IJARAI Volume 5 Issue 5

Copyright Statement: This is an open access publication licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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Paper 1: Parameter Optimization for Nadaraya-Watson Kernel Regression Method with Small Samples

Abstract: Many current regression algorithms have unsatisfactory prediction accuracy with small samples. To solve this problem, a regression algorithm based on Nadaraya-Watson kernel regression (NWKR) is proposed. The proposed method advocates parameter selection directly from the standard deviation of training data, optimized with leave-one-out cross- validation (LOO-CV). Good generalization performance of the proposed parameter selection is demonstrated empirically using small sample regression problems with Gaussian noise. The results show that proposed parameter optimization method is more robust and accurate than other methods for different noise levels and different sample sizes, and indicate the importance of Vapnik’s e-insensitive loss for regression problems with small samples.

Author 1: Li Fengping
Author 2: Zhou Yuqing
Author 3: Xue Wei

Keywords: small samples regression; Nadaraya-Watson kernel regression; parameter optimization; loss function; cross validation

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Paper 2: Outlier-Tolerance RML Identification of Parameters in CAR Model

Abstract: The measured data inevitably contain abnormal data under the normal operating conditions. Most of the existing algorithms, such as least squares identification and maximum likelihood estimation, are easily affected by abnormal data and appear large indentation deviation. It is a difficult task needed to be addressed that how to improve the sensitivity of the existing algorithm or build a new parameter identifying algorithm with outlier-tolerance ability to abnormal data in system identification technology application. In this paper, the sensitivity of the RML to the sampled abnormal data was analyzed and a new improvement algorithm of CAR process is established to improve outlier-tolerance ability of the RML identification when there are outliers in the sampling series. The improved algorithm not only effectively inhibits the negative impact of the abnormal data but also effectively improve the quality of the parameter identification results. Some simulation given in this paper shows that the improved RML algorithm has strong outlier-tolerance. This paper’s research results play an important role in engineering control, signal processing, industrial automation and aerospace or other fields.

Author 1: Hong Teng-teng
Author 2: Hu Shaolin

Keywords: recursive maximum likelihood identification; parameter identification; outliers; outlier-tolerance identification

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Paper 3: Method for Reducing the Number of Wild Animal Monitors by Means of Kriging

Abstract: Method for reducing the number of wild animal monitors is proposed by means of Kriging. Through wild animal route of simulations with 128 by 128 cells, the required number of wild animal monitors is clarified. Then it is found that the number of wild animal monitors can be reduced based on Kriging by using variograms and semi-variograms among the neighboring monitors. Also, it is found that the number of wild animal monitors by the factor of a by means of the proposed method.

Author 1: Kohei Arai
Author 2: Takashi Higuchi

Keywords: Kriging; Variogram; Semi-Variogram; Wild animal; Wild pig

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Paper 4: Comparative Study on Cloud Parameter Estimation Among GOSAT/CAI, MODIS, CALIPSO/CALIOP and Landsat-8/OLI with Laser Radar: Lidar as Truth Data

Abstract: A comparative study on cloud parameter estimation among GOSAT/CAI, MODIS, CALIPSO/CALIOP and Landsat-8/OLI is carried out using Laser Radar: Lidar as a truth data. Optical depth, size distribution, as well as cirrus type of clouds are cloud parameters. In particular, cirrus cloud detection is tough issue. 1.38 µm channel is required for its detection. Although MODIS and Landsat-8/OLI have such channel, the other mission instruments, CAI and CALIPSO/CALIOP do not have such channel. As a truth data of cloud parameter, ground based Lidar is used in this comparative study. From the Lidar, backscattered echo signal and depolarization coefficient are obtained as a function of altitude. Therefore, cloud type, vertical profile can be derived from the Lidar data. CALIPSO/CALIOP is satellite based Lidar which allows observation of clouds from space. Although the directions of laser light emissions between CALIPSO/CALIOP and the ground based Lidar are different, their principles are same. Therefore, it is expected that CALIPSO/CALIOP data derived cloud parameters are similar to the ground based Lidar data derived cloud parameters. The experimental results show the aforementioned facts and are useful for improvement of cloud parameter estimation accuracy with several sensor data combinations.

Author 1: Kohei Arai
Author 2: Masanori Sakashita
Author 3: Hiroshi Okumura
Author 4: Shuji Kawakami
Author 5: Kei Shiomi
Author 6: Hirofumi Ohyama

Keywords: Cirrus cloud; GOSAT/CAI; Landsat; LiDAR; Sky view camera; CALIPSO/CALIOP; topogramphic representation of 3D clouds

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Paper 5: The Mobile Version of the Predicted Energy Efficient Bee-Inspired Routing (PEEBR)

Abstract: In this paper, the previously proposed Predictive Energy Efficient Bee-inspired Routing (PEEBR) family of routing optimization algorithms based on the Artificial Bees Colony (ABC) Optimization model is extended from a random static mobility model, as employed by its first version (PEEBR-1), into a random mobility model in its second version (PEEBR-2). This random mobility model used by PEEBR-2 algorithm is proposed and described. Then, PEEBR-2’s was simulated in order to compare its performance relative to the first version (PEEBR-1) in terms of predicted optimal path energy consumption, nodes batteries residual power and fitness. The simulation results have shown that PEEBR-2’s optimal path is predicted to consume less energy and realizing higher fitness. On the other hand, PEEBR-1’s optimal paths nodes possess higher batteries residual power. At last, the impact of mobile nodes speeds was studied for PEEBR-2 in terms of optimal path’s predicted energy consumption and path nodes batteries residual power showing its performance stability relative to nodes mobility speed.

Author 1: Imane M. A. Fahmy
Author 2: Hesham A. Hefny
Author 3: Laila Nassef

Keywords: PEEBR; PEEBR-1; PEEBR-2; Energy Efficient Routing; Bee-inspired; Artificial Bee Colony (ABC) optimization; Random Mobility Model

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Paper 6: Brainstorming Versus Arguments Structuring in Online Forums

Abstract: We characterize electronic discussion forums as being of one of the following two types: Brainstorming Forums and Arguments Structuring Forums. In this work we analyze and classify the types of threading models occurring as a function of the type of forum. For our analysis we study forums attached to the 25 news sources most used by the aggregator Google News, as detected by a 2007 study. Most discussion forums associated with articles on these news sources seem to be designed not with the purpose of structuring arguments but mainly with the purpose of helping readers brainstorm easily their reactions to the corresponding news item. The forums were classified as to what user-supported metadata they gather and use in comment presentation. We compare the features observed for brainstorming forums, as learned via the aforementioned procedure, with the fea-tures of dedicated argument structuring forums. The argument structuring forums that were used as basis of the comparison are: YourView, DebateDecide, and Opinion Space. We notice significant differences in the obtained models for the two types of forums, as well as significant differences with respect to the the structuring of user submitted data in polls associated with major news channels. We believe that this is the first kind of work that deals with the issue above.

Author 1: Abdulrahman Alqahtani
Author 2: Marius Silaghi

Keywords: Knowledge Representation, Threading Models for Ar-guments in Electronic Debates, Threading Model Classification, Debate Threading Model, Comparison Online news Platforms

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Paper 7: Factor Analysis Based Selections

Abstract: Merger in higher education has been of scholarly interest to researchers in various fields. This work is devoted to challenges related to partner selection for an feasible merger. A systematic approach is proposed based on describing educational organizations via several predefined key numbers from the one hand and their expectations from the other hand. Methods from Boolean factor analysis, formal concept analysis, and Belnap’s logic are further employed in an attempt of drawing meaningful conclusions.

Author 1: Sylvia Encheva

Keywords: Boolean factor analysis; Formal concept analysis; Belnap’s logic

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