=========== Expressions =========== ARIMA is first appeared without explanation. ------------------------------------- 2.1 Association rule mining technique ------------------------------------- The subsection 2.1.1 can be understood but should be improved for easier understanding. For example, authors' expression: "The rule x->i_j is satisfied in the set of transactions T with the confidence factor 0<=c<=1 if f at least c% of transactions in T that satisfy X also satisfy I_j." is unclear. what is f in the phrase "if f at least c% ..." or you mean iff? "a rule is satisfied with a confidence c%" is O.K. but what about both "(a set of) t satisfy X" and "(a set of) t satisfy I_j"? Both are not defined. The verb "bought" in "if it bought the item" is not appropriate as a theory description, easy example to understand though. "(may be considered later)" is not appropriate. If actually considered later, it would be "(See later more in detail.) of something like that. "To do this task efficiently, we use some estimation tools and some pruning techniques." should be more specifically. That is, "some" makes the explanation unclear. The algorithm shown in Fig. 1 is authors' original or a proposal by others (maybe by [18])? In what author wrote "The connection weights are assigned initial values first," what are initial values? A random value? Authors wrote "The error between the predicted and actual output values is back-propagated via the network for updating the weights [19]." But the original idea is not by [19]. The same holds [20] and [21]. These papers might be just papers in which author learned what the BP is. Author wrote "Theoretically, neural networks can simulate any kind of data pattern given sufficient training." Who claim this? Not authors' theory right? Then reference should be shown. No description about W_{ij} which is important because connection from i to j or opposite is crucial. Authors notation is different than standard usage. ---------------------- 2.2. BP neural network ---------------------- what is D-day? in Fig. 2 (should be the day when data were taken). -------------------------------------------------- 2.3. Chaotic particle Swarm Optimization Algorithm -------------------------------------------------- Who proposed PSO for the first time? It should be added in the Reference. Otherwise it looks like authors propose. The variable g in (9) is not explicitly explained. Also in (9) and others we read "where k = C, epsilon, sigma" but what is this? Describe what happens when V_ki becomes the range. The description "until the pre-defined termination criterion is satisfied" is not the idea only in [17], but quite general criterion. So delete [17]. Is [15] the original proposal of CPSO? It is not, isn't it? ------------------------- 2.3.2. Chaotic PSO Method ------------------------- The first paragraph of 2.3.2 should be refined. What is l or what should it be called in (11)? What is globalbesti? According to the description of CLS in the 3rd paragraph, the CPSO the authors used is the one proposed by [22], right? what are "the three parameters in the model?" Delete [24] in "The BP algorithm for multi-layer neural network [24]," for the reason already mentioned. ------------------------ 2.4. Performance Indices ------------------------ ------------------------------- 3.3 Instruments and Measurement ------------------------------- what is ML9800, ML9810B etc? ------------------------------ 4.1 Independent Input Variable ------------------------------ "None of the inference methods described above performs reliably if factors are missing from the model." might be better to be "None of the inference methods described above performs reliably if any of onf the input factors are missing from the model." "We therefore briefly discuss methods helpful for developing parsimonious models." might be better to be "We therefore briefly discuss methods which help us develop a parsimonious model." What does it mean by "the regression sum of squares sum of squares due to regression"? Also "the regression sum of squares sum of squares due to regression" is difficult to understand. Authors wrote "Firstly, we prepare seven factors for selection." Then what comes secondly? Or, why these seven factors are chosen firstly? Table 1 should be explained what are X1 ... X7 and what is R? what ppbv and ppb stand for? what is S1(i) and T1(i) in table 2? what is sigh? The similar ambiguity exists also in table 2. Table 2 - 4 are not understandable at all. Table 3 is not understandable. These three tables are more like an author's memo for the raw data to prepare for their paper. Caption of Fig.4. should be "Comparisons of three models through the prediction of the ozone levels (1-24 July 2009) using the testing data set (25-30 July 2009)." or "Comparisons of three models through the prediction of the ozone levels using the testing data set." ------------------------- 4.2 Result and discussion ------------------------- Title of this subsection should be "Results and discussion." What actually means by "A range of evaluative statistics"? Reviewer understand what it means, but as an expression it is not good at all. ===== Typos ===== In the following memo '=>' indicates "should be," "might be," "is better to be," or comment follows after this symbol First, all equations if not in the text should be end with period. theory . => theory. wellbeing => well being urban areas . => urban areas. [8]proposed => [8] proposed Victor R. et al. [12] => Prybutok et al. [12] out performs => outperforms trial approaches => trial and error approaches An improved PSO ... => better to be particle swarm optimization because it is firstly appeared capability.Xianlun => capability. Xianlun Xianlun Tang et al. [16] => Tang et al. [16] chaotic swarm optimization algorithm (CPSO) => chaotic particle swarm optimization algorithm (CPSO) determine the optimal non-linear mapping parameters between ... => determine the optimal non-linear mapping between ... meteorological factory => meteorological factor I_x appearing in the consequent => I_x in the consequent I_y appearing in the antecedent => I_x in the antecedent X appearing in the consequent => X in the consequent Y appearing in the antecedent => Y in the antecedent possible- => possible predefine itemset => predefined itemset ,k>2 => , k>2 at the most k => at most k minsuppori (in Fig.1)=> minsupport algorith => algorithm set[18]. => set [18]. network.The => network. The Steps in training BP network as followed => Steps in training BP network are as follows Select training pair from the training set and apply the input vector to the inputlayer. => Select training pair from the training set and apply them one by one as the input vector to the input layer. Each particle flies => Each swarm particle flies can be founded => can be found in respect with => with respect to electiveness => effectiveness? in noon => at noon 13:00 in the noon => 13:00 (in the afternoon) 0.00mm => 0.00 mm about 30 days => for 30 days "there have 48 times measures" => ? "This method is developed based on association rule mining technique, chaotic particle swarm optimization algorithm (CPSO) and BP neural network." => Be consistent. Sciences,2008