Rohani S Mohamed Farook
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Rohani S Mohamed Farook
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Rohani, S Mohamed Farook
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Farook, Rohani S.Mohamed
Mohamed Farook, Rohani S.
Farook, Rohani S.M.
Farook, Rohani
Farook, R. S.M.
Farook, Rohani S.
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26428761700

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Cross-layer optimization for VANET city scenario using Taguchi mechanism

2022-02-14 , Aznor Hanah Abdul Halim , Rohani S Mohamed Farook , Mohamad Rizal Abdul Rejab , Mohd Fairuz Muhamad Fadzil , Elias S.J. , Dawam S.R.M.

Because of the dynamic vehicle density, vehicle speed, and unexpected and harsh communication environment, the Vehicular Ad Hoc Network (VANET) differs from conventional ad hoc networks in terms of functioning. VANET has two primary applications: safety and non-safety. Network optimization is one method of preserving current protocols and other network characteristics rather than inventing and implementing new enhanced protocols, which is prohibitively expensive. As a result, this study proposes an optimization city scenario of VANET for maximizing throughput and packet delivery ratio (PDR) while reducing delay using the Taguchi method. For three target performances evaluated, packet size has the highest rank control factors for two conditions. Packet size plays the major control factor in contributing to lower delay and maximizing throughput. Therefore, packet size is a major control factor and is applicable for optimizing both safety and non-safety applications focusing on the city scenarios of VANET.

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The Stability of The Combat Technique in Seni Silat Cekak Malaysia

2024-06-07 , Aznor Hanah Abdul Halim , Rohani S Mohamed Farook , Mohamad Rizal Abdul Rejab , Mohd Fairuz M. Fadzil , Mohammad Shahril Salim

This paper presents the stability of the performer of Seni Silat Cekak Malaysia (SSCM) when performing the combat technique of SSCM buah jatuh, especially the Buah Kilas Hadapan (BKH). Comparisons are made when SSCM practitioners perform the movement with and without a sparring partner. The analysis of the study refers to the Center of Gravity (COG-Centre of Gravity) on the sole of the left foot that supports the SSCM performer's body when performing the movement of BKH. This study was conducted using Motion Capture System. The Oqus (camera) is placed around the platform which is used to record the movement of the human body through markers attached to the body. The data obtained is stored and processed through Qualisys Track Manager (QTM) and Visual 3D software. Data on the movement of BKH was recorded on two SSCM respondents of different genders. The Center of Gravity Point on the left foot of the SSCM practitioner was compared and analyzed. Axis changes on the left foot of SSCM respondents are mainly focused on the medial and lateral axis (left and right) when performing the BKH. The results of this study show a minimal coordinate change on the axis (0.02m to 0.06m) from the medial and lateral angles of the Center of Gravity on the left leg of the SSCM respondents proving that the BKH is stable.

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Analusis of Harumanis mango flowering prediction through biotic and abiotic factors using machine learning

2018 , Rohani S Mohamed Farook

Harumanis Mango (Mangifera indica) is known as one of the best table tropical fruit, due to its aroma and sweetness. Harumanis mango cultivar is included in the national agenda as a specialty fruit from Perlis, Malaysia for the world. Despite its overwhelming local demand in Malaysia and also internationally, the fruit supply never meets the demand. Mango flowering prediction is important as one of the factors to predict mango yield in order to implement effective forward marketing. Forward marketing is a contract that is signed between supplier and client based on the amount of delivery and the price of delivery in future, based on the predicted yield. Harumanis mango is a species that only bear fruit once a year. The biotic and environmental factors are reported in the literature as the factors that influence the mango trees flowering and fruit-bearing. The pre-processing and analysis done shows that the biotic and abiotic factors have non-linear relation with the yield. It is essential to develop, train and test the Harumanis mango tree flowering prediction model through machine learning approaches such as K-Nearest Neighbors (k-NN), Naive Bayes, Support Vector Machine (SVM), Classification Trees (CAT) and Random Forests (RF). Harumanis flowering predictive model on biotic and abiotic factors developed, trained and tested through the data accumulated from Harumanis trees in the greenhouse. The biotic factors are lysimeter, Length of First Whorl (LFW), Length of Second Whorl (LSW), Length of Third Whorl (LTW) and Diameter of the Whorl (DW). The Harumanis flowering prediction on biotic factors indicates that SVM technique prediction accuracy is at 79.9% as compared to k-NN, Naïve Bayes, CAT and RF at 66.5%, 74%. 71.3% and 72%, respectively. The SVM predictive model further tested on several kernels which are linear, polynomial, radial basis and sigmoid. The radial basis kernel accuracy is at 79.9% compared to linear, polynomial, and sigmoid at 65%, 65.7% and 59.8% respectively. The environmental data from Perlis Meteorology Department and the yield from Bukit Bintang Orchard were analyzed to identify the significant abiotic factors in predicting the Harumanis mango yield. Later, the abiotic factors which are average minimum temperature and average soil moisture of the 10 days from Harumanis greenhouse are calculated and utilized in developing the Harumanis flowering predictive model. The Harumanis mango tree flowering prediction on abiotic factors shows that SVM technique is at 90.6% accuracy rate compared to k-NN, Naive Bayes, CAT and RF at 76.6%, 75%, 82.1% and 72% respectively. Concluded that the prediction model using SVM on radial basis kernel through biotic and abiotic factors displays the highest prediction accuracy at 79.9 % and 90.6% accordingly. The SVM with radial basis kernel model able to perform flowering prediction although using a limited data due to the nature of the agricultural domain where the data collection and observation require a longer period of time.

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Design and development of stingless beehive air pollutant monitoring system

2021-12 , Mohammad Fadzli Ramli , Azizi Harun , Rizalafande Che Ismail , Ndzi, David Lorater , Turner, Jamie Siregar Cynthia , Rohani S Mohamed Farook , Mohd Nazrin Md Isa , Sohiful Anuar Zainol Murad

Currently, the presence and levels of air pollution in most stingless bee farms are not measured periodically nor there are proper monitoring levels that can be attributed to hive productivity. Long-term measurement and monitoring of air pollution for hive productivity and honey yield have not been performed. Therefore, this research proposes long-term and real-time air pollution measurement and monitoring techniques that are important for correlation analysis and parameter modeling. By using air pollution detection sensors through a wireless sensor network topology in stingless bee farms, details of pollutant presence and levels will be available in real-time over a long period of time for hive productivity correlation analysis. With such deployment using IoT -based systems, data can be easily accessed from anywhere thus ensuring data continuity. This paper describes a preliminary study on the design, development, and testing of real-time air pollution measurement and monitoring systems capable of determining the health status of stingless bee nests in mixed livestock crop farms. The goal of this research is to help beekeepers/users maintain control and be able to take quick action on the hive from the information obtained.

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Development of automated hybrid intelligent system for herbs plant classification and early herbs plant disease detection

2020-08-01 , Muhamad Sani Mustafa , Zulkifli Husin , Tan Wei Keong , Mavi Muhamad Farid , Rohani S Mohamed Farook

Plants such as herbs are widely used in the medical and cosmetic industry. Recognizing a species and detecting an early disease of a plant are quite challenging and difficult to implement as an automated device. The manual identification process is a lengthy process and requires a prior understanding about the plant itself, such as shape, odour, and texture. Thus, this research aimed to realize the computerized method to recognize the species and detect early disease of the herbs by referring to these characteristics. This research has been developed a system for recognizing the species and detecting the early disease of the herbs using computer vision and electronic nose, which focus on odour, shape, colour and texture extraction of herb leaves, together with a hybrid intelligent system that are involved fuzzy inference system, naïve Bayes (NB), probabilistic neural network (PNN) and support vector machine (SVM) classifier. These techniques were used to perform a convenient and effective herb species recognition and early disease detection on ten different herb species samples. The species recognition accuracy rate among ten different species using computer vision and electronic nose is archived 97% and 96%, respectively, in SVM, 98% and 98%, respectively, in PNN and both 94% in NB. In the early disease detection, the detection rate among ten different herb’s species using computer vision and electronic nose are 98% and 97%, respectively, in SVM, both 98% in PNN, 95% and 94%, respectively, in NB. Integrated three machine learning approaches have successfully achieved almost 99% for recognition and detection rate.

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Real-Time Flood Monitoring System Using Raspberry PI

2020-12-18 , Fairuz M.F.M. , Mohamad Rizal Abdul Rejab , Rohani S Mohamed Farook , Aznor Hanah Abdul Halim , Najihah Abdul Rashid , Rifaat M.N.M.

Flood has been a major concern for a very long time and the inability to monitor it in real-time has been a major disadvantage in maintaining a healthy hydrologic process. The main problem in monitoring flood is the amount of time taken for data to reach users and how long the data is relevant for as in monitoring flood, timing is the crucial key. This research proposes a Real-Time Flood Monitoring System that can aid in monitoring flood more efficiently. The system utilizes a set of sensors connected to a single-board computer that determines values in which is vital in monitoring flood. To ensure a fast transmission of data, the values are transferred over Wide Area Network (WAN) to host these values on a remote server. The remote server hosts these data on a website and application which is made accessible for the public with an ease of access. As a result, it can be viewed by users who wish to know the necessary values in determining danger level and further actions can be taken in ensuring their safety. Data which is transferred on real-time allow less time to be taken in order for the news to spread around as time is very crucial in saving people from natural disasters. These data also have a great importance for safety enforcement to be used in determining safety precautions that can be taken in order to ensure the safety of people around a particular area.

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Wireless sensor network coverage measurement and planning in mixed crop farming

2014-04-17 , David L. Ndzi , Azizi Harun , Fitri M. Ramli , Latifah Munirah Kamarudin , Ammar Zakaria , Ali Yeon Md Shakaff , Mahmad N. Jaafar , Shikun Zhou , Rohani S Mohamed Farook

Wireless sensor network technology holds great promise for application in a wide range of areas, both to monitor and control a variety of systems. Whilst the use of sensors has found natural applications within the manufacturing sector, application in agriculture is still in its infancy and has been used largely to only monitor the environment. The use of technology in the agricultural sector to improve crop yield, quality and to foster sustainable agriculture can be regarded as one of the areas that will provide food security to the expanding global population and to mitigate food shortage precipitated by unpredictable weather patterns. This paper presents a Wireless Sensor Network coverage measurements in a mixed crop farming, modeling and deployment architecture taking into account the different signal propagation scenarios and attenuation factor of different crops. Most importantly, the paper presents wireless sensor network deployment architecture for a mixed crop trial field over an area of 54,432m2 , which is 4% of the total area to be covered by the final network.

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Motion detection system for recognition of early sign of depression

2022-02-14 , Aliza Ya'Kob N.A. , Rohani S Mohamed Farook , Aznor Hanah Abdul Halim , Mohd Fairuz Muhamad Fadzil , Mohamad Rizal Abdul Rejab , Elias S.J.

Depression is one of the mental health disorders that affect many humans, especially campus students. In certain cases, people with depression-prone to commit suicide without any warning signs and symptoms observed by family and friends. There is a need to be able to identify and proceed for treatment from the professionals as soon as possible. There is a lack of tools to identify students' depression behavior through quantified motion characteristics. The advancement of algorithms could be used in detecting such behaviors. This research is motivated to classify depression among students using artificial intelligence. The motion characteristics are quantified using accelerometer and GPS data and trained using neural networks to enable human activity prediction. Once predicted the prone to depression behavior notification will be sent to alert the user on their mental health condition. The user should react and respond to the alert and meet their doctors for further treatment.

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