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SimpleScan: Technical Report
No full textThis Report shows how I developed and deployed my web-based web app and network scanning tool that can be used to increase security of websites or networks for business owners or individuals, this project aims to make a simple user-friendly experience for people looking to perform security scans such as spidering or active scans on websites using ZAP, as well as performing networks scans on networks using Nmap.
The Key functionalities of my tools is that you can monitor different types of scans you have made, view the scan results, and generate reports in XML. All functionality is managed by sessions and secure user authentication which also allows you to find scan results you have made in the past. OWASP Zap uses its API to perform the security assessments while Nmap is used to perform network scans.
This project demonstrates how to perform network and web security scans through a web app in simple way while also being secure and making results easily accessible. I would like to expand the project in the future to make it more comprehensive and add more features
An Evaluation of Racially Diverse Skilled Immigrants’ Experiences of Career Advancement in the Irish HR Industry
No full textGlobalisation has dramatically underscored the importance of diversity and inclusion within workplaces around the globe, particularly enhancing organisational productivity through the diverse perspectives that come with international migration. However, despite these potential benefits, skilled immigrants frequently encounter substantial barriers in accessing employment and advancing their careers. This qualitative study investigates the experiences of skilled immigrants, focusing on their access to employment, career advancement, and the availability of support programs designed to facilitate professional growth in Ireland's HR sector. This is in the bid to fill the gap that exist in the Irish literature as there is a dearth of studies that explore the perceptions, thoughts and experiences of racially diverse immigrants, while adopting an occupation specific approach.
Data from semi-structured interviews with seven HR professionals was analysed using thematic analysis. The findings suggest that immigrants experience significant obstacles such as stringent visa restrictions, difficulty securing interviews and discrimination during interviews which impedes access to employment opportunities. Despite these hurdles, the findings suggest that with substantial effort, career advancement is achievable. Immigrants must often perform significantly better than their local counterparts to gain recognition and progress within their careers. Importantly, the research identifies the availability of structured support programs that are crucial in aiding immigrants' professional integration and advancement within the sector.
Recommendations based on the findings include policy reforms aimed at expanding the definition of critical skills, simplifying visa sponsorship processes, and improving employer education to promote more inclusive hiring practices. Future research should aim to quantitatively evaluate the impact of support programs and further explore persistent barriers, thereby improving the integration and success of skilled immigrants in the HR sector
Working Remotely and Employee Wellbeing: A Qualitative Study of Irish Workers within the Pharmaceutical Sector
No full textEmployee wellbeing is becoming more important in the rapidly growing remote work environment. Previous research has established that employees with varied work arrangements (fully remote, hybrid remote) can behave differently to remote working. It has also been acknowledged that recent literature concerning the effects of telecommuting on the wellbeing of workers is divisive as well as dividing. It is recognised by several studies that remote working has various positive aspects for employees. However, other research also emphasises negative implications for employees.
Furthermore, additional investigation into the correlation between remote work and employee wellbeing is necessary due to a scarcity of literature on this topic in the Irish pharmaceutical sector. This indicates a gap in the literature and as a result, there is a need to conduct research into this topic by gathering remote employee’s experiences and opinions concerning their wellbeing specifically within the pharmaceutical industry in Ireland. Therefore, the purpose of this study is to bridge the gap via examining the effects of working remotely on the wellbeing of workers.
This research approach adopted a qualitative method, through means of semi-structured interviews. Eight participants were interviewed, representing a variety of fully remote and hybrid remote employees within the pharmaceutical industry. This approach allowed the investigator to critique this topic thoroughly, in addition to deriving experiences and views from employees regarding their wellbeing. The semi-structured interviews were recorded using Microsoft Teams and subsequently transcribed. Thematic analysis was used to evaluate the data, allowing further coding, and grouping into common themes. Additionally, the study’s ethical concerns were identified and investigated to fulfil the criteria of a competent researcher.
The findings emerged, indicating that working from home has a favourable impact on overall wellbeing. Nonetheless, it is also clear from this research that isolation had an adverse impact on remote employees’ societal wellbeing. The sample size and participant demographics may be a research limitation
A Deep Learning Framework to Identify Interconnectivity of Citation Networks
No full textCitation networks are graphs that describe citations within a collection of scholarly articles. Citation analysis can be used to identify the amount of interconnectivity such as the citation structure, word refreshes, influence, and quality of research. Identifying the related citation to an author’s research is a challenge due to the number of papers that exist, and the datasets that are available have a lot of papers which leads to datasets being very huge.
This research proposes a Deep Learning Framework to identify interconnectivity of citation networks. The proposed framework combines Transformer models and Interconnectivity. The transformer models are trained on three different datasets C2D-I, ACL-ARC, and SciCite. This research compares transformer models BERT_base, BERT_large, and DistilBERT to identify the model with the highest accuracy. This research is of interest to research students who require help in identifying the most relevant research papers for their research area
Optimizing Load Balancing in cloud computing using Enhanced Firefly Algorithm (EFA) Method
No full textCloud computing has seen a surge in interest from both the academic and business sectors due to its scalability and virtualization benefits. Efficient load balancing within cloud data centers is essential to maintain system stability and performance. Load Balancing (LB) is particularly critical in cloud environments where providers serve a multitude of clients, necessitating robust task scheduling that ensures equitable load distribution. Various strategies and algorithms have been developed to advance LB in cloud services, focusing on minimizing task execution time, reducing energy consumption, optimizing resource use, and rapidly allocating tasks among clusters of Virtual Machines (VMs). Nonetheless, these solutions often overlook the existing load on VMs, potentially leading to overload issues. To address this, the research introduces the Enhanced Firefly Algorithm (EFA), a refined approach leveraging Metaheuristic Optimization. EFA intelligently evaluates VM workloads in cloud infrastructures to distribute tasks without overloading any single VM. It is compared against traditional LB algorithms like Round Robin Algorithm (RRA) and Ant Colony Algorithm (ACO), using metrics such as resource consumption, computational speed, and time efficiency. The results demonstrate EFA’s superior performance over RRA and ACO, particularly as the number of task units (cloudlets) increases. EFA achieves better execution times, enhanced resource utilization, and more efficient LB solutions. This study underscores the necessity for LB mechanisms that consider the current load of VMs alongside resource, timing, and energy metrics, presenting EFA as a viable solution for dynamic and efficient load distribution in cloud computing
A comparative study of AES encryption modes and hashing for blockchain applications
No full textThis paper gives a complete examination of multiple modes of Advanced Encryption Standard (AES) algorithms, concentrating on their performance features and security consequences. A comparative analysis was conducted on all modalities to determine the fastest and most efficient option in terms of throughput. The Electronic Code Book (ECB) mode has been determined to be the fastest one thorough experimentation and analysis. However, it is important to note that this mode may have security weaknesses due to its block-independent nature. Conversely, Cipher Block Chaining (CBC) mode offers greater security through chaining techniques and competitive throughput. the findings underline the significance of balancing speed and security factors when selecting an AES encryption option. A comparative analysis was conducted on all modalities to determine the fastest and most efficient option in terms of throughput. Integration of Simple Mail Transfer Protocol (SMTP) with the Gmail API provides secure key distribution, boosting secrecy and integrity in email communication. This research gives significant insights for enhancing AES encryption and strengthening data security against evolving cyber threats
Unveiling the Power of CNNs with Attention for URL Phishing Detection
No full text91% of cybercrimes is initiated using phishing emails where the victim's personal sensitive information is achieved by the attacker.URL poses prime threats to online security which leads to financial losses and privacy which motivates us to investigate and propose a robust solution. This study is conducted to investigate URL phishing detection methods that traditional methods didn’t achieve and focuses on the comparison between machine learning and deep learning approaches. This study will explore the effectiveness of both ML and DL models using datasets containing benign and phishing URLs sourced from online repositories. The dataset has been split into training and testing stages in a ratio of 90:10 which is applied to 3 models. Random Forest Classifier and Extra Tree Classifier form Machine learning models, alongside Deep learning models such as Convolutional Neural Network with Attention Mechanism were implemented. Performance evaluation was done with the help of a confusion matrix and classification report. Further, an application using Flask is developed for testing phishing URLs. This web application will show URL phishing detection systems which will identify whether the entered URL is phishing or safe. Lastly, it was observed that CNN model performs best with superior and good accuracy of 91%
Predicting Fatalities: Enhancing Construction Site Safety Through Advanced Machine Learning
No full textIn the construction industry, ensuring workplace safety is a critical challenge. Jaby Mohammed and Md Jubaer Mahmud, from the Department of Technology at Illinois State University, address this issue by analyzing Occupational Safety and Health Administration (OSHA) Accident and Injury Data from 2015 to 2017. They use machine learning techniques, exploring nine algorithms, including XGBoost and Random Forest, which achieve accuracies of 65.29% and 58.24%, with AUC values of 78.83% and 69.52%, respectively. To improve their model, this research pays attention to specific details in the data and fine-tunes their methods, focusing on precision. After this adjustment, this research achieves an accuracy of 85.7% and an AUC of 92.5% for XGBoost, and an accuracy of 84.9% and an AUC of 91.3% for AdaBoost. This signifies that the methods employed in this research are more effective in predicting outcomes.
The study aims to transform safety management in the construction industry by establishing a data-driven system. By uncovering injury patterns, causal factors, and areas requiring improved safety measures in the unstructured OSHA data, the research contributes significantly to the workplace safety literature. This work envisions a safer future by combining advanced machine learning methods, detailed data analysis, and refined predictive models. The insights from this research offer practical guidance for safety-conscious organizations, with the potential to positively impact workplace safety practices and decision-making
Deep Learning-Based Lung Cancer Detection and Classification from Medical Images
No full textLung cancer is one of the most common causes of cancer-related deaths, and early detection is critical for effective treatment. However, traditional diagnostic methods that include manual inspection of the medical imaging slides have limitations in their accuracy and can be time-consuming. To overcome these challenges, deep learning approaches like convolutional neural networks (CNN) have shown great potential for providing fast, accurate, and noninvasive diagnostic tools. However, to improve the accuracy and efficiency of these models, a standardised image pre-processing technique is necessary. Such techniques could help reduce the cost and time associated with traditional diagnostic methods. By leveraging the power of deep learning, we can enhance the accuracy of lung cancer diagnosis, reduce the need for invasive tests, and ultimately improve patient outcomes. The empirical evidence from this investigation underscores the superior performance of the InceptionNetV3 model, and VGG19 model, achieving a 84.47%, and 85.84% accuracy rate in lung cancer detection. These findings not only underscore the significance of model selection in deep learning applications but also illuminate the path for further refinements in automated diagnostic technologies
Forex Rate Forecasting Based on Deep Learning Ensembled Predictions
No full textThe aim of this paper is to improve foreign exchange rate forecasting by evaluating the capabilities of Recurrent Neural Networks (RNNs), which are known for their ability to analyze sequential data. Four LSTM models with different architectures were created and compared to discover the most suitable architecture based on their performances.
Accurate forecasting of Forex rates is critical across industries, influencing the valuation of foreign investments and driving the dynamics of international trade. Given the complexity and volatility inherent in Forex markets, which are defined by detailed time-series data, traditional forecasting approaches hardly capture the full range of market dynamics and other macroeconomic factors and behaviours.
By overcoming frequent RNN time series predictors that only consider historical data from the predicted variable as input, the models in this paper perform a multidimensional approach that aims to provide a more comprehensive understanding of the factors that influence the Mexican currency (MXN) price against the American dollar (USD). The most distinctive aspect of this approach is the use of the Keras Functional API, which allows more flexible model architectures, including the integration of different input streams to allow for a wider range of factors that influence the MXN. In this case, there are included external macroeconomic variables such as crude oil international prices and the performance of the Mexican stock market.
The provided findings are that by considering the previously mentioned macroeconomic factors correlated to the Mexican currency, into the LSTM models, it was possible to get more reliable predictions with a lower error rate, than the models that do not consider these macroeconomic factors and only consider historical data from the predicted currency