International Journal of Advanced Science and Computer Applications

Critical Success Factors of Digital Transformation in the Higher Education Sector

2024-06-25T21:25:08+00:00

Digital transformation (DT) has a significant impact on higher education institutions (HEIs), which is directly related to the development and performance improvement. There is, however, lack of understanding of the critical success factors of digital transformation in HEIs. Based on a review of the related literature, the study identified the critical success factors of digital transformation including digital literacy as central to DT process. This identification led to the development of the initial conceptual framework. Such findings can help to develop appropriate strategies and policies for better implementation of digital transformation programs for improving HEIs (managers, academics, and staff) in their DT.

Examining Blockchain Platforms: Finding the Perfect Fit for Various Topics

2024-07-11T12:39:38+00:00

This research compares some of the most well-liked Blockchain platforms. Depending on the orientation and the target domain, it greatly varies from one to the next. To offer readers a thorough grasp of the advantages and disadvantages of each platform, essential elements, including scalability, security, interoperability, and administration, are explored. The researchers who want to investigate the best user interface for developing and generating their Blockchain architecture quickly, analysing the nodes, saving transactions, and spreading data to all network members will benefit from comparisons between Blockchain platforms before and after. This paper will assist researchers in learning more about Blockchain platforms, their configuration/installation difficulty, and other details like the programming languages used in the structure, the description, and the outcome of a medium to expert IT researcher and the challenges that surpass him during the installation phase. Researchers will value this concept since it will save them money and time.

NEW INTEGRAL TRANSFORM AND SOME OF ITS RELATIONS AND APPLICATIONS

2024-06-26T17:57:26+00:00

In this manuscript, we introduce a new integral transform called
A. M. Abdallah transform which is a generalization of the Jafari and polynomial
integral transform for solving differential, partial and integral equations. The
proposed integral transform is applied to show high accuracy, efficiency and
simplicity.

Performance Analysis of Quicksort Algorithm: An Experimental Study of Its variants

2024-06-14T00:47:25+00:00

The Quicksort algorithm is often the best practice choice for sorting due to its remarkable efficiency on average cases, small constant factors hidden in the θ(n log n) notation, and its in-place sorting nature. This paper provides a comprehensive study and empirical results of the Quicksort algorithm and its variants. The study encompasses all Quicksort variants from 1961 to the present. Additionally, the paper compares the performance of different versions of Quicksort in terms of running time on integer arrays that are sorted, reversed, and randomly generated. Our work will be invaluable to anyone interested in studying and understanding the Quicksort algorithm and its various versions.

Developing Semantic Textual Similarity for Guragigna Language Using Deep Learning Approach

2024-09-19T08:12:50+00:00

Semantic Similarity is one of the highest levels of NLP. STS has significant advantages in NLP applications like information retrieval, information extraction, text summarization, data mining, machine translation, and other tasks. This research aims to present a deep learning approach for capturing semantic textual similarity (STS) in the Guragigna. The methodology involves collecting a Guragigna language corpus and preprocessing the text data and text representation is done using the Universal Sentence Encoder (USE), along with word embedding techniques including Word2Vec and GloVe and mean Square Error (MSE) is used to measure the performance. In the experimentation phase, models like LSTM, Bidirectional RNN, GRU, and Stacked RNN are trained and evaluated using different embedding techniques. The results demonstrate the efficacy of the developed models in capturing semantic textual similarity in the Guragigna language. Across different embedding techniques, including Word2Vec, GloVe, and USE, the Bidirectional RNN model with USE embedding achieves the lowest MSE of 0.0950 and the highest accuracy of 0.9244. GloVe and Word2Vec embedding also show competitive performance with slightly higher MSE and lower accuracy. The Universal Sentence Encoder consistently emerges as the top-performing embedding across all RNN architectures. The research results demonstrate the effectiveness of LSTM, GRU, Bi RNN, and Stacked RNN models in measuring semantic textual similarity in the Guragigna language.

SOLAR POWER INTEGRATED GREEN CAMPUS FRAMEWORK FOR ELECTRIC VEHICLE CHARGING INFRASTRUCTURE

2024-07-08T07:27:40+00:00

Global warming presents a serious threat to the environment and human livelihoods, with the residential building and transportation sectors being major contributors to greenhouse gas emissions. Electric vehicles (EVs) have gained prominence as a sustainable alternative to traditional fossil fuel-powered vehicles. The success of EVs hinges on efficient charging infrastructure. This research focuses on transportation pollution and greenhouse gas emissions, emphasizing the role of EVs. The study explores the importance of Electric Vehicle Charging Station (EVCS) location selection and introduces the concept of a Green Campus (GC) approach to enhance sustainability. As the world phases out carbon-producing vehicles like trains and buses, electrified transportation offers a greener alternative. However, to support the growing adoption of electric vehicles, charging infrastructure must expand and become more seamless. Some entities are exploring solar panels to power EVs, reducing their carbon footprint. The study proposes an EVSC-GC service architecture that aims to minimize carbon dioxide emissions, reduce electricity costs, and enhance charging efficiency. It leverages telematics, digital systems, and roadside cameras to optimize fuel consumption. Additionally, electronic wallets facilitate convenient payment for charging costs. This suggested EVSC-GC model improves charging demand, charging time, time distribution, and traveling velocity compared to existing methods, making electric mobility more sustainable and efficient.