scholarly journals Reliable E-Nose System using the Improved Optimization Technique based ANN

2020 ◽  
Vol 9 (4) ◽  
pp. 1726-1731
Keyword(s):  
Machine Learning ◽  
Optimization Technique ◽  
Research Work ◽  
Error Rates ◽  
Human Beings ◽  
Training Process ◽  
Accuracy Rate ◽  
Machine Learning Model ◽  
Artificial Neural Network Ann ◽  
Pollutant Gases

(Since from last decade, there is a growing interest in a system that detects the pollutant gases and other environmental information is called Electronic Nose (E-Nose) networks. The gases such as methanol, Liquid Petroleum Gases, ammonia, etc. are harmful for human beings; therefore, such frailness required detecting automatically as well as safety alarm promoted in a specific field. The critical challenges of the E-nose system are efficient to detect with minimum error and overhead. In this paper, we targeted to design the optimized machine learning-based algorithm to detect and alert the pollutant gases, Humidity, O2 Level, and Air Temperature in the real-time datasets. We initiated E-nose design using Artificial Neural Network (ANN). Using essential ANN leads to poor accuracy and error rates, as they failed to select the best solutions during the training process. Thus, we next use the Particle Swarm Optimization (PSO) based ANN called ANN-PSO to improve the accuracy rate and error performances for E-Nose systems. Finally, the proposed Improved Optimization Technique based ANN (IOT-ANN) machine learning model designed and evaluated in current this research work. The IoT-ANN it is based on a bio-inspired algorithm to achieve reliable training during the E-Nose prediction

scholarly journals Automated Retraining of Machine Learning Models

2019 ◽  
Vol 8 (12) ◽  
pp. 445-452
Keyword(s):  
Machine Learning ◽  
Input Data ◽  
Research Work ◽  
Learning Models ◽  
Machine Learning Methods ◽  
Machine Learning Model ◽  
Crucial Component ◽  
Conventional Machine ◽  
Over Time ◽  
Machine Learning Models

Data is the most crucial component of a successful ML system. Once a machine learning model is developed, it gets obsolete over time due to presence of new input data being generated every second. In order to keep our predictions accurate we need to find a way to keep our models up to date. Our research work involves finding a mechanism which can retrain the model with new data automatically. This research also involves exploring the possibilities of automating machine learning processes. We started this project by training and testing our model using conventional machine learning methods. The outcome was then compared with the outcome of those experiments conducted using the AutoML methods like TPOT. This helped us in finding an efficient technique to retrain our models. These techniques can be used in areas where people do not deal with the actual working of a ML model but only require the outputs of ML processes

scholarly journals Machine Learning Based Clinical Diagnosis of Liver Patients with Instance Replacement

2021 ◽  
Author(s):  
J. V. D. Prasad ◽  
A. Raghuvira Pratap ◽  
Babu Sallagundla
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Research Work ◽  
Feature Selection Method ◽  
Learning Model ◽  
Disease Classification ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Huge Data ◽  
Machine Learning Model

With the rapid increase in number of clinical data and hence the prediction and analysing data becomes very difficult. With the help of various machine learning models, it becomes easy to work on these huge data. A machine learning model faces lots of challenges; one among the challenge is feature selection. In this research work, we propose a novel feature selection method based on statistical procedures to increase the performance of the machine learning model. Furthermore, we have tested the feature selection algorithm in liver disease classification dataset and the results obtained shows the efficiency of the proposed method.

Pulomonary Nodule Classification From CT Scan Images Using Machine Learning Method

2021 ◽  
pp. 99-115
Author(s):  
Rekha K. V. ◽  
Anirudh Itagi ◽  
Bharath K. P. ◽  
Balaji Subramanian ◽  
Rajesh Kumar M.
Keyword(s):  
Machine Learning ◽  
Ct Scan ◽  
Learning Algorithm ◽  
Research Work ◽  
Early Period ◽  
Pulmonary Nodules ◽  
Artificial Neural Network Ann ◽  
Benign Nodules ◽  
Occurrence Matrix ◽  
Nodule Classification

The research work is to enhance the classification accuracy of the pulmonary nodules with the limited number of features extracted using Gray level co-occurrence matrix and linear binary pattern. The classification is done using the machine learning algorithm such as artificial neural network (ANN) and the random forest classifier (RF). In present, lung cancer seems to be the most deadly disease in the world which can be detected only after the computerized tomography (i.e., CT scan images of the person). Detecting the infected portion at the early period is the challenging task. Hence, the recent researchers where under the detection of pulmonary nodules to categorize it either as benign nodules which named as non-cancerous or as malignant nodules which are named as cancerous. When associated the results with the recent papers, the accuracy has been improved in classifying the lung nodules.

scholarly journals Eye-color and Type-2 diabetes phenotype prediction from genotype data using deep learning methods

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Muhammad Muneeb ◽  
Andreas Henschel
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Statistical Techniques ◽  
Human Beings ◽  
Eye Color ◽  
Machine Learning Model ◽  
Extreme Gradient Boosting

Abstract Background Genotype–phenotype predictions are of great importance in genetics. These predictions can help to find genetic mutations causing variations in human beings. There are many approaches for finding the association which can be broadly categorized into two classes, statistical techniques, and machine learning. Statistical techniques are good for finding the actual SNPs causing variation where Machine Learning techniques are good where we just want to classify the people into different categories. In this article, we examined the Eye-color and Type-2 diabetes phenotype. The proposed technique is a hybrid approach consisting of some parts from statistical techniques and remaining from Machine learning. Results The main dataset for Eye-color phenotype consists of 806 people. 404 people have Blue-Green eyes where 402 people have Brown eyes. After preprocessing we generated 8 different datasets, containing different numbers of SNPs, using the mutation difference and thresholding at individual SNP. We calculated three types of mutation at each SNP no mutation, partial mutation, and full mutation. After that data is transformed for machine learning algorithms. We used about 9 classifiers, RandomForest, Extreme Gradient boosting, ANN, LSTM, GRU, BILSTM, 1DCNN, ensembles of ANN, and ensembles of LSTM which gave the best accuracy of 0.91, 0.9286, 0.945, 0.94, 0.94, 0.92, 0.95, and 0.96% respectively. Stacked ensembles of LSTM outperformed other algorithms for 1560 SNPs with an overall accuracy of 0.96, AUC = 0.98 for brown eyes, and AUC = 0.97 for Blue-Green eyes. The main dataset for Type-2 diabetes consists of 107 people where 30 people are classified as cases and 74 people as controls. We used different linear threshold to find the optimal number of SNPs for classification. The final model gave an accuracy of 0.97%. Conclusion Genotype–phenotype predictions are very useful especially in forensic. These predictions can help to identify SNP variant association with traits and diseases. Given more datasets, machine learning model predictions can be increased. Moreover, the non-linearity in the Machine learning model and the combination of SNPs Mutations while training the model increases the prediction. We considered binary classification problems but the proposed approach can be extended to multi-class classification.

scholarly journals The role of ambient parameters on transmission rates of the COVID-19 outbreak: A machine learning model

Work ◽  
2021 ◽  
pp. 1-9
Author(s):  
Amir Jamshidnezhad ◽  
Seyed Ahmad Hosseini ◽  
Leila Ibrahimi Ghavamabadi ◽  
Seyed Mahdi Hossaeini Marashi ◽  
Hediye Mousavi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Climatic Factors ◽  
Ambient Air ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Machine Learning Model ◽  
Testing Stage ◽  
Artificial Neural Network Ann ◽  
Air Conditioning Systems

BACKGROUND: In recent years the relationship between ambient air temperature and the prevalence of viral infection has been under investigation. OBJECTIVE: The study was aimed at providing the statistical and machine learning-based analysis to investigate the influence of climatic factors on frequency of COVID-19 confirmed cases in Iran. METHOD: The data of confirmed cases of COVID-19 and some climatic factors related to 31 provinces of Iran between 04/03/2020 and 05/05/2020 was gathered from official resources. In order to investigate the important climatic factors on the frequency of confirmed cases of COVID-19 in all studied cities, a model based on an artificial neural network (ANN) was developed. RESULTS: The proposed ANN model showed accuracy rates of 87.25%and 86.4%in the training and testing stage, respectively, for classification of COVID-19 confirmed cases. The results showed that in the city of Ahvaz, despite the increase in temperature, the coefficient of determination R2 has been increasing. CONCLUSION: This study clearly showed that, with increasing outdoor temperature, the use of air conditioning systems to set a comfort zone temperature is unavoidable. Thus, the number of positive cases of COVID-19 increases. Also, this study shows the role of closed-air cycle condition in the indoor environment of tropical cities.

scholarly journals A New Ensenble Learning based Optimal Prediction Model for Cardiovascular Diseases

2021 ◽  
Vol 309 ◽  
pp. 01007
Author(s):  
B. Srınıvasa Rao
Keyword(s):  
Machine Learning ◽  
Cardiovascular Diseases ◽  
Present Model ◽  
Research Work ◽  
Learning Model ◽  
Ensemble Technique ◽  
Machine Learning Model ◽  
Learning Technique ◽  
Traditional Approaches ◽  
Optimal Machine

The present paperreports an optimal machine learning model for an effective prediction of cardiovascular diseases that uses the ensemble learning technique. The present research work gives an insight about the coherent way of combining Naive Bayes and Random Forest algorithm using ensemble technique. It also discusses how the present model is different from other traditional approaches. The present experimental results manifest that the present optimal machine learning model is more efficient than the other models.

Eye-Color and Type-2 Diabetes Phenotype Prediction From Genotype Data Using Deep Learning Methods

2020 ◽  
Author(s):  
Muhammad Muneeb ◽  
Andreas Henschel
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Statistical Techniques ◽  
Human Beings ◽  
Eye Color ◽  
Machine Learning Model ◽  
Extreme Gradient Boosting

Abstract Background: Genotype-Phenotype predictions are of great importance in genetics. These predictions can help to find genetic mutations causing variations in human beings. There are many approaches for finding the association which can be broadly categorized into two classes, statistical techniques, and machine learning. Statistical techniques are good for finding the actual SNPs causing variation where Machine Learning techniques are good where we just want to classify the people into different categories. In this article, we examined the Eye-color and Type-2 diabetes phenotype. The proposed technique is a hybrid approach consisting of some parts from statistical techniques and remaining from Machine learning. Results: The main dataset for Eye-color phenotype consists of 806 people. 404 people have Blue-Green eyes where 402 people have Brown eyes. After preprocessing we generated 8 different datasets, containing different numbers of SNPs, using the mutation difference and thresholding at individual SNP. We calculated three types of mutation at each SNP no mutation, partial mutation, and full mutation. After that data is transformed for machine learning algorithms. We used about 9 classifiers, RandomForest, Extreme Gradient boosting, ANN, LSTM, GRU, BILSTM, 1DCNN, ensembles of ANN, and ensembles of LSTM which gave the best accuracy of 0.91, 0.9286, 0.945, 0.94, 0.94, 0.92, 0.95, and 0.96 percent respectively. Stacked ensembles of LSTM outperformed other algorithms for 1560 SNPs with an overall accuracy of 0.96, AUC = 0.98 for brown eyes, and AUC = 0.97 for Blue-Green eyes. The main dataset for Type-2 diabetes consists of 107 people where 30 people are classified as cases and 74 people as controls. We used different linear threshold to find the optimal number of SNPs for classification. The final model gave an accuracy of 0.97 percent. Conclusion: Genotype-phenotype predictions are very useful especially in forensic. These predictions can help to identify SNP variant association with traits and diseases. Given more datasets, machine learning model predictions can be increased. Moreover, the non-linearity in the Machine learning model and the combination of SNPs Mutations while training the model increases the prediction. We considered binary classification problems but the proposed approach can be extended to multi-class classification.

Machine Learning Approach to Task Scheduling in Cloud Computing

2020 ◽  
Vol 17 (9) ◽  
pp. 4213-4218
Author(s):  
H. S. Madhusudhan ◽  
T. Satish Kumar ◽  
G. Mahesh
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Research Work ◽  
Service Level Agreement ◽  
Service Level ◽  
Machine Learning Approach ◽  
Artificial Neural Network Ann

Cloud computing provides on demand service on internet using network of remote servers. The pivotal role for any cloud environment would be to schedule tasks and the virtual machine scheduling have key role in maintaining Quality of Service (QOS) and Service Level Agreement (SLA). Task scheduling is the process of scheduling task (user requests) to certain resources and it is an NP-complete problem. The primary objectives of scheduling algorithms are to minimize makespan and improve resource utilization. In this research work an attempt is made to implement Artificial Neural Network (ANN), which is a methodology in machine learning technique and it is applied to implement task scheduling. It is observed that neural network trained with genetic algorithm will outperforms default genetic algorithm by an average efficiency of 25.56%.

scholarly journals Power Usage Efficiency (PUE) Optimization with Counterpointing Machine Learning Techniques for Data Center Temperatures

2021 ◽  
Vol 6 (6) ◽  
pp. 1594-1611
Author(s):  
Rajendra Kumar ◽  
Sunil Kumar Khatri ◽  
Mario José Diván
Keyword(s):  
Machine Learning ◽  
Data Center ◽  
Data Centers ◽  
Optimization Technique ◽  
Electrical Power ◽  
Research Work ◽  
Machine Learning Techniques ◽  
Optimization Approach ◽  
Information And Communication ◽  
Usage Efficiency

The rapid increase in the IT infrastructure has led to demands in more Data Center Space & Power to fulfil the Information and Communication Technology (ICT) services hosting requirements. Due to this, more electrical power is being consumed in Data Centers therefore Data Center power & cooling management has become quite an important and challenging task. Direct impacting aspects affecting the power energy of data centers are power and commensurate cooling losses. It is difficult to optimise the Power Usage Efficiency (PUE) of the Data Center using conventional methods which essentially need knowledge of each Data Center facility and specific equipment and its working. Hence, a novel optimization approach is necessary to optimise the power and cooling in the data center. This research work is performed by varying the temperature in the data center through a machine learning-based linear regression optimization technique. From the research, the ideal temperature is identified with high accuracy based on the prediction technique evolved out of the available data. With the proposed model, the PUE of the data center can be easily analysed and predicted based on temperature changes maintained in the Data Center. As the temperature is raised from 19.73 oC to 21.17 oC, then the cooling load is decreased in the range 607 KW to 414 KW. From the result, maintaining the temperature at the optimum value significantly improves the Data Center PUE and same time saves power within the permissible limits.

scholarly journals The Role of Ambient parameters on Transmission Rates of the COVID-19 Outbreak: A machine learning model

2021 ◽  
Author(s):  
Amir Jamshidnezhad ◽  
Seyed Ahmad Hosseini ◽  
Seyed Mahdi Hossaeini Marashic ◽  
Leila Ibrahimi Ghavamabadi ◽  
Hediye Mousavi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Climatic Factors ◽  
Ambient Air ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Machine Learning Model ◽  
Testing Stage ◽  
Artificial Neural Network Ann ◽  
Air Conditioning Systems

Abstract The relation between ambient air temperature and prevalence of viral infection has been under investigation in recent years. The study was aimed at providing the statistical and machine learning-based analysis to investigate the influence of climatic factors on frequency of COVID-19 confirmed cases in Iran. The data of confirmed cases of COVID-19 and some climatic factors related to 31 provinces of Iran during 04/03/2020 to 05/05/2020 was gathered from the official resources. In order to investigate the important climatic factors on the frequency of confirmed cases of COVID-19 in all studied cities, a model based on an artificial neural network (ANN) was developed. The proposed ANN model showed accuracy rates of 87.25% and 86.4% in the training and testing stage, respectively, for classification of COVID-19 confirmed cases. The results showed that in the city of Ahvaz, despite the increase in temperature, the coefficient of determination R2 has been increasing. This study clearly showed that, with increasing outdoor temperature, the use of air conditioning systems to set a comfort zone temperature is unavoidable; thus, the number of positive cases of COVID-19 increases. Also, this study shows the role of closed-air cycle condition in the indoor environment of tropical cities.

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