An Overview of Data Storage in Cloud Computing

Cloud computing is a functional paradigm that is evolving and making IT utilization easier by the day for consumers. Cloud computing offers standardized applications to users online and in a manner that can be accessed regularly. Such applications can be accessed by as many persons as permitted within an organisation without bothering about the maintenance of such application. The Cloud also provides a channel to design and deploy user applications including its storage space and database without bothering about the underlying operating system. The application can run without consideration for on-premise infrastructure. Also, the Cloud makes massive storage available both for data and databases. Storage of data on the Cloud is one of the core activities in Cloud computing. Storage utilizes infrastructure spread across several geographical locations. Storage on the Cloud makes use of the internet, virtualization, encryption and others technologies to ensure security of data. This paper presents the state of the art from some literature available on Cloud storage. The study was executed by means of review of literature available on Cloud storage. It examines present trends in the area of Cloud storage and provides a guide for future research. The objective of this paper is to answer the question of what the current trend and development in Cloud storage is? The expected result at the end of this review is the identification of trends in Cloud storage, which can beneficial to prospective Cloud researches, users and even providers

Developing a Multi-modal Listing Service for Real Estate Agency Practice in Nigeria

Fraudsters posing as real estate agents threaten the reputation of real estate agencies in Nigeria. These fraudsters have continually defrauded unsuspecting members of the public. The major cause of this lapse is due largely to the fact that there is no known platform provided in the country that allows members of the public to verify a given real estate agent. This paper aims to provide support to real estate agency practice in Nigeria by developing a multi-modal listing service for verifying registered real estate agents and to also provide information on real estates available for sale, lease or rent. The requirements for the system were gathered through observation, literature survey and user survey. These requirements were then modelled using the Unified Modelling Language (UML). The system is developed both as a web and mobile application using an open source content management system (WordPress). This paper essentially presents the: requirements gathering process, design and implementation of the multimodal listing service as well as how it compares with other similar services developed elsewhere. The multi-modal listing service developed in this study is a welcome development due to the availability and widespread adoption of the Internet and Internet-enabled mobile devices in Nigeria. The tool can be of use to the National Association of Estate Agents in Nigeria - a body saddled with the responsibility of rebranding the real estate agency profession in Nigeria

A Machine Learning Prediction of Automatic Text Based Assessment for Open and Distance Learning: A Review

In this systematic literature review, automatic text-based and easy type assessment grading system using Machine Learning and Natural Language Processing (NLP) techniques was investigated. The major focus is on text-based and essay type assessment in ODL courses. Text-based and essay type questions is an important tool for performing quality examination and assessment to help the students gain mastery over the task and widen their horizon of knowledge and increase the learner’s development and learning than, for instance subjective question type, single choice question (SCQ), multiple choice question (MCQ) and true/false question type. Automatic text-based and essay type assessment grading system can be used as an important tool in ODL institutions, where assessment and examination can be quickly and easily evaluated for the purpose of efficient feedback. We carried out this study using quality, exclusion and inclusion criteria by selecting only studies that focuses on NLP and Machine Learning techniques for automatic text-based and essay type assessment grading task. Searches in ACM Digital Library, Semantic Scholar, Scopus, IEEE Xplore, Google Scholar, Microsoft Academic, Learn Tech Library and Springer is performed in order to retrieve important and relevant literature in this research domain. Conference papers, journals and articles between the year 2011 and 2019 were considered in this study. This study found 34 published articles describing automatic text-based and essay type assessment and examination grading task out of a total of 1260 articles that met our search criteria

Spatial Analysis of Violent Crime Dataset Using Machine Learning

The monster called crime has been living with us from the beginning of human existence and impacts negatively on the general health of a nation. Different approaches were employed in the past studies for predicting occurrence of violent crime to aid predictive policing, which makes conventional policing more efficient and proactive. This paper investigates the accuracy of Machine Learning-based crime prediction approaches, which were used previously by other researchers. This study presents Machine Learning approaches to violent crime prediction. Five years’ historical dataset between July 2014 and July 2019 were collected from Nigerian Police Lagos, analyzed and used for training the models built. Two different Machine Learning predictive models, Decision Tree and K-Nearest Neighbor, were implemented using IBM Watson Studio and violent crime prediction accuracy of 79.65%, and 81.45% were obtained, respectively, with the real-life dataset collected from Nigerian Police Obalende Lagos and online crime reported portal during violent crime prediction in Lagos. This could be used to enhance crime prevention and control strategies in curbing the worrisome crime rate in the country

Cloud Providing on Demand Storage Services Enforcing Techno Commercial Service Level Agreement for an improved QoS

Advancements in the application service provisioning have created stringent scenarios in the world of cloud. The framework now needs to comply with specific requirements of consumers with specific demands. The diversified needs of storage and other related services in the dynamically changing needs of consumers. Such application requirements are becoming frequent as a consequence to high performance computing and large data/information retrievals. At the same time, understandings between the Cloud and its consumers have to be more transparent and should work under pre-defined terms. This leads to a committed Quality of Service delivered which directly effects revenues. To bring in transparency and durability between the service providers and service seekers, there is a need of a Techno Commercial Service Level Agreement that fixes responsibility of both parties. In this work, authors propose and implement an algorithm for meta scheduling that envisages enforcement of SLA apart from managing the distinct cloud applications such as storage. The work assesses the changes brought in terms of revenues, QoS enhancement, recoveries, building long term ties etc.. The results have been found to be encouraging while testing the scheduler proposed with stringent service level conditions well. The agreement imposes clear by laws and penalties to the bi-part irate redressed on service provider cloud & the consumers. The frame work is tested and achieves objectives & goals with specific features suitable for storage cloud

Particle swarm optimization of the spectral and energy efficiency of an SCMA-based heterogeneous cellular network

Background The effect of stochastic small base station (SBS) deployment on the energy efficiency (EE) and spectral efficiency (SE) of sparse code multiple access (SCMA)-based heterogeneous cellular networks (HCNs) is still mostly unknown. Aim This research study seeks to provide insight into the interaction between SE and EE in SBS sleep-mode enabled SCMA-based HCNs. Methodology A model that characterizes the energy-spectral-efficiency (ESE) of a two-tier SBS sleep-mode enabled SCMA-based HCN was derived. A multiobjective optimization problem was formulated to maximize the SE and EE of the SCMA-based HCN simultaneously. The multiobjective optimization problem was solved using a proposed weighted sum modified particle swarm optimization algorithm (PSO). A comparison was made between the performance of the proposed weighted sum modified PSO algorithm and the genetic algorithm (GA) and the case where the SCMA-based HCN is unoptimized. Results The Pareto-optimal front generated showed a simultaneous maximization of the SE and EE of the SCMA-based HCN at high traffic levels and a convex front that allows network operators to select the SE-EE tradeoff at low traffic levels flexibly. The proposed PSO algorithm offers a higher SBS density, and a higher SBS transmit power at high traffic levels than at low traffic levels. The unoptimized SCMA-based HCN achieves an 80% lower SE and a 51% lower EE than the proposed PSO optimized SCMA-based HCN. The optimum SE and EE achieved by the SCMA-based HCN using the proposed PSO algorithm or the GA are comparable, but the proposed PSO uses a 51.85% lower SBS density and a 35.96% lower SBS transmit power to achieve the optimal SE and EE at moderate traffic levels. Conclusion In sleep-mode enabled SCMA-based HCNs, network engineers have to decide the balance of SBS density and SBS transmit power that helps achieve the desired SE and EE.publishedVersio

Intrusion Detection Using Anomaly Detection Algorithm and Snort

Many organizations and businesses are all delving into crafting out an online presence for themselves. This could either be in the form of websites or mobile apps. Many advantages come from an online presence; however, there are some drastic disadvantages that, if left unchecked, could disrupt any business or organization. Chief amongst these disadvantages is the aspect of security. However, many of the techniques that some organizations utilize to guard against unwanted access have been inadequate, and as a result, many unauthorized system break-ins have been reported. This is not made any better by the fact that certain applications used in hacking or system breach are now commonplace. Therefore, the focus of this work is to take an Intrusion Detection System (IDS) for a local network to detect network intrusion. A statistical approach, as well as a binomial classification, was used for simplicity in classification. The result shows the outlier value for each item considered; a 1 depicts an attack, a 0 depicts normalcy. The results are promising in dictating intrusion and anomalies in an IDS system

Employability Skills: A Web-Based Employer Appraisal System for Construction Students

In order to increase employability skills among construction students, industrial training is essential. These trainings should be accurately appraised to ensure effectiveness and accountability. Therefore, this study developed a Webbased employer appraisal system for construction students with a view of improving employability skills. The behavior of the web-based system was modeled using a system block diagram, use case diagram and activity diagram. Using HTML, CSS, MySQL and C-Sharp programming language the Web-based employer appraisal system was developed and presented in screenshot. Employers are able to appraise students on industrial training schemes within their firm using the platform and upload recommendation letters. The Web-based employer appraisal system ensures speed, accuracy, proper storage of appraisal information and ease of retrieval of data