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Rashedul Islam

Bio: Rashedul Islam is an academic researcher from University of Aizu. The author has contributed to research in topics: Feature extraction & Support vector machine. The author has an hindex of 8, co-authored 32 publications receiving 265 citations. Previous affiliations of Rashedul Islam include College of Business Administration & University of Ulsan.

Papers
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Journal ArticleDOI
06 Jan 2020-Sensors
TL;DR: This research has proposed a hybrid method feature selection process, which includes a filter and wrapper method, that works efficiently with limited hardware resource and provides satisfactory activity identification.
Abstract: Human activity recognition (HAR) techniques are playing a significant role in monitoring the daily activities of human life such as elderly care, investigation activities, healthcare, sports, and smart homes. Smartphones incorporated with varieties of motion sensors like accelerometers and gyroscopes are widely used inertial sensors that can identify different physical conditions of human. In recent research, many works have been done regarding human activity recognition. Sensor data of smartphone produces high dimensional feature vectors for identifying human activities. However, all the vectors are not contributing equally for identification process. Including all feature vectors create a phenomenon known as ‘curse of dimensionality’. This research has proposed a hybrid method feature selection process, which includes a filter and wrapper method. The process uses a sequential floating forward search (SFFS) to extract desired features for better activity recognition. Features are then fed to a multiclass support vector machine (SVM) to create nonlinear classifiers by adopting the kernel trick for training and testing purpose. We validated our model with a benchmark dataset. Our proposed system works efficiently with limited hardware resource and provides satisfactory activity identification.

122 citations

Journal ArticleDOI
TL;DR: The proposed hybrid feature selection model with a novel discriminant feature distribution analysis-based feature evaluation method outperforms the existing methods with regard to classification accuracy and is compared with those of existing state of theart average distance-based approaches.
Abstract: Optimal feature distribution and feature selection are of paramount importance for reliable fault diagnosis in induction motors. This paper proposes a hybrid feature selection model with a novel discriminant feature distribution analysis-based feature evaluation method. The hybrid feature selection employs a genetic algorithm- (GA-) based filter analysis to select optimal features and a -NN average classification accuracy-based wrapper analysis approach that selects the most optimal features. The proposed feature selection model is applied through an offline process, where a high-dimensional hybrid feature vector is extracted from acquired acoustic emission (AE) signals, which represents a discriminative fault signature. The feature selection determines the optimal features for different types and sizes of single and combined bearing faults under different speed conditions. The effectiveness of the proposed feature selection scheme is verified through an online process that diagnoses faults in an unknown AE fault signal by extracting only the selected features and using the -NN classification algorithm to classify the fault condition manifested in the unknown signal. The classification performance of the proposed approach is compared with those of existing state-of-the-art average distance-based approaches. Our experimental results indicate that the proposed approach outperforms the existing methods with regard to classification accuracy.

55 citations

Journal ArticleDOI
TL;DR: A video-based approach for alarm systems that detects smoke based on temporal features extracted from optical smoke flow pattern analysis and spatial-temporal energy analysis, which considers various optical characteristics such as the diffusion, color, and semi-transparency of smoke.

41 citations

Proceedings ArticleDOI
01 Sep 2018
TL;DR: In this proposed model, Deep Convolutional Neural Network (DCNN) is used for extracting efficient hand features to recognize the American Sign Language (ASL) using hand gestures and the Multi-class Support Vector Machine (MCSVM) is use for identifying the hand sign.
Abstract: In this era, Human-Computer Interaction (HCI) is a fascinating field about the interaction between humans and computers. Interacting with computers, human Hand Gesture Recognition (HGR) is the most significant way and the major part of HCI. Extracting features and detecting hand gesture from inputted color videos is more challenging because of the huge variation in the hands. For resolving this issue, this paper introduces an effective HGR system for low-cost color video using webcam. In this proposed model, Deep Convolutional Neural Network (DCNN) is used for extracting efficient hand features to recognize the American Sign Language (ASL) using hand gestures. Finally, the Multi-class Support Vector Machine (MCSVM) is used for identifying the hand sign, where CNN extracted features are used to train up the machine. Distinct person hand gesture is used for validation in this paper. The proposed model shows satisfactory performance in terms of classification accuracy, i.e., 94.57%

35 citations

Journal ArticleDOI
TL;DR: A hybrid segmentation technique including YCbCr and SkinMask segmentation is developed to identify the hand and extract the feature using the feature fusion of the convolutional neural network (CNN).
Abstract: Hand gesture-based sign language recognition is a prosperous application of human– computer interaction (HCI), where the deaf community, hard of hearing, and deaf family members communicate with the help of a computer device. To help the deaf community, this paper presents a non-touch sign word recognition system that translates the gesture of a sign word into text. However, the uncontrolled environment, perspective light diversity, and partial occlusion may greatly affect the reliability of hand gesture recognition. From this point of view, a hybrid segmentation technique including YCbCr and SkinMask segmentation is developed to identify the hand and extract the feature using the feature fusion of the convolutional neural network (CNN). YCbCr performs image conversion, binarization, erosion, and eventually filling the hole to obtain the segmented images. SkinMask images are obtained by matching the color of the hand. Finally, a multiclass SVM classifier is used to classify the hand gestures of a sign word. As a result, the sign of twenty common words is evaluated in real time, and the test results confirm that this system can not only obtain better-segmented images but also has a higher recognition rate than the conventional ones.

29 citations


Cited by
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Journal ArticleDOI
TL;DR: This survey presents various ML-based algorithms for WSNs with their advantages, drawbacks, and parameters effecting the network lifetime, covering the period from 2014–March 2018.

434 citations

Journal ArticleDOI
TL;DR: In this article, an early fault diagnostic technique based on acoustic signals was used for a single-phase induction motor, which can be also used for other types of rotating electric motors.

286 citations

Journal ArticleDOI
TL;DR: A state-of-art of lightweight cryptographic primitives which include lightweight block cipher, hash function, stream ciphers, high performance system, and low resources device for IoT environment are discussed in details.
Abstract: There are many emerging areas in which highly constrained devices are interconnected and communicated to accomplish some tasks Nowadays, Internet of Things (IoT) enables many low resources and constrained devices to communicate, compute process and make decision in the communication network In the heterogeneous environments for IoT, there are many challenges and issues like power consumption of devices, limited battery, memory space, performance cost, and security in the Information Communication Technology (ICT) network In this paper, we discuss a state-of-art of lightweight cryptographic primitives which include lightweight block ciphers, hash function, stream ciphers, high performance system, and low resources device for IoT environment in details We analyze many lightweight cryptographic algorithms based on their key size, block size, number of rounds, and structures In addition, we discuss the security architecture in IoT for constrained device environment, and focus on research challenges, issues and solutions Finally, a proposed security scheme with a service scenario for an improvement of resource constrained IoT environment and open issues are discussed

252 citations

Journal ArticleDOI
TL;DR: The proposed methods had good results for diagnosis of bearing, stator and rotor faults of the single-phase induction motor and can find applications for fault diagnosis of other types of rotating machines.

247 citations