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IT PROFESSIONAL 2017年第4期19卷 16-20页

作者： Demirkan, Haluk Earley, Seth Harmon, Robert R. Univ Washington Milgard Sch Business Serv Innovat & Business Analyt Tacoma WA 98402 USA Univ Washington Milgard Sch Business Ctr Business Analyt Tacoma WA 98402 USA Earley Informat Sci Carlisle MA USA Portland State Univ Mkt & Serv Innovat Portland OR 97207 USA Portland State Univ Portland OR 97207 USA

cognitive computing refers to smart systems that learn at scale, reason with purpose, and interact with humans and other smart systems naturally. This special issue seeks to provide readers with an overview of the current topics and practices related to cognitive computing, and looks at the future for IT professionals as these technologies become indispensable and essential enablers of product and service development and the creation of new markets.

关键词： Special Issues And Sections cognitive Systems Learning Systems Artificial Intelligence cognitive computing Artificial Intelligence Big Data Machine Learning Neural Networks

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cognitive computing and Business Intelligence Applications in Accounting, Finance and Management

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BIG DATA AND cognitive computing 2025年第3期9卷 54-54页

作者： Ao, Sio-Iong Hurwitz, Marc Palade, Vasile Int Assoc Engineers Unit 11-FHung Rd Hong Kong Peoples R China Univ Waterloo Conrad Sch Entrepreneurship & Business Engn Bldg 72316 Waterloo ON N2L 3G1 Canada Coventry Univ Ctr Computat Sci & Math Modelling Innovat Village 10 Coventry CV1 2TL England

cognitive computing encompasses computing tools and methods that simulate and mimic the process of human thinking, without human supervision. Deep neural network architectures, natural language processing, big data tools, and self-learning tools based on pattern recognition have been widely deployed to solve highly complex problems. Business intelligence enhances collaboration among different organizational departments with data-driven conversations and provides an organization with meaningful data interpretation for making strategic decisions on time. Since the introduction of ChatGPT in November 2022, the tremendous impacts of using Large Language Models have been rippling through cognitive computing, business intelligence, and their applications in accounting, finance, and management. Unlike other recent reviews in related areas, this review focuses precisely on the cognitive computing perspective, with frontier applications in accounting, finance, and management. Some current limitations and future directions of cognitive computing are also discussed.

关键词： cognitive computing business intelligence accounting finance management explainable AI continual learning ChatGPT

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Next-Generation Business Process Management (BPM): A Systematic Literature Review of cognitive computing and Improvements in BPM 15th

Next-Generation Business Process Management (BPM): A Systema...

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15th International Conference on Subject Oriented Business Process Management

作者： Hildebrand, Dennis Roesl, Stefan Auer, Thomas Schieder, Christian Tech Univ Appl Sci Amberg Weiden Hetzenrichter Weg 15 D-92637 Weiden Germany

ISBN: (纸本)9783031720406;9783031720413

cognitive computing (CC) applied to business process management (BPM) utilizes intelligent capabilities to structure and organize the management of business processes. BPM in organizations remains a crucial pillar for increasing operational efficiency and accelerating routine-based processes. By adopting CC, organizations can leverage intelligent methods and proactive decision-making, ushering in a new era of cognitive BPM. This raises the question of how the challenges and benefits of cognitive BPM compare to traditional BPM. Despite growing interest in the practical deployment of cognitive technologies, there has been limited research conducted in this area. To address this research gap, a systematic literature review (SLR) was conducted to examine the current state of research on the use of cognitive technologies for improving BPM. Based on an initial classification matrix, we divide the topic of cognitive BPM into five dimensions and 16 characteristics. This solid theoretical foundation enables us to provide practitioners and researchers with a comprehensive overview and present the current state of the literature.

关键词： cognitive computing Process improvement cognitive approach Business process management cognitive BPM

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Integrating chemical artificial intelligence and cognitive computing for predictive analysis of biological pathways: a case for intrinsically disordered proteins

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BIOPHYSICAL REVIEWS 2025年 1-22页

作者： Coskuner-Weber, Orkid Gentili, Pier Luigi Uversky, Vladimir N. Turkish German Univ Mol Biotechnol Sahinkaya Caddesi 106 TR-34820 Istanbul Turkiye Univ Perugia Dept Chem Biol & Biotechnol I-06123 Perugia Italy Univ S Florida Morsani Coll Med Dept Mol Med 12901 Bruce B Downs BlvdMDC07 Tampa FL 33612 USA Univ S Florida USF Hlth Byrd Alzheimers Inst Morsani Coll Med 12901 Bruce B Downs BlvdMDC07 Tampa FL 33612 USA

Incorporating biological molecular interactions into cognitive computing through chemical artificial intelligence (AI) presents a transformative approach with far-reaching implications for various fields, such as protein engineering, drug discovery, bioinformatics, synthetic biology, and unconventional computing. cognitive computing, designed to emulate human thought processes and enhance decision-making, utilizes technologies, such as machine learning, natural language processing, and speech recognition for better human-system interactions. Despite advancements, the integration of biological processes with cognitive computing remains fraught with challenges, particularly due to the complexity and scale of biological data. Here, we explore the possible benefits of connecting cognitive computing with biological knowledge, including more precise models of protein interactions, gene regulation, and metabolic pathways, which could lead to personalized treatments and early disease detection. Furthermore, we discuss the intersection of cognitive computing and biophysical research techniques, examining how analogies from neuroscience-like synaptic communication and neural plasticity-can inform the development of neuromorphic chips and enhance predictive models. Additionally, the study delves into intrinsically disordered proteins (IDPs) and their crucial roles in brain function and information processing. These insights are pivotal for advancing neuroinformatics and creating more adaptive, context-aware cognitive computing algorithms. By leveraging biophysical investigations and the unique properties of IDPs, the research aims to bridge the gap between the biological processes and their computational analogs, proposing novel methods, such as chemical AI implemented in liquid solutions as promising avenues for future advancements.

关键词： Artificial intelligence cognitive computing Intrinsically disordered proteins

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cognitive computing Models in Artificial Intelligence Education: From Theory to Practice

Artificial Intelligence Education Studies

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Artificial Intelligence Education Studies 2025年第1期1卷 1-14页

作者： Changkui LI Creative Publishing Co. Limited

cognitive computing has emerged as a transformative force in artificial intelligence(AI)education,bridging theoretical advancements with practical *** article explores the role of cognitive models in en-hancing learning systems,from intelligent tutoring and personalized recommendations to virtual laboratories and special education *** examines key technologies—such as knowledge graphs,natural language processing,and multimodal data analysis—that enable adaptive,human-like *** study also ad-dresses technical challenges like interpretability and data privacy,alongside ethical concerns including equity and *** forward,it discusses how cognitive computing could reshape future learning modalities and aligns with trends like artificial general intelligence and interdisciplinary learning *** tracing the path from theory to practice,this work underscores the potential of cognitive computing to create an inclusive,dy-namic educational landscape,while highlighting the need for ethical and technical rigor to ensure its responsible evolution.

关键词： cognitive computing Artificial Intelligence Education Adaptive Learning Ethical AI Future Learning

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Harnessing the Power of cognitive computing: Assessing Point-of-Care Decision Support Tools in Oncology Practice 1st

Harnessing the Power of Cognitive Computing: Assessing Point...

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1st International Conference on Artificial Intelligence and its Applications, ICAIA 2023

作者： Kushwaha, Ravindra Kumar Kumar, Vikash Mishra, Ganesh Yadav, Mukesh Kumar Singh, Reena Tripathi, Arpan Kumar Sundramurthy, Venkatesa Prabhu Department of Teacher Training Halim Muslim PG College Kanpur208001 India Department of Teacher Training Nirmal Special Teacher Training College Rajasthan Jhalawar326001 India Department of Teacher Training Shri P K Mehta College of Special Education Banaskantha385001 India Department of Teacher Training Nirmal Special Teacher Training College Jhalawar326001 India Shree Sai Baba Special Education Centre Kanpur208019 India Faculty of Pharmaceutical Sciences Shri Shankaracharya Technical Campus Bhilai Chhattisgarh India Center of Excellence for Bioprocess and Biotechnology Department of Chemical Engineering College of Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia

ISBN: (纸本)9783031843969

As cognitive computing healthcare decisional support tools can read patient characteristics from microelectronic health records and then present treatment selections based on these characteristics, medical literature, and real-clinical evidence (RCE), they may be useful for both seasoned and inexperienced oncologists. The extent to which augmented intelligence systems conform to established medical practices and how they could influence surgical decision-making is still poorly understood. Experts in the field of Breast Oncology agreed with the "recommended" choice made by cognitive computing in 79.6% of cases, and with the "for consideration" choice made in 10.2% of cases, for a total agreement rate of 87.9%. The 8% of the situations yielded 59% of the replies that did not agree. According to the Cota observational database, "recommended" was given to 70.2% of matched controls, "for consideration" to 13.5%, and "not recommended" to 20.4%. Rookie oncologists choose 75.5% of recommended/for consideration medicines without Watson for Oncology (WFO)/Cota RCE and 95.3% with it. As a result of not using Watson for Oncology/Cota RCE, novices were 39% more likely to select an unrecommended option than experts. Treatment decisions made by Breast Oncology novices were shown to be significantly enhanced when Watson for Oncology was used in conjunction with Cota RCE. Offer a concise description of "Watson for Oncology" as an AI-driven system that provides treatment recommendations based on up-to-date scientific and clinical information. The fact that almost 20% of people with similar sickness characteristics in a real-world database were given non-recommended options demonstrates the necessity for decision help. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Breast Oncology cognitive computing experts real clinical evidence Watson for oncology

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cognitive computing, Analytics, and Personalization

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IT PROFESSIONAL 2015年第4期17卷 12-18页

作者： Earley, Seth Earley Informat Sci Carlisle MA 01741 USA

This article discusses the increasing sophistication of search technology and its role in cognitive computing. It provides a concrete example of a personal assistant that is context-aware and can retrieve the information most likely to solve users' problems. It also addresses how search engines predict what information users are likely to need, and how the software retrieves and presents the information. Finally, the article discusses personalization and how machine learning can contribute to this process.

关键词： information retrieval learning (artificial intelligence) search engines cognitive computing context-aware personal attendant information retrieval machine learning search engines search technology Cognition Context modeling Information retrieval Machine learning algorithms Ontologies Search methods Semantics artificial intelligence cognitive computing data analysis information retrieval search

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cognitive computing and 3D facial tracking method to explore the ethical implication associated with the detection of fraudulent system in online examination

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第5期45卷 8449-8463页

作者： Sultanuddin, S. J. Sudhee, Devulapalli Satve, Priyanka Prakash Sumithra, M. Sathyanarayana, K. B. Kumari, R. Krishna Narasimharao, Jonnadula Reddy, R. Vijaya Kumar Rajkumar, R. Dhanalakshmi Coll Engn Dept Cyber Secur Tambaram Tamilnadu India Vallurupalli Nageswara Rao Vignana Jyothi Inst En Dept Comp Sci & Engn Hyderabad Telangana India Dr Babasaheb Ambedkar Technol Univ Dept Comp Sci & Engn Raigad Maharashtra India Panimalar Engn Coll Dept Informat Technol Chennai Tamil Nadu India Jawaharlal Nehru New Coll Engn Dept Informat Sci & Engn Shivamogga India SRM Inst Sci & Technol Coll Engn & Technol Dept Career Dev Ctr Kattankulathur Tamil Nadu India CMR Techn Campus Dept Comp Sci & Engn Hyderabad Telangana India Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram Andhra Pradesh India Kongu Engn Coll Dept Elect & Instrumentat Engn Erode Tamilnadu India

Following the Covid-19 pandemic, the rapid spread of online education and tests demanded the implementation of cheating detection tools to ensure academic integrity. While advances in technology such as face recognition, face expression recognition, head posture analysis, eye gaze tracking, network data traffic analysis, and IP spoofing detection have shown promising results in detecting fraudulent behavior, their integration raises ethical concerns that must be carefully considered. This work presents a cognitive computing strategy for investigating the ethical implications of using cheating detection systems in online tests. This study attempts to examine the potential impact on students' privacy, fairness, and trust in the examination process by employing cognitive computing, which models human cognitive capacities. A thorough literature review is used in the process to uncover existing ethical norms and regulatory frameworks linked to online assessments and cheating detection. Soft computing approaches are also used to evaluate the effectiveness and dependability of the aforementioned cheating detection strategies. The study looks into how far facial recognition and expression analysis can go in terms of privacy, as well as the possibility of bias in head posture analysis and eye gaze tracking algorithms. Furthermore, it investigates the ethical implications of monitoring network data traffic and detecting IP spoofing, with a focus on data security and user permission. The cognitive computing model, based on the analysis, presents a comprehensive framework for ethical decision-making when installing cheating detection technologies. The findings of this study contribute to the continuing discussion about the ethical concerns of using modern technologies to identify cheating in online exams. It provides educational institutions and policymakers with practical ideas for striking a balance between academic integrity and protecting students' rights and dignity. By em

关键词： cognitive computing 3D facial tracking method ethical implication detection of fraudulent system online examination

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cognitive computing and Rule Extraction in Generalized One-sided Formal Contexts

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cognitive COMPUTATION 2022年第6期14卷 2087-2107页

作者： Hu, Zhiyong Shao, Mingwen Liu, Huan Mi, Jvsheng China Univ Petr East China Coll Comp Sci & Technol Qingdao Peoples R China China Univ Petr East China Coll Control Sci & Engn Qingdao Peoples R China Hebei Normal Univ Sch Math Sci Shijiazhuang Hebei Peoples R China

As an effective tool for analyzing human behavior and social cognition, rule extraction is a key issue in cognitive computing. However, the existing association rules state whether an attribute is possessed only without considering the order relation of attribute values. In this paper, we propose a novel method for quantitative association rule mining based on a generalized one-sided context and solid cognitive foundations. The extracted rule indicates the order relation of the attribute values and the structure of truth values for different attributes. We also propose specific algorithms to extract generalized one-sided quantitative association rules and non-redundant generalized one-sided quantitative association rules. The scale of data also needs to be considered by cognitive computing. An object may possess different values for the same attribute according to different measuring scales. The relationship between generalized one-sided quantitative association rules at different scales is also discussed. Rather than converting the multi-valued formal context into a binary formal context, the generalized one-sided quantitative association rule is extracted directly in a multi-valued formal context. The experimental results show the presented algorithms reduce both time and space complexity compared with the classical quantitative association rule-mining algorithm.

关键词： Concept lattice Generalized one-sided formal context Multi-scale cognitive computing Rule extraction

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cognitive computing: Architecture,Technologies and Intelligent Applications

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IEEE ACCESS 2018年 6卷 19774-19783页

作者： Chen, Min Herrera, Francisco Hwang, Kai Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China Wuhan Natl Lab Optoelect Wuhan 430074 Peoples R China Univ Granada Dept Comp Sci & Artificial Intelligence E-1807 Granada Spain Univ Southern Calif Los Angeles CA 90089 USA

With the development of network-enabled sensors and artificial intelligence algorithms, various human-centered smart systems are proposed to provide services with higher quality, such as smart healthcare, affective interaction, and autonomous driving. Considering cognitive computing is an indispensable technology to develop these smart systems, this paper proposes human-centered computing assisted by cognitive computing and cloud computing. First, we provide a comprehensive investigation of cognitive computing, including its evolution from knowledge discovery, cognitive science, and big data. Then, the system architecture of cognitive computing is proposed, which consists of three critical technologies, i.e., networking (e.g., Internet of Things), analytics (e.g., reinforcement learning and deep learning), and cloud computing. Finally, it describes the representative applications of human-centered cognitive computing, including robot technology, emotional communication system, and medical cognitive system.

关键词： cognitive computing big data analysis Internet of Things cloud computing

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