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Training of Convolutional Neural Network using Transfer Learning for Aedes Aegypti Larvae Mohamad Aqil Mohd Fuad; Mohd Ruddin Ab Ghani; Rozaimi Ghazali; Tarmizi Ahmad Izzuddin; Mohamad Fani Sulaima; Zanariah Jano; Tole Sutikno
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 4: August 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i4.8744

Abstract

The flavivirus epidemiology has reached an alarming rate which haunts the world population including Malaysia. World Health Organization has proposed and practised various methods of vector control through environmental management, chemical and biological orientations. However, from the listed control vectors, the most crucial part to be heeded are non-accessible places like water storage and artificial container. The objective of the study was to acquire and compare various accuracies and cross-entropy errors of the training sets within different learning rates in water storage tank environment which was essential for detection. This experiment performed transfer learning where Inception-V3 was implemented. About 534 images were trained to classify between Aedes Aegypti larvae and float valve within 3 different learning rates. For training accuracy and validation accuracy, learning rates were 0.1; 99.98%, 99.90% and 0.01; 99.91%, 99.77% and 0.001; 99.10%, 99.93%. Cross-entropy errors for training and validation for 0.1 were 0.0021, 0.0184 whereas for 0.01 were 0.0091, 0.0121 and 0.001; 0.0513, 0.0330. Various accuracies and cross-entropy errors of the training sets within the different learning rates were successfully acquired and compared.
A Review on Methods of Identifying and Counting Aedes Aegypti Larvae using Image Segmentation Technique Mohamad Aqil Mohd Fuad; Mohd Ruddin Ab Ghani; Rozaimi Ghazali; Mohamad Fani Sulaima; Mohd Hafiz Jali; Tole Sutikno; Tarmizi Ahmad Izzuddin; Zanariah Jano
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 3: September 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i3.6422

Abstract

Aedes aegypti mosquitoes are a small slender fly insect that spreads the arbovirus from flavivirus vector through its sucking blood. An early detection of this species is very important because once these species turn into adult mosquitoes a population control becomes more complicated. Things become worse when difficult access places like water storage tank becomes one of the breeding favorite places for Aedes aegypti mosquitoes. Therefore, there is a need to help the field operator during the routine inspection for an automated identification and detection of Aedes aegypti larvae, especially at difficult access places. This paper reviews different methodologies that have been used by various researchers in identifying and counting Aedes aegypti. The objective of the review was to analyze the techniques and methods in identifying and counting the Aedes Aegypti larvae of various fields of study from 2008 and above by taking account their performance and accuracy. From the review, thresholding method was the most widely used with high accuracy in image segmentation followed by hidden Markov model, histogram correction and morphology operation region growing.
Smart irrigation system with photovoltaic supply Elia Erwani Hassan; Leong Lek Chung; Mohamad Fani Sulaima; Nazrulazhar Bahaman; Aida Fazliana Abdul Kadir
Bulletin of Electrical Engineering and Informatics Vol 11, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i1.3338

Abstract

Maximizing crop yielding is an extensive problem faced by the population in a country. The main issue comes from the farmer who still implemented the conventional method of irrigation that required human actions, especially for water pump operation. As an alternative, the automatic solution becomes a demand with the internet of things (IoT) support system to overcome the agriculture scenario. Meanwhile, multiple sensors controlled by the ESP32 microcontroller are also used to measure the crucial parameters that influenced the living conditions of crops and are called input parameters. Meanwhile, the implementation of a fuzzy logic controller is to control the timing of water volume based on the inputs data obtained through the sensors' responses. Solar energy is the main supply because of the zero-cost expense and environmentally friendly energy generation. In large, this research developed the smart irrigation system (SIS) with photovoltaic (PV) panels as a supply to sustain the energy required for empowering the entire process. As a result, the SIS is found as a successful system in controlling the best suitable time of water irrigation. The soil evaporation contents obtained from the experiment were also close to actual accurate data reference for Melaka state to verify the solution.
Optimal load management strategy under off-peak tariff riders in UTeM: a case study Mohamad Fani Sulaima; Musthafah Mohd Tahir; Aida Fazliana Abdul Kadir; Mohamad Firdaus Shukri; Mohd Rahimi Yusoff; Ainuddin Abu Kasim; Luqman Ali
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3556

Abstract

Demand response (DR) program through tariff initiative has been established in Malaysia since 1990. The available time of use (TOU) tariff focuses on providing price signals to consumers, especially from industrial and commercial sectors. In achieving a certain standard for off-peak tariff rider (OPTR) initiative to receive discount rate, consumers must improve load factors compared to the baseline declared. However, not all consumers are able to commit. In Universiti Teknikal Malaysia Melaka (UTeM), the TOU (C1-OPTR) tariff is proposed and applied when the available cost discount of 20% can be enjoyed by sustaining the load factor improvement (LFI). A simulator projected a flexible optimal load profile referred by the energy management team to achieve the university's sustainable energy management goal. Thus, securing the LFI would allow the energy consumption (kWh) and peak demand (kW) to be managed concurrently. As for testing results for two buildings, the load factor improves to 0.40, and the maximum demand reduces by about 35 kW. When getting the 20% discount for the OPTR scheme, the total cost saving is forecasted approximately USD 29,441.40 yearly. The current pilot project presents a positive sign with the peak demand reduction and load factor improvement close to the simulator's optimal profile.
A review of electricity consumer behavioural change under sustainable energy management scheme Mohamad Fani Sulaima; Nurul Fasihah Jumidey; Arfah Ahmad; Aida Fazliana Abdul Kadir; Mohamad Firdaus Sukri; Musthafah Mohd Tahir
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3800

Abstract

Many authorities launched their energy sustainability plan that involve the sustainable energy management scheme to improve energy efficiency. The sustainable energy management scheme consists of several measures to encourage energy efficiency in three primary energy consumers by pursuing implementation measures in the industrial, commercial, and residential sectors. Meanwhile, energy performance is quantifiable in energy efficiency and energy consumption become one of scheme measure aspects. In this review, the ASEAN Energy Management Scheme (AEMAS) was discussed as a regionally structured training and certification system for ASEAN Energy Managers. Besides that, Energy Management Gold Standard (EMGS) is AEMAS's first regional achievement certification for global excellence in energy management systems. Previous literatures exposed the key to energy efficiency goals is behavioural change, which means individual attitudes affect energy consumption.
Firefly analytical hierarchy algorithm for optimal allocation and sizing of DG in distribution network Noor Ropidah Bujal; Aida Fazliana Abdul Kadir; Marizan Sulaiman; Sulastri Manap; Mohamad Fani Sulaima
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 13, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v13.i3.pp1419-1429

Abstract

Distributed generation (DG) can be beneficially allocated in distribution power systems to improve the power system's efficiency. However, specious DG's allocation and sizing may cause more power loss and voltage profile issues for distribution feeders. Therefore, optimization algorithms are vital for future intelligent power distribution network planning. Hence, this study proposes a multi-objective firefly analytical hierarchy algorithm (FAHA) for determining the optimal allocation and sizing of DG. The multi-objective function formulation is improved further by integrating analytical hierarchy process (AHP) with FA to obtain the weight of the coefficient factor (CF). The performance of the proposed approach is verified on the 118-bus radial distribution network with different bus voltage at DG location (VDG) as regulated PV-bus during load flow calculations. The calculated CF and impact of the unregulated voltage at the PV-bus on the objectives function have been analysed. The findings show that the proposed techniques could allocate the DG at the most voltage deviation while minimizing the power loss and improving the radial distribution’s voltage stability index (VSI). The experimental results indicate that the approach is able to improve the overall voltage profile, especially at PQ-buses, minimize the power loss while improving the network's stability index simultaneously.
Coronavirus disease 2019; pandemic; Data analysis; Energy demand; Neural network; Self-organizing mapping; Mohamad Fani Sulaima; Sharizad Saharani; Arfah Ahmad; Elia Erwani Hassan; Zul Hasrizal Bohari
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

The world faces a significant impact from the coronavirus disease 2019 (Covid-19) pandemic, which also influences energy consumption. This study investigates the substantial connection of the classified data between power consumption, cooling degree days, average temperature, and covid-19 cases information using mathematical and neural network approaches regression analysis, and self-organizing maps. It is well established that various data mining methods have revamped the classification process of data analytics. Specifically, this study investigates the correlation between the collected variables using regression analysis and selecting the best-matching unit under the normalization method using self-organizing maps. The selforganizing maps become better when the datasets have variations; the result denotes that this method produced high mapping quality based on the map size and normalization method. Furthermore, the data crossing connection is indicated using the regression analysis method. Finally, the classified data results during the movement control order are validated in self-organizing maps to achieve the study objective. By performing these methods, this study established that the correlation between the energy demand towards cooling degree days, average temperature, and covid-19 cases is very weak. The verification has been made where the ‘logistic’ normalization method has produced the best classification result.
Investigation of electricity load shifting under various tariff design using ant colony optimization algorithm Mohamad Fani Sulaima; Nurliyana Binti Baharin; Aida Fazliana Abdul Kadir; Norhafiz Bin Salim obtained; Elia Erwani Hassan
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 1: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i1.pp1-11

Abstract

A price-based program through a time of use tariff (TOU) program is one of the initiatives to offer sufficient benefit for both consumers and generations sides. However, without any strategy for implementing optimal load management, a new tariff design structure will lead to the miss perception by electricity consumers. Therefore, this study offers an investigation toward appropriate TOU tariff design to reflect load profiles. Concurrently, the ant colony optimization (ACO) algorithm was proposed to deal with the load shifting strategy to determine the best load profiles and reducing the consumers’ electricity cost. The sample load profiles data is obtained from various residential houses, such as single-story, double-story, semi-D, apartment, and bungalow houses. The significant comparison between baseline flat tariffs to several TOU tariffs has shown an improvement in the percentage of cost saving for approximately 7 to 40%. Furthermore, the identified load management was observed where the maximum load shifting weightage was set up to 30% to reflect the consumers’ effort towards energy efficiency (EE) program. The previously proposed TOU design was identified to be a suitable structure that can promote balancing of EE and demand response (DR) program effort in most consumers' houses category in Malaysia.