Effective M2M gateway selection algorithms for geographical region-based query

Author(s):  
Minsu Kim ◽  
Suhan Choi
2013 ◽  
pp. 171-208
Author(s):  
Ghayet el ouna Zhioua ◽  
Houda Labiod ◽  
Nabil Tabbane ◽  
Sami Tabbane

Author(s):  
Manpreet Kaur ◽  
Chamkaur Singh

Educational Data Mining (EDM) is an emerging research area help the educational institutions to improve the performance of their students. Feature Selection (FS) algorithms remove irrelevant data from the educational dataset and hence increases the performance of classifiers used in EDM techniques. This paper present an analysis of the performance of feature selection algorithms on student data set. .In this papers the different problems that are defined in problem formulation. All these problems are resolved in future. Furthermore the paper is an attempt of playing a positive role in the improvement of education quality, as well as guides new researchers in making academic intervention.


Author(s):  
Mirza Sangin Beg

Finally, Mirza Sangin Beg tackles a huge assemblage of eclectic human exertions in the environs, centred around areas of trade and commerce, piety, landscaped spaces, cemeteries, and natural surroundings of rivers and hillocks. While structures such as the Jantar Mantar and the Firoz Shah’s lat are alluded to, it is stories about the human agencies that are privileged above these spaces. There are detailed renderings of activities in areas such as Pahar Ganj, Subzi Mandi, and Qadam Sharif, the biannual fair at Hanuman Temple, celebrations of Salono, numerous chhariyan melas, and worship of Goga. A fantastic account of Makhdum Jahanian Jahan Gasht coexists with an intense belief in relics of Prophet Muhammad, Hazrat Ali, and Imam Husain. Mirza Sangin Beg goes beyond the geographical region of Delhi towards north, west, and southwest. He writes of Bu Ali Shah Qalander’s dargah in Panipat and of the English platoon, officers, and gentlemen stationed between Gurgaon and Pataudi. The author has placed a variety of inscriptions and epitaphs from equally diverse structures and graveyards in a fatuous manner. Certain inscriptions seem to satisfy the self-esteem of the builders, some are laudatory while several are informative.


Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3995 ◽  
Author(s):  
Ning Liu ◽  
Ruomei Zhao ◽  
Lang Qiao ◽  
Yao Zhang ◽  
Minzan Li ◽  
...  

Potato is the world’s fourth-largest food crop, following rice, wheat, and maize. Unlike other crops, it is a typical root crop with a special growth cycle pattern and underground tubers, which makes it harder to track the progress of potatoes and to provide automated crop management. The classification of growth stages has great significance for right time management in the potato field. This paper aims to study how to classify the growth stage of potato crops accurately on the basis of spectroscopy technology. To develop a classification model that monitors the growth stage of potato crops, the field experiments were conducted at the tillering stage (S1), tuber formation stage (S2), tuber bulking stage (S3), and tuber maturation stage (S4), respectively. After spectral data pre-processing, the dynamic changes in chlorophyll content and spectral response during growth were analyzed. A classification model was then established using the support vector machine (SVM) algorithm based on spectral bands and the wavelet coefficients obtained from the continuous wavelet transform (CWT) of reflectance spectra. The spectral variables, which include sensitive spectral bands and feature wavelet coefficients, were optimized using three selection algorithms to improve the classification performance of the model. The selection algorithms include correlation analysis (CA), the successive projection algorithm (SPA), and the random frog (RF) algorithm. The model results were used to compare the performance of various methods. The CWT-SPA-SVM model exhibited excellent performance. The classification accuracies on the training set (Atrain) and the test set (Atest) were respectively 100% and 97.37%, demonstrating the good classification capability of the model. The difference between the Atrain and accuracy of cross-validation (Acv) was 1%, which showed that the model has good stability. Therefore, the CWT-SPA-SVM model can be used to classify the growth stages of potato crops accurately. This study provides an important support method for the classification of growth stages in the potato field.


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