Abdul Hamid Adom
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Abdul Hamid Adom
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Adom, Abdul Hamid
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6506600412

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Medium Optimization for Biobutanol Production From Palm Kernel Cake (PKC) Hydrolysate By Clostridium saccharoperbutylacetonicum N1-4

2024-03-01 , Amin M.A. , Shukor H. , Shoparwe N.F. , Makhtar M.M.Z. , Abdul Hamid Adom , Rongwong W.

The study aims to optimize the medium composition for biobutanol production using a Palm Kernel Cake (PKC) hydrolysate by Clostridium saccharoperbutylacetonicum N1-4. Various nutrient factors affecting biobutanol production were screened using the Plackett-Burman design. These factors included: NH4 NO3, KH2 PO4, K2 HPO4, MgSO4.7H2 O, MnSO4.7H2 O, FeSO4.7H2 O, yeast extract, cysteine, PABA, biotin, and thiamin. The results were analyzed by an analysis of variance (ANOVA), which showed that cysteine (P=0.008), NH4 NO3 (P=0.011) dan yeast extract (P=0.036) had significant effects on biobutanol production. The established model from the ANOVA analysis had a significant value of Pmodel >F = 0.0299 with an F-value of 32.82 which explains that the factors can explain in detail the variation in the data about the average and the interpretation is true with an R2 value of 0.993. The estimated maximum biobutanol production was 10.56 g/L, whereas the optimized medium produced 15.49 g/L of biobutanol. Process optimizations with optimum concentration of cysteine, NH4 NO3, and yeast extract have produced 21.33 g/L biobutanol which is a 37.7% improvement from the non-optimized medium. The findings show that PKC hydrolysate with the addition of optimal concentrations of the three types of medium namely, cysteine (0.15 g/L), NH4 NO3 (0.50 g/L), and yeast extract (1.5 g/L) during ABE fermentation, yielded a maximum biobutanol concentration of 21.33 g/L. Therefore, the results of this study provide good indications for promoting PKC hydrolysate as a new source of novel substrates with great potential in producing high biobutanol through ABE fermentation by C. saccharoperbutylacetonicum N1-4.

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A Review on the Stingless Beehive Conditions and Parameters Monitoring using IoT and Machine Learning

2021-12-01 , Ali M.A.A.C. , Bukhari Ilias , Norasmadi Abdul Rahim , Shazmin Aniza Abdul Shukor , Abdul Hamid Adom , Saad M.A.H.

One of the stingless bee types named Heterotrigona Itama are widespread in the tropics and subtropics especially in Malaysia. Due to its excellent nutritional content, stingless bee honey has gained favour in recent years. According to some studies, stingless bee honey has been used to cure eye infections, open wounds, diabetes, hypertension, and a variety of other diseases. Additionally, this stingless bee is non-venomous and smaller in size than common bees. Nevertheless, beekeepers may encounter a number of obstacles that may result in colony failure and under-production. These problems can be attributed to a variety of factors such as surrounding temperature, surrounding humidity and predators. Numerous stingless bee colonies and other bee species lost in 2006 due to Colony Collapse Disorder as a result of this problem. Therefore, this article will review previous research on optimizing stingless beehive conditions via the use of the Internet of Things (IoT) and machine learning to minimise this issue. To begin, a review of existing research on the characteristics of stingless bees, particularly the Heterotrigona Itama species, has been conducted to understand the natural habitat of Heterotrigona Itama. Following that, the articles on colony division was reviewed in order to transition the colony from the conventional hive to the artificial hive which also reviewed its design from the past article to simplify the sensors installation, IoT monitoring system and honey harvesting. Then, the prior article on sensors and IoT deployment was examined to monitor and analysis the data online without disturbing the colony activity inside the beehives. Finally, the article on the application of machine learning with the beehive dataset was reviewed the most precise and accurate machine learning method to predict the existence of bee activity in the hives and the future condition of beehive.

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Correction Model for Metal Oxide Sensor Drift Caused by Ambient Temperature and Humidity

2022-05-01 , Abdulnasser Nabil Abdullah , Kamarulzaman Kamarudin , Latifah Munirah Kamarudin , Abdul Hamid Adom , Syed Muhammad Mamduh Syed Zakaria , Zaffry Hadi Mohd Juffry , Bennetts V.H.

For decades, Metal oxide (MOX) gas sensors have been commercially available and used in various applications such as the Smart City, gas monitoring, and safety due to advantages such as high sensitivity, a high detection range, fast reaction time, and cost-effectiveness. However, several factors affect the sensing ability of MOX gas sensors. This article presents the results of a study on the cross-sensitivity of MOX gas sensors toward ambient temperature and humidity. A gas sensor array consisting of temperature and humidity sensors and four different MOX gas sensors (MiCS-5524, GM-402B, GM-502B, and MiCS-6814) was developed. The sensors were subjected to various relative gas concentrations, temperatures (from 16◦C to 30◦C), and humidity levels (from 75% to 45%), representing a typical indoor environment. The results proved that the gas sensor responses were significantly affected by the temperature and humidity. The increased temperature and humidity levels led to a decreased response for all sensors, except for MiCS-6814, which showed the opposite response. Hence, this work proposed regression models for each sensor, which can correct the gas sensor response drift caused by the ambient temperature and humidity variations. The models were validated, and the standard deviations of the corrected sensor response were found to be 1.66 kΩ, 13.17 kΩ, 29.67 kΩ, and 0.12 kΩ, respectively. These values are much smaller compared to the raw sensor response (i.e., 18.22, 24.33 kΩ, 95.18 kΩ, and 2.99 kΩ), indicating that the model provided a more stable output and minimised the drift. Overall, the results also proved that the models can be used for MOX gas sensors employed in the training process, as well as for other sets of gas sensors.

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Development of Artificial Stingless Bee Hive Monitoring using IoT System on Developing Colony

2024-01-01 , Ali M.A.A.C. , Bukhari Ilias , Norasmadi Abdul Rahim , Shazmin Aniza Abdul Shukor , Abdul Hamid Adom , Saad M.A.H. , Hassan M.F.A.

The trend of stingless bees’ farm in Malaysia has increased recently as it has been proven that its honey gives various benefits to human beings. This trend requires beekeepers to do more frequent inspections of beehives. However, the current practice of opening the cover to inspect the colony and honey will disrupt colony activity. According to a recent study, these stingless bees can only survive between 22 and 38 degrees Celsius, and harsh weather conditions might lead to the collapse of bee colonies. In order to ensure a consistent honey production, the IoT monitoring system will be implemented on an artificial stingless beehive. The system is equipped with an embedded system that utilizes a NodeMCU ESP8266, temperature and humidity sensors, and load cell sensors. Next, honey compartment weight, temperature and humidity inside stingless beehive, and temperature and humidity outside stingless beehive will be uploaded to the Internet of Things (IoT) platforms, namely Thingspeak and Cayenne. The data is sent to Thingspeak via the REST API while to Cayenne by the MQTT API. All data from the artificial stingless bee hive indicating the occurrence of colony rising and has been uploaded to the IoT platform. By analysing the data that were recorded for 13 days, all of the input data such as the weight of the honey compartment, the temperature in the hive, and the humidity in the hive, display its respective characteristics. For the honey compart weight, it has been found that the stingless bee colony is rising as a result of the increasing honey and colony in the compartment weight. Regarding the hive temperature, it has been determined that the temperature inside the hive is stable around 26°C to 38°C in normal weather conditions. Whereas for humidity inside the hive, it is remained between 76.5% and 85.6% due to the moisture from the honey inside the compartment. Lastly, these results indicate that the colony living in the artificial hive of stingless bees is healthy and growing.

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An emotion assessment of stroke patients by using bispectrum features of EEG signals

2020-10-01 , Yean, Choong Wen , Wan Khairunizam Wan Ahmad , Wan Azani Wan Mustafa , Murugappan M. , Rajamanickam Y. , Abdul Hamid Adom , Omar, Mohammad Iqbal , Zheng, Bong Siao , Ahmad Kadri Junoh , Zuradzman Mohamad Razlan , Shahriman Abu Bakar

Emotion assessment in stroke patients gives meaningful information to physiotherapists to identify the appropriate method for treatment. This study was aimed to classify the emotions of stroke patients by applying bispectrum features in electroencephalogram (EEG) signals. EEG signals from three groups of subjects, namely stroke patients with left brain damage (LBD), right brain damage (RBD), and normal control (NC), were analyzed for six different emotional states. The estimated bispectrum mapped in the contour plots show the different appearance of nonlinearity in the EEG signals for different emotional states. Bispectrum features were extracted from the alpha (8–13) Hz, beta (13–30) Hz and gamma (30–49) Hz bands, respectively. The k-nearest neighbor (KNN) and probabilistic neural network (PNN) classifiers were used to classify the six emotions in LBD, RBD and NC. The bispectrum features showed statistical significance for all three groups. The beta frequency band was the best performing EEG frequency-sub band for emotion classification. The combination of alpha to gamma bands provides the highest classification accuracy in both KNN and PNN classifiers. Sadness emotion records the highest classification, which was 65.37% in LBD, 71.48% in RBD and 75.56% in NC groups.

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Investigation of Different Classifiers for Stress Level Classification using PCA-Based Machine Learning Method

2023-01-01 , Mazlan M.R.B. , Abdul Syafiq Abdull Sukor , Abdul Hamid Adom , Jamaluddin R.B. , Saidatul Ardeenawatie Awang

Undergraduate students experience several changes and face various problems during their time transitioning from adolescence to adulthood. One of the issues during this time is a mental stress disorder. Stress burdens the students either through mental or physical capabilities. The common method of determining stress includes physical examination and clinical diagnosis. However, the method is subjective and time-consuming as doctors need to make sure that their diagnosis is accurate. Thus, the severity of the stress stages could not be easily determined. A new method using machine learning-based algorithms coupled with EEG devices promises to overcome the issues with the current approaches. This paper presents an investigation using machine learning techniques based on Principal Component Analysis (PCA) which allows for the reduction in the dimensionality of datasets to enhance their interpretability while minimizing information loss. The pre-processed EEG data and PCA-based EEG data were compared and analyzed using three machine learning classifiers such as K-Nearest Network (KNN), Naive Bayes (NB) and Multilayer Perceptron (MLP). The results indicate that KNN demonstrated the highest average classification accuracy of 99%, while the other approaches mentioned above averaged around 50% and 80% for NB and MLP respectively. This investigation shows that the KNN classifier is most suitable for the proposed approach.

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Application of Deep Neural Network for Gas Source Localization in an Indoor Environment

2023-01-01 , Juffry Z.H.M. , Kamarulzaman Kamarudin , Abdul Hamid Adom , Miskon M.F. , Latifah Munirah Kamarudin , Ammar Zakaria , Syed Muhammad Mamduh Syed Zakaria , Abdullah A.N.

Nowadays, the quality of air in the environment has been impacted by the industry. It is important to make sure our ambient air especially in an indoor environment is clean from contaminating particles or harmful gases. Therefore, the air quality inside the indoor environment should be monitored regularly. One of the major problems, when a particular environment has been contaminated by harmful gases, is finding the source of the emission. If the indoor environment has been contaminated by a harmful source it should be instantly localized and eliminated to prevent severe casualties. In this paper, we propose the utilization of synthetic data generated by the Computational Fluid Dynamic (CFD) approach to train the Deep Neural Network (DNN) model called CFD-DNN to perform gas source localization in an indoor environment. The model is capable to locate the contaminated source within a small area of an indoor environment. A total of 361 datasets with different locations of contaminated source release have been obtained using the CFD approach. The obtained dataset was divided into training and testing datasets. The training dataset was used for the model training process while the testing dataset is fed into the model to test model reliability to predict the gas source location. The Euclidian distance equation was used to measure the distance error between the actual and predicted location of the source. The result shows that the model is capable to locate the gas source within a minimum and maximum error of 0.03m to 0.46m respectively.

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