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5th International Conference on Smart systems and Inventive Technology, ICSSIT 2023

作者： Vijayalakshmi, V. Selvakumar, A. Panimalar, K. Cmr Institute of Technology Department of Information Science and Engineering Bengaluru India Simats School of Engineering Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai India

ISBN: (纸本)9781665474672

Medical expenses are increasing day by day due to unexpected epidemic diseases. People have awareness about the medical insurance and claiming processes which is helping in critical situations. Predicting the medical insurance cost is very important work in health care sectors. Many researcheis applied different machine learning algoiithms to predict the insurance premium in Kaggle data set with seven attributes such as age, sex, bmi, children, smoker, region and charges. But these features only not suitable for prediction. In this paper, the dataset with 24 features including all relevant attributes needed for prediction of insurance cost was used. The implementation done using regression algorithms such as Linear Regression, Decision Tree Regression, Lasso Regression, Ridge Regression, Random Forest Regression, ElasticNet Regression, Support Vector Regression, K Nearest Neighbor Regression and Neural Network Regression in R Programming. There were seven metiices applied to measure the performance of the model namely Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), RSquared (R2), Adjusted R-Squared (Adj. R2), and Explained Variance Score (EVS). Random Forest Regression outperformed with 0.9533 as RS quared value. This prediction system will reduce the manual work and give the prediction accurately in medical field. © 2023 IEEE.

关键词： Forecasting

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Harmonic Long-Range Backscatter with Frequency-Shifted Lightweight Tag 100

Harmonic Long-Range Backscatter with Frequency-Shifted Light...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Lin, Junliang Zhang, Xiannan Xu, Rongtao Wang, Gongpu Quek, Tony Q. S. Beijing Jiaotong University Beijing Key Laboratory of Transportation Data Analysis and Mining Beijing China Beijing Jiaotong University School of Electronic and Information Engineering Beijing China Singapore University of Technology and Design Department of Information Systems Technology and Design Singapore

ISBN: (纸本)9798331517786

In this paper, we introduce a simplified long-range (LoRa) backscatter system that enables a lightweight tag to communicate with a remote transceiver using chirp carrier and harmonic backscatter. The key idea is twofold: first, we delegate the chirp waveform generation from the tag to the transceiver, thereby maintaining lightweight tag design;second, we introduce a frequency shift on the tag during its carrier modulation to create harmonic backscatter, thereby mitigating self-interference. To refine the framework, we first detail the system model, including the design for harmonic backscatter. We then present an efficient method to address synchronization and detection issues at the transceiver. Finally, we prototype the proposed system and evaluate its performance. Experimental results demonstrate that in a corridor environment with a carrier power of 0 dBm, the tag can backscatter a signal from the transceiver at a maximum bit rate of 4 kb/s over a distance of 50 meters. © 2024 IEEE.

关键词： Harmonic analysis

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Semantic Template-based Convolutional Neural Network for Text Classification

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ACM Transactions on Asian and Low-Resource Language information Processing 2023年第11期22卷 1-21页

作者： Chang, Yung-Chun Ng, Siu Hin Chen, Jung-Peng Liang, Yu-Chi Hsu, Wen-Lian Graduate Institute of Data Science Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan Snhcc Tigp Academia Sinica National Cheng Chi University Taipei Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering College of Information and Electrical Engineering Asia University Taichung Taiwan Pervasive Ai Research Labs Ministry of Science and Technology Hsinchu City Taiwan

We propose a semantic template-based distributed representation for the convolutional neural network called Semantic Template-based Convolutional Neural Network (STCNN) for text categorization that imitates the perceptual behavior of human comprehension. STCNN is a highly automatic approach that learns semantic templates that characterize a domain from raw text and recognizes categories of documents using a semantic-infused convolutional neural network that allows a template to be partially matched through a statistical scoring system. Our experiment results show that STCNN effectively classifies documents in about 140,000 Chinese news articles into predefined categories by capturing the most prominent and expressive patterns and achieves the best performance among all compared methods for Chinese topic classification. Finally, the same knowledge can be directly used to perform a semantic analysis task. © 2023 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Semantics

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Geospatial Analysis of Extreme Temperature Impacts in Agricultural systems Using Machine Learning 10

Geospatial Analysis of Extreme Temperature Impacts in Agricu...

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10th IEEE International Symposium on systems engineering, ISSE 2024

作者： Wheeler, Rayshaun L. Miller, John S. Smith, Gia N. Lambert, James H. Pennetti, Cody A. Louis, Garrick E. Johnson, Deandre A. University of Virginia Department of Civil & Environmental Engineering CharlottesvilleVA United States University of Virginia School of Data Science CharlottesvilleVA United States University of Virginia Department of Systems & Information Engineering CharlottesvilleVA United States University of Virginia Department of Engineering & Society CharlottesvilleVA United States

ISBN: (纸本)9798350353723

Recent higher temperatures, observed compared to previous years, have led to more frequent and intense heat waves, adversely affecting agriculture and human health. This paper explores using Inverse Distance Weighting (IDW) and Kriging to develop GIS layers for a heat-based risk index. Recent increases in global mean temperatures have led to more frequent and intense heat waves, impacting food supply, vegetation, and human health. Optimal plant growth temperatures in temperate climates range from 20°C to 30°C, with nighttime temperatures also being critical. Exceeding these temperature ranges can reduce plant immunity, cause disease, and result in poor crop yields. For example, mango cultivars need a "safe"temperature range of 10°C to 12°C to avoid damage. The intensification of higher temperatures established July 2023 record the hottest days on Earth, highlighting the need for effective monitoring and management tools. IDW, a deterministic method, offers a straightforward and efficient approach by assuming closer points have more influence. Kriging, a probabilistic geostatistical technique, accounts for spatial autocorrelation and prediction uncertainty, making it suitable for complex datasets. The study compares these methods' performance in creating continuous surfaces representing heat-based risk. IDW is simple and efficient but less accurate in sparsely populated areas, while Kriging is more accurate and detailed but requires more computational resources and expertise. An automated workflow for data preprocessing, model training, and cross-validation ensures consistent results. The study recommends adopting IDW for initial assessments and transitioning to Kriging as expertise grows. Enhanced data collection on temperature and population density is also advised to improve GIS layer accuracy, aiding better decision-making and resource allocation to mitigate extreme heat effects on agriculture and public health. © 2024 IEEE.

关键词： Anomaly detection Climate change Complex systems Risk management System identification

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Social inequity of park accessibility in Taiyuan:highlighting the unfair layout of parks in second-tier cities of China and the relative role of contributors

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地球空间信息科学学报（英文版） 2024年第4期27卷 1118-1140页

作者： Jing He Fu Ren Jiaxin Dong Huihui Zhang Wenhao Yan Jiping Liu School of Resource and Environmental Sciences Wuhan UniversityWuhanChina School of Resource and Environmental Sciences Wuhan UniversityWuhanChina Key Laboratory of Geographic Information Systems Ministry of EducationWuhan UniversityWuhanChina School of Remote Sensing and Information Engineering Wuhan UniversityWuhanChina Guangdong Provincial Department of Natural Resources Land and Resources Technology CenterGuangzhouChina Research Center of Geospatial Big Data Application Chinese Academy of Surveying and MappingBeijingChina

The scarcity of public resources and environmental pollution caused by rapid urbanization highlight the practical significance of parks in ensuring the sustainable development of a ***,the social equity of parks warrants further *** paper proposes a fine-grained comprehensive evaluation framework that combines geographic accessibility models,geo-statistical analysis,and machine learning algorithms to explore social inequity in Taiyuan,*** this framework,gini coefficient and lorentz curve express spatial equality,accessibility shows spatial equity,and ridge regression model handles the interdependence of variables with different dimensions to quantify the relative effects of local participants on changes in park *** this basis,the imbalance between vulnerable groups and park supply is analyzed to further understand the core concept of social *** highlight serious spatial inequality in all three types of parks allocation of six urban areas,especially in commu-nity *** actual access level of people to parks is also stratified by their demographic and socioeconomic characteristics,revealing the social inequity in access to *** distribution is indeed not conducive to some social vulnerable groups,whose contradiction between supply and demand is highly prominent in urban-rural junctions and new urban *** paper also confirms the unfair layout of public facilities can be observed in second-tier cities of China by highlighting the social inequity of parks in *** findings of this work have profound implications for urban planning and sustainable development.

关键词： Parks social inequity spatial equality accessibility social vulnerable groups Taiyuan

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Brain Tumor Diagnosis Using Sparrow Search Algorithm Based Deep Learning Model

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Computer systems Science & engineering 2023年第2期44卷 1793-1806页

作者： G.Ignisha Rajathi R.Ramesh Kumar D.Ravikumar T.Joel Seifedine Kadry Chang-Won Jeong Yunyoung Nam Department of Computer Science and Business Systems Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of Information Technology Sri Krishna College of TechnologyCoimbatore641008India Department of Electronics and Communication Engineering Kings Engineering CollegeChennai602117India Department of Electronics and Communication Engineering R.M.K.Engineering CollegeChennai601206India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Medical Convergence Research Center Wonkwang UniversityIksanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Recently,Internet of Medical Things(IoMT)has gained considerable attention to provide improved healthcare services to *** earlier diag-nosis of brain tumor(BT)using medical imaging becomes an essential task,auto-mated IoMT and cloud enabled BT diagnosis model can be devised using recent deep learning *** this motivation,this paper introduces a novel IoMT and cloud enabled BT diagnosis model,named *** IoMTC-HDBT model comprises the data acquisition process by the use of IoMT devices which captures the magnetic resonance imaging(MRI)brain images and transmit them to the cloud ***,adaptive windowﬁltering(AWF)based image preprocessing is used to remove *** addition,the cloud server executes the disease diagnosis model which includes the sparrow search algorithm(SSA)with GoogleNet(SSA-GN)*** IoMTC-HDBT model applies functional link neural network(FLNN),which has the ability to detect and classify the MRI brain images as normal or ***ﬁnds useful to generate the reports instantly for patients located in remote *** validation of the IoMTC-HDBT model takes place against BRATS2015 Challenge dataset and the experimental analysis is car-ried out interms of sensitivity,accuracy,and speciﬁ*** experimentation out-come pointed out the betterment of the proposed model with the accuracy of 0.984.

关键词： Internet of medical things healthcare brain tumor disease classiﬁcation deep learning metaheuristics

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Multiclass Classification and Defect Detection of Steel Tube Using Modified YOLO 30th

Multiclass Classification and Defect Detection of Steel Tu...

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30th International Conference on Neural information Processing, ICONIP 2023

作者： G, Deepti Raj B, Prabadevi Gadekallu, Thippa Reddy Bhatia Khan, Surbhi Saraee, Mohammed School of Computer Science Engineering and Information Systems VIT University Vellore India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon College of Information Science and Engineering Jiaxing University Jiaxing314001 China Department of Data Science School of Science Engineering and Environement University of Salford Manchester United Kingdom School of science engineering and environment University of Salford Salford United Kingdom

ISBN: (纸本)9789819981830

Steel tubes are widely used in hazardous high pressure environments such as petroleum, chemicals, natural gas and shale gas. Defects in steel tubes have serious negative consequences. Using deep learning object recognition to identify and detect defects can greatly improve inspection efficiency and drive industrial automation. In this work, we use a well-known YOLOv7(You Only Look Once version7) deep learning model and propose to improve it to achieve accurate defects detection of steel tube images. First, the classification of the dataset is checked using a sequential model and AlexNet. A Coordinate Attention (CA) mechanism is then integrated into the YOLOv7 backbone network to improve the expressive power of the feature graph. Additionally, the SIoU (SCYLLA-Intersection over Union) loss function is used to speed up convergence due to class imbalance in the dataset. Experimental results show that the evaluation index of the optimized and modified YOLOv7 algorithm outperforms other models. This study demonstrates the effectiveness of using this method in improving the model’s detection performance and providing a more effective solution to steel tube defects. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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Enabling Efficient NVM-Based Text Analytics without Decompression

Enabling Efficient NVM-Based Text Analytics without Decompre...

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International Conference on data engineering

作者： Xiaokun Fang Feng Zhang Junxiang Nong Mingxing Zhang Puyun Hu Yunpeng Chai Xiaoyong Du Key Laboratory of Data Engineering and Knowledge Engineering (MOE) and School of Information Renmin University of China Department of Computer Science and Engineering Tsinghua University

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Text analytics directly on compression (TADOC) is a promising technology designed for handling big data analytics. However, a substantial amount of DRAM is required for high performance, which limits its usage in many important scenarios where the capacity of DRAM is limited, such as memory-constrained systems. Non-volatile memory (NVM) is a novel storage technology that combines the advantage of reading per-formance and byte addressability of DRAM with the durability of traditional storage devices like SSD and HDD. Unfortunately, no research demonstrates how to use NVM to reduce DRAM utilization in compressed data analytics. In this paper, we propose N-TADOC, which substitutes DRAM with NVM while maintaining TADOC's analytics performance and space savings. Utilizing an NVM block device to reduce DRAM utilization presents two challenges, including poor data locality in traversing datasets and auxiliary data structure reconstruction on NVM. We develop novel designs to solve these challenges, including a pruning method with NVM pool management, bottom-up upper bound estimation, correspondent data structures, and persistence strategy at different levels of cost. Experimental results show that on four real-world datasets, N-TADOC achieves 2.04× performance speedup compared to the processing directly on the uncompressed data and 70.7% DRAM space saving compared to the original TADOC.

关键词： Performance evaluation Upper bound data analysis Costs Nonvolatile memory Random access memory Estimation

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A Non-Parametric Scheme for Identifying data Characteristic Based on Curve Similarity Matching

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1424-1437页

作者： Quanbo Ge Yang Cheng Hong Li Ziyi Ye Yi Zhu Gang Yao School of Automation Nanjing University of Information Science&TechnologyJiangsu Provincial University Key Laboratory of Big Data Analysis and Intelligent SystemsNanjing University of Information Science&Technologyand the Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)Nanjing University of Information Science&TechnologyNanjing 210044China School of Department of Logistics Engineering Shanghai Maritime UniversityShanghai 201306China China Classification Society Nantong Office Nantong 226006China Chinese Flight Test Establishment Xi’an 710089China School of Mathematics Tsinghua UniversityBeijing 100084China School of Automation Nanjing University of Information Science&TechnologyNanjing 210044China Department of Logistics Engineering Shanghai Maritime UniversityShanghai 201306China IEEE

For accurately identifying the distribution charac-teristic of Gaussian-like noises in unmanned aerial vehicle(UAV)state estimation,this paper proposes a non-parametric scheme based on curve similarity *** the framework of the pro-posed scheme,a Parzen window(kernel density estimation,KDE)method on sliding window technology is applied for roughly esti-mating the sample probability density,a precise data probability density function(PDF)model is constructed with the least square method on K-fold cross validation,and the testing result based on evaluation method is obtained based on some data characteristic analyses of curve shape,abruptness and *** com-parison simulations with classical methods and UAV flight exper-iment shows that the proposed scheme has higher recognition accuracy than classical methods for some kinds of Gaussian-like data,which provides better reference for the design of Kalman filter(KF)in complex water environment.

关键词： Curve similarity matching Gaussian-like noise non-parametric scheme parzen window.

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Deep Learning for Automated Classification of Kidney Lesions in Chronic Kidney Disease Patients 2

Deep Learning for Automated Classification of Kidney Lesions...

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2nd IEEE International Conference on Integrated Circuits and Communication systems, ICICACS 2024

作者： Lakshmi, B. Seetha Anand, Vijay Omotunde, Habeeb Birundadevi, M. Mahalakshmi, S. Brahmam, Madala Guru Sri Shanmugha College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamilnadu Namakkal India School of Computer Science Engineering & Information Systems Vellore Institute of Technology Vellore India College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University Department of Information Systems Riyadh Saudi Arabia Sri Sairam Siddha Medical College and Research Centre Department of Maruthuvam Chennai India R.M.K. Engineering College Department of Computer Science and Design Tamilnadu Chennai India

ISBN: (纸本)9798350317558

Deep Learning was utilized to autonomously classify renal abnormalities, which improved the diagnostic accuracy for individuals with chronic kidney disease (CKD), according to this study. During training on a meticulously selected dataset, the proposed model outperformed the most recent approaches. By utilizing medical imaging data - specifically quantitative and qualitative data - to differentiate between discrete kidney abnormalities, the model demonstrated exceptional levels of accuracy, precision, and recall. Furthermore, apart from effectively resolving ethical considerations pertaining to data privacy, this innovative methodology has the potential to fundamentally transform the existing landscape of chronic kidney disease (CKD) diagnosis and treatment. The results demonstrate that the model effectively manages intricate renal disorders, signifying a noteworthy progression in the field of medical image processing. This research paves the way for additional fruitful lines of inquiry, which ought to enhance nephrology diagnostics and patient care. © 2024 IEEE.

关键词： Automated Classification Chronic Kidney Disease Deep Learning Diagnostic Accuracy Kidney Lesions Medical Image Analysis

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