Search results for ""Author Kamal I. M. Al-Malah""

MACHINE AND DEEP LEARNING In-depth resource covering machine and deep learning methods using MATLAB tools and algorithms, providing insights and algorithmic decision-making processes Machine and Deep Learning Using MATLAB introduces early career professionals to the power of MATLAB to explore machine and deep learning applications by explaining the relevant MATLAB tool or app and how it is used for a given method or a collection of methods. Its properties, in terms of input and output arguments, are explained, the limitations or applicability is indicated via an accompanied text or a table, and a complete running example is shown with all needed MATLAB command prompt code. The text also presents the results, in the form of figures or tables, in parallel with the given MATLAB code, and the MATLAB written code can be later used as a template for trying to solve new cases or datasets. Throughout, the text features worked examples in each chapter for self-study with an accompanying website providing solutions and coding samples. Highlighted notes draw the attention of the user to critical points or issues. Readers will also find information on: Numeric data acquisition and analysis in the form of applying computational algorithms to predict the numeric data patterns (clustering or unsupervised learning) Relationships between predictors and response variable (supervised), categorically sub-divided into classification (discrete response) and regression (continuous response) Image acquisition and analysis in the form of applying one of neural networks, and estimating net accuracy, net loss, and/or RMSE for the successive training, validation, and testing steps Retraining and creation for image labeling, object identification, regression classification, and text recognition Machine and Deep Learning Using MATLAB is a useful and highly comprehensive resource on the subject for professionals, advanced students, and researchers who have some familiarity with MATLAB and are situated in engineering and scientific fields, who wish to gain mastery over the software and its numerous applications.

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ASPEN PLUS® Comprehensive resource covering Aspen Plus V12.1 and demonstrating how to implement the program in versatile chemical process industries Aspen Plus®: Chemical Engineering Applications facilitates the process of learning and later mastering Aspen Plus®, the market-leading chemical process modeling software, with step-by-step examples and succinct explanations. The text enables readers to identify solutions to various process engineering problems via screenshots of the Aspen Plus® platforms in parallel with the related text. To aid in information retention, the text includes end-of-chapter problems and term project problems, online exam and quiz problems for instructors that are parametrized (i.e., adjustable) so that each student will have a standalone version, and extra online material for students, such as Aspen Plus®-related files, that are used in the working tutorials throughout the entire textbook. The second edition of Aspen Plus®: Chemical Engineering Applications includes information on: Various new features that were embedded into Aspen Plus V12.1 and existing features which have been modified Aspen Custom Modeler (ACM), covering basic features to show how to merge customized models into Aspen Plus simulator New updates to process dynamics and control and process economic analysis since the first edition was published Vital areas of interest in relation to the software, such as polymerization, drug solubility, solids handling, safety measures, and energy saving For chemical engineering students and industry professionals, the second edition of Aspen Plus®: Chemical Engineering Applications is a key resource for understanding Aspen Plus and the new features that were added in version 12.1 of the software. Many supplementary learning resources help aid the reader with information retention.

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