Artificial Intelligence

1910 Submissions

[36] viXra:1910.0632 [pdf] submitted on 2019-10-30 01:57:43

Latest Editions Of Original Research Books by Mr. Ramesh Chandra Bagadi as on October 30th 2019 AD.

Authors: B Ananda Rao Ramesh Chandra
Comments: 6 Pages.

Latest Editions Of Original Research Books by Mr. Ramesh Chandra Bagadi as on October 30th 2019 AD.
Category: Artificial Intelligence

[35] viXra:1910.0597 [pdf] submitted on 2019-10-29 10:51:31

Facial Expression Analysis by K-Means Clustering on Fiducial Points of Face

Authors: Previte Modesto, Saul Reitano
Comments: 5 Pages.

Human beings produce thousands of facial actions and emotions in a single day. These come up while communicating with someone and at times even when alone. These expressions vary in complexity, intensity, and meaning. This paper proposes a novel method to predict what emotion is being expressed by analyzing the face. The algorithm, because of the speed of execution, could also be used for micro expression analysis. 11 fiducial points are taken on the image after a face recognition algorithm is used. 7 classes of images are formed. These classes are the main expressions: sadness, happiness, anger, fear, disgust, surprise and neutral. Training is done by studying the relationship between the fiducial points for each class of image. Using this relationship a new image is classified by making use of the k-means algorithm.
Category: Artificial Intelligence

[34] viXra:1910.0584 [pdf] submitted on 2019-10-28 07:00:07

Neural Networks Detect DNA Damage

Authors: George Rajna
Comments: 31 Pages.

Researchers at Tomsk Polytechnic University jointly with the University of Chemistry and Technology (Prague) conducted a series of experiments which proved that artificial neural networks can accurately identify DNA damage caused by UV radiation. [20] Computer scientists at Carnegie Mellon University say neural networks and supervised machine learning techniques can efficiently characterize cells that have been studied using single cell RNA-sequencing (scRNA-seq). [19] Researchers at Delft University of Technology, in collaboration with colleagues at the Autonomous University of Madrid, have created an artificial DNA blueprint for the replication of DNA in a cell-like structure. [18]
Category: Artificial Intelligence

[33] viXra:1910.0578 [pdf] submitted on 2019-10-28 08:51:06

Unsupervised Decomposition of Multi-Author Document

Authors: Kautsya Kanu, Sayantan Sengupta
Comments: 4 Pages.

This paper proposes an improvement over a paper[A generic unsupervised methods for decomposing multi-author documents, N. Akiva and M. Koppel 2013]. We have worked on two aspects, In the first aspect, we try to capture writing style of author by ngram model of words, POS Tags and PQ Gram model of syntactic parsing over used basic uni-gram model. In the second aspect, we added some layers of refinements in existing baseline model and introduce new term ”similarity index” to distinguish between pure and mixed segments before unsupervised labeling. Similarity index uses overall and sudden change of writing style by PQ Gram model and words used using n-gram model between lexicalised/unlexicalised sentences in segments for refinement. In this paper, we investigate the role of feature selection that captures the syntactic patterns specific to an author and its overall effect in the final accuracy of the baseline system. More specifically, we insert a layer of refinement to the baseline system and define a threshold based on the similarity measure among the sentences to consider the purity of the segments to be given as input to the GMM.The key idea of our approach is to provide theGMMclustering with the ”good segments” so that the clustering precision is maximised which is then used as labels to train a classifier. We also try different features set like bigrams and trigrams of POS tags and an PQ Grams based feature on unlexicalised PCFG to capture the distinct writing styles which is then given as an input to a GMM trained by iterative EM algorithm to generate good clusters of the segments of the merged document.
Category: Artificial Intelligence

[32] viXra:1910.0568 [pdf] submitted on 2019-10-27 18:08:55

Sentiment Classification Over Brazilian Supreme Court Decisions Using Multi-Channel CNN

Authors: Marcus Oliveira da Silva
Comments: 11 Pages.

Sentiment analysis seeks to identify the viewpoint(s) underlying a text document; In this paper, we present the use of a multichannel convolutional neural network which, in effect, creates a model that reads text with different n-gram sizes, to predict with good accuracy sentiments behind the decisions issued by the Brazilian Supreme Court, even with a very imbalanced dataset we show that a simple multichannel CNN with little to zero hyperparameter tuning and word vectors, tuned on network training, achieves excellent results on the Brazilian Supreme Court data. We report results of 97% accuracy and 84% average F1- score in predicting multiclass sentiment dimensions. We also compared the results with classical classification machine learning models like Naive Bayes and SVM.
Category: Artificial Intelligence

[31] viXra:1910.0522 [pdf] submitted on 2019-10-25 05:08:51

Prisoner's Dilemma Game

Authors: George Rajna
Comments: 29 Pages.

Game theory is a field which applies mathematics to understand the science behind logical decision-making behavior and social structures. [20] It sounds like science fiction: controlling electronic devices with brain waves. But researchers have developed a new type of electroencephalogram (EEG) electrode that can do just that, without the sticky gel required for conventional electrodes. [19] U.S. Army Research Laboratory scientists have discovered a way to leverage emerging brain-like computer architectures for an age-old number-theoretic problem known as integer factorization. [18]
Category: Artificial Intelligence

[30] viXra:1910.0514 [pdf] submitted on 2019-10-25 09:44:59

Review Highlights: Opinion Mining on Reviews: a Hybrid Model for Rule Selection in Aspect Extraction

Authors: A Kushwaha, S Chaudhary
Comments: 6 Pages.

This paper proposes a methodology to extract key insights from user generated reviews. This work is based on Aspect Based Sentiment Analysis (ABSA) which predicts the sentiment of aspects mentioned in the text documents. The extracted aspects are fine-grained for the presentation form known as Review Highlights. The syntactic approach for extraction process suffers from the overlapping chunking rules which result in noise extraction. We introduce a hybrid technique which combines machine learning and rule based model. A multi-label classifier identifies the effective rules which efficiently parse aspects and opinions from texts. This selection of rules reduce the amount of noise in extraction tasks. This is a novel attempt to learn syntactic rule fitness from a corpus using machine learning for accurate aspect extraction. As the model learns the syntactic rule prediction from the corpus, it makes the extraction method domain independent. It also allows studying the quality of syntactic rules in a different corpus.
Category: Artificial Intelligence

[29] viXra:1910.0501 [pdf] submitted on 2019-10-24 22:03:01

A Simple Suggestion based on Understanding & Using a Combination of Python based Tools in the Context of Rabin Fingerprint Computing.

Authors: Nirmal Tej Kumar
Comments: 5 Pages. Short Communication & Technical Notes

A Simple Suggestion based on Understanding & Using a Combination of Python based Tools in the Context of Rabin Fingerprint Computing. [ Exploring - Medical Imaging/Electron Microscopy Imaging R&D ]
Category: Artificial Intelligence

[28] viXra:1910.0445 [pdf] submitted on 2019-10-23 11:11:32

Automatic Retinal Disease Classification Using Machine Learning and AI

Authors: Tapesh Santra
Comments: 8 Pages. None

patients get preventive care. Yet, due to lack of infrastructure and resources millions of patients do not avail such diagnosis. In this paper, I explore the possibility of developing an automatic retinal disease classifier using computer vision algorithms. Two different classes of algorithms are tested; (a) traditional computer vision approach of hand crafting features followed by developing machine learning (ML) models (b) automatic feature engineering and classification using more modern convolutional neural networks (CNN). The above algorithms were used to build both multi-class classifiers, i.e. the models which are trained to identify the correct disease, and binary classifiers, i.e. models that are trained to determine if a patient has a specific disease or not. A set of 600 pre-labelled retinal scan images were used to train the models. Both the ML and CNN models had relatively modest success in the multiclass scenario. However, the ML models were found to be reliably accurate in binary classification scenario, achieving >90% accuracy in identifying cataract.
Category: Artificial Intelligence

[27] viXra:1910.0434 [pdf] submitted on 2019-10-22 23:10:12

Machine Learning Alternatives for the Diagnosis of Adhd from Functional Connectivity and Phenotypic Information

Authors: Amrit Baveja
Comments: 43 Pages.

Current estimates are that 5-10% of school age children (including the author) suffer from ADHD, costing the US healthcare system alone over $36B. However, factors such as a revenue-motivated healthcare system and researcher confirmation bias make ADHD overdiagnosis a very real issue, and today, there continues to be no reliable technique for automated ADHD diagnosis in clinical use. This work’s objective was to improve upon previous attempts to develop machine learning models for automated ADHD diagnosis. For this project, the author used the ADHD200 dataset which was generated for an international competition in 2011. The author extended the competition’s approach in several ways: First, the author combined high coverage phenotypic features with the functional connectome features used previously. Next, the author used a random 20% test/train split cross-validated five times to avoid overfitting, rather than the previously used fixed test/train split. Third, the author used a broad range of newer models such as neural networks and deep forest. Finally, the author used newer hyperparameter optimization techniques to identify the best model parameters. The best model explored was the more recent gcForest model with automated optimization -- it improved the previous best ADHD F1 from 0.32 to 0.52, a substantial improvement in binary diagnostic performance.
Category: Artificial Intelligence

[26] viXra:1910.0433 [pdf] submitted on 2019-10-22 00:14:26

RTOP: A Conceptual and Computational Framework for General Intelligence

Authors: Shilpesh Garg
Comments: 17 Pages.

A novel general intelligence model is proposed with three types of learning. A unified sequence of the foreground percept trace and the command trace translates into direct and time-hop observation paths to form the basis of Raw learning. Raw learning includes the formation of image-image associations, which lead to the perception of temporal and spatial relationships among objects and object parts; and the formation of image-audio associations, which serve as the building blocks of language. Offline identification of similar segments in the observation paths and their subsequent reduction into a common segment through merging of memory nodes leads to Generalized learning. Generalization includes the formation of interpolated sensory nodes for robust and generic matching, the formation of sensory properties nodes for specific matching and superimposition, and the formation of group nodes for simpler logic pathways. Online superimposition of memory nodes across multiple predictions, primarily the superimposition of images on the internal projection canvas, gives rise to Innovative learning and thought. The learning of actions happens the same way as raw learning while the action determination happens through the utility model built into the raw learnings, the utility function being the pleasure and pain of the physical senses.
Category: Artificial Intelligence

[25] viXra:1910.0429 [pdf] submitted on 2019-10-22 03:23:29

An Insight Into Mathematics Behind Rabin Finger Printing to Develop QRNG/ ML-Machine Learning/c/ruby/ruby-LLVM/LLVM-Polly/iot/aot/hpc – High Performance Computing Heterogeneous Systems/environments.

Authors: Nirmal Tej Kumar
Comments: 2 Pages. Short Communication & Technical Notes

An Insight into Mathematics behind Rabin Finger Printing to Develop QRNG/ML-Machine Learning/C/Ruby/Ruby-LLVM/LLVM-Polly/IoT/AoT/HPC – High Performance Computing Heterogeneous Systems/Environments.
Category: Artificial Intelligence

[24] viXra:1910.0409 [pdf] submitted on 2019-10-21 05:44:41

Data Mining Earth's Biodiversity

Authors: George Rajna
Comments: 38 Pages.

Once the data are available, they are immediately distributed to global biodiversity platforms, such as GBIF—the Global Biodiversity Information Facility. [24] Researchers at Oregon State University have used deep learning to decipher which ribonucleic acids have the potential to encode proteins. [23] A new method allows researchers to systematically identify specialized proteins that unpack DNA inside the nucleus of a cell, making the usually dense DNA more accessible for gene expression and other functions. [22]
Category: Artificial Intelligence

[23] viXra:1910.0400 [pdf] submitted on 2019-10-21 10:01:20

On the Maximum X Entropy Negation of a Complex-Valued Distribution

Authors: Fuyuan Xiao
Comments: 2 Pages.

In this paper, we propose a generalized model of the negation function, so that it can has more powerful capability to represent the knowledge and uncertainty measure. In particular, we first define a vector representation of complex-valued distribution. Then, an entropy measure is proposed for the complex-valued distribution, called X entropy. After that, a transformation function to acquire the negation of the complex-valued distribution is exploited. Finally, we verify that the proposed negation method has a maximal entropy.x
Category: Artificial Intelligence

[22] viXra:1910.0382 [pdf] replaced on 2019-10-28 00:59:37

Intrusion Detection using Sequential Hybrid Model

Authors: Aditya Pandey, Abhishek Sinha, Aishwarya PS
Comments: 6 Pages.

A large amount of work has been done on the KDD 99 dataset, most of which includes the use of a hybrid anomaly and misuse detection model done in parallel with each other. In order to further classify the intrusions, our approach to network intrusion detection includes use of two different anomaly detection models followed by misuse detection applied on the combined output obtained from the previous step. The end goal of this is to verify the anomalies detected by the anomaly detection algorithm and clarify whether they are actually intrusions or random outliers from the trained normal (and thus to try and reduce the number of false positives). We aim to detect a pattern in this novel intrusion technique itself, and not the handling of such intrusions. The intrusions were detected to a very high degree of accuracy.
Category: Artificial Intelligence

[21] viXra:1910.0362 [pdf] submitted on 2019-10-19 20:41:22

Walkrnn: Reading Stories from Property Graphs

Authors: Deborah Tylor, Joseph Haaga, Mirco Mannucci
Comments: 7 Pages.

WalkRNN, the approach described herein, leverages research in learning continuous representations for nodes in networks, layers in features captured in property graph attributes and labels, and uses Deep Learning language modeling via Recurrent Neural Networks to read the grammar of an enriched property graph. We then demonstrate translating this learned graph literacy into actionable knowledge through graph classification tasks.
Category: Artificial Intelligence

[20] viXra:1910.0335 [pdf] submitted on 2019-10-18 03:25:08

Computers Models of Wind Turbines

Authors: George Rajna
Comments: 37 Pages.

Researchers have modeled the fluid dynamics of multi-rotor wind turbines, and how they interact in wind farms. The research demonstrates a clear advantage for a turbine model with four rotors. [24] This makes it a potential candidate for the next generation of larger and more powerful quantum computers," adds Ulrik Lund Andersen. [23] An international team of scientists from Australia, Japan and the United States has produced a prototype of a large-scale quantum processor made of laser light. [22]
Category: Artificial Intelligence

[19] viXra:1910.0284 [pdf] submitted on 2019-10-16 18:42:13

Harnessing Machine Learning to Anticipate Infectious Disease Threats with Pandemic Potential

Authors: Chandra Swarathesh Addanki
Comments: 8 Pages.

With the massive emergence and exponential increase in the number of infectious diseases with pandemic potential spreading across the globe, we need a modern approach to find these sort of infectious diseases before they reach their pandemic potential , Our objective is to address this challenge by creating an IVR (interactive Voice Response) system which takes a voice input from user then queries, filters and classifies using big data whether the disease is pandemic and informs the user the results of the prediction and keeps track of it.
Category: Artificial Intelligence

[18] viXra:1910.0280 [pdf] submitted on 2019-10-16 02:09:26

R+Java+Renjin+ImageJ+JikesRVM in the Context of Sparse Matrices/Machine Learning/Medical Imaging R&D in HPC – High Performance Computing Environments.

Authors: Nirmal Tej Kumar
Comments: 2 Pages. Short Communication & Technical Notes

R+Java+Renjin+ImageJ+JikesRVM in the Context of Sparse Matrices/Machine Learning/Medical Imaging R&D in HPC – High Performance Computing Environments.
Category: Artificial Intelligence

[17] viXra:1910.0257 [pdf] submitted on 2019-10-15 02:19:06

[ Gentle Compiler Construction System/cool-Spe ] in the Context of [ Minsky Machines/nlp ] Towards Big Data Testing on Iot/hpc [ Hardware+software+firmware ] R&D Platforms – a General Approach in Using Minsky Machines+nlp.

Authors: Nirmal Tej Kumar
Comments: 2 Pages. Short Communication & Technical Notes

[ Gentle Compiler Construction System/CooL-SPE ] in the Context of [ Minsky Machines/NLP ] towards BIG DATA Testing on IoT/HPC - [ Hardware+Software+Firmware ] R&D Platforms – A General Approach in Using Minsky Machines+NLP.
Category: Artificial Intelligence

[16] viXra:1910.0255 [pdf] submitted on 2019-10-15 04:07:04

A Deep Neural Network as Surrogate Model for Forward Simulation of Borehole Resistivity Measurements

Authors: M. Shahriari, D. Pardo, B. Moser
Comments: 7 Pages.

Inverse problems appear in multiple industrial applications. Solving such inverse problems require the repeated solution of the forward problem. This is the most time-consuming stage when employing inversion techniques, and it constitutes a severe limitation when the inversion needs to be performed in real-time. In here, we focus on the real-time inversion of resistivity measurements for geosteering. We investigate the use of a deep neural network (DNN) to approximate the forward function arising from Maxwell's equations, which govern the electromagnetic wave propagation through a media. By doing so, the evaluation of the forward problems is performed offline, allowing for the online real-time evaluation (inversion) of the DNN.
Category: Artificial Intelligence

[15] viXra:1910.0242 [pdf] replaced on 2019-10-28 08:02:52

An Empirical Study of Active Learning Strategies for Supervised Classification

Authors: Louis Desreumaux
Comments: 79 Pages.

Modern supervised learning methods are known to require large amounts of training examples to reach their full potential. Since these examples are mainly obtained through human experts who manually label samples, the labeling process may have a high cost. Active learning aims to reduce annotation cost by predicting which samples are useful for a human expert to label. Although this field is quite old, several important challenges to using active learning in real-world settings still remain unsolved. In particular, most selection strategies are hand-designed, and it has become clear that there is no best active learning strategy. This has motivated research into meta-learning algorithms for "learning how to actively learn". In this report, a comprehensive comparison of active learning strategies is presented, including a strategy learnt using reinforcement learning.
Category: Artificial Intelligence

[14] viXra:1910.0234 [pdf] submitted on 2019-10-14 03:51:15

The Pascal Triangle of Maximum Deng Entropy

Authors: Xiaozhuan Gao, Yong Deng
Comments: 19 Pages.

Pascal-Triangle (known as Yang Hui Triangle) is an important structure in mathematics, which has been used many fields. Entropy plays an essential role in physics. In various, information entropy is used to measure the uncertainty of information. Hence, setting the connection between Pascal Triangle and information uncertainty is a question worth exploring. Deng proposed the Deng entropy that it can measure non-specificity and discord of basic probability assignment (BPA) in Dempster-Shafer (D-S) evidence theory. D-S evidence theory and power set are very closely related. Hence, by analysing the maximum Deng entropy, the paper find that there is an potential rule of BPA with changes of frame of discernment. Finally, the paper set the relation between the maximum Deng entropy and PascalTriangle.
Category: Artificial Intelligence

[13] viXra:1910.0197 [pdf] submitted on 2019-10-12 21:56:48

[ OCaml-LLVM/Polly-Owl-Satallax(Theorem Prover in OCaml) - CoqTP/OCaml - Q*cert/OCaml/Eigen ] in the Context of IoT/HPC-High Performance Computing - Heterogeneous Environments to [ Design+Test+Implement DNA based Theoretical Gene Therapy Informatics ] R&D

Authors: Nirmal Tej Kumar
Comments: 4 Pages. Short Communication & Technical Notes

[ OCaml-LLVM/Polly-Owl-Satallax(Theorem Prover in OCaml) - CoqTP/OCaml - Q*cert/OCaml/Eigen ] in the Context of IoT/HPC-High Performance Computing - Heterogeneous Environments to[Design+Test+Implement DNA based Theoretical Gene Therapy Informatics ] R&D Framework – A Simple Suggestion & Novel Approach.
Category: Artificial Intelligence

[12] viXra:1910.0191 [pdf] submitted on 2019-10-12 03:54:59

Artificial Intelligence Determine Exoplanets Sizes

Authors: George Rajna
Comments: 40 Pages.

Using a machine learning technique, a team of Instituto de Astrofísica e Ciências do Espaço researchers constrained the radius of an exoplanet with known mass. [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

[11] viXra:1910.0166 [pdf] submitted on 2019-10-10 16:35:34

Unbias Coding(™): a Meditation.

Authors: Bheemaiah, Anil Kumar
Comments: 4 Pages.

The blue ribbon of free speech was taunted, may be it is the Unbias Coding(™) by A.B, my company. ChatterBoxing with the network, I meditate in this publication on OOPS, NLP and the wigner universe, can we evolve self conscious machines, without the diagonal paradox? Read my reflections and share yours at the miyawaki spam free mail address. Keywords: Approximation, iSense, metamodels, machine evolution, code generation, OOPS, NLP, Cheap Talk, Sand Boxing, Wolfram Cloud and Script, Wisdom Capital.
Category: Artificial Intelligence

[10] viXra:1910.0163 [pdf] submitted on 2019-10-10 01:52:58

[ Julia+flux-ML Library+polly-LLVM+QRNG Services/quantum Devices+qrng Library ] in the Context of Understanding [ Machine Learning-Ml/internet of Things-Iot/hpc High Performance Computing ] Based Advanced Medical Imaging Software R&D

Authors: Nirmal Tej Kumar
Comments: 3 Pages. Short Communication & Technical Notes

[ Julia+Flux-ML Library+Polly-LLVM+QRNG Services/Quantum Devices+qrng library ] in the Context of Understanding [ Machine Learning-ML/Internet of Things-IoT/HPC -High Performance Computing ] based Advanced Medical Imaging Software R&D – A Simple & Useful Suggestion.
Category: Artificial Intelligence

[9] viXra:1910.0122 [pdf] replaced on 2019-11-20 15:06:38

Confusion in the Matrix: Going Beyond the Roc Curve

Authors: Stephen Borstelmann, Saurabh Jha
Comments: 17 Pages. v3: spelling corrections, added Cohen's Kappa, image and formula corrections

Artificial intelligence algorithms are being created both investigationally and commercially. Evaluation of their performance is important for developers, investigators, clinical physicians, and regulatory agencies. No clear consensus exists on what metrics are best for algorithmic evaluation for AI and ML applications in radiology. We review the basics of the confusion matrix, continue to single number summary values such as accuracy, F1 score, and ɸ coefficient, and then discuss Receiver Operator Curves and their derivatives, Precision Recall Curves, and Cost Curves. Recommendations are made for potential future directions and what currently may be best practices in algorithmic evaluation metrics.
Category: Artificial Intelligence

[8] viXra:1910.0108 [pdf] submitted on 2019-10-08 09:17:39

Artificial Intelligence Cytometer in Blood

Authors: George Rajna
Comments: 42 Pages.

Detection of rare cells in blood and other bodily fluids has numerous important applications including diagnostics, monitoring disease progression and evaluating immune response. [25] But making those quantum leaps from science fiction to reality required hard work from computer scientists like Yoshua Bengio, Geoffrey Hinton and Yann LeCun. [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]
Category: Artificial Intelligence

[7] viXra:1910.0101 [pdf] submitted on 2019-10-07 09:42:55

[ JI Prolog/JikesRVM-Research Virtual Machine/Java-NLP/Java-Genetic Algorithms ] & its Important Uses in the R&D Domains of [ Bio-informatics ] – A Simple Suggestion Once Again in the Context of Complex Systems by Adding Java based AI Dimension.

Authors: Nirmal Tej Kumar
Comments: 4 Pages. Short Communication & Technical Notes

[ JI Prolog/JikesRVM-Research Virtual Machine/Java-NLP/Java-Genetic Algorithms ] & its Important Uses in the R&D Domains of [ Bio-informatics ] – A Simple Suggestion Once Again in the Context of Complex Systems by Adding Java based AI Dimension.
Category: Artificial Intelligence

[6] viXra:1910.0087 [pdf] submitted on 2019-10-06 21:42:02

An Insight Into [ Dlib C++ Machine Learning Library + Pulseseq+cplusplus-NLP-Library/python NLTK Library ] Software to Understand MR Sequences in the Context of [ Iot/hpc/nlp ] Informatics Environments.

Authors: Nirmal Tej Kumar
Comments: 2 Pages. Short Communication & Technical Notes

An Insight into [ Dlib C++ Machine Learning Library + PulseSeq+CPlusPlus-NLP-Library/python NLTK library ]Software to Understand MR Sequences in the Context of [ IoT/HPC/NLP ] Informatics Environments.
Category: Artificial Intelligence

[5] viXra:1910.0066 [pdf] submitted on 2019-10-06 09:27:07

Breaking the Wall of Unfamiliarity of One's Own Voice

Authors: Sun Ruikang
Comments: 2 Pages.

The voice someone heard inside their head is generally different from it heard on record. This problem makes it difficult to sing correctly for many people. Due to the development of Transfer Learning and Neural Networks, we can transfer the voice we really made to the voice we intend to with models like Generative Adversarial Networks (GAN).
Category: Artificial Intelligence

[4] viXra:1910.0060 [pdf] submitted on 2019-10-05 14:14:25

Study on Application of Machine Learning Intelligence for Identification and Control of Nonlinear Dynamical Systems: Case Study Robotic Manipulators

Authors: Divya Rao Ashok Kumar, Krishna Vijayaraghavan
Comments: 50 Pages.

In the literature, machine learning has been referred to as deeply structured learning, hierarchical learning and feature based learning which can provide computational models from high-level data abstractions. One of the most used learning structures is the multiple-layered models of inputs, commonly known as neural networks, which comprise multiple levels of non-linear operations. The machine learning algorithms are able solve many problems around fault detection, isolation and recovery. There has been a growing interest in using learning architectures in advanced robotics applications, e.g., object detection, scene semantic segmentation, and grasping. The real-time learning of high-dimensional features for robotics applications via machine learning techniques is another important topic. In addition, other topics in robotics such as obstacle detection and context-dependent social mapping are also being addressed by researchers through machine learning methods. Machine learning algorithms provide real time driving decisions for automated vehicles (self-driving vehicles or driverless cars) from integration of numerous sensors onboard the vehicle. The advancement of autonomous navigation and situational awareness systems adapt neural networks for analyzing the multi-modal sensor inputs. We observe that machine learning algorithms influence largely in decision making process. But, there is need to understand the control system consequences for adapting the outcome of the machine learning algorithm. This proposal presents the detailed study on the influences of machine learning architectures and algorithms for modeling and control of nonlinear dynamical system. Research Outcome: · Knowledge on machine learning architectures (Support Vector Machines (SVMs), Conditional Random Field, supervised neural network) · Understanding the constraints on applicability of ML architectures for nonlinear dynamical system · Study on real time control of nonlinear dynamical system with ML algorithm in closed loop.
Category: Artificial Intelligence

[3] viXra:1910.0046 [pdf] submitted on 2019-10-05 05:49:58

Machine Learning Cosmic and Subatomic Scales

Authors: George Rajna
Comments: 41 Pages.

While high-energy physics and cosmology seem worlds apart in terms of sheer scale, physicists and cosmologists at Argonne are using similar machine learning methods to address classification problems for both subatomic particles and galaxies. [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]
Category: Artificial Intelligence

[2] viXra:1910.0019 [pdf] submitted on 2019-10-01 19:06:48

A Harmless Wireless Quantum Alternative to Cell Phones Based on Quantum Noise

Authors: Robert Neil Boyd, Victor Christianto, Florentin Smarandache
Comments: 5 Pages. This paper has been accepted and published by EC Neurology Journal, 2019. Comments are welcome

In the meantime we know that 4G and 5G technologies cause many harms to human health. Therefore, here we submit a harmless wireless quantum alternative to cell phones. It is our hope that this alternative communication method can find its way to realization, while the existing RF based wireless technologies (4G, 5G) are being phased out.
Category: Artificial Intelligence

[1] viXra:1910.0009 [pdf] submitted on 2019-10-01 01:35:41

[ Python Metaprogramming+Z3Py-Python Theorem Prover+ImageAI ] in the Context of Radiation Oncology & [ IoT/HPC ]- High Performance Computing Heterogeneous Informatics R&D – An Interesting Insight into the World of Metaprogramming Concepts

Authors: Nirmal Tej Kumar
Comments: 3 Pages. Short Communication-Review

[ Python Metaprogramming+Z3Py-Python Theorem Prover+ImageAI ] in the Context of Radiation Oncology & [ IoT/HPC ]- High Performance Computing Heterogeneous Informatics R&D – An Interesting Insight into the World of Metaprogramming Concepts With a Useful Review. [ Exploring Metaprogramming+AI to Probe Complex Design Patterns Next Generation Medical Software R&D ]
Category: Artificial Intelligence