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714 research outputs found
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Security Challenges in the Application of Blockchain Technology in Energy Trading
No full textThis paper examines the interplay between blockchain technology and the energy sector, focusing on security limits, barriers, and challenges. The authors discusses the primary components of cyber risks, including threats, vulnerabilities, and impacts that plague blockchain systems and their application, network, and data layers. Further, anonymity is a key feature of blockchain, ensuring that blockchain users, nodes, and miners remain unidentifiable by any measure. Therefore, perpetrator-focused measures are not viable when assigning responsibility for dangerous and illegal conduct. There are concerns that the concealment of identity will broaden blockchain attack surfaces and pose risks to energy security. The authors also emphasises the need for a well-defined and consistent legal and regulatory framework to address the complexities of blockchain development in the energy sector and assert that the maturity of blockchain in this industry will depend on balancing security and user rights and suggest implementing ex-ante and ex-post measures. This paper is novel; the author seeks to provide an in-depth analysis of the security challenges faced by blockchain-based energy applications and offer practical solutions for mitigating these cybersecurity threats and vulnerabilities
Factors Influencing Users’ Interest in Using Online Dating Apps with Age as the Moderator
No full textA mobile dating software app advertises itself as a social discovery tool and helps users start new, possibly romantic interactions. Online dating apps’ entry into personal life is promoted as a well-liked and distinctive method of establishing relationships. It is a sign of rising information and communication technology (ICT) use in this area. Previous studies have also shown that age may be a significant factor that influences individuals in using dating apps, especially among the younger generations. Various studies have been conducted in different countries but there is a lack of evidence that focuses on Malaysians and age. Thus, this research was conducted with 103 respondents through convenience sampling, in which participants completed an online survey. Using the SmartPLS, the analysis for the direct relationships and moderating relationships was conducted. The results showed that attraction, mood and selective swipers trigger users' interest in using dating apps although age did not interfere with the relationships. Age may not seem to be a vital factor in using dating apps. This study recognised the evolution of dating apps for mobile devices, the need for more data on how dating apps facilitated user relationship initiation and any possible implications for relationship developmental stages
To Vaccinate or Not? Perceived Benefits and Social Media Exposure as Predictors of COVID-19 Booster Jab Vaccination Practices with Self-Efficacy as Moderator
No full textVaccination acceptance and its rates among Malaysians have generally been shown to be high during the COVID-19 pandemic; however, statistics from the Ministry of Health Malaysia show that the uptake for booster jab vaccination among Malaysians is significantly lower than the rate of uptake when the first dose of vaccinations was offered. Thus, this study examined the perceived benefits and social media exposure of booster jab vaccination practices among Malaysians, moderated by self-efficacy. The study used the Stimulus-Response model as a theoretical basis. A quantitative method was applied by conducting an online survey and gathering 300 valid data. The study employed a non-probability sampling procedure that combined purposive and convenience samplings. The data was analysed using the structural equation modeling via Partial Least Square Structural Equation Modeling (PLS-SEM). The results affirmed that perceived benefits and exposure to social media have a positive and significant relationship with booster jab practices. Meanwhile, the moderating role of self-efficacy established a negative relationship between perceived benefits and booster jab practices, and self-efficacy does not moderate the path between social media exposure and booster jab practices. The research findings provide an opportunity for more comprehensive educational programmes that focus on consistency in providing the public with adequate knowledge and communicating consistent health messages to curb the spread of the COVID-19 virus. Conclusion, implications, and future research pathways are also discussed
Sentiment Analysis using Support Vector Machine and Random Forest
No full textSentiment analysis, is commonly known as opinion mining, is a vital field in natural language processing (NLP) that claims to find out the sentiment or emotion expressed in a given text. This research paper demonstrates an exhaustive survey of sentiment analysis, focusing on the application of machine learning techniques. Comprehensive parametric literature review has been completed to determine the sentiment analysis using SVM and Random Forest. Additionally, the paper covers preprocessing techniques, feature extraction, model training, evaluation, and challenges encountered in sentiment analysis. The findings of this research contribute to a deeper understanding of sentiment analysis and provide insights into the effectiveness of machine learning approaches in this domain. Based on the results obtained, two machine learning algorithms named as Random Forest and SVM were evaluated based on their accuracy in a classification task. The Random Forest algorithm achieved an accuracy of 0.78564, while SVM outperformed it slightly with an accuracy of 0.80394. Both Random Forest and SVM have demonstrated their strengths in achieving respectable accuracies in the given classification task. These results suggest that SVM, with its slightly higher accuracy of 0.80394, may be a more suitable choice when accuracy is the primary concern. However, the basic configuration need and characteristics of the problem at hand should be considered when choosing the better algorithm with better results
Emojis and Miscommunication in Text-Based Interactions Among Nigerian Youths
No full textThis paper explores the dynamic role of emojis in text-based communication among Nigerian youths and the potential implications for miscommunication. Emojis have become integral to contemporary digital conversations, offering users a visual means of expressing emotions, tone, and context within the constraints of text-based interactions. In the context of Nigeria, a country with a diverse linguistic landscape and a youthful population heavily engaged in online communication, understanding the impact of emojis on interpersonal exchanges becomes particularly pertinent. This paper examines the prevalence and patterns of emoji usage among Nigerian youths across various digital platforms. It investigates the cultural nuances and interpretations associated with emojis within the Nigerian context, considering factors such as regional differences, linguistic diversity, and socio-cultural influences. Furthermore, the study examines instances where emojis may contribute to miscommunication or misunderstanding, potentially exacerbating conflicts or hindering effective communication. Through a comprehensive review of existing literature, online discourse analysis, and case studies, this research aims to shed light on the ways in which emojis influence the interpretation of textual messages and the potential challenges they pose to clear and accurate communication. The study concludes that as digital communication continues to be a primary mode of interaction, it is essential for users to recognize the potential for misinterpretation, prompting the need for increased emoji literacy and awareness
An In-Depth Analysis on Efficiency and Vulnerabilities on a Cloud-Based Searchable Symmetric Encryption Solution
No full textSearchable Symmetric Encryption (SSE) has come to be as an integral cryptographic approach in a world where digital privacy is essential. The capacity to search through encrypted data whilst maintaining its integrity meets the most important demand for security and confidentiality in a society that is increasingly dependent on cloud-based services and data storage. SSE offers efficient processing of queries over encrypted datasets, allowing entities to comply with data privacy rules while preserving database usability. Our research goes into this need, concentrating on the development and thorough testing of an SSE system based on Curtmola’s architecture and employing Advanced Encryption Standard (AES) in Cypher Block Chaining (CBC) mode. A primary goal of the research is to conduct a thorough evaluation of the security and performance of the system. In order to assess search performance, a variety of database settings were extensively tested, and the system's security was tested by simulating intricate threat scenarios such as count attacks and leakage abuse. The efficiency of operation and cryptographic robustness of the SSE system are critically examined by these reviews
Sentiment Analysis in Social Media: A Case Study of Hike in University School Fees in Selected Nigerian Universities
No full textFaced with escalating operational costs and government disinvestment, Nigerian public universities are implementing tuition fee increases to maintain institutional functionality. This necessary fiscal measure comes in the wake of 2022 industrial action, which exacerbated pre-existing financial strain through extended work stoppages and potentially higher costs associated with resuming activities, while leaving unaddressed the longstanding demands of academics for improved welfare and working conditions. The court-mandated resumption of academic activities without resolution of these core issues further strained university finances, leading to a significant increase in tuition fees. Using VADER, this study investigated social media sentiments related to the increase in university school fees at Usmanu Danfodiyo University, Sokoto, and the University of Maiduguri. The results revealed that students' sentiments regarding the rise in tuition fees at the two universities were largely neutral, with 4.6% positive sentiment, 7.9% negative sentiment, and 87.5% neutral sentiment identified for Usmanu Danfodiyo University, Sokoto. In contrast, the University of Maiduguri had 0% positive sentiment, 19.8% negative sentiment, and 80.2% neutral sentiment. The study recommends seeking feedback through surveys or student leaders and offering scholarships to indigent students to address fee hike concerns at the two universities. While VADER is designed to handle social media textual data, few misclassifications of sentiments were noted and discussed
Prediction of Student’s Academic Performance through Data Mining Approach
No full textThe universities and institutes produce a large amount of student data that can be used in a disciplinary way and useful information can be extracted by using an automated approach. Educational Data Mining (EDM) is an emerging discipline used in the educational environment to deal with big student data and extract useful information. The data mining of students’ data can help the At-risk students as well as the stakeholders by the early warning. This study aims to predict the performance of the students based on student-related data to increase the overall performance. In existing studies, insufficient attributes and complexity of network models is a problem. The student’s current records and grades need to be analyzed. In this approach, the Levenberg Marquardt Algorithm (MLA) deep learning algorithm is used. The data consists of the class test, attendance, assignment and midterm scores. The neural network model consists of four input variables, three hidden and one output layer. The performance of the deep neural network is evaluated by accuracy, precision, recall and F1 score. The proposed model gained a higher accuracy of 88.6% than existing studies. The study successfully predicts the student's final grades using current academic records. This research will be beneficial to the students, educators and educational authorities as a whole
Hybrid Crow Search and RBFNN: A Novel Approach to Medical Data Classification
No full textThe Radial Basis Function Neural Network (RBFNN) is frequently employed in artificial neural networks for diverse classification tasks, yet it encounters certain limitations, including issues related to network latency and local minima. To tackle these challenges, researchers have explored various algorithms to enhance learning performance and alleviate local minima problems. This study introduces a novel approach that integrates the Crow Search Algorithm (CSA) with RBFNN to augment the learning process and address the local minima issue associated with RBFNN. The study evaluates the performance of this innovative model by comparing it to state-of-the-art models like Flower-pollination-RBNN (FP-NN), Artificial Neural Network (ANN), and the conventional RBFNN. To assess the efficacy of the proposed model, the study employs specific datasets, such as the Breast Cancer and Thyroid Disease datasets from the UCI Machine Repository. The simulation results illustrate that the proposed model surpasses other models in terms of accuracy, exhibiting lower Mean Squared Error (MSE) and Mean Absolute Error (MAE) values. Specifically, for the Breast Cancer dataset, the proposed model attains an accuracy of 99.9693%, MSE of 0.000307024, and MAE of 0.00789449. Likewise, for the Thyroid Disease dataset, the proposed model achieves an accuracy of 99.9535%, along with MSE of 0.000464932 and MAE of 0.0057098. For the diabetes dataset, the proposed model demonstrates an accuracy of 98.8073%, MSE of 0.003024, and MAE of 0.009449. In summary, this analysis underscores the enhanced accuracy and effectiveness of the proposed model when compared to traditional approaches
Knowledge-based Word Tokenization System for Urdu
No full textWord tokenization, a foundational step in natural language processing (NLP), is critical for tasks like part-of-speech tagging, named entity recognition, and parsing, as well as various independent NLP applications. In our tech-driven era, the exponential growth of textual data on the World Wide Web demands sophisticated tools for effective processing. Urdu, spoken widely across the globe, is experiencing a surge in, presents unique challenges due to its distinct writing style, the absence of capitalization features, and the prevalence of compound words. This study introduces a novel knowledge-based word tokenization system tailored for Urdu. Central to this system is a maximum matching model with forward and reverse variants, setting it apart from conventional approaches. The novelty of our system lies in its holistic approach, integrating knowledge-based techniques, dual-variant maximum matching, and heightened adaptability to low-resource language speakers, emphasizing the urgent need for advanced Urdu Language Processing (ULP) systems. However, Urdu, labeled as a low-resource language challenges compared to traditional machine learning (ML) approaches. Significantly, our system eliminates the need for a features file and pre-labelled datasets, streamlining the tokenization process. To evaluate the proposed model's efficacy, a comprehensive analysis was conducted on a dataset comprising 100 sentences with 5,000 Urdu words, yielding an impressive accuracy of 97%. This research makes a substantial contribution to Urdu language processing, providing an innovative solution to the complexities posed by the unique linguistic attributes of Urdu tokenization