2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)最新文献_第9页

Health Care and Management based on Block chain and Machine Learning 基于区块链和机器学习的医疗保健和管理

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083757

Ruhi Bakh, VeenaNair Sarkar, Jerlin Priya Lovelin Auguskani, Galiveeti Poornima, Monali N. Shetty, Meerjumla Govind Raj

{"title":"Health Care and Management based on Block chain and Machine Learning","authors":"Ruhi Bakh, VeenaNair Sarkar, Jerlin Priya Lovelin Auguskani, Galiveeti Poornima, Monali N. Shetty, Meerjumla Govind Raj","doi":"10.1109/ICECONF57129.2023.10083757","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083757","url":null,"abstract":"With the coming of technology, we currently have an immense measure of information accessible in each field, making it conceivable to offer responses to many issues. We will utilize machine learning and block chain technology to resolve issues with healthcare information the executives in this review. With the guide of machine learning, separating just the relevant information from the data is plausible. Using prepared calculations, this is finished. The reliability of information trade turns into a test after this information has been put away. This is where block chain technology is valuable. Block chain technology's agreement guarantees that information is credible and exchanges are secure. By putting the patient at the focal point of the healthcare framework and upgrading the security and interoperability of health information, block chain technology can possibly further develop health care organization. This article fundamentally centres around settling issues with healthcare information the board using Block chain technology and including a few urgent highlights utilizing Machine Learning. The freshest technology, block chain, offers a strong and secure groundwork. With these offices, block chain has gotten some momentum in an assortment of use areas, including the food business, store network the board, energy, government support offices, and healthcare.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"206 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121849009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Implementation of Fog-IoT Framework to Deal with the Performance metrics in various IoT Devices 实现Fog-IoT框架以处理各种物联网设备中的性能指标

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083823

T. D. Subha, Kommi Sai Manasa, Kollareddy Akhila, Nekkantl Satya Saranya, K. Tejaswini, Kannanoor Sannuthi

{"title":"Implementation of Fog-IoT Framework to Deal with the Performance metrics in various IoT Devices","authors":"T. D. Subha, Kommi Sai Manasa, Kollareddy Akhila, Nekkantl Satya Saranya, K. Tejaswini, Kannanoor Sannuthi","doi":"10.1109/ICECONF57129.2023.10083823","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083823","url":null,"abstract":"The number of wireless gadgets linked to the Internet of Things (IoT) has exploded as a result of commercial and technological shifts. Internet of Things networks keep delay to a minimum while yet allowing devices to talk to one another. Effective data transmission strategies are essential for orchestrations of distributed fog-IoT networks to reduce maintenance windows. In this study, we compare cloud and fog performance measures over a range of Internet of Things (IoT) device densities to determine fog's practical usefulness in the wild. Moreover, we test the effects of employing two distinct fog implementation frameworks on performance by implementing the fog layer in both of them. However, a major difficulty in choreographing complex services is how to effectively manage dynamic fluctuations and temporary operating behavior. Moreover, the variety, dynamics, and unpredictability inside Fog settings, as well as the increasing computing complexity, further substantially worsen this difficulty as the size of IoT installations rapidly expands. Adding a fog layer with semi-heavyweight compute capabilities increases upfront expenditures, but reduces operational expenses, time, and material over the long term.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121772788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Maximum Energy Productivity for Concurrent Wireless Data and Power Shifting-Enabled IoT Network with Energy Coordination 具有能量协调功能的并发无线数据和功率转换物联网网络的最大能量生产力

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10084337

Sneha Joseph, S. R, Angelina Royappa, Anandakumar D, Gururaj D, K. Karthikeyan

{"title":"Maximum Energy Productivity for Concurrent Wireless Data and Power Shifting-Enabled IoT Network with Energy Coordination","authors":"Sneha Joseph, S. R, Angelina Royappa, Anandakumar D, Gururaj D, K. Karthikeyan","doi":"10.1109/ICECONF57129.2023.10084337","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10084337","url":null,"abstract":"The online phase may be successfully extended by simultaneous wireless data, internet of things (IoT) components, and sophisticated technologies. The development support base station is developed to accomplish the exchange of renewable electricity to manage the volatility of power generation by the hybrid access points. In this research, we jointly investigate the cooperative SWIPT-enabled IoT systems. While maximizing the program's energy consumption, we must also adhere to maximize the transmission limits, thermoelectric generator restrictions, and customer quality of service (QoS) requirements. We collaborate to find solutions to the challenges of power-sharing, period shifting, and ecological collaboration. The incessant algorithm is employed to address the load distribution and duration swapping problems, the matching algorithm is employed to fix the cooperation agreement issue, and since this trouble is a nonlinear optimization issue, it is challenging to address directly. Instead, we are using the interchanging differential technique. The outcomes of the simulations demonstrate that the suggested algorithm performs with a considerable advantage in terms of energy conservation compared to the comparative method. Also, it has been shown that using energy collaboration technologies can reduce the amount of power a system uses and make it run better.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122280671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Horticulture Image Based Segmentation with Feature Selection Using U-ConVolNet with Boltzmann Machine Using Deep Learning Architectures 基于U-ConVolNet和Boltzmann机器的园艺图像特征选择分割

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10084231

Divya A, Sungeetha D

引用次数: 0

Tracking of Food Waste in Food Supply Chain Using Machine Learning 利用机器学习跟踪食品供应链中的食物浪费

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083755

S. B, S. R, S. R, Saras Prasad Raju V, Ruma Prasad H, Sailendiran R

引用次数: 0

Classification of Epileptic Seizures using Optimized TQWT and Hybrid Models 基于优化TQWT和混合模型的癫痫发作分类

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10084140

V. P, R. V., Caushik Subramaniam C, Aditya Vishwakarma R I, Sakthi Jaya Sundar Rajasekar

{"title":"Classification of Epileptic Seizures using Optimized TQWT and Hybrid Models","authors":"V. P, R. V., Caushik Subramaniam C, Aditya Vishwakarma R I, Sakthi Jaya Sundar Rajasekar","doi":"10.1109/ICECONF57129.2023.10084140","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10084140","url":null,"abstract":"Epilepsy is a Central Nervous System (CNS) disorder that can cause chronic seizures at any time. The electroencephalogram (EEG) records the electrical activities caused by the postsynaptic potentials that can be used to diagnose any disorder in the brain. This study identifies the best methods of forecasting epileptic seizures by comparing different approaches. The EEG usually contains enormous data, which becomes time-consuming and laborious for data interpretation. This study proposes to develop a learner with an automated signal interpretation technique using advanced signal processing methods that can predict seizures from the EEG recordings. The extracted EEG signals from the patient are subjected to an optimized tunable Q-factor wavelet transformation. The global, temporal, and entropy-based features are extracted from the sub-bands and fused. An ANN model is trained with the fused features. Also, from the TQWT subbands, EEG scalograms are generated and used to train a CNN model. These models are trained in such a way that they can differentiate between the normal, ictal, and interictal classes. The performance of the CNN model trained with scalogram images by the proposed approach is compared to the performance of deep hybrid models. The ANN hybrid model produced an accuracy of 98% using different categories of features extracted, and the CNN model produced an accuracy of 91 % using scalogram images of EEG signals, which outperformed the hybrid model in terms of speed and computation.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126340089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Divination of Air Quality Assessment using Ensembling Machine Learning Approach 基于集成机器学习方法的空气质量评估预测

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083751

P. William, Deepak Paithankar, P. Yawalkar, Sachin K. Korde, Abhijeet Rajendra, Pabale, D. Rakshe

{"title":"Divination of Air Quality Assessment using Ensembling Machine Learning Approach","authors":"P. William, Deepak Paithankar, P. Yawalkar, Sachin K. Korde, Abhijeet Rajendra, Pabale, D. Rakshe","doi":"10.1109/ICECONF57129.2023.10083751","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083751","url":null,"abstract":"Smart cities must address air pollution as a top environmental concern. Real-time monitoring of pollution data enables metropolitan authorities to analyze the city's current traffic conditions and implement necessary corrective actions. The increased usage of Internet of things (IoT)-based sensors has altered the dynamics of air quality prediction significantly. While earlier research has used a number of machine learning techniques to anticipate pollution, it is usually necessary to compare various tactics in order to better understand how long they take to analyse different datasets. The best model for accurately predicting air quality given the amount of data available and the processing time required was determined by a comparative study of four different advanced regression algorithms. Apache Spark was used to perform tests and estimate pollution levels from a range of publicly available data sources. MAE and the root mean square error (RMSE) are often used to compare regression models. In order to find the best-fitting mode on Apache Spark, each method was tested in terms of processing time and error rate using a mix of standalone learning and fitting the hyperparameter tweaks on Apache Spark.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"75 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121016760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 20

Empirical Copula based Naive Bayes Classifier 基于经验Copula的朴素贝叶斯分类器

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083573

Tanishi Srivastava, Dristi De, Prerna Sharma, Debarka Sengupta

{"title":"Empirical Copula based Naive Bayes Classifier","authors":"Tanishi Srivastava, Dristi De, Prerna Sharma, Debarka Sengupta","doi":"10.1109/ICECONF57129.2023.10083573","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083573","url":null,"abstract":"In this paper, we propose a Bayesian model with enhanced performance on statistical datasets by incorporating the concept of Empirical copulas to compute the joint probability distribution of features present in the data. Copulas are defined as cumulative distribution functions deemed popular in highdimensional statistical applications since they easily enable one to model and estimate the distribution of random vectors by estimating the marginals and copulae separately. The key idea of this method is to replace the joint probability, which is defined as the probability of occurrence of two or more simultaneous events, with the cumulative distribution generated by the nonparametric empirical copula function and utilize it on bivariate and multivariate data to assess the performance of the model thus generated. Through extensive research on the topic of nonparametric empirical copulas and tuning the model with various smoothing techniques, we have achieved significant accuracy with a more robust statistical hold in the predictive analysis of different datasets in comparison to the simple Gaussian Naïve Bayes technique.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121431474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Neural Machine Translation Using Attention 利用注意力的神经机器翻译

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083569

Dafni Rose, K. Vijayakumar, D. Kirubakaran, R. Pugalenthi, Gotti Balayaswantasaichowdary

{"title":"Neural Machine Translation Using Attention","authors":"Dafni Rose, K. Vijayakumar, D. Kirubakaran, R. Pugalenthi, Gotti Balayaswantasaichowdary","doi":"10.1109/ICECONF57129.2023.10083569","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083569","url":null,"abstract":"Machine Translation pertains to translation of one natural language to other by using automated computing. The most common method for dealing with the machine translation problem is Statistical machine translation. This method is convenient for language pairs with similar grammatical structures even so it taken vast datasets.N evertheless, the conventional models do not perform well for languages without similar grammar and contextual meaning. Lately this problemhas been resolved by the neural machine translation (NMT) that has proved to be an effective curative. Only a little amount of data is required for training in NMT and it can translate only a small number of training words. A fixed-length vector is used to identify the important words that contribute for the translation of text, and assigns weights to each word in our proposed system. The Encoder-Decoder architecture with Long- Term and Short- Term Memory (LSTM) Neural Network and trained modelsare employed by calling the previous sequences and states. The proposed model ameliorates translation performance with attention vector and by returning the sequences of previous states unlike LSTM.English-Hindi sentences corpus data for implementing a Model with attention and without attention is considered here. By evaluating the results, the proposed solution, overcomes complexity of training a Neural Network and increases translation performance.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124417179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Improved Accuracy in Speech Recognition System for Detection of Covid-19 using K Nearest Neighbour and Comparing with Artificial Neural Network 基于K近邻的新型冠状病毒语音识别系统及其与人工神经网络的比较

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083858

Rallapalli Jhansi, G. Uganya

{"title":"Improved Accuracy in Speech Recognition System for Detection of Covid-19 using K Nearest Neighbour and Comparing with Artificial Neural Network","authors":"Rallapalli Jhansi, G. Uganya","doi":"10.1109/ICECONF57129.2023.10083858","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083858","url":null,"abstract":"Aim: This study focuses on the detection of Covid-19 via the use of cutting-edge speech recognition technology known as K Nearest Neighbor (KNN), and comparing its accuracy with that of an Artificial Neural Network (ANN). Both the Materials and the Methods: In this case, speech recognition through the use of KNN is deemed to be group 1, while speech recognition via the use of an artificial neural network is considered to be group 2. ANN is comprised of several different components that are responsible for gathering the input signals and predefined functions that are responsible for creating the output signals. KNN works by calculating the distance between the query and the data and then picking the samples that are geographically closest to the requests. These various samples using algorithms were computationally assessed by a sampling test with 5% of alpha error and 0.95 of confidence interval. The results of this analysis are shown below. The findings show that ANN performs at a level of mean accuracy of 83.5%, whereas KNN performs at a level of mean accuracy of 91.49% with an error significance value of 0.03 (p 0.05). The findings that were acquired using KNN have shown much improved performance in terms of accuracy compared to those obtained using ANN.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121334889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0