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International Conference on e-health Networking, Applications and Services (HealthCom)

作者： Mengting Wang Yen Le Chen-Hsiang Yu School of Computing and Data Science Wentworth Institute of Technology Boston MA U.S.A Multidisciplinary Graduate Engineering College of Engineering Northeastern University Boston MA U.S.A

Managing healthcare data is essential for everyone, but it can be challenging and time-consuming. Although several Web and mobile applications existed for personal medical record management, there were few home-based, family-oriented solutions. In this research, we proposed to design an easy-to-use solution for a family. We designed and implemented an innovative Home Medical Care System, MedCare4Home, by deploying a MERN stack Web application to a minicomputer, Raspberry Pi 4, and enabling a local network to connect different devices at home. The users can access the Web application using either a desktop, tablet, mobile phone, or simply a 7-inch LCD touchscreen. MedCare4Home not only allows users to keep track of appointments and organize healthcare documents, but it can also help set up medication reminders and provide a self-report interface for symptom collection for family members easily and securely in one place. The final developed system demonstrated that we achieved the original goal and beyond. We expect to extend our current hardware design and software development to support physiological data collection with external sensors in the future.

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eVision - A technological solution to assist vision impaired in self-navigation 7

eVision - A technological solution to assist vision impaired...

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7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Hewagama, K.G. Suwandarachchi, T.D. Hettiarachchi, C.R. Alwis, P.L.D.N. Karunasena, A. Weerasinghe, K.M.L.P. Sri Lanka Institute of Information Technology Department of Information Technology Specializes in Data Science Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Specializes in Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9781665421683

Visually impaired people face many difficulties in navigation such as crossing the road, identifying signs and text in indoor and outdoor environments and avoiding obstacles. Even though much research has been done to assist visually impaired people, most methods are unpopular, and almost all visually impaired people still only rely on the white cane. This paper proposes eVision which consists of a mobile app as well as a wearable tool that enables visually impaired people to detect obstacles and objects such as moving vehicles and staircases, identify signs, provide assistance with road crossing and natural scene text recognition, using Convolutional Neural Networks and image processing techniques. The CNN architecture used for object detection was SSD Mobilenet V2, since it provided around 95% accuracy for most objects with good performance on mobile. Mobilenet V2 transfer learning model was used for classification of objects, which provided around 94% accuracy. For text detection, the EAST algorithm was used, and the method resulted an accuracy around 98%. From the generated data from models, eVision provides audio feedback to the user using a text to speech(TTS) system. © 2022 IEEE.

关键词： Navigation

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Building a data Lake for Power BI in the Cloud: A Review on Utilizing Cloud Storage Services for Large datasets

Building a Data Lake for Power BI in the Cloud: A Review on ...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Viraj Vijay Gurbade Prateek Verma Swapnil Gundewar Vijendra Singh Bramhe Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India School of Computing Science and Engineering VIT Bhopal University Sehore Madhya Pradesh India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

A data lake is a centralized repository where you may store both structured and unstructured data at any scale. Unlike typical data warehouses, which need data structure for storage, data lakes allow you to store raw data in its natural state. This adaptability makes data lakes suitable for storing large volumes of various data kinds, including photographs, videos, log files, sensor data, and others. When integrating a data lake with Power Business Intelligence (BI), organizations can harness the power of both technologies to gain valuable insights from their data. In the realm of data-driven decision-making, organizations face the challenge of managing and extracting insights from vast and diverse datasets, a task conventional on-premises storage solutions often struggle with due to the exponential growth of data. Thus, the adoption of more scalable and flexible alternatives becomes imperative. Cloud-based data lakes emerge as transformative solutions, offering unparalleled scalability, accessibility, cost-effectiveness, and advanced security protocols. This paper discuss the concept of constructing a cloud-based data lake optimized for Power BI, leveraging leading cloud storage services like Azure data Lake Storage (ADLS) and Amazon S3. By seamlessly integrating Power BI with the cloud data lake, organizations can overcome traditional storage limitations and reap numerous benefits. The practical applications in advanced analytics, customer insights, IoT data management, and data discovery underscore the real-world utility of cloud data lakes. The integration of Power BI with a cloud data lake augments efficient data analysis and self-service analytics potential, offering organizations a comprehensive solution for deriving actionable insights from their data assets.

关键词： Cloud computing Scalability Decision making Buildings Organizations Big data applications Security data mining Business intelligence Self-service

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Multi-Stage Vehicle Dispatch for Community Group-buying Logistics via Deep Reinforcement Learning

Multi-Stage Vehicle Dispatch for Community Group-buying Logi...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Lingyi Xu Xingyuan Liang Xiaolei Zhou Tian He School of Computer Science and Engineering Southeast University The Sixty-Third Research Institute National University of Defense Technology Department of Technology and Data Intelligence JD Logistics

ISBN: (数字)9798350358261

ISBN: (纸本)9798350358278

Community group-buying is an emerging shopping model in which individuals within the same community collectively purchase daily necessities at lower prices. The logistics transportation in this scenario requires higher turnover rates and timeliness compared to traditional online shopping. Therefore, making efficient vehicle dispatch decisions is necessary to improve delivery efficiency and reduce transportation costs. However, uncertainties in orders and complex dependencies between multi-stage decisions in the community group-buying scenario challenge the scheduling of delivery vehicles. In this paper, we propose a vehicle dispatch system for community group-buying logistics based on a predict-then-optimize process, which incorporates a demand prediction module and a vehicle dispatch module. In the prediction module, we categorize goods and predict the total quantity of each category using a multi-layer perceptron (MLP). In the vehicle dispatch module, we model the vehicle dispatch process as a reinforcement learning problem and use an action masking mechanism to prune the policy search space. Experimental results demonstrate that the vehicle dispatch system achieves state-of-the-art performance compared to several popular baselines across three different datasets. Our system reduces transportation costs by at least 8.89% and improves decision efficiency by at least 10%. Moreover, it behaves close to the approximate optimal solution in most cases.

关键词： Space vehicles Costs Uncertainty Transportation Predictive models Search problems Sensors

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The Second Order Asymptotics of Covert Communication over AWGN Channels

arXiv

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arXiv 2023年

作者： Yu, Xinchun Wei, Shuangqing Huang, Shao-Lun Zhang, Xiao-Ping The Institute of information and Data Science Tsinghua Shenzhen International Graduate School Shenzhen China The Division of Electrical and Computer Engineering School of Electrical Engineering and Computer Science Louisiana State University Baton RougeLA70803 United States

This paper investigates the asymptotics of the maximal throughput of communication over AWGN channels by n channel uses under a covert constraint in terms of an upper bound δ of Kullback-Leibler divergence (KL divergence). It is shown that the first and second order asymptotics of the maximal throughput are √nδ log e and (2)1/2(nδ)1/4(log e)3/4 · Q−1(ϵ), respectively. The technique we use in the achievability is quasiΕ-neighborhood notion from information geometry. For finite blocklength n, the generating distributions are chosen to be a family of truncated Gaussian distributions with decreasing variances. The law of decreasing is carefully designed so that it maximizes the throughput at the main channel in the asymptotic sense under the condition that the output distributions satisfy the covert constraint. For the converse, the optimality of Gaussian distribution for minimizing KL divergence under the second order moment constraint is extended from dimension 1 to dimension n. Based on that, we establish an upper bound on the average power of the code to satisfy the covert constraint, which further leads to the direct converse bound in terms of covert metric. © 2023, CC BY-NC-ND.

关键词： Gaussian distribution

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Human Resource Management based Economic analysis using data Mining 3

Human Resource Management based Economic analysis using Data...

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3rd International Conference on Intelligent engineering and Management, ICIEM 2022

作者： Sindhura, K. Sabarirajan, A. Kumar Siddamallappa, U. Narang, Pravin Bhanushali, Mahesh Manohar Turai, Anjaneya Krishna Studies University of Madras Department of Management Tamilnadu Chennai India PSNA College of Engineering & Technology Department of Management Studies Tamilnadu Dindigul India Devangere University DOS in Computer Science Karnataka Davanagere India VPM's Dr. V. N. Bedekar Institute of Management Studies Management Studies Maharashtra Thane India Data Science Student Data Science Symbiosis Skill and Professional University Maharashtra Mumbai India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

Researchers in this field are looking at how data mining and HRM are intertwined to manage human resources better. An Ensemble Classifier-Decision Tree (EC-DT) approach may analyse Human Resource Management (HRM) data. C4.5, Random Tree, J48, and Simple Cart are the single decision tree algorithms employed. Based on the algorithm design, algorithms for evaluation and talent recommendation are then assessed in an HRM system. The algorithm under development is put through its last paces in terms of comparisons and testing. The EC-DT method has a classification accuracy of 79.97 %, whereas C4.5 has a classification accuracy of 76.69 %. DMRM's accuracy and recall were 35.2 % and 41.6 % better than CRM's and CFR's combined, proving that data mining may be a powerful tool for recommending products and services (CFRM). Thus, the data mining-based human resources management system may help organisations expand due to quantitative analyses. In this way, the data mining-based HRM system may encourage and direct businesses to grow under the outcomes of quantitative assessments. A starting point for further research on data mining-based HRM systems may be found in the abovementioned findings © 2022 IEEE.

关键词： data mining

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Big data Analytics for Quality Control in The Pharmaceutical Industry

Big Data Analytics for Quality Control in The Pharmaceutical...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： P. Malathi S. Jansi M.V. Prabhakaran K. Senbagam M.P. Sujatha R Sadaieswaran Department of Data Analytics Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer science and Engineering Acharya college of Engineering and Technology Achariyapuram Puducherry India Department of Computer Science and Engineering Dhanalakshmi Srinivasan College of Engineering and Technology Mamallapuram Tamil Nadu India Department of Computer Science and Engineering Agni College of Technology Chennai India Department of Computer Applications Tagore College of arts and science Chennai India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

In order to ensure the safety and manufacturing efficiency of pharmaceuticals, rigorous quality control measures are necessary. However, existing quality control systems often struggle with industrial process complexity and regulatory requirements. The proposed study highlights the need for a revolutionary strategy to overcome the limitations of existing systems. Quality control systems in existing systems have limitations such as real-time monitoring, insufficient data analysis for pattern identification, and limited predictive capabilities. These limitations have an impact on the industry's ability to recognize and manage quality concerns, posing a danger to medicine manufacturing. For addressing these issues, a novel approach to maintain quality control in the pharmaceutical sector using big data analytics is proposed. Advanced analytics are used for real-time monitoring, data analysis and predictive modeling in the proposed system. The results and analysis through extensive comparisons shows proposed big data analytics system achieves 95% fault detection, 40% downtime reduction, and 35% waste reduction in pharmaceutical manufacturing. The proposed system's ability to handle massive datasets allows it to identify complicated patterns and abnormalities, enabling proactive quality control. The implementation exceeds industry standards, addressing data privacy and security concerns.

关键词： Waste reduction Technological innovation data analysis Fault detection Quality control Big data Real-time systems Manufacturing Predictive analytics Pharmaceuticals

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Graph-Based Temporal Attention for Coronary Artery Disease Prediction Using Electronic Health Records

Graph-Based Temporal Attention for Coronary Artery Disease P...

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International Computer Symposium (ICS)

作者： Kun-Wei Lin Kuan-Hui Liu Hsin-Yao Wang Yi-Ju Tseng Institute of Computer Science and Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Data Science and Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Laboratory Medicine Chang Gung Memorial Hospital (CGMH) at Linkou Taoyuan Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan

ISBN: (数字)9798331509071

ISBN: (纸本)9798331509088

Deep learning has significantly enhanced the ability to predict health outcomes using Electronic Health Records (EHR). However, the challenges posed by EHR data, such as sparsity, heterogeneity, and temporal complexity, necessitate innovative representation techniques. This research introduces a novel graph-based approach to effectively represent EHR data for the early prediction of coronary artery disease (CAD). We proposed a dual-graph approach by constructing then fusing two distinct graphs: a diagnosis graph and a lab graph. The diagnosis graph employs diagnosis nodes and visit nodes to capture patient diagnoses over time, while the lab graph discretizes continuous laboratory measurement results into four quantile levels, represented as lab nodes connected to visit nodes. Graph convolutional networks (GCN) are used separately on each graph to manage data sparsity, and a time-aware transformer layer captures the temporal dynamics of patient visits. The resulting visit embeddings from both graphs are concatenated and processed through fully connected layers for dimensionality reduction and final classification. Our model was evaluated on a dataset of 6,272 CAD patients and 47,295 non-CAD patients, using three different observation periods. Ablation studies were also performed to assess the impact of using only diagnosis data or only laboratory measurement data. The results demonstrate that our dual-graph approach effectively integrates diagnosis and laboratory measurement data, capturing both spatial and temporal information to accurately predict CAD, achieving a best AUROC (area under the receiver operation characteristic) of 0.908 ± 0.008. This method simplifies the graph structure, enhances interpretability, and improves predictive performance compared to previous techniques.

关键词： Dimensionality reduction Solid modeling Graph convolutional networks Receivers Transformers Time measurement Spatial databases Electronic medical records Arteries Diseases

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Deviations in Efficiencies of Logarithmically Growing Sorting Algorithms at Infinitely Large datasets 3

Deviations in Efficiencies of Logarithmically Growing Sortin...

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3rd IEEE International Virtual Conference on Innovations in Power and Advanced Computing Technologies, i-PACT 2021

作者： Mathew, D.A. Steve School of Information Technology and Engineering Vellore Institute of Technology Vellore632 014 India Programming and Data Science Indian Institute of Technology Madras Chennai600 036 India

ISBN: (纸本)9781665426916

Asymptotic Notations have been very effective in representing the efficiencies of algorithms by analyzing the number of steps and the nature of traversals and operations that the algorithm is bound to perform. Though they represent the nature of growth of the time or space consumed by the algorithms, they only consider the fastest-growing term and not the other terms. The result from this kind of analysis often seems to be fruitful in grading the algorithms based on their efficiency, but theoretically, there seem to be evident deviations when these complexities are extrapolated to larger datasets, hypothetically 'infinitely large' datasets. This deviation is tracked down when the constant and slow-growing terms involved are considered. The practical difficulty of empirically verifying this fact due to the ambiguity in defining a very large number of inputs is overcome by theoretically calculating the ratio between the time complexities of algorithms of interest and applying their limit to be infinity. In this study, the algorithms heapsort and merge sort are analyzed and the drastic deviation between them is shown mathematically though they both have the complexity of $n(log(n))$, concluding that the merge sort is actually twice as fast as heap sort when approaching infinitely large data elements. © 2021 IEEE.

关键词： Large dataset

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Inbuilt Framework Using IoT and Computer Vision Technique for Automatic Visitor Management 7

Inbuilt Framework Using IoT and Computer Vision Technique fo...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Yedukondalu, Gangolu Rajesh, Nichenametla Kumar, B Krishna Gowd, G.Sudha Suguna, R. Gupta, K.Gurnadha Anurag University Department of Data Science Hyderabad India KL Deemed to Be University Dept of AI&DS AP Vaddeswaram India Nalla Malla Reddy Engineering College Department of CSE Hyderabad India Srinivasa Ramanujan Institute of Technology CSE Department Anantapuram India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of CSE Chennai India K L Deemed to Be University Department of CSE AP Vaddeswaram India

ISBN: (纸本)9798350340600

The Visitor Monitoring (VM) concept is based on the use of systems and gadgets to improve occupant comfort, energy efficiency, privacy, and safety of its inhabitants. Visitor monitoring system is a system through which we can monitor the people who are entering into the workplaces or offices in a digital way. These highly developed electronic dwellings are multifaceted, intricate systems that require research to improve upon those procedures. However, there is lack of consensus in the existing VM concept. So, there is a requirement to research and innovate more effective VM systems to countermeasure new and emerging processes. This research work proposes an automated visitor management tool to manage and coordinate the visitors by using IoT and computer vision techniques. Experiments were carried out on 100 visitors by using putty platform and observed noticeable result. © 2023 IEEE.

关键词： Computer vision

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