Artificial Intelligence Advances

Users’ Evaluation of Traffic Congestion in LTE Networks using Machine Learning Techniques

Fri, 07 Apr 2023 00:00:00 +0800

Over time, higher demand for data speed and quality of service by an increasing number of mobile network subscribers has been the major challenge in the telecommunication industry. This challenge is the result of an increasing population of human race and the continuous advancement in mobile communication industry, which has led to network traffic congestion. In an effort to solve this problem, the telecommunication companies released the Fourth Generation Long Term Evolution (4G LTE) network and afterwards the Fifth Generation Long Term Evolution (5G LTE) network that laid claims to have addressed the problem. However, machine learning techniques, which are very effective in prediction, have proven to be capable of great importance in the extraction and processing of information from the subscriber’s perceptions about the network. The objective of this work is to use machine learning models to predict the existence of traffic congestion in LTE networks as users perceived it. The dataset used for this study was gathered from some students over a period of two months using Google form and thereafter, analysed using the Anaconda machine learning platform. This work compares the results obtained from the four machine learning techniques employed that are k-Nearest Neighbour, Support Vector Machine, Decision Tree and Logistic Regression. The performance evaluation of the ML techniques was done using standard metrics to ascertain the real existence of congestion. The result shows that k-Nearest Neighbour outperforms all other techniques in predicting the existence of traffic congestion. This study therefore has shown that the majority of LTE network users experience traffic congestion.

Development of an Extended Medical Diagnostic System for Typhoid and Malaria Fever

Temitope Apanisile, Joshua Ayobami Ayeni — Tue, 26 Sep 2023 00:00:00 +0800

In developing countries like Nigeria, malaria and typhoid fever are major health challenges in society today. The symptoms vary and can lead to other illnesses in the body which include prolonged fever, fatigue, nausea, headaches, and the risk of contracting infection occurring concurrently if not properly diagnosed and treated. There is a strong need for cost-effective technologies to manage disease processes and reduce morbidity and mortality in developing countries. Some of the challenging issues confronting healthcare are lack of proper processing of data and delay in the dissemination of health information, which often causes delays in the provision of results and poor quality of service delivery. This paper addressed the weaknesses of the existing system through the development of an Artificial Intelligence (AI) driven extended diagnostic system (EDS). The dataset was obtained from patients’ historical records from the Lagos University Teaching Hospital (LUTH) and contained two-hundred and fifty (250) records with five (5) attributes such as risk level, gender, symptom 1, symptom 2, and ailment type. The malaria and typhoid dataset was pre-processed and cleansed to remove unwanted data and information. The EDS was developed using the Naive Bayes technique and implemented using software development tools. The performance of the system was evaluated using the following known metrics: accuracies of true positive (TP), true negative (TN), false positive (FP), and false negative (FN). The performance of the EDS was substantially significant for both malaria and typhoid fevers.

AI Assists Operation and Maintenance of Future Cities

Han-Wei Zhao — Mon, 19 Jun 2023 00:00:00 +0800

Attitudes About Cryptocurrency Incentives for Research Participation

Dominic Arjuna Ugarte, Sean Young — Fri, 31 Mar 2023 00:00:00 +0800

It is essential to continually assess and find new ways to recruit and retain participants for research studies. Cryptocurrency is growing in popularity and may be a novel way to incentivize research participants. 100 participants, 50 of whom already had a cryptocurrency wallet and 50 of whom did not have a cryptocurrency wallet, were recruited through Facebook ads and completed a survey that asked about their experience with cryptocurrency and non-fungible tokens (NFTs) and potential interest in use of it for compensating research participants. The majority of respondents (79%) had some experience with cryptocurrency and 85% said they were comfortable trading cryptocurrency. Many participants had exchanged cryptocurrency within the past month (62%) and over their lifetime (70%). Respondents, however, were less familiar with NFTs, with only half having some experience with them. 18% of those without a cryptocurrency wallet and 42% of those with a cryptocurrency wallet chose to be compensated by cryptocurrency and NFT. Results suggest that, although cash and gift card incentives are preferred, there is an interest in cryptocurrency and NFTs. More studies will need to be done on a larger sample size and some of the challenges discussed (like cryptocurrency volatility) need to be addressed.