TreeLoc: An Ensemble Learning-based Approach for Range Based Indoor Localization

Maduranga, Mwp; Abeysekera, Ruvan

doi:10.5815/ijwmt.2021.05.03

Cited by 13 publications

(9 citation statements)

References 19 publications

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“…The research team has highlighted the suggested system's efficiency, claiming that it will benefit a real-time system due to its performance. To take things a step beyond, T Kalansuriya and others have used pictures of people's faces to discuss a gender recognition system that determines age [6] This method utilized image processing methods for pre-processing and feature extraction, while ANNs were employed for classification. Four age categories have been established to train and evaluate the algorithm for faces from Asian and non-Asian countries.…”

Section: Related Workmentioning

confidence: 99%

Mobile-Based Skin Disease Diagnosis System Using Convolutional Neural Networks (CNN)

Maduranga¹,

Nandasena²

2022

IJIGSP

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This paper presents a design and development of an Artificial Intelligence (AI) based mobile application to detect the type of skin disease. Skin diseases are a serious hazard to everyone throughout the world. However, it is difficult to make accurate skin diseases diagnosis. In this work, Deep learning algorithms Convolution Neural Networks (CNN) is proposed to classify skin diseases on the HAM10000 dataset. An extensive review of research articles on object identification methods and a comparison of their relative qualities were given to find a method that would work well for detecting skin diseases. The CNN-based technique was recognized as the best method for identifying skin diseases. A mobile application, on the other hand, is built for quick and accurate action. By looking at an image of the afflicted area at the beginning of a skin illness, it assists patients and dermatologists in determining the kind of disease present. Its resilience in detecting the impacted region considerably faster with nearly 2x fewer computations than the standard MobileNet model results in low computing efforts. This study revealed that MobileNet with transfer learning yielding an accuracy of about 85% is the most suitable model for automatic skin disease identification. According to these findings, the suggested approach can assist general practitioners in quickly and accurately diagnosing skin diseases using the smart phone.

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Section: Related Workmentioning

confidence: 99%

Mobile-Based Skin Disease Diagnosis System Using Convolutional Neural Networks (CNN)

Maduranga¹,

Nandasena²

2022

IJIGSP

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“…Namely classification type problems and regression type problems. This particular research work comes under a classification type problem [21][22]. In classification type problems, the evaluation process typically done by creating the confusion matrix to measure the model's performance.…”

Section: Testing and Evaluationmentioning

confidence: 99%

Towards Ambient Assisted Living (AAL): Design of an IoT-based Elderly Activity Monitoring System

Rupasinghe¹,

Maduranga²

2022

IJEM

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This paper presents a design and development of an IoT-based system to real-time track elders' physical activities using accelerometer sensor data. The objective behind conducting such research is to overcome the lack of ability to monitor physical activities. Especially with the development of the socio-economic sector, the number of elders who live in isolated areas such as elderly homes have increased rapidly. In such a case with declining cognitive abilities, the healthcare of these elderly personalities becomes vulnerable. This research project fulfilled the necessity of a system to capture the vital details about those people. The Internet of Things (IoT) and cloud-based applications have become a significant part of the Information and Technology sector. Realtime monitoring is a concept tightly coupled with IoT cloud cloud-native application for this application is an excellent example of that.Further, the requirement of a low-cost system was fulfilled by using hardware components such as NodeMCU and accelerometer sensors. The designed and developed system is composed of a cost-effective wrist-worn device capable of capturing hand movement on three different arises. Hence, the detected signals are transmitted to a master node to process and recognize the activity according to the detected signal. Another significant aspect of the project is using machine learning techniques to recognize the four different activities such as walking, sitting, sleeping, and standing. The use of supervised machine learning techniques is evaluated to overcome the barriers of real-time activity recognition. Further different supervised machine learning algorithms were used and evaluated, which were extracted from existing literature. The project was conducted while accomplishing the machine learning life cycle stages, and it has significantly benefitted from generating highly accurate final results for the overall system. Further different supervised machine learning algorithms were used and evaluated, which were extracted from existing literature. The supervised machine learning algorithm Decision Tree Classifier used for this study. Using the Decision Classifier Tree algorithm succeeded in gaining more than 80% of model accuracy. Since the research topic comes under a classification type-oriented problem, the testing process of the model has been done using the confusion matrix for the trained model.

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“…All waves cannot be defined as wavelets. Wavelets need to satisfy the number of conditions mentioned below [14,17,18].…”

Section: Wavelet Transform and Semg Signal Processing Using Wavelet T...mentioning

confidence: 99%

“…When transforming a signal to the frequency domain, time information will be lost. When looking at an FT of a signal, it is impossible to emphasize when a particular event took place [11], [12], [14]. To analyze the signal in time-frequency joint representation, it is needed to divide the signal into several parts.…”

Section: Introductionmentioning

confidence: 99%

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Surface Electromyography Signal Acquisition and Classification Using Artificial Neural Networks (ANN)

Rasnayake¹,

Maduranga²,

Sithara³

2022

IJMECS

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An electromyography (EMG) is an analytical tool used to record muscles' electrical activity, which produces an electrical signal proportional to the level of muscle activity. EMG signal plays a vital role in bio-mechatronic engineering for designing intelligent prostheses and other rehabilitation devices. Analysis of EMG signals with powerful and advanced methodologies is an essential requirement in EMG signal processing, as the EMG signal is a complex nonlinear, non-stationary signal in nature. It is required to use advanced signal processing techniques rather than conventional methods to exact EMG signals' features. Fourier transforms (FT) are not the most appropriate tool for analyzing non-stationary signals such as EMG. In this work, we have developed a system that can be useful for disabled persons to get a regular lifestyle using a functioning part of the body. Here, we studied the electrocution gram behavior of human body parts to feature extraction and trained the neural network to simulate the movements of mechanical actuators such as robotic arms. The wavelet transformation has been used to get high-quality feature extraction from electro cardio grapy and develops proper faltering methods for cardio systems' electrical signals. Finally, an artificial neural network (ANN) is used to classify the EMG signals through exacted features. Classification results are presented in this paper.

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TreeLoc: An Ensemble Learning-based Approach for Range Based Indoor Localization

Cited by 13 publications

References 19 publications

Mobile-Based Skin Disease Diagnosis System Using Convolutional Neural Networks (CNN)

Mobile-Based Skin Disease Diagnosis System Using Convolutional Neural Networks (CNN)

Towards Ambient Assisted Living (AAL): Design of an IoT-based Elderly Activity Monitoring System

Surface Electromyography Signal Acquisition and Classification Using Artificial Neural Networks (ANN)

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