An Evaluation of Supervised Dimensionality Reduction For Large Scale Data

Experimenters today frequently quantify millions or even billions of characteristics (measurements) each sample to address critical biological issues, in the hopes that machine learning tools would be able to make correct data-driven judgments. An efficient analysis requires a low-dimensional representation that preserves the differentiating features in data whose size and complexity are orders of magnitude apart (e.g., if a certain ailment is present in the person's body). While there are several systems that can handle millions of variables and yet have strong empirical and conceptual guarantees, there are few that can be clearly understood. This research presents an evaluation of supervised dimensionality reduction for large scale data. We provide a methodology for expanding Principal Component Analysis (PCA) by including category moment estimations in low-dimensional projections. Linear Optimum Low-Rank (LOLR) projection, the cheapest variant, includes the class-conditional means. We show that LOLR projections and its extensions enhance representations of data for future classifications while retaining computing flexibility and reliability using both experimental and simulated data benchmark. When it comes to accuracy, LOLR prediction outperforms other modular linear dimension reduction methods that require much longer computation times on conventional computers. LOLR uses more than 150 million attributes in brain image processing datasets, and many genome sequencing datasets have more than half a million attributes.

Analysis of Membrane Process Model from Black Box to Machine Learning

The membrane processes include the complex frameworks, typically integrating various physio-chemical aspects, and the biological activities, based on the systems researched. In that regard, the process modeling is essential to predict and simulate the process and the performance of membranes, to infer concerning the optimum process aspects, meant to analyze fouling developments, and principally, the controls and monitoring of processes. Irrespective of the real terminological dissemination such as Machine Learning (ML), the application of computing instruments to the processes of model membrane was considered in the past are insignificant from the scholarly perspective, not contributing to our knowledge of the aspects included. Irrespective of the controversies, in the past two decades, non-mechanistic and data-driven modeling is applicable to illustrate various membrane process, and in the establishment of novel tracking and modeling approaches. In that regard, this paper concentrates on the provision of a custom aspect regarding the use of Non-Mechanistic Modeling (NMM) in membrane processing, assessing the transformations endorsed by our experience, accomplished as a research segment operational in the membrane process segment. Furthermore, the guidelines are the problems for the application of the state-of-the-art computational instruments Membrane Computing (MC).

Survey on Technique and User Profiling in Unsupervised Machine Learning Method

In order to generate precise behavioural patterns or user segmentation, organisations often struggle with pulling information from data and choosing suitable Machine Learning (ML) techniques. Furthermore, many marketing teams are unfamiliar with data-driven classification methods. The goal of this research is to provide a framework that outlines the Unsupervised Machine Learning (UML) methods for User-Profiling (UP) based on essential data attributes. A thorough literature study was undertaken on the most popular UML techniques and their dataset attributes needs. For UP, a structure is developed that outlines several UML techniques. In terms of data size and dimensions, it offers two-stage clustering algorithms for category, quantitative, and mixed types of datasets. The clusters are determined in the first step using a multilevel or model-based classification method. Cluster refining is done in the second step using a non-hierarchical clustering technique. Academics and professionals may use the framework to figure out which UML techniques are best for creating strong profiles or data-driven user segmentation.

Analysis of Inverse Methods in Empirical Structural Mechanics

The application of inverse methods in empirical structural mechanics is the subject of this study. After a broad introduction to Inverse Problems (IPs), which includes a discussion of the many domains of application in general structural mechanics, the focus is limited to the critical area of material identification, with a special focus on the use of complete surveys. In this example, a more detailed explanation of the IPs to solve is provided, as well as the primary approaches to solving it. Lastly, there are several illustrations of exploratory uses of such techniques.

Critical Evaluation of the Cloud-Based Design and Manufacture

This article examines Cloud-based Design and Manufacturing from a critical standpoint (CBDM). Cloud technology has lately found its way into the realm of computer-assisted product creation. Corporations could explore substituting existing own CAD software licences with Design software as a cloud - based service as the first part of implementation. Installing a CAD program via the cloud on a carrier's server and incurring a miniscule proportion of the initial licensing price on a pay-per-use basis is definitely tempting. Furthermore, time and money-consuming software upgrades and operational issues are no longer an issue. We provide an introduction of cloud technology and the intrinsic features that drive its usage in both the business and education domains for dispersed and interactive design and production. Cloud Technology is a hotly debated Information Technology (IT) model that is expected to have a major effect on how businesses are run in the future. While cloud technology was first proposed in the late 60s, it was only recently that it became a viable part of day-to-day IT systems, thanks to the Internet's increasing prevalence and other modern improvements in information communication technology (ICT).

Critical Analysis of Complex Systems Science and Mechanics

For many highly complex, ecological, physical and societal structures, the conventional hypotheses that underpin many theoretical and analytical frameworks are false. Complex systems research elucidates why and when such preconceptions are incorrect, as well as alternate paradigms for comprehending complex series characteristics. Complexity characteristics, the tradeoff between effectiveness and adaptation, the need of matching the complexities of networks to that of their surroundings, multiresolution assessment, and evolution are among the fundamental concepts of Complex Systems (CS) research introduced in this study. Instead of simulating particular dynamics, we concentrate on the general characteristics of systems. We didactically explain a theoretical and analytic strategy for comprehending and engaging with the complicated processes of our environment rather than giving a complete overview. This article requires just a middle school mathematics and science foundation in order to make it approachable to researchers from all disciplines, decision-makers from business, government, and charity, and anybody engaged in networks and civilization.

Machine Learning Technique and Applications – An Classification Analysis

The digitally-enhanced environment is susceptible to massive data, such as information security data, internet technology data, cellular internet, patient records, media data, corporate data, and so on, in the current era of Industry 4.0. Understanding of Machine Learning (ML) is essential for intelligently evaluating these sets of data and developing related "intelligent" and "automated" solutions. Different forms of ML algorithms e.g. reinforcement learning, semi-supervised, unsupervised and supervised learning exist in this segment. In addition, deep learning, which is a wider segment of ML techniques, can smartly evaluate datasets on a massive scale. In this research, a comprehensive analysis of ML techniques and classification analysis algorithms that are applicable to develop capabilities and intelligence of applications are analyzed. Therefore, this research’s contribution is illustrating the key principles of various ML techniques and their application in different real-life application realms e.g. e-commerce, healthcare, agriculture, smart cities, cyber-security systems etc. Lastly, this paper presents a discussion of the challenges and future research based on this research.

Idea of Knowledge-Based Engineering using CAD Model

Manufacturing firms have been compelled to invest heavily in digitizing and optimizing their technical and manufacturing operations as a result of mass customization. When developing and introducing new goods, not only must manufacturing procedures be computerized, but also information of how the products must be developed and manufactured based on client needs must be applied. One major academic issue is to assist the industry in ensuring that stakeholders understand the background information of automated engineering all through the production process. The goal of the study described in this article is to provide a foundation for a connectivity perspective of Knowledge-Based Engineering (KBE). The use of graph theory in conjunction with content-based filtering methods is used to handle network creation and contextualization, which are fundamental ideas in connectivism. To enable a connectivity management culture, the article demonstrates how engineering information in spreadsheet, knowledge representation, and Computer Aided Design (CAD) models may be infiltrated and displayed as filtering graphs.

Human Machine Integrated Interactions and Internet of Things

More applications are being developed for the Internet of Things (IoT) these days. What resources are available to help with the software design phase, particularly for those programs that emphasize interplay? The incorporation of cognitive approach can aid in the understanding of people and the application of these discoveries in the design phase. The goal of this article is to introduce the idea of blending and the architecture of Integrated Interactions, and to apply those principles to the IoT. This article evaluates the intellectual foundations of conceptual integration and the layering process. After that, this article describes the Integrated Interaction framework and shows an Affinity Table in intervention. This paper also evaluates the fundamentals of the Internet of Things and how it can be used in various industries. According to the findings, this application offers a distinct blend.

Methodology to Design for Product Service Supportability

In today’s world, processors must expertly structure sophisticated integrated remedies using modern technologies based on multiple functionalities demands and the rapidly changing perceptions of consumers. Due to this, it is considerably complete for the givers of Product Service Systems (PSSs) to attain all the essential designing requirements. Product designers have to focus on the essential objectives required by PSSs to attain in the whole lifecycle process based on various criteria and approach typically considered in trade-off balancing. Presently, Design-for-X (DfX) approach signifies the most fundamental projection to facilitate production developments based on features and stages of product lifecycle. It is considered that these stages and features support the designing of PSSs, product redesigning and developing engineered products based on x dimensionality with respect to supportability of services. In this paper, a methodology has been proposed for the generation of novel DfX protocols. As such, an application case in mold industry will be used to represent physical engineered productions, which are developed when services have been integrated or added.