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Optimizing Patient Satisfaction Surveys and Feedback with Cloud Computing and NLP Techniques

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Optimizing Patient Satisfaction Surveys and Feedback with Cl...

International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： L. N. Jayanthi Kannan Shanmugam Challapalli Suma Sagar B S S. P. Maniraj T. R. GaneshBabu Department of Commerce Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamil Nadu India Department of Gaming Technology School of Computing Science and Engineering VIT Bhopal University Bhopal Madhapradesh India Department of Computer Science and Engineering Sagi Rama Krishnam Raju Engineering College (A) Bhimavaram Andhra Pradesh India Department of Electrical and Electronics Engineering REVA University Bengaluru Karnataka India Department of Data Science and Business Systems Faculty of Engineering & Technology Kattankulathur - Chennai Chennai Tamil Nadu India Department of Electronics and Communication Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

Healthcare quality assessment and identification of opportunities for improvement are crucially facilitated by patient satisfaction surveys. Traditional approaches sometimes face difficulties in handling substantial amounts of written comments and obtaining real useful data. It presents a new method that combines cloud computing with Natural Language Processing (NLP) approaches to enhance the analysis of patient satisfaction data. By using cloud platforms, it can effectively manage large datasets and provide scalable, cost-efficient processing. The use of NLP methods, such as text mining and sentiment analysis, enables the conversion of unstructured input into structured insights, thereby uncovering the fundamental patient feelings and patterns. This connection enables the analysis of data in real-time also improving the accuracy of interpreting feedback and facilitating fast enhancements in healthcare services. Future research will prioritise the improvement of NLP algorithms and the investigation of supplementary cloud-based technologies to enhance the accuracy and efficiency of patient satisfaction analysis to improved patient care and experience.

关键词： Surveys Text mining Cloud computing Technological innovation Accuracy Medical services Strategic planning Market research Natural language processing Real-time systems

Bankruptcy Prediction using Emperor Penguin Optimizer with Deep Learning Model on Qualitative dataset

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Bankruptcy Prediction using Emperor Penguin Optimizer with D...

International Conference on Smart Systems and Inventive Technology (ICSSIT)

作者： Vijay Abrol Priyanka Singh R Subhashini A.Sasi Kumar Kamlesh Singh B. Raja Mannar Department of Computer Science Uttam Group of Institutions Agra Uttar Pradesh India Department of Technical Education Sgsj Government Polytechnic Bulandshahr Uttar Pradesh India Department of Information Technology Sona College of Technology Salem Tamil Nadu India Department of Cloud Technology & Data Science (Mentor-IT – inurture Education Solutions Pvt LtdBangalore) Institute of Engineering & Technology Srinivas UniversitySrinivas NagarMukka SurathkalMangalore Dakshina Kannada District Karnataka State India Department of Computer Science and Engineering Graphic Era Hill UniversityDehradun Campus Dehradun India Department of Commerce Sri Venkateswara University Tirupati Andhra Pradesh India

Bankruptcy prediction and credit risk study are amongstthe most important problems in the domain of financial and accounting decision making. Evolving an effectual Classification Rule Induction (CRD) structure for bankruptcy prediction and credit risk examination at suitable time is vital to avoid the business communities being bankrupt. Classical statistical approaches and Artificial Intelligence (AD) play an important role in bankruptcy prediction and credit risks. Most previous research works can be executed on quantitative approaches but some studies are presented on qualitative approaches for improvising the efficiency of bankruptcy prediction techniques. This study focuses on the development of Bankruptcy Prediction using Emperor Penguin optimizer with Deep Learning (BP-EPODL) model on Qualitative dataset. The projected BP-EPODL technique identifies the existence of bankruptcy or not. Here, the BP-EPODL technique exploits EPO algorithm for the choice of optimum features. For bankruptcy prediction, the BP-EPODL technique applies Long Short Term Memory (LS TM) model. At last, Adam optimizer is applied as a hyperparameter tuning technique for the IS TM model. The experimental validation of the BP-EPODL technique is tested on benchmark qualitative dataset. The obtained values inferred that the BP-EPODLtechnique has shown effectual results over other models.

关键词： Deep learning Decision making Predictive models Benchmark testing Prediction algorithms Artificial intelligence Bankruptcy

Pushing AI to wireless network edge: An overview on integrated sensing, communication, and computation towards 6G

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arXiv 2022年

作者： Zhu, Guangxu Lyu, Zhonghao Jiao, Xiang Liu, Peixi Chen, Mingzhe Xu, Jie Cui, Shuguang Zhang, Ping Shenzhen Research Institute of Big Data Shenzhen518172 China Shenzhen518172 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables33146 United States State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Peng Cheng Laboratory Shenzhen518066 China

Pushing artificial intelligence (AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things (AIoT) in the sixth-generation (6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the huge amount of data scattered at wireless network edge. In general, realizing edge intelligence corresponds to the process of sensing, communication, and computation, which are coupled ingredients for data generation, exchanging, and processing, respectively. However, conventional wireless networks design the sensing, communication, and computation separately in a task-agnostic manner, which encounters difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications such as auto-driving. This thus prompts a new design paradigm of seamless integrated sensing, communication, and computation (ISCC) in a task-oriented manner, which comprehensively accounts for the use of the data in the downstream AI applications. In view of its growing interest, this article provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art development, and shedding light on the road ahead. Copyright © 2022, The Authors. All rights reserved.

关键词： Artificial intelligence

Correction to: Automatic COVID-19 detection mechanisms and approaches from medical images: a systematic review

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Multimedia tools and applications 2022年第20期81卷 28799-28800页

作者： Amir Masoud Rahmani Elham Azhir Morteza Naserbakht Mokhtar Mohammadi Adil Hussein Mohammed Aldalwie Mohammed Kamal Majeed Sarkhel H Taher Karim Mehdi Hosseinzadeh Future Technology Research Center National Yunlin University of Science and Technology Douliu Yunlin Taiwan. Research and Development Center Mobile Telecommunication Company of Iran Tehran Iran. Mental Health Research Center Psychosocial Health Research Institute Iran University of Medical Sciences Tehran Iran. Department of Information Technology College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq. Department of Communication and Computer Engineering Faculty of Engineering Cihan University-Erbil Kurdistan Region Iraq. Information Technology Department Faculty of Applied Science Tishk International University Erbil Iraq. Computer Department College of Science University of Halabja Halabja Iraq. Computer Networks Department Sulaimani Polytechnic University Technical College of Informatics Sulaymaniyah Iraq. Computer Science University of Human Development Sulaymaniyah Iraq.

[This corrects the article DOI: 10.1007/s11042-022-12952-7.].

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Accurate Ab-initio Neural-network Solutions to Large-Scale Electronic Structure Problems

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arXiv 2025年

作者： Scherbela, Michael Gao, Nicholas Grohs, Philipp Günnemann, Stephan Faculty of Mathematics University of Vienna Oskar-Morgenstern-Platz 1 ViennaA-1090 Austria Department of Computer Science Munich Data Science Institute Technical University of Munich Germany Johann Radon Institute for Computational and Applied Mathematics Austrian Academy of Sciences Altenbergerstrasse 69 Linz4040 Austria

We present finite-range embeddings (FiRE), a novel wave function ansatz for accurate large-scale ab-initio electronic structure calculations. Compared to contemporary neural-network wave functions, FiRE reduces the asymptotic complexity of neural-network variational Monte Carlo (NN-VMC) by ∼ nel, the number of electrons. By restricting electron-electron interactions within the neural network, FiRE accelerates all key operations – sampling, pseudopotentials, and Laplacian computations – resulting in a real-world 10× acceleration in now-feasible 180-electron calculations. We validate our method’s accuracy on various challenging systems, including biochemical compounds, conjugated hydrocarbons, and organometallic compounds. On these systems, FiRE’s energies are consistently within chemical accuracy of the most reliable data, including experiments, even in cases where high-accuracy methods such as CCSD(T), AFQMC, or contemporary NN-VMC fall short. With these improvements in both runtime and accuracy, FiRE represents a new ‘gold-standard’ method for fast and accurate large-scale ab-initio calculations, potentially enabling new computational studies in fields like quantum chemistry, solid-state physics, and material design. Copyright © 2025, The Authors. All rights reserved.

关键词： Organometallics

Adjacent Context Coordination Network for Salient Object Detection in Optical Remote Sensing Images

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arXiv 2022年

作者： Li, Gongyang Liu, Zhi Zeng, Dan Lin, Weisi Ling, Haibin Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China School of Communication and Information Engineering Shanghai University Shanghai200444 China Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai University Shanghai200444 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The Department of Computer Science Stony Brook University Stony BrookNY11794 United States

Salient object detection (SOD) in optical remote sensing images (RSIs), or RSI-SOD, is an emerging topic in understanding optical RSIs. However, due to the difference between optical RSIs and natural scene images (NSIs), directly applying NSI-SOD methods to optical RSIs fails to achieve satisfactory results. In this paper, we propose a novel Adjacent Context Coordination Network (ACCoNet) to explore the coordination of adjacent features in an encoder-decoder architecture for RSI-SOD. Specifically, ACCoNet consists of three parts: an encoder, Adjacent Context Coordination Modules (ACCoMs), and a decoder. As the key component of ACCoNet, ACCoM activates the salient regions of output features of the encoder and transmits them to the decoder. ACCoM contains a local branch and two adjacent branches to coordinate the multilevel features simultaneously. The local branch highlights the salient regions in an adaptive way, while the adjacent branches introduce global information of adjacent levels to enhance salient regions. Additionally, to extend the capabilities of the classic decoder block (i.e., several cascaded convolutional layers), we extend it with two bifurcations and propose a Bifurcation-Aggregation Block to capture the contextual information in the decoder. Extensive experiments on two benchmark datasets demonstrate that the proposed ACCoNet outperforms 22 state-of-the-art methods under nine evaluation metrics, and runs up to 81 fps on a single NVIDIA Titan X GPU. The code and results of our method are available at https://***/MathLee/ACCoNet. © 2022, CC BY-NC-ND.

关键词： Signal encoding

The Future of data Protection: Empowering data Security with Identity-Centric Blockchain Technology

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The Future of Data Protection: Empowering Data Security with...

Communication & Computing (WCONF), World Conference on

作者： A. Heney Juliet Vinithra Banu T K. Suresh Kumar D Kirubakaran M Dinesh K. Thilagam Department of Data Analytics Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of MBA Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering R.M.K Engineering College Chennai Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Tamil Nadu India Department of Electronics and Communication Engineering Velammal Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

In response to the formidable challenges surrounding data security, particularly in the face of evolving threats and vulnerabilities, this study introduces the Advanced Blockchain-Enhanced data Security Protocol (ABE-DSP) as a cutting-edge solution. Acknowledging the complexities of safeguarding sensitive information, ABE-DSP is a meticulously crafted and sophisticated framework that aims to fortify the security and integrity of data transactions through the integration of blockchain technology. This protocol addresses the pressing challenge of ensuring robust data security by placing a primary focus on enhancing overall system security and refining user authentication processes. Within the context of these challenges, the study explores the intricacies of ABE-DSP's design and implementation. Furthermore, the proposed method demonstrates its effectiveness by achieving a remarkable success rate, processing 97% of received packets with precision. This high percentage underscores the protocol's efficacy and reliability, positioning it as a robust and advanced tool for overcoming the contemporary challenges associated with data security. This study contributes valuable insights to the ongoing discourse on leveraging blockchain technology for identity-centric data security, offering a promising avenue for addressing and mitigating the challenges faced in the dynamic landscape of information protection.

关键词： Protocols Navigation data security Face recognition Refining Authentication Pressing Blockchains Reliability Protection

Cloud Det ection Using Super Pixel Classification and Semantic Segmentation

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Journal of computer science & Technology 2019年第3期34卷 622-633页

作者： Han Liu Hang Du Dan Zeng Qi Tian Key Laboratorij of Specialty Fiber Optics and Optical Access Networks Shanghai UniversityShanghai 200444China Shanghai Institute of Advanced Communication and Data Science Shanghai UniversityShanghai 200444China Department of Computer Science The University of Texas at San AntonioSan AntonioU.S.A.

Cloud detection plays a very significant role in remote sensing image *** paper introduces a cloud detection method based on super pixel level classification and semantic ***,remote sensing images are segmented into super *** super pixels compose a super pixel level remote sensing image *** cloud detection is essentially a binary classification task,our database is labeled into four categories to improve the generalization ability:thick cloud,cirrus cloud,building,and other ***,the super pixel level database is used to train our cloud detection models based on convolution neural network(CNN)and deep *** fusion CNN is proposed considering super pixel level images contain less semantic information than normal *** full advantage of low-level features like color and texture information,it is more applicable for super pixel level ***,a distance metric is proposed to refine ambiguous super ***,an end-to-end cloud detection model based on semantic segmentation is *** model has no restrictions on the input size,and takes less *** results show that compared with other cloud detection *** proj)osed method acliieves better performance.

关键词： cloud detection convolution neural network deep forest semantic segmentation

A Survey on Indoor Visible Light Positioning Systems: Fundamentals, Applications, and Challenges

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arXiv 2024年

作者： Zhu, Zhiyu Yang, Yang Chen, Mingzhe Guo, Caili Cheng, Julian Cui, Shuguang The Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Faculty of Applied Science School of Engineering The University of British Columbia KelownaBCV1V 1V7 Canada The Chinese University of Hong Kong Shenzhen518172 China

The growing demand for location-based services in areas like virtual reality, robot control, and navigation has intensified the focus on indoor localization. Visible light positioning (VLP), leveraging visible light communications (VLC), becomes a promising indoor positioning technology due to its high accuracy and low cost. This paper provides a comprehensive survey of VLP systems. In particular, since VLC lays the foundation for VLP, we first present a detailed overview of the principles of VLC. Then, we provide an in-depth overview of VLP algorithms. The performance of each positioning algorithm is also compared in terms of various metrics such as accuracy, coverage, and orientation limitation. Beyond the physical layer studies, the network design for a VLP system is also investigated, including multi-access technologies, resource allocation, and light-emitting diode (LED) placements. Next, the applications of the VLP systems are overviewed. Finally, this paper outlines open issues, challenges, and opportunities for the research field. In a nutshell, this paper constitutes the first holistic survey on VLP from state-of-the-art studies to practical uses. Copyright © 2024, The Authors. All rights reserved.

关键词： Resource allocation