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| viXra.org > Artificial Intelligence > 2003 |
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[20] viXra:2003.0652 [pdf] submitted on 2020-03-30 07:45:49
Authors: George Rajna
Comments: 44 Pages.
Researchers from Tokyo Metropolitan University have used machine learning to analyze spin models, which are used in physics to study phase transitions. [26] We are still far off from achieving Quantum Advantage for machine learning-the point at which quantum computers surpass classical computers in their ability to perform AI algorithms. [25] Physicists in the US have used machine learning to determine the phase diagram of a system of 12 idealized quantum particles to a higher precision than ever before. [24] The research group took advantage of a system at SLAC's Stanford Synchrotron Radiation Lightsource (SSRL) that combines machine learning-a form of artificial intelligence where computer algorithms glean knowledge from enormous amounts of data-with experiments that quickly make and screen hundreds of sample materials at a time. [23] Researchers at the UCLA Samueli School of Engineering have demonstrated that deep learning, a powerful form of artificial intelligence, can discern and enhance microscopic details in photos taken by smartphones. [22] Such are the big questions behind one of the new projects underway at the MIT-IBM Watson AI Laboratory, a collaboration for research on the frontiers of artificial intelligence. [21]
Category: Artificial Intelligence
[19] viXra:2003.0583 [pdf] submitted on 2020-03-26 11:48:51
Authors: Egger Mielberg
Comments: 15 Pages.
Like each neuron of the human brain may be connected to up to 10,000 other neurons, passing signals to each other via as many as 1,000 trillion synaptic connections, in Sense Theory there is a possibility for connecting over 1,000 trillion heterogeneous objects.An object in Sense Theory is like a neuron in the human brain. Properties of the object are like dendrites of the neuron. Changing object in the process of addition or deletion of its properties is like forming a new knowledge in the process of synaptic connections of two or more neurons. In Sense Theory, we introduced a mechanism for determining possible semantic relationships between objects by connecting-disconnecting different properties. This mechanism is Sense Integral.In this article, we describe one of the instruments, sense antiderivative, that sheds light on the nature of forming new knowledge in the field of Artificial Intelligence.
Category: Artificial Intelligence
[18] viXra:2003.0565 [pdf] submitted on 2020-03-26 10:23:15
Authors: George Rajna
Comments: 51 Pages.
An Australian-German collaboration has demonstrated fully-autonomous SPM operation, applying artificial intelligence and deep learning to remove the need for constant human supervision. [30] Now, researchers have tested the first artificial intelligence model to identify and rank many causes in real-world problems without time-sequenced data, using a multi-nodal causal structure and Directed Acyclic Graphs. [29] A country that thinks its adversaries have or will get AI weapons will want to get them too. Wide use of AI-powered cyberattacks may still be some time away. [28] Following the old saying that "knowledge is power", companies are seeking to infer increasingly intimate properties about their customers as a way to gain an edge over their competitors. [27] Researchers from Human Longevity, Inc. (HLI) have published a study in which individual faces and other physical traits were predicted using whole genome sequencing data and machine learning. [26] Artificial intelligence can improve health care by analyzing data from apps, smartphones and wearable technology. [25] Now, researchers at Google's DeepMind have developed a simple algorithm to handle such reasoning-and it has already beaten humans at a complex image comprehension test. [24] A marimba-playing robot with four arms and eight sticks is writing and playing its own compositions in a lab at the Georgia Institute of Technology. The pieces are generated using artificial intelligence and deep learning. [23] Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations. [22] Physicists have found that the structure of certain types of quantum learning algorithms is very similar to their classical counterparts-a finding that will help scientists further develop the quantum versions. [21] We should remain optimistic that quantum computing and AI will continue to improve our lives, but we also should continue to hold companies, organizations, and governments accountable for how our private data is used, as well as the technology's impact on the environment. [20]
Category: Artificial Intelligence
[17] viXra:2003.0557 [pdf] submitted on 2020-03-25 19:23:46
Authors: Ayoub Abraich
Comments: 4 Pages. Code : https://github.com/abraich/COVID-19
In this article we present a naive model for the prediction of the number of COVID-19 infections, with illustrations of real data on the evolution of COVID-19 in France.
Category: Artificial Intelligence
[16] viXra:2003.0508 [pdf] submitted on 2020-03-24 09:40:36
Authors: George Rajna
Comments: 52 Pages.
In a paper published in Nature Nanotechnology on 23 March 2020, the researchers from the NUS Nanoscience and Nanotechnology Initiative (NUSNNI) reported the invention of a nanoscale device based on a unique material platform that can achieve optimal digital in-memory computing while being extremely energy efficient. [35]
University of Central Florida researchers are helping to close the gap separating human and machine minds. [34]
Brain-machine interfaces provide one way to connect with this puzzling organ system, including the brain. [33]
Category: Artificial Intelligence
[15] viXra:2003.0484 [pdf] replaced on 2023-03-17 02:54:10
Authors: Qing Tian, Guangjun Tian
Comments: 4 Pages. In Chinese
This manuscript sketch first describes neural networks’ effect from the perspective of data space transformation, which is transforming data in a complicated raw space into an easily (e.g. linearly) separable space. We use a simple paper wrapping example to illustrate this point. In addition, this sketch also discusses some similarities between neural networks and ensemble classification.
Category: Artificial Intelligence
[14] viXra:2003.0419 [pdf] submitted on 2020-03-20 05:16:19
Authors: George Rajna
Comments: 51 Pages.
After testing prototype AI software on over 140 patients, a multinational team of researchers found that the algorithm showed very strong correlation with traditional pulmonary function tests. [32] A new artificial-intelligence tool captures strategies used by top players of an internet-based videogame to design new RNA molecules. [31] A team of EPFL scientists has now written a machine-learning program that can predict, in record time, how atoms will respond to an applied magnetic field. [30] Researchers from the University of Luxembourg, Technische Universität Berlin, and the Fritz Haber Institute of the Max Planck Society have combined machine learning and quantum mechanics to predict the dynamics and atomic interactions in molecules. [29] For the first time, physicists have demonstrated that machine learning can reconstruct a quantum system based on relatively few experimental measurements. [28] AlphaZero plays very unusually; not like a human, but also not like a typical computer. Instead, it plays with "real artificial" intelligence. [27] Predictions for an AI-dominated future are increasingly common, but Antoine Blondeau has experience in reading, and arguably manipulating, the runes-he helped develop technology that evolved into predictive texting and Apple's Siri. [26] Artificial intelligence can improve health care by analyzing data from apps, smartphones and wearable technology. [25] Now, researchers at Google's DeepMind have developed a simple algorithm to handle such reasoning-and it has already beaten humans at a complex image comprehension test. [24] A marimba-playing robot with four arms and eight sticks is writing and playing its own compositions in a lab at the Georgia Institute of Technology. The pieces are generated using artificial intelligence and deep learning. [23] Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations. [22] Physicists have found that the structure of certain types of quantum learning algorithms is very similar to their classical counterparts-a finding that will help scientists further develop the quantum versions. [21]
Category: Artificial Intelligence
[13] viXra:2003.0378 [pdf] submitted on 2020-03-18 05:10:54
Authors: George Rajna
Comments: 51 Pages.
This capability will be crucial for ITER, the large international tokamak under construction in France to demonstrate the practicality of fusion energy. [32] An artificial intelligence (AI) algorithm can transform low-dose CT (LDCT) scans into high-quality exams that radiologists may even prefer over LDCT studies produced via commercial iterative reconstruction techniques. [31] A team of EPFL scientists has now written a machine-learning program that can predict, in record time, how atoms will respond to an applied magnetic field. [30] Researchers from the University of Luxembourg, Technische Universität Berlin, and the Fritz Haber Institute of the Max Planck Society have combined machine learning and quantum mechanics to predict the dynamics and atomic interactions in molecules. [29] For the first time, physicists have demonstrated that machine learning can reconstruct a quantum system based on relatively few experimental measurements. [28] AlphaZero plays very unusually; not like a human, but also not like a typical computer. Instead, it plays with "real artificial" intelligence. [27] Predictions for an AI-dominated future are increasingly common, but Antoine Blondeau has experience in reading, and arguably manipulating, the runes-he helped develop technology that evolved into predictive texting and Apple's Siri. [26] Artificial intelligence can improve health care by analyzing data from apps, smartphones and wearable technology. [25] Now, researchers at Google's DeepMind have developed a simple algorithm to handle such reasoning-and it has already beaten humans at a complex image comprehension test. [24] A marimba-playing robot with four arms and eight sticks is writing and playing its own compositions in a lab at the Georgia Institute of Technology. The pieces are generated using artificial intelligence and deep learning. [23] Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations. [22] Physicists have found that the structure of certain types of quantum learning algorithms is very similar to their classical counterparts-a finding that will help scientists further develop the quantum versions. [21]
Category: Artificial Intelligence
[12] viXra:2003.0373 [pdf] submitted on 2020-03-18 06:16:14
Authors: George Rajna
Comments: 37 Pages.
Models based on artificial intelligence can significantly change the way we approach chemical syntheses. But we are still at the very beginning." [26] A new tool is drastically changing the face of chemical research-artificial intelligence. In a new paper published in Nature, researchers review the rapid progress in machine learning for the chemical sciences. [25] A new type of artificial-intelligence-driven chemistry could revolutionise the way molecules are discovered, scientists claim. [24] Tired of writing your own boring code for new software? Finally, there's an AI that can do it for you. [23] Welcome to Move Mirror, where you move in front of your webcam. [22] Understanding how a robot will react under different conditions is essential to guaranteeing its safe operation. [21] Marculescu, along with ECE Ph.D. student Chieh Lo, has developed a machine learning algorithm-called MPLasso-that uses data to infer associations and interactions between microbes in the GI microbiome. [20] A team of researchers from the University of Muenster in Germany has now demonstrated that this combination is extremely well suited to planning chemical syntheses-so-called retrosyntheses-with unprecedented efficiency. [19] Two physicists at ETH Zurich and the Hebrew University of Jerusalem have developed a novel machine-learning algorithm that analyses large data sets describing a physical system and extract from them the essential information needed to understand the underlying physics. [18]
Category: Artificial Intelligence
[11] viXra:2003.0304 [pdf] submitted on 2020-03-14 01:21:27
Authors: Nirmal Tej Kumar
Comments: 7 Pages. Short Communication
A Short & Simple Technical Communication on Algorithms Design Using Python Based [ Applied Physics+AI+Imaging Mathematics+Data Bases ] →
Image Processing Software R&D.
Category: Artificial Intelligence
[10] viXra:2003.0193 [pdf] submitted on 2020-03-09 15:22:53
Authors: George Rajna
Comments: 54 Pages.
This study is part of a larger, coordinated effort across all the LHC experiments to use modern machine techniques to improve how the large data samples are recorded by the detectors and the subsequent data analysis. [28] Machine learning and automation technologies are gearing up to transform the radiation-therapy workflow while freeing specialist clinical and technical staff to dedicate more time to patient care. [27] Navid Borhani, a research-team member, says this machine learning approach is much simpler than other methods to reconstruct images passed through optical fibers, which require making a holographic measurement of the output. [26]
Category: Artificial Intelligence
[9] viXra:2003.0138 [pdf] submitted on 2020-03-07 07:22:48
Authors: George Rajna
Comments: 39 Pages.
A Cornell collaboration led by physicist Brad Ramshaw, the Dick & Dale Reis Johnson Assistant Professor in the College of Arts and Sciences, used a combination of ultrasound and machine learning to narrow the possible explanations for what happens to this quantum material when it enters this so-called "hidden order." [27]
A new study from the U.S. Department of Energy's (DOE) Argonne National Laboratory has achieved a breakthrough in the effort to mathematically represent how water behaves. [26]
A new tool is drastically changing the face of chemical research – artificial intelligence. In a new paper published in Nature, researchers review the rapid progress in machine learning for the chemical sciences. [25]
A new type of artificial-intelligence-driven chemistry could revolutionise the way molecules are discovered, scientists claim. [24]
Category: Artificial Intelligence
[8] viXra:2003.0033 [pdf] submitted on 2020-03-02 08:54:35
Authors: George Rajna
Comments: 31 Pages.
By using machine learning as an image processing technique, scientists can dramatically accelerate the heretofore laborious manual process of quantitatively looking for and at interfaces without having to sacrifice accuracy. [21] Marculescu, along with ECE Ph.D. student Chieh Lo, has developed a machine learning algorithm-called MPLasso-that uses data to infer associations and interactions between microbes in the GI microbiome. [20] A team of researchers from the University of Muenster in Germany has now demonstrated that this combination is extremely well suited to planning chemical syntheses-so-called retrosyntheses-with unprecedented efficiency. [19] Two physicists at ETH Zurich and the Hebrew University of Jerusalem have developed a novel machine-learning algorithm that analyses large data sets describing a physical system and extract from them the essential information needed to understand the underlying physics. [18]
Category: Artificial Intelligence
[7] viXra:2003.0032 [pdf] submitted on 2020-03-02 09:04:51
Authors: George Rajna
Comments: 34 Pages.
The new method could allow researchers to artificially synthesize the low-frequency waves that are hidden in seismic data, which can then be used to more accurately map the Earth's internal structures. [22] By using machine learning as an image processing technique, scientists can dramatically accelerate the heretofore laborious manual process of quantitatively looking for and at interfaces without having to sacrifice accuracy. [21] Marculescu, along with ECE Ph.D. student Chieh Lo, has developed a machine learning algorithm-called MPLasso-that uses data to infer associations and interactions between microbes in the GI microbiome. [20] A team of researchers from the University of Muenster in Germany has now demonstrated that this combination is extremely well suited to planning chemical syntheses-so-called retrosyntheses-with unprecedented efficiency. [19] Two physicists at ETH Zurich and the Hebrew University of Jerusalem have developed a novel machine-learning algorithm that analyses large data sets describing a physical system and extract from them the essential information needed to understand the underlying physics. [18]
Category: Artificial Intelligence
[6] viXra:2003.0019 [pdf] submitted on 2020-03-01 02:57:08
Authors: M. Hanefi CALP
Comments: 13 Pages.
Günümüzde kamu veya özel kurumların birçoğu, bünyelerinde çalışan personeller için profesyonel yemek hizmeti vermektedir. Söz konusu hizmetin planlanması konusunda, kurumlarda çalışan personel sayısının genel olarak fazla olması ve personellerin şahsi veya kuruma ait sebeplerle kurum dışında olmalarından dolayı birtakım aksamalar yaşanmaktadır. Bu yüzden, günlük yemek talebinin belirlenmesi zorlaşmakta ve bu durum kurumlar için maliyet, zaman ve emek kaybına sebep olmaktadır. Bu kayıpları ortadan kaldırmak veya en azından minimuma indirmek amacıyla istatistiksel veya sezgisel yöntemler kullanılmaktadır. Bu çalışmada, işletmeler için yapay sinir ağları kullanılarak günlük yemek talebini tahmin eden yapay zekâ tabanlı bir model önerilmiştir. Veriler, günlük yemek çıkaran ve farklı kademlerde görev alan 110 kişilik bir personel kapasitesine sahip özel bir işletmenin yemekhane veritabanından elde edilmiş olup son 2 yıllık (2016-2018) veriyi kapsamaktadır. Model, MATLAB paket programı kullanılarak oluşturulmuştur. Modelin performansı, Regresyon değerleri, Ortalama Mutlak Hata Yüzdesi (OMHY-MAPE) ve Ortalama Karesel Hata (OKH-MSE) dikkate alınarak belirlenmiştir. Ağın eğitiminde, ileri beslemeli geri yayılımlı ağ mimarisi kullanılmıştır. Denemeler sonucunda elde edilen en iyi model, sırasıyla eğitim R oranı: 0,9948, test R oranı: 0,9830 ve hata oranı ise 0,003783 olup çok katmanlı (8-10-10-1) bir yapıya sahiptir. Deney sonuçları, modelin hata oranının düşük, performansının yüksek olduğunu ve talep tahmini için yapay sinir ağları kullanımının olumlu etkisini ortaya koymuştur.
Category: Artificial Intelligence
[5] viXra:2003.0018 [pdf] submitted on 2020-03-01 02:59:47
Authors: M. Hanefi CALP
Comments: 10 Pages.
Artificial Intelligence has an important place in the scientific community as a result of its
successful outputs in terms of different fields. In time, the field of Artificial Intelligence has been divided into many sub-fields because of increasing number of different solution approaches, methods, and techniques. Machine Learning has the most remarkable role with its functions to learn
from samples from the environment. On the other hand, intelligent optimization done by inspiring from nature and swarms had its own unique scientific literature, with effective solutions provided for optimization problems from different fields. Because intelligent optimization can be applied in different fields effectively, this study aims to provide a general discussion on multidisciplinary effects of Artificial Intelligence by considering its optimization oriented solutions. The study briefly focuses on background of the intelligent optimization briefly and then gives application examples of intelligent optimization from a multidisciplinary perspective.
Category: Artificial Intelligence
[4] viXra:2003.0017 [pdf] submitted on 2020-03-01 03:00:58
Authors: M. Hanefi CALP
Comments: 18 Pages.
Effective use and management of ever-diminishing water resources are critically important to the future of humanity. At this point, rainfall is one of the most important factors that supply water resources, but the fact that the rainfall higher is more than normal causes many disasters such as flood, erosion. Therefore, rainfall amount must be analyzed mathematically, statistically or heuristically in order to take precautions, in the region. In this study, an Adaptive Neuro Fuzzy Inference System - Genetic Algorithm (ANFIS-GA) based hybrid model was proposed for estimation of regional rainfall amount. Purpose of the study is to minimize the loss of life and goods for people of the region by estimating the amount of annual rainfall and ensuring effective management of water resources and allowing some evaluations and preparations according to possible climate changes. The estimation model was developed by coding in the MATLAB package program. In the development of the model, 3650 meteorological data from 2008-2018 years belonging to Basel, a Swiss city, were utilized. The real data were tested on both the Artificial Neural Network (ANN) and the hybrid ANFIS-GA model. The obtained results demonstrated that the training R-value of the suggested ANFIS-GA model was 0.9920, the testing R-value was 0.9840 and the error ratio was 0.0011. This clearly shows that predictive performance of the model is high and error level is low, and therefore that hybrid approaches such as ANFIS-GA can be easily used in predicting meteorological events.
Category: Artificial Intelligence
[3] viXra:2003.0015 [pdf] submitted on 2020-03-01 03:03:48
Authors: M. Hanefi CALP
Comments: 13 Pages.
Projects consist of interconnected dimensions such as objective, time, resource and environment. Use of these dimensions in a controlled way and their effective scheduling brings the project success. Project scheduling process includes defining project activities, and estimation of time and resources to be used for the activities. At this point, the project resource-scheduling problems have begun to attract more attention after Program Evaluation and Review Technique (PERT) and Critical Path Method (CPM) are developed one after the other. However, complexity and difficulty of CPM and PERT processes led to the use of these techniques through artificial intelligence methods such as Genetic Algorithm (GA). In this study, an algorithm was proposed and developed, which determines critical path, critical activities and project completion duration by using GA, instead of CPM and PERT techniques used for network analysis within the scope of project management. The purpose of using GA was that these algorithms are an effective method for solution of complex optimization problems. Therefore, correct decisions can be made for implemented project activities by using obtained results. Thus, optimum results were obtained in a shorter time than the CPM and PERT techniques by using the model based on the dynamic algorithm. It is expected that this study will contribute to the performance field (time, speed, low error etc.) of other studies.
Category: Artificial Intelligence
[2] viXra:2003.0014 [pdf] submitted on 2020-03-01 03:04:53
Authors: Murat Dener, M. Hanefi CALP
Comments: 14 Pages.
It is the efficient use of resources expected from an exam scheduling application. There are various criteria for efficient use of resources and for all tests to be carried out at minimum cost in the shortest possible time. It is aimed that educational institutions with such criteria successfully carry out central examination organizations. In the study, a two-stage genetic algorithm was developed. In the first stage, the assignment of courses to sessions was carried out. In the second stage, the students who participated in the test session were assigned to examination rooms. Purposes of the study are increasing the number of joint students participating in sessions, using the minimum number of buildings in the same session, and reducing the number of supervisors using the minimum number of classrooms possible. In this study, a general purpose exam scheduling solution for educational institutions was presented. The developed system can be used in different central examinations to create originality. Given the results of the sample application, it is seen that the proposed genetic algorithm gives successful results.
Category: Artificial Intelligence
[1] viXra:2003.0013 [pdf] submitted on 2020-03-01 03:08:39
Authors: M. Hanefi CALP
Comments: 11 Pages.
Machine Learning is an important sub-field of the Artificial Intelligence and it has been become a very critical task to train Machine Learning techniques via effective method or techniques. Recently, researchers try to use alternative techniques to improve ability of Machine Learning techniques. Moving from the explanations, objective of this study is to introduce a novel SVM-CoDOA (Cognitive Development Optimization Algorithm trained Support Vector Machines) system for general medical diagnosis. In detail, the system consists of a SVM, which is trained by CoDOA, a newly developed optimization algorithm. As it is known, use of optimization algorithms is an essential task to train and improve Machine Learning techniques. In this sense, the study has provided a medical diagnosis oriented problem scope in order to show effectiveness of the SVM-CoDOA hybrid formation.
Category: Artificial Intelligence
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