New methods for quantitative climate reconstructions applied to the Levant

2021 ◽  
Author(s):  
Timon Netzel ◽  
Andreas Hense ◽  
Thomas Litt ◽  
Andrea Miebach
Keyword(s):  
Mathematical Formulation ◽  
Dead Sea ◽  
Modern Human ◽  
Climate Reconstruction ◽  
Machine Learning Techniques ◽  
Climate Data ◽  
Jordan Valley ◽  
Bayesian Hierarchical ◽  
Climate Reconstructions ◽  
Learning Techniques

<p align="justify">On his migration out of Africa, anthropological modern human had to cross the Levant, among other places. Today, there are many different climatic zones, which are particularly evident <span>along</span> the Jordan Valley. For example, the Dead Sea and the Sea of Galilee in Israel are reservoirs of climate proxies and reflect climate variability during the Holocene, based on pollen and plant remains in their lake sediments.</p><p align="justify">In addition to plant information, speleothems are also useful as climatic proxies. They have been studied in many caves in the Levant. From their isotopic data, conclusions can be drawn about the climate in specific periods and areas. One task is their appropriate use in terms of quantitative climate reconstruction.</p><p align="justify">Another topic is the consideration of age uncertainties in paleoclimatology and their influence on reconstruction techniques. For this purpose, it is advantageous to use mathematical formulations that are easy to implement and calculate.</p><p align="justify"><span>B</span>ased on data from a sediment core of <span>Sea of Galilee</span> we will discuss and present results for the following sequence of points: the mathematical formulation of climate reconstruction using Bayesian hierarchical models, the computation of transfer function connecting proxy information with physical climate data using machine learning techniques, and the inclusion of age uncertainty based on the output from the latest BACON version.</p>

Machine Learning Based Assembly of Fragments of Ancient Papyrus

2021 ◽  
Vol 14 (3) ◽  
pp. 1-21
Author(s):  
Roy Abitbol ◽  
Ilan Shimshoni ◽  
Jonathan Ben-Dov
Keyword(s):  
Machine Learning ◽  
Spatial Information ◽  
Real Life ◽  
Dead Sea ◽  
Machine Learning Techniques ◽  
Automated Classification ◽  
Learning Techniques ◽  
Test Batch ◽  
Validation Set ◽  
Local Edge

The task of assembling fragments in a puzzle-like manner into a composite picture plays a significant role in the field of archaeology as it supports researchers in their attempt to reconstruct historic artifacts. In this article, we propose a method for matching and assembling pairs of ancient papyrus fragments containing mostly unknown scriptures. Papyrus paper is manufactured from papyrus plants and therefore portrays typical thread patterns resulting from the plant’s stems. The proposed algorithm is founded on the hypothesis that these thread patterns contain unique local attributes such that nearby fragments show similar patterns reflecting the continuations of the threads. We posit that these patterns can be exploited using image processing and machine learning techniques to identify matching fragments. The algorithm and system which we present support the quick and automated classification of matching pairs of papyrus fragments as well as the geometric alignment of the pairs against each other. The algorithm consists of a series of steps and is based on deep-learning and machine learning methods. The first step is to deconstruct the problem of matching fragments into a smaller problem of finding thread continuation matches in local edge areas (squares) between pairs of fragments. This phase is solved using a convolutional neural network ingesting raw images of the edge areas and producing local matching scores. The result of this stage yields very high recall but low precision. Thus, we utilize these scores in order to conclude about the matching of entire fragments pairs by establishing an elaborate voting mechanism. We enhance this voting with geometric alignment techniques from which we extract additional spatial information. Eventually, we feed all the data collected from these steps into a Random Forest classifier in order to produce a higher order classifier capable of predicting whether a pair of fragments is a match. Our algorithm was trained on a batch of fragments which was excavated from the Dead Sea caves and is dated circa the 1st century BCE. The algorithm shows excellent results on a validation set which is of a similar origin and conditions. We then tried to run the algorithm against a real-life set of fragments for which we have no prior knowledge or labeling of matches. This test batch is considered extremely challenging due to its poor condition and the small size of its fragments. Evidently, numerous researchers have tried seeking matches within this batch with very little success. Our algorithm performance on this batch was sub-optimal, returning a relatively large ratio of false positives. However, the algorithm was quite useful by eliminating 98% of the possible matches thus reducing the amount of work needed for manual inspection. Indeed, experts that reviewed the results have identified some positive matches as potentially true and referred them for further investigation.

scholarly journals Fiscore Package: Effective Protein Structural Data Visualisation and Exploration

2021 ◽  
Author(s):  
Auste Kanapeckaite
Keyword(s):  
Machine Learning ◽  
Protein Function ◽  
Mathematical Formulation ◽  
Structural Data ◽  
R Package ◽  
Gaussian Mixture ◽  
Structural Features ◽  
Machine Learning Techniques ◽  
Topological Features ◽  
Learning Techniques

Lack of bioinformatics tools to quickly assess protein conformational and topological features motivated to create an integrative and user-friendly R package. Moreover,Fiscore package implements a pipeline for Gaussian mixture modelling making such machine learning techniques readily accessible to non-experts. This is especially important since probabilistic machine learning techniques can help with a better interpretation of complex biological phenomena when it is necessary to elucidate various structural features that might play a role in protein function. Thus,Fiscore package builds on the mathematical formulation of protein physicochemical properties that can aid in drug discovery, target evaluation, or relational database building. Moreover, the package provides interactive environments to explore various features of interest. Finally, one of the goals of this package was to engage structural bioinformaticians and develop more R tools that could help researchers not necessarily specialising in this field. Package Fiscore(v.0.1.2) is distributed via CRAN and Github.

scholarly journals Enhancement of the Ball Balancing on the Plate using hybrid PD/Machine learning techniques

2021 ◽  
Vol 2128 (1) ◽  
pp. 012028
Author(s):  
Mohamed R. Elshamy ◽  
Essam Nabil ◽  
Amged Sayed ◽  
Belal Abozalam
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Mathematical Formulation ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Servo Motor ◽  
Random Forest Regression ◽  
Learning Techniques ◽  
Motor Parameter ◽  
Simulation Results

Abstract This paper discusses an efficient method to improve the balancing and tracking of the trajectory of the BOPS based on machine learning (ML) algorithm with the Pseudo proportional-derivative (PPD) controller. The proposed controller depends on a ML technique that detect the angle of the servo motor required to correct the ball position on the plate. This paper presents three different ML algorithms for the servo motor angle prediction and achieved higher accuracy which are 99.855%, 99.999%, and 99.998% for support vector regression, decision tree regression, and random forest regression, respectively. The simulation results demonstrate that the proposed strategy has significantly improved the settling time and overshoot of the system. The mathematical formulation can be obtained using the Lagrangian formulation and the servo motor parameter obtained by a practical identification experiment.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

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