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Intelligent video surveillance systems : an algorithmic approach
This book will provide an overview of techniques for visual monitoring including video surveillance and human activity understanding. It will present the basic techniques of processing video from static cameras, starting with object detection and tracking. The author will introduce further video analytic modules including face detection, trajectory analysis and object classification. Examining system design and specific problems in visual surveillance, such as the use of multiple cameras and moving cameras, the author will elaborate on privacy issues focusing on approaches where automatic processing can help protect privacy-- Provided by publisher.
Book
Artificial intelligence‐powered spatial analysis of tumor‐infiltrating lymphocytes as a biomarker in locally advanced unresectable thymic epithelial neoplasm: A single‐center, retrospective, longitudinal cohort study
BackgroundThymic epithelial tumors (TET) are rare malignancies and lack well-defined biomarkers for neoadjuvant therapy. This study aimed to evaluate the clinical utility of artificial intelligence (AI)-powered tumor-infiltrating lymphocyte (TIL) analysis in TET.MethodsPatients initially diagnosed with unresectable thymoma or thymic carcinoma who underwent neoadjuvant therapy between January 2004 and December 2021 formed our study population. Hematoxylin and eosin-stained sections from the initial biopsy and surgery were analyzed using an AI-powered spatial TIL analyzer. Intratumoral TIL (iTIL) and stromal TIL (sTIL) were quantified and their immune phenotype (IP) was identified.ResultsThirty-five patients were included in this study. The proportion of patients with partial response to neoadjuvant therapy was higher in the group with nondesert IP in preneoadjuvant biopsy (63.6% vs. 17.6%, p = 0.038). A significant increase in both iTIL (median 22.18/mm2 vs. 340.69/mm2, p < 0.001) and sTIL (median 175.19/mm2 vs. 531.02/mm2, p = 0.004) was observed after neoadjuvant therapy. Patients with higher iTIL (>147/mm2) exhibited longer disease-free survival (median, 29 months vs. 12 months, p = 0.009) and overall survival (OS) (median, 62 months vs. 45 months, p = 0.002). Patients with higher sTIL (>232.1/mm2) exhibited longer OS (median 62 months vs. 30 months, p = 0.021).ConclusionsNondesert IP in initial biopsy was associated with a better response to neoadjuvant therapy. Increased infiltration of both iTIL and sTIL in surgical specimens were associated with longer OS in patients with TET who underwent resection followed by neoadjuvant therapy.
Journal Article
Image processing and intelligent computing systems
\"There is a drastic growth in multimedia data. Even during the Covid-19 pandemic, we observed that the images helped doctors immensely in fast detection of Covid-19 infection in patients. There are many critical applications where images play a vital role. These applications use raw image data to extract some useful information about the world around us. Quick extraction of valuable information from raw images is one challenge that academicians and professionals face nowadays. This is where image processing comes into action. Image processing's primary purpose is to get an enhanced image or extract some useful information from it. Therefore, there is a major need for some technique or system that addresses this challenge. Intelligent Systems have emerged as a solution to address quick image information extraction. In simple words, an Intelligent System can be defined as a mathematical model that adapts itself to deal with the problems' dynamicity. These systems learn how to act so it can reach their objectives. Intelligent System helps accomplish various image processing functions like enhancement, segmentation, reconstruction, object detection, and morphing. The advent of Intelligent Systems in the image processing field has leveraged many critical applications for humankind. These critical applications include factory automation, biomedical imaging analysis, and decision-econometrics, Intelligent Systems and challenges\"-- Provided by publisher.
Book
Entrepreneurial orientation, competitive advantage and strategic knowledge management capability in Malaysian family firms
Purpose
The purpose of this study is to test the thesis that the family firm’s success hinges on effective strategic knowledge management (SKM) capability coupled with an entrepreneurial orientation (EO). Contingency theory holds that entrepreneurial success is contingent on strategic capabilities and resource orchestration theory explains how well family firms nurture capabilities to structure, bundle and leverage resources that define competitive advantage (CA). This study combines these two theoretical viewpoints to propose the effects of EO and SKM capability on CA to achieve successful performance in family firms.
Design/methodology/approach
This study uses a hybrid approach applying structural equation modelling (SEM) and deep-learning artificial intelligence (DL-AI) analysis to survey data on 268 Malaysian family firms.
Findings
SEM results confirm that CA mediates the relationship between innovativeness, proactiveness and risk-taking dimensions of EO and firm performance. Autonomy and competitive aggressiveness have no bearing, however. The relationships among innovativeness, proactiveness and risk-taking with CA and performance are positively moderated by SKM capability, becoming more potent at higher levels. Moreover, four additional DL-AI models reveal the necessity of specific EO dimensions and the interacting effects of EO–SKM capability to influence CA and to attain performance success subsequently.
Originality/value
This study theorizes and presents two new boundary conditions to a knowledge-based theory of the family firm and its firm performance. First, CA mediates the relationship between EO and performance; and second, SKM capability moderates the relationships between EO and CA and between EO and family firm performance. Methodologically, this study uses DL-AI to embrace non-linearity and prioritize predictor variables based on normalized importance to produce greater accuracy over regression analysis. Hence, DL-AI adds methodological novelty to the knowledge management and family firm literature.
Journal Article
Bridging the gap between AI, cognitive science, and narratology with narrative generation
\"This book combines traditional theories of narratology with current narrative generation practices using artificial intelligence\"-- Provided by publisher.
BOOK
Emerging role of deep learning‐based artificial intelligence in tumor pathology
The development of digital pathology and progression of state‐of‐the‐art algorithms for computer vision have led to increasing interest in the use of artificial intelligence (AI), especially deep learning (DL)‐based AI, in tumor pathology. The DL‐based algorithms have been developed to conduct all kinds of work involved in tumor pathology, including tumor diagnosis, subtyping, grading, staging, and prognostic prediction, as well as the identification of pathological features, biomarkers and genetic changes. The applications of AI in pathology not only contribute to improve diagnostic accuracy and objectivity but also reduce the workload of pathologists and subsequently enable them to spend additional time on high‐level decision‐making tasks. In addition, AI is useful for pathologists to meet the requirements of precision oncology. However, there are still some challenges relating to the implementation of AI, including the issues of algorithm validation and interpretability, computing systems, the unbelieving attitude of pathologists, clinicians and patients, as well as regulators and reimbursements. Herein, we present an overview on how AI‐based approaches could be integrated into the workflow of pathologists and discuss the challenges and perspectives of the implementation of AI in tumor pathology.
Journal Article
Smart proxy modeling : artificial intelligence and machine learning in numerical simulation
\"Numerical simulation models are used in all engineering disciplines for modeling physical phenomena to learn how the phenomena work, and to identify problems and optimize behavior. Smart proxy models provide an opportunity to replicate numerical simulations with very high accuracy and can be run on a laptop within a few minutes, thereby simplifying the use of complex numerical simulations which can otherwise take tens of hours. This book focuses on smart proxy modeling and provides readers with all the essential details on how to develop smart proxy models using artificial intelligence and machine learning, as well as how it may be used in real-world cases. Covers replication of highly accurate numerical simulations using artificial intelligence and machine learning. Details application in reservoir simulation and modeling, and computational fluid dynamics. Includes real case studies based on commercially available simulators. Smart Proxy Modeling is ideal for petroleum, chemical, environmental, and mechanical engineers, as well as statisticians and others working with applications of data-driven analytics\"-- Provided by publisher.
Book
Improving Intelligence Analysis With Decision Science
Intelligence analysis plays a vital role in policy decision making. Key functions of intelligence analysis include accurately forecasting significant events, appropriately characterizing the uncertainties inherent in such forecasts, and effectively communicating those probabilistic forecasts to stakeholders. We review decision research on probabilistic forecasting and uncertainty communication, drawing attention to findings that could be used to reform intelligence processes and contribute to more effective intelligence oversight. We recommend that the intelligence community (IC) regularly and quantitatively monitor its forecasting accuracy to better understand how well it is achieving its functions. We also recommend that the IC use decision science to improve these functions (namely, forecasting and communication of intelligence estimates made under conditions of uncertainty). In the case of forecasting, decision research offers suggestions for improvement that involve interventions on data (e.g., transforming forecasts to debias them) and behavior (e.g., via selection, training, and effective team structuring). In the case of uncertainty communication, the literature suggests that current intelligence procedures, which emphasize the use of verbal probabilities, are ineffective. The IC should, therefore, leverage research that points to ways in which verbal probability use may be improved as well as exploring the use of numerical probabilities wherever feasible.
Journal Article