Advances in Machine Learning & Artificial Intelligence最新文献_第2页

Straight Forward Constructive Deep Learning Neural Network (SFC-DLNN) Algorithm 直接建设性深度学习神经网络(SFC-DLNN)算法

Advances in Machine Learning & Artificial Intelligence Pub Date : 2022-08-27 DOI: 10.33140/amlai.03.02.04

引用次数: 0

Unmanned Aerial Vehicle Application in Mining User case in Rwanda 无人机在卢旺达采矿用户案例中的应用

Advances in Machine Learning & Artificial Intelligence Pub Date : 2022-08-27 DOI: 10.33140/amlai.03.02.05

{"title":"Unmanned Aerial Vehicle Application in Mining User case in Rwanda","authors":"","doi":"10.33140/amlai.03.02.05","DOIUrl":"https://doi.org/10.33140/amlai.03.02.05","url":null,"abstract":"Drones have piqued the interest of the mining industry, which has expressed a strong interest in the usage of UAVs/drones for regular tasks Unmanned Aerial Vehicles(UAVs)/drones, sometimes known as Micro Air Vehicles (MAVs), are mostly drones that are used for a number of commercial and military applications, including surveillance and reconnaissance. These unmanned aerial vehicles (UAVs)/drones are capable of transporting of a wide range sensors depending on the nature of their missions, including acoustic, optical, biochemical, and bio sensors. In order to improve the performance and efficiency of drones/UAV, researchers have concentrated on the design optimization of drones, which has resulted in the creation and construction of a variety of Aerial Vehicles/drones with diverse abilities and capabilities. As a consequence, previous research as well as information from firms that supply drones for the mining industry are being explored further. An investigation of the application of drone/UAVs in surface and subsurface mines is presented in this research. The usage of drones/UAVs in abandoned mines, both on the surface and below, is also discussed. It also includes a thorough discussion of the instruments or sensors that are frequently used in mining drones. In this paper/article, we address the difficulties linked with the usage of drones technologies in underground mines, as well as potential solutions to these difficulties.","PeriodicalId":186756,"journal":{"name":"Advances in Machine Learning & Artificial Intelligence","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131312380","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

A Deep Learning Approach for the Detection of COVID-19 from Chest X-Ray images using Convolutional Neural Networks 基于卷积神经网络的胸部x线图像中COVID-19检测的深度学习方法

Advances in Machine Learning & Artificial Intelligence Pub Date : 2022-04-19 DOI: 10.33140/amlai.03.02.01

Aditya Singh Shamsheer Pal Saxena

{"title":"A Deep Learning Approach for the Detection of COVID-19 from Chest X-Ray images using Convolutional Neural Networks","authors":"Aditya Singh Shamsheer Pal Saxena","doi":"10.33140/amlai.03.02.01","DOIUrl":"https://doi.org/10.33140/amlai.03.02.01","url":null,"abstract":"The COVID-19 (coronavirus) is an ongoing pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus was first identified in mid-December 2019 in the Hubei province of Wuhan, China and by now has spread throughout the planet with more than 75.5 million confirmed cases and more than 1.67 million deaths. With limited number of COVID-19 test kits available in medical facilities, it is important to develop and implement an automatic detection system as an alternative diagnosis option for COVID-19 detection that can used on a commercial scale. Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Computer vision and deep learning techniques can help in determining COVID-19 virus with Chest X-ray Images. Due to the high availability of large-scale annotated image datasets, great success has been achieved using convolutional neural network for image analysis and classification. In this research, we have proposed a deep convolutional neural network trained on five open access datasets with binary output: Normal and Covid. The performance of the model is compared with four pre-trained convolutional neural network- based models (COVID-Net, ResNet18, ResNet and MobileNet-V2) and it has been seen that the proposed model provides better accuracy on the validation set as compared to the other four pre-trained models. This research work provides promising results which can be further improvise and implement on a commercial scale.","PeriodicalId":186756,"journal":{"name":"Advances in Machine Learning & Artificial Intelligence","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134127789","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

Efficient Resource Optimization of 5G Networks Enabling QoS and QoE in IoT Applications 5G网络高效资源优化，实现物联网应用的QoS和QoE

Advances in Machine Learning & Artificial Intelligence Pub Date : 2022-03-20 DOI: 10.33140/amlai.03.01.05

引用次数: 0

Blockchain Towards Prioritization-Based Distributed Storage of Big Data for Internet of Vehicles 面向车联网大数据优先级分布式存储的区块链

Advances in Machine Learning & Artificial Intelligence Pub Date : 2022-03-20 DOI: 10.33140/amlai.03.01.04

引用次数: 0

Letter To Feynman, Einstein, Wallace, Darwin, Maxwell and Mendeleev 致费曼、爱因斯坦、华莱士、达尔文、麦克斯韦和门捷列夫的信

Advances in Machine Learning & Artificial Intelligence Pub Date : 2022-03-20 DOI: 10.33140/amlai.03.01.07

{"title":"Letter To Feynman, Einstein, Wallace, Darwin, Maxwell and Mendeleev","authors":"","doi":"10.33140/amlai.03.01.07","DOIUrl":"https://doi.org/10.33140/amlai.03.01.07","url":null,"abstract":"The Roberts-Janet Nuclear Periodic Table has emerged recently. The inversion of the Periodic Table to accommodate spatial variation of atomic energy levels relative to the nucleus has subsequently been underwritten by Quantum Field Theory’s U (1) X SU (2) x SU (3) group symmetry and Clifford Algebra resulting in a one-toone mapping between the Roberts-Janet Table and The Quantum Mechanical Table. This manuscript attempts to show the over-arching nature of the Roberts-Janet Table epitomised by two cycles. The first of these is the role of causality within the lower half of the table in nucleosynthesis and cosmology whilst the second attempts to outline causality’s path in the upper half of the table in biochemical settings. The link between the cycles is the set of elements themselves; within theoretically an infinite group of elements as radioactivity is reignited having been extinguished temporarily in the ebb and flow of production and annihilation of white dwarfs, neutron stars and black holes. The current scientific landscape is outlined to create a platform from which to proceed. Various sizes of black hole production suggest a hierarchy of outcomes which produces a reignition of radioactivity and potentially a creation of other universes from the explosions of larger supermassive black holes as energies increase to the Planck scale resulting in periods of inflation and condensation that predate quark production. Universes could be superimposed on previous universes explaining why some supermassive black holes appear nearer than current theoretical models.","PeriodicalId":186756,"journal":{"name":"Advances in Machine Learning & Artificial Intelligence","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122610269","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

Functional Connectivity And Regional Homogeneity Alterations In Migraine Patients: A Protocol of Systematic Review And Meta-Analysis 偏头痛患者的功能连通性和区域同质性改变:一个系统回顾和荟萃分析的方案

Advances in Machine Learning & Artificial Intelligence Pub Date : 2021-12-21 DOI: 10.21203/rs.3.rs-1150880/v1

Yuzhong Cui, Q. Xu, Yu-Ting Li, Yanguo Zhang, Jing-Ting Sun, Ze-Yang Li, Min-Hua Ni, Teng Ma, Lin-Feng Yan, G. Cui, Wen Wang, Zhuanghong Chen

{"title":"Functional Connectivity And Regional Homogeneity Alterations In Migraine Patients: A Protocol of Systematic Review And Meta-Analysis","authors":"Yuzhong Cui, Q. Xu, Yu-Ting Li, Yanguo Zhang, Jing-Ting Sun, Ze-Yang Li, Min-Hua Ni, Teng Ma, Lin-Feng Yan, G. Cui, Wen Wang, Zhuanghong Chen","doi":"10.21203/rs.3.rs-1150880/v1","DOIUrl":"https://doi.org/10.21203/rs.3.rs-1150880/v1","url":null,"abstract":"\u0000 Objective: There are numerous functional magnetic resonance imaging (fMRI) studies examining the cerebral function of migraine patients using regional homogeneity (ReHo) and functional connectivity (FC) measurements. However, these studies generally report inconsistent conclusions. We will performed a systematic review and meta-analysis of this body of literature, aiming to identify consistent conclusions regarding cerebral functional changes in migraine patients and to describe potential future directions.Methods: Two investigators will independently screen studies published in online databases (i.e., Medline, Cochrane Library, PubMed, and Web of Science) from the database inception to June 1, 2021. By discussing with a third investigator, any disagreement will be resolved and will attain consensus. A coordinate-based meta-analysis will then be performed with an activation likelihood estimate (ALE) random-effects model.Results: The cerebral FC and ReHo altered regions in migraine patients will be elucidated in this meta-analysis.Conclusion: This study will reveal cerebral functional changes of migraine patients based on current literature to identify consistent conclusions and to describe potential future direction.Registration number: CRD42021257300.","PeriodicalId":186756,"journal":{"name":"Advances in Machine Learning & Artificial Intelligence","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128647721","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

Newly Proposed Matrix Reduction technique Under Mean Ranking Method for Solving Trapezoidal Fuzzy Transportation problems Under Fuzzy Environment 基于平均排序法的矩阵约简新方法求解模糊环境下梯形模糊运输问题

Advances in Machine Learning & Artificial Intelligence Pub Date : 2021-06-23 DOI: 10.20944/preprints202106.0573.v1

Tekalign Regasa Ashale

引用次数: 0

Advances in Machine Learning &amp; Artificial Intelligence最新文献

Advances in Machine Learning & Artificial Intelligence最新文献