2076 hits

Aggregate production planning (APP) is a significant level that seeks efficient production systems. In actual condition, APP decisions, production inputs, and relevant planning parameters are intrinsically imprecise, which results in significant complexities in the generation of master production schedules. Thus, this paper proposes a hybridization of a fuzzy programming, simulated annealing (SA), and simplex downhill (SD) algorithm called fuzzy– SASD to establish multiple-objective linear programming models and consequently resolve APP problems in a fuzzy environment. The proposed strategy is dependent on Zimmerman’s approach for handling all inexact operating costs, data capacities, and demand variables. The SD algorithm is employed to balance exploitation and exploration in SA, thereby efficient and effective (speed and quality) solution for the APP model. The proposed approach produces rates for efficient solutions of APP in large-scale problems that are 33, 83, and 89% more ef.....

1241 hits

This paper presents a new approach based on multi-dimensional evaluation and benchmarking for data hiding techniques, i.e., watermarking and steganography. The novelty claim is the use of evaluation matrix (EM) for performance evaluation of data hiding techniques; however, one major problem with performance evaluation of data hiding techniques is to ¯nd reasonable thresholds for performance metrics and the trade-o® among them in di®erent data hiding applications. Two experiments are conducted. The ¯rst experiment included LSB techniques (eight approaches) based on di®erent payload results and the noise gate approach; a total of nine approaches were used. Five audio samples with di®erent audio styles are tested using each of the nine approaches and considering three evaluation criteria, namely, complexity, payload, and quality, to generate watermarked samples. The second experiment involves the use of various decision-making techniques simple additive weighting (SAW), multiplicati.....

1238 hits

Evaluating and benchmarking software and hardware ¯eld programmable gate array (FPGA)-based digital watermarking are considered challenging tasks because of multiple and con°icting evaluation criteria. A few evaluation and benchmarking techniques/frameworks have been implemented to digital watermarking or steganography; however, these approaches still present certain limitations. In particular, ¯xing some attributes on account of other attributes and wellknown benchmarking approaches are limited to robust watermarking techniques. Thus, this study aims toward a new methodology for evaluation and benchmarking using multi-criteria analysis for software and hardware \FPGA"-based digital watermarking or steganography. To achieve this objective, two iterations are conducted. The ¯rst iteration consists of two stages: discussing software and hardware \FPGA"-based digital watermarking or steganography to create a dataset with various samples for benchmarking and discussing the evaluation m.....

900 hits

Innovative technology on intelligent processes for smart home applications that utilize Internet of Things (IoT) is mainly limited and dispersed. The available trends and gaps wereinvestigated in this study to provide valued visions for technical environments and researchers. Thus, a survey was conducted to create a coherent taxonomy on the research landscape. An extensive search was conducted for articles on (a) smart homes, (b) IoT and (c) applications. Three databases, namely, IEEE Explore, ScienceDirect and Web of Science, were used inthe article search. These databases comprised comprehensive literature that concentrate on IoT-based smart home applications. Subsequently, filtering process was achieved on the basis of intelligent processes. The final classification scheme outcome of the dataset contained 40 articles that were classified into four classes. The first class includes the knowledge engineering process that examines data representation to identify the means of accomplish.....

1551 hits

According to the Malaysian Institute of Road Safety Research (MIROS), over 500,000 car accidents occurred in 2016, making cars an unsafe means of transportation. This research aimed to collect driver behaviour-related data for Malaysian drivers to provide useful insights for Malaysian driving profile and to modulate machine learning for classification tasks. Twenty-one drivers (11 male and 10 female) were studied and compared for their driving style in Lebuhraya Behrang Stesen-tg malim (11 km per driver). Drivers were asked to drive naturally while considering their safety. Two analysis techniques were utilized (i.e. Statistical and Machine Learning-Based). Different conclusions were drawn from each analysis. The number of driving events for each driver was calculated (i.e. aggressive, normal and safe) and statistical tests (i.e. Mean, Standard Deviation, Correlation analysis, Oneway ANOVA and T-test) presented significant differences between each driver from the same gende.....

1625 hits

Road accidents are increasing every year in Malaysia, and it is always challenging to collect reliable pre-crash data in the transportation community. Existing studies relied on simulators, police crash reports, questionnaires, and surveys to study Malaysia’s drivers’ behavior. Researchers previously criticized such methods for being biased and unreliable. To fill in the literature gap, this study presents the first naturalistic driving study in Malaysia. Thirty drivers were recruited to drive an instrumented vehicle for 750 km while collecting continuous driving data. The data acquisition system consists of various sensors such as OBDII, lidar, ultrasonic sensors, IMU, and GPS. Irrelevant data were filtered, and experts helped identify safety criteria regarding multiple driving metrics such as maximum acceptable speed limits, safe accelerations, safe decelerations, acceptable distances to vehicles ahead, and safe steering behavior. These thresholds were used to investigate the inf.....

1827 hits

This work proposes a pattern recognition model for static gestures in Malaysian Sign Language (MSL) based on Machine Learning (ML) techniques. The proposed model is divided into two phases, namely, data acquisition and data processing. The first phase involves capturing the required sign data, such as the shape and orientation of the hand, to construct a sensor-based SL dataset. The dataset is collected using a DataGlove device. This device is used to measure the motions of the fingers and wrists. Sixty-four features represent each sign in the dataset. The collected sensory dataset is cleaned in the second phase by removing redundant data. Then, the features are scaled and normalised to exhibit symmetrical behaviour and eliminate outliers. Then, ten different ML techniques are utilised based on real-time data for SL gesture recognition. Experimental results confirmed the efficacy of the proposed pattern recognition model compared with previous work. ? 2021..

1613 hits

This study proposes a new real-time sign recognition system based on a wearable sensory glove, which has 17 sensors with 65 channels. We introduce the DataGlove to recognize various, possibly complex hand gestures of the Malaysian Sign Language (MSL). With 65 data channels, DataGlove can satisfy the requirement suggested by the analysis of hand anatomy, kinematic and gestures. Four groups of sensors were tested to select the optimal sensors that can capture hand gesture information. Also, a 3D-printed humanoid arm is used to validate the sensor mounted on the glove. In an extensive set of experiments to test our system, five well-known MSL participants were chosen to perform 75 gestures, all taken from the MSL numbers, alphabet, and words. The error rate was used to check system performance. Discussion confirms that the proposed system competes well with an advanced benchmark of previous works is up 100% based on 14 criteria in term of the type of captured signals, recognised gestures,.....

850 hits

As coronavirus disease 2019 (COVID-19) spreads across the world, the transfusion of efficient convalescent plasma (CP) to the most critical patients can be the primary approach to preventing the virus spread and treating the disease, and this strategy is considered as an intelligent computing concern. In providing an automated intelligent computing solution to select the appropriate CP for the most critical patients with COVID-19, two challenges aspects are bound to be faced: (1) distributed hospital management aspects (including scalability and management issues for prioritising COVID-19 patients and donors simultaneously), and (2) technical aspects (including the lack of COVID-19 dataset availability of patients and donors and an accurate matching process amongst them considering all blood types). Based on previous reports, no study has provided a solution for CP-transfusion-rescue intelligent framework during this pandemic that has addressed said challenges and issues. This study ai.....

484 hits