scholarly journals Cuckoo Search-based SVM (CS-SVM) Model for Real-Time Indoor Position Estimation in IoT Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Amjad Khan ◽  
Asfandyar Khan ◽  
Javed Iqbal Bangash ◽  
Fazli Subhan ◽  
Abdullah Khan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Real Time ◽  
Cuckoo Search ◽  
Position Estimation ◽  
Support Vector ◽  
Hybrid Technique ◽  
Positioning Systems ◽  
Proposed Model ◽  
Radio Map

Internet of Things (IoT), an emerging technology, is becoming an essential part of today’s world. Machine learning (ML) algorithms play an important role in various applications of IoT. For decades, the location information has been extremely useful for humans to navigate both in outdoor and indoor environments. Wi-Fi access point-based indoor positioning systems get more popularity, as it avoids extra calibration expenses. The fingerprinting technique is preferred in an indoor environment as it does not require a signal’s Line of Sight (LoS). It consists of two phases: offline and online phase. In the offline phase, the Wi-Fi RSSI radio map of the site is stored in a database, and in the online phase, the object is localized using the offline database. To avoid the radio map construction which is expensive in terms of labor, time, and cost, machine learning techniques may be used. In this research work, we proposed a hybrid technique using Cuckoo Search-based Support Vector Machine (CS-SVM) for real-time position estimation. Cuckoo search is a nature-inspired optimization algorithm, which solves the problem of slow convergence rate and local minima of other similar algorithms. Wi-Fi RSSI fingerprint dataset of UCI repository having seven classes is used for simulation purposes. The dataset is preprocessed by min-max normalization to increase accuracy and reduce computational speed. The proposed model is simulated using MATLAB and evaluated in terms of accuracy, precision, and recall with K-nearest neighbor (KNN) and support vector machine (SVM). Moreover, the simulation results show that the proposed model achieves high accuracy of 99.87%.

Deep Learning in Engineering Education

2020 ◽  
pp. 187-218
Author(s):  
Deepali R. Vora ◽  
Kamatchi R. Iyer
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Deep Learning ◽  
Information Systems ◽  
Engineering Education ◽  
Hybrid Model ◽  
Cuckoo Search ◽  
Support Vector ◽  
Proposed Model ◽  
Class Labels

The goodness measure of any institute lies in minimising the dropouts and targeting good placements. So, predicting students' performance is very interesting and an important task for educational information systems. Machine learning and deep learning are the emerging areas that truly entice more research practices. This research focuses on applying the deep learning methods to educational data for classification and prediction. The educational data of students from engineering domain with cognitive and non-cognitive parameters is considered. The hybrid model with support vector machine (SVM) and deep belief network (DBN) is devised. The SVM predicts class labels from preprocessed data. These class labels and actual class labels acts as input to the DBN to perform final classification. The hybrid model is further optimised using cuckoo search with Levy flight. The results clearly show that the proposed model SVM-LCDBN gives better performance as compared to simple hybrid model and hybrid model with traditional cuckoo search.

Deep Learning in Engineering Education

2021 ◽  
pp. 222-255
Author(s):  
Deepali R. Vora ◽  
Kamatchi R. Iyer
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Deep Learning ◽  
Information Systems ◽  
Engineering Education ◽  
Hybrid Model ◽  
Cuckoo Search ◽  
Support Vector ◽  
Proposed Model ◽  
Class Labels

The goodness measure of any institute lies in minimising the dropouts and targeting good placements. So, predicting students' performance is very interesting and an important task for educational information systems. Machine learning and deep learning are the emerging areas that truly entice more research practices. This research focuses on applying the deep learning methods to educational data for classification and prediction. The educational data of students from engineering domain with cognitive and non-cognitive parameters is considered. The hybrid model with support vector machine (SVM) and deep belief network (DBN) is devised. The SVM predicts class labels from preprocessed data. These class labels and actual class labels act as input to the DBN to perform final classification. The hybrid model is further optimised using cuckoo search with levy flight. The results clearly show that the proposed model SVM-LCDBN gives better performance as compared to simple hybrid model and hybrid model with traditional cuckoo search.

Estimation of CO2 Diffusivity in Brine by Use of the Genetic Algorithm and Mixed Kernels-Based Support Vector Machine Model

2018 ◽  
Vol 141 (4) ◽  
Author(s):  
Qihong Feng ◽  
Ronghao Cui ◽  
Sen Wang ◽  
Jin Zhang ◽  
Zhe Jiang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Diffusion Coefficient ◽  
Co2 Storage ◽  
Co2 Injection ◽  
Support Vector ◽  
Hybrid Technique ◽  
Machine Model ◽  
Proposed Model ◽  
Wide Range

Diffusion coefficient of carbon dioxide (CO2), a significant parameter describing the mass transfer process, exerts a profound influence on the safety of CO2 storage in depleted reservoirs, saline aquifers, and marine ecosystems. However, experimental determination of diffusion coefficient in CO2-brine system is time-consuming and complex because the procedure requires sophisticated laboratory equipment and reasonable interpretation methods. To facilitate the acquisition of more accurate values, an intelligent model, termed MKSVM-GA, is developed using a hybrid technique of support vector machine (SVM), mixed kernels (MK), and genetic algorithm (GA). Confirmed by the statistical evaluation indicators, our proposed model exhibits excellent performance with high accuracy and strong robustness in a wide range of temperatures (273–473.15 K), pressures (0.1–49.3 MPa), and viscosities (0.139–1.950 mPa·s). Our results show that the proposed model is more applicable than the artificial neural network (ANN) model at this sample size, which is superior to four commonly used traditional empirical correlations. The technique presented in this study can provide a fast and precise prediction of CO2 diffusivity in brine at reservoir conditions for the engineering design and the technical risk assessment during the process of CO2 injection.

scholarly journals PSO-Based Support Vector Machine with Cuckoo Search Technique for Clinical Disease Diagnoses

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Xiaoyong Liu ◽  
Hui Fu
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Cuckoo Search ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Svm Model ◽  
Two Stages ◽  
Best Parameters

Disease diagnosis is conducted with a machine learning method. We have proposed a novel machine learning method that hybridizes support vector machine (SVM), particle swarm optimization (PSO), and cuckoo search (CS). The new method consists of two stages: firstly, a CS based approach for parameter optimization of SVM is developed to find the better initial parameters of kernel function, and then PSO is applied to continue SVM training and find the best parameters of SVM. Experimental results indicate that the proposed CS-PSO-SVM model achieves better classification accuracy and F-measure than PSO-SVM and GA-SVM. Therefore, we can conclude that our proposed method is very efficient compared to the previously reported algorithms.

scholarly journals Intelligent Plant Disease Identification System Using Machine Learning

2020 ◽  
Vol 2 (1) ◽  
pp. 49
Author(s):  
Paramasivam Alagumariappan ◽  
Najumnissa Jamal Dewan ◽  
Gughan Narasimhan Muthukrishnan ◽  
Bhaskar K. Bojji Raju ◽  
Ramzan Ali Arshad Bilal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Real Time ◽  
Extreme Learning Machine ◽  
Plant Disease ◽  
Agricultural Sector ◽  
Plant Diseases ◽  
Support Vector ◽  
Identification System ◽  
Learning Machine

Agriculture is the backbone of every country in the world. In India, most of the rural population still depends on agriculture. The agricultural sector provides major employment in rural areas. Furthermore, it contributes a significant amount to India’s gross domestic product (GDP). Therefore, protecting and enhancing the agricultural sector helps in the development of India’s economy. In this work, a real-time decision support system integrated with a camera sensor module was designed and developed for identification of plant disease. Furthermore, the performance of three machine learning algorithms, such as Extreme Learning Machine (ELM) and Support Vector Machine (SVM) with linear and polynomial kernels was analyzed. Results demonstrate that the performance of the extreme learning machine is better when compared to the adopted support vector machine classifier. It is also observed that the sensitivity of the support vector machine with a polynomial kernel is better when compared to the other classifiers. This work appears to be of high social relevance, because the developed real-time hardware is capable of detecting different plant diseases.

scholarly journals Hybrid Intelligent Predictive Maintenance Model for Multiclass Fault Classification

2021 ◽  
Author(s):  
Albert Buabeng ◽  
Anthony Simons ◽  
Nana Kena Frimpong ◽  
Yao Yevenyo Ziggah
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Predictive Maintenance ◽  
Machine Learning Techniques ◽  
Fault Classification ◽  
Support Vector ◽  
Hybrid Technique ◽  
Intrinsic Mode Functions ◽  
Linear Discriminant ◽  
Maintenance Model

Abstract Data recorded from monitoring the health condition of industrial equipment are often high-dimensional, nonlinear, nonstationary and characterised by high levels of uncertainty. These factors limit the efficiency of machine learning techniques to produce desirable results when developing effective fault classification frameworks. This paper sought to propose a hybrid artificial intelligent predictive maintenance model based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Principal Component Analysis (PCA) and Least Squares Support Vector Machine (LSSVM) optimised by the combination of Coupled Simulated Annealing and Nelder-Mead Simplex optimisation algorithms (ICEEMDAN-PCA-LSSVM). Here, ICEEMDAN was first employed as a denoising technique to decompose signals into series of Intrinsic Mode Functions (IMFs) of which only relevant IMFs containing the relevant fault features were retained for signal reconstruction. PCA was then employed as a dimension reduction technique through which the resulting set of uncorrelated features extracted served as input for LSSVM for classifying various fault types. The proposed technique is compared with three established methods (Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and Artificial Neural Network (ANN)) with multiclass classification capabilities. The various techniques were tested on an experimental UCI machine learning benchmark data obtained from multi-sensors of a hydraulic test rig. The results from the analysis revealed that the proposed ICEEMDAN-PCA-LSSVM technique is versatile and outperformed all the compared classifiers in terms of accuracy, error rate and other evaluation metrics considered. The proposed hybrid technique drastically reduced the redundancies and the dimension of features, allowing for the efficient consideration of relevant features for the enhancement of classification accuracy and convergence speed.

Development of a machine learning-based real-time location system to streamline acute endovascular intervention in acute stroke: a proof-of-concept study

2021 ◽  
pp. neurintsurg-2021-017858
Author(s):  
Dee Zhen Lim ◽  
Melissa Yeo ◽  
Ariel Dahan ◽  
Bahman Tahayori ◽  
Hong Kuan Kok ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Acute Stroke ◽  
Real Time ◽  
Endovascular Intervention ◽  
Support Vector ◽  
Proof Of Concept ◽  
Location System ◽  
Real Time Location System

BackgroundDelivery of acute stroke endovascular intervention can be challenging because it requires complex coordination of patient and staff across many different locations. In this proof-of-concept paper we (a) examine whether WiFi fingerprinting is a feasible machine learning (ML)-based real-time location system (RTLS) technology that can provide accurate real-time location information within a hospital setting, and (b) hypothesize its potential application in streamlining acute stroke endovascular intervention.MethodsWe conducted our study in a comprehensive stroke care unit in Melbourne, Australia that offers a 24-hour mechanical thrombectomy service. ML algorithms including K-nearest neighbors, decision tree, random forest, support vector machine and ensemble models were trained and tested on a public WiFi dataset and the study hospital WiFi dataset. The hospital dataset was collected using the WiFi explorer software (version 3.0.2) on a MacBook Pro (AirPort Extreme, Broadcom BCM43x×1.0). Data analysis was implemented in the Python programming environment using the scikit-learn package. The primary statistical measure for algorithm performance was the accuracy of location prediction.ResultsML-based WiFi fingerprinting can accurately predict the different hospital zones relevant in the acute endovascular intervention workflow such as emergency department, CT room and angiography suite. The most accurate algorithms were random forest and support vector machine, both of which were 98% accurate. The algorithms remain robust when new data points, which were distinct from the training dataset, were tested.ConclusionsML-based RTLS technology using WiFi fingerprinting has the potential to streamline delivery of acute stroke endovascular intervention by efficiently tracking patient and staff movement during stroke calls.

scholarly journals Prediction of Employee Attrition Using Machine Learning Approach

2021 ◽  
Vol 9 (8) ◽  
pp. 2479-2483
Author(s):  
M. B. Shete
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
False Positive Rate ◽  
Time Trial ◽  
Support Vector ◽  
Development Factor ◽  
Proposed Model ◽  
Machine Learning Approach ◽  
Employee Attrition ◽  
Positive Rate

Abstract: In the world of technology, there are various zones through which different companies may adopt technologies which sustenance decision-making, Artificial Intelligence is the most creative advancement, generally used to help various companies and institutions in business approaches, authoritative aspects and individual’s administration. As of late, consideration has progressively been paid to Human Resources (HR), since professional excellence and capabilities address a development factor and a genuine upper hand for organizations. Subsequent to having been acquainted with deals and showcasing offices, manmade brainpower is additionally beginning to direct representative related choices inside HR the board. The reason for existing is to help choices that are put together not with respect to emotional viewpoints but rather on target information investigation. The objective of this work is to break down how target factors impact representative weakening, to distinguish the fundamental driver that add to a specialist's choice to leave an organization, and to have the option to foresee whether a specific worker will leave the organization. After the testing, the proposed model of an algorithm for the prediction of workers in any industry, attrition is tested on actual dataset with almost 150 samples. With this algorithm best results are generated in terms of all experimental parameters. It uncovers the best review rate, since it estimates the capacity of a classifier to track down every one of the True positive rates and accomplishes a generally false positive rate. The introduced result will help us in distinguishing the conduct of representatives who can be attired throughout the following time. Trial results uncover that the strategic relapse approach can reach up to 86% exactness over another. There are the few algorithms that can be used for processing the data, KNearest Neighbour, logistic regression, decision Tree, random Forest, Support Vector Machine etc. Keywords: Employees Attrition, Machine Learning, Support vector machine (SVM), KNN (K-Nearest Neighbour)

scholarly journals Real time Drowsy Driver Detection using Polynomial Kernel based Support Vector Machine

2021 ◽  
Vol 9 (9) ◽  
pp. 1389-1396
Author(s):  
Makhan Ahirwar
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Support Vector Machine ◽  
Real Time ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Human Errors ◽  
Drowsiness Detection ◽  
Non Linear ◽  
Data Separation

Abstract: Casualty increases from road accidents day by day. There are so many reasons that accident causes and mostly due to human errors. Driver drowsiness is one of them. A small drowsiness may turn it into a big accident that resulted heavy casualties. If any of the system automatically detects the driver’s drowsiness and alert at real time may secure many lives. Drowsiness can be recognized by different situations such as by opening full mouth, by closing both the eyes and a combination of both. This may advised not to drive at drowsy state. There are various techniques through which drowsiness can be detected at real time but accuracy matters. OpenCV is a highly utilized open source computer vision library through which facial features can be recognized effectively. Polynomial kernel based support vector machine (SVM) is an advanced classification technique through which drowsiness can be classified from face. SVM is advanced machine learning approach through which linear and non-linear data can be classified with higher level of accuracy. System pertained 96.17 % of accuracy. Polynomial kernel is useful for non-linear data separation. Here system classifies the expressional features of face and result accordingly for drowsiness detection. Keywords: Support Vector Machine (SVM), OpenCV, Machine Learning, Non-Linear SVM Model, Drowsiness Detection, Face Detection, Computer Vision.

IMPLEMENTASI SUPPORT VECTOR MACHINE PADA PREDIKSI HARGA SAHAM GABUNGAN (IHSG)

2020 ◽  
Vol 25 (1) ◽  
pp. 24-38
Author(s):  
Eka Patriya
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector Regression ◽  
Support Vector

Saham adalah instrumen pasar keuangan yang banyak dipilih oleh investor sebagai alternatif sumber keuangan, akan tetapi saham yang diperjual belikan di pasar keuangan sering mengalami fluktuasi harga (naik dan turun) yang tinggi. Para investor berpeluang tidak hanya mendapat keuntungan, tetapi juga dapat mengalami kerugian di masa mendatang. Salah satu indikator yang perlu diperhatikan oleh investor dalam berinvestasi saham adalah pergerakan Indeks Harga Saham Gabungan (IHSG). Tindakan dalam menganalisa IHSG merupakan hal yang penting dilakukan oleh investor dengan tujuan untuk menemukan suatu trend atau pola yang mungkin berulang dari pergerakan harga saham masa lalu, sehingga dapat digunakan untuk memprediksi pergerakan harga saham di masa mendatang. Salah satu metode yang dapat digunakan untuk memprediksi pergerakan harga saham secara akurat adalah machine learning. Pada penelitian ini dibuat sebuah model prediksi harga penutupan IHSG menggunakan algoritma Support Vector Regression (SVR) yang menghasilkan kemampuan prediksi dan generalisasi yang baik dengan nilai RMSE training dan testing sebesar 14.334 dan 20.281, serta MAPE training dan testing sebesar 0.211% dan 0.251%. Hasil penelitian ini diharapkan dapat membantu para investor dalam mengambil keputusan untuk menyusun strategi investasi saham.

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