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cognitive computing-Based Mammographic Image Classification on an Internet of Medical

Computers, Materials & Continua 2022年第8期72卷 3945-3959页

作者： Romany F.Mansour Maha M.Althobaiti Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Recently,the Internet of Medical Things(IoMT)has become a research hotspot due to its various applicability in medical ***,the data analysis and management in IoMT remain challenging owing to the existence of a massive number of devices linked to the server environment,generating a massive quantity of healthcare *** such cases,cognitive computing can be employed that uses many intelligent technologies-machine learning(ML),deep learning(DL),artificial intelligence(AI),natural language processing(NLP)and others-to comprehend data ***,breast cancer(BC)has been found to be a major cause of mortality among ladies *** detection and classification of BC using digital mammograms can decrease the mortality *** paper presents a novel deep learning-enabled multi-objective mayfly optimization algorithm(DLMOMFO)for BC diagnosis and classification in the IoMT *** goal of this paper is to integrate deep learning(DL)and cognitive computing-based techniques for e-healthcare applications as a part of IoMT technology to detect and classify *** proposed DL-MOMFO algorithm involved Adaptive Weighted Mean Filter(AWMF)-based noise removal and contrast-limited adaptive histogram equalisation(CLAHE)-based contrast improvement techniques to improve the quality of the digital *** addition,a U-Net architecture-based segmentation method was utilised to detect diseased regions in the ***,a SqueezeNet-based feature extraction and a fuzzy support vector machine(FSVM)classifier were used in the presented *** enhance the diagnostic performance of the presented method,the MOMFO algorithm was used to effectively tune the parameters of the SqueezeNet and FSVM *** DL-MOMFO technique was tested on the MIAS database,and the experimental outcomes revealed that the DL-MOMFO technique outperformed existing techniques.

关键词： cognitive computing breast cancer digital mammograms image processing internet of medical things smart healthcare

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An integrated cognitive computing approach for systematic conceptual design

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2016年第4期17卷 286-294页

作者： Hao ZHENG Yi-xiong FENG Jian-rong TAN Zhi-feng ZHANG Zi-xian ZHANG State Key Laboratory of Fluid Power and Mechatronic Systems Zhefiang University Hangzhou 310027 China Hitachi Research Laboratory Tokyo 152 Japan

Conceptual design plays an important role in product life cycle, which requires engineers to use sound design theory, cross-disciplinary knowledge, and complex technical support to acquire design concepts. However, the lack of sufficient computational tools makes it difficult for designers to fully explore in the wide design solution spaces. Therefore, this paper proposes an integrated cognitive computing approach to formalize the cognitive activities of conceptual design. A cognitive computing model composed of concept associative memory, concept generation, and decision-making process is established based on the integration of cognitive psychology and engineering design. First of all, the Hopfield neural network is used to acquire similar concept solutions for specific subfunctions from a knowledge base. Then, morphological matrix and genetic algorithm are introduced to produce a set of feasible candidate solutions in the concept generation process. Furthermore, a technique for order preference by similarity to an ideal solution is applied to evaluate the generated concept solutions and obtain the optimal solution automatically. Finally, a case study is given to demonstrate the effectiveness and efficiency of the proposed approach.

关键词： Conceptual design cognitive computing Genetic algorithm (GA) Technique for order preference by similarity to ideal solution (TOPSIS)

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Transforming Data into Actionable Insights with cognitive computing and AI

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Journal of Software Engineering and Applications 2023年第6期16卷 211-222页

作者： Saleimah Al Mesmari Department of Computer Science Higher Colleges of Technology Fujairah United Arab Emirates

How organizations analyze and use data for decision-making has been changed by cognitive computing and artificial intelligence (AI). cognitive computing solutions can translate enormous amounts of data into valuable insights by utilizing the power of cutting-edge algorithms and machine learning, empowering enterprises to make deft decisions quickly and efficiently. This article explores the idea of cognitive computing and AI in decision-making, emphasizing its function in converting unvalued data into valuable knowledge. It details the advantages of utilizing these technologies, such as greater productivity, accuracy, and efficiency. Businesses may use cognitive computing and AI to their advantage to obtain a competitive edge in today’s data-driven world by knowing their capabilities and possibilities [1].

关键词： Business Growth Technology Natural Language Processing Neural Networks Data Analysis Pattern Recognition Automation cognitive computing Artificial Intelligence Actionable Insights Machine Learning Natural Language Virtual Assistants Chatbots Voice-Activated Devices

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Robotic Process Automation with New Future Trends

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Journal of Computer and Communications 2024年第6期12卷 12-24页

作者： Abu Tayab Yanwen Li School of Mechanical Engineering Yanshan University Qinhuangdao China

The invention concept of Robotic Process Automation (RPA) has emerged as a transformative technology that has revolved the local business processes by programming repetitive task and efficiency adjusting the operations. This research had focused on developing the RPA environment and its future features in order to elaborate on the projected policies based on its comprehensive experiences. The current and previous situations of industry are looking for IT solutions to fully scale their company Improve business flexibility, improve customer satisfaction, improve productivity, accuracy and reduce costs, quick scalability in RPA has currently appeared as an advance technology with exceptional performance. It emphasizes future trends and foresees the evolution of RPA by integrating artificial intelligence, learning of machine and cognitive automation into RPA frameworks. Moreover, it has analyzed the technical constraints, including the scalability, security issues and interoperability, while investigating regulatory and ethical considerations that are so important to the ethical utilization of RPA. By providing a comprehensive analysis of RPA with new future trends in this study, researcher’s ambitions to provide valuable insights the benefits of it on industrial performances from the gap observed so as to guide the strategic decision and future implementation of the RPA.

关键词： Robotic Process Automation Artificial Intelligence Machine Learning cognitive computing Interoperability Data Security

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A cognitive Memory-Augmented Network for Visual Anomaly Detection

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IEEE/CAA Journal of Automatica Sinica 2021年第7期8卷 1296-1307页

作者： Tian Wang Xing Xu Fumin Shen Yang Yang the Center for Future Multimedia and School of Computer Science and Engineering University of Electronic Science and Technology of China(UESTC)Chengdu 611731China the Center for Future Multimedia and School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731 the Institute of Electronic and Information Engineering of UESTC in Guangdong Dongguan 523808China

With the rapid development of automated visual analysis,visual analysis systems have become a popular research topic in the field of computer vision and automated *** analysis systems can assist humans to detect anomalous events(e.g.,fighting,walking alone on the grass,etc).In general,the existing methods for visual anomaly detection are usually based on an autoencoder architecture,i.e.,reconstructing the current frame or predicting the future ***,the reconstruction error is adopted as the evaluation metric to identify whether an input is abnormal or *** flaws of the existing methods are that abnormal samples can also be reconstructed *** this paper,inspired by the human memory ability,we propose a novel deep neural network(DNN)based model termed cognitive memory-augmented network(CMAN)for the visual anomaly detection *** proposed CMAN model assumes that the visual analysis system imitates humans to remember normal samples and then distinguishes abnormal events from the collected ***,in the proposed CMAN model,we introduce a memory module that is able to simulate the memory capacity of humans and a density estimation network that can learn the data *** reconstruction errors and the novelty scores are used to distinguish abnormal events from *** addition,we develop a two-step scheme to train the proposed model so that the proposed memory module and the density estimation network can cooperate to improve *** experiments evaluated on various popular benchmarks show the superiority and effectiveness of the proposed CMAN model for visual anomaly detection comparing with the state-of-the-arts *** implementation code of our CMAN method can be accessed at https://***/CMANcode/CMAN_pytorch.

关键词： cognitive computing density estimation memory visual analysis systems visual anomaly detection.

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Exercise Recommendation with Preferences and Expectations Based on Ability Computation

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Computers, Materials & Continua 2023年第10期77卷 263-284页

作者： Mengjuan Li Lei Niu Faculty of Artificial Intelligence in Education Central China Normal UniversityWuhan430079China

In the era of artificial intelligence,cognitive computing,based on cognitive science;and supported by machine learning and big data,brings personalization into every corner of our social *** systems are essential applications of cognitive computing in educational *** help learners personalize their learning better by computing student and exercise characteristics using data generated from relevant learning *** paper introduces a Learning and Forgetting Convolutional Knowledge Tracking Exercise Recommendation model(LFCKT-ER).First,the model computes students’ability to understand each knowledge concept,and the learning progress of each knowledge concept,and the model consider their forgetting behavior during learning ***,students’learning stage preferences are combined with filtering the exercises that meet their learning progress and *** students’ability is used to evaluate whether their expectations of the difficulty of the exercises are ***,the model filters the exercises that best match students’expectations again by students’***,we use a simulated annealing optimization algorithm to assemble a set of exercises with the highest *** the experimental results,the LFCKT-ER model can better meet students’personalized learning needs and is more accurate than other exercise recommendation systems under various metrics on real online education public datasets.

关键词： cognitive computing personalized learning forgetting behavior exercise recommendation

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Hybrid-augmented intelligence： collaboration and cognition

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Frontiers of Information Technology & Electronic Engineering 2017年第2期18卷 153-179页

作者： Nan-ning ZHENG Zi-yi LIU Peng-ju REN Yong-qiang MA Shi-tao CHEN Si-yu YU Jian-ru XUE Ba-dong CHEN Fei-yue WANG Institute of Artificial Intelligence and Robotics Xi ' an Jiaotong University Xi ' an 710049 China National Engineering Laboratory of Visual Information Processing Applications Xi 'an Jiaotong University Xi'an 710049 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

The long-term goal of artificial intelligence （AI） is to make machines learn and think like human beings. Due to the high levels of uncertainty and vulnerability in human life and the open-ended nature of problems that humans are facing, no matter how intelligent machines are, they are unable to completely replace humans. Therefore, it is necessary to introduce human cognitive capabilities or human-like cognitive models into AI systems to develop a new form of AI, that is, hybrid-augmented intelligence. This form of AI or machine intelligence is a feasible and important developing model. Hybrid-augmented intelligence can be divided into two basic models： one is human-in-the-loop augmented intelligence with human-computer collaboration, and the other is cognitive computing based augmented intelligence, in which a cognitive model is embedded in the machine learning system. This survey describes a basic framework for human-computer collaborative hybrid-augmented intelligence, and the basic elements of hybrid-augmented intelligence based on cognitive computing. These elements include intuitive reasoning, causal models, evolution of memory and knowledge, especially the role and basic principles of intuitive reasoning for complex problem solving, and the cognitive learning framework for visual scene understanding based on memory and reasoning. Several typical applications of hybrid-augmented intelligence in related fields are given.

关键词： Human-machine collaboration Hybrid-augmented intelligence cognitive computing Intuitivereasoning Causal model cognitive mapping Visual scene understanding Self-driving cars

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Improvements in Weather Forecasting Technique Using cognitive Internet of Things

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Computer Systems Science & Engineering 2023年第9期46卷 3767-3782页

作者： Kaushlendra Yadav Anuj Singh Arvind Kumar Tiwari KNIT Sultanpur Sultanpur228118India

Forecasting the weather is a challenging task for human beings because of the unpredictable nature of the ***,effective forecasting is vital for the general growth of a country due to the significance of weather forecasting in science and *** primary motivation behind this work is to achieve a higher level of forecasting accuracy to avoid any ***,most weather forecasting work is based on initially observed numerical weather data that cannot fully cover the changing essence of the *** this work,sensors are used to collect real-time data for a particular location to capture the varying nature of the *** solution can give the anticipated results with the least amount of human engagement by combining human intelligence and machine learning with the help of the cognitive Internet of *** Authors identified weatherrelated parameters such as temperature,humidity,wind speed,and rainfall and then applied cognitive data collection methods to train and validate their *** addition,the Authors have examined the efficacy of various machine learning algorithms by using them on both data sets i.e.,pre-recorded metrological data sets and live sensor data sets collected from multiple *** Authors noticed that the results were superior on the sensor *** Authors developed ensemble learning model using stacked method that achieved 99.25%accuracy,99%recall,99%precision,and 99%F1-score for Sensor *** also achieved 85%accuracy,86%recall,85%precision,and 86%F1 score for Australian rainfall data.

关键词： Internet of Things machine learning weather forecast cognitive computing predictors

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认知情报学：大数据背景下情报分析的新机遇

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图书馆学情报学 2019年第3期

作者：庞娜

[目的/意义]面对大数据的兴起为情报学领域带来新机遇的同时带来的极大挑战，传统的情报分析已经不足以对高速生产的、多种类型的、海量的数据进行有效处理与分析。融合多个学科的认知情报学，在很多层面上可以用来促进情报分析的变革... 详细信息

[目的/意义]面对大数据的兴起为情报学领域带来新机遇的同时带来的极大挑战，传统的情报分析已经不足以对高速生产的、多种类型的、海量的数据进行有效处理与分析。融合多个学科的认知情报学，在很多层面上可以用来促进情报分析的变革，为用户提供更为优质与具有创新性的知识服务。[方法/过程]从认知计算与情报分析的概念出发，探索了国外目前认知情报学的研究情况与现有的理论框架，从理论、技术层面分析了认知情报学对情报学的支撑，并分析了认知情报学对情报分析带来的新机遇以及新的应用场景展望。[结果/结论]认知情报学在情报分析中，有着其独特的优势，将会大大加快信息转化为知识与行动的过程，为情报分析面临的当前信息环境迅猛变革提供新的解决方案，促进知识发现，为整个行业的服务业态探索提供新思路。

关键词：认知科学认知情报学认知计算情报分析知识服务 cognitive science cognitive informatics cognitive computing Information analysis Knowledge service

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