The reduction of dimensionality has become inevitable for the analysis of a large data set in order to extract useful information for data analysis. To achieve this, two-dimensional reduction methods are proposed. Main content analysis (PCA) and linear discriminant analysis (LDA) are compared. Both are used to extract efficient characteristics of large vectors in the input data. PCA is an unsupervised algorithm because it ignores class tags and its purpose is to look for addresses (main components) that exploit the modification of a data set. Unlike the PCA, the LDA monitors and calculates the directions (linear discriminators) that represent the axes that exploit the separation between various classes.

A smart antenna system allows combination of antenna arrays through signal processing capacity for transmission and adaptive reception. Smart antennas normally complement the signal quality of wireless by enhancing capacity through increased radio frequency. This paper focused on the quantitative analysis and performance experiment of smart antennas for signal quality in wireless network. Virtual wireless network was created for signal propagation and simulated in MATLAB using adaptive array. Performance analysis was examined, by measuring the signal intensity against noise ratio and throughput. The simulation results showed that adaptive beam array produces weight vectors to optimize radiation pattern at steady flow of the amplitude by minimizing the interference.

Computer programming and programming language is relatively machine-dependent, as it spring up control element called software through computational algorithms. Introductory programming is quite fundamental to study of computing and computational theories, and as such it is offered /taught at elementary or intermediate level for computer science majors across tertiary institutions. Though, different programming languages had came into being with focus and diversity for problem solving in different domains. Exploratory context of lexical and semantic similarities between BASIC and Python were provided in this paper.

Programming languages are the way developers communicate with computers. In the past two or three decades, multiple languages have evolved. Today, a common topic for discussion among software engineers is the comparison of these programming languages. Often times, questions or enquiries are made by students or people who wishes to venture into application development on which programming language will be more effective to learn and use for their programming. However, most times, good answers to these enquiries or questions are not easily found. This paper, presents a comparative analysis between two amongst the several programming languages we have. There two programming language compared in this paper are the go programming language and swift programming language. These languages are compared in terms of parameters such as speed, efficiency as per memory, compilation, usage, OS support and paradigm.

Client-server model for network architecture and connectivity are considered insecure with security threats due to common traffic with exposed rogue devices in Virtual Private Network (VPN). The weaknesses of client-server web application or domain hotspot includes security invasion due to open synchronization among clients or from client device to web or network server. Any unauthorized client can hack client account and can change the data, by pretending to be the legitimate source with masquerading attack or SQL injection. In this paper, architectural design of secure middleware for client-server communication was demonstrated with logical authentication in Virtual Private Network (VPN) to avoid privacy.

Distributed Database Systems as the name implies, is known for its ability to be geographically dispersed on separate computers but still communicate remotely via a given computer network. Thus, query execution on a distributed database will have to access data from different geographical sites. It is based on this premise that’s one has to worry about the speed, reliability, security of query operations carried out on this database since its execution affects different sites in the organization. This paper discusses various techniques used to search a distributed database system in a bid to compare them to identify their efficiency in terms of speed, reliability and security

With the advent of digital technology and the accompanying gains in processing speed and data storage, techniques in signal processing have become increasingly sought after in the finance industry. Predicting cryptocurrency has become an increasingly interesting research area for both researchers and investors, and many prediction models have been proposed. This paper evaluates the accuracy of ARIMA as linear models and GARCH as non-linear models to predict the weekly price of cryptocurrency Bitcoin. We selected the best ARIMA models and the best GARCH model based on model selection criteria AIC, AICc, and BIC, then make a comparison between ARIMA and ARMA−GARCH models to determine which is better to use in the similar situation. The analysis of this study is carried out with the assistant of R software. The accuracy of GARCH and ARIMA models for predicting the weekly price of cryptocurrency Bitcoin was compared using different statistical forecasting evaluation criteria. We found that linear models perform better than nonlinear models and the ARIMA model is better than the ARMA−GARCH model

The purpose of the study is to investigate the challenges of Taxation on Commercial property investment in Obio/Akpor L. G. A, Rivers State of Nigeria. Open ended questionaires, structured on the 2-point yes /no was used because of flexibility and simplicity benefits in data interpretation.  A total of 102 questionaires were administered to commercial property investors, Estate Surveyor and Valuers and staffs of Rivers State Board of Internal Revenue (Estate and Tax department) and of these number 92 questionaires were completed and returned representing a response rate of 90.2%. The Data collected was analyzed using the SPSS package of descriptive frequency and mean. Results revealed that the forms of property tax in the study area include stamp duty, consent fees, title registration, capital gain tax, withholding tax, and inheritance/gift tax, betterment tax, planning rate, tenement rate, land use changes, development levy, operational permit and signage levy; the forms of commercial property investments include office buildings, retail outlets, multi-family buildings, land and non-residential properties such as hotels, hospitality, and medical and self-storage development; the challenges to commercial property taxes includes assessment and valuation inconsistencies, illiteracy and ignorance, illegal documents, attitude of the tax payer and taxing authority and defective policies. Based on the findings, the need for relevant stakeholders in tax administration to formulate or modify existing tax policies to fit effectively into different business sectors including the commercial property sector was recommended

The massive increase in computation power in the years past has substantially increased our ability to solve more complex problems with their performance evaluations in diverse areas. Advanced programming approaches are now becoming profitable to make decisions with the latest developments in the area of optimization. One of the standards of elegant algorithm design and also a useful tool that delivers classic algorithms for a wide range of combinatorial optimization issues is the dynamic programming. Our experiment will be based on using Python to solve Dynamic Programming.

There is an alarming rate of data leakage in most social networks and not every social media user takes data security seriously. A major challenge faced by most Social Media users is the problem of data leakage. There are basically two major data leakage problems, they include Malicious Data Leakage (MDL) and Inadvertent Data Leakage (IDL) respectively. Despite the various improvements of data security by different encryption algorithms, there are still open problems and occurrences of data leakages on Social Media. In this work, we developed an improved Data Leakage Detector for Social Media Users. We adopted Rapid Application Development Methodology (RAD) in this approach. We implemented with Hypertext Preprocessor (PHP) programming language using Ant Bee Colony Algorithm and MySQL Relational Database Management System as backend. The results obtained show that fraudulent data leakage can be blocked through Internet Protocol (IP) address validation and message notification to the Social Media User. The work could be beneficial to Social Media users, to Software Developers and to Ministries, Departments and Agencies that require relevant information on the prevention of data leakages to hackers.

Artificial intelligence is sometimes referred to as synthetic intelligence due to enormous task and artificial entities with real-world applications. Extensive review of relevant literature iterated the application areas of informed and uninformed search strategies. A comparative survey of selected search algorithms was provided in this paper, for problem solving based on completeness, logic structure, time complexity and space complexity.

The management of big data for youth empowerment programmes in Nigeria has become an alarming issue. Youth empowerment is an attitudinal, structural, and cultural process whereby young people gain the ability, authority, and agency to make decisions and implement change in their own lives and the lives of other people, including youth and adults. This study recommended an improved database model for big data analytics and management using a MySQL-based approach. The relevant details from the study also highlighted the need for complementing big data management with MySQL-based concepts.

Image edification method makes it possible to improve the quality of image for visual perception or to aid projected analysis of the image for feature extraction. The main problem in image processing is attributed to signal representation and modeling, enhancement, restoration and reconstruction from projections. Characterized elements of an image could be a large array of discrete dots or various luminous objects; and plays significant roles in quantizing and sampling for computer graphics. Image model is a considerable factor in choosing appropriate de-noising technique and processing domain for image enhancement. This paper presents the use of Gaussian filter to improve image model with instrumentality of Matrix Laboratory (MATLAB). The scientific demonstration showed that, the visual perception was enhanced, when the image quality was improved.

As part of globalization of IT industry, software development outsourcing has become a very common practice and even a separate industry that offers growth opportunities. USA and Europe have been recognized as most common clients that outsource their software development mostly to developing countries to reduce production costs. But costs are not the only factor that clients consider when choosing a vendor and with more countries on the market that offer software development services, the decision for clients become even harder. This topic had been covered in several research papers and by analyzing available literature, the purpose of this article is to try to create a conceptual model of factors that are influencing outsourcing patterns, focusing on the factors that are significant on a country/destination level. By reviewing the literature, following groups of factors have been identified: economic, administrative, human capital, political and security factors. Each group lists several factors that are further elaborated based on available literature.

Continuous increase and complexity of transaction data for stock prices, now paves way for big data computing. Financial market is somehow dynamic and tends to be challenging as stock trading system, where investors could make money or lose their entire life savings. Efficient classification and analysis of huge amount of financial data can facilitate extraction of significant information to support business decision. Clustering provides insight into large dataset or huge volume of data which may be tedious for human analysis by grouping similar and related objects to form clusters. Nowadays, business environment is associated with big data which requires a good number of clusters and computational tool for processing. This paper provided k-means clustering approach for analyzing large amount of financial / stock data.