Detection and Classification of Human Activity for Emergency Response in Smart Factory Shop Floor

Factory shop floor workers are exposed to threats and accidents due to their encounters with tools, equipment, and toxic materials. There are cases of occupational accidents resulting in injuries to workers and precipitating lawsuits, which on the other hand affect company’s operational cost. To ensure the safety of workers within the shop floor, there is a need for proactive activity monitoring. Such activities include detection of falling objects, abnormal vibration, and movement of humans within an acceptable area of the factory floor. Breathing sensor-based monitoring of workers in the smart factory shop floor can also be implemented. This is for the detection of human activity, especially in cases where workers are in isolation with no available emergency assistance. Internet of Things (IoT), Industrial Internet of Things (IIoT), and machine learning (ML) have enabled so many possibilities in this area. In this study, we present a simple test-bed, which is made up of a vibration sensor, a breathing and movement sensor, and a Light Detection and Ranging (LIDAR) sensor. These sensors were used to gather normal and abnormal data of human activities at the factory. We developed a dataset based on possible real-life situations and it is made up of about 10,000 data points. The data was split with a ratio of 75:25 for training and testing the model. We investigated the performance of different ML algorithms, including support vector machine (SVM), linear regression, naive Bayes (NB), K-nearest neighbor (KNN), and convolutional neural network (CNN). From our experiments, the CNN model outperformed other algorithms with an accuracy of 99.45%, 99.78%,100%, and 100%, respectively, for vibration, movement, breathing, and distance. We have also successfully developed a dataset to assist the research community in this field.

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Wavelet-Based Multiresolution Bispectral Analysis for Detection and Classification of Helicopter Drive-Shaft Problems

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.4038243 ◽

2017 ◽

Vol 140 (6) ◽

Cited By ~ 3

Author(s):

Mohammed A. Hassan ◽

Michael R. Habib ◽

Rania A. Abul Seoud ◽

Abdel M. Bayoumi

Keyword(s):

Search Algorithm ◽

Drive Shaft ◽

Superior Performance ◽

Support Vector ◽

Classification Algorithms ◽

Test Bed ◽

K Nearest Neighbor ◽

Safety Level ◽

Rotor Drive ◽

Minimum Number

Condition monitoring and fault diagnostics in rotorcraft have significant effect on improving safety level and reducing operational and maintenance costs. In this paper, a new method is proposed for fault detection and diagnoses of AH-64D (Apache helicopter) tail rotor drive-shaft problems. The proposed method depends on decomposing signal into different frequency ranges using mother wavelet. The most informative part of the vibration signal is then determined by calculating Shannon entropy of each part. Bispectrum is calculated for this part to investigate quadratic nonlinearities in this segment. Then, search algorithm is used to extract minimum number of indicative features from the bispectrum, which are then fed to classification algorithms. In order to quantitatively evaluate the proposed method, six classification algorithms are compared against each other such as fine K-nearest neighbor (KNN), cubic KNN, quadratic discriminant analysis, linear support vector machine (SVM), Gaussian SVM, and neural network. Comparison criteria include accuracy, precision, sensitivity, F score, true alarm, recall, and error classification accuracy (ECA). The proposed method is verified using real-world vibration data collected from a dedicated AH-64D helicopter tail rotor drive train (TRDT) research test bed. The proposed algorithm proves its ability in finding minimum number of indicative features and classifying the shaft faults with superior performance.

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Diagnosis of Problems in Truck Ore Transport Operations in Underground Mines Using Various Machine Learning Models and Data Collected by Internet of Things Systems

Minerals ◽

10.3390/min11101128 ◽

2021 ◽

Vol 11 (10) ◽

pp. 1128

Author(s):

Sebeom Park ◽

Dahee Jung ◽

Hoang Nguyen ◽

Yosoon Choi

Keyword(s):

Machine Learning ◽

Internet Of Things ◽

Production Management ◽

Classification And Regression Tree ◽

Underground Mines ◽

Validation Dataset ◽

Support Vector ◽

Learning Models ◽

K Nearest Neighbor ◽

Machine Learning Models

This study proposes a method for diagnosing problems in truck ore transport operations in underground mines using four machine learning models (i.e., Gaussian naïve Bayes (GNB), k-nearest neighbor (kNN), support vector machine (SVM), and classification and regression tree (CART)) and data collected by an Internet of Things system. A limestone underground mine with an applied mine production management system (using a tablet computer and Bluetooth beacon) is selected as the research area, and log data related to the truck travel time are collected. The machine learning models are trained and verified using the collected data, and grid search through 5-fold cross-validation is performed to improve the prediction accuracy of the models. The accuracy of CART is highest when the parameters leaf and split are set to 1 and 4, respectively (94.1%). In the validation of the machine learning models performed using the validation dataset (1500), the accuracy of the CART was 94.6%, and the precision and recall were 93.5% and 95.7%, respectively. In addition, it is confirmed that the F1 score reaches values as high as 94.6%. Through field application and analysis, it is confirmed that the proposed CART model can be utilized as a tool for monitoring and diagnosing the status of truck ore transport operations.

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A Hadoop Based Framework Integrating Machine Learning Classifiers for Anomaly Detection in the Internet of Things

Electronics ◽

10.3390/electronics10161955 ◽

2021 ◽

Vol 10 (16) ◽

pp. 1955

Author(s):

Ikram Sumaiya Thaseen ◽

Vanitha Mohanraj ◽

Sakthivel Ramachandran ◽

Kishore Sanapala ◽

Sang-Soo Yeo

Keyword(s):

Machine Learning ◽

Internet Of Things ◽

Experimental Analysis ◽

Parameter Tuning ◽

Computational Time ◽

Support Vector ◽

Learning Approaches ◽

K Nearest Neighbor ◽

Machine Learning Classifiers ◽

Learning Classifiers

In recent years, different variants of the botnet are targeting government, private organizations and there is a crucial need to develop a robust framework for securing the IoT (Internet of Things) network. In this paper, a Hadoop based framework is proposed to identify the malicious IoT traffic using a modified Tomek-link under-sampling integrated with automated Hyper-parameter tuning of machine learning classifiers. The novelty of this paper is to utilize a big data platform for benchmark IoT datasets to minimize computational time. The IoT benchmark datasets are loaded in the Hadoop Distributed File System (HDFS) environment. Three machine learning approaches namely naive Bayes (NB), K-nearest neighbor (KNN), and support vector machine (SVM) are used for categorizing IoT traffic. Artificial immune network optimization is deployed during cross-validation to obtain the best classifier parameters. Experimental analysis is performed on the Hadoop platform. The average accuracy of 99% and 90% is obtained for BoT_IoT and ToN_IoT datasets. The accuracy difference in ToN-IoT dataset is due to the huge number of data samples captured at the edge layer and fog layer. However, in BoT-IoT dataset only 5% of the training and test samples from the complete dataset are considered for experimental analysis as released by the dataset developers. The overall accuracy is improved by 19% in comparison with state-of-the-art techniques. The computational times for the huge datasets are reduced by 3–4 hours through Map Reduce in HDFS.

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Machine Learning Methods as a Test Bed for EEG Analysis in BCI Paradigms

Cognitive Analytics ◽

10.4018/978-1-7998-2460-2.ch081 ◽

2020 ◽

pp. 1577-1597

Author(s):

Kusuma Mohanchandra ◽

Snehanshu Saha

Keyword(s):

Machine Learning ◽

Nearest Neighbor ◽

Machine Learning Techniques ◽

Analytical Models ◽

Support Vector ◽

Test Bed ◽

K Nearest Neighbor ◽

Eeg Signals ◽

Broad Perspective ◽

Learning Techniques

Machine learning techniques, is a crucial tool to build analytical models in EEG data analysis. These models are an excellent choice for analyzing the high variability in EEG signals. The advancement in EEG-based Brain-Computer Interfaces (BCI) demands advanced processing tools and algorithms for exploration of EEG signals. In the context of the EEG-based BCI for speech communication, few classification and clustering techniques is presented in this book chapter. A broad perspective of the techniques and implementation of the weighted k-Nearest Neighbor (k-NN), Support vector machine (SVM), Decision Tree (DT) and Random Forest (RF) is explained and their usage in EEG signal analysis is mentioned. We suggest that these machine learning techniques provides not only potentially valuable control mechanism for BCI but also a deeper understanding of neuropathological mechanisms underlying the brain in ways that are not possible by conventional linear analysis.

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A Method for Sensor-Based Activity Recognition in Missing Data Scenario

Sensors ◽

10.3390/s20143811 ◽

2020 ◽

Vol 20 (14) ◽

pp. 3811

Author(s):

Tahera Hossain ◽

Md. Atiqur Rahman Ahad ◽

Sozo Inoue

Keyword(s):

Machine Learning ◽

Missing Data ◽

Activity Recognition ◽

Human Activity ◽

Human Activities ◽

Real Life ◽

Sensor Data ◽

Quality Data ◽

Support Vector ◽

Random Pattern

Sensor-based human activity recognition has various applications in the arena of healthcare, elderly smart-home, sports, etc. There are numerous works in this field—to recognize various human activities from sensor data. However, those works are based on data patterns that are clean data and have almost no missing data, which is a genuine concern for real-life healthcare centers. Therefore, to address this problem, we explored the sensor-based activity recognition when some partial data were lost in a random pattern. In this paper, we propose a novel method to improve activity recognition while having missing data without any data recovery. For the missing data pattern, we considered data to be missing in a random pattern, which is a realistic missing pattern for sensor data collection. Initially, we created different percentages of random missing data only in the test data, while the training was performed on good quality data. In our proposed approach, we explicitly induce different percentages of missing data randomly in the raw sensor data to train the model with missing data. Learning with missing data reinforces the model to regulate missing data during the classification of various activities that have missing data in the test module. This approach demonstrates the plausibility of the machine learning model, as it can learn and predict from an identical domain. We exploited several time-series statistical features to extricate better features in order to comprehend various human activities. We explored both support vector machine and random forest as machine learning models for activity classification. We developed a synthetic dataset to empirically evaluate the performance and show that the method can effectively improve the recognition accuracy from 80.8% to 97.5%. Afterward, we tested our approach with activities from two challenging benchmark datasets: the human activity sensing consortium (HASC) dataset and single chest-mounted accelerometer dataset. We examined the method for different missing percentages, varied window sizes, and diverse window sliding widths. Our explorations demonstrated improved recognition performances even in the presence of missing data. The achieved results provide persuasive findings on sensor-based activity recognition in the presence of missing data.

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Predicting Stock Exchange using Supervised Learning Algorithms

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.a4144.119119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 4081-4090

Keyword(s):

Machine Learning ◽

Random Forest ◽

Stock Market ◽

Supervised Learning ◽

Nearest Neighbor ◽

Market Price ◽

Real Life ◽

Support Vector ◽

K Nearest Neighbor ◽

Future Value

The stock market price trend is one of the brightest areas in the field of computer science, economics, finance, administration, etc. The stock market forecast is an attempt to determine the future value of the equity traded on a financial transaction with another financial system. The current work clearly describes the prediction of a stock using Machine Learning. The adoption of machine learning and artificial intelligence techniques to predict the prices of the stock is a growing trend. More and more researchers invest their time every day in coming up with ways to arrive at techniques that can further improve the accuracy of the stock prediction model. This paper is mainly concerned with the best model to predict the stock market value. During the mechanism of contemplating the various techniques and variables that can be taken into consideration, we discovered five models Which are based on supervised learning techniques i.e.., Support Vector Machine (SVM), Random Forest, K-Nearest Neighbor (KNN), Bernoulli Naïve Bayes.The empirical results show that SVC performs the best for large datasets and Random Forest, Naïve Bayes is the best for small datasets. The successful prediction for the stock will be a great asset for the stock The stock market price trend is one of the brightest areas in the field of computer science, economics, finance, administration, etc. The stock market forecast is an attempt to determine the future value of the equity traded on a financial transaction with another financial system. The current work clearly describes the prediction of a stock using Machine Learning. The adoption of machine learning and artificial intelligence techniques to predict the prices of the stock is a growing trend. More and more researchers invest their time every day in coming up with ways to arrive at techniques that can further improve the accuracy of the stock prediction model. This paper is mainly concerned with the best model to predict the stock market value. During the mechanism of contemplating the various techniques and variables that can be taken into consideration, we discovered five models Which are based on supervised learning techniques i.e.., Support Vector Machine (SVM), Random Forest, K-Nearest Neighbor (KNN), Bernoulli Naïve Bayes.The empirical results show that SVC performs the best for large datasets and Random Forest, Naïve Bayes is the best for small datasets. The successful prediction for the stock will be a great asset for the stock market institutions and will provide real-life solutions to the problems that stock investors face.market institutions and will provide real-life solutions to the problems that stock investors face.

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Machine Learning Methods as a Test Bed for EEG Analysis in BCI Paradigms

Handbook of Research on Applied Cybernetics and Systems Science - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-2498-4.ch008 ◽

2017 ◽

pp. 186-206

Author(s):

Kusuma Mohanchandra ◽

Snehanshu Saha

Keyword(s):

Machine Learning ◽

Nearest Neighbor ◽

Machine Learning Techniques ◽

Analytical Models ◽

Support Vector ◽

Test Bed ◽

K Nearest Neighbor ◽

Eeg Signals ◽

Broad Perspective ◽

Learning Techniques

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Project Teamwork Assessment and Success Rate Prediction Through Meta-Heuristic Algorithms

Interdisciplinary Approaches to Information Systems and Software Engineering - Advances in Systems Analysis, Software Engineering, and High Performance Computing ◽

10.4018/978-1-5225-7784-3.ch003 ◽

2019 ◽

pp. 33-61

Author(s):

Soumen Mukherjee ◽

Arup Kumar Bhattacharjee ◽

Arpan Deyasi

Keyword(s):

Success Rate ◽

Association Rule ◽

Nearest Neighbor ◽

Heuristic Algorithms ◽

Real Life ◽

Machine Learning Algorithms ◽

Support Vector ◽

Software Project ◽

K Nearest Neighbor ◽

Soft Skill

In this chapter, machine learning algorithms along with association rule analysis are applied to measure how the project teamwork success rate depends on various technical and soft skill factors of a software project. A real-life dataset is taken form UCI archive on project teamwork, which comprises of 84 features or attributes with 64 samples. The most effective feature set is therefore selected using meta-heuristic algorithms (i.e., particle swarm optimization [PSO] and simulated annealing [SA]) and then the data are given to support vector machine (SVM) and k-nearest neighbor (KNN) classifier for classification. Association rule mining is also used for rule generation among the different features of software project team to determine support and confidence. This chapter deals with how the project-based learning helps to manifest the students towards professionalism.

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Smartphone Naïve Bayes Human Activity Recognition Using Personalized Datasets

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2020.p0685 ◽

2020 ◽

Vol 24 (5) ◽

pp. 685-702

Author(s):

Moses L. Gadebe ◽

◽

Okuthe P. Kogeda ◽

Sunday O. Ojo

Keyword(s):

Support Vector Machines ◽

Real Time ◽

Activity Recognition ◽

Human Activity ◽

Nearest Neighbor ◽

Human Activity Recognition ◽

Training Dataset ◽

Support Vector ◽

K Nearest Neighbor ◽

Vector Machines

Recognizing human activity in real time with a limited dataset is possible on a resource-constrained device. However, most classification algorithms such as Support Vector Machines, C4.5, and K Nearest Neighbor require a large dataset to accurately predict human activities. In this paper, we present a novel real-time human activity recognition model based on Gaussian Naïve Bayes (GNB) algorithm using a personalized JavaScript object notation dataset extracted from the publicly available Physical Activity Monitoring for Aging People dataset and University of Southern California Human Activity dataset. With the proposed method, the personalized JSON training dataset is extracted and compressed into a 12×8 multi-dimensional array of the time-domain features extracted using a signal magnitude vector and tilt angles from tri-axial accelerometer sensor data. The algorithm is implemented on the Android platform using the Cordova cross-platform framework with HTML5 and JavaScript. Leave-one-activity-out cross validation is implemented as a testTrainer() function, the results of which are presented using a confusion matrix. The testTrainer() function leaves category K as the testing subset and the remaining K-1 as the training dataset to validate the proposed GNB algorithm. The proposed model is inexpensive in terms of memory and computational power owing to the use of a compressed small training dataset. Each K category was repeated five times and the algorithm consistently produced the same result for each test. The result of the simulation using the tilted angle features shows overall precision, recall, F-measure, and accuracy rates of 90%, 99.6%, 94.18%, and 89.51% respectively, in comparison to rates of 36.9%, 75%, 42%, and 36.9% when the signal magnitude vector features were used. The results of the simulations confirmed and proved that when using the tilt angle dataset, the GNB algorithm is superior to Support Vector Machines, C4.5, and K Nearest Neighbor algorithms.

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Combination of DNN and Improved KNN for Indoor Location Fingerprinting

Wireless Communications and Mobile Computing ◽

10.1155/2019/4283857 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 4

Author(s):

Peng Dai ◽

Yuan Yang ◽

Manyi Wang ◽

Ruqiang Yan

Keyword(s):

Nearest Neighbor ◽

Support Vector ◽

Time Of Arrival ◽

Test Bed ◽

K Nearest Neighbor ◽

Angle Of Arrival ◽

Access Points ◽

Indoor Location ◽

Location Fingerprinting ◽

Lower Accuracy

Fingerprinting based on Wi-Fi Received Signal Strength Indicator (RSSI) has been widely studied in recent years for indoor localization. While current algorithms related to RSSI Fingerprinting show a much lower accuracy than multilateration based on time of arrival or the angle of arrival techniques, they highly depend on the number of access points (APs) and fingerprinting training phase. In this paper, we present an integrated method by combining the deep neural network (DNN) with improved K-Nearest Neighbor (KNN) algorithm for indoor location fingerprinting. The improved KNN is realized by boosting the weights on K-nearest neighbors according to the number of matching access points. This will overcome the limitation of the original KNN algorithm on ignoring the influence of the neighboring points, which directly affect localization accuracy. The DNN algorithm is first used to classify the Wi-Fi RSSI Fingerprinting dataset. Then these possible locations in a certain class are also classified by the improved KNN algorithm to determine the final position. The proposed method is validated inside a room within about 13⁎9 m2. To examine its performance, the presented method has been compared with some classical algorithms, i.e., the random forest (RF) based algorithm, the KNN based algorithm, the support vector machine (SVM) based algorithm, the decision tree (DT) based algorithm, etc. Our real-world experiment results indicate that the proposed method is less dependent on the dense of access points and indoor radio propagation interference. Furthermore, our method can provide some preliminary guidelines for the design of indoor Wi-Fi test bed.

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