检索结果-内蒙古大学图书馆

Computer Science and Emerging Technologies (CSET), International Conference on

作者： Fayiz Momani Amer Ahmad Hatamleh Wan Mohd Amir Fazamin Wan Hamzah Julaily Aida Jusoh Muhammad D. Zakaria Sunanda Das E-business & Commerce Department Faculty of Management and Financial Sciences University of Petra (UOP) Jordan Head of Departments Faculty of Business and Financial Science MIS BI and Data Analysis Irbid National University Irbid Jordan Faculty of Informatics and Computing (UniSZA) Malaysia Faculty of Informatics and Computing UniSZA Malaysia Cyber Security School of Computer Science and Engineering Jain University Bangalore India

Education is developing with the economy. This led to the creation of e-Iearning tools for instructors and students. In e-learning, electronic resources are used to teach. E-learning can be taught in or out of school, but computers and the Internet are essential. That is, e-learning involves delivering education to a big group of people at once or over time. So, overall, in this research is about E-learning that make a successful environment. We chose this term since it is widely used in E-learning research to improve student academic achievement. Due to the difficulties of accessing adequate study materials during the Covid-19 epidemic, distance learning has grown more popular among students. This study proposes a unique learning technique that involves choosing and organizing important learning items using a recommender system. We also studied our method's efficacy. Using a recommended system to help online learning activities was proven to be effective. Software development methodology is a collection of principles and processes used to build software. Systematic software development is the goal. The research approach utilized for this topic is Lean Development Methodology. We also examine the advantages and disadvantages of e-learning. This research also introduces various new strategies and methodologies that may be employed in the future to improve system-learning. It also presents system that we choose and why we choose it. So, it aims to eliminate waste and increase production. Following the principles can help developers avoid nonproductive chores while still producing high-quality results. The ultimate objective is to develop an efficient and error-free system. Finally, E-learning is a social revolution. It's part of a bigger rethinking of how we teach future employees and students. The eLearning platform enabled the learning data visualization dashboard beautifully. User-centered design lets the dashboard adapt to its users' demands.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Cloud Computing in Smart Cities: Privacy, Ethical and Social Issues

Cloud Computing in Smart Cities: Privacy, Ethical and Social...

引用

Computer Science and Emerging Technologies (CSET), International Conference on

作者： Abdel Rahman Mohammad Alkhazali Ahmad M. Khasawneh Sharaf Alzoubi Murad Magableh Rajina R. Mohamed Bishwajeet Pandey MIS BI and Data Analysis Department Faculty of Business and Financial Science Irbid National University Irbid Jordan Department of Information Technology College of Computer Science and Informatics Amman Arab University (AAU) Jordan Information Technology department College of Computer Science and Informatics Amman Arab University (AAU) Jordan College of Computing dan Informatics Universiti Tenaga Nasional Malaysia Eurasian National University & Astana IT University Astana Kazaksthan & Jain Deemed To Be University Bangalore India

The rapid development of cloud computing technologies has revolutionized various sectors, and smart cities are no exception. Smart cities leverage cloud computing to optimize urban services, enhance resource management, and improve the overall quality of life for their inhabitants. However, as these technological advancements proliferate, concerns about privacy, ethical considerations, and social implications have emerged. This research paper critically examines the multifaceted challenges associated with the integration of cloud computing in smart cities, shedding light on the potential risks and highlighting the need for comprehensive solutions. The research employs a mixed-methods approach, combining quantitative data analysis and qualitative case studies to offer a comprehensive perspective on the identified issues. The primary focus lies in identifying privacy risks, ethical dilemmas, and social disparities that arise due to the extensive use of cloud-based systems and data in smart cities. Furthermore, the study investigates the role of key stakeholders, including governments, technology providers, and citizens, in mitigating or exacerbating these challenges. Key findings reveal that while cloud computing empowers smart cities with unparalleled capabilities, it also exposes residents' personal information to potential breaches and misuse. Ethical concerns arise from the handling of sensitive data, data ownership, and algorithmic biases that could perpetuate discrimination. Moreover, social issues like the digital divide and access disparities may further exacerbate existing inequalities in smart city implementation. This research paper concludes by proposing a comprehensive framework of guidelines and best practices to address the identified issues effectively. These recommendations encompass enhanced data privacy measures, transparent and accountable data governance, the promotion of ethical data usage, and inclusive strategies to bridge social disparities. B

关键词：

来源：评论

学校读者我要写书评

暂无评论

Using Arduino Iot Modules As A Low Cost Environmental Research Monitoring System

Using Arduino Iot Modules As A Low Cost Environmental Resear...

引用

Computer Science and Emerging Technologies (CSET), International Conference on

作者： Murad Magableh Ziyad Marie Rajina R. Mohamed Mohd Hafizuddin Bin Ibrahim Julaily Aida Jusoh Rohit Kumar MIS BI and Data Analysis Department Faculty of Business and Financial Science Irbid National University Irbid Jordan Public administration Department Faculty of Administrative sciences and Informatics Al-Istiqlal University Palestine College of Computing dan Informatics Universiti Tenaga Nasional Malaysia Department of Electrical Engineering Politeknik Kuala Terenngganu Kuala Terengganu Malaysia Faculty of Informatics and Computing UniSZA Malaysia School of Computer Science and Engineering Jain Deemed To Be University Bangalore India

Environmental monitoring plays a crucial role in understanding the impact of human activities on our ecosystems. However, the cost and complexity of traditional monitoring systems often hinder widespread deployment, especially in resource-constrained regions. This research paper explores the feasibility and effectiveness of employing Arduino Internet of Things (IoT) modules as a low-cost alternative for environmental research monitoring. The study focuses on designing and implementing an Arduino-based environmental monitoring system capable of gathering real-time data on key environmental parameters, such as temperature, humidity, air quality, and soil moisture. The hardware setup comprises Arduino microcontrollers interfaced with a range of environmental sensors and connectivity modules for data transmission. To assess the system's reliability and accuracy, a series of field experiments were conducted in diverse environmental settings, including urban areas, agricultural zones, and natural habitats. The collected data was compared against measurements from conventional monitoring equipment to validate the Arduino-based system's performance. The research demonstrates that the Arduino IoT modules can effectively monitor and transmit environmental data with a reasonable level of accuracy and stability, making them suitable for various environmental research applications. The low-cost nature of Arduino components and the open-source ecosystem facilitate customization and scalability, enabling researchers and organizations to develop tailored monitoring solutions for specific environmental contexts. Additionally, the paper discusses the challenges and limitations encountered during the deployment of Arduino-based monitoring systems, including power constraints, sensor calibration, and data processing techniques. Furthermore, it explores potential avenues for improvement and optimization to enhance the system's capabilities and data accuracy. In conclusion, this research

关键词：

来源：评论

学校读者我要写书评

暂无评论

Early Diabetes Prediction: A Comparative Study Using Machine Learning Techniques

Early Diabetes Prediction: A Comparative Study Using Machine...

引用

作者： Poly, Tahmina Nasrin Islam, Md Mohaimenul Li, Yu-Chuan Jack Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei110 Taiwan AESOP Technology Songshan District Taipei105 Taiwan Taipei Medical University Taipei110 Taiwan Research Center of Big Data and Meta-Analysis Wan Fang Hospital Taipei Medical University Taipei116 Taiwan Department of Dermatology Wan Fang Hospital Taipei116 Taiwan

ISBN: (纸本)9781643682907

Most screening tests for Diabetes Mellitus (DM) in use today were developed using electronically collected data from Electronic Health Record (EHR). However, developing and under-developing countries are still struggling to build EHR in their hospitals. Due to the lack of HER data, early screening tools are not available for those countries. This study develops a prediction model for early DM by direct questionnaires for a tertiary hospital in Bangladesh. Information gain technique was used to reduce irreverent features. Using selected variables, we developed logistic regression, support vector machine, K-nearest neighbor, Naïve Bayes, random forest (RF), and neural network models to predict diabetes at an early stage. RF outperformed other machine learning algorithms achieved 100% accuracy. These findings suggest that a combination of simple questionnaires and a machine learning algorithm can be a powerful tool to identify undiagnosed DM patients. © 2022 The authors and IOS Press.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Interpreting Industrial IoT data Streams Through Fuzzy Querying With Hysteretic Fuzzy Sets on Apache Kafka

引用

IEEE Transactions on Fuzzy Systems 2024年第8期32卷 4671-4684页

作者： Bożena Małysiak-Mrozek Bartłomiej Ryba Marek Molęda Che-Lun Hung Witold Pedrycz Weiping Ding Dariusz Mrozek Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland Department of Applied Informatics Silesian University of Technology Gliwice Poland Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei City Taiwan Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Department of Measurements and Control Systems Silesian University of Technology Gliwice Poland Research Center of Performance and Productivity Analysis Istinye University Istanbul Türkiye School of Artificial Intelligence and Computer Science Nantong University Nantong China Faculty of Data Science City University of Macau Macau China

In industrial settings, querying data streams from Internet of Things (IoT) devices benefits from utilizing elastic criteria to enhance the interpretability of the current state of the monitored environment. Fuzzy sets provide this elasticity, enabling the aggregation and representation of similar values in a human-comprehensible manner. However, many sensor signals exhibit temporal oscillations, leading to varying interpretations of the signal based on its current trend (rising or falling). This hysteresis in signal (and subsequently of the production device) interpretation inspired us to introduce this phenomenon into data stream processing, resulting in the novel concept of hysteretic fuzzy sets. This article demonstrates how fuzzy searching and grouping can be applied to IoT sensor signals in flexible Big data stream processing on Apache Kafka. We illustrate the impact of data stream querying with KSQL queries involving fuzzy sets (encompassing fuzzy filtering of data stream events, fuzzy transformation of data stream attributes, fuzzy grouping, and joining) on the flexibility of executed operations and computational resources utilized by the Kafka processing engine. Finally, our experiments with hysteretic fuzzy sets while analyzing sensor signals in power plants demonstrate that this novel approach effectively reduces the number of alarms while monitoring the state of the production machine.

关键词： Streams Fuzzy sets Internet of Things Big data Monitoring Production Real-time systems

来源：评论

学校读者我要写书评

暂无评论

DermoSegDiff: A Boundary-aware Segmentation Diffusion Model for Skin Lesion Delineation

arXiv

引用

arXiv 2023年

作者： Bozorgpour, Afshin Sadegheih, Yousef Kazerouni, Amirhossein Azad, Reza Merhof, Dorit Institute of Image Analysis and Computer Vision Faculty of Informatics and Data Science University of Regensburg Regensburg Germany School of Electrical Engineering Iran University of Science and Technology Iran Faculty of Electrical Engineering and Information Technology RWTH Aachen University Aachen Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

Skin lesion segmentation plays a critical role in the early detection and accurate diagnosis of dermatological conditions. Denoising Diffusion Probabilistic Models (DDPMs) have recently gained attention for their exceptional image-generation capabilities. Building on these advancements, we propose DermoSegDiff, a novel framework for skin lesion segmentation that incorporates boundary information during the learning process. Our approach introduces a novel loss function that prioritizes the boundaries during training, gradually reducing the significance of other regions. We also introduce a novel U-Net-based denoising network that proficiently integrates noise and semantic information inside the network. Experimental results on multiple skin segmentation datasets demonstrate the superiority of DermoSegDiff over existing CNN, transformer, and diffusion-based approaches, showcasing its effectiveness and generalization in various scenarios. The implementation is publicly accessible on GitHub. © 2023, CC BY.

关键词： Diffusion

来源：评论

学校读者我要写书评

暂无评论

Overcoming data Scarcity in Biomedical Imaging with a Foundational Multi-Task Model

arXiv

引用

arXiv 2023年

作者： Schäfer, Raphael Nicke, Till Höfener, Henning Lange, Annkristin Merhof, Dorit Feuerhake, Friedrich Schulz, Volkmar Lotz, Johannes Kiessling, Fabian Fraunhofer Institute for Digital Medicine MEVIS Germany Institute for Experimental Molecular Imaging RWTH Aachen University Germany Institute of Image Analysis and Computer Vision Faculty of Informatics and Data Science University of Regensburg Germany Institute for Pathology Hannover Medical School Germany Institute for Neuropathology University Clinic Freiburg Germany

Foundational models, pretrained on a large scale, have demonstrated substantial success across non-medical domains. However, training these models typically requires large, comprehensive datasets, which contrasts with the smaller and more heterogeneous datasets common in biomedical imaging. Here, we propose a multi-task learning strategy that decouples the number of training tasks from memory requirements. We trained a Universal bioMedical PreTrained model (UMedPT) on a multi-task database including tomographic, microscopic, and X-ray images, with various labelling strategies such as classification, segmentation, and object detection. The UMedPT foundational model outperformed ImageNet pretraining and the previous state-of-the-art models. For tasks related to the pretraining database, it maintained its performance with only 1% of the original training data and without fine-tuning. For out-of-domain tasks it required not more than 50% of the original training data. In an external independent validation imaging features extracted using UMedPT proved to be a new standard for cross-center transferability. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Internet of Thing (IoT) and data Analytics with Challenges and Future Applications

Internet of Thing (IoT) and Data Analytics with Challenges a...

引用

Computer Science and Emerging Technologies (CSET), International Conference on

作者： Mohammmadnour Mosbah Aljarrah Farah H. Zawaideh Murad Magableh Hassan Al Wahshat Rajina R. Mohamed Archana K V MIS BI and Data Analysis Department Faculty of Business and Financial Science Irbid National University Irbid Jordan Department of BI and Data Analysi Faculty of Financial Sciences and Business Irbid National University Irbid Jordan Department of Management Information System Faculty of Business Ajloun National University Ajloun Jordan College of Computing dan Informatics Universiti Tenaga Nasional Malaysia Jain Deemed To Be University Bangalore India

The Internet of Things (IoT) has emerged as a transformative technology, enabling seamless connectivity and communication between smart devices and the digital world. IoT's integration with data analytics has opened up a myriad of possibilities for extracting valuable insights from the vast volumes of data generated by connected devices. This research paper explores the convergence of IoT and data analytics, shedding light on the challenges encountered during their integration and envisioning future applications that can revolutionize various domains. The paper begins with a comprehensive review of the IoT ecosystem and its significance in shaping the modern era of interconnected devices. It highlights the diverse range of IoT applications across industries, including smart cities, healthcare, agriculture, manufacturing, transportation, and more. Moreover, it delves into the underlying technologies and communication protocols that enable IoT devices to gather, transmit, and process data efficiently. As data generated by IoT devices continues to escalate exponentially, effective data analytics becomes paramount to extract meaningful insights and support informed decision-making. The research delves into various data analytics techniques, such as machine learning, artificial intelligence, and big data analytics, illustrating their relevance in harnessing the potential of IoT-generated data. However, amidst the promises, several challenges impede the seamless amalgamation of IoT and data analytics. Issues concerning data privacy, security, interoperability, scalability, and data quality are thoroughly examined. The research emphasizes the need for robust frameworks and standards to address these challenges and ensure the secure and efficient functioning of IoT ecosystems. In the final section, this paper envisions the future applications of IoT and data analytics. It envisions intelligent and autonomous systems that adapt to dynamic environments, leading to advancement

关键词：

来源：评论

学校读者我要写书评

暂无评论

Selection of Neuroprotective Therapy in Patients with Chronic Cerebral Ischemia Taking Account of the Synergism of Drug Interactions

引用

Neuroscience and Behavioral Physiology 2021年第4期51卷 430-437页

作者： Gromova, O.A. Torshin, I. Yu. Putilina, M.V. Semenov, V.A. Rudakov, K.V. Institute of Pharmacoinformatics Informatics and Management Federal Research Center Russian Academy of Sciences Moscow Russian Federation Center for Storage and Analysis of Big Data Lomonosov Moscow State University Moscow Russian Federation Pirogov Russian National Research Medical University Russian Ministry of Health Moscow Russian Federation Kemerovo State Medical University Russian Ministry of Health Kemerovo Russian Federation

Objective. To optimize selection of neuroprotective treatment protocols in patients with chronic cerebral ischemia taking account of synergism in drug interactions for an individual personalized approach to increase treatment efficacy. Materials and Methods. Differential chemoreactome analysis of synergism between ethylmethylhydroxypyridine succinate (EMHPS) and a number of single-component neuroprotective substances (piracetam, vinpocetine, citicoline, choline alfoscerate);proteome analysis of polypeptide neuroprotectors (Cerebrolysin and others);expert analysis of the composition of a multicomponent neuroprotector (Cytoflavin). Results. Piracetam, citicoline (Neupilept), and choline alfoscerate (Cereton) effectively enhanced the pharmacological properties of EMHPS. Expert assessments of synergism between the properties of EMHPS, polypeptide neuroprotectors (Cerebrolysin), and other multicomponent formulations (Cytoflavin), which are also used in the adjuvant therapy in combination with EMHPS, are presented. Conclusions. In real clinical practice there is particular interest in objective evaluation of combined treatment protocols. EMHPS can provide a favorable background for maximizing treatment efficacy using additional drugs. © 2021, Springer Science+Business Media, LLC, part of Springer Nature.

关键词： choline alfoscerate Citicoline Cytoflavin ethylmethylhydroxypyridine succinate intelligent data analysis peptide drugs pharmacoinformatics piracetam synergism

来源：评论

学校读者我要写书评

暂无评论

Direct and Indirect Neurological Signs of COVID-19

引用

Neuroscience and Behavioral Physiology 2021年第7期51卷 856-866页

作者： Gromova, O.A. Torshin, I. Yu. Semenov, V.A. Putilina, M.V. Chuchalin, A.G. Institute of Pharmacoinformatics “Informatics and Control” Federal Research Center Russian Academy of Sciences Moscow Russian Federation Big Data Storage and Analysis Center Moscow State University Moscow Russian Federation Kemorovo State Medical University Russian Ministry of Health Kemerovo Russian Federation Pirogov Russian National Research Medical University Russian Ministry of Health Moscow Russian Federation

Objective. To systematize the neurological manifestations of COVID-19. Materials and methods. A systematic computerized analysis of all currently available publications on the neurological manifestations of COVID-19 was undertaken (2374 reports in PubMed) by topological data analysis. Results. A set of interactions between infection with SARS-CoV-2, metabolic impairments affecting neurotransmitters (acetylcholine, dopamine, serotonin, and GABA), enkephalins, and neurotrophins, micronutrients, chronic and acute inflammation, encephalopathy, cerebral ischemia, and neurodegeneration (including demyelination) was described. The most typical neurological manifestations of COVID-19 were anosmia/ageusia due to ischemia, neurodegeneration, and/or systematic increases in proinflammatory cytokine levels. COVID-19 provoked ischemic stroke, Guillain–Barré syndrome, polyneuropathy, encephalitis, meningitis, and parkinsonism. Coronavirus infection increased the severity of multiple sclerosis and myopathies. The possible roles of the human virome in the pathophysiology of COVID-19 are considered. A clinical case of a patient with neurological complications of COVID-19 is described. Conclusions. In the long-term perspective, COVID-19 promotes increases in neurodegenerative changes, which requires special neurological rehabilitation programs. Use of cholinergic drugs and antihypoxic agents compatible with COVID-19 therapy is advised. © 2021, Springer Science+Business Media, LLC, part of Springer Nature.

关键词： cholinergic drugs complications COVID-19 neurological symptoms

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：