Usability Issues with E-Commerce Websites in Nigeria

Usability issues are vital components for online-based businesses. With Nigeria integrating electronic payment into its financial system coupled with rising internet penetration in the country, several businesses have created an online presence and are cashing in on the opportunities. This has created a form of online competition among e-commerce businesses. This study employs the user test method to test the usability issues associated with E-commerce websites in Nigeria and how this affects the success of e-commerce businesses. We find several usability issues with all e-commerce websites tested and a general need for user-focused improvement on all the websites. We also find the issues of security and trust as salient to expand the e-commerce business in Nigerians. Based on the result and analysis, recommendations on usability, data policy, security, registration and other vital issues are offered.

Ad Hoc Vehicle Networks: Recapitulation and Challenges

Being ad-hoc in design, VA NET is a form of networks generated by the idea of building up a network of cars for a specific needs or circumstance. In addition to the benefits, VANET poses a large number of challenges such as providing QoS, high bandwidth and connectivity, and vehicle and individual privacy security. Each report discusses VANET 's state-of-the-art, explaining the relevant problems. We address in depth network design, signal modelling and propagation mechanisms m, usability modeling, routing protocols and network security. The paper's key results are that an effective and stable VANET satisfies all architecture criteria such as QoS, minimal latency, low BER and high PDR. At the end of the paper are addressed several primary work areas and challenges at VANET.

An Innovative Performance of Refuge using Stowage Main Servers in Cloud Computing Equipment

Distributed computing has been imagined as the cutting edge engineering of IT Enterprise. It moves the application programming and information bases to the incorporated enormous server farms, where the administration of the information and administrations may not be completely dependable. There are various security issues for distributed computing as it envelops numerous innovations including networks, information bases, working frameworks, virtualization, asset planning, exchange the board, load adjusting, simultaneousness control and memory the executives. Putting away information in an outsider's cloud framework causes genuine worry over information secrecy. Hence, security issues for a large number of these frameworks and advancements are material to distributed computing. We propose a key worker encryption conspire and incorporate it with a decentralized deletion code with the end goal that a safe conveyed stockpiling key framework is defined respectively.

Role of Artificial Intelligence in Distinct Sector: A Study

The artificial intelligence (AI) plays a significant role in distinct fields to solve the complex problems. The digital technical field is increasing day by day in the world and it makes an internal part of our life. Hence, the knowledge of emerging technology is must for making our life easy. However, there are some major areas which are creating the problems to human such as agriculture field that comprises crop diseases, lack of storage management, pesticide control etc. These problems can be solved by artificial intelligence, IOT, machine learning and deep learning. Hence, the aim of this paper is to discuss the role of artificial intelligence to solve different issues of distinct sectors (medical, engineering, agriculture, business, defenses etc.) especially in medicine (COVID-19). Finally, future scope, challenges and application domain of artificial intelligence is also described.

Automated Detection of Multiple Sclerosis Lesions in Normal Appearing White Matter from Brain MRI: A Survey

Multiple Sclerosis is an inoperable disease of the Central Nervous System (CNS) that irritates the myelin sheath by forming lesions. This affects all organs of the CNS; the vital of them is the brain. This disease can be detected by diagnosis like Magnetic Resonance Imaging (MRI). It is a non-invasive diagnostic test that provides detailed images of the soft tissues of the body. Out of the different variations of MRI, MS lesions are predominantly visible in the DTI (Diffusion Tensor Imaging) variant of MRI. DTI gives enhanced visualization of normal-appearing white matter tracts of the organs, hence providing a better image of the MS lesion. In this paper, the latest methodologies regarding the identification of the MS lesions in MRI scans like T2 FLAIR or DTI, using automated techniques like deep learning, computer vision, neural network and many more are surveyed. Furthermore, this paper consists of a proposed model which would focus on correlating the lesions found in DTI scan with the basic MRI scan like T2. It would identify the MS lesion in DTI scan and eventually highlight that lesion position in the T2 image scan. This would help radiologist in a way to effectively handle multiple MRI scans.

Analysis on Machine Learning Techniques for Stress Detection among Employees

Mental stress is a common and major issue nowadays especially among working professional, because employees have family commitments with their over workload, target, achievements, etc. Stress tends various health issues like heart attack, stroke, depression, and suicide. Mental stress is not only in employees even normal people also face this problem but the employees has so many stress management techniques to manage the stress like yoga, meditation etc., but still employees suffer from the stress. Stress calculated by the Traditional stress detection method has two types of physiological parameters one is questionnaire format and another one is physiological signals based on Heart rate variability, galvanic skin response, BP, and electrocardiography, etc., Machine learning techniques are applied to analyze and anticipate stress in employees. In this paper, we mainly focus on different machine learning techniques and physiological parameters for stress detection.

An Efficient Reverse Converter for the Four Non Coprime Moduli Set {2n,2n-1,2n-2,2n-3}

In this paper, residue to binary conversion is presented for the four moduli setsharing a common factor. A new and efficient converter for the moduli set using multipliers, carry saves and modular adders is proposed based on a cyclic jump approach. A theoretical hardware implementation and comparison with a state-of- the- art scheme showed that the proposed scheme performed better. The 4- moduli set selected provides a larger dynamic range which is needed for Digital Signal Processing (DSP) applications [7].

New Algorithm for Reverse Conversion in Residue Number System

Reverse conversion is an important exercise in achieving the properties of Residue Number System (RNS). Current algorithms available for reverse conversion exhibits greater computational overhead in terms of speed and area. In this paper, we have developed a new algorithm for reverse conversion for two-moduli set and three-moduli set that are very simple and with fewer multiplicative inverse operations than there are in the traditional algorithms like the Chinese Remainder Theorem (CRT) and Mixed Radix Conversion (MRC).

A Novel Pre-Processing Approach for the Denoising of Alzheimer Disease Image Dataset

Medical imaging doing an indispensable part in the area of medicine. Noise in the image is maddening as it worsens the quality of image. Thus, removal of noise is perpetually a problematic work in the images of all domain. Alzheimer’s disease, a neurological dysfunction in which destruction of cells in brain creates mental weakening and memory loss. The distinguished reason for Alzheimer’s disease is low brain activity and low blood flow. We proposed a framework for removal of noise in Alzheimer disease image using histogram equalization, thresholding, open morphological operation and a wavelet transform. This framework reduces the noise and significantly better than the existing methods used for Alzheimer disease images.

Data Replication Using Data Mining Techniques

Database Replication is the successive electronic duplicating of information from a database in one PC or server to a database in another with the goal that all clients share a similar dimension of data. The outcome is a conveyed database in which clients can get to information significant to their assignments without meddling with crafted by others. Anyway information replication is an entrancing theme for both hypothesis and practice. On the hypothetical side, numerous solid outcomes requirement what should be possible as far as consistency: e.g., the difficulty of achieving agreement in offbeat frameworks the blocking idea of CAP hypothesis, and the requirement for picking an appropriate rightness foundation among the numerous conceivable. On the pragmatic side, information replication assumes a key job in a wide scope of settings like storing, back-up, high accessibility, wide territory content dissemination, expanding versatility, parallel preparing, and so forth. Finding a replication arrangement that is reasonable in whatever number such settings as could reasonably be expected remains an open test.