A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU

Deep learning (DL) has emerged as a powerful subset of machine learning (ML) and artificial intelligence (AI), outperforming traditional ML methods, especially in handling unstructured and large datasets. Its impact spans across various domains, including speech recognition, healthcare, autonomous vehicles, cybersecurity, predictive analytics, and more. However, the complexity and dynamic nature of real-world problems present challenges in designing effective deep learning models. Consequently, several deep learning models have been developed to address different problems and applications. In this article, we conduct a comprehensive survey of various deep learning models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Models, Deep Reinforcement Learning (DRL), and Deep Transfer Learning. We examine the structure, applications, benefits, and limitations of each model. Furthermore, we perform an analysis using three publicly available datasets: IMDB, ARAS, and Fruit-360. We compare the performance of six renowned deep learning models: CNN, Simple RNN, Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit (GRU), and Bidirectional GRU.Comment: 16 pages, 29 figure

Conflict resolution using enhanced label combination method for complex activity recognition in smart home environment

Activity recognition in smart home environment is becoming challenging when it is involving more than one resident living in the same space. It is not merely recognizing and tracking the multi-resident activity, but the interaction between them are also need to address in order to provide the great autonomous ambient intelligence (AmL) system. It is a challenging task due to diversity and complexity level of human activity and resident interaction using only binary data from ambient-based type sensors. Strong approach is needed to identify types of interaction based on activity performed either it is individual, parallel or cooperative. Previously, researchers tend to simplify the problem and define the parallel as individual activity due to the sensors type are unobtrusive and open to noise in nature. Hence, we address this issue as separate interaction. This research presents the rule-based approach to recognize complex activity recognition in multi-resident scenario in a smart home setting. It has been tested on the real smart home datasets using multi-label classification technique using Enhanced Label Combination method with random forest as its base classifier. The quality of the classification is selected as evaluation metrics to measure the proposed solution

Two-stage feature selection using ranking self-adaptive differential evolution algorithm for recognition of acceleration activity

Widespread research on activity recognition is becoming an imperative topic for improving the quality of human health. The fast development of sensing technology has become a fundamental platform for researchers to implement a system that could fulfill human needs. Due to privacy interests and low cost, wearable sensing technology is used in numerous physical activity monitoring and recognition systems. While these systems have proved to be successful, it is crucial to pay attention to the less relevant features to be classified. In such circumstances, it might happen that some features are less meaningful for describing the activity. Less complex and easy to understand, feature ranking is gaining a lot of attention in most feature dimension problems such as in bioinformatics and hyperspectral images. However, the improvement of ranking features in activity recognition has not yet been achieved. On the other hand, an evolutionary algorithm has proven its effectiveness in searching the best feature subsets. An exhaustive searching process of finding an optimal parameter value is another challenge. Consequently, this paper proposes a ranking self-adaptive differential evolution (rsaDE) feature selection algorithm. The proposed algorithm is capable of selecting the optimal feature subsets while improving the recognition of acceleration activity using a minimum number of features. The experiments employed real-world physical acceleration data sets: WISDM and PAMAP2. As a result, rsaDE performed better than the current methods in terms of model performance and its efficiency in the context of random forest ensemble classifiers

Solving classification problem using ensemble binarization classifier

Binarization strategy is broadly applied in solving various multi-class classification problems. However, the classifier model learning complexity tends to increase when expanding the number of problems into several replicas. One-Versus-All (OVA) is one of the strategies which transforming the ordinal multi-class classification problems into a series of two-class classification problems. The final output from each classifier model is combined in order to produce the final prediction. This binarization strategy has been proven as superior performance in accuracy than ordinal multi-class classifier model. However, learning model complexity (eg. Random Forest-RF ensemble decision trees) tends to increase when employing a large number of trees. Even though a large number of trees might produce a decent accuracy, generating time of the learning model is significantly longer. Hence, self-tuning tree parameter is introduced to tackle this matter. In such circumstances, a number of trees in the RF classifier are defined according to the number of class problem. In this paper, the OVA with self-tuning is evaluated based on parameter initialization in the context of RF ensemble decision tree. At the same time, the performance has also been compared with two classifier models such J48 and boosting for several well-known datasets

Parallel Algorithm for Brain Tissues Segmentation in T1-Weighted MR Images on 3D Reconfigurable Mesh Computer

In this paper, we propose a parallel algorithm for brain tissues segmentation from T1-weighted Magnetic Resonance Images (MRI) on Massively Parallel architecture named reconfigurable mesh computer (MCR), this brain tissues are already extracted using our method named Threshold Morphologic Brain Extraction method (TMBE)[1]. The use of this massively parallel architecture is introduced in order to improve the complexities of the corresponding algorithms. The image of size (M x N x K) to be processed must be stored on the RMC of the same size, one Voxel per Processing Element (PE). The proposed method consists in the brain tissues segmentation using parallel version of the modified fuzzy c-means MFCM [2], named PMFCM. This algorithm is directly applied on the extracted volume. The corresponding parallel program of the proposed algorithm is validated on a 3D Reconfigurable Mesh emulator [3]

A Survey on Multi-Resident Activity Recognition in Smart Environments

Human activity recognition (HAR) is a rapidly growing field that utilizes smart devices, sensors, and algorithms to automatically classify and identify the actions of individuals within a given environment. These systems have a wide range of applications, including assisting with caring tasks, increasing security, and improving energy efficiency. However, there are several challenges that must be addressed in order to effectively utilize HAR systems in multi-resident environments. One of the key challenges is accurately associating sensor observations with the identities of the individuals involved, which can be particularly difficult when residents are engaging in complex and collaborative activities. This paper provides a brief overview of the design and implementation of HAR systems, including a summary of the various data collection devices and approaches used for human activity identification. It also reviews previous research on the use of these systems in multi-resident environments and offers conclusions on the current state of the art in the field.Comment: 16 pages, to appear in Evolution of Information, Communication and Computing Systems (EICCS) Book Serie

Multi resident complex activity recognition in smart home: a literature review

This paper presents an overview of state of art of multi resident activity recognition in smart home environment. Generally wearable sensors as well as bespoke sensors are used for tracing the pattern of activity recognition among home dwellers in smart home scenario. Unlike wearable sensor, deployment of bespoke sensors embedded into the environment could be challenging to infer user activities. However, this type of sensors is selected due to human centric concerns, non-obtrusive, inviolate residents’ privacy and pervasive concern. Moreover, as human activity is becoming complex when dealing with multi resident, affected that inference activity in smart home scenario are also becoming complicated. Hence, this paper highlight the review of intelligent of smart home including technology sensing involved, previous research on activity recognition area specifically multi resident complex activity recognition in the same environment. We highlighted the multi resident activity recognition including concurrent, interleave and cooperative interaction activity. We present methods behind the main stream of multi resident activity recognition models and algorithms that deploys machine learning as the core subject. Furthermore, this paper also provides potential area for future research

Multi-resident activity recognition using label combination approach in smart home environment

Activity recognition in smart home environment is becoming challenging when it is involving more than one resident living in the same space. It is not merely recognizing the activity performed nevertheless to track and identify the performer of specific activity also need to address in order to provide the great autonomous for ambient intelligence system (AmI). It is a challenging task due to diversity and complexity of sensor fusion that only using the binary data from single type technology of ambient sensors. Strong approach is needed to identify types of activities performed at the same time to track which resident are performing that particular activity. Previously, researchers build the multi-resident activity model regardless the performer, thus the data association also fails to tackle the problem applicably. This research presents the multi-label classification approach to recognize the activity at the same is able to track the resident in multi-resident in a smart home setting. It has been tested on the real smart home datasets using Label Combination method of multi-label classification technique using random forest as its base classifier. The Hamming score, accuracy and exact match are selected as evaluation metrics to measure the proposed solution

Recognizing Complex Human Activities using Hybrid Feature Selections based on an Accelerometer Sensor

Wearable sensor technology is evolving in parallel with the demand for human activity monitoring applications. According to World Health Organization (WHO), the percentage of health problems occurring in the world population, such as diabetes, heart problem, and high blood pressure rapidly increases from year-to-year. Hence, regular exercise, at least twice a week, is encouraged for everyone, especially for adults and the elderly. An accelerometer sensor is preferable, due to privacy concerns and the low cost of installation. It is embedded within smartphones to monitor the amount of physical activity performed. One of the limitations of the various classifications is to deal with the large dimension of the feature space. Practically speaking, a large amount of memory space is demanded along with high processor performance to process a large number of features. Hence, the dimension of the features is required to be minimized by selecting the most relevant feature before it is classified. In order to tackle this issue, the hybrid feature selection using Relief-f and differential evolution is proposed. The public domain activity dataset from Physical Activity for Ageing People (PAMAP2) is used in the experimentation to identify the quality of the proposed method. Our experimental results show outstanding performance to recognize different types of physical activities with a minimum number of features. Subsequently, our findings indicate that the wrist is the best sensor placement to recognize the different types of human activity. The performance of our work also been compared with several state-of-the-art of features for selection algorithms

Resolution mechanism model for heterogeneous systems in smart home environment

Emerging growth of heterogeneous devices can be seen in smart home environment. These devices are diversified and highly heterogeneous in nature. Hence, there is a need of multiple devices in the same environment are orchestrating with one another in harmonic way. However, this thing becoming complicated whenever more heterogeneous systems are introducing into the same environment from time to time. This condition leads to system dependencies with each other and eventually leading towards conflict occurrences among them. In this work, we present a conflict resolution mechanism for heterogeneous devices in home environment using Resolution Mechanism model. The performance of the proposed model verified and justified within the need of smart home context