A Comparative Review of Information Technology Project Management in Private and Public Sector Organization

Both private and public sector organizations tend to recognize the prominence of information technology within project management techniques and practices. The primary objective of this paper is to present a comparative review of information technology within project management in private and public sectors. Moreover, this research provides an extensive review of related topics such as the evolution of information technology, factors contributing to project abandonment, and the tools and techniques of management that effect project success. In conclusion, the authors present a variety of practical and effective guidelines and recommend approaches for the successful deployment of information technology within project management for both private and public sectors

DRIP - Data Rich, Information Poor: A Concise Synopsis of Data Mining

As production of data is exponentially growing with a drastically lower cost, the importance of data mining required to extract and discover valuable information is becoming more paramount. To be functional in any business or industry, data must be capable of supporting sound decision-making and plausible prediction. The purpose of this paper is concisely but broadly to provide a synopsis of the technology and theory of data mining, providing an enhanced comprehension of the methods by which massive data can be transferred into meaningful information

Business Intelligence Technology, Applications, and Trends

Enterprises are considering substantial investment in Business Intelligence (BI) theories and technologies to maintain their competitive advantages. BI allows massive diverse data collected from virus sources to be transformed into useful information, allowing more effective and efficient production. This paper briefly and broadly explores the business intelligence technology, applications and trends while provides a few stimulating and innovate theories and practices. The authors also explore several contemporary studies related to the future of BI and surrounding fields

How Do Business Students in the U.S. and in Cameroon Perceive Faculty Attributes? A Comparative Study

This study investigates student perceptions of ten selected attributes embedded in faculty behavior. These attributes are classified as primary and secondary attributes. The 4 primary attributes include effective communication (ability to communicate information effectively), ability to combine knowledge and application in real world cases and examples, high level of knowledge in presented materials, and substantial business experience in the area taught. The 6 secondary attributes include active association with the business community, active participation in academic organizations, active participation in business organizations, extensive publication of business research in scientific/scholarly journals, extensive publication of business articles in practitioner/trade oriented journals, and the college or university degree from which the faculty earned their highest degree. This study also investigates potential difference in the emphasis placed on the ten attributes between the surveyed business students in both countries. Utilizing two samples (graduate and under graduate students) from business schools (at public, private, and proprietary universities) in the United States and in Cameroon, Africa, the surveyed students revealed stronger support for the primary attributes than for the secondary attributes. The results of this study also indicated that the ability to communicate effectively, the application of knowledge to real world cases, substantial business experience in the discipline area taught, and knowledge of the materials being presented are considered the most important attributes in assessing teaching effectiveness. While students in both countries have similar mean rankings of the selected ten attributes, they significantly differ in their ratings of six attributes: actively participates in academic organizations, publications in practice/trade journals, actively participates in practice related organizations, college from which the professor earned their highest degree, and association with the business community. Further investigation using exploratory factor analysis revealed that students in both countries have moderate agreement with the two component conceptualized model: the primary and secondary business faculty attributes

Efficient Link Prediction Model For Real-World Complex Networks Using Matrix-Forest Metric With Local Similarity Features

Link prediction in a complex network is a difficult and challenging issue to address. Link prediction tries to better predict relationships, interactions and friendships based on historical knowledge of the complex network graph. Many link prediction techniques exist, including the common neighbour, Adamic-Adar, Katz and Jaccard coefficient, which use node information, local and global routes, and previous knowledge of a complex network to predict the links. These methods are extensively used in various applications because of their interpretability and convenience of use, irrespective of the fact that the majority of these methods were designed for a specific field. This study offers a unique link prediction approach based on the matrix-forest metric and vertex local structural information in a real-world complex network. We empirically examined the proposed link prediction method over 13 real-world network datasets obtained from various sources. Extensive experiments were performed that demonstrated the superior efficacy of the proposed link prediction method compared to other methods and outperformed the existing state-of-the-art in terms of prediction accuracy

A Systematic Analysis of Community Detection in Complex Networks

Numerous techniques have been proposed by researchers to uncover the hidden patterns of real-world complex networks. Finding a hidden community is one of the crucial tasks for community detection in complex networks. Despite the presence of multiple methods for community detection, identification of the best performing method over different complex networks is still an open research question. In this article, we analyzed eight state-of-the-art community detection algorithms on nine complex networks of varying sizes covering various domains including animal, biomedical, terrorist, social, and human contacts. The objective of this article is to identify the best performing algorithm for community detection in real-world complex networks of various sizes and from different domains. The obtained results over 100 iterations demonstrated that the multi-scale method has outperformed the other techniques in terms of accuracy. Multi-scale method achieved 0.458 average value of modularity metric whereas multiple screening resolution, unfolding fast, greedy, multi-resolution, local fitness optimization, sparse Geosocial community detection algorithm, and spectral clustering, respectively obtained the modularity values 0.455, 0.441, 0.436, 0.421, 0.368, 0.341, and 0.340.

Leveraging brain–computer interface for implementation of a bio-sensor controlled game for attention deficit people

In video games, neurofeedback via Electroencephalogram (EEG) has emerged as a method for treating attention deficit, alongside preventative measures such as behavioral therapy. By 2020–21, the Neuro-Gaming industry has reached USD 6.29 billion. As a remedy to attention deficit and to take advantage of the ever-growing EEG-based gaming industry, this research work presents the design and implementation of an EEG-controlled 2D game built in the Unity 3D game engine. Our research includes steps like dataset creation, training the learning algorithms, classification, and deciding on those results in the designed game whether to shoot a target or not. We read signals from the Neurosky sensor, user orientation, and linear acceleration. We pre-process them via transforms into a processed input for various learning algorithms. The results are then exported to the game engine and used in the game. In classification, we have achieved 89% accuracy and F1 score of 87% with LSTM

Integrating visual stimuli for enhancing neural text style transfer with EEG sensors

Font Designers need to create each font character by hand. With the help of what we propose, designers finish the process quickly and automatically. We use a neural network to learn and generate new fonts. We provide two vectors as input (Bigrams and Style Vectors) of Urdu language, encoded manually with one-hot encoding and t-distributed neighbor embedding. The transposed convolution neural network takes care of learning from input, where it decodes the input into beautiful fonts. Thus, by changing the style vector, the required changes are reflected in the resultant font style. Additionally, with the help of simulated annealing, we generate meaningful and full-length sentences. To evaluate whether the fonts generated are aesthetically sound, we provide the generated sentences to the end-users as visual stimuli and measure their responses in terms of their attention and meditation levels with EEG sensors. Higher sensor levels suggest the font quality and visual appeal

Real-World Protein Particle Network Reconstruction Based on Advanced Hybrid Features

Biological network proteins are key operational particles that substantially and operationally cooperate to bring out cellular progressions. Protein links with some other biological network proteins to accomplish their purposes. Physical collaborations are commonly referred to by the relationships of domain-level. The interaction among proteins and biological network reconstruction can be predicted based on various methods such as social theory, similarity, and topological features. Operational particles of proteins collaboration can be indirect among proteins based on mutual fields, subsequently particles of proteins involved in an identical biological progression be likely to harbor similar fields. To reconstruct the real-world network of proteins particles, some methods need only the notations of proteins domain, and then, it can be utilized to multiple species. A novel method we have introduced will analyze and reconstruct the real-world network of protein particles. The proposed technique works based on protein closeness, algebraic connectivity, and mutual proteins. Our proposed method was practically tested over different data sets and reported the results. Experimental results clearly show that the proposed technique worked best as compared to other state-of-the-art algorithms

Parallel tensor factorization for relational learning

Link prediction is a statistical relational learning problem that has a variety of applications in recommender systems, expert systems, and knowledge bases. Numerous approaches have already been devised to solve the problem. Tensor factorization is one of the ways to solve the link prediction problem. Many tensor factorization techniques have been devised in the last few decades, including Tucker, CANDECOMP/PARAFAC, and DEDICOM. RESCAL is one of the famous tensor factorization technique that can solve large scale problems with relatively less time and space complexity. The time complexity of RESCAL can further be reduced by making it parallel. This variant can also be applied to large scale datasets. This article focuses on devising a parallel version for RESCAL. A decent decrease in execution time has been observed in the execution of parallel RESCAL