Semantic eSystems : Engineering methods, techniques, and tools

We are delighted to present this novel special issue, which emphasizes semantic eSystems and their engineering methods, techniques, tools. “eSystems” those interdisciplinary cost-effective interconnected solutions that leverage advanced information communication technologies' techniques tools gain competitive advantage. Recent years have witnessed increasing interest in the design development of various eSystem-based applications, ranging from real-world machine robotic applications. An essential feature is intelligent ability “speak” interact with one another support function “extensibility” so complex interactions could be established other eSystems. Therefore, connecting disparate “on fly” necessitates a dialog channel such it enables data fusion, exchange, link unified understandable way. While emitted different formats, sizes, types, adopting technologies natural way address these differences. Simply put, semantics about agreeing on common understanding need exchanged between systems humans systems. This issue received 22 submissions, were aligned theme eSystems' Those submissions came diverse researchers academia, industry, individuals all over globe. First, screened closely by editors check suitability issue's list topics. Second, after multiple rounds peer review, only 11 high-quality accepted for publication issue. papers perspectives including, instance, efficient semantic–visual indexing model large-scale image retrieval cloud environment, trilateration-based indoor localization technique visible light system, graph-based system enable transformation enterprise infrastructures, recognizing physical activities having interclass variations using smartphone, spatiotemporal-based sentiment analysis tweets risk assessment an event deep-learning approach, sentiment-based eSystem hybridized fuzzy deep neural network measuring customer satisfaction, fusion emotion recognition thermal Internet Thing devices, framework manage cybersecurity safety manufacturing convolutional stock price prediction leading indicators, author classification transfer learning predicting stars coauthor networks, DNA signal tool: noise suppression window filter. The authors express sincere thanks Editor-in-Chief allowing them organize editorial office staff members excellent thanked support. also thankful contributors who made possible reviewers thoughtful contributions.