This study was conducted during two different seasons to determine the best concentration of gibberellic acid (GA3) that
could result in better growth and higher yield of groundnut (Arachis hypogaea L.). Experiments were conducted during
the 2015 dry season and 2016 wet season at the field of the Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor,
Malaysia. The purpose of the experiments was to investigate the response of the groundnut plants to four levels of GA3
(0, 50, 100 and 150 mg L−1) as foliar spray at 21 and 42 days after sowing. The treatments were laid out in a randomized
complete block design and replicated thrice. The results showed that the treatment of 150 mg L−1 GA3 significantly
(p<0.05) increased plant height, number of branches per plant, total dry weight, number of pods per plant, pod yield,
100 seed weight, % shelling, oil content, protein content, seed moisture and germination percentage during the wet and
dry seasons. In conclusion, the 150 mg L−1 GA3 concentration is the optimum level required to enhance the growth and
yield in groundnuts during the wet and dry seasons.
Active appearance model (AAM) is one of the most popular model-based approaches that have been extensively used to extract features by highly accurate modeling of human faces under various physical and environmental circumstances. However, in such active appearance model, fitting the model with original image is a challenging task. State of the art shows that optimization method is applicable to resolve this problem. However, another common problem is applying optimization. Hence, in this paper we propose an AAM based face recognition technique, which is capable of resolving the fitting problem of AAM by introducing a new adaptive ABC algorithm. The adaptation increases the efficiency of fitting as against the conventional ABC algorithm. We have used three datasets: CASIA dataset, property 2.5D face dataset, and UBIRIS v1 images dataset in our experiments. The results have revealed that the proposed face recognition technique has performed effectively, in terms of accuracy of face recognition.
Existing face recognition methods utilize particle swarm optimizer (PSO) and opposition based particle swarm optimizer (OPSO) to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO) technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM). In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented.