Asian Journal of Research in Computer Science

Wireless mobile internet is migrating towards an integrated system of internet and mobile communication network to fulfill the future of mobile network requirement: ubiquitous communication, where mobile users move freely almost anywhere and have access to the internet, communicate with anyone, anytime with any application using the best service available. This demands rapid progress in mobile communication networks and their internet technologies. This research project on the analysis of the Quality of Service (QoS) provided by Mobile Network Operators (MNOs) in the cellular internet networks. It aims to analyze and address the QoS of the internet network experienced by the user. QoS monitoring and analysis require the processing of large amounts of data and knowledge of which kinds of applications the traffic is generated by. To obtain satisfactory QoS based on internet network analysis, some network metrics must be measured and monitored at a regular time interval. In this research work, the network metric that will be measured for the internet service are signal strength, download speed, upload speed, and network transaction latency. This network metrics data will be collected from the consumer’s mobile device by the use of a crowdsourcing mobile application that is installed on the consumer’s mobile device. The investigation will cover mobile communication network providers in Nigeria. The data collated is then evaluated and analyzed.

The development of machine learning systems that used for diagnosis of chronic diseases is challenging mainly due to lack of data and difficulty of diagnosing. This paper compared between two proposed systems for computer-aided diagnosis (CAD) to detect and classify three types of white blood cells which are fundamental of an acute leukemia diagnosis. Both systems depend on the You Only Look Once (YOLOv2) algorithm based on Convolutional Neural Network (CNN). The first system detects and classifies leukocytes at the same time called computer-aided diagnosis with one model (CADM1). The second system separates detection and classification by using two models called computer-aided diagnosis with two models (CADM2). The main purpose of the paper is proving the high performance and accuracy by fragmentation of the main task into sub-tasks through comparing between CADM1 and CADM2. Also, the paper proved that can be depending only on deep learning without any traditional segmentation and preprocessing on the microscopic image. The (CADM1) achieved average precision for detection and classification class1=56%, class2=69% and class3 72% while (CADM2) achieved average precision up to 94% for detect leukocytes and accuracy 92.4% for classification. The result of the second system is very suitable for diagnosis leukocytes in leukemia.

A major cause of human vision loss worldwide is Diabetic retinopathy (DR). The disease requires early screening for slowing down the progress. However, in low-resource settings where few ophthalmologists are available to care for all patients with diabetes, the clinical diagnosis of DR will be a considerable challenge. This paper, review the most recent studies on the detection of DR by using one of the efficient algorithms of deep learning, which is Convolutional Neural Networks (CNN), which highly used to detect DR features from retinal images. CNNs approach to DR detection saves time and expense, and is more efficient and accurate than manual diagnostics. Therefore, CNN is essential and beneficial for DR detection.

Various operating systems (OS) with numerous functions and features have appeared over time. As a result, they know how each OS has been implemented guides users' decisions on configuring the OS on their machines. Consequently, a comparative study of different operating systems is needed to provide specifics on the same and variance in novel types of OS to address their flaws. This paper's center of attention is the visual operating system based on the OS features and their limitations and strengths by contrasting iOS, Android, Mac, Windows, and Linux operating systems. Linux, Android, and Windows 10 are more stable, more compatible, and more reliable operating systems. Linux, Android, and Windows are popular enough to become user-friendly, unlike other OSs, and make more application programs. The firewalls in Mac OS X and Windows 10 are built-in. The most popular platforms are Android and Windows, specifically the novelist versions. It is because they are low-cost, dependable, compatible, safe, and easy to use. Furthermore, modern developments in issues resulting from the advent of emerging technology and the growth of the cell phone introduced many features such as high-speed processors, massive memory, multitasking, high-resolution displays, functional telecommunication hardware, and so on.

Automation frees workers from excessive human involvement to promote ease of use while still reducing their input of labor. There are about 2 billion people on Earth who live in cities, which means about half of the human population lives in an urban environment. This number is rising which places great problems for a greater number of people, increased traffic, increased noise, increased energy consumption, increased water use, and land pollution, and waste. Thus, the issue of security, coupled with sustainability, is expected to be addressed in cities that use their brain. One of the most often used methodologies for creating a smart city is the Internet of Things (IoT). IoT connectivity is understood to be the very heart of the city of what makes a smart city. such as sensor networks, wearables, mobile apps, and smart grids that have been developed to harness the city's most innovative connectivity technology to provide services and better control its citizens The focus of this research is to clarify and showcase ways in which IoT technology can be used in infrastructure projects for enhancing both productivity and responsiveness.