR), Decision Tree (DT), Random Forest (RF) and Gradient Boosted Tree (GBT). We compare the accuracy and AUC metrics of those ML Methods. We use a large dataset as Higgs Boson, collected from public site UCI and Higgs dataset downloaded from Kaggle site, in the experimentation stage.

关键词： Boson Higgs Spark Pyspark Machine Learning (ML) Logistic Regression (LR) Decision Tree (DT) Random Forest (RF) Gradient Boosted Tree (GBT) AUC Accuracy

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Edge-centric functional network reveals new spatiotemporal biomarkers of early mild cognitive impairment

Brain-X

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Brain-X 2023年第3期1卷

作者： Wang, Weiping Du, Ruiying Wang, Zhen Luo, Xiong Zhao, Haiyan Luan, Ping Ouyang, Jipeng Liu, Song School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Shunde Graduate School Beijing University of Science and Technology Guangdong Shunde China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing China The Center for Optical Imagery Analysis and Learning and School of Mechanical Engineering Northwestern Polytechnical University Shaanxi Xi'an China Department of Neurology Peking University Third Hospital Beijing China Guangdong Second People's Hospital Guangdong Guangzhou China Department of Neurology Shunde Hospital Southern Medical University Guangdong Shunde China Southeast Asia Project Manager Department of China Petroleum Pipeline Engineering Co. Ltd. Hebei Langfang China

Most neuroimaging studies of the pathogenesis of early mild cognitive impairment (EMCI) rely on a node-centric network model, which only calculates correlations between brain regions. Considering the interaction of low-order correlations between pairs of brain regions, we use an edge-centric network model to study high-order functional network correlations. Here, we compute edge time series (eTS) to obtain overlapping communities and study the relationship between subnetworks and communities in space. Then, based on the overlapping communities, we calculate the normalized entropy to measure the diversity of each node. Next, we compute the high-amplitude co-fluctuation of the eTS to explore the pattern of brain activity with temporal precision. Our results show that the normal control and EMCI patients differ in brain regions, subnetworks, and the whole brain. In particular, entropy values show a gradual decrease, and brain network co-fluctuation increases with disease progression. Our study is the first to investigate the pathogenesis of EMCI from the perspective of spatiotemporal flexibility and cognitive diversity based on high-order edge connectivity, further characterizing brain dynamics and providing new insights into the search for biomarkers of EMCI. © 2023 The Authors. Brain-X published by John Wiley & Sons Australia, Ltd on behalf of Ainuohui Medical Technology.

关键词： co-fluctuation edge-centric functional flexibility overlapping community spatiotemporal

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