Application of Data Mining Techniques on Tourist Expenses in Malaysia

تلعب السياحة دورًا مهمًا في التنمية الاقتصادية لماليزيا حيث يمكنها تعزيز فرص العمل في الاقتصاد المحيط بها. من خلال تطبيق استخراج البيانات على بيانات السياحة للتنبؤ بمجال الفرص التجارية وهذا يعد اختيارًا جيدًا. استخراج البيانات هو العملية التي تأخذ البيانات كمدخلات وتنتج معرفة المخرجات. بسبب ازدياد عدد السكان الذين يسافرون في بلد آسيا  في هذه السنوات القليلة. يبدأ العديد من رواد الأعمال أعمالهم الخاصة ولكن هناك بعض المشاكل مثل الاستثمار الخاطئ في مجالات الأعمال  الخدمات السيئة التي أثرت على دخل أعمالهم. الهدف من هذه البحث هو استخدام تقنية استخراج البيانات لتلبية احتياجات العمل واحتياجات العملاء للمؤسسات السياحية والعثور على تكنولوجيا استخراج البيانات الأكثر فعالية. بالإضافة إلى ذلك ، تم تجربة تنفيذ هذا البحث لأربع تقنيات تصنيف استخراج البيانات لاستخراج رؤى مهمة من مجموعة البيانات السياحية. كانت الأهداف هي معرفة أفضل الخوارزمية أداءً من بين النتائج المقارنة لتحسين فرص العمل في المجالات المتعلقة بالسياحة. كانت نتائج المصنفات الأربعة الصحيحة هي JRIP (84.09٪) ، Random Tree (83.66٪) ، J48 (85.50٪) ، وشجرة REP (82.47٪). سيتم تحليل جميع النتائج ومناقشتها في البحث.Tourism plays an important role in Malaysia’s economic development as it can boost business opportunity in its surrounding economic. By apply data mining on tourism data for predicting the area of business opportunity is a good choice. Data mining is the process that takes data as input and produces outputs knowledge. Due to the population of travelling in Asia country has increased in these few years. Many entrepreneurs start their owns business but there are some problems such as wrongly invest in the business fields and bad services quality which affected their business income. The objective of this paper is to use data mining technology to meet the business needs and customer needs of tourism enterprises and find the most effective data mining technology. Besides that, this paper implementation of 4 data mining classification techniques was experimented for extracting important insights from the tourism data set. The aims were to find out the best performing algorithm among the compared on the results to improve the business opportunities in the fields related to tourism. The results of the 4 classifiers correctly classifier the attributes were JRIP (84.09%), Random Tree (83.66%), J48 (85.50%), and REP Tree (82.47%). All the results will be analyzed and discussed in this paper

High-dimensional Schwarzschild black holes in scalar-tensor-vector gravity theory

We obtain a high-dimensional Schwarzschild black hole solution in the scalar-tensor-vector gravity (STVG), and then analyze the influence of parameter $\alpha$ associated with a deviation of the STVG theory from General Relativity on event horizons and Hawking temperature. We calculate the quasinormal mode frequencies of massless scalar field perturbations for the high-dimensional Schwarzschild STVG black hole by using the sixth-order WKB approximation method and the unstable null geodesic method in the eikonal limit. The results show that the increase of parameter $\alpha$ makes the scalar waves decay slowly, while the increase of the spacetime dimension makes the scalar waves decay fast. In addition, we study the influence of parameter $\alpha$ on the shadow radius of this high-dimensional Schwarzschild STVG black hole and find that the increase of parameter $\alpha$ makes the black hole shadow radius increase, but the increase of the spacetime dimension makes the black hole shadow radius decrease. Finally, we investigate the energy emission rate of the high-dimensional Schwarzschild STVG black hole, and find that the increase of parameter $\alpha$ makes the evaporation process slow, while the increase of the spacetime dimension makes the process fast.Comment: v1: 18 pages, 6 figures, 4 tables; v2: clarifications and references added; v3: 19 pages, final version to appear in European Physical Journal

Diagnosis of ball bearing with various degree rolling element defects based on local mean decomposition

This paper aims to explore fault characteristics extraction method to deal with the light and severe rolling element faults based on local mean decomposition (LMD) and Fourier transform (FT). The characteristics of LMD are studied by processing multi-component frequency modulation and amplitude modulation (FM-AM) signal, which are usually used to describe the bearing fault signals. Based on the simulation analysis, the envelope spectrum method called LMD-FT is used to deal with the vibration signals of rolling balling bearing with various element faults. Moreover, the fault features extracted by LMD are compared with those obtained from conventional Hilbert transform (HT). The results demonstrate that LMD method is an effective way to identify the characteristics of rolling element defect generated at the initial stage

Non-Commutativity, Teleology and GRB Time Delay

We propose a model in which an energy-dependent time delay of a photon originates from space-time non-commutativity, the time delay is due to a noncommutative coupling between dilaton and photon. We predict that in our model, high energy photons with different momenta can either be delayed or superluminal, this may be related to a possible time delay reported by the Fermi LAT and Fermi GBM Collaborations.Comment: 8 pages, 1 figure, typo revised, contents and reference adde