The document describes a study that used a self-attention neural network architecture for human activity recognition using smartphone sensor data. The study compared the proposed self-attention model to convolutional neural network (CNN) and long short-term memory (LSTM) baselines. The self-attention model achieved a test accuracy of 91.75% for classifying six human activities, which was comparable to the baseline models. The study investigated components of the self-attention model like dropout rate, positional encoding, and scaling factors to determine the best performing model.
Health monitoring catalogue based on human activity classification using mac...IJECEIAES
In recent times, fitness trackers and smartphones equipped with different sensors like gyroscopes, accelerometers, global positioning system sensors and programs are used for recognizing human activities. In this paper, the results collected from these devices are used to design a system that can assist an application in monitoring a person’s health. The proposed system takes the raw sensor signals as input, preprocesses it and using machine learning techniques outputs the state of the user with minimum error. The objective of this paper is to compare the performance of different algorithms logistic regression (LR), support vector machine (SVM), k-nearest neighbor (k-NN) and random forest (RF). The algorithms are trained and tested with an original number of features as well as with transformed number of features (using linear discriminant analysis). The data with a smaller number of features is then used to visualize the high dimensional data. In this paper, each data point is mapped in the high dimensional data to two-dimensional data using t-distributed stochastic neighbor embedding technique. Overall, the first high dimensional data is visualized and compared with model’s performance with different algorithms and different number of coordinates.
Wearable sensor-based human activity recognition with ensemble learning: a co...IJECEIAES
The spectacular growth of wearable sensors has provided a key contribution to the field of human activity recognition. Due to its effective and versatile usage and application in various fields such as smart homes and medical areas, human activity recognition has always been an appealing research topic in artificial intelligence. From this perspective, there are a lot of existing works that make use of accelerometer and gyroscope sensor data for recognizing human activities. This paper presents a comparative study of ensemble learning methods for human activity recognition. The methods include random forest, adaptive boosting, gradient boosting, extreme gradient boosting, and light gradient boosting machine (LightGBM). Among the ensemble learning methods in comparison, light gradient boosting machine and random forest demonstrate the best performance. The experimental results revealed that light gradient boosting machine yields the highest accuracy of 94.50% on UCI-HAR dataset and 100% on single accelerometer dataset while random forest records the highest accuracy of 93.41% on motion sense dataset.
This document discusses a human activity recognition system using machine learning techniques. It provides an overview of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks for human activity recognition using data from sensors like accelerometers. CNNs use spatial correlations to process data through convolutional and pooling layers, while LSTMs are useful for processing large datasets and retaining memory of previous data due to their gated structure. The document compares CNNs and LSTMs, discusses relevant literature, and machine learning algorithms that can be used like K-nearest neighbors, support vector machines, and random forests. The goal is to classify human activities in real-time using supervised learning techniques and sensor data.
Human Activity Recognition Using Recurrent Neural Networkmlaij
The document summarizes a research paper that proposes using a recurrent neural network for human activity recognition using smartphone sensor data. Specifically:
- It aims to overcome limitations of previous methods that divided data into fixed time windows and could not perform inference until a full window of data was collected.
- The proposed method normalizes sensor data and feeds it continuously into a recurrent neural network without dividing into time windows.
- It achieves higher accuracy than previous convolutional neural network and LSTM methods, and accuracy comparable to previous "handcrafted" feature engineering methods.
- By not requiring fixed time windows or complex feature engineering, it allows for real-time inference as data is streamed from sensors.
An Efficient Activity Detection System based on Skeleton Joints Identification IJECEIAES
The increasing criminal activities in the current world has drawn lot of interest activity recognition techniques which helps to perform the sophistical analytical operations on human activity and also helps to interface the human and computer interactions. From the existing review analysis it is found that most of the existing systems are not emphasize on computational performance but are more application specific by identifying specific problems. Hence, it is found that all the features are not required for accurate and cost effective human activity detection. Thus, the human skelton action can be considered and presented a simple and accurate process to identify the significant joints only. From the outcomes it is found that the proposed system is cost effective and computational efficient activity recognition technique for human actions.
IRJET - Creating a Security Alert for the Care Takers Implementing a Vast Dee...IRJET Journal
This document presents a proposed system for creating a security alert for caregivers by implementing a vast deep learning model to recognize human activities and gestures. The system would collect a dataset of skeleton images of human actions and gestures. It would then train models using deep learning algorithms like AlexNet, VGG16, GoogleNet, and ResNet to accurately recognize activities and gestures. This would help monitor senior citizens and detect any health issues or untrustworthy individuals. The proposed system aims to optimize techniques such as stochastic gradient descent and regularizers like ReLU and ELU to increase prediction accuracy and provide low-cost, high-accuracy monitoring to improve senior citizen safety.
Draft activity recognition from accelerometer dataRaghu Palakodety
This document describes a framework for classifying human activities like standing, walking, and running using data from an accelerometer sensor on a smartphone. It discusses collecting raw sensor data, preprocessing the data through smoothing and feature extraction, training classifiers on extracted features, and classifying new data in real-time. Random forest classification achieved 83.49% accuracy on this activity recognition task using accelerometer data from an Android application.
This document reviews various object counting techniques discussed in recent research. It begins with an introduction to object detection and counting, describing how computer vision algorithms can now perform tasks humans struggle with at scale, like counting objects in large crowds. The literature review then summarizes 12 research papers describing different approaches to object counting, such as using YOLO, Faster R-CNN, and other deep learning models on datasets of vehicles, crops, and people to achieve accurate real-time counting in applications like traffic monitoring, agriculture, and search and rescue operations. In conclusion, the document discusses how image processing systems can help count objects effectively while reducing time, though accuracy depends on factors like camera quality, object size, image clarity, and illumination conditions
Health monitoring catalogue based on human activity classification using mac...IJECEIAES
In recent times, fitness trackers and smartphones equipped with different sensors like gyroscopes, accelerometers, global positioning system sensors and programs are used for recognizing human activities. In this paper, the results collected from these devices are used to design a system that can assist an application in monitoring a person’s health. The proposed system takes the raw sensor signals as input, preprocesses it and using machine learning techniques outputs the state of the user with minimum error. The objective of this paper is to compare the performance of different algorithms logistic regression (LR), support vector machine (SVM), k-nearest neighbor (k-NN) and random forest (RF). The algorithms are trained and tested with an original number of features as well as with transformed number of features (using linear discriminant analysis). The data with a smaller number of features is then used to visualize the high dimensional data. In this paper, each data point is mapped in the high dimensional data to two-dimensional data using t-distributed stochastic neighbor embedding technique. Overall, the first high dimensional data is visualized and compared with model’s performance with different algorithms and different number of coordinates.
Wearable sensor-based human activity recognition with ensemble learning: a co...IJECEIAES
The spectacular growth of wearable sensors has provided a key contribution to the field of human activity recognition. Due to its effective and versatile usage and application in various fields such as smart homes and medical areas, human activity recognition has always been an appealing research topic in artificial intelligence. From this perspective, there are a lot of existing works that make use of accelerometer and gyroscope sensor data for recognizing human activities. This paper presents a comparative study of ensemble learning methods for human activity recognition. The methods include random forest, adaptive boosting, gradient boosting, extreme gradient boosting, and light gradient boosting machine (LightGBM). Among the ensemble learning methods in comparison, light gradient boosting machine and random forest demonstrate the best performance. The experimental results revealed that light gradient boosting machine yields the highest accuracy of 94.50% on UCI-HAR dataset and 100% on single accelerometer dataset while random forest records the highest accuracy of 93.41% on motion sense dataset.
This document discusses a human activity recognition system using machine learning techniques. It provides an overview of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks for human activity recognition using data from sensors like accelerometers. CNNs use spatial correlations to process data through convolutional and pooling layers, while LSTMs are useful for processing large datasets and retaining memory of previous data due to their gated structure. The document compares CNNs and LSTMs, discusses relevant literature, and machine learning algorithms that can be used like K-nearest neighbors, support vector machines, and random forests. The goal is to classify human activities in real-time using supervised learning techniques and sensor data.
Human Activity Recognition Using Recurrent Neural Networkmlaij
The document summarizes a research paper that proposes using a recurrent neural network for human activity recognition using smartphone sensor data. Specifically:
- It aims to overcome limitations of previous methods that divided data into fixed time windows and could not perform inference until a full window of data was collected.
- The proposed method normalizes sensor data and feeds it continuously into a recurrent neural network without dividing into time windows.
- It achieves higher accuracy than previous convolutional neural network and LSTM methods, and accuracy comparable to previous "handcrafted" feature engineering methods.
- By not requiring fixed time windows or complex feature engineering, it allows for real-time inference as data is streamed from sensors.
An Efficient Activity Detection System based on Skeleton Joints Identification IJECEIAES
The increasing criminal activities in the current world has drawn lot of interest activity recognition techniques which helps to perform the sophistical analytical operations on human activity and also helps to interface the human and computer interactions. From the existing review analysis it is found that most of the existing systems are not emphasize on computational performance but are more application specific by identifying specific problems. Hence, it is found that all the features are not required for accurate and cost effective human activity detection. Thus, the human skelton action can be considered and presented a simple and accurate process to identify the significant joints only. From the outcomes it is found that the proposed system is cost effective and computational efficient activity recognition technique for human actions.
IRJET - Creating a Security Alert for the Care Takers Implementing a Vast Dee...IRJET Journal
This document presents a proposed system for creating a security alert for caregivers by implementing a vast deep learning model to recognize human activities and gestures. The system would collect a dataset of skeleton images of human actions and gestures. It would then train models using deep learning algorithms like AlexNet, VGG16, GoogleNet, and ResNet to accurately recognize activities and gestures. This would help monitor senior citizens and detect any health issues or untrustworthy individuals. The proposed system aims to optimize techniques such as stochastic gradient descent and regularizers like ReLU and ELU to increase prediction accuracy and provide low-cost, high-accuracy monitoring to improve senior citizen safety.
Draft activity recognition from accelerometer dataRaghu Palakodety
This document describes a framework for classifying human activities like standing, walking, and running using data from an accelerometer sensor on a smartphone. It discusses collecting raw sensor data, preprocessing the data through smoothing and feature extraction, training classifiers on extracted features, and classifying new data in real-time. Random forest classification achieved 83.49% accuracy on this activity recognition task using accelerometer data from an Android application.
This document reviews various object counting techniques discussed in recent research. It begins with an introduction to object detection and counting, describing how computer vision algorithms can now perform tasks humans struggle with at scale, like counting objects in large crowds. The literature review then summarizes 12 research papers describing different approaches to object counting, such as using YOLO, Faster R-CNN, and other deep learning models on datasets of vehicles, crops, and people to achieve accurate real-time counting in applications like traffic monitoring, agriculture, and search and rescue operations. In conclusion, the document discusses how image processing systems can help count objects effectively while reducing time, though accuracy depends on factors like camera quality, object size, image clarity, and illumination conditions
Human Activity Recognition Using SmartphoneIRJET Journal
The document discusses human activity recognition using smartphone sensors. It proposes using a CNN-LSTM model to classify activities like walking, running, and sitting based on accelerometer and gyroscope sensor data from a smartphone. The CNN extracts features from the sensor data, while the LSTM recognizes sequences of activities over time. The model is implemented in an Android application that recognizes activities in real-time and also counts steps, distance, and calories burned. The application uses built-in smartphone sensors like accelerometer, gyroscope, and pedometer to recognize activities affordably and with high availability without external devices. The CNN-LSTM model achieves accurate activity recognition compared to other machine learning techniques.
Schematic model for analyzing mobility and detection of multipleIAEME Publication
The document discusses a schematic model for analyzing mobility and detecting multiple objects in traffic scenes. It aims to not only detect and count moving objects, but also understand crowd behavior and reduce issues with objects occluding each other. Previous work on object detection is reviewed, noting that most approaches do not integrate detecting multiple objects simultaneously or address problems of object occlusion. The proposed model uses background subtraction and unscented Kalman filtering to increase detection accuracy and reduce false positives when analyzing image sequences of traffic scenes to detect multiple moving objects. It was tested in MATLAB and results showed highly accurate detection rates.
A Study of Mobile User Movements Prediction Methods IJECEIAES
For a decade and more, the Number of smart phone users count increasing day by day. With the drastic improvements in Communication technologies, the prediction of future movements of mobile users needs also have important role. Various sectors can gain from this prediction. Communication management, City Development planning, and locationbased services are some of the fields that can be made more valuable with movement prediction. In this paper, we propose a study of several Location Prediction Techniques in the following areas.
A Time Series ANN Approach for Weather Forecastingijctcm
Weather forecasting is most challenging problem around the world. There are various reason because of its experimented values in meteorology, but it is also a typical unbiased time series forecasting problem in scientific research. A lots of methods proposed by various scientists. The motive behind research is to predict more accurate. This paper contribute the same using artificial neural network (ANN) and simulated in MATLAB to predict two important weather parameters i.e. maximum and minimum temperature. The model has been trained using past 60 years of real data collected from(1901-1960) and tested over 40 years to forecast maximum and minimum temperature. The results based on mean square error function (MSE) confirm, this model which is based on multilayer perceptron has the potential to successful application to weather forecasting
Mining Frequent Patterns and Associations from the Smart meters using Bayesia...Eswar Publications
In today’s world migration of people from rural areas to urban areas is quite common. Health care services are one of the most challenging aspect that is must require to the people with abnormal health. Advancements in the technologies lead to build the smart homes, which contains various sensor or smart meter devices to automate the process of other electronic device. Additionally these smart meters can be able to capture the daily activities of the patients and also monitor the health conditions of the patients by mining the frequent patterns and
association rules generated from the smart meters. In this work we proposed a model that is able to monitor the activities of the patients in home and can send the daily activities to the corresponding doctor. We can extract the frequent patterns and association rules from the log data and can predict the health conditions of the patients and can give the suggestions according to the prediction. Our work is divided in to three stages. Firstly, we used to record the daily activities of the patient using a specific time period at three regular intervals. Secondly we applied the frequent pattern growth for extracting the association rules from the log file. Finally, we applied k means clustering for the input and applied Bayesian network model to predict the health behavior of the patient and precautions will be given accordingly.
MOTION PREDICTION USING DEPTH INFORMATION OF HUMAN ARM BASED ON ALEXNETgerogepatton
This document describes a study that uses a convolutional neural network model based on AlexNet to predict the drop point of a ball thrown by experimenters based on analyzing depth map images of their arm motion captured by a Kinect sensor. The researchers collected depth map data from 400 throwing trials, trained and tested their CNN model on this data, and made modifications to improve prediction accuracy, such as adjusting the network scale, loss function, and adding regularization. Their best model achieved over 70% accuracy in predicting the drop area of test examples based on the motion seen in the first 10 frames of depth maps capturing the throw.
Motion Prediction Using Depth Information of Human Arm Based on Alexnetgerogepatton
This document describes a study that uses a convolutional neural network based on AlexNet to predict the drop point of a ball thrown by experimenters based on analyzing depth information captured of their arm motion. The study involved collecting depth map data using a Kinect sensor as experimenters threw balls into different areas. The AlexNet model was modified and trained on this data, with improvements made such as adding regularization to the loss function to reduce overfitting. Experimental results showed the modified model could predict the drop point with improved accuracy compared to the original AlexNet.
Analytical framework for optimized feature extraction for upgrading occupancy...IJECEIAES
The adoption of the occupancy sensors has become an inevitable in commercial and non-commercial security devices, owing to their proficiency in the energy management. It has been found that the usages of conventional sensors is shrouded with operational problems, hence the use of the Doppler radar offers better mitigation of such problems. However, the usage of Doppler radar towards occupancy sensing in existing system is found to be very much in infancy stage. Moreover, the performance of monitoring using Doppler radar is yet to be improved more. Therefore, this paper introduces a simplified framework for enriching the event sensing performance by efficient selection of minimal robust attributes using Doppler radar. Adoption of analytical methodology has been carried out to find that different machine learning approaches could be further used for improving the accuracy performance for the feature that has been extracted in the proposed system of occuancy system.
The document discusses using deep learning models to classify different types of eye movements from raw eye tracking data. Specifically, it explores using an attention convolutional neural network (ACNN) to classify samples as fixations, saccades, smooth pursuit, or noise. The ACNN outperforms current state-of-the-art models on labeled eye tracking datasets. Additionally, the document investigates using unsupervised pre-training of an autoencoder on a different eye tracking dataset to improve the generalization of deep learning models to new datasets.
APPLICATION OF ARTIFICIAL NEURAL NETWORKS IN ESTIMATING PARTICIPATION IN ELEC...Zac Darcy
This document discusses using artificial neural networks to estimate voter participation rates in future elections in Iran. Specifically, it describes using a two-layer feed-forward neural network to predict voter turnout in the Kohgiluyeh and Boyer-Ahmad province with 91% accuracy. The neural network was trained on past electoral data from the province. The document also provides background on artificial neural networks and reviews their use in predicting outcomes in various domains, including economics, politics, tourism, the environment, and information technology.
Human activity detection based on edge point movements and spatio temporal fe...IAEME Publication
This document summarizes a research paper on human activity detection using edge point movements and spatiotemporal features from video recorded in indoor environments. The proposed method first extracts foreground objects from video frames using background subtraction. It then analyzes object shapes and extracts edge points using edge detection. Edge points across frames are stored in a database and compared to detect changes indicating activities. Activities are recognized by matching to stored patterns, or adding new patterns if unknown. In addition to movements, spatiotemporal features of location and time are considered to better detect activities as normal or abnormal. The goal is to develop a simple and accurate system for activity recognition to assist elderly or disabled people living independently.
Principal coefficient encoding for subject-independent human activity analysis IJECEIAES
Tracking human physical activity using smartphones is an emerging trend in healthcare monitoring and healthy lifestyle management. Neural networks are broadly used to analyze the inertial data of activity recognition. Inspired by the autoencoder neural networks, we propose a layer-wise network, namely principal coefficient encoder model (PCEM). Unlike the vanilla neural networks which apply random weight initialization and back-propagation for parameter updating, an optimized weight initialization is implemented in PCEM via principal coefficient learning. This principal coefficient encoding allows rapid data learning with no back-propagation intervention and no gigantic hyperparameter tuning. In PCEM, the most principal coefficients of the training data are determined to be the network weights. Two hidden layers with principal coefficient encoding are stacked in PCEM for the sake of deep architecture design. The performance of PCEM is evaluated based on a subject-independent protocol where training and testing samples are from different users, with no overlapping subjects in between the training and testing sets. This subject-independent protocol can better assess the generalization of the model to new data. Experimental results exhibit that PCEM outperforms certain state-of-the-art machine learning and deep learning models, including convolutional neural network, and deep belief network. PCEM can achieve ~97% accuracy in subject-independent human activity analysis.
IRJET- Surveillance of Object Motion Detection and Caution System using B...IRJET Journal
This document describes a proposed surveillance system using a block matching algorithm for motion detection. The system would use IP cameras to stream video that is monitored for unauthorized activity. Motion detection is performed by comparing frames using the block matching algorithm to detect changes in pixel intensity values, which would trigger an alarm. The block matching algorithm divides frames into blocks of pixels and validates the maximum and minimum intensity of each pixel. Comparing blocks between frames identifies motion if intensity values change beyond a threshold. If motion is detected in a designated sensitive area, the system saves the video and sends alerts by email and mobile notification to users.
Crowd Recognition System Based on Optical Flow Along with SVM classifierIJECEIAES
The manuscript discusses about abnormalities in a crowded scenario. To prevent the mishap at a public place, there is no much mechanism which could prevent or alert the concerned authority about suspects in a crowd. Usually in a crowded scene, there are chances of some mishap like a terrorist attack or a crime. Our target is finding techniques to identify such activities and to possibly prevent them. If the crowd members exhibit abnormal behavior, we could identify and say that this particular person is a suspect and then the concerned authority would look into the matter. There are various methods to identify the abnormal behavior. The proposed approach is based on optical flow model. It has an ability to detect the sudden changes in motion of an individual among the crowd. First, the main region of motion is extracted by the help of motion heat map. Harris corner detector is used for extracting point of interest of extracted motion area. Based on the point of interest an optical flow is estimated here. After analyzing this optical flow model, a threshold value is fixed. Basically optical flow is an energy level of individual frame. The threshold value is forwarded to SVM classifier, which produces a better result with 99.71% accuracy. This approach is very useful in real time video surveillance system where a machine can monitor unwanted crowd activity.
A novel enhanced algorithm for efficient human trackingIJICTJOURNAL
This paper introduces a novel enhanced algorithm for efficient human tracking based on background subtraction. The algorithm operates in four steps: 1) identifying a fixed background and removing noise, 2) subtracting the background from movable objects, 3) filtering the image to remove shadows and noise, and 4) separating and tracking movable objects using bubble routing. Experimental results found that the proposed algorithm improved motion and trajectory estimation of objects in terms of speed and accuracy compared to previous methods. The algorithm provides a fast and accurate way to track humans which has applications in video surveillance, human action recognition, and human-computer interaction.
Artificial neural networks (ANN) consider classification as one of the most dynamic research and
application areas. ANN is the branch of Artificial Intelligence (AI). The neural network was trained by
back propagation algorithm. The different combinations of functions and its effect while using ANN as a
classifier is studied and the correctness of these functions are analyzed for various kinds of datasets. The
back propagation neural network (BPNN) can be used as a highly successful tool for dataset classification
with suitable combination of training, learning and transfer functions. When the maximum likelihood
method was compared with backpropagation neural network method, the BPNN was more accurate than
maximum likelihood method. A high predictive ability with stable and well functioning BPNN is possible.
Multilayer feed-forward neural network algorithm is also used for classification. However BPNN proves to
be more effective than other classification algorithms.
IRJET- Human Activity Recognition using Flex SensorsIRJET Journal
This document discusses a system for human activity recognition using flex sensors. Flex sensors are attached to the body and can detect movements. The flex sensor data is fed into a neural network model to recognize activities. The model is trained using flex sensor data from various human activities. The trained model can then accurately recognize activities based on new flex sensor input data. The system is meant to help elderly people or those with disabilities by allowing them to control devices with body movements detected by flex sensors. It aims to provide a modular system that can adapt to new users and disabilities. Flex sensors make the system customizable while neural networks enable accurate activity recognition.
Influence of time and length size feature selections for human activity seque...ISA Interchange
In this paper, Viterbi algorithm based on a hidden Markov model is applied to recognize activity sequences from observed sensors events. Alternative features selections of time feature values of sensors events and activity length size feature values are tested, respectively, and then the results of activity sequences recognition performances of Viterbi algorithm are evaluated. The results show that the selection of larger time feature values of sensor events and/or smaller activity length size feature values will generate relatively better results on the activity sequences recognition performances.
Recently, the demand for surveillance system is increasing in real time application to enhance the security system. These surveillance systems are mainly used in crowded places such as shopping malls, sports stadium etc. In order to support enhance the security system, crowd behavior analysis has been proven a significant technique which is used for crowd monitoring, visual surveillance etc. For crowd behavior analysis, motion analysis is a crucial task which can be achieved with the help of trajectories and tracking of objects. Various approaches have been proposed for crowd behavior analysis which has limitation for densely crowded scenarios, a new object entering the scene etc. In this work, we propose a new approach for abnormal crowd behavior detection. Proposed approach is a motion based spatio-temporal feature analysis technique which is capable of obtaining trajectories of each detected object. We also present a technique to carry out the evaluation of individual object and group of objects by considering relational descriptors based on their environmental context. Finally, a classification is carried out for detection of abnormal or normal crowd behavior by following patch based process. In the results, we have reported that proposed model is able to achieve better performance when compared to existing techniques in terms of classification accuracy, true positive rate, and false positive rate.
SCCAI- A Student Career Counselling Artificial Intelligencevivatechijri
As education is growing day by day, the competition has prompted a need for the student to
understand more about the educational field. Many times the counselor isn’t available all the time and
sometimes due to the lack of proper knowledge about some educational field. Due to this, it creates an issue of
misconception of that field. This creates a problem for the student to decide a proper educational trajectory and
guidance is not always useful. The proposed paper will overcome all these problem using machine learning
algorithm. Various algorithms are being considered and amongst them the best suitable for our project are used
here. There are 3 major problems that come across our path and they are solved using Random forest, Linear
regression and Searching algorithm using Google API. At first Searching algorithm solves the problem of
location by segregating the college’s location vice, then Random Forest provides the list of colleges by using
stream and range of percentage and finally Linear Regression predicts the current cutoff using previous years’
data. Rather than this, the proposed system also provides information regarding all fields of education helping
students to understand and know about their field of interest better. The following idea is a total fresh idea with
no existing projects of similar kind. This project will help students guide them throughout.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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For a decade and more, the Number of smart phone users count increasing day by day. With the drastic improvements in Communication technologies, the prediction of future movements of mobile users needs also have important role. Various sectors can gain from this prediction. Communication management, City Development planning, and locationbased services are some of the fields that can be made more valuable with movement prediction. In this paper, we propose a study of several Location Prediction Techniques in the following areas.
A Time Series ANN Approach for Weather Forecastingijctcm
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Mining Frequent Patterns and Associations from the Smart meters using Bayesia...Eswar Publications
In today’s world migration of people from rural areas to urban areas is quite common. Health care services are one of the most challenging aspect that is must require to the people with abnormal health. Advancements in the technologies lead to build the smart homes, which contains various sensor or smart meter devices to automate the process of other electronic device. Additionally these smart meters can be able to capture the daily activities of the patients and also monitor the health conditions of the patients by mining the frequent patterns and
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MOTION PREDICTION USING DEPTH INFORMATION OF HUMAN ARM BASED ON ALEXNETgerogepatton
This document describes a study that uses a convolutional neural network model based on AlexNet to predict the drop point of a ball thrown by experimenters based on analyzing depth map images of their arm motion captured by a Kinect sensor. The researchers collected depth map data from 400 throwing trials, trained and tested their CNN model on this data, and made modifications to improve prediction accuracy, such as adjusting the network scale, loss function, and adding regularization. Their best model achieved over 70% accuracy in predicting the drop area of test examples based on the motion seen in the first 10 frames of depth maps capturing the throw.
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This document describes a study that uses a convolutional neural network based on AlexNet to predict the drop point of a ball thrown by experimenters based on analyzing depth information captured of their arm motion. The study involved collecting depth map data using a Kinect sensor as experimenters threw balls into different areas. The AlexNet model was modified and trained on this data, with improvements made such as adding regularization to the loss function to reduce overfitting. Experimental results showed the modified model could predict the drop point with improved accuracy compared to the original AlexNet.
Analytical framework for optimized feature extraction for upgrading occupancy...IJECEIAES
The adoption of the occupancy sensors has become an inevitable in commercial and non-commercial security devices, owing to their proficiency in the energy management. It has been found that the usages of conventional sensors is shrouded with operational problems, hence the use of the Doppler radar offers better mitigation of such problems. However, the usage of Doppler radar towards occupancy sensing in existing system is found to be very much in infancy stage. Moreover, the performance of monitoring using Doppler radar is yet to be improved more. Therefore, this paper introduces a simplified framework for enriching the event sensing performance by efficient selection of minimal robust attributes using Doppler radar. Adoption of analytical methodology has been carried out to find that different machine learning approaches could be further used for improving the accuracy performance for the feature that has been extracted in the proposed system of occuancy system.
The document discusses using deep learning models to classify different types of eye movements from raw eye tracking data. Specifically, it explores using an attention convolutional neural network (ACNN) to classify samples as fixations, saccades, smooth pursuit, or noise. The ACNN outperforms current state-of-the-art models on labeled eye tracking datasets. Additionally, the document investigates using unsupervised pre-training of an autoencoder on a different eye tracking dataset to improve the generalization of deep learning models to new datasets.
APPLICATION OF ARTIFICIAL NEURAL NETWORKS IN ESTIMATING PARTICIPATION IN ELEC...Zac Darcy
This document discusses using artificial neural networks to estimate voter participation rates in future elections in Iran. Specifically, it describes using a two-layer feed-forward neural network to predict voter turnout in the Kohgiluyeh and Boyer-Ahmad province with 91% accuracy. The neural network was trained on past electoral data from the province. The document also provides background on artificial neural networks and reviews their use in predicting outcomes in various domains, including economics, politics, tourism, the environment, and information technology.
Human activity detection based on edge point movements and spatio temporal fe...IAEME Publication
This document summarizes a research paper on human activity detection using edge point movements and spatiotemporal features from video recorded in indoor environments. The proposed method first extracts foreground objects from video frames using background subtraction. It then analyzes object shapes and extracts edge points using edge detection. Edge points across frames are stored in a database and compared to detect changes indicating activities. Activities are recognized by matching to stored patterns, or adding new patterns if unknown. In addition to movements, spatiotemporal features of location and time are considered to better detect activities as normal or abnormal. The goal is to develop a simple and accurate system for activity recognition to assist elderly or disabled people living independently.
Principal coefficient encoding for subject-independent human activity analysis IJECEIAES
Tracking human physical activity using smartphones is an emerging trend in healthcare monitoring and healthy lifestyle management. Neural networks are broadly used to analyze the inertial data of activity recognition. Inspired by the autoencoder neural networks, we propose a layer-wise network, namely principal coefficient encoder model (PCEM). Unlike the vanilla neural networks which apply random weight initialization and back-propagation for parameter updating, an optimized weight initialization is implemented in PCEM via principal coefficient learning. This principal coefficient encoding allows rapid data learning with no back-propagation intervention and no gigantic hyperparameter tuning. In PCEM, the most principal coefficients of the training data are determined to be the network weights. Two hidden layers with principal coefficient encoding are stacked in PCEM for the sake of deep architecture design. The performance of PCEM is evaluated based on a subject-independent protocol where training and testing samples are from different users, with no overlapping subjects in between the training and testing sets. This subject-independent protocol can better assess the generalization of the model to new data. Experimental results exhibit that PCEM outperforms certain state-of-the-art machine learning and deep learning models, including convolutional neural network, and deep belief network. PCEM can achieve ~97% accuracy in subject-independent human activity analysis.
IRJET- Surveillance of Object Motion Detection and Caution System using B...IRJET Journal
This document describes a proposed surveillance system using a block matching algorithm for motion detection. The system would use IP cameras to stream video that is monitored for unauthorized activity. Motion detection is performed by comparing frames using the block matching algorithm to detect changes in pixel intensity values, which would trigger an alarm. The block matching algorithm divides frames into blocks of pixels and validates the maximum and minimum intensity of each pixel. Comparing blocks between frames identifies motion if intensity values change beyond a threshold. If motion is detected in a designated sensitive area, the system saves the video and sends alerts by email and mobile notification to users.
Crowd Recognition System Based on Optical Flow Along with SVM classifierIJECEIAES
The manuscript discusses about abnormalities in a crowded scenario. To prevent the mishap at a public place, there is no much mechanism which could prevent or alert the concerned authority about suspects in a crowd. Usually in a crowded scene, there are chances of some mishap like a terrorist attack or a crime. Our target is finding techniques to identify such activities and to possibly prevent them. If the crowd members exhibit abnormal behavior, we could identify and say that this particular person is a suspect and then the concerned authority would look into the matter. There are various methods to identify the abnormal behavior. The proposed approach is based on optical flow model. It has an ability to detect the sudden changes in motion of an individual among the crowd. First, the main region of motion is extracted by the help of motion heat map. Harris corner detector is used for extracting point of interest of extracted motion area. Based on the point of interest an optical flow is estimated here. After analyzing this optical flow model, a threshold value is fixed. Basically optical flow is an energy level of individual frame. The threshold value is forwarded to SVM classifier, which produces a better result with 99.71% accuracy. This approach is very useful in real time video surveillance system where a machine can monitor unwanted crowd activity.
A novel enhanced algorithm for efficient human trackingIJICTJOURNAL
This paper introduces a novel enhanced algorithm for efficient human tracking based on background subtraction. The algorithm operates in four steps: 1) identifying a fixed background and removing noise, 2) subtracting the background from movable objects, 3) filtering the image to remove shadows and noise, and 4) separating and tracking movable objects using bubble routing. Experimental results found that the proposed algorithm improved motion and trajectory estimation of objects in terms of speed and accuracy compared to previous methods. The algorithm provides a fast and accurate way to track humans which has applications in video surveillance, human action recognition, and human-computer interaction.
Artificial neural networks (ANN) consider classification as one of the most dynamic research and
application areas. ANN is the branch of Artificial Intelligence (AI). The neural network was trained by
back propagation algorithm. The different combinations of functions and its effect while using ANN as a
classifier is studied and the correctness of these functions are analyzed for various kinds of datasets. The
back propagation neural network (BPNN) can be used as a highly successful tool for dataset classification
with suitable combination of training, learning and transfer functions. When the maximum likelihood
method was compared with backpropagation neural network method, the BPNN was more accurate than
maximum likelihood method. A high predictive ability with stable and well functioning BPNN is possible.
Multilayer feed-forward neural network algorithm is also used for classification. However BPNN proves to
be more effective than other classification algorithms.
IRJET- Human Activity Recognition using Flex SensorsIRJET Journal
This document discusses a system for human activity recognition using flex sensors. Flex sensors are attached to the body and can detect movements. The flex sensor data is fed into a neural network model to recognize activities. The model is trained using flex sensor data from various human activities. The trained model can then accurately recognize activities based on new flex sensor input data. The system is meant to help elderly people or those with disabilities by allowing them to control devices with body movements detected by flex sensors. It aims to provide a modular system that can adapt to new users and disabilities. Flex sensors make the system customizable while neural networks enable accurate activity recognition.
Influence of time and length size feature selections for human activity seque...ISA Interchange
In this paper, Viterbi algorithm based on a hidden Markov model is applied to recognize activity sequences from observed sensors events. Alternative features selections of time feature values of sensors events and activity length size feature values are tested, respectively, and then the results of activity sequences recognition performances of Viterbi algorithm are evaluated. The results show that the selection of larger time feature values of sensor events and/or smaller activity length size feature values will generate relatively better results on the activity sequences recognition performances.
Recently, the demand for surveillance system is increasing in real time application to enhance the security system. These surveillance systems are mainly used in crowded places such as shopping malls, sports stadium etc. In order to support enhance the security system, crowd behavior analysis has been proven a significant technique which is used for crowd monitoring, visual surveillance etc. For crowd behavior analysis, motion analysis is a crucial task which can be achieved with the help of trajectories and tracking of objects. Various approaches have been proposed for crowd behavior analysis which has limitation for densely crowded scenarios, a new object entering the scene etc. In this work, we propose a new approach for abnormal crowd behavior detection. Proposed approach is a motion based spatio-temporal feature analysis technique which is capable of obtaining trajectories of each detected object. We also present a technique to carry out the evaluation of individual object and group of objects by considering relational descriptors based on their environmental context. Finally, a classification is carried out for detection of abnormal or normal crowd behavior by following patch based process. In the results, we have reported that proposed model is able to achieve better performance when compared to existing techniques in terms of classification accuracy, true positive rate, and false positive rate.
SCCAI- A Student Career Counselling Artificial Intelligencevivatechijri
As education is growing day by day, the competition has prompted a need for the student to
understand more about the educational field. Many times the counselor isn’t available all the time and
sometimes due to the lack of proper knowledge about some educational field. Due to this, it creates an issue of
misconception of that field. This creates a problem for the student to decide a proper educational trajectory and
guidance is not always useful. The proposed paper will overcome all these problem using machine learning
algorithm. Various algorithms are being considered and amongst them the best suitable for our project are used
here. There are 3 major problems that come across our path and they are solved using Random forest, Linear
regression and Searching algorithm using Google API. At first Searching algorithm solves the problem of
location by segregating the college’s location vice, then Random Forest provides the list of colleges by using
stream and range of percentage and finally Linear Regression predicts the current cutoff using previous years’
data. Rather than this, the proposed system also provides information regarding all fields of education helping
students to understand and know about their field of interest better. The following idea is a total fresh idea with
no existing projects of similar kind. This project will help students guide them throughout.
Similar to Human activity recognition with self-attention (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Blood finder application project report (1).pdfKamal Acharya
Blood Finder is an emergency time app where a user can search for the blood banks as
well as the registered blood donors around Mumbai. This application also provide an
opportunity for the user of this application to become a registered donor for this user have
to enroll for the donor request from the application itself. If the admin wish to make user
a registered donor, with some of the formalities with the organization it can be done.
Specialization of this application is that the user will not have to register on sign-in for
searching the blood banks and blood donors it can be just done by installing the
application to the mobile.
The purpose of making this application is to save the user’s time for searching blood of
needed blood group during the time of the emergency.
This is an android application developed in Java and XML with the connectivity of
SQLite database. This application will provide most of basic functionality required for an
emergency time application. All the details of Blood banks and Blood donors are stored
in the database i.e. SQLite.
This application allowed the user to get all the information regarding blood banks and
blood donors such as Name, Number, Address, Blood Group, rather than searching it on
the different websites and wasting the precious time. This application is effective and
user friendly.
Build the Next Generation of Apps with the Einstein 1 Platform.
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memory units within the NoC router, assessing their performance
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reach in time. Our project will provide an optimum solution to this draw back. A piezo electric
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signal or if a car rolls over. Then with the help of GSM module and GPS module, the location
will be sent to the emergency contact. Then after conforming the location necessary action will
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1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 2, April 2023, pp. 2023~2029
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i2.pp2023-2029 2023
Journal homepage: http://ijece.iaescore.com
Human activity recognition with self-attention
Yi-Fei Tan, Soon-Chang Poh, Chee-Pun Ooi, Wooi-Haw Tan
Faculty of Engineering, Multimedia University, Cyberjaya, Malaysia
Article Info ABSTRACT
Article history:
Received Mar 31, 2022
Revised Sep 15, 2022
Accepted Oct 13, 2022
In this paper, a self-attention based neural network architecture to address
human activity recognition is proposed. The dataset used was collected using
smartphone. The contribution of this paper is using a multi-layer multi-head
self-attention neural network architecture for human activity recognition and
compared to two strong baseline architectures, which are convolutional
neural network (CNN) and long-short term network (LSTM). The dropout
rate, positional encoding and scaling factor are also been investigated to find
the best model. The results show that proposed model achieves a test
accuracy of 91.75%, which is a comparable result when compared to both
the baseline models.
Keywords:
Convolution neural network
Human activity recognition
Long short-term memory
Self-attention
This is an open access article under the CC BY-SA license.
Corresponding Author:
Yi-Fei Tan
Faculty of Engineering, Multimedia University
63100 Cyberjaya, Selangor, Malaysia
Email: yftan@mmu.edu.my
1. INTRODUCTION
Around the world, the population of elderly is increasing rapidly. According to United Nations
analysis of recent trends of elderly population, the population is predicted to increase to 2.1 billion by 2050
[1]. Department of Statistics Malaysia projected the elderly population to reach about 20% of total population
[2]. Therefore, there are research which focused on improving elderly via assisted living technology [3]. One
of the key technologies is activity recognition. Activity recognition is a task of recognizing human activities
based on a series of observations of human actions [4]. Computer scientists addressed this task by using
various machine learning algorithms which feed on the observational data of human actions and classify the
given data into specific activity type. The observational data of activities can be collected by using different
means such as sensors or cameras [5]–[7]. In this paper, the focus is on sensor-based activity recognition
algorithm because sensory-based system is much less intrusive and lightweight when compared to
vision-based methods. On-body sensors such as accelerometer and gyroscope can be used to collect
acceleration and angular velocity of human body in various axis [8]–[11]. On-body sensors as data collectors
are ubiquitous and prevalent in human activity recognition research because of their relatively low cost. For
example, electronic devices such as smartphones, smart watches and wearable activity tracker have
embedded accelerometer and gyroscope. Besides, they are not limited to any location because people can
carry them everywhere. One of the limitations of using on-body sensors is the battery issue. The pattern of
the time series sensory data for each type of activity is distinct. For example, time series acceleration of
running should have a higher rate of change compared to walking. This unique distinction in pattern allows
machine learning algorithm to classify them.
The epplication of neural network architecture in human activity recognition has grown in recent
years. Besides neural networks, there are several machine learning algorithms that were used for human
activity recognition which include support vector machine (SVM) [12] and random forest (RF) [13]. Anguita
et al. [12] used a dataset collected using smartphone’s inertial sensors. In this research, SVM which exploits
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fixed-point arithmetic was used as a multiclass classifier. Contrary to traditional SVM, this approach
demonstrated a considerable improvement in computational cost while achieving similar accuracy. Xu et al.
[13] proposed a RF model which classify human activity based on sensory data from wearable devices. RF
model managed to obtain an overall accuracy of 90%.
The prevalent neural network architecture includes CNN and recurrent neural network (RNN).
Wu and Zhang [14] collected a 128-dimensional time series sensory data from accelerometer and gyroscope
of a smartphone. In this study, they trained an 8-layer CNN network to classify the sensory data into type of
activities. Their method achieved comparable results with conventional and state of the art models. Xi et al.
[15] reasoned using pooling after convolution to expand the receptive fields is not advisable because it could
bring about information loss. They proposed an alternative model based on dilated convolution which can
expand receptive field without compromising on information loss.
Long-short term network (LSTM) [16] is a widely used variant of RNN. Güney and Erdas [17] used
LSTM to classify raw sensory data collected from three-axis accelerometer. The proposed method managed
to obtain a classification accuracy of 91.34%. Mahmud et al. [18] used a multi-stage LSTM to extract
temporal features from sensory data and achieved a F1 score of 83.9%. Each stage of LSTM is used to
extract features from each sensor’s data. Then, the output of every stage of LSTM are aggregated using fully
connected layers.
There are also research works which combine both CNN and RNN for human activity recognition.
Mutegeki and Han [19] introduced a hybrid model of CNN and LSTM which achieved high classification
accuracy and reduced the complexity of the model. In [20], a hybrid model based on CNN and gated
recurrent unit (GRU) is proposed. GRU is another variant of RNN. In this work, the authors trained the
model on recognition of pairwise similar activities. This means that this model can be trained on data of one
type of activity and classify the data of another similar activity type.
In this work, we developed a human activity classification algorithm based on multi-head
self-attention neural network architecture. We trained and evaluated our models on a dataset which consists
of six activities. The proposed model was compared with two strong baselines based on CNN and LSTM
respectively. This work consists of ablation study of various component that is commonly used in
self-attention model such as dropout, scaling study and positional encoding. Our results show that the
proposed self-attention neural network architecture delivered a test accuracy on unseen data which is on-par
with both the baseline architectures. The organization of this paper is: in section 2 descriptions of the dataset
are given; in section 3 we present the detailed architecture of baseline CNN and LSTM model, as well as
proposed self-attention model; section 4, the experimental result and discussion are discussed; section 5, we
conclude the paper with a conclusion and future work.
2. DATASET
The dataset used in this research is a publicly available human activity recognition dataset called
UCI-HAR dataset [21]. This data is collected from 30 person aged 19 to 48 years old. Each volunteer was
asked to carry out six types of activities (walking, walking upstairs, walking downstairs, standing, sitting, and
laying) wearing a Samsung Galaxy smartphone on the waist. The dataset is not raw sensory data of tri-axis
accelerometer and gyroscope. It was pre-processed with noise filters and sampled with a sliding window of
2.56 seconds with 50% overlap. Eventually, each instance of sensory data consists of 128 readings or time
steps. The dataset consists of 10,299 instances in total. The authors of the dataset split the dataset into 70%
training set and 30% test set based on the volunteers. This means that volunteers for training set are not
included in test set and vice versa. The percentage of how much each activity type made up the train and test
set and other details of the dataset is summarized in Table 1.
Table 1. Class percentage for train and test set
Activity Type Train (%) Train (%)
Walking 16.68 16.83
Walking upstairs 14.59 15.98
Walking downstairs 13.41 14.25
Sitting 17.49 16.66
Standing 18.69 18.05
Laying 19.14 18.22
Total number of instances 7352 2947
There are nine features available for each instance of data which include triaxial acceleration from
the accelerometer (total acceleration), the estimated body acceleration and the triaxial angular velocity from
3. Int J Elec & Comp Eng ISSN: 2088-8708
Human activity recognition with self-attention (Yi-Fei Tan)
2025
the gyroscope. Each of these three categories contribute 3 features in x, y and z axis. Each instance of data
consists of 128 time steps and 9 dimensions per time step for each of the features and is a matrix of size
128×9.
3. RESEARCH METHOD
Time series classification problem is usually solved using certain type of RNN architecture such as
LSTM and GRU. However, RNN computation cannot be done in parallel for all the time steps. RNN
architecture design requires computation to be computed sequentially. Due to this fact, CNN is explored as
an alternative for time series data because CNN can compute all the time steps in parallel with convolution.
In this work, we developed two baselines, one based on LSTM and another based on CNN. We compared our
multi-head self-attention model to these baselines. Self-attention models are widely used model in natural
language processing (NLP) tasks which are also sequential in nature.
3.1. CNN architecture
The CNN baseline model has 2 layers of 1D convolutional layer with filter of size 3. The number of
filters for both layers are 64. Both of the convolutional layers use non-linear activation function called
rectified linear unit (ReLU). The output of second convolutional layer is passed to a 1D max pooling layer
with pooling size of 2. The output is then flattened and connected to two layers of fully connected layers. The
first fully-connected layer has 128 hidden units and uses ReLU activation function. The final layer has 6 units
corresponding to 6 classes of activities and uses a SoftMax activation function. Figure 1 visualized the CNN
model which serves as one of our baseline models.
3.2. LSTM architecture
The LSTM baseline model has 3 layers of LSTM. The first LSTM layer has 32 hidden units. The
second LSTM layer has 64 hidden units. The third LSTM layer has 32 hidden units. The final time step of the
third LSTM layer is connected to a fully connected layer. The fully connected layer has 6 units corresponding
to 6 classes of activities and uses a SoftMax activation function. Figure 2 summarizes the LSTM model we
used.
Figure 1. CNN model Figure 2. LSTM model
3.3. Multi-head self-attention architecture
3.3.1. Multi-head self-attention layer
Recently, self-attention neural network architecture [22] is the go-to neural network architecture for
many NLP tasks. Large language models with lots of self-attention layers such as bidirectional encoder
representations from transformers (BERT) can be trained on large text corpus without annotations [23]. Then,
it acts a pre-trained model for downstream NLP tasks such as question answering (QA) [24]. Recently,
researchers are investigating the possibility of using attention-based model for computer vision tasks such as
image classification [25] and object detection [26].
In [22], Google scientists introduced fully attention-based sequence-to-sequence model called
transformer. Transformer consists of a basic building block of self-attention called scaled dot product
attention which is given by equations listed at (1). The input sequence, X is a tensor of N×T×F where N is the
number of data examples, T is the number of time steps and F is the number of features. For UCI-HAR
dataset, T=128 and F=9. The input X is passed through three fully connected layer to produce Q, K ad V. All
three fully connected layers are linear without any non-linear activation function. Their parameters include
the weights denoted as Wq, Wk, Wv and biases denoted as bq, bk and bv. Each of the weight term is a matrix of
F×H where H is the number of hidden units of the fully connected layer. In addition, each of the bias term is
a vector of H dimension. These fully connected layers produce three output tensors denoted by Q, K and V.
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Each of these output tensors is a tensor of N×T×H. We then carry out dot product for both Q and K with a
scaling factor of
1
√dk
.
Q = XWq + bq
K = XWk + bk
V = XWv + bv
Attention(Q,K,V)=SoftMax (
Q ∙KT
√dk
)V (1)
The output is then passed through a SoftMax activation and multiplied with V which produce a final tensor of
N×T×H.
3.3.2. Multi-head self-attention model
Figure 3 shows the multi-head self-attention model for human activity recognition for one data
instance. A data instance of 128×9 is expanded in dimension to 128×128 after passing through a fully
connected layer (labelled FC in Figure 3) with ReLU activation. Then, the matrix of 128×128 is passed to
2 blocks of “4-head self-attention block”. Each 4-head self-attention block consists of 2 layers. The first layer
is a 4-head self-attention layer described in Part 1 of this subsection. Each individual self-attention layer
outputs a 128×128 matrix. The 4-head self-attention layer results in four 128×128 matrices which is then
concatenated to a matrix of 128×512. The output matrix of this concatenated matrix is passed to a fully
connected layer with 128 hidden units, which then outputs a 128×128 matrix. The output matrix of 2 blocks
of self-attention is matrix of 128×128, which is then unrolled to a column vector of 16384 dimensions. The
column vector is then passed through a FC layer with 128 units using ReLU and then to the prediction layer
with six units with SoftMax activation. The output is a vector of 6. Finally, we added dropout for some FC
layer. Table 2 summarizes the model and listed the number of parameters for each layer.
Figure 3. Multi-head self-attention model used for time series activity recognition
Table 2. Model summary
Layer Output Shape Number of parameters
FC (128 units, ReLU) 128×128 1280
4-head attention 4×128×128 4×49,536
Concatenate 128×512 -
FC (128 units, ReLU, dropout) 128×128 65,664
4-head attention 4×128×128 4×49,536
Concatenate 128×512 -
FC (128 units, ReLU, dropout) 128×128 65,664
Flatten 16384 -
FC (128 units, ReLU, dropout) 128 2,097,280
FC (6 units, Softmax) 6 774
FC (128 units, ReLU) 128×128 1,280
Total - 2,626,950
5. Int J Elec & Comp Eng ISSN: 2088-8708
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3.3.3. Positional encoding
Self-attention layer does not have any convolution and recurrence. For an instance of data, LSTM
takes in each time step of the data recurrently. As for CNN, each position of the output feature map of
convolutional layer corresponds to a receptive field in the input. Both LSTM and CNN have location
information of each time step of the sequential input. On the contrary, the output at each time step of the
self-attention layer is independent of computation at other time steps. This results in self-attention is not
location or positionally aware. For example, if the input is the word “apple”. To self-attention, “apple” or its
anagrams such as “papel” are the same. Therefore, the authors proposed that it is vital to inject some
positional information to the input.
In this work, we tried the positional encoding function introduced in [22]. The positional encoding
function generates a matrix PE of same shape as input X. The positional information is injected using
elementwise addition of X and PE as given in (2).
𝑋 = 𝑋 + 𝑃𝐸 (2)
4. RESULTS AND DISCUSSION
We evaluated the proposed self-attention on test set using the evaluation metric, accuracy. For every
experiment, we used Adam optimizer and run the training for 50 epochs. We picked the best test accuracy out
of all the 50 epochs to be presented.
4.1. Dropout
Dropout is a regularization method [27]. We added dropout at some of the FC layer. The detailed
position of dropout in the self-attention model is given in Table 2. Since the dropout rate is a hyperparameter,
we have tried out a few choices of dropout rate. Table 3 listed the test accuracy of our self-attention model
for difference choices of dropout rate. It was discovered that self-attention model with dropout rate of 0.2 has
the highest test accuracy of 0.9175. Therefore, we fixed dropout rate to 0.2 for later experiments.
Table 3. Choice of dropout rate and test accuracy
Dropout Rate Test Accuracy
None 0.9070
0.1 0.9152
0.2 0.9175
0.3 0.9104
0.5 0.9023
4.2. Positional encoding
Experiment was carried out to investigate positional encoding. Our experiment result is tabulated in
Table 4. Addition of positional encoding results in a test accuracy of 0.8985 which is less accurate than the
model without addition of positional encoding. Positional encoding does not improve the performance of our
proposed self-attention model. We believed the reason behind this is due to the sequence length of 128 for
this dataset is relatively shorter compared to NLP tasks. Besides, we conjecture that the activity recognition
dataset is sensitive to noise. Addition of positional encoding may have brought about a regularizing side
effect.
4.3. Scaling factor
In this part, we evaluated the necessity of scaling factor. Scaling factor
1
√dk
is part of the
self-attention layer given in (1). For our proposed model, dk=128. By setting dk=1, the scaling factor’s effect
disappear as it is multiplied by 1. We discovered adding scaling factor will hurt the accuracy of the model.
Table 5 shows the effect of scaling factor on test accuracy. Addition of scaling factor results in a model with
test accuracy of 0.8853 which is lower than a model without scaling factor. Therefore, the model that was
used to compare with the two baselines did not include scaling factor.
Table 4. Effects of positional encoding test accuracy
With Positional Encoding Test Accuracy
Yes 0.8985
No 0.9175
Table 5. Effects of scaling factor on test accuracy
dk Train Accuracy Test Accuracy
1 0.9643 0.9175
128 0.9519 0.8853
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4.4. Comparison with baseline models
In this paper, we introduced two baseline models which include CNN and LSTM. We compared the
performance of our proposed self-attention model with these two baselines. Table 6 listed the train and test
accuracies of self-attention model and two baseline models: CNN and LSTM. The model with the highest
test accuracy is LSTM followed by CNN. Our proposed model has the lowest test accuracy of 0.9175 with
less than 1% difference from LSTM’s test accuracy of 0.9237. Our proposed model is comparable to both the
baseline models.
Table 6. Train and test accuracy for self-attention model, CNN and LSTM
Model Train Accuracy Test Accuracy
CNN 0.9536 0.9223
LSTM 0.9559 0.9237
Self-attention 0.9643 0.9175
5. CONCLUSION AND FUTURE WORK
In this paper, an activity recognition algorithm using multi-head self-attention neural network
architecture is introduced. The experiment to evaluate this proposed model was conducted using UCI-HAR
dataset. The results demonstrated that the proposed model has a test accuracy of 0.9175 which is comparable
to two baseline models: CNN and LSTM. In addition, we also discovered some of the common components
of self-attention layer such as scaling factor and positional encoding actually reduce test accuracy. In future
work, we would like to apply our model on other datasets and collect our own dataset.
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BIOGRAPHIES OF AUTHORS
Yi-Fei Tan received her B.Sc. (Hons), M.Sc. and Ph.D. from University of Malaya
(UM), Malaysia. She is currently a senior lecturer at the Faculty of Engineering in Multimedia
University (MMU), Cyberjaya, Malaysia. Her research areas include machine learning, deep
learning, image processing, big data analytics and queueing theory. She can be contacted at
email: yftan@mmu.edu.my.
Soon-Chang Poh obtained his B. Eng. (Hons.) Electronics and Master of
Engineering Science from Multimedia University, Malaysia. He is currently a research officer at
the Faculty of Engineering in Multimedia University (MMU), Cyberjaya, Malaysia. His research
interests include deep learning and anomaly detection. He can be contacted at email:
psoonchang@gmail.com.
Chee-Pun Ooi obtained his Ph.D. in Electrical Engineering from University of
Malaya, Malaysia, in year 2010. His current position is an Assoc. professor the Faculty of
Engineering in Multimedia University, Malaysia. He is a chartered engineer registered with
Engineering Council (the British regulatory body for Engineers), member of IET. His research
areas are FPGA based embedded systems and embedded systems. He can be contacted at email:
cpooi@mmu.edu.my.
Wooi-Haw Tan received his M.Sc. in Electronics from Queen’s University of
Belfast, UK and a Ph.D. in Engineering from Multimedia University. He is currently a senior
lecturer at Multimedia University. Dr. Tan’s areas of expertise include image processing,
embedded system design, internet of things (IoT), machine learning and deep learning. He can be
contacted at email: twhaw@mmu.edu.my.