scholarly journals A K-Nearest Neighbour Algorithm-Based Recommender System for the Dynamic Selection of Elective Undergraduate Courses

2019 ◽  
Vol 5 (6) ◽  
pp. 128
Author(s):  
Adewale Opeoluwa Ogunde ◽  
Emmanuel Ajibade
Keyword(s):  
Recommender System ◽  
Nearest Neighbour ◽  
Dynamic Selection ◽  
Selection Of

Dynamic selection of dispatching rules for job shop scheduling

2000 ◽  
Vol 11 (1) ◽  
pp. 73-81 ◽  
Author(s):  
V. Subramaniam ◽  
G. K. Lee ◽  
G. S. Hong ◽  
Y. S. Wong ◽  
T. Ramesh
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Dispatching Rules ◽  
Dynamic Selection ◽  
Shop Scheduling ◽  
Selection Of

scholarly journals Distribusi Keruangan Penanaman Modal di Kabupaten Bantul

2020 ◽  
Vol 34 (1) ◽  
pp. 25
Author(s):  
Aprilia Prasmudika Sighita ◽  
Bambang Sriyanto Eko Prakoso
Keyword(s):  
Economic Growth ◽  
Spatial Distribution ◽  
Descriptive Analysis ◽  
Analytical Techniques ◽  
Nearest Neighbour ◽  
Investment Climate ◽  
R Value ◽  
Investment Firm ◽  
Neighbour Analysis ◽  
Selection Of

Kabupaten Bantul meraih prestasi tingkat nasional di tahun 2008 yakni memperoleh penghargaan dalam KPPOD Award. Penghargaan yang diperoleh menjadi awal yang baik bagi Kabupaten Bantul dalam memperbaiki iklim penanaman modal. Tujuan dari penelitian ini untuk mengidentifikasi dan menganalisis distribusi keruangan penanaman modal dan pengaruh karakteristik wilayah terhadap pemilihan lokasi penanaman modal. Teknik analisis yang digunakan adalah analisis deskriptif, analisis tetangga terdekat, dan analisis regresi berganda. Berdasarkan hasil kajian dapat disimpulkan bahwa distribusi keruangan penanaman modal di Kabupaten Bantul terdistribusi di 10 kecamatan, sedangkan 7 kecamatan lainnya belum menjadi destinasi penanaman modal. Untuk lokasi perusahaan penanaman modal membentuk pola dispersed atau merata dengan nilai R sebesar 5,920887 (R>1). Sebagian dari lokasi penanaman modal berada di tepi jalan raya. Pemilihan lokasi penanaman modal di Kabupaten Bantul dipengaruhi oleh faktor daya tarik karakteristik wilayah seperti pertumbuhan ekonomi dan jumlah objek wisata. Bantul Regency won the national award of KPPOD in 2008. That award was a good commencement to improve Bantul Regency’s investment climate. The aims of this research were to identify and analyse the spatial distribution of investment and the effect of regional characteristics on the selection of investment site. The analytical techniques used in the research are descriptive analysis, nearest neighbour analysis, and multiple regression analysis. Based on the analysis, it can be concluded that distribution of investment in Bantul Regency distributed in 10 sub-districts, while 7 others are not yet be destination of investment. For the location of investment firms forms a dispersed pattern with R value 5,920887 (R>1). Some investment firm are located on the edge of highway. The selection of investment sites in Bantul regency is affected by the attraction factors of the region characteristics such as economic growth and number of tourism objects.   

scholarly journals Improving the energy efficiency of VoIP applications in IEEE 802.11 networks through control of the packetization rate

2017 ◽  
Author(s):  
Rafael Estepa ◽  
Antonio Estepa ◽  
Germán Madinabeitia ◽  
Mark Davis
Keyword(s):  
Energy Efficiency ◽  
Ieee 802.11 ◽  
Rate Control ◽  
Control Algorithm ◽  
Energy Savings ◽  
Dynamic Selection ◽  
802.11 Networks ◽  
Ieee 802.11 Networks ◽  
Rate Control Algorithm ◽  
Selection Of

This paper presents an adaptive algorithm that improves the energy efficiency of VoIP applications over IEEE 802.11 networks. The algorithm seeks to achieve the largest energy savings subject to reaching a minimum speech quality under the prevailing network conditions. The control mechanism used is the dynamic selection of the packet size during the communication.This algorithm has been implemented in an experimental testbed and the results demonstrate that our packetization rate control algorithm can provide energy savings in uncongested IEEE 802.11 networks (up to 30%). Furthermore, under poor network conditions the algorithm can prolong the duration of the call before it is dropped at the expense of a higher energy consumption.

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