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A New Embedded Surveillance System for Reducing COVID-19 Outbreak in Elderly Based on Deep Learning and IoT

A New Embedded Surveillance System for Reducing COVID-19 Out...

2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020

作者： Othman, Nashwan Adnan Al-Dabagh, Mustafa Zuhaer Nayef Aydin, Ilhan Knowledge University College of Science Department of Computer Science Erbil Kurdistan Region446015 Iraq Firat University Computer Engineering Department Elazig Turkey

ISBN: (纸本)9781728196756

As a result of the fast spread of Coronavirus disease (COVID-19) throughout the world, it became urgent to evolve an aided-intelligent system to help healthcare organizations to control and early detect COVID-19 outbreak, especially after massive development in the computing. Artificial Intelligence and Deep Learning methods could introduce real assistance to many healthcare organizations in this global problem by monitoring, detecting, and reporting on infected persons in early-stage. As is well known, the elderly are most vulnerable to the effects of COVID-19, therefore, we aim through this paper is to present the embedded system has the ability to detect and report on the elderly in the endemic areas. An age estimation from a facial image based on deep learning methods and Internet of Things is also applied to send notification to mobile or any other device systems that the application is embedded on it based on IoT. Depending on the experimental results on the proposed system through the Mean Average Error rate, the proposed system gave better or equivalent to the results of the state-of-the-art techniques, and the prediction results of an average accuracy achieved 89.45%. Finally, the proposed system has the capacity to help reduce the intensity of spread COVID-19 by identified older people and prevent them from prohibited in dangerous areas. © 2020 IEEE.

关键词： COVID-19

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Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model

arXiv

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arXiv 2023年

作者： Liu, Yixuan Zhao, Suyun Xiong, Li Liu, Yuhan Chen, Hong Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Renmin University of China China Engineering Research Center Ministry of Education on Database and BI China Information School Renmin University of China China Department of Computer Science Emory University United States

Federated Learning, as a popular paradigm for collaborative training, is vulnerable against privacy attacks. Different privacy levels regarding users’ attitudes need to be satisfied locally, while a strict privacy guarantee for the global model is also required centrally. Personalized Local Differential Privacy (PLDP) is suitable for preserving users’ varying local privacy, yet only provides a central privacy guarantee equivalent to the worst-case local privacy level. Thus, achieving strong central privacy as well as personalized local privacy with a utility-promising model is a challenging problem. In this work, a general framework (APES) is built up to strengthen model privacy under personalized local privacy by leveraging the privacy amplification effect of the shuffle model. To tighten the privacy bound, we quantify the heterogeneous contributions to the central privacy user by user. The contributions are characterized by the ability of generating "echos" from the perturbation of each user, which is carefully measured by proposed methods Neighbor Divergence and Clip-Laplace Mechanism. Furthermore, we propose a refined framework (S-APES) with the post-sparsification technique to reduce privacy loss in high-dimension scenarios. To the best of our knowledge, the impact of shuffling on personalized local privacy is considered for the first time. We provide a strong privacy amplification effect, and the bound is tighter than the baseline result based on existing methods for uniform local privacy. Experiments demonstrate that our frameworks ensure comparable or higher accuracy for the global model. © 2023, CC BY.

关键词： Learning systems

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Algorithmic Intelligence Instantaneously for Complex Real-Time Supply Chain Optimization

Algorithmic Intelligence Instantaneously for Complex Real-Ti...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： G Lalitha Himanshu Kumar Rajora Asha V Ginni Nijhawan Praveen Karrar.R.Al- Shami Institute of Aeronautical Engineering Dundigal Hyderabad Docse GyanGanga Institute of Technology and Sciences Jabalpur M.P India Department of Computer Applications New Horizon College of Engineering Bangalore Lovely Professional University Phagwara Knowledge Park II Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh Department of Forensic Sciences College of Science National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

The modern world of supply chain management is complex and ever-evolving, thus the ability to make informed decisions and react quickly are of the utmost importance. In response to these issues, we have created a ground-breaking method that we call “Dynamic Real-Time Supply Chain Optimization with Algorithmic Intelligence (DRT-SCAI).” Better real-time visibility, autonomous decision-making, predictive accuracy, efficiency, customer happiness, risk mitigation, and sustainability are all outcomes of this suggested methodology’s use of state-of-the-art algorithmic intelligence, which simultaneously optimizes the supply chain in an instant. By using this approach, several enhancements are realized. To achieve these improvements, it is necessary to use this strategy.

关键词： Technological innovation Supply chain management Heuristic algorithms Supply chains Decision making Prediction algorithms Real-time systems

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S3map: Semi-Supervised Aspect-Based Sentiment Analysis with Masked Aspect Prediction

SSRN

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SSRN 2022年

作者： Yang, Zhiyao Wang, Bing Li, Ximing Wang, Wenting Ouyang, Jihong College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Department of Computer Science University of Texas Dallas United States

Aspect-Based Sentiment Analysis (ABSA) refers to a fine-grained task of detecting the sentiment polarities of sentences at the aspect level. To resolve this task, training samples of ABSA must be annotated with aspect words and the corresponding sentiment polarities. However, collecting such fine-grained training samples is expensive and time consuming. Therefore the available ABSA training samples are often scarce. To break the data scarcity challenge of ABSA, we investigate Semi-supervised Aspect-Based Sentiment Analysis (SemiABSA), which trains ABSA models by using a limited amount of expensive labeled sentences and more unlabeled-yet-cheaper ones. We propose a novel SemiABSA framework, namely Semi-supervised aspect-based Sentiment analysis with Masked aspect prediction (S3MAP), built on the self-training paradigm. For unlabeled sentences, we form pseudo-aspect words and pseudo-sentiment polarities and improve model training. Specifically, a BERT-Encoder-based Masked Aspect Prediction (MAP) task achieves the pseudo-aspect words generation. Based on S3MAP, we thoroughly investigate the potential of SemiABSA from various perspectives. Empirical results show that S3MAP can consistently improve the performance by leveraging unlabeled sentences even with those from different domains. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Sparse Training Data-Based Hyperspectral Image Super Resolution Via ANFIS Interpolation

Sparse Training Data-Based Hyperspectral Image Super Resolut...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Jing Yang Changjing Shang Lu Chen Pan Su Qiang Shen School of Automation and Software Engineering Shanxi University Taiyuan China Department of Computer Science Aberystwyth University Aberystwyth UK Insti. of Big Data Science & Industry Shanxi University Taiyuan China Department of Computer Hebei Key Laboratory of Knowledge Computing for Energy & Power Baoding North China Electric Power University China

Hyperspectral image super resolution aims to improve the spatial resolution of given hyperspectral images, which has become a highly attractive topic in the field of image processing. Existing techniques typically focus on super resolution with sufficient training data. However, restricted by data acquisition conditions, certain hyperspectral images or band images are very different to obtain, resulted in insufficient training data. In order to solve this problem, a new hyperspectral image super resolution method is proposed in this paper in an effort to conduct the super resolution task over insufficient (sparse) training data, by applying the recently introduced ANFIS (Adaptive Network-based Fuzzy Inference System) interpolation method. Particularly, the training dataset is divided into several subsets. For the subsets with sufficient training data, the relevant ANFIS models are trained using standard ANFIS learning algorithm, while for the subsets with sparse training data, the corresponding ANFIS models are interpolated through the use of ANFIS interpolation. Experimental results indicate that compared with the methods using sufficient training data, the proposed method can achieve very similar result, showing its effectiveness for situations where only sparse training data is available.

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Dual UAV Cluster-Assisted Maritime Physical Layer Secure Communications via Collaborative Beamforming

arXiv

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arXiv 2024年

作者： Huang, Jiawei Wang, Aimin Sun, Geng Li, Jiahui Wang, Jiacheng Du, Hongyang Niyato, Dusit College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering Hong Kong University 999077 Hong Kong

Unmanned aerial vehicles (UAVs) can be utilized as relay platforms to assist maritime wireless communications. However, complex channels and multipath effects at sea can adversely affect the quality of UAV transmitted signals. Collaborative beamforming (CB) can enhance the signal strength and range to assist the UAV relay for remote maritime communications. However, due to the open nature of UAV channels, security issue requires special consideration. This paper proposes a dual UAV cluster-assisted system via CB to achieve physical layer security in maritime wireless communications. Specifically, one UAV cluster forms a maritime UAV-enabled virtual antenna array (MUVAA) relay to forward data signals to the remote legitimate vessel, and the other UAV cluster forms an MUVAA jammer to send jamming signals to the remote eavesdropper. In this system, we formulate a secure and energy-efficient maritime communication multi-objective optimization problem (SEMCMOP) to maximize the signal-to-interference-plus-noise ratio (SINR) of the legitimate vessel, minimize the SINR of the eavesdropping vessel and minimize the total flight energy consumption of UAVs. Since the SEMCMOP is an NP-hard and large-scale optimization problem, we propose an improved swarm intelligence optimization algorithm with chaotic solution initialization and hybrid solution update strategies to solve the problem. Simulation results indicate that the proposed algorithm outperforms other comparison algorithms, and it can achieve more efficient signal transmission by using the CB-based method. Copyright © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Navigating the Generative AI Project Ecosystem with a Focus on Addressing Data Architecture Complexities and Strategic Model Selection for Optimal Outcomes

Navigating the Generative AI Project Ecosystem with a Focus ...

引用

Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Priyanka Gupta I. Vasantha Kumari B Rajalakshmi Ginni Nijhawan Praveen Zahraa Sahib Mazaal Institute of Aeronautical Engineering Dundigal Hyderabad Medchal Malkajgiri District Siddhartha Institute Of Technology And Sciences Hyderabad Telangana Department of Computer Science New Horizon College of Engineering Bangalore Lovely Professional University Phagwara Knowledge Park II Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh Collage of Dentist National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

This article discusses the Generative Artificial Intelligence (AI) project’s complex ecology and how to pick the optimal models and data design. Creative AI in many commercial and academic fields presents new issues and opportunities. Knowing your way around generative AI projects is crucial. Choose models carefully since data design is hard to grasp. This research provides comprehensive guidance on how to address these areas and ensure project success. Data architecture study is the first step towards generative artificial intelligence since it identifies common issues and viable solutions. Next, to determine which generative models—in terms of complexity, scalability, and ethics—are best suited for a range of tasks, we must evaluate and compare many of them. Researchers were able to validate these findings using case studies and expert interviews. The results suggest that an ordered data infrastructure is required for creative AI. This study indicated the need to boost generational artificial intelligence programs by making practitioners and researchers more aware of the challenges. The study argues that further research and development are necessary to keep up with the fast progress of creative artificial intelligence.

关键词： Ethics Technological innovation Generative AI Biological system modeling Software algorithms computer architecture Data models

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Digital Imaging Analyzing and Reproducing Color, Texture, and Material Appearance

Digital Imaging Analyzing and Reproducing Color, Texture, an...

引用

International Conference on Contemporary Computing and Informatics (IC3I)

作者： Sujin Jose Arul Sajja Suneel Manish Gupta K. Varada Rajkumar Monica Garg Ali Abduhussien Hameed Department of Automobile Engineering Engineering New Horizon College of Engineering Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana India Lloyd Law College Plot No. 11 Knowledge Park II Greater Noida Uttar Pradesh Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Digital photography has made great strides in showing colour and texture along with specific material details. Standard approaches usually do not attain realistic representations. The Multifaceted Realism Synthesis presented in this document resolves these problems and outshines classic strategies in accuracy and realism. The MRS framework incorporates three core algorithms: With this approach we can obtain more consistent and accurate colour reproduction using the Colour Harmonisation Algorithm. The Physically Informed Material Rendering and Typical Texture Synthesis (ATS) generate more natural textures that adapt to local details rather than using median filtering or morphological methods. The MRS approach adjusts materials appearance texture and colour to deliver superior realism adaptability and precision beating current methods. This new approach can change digital photography across different areas such as entertainment and healthcare. The technique of MRS has made a substantial improvement in the quest for hyper-real visuals in digital photography.

关键词： Measurement Visualization Accuracy Image color analysis MIMICs Transforms Medical services Rendering (computer graphics) Digital photography Standards

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An Exhaustive Meta-Analysis on Consumer Retention Forecasting Using Advanced Machine Learning Techniques

An Exhaustive Meta-Analysis on Consumer Retention Forecastin...

引用

Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Sorabh Lakhanpal V Alekhya B Rajalakshmi Nida Khan Irfan Khan Saad Khudhur Mohammed Lovely Professional University Phagwara Institute of Aeronautical Engineering Dundigal Hyderabad Department of Computer Science New Horizon College of Engineering Bangalore DoCSE GyanGanga Institute of Technology and Sciences Jabalpur M.P India Knowledge Park II Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh College of Pharmacy National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

In this meta-analysis, we present a robust approach for consumer retention forecasting by integrating advanced machine learning algorithms. The method comprises three core algorithms: Consumer Behavior Neural Network (CBNN), Retention Decision Trees (RDT), and Long ShortTerm Memory for Retention (LSTM-R). Our objective is to model intricate relationships within consumer behaviors and accurately predict their retention probabilities. Consumer Behavior Neural Network (CBNN): CBNN, a key component, adeptly captures intricate patterns within consumer behavior data. It involves multiple hidden layers with activation functions, enabling accurate prediction of retention probabilities based on input features. Retention Decision Trees (RDT): This method utilizes decision tree algorithms to predict consumer retention decisions. By recursively splitting the dataset based on information gain, RDT efficiently models consumer behavior, as depicted in the flowchart. Information gain is maximized to create a robust predictive model. Long Short-Term Memory for Retention (LSTM-R): LSTM-R, tailored for temporal data in consumer retention prediction, leverages specialized LSTM architecture. It effectively processes sequential consumer interactions, crucial for accurate prediction of consumer retention probabilities. The proposed method integrates these algorithms, leveraging historical consumer data, and conducts a comprehensive meta-analysis to evaluate their predictive performance.

关键词： Consumer behavior Accuracy Machine learning algorithms Neural networks Machine learning Predictive models Prediction algorithms Decision trees Forecasting Long short term memory

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Location Optimization of Fresh Agricultural Products Distribution Center Based on Particle Swarm Optimization Neural Network

Location Optimization of Fresh Agricultural Products Distrib...

引用

International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： Minu Inba Shanthini Watson Benjamin R Bhuvana Venkata Reddy Adama E. Kiruthika S. Manohar Pradeep Kumar Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu Dept of Computer Science AM JAIN College Chennai Department of ECE Vaageswari College of Engineering Karimnagar Telangana India Department of CSE Knowledge Institute of Technology Kakapalayam Salem(Dt) Department of CSE Srm Institute Of Science And Technology Vadapalani Chennai ssDepartment of ECE CMR Institute of Technology Hyderabad Kandlakoya Telangana

High transportation cost is a major feature of fresh agricultural products distribution, how to effectively optimize the path, and then reduce transportation cost is the key method to promote the development of fresh logistics. In contrast to the good development prospects of the cold chain logistics market of fresh agricultural products, the development of India's cold chain logistics industry has entered a bottleneck period. According to the characteristics of fresh agricultural products with short preservation period, the mathematical model of location problem of multiple distribution centers with time constraint was established in order to save various operating costs and minimize investment costs. Traffic flow prediction is the basis of traffic control and guidance. The traditional traffic flow prediction technology has been unable to meet the actual demand, and some prediction methods with higher accuracy, especially intelligent calculation methods, keep emerging. Through the optimization of the whole system, to achieve the standardization of the whole system, standardized development, to improve the circulation quality and efficiency of fresh agricultural products, promote the rapid and healthy development of Modern agriculture in India, is of great significance. The key of cooperative distribution is to distribute the profit reasonably among distribution centers. Therefore, the topic of this paper has important practical significance in solving practical problems.

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