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Full-Text Articles in Engineering

An Approach To Fast Multi-Robot Exploration In Buildings With Inaccessible Spaces, Matt Mcneill, Damian Lyons Dec 2019

An Approach To Fast Multi-Robot Exploration In Buildings With Inaccessible Spaces, Matt Mcneill, Damian Lyons

Faculty Publications

The rapid exploration of unknown environments is a common application of autonomous multi-robot teams. For some types of exploration missions, a mission designer may possess some rudimentary knowledge about the area to be explored. For example, the dimensions of a building may be known, but not its floor layout or the location of furniture and equipment inside. For this type of mission, the Space- Based Potential Field (SBPF) method is an approach to multirobot exploration which leverages a priori knowledge of area bounds to determine robot motion. Explored areas and obstacles exert a repulsive force, and unexplored areas exert an ...


Fkrr-Mvsf: A Fuzzy Kernel Ridge Regression Model For Identifying Dna-Binding Proteins By Multi-View Sequence Features Via Chou's Five-Step Rule, Yi Zou, Yije Ding, Jijun Tang, Fei Guo, Li Peng Sep 2019

Fkrr-Mvsf: A Fuzzy Kernel Ridge Regression Model For Identifying Dna-Binding Proteins By Multi-View Sequence Features Via Chou's Five-Step Rule, Yi Zou, Yije Ding, Jijun Tang, Fei Guo, Li Peng

Faculty Publications

DNA-binding proteins play an important role in cell metabolism. In biological laboratories, the detection methods of DNA-binding proteins includes yeast one-hybrid methods, bacterial singles and X-ray crystallography methods and others, but these methods involve a lot of labor, material and time. In recent years, many computation-based approachs have been proposed to detect DNA-binding proteins. In this paper, a machine learning-based method, which is called the Fuzzy Kernel Ridge Regression model based on Multi-View Sequence Features (FKRR-MVSF), is proposed to identifying DNA-binding proteins. First of all, multi-view sequence features are extracted from protein sequences. Next, a Multiple Kernel Learning (MKL) algorithm ...


Fkrr-Mvsf: A Fuzzy Kernel Ridge Regression Model For Identifying Dna-Binding Proteins By Multi-View Sequence Features Via Chou's Five-Step Rule, Yi Zou, Yijie Ding, Jijun Tang, Fei Guo, Li Peng Sep 2019

Fkrr-Mvsf: A Fuzzy Kernel Ridge Regression Model For Identifying Dna-Binding Proteins By Multi-View Sequence Features Via Chou's Five-Step Rule, Yi Zou, Yijie Ding, Jijun Tang, Fei Guo, Li Peng

Faculty Publications

DNA-binding proteins play an important role in cell metabolism. In biological laboratories, the detection methods of DNA-binding proteins includes yeast one-hybrid methods, bacterial singles and X-ray crystallography methods and others, but these methods involve a lot of labor, material and time. In recent years, many computation-based approachs have been proposed to detect DNA-binding proteins. In this paper, a machine learning-based method, which is called the Fuzzy Kernel Ridge Regression model based on Multi-View Sequence Features (FKRR-MVSF), is proposed to identifying DNA-binding proteins. First of all, multi-view sequence features are extracted from protein sequences. Next, a Multiple Kernel Learning (MKL) algorithm ...


Limited Data Rolling Bearing Fault Diagnosis With Few-Shot Learning, Ansi Zhang, Shaobo Li, Yuxin Cui, Wanli Yang, Rongzhi Dong, Jianjun Hu Aug 2019

Limited Data Rolling Bearing Fault Diagnosis With Few-Shot Learning, Ansi Zhang, Shaobo Li, Yuxin Cui, Wanli Yang, Rongzhi Dong, Jianjun Hu

Faculty Publications

This paper focuses on bearing fault diagnosis with limited training data. A major challenge in fault diagnosis is the infeasibility of obtaining sufficient training samples for every fault type under all working conditions. Recently deep learning based fault diagnosis methods have achieved promising results. However, most of these methods require large amount of training data. In this study, we propose a deep neural network based few-shot learning approach for rolling bearing fault diagnosis with limited data. Our model is based on the siamese neural network, which learns by exploiting sample pairs of the same or different categories. Experimental results over ...


A Review Of Text Corpus-Based Tourism Big Data Mining, Qin Li, Shaobo Li, Sen Zhang, Jie Hu, Jianhun Hu Aug 2019

A Review Of Text Corpus-Based Tourism Big Data Mining, Qin Li, Shaobo Li, Sen Zhang, Jie Hu, Jianhun Hu

Faculty Publications

With the massive growth of the Internet, text data has become one of the main formats of tourism big data. As an effective expression means of tourists’ opinions, text mining of such data has big potential to inspire innovations for tourism practitioners. In the past decade, a variety of text mining techniques have been proposed and applied to tourism analysis to develop tourism value analysis models, build tourism recommendation systems, create tourist profiles, and make policies for supervising tourism markets. The successes of these techniques have been further boosted by the progress of natural language processing (NLP), machine learning, and ...


Subsurface Mimo: A Beamforming Design In Internet Of Underground Things For Digital Agriculture Applications, Abdul Salam Aug 2019

Subsurface Mimo: A Beamforming Design In Internet Of Underground Things For Digital Agriculture Applications, Abdul Salam

Faculty Publications

In underground (UG) multiple-input and multiple-output (MIMO), the transmit beamforming is used to focus energy in the desired direction. There are three different paths in the underground soil medium through which the waves propagates to reach at the receiver. When the UG receiver receives a desired data stream only from the desired path, then the UG MIMO channel becomes three path (lateral, direct, and reflected) interference channel. Accordingly, the capacity region of the UG MIMO three path interference channel and degrees of freedom (multiplexing gain of this MIMO channel requires careful modeling). Therefore, expressions are required for the degree of ...


Tutorial: Are You My Neighbor?: Bringing Order To Neighbor Computing Problems, David Anastasiu, Huzefa Rangwala, Andrea Tagarelli Aug 2019

Tutorial: Are You My Neighbor?: Bringing Order To Neighbor Computing Problems, David Anastasiu, Huzefa Rangwala, Andrea Tagarelli

Faculty Publications

Finding nearest neighbors is an important topic that has attracted much attention over the years and has applications in many fields, such as market basket analysis, plagiarism and anomaly detection, community detection, ligand-based virtual screening, etc. As data are easier and easier to collect, finding neighbors has become a potential bottleneck in analysis pipelines. Performing pairwise comparisons given the massive datasets of today is no longer feasible. The high computational complexity of the task has led researchers to develop approximate methods, which find many but not all of the nearest neighbors. Yet, for some types of data, efficient exact solutions ...


Underground Soil Sensing Using Subsurface Radio Wave Propagation, Abdul Salam, Akhlaque Ahmad Jul 2019

Underground Soil Sensing Using Subsurface Radio Wave Propagation, Abdul Salam, Akhlaque Ahmad

Faculty Publications

Continuous sensing of soil moisture is essential for smart agriculture variable rate irrigation (VRI), real-time agricultural decision making, and water conservation. Therefore, development of simple techniques to measure the in-situ properties of soil is of vital importance. Moreover, permittivity estimation has applications in electromagnetic (EM) wave propagation analysis in the soil medium, depth analysis, subsurface imaging, and UG localization. Different methods for soil permittivity and moisture estimation are time-domain reflectometry (TDR), ground-penetrating radar (GPR) measurements, and remote sensing. One major bottleneck in the current laboratory-based permittivity estimation techniques is off-line measurement of the collected soil samples. At that, the remote ...


A Path Loss Model For Through The Soil Wireless Communications In Digital Agriculture, Abdul Salam Jul 2019

A Path Loss Model For Through The Soil Wireless Communications In Digital Agriculture, Abdul Salam

Faculty Publications

In this paper, a path loss model is developed to predict the impact of soil type, soil moisture, operation frequency, distance, and burial depth of sensors for through-the-soil wireless communications channel. The soil specific model is developed based on empirical measurements in a testbed and field settings. The model can be used in different soils for a frequency range of 100MHz to 1GHz. The standard deviation between measured and predicted path loss is from 4-6dB in the silt loam, sandy, and silty clay loam soil types. The model leads to development of sensor-guided irrigation system in the field of digital ...


A Comparison Of Path Loss Variations In Soil Using Planar And Dipole Antennas, Abdul Salam Jul 2019

A Comparison Of Path Loss Variations In Soil Using Planar And Dipole Antennas, Abdul Salam

Faculty Publications

In this paper, an empirical investigation of propagation path loss variations with frequency in sandy and silty clay loam soils has been done using planar and dipole antennas. The path loss experiments are conducted using vector network analyzer (VNA) in sandy soil testbed, and greenhouse outdoor silty clay loam testbed for different operation frequencies and communication distances. The results show that the planar antenna can be used for subsurface communications in a wide range of operation frequencies. The comparison paves the way for development of sensor-guided irrigation system in the field of digital agriculture.


Evaluation Of Field Of View Width In Stereo-Vision-Based Visual Homing, Damian Lyons, Benjamin Barriage, Luca Del Signore Jul 2019

Evaluation Of Field Of View Width In Stereo-Vision-Based Visual Homing, Damian Lyons, Benjamin Barriage, Luca Del Signore

Faculty Publications

Visual homing is a local navigation technique used to direct a robot to a previously seen location by comparing the image of the original location with the current visual image. Prior work has shown that exploiting depth cues such as image scale or stereo-depth in homing leads to improved homing performance. While it is not unusual to use a panoramic field of view (FOV) camera in visual homing, it is unusual to have a panoramic FOV stereo-camera. So, while the availability of stereo-depth information may improve performance, the concomitant-restricted FOV may be a detriment to performance, unless specialized stereo hardware ...


Personalized Product Evaluation Based On Gra-Topsis And Kansei Engineering, Huafeng Quan, Shaobo Li, Hongjing Wei, Jianjun Hu Jul 2019

Personalized Product Evaluation Based On Gra-Topsis And Kansei Engineering, Huafeng Quan, Shaobo Li, Hongjing Wei, Jianjun Hu

Faculty Publications

With the improvement of human living standards, users’ requirements have changed from function to emotion. Helping users pick out the most suitable product based on their subjective requirements is of great importance for enterprises. This paper proposes a Kansei engineering-based grey relational analysis and techniques for order preference by similarity to ideal solution (KE-GAR-TOPSIS) method to make a subjective user personalized ranking of alternative products. The KE-GRA-TOPSIS method integrates five methods, including Kansei Engineering (KE), analytic hierarchy process (AHP), entropy, game theory, and grey relational analysis-TOPSIS (GRA-TOPSIS). First, an evaluation system is established by KE and AHP. Second, we define ...


Towards Lakosian Multilingual Software Design Principles, Damian Lyons, Saba Zahra, Thomas Marshall Jul 2019

Towards Lakosian Multilingual Software Design Principles, Damian Lyons, Saba Zahra, Thomas Marshall

Faculty Publications

Large software systems often comprise programs written in different programming languages. In the case when cross-language interoperability is accomplished with a Foreign Function Interface (FFI), for example pybind11, Boost.Python, Emscripten, PyV8, or JNI, among many others, common software engineering tools, such as call-graph analysis, are obstructed by the opacity of the FFI. This complicates debugging and fosters potential inefficiency and security problems. One contributing issue is that there is little rigorous software design advice for multilingual software. In this paper, we present our progress towards a more rigorous design approach to multilingual software. The approach is based on the ...


Using Big Data Analytics To Improve Hiv Medical Care Utilisation In South Carolina: A Study Protocol, Bankole Olatosi, Jiajia Zhang, Sharon Weissman, Jianjun Hu, Mohammad Rifat Haider, Xiaoming Li Jun 2019

Using Big Data Analytics To Improve Hiv Medical Care Utilisation In South Carolina: A Study Protocol, Bankole Olatosi, Jiajia Zhang, Sharon Weissman, Jianjun Hu, Mohammad Rifat Haider, Xiaoming Li

Faculty Publications

Introduction Linkage and retention in HIV medical care remains problematic in the USA. Extensive health utilisation data collection through electronic health records (EHR) and claims data represent new opportunities for scientific discovery. Big data science (BDS) is a powerful tool for investigating HIV care utilisation patterns. The South Carolina (SC) office of Revenue and Fiscal Affairs (RFA) data warehouse captures individual-level longitudinal health utilisation data for persons living with HIV (PLWH). The data warehouse includes EHR, claims and data from private institutions, housing, prisons, mental health, Medicare, Medicaid, State Health Plan and the department of health and human services. The ...


Deep Autoencoder Neural Networks For Short-Term Traffic Congestion Prediction Of Transportation Networks, Sen Zhang, Yong Yao, Jie Hu, Yong Zhao, Shaobo Li, Jianjun Hu May 2019

Deep Autoencoder Neural Networks For Short-Term Traffic Congestion Prediction Of Transportation Networks, Sen Zhang, Yong Yao, Jie Hu, Yong Zhao, Shaobo Li, Jianjun Hu

Faculty Publications

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available ...


Deep Autoencoder Neural Networks For Short-Term Traffic Congestion Prediction Of Transportation Networks, Sen Zhang, Yong Yao, Jie Hu, Yong Zhao, Shaobo Li, Jianjun Hu May 2019

Deep Autoencoder Neural Networks For Short-Term Traffic Congestion Prediction Of Transportation Networks, Sen Zhang, Yong Yao, Jie Hu, Yong Zhao, Shaobo Li, Jianjun Hu

Faculty Publications

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available ...


Deep Autoencoder Neural Networks For Short-Term Traffic Congestion Prediction Of Transportation Networks, Sen Zhang, Yong Yao, Jie Hu, Yong Zhao, Shaobo Li, Jianjun Hu May 2019

Deep Autoencoder Neural Networks For Short-Term Traffic Congestion Prediction Of Transportation Networks, Sen Zhang, Yong Yao, Jie Hu, Yong Zhao, Shaobo Li, Jianjun Hu

Faculty Publications

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available ...


Urban Underground Infrastructure Monitoring Iot: The Path Loss Analysis, Abdul Salam, Syed Shah Apr 2019

Urban Underground Infrastructure Monitoring Iot: The Path Loss Analysis, Abdul Salam, Syed Shah

Faculty Publications

The extra quantities of wastewater entering the pipes can cause backups that result in sanitary sewer overflows. Urban underground infrastructure monitoring is important for controlling the flow of extraneous water into the pipelines. By combining the wireless underground communications and sensor solutions, the urban underground IoT applications such as real time wastewater and storm water overflow monitoring can be developed. In this paper, the path loss analysis of wireless underground communications in urban underground IoT for wastewater monitoring has been presented. It has been shown that the communication range of up to 4 kilometers can be achieved from an underground ...


Lightcpg: A Multi-View Cpg Sites Detection On Single-Cell Whole Genome Sequence Data, Limin Jiang, Chongqing Wang, Jijun Tang, Fei Gu Apr 2019

Lightcpg: A Multi-View Cpg Sites Detection On Single-Cell Whole Genome Sequence Data, Limin Jiang, Chongqing Wang, Jijun Tang, Fei Gu

Faculty Publications

Background DNA methylation plays an important role in multiple biological processes that are closely related to human health. The study of DNA methylation can provide an insight into the mechanism behind human health and can also have a positive effect on the assessment of human health status. However, the available sequencing technology is limited by incomplete CpG coverage. Therefore, it is crucial to discover an efficient and convenient method capable of distinguishing between the states of CpG sites. Previous studies focused on identifying methylation states of the CpG sites in single cell, which only evaluated sequence information or structural information ...


An Underground Radio Wave Propagation Prediction Model For Digital Agriculture, Abdul Salam Apr 2019

An Underground Radio Wave Propagation Prediction Model For Digital Agriculture, Abdul Salam

Faculty Publications

Underground sensing and propagation of Signals in the Soil (SitS) medium is an electromagnetic issue. The path loss prediction with higher accuracy is an open research subject in digital agriculture monitoring applications for sensing and communications. The statistical data are predominantly derived from site-specific empirical measurements, which is considered an impediment to universal application. Nevertheless, in the existing literature, statistical approaches have been applied to the SitS channel modeling, where impulse response analysis and the Friis open space transmission formula are employed as the channel modeling tool in different soil types under varying soil moisture conditions at diverse communication distances ...


Convolutional Neural Networks For Crystal Material Property Prediction Using Hybrid Orbital-Field Matrix And Magpie Descriptors, Zhuo Cao, Yabo Dan, Zheng Xiong, Chengcheng Niu, Xiang Li, Songrong Qian, Jianjun Hu Apr 2019

Convolutional Neural Networks For Crystal Material Property Prediction Using Hybrid Orbital-Field Matrix And Magpie Descriptors, Zhuo Cao, Yabo Dan, Zheng Xiong, Chengcheng Niu, Xiang Li, Songrong Qian, Jianjun Hu

Faculty Publications

Computational prediction of crystal materials properties can help to do large-scale in-silicon screening. Recent studies of material informatics have focused on expert design of multi-dimensional interpretable material descriptors/features. However, successes of deep learning such as Convolutional Neural Networks (CNN) in image recognition and speech recognition have demonstrated their automated feature extraction capability to effectively capture the characteristics of the data and achieve superior prediction performance. Here, we propose CNN-OFM-Magpie, a CNN model with OFM (Orbital-field Matrix) and Magpie descriptors to predict the formation energy of 4030 crystal material by exploiting the complementarity of two-dimensional OFM features and Magpie features ...


Convolutional Neural Networks For Crystal Material Property Prediction Using Hybrid Orbital-Field Matrix And Magpie Descriptors, Zhuo Cao, Yabo Dan, Zheng Xiong, Chengcheng Niu, Xiang Li, Songrong Qian, Jianjun Hu Apr 2019

Convolutional Neural Networks For Crystal Material Property Prediction Using Hybrid Orbital-Field Matrix And Magpie Descriptors, Zhuo Cao, Yabo Dan, Zheng Xiong, Chengcheng Niu, Xiang Li, Songrong Qian, Jianjun Hu

Faculty Publications

Computational prediction of crystal materials properties can help to do large-scale in-silicon screening. Recent studies of material informatics have focused on expert design of multi-dimensional interpretable material descriptors/features. However, successes of deep learning such as Convolutional Neural Networks (CNN) in image recognition and speech recognition have demonstrated their automated feature extraction capability to effectively capture the characteristics of the data and achieve superior prediction performance. Here, we propose CNN-OFM-Magpie, a CNN model with OFM (Orbital-field Matrix) and Magpie descriptors to predict the formation energy of 4030 crystal material by exploiting the complementarity of two-dimensional OFM features and Magpie features ...


Underground Environment Aware Mimo Design Using Transmit And Receive Beamforming In Internet Of Underground Things, Abdul Salam Apr 2019

Underground Environment Aware Mimo Design Using Transmit And Receive Beamforming In Internet Of Underground Things, Abdul Salam

Faculty Publications

In underground (UG) multiple-input and multiple-output (MIMO), the transmit beamforming is used to focus energy in the desired direction. There are three different paths in the underground soil medium through which the waves propagates to reach at the receiver. When the UG receiver receives a desired data stream only from the desired path, then the UG MIMO channel becomes three path (lateral, direct, and reflected) interference channel. Accordingly, the capacity region of the UG MIMO three path interference channel and degrees of freedom (multiplexing gain of this MIMO channel requires careful modeling). Therefore, expressions are required derived the degrees of ...


High-Performing Pgm-Free Aemfc Cathodes From Carbon-Supported Cobalt Ferrite Nanoparticles, Xiong Peng, Varchaswal Kashyap, Benjamin Ng, Sreekumar Kurungot, Lianqin Wang, John R. Varcoe, Mustain E William Mar 2019

High-Performing Pgm-Free Aemfc Cathodes From Carbon-Supported Cobalt Ferrite Nanoparticles, Xiong Peng, Varchaswal Kashyap, Benjamin Ng, Sreekumar Kurungot, Lianqin Wang, John R. Varcoe, Mustain E William

Faculty Publications

Efficient and durable non-precious metal electrocatalysts for the oxygen reduction reaction (ORR) are highly desirable for several electrochemical devices, including anion exchange membrane fuel cells (AEMFCs). Here, cobalt ferrite (CF) nanoparticles supported on Vulcan XC-72 carbon (CF-VC) were created through a facile, scalable solvothermal method. The nano-sized CF particles were spherical with a narrow particle size distribution. The CF-VC catalyst showed good ORR activity, possessing a half-wave potential of 0.71 V. Although the intrinsic activity of the CF-VC catalyst was not as high as some other platinum group metal (PGM)-free catalysts in the literature, where this catalyst really ...


Diagnosis Of Brain Diseases Via Multi-Scale Time-Series Model, Zehua Zhang, Junhai Xu, Jijun Tang, Quan Zou, Fei Guo Mar 2019

Diagnosis Of Brain Diseases Via Multi-Scale Time-Series Model, Zehua Zhang, Junhai Xu, Jijun Tang, Quan Zou, Fei Guo

Faculty Publications

The functional magnetic resonance imaging (fMRI) data and brain network analysis have been widely applied to automated diagnosis of neural diseases or brain diseases. The fMRI time series data not only contains specific numerical information, but also involves rich dynamic temporal information, those previous graph theory approaches focus on local topology structure and lose contextual information and global fluctuation information. Here, we propose a novel multi-scale functional connectivity for identifying the brain disease via fMRI data. We calculate the discrete probability distribution of co-activity between different brain regions with various intervals. Also, we consider nonsynchronous information under different time dimensions ...


Application Of The Second-Order Comprehensive Adjoint Sensitivity Analysis Methodology To Compute First- And Second-Order Sensitivities Of Flux Functionals In A Multiplying System With Source, Dan Gabriel Cacuci Feb 2019

Application Of The Second-Order Comprehensive Adjoint Sensitivity Analysis Methodology To Compute First- And Second-Order Sensitivities Of Flux Functionals In A Multiplying System With Source, Dan Gabriel Cacuci

Faculty Publications

This work presents an application of the Second-Order Adjoint Sensitivity Analysis Methodology (2nd-ASAM) to the neutron transport Boltzmann equation that models a multiplying subcritical system comprising a nonfission neutron source to compute efficiently and exactly all of the first- and second-order functional derivatives (sensitivities) of a detector’s response to all of the model’s parameters, including isotopic number densities, microscopic cross sections, fission spectrum, sources, and detector response function. As indicated by the general theoretical considerations underlying the 2nd-ASAM, the number of computations required to obtain the first and second orders increases linearly in augmented Hilbert spaces as opposed ...


Discovering Cancer Subtypes Via An Accurate Fusion Strategy On Multiple Profile Data, Limin Jiang, Yongkang Xiao, Yijie Ding, Jijun Tang, Fei Guo Feb 2019

Discovering Cancer Subtypes Via An Accurate Fusion Strategy On Multiple Profile Data, Limin Jiang, Yongkang Xiao, Yijie Ding, Jijun Tang, Fei Guo

Faculty Publications

Discovering cancer subtypes is useful for guiding clinical treatment of multiple cancers. Progressive profile technologies for tissue have accumulated diverse types of data. Based on these types of expression data, various computational methods have been proposed to predict cancer subtypes. It is crucial to study how to better integrate these multiple profiles of data. In this paper, we collect multiple profiles of data for five cancers on The Cancer Genome Atlas (TCGA). Then, we construct three similarity kernels for all patients of the same cancer by gene expression, miRNA expression and isoform expression data. We also propose a novel unsupervised ...


Influence Propagation For Social Graph-Based Recommendations, Avni Gulati, Magdalini Eirinaki Jan 2019

Influence Propagation For Social Graph-Based Recommendations, Avni Gulati, Magdalini Eirinaki

Faculty Publications

Social networking is an inevitable behavior of humans living in a society. In recent years, and with the rise of online social networks, personalized recommendations that leverage the social aspect have become a very intriguing domain for researchers. In this work, we explore how influence propagation and the decay in the cascading effect of influence from influential users can be leveraged to generate social graph-based recommendations. Understanding how influence propagates within a social network is itself a challenging problem. Few researchers have considered influence propagation and even fewer have considered decay in the cascading effect of influence in a social ...


Multivariate Information Fusion With Fast Kernel Learning To Kernel Ridge Regression In Predicting Lncrna-Protein Interactions, Cong Shen, Yijie Ding, Jijun Tang, Fei Guo Jan 2019

Multivariate Information Fusion With Fast Kernel Learning To Kernel Ridge Regression In Predicting Lncrna-Protein Interactions, Cong Shen, Yijie Ding, Jijun Tang, Fei Guo

Faculty Publications

Long non-coding RNAs (lncRNAs) constitute a large class of transcribed RNA molecules. They have a characteristic length of more than 200 nucleotides which do not encode proteins. They play an important role in regulating gene expression by interacting with the homologous RNA-binding proteins. Due to the laborious and time-consuming nature of wet experimental methods, more researchers should pay great attention to computational approaches for the prediction of lncRNA-protein interaction (LPI). An in-depth literature review in the state-of-the-art in silico investigations, leads to the conclusion that there is still room for improving the accuracy and velocity. This paper propose a novel ...


Internet Of Things In Smart Agriculture: Enabling Technologies, Abdul Salam, Syed Shah Jan 2019

Internet Of Things In Smart Agriculture: Enabling Technologies, Abdul Salam, Syed Shah

Faculty Publications

In this paper, an IoT technology research and innovation roadmap for the field of precision agriculture (PA) is presented. Many recent practical trends and the challenges have been highlighted. Some important objectives for integrated technology research and education in precision agriculture are described. Effective IoT based communications and sensing approaches to mitigate challenges in the area of precision agriculture are presented.