The technology, Long Term Evolution (LTE) developed by 3rd Generation Partnership Project is considered an improved standard in mobile communications when compared to previously attained network standards. LTE with prospects of decreased latency levels and support of downlink and uplink transmission at data rates exceeding 100Mbps and 50Mbps, an effective handover framework needs to be put in place to improve quality of service rendered to the network users and decrease wastage of network resources. This study examines several works carried out on a handover criteria (hysteresis margin) needed for designing an effective handover framework. This margin is based on the received signal strength between both target and serving eNodeBs, and its proper determination amongst other advantages mitigates the rate of unnecessary and repeated handover (ping-pong effect). The model presented in this research integrates the artificial neural network (ANN) mechanism into the determination of hysteresis margin in the LTE handover process which is to minimize handover delay and ping-pong taking into consideration the speed of the user equipment (UE).
Background and context: Prostate cancer is the commonest cancer affecting Nigerian men, with worse outcome compared with men from the developed world. There is limited public awareness about prostate cancer in Nigeria. Oga Blue 4 Prostate Awareness (OB4PA) was created by a consortium of Nigerian nonprofits for prostate cancer advocacy (PCA). Aims: -Design PCA using videos, printed brochure and social media -Implement PCA in five Nigerian states -Evaluate the reach and impact of PCA campaign Program/Policy process: Community-based participatory process, involving the medical community, prostate cancer survivor, and the public was used. Multimedia teaching was used to enhance learning and retention; social media was used to engage groups and individuals. Content development involved iterative consultation among project leaders, medical experts and target audience, often on social media. High-quality teaching videos were recorded in English and Nigerian Pidgin languages. Videos ensured consistency and ease of broadcast. Videos were accessed by OB4PA partners through cloud computing (OneDrive). Facebook was used to promote the campaign, engage local audience, and for Facebook Live presentation. Local health professionals projected the video in appropriate language to audiences in religious and community groups. Brochure detailing clinical features and local service providers was distributed. Audience evaluation was obtained following each interaction. Outcomes: In 6 weeks, 20 presentations were made to 1800 persons. The Facebook Live presentation had 1500 views, reached 9302 people and was shared 107 times. A total of 25 Facebook posts were made, resulting in 628 like, 1908 video views, 160 shares, and reached 14,222 people. Almost all participants had positive feedback on the free and detailed advocacy. Most questions focused on the causes and prevention of prostate cancer, especially on the use of nutritional supplements. What was learned: Cloud computing enabled us to have one presenter; this eliminated the need to find a presenter for each organization. Audience appreciated simplified videos used in explaining the disease process and need for personalized early detection. Facebook live presentation attracted the most reactions on social media, with most comments showing that people liked the intervention. Audience feedback showed that adding advocacy cellphone video by a survivor helped demystify prostate cancer. Having the main presentation video in different file formats and sizes enhanced sharing on social media, as most Nigerians access the Internet on cell phones. Reliable access to projectors was challenging, especially in remote areas. Overall, use of cloud computing and social media were crucial in the success of the PCA project. Lessons from OB4PA informed the design of the current We Can, I Can Conquer Cervical Cancer Awareness project in Nigeria.
With the rise in smart devices communicating on internet, there is a huge demand in the delivery of internet bandwidth to fulfil subscribers’ aspirations. It is therefore important for internet network providers to understand the subscribers’ behavioural pattern in terms of internet bandwidth usage so as to meet up with the continuous rise in its demand. This research introduces the schematics of an android application effectively communicating with a remote database; Firebase cloud service. The classification of the subscribers’ internet traffic bandwidth consumption enables the android application to generate dataset of bandwidth utilization patterns of volunteered subscribers’ device which are grouped into 4 classes; A, B, C and D representing very high, high, medium and low data usage respectively. The collection of internet bandwidth usage of subscribers was recorded at intervals of every hour into the remote database in Firebase cloud.
Cellular networks are highly prone to congestion especially at peak traffic periods. This is compounded by the fact that the blocking probability increases. In this study, a machine learning based subscriber classification along with an adaptive Wi-Fi offloading scheme is proposed to improve the throughput and lower the blocking probability of the network. The proposed subscriber classification was implemented using a back propagation based artificial neural network. The result of the subscriber classification was used to develop an adaptive Wi-Fi offloading algorithm based on bandwidth utilization and system throughput. The developed neural network models are shown to be effective, with 94.6% in one experiment, in classifying a user into user classes or levels based on previous data usage. The levenberg–marquardt (LM) algorithm gave the highest accuracy in categorizing the four classes. A relatively large sample size was used for the neural network training cycle and the resulting neural network was then made to use many neurons in its hidden layer. The implementation of the proposed subscriber classification and adaptive Wi-Fi offloading scheme led to a 20% drop in blocking probability and a 50.53% increase in the system throughput.
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