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6th International Conference on Computer-Human Interaction Research and Applications, CHIRA 2022

作者： Chessum, Karen Liu, Haiming Frommholz, Ingo School of Computer Science and Technology University of Bedfordshire Luton United Kingdom School of Electronics and Computer Science University of Southampton United Kingdom School of Engineering Computing and Mathematical Sciences University of Wolverhampton Wolverhampton United Kingdom

ISBN: (纸本)9789897586095

An information seeker's cultural background could influence their preference for search user interface (UI) design. To study cultural influences Geert Hofstede's cultural dimensions have been applied to website design for a number of years. In this paper, we examine if Hofstede's six cultural dimension can be applied to inform the design of search engine user interfaces. The culturally designed search user interfaces have been evaluated in a study with 148 participants of different cultural backgrounds. The results have been analysed to determine if Hofstede's cultural dimensions are appropriate for understanding users' preferences on search user interface design. Whilst the key findings from the study suggest Hofstede cross-cultural dimensions can be used to model users' preferences on search interface design, further work is still needed for particular cultural dimensions to reinforce the conclusions. Copyright © 2022 by SCITEPRESS - Science and technology Publications, Lda. All rights reserved.

关键词： Web Design

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Leveraging Machine Learning for Classification of Rheumatoid Arthritis: A Comparative Analysis of Classifier Performance

Leveraging Machine Learning for Classification of Rheumatoid...

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Technological Advancements in Computational Sciences (ICTACS), International Conference on

作者： Naresh Kumar Trivedi Raj Gaurang Tiwari Himani Maheshwari Vinay Gautam Ambuj Kumar Agarwal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computing Graphic Era Hill University Dehradun Uttrakhand India Department of Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Greater Naida India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

A systemic inflammatory illness affecting the musculoskeletal system, rheumatoid arthritis (RA) is characterized by chronic, systemic symptoms. Injuries to joints and muscles from wear and tear are expected outcomes of rheumatoid arthritis (RA)., a degenerative illness that worsens with time. Arthritis rheumatoid generally degenerates muscles and joints while damaging bone and cartilage. To categorize medical conditions according to RA, this study employs machine learning approaches. Real-time RA data from the Sakthi Rheumatology Center, which keeps track of one thousand patient profiles has been used in this study. Multiple numerical features of the dataset are used for the classification task RA. The machine learning approaches used in this experiment were support vector machine (SVM), logistic regression, SDG, kNN, and ada-boosting. Ada-boost outperforms other classifiers regarding prediction accuracy rate and generating 98.7% classification accuracy. To classify the data, 20-fold cross-validation is used, and its efficacy is evaluated with metrics like precision, recall, sensitivity, and accuracy. The values of these measures were compared to a range of classifiers and baseline techniques.

关键词： Support vector machines Logistic regression Accuracy Sensitivity Machine learning Rheumatology Muscles Arthritis Real-time systems Vectors

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Memory-Guided Contrastive and Triplet Separation for Weakly-Supervised Disease Detection

Memory-Guided Contrastive and Triplet Separation for Weakly-...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： She, Jinwen Li, Qiong Ma, Andy J. Sun Yat-sen University Cancer Center Guangzhou China Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Ministry of Education Guangdong Province Key Laboratory of Information Security Technology Key Laboratory of Machine Intelligence and Advanced Computing China

ISBN: (纸本)9798350386226

Weakly-supervised disease detection has the great potential to alleviate the time-consuming and labor-intensive burden of manual annotations in instance level. While existing methods extract normality prototypes encoding normal patterns to improve the detection performance, they may fail to detect subtle anomalies without considering the diverse abnormal patterns. In this paper, we propose to recognize both normal and abnormal patterns for feature representation learning in disease detection based on a dual memory network. To learn discriminative memory banks and classifiers, dual memory loss is incorporated with a feature magnitude separation loss for model training. With the proposed contrastive feature separation loss and triplet feature separation loss, feature discriminability is further improved to obtain a more separable decision boundary. Additionally, we collect a large-scale CT dataset to evaluate lung tumor detection. Extensive experiments on the publicly available PANDA-MIL dataset and the collected LUNG-MIL dataset demonstrate the superiority of our proposed method compared to the state-of-the-art approaches for weakly-supervised disease detection. © 2024 IEEE.

关键词： Contrastive Learning

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Evolution of Service-Oriented computing: Integrating Quantum Techniques for Integer Factorization 9

Evolution of Service-Oriented Computing: Integrating Quantum...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Salama, Reda Alghamdi, Wajdi Alsalmi, Hany M. Mayakannan, S. Taqui, Syed Noeman Deepalakshmi, R. King Abdulaziz University Faculty of Computing And Information Technology Department of Information Technology Jeddah21589 Saudi Arabia Department of Digital Transformation And Information Riyadh Saudi Arabia Vidyaa Vikas College of Engineering And Technology Department of Mechanical Engineering Namakkal Tamilnadu Tiruchengode India Saveetha Institute of Medical And Technical Sciences Saveetha School of Engineering Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India The Tamilnadu Dr. Ambedkar Law University Department of Inter-Disciplinary Studies Tamilnadu Chennai India

ISBN: (纸本)9798350394474

The development of quantum computing has caught the attention of businesses that could profit from implementing quantum software. Given this, it is not out of the question that in the coming years, hybrid systems will begin to emerge, merging together both the traditional software systems of organization and new quantum ones, giving solutions to difficulties that are today intractable. Service oriented computing (soc) is a logical technique to facilitate such integration. There are some conceptual similarities between calling on a classical service and calling on a quantum service, but there are significant technological differences. This research uses the amazon braket quantum service platform and a well-known problem with a quantum solution to highlight these distinctions and the difficulties inherent in developing high-quality quantum services (integer factorization). To practice providing the factorization as a quantum service, we perform the exercise. This case study illustrates the challenges associated with adopting service-oriented computing for integrating hybrid systems based on classical and quantum principles. After conducting this study, we were able to make suggestions on where to place effort in order to bring about the most effective realization of quantum service-oriented computing. © 2024 IEEE.

关键词： Hybrid systems

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Brain Tumor Identification Using Data Augmentation and Transfer Learning Approach

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Computer Systems Science & engineering 2023年第8期46卷 1845-1861页

作者： K.Kavin Kumar P.M.Dinesh P.Rayavel L.Vijayaraja R.Dhanasekar Rupa Kesavan Kannadasan Raju Arfat Ahmad Khan Chitapong Wechtaisong Mohd Anul Haq Zamil S.Alzamil Ahmed Alhussen Department of Electronics and Communication Engineering Kongu Engineering College638060Tamil NaduIndia Department of Electronics and Communication Engineering Sona College of Technology636005Tamil NaduIndia Department of Computer Science and Engineering Sri Sairam Institute of TechnologyChennai600044Tamil NaduIndia Department of Electrical and Electronics Engineering Sri Sairam Institute of TechnologyChennai600044Tamil NaduIndia Department of Computer Science and Engineering Sri Venkateswara College of EngineeringSriperumbudur602117Tamil NaduIndia Department of Electrical and Electronics Engineering Sri Venkateswara College of EngineeringSriperumbudur602117Tamil NaduIndia Department of Computer Science College of ComputingKhon Kaen UniversityKhonKaen40002Thailand School of Telecommunication Engineering Suranaree University of TechnologyNakhon Ratchasima30000Thailand Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia

A brain tumor is a lethal neurological disease that affects the average performance of the brain and can be *** India,around 15 million cases are diagnosed *** mitigate the seriousness of the tumor it is essential to diagnose at the ***,the manual evaluation process utilizing Magnetic Resonance Imaging(MRI)causes a few worries,remarkably inefficient and inaccurate brain tumor ***,the examination process of brain tumors is intricate as they display high unbalance in nature like shape,size,appearance,and ***,a precise and expeditious prognosis of brain tumors is essential for implementing the of an implicit *** computer models adapted to diagnose the tumor,but the accuracy of the model needs to be *** all the above mentioned things,this work aims to identify the best classification system by considering the prediction accuracy out of Alex-Net,ResNet 50,and Inception *** augmentation is performed on the database and fed into the three convolutions neural network(CNN)models.A comparison line is drawn between the three models based on accuracy and *** accuracy of 96.2%is obtained for AlexNet with augmentation and performed better than ResNet 50 and Inception V3 for the 120th *** the suggested model with higher accuracy,it is highly reliable if brain tumors are diagnosed with available datasets.

关键词： AlexNet brain tumor data augmentation inception V3 ResNet 50

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Cloud computing based Smart Traffic Management System with Priority Switching for Health Care Services 2

Cloud Computing based Smart Traffic Management System with P...

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2nd International Conference on Applied Artificial Intelligence and computing, ICAAIC 2023

作者： Shiny, X.S. Asha Ravikumar, D. Chinnasamy, A. Hemavathi, S. Department of Computer Science and Engineering Telangana Hyderabad India Kings Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Chennai India Sri Sairam Engineering College Department of Computer Science Engineering Tamil Nadu Chennai India

ISBN: (纸本)9781665456302

Regular cities can be transformed into intelligent structures by leveraging information and communication technologies. Innovative city development could be significantly impacted by the Internet of Things paradigm, commonly called cloud computing. All urban locations have a lot of traffic. In this study, Internet-of- Things (IoT) based system is proposed for health care services to organize and to establish the traffic signaling and pick up a route under which road congestion can be administrated. Vehicle accident contraventions are identified by the traffic officers using an online service that is hierarchically carefully monitored or constrained. However, the suggested approach is generic and may be utilized in any major metropolis without losing its generality. Traffic lights linked to cameras in metropolitan areas can be upgraded by connecting to IoT. During a pandemic, this approach is precious. The police can easily regulate traffic from their homes using their cell phones and identify defaulters. The suggested technique aids in the separation of ambulance and rescue engines from ordinary traffic. © 2023 IEEE.

关键词： Internet of things

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AI-Based Mental Fatigue Recognition and Responsive Recommendation System

AI-Based Mental Fatigue Recognition and Responsive Recommend...

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International Conference on Data Analytics, Intelligent computing, and Cyber Security, ICDIC 2020

作者： Kumar, Korupalli V. Rajesh Devi, B. Rupa Sudhakara, M. Keerthi, Gabbireddy Madhavi, K. Reddy Woxsen University Telangana Hyderabad India Annamacharya Institute of Technology and Sciences AP Tirupati India School of Computing and Information Technology Reva University Bangalore India CSE Vignan’s Foundation for Science Technology and Research A.P Guntur India Sree Vidyanikethan Engineering College AP Tirupati India

ISBN: (纸本)9789811941610

Complete comfort is considered the definite one among all the foremost concerns in the present scenario. The organization has been proceeding with more contemplation in upgrading their work comfort. The workers also proceed with many perspectives to enhance their ongoing comfort status. However, comfort is generally related to their everyday activity and conduct, mainly in the worksites where it influences both the levels of stress and mood, resulting in mental fatigue. Specifically, the characteristic of the person’s comfort will be affected by the conduct in a worksite. We suggested a mental fatigue identification system that embraced deep learning techniques for supplying non-intrusive observing systems. The comfort level has been classified based on surveys: pressure and mood. This preparatory study instructs the model to generalized and personalized models of classifications. The model as personalized perspective has been taken as one of the steps to supply a personalized health resolution support system that helps in elevating the awareness in customers and motivates them to upgrade their conduct and eventually put up to the best comfort system. We have attained an accuracy of 87% on the model generic and 94% on the model personalized. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Recommender systems

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Simulate the Machine Learning Algorithm to Organize the CRAHN Network System 3

Simulate the Machine Learning Algorithm to Organize the CRAH...

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Prabakaran, P. Chandra Sekhar Reddy, L. Ravikumar, L.V.D. Verma, Manish Kumar School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Department of Computer Applications Chennai India Hyderabad Medchal India Shri Vishnu Engineering College for Women Department of Information Technology Bhimavaram India Lovely Professional University Department of Psychology Phagwara India

ISBN: (纸本)9798350330724

Using available channels in a wireless spectrum, cognitive radio (CR) may automatically adjust transmission settings to optimize radio operational behaviour. To function properly, a CR ad hoc network (CRAHN) has to be dynamically capable construct autonomous and decentralized networks without negatively impacting licensed main user (PU) systems. For this reason, an effective spectrum necessitates a system structure based on artificial intelligence. This research provides a model for network planning, learning, and dynamic configuration that is based on a distributed autonomous CRAHN network system that uses reinforcement learning. The proposed optimization techniques for spectrum sensing, ad hoc network design, and context-aware signal categorization are all derived from the system model and are based on machine learning. The cognitive and detection engines may be used to examine the spectrum utilization and neighbour network status in the immediate area. To adapt to the ever-changing nature of the wireless environment, the suggested policy engine may generate network operating policies, identify policy conflicts, and infer the best course of action. Together with the erudition engine, whereby apply the recommended machine-learning methods, the decision engine arrives at the best possible settings for the CRAHN. In addition, guarantee peaceful cohabitation with surrounding systems to have excellent signal context recognition ability. © 2023 IEEE.

关键词： Ad hoc networks

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Multi-Task Driven Multi-Level Dynamical Fusion for Single-Cell Multi-Omics Cell Type Annotation

Multi-Task Driven Multi-Level Dynamical Fusion for Single-Ce...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Jiawei Chen, Shizhan Han, Zongbo Li, Wei Tang, Jijun Guo, Fei Tianjin University College of Intelligence and Computing Tianjin300072 China Nankai University College of Artificial Intelligence Tianjin518055 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518107 China Central South University School of Computer Science and Engineering Changsha410083 China

ISBN: (纸本)9798350386226

The emergence of single-cell multi-omics sequencing technology has enabled the simultaneous profiling of diverse omics data within individual cells. It offers a more comprehensive perspective on cellular phenotypes and heterogeneity. However, single-cell multi-omics data are inherently high-dimensional and heterogeneous. Due to technical limitations and scarce starting materials, the data are often affected by noise and dropout effects. To address these challenges, we propose a novel multitask driven multi-level dynamical fusion algorithm for single-cell multi-omics cell type annotation, named scMMDyn. Our approach incorporates reconstruction and classification auxiliary tasks to guide the training of trustworthy modules at both the feature and modality levels. It executes dynamical fusion during these stages and finally achieves cross-modality fusion via an attention mechanism. This method effectively mitigates data quality issues through reconstruction tasks and feature-level dynamical fusion while providing interpretability at both feature and modality levels. Experimental results across diverse single-cell multi-omics datasets show that our method surpasses existing approaches in cell type annotation. © 2024 IEEE.

关键词： cell type annotation multi-task mutli-level dynamical fusion single-cell multi-omics trustworthy

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Internet-of-Things for Smart Agriculture: Current Applications, Future Perspectives, and Limitations

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Agricultural Sciences 2024年第12期15卷 1446-1475页

作者： Nastaran Rizan Siva K. Balasundram Arash Bayat Shahbazi Unetha Balachandran Redmond R. Shamshiri Department of Agriculture Technology Faculty of Agriculture University of Putra Malaysia Serdang Malaysia Department of Information System School of Computing Faculty of Engineering University of Technology Malaysia Johor Bahru Malaysia Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB) Potsdam Germany

The increasing global population has led to a higher demand for food production, while a decrease in rural labor and a rise in production costs present complex challenges for the food industry. Smart agriculture is a farm management concept that considers the deployment of Internet of Things (IoT) to address current food production challenges. In this regard, the agricultural sector is becoming increasingly data-focused, and requires data and technologies that are more precise, advanced, and cutting-edge than in the past. IoT enables agriculture to become data-driven, resulting in timely and more cost-effective farm intervention while reducing environmental impact. This review provides an analytical survey of the current and potential applications of IoT in smart agriculture to overcome challenges posed by spatio-temporal variability under varying environments and task diversity. This review also discusses the challenges that may arise from IoT deployment and presents an overview of the existing applications and those that may be developed in the future.

关键词： Smart Agriculture Internet of Things (IoT) Precision Farming Data-Driven Agriculture Agricultural technology Sustainable Food Production

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