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An Innovative Internet of things (IoT) computing-Based Health Monitoring System with the Aid of machine learning Approach 5

An Innovative Internet of Things (IoT) Computing-Based Healt...

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5th International conference on Contemporary computing and Informatics, IC3I 2022

作者： Kumar, Bhupendra Rajput, Namita Yagnam, Nagesh Debre Tabor University Debre Tabor College of Business & Economics Department of Accounting & Finance Ethiopia Sri Aurobindo College Department of Commerce University of Delhi Delhi India Debre Tabor University Department of Information & Technology Africa Ethiopia

ISBN: (纸本)9798350398267

the community health area comprises a massive amount of information, and particular approaches are used to handle that information. One of the most common approaches is handling as well as processing. this technique forecasts the likely consequences of cardiovascular disease. the outcome of this method is to predict the preceding heart illness. the job controls IOT using a sensor (a pulse sensor to monitor pulses) and Arduino, and the results may be seen on a sequential screen. IFTTT is used to analyze sensor readings in Google Sheets, which are subsequently converted into CSV go-like data. the datasets used are classified according to treatment parameters, in addition to being used for data preparation and testing. this technique evaluates those parameters using the information preparation order method. With AI computations and categorization work. the dataset is first dissected, examined, and screened, after which the gathered data is processed in Python programming using machine learning Algorithms, namely Decision Tree Algorithm with Random Backwoods Classification Algorithm. SVM (Support vector machine) produces the best results in terms of detecting heart disease. As a result, the suggested paradigm is shown to be a reliable one for predicting past heart disease. the suggested hardware and software technology assists patients in predicting cardiac disease in its initial stages. © 2022 IEEE.

关键词： Internet of things

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A Unified Framework and a Case Study for Hyperparameter Selection in machine learning via Bilevel Optimization 5

A Unified Framework and a Case Study for Hyperparameter Sele...

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5th International conference on Data Science and Information Technology, DSIT 2022

作者： Li, Zhen Qian, Yaru Li, Qingna Beijing Institute of Technology School of Mathematics and Statistics Beijing China Beijing Institute of Technology Beijing Key Laboratory on MCAACI Beijing China

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

Support vector classification (SVC) is a classical and widely used method for classification problems. A regularization parameter, which significantly affects the classification performance, has to be chosen. In this paper, we propose a unified framework for hyperparameter selection in machine learning via bilevel optimization. Specially, we study the case for l2-loss SVC in which the upper-level objective function is the hinge loss function, and the lower-level problems are l2-loss SVC models. Following the same way as in our recent work, we reformulate the bilevel optimization model as a mathematical program with equilibrium constraints (MPEC). Similarly to that in our recent work, the MPEC-tailored version of the Mangasarian-Fromovitz constraint qualification (MPEC-MFCQ) automatically holds at each feasible point of the MPEC. Extensive numerical results verify that the global relaxation cross-validation algorithm for l2-loss SVC (GRCV-l2) enjoys superior generalization performance over almost all the datasets used in this paper. © 2022 IEEE.

关键词： machine learning

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A Novel Performance Evaluation Strategy of Automatic machine learning on Electricity Services 5

A Novel Performance Evaluation Strategy of Automatic Machine...

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5th International conference on Data Science and Information Technology, DSIT 2022

作者： Ding, Qi Wang, Qingjuan Jin, Liangfeng Shen, Ran State Grid ZheJiang Marketing Service Center Hangzhou China

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

In electricity services, the customers have many different requests, and different solutions need to be designed for filling these demands. For example, the customer sentiment classification task and meter image classification task requires experts to design different models to solve them respectively. However, it is time comsuming to design a specific model for machine learning experts, which largely limits the application of AI in electricity operation. In recent years, with the development of neural structure search algorithms, people have started to apply this technique to various scenarios. In this paper, based on the latest academic research on the neural structure search algorithm, we innovatively improve the neural structure search by a novel performance evaluation strategy in terms of model size and time consumption to effectively balance model accuracy, size, and latency for the large task volume and short response time of electricity services. the experimental results show that the algorithm can search for models with almost no loss of model accuracy while effectively constraining the memory size and reducing the computational latency of the models, thus better adapting to the tasks related to electricity services. © 2022 IEEE.

关键词： machine learning

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Implementation of a machine learning-based Trust Management System in Social Internet of things 5

Implementation of a Machine Learning-based Trust Management ...

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5th International conference on Contemporary computing and Informatics, IC3I 2022

作者： Goswami, Chandrashekhar Raman, Ramakrishnan Pillai, Biju G. Singh, Rajesh Dhanne, Basava Kapila, Dhiraj Amity University Madhya Pradesh Gwalior India Maharashtra Pune India Pune India Uttaranchal Institute of Technology Uttaranchal University Uttarakhand Dehradun India St. Matrins Engineering College Electronics and Communication Engineering Hyderabad Telangana Secunderabad India Lovely Professional University Department of Computer Science & Engineering Punjab Phagwara India

ISBN: (纸本)9798350398267

IoT (Internet of things) is a constantly expanding network made up of billions of connected physical things, including many sensors, embedded devices, smartphones, and wearables. these actual things are typically referred to as 'smart objects'. A development of the Internet of things, the Social Internet of things (SIoT) combines ideas of social networking to create social networks of linked smart items. they sift through the SIoT looking for services and relevant information. In the early stages of research, the concept of trust and trustworthy in social communities created by SIoT is still relatively new. We outline the foundations of SIoT and the ideas of trust in SIoT while highlighting the parallels and discrepancies between IoT and SIoT. In order to determine an aggregate trust score, all of the trust features are aggregated using a machine learning-based algorithm. Results show that the suggested trust-based model effectively separates the network's trustworthy and untrustworthy nodes. © 2022 IEEE.

关键词： Internet of things

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A Review: Heart Disease Prediction Based on Conventional and machine learning Algorithms 5

A Review: Heart Disease Prediction Based on Conventional and...

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5th International conference on Contemporary computing and Informatics, IC3I 2022

作者： Kumar, Sandeep Bhola, Abhishek Satvika, Vandavasi Venkata Akshaya, Palapati Vaishnavi Reshma, Nannapaneni Kumar, Maka Hemanth Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350398267

In our daily life, heart-related diseases are the most dangerous diseases in the world, some of them can be cured even though it is time taking, but others have high-risk factors that make them difficult to treat. Considering these risk factors, there is a chance that a patient can die. this is the main criterion to focus on this disease because of death rate is high. As a team, we mainly focused on four diseases that have recorded increased deaths: Heart disease, Depression, Kidney disease, and Lung Cancer. Based on this, we have surveyed the disorders to predict them at the earliest, making them easy to cure. From this survey, we learned that a high number of deaths are recorded due to heart-related issues, which 17.9 million people globally each year. It is known that diagnosis of heart disease is quite challenging, but that doesn't mean that person is on the verge of their life. there are effective ways in which a person can be gone through an automated prediction of their heart condition. machine and deep learning algorithms are practical and efficient in producing the most accurate results. these algorithms are helpful in many ways and different fields, mainly in the medical domain, for predicting diseases based on symptoms. © 2022 IEEE.

关键词： Diseases

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machine learning Based Corporate Climate Change Disclosure in Integrating Institutional and Resource Approach based on Computational Intelligence Method 5

Machine Learning Based Corporate Climate Change Disclosure i...

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5th International conference on Contemporary computing and Informatics, IC3I 2022

作者： Supreeth, B.R. Ansari, Mohammed Saleh Al Jasim, Nada Hasan Swain, Prakash Chandra Srinivasan, Dhamodharan Pundir, Sumit Oxford Degree and PG College Karnataka Bangalore India University of Bahrain Bahrain Bahrain Imam ja'Afar Al-Sadiq University College of Information Technology Iraq Centurion University of Technology and Management Odisha R. Sitapur India Sri Eshwar College of Engineering Tamilnadu Coimbatore India Graphic Era Deemed to Be University Uttarakhand Dehradun India

ISBN: (纸本)9798350398267

there has to be a coordinated effort between academia, government, and business to evaluate and shape the impact of AI and ML on efforts to slow global warming. It's not clear how ML will affect GHG emissions in the future, but it's already having an impact now. this is due in part to the fact that assessing and predicting the effects of these emissions is complicated by the fact that their various processes are not well characterised. As a result, we present a structured approach for characterising the consequences of ML on GHG emissions, which is comprised of three levels: (1) compute-related implications;(2) immediate repercussions of using ML;and (3) effects on the whole system. this approach is used to assess and rank the importance of the information and studies required for carrying out an effect evaluation, doing a scenario analysis, or identifying crucial policy levers. © 2022 IEEE.

关键词： machine learning

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Computational learning Model for Prediction of Parkinson’s Disease Using machine learning 5th

Computational Learning Model for Prediction of Parkinson’s ...

引用

5th International conference on Soft computing and Signal Processing, ICSCSP 2022

作者： Swathi, Ch. Cheripelli, Ramesh Department of CSE G. Narayanamma Institute of Technology and Science Telangana Hyderabad India Department of IT G. Narayanamma Institute of Technology and Science Telangana Hyderabad India

ISBN: (纸本)9789811986680

Parkinson’s disease (PD) is a chronic and progressive condition;a wide variety of motor and cognitive symptoms may present. Parkinson’s disease symptoms may coexist with other illnesses such as age-related decline and essential tremor. In order to aid in the identification of this ailment, many researches have been conducted in the last few years. Deep learning, recursive neural networks, and convolutional neural networks will be employed in this study to draw a distinction between healthy individuals and those with PD. A secondary objective of this research is to see whether the PD test can better distinguish between individuals by analysing a variety of imaging and movement data sets (notably cube and spiral pentagon data sets). We’ll see time series or imaging data sets are better at detecting Parkinson’s disease as a result of this study. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Principal component analysis

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Research on kiwifruit grading detection system based on depth limit learning machine 5

Research on kiwifruit grading detection system based on dept...

引用

5th International conference on Algorithms, computing and Artificial Intelligence, ACAI 2022

作者： Wang, Liang Department of Engineering Management International College Krirk University Bangkok10220 Thailand

ISBN: (纸本)9781450398343

Aiming at the low recognition rate and low accuracy of traditional kiwifruit grading detection, a feature extraction method based on machine vision kiwifruit detection system is proposed. In order to improve the recognition accuracy, the R component map is used as the input map, and the median filtering method is used to denoise and extract the surface defect features of kiwifruit. On this basis, the least square method is used to establish the ellipse fitting curve to extract the size features, and the mean and standard deviation of H, I, S are used to extract the color features;At the same time, 30 models were selected to establish the deep extreme learning machine (DELM) model to identify kiwifruit defects. Finally, 326 kiwi images were constructed, and the results showed that the correct recognition rate of the depth limit learning machine model was 97.5%, which could achieve accurate grading. © 2022 ACM.

关键词： Feature extraction

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Contrastive learning for Multiple Models in One Supernet 5th

Contrastive Learning for Multiple Models in One Supernet

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5th International conference on Cognitive computing (ICCC) Held as Part of the 19th Services conference Federation (SCF)

作者： Xu, Sean Li, Yihui Zhang, Yice Xu, Ruifeng Li, Jianxin Shi, Guozhong Hu, Feiran Harbin Inst Technol Shenzhen Joint Lab HITSZ CMS Shenzhen Peoples R China China Merchants Secur Co Ltd Shenzhen Peoples R China

ISBN: (纸本)9783030964191;9783030964184

Autonomous machine learning (AML) alludes to a learning framework having an adaptable trademark to develop the construction and boundaries on the fly. It is empowered by the way that AMLs means to adjust among stability and plasticity of a learning system. At present, deep learning-based unsupervised learning is another intriguing issue in the field of Autonomous machine learning, among which multi-architecture optimization is of extraordinary trouble in this research area. At the point when the current calculations face multi-intellectual model issues, it frequently sets aside a great deal of effort to ceaselessly set diverse looking through functions of different boundaries to look for the ideal model. In this work, we propose a novel technique for multi-model contrastive learning, which can get diverse unsupervised learning structures that are fit for deploying under different budgets, through a single shot super-network. Experiments reveal that our algorithm can boost the performance of the existing methods.

关键词： Neural Architecture Search Unsupervised learning Model compression

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Identifying Face Masks with OpenCv and Deep learning 5

Identifying Face Masks with OpenCv and Deep Learning

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5th International conference on Contemporary computing and Informatics, IC3I 2022

作者： Kumari, Alka Shekokar, Kishori ARKA JAIN University Department of Computer Science Jharkhand Jamshedpur India PIET Department of CSE Parul University Gujarat Vadodara India

ISBN: (纸本)9798350398267

During the course of this epidemic, the Corona virus had a significant influence not only regular lives but also on international business. Protecting one's appearance has recently emerged as a widespread fashion trend and can now be considered the norm. In the present day or in the future, a large number of individuals will be obliged to wear masks in order to protect not only themselves but also the people around as well as the surrounding area. Face recognition has emerged as an increasingly vital tool in the fight against global terrorism. As part of this work, we are developing an AI system that will be able to determine whether or not a person is concealing their identity by wearing a mask. It will be of assistance to us in preventing the virus from spreading across the environment. In order to construct this work, we require the assistance of machine learning (ML), deep learning (DL), and Neural Network (NN), all of which will assist us in realizing the purpose of this work. We needed jupyter notebook in order to complete this work, and we also needed to install numpy, opencv, tensorflow, and numpy as well as a learning tool. this strategy will assist us in identifying the individual who is concealing their identity by wearing a mask in the imageand in real life picture. Additionally, it is able to recognize and distinguish a moving mask or face. © 2022 IEEE.

关键词： Viruses

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