Now showing items 1-20 of 39

    • A Logic Approach to Granular computing 

      Bing, Zhou; Yiyu, Yao (International Journal of Cognitive Informatics and Natural Intelligence, 2008-04)
      Granular computing is an emerging field of study that attempts to formalize and explore methods and heuristics of human problem solving with multiple levels of granularity and abstraction. A fundamental issue of granular ...
    • In Search of Effective Granulization with DTRS for Ternary Classification 

      Bing, Zhou; Yiyu, Yao (International Journal of Cognitive Informatics and Natural Intelligence, 2011)
      Decision-Theoretic Rough Set (DTRS) model provides a three-way decision approach to classification problems, which allows a classifier to make a deferment decision on suspicious examples, rather than being forced to make ...
    • Supervised learning-based tagSNP selection for genome-wide disease classifications 

      Liu, Qingzhong; Sung, Andrew H.; Chen, Zhongxue; Yang, Mary Qu; Huang, Xudong; Yang, Jack (BIomed Central, 2007-07-25)
      Comprehensive evaluation of common genetic variations through association of single nucleotide polymorphisms (SNPs) with complex human diseases on the genome-wide scale is an active area in human genome research. One of ...
    • Independent component analysis of Alzheimer's DNA microarray gene expression data 

      Liu, Qingzhong; Chen, Zhongxue; Vanderburg, Charles R; Rogers, Jack T.; Huang, Xudong; Mou, Xiaoyang; Wei, Kong (Molecular Neurodegeneration, 2009-01-28)
      Background: Gene microarray technology is an effective tool to investigate the simultaneous activity of multiple cellular pathways from hundreds to thousands of genes. However, because data in the colossal amounts generated ...
    • Comparison of feature selection and classification for MALDI-MS data 

      Liu, Qingzhong; Sung, Andrew H.; Chen, Zhongxue; Yang, Jack Y; Qiao, Mengyu; Yang, Mary Qu; Huang, Xudong; Deng, Youping (BMC Genomics, 2009-07-07)
      Introduction: In the classification of Mass Spectrometry (MS) proteomics data, peak detection, feature selection, and learning classifiers are critical to classification accuracy. To better understand which methods are ...
    • Feature Selection and Classification of MAQC-II Breast Cancer and Multiple Myeloma Microarray Gene Expression Data 

      Liu, Qingzhong; Sung, Andrew H.; Chen, Zhongxue; Huang, Xudong; Deng, Youpin; Liu, Jianzhong (PLoS ONE, 2009-12-11)
      Microarray data has a high dimension of variables but available datasets usually have only a small number of samples, thereby making the study of such datasets interesting and challenging. In the task of analyzing microarray ...
    • A gene selection method for GeneChip array data with small sample sizes 

      Chen, Zhongxue; Liu, Qingzhong; McGee, Monnie; Kong, Megan; Huang, Xudong; Deng, Youpin; Scheuermann, Richard H. (2010-07)
      In microarray experiments with small sample sizes, it is a challenge to estimate p-values accurately and decide cutoff p-values for gene selection appropriately. Although permutation-based methods have proved to have ...
    • Gene selection and classification for cancer microarray data based on machine learning and similarity measures 

      Liu, Qingzhong; Sung, Andrew H.; Chen, Zhongxue; Liu, Jianzhong; Chen, Lei; Deng, Youpin; Wang, Zhaohui; Huang, Xudong; Qiao, Mengyu (BMC Genomics, 2011)
      Background: Microarray data have a high dimension of variables and a small sample size. In microarray data analyses, two important issues are how to choose genes, which provide reliable and good prediction for disease ...
    • Assessment of gene order computing methods for Alzheimer’s disease 

      Liu, Qingzhong; Hu, Benqiong; Pang, Chaoyang; Wang, Shipend; Chen, Zhongxue; Vanderburg, Charles R; Rogers, Jack T.; Deng, Youping; Huang, Xudong; Jiang, Gang (BMC Medical Genomics, 2013-01-23)
      Background: Computational genomics of Alzheimer disease (AD), the most common form of senile dementia, is a nascent field in AD research. The field includes AD gene clustering by computing gene order which generates higher ...
    • Influence of Machine Learning vs. Ranking Algorithm on the Critical Dimension 

      Sung, Andrew H.; Liu, Qingzhong; Suryakumar, Divya (International Journal of Future Computer and Communication, 2013-06)
      The critical dimension is the minimum number of features required for a learning machine to perform with “high” accuracy, which for a specific dataset is dependent upon the learning machine and the ranking algorithm. ...
    • “Meta Cloud Discovery” Model: An Approach to Integrity Monitoring for Cloud-Based Disaster Recovery Planning 

      Wilbert, Brittany M.; Liu, Qingzhong (International Journal of Information and Education Technology, 2013-10)
      A structure is required to prevent the malicious code from leaking onto the system. The use of sandboxes has become more advance, allowing for investigators to access malicious code while minimizing the risk of infecting ...
    • A new statistical approach to combining p-values using gamma distribution and its application to genome-wide association study 

      Liu, Qingzhong; Chen, Zhongxue; Yang, William; Yang, Jack Y; Li, Jing; Yang, Mary Qu (BMC Bioinformatics, 2014-12-16)
      Background: Combining information from different studies is an important and useful practice in bioinformatics, including genome-wide association study, rare variant data analysis and other set-based analyses. Many statistical ...
    • A Method to Detect AAC Audio Forgery 

      Zhang, Jing; Liu, Qingzhong; Yang, Ming; Sung, Andrew H.; Liu, Yanxin; Chen, Lei; Chen, Zhongxue (Endorsed Transactions on Security and Safety, 2015-05-25)
      Advanced Audio Coding (AAC), a standardized lossy compression scheme for digital audio, which was designed to be the successor of the MP3 format, generally achieves better sound quality than MP3 at similar bit rates. ...
    • Mobile Watermarking against Geometrical Distortions 

      Liu, Qingzhong; Zhang, Jing; Su, Yuting; Zhi, Meili (EAI Endorsed Transactions on Security and Safety, 2015-05-25)
      Mobile watermarking robust to geometrical distortions is still a great challenge. In mobile watermarking, efficient computation is necessary because mobile devices have very limited resources due to power consumption. In ...
    • A Comparison Study using Stegexpose for Steganalysis. 

      Olson, Eric; Carter, Larry; Liu, Qingzhong (International Journal of Knowledge Engineering, 2017-06)
      Steganography is the art of hiding secret message in innocent digital data files. Steganalysis aims to expose the existence of steganograms. While internet applications and social media has grown tremendously in recent ...
    • Smartphone Forensic Challenges 

      Krishnan, Sundar; Zhou, Bing; An, Min Kyung (International Journal of Computer Science and Security, 2019)
      Globally, the extensive use of smartphone devices has led to an increase in storage and transmission of enormous volumes of data that could be potentially be used as digital evidence in a forensic investigation. Digital ...
    • Android System Partition to Traffic Data? 

      Bing, Zhou; Liu, Qingzhong; Byrd, Brittany (International Journal of Knowledge Engineering, 2017-12)
      The familiarity and prevalence of mobile devices inflates their use as instruments of crime. Law enforcement personnel and mobile forensics investigators, are constantly battling to gain the upper-hand at developing a ...
    • Smartphone Sensor-Based Activity Recognition by Using Machine Learning and Deep Learning Algorithms 

      Liu, Qingzhong; Zhaoxian, Zhou; Sarbagya, Shakya Ratna; Prathyusha, Uduthalapally; Mengyu, Qiao; Andrew, Sung H. (International Journal of Machine Learning and Computing, 2018)
      Smartphones are widely used today, and it becomes possible to detect the user's environmental changes by using the smartphone sensors, as demonstrated in this paper where we propose a method to identify human activities ...
    • Security, Privacy and Steganographic Analysis of FaceApp and TikTo 

      Krishnan, Sundar; Liu, Qingzhong; Neyaz, Ashar; Kumar, Avinash; Placker, Jessica (International Journal of Computer Science and Security, 2020)
      Smartphone applications (Apps) can be addictive for users due to their uniqueness, ease-of-use, trendiness, and growing popularity. The addition of Artificial Intelligence (AI) into their functionality has rapidly gained ...
    • IoT Network Attack Detection using Supervised Machine Learning 

      Krishnan, Sundar; Neyaz, Ashar; Liu, Qingzhong (International Journal of Artificial Intelligence and Expert Systems, 2021)
      The use of supervised learning algorithms to detect malicious traffic can be valuable in designing intrusion detection systems and ascertaining security risks. The Internet of things (IoT) refers to the billions of physical, ...