Neural network prediction for efficient waste management in Malaysia

Maintaining current municipal solid waste management (MSWM) for the next ten years would not be efficient anymore as it has brought many environmental issues such as air pollution. This project has proposed Artificial Neural Network (ANN) based prediction algorithm that can forecast Solid Waste Generation (SWG) based on population growth factor. This study uses Malaysian population as sample size and the data for weight is acquired via authorized Malaysia statistics’ websites. All data will be normalized in the pre-processing stage before proceeding to the prediction using Visual Gene Developer. This project evaluated the performance using R2 value. Two hidden layers with ten and five nodes were used respectively. The result portrayed that there will be an increase of 29.03 percent of SWG in year 2031 compared to 2012. The limitation to this study is that the data was not based on real time as it was restricted by the government

Design of smart waste bin and prediction algorithm for waste management in household area

Maintaining current municipal solid waste management (MSWM) for the next ten years would not be efficient anymore as it has brought many environmental issues such as air pollution. This project has proposed Artificial Neural Network (ANN) based prediction algorithm that can forecast Solid Waste Generation (SWG) based on household size factor. Kulliyyah of Engineering (KOE) in International Islamic University Malaysia (IIUM) has been chosen as the sample size for household size factor. A smart waste bin has been developed that can measure the weight, detect the emptiness level of the waste bin, stores information and have direct communication between waste bin and collector crews. This study uses the information obtained from the smart waste bin for the waste weight while the sample size of KOE has been obtained through KOE’s department. All data will be normalized in the pre-processing stage before proceeding to the prediction using Visual Gene Developer. This project evaluated the performance using R2 value. Two hidden layers with five and ten nodes were used respectively. The result portrayed that the average rate of increment of waste weight is 2.05 percent from week one until week twenty. The limitation to this study is that the amount of smart waste bin should be replicated more so that all data for waste weight is directly collected from the smart waste bin

Study of Emotional Variability Using Photoplethysmogram Signal

This study investigates the feasibility of photoplethysmogram (PPG) signals in recognizing variability in human’s funny, fear and sad emotions. Undoubtedly, Easy Pulse data acquisition device which is used to perceive the PPG signals have superior criterions which are small in size, low power consumption as well as low in cost. Thus, this study will prove the robustness and reliability of PPG signals as an emotion recognition mechanism. A total of ten subjects were chosen randomly which ranged from twenty-one to twenty-four years old. A total of five male and five female students were given three different videos to stimulate different emotions during the given time. Easy Pulse sensor, which has the ability in filtering the unwanted signals has made the study easier. Discriminative features are then extracted from the PPG morphology. PPI, maximum amplitude, as well as the Cardioid pattern of the signals. Finally, four methods of classification have been used to identify the variability in emotions. PPI, maximum amplitude, area and maximum radius of the Cardioid loop were used as the classifiers. These methods have clearly shown great results in differentiating between funny, fear and sad emotions. It was discovered that every human has different rate of sensitivity to fear and sad. Some have the tendency to be very sensitive to fear and some to sad. The experimental results demonstrated that the physiological signals such as PPG have great potentials where the system provides high classification performance

Analysis on smart waste management in Malaysia

Human overpopulation is among the most pressing environmental issues. As the population growth is rapidly increasing, there will be more people who will consume more resources. On the other hand, public waste bins are filling up faster than ever and inevitably, many of the bins end up overflowing before collected, causing not only cluttered streets and bad odors, but also negative health and environmental impacts. The question that everyone has in mind but not knows what the solution is, how to manage the issue of overflowing bins? To cater this problem, a new effective waste disposal management can be proposed. With the title of “Analysis on Smart Waste Management in Malaysia”, the objective of this research is mainly to conduct a survey that shows the current household waste management in Malaysia, to analyze the conducted survey and to propose a subtle solution for the garbage overflowing issues. This research will focus on the implementation of few sensors and microcontroller that act as the internet of things’ (IoT) devices in the dustbin. Of the biggest benefit to develop a smart waste management system is to notify the waste collector of the real-time status of the waste level inside a bin through short message services (SMS), which later will assign the waste collector crews to collect the waste inside the bin. This system will decrease the cost and increase the time efficiency of the services. In addition, this study is vital to provide statistical amount of waste collected in Malaysia within the stipulated time and to prevent overflow of the garbage from the unattended bins. Hence, it can lead to the prevention of environmental pollution

Energy management in IIUM library considering user behaviour

Electricity usage in a commercial building is a crucial issue and any over consumption should be prevented. Energy consumption in academic building is one of the causes that lead to high carbon emission. High carbon emission will cause global warming thus affects the environmental and health of the society. Variety users’ activity and room activities in academic building relatively related to the energy consumption due to the use of electricity. Therefore, this project analyzed users’ behavior and electricity consumption. It is done in order to determine the main factor that lead to high energy usage. Power consumption of identified appliances were analyzed. Through energy analysis, saving of electricity can be done by minimizing the power consumption of electrical appliances and avoiding behavior that lead to wastage of energy. Based on this issue, data regarding all activities in the building has been collected. Questionnaires and online surveys were distributed among students and staffs of the library to gain insight observation on how electricity inside the building is utilized. A system has been designed to simulate the scenario and to give out alert on power consumption to the management team

Study of emotional variability using photoplethysmogram signal

This study investigates the feasibility of photoplethysmogram (PPG) signals in recognizing variability in human’s funny, fear and sad emotions. Undoubtedly, Easy Pulse data acquisition device which is used to perceive the PPG signals have superior criterions which are small in size, low power consumption as well as low in cost. Thus, this study will prove the robustness and reliability of PPG signals as an emotion recognition mechanism. A total of ten subjects were chosen randomly which ranged from twenty-one to twenty-four years old. A total of five male and five female students were given three different videos to stimulate different emotions during the given time. Easy Pulse sensor, which has the ability in filtering the unwanted signals has made the study easier. Discriminative features are then extracted from the PPG morphology. PPI, maximum amplitude, as well as the Cardioid pattern of the signals. Finally, four methods of classification have been used to identify the variability in emotions. PPI, maximum amplitude, area and maximum radius of the Cardioid loop were used as the classifiers. These methods have clearly shown great results in differentiating between funny, fear and sad emotions. It was discovered that every human has different rate of sensitivity to fear and sad. Some have the tendency to be very sensitive to fear and some to sad. The experimental results demonstrated that the physiological signals such as PPG have great potentials where the system provides high classification performance