Classification of frontal alpha asymmetry using k-Nearest Neighbor

Frontal alpha asymmetry is used as the EEG feature in this study. Total number of 43 students participated in EEG data collections of relax and non-relax conditions. The spectral power of the alpha band for both left and right brain are extracted using data segmentations and then the Asymmetry Score (AS) is computed. Subtractive clustering is used to predetermine the number of cluster center that are presented in the data. While Fuzzy C-Means (FCM), is used to discriminate the EEG data into an appropriate cluster after the total number of cluster had been determined. The classification rate obtained from the k-Nearest Neighbor (k-NN) classifier is 84.62% which gives the highest classification rate

Advanced signal processing of EEG sub-band frequencies in characterizing psychophysiological calmness / Siti Armiza Mohd Aris

Electroencephalographic signals or EEG signals are very closely related to psychophysiological. At present, many types of diseases caused by the psycho-physiological have risen in the society. Early detection of psycho-physiological related problem such as stress, hypertension, depression and others has now become very important. This is due to the modern society lifestyle which contributes to major psychophysiological problems. The existing practice to patients, who have been detected with psycho-physiological problem, used the assessment methods such as questionnaires and interviews which need expertise to handle the case and resulted in time consuming. Thus, a quick, reliable and simple method to determine the level of psycho-physiological condition is necessary during the treatments which can abridge the time needed to diagnose individuals. This study aim is to demonstrate some mental characteristics related to calmness can be grouped and categorized, hence produced EEG calmness index. To materialize this, asymmetry index was chosen as EEG feature in distinguishing unique characteristics of EEG signals from different brain behaviours named as relaxed state and non-relaxed state. The EEG signals were preprocessed through Fast Fourier Transform and were segmented before the energy spectral density (ESD) was derived. The asymmetry index was calculated using the derived ESD. The different data behaviour between relaxed state and non-relaxed state were verified by means of linear regression method to confirm the data discrepancy. In the EEG calmness categorization subtractive clustering was used to identify the total number of EEG behaviour existed within the data features. The Fuzzy C-Means (FCM) was used to place the data features into the same group and the classification was proven through k-Nearest Neighbour (k-NN). Statistical analysis was also employed to confirm the group selected by the FCM provides significant cluster selectivity. Model of Z-score was used to label the calmness indices and set the Zvalue equal to 2a as the minimum level to become EEG calmness index. From the statistical analysis and Z-score results, three indices can be proposed as calmness indices. This was supported with the k-NN performance measures which confirmed the selection for the three indices with 100% accuracy. Therefore, the number of calmness indices which could be used to represent the EEG calmness is three. The results obtained in this study also suggested that the EEG behaviour during calmness can be categorized. The chosen techniques used in the study and the calmness index have brought a novel finding in the EEG research. It is expected that the established EEG calmness index can be projected for the psycho-physiological diagnosis

COVID-19 Confirmed Cases Forecasting in Malaysia Using Linear Regression and Holt's Winter Algorithm

The 2019 coronavirus disease pandemic (COVID-19) has emerged and is spreading rapidly over the world. Therefore, it may be highly significant to have the general population tested for COVID-19. There has been a rapid surge in the use of machine learning to combat COVID-19 in the past few years, owing to its ability to scale up quickly, its higher processing power, and the fact that it is more trustworthy than people in certain medical tasks. In this study, we compared between two different models: the Holt’s Winter (HW) model and the Linear Regression (LR) model. To obtain the data set of COVID-19, we accessed the website of the Malaysian Ministry of Health. From January 24th, 2020, through July 31st, 2021, daily confirmed instances were documented and saved in Microsoft Excel. Case forecasts for the next 14 days were generated in the Waikato Environment for Knowledge Analysis (WEKA), and the accuracy of the forecasting models was measured by means of the Mean Absolute Percentage Error (MAPE). According to the lowest value of performance indicators, the best model is picked. The results of the comparison demonstrate that Holt's Winter showed better forecasting outcome than the Linear Regression model. The obtained result depicted the forecasted model can be further analyzed for the purpose of COVID-19 preparation and control

COVID-19 Confirmed Cases Forecasting in Malaysia Using Linear Regression and Holt's Winter Algorithm

The 2019 coronavirus disease pandemic (COVID-19) has emerged and is spreading rapidly over the world. Therefore, it may be highly significant to have the general population tested for COVID-19. There has been a rapid surge in the use of machine learning to combat COVID-19 in the past few years, owing to its ability to scale up quickly, its higher processing power, and the fact that it is more trustworthy than people in certain medical tasks. In this study, we compared between two different models: the Holt’s Winter (HW) model and the Linear Regression (LR) model. To obtain the data set of COVID-19, we accessed the website of the Malaysian Ministry of Health. From January 24th, 2020, through July 31st, 2021, daily confirmed instances were documented and saved in Microsoft Excel. Case forecasts for the next 14 days were generated in the Waikato Environment for Knowledge Analysis (WEKA), and the accuracy of the forecasting models was measured by means of the Mean Absolute Percentage Error (MAPE). According to the lowest value of performance indicators, the best model is picked. The results of the comparison demonstrate that Holt's Winter showed better forecasting outcome than the Linear Regression model. The obtained result depicted the forecasted model can be further analyzed for the purpose of COVID-19 preparation and control

Human-computer interaction in mobile learning: a review

Mobile learning mainly concerns mobility and high-quality education, regardless of location or time. Humancomputer interaction comprises the concepts and methods in which humans interact with computers, including designing, implementing, and evaluating computer systems that are accessible and provide an intuitive user interface. Some studies showed that mobile learning could help overcome multiple limitations and improve learning in educational systems. The study investigates the HCI design challenges, including the guidelines and methods in mobile HCI for education. An existing mobile learning tool was discussed on the current and future design enhancements of Udemy. Next is the further discussion on future mobile learning to provide the possible improvements for learners based on the challenges of mobile HCI in education

Statistical feature analysis of EEG signals for calmness index establishment

Electroencephalographic (EEG) signals are very closely related to psychophysiological. The EEG signals displayed few responses which can be categorized. This article discussed the use of statistics over the EEG features which confirm the different mental characteristics. Two different type of stimulus was given named as relaxed state and non-relaxed state. Asymmetry index was computed as the EEG features via the alpha waves and was extracted during the relaxed state and the non-relaxed state. The EEG features were clustered to a group of three, four and five using Fuzzy C-Means. During the relaxed state, the alpha wave showed a higher response as compared to the non-relaxed state. This is observed by using the mean relative energy between the relaxed state and non-relaxed state. To ensure which EEG features in the clusters showed a significant difference, p < .05, a statistical test was used. Wilcoxon Signed Ranks test is the best-statistical test to verify the selected clusters as it is suitable to analyze the small sample of data. Wilcoxon Signed Ranks test used a hypothesis testing which using the same method as paired sample t-test. The advantage in using Wilcoxon Signed Ranks test is that, it uses the median to get the difference between two samples of data. Analytical results showed that the data features of four clusters and three clusters give a significant difference, thus the obtained results can be used to further up the study. The Wilcoxon Signed Ranks test results confirmed that the proposed technique has potential in establishing the calmness index

Denoising of impulse noise using partition-supported median, interpolation and DWT in dental X-ray images

The impulse noise often damages the human dental X-Ray images, leading to improper dental diagnosis. Hence, impulse noise removal in dental images is essential for a better subjective evaluation of human teeth. The existing denoising methods suffer from less restoration performance and less capacity to handle massive noise levels. This method suggests a novel denoising scheme called "Noise removal using Partition supported Median, Interpolation, and Discrete Wavelet Transform (NRPMID)" to address these issues. To effectively reduce the salt and pepper noise up to a range of 98.3 percent noise corruption, this method is applied over the surface of dental X-ray images based on techniques like mean filter, median filter, Bi-linear interpolation, Bi-Cubic interpolation, Lanczos interpolation, and Discrete Wavelet Transform (DWT). In terms of PSNR, IEF, and other metrics, the proposed noise removal algorithm greatly enhances the quality of dental X-ray images

Heat exposure assessment among warship technicians in machinery room

Some warship compartments are undoubtedly considered severe hot environment due to high-temperature values produced by rotating machinery. Besides, it also depended on the external conditions such as weather and design of the warship, which contributes to high-temperature in specific compartments. Such inconvenient situations which related to space, noise, vibration and poor air quality inside the warship compartment further increase high prevalence risk to the associated technicians. Thus, this study was to examine the awareness state among technicians regarding the heat exposure they faced in machinery room during the daily routine and to propose an action plan to increase awareness state among technicians regarding heat exposure in the workplace. The variables that have been chosen in the study were knowledge awareness, personal influences, environmental influences, interpersonal influences and management influences. The statistical analysis technique was applied in the study by using The Pearson-correlation coefficient. Result shows that the environmental impacts and management authorities have a significant positive relationship with awareness state among technicians

Feasibility Study of a Low Cost Saltwater Lamp for Rural Area

Renewable energy is energy generated from natural resources and cannot be depleted. Solar energy is the fastest growing source of renewable energy but the high installation and maintenance cost of a solar system has restrained the consumers from adopting this technology at their home or commercial building. This is especially true for those in developing countries. A new promising renewable energy source known as saltwater energy that takes advantage of the conductive nature of salt water to generate electricity, has intrigued many people. A study has been conducted to develop and produce saltwater-powered devices especially for rural and remote communities in Malaysia as well as worldwide. To main objective of this study is to determine the factors that affect the performance of the saltwater energy generation such as electrode’s combinations, number of cells and the durability of the electrodes. It was found that the choice of electrodes as anode and cathode does affect the voltage output. However, due to the small power produce, the number of cells must be increased to produce enough power to light up a led light and to provide power to USB port. This paper also conducted a cost analysis of using the saltwater lamp and compared it with a solar system. Although the difference in the cost per hour is very small, there are a number of disadvantages of solar system that need to be aware of. The findings obtained from these experiments will be used to design a prototype of the illumination technology for further product development

Hazard control management on optimization layout of vent stack at offshore platform

The flaring is a normal practice in the oil and gas industry to achieve a safe and reliable process during the emergency situation. This situation is a routine practice for oil and gas production by controlled burning of natural gas. The burning process can cause hazards by explosion or at the very least surrounding environment will be affected by heat radiation during vent stack burning operation. Hence, investigation of the gas flaring produced by the vent stack is needed to tackle these problems. This paper presents designing a safe vent stack position in the limited space of oil and gas platform with considered the heat radiation produced by the vent stack. The simulation will be done by using flaresim software to predict the heat contour, heat radiation, and gas dispersion. The results proved that the optimal position of vent stack with water shield gives a better heat radiation