Big Data Analytics for Complex Systems

The evolution of technology in all fields led to the generation of vast amounts of data by modern systems. Using data to extract information, make predictions, and make decisions is the current trend in artificial intelligence. The advancement of big data analytics tools made accessing and storing data easier and faster than ever, and machine learning algorithms help to identify patterns in and extract information from data. The current tools and machines in health, computer technologies, and manufacturing can generate massive raw data about their products or samples. The author of this work proposes a modern integrative system that can utilize big data analytics, machine learning, super-computer resources, and industrial health machines’ measurements to build a smart system that can mimic the human intelligence skills of observations, detection, prediction, and decision-making. The applications of the proposed smart systems are included as case studies to highlight the contributions of each system. The first contribution is the ability to utilize big data revolutionary and deep learning technologies on production lines to diagnose incidents and take proper action. In the current digital transformational industrial era, Industry 4.0 has been receiving researcher attention because it can be used to automate production-line decisions. Reconfigurable manufacturing systems (RMS) have been widely used to reduce the setup cost of restructuring production lines. However, the current RMS modules are not linked to the cloud for online decision-making to take the proper decision; these modules must connect to an online server (super-computer) that has big data analytics and machine learning capabilities. The online means that data is centralized on cloud (supercomputer) and accessible in real-time. In this study, deep neural networks are utilized to detect the decisive features of a product and build a prediction model in which the iFactory will make the necessary decision for the defective products. The Spark ecosystem is used to manage the access, processing, and storing of the big data streaming. This contribution is implemented as a closed cycle, which for the best of our knowledge, no one in the literature has introduced big data analysis using deep learning on real-time applications in the manufacturing system. The code shows a high accuracy of 97% for classifying the normal versus defective items. The second contribution, which is in Bioinformatics, is the ability to build supervised machine learning approaches based on the gene expression of patients to predict proper treatment for breast cancer. In the trial, to personalize treatment, the machine learns the genes that are active in the patient cohort with a five-year survival period. The initial condition here is that each group must only undergo one specific treatment. After learning about each group (or class), the machine can personalize the treatment of a new patient by diagnosing the patients’ gene expression. The proposed model will help in the diagnosis and treatment of the patient. The future work in this area involves building a protein-protein interaction network with the selected genes for each treatment to first analyze the motives of the genes and target them with the proper drug molecules. In the learning phase, a couple of feature-selection techniques and supervised standard classifiers are used to build the prediction model. Most of the nodes show a high-performance measurement where accuracy, sensitivity, specificity, and F-measure ranges around 100%. The third contribution is the ability to build semi-supervised learning for the breast cancer survival treatment that advances the second contribution. By understanding the relations between the classes, we can design the machine learning phase based on the similarities between classes. In the proposed research, the researcher used the Euclidean matrix distance among each survival treatment class to build the hierarchical learning model. The distance information that is learned through a non-supervised approach can help the prediction model to select the classes that are away from each other to maximize the distance between classes and gain wider class groups. The performance measurement of this approach shows a slight improvement from the second model. However, this model reduced the number of discriminative genes from 47 to 37. The model in the second contribution studies each class individually while this model focuses on the relationships between the classes and uses this information in the learning phase. Hierarchical clustering is completed to draw the borders between groups of classes before building the classification models. Several distance measurements are tested to identify the best linkages between classes. Most of the nodes show a high-performance measurement where accuracy, sensitivity, specificity, and F-measure ranges from 90% to 100%. All the case study models showed high-performance measurements in the prediction phase. These modern models can be replicated for different problems within different domains. The comprehensive models of the newer technologies are reconfigurable and modular; any newer learning phase can be plugged-in at both ends of the learning phase. Therefore, the output of the system can be an input for another learning system, and a newer feature can be added to the input to be considered for the learning phase

A Machine Learning Approach for Identifying Gene Biomarkers Guiding the Treatment of Breast Cancer

Genomic profiles among different breast cancer survivors who received similar treatment may provide clues about the key biological processes involved in the cells and finding the right treatment. More specifically, such profiling may help personalize the treatment based on the patients’ gene expression. In this paper, we present a hierarchical machine learning system that predicts the 5-year survivability of the patients who underwent though specific therapy; The classes are built on the combination of two parts that are the survivability information and the given therapy. For the survivability information part, it defines whether the patient survives the 5-years interval or deceased. While the therapy part denotes the therapy has been taken during that interval, which includes hormone therapy, radiotherapy, or surgery, which totally forms six classes. The Model classifies one class vs. the rest at each node, which makes the tree-based model creates five nodes. The model is trained using a set of standard classifiers based on a comprehensive study dataset that includes genomic profiles and clinical information of 347 patients. A combination of feature selection methods and a prediction method are applied on each node to identify the genes that can predict the class at that node, the identified genes for each class may serve as potential biomarkers to the class’s treatment for better survivability. The results show that the model identifies the classes with high-performance measurements. An exhaustive analysis based on relevant literature shows that some of the potential biomarkers are strongly related to breast cancer survivability and cancer in general

A Novel Approach for Identifying Relevant Genes for Breast Cancer Survivability on Specific Therapies

Analyzing the genetic activity of breast cancer survival for a specific type of therapy provides a better understanding of the body response to the treatment and helps select the best course of action and while leading to the design of drugs based on gene activity. In this work, we use supervised and nonsupervised machine learning methods to deal with a multiclass classification problem in which we label the samples based on the combination of the 5-year survivability and treatment; we focus on hormone therapy, radiotherapy, and surgery. The proposed nonsupervised hierarchical models are created to find the highest separability between combinations of the classes. The supervised model consists of a combination of feature selection techniques and efficient classifiers used to find a potential set of biomarker genes specific to response to therapy. The results show that different models achieve different performance scores with accuracies ranging from 80.9% to 100%. We have investigated the roles of many biomarkers through the literature and found that some of the discriminative genes in the computational model such as ZC3H11A , VAX2 , MAF1 , and ZFP91 are related to breast cancer and other types of cancer

New Architecture of Optical Interconnect for High-Speed Optical Computerized Data Networks (Nonlinear Response)

Although research into the use of optics in computers has increased in the last and current decades, the fact remains that electronics is still superior to optics in almost every way. Research into the use of optics at this stage mirrors the research into electronics after the 2nd World War. The advantages of using fiber optics over wiring are the same as the argument for using optics over electronics in computers. Even through totally optical computers are now a reality, computers that combine both electronics and optics, electro-optic hybrids, have been in use for some time. In the present paper, architecture of optical interconnect is built up on the bases of four Vertical-Cavity Surface- Emitting Laser Diodes (VCSELD) and two optical links where thermal effects of both the diodes and the links are included. Nonlinear relations are correlated to investigate the power-current and the voltage-current dependences of the four devices. The good performance (high speed) of the interconnect is deeply and parametrically investigated under wide ranges of the affecting parameters. The high speed performance is processed through three different effects, namely the device 3-dB bandwidth, the link dispersion characteristics, and the transmitted bit rate (soliton). Eight combinations are investigated; each possesses its own characteristics. The best architecture is the one composed of VCSELD that operates at 850 nm and the silica fiber whatever the operating set of causes. This combination possesses the largest device 3-dB bandwidth, the largest link bandwidth and the largest soliton transmitted bit rate. The increase of the ambient temperature reduces the high-speed performance of the interconnec