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

Simulating Distributed and Global Consciousness Under Spatial Grasp Paradigm 空间把握范式下的分布式和全局意识模拟

Advances in Machine Learning & Artificial Intelligence Pub Date : 2020-10-03 DOI: 10.34121/1028-9763-2020-4-49-61

P. Sapaty

{"title":"Simulating Distributed and Global Consciousness Under Spatial Grasp Paradigm","authors":"P. Sapaty","doi":"10.34121/1028-9763-2020-4-49-61","DOIUrl":"https://doi.org/10.34121/1028-9763-2020-4-49-61","url":null,"abstract":"The paper is investigating the possibility of using developed and tested in different countries Spatial Grasp model and technology (SGT) for simulating global awareness and consciousness in distributed dynamic systems, with potential applications in intelligent system management, industrial development, space research, security, and defense. The main technology component, Spatial Grasp Language (SGL), allows us to obtain powerful and compact spatial solutions of different problems by directly expressing their top semantics while hiding traditional system organization and management routines inside efficient networked implementation. The paper describes in SGL a traditional organization of two opposing swarms, called “chasers” and “targets”, randomly operating on expected area. It then enriches the chasers swarm with global awareness and a sort of migrating consciousness, further strengthened by external super-consciousness capability, which allows it to drastically improve performance and make important nonlocal decisions, while putting it to a superior position over the opposing targets swarm. Despite simplicity of the shown practical example, it gives hope for the use of SGT for simulation of much broader and complex areas linked with consciousness like, for example, brain’s bimolecular processes and the basic structure of the universe. The developed networking technology can be readily implemented even in traditional university environments, as was done in the past for its previous versions in different countries under the author’s supervision.","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":"134440260","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: 6

Development of Novel Average Neuro Fuzzy Hybrid Control Technique for Robot Navigation in Unknown Environments 未知环境下机器人导航的新型平均神经模糊混合控制技术研究

Advances in Machine Learning & Artificial Intelligence Pub Date : 2020-10-03 DOI: 10.33140/amlai.01.01.08

引用次数: 0

New Newtonian Mechanics and New Laws of Motion 新牛顿力学和新运动定律

Advances in Machine Learning & Artificial Intelligence Pub Date : 2015-05-01 DOI: 10.33140/amlai.02.01.01

G. Yu

{"title":"New Newtonian Mechanics and New Laws of Motion","authors":"G. Yu","doi":"10.33140/amlai.02.01.01","DOIUrl":"https://doi.org/10.33140/amlai.02.01.01","url":null,"abstract":"Newton’s third law has been proved to be wrong, there are experimental evidence of the video, there are rigorous proof of a strong paper. Further obtained based on this, that is, Newton’s second law to prove is wrong. Therefore, the Newton law of correcting wrong, there are new second law of motion and new third law of motion, to be produces. So including Newton’s first law the New three laws of motion, will become more accurate, more efficient mechanical principles, guiding the new mechanical system is derived and the establishment. No one would doubt that Newton’s second law and Newton’s third law would be wrong. But a surprising discovery was produced in a simple mechanic’s experiment. The earliest experiments showed that two objects interact, acting force and reaction force, Is not the same size. Therefore, Newton’s third law seems to be wrong. Using conventional methods, considering objects with different masses, the inertia is also different. It can also provide a reasonable explanation for the unequal force and reaction force. But when It was further discovered that when Newton’s second law was also wrong, the introduction of the new second law made the establishment of the new third law also perfect. A series of extremely important new discoveries were successively produced and realized.","PeriodicalId":377073,"journal":{"name":"Advances in Machine Learning & Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128889855","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: 3

AI Uncertainty Based on Rademacher Complexity and Shannon Entropy 基于Rademacher复杂度和Shannon熵的人工智能不确定性

Advances in Machine Learning & Artificial Intelligence Pub Date : 1900-01-01 DOI: 10.33140/amlai.02.01.02

{"title":"AI Uncertainty Based on Rademacher Complexity and Shannon Entropy","authors":"","doi":"10.33140/amlai.02.01.02","DOIUrl":"https://doi.org/10.33140/amlai.02.01.02","url":null,"abstract":"In this paper from communication channel coding perspective we are able to present both a theoretical and practical discussion of AI’s uncertainty, capacity and evolution for pattern classification based on the classical Rademacher complexity and Shannon entropy. First AI capacity is defined as in communication channels. It is shown qualitatively that the classical Rademacher complexity and Shannon rate in communication theory is closely related by their definitions. Secondly based on the Shannon mathematical theory on communication coding, we derive several sufficient and necessary conditions for an AI’s error rate approaching zero in classifications problems. A 1/2 criteria on Shannon entropy is derived in this paper so that error rate can approach zero or is zero for AI pattern classification problems. Last but not least, we show our analysis and theory by providing examples of AI pattern classifications with error rate approaching zero or being zero. Impact Statement: Error rate control of AI pattern classification is crucial in many lives related AI applications. AI uncertainty, capacity and evolution are investigated in this paper. Sufficient/necessary conditions for AI’s error rate approaching zero are derived based on Shannon’s communication coding theory. Zero error rate and zero error rate approaching AI design methodology for pattern classifications are illustrated using Shannon’s coding theory. Our method shows how to control the error rate of AI, how to measure the capacity of AI and how to evolve AI into higher levels. Index Terms: Rademacher Complexity, Shannon Theory, Shannon Entropy, Vapnik-Cheronenkis (VC) dimension.","PeriodicalId":377073,"journal":{"name":"Advances in Machine Learning & Artificial Intelligence","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115398157","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

Ensemble Machine Learning Model for Software Defect Prediction 软件缺陷预测的集成机器学习模型

Advances in Machine Learning & Artificial Intelligence Pub Date : 1900-01-01 DOI: 10.33140/amlai.02.01.03

{"title":"Ensemble Machine Learning Model for Software Defect Prediction","authors":"","doi":"10.33140/amlai.02.01.03","DOIUrl":"https://doi.org/10.33140/amlai.02.01.03","url":null,"abstract":"Software defect prediction is a significant activity in every software firm. It helps in producing quality software by reliable defect prediction, defect elimination, and prediction of modules that are susceptible to defect. Several researchers have proposed different software prediction approaches in the past. However, these conventional software defect predictions are prone to low classification accuracy, time-consuming, and tasking. This paper aims to develop a novel multi-model ensemble machine-learning for software defect prediction. The ensemble technique can reduce inconsistency among training and test datasets and eliminate bias in the training and testing phase of the model, thereby overcoming the downsides that have characterized the existing techniques used for the prediction of a software defect. To address these shortcomings, this paper proposes a new ensemble machine-learning model for software defect prediction using k Nearest Neighbour (kNN), Generalized Linear Model with Elastic Net Regularization (GLMNet), and Linear Discriminant Analysis (LDA) with Random Forest as base learner. Experiments were conducted using the proposed model on CM1, JM1, KC3, and PC3 datasets from the NASA PROMISE repository using the RStudio simulation tool. The ensemble technique achieved 87.69% for CM1 dataset, 81.11% for JM1 dataset, 90.70% for PC3 dataset, and 94.74% for KC3 dataset. The performance of the proposed system was compared with that of other existing techniques in literature in terms of AUC. The ensemble technique achieved 87%, which is better than the other seven state-of-the-art techniques under consideration. On average, the proposed model achieved an overall prediction accuracy of 88.56% for all datasets used for experiments. The results demonstrated that the ensemble model succeeded in effectively predicting the defects in PROMISE datasets that are notorious for their noisy features and high dimensions. This shows that ensemble machine learning is promising and the future of software defect prediction.","PeriodicalId":377073,"journal":{"name":"Advances in Machine Learning & Artificial Intelligence","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115404483","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: 10