Ranking with Genetics

2020 ◽  
Vol 10 (3) ◽  
pp. 20-34
Author(s):  
Lawrence Master
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Question Answering ◽  
Learning To Rank ◽  
Approaches To Learning ◽  
Genetic Optimization ◽  
Regularization Technique ◽  
The Past ◽  
Ranking Algorithms ◽  
Ranking Model

There are many applications for ranking, including page searching, question answering, recommender systems, sentiment analysis, and collaborative filtering, to name a few. In the past several years, machine learning and information retrieval techniques have been used to develop ranking algorithms and several list wise approaches to learning to rank have been developed. We propose a new method, which we call GeneticListMLE++ and GeneticListNet++, which build on the original ListMLE and ListNet algorithms. Our method substantially improves on the original ListMLE and ListNet ranking approaches by incorporating genetic optimization of hyperparameters, a nonlinear neural network ranking model, and a regularization technique.

scholarly journals CAN A MACHINE LEARNING ALGORITHM IDENTIFY SARS-COV-2 VARIANTS BASED ON CONVENTIONAL rRT-PCR? PROOF OF CONCEPT

2021 ◽  
Author(s):  
jorge cabrera Alvargonzalez ◽  
Ana Larranaga Janeiro ◽  
Sonia Perez ◽  
Javier Martinez Torres ◽  
Lucia martinez lamas ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Supervised Classification ◽  
Learning Algorithm ◽  
Support Vector ◽  
Classification Algorithms ◽  
Machine Learning Algorithm ◽  
Proof Of Concept ◽  
The Past ◽  
Number Of Cycles

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been and remains one of the major challenges humanity has faced thus far. Over the past few months, large amounts of information have been collected that are only now beginning to be assimilated. In the present work, the existence of residual information in the massive numbers of rRT-PCRs that tested positive out of the almost half a million tests that were performed during the pandemic is investigated. This residual information is believed to be highly related to a pattern in the number of cycles that are necessary to detect positive samples as such. Thus, a database of more than 20,000 positive samples was collected, and two supervised classification algorithms (a support vector machine and a neural network) were trained to temporally locate each sample based solely and exclusively on the number of cycles determined in the rRT-PCR of each individual. Finally, the results obtained from the classification show how the appearance of each wave is coincident with the surge of each of the variants present in the region of Galicia (Spain) during the development of the SARS-CoV-2 pandemic and clearly identified with the classification algorithm.

Review and Applications of Machine Learning and Artificial Intelligence in Engineering

2020 ◽  
pp. 1-12
Author(s):  
Melda Yucel ◽  
Gebrail Bekdaş ◽  
Sinan Melih Nigdeli
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Development Process ◽  
Current Trend ◽  
Engineering Applications ◽  
The Past ◽  
Applications Of Machine Learning ◽  
Basic Features

This chapter presents a summary review of development of Artificial Intelligence (AI). Definitions of AI are given with basic features. The development process of AI and machine learning is presented. The developments of applications from the past to today are mentioned and use of AI in different categories is given. Prediction applications using artificial neural network are given for engineering applications. Usage of AI methods to predict optimum results is the current trend and it will be more important in the future.

scholarly journals Ecg Heartbeat Classification: Conceptual Understanding through Cnn & Rnn – A Machine Learning Approach

2020 ◽  
Vol 10 (2) ◽  
pp. 143-147
Author(s):  
P. Rama Santosh Naidu ◽  
K.Venkata Ramana ◽  
G. Lavanya Devi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Dominant Role ◽  
Machine Learning Techniques ◽  
Heartbeat Classification ◽  
Ecg Signals ◽  
The Past ◽  
Learning Techniques ◽  
Machine Learning Approach

In recent days Machine Learning has become major study aspect in various applications that includes medical care where convenient discovery of anomalies in ECG signals plays an important role in monitoring patient's condition regularly. This study concentrates on various MachineLearning techniques applied for classification of ECG signals which include CNN and RNN. In the past few years, it is being observed that CNN is playing a dominant role in feature extraction from which we can infer that machine learning techniques have been showing accuracy and progress in classification of ECG signals. Therefore, this paper includes Convolutional Neural Network and Recurrent Neural Network which is being classified into two types for better results from considerably increased depth.

scholarly journals Opening closed doors: using machine learning to explore factors associated with marital sexual violence in a cross-sectional study from India

BMJ Open ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. e053603
Author(s):  
Lotus McDougal ◽  
Nabamallika Dehingia ◽  
Nandita Bhan ◽  
Abhishek Singh ◽  
Julian McAuley ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Sexual Violence ◽  
Thematic Analysis ◽  
Health Knowledge ◽  
Machine Learning Algorithms ◽  
Paper Machine ◽  
Cross Sectional ◽  
The Past ◽  
Modifiable Factors

ObjectivesSexual violence against women is pervasive in India. Most of this violence is experienced in the context of marriage, and rates of marital sexual violence (MSV) have been relatively stagnant over the past decade. This paper machine learning algorithms paired with qualitative thematic analysis to identify new and potentially modifiable factors influencing MSV in India.Design, setting and participantsThis cross-sectional analysis of secondary data used data from in-person interviews with ever-married women aged 15–49 who responded to gender-based violence questions in the nationally representative 2015–2016 National Family Health Survey (N=66 013), collected between 20 January 2015 and 4 December 2016. Analyses included iterative thematic analysis (L-1 regularised regression followed by iterative qualitative thematic coding of L-2 regularised regression results) and neural network modelling.Outcome measureParticipants reported their experiences of sexual violence perpetrated by their current (or most recent) husband in the previous 12 months. These responses were aggregated into any vs no recent MSV.ResultsNearly 7% of women experienced MSV in the past 12 months. Major themes associated with MSV through iterative thematic analysis included experiences of/exposure to violence, sexual behaviour, decision making and freedom of movement, sociodemographics, access to media, health knowledge, health system interaction, partner control, economic agency, reproductive and maternal history, and health status. A neural network model identified variables that largely corresponded to these themes.ConclusionsThis analysis identified several themes that may be promising avenues to identify and support women experiencing MSV, and to mitigate these traumatic experiences. In particular, amplifying screening activities at health encounters, especially among women who appear to have compromised health or restricted agency, may enable a greater number of women access to essential physical and emotional support services, and merits further consideration.

scholarly journals Cross-situational word learning with multimodal neural networks

2021 ◽  
Author(s):  
Wai Keen Vong ◽  
Brenden M. Lake
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Word Learning ◽  
Network Architecture ◽  
Question Answering ◽  
Cognitive Models ◽  
Learning Approaches ◽  
Neural Network Learning ◽  
Single Epoch

In order to learn the mappings from words to referents, children must integrate co-occurrence information across individually ambiguous pairs of scenes and utterances, a challenge known as cross-situational word learning. In machine learning, recent multimodal neural networks have been shown to learn meaningful visual-linguistic mappings from cross-situational data, as needed to solve problems such as image captioning and visual question answering. These networks are potentially appealing as cognitive models because they can learn from raw visual and linguistic stimuli, something previous cognitive models have not addressed. In this paper, we examine whether recent machine learning approaches can help explain various behavioral phenomena from the psychological literature on cross-situational word learning. We consider two variants of a multimodal neural network architecture, and look at seven different phenomena associated with cross-situational word learning, and word learning more generally. Our results show that these networks can learn word-referent mappings from a single epoch of training, matching the amount of training found in cross-situational word learning experiments. Additionally, these networks capture some, but not all of the phenomena we studied, with all of the failures related to reasoning via mutual exclusivity. These results provide insight into the kinds of phenomena that arise naturally from relatively generic neural network learning algorithms, and which word learning phenomena require additional inductive biases.

scholarly journals Fairness-Aware Neural Rényi Minimization for Continuous Features

2020 ◽  
Author(s):  
Vincent Grari ◽  
Sylvain Lamprier ◽  
Marcin Detyniecki
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Network Architecture ◽  
Regression Models ◽  
Discrete Variables ◽  
Neural Network Architecture ◽  
The Past ◽  
Maximal Correlation ◽  
Dramatic Rise ◽  
Societal Interest

The past few years have seen a dramatic rise of academic and societal interest in fair machine learning. While plenty of fair algorithms have been proposed recently to tackle this challenge for discrete variables, only a few ideas exist for continuous ones. The objective in this paper is to ensure some independence level between the outputs of regression models and any given continuous sensitive variables. For this purpose, we use the Hirschfeld-Gebelein-Rényi (HGR) maximal correlation coefficient as a fairness metric. We propose to minimize the HGR coefficient directly with an adversarial neural network architecture. The idea is to predict the output Y while minimizing the ability of an adversarial neural network to find the estimated transformations which are required to predict the HGR coefficient. We empirically assess and compare our approach and demonstrate significant improvements on previously presented work in the field.

scholarly journals Disease Identification in Chilli Leaves using Machine Learning Techniques

2019 ◽  
Vol 9 (1S3) ◽  
pp. 325-329
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Detection System ◽  
Network Models ◽  
Detection Algorithm ◽  
Machine Learning Techniques ◽  
Neural Network Models ◽  
The Past ◽  
Learning Techniques ◽  
Proposed Model

Crop diseases reduce the yield of the crop or may even kill it. Over the past two years, as per the I.C.A.R, the production of chilies in the state of Goa has reduced drastically due to the presence of virus. Most of the plants flower very less or stop flowering completely. In rare cases when a plant manages to flower, the yield is substantially low. Proposed model detects the presence of disease in crops by examining the symptoms. The model uses an object detection algorithm and supervised image recognition and feature extraction using convolutional neural network to classify crops as infected or healthy. Google machine learning libraries, TensorFlow and Keras are used to build neural network models. An Android application is developed around the model for the ease of using the disease detection system.

scholarly journals H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks

2020 ◽  
Author(s):  
Thomas Limbacher ◽  
Robert Legenstein
Keyword(s):  
Neural Network ◽  
Synaptic Plasticity ◽  
Question Answering ◽  
Network Models ◽  
Neural Network Models ◽  
The Past ◽  
Hebbian Plasticity ◽  
Long Time ◽  
Simple Neural Network ◽  
The Brain

AbstractThe ability to base current computations on memories from the past is critical for many cognitive tasks such as story understanding. Hebbian-type synaptic plasticity is believed to underlie the retention of memories over medium and long time scales in the brain. However, it is unclear how such plasticity processes are integrated with computations in cortical networks. Here, we propose Hebbian Memory Networks (H-Mems), a simple neural network model that is built around a core hetero-associative network subject to Hebbian plasticity. We show that the network can be optimized to utilize the Hebbian plasticity processes for its computations. H-Mems can one-shot memorize associations between stimulus pairs and use these associations for decisions later on. Furthermore, they can solve demanding question-answering tasks on synthetic stories. Our study shows that neural network models are able to enrich their computations with memories through simple Hebbian plasticity processes.

scholarly journals Personalized decision making for QOS-based web service selection

2021 ◽  
Author(s):  
Muhammad S Saleem
Keyword(s):  
Decision Making ◽  
Learning To Rank ◽  
Service Selection ◽  
Decision Strategy ◽  
The Past ◽  
Web Service Selection ◽  
Selection Systems ◽  
Ranking Model ◽  
Service Ranking ◽  
Multiple Qos

In order to choose from a list of functionally similar services, users often need to make their decisions based on multiple QoS criteria they require on the target service. In this process, different users may follow different decision making strategies, some are compensatory and some are non-compensatory. Most of the current QoS-based service selection systems do not consider these decision strategies in the ranking process, which we believe are crucial for generating accurate ranking results for individual users. In this thesis, we propose a decision strategy based service ranking model. Furthermore, considering that different users follow different strategies in different contexts at different times, we apply a learning to rank algorithm to learn a personalized ranking model for individual users based on how they select services in the past. Our experiment result shows the effectiveness of the proposed approach.

A RELATIONAL RANKING METHOD WITH GENERALIZATION ANALYSIS

2012 ◽  
Vol 21 (03) ◽  
pp. 1250021
Author(s):  
ZEWU PENG ◽  
YAN PAN ◽  
YONG TANG ◽  
GUOHUA CHEN
Keyword(s):  
Machine Learning ◽  
Learning Communities ◽  
Learning To Rank ◽  
Superior Performance ◽  
Ranking Algorithm ◽  
Research Topics ◽  
Ranking Algorithms ◽  
Ranking Problems ◽  
Generalization Bound ◽  
Conventional Algorithm

Recently, learning to rank, which aims at constructing a model for ranking objects, is one of the hot research topics in information retrieval and machine learning communities. Most of existing learning to rank approaches are based on the assumption that each object is independently and identically distributed. Although this assumption simplifies ranking problems, the implicit interconnections between objects are ignored. In this paper, a graph based ranking framework is proposed, which takes advantage of implicit correlations between objects. Furthermore, the derived relational ranking algorithm from this framework, called GRSVM, is developed based on the conventional algorithm RankSVM-primal. In addition, generalization properties of different relational ranking algorithms are analyzed using Rademacher Average. Based on the analysis, we find that GRSVM can achieve tighter generalization bound than existing relational ranking algorithms in most cases. Finally, a comparison of experimental results produced by improved and conventional algorithms shows the superior performance of the former.

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