发布求助
文献互助 智能选刊 最新文献

Advances in Machine Learning & Artificial Intelligence最新文献

筛选
英文 中文
Intentional First Order Logic for Strong-AI Generation of Robots 强人工智能机器人生成的一阶逻辑
Advances in Machine Learning & Artificial Intelligence Pub Date : 2023-05-27 DOI: 10.33140/amlai.04.01.03
{"title":"Intentional First Order Logic for Strong-AI Generation of Robots","authors":"","doi":"10.33140/amlai.04.01.03","DOIUrl":"https://doi.org/10.33140/amlai.04.01.03","url":null,"abstract":"Neuro-symbolic AI attempts to integrate neural and symbolic architectures in a manner that addresses strengths and weaknesses of each, in a complementary fashion, in order to support robust strong AI capable of reasoning, learning, and cognitive modeling. We consider the robot’s four-levels knowledge structure: The syntax level of particular natural language (Italian, French, etc..), two universal language levels: its semantic logic structure (based on virtual predicates of FOL and logic connectives), and its corresponding conceptual PRP structure level which universally represents the composite mining of FOL formulae grounded on the last robot’s neuro system level. Therefore, this paper we consider the intentional First Order Logic as a symbolic architecture of modern robots, able to use natural languages to communicate with humans and to reason about their own knowledge with self-reference and abstraction language property","PeriodicalId":377073,"journal":{"name":"Advances in Machine Learning & Artificial Intelligence","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135946690","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 Novel Approach to Adopt Explainable Artificial Intelligence in X-ray Image Classification 采用可解释人工智能进行x射线图像分类的新方法
Advances in Machine Learning & Artificial Intelligence Pub Date : 2022-01-25 DOI: 10.33140/amlai.03.01.01
{"title":"A Novel Approach to Adopt Explainable Artificial Intelligence in X-ray Image Classification","authors":"","doi":"10.33140/amlai.03.01.01","DOIUrl":"https://doi.org/10.33140/amlai.03.01.01","url":null,"abstract":"Robust “Blackbox” algorithms such as Convolutional Neural Networks (CNNs) are known for making high prediction performance. However, the ability to explain and interpret these algorithms still require innovation in the understanding of influential and, more importantly, explainable features that directly or indirectly impact the performance of predictivity. In view of the above needs, this study proposes an interaction- based methodology – Influence Score (I-score) – to screen out the noisy and non-informative variables in the images hence it nourishes an environment with explainable and interpretable features that are directly associated to feature predictivity. We apply the proposed method on a real-world application in Pneumonia Chest X-ray Image data set and produced state- of-the-art results. We demonstrate how to apply the proposed approach for more general big data problems by improving the explain ability and interpretability without sacrificing the prediction performance. The contribution of this paper opens a novel angle that moves the community closer to the future pipelines of XAI problems.","PeriodicalId":377073,"journal":{"name":"Advances in Machine Learning & Artificial Intelligence","volume":"291 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114054905","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
Proposed Enhanced Security on Face Recognition Technology for Mobile Devices 针对移动设备的人脸识别技术的增强安全性建议
Advances in Machine Learning & Artificial Intelligence Pub Date : 2022-01-25 DOI: 10.33140/amlai.03.01.02
{"title":"Proposed Enhanced Security on Face Recognition Technology for Mobile Devices","authors":"","doi":"10.33140/amlai.03.01.02","DOIUrl":"https://doi.org/10.33140/amlai.03.01.02","url":null,"abstract":"Although Face Recognition has many advantages over traditional technology (fingerprint, keystroke, Passwords, Pins, Patterns and voice) like feature used for protection and reduction risks of accessing other’s information without permission and provide fast and easy accessibility of the information stored in the device for authorized user. However, some of the systems can be fooled with a picture or video of a user’s face that can be unlocked by someone that looks similar to you (such as a twin), other point if the mobile phone user was kidnapped and be forced to unlock his /her device for Froude use. As mentioned above issues, they are still having a leakage about the security of information stored in the device according to their working algorithm. In this paper, we are proposing a way to enhance the security on face recognition for mobile devices by connecting the mobile device to the other wearable device in order to increase security on face recognition to the mentioned issues and ensure that the information is more secured.","PeriodicalId":377073,"journal":{"name":"Advances in Machine Learning & Artificial Intelligence","volume":"196 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134445501","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
The Comparison Between Gumbel and Exponentiated Gumbel Distributions and Their Applications in Hydrological Process 甘贝尔分布与指数甘贝尔分布的比较及其在水文过程中的应用
Advances in Machine Learning & Artificial Intelligence Pub Date : 2021-09-10 DOI: 10.33140/amlai.02.01.08
{"title":"The Comparison Between Gumbel and Exponentiated Gumbel Distributions and Their Applications in Hydrological Process","authors":"","doi":"10.33140/amlai.02.01.08","DOIUrl":"https://doi.org/10.33140/amlai.02.01.08","url":null,"abstract":"The Exponentiated Gumbel (EG) distribution has been proposed to capture some aspects of the data that the Gumbel distribution fails to specify. it has an increasing hazard rate. The Exponentiated Gumbel distribution has applications in hydrology, meteorology, climatology, insurance, finance and geology, among many others. In this paper Firstly, the mathematical and statistical characteristics of the gumbel and Exponentiated Gumbel distribution are presented, then the applications of this distributions are studied using the real data set. Its first moment about origin and moments about mean have been obtained and expressions for skewness, kurtosis have been given. Estimation of its parameter has been discussed using the method of maximum likelihood. In the end, two applications of the gumbel and exponentiated gumbel distribution have been discussed with two real lifetime data sets. The results also confirmed the suitability of the Exponentiated Gumbel distribution for real data collection.","PeriodicalId":377073,"journal":{"name":"Advances in Machine Learning & Artificial Intelligence","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132739497","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: 1
Safety Behavior Analysis of a Delayed Control System 时滞控制系统的安全行为分析
Advances in Machine Learning & Artificial Intelligence Pub Date : 2021-09-10 DOI: 10.33140/amlai.02.01.06
{"title":"Safety Behavior Analysis of a Delayed Control System","authors":"","doi":"10.33140/amlai.02.01.06","DOIUrl":"https://doi.org/10.33140/amlai.02.01.06","url":null,"abstract":"Time delays in systems are becoming important phenomena now-a-days in regards to its safety issues. A continuous delayed system proposed by A. Uçar is considered for this work. Detailed works are concentrated on finding behavior of this system of continuous delayed system with respect to different system parameters. Self-written code is used to observe the behavior of the system. Self-written code gives flexibility to see behaviors of the system in more in depth. System behavior is observed for a very large range of parameters and comparison is made with others works. Results indicate that for a certain range of values of parameters the system show predictable behavior but after certain range of parameter values the system goes to unpredictable chaotic behavior. In addition, parametric relation is shown for same type of chaotic behavior. It is expected that this finding will increase understanding of complex phenomena involved in delayed dynamical system when safety is prime importance.","PeriodicalId":377073,"journal":{"name":"Advances in Machine Learning & Artificial Intelligence","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115113310","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
Switching Towards a Proactive Grid Based Data Management Approach 转向主动的基于网格的数据管理方法
Advances in Machine Learning & Artificial Intelligence Pub Date : 2021-09-10 DOI: 10.33140/amlai.02.01.07
{"title":"Switching Towards a Proactive Grid Based Data Management Approach","authors":"","doi":"10.33140/amlai.02.01.07","DOIUrl":"https://doi.org/10.33140/amlai.02.01.07","url":null,"abstract":"Over time, an exorbitant data quantity is generating which indeed requires a shrewd technique for handling such a big database to smoothen the data storage and disseminating process. Storing and exploiting such big data quantities require enough capable systems with a proactive mechanism to meet the technological challenges too. The available traditional Distributed File System (DFS) becomes inevitable while handling the dynamic variations and requires undefined settling time. Therefore, to address such huge data handling challenges, a proactive grid base data management approach is proposed which arranges the huge data into various tiny chunks called grids and makes the placement according to the currently available slots. The data durability and computation speed have been aligned by designing data disseminating and data eligibility replacement algorithms. This approach scrumptiously enhances the durability of data accessing and writing speed. The performance has been tested through numerous grid datasets and therefore, chunks have been analysed through various iterations by fixing the initial chunks statistics, then making a predefined chunk suggestion and then relocating the chunks after the substantial iterations and found that chunks are in an optimal node from the first iteration of replacement which is more than 21% of working clusters as compared to the traditional approach.","PeriodicalId":377073,"journal":{"name":"Advances in Machine Learning & Artificial Intelligence","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125459500","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
Classification of Heart Rate Time Series Using Machine Learning Algorithms 使用机器学习算法的心率时间序列分类
Advances in Machine Learning & Artificial Intelligence Pub Date : 2021-09-10 DOI: 10.33140/amlai.02.01.09
{"title":"Classification of Heart Rate Time Series Using Machine Learning Algorithms","authors":"","doi":"10.33140/amlai.02.01.09","DOIUrl":"https://doi.org/10.33140/amlai.02.01.09","url":null,"abstract":"An important diagnostic method for diagnosing abnormalities in the human heart is the electrocardiogram (ECG). A large number of heart patients increase the assignment of physicians. To reduce their assignment, an automatic computer detection system is needed. In this study, a computer system for classifying ECG signals is presented. The MIT-BIH, ECG arrhythmia database is used for analysis. After the ECG signal is noisy in the preprocessing stage, the data feature is extracted. In the feature extraction step, the decision tree is used and the support vector machine (SVM) is constructed to classify the ECG signal into two categories. It is normal or abnormal. The results show that the system classifies the given ECG signal with 90% sensitivity.","PeriodicalId":377073,"journal":{"name":"Advances in Machine Learning & Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115970676","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: 1
Semantic NLP Technologies in Information Retrieval Systems for Legal Research 语义NLP技术在法律研究信息检索系统中的应用
Advances in Machine Learning & Artificial Intelligence Pub Date : 2021-08-05 DOI: 10.33140/amlai.02.01.05
{"title":"Semantic NLP Technologies in Information Retrieval Systems for Legal Research","authors":"","doi":"10.33140/amlai.02.01.05","DOIUrl":"https://doi.org/10.33140/amlai.02.01.05","url":null,"abstract":"Companies involved in providing legal research services to lawyers, such as LexisNexis or Westlaw, have rapidly incorporated natural language processing (NLP) into their database systems to deal with the massive amounts of legal texts contained within them. These NLP techniques, which perform analysis on natural language texts by taking advantage of methods developed in the fields of computational linguistics and artificial intelligence, have potential applications ranging from text summarization all the way to the prediction of court judgments. However, a potential concern with the use of this technology is that professionals will come to depend on systems, over which they have little control or understanding, as a source of knowledge. While recent strides in AI and deep learning have led to increased effectiveness in NLP techniques, the decision-making processes of these algorithms have progressively become less intuitive for humans to understand. Concerns about the interpretability of patented legal services such as LexisNexis are more pertinent than ever. The following survey conducted for current NLP techniques shows that one potential avenue to make algorithms in NLP more explainable is to incorporate symbol-based methods that take advantage of knowledge models generated for specific domains. An example of this can be seen in NLP techniques developed to facilitate the retrieval of inventive information from patent applications.","PeriodicalId":377073,"journal":{"name":"Advances in Machine Learning & Artificial Intelligence","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127485252","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
Artificial Intelligence and Advances 人工智能及其发展
Advances in Machine Learning & Artificial Intelligence Pub Date : 2020-10-03 DOI: 10.33140/amlai.01.01.03
{"title":"Artificial Intelligence and Advances","authors":"","doi":"10.33140/amlai.01.01.03","DOIUrl":"https://doi.org/10.33140/amlai.01.01.03","url":null,"abstract":"This research abstract shares open source theme smart materials and technology for the Current and Future Global Challenges in relation with artificial intelligence which is the simulation of human intelligence processes by machines, especially computer systems. AI will also have a major impact on illegal/illicit /legal harmful drugs, chemicals, toxic herbs and others Control Intervention. the patent pending this scientific computing innovation; will include illegal/illicit/ legal harmful drugs, chemicals, toxic herbs and others Control security system and special purpose computer connected intelligent equipment’s, with sensors capable of taking thousands of measurements throughout the production process and generating billions of data points used to monitor, analyze and control the trafficking process. AI and machine learning are also contributing to the development of next-generation security system, accelerating the development of security intervention for conditions where there are no viable options today. Artificial intelligence promises both to improve existing goods and services, by enabling the automation of many tasks, to greatly increase the efficiency with which they are produced. But it may have an even larger impact on the economy by serving as a new general-purpose method of invention for unique or special tasks; Where Artificial intelligence (AI) clearly lead to better outcomes, already producing benefits and optimizing processes, increasingly sophisticated algorithms and machine learning techniques of data on a particular issue, generate insights, detections and resulting with more efficiency than teams of humans ever could.","PeriodicalId":377073,"journal":{"name":"Advances in Machine Learning & Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114758281","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: 1
Is AI Forcing the Reincarnation of Quality? 人工智能是否推动了质量的转世?
Advances in Machine Learning & Artificial Intelligence Pub Date : 2020-10-03 DOI: 10.33140/amlai.01.01.04
{"title":"Is AI Forcing the Reincarnation of Quality?","authors":"","doi":"10.33140/amlai.01.01.04","DOIUrl":"https://doi.org/10.33140/amlai.01.01.04","url":null,"abstract":"You may remember well when you truly believed you have done something absolutely right although it turned out to be wrong? It´s sometime hard to realize how convinced you were and how upsetting it can be to submit you were wrong.","PeriodicalId":377073,"journal":{"name":"Advances in Machine Learning & Artificial Intelligence","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132151170","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
下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信