1,721,110 research outputs found
Exploring the Application of Particle Swarm Optimization in Vegetation Remote Sensing
No full textParticle swarm optimization (PSO) is an algorithm belonging to the family of swarm
intelligence and metaheuristics, designed to solve optimization problems. It is a nature inspired
algorithm. Specifically, PSO mimics the collective behaviour of fish and birds. These organisms
are simple organisms that achieved complex tasks through information sharing and learning from
experience. The collective and cognitive behaviours are imitated in PSO using only two simple
mathematical equations. Owing to the simplicity of the algorithm, PSO had been widely applied
to various real-world problems. Despite its simplicity PSO reported a good performance. This
study aims to examine the application of PSO in the field of remote sensing focusing on
vegetation. Vegetation remote sensing focusses on vegetation data from satellite. This data is
used for monitoring and managing agriculture, forestry, environmental condition, and land
usage. The findings show that PSO has been popularly used by researchers in vegetation remote
sensing field. The applications cover multiple areas; nonetheless, the topic remains relevant, and
further research opportunities can be explored
Anthelmintic Resistance, Validation of FAMACHA and Effects of Management Practices in Selected Goat Farms in Terengganu, Malaysia.
No full textA total of 230 goats from six goat farms in Marang, Kuala Berang and Setiu, Terengganu were chosen for this study. Distribution of anthelmintic resistance is reported to be widespread in Malaysia. Therefore, it is important to evaluate the present anthelmintic resistance status f goats in Terengganu. Fecal samples were taken for fecal egg count (FEC). From 230 goats, 141 goats were chosen for detection of resistance by using Fecal Egg Count Reduction test, using levamisole, ivermectin, benzimidazole and closantel with each group having at least five goats including control. All farms had resistance towards benzimidazole and closantel while only two farms were still susceptible to levamisole nd one farm had suspected resistance to ivermectin. There were four out of six farms that has resistance to all anthelmintics tested. The strongyles which had developed anthelmintic resistance were were predominantly ahsrmonchus contortus followed by Trachostrongylus spp. Results obtained from this study showed that anthelmintic resistance is escalating and the need for effective action is very important for the small ruminant industry. Due to importance of helminthiasis in small ruminant industry, a quick, easy and useful field diagnostic method have developed in South Africa which is known as FAMACHA© eye color chart. The ocular mucous membrane of sheep and goats are classified by comparison with a laminated color chart bearing pictures of conjunctiva classified into five categories ranging from normal red through pink to practically white in severe anemia. Before the chart can be implemented to be use in this country, it is important to validate the system. Eye color based on FAMACHA© grading, blood and fecal samples were taken and subjected to packed cell volume (PCV) and FEC respectively. Two separate FAMACHA© scores defined as anemia were ≥ 3 and ≥ 4. The correlation between PCV and FAMACHA© eye score, and PCV and FEC were highly significant (P<0.01), but the correlation between FEC and FAMACHA© eye score were not significant. Sensitivity was 100% when FAMACHA© scores of 3 and above were considered and anemic, while specificity increased (63.08%) when FAMACHA© scores of 4 and above were considered as anemic. The data obtained strongly suggest that FAMACHA© method is a valuable diagnostic tool for identifying anemic goats. Due to widespread anthelmintic resistance, other options need to be investigated besides chemical control. Good management practices has been reported to reduce worm burden and reduces the frequency of anthelmintic use. Therefore, it is important to study the effect of some management practices to control helminthiasis. The practices chosen were grazing time, mineral block supplementation, type of drug used, breed, source of animal, additional feed and drenching personnel. Data on management practices adopted by the six farms were obtained from a survey based on a questionnaire. Worm burden was extrapolated from fecal egg counts (FEC). The data analysis was done by systematic using t-test, Spearman correlation and ANOVA. Afternoon grazing reduced the mean FEC nearly five-fold compared to morning grazing and mineral block supplementation reduced FEC two-fold compared to unsupplemented goats (P<0.05). Anthelmintic resistance in Terengganu is alarmingly increasing and this is a big threat to small ruminant production. FAMACHA© is a useful technique to diagnose anemia cause by J. contortus. By selective treatment, it will reduces anthelmintic frequency, thus delays the resistance development management practices is an important option for controlling parasitism in small ruminant
Performance evaluation of vector evaluated gravitational search algorithms based on ZDT test functions
No full textThis paper presents a performance evaluation of Vector Evaluated Gravitational Search Algorithm (VEGSA), namely VEGSA-I and VEGSA-II algorithms, for multi-objective optimization problems. The VEGSA algorithms use a number of populations of particles. In particular, a population of particles corresponds to one objective function to be minimized or maximized. Simultaneous minimization or maximization of every objective function is realized by exchanging a variable between populations. Performance evaluation is done based on ZDT test functions, which is a common benchmark problem for multi-objective optimization. The results shows that both VEGSA algorithms are outperformed by other multi-objective optimization algorithms and further enhancements are needed before it can be employed in any application
Simultaneous computation of model order and parameter estimation of a heating system based on particle swarm optimization for autoregressive with exogenous model
No full textSystem identification is a method used to obtain a mathematical model of a system by performing analysis of input-output behavior of the system. In system identification, the procedure can be separated into four main parts. The first part is constructing an experiment to collect the input-output data of the system. Then, based on some criteria, the model order and structure are selected. The next part is to estimate the parameters of the model. For the final part, the mathematical model is verified. In this study, a new approach called simultaneous model order and parameter estimation (SMOPE), which is based on Particle Swarm Optimization (PSO), is proposed to combine model order selection and parameter estimation in one platform. In this approach, both the model order and the parameters of the system are searched simultaneously by a particle. Similar to other PSO implementation, a number of particles are utilized in the search process. In order to realize the simultaneous search of the best model order and the associated parameters, a suitable particle representation is employed. Based on a heating system case study, it is proven that the proposed approach is superior compared to some other methods in literature
Synchronous vs asynchronous gravitational search algorithm
No full textGravitational search algorithm (GSA) is a new member of swarm intelligence algorithms. It stems from Newtonian law of gravity and mass interaction. Typically the agents in GSA are updated synchronously, where the whole population is updated together after every member's performance is evaluated. However, asynchronous update of agent has been used by other optimization algorithms. Therefore the performance of asynchronous GSA (A-GSA) is studied in this work. An agent in A-GSA is updated immediately after its performance evaluation. Hence an agent in A-GSA is updated without using complete and updated information of its entire population. Asynchronous update is more attractive from the perspective of parallelization. The results show that improvement to the straight forward implementation of A-GSA is needed
Swarm Intelligence Based Coverage And Energy Optimization In Wireless Sensor Networks
No full textThis research investigates the application of particle swarm optimization (PSO) in wireless sensor networks (WSN) and three new algorithms are proposed to solve the problem of coverage in WSN. The main objectives of the research are to formulate the coverage problem in WSN as an optimization problem,to investigate the applicability of PSO to solve the coverage problem in WSN. This research also investigate the applicability of PSO to solve the coverage/energy conservation problem as; first a multiobjective optimization problem and second as a constrained optimization problem
Performance evaluation of vector evaluated gravitational search algorithm II
No full textThis paper presents a performance evaluation of a novel Vector Evaluated Gravitational Search Algorithm II (VEGSAII) for multi-objective optimization problems. The VEGSAII algorithm uses a number of populations of particles. In particular, a population of particles corresponds to one objective function to be minimized or maximized. Simultaneous minimization or maximization of every objective function is realized by exchanging a variable between populations. The results shows that the VEGSA is outperformed by other multi-objective optimization algorithms and further enhancements are needed before it can be employed in any application
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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