scholarly journals A novel approach for federated machine learning using Raspberry Pi

2021 ◽  
Vol 6 (3) ◽  
pp. 063-068
Author(s):  
Barida Baah ◽  
Onate Egerton Taylor ◽  
Chioma Lizzy Nwagbo
Keyword(s):  
Machine Learning ◽  
Research Work ◽  
Cost Effective ◽  
Training Model ◽  
Learning System ◽  
Raspberry Pi ◽  
Application Development ◽  
Privacy And Security ◽  
Data Set ◽  
Novel Approach

The problems of privacy and security is becoming a major challenge when it comes to the distributed systems, federated machine learning system especially when data are been transmitted or learned on a network , this necessitated the reasons for this research work which is all about wireless federated machine learning process using a Raspberry Pi. The Raspberry Pi 4 is a single hardware board with built in Linux operating system. We used data set of names from nine (9) different languages and then develop a training model using recurrent neural network to train this names compare to the names in the existing language like French, Scottish to predict if the names are from any of this language, this is done wirelessly with the Wi-Fi network in a federated machine learning environment for experimental setup with PySft’s that is installed in the python environment. The system was able to predict that name from which the language it originate from, the methodology that is implore in the research work is the Rapid Application Development (RAD). The benefits of this system are to ensure privacy, reduces the computing power, ensure real time learning and most importantly it is cost effective.

Knee Muscle Force Estimating Model Using Machine Learning Approach

2020 ◽  
Author(s):  
Anurag Sohane ◽  
Ravinder Agarwal
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Muscle Force ◽  
Vastus Lateralis ◽  
Input Parameter ◽  
Research Work ◽  
Cost Effective ◽  
Coefficient Of Determination ◽  
Muscle Forces ◽  
Knee Muscle

Abstract Various simulation type tools and conventional algorithms are being used to determine knee muscle forces of human during dynamic movement. These all may be good for clinical uses, but have some drawbacks, such as higher computational times, muscle redundancy and less cost-effective solution. Recently, there has been an interest to develop supervised learning-based prediction model for the computationally demanding process. The present research work is used to develop a cost-effective and efficient machine learning (ML) based models to predict knee muscle force for clinical interventions for the given input parameter like height, mass and angle. A dataset of 500 human musculoskeletal, have been trained and tested using four different ML models to predict knee muscle force. This dataset has obtained from anybody modeling software using AnyPyTools, where human musculoskeletal has been utilized to perform squatting movement during inverse dynamic analysis. The result based on the datasets predicts that the random forest ML model outperforms than the other selected models: neural network, generalized linear model, decision tree in terms of mean square error (MSE), coefficient of determination (R2), and Correlation (r). The MSE of predicted vs actual muscle forces obtained from the random forest model for Biceps Femoris, Rectus Femoris, Vastus Medialis, Vastus Lateralis are 19.92, 9.06, 5.97, 5.46, Correlation are 0.94, 0.92, 0.92, 0.94 and R2 are 0.88, 0.84, 0.84 and 0.89 for the test dataset, respectively.

scholarly journals MACHINE LEARNING APPROACH FOR CRYPTOSYSTEM SUGGEST IN EDUCARE OVER CLOUDS

2021 ◽  
Vol 9 (6) ◽  
pp. 98-103
Author(s):  
Ravish G K ◽  
Thippeswamy K
Keyword(s):  
Machine Learning ◽  
Research Work ◽  
Cloud Services ◽  
Learning System ◽  
Security And Privacy ◽  
Great Promise ◽  
Sensitive Information ◽  
Online Systems ◽  
Machine Learning Approach ◽  
Time Requirements

In the current situation of the pandemic, global organizations are turning to online functionality to ensure survival and sustainability. The future, even though uncertain, holds great promise for the education system being online. Cloud services for education are the center of this research work as they require security and privacy. The sensitive information about the users and the institutions need to be protected from all interested third parties. since the data delivery on any of the online systems is always time sensitive, the have to be fast. In previous works some of the algorithms were explored and statistical inference based decision was presented. In this work a machine learning system is designed to make that decision based on data type and time requirements.

scholarly journals Implementation of modified SARSA learning technique in EMCAP

2017 ◽  
Vol 7 (1.5) ◽  
pp. 274
Author(s):  
D. Ganesha ◽  
Vijayakumar Maragal Venkatamuni
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Process ◽  
Learning Algorithm ◽  
Research Work ◽  
Learning System ◽  
State Action ◽  
Learning Technique ◽  
Markov Decision ◽  
Experiment Analysis

This research work presents analysis of Modified Sarsa learning algorithm. Modified Sarsa algorithm.  State-Action-Reward-State-Action (SARSA) is an technique for learning a Markov decision process (MDP) strategy, used in for reinforcement learning int the field of artificial intelligence (AI) and machine learning (ML). The Modified SARSA Algorithm makes better actions to get better rewards.  Experiment are conducted to evaluate the performace for each agent individually. For result comparison among different agent, the same statistics were collected. This work considered varied kind of agents in different level of architecture for experiment analysis. The Fungus world testbed has been considered for experiment which is has been implemented using SwI-Prolog 5.4.6. The fixed obstructs tend to be more versatile, to make a location that is specific to Fungus world testbed environment. The various parameters are introduced in an environment to test a agent’s performance. This modified   SARSA learning algorithm can   be more suitable in EMCAP architecture.  The experiments are conducted the modified   SARSA Learning system gets   more rewards compare to existing  SARSA algorithm.

A hierarchical approach to mood classification in blogs

2011 ◽  
Vol 18 (1) ◽  
pp. 61-81 ◽  
Author(s):  
FAZEL KESHTKAR ◽  
DIANA INKPEN
Keyword(s):  
Machine Learning ◽  
Error Analysis ◽  
Learning Approach ◽  
Hierarchical Approach ◽  
Data Set ◽  
Novel Approach ◽  
Machine Learning Approach ◽  
Sentiment Orientation ◽  
Mood Classification

AbstractIn this article, we explore the task of mood classification for blog postings. We propose a novel approach that uses the hierarchy of possible moods to achieve better results than a standard machine learning approach. We also show that using sentiment orientation features improves the performance of classification. We used the Livejournal blog corpus as a data set to train and evaluate our method. We present extensive error analysis and discuss the difficulty of the task.

scholarly journals Bituminous Mixtures Experimental Data Modeling Using a Hyperparameters-Optimized Machine Learning Approach

2021 ◽  
Vol 11 (24) ◽  
pp. 11710
Author(s):  
Matteo Miani ◽  
Matteo Dunnhofer ◽  
Fabio Rondinella ◽  
Evangelos Manthos ◽  
Jan Valentin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bayesian Optimization ◽  
Automatic Identification ◽  
Learning Approach ◽  
Ann Model ◽  
Data Set ◽  
Bituminous Mixtures ◽  
Novel Approach ◽  
The Neural Network ◽  
Machine Learning Approach

This study introduces a machine learning approach based on Artificial Neural Networks (ANNs) for the prediction of Marshall test results, stiffness modulus and air voids data of different bituminous mixtures for road pavements. A novel approach for an objective and semi-automatic identification of the optimal ANN’s structure, defined by the so-called hyperparameters, has been introduced and discussed. Mechanical and volumetric data were obtained by conducting laboratory tests on 320 Marshall specimens, and the results were used to train the neural network. The k-fold Cross Validation method has been used for partitioning the available data set, to obtain an unbiased evaluation of the model predictive error. The ANN’s hyperparameters have been optimized using the Bayesian optimization, that overcame efficiently the more costly trial-and-error procedure and automated the hyperparameters tuning. The proposed ANN model is characterized by a Pearson coefficient value of 0.868.

scholarly journals Deep Neural Network for Reducing the Screening Workload in Systematic Reviews for Clinical Guidelines: Algorithm Validation Study

10.2196/22422 ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. e22422
Author(s):  
Tomohide Yamada ◽  
Daisuke Yoneoka ◽  
Yuta Hiraike ◽  
Kimihiro Hino ◽  
Hiroyoshi Toyoshiba ◽  
...  
Keyword(s):  
Machine Learning ◽  
Randomized Controlled Trials ◽  
Systematic Reviews ◽  
Clinical Guidelines ◽  
Learning System ◽  
Controlled Trials ◽  
Data Set ◽  
Randomized Controlled ◽  
Fold Reduction ◽  
Meta Analyses

Background Performing systematic reviews is a time-consuming and resource-intensive process. Objective We investigated whether a machine learning system could perform systematic reviews more efficiently. Methods All systematic reviews and meta-analyses of interventional randomized controlled trials cited in recent clinical guidelines from the American Diabetes Association, American College of Cardiology, American Heart Association (2 guidelines), and American Stroke Association were assessed. After reproducing the primary screening data set according to the published search strategy of each, we extracted correct articles (those actually reviewed) and incorrect articles (those not reviewed) from the data set. These 2 sets of articles were used to train a neural network–based artificial intelligence engine (Concept Encoder, Fronteo Inc). The primary endpoint was work saved over sampling at 95% recall (WSS@95%). Results Among 145 candidate reviews of randomized controlled trials, 8 reviews fulfilled the inclusion criteria. For these 8 reviews, the machine learning system significantly reduced the literature screening workload by at least 6-fold versus that of manual screening based on WSS@95%. When machine learning was initiated using 2 correct articles that were randomly selected by a researcher, a 10-fold reduction in workload was achieved versus that of manual screening based on the WSS@95% value, with high sensitivity for eligible studies. The area under the receiver operating characteristic curve increased dramatically every time the algorithm learned a correct article. Conclusions Concept Encoder achieved a 10-fold reduction of the screening workload for systematic review after learning from 2 randomly selected studies on the target topic. However, few meta-analyses of randomized controlled trials were included. Concept Encoder could facilitate the acquisition of evidence for clinical guidelines.

scholarly journals Big Data Analytics using Swarm Intelligence based Framework for Prediction on Datasets

2019 ◽  
Vol 8 (4) ◽  
pp. 7356-7360
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Information Source ◽  
Research Work ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Raw Data

Data Analytics is a scientific as well as an engineering tool used to investigate the raw data to revamp the information to achieve knowledge. This is normally connected with obtaining knowledge from reliable information source and rapidity in information processing, and future prediction of the data analysis. Big Data analytics is strongly evolving with different features of volume, velocity and Vectors. Most of the organizations are now concentrating on analyzing information or raw data that are fascinated in deploying analytics to survive forthcoming issues and challenges. The prediction model or intelligent model is proposed in this research to apply machine learning algorithms in the data set. Then it is interpreted and to analyze the better forecast value of the study. The major objective of this research work is to find the optimum prediction from the medical data set using the machine learning techniques.

Automated App for Mental Health Analysis

2021 ◽  
pp. 104-131
Author(s):  
Rohit Rastogi ◽  
Devendra Kumar Chaturvedi ◽  
Mayank Gupta ◽  
Parul Singhal
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Experimental Research ◽  
Research Work ◽  
Tension Type Headache ◽  
Data Set ◽  
Closed Set ◽  
Sf 36 ◽  
Skin Response ◽  
Type Headache

Many apps and analyzers based on machine learning have been designed already to help and cure the stress issue, which is an epidemic. The project is based on an experimental research work that the authors have performed at Research Labs and Scientific Spirituality Centers of Dev Sanskriti VishwaVidyalaya, Haridwar and Patanjali Research Foundations, Uttarakhand. In their research work, the correctness and accuracy have been studied and compared for two biofeedback devices named as electromyography (EMG) and galvanic skin response (GSR), which can operate in three modes—audio, visual, and audio-visual—with the help of data set of tension type headache (TTH) patients. They have realized by their research work that these days people have lot of stress in their life so they planned to make an effort for reducing the stress level of people by their technical knowledge of computer science. In their project, the authors have a website that contains a closed set of questionnaires from SF-36, which have some weight associated with each question.

scholarly journals Innovative Algorithm for Managing the Number of Clusters

2020 ◽  
Vol 8 (5) ◽  
pp. 310-315
Keyword(s):  
Clustering Algorithm ◽  
Research Work ◽  
Cost Effective ◽  
Integrated Approach ◽  
Initial Distribution ◽  
Optimal Number ◽  
Data Set ◽  
Number Of Clusters ◽  
The Neural Network ◽  
Traditional Approaches

This research work proposed an integrated approach using Fuzzy Clustering to discover the optimal number of clusters. The proposed technique is a great technological innovation clustering algorithm in marketing and could be used to determine the best group of customers, similar items and products. The new approach can independently determine the initial distribution of cluster centers. The task of finding the number of clusters is converted into the task of determining the size of the neural network, which later translated to identify the optimal groups of clusters. This approach has been tested using four business data set and shows outstanding results compared to traditional approaches. The proposed method is able to find without any significant error the expected exact number of clusters. Further, we believe that this work is a business value to increase market efficiency in finding out what group of clusters is more cost-effective.

scholarly journals AI Powered Glasses for Visually Impaired Person

2020 ◽  
Vol 9 (2) ◽  
pp. 416-421
Keyword(s):  
Visual Impairment ◽  
Visually Impaired ◽  
Research Work ◽  
Image Data ◽  
Cost Effective ◽  
Daily Basis ◽  
Assistive Technologies ◽  
Raspberry Pi ◽  
Current Time ◽  
Impaired Person

This dissertation presents a system that can assist a person with a visual impairment in both navigation and movability. Meanwhile, number of solutions are available in current time. We described some of them in the later part of the paper. But to date, a reliable and cost-effective solution has not been put forward to replace the legacy devices currently used in mobilizing on a daily basis for people with a visual impairment. This report first examines the problem at hand and the motivation behind addressing it. Later, it explores relative current technologies and research in the assistive technologies industry. Finally, it proposes a system design and implementation for the assistance of visually impaired people. The proposed device is equipped with hardware like raspberry pi processor, camera, battery, goggles, earphone, power bank and connector. Objects will be captured with the help of camera. Image processing and detecting would be done with the help of deep learning, R-CNN like modules on the device itself. However, final output would be delivered by the earphone into the visually impaired person’s ear. The research work contains the methodology and the solutions of above mention problem. The research works can be used in practical use cases, for visually impaired person. The system proposed in this project includes the use of a region based convolutional neural network as well as the use of a raspberry pi for processing the image data. System includes tesseract library of programming language python for OCR and give output to the user. The detailed methodology and result are elaborated later in this paper.

Sign in / Sign up

Export Citation Format

Share Document