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Impact of funding and geographical factors on software startups' success
No full textThe purpose of this study was to investigate and adress two important questions regarding the success of software startups. To begin, it investigated the level of impact that different types of funding structures and geographical factors have on the success of the software startup companies. It explored various machine learning models to predict the outcomes of startup ventures, taking into account important features, model performance and cost-effectiveness. As demonstrated in this report, the research provided answers to these questions.
The study identified the primary factors that contribute to over 64 per cent of the success or failure of a software startup company. Location-region accounts for 18%, Time initial funding has received accounts for 16%, timing of Final Funding accounts for 15%, Access to Venture Capital accounts for 11%, and Location - Country accounts for 5%.
Logistic regression emerged as the most suitable model for deployment, achieving an accuracy of 96.58%, precision of 96.51%, recall of 100%, and an F1 score of 98.22%. This is by utilising the CRISP-DM methodology, Python code, and Power BI for data scrutiny and analysis. In addition, this model provides significant cost savings, which amount to 73.81 million dollars. The study does, however, acknowledge that there are challenges associated with limitations in the dataset scope and timeframe. These findings highlight the significance of conducting a comprehensive analysis of a startup, which should include aspects such as financial evaluation, geographical analysis, and predictive modelling
The Role of Standardization in Cost Control for the Overall Performance of Construction Projects in the UK and Ireland
No full textThis research explores the critical role of standardization in cost control for construction projects in the UK and Ireland. Utilizing a qualitative approach, five interviews with industry professionals aged 25 to 60 were conducted, employing NVivo software for thematic analysis. The study systematically addresses three objectives: assessing current standardization practices, exploring the relationship between standardization and cost control, and identifying challenges and opportunities in implementation. The thematic analysis revealed eight key interconnected themes, including Safety Standards, Standardized Cost Control Methods, and Standardization Awareness. Participants, such as civil engineers and construction safety managers, shared insights on the positive impact of standardization on budget accuracy and financial outcomes. Despite regional differences, common challenges and opportunities emerged, emphasizing the need for transparent communication and improved collaboration. This research provides valuable insights for stakeholders and policymakers, offering a nuanced understanding of the interconnected elements influencing construction projects and laying the foundation for enhanced cost control strategies
Safeguarding of Financial Organization from Cyber-Attack using Next Generation Firewall (NGFW), Security Information & Event Management (SIEM) and Honeypot
Get PDFThis project explores a robust cybersecurity initiative aimed at fortifying financial organizations against cyber-attacks through the strategic integration of Next Generation Firewall (NGFW), Security Information & Event Management (SIEM), and Honeypot technologies. Conducted on the EVE-NG VM platform, the project employs a multi-zoned strategy, encompassing Outside/Attacker, DMZ, Core/Production, and Branch zones, establishing a secure network design. The NGFW emerges as a linchpin in preventing unauthorized access, specifically shielding critical web servers from potential cyber threats such as unauthorized SSH, FTP, RDP, and Telnet access. Controlled interactions are maintained, allowing real user engagement, while stringent measures block social media sites to enhance security policies and productivity.
Within the DMZ, an Intrusion Detection System (IDS) showcases real-time monitoring and alerting capabilities, swiftly identifying and notifying administrators of suspected intrusion attempts. Simultaneously, Wazuh SIEM and HFish honeypot contribute significantly to IT asset inventory, log collection, reporting, management, and attack pattern analysis. This multi layered defense approach not only safeguards sensitive financial data but also empowers organizations to stay one step ahead of cyber adversaries. The amalgamation of NGFW, SIEM, and Honeypot technologies creates a proactive and adaptive defense system, allowing financial
organizations to navigate the complex challenges of cybersecurity with resilience and confidence in the face of an ever-evolving digital landscape
Predicting Energy Consumption Using Machine Learning
Get PDFElectricity is the primary source of energy all around the world, and predicting the given commodity is a substantial topic. Previous research has shown low accuracy in the given topic because of the non-linear electricity consumption pattern. Our research tries to incorporate data mining methodology- KDD to predict the power consumption based on various features. The study has used various feature Engineering techniques like F regression to find the most critical variables and build statistical and deep learning models like linear regression k-nearest neighbour and Random forest. The study has incorporated statistical models to understand the relationship between the dependent and independent variables and justify the results of the machine learning models, building a comparative study. These models are evaluated on different metrics like RMSE, MAE and MAPE, and linear regression has achieved the Minimum error and can be used for the power consumption
Customer satisfaction with the chatbot service: A study on HDFC Bank of India
No full textIndian banks are currently investing in chatbot technology to enhance customer service and offer virtual support. This study aims to examine customer satisfaction with the chatbot service of HDFC Bank of India. A cross-sectional quantitative research design was used. Primary data was gathered directly from HDFC Bank clients using a close-ended structured questionnaire through Google Forms. The participants were chosen through convenient sampling via an online platform. The research uses an inferential quantitative research method by utilizing regression analysis. Situational Factors positively but non-significantly affect customer happiness. System quality greatly affects client satisfaction. Service Quality has little effect, defying expectations. Trust is crucial, positively affecting consumer pleasure. However, the Intention of Use is non-significant, indicating chatbot services require reevaluation. System dependability, service quality measurements, trust, situational dynamics, and user feedback-based strategy adjustments are recommended to improve customer satisfaction towards the HDFC Bank of India chatbot services
Coaching’s Effectiveness in Global Technology Organisations in Ireland - an Exploration of Factors and Barriers
No full textThis research provides an innovative exploration of coaching practices within the rapidly evolving Irish tech sector, focusing on the implementation, effectiveness, challenges, and outcomes of coaching in global tech entities in Ireland, a burgeoning hub of tech innovation, investment and growth.
This study methodically examines the prevalence, types, and perceived benefits of coaching programmes, alongside factors affecting their effectiveness. Its significance lies in its unique multidimensional insights into coaching's complexity and interconnectivity from various stakeholder perspectives, encompassing strategic alignment, cultural integration, and diverse methodologies.
The findings aid tech organisations in creating effective coaching strategies tailored to industry-specific challenges and opportunities, such as developing HR strategies, improving employee engagement and retention, and adapting coaching for varied cultural contexts. This study links theory with practice, shaping coaching in the competitive digital landscape, and enhancing tech sector success. It also notably enriches the limited academic research on coaching effectiveness within the global tech industry, particularly focusing on Ireland
Analysis of factors affecting investment in stock market in Udaipur city (India)
No full textThis study explores the factors influencing stock market investment decisions among investors in Udaipur, a city in India, focusing on the impact of socio-economic characteristics and sustainability considerations. Utilizing a quantitative methodology, data were gathered from 154 respondents through structured questionnaires. The analysis reveals that expected returns, past stock performance, company goodwill and low level of risk are the most significant factors driving investment decisions, with company goodwill and low risk also being highly valued. In contrast, religious reasons, rumours, losses from other investments and stockbrokers/financial advisors/ analysts recommendation are deemed least significant. The study highlights the role of demographic variables and sustainability awareness in shaping investment preferences, providing insights that can inform tailored financial advisory services and policy-making aimed at promoting informed and sustainable investment practices
Detecting and Mitigating Advanced Persistent Threats using Machine Learning Techniques
No full textIntrusion detection systems play a pivotal role in safeguarding networks by analyzing network data to identify potential intrusions. The effectiveness of these systems relies on achieving high accuracy and detection rates while maintaining low false alarm rates. To analyze network data, a variety of techniques, including expert systems, data mining, and state transition analysis, are commonly employed. This project focuses on feature selection utilizing Recursive Feature Elimination (RFE) to enhance the performance of intrusion detection systems. Subsequently, attack detection is carried out on the NSL-KDD dataset using various machine learning algorithms, namely Random Forest, K-Neighbors, and Support Vector Classifier. Additionally, an Ensemble Learning approach is implemented, combining the outputs of all models for classification purposes. The primary aim is to conduct accuracy comparisons among the individual machine learning models and the Ensemble Learning framework.
The study demonstrates the application of machine learning algorithms for intrusion detection on the NSL-KDD dataset. Random Forest, K-Neighbors, and Support Vector Classifier serve as standalone models in this project. Moreover, an Ensemble Learning methodology is utilized to harness the collective decision-making capabilities of all models. The project rigorously evaluates the accuracy of each model and the Ensemble Learning technique. Through meticulous accuracy comparisons conducted on the NSL-KDD dataset, this project provides insights into the efficacy of diverse machine learning approaches for intrusion detection. By employing feature selection techniques and evaluating various models, the study contributes to understanding the strengths and limitations of individual algorithms
and highlights the potential advantages offered by Ensemble Learning in enhancing the accuracy of intrusion detection systems
An analysis of the adoption of risk management practices in ongoing public sector solar projects in Kerala
No full textThis research evolved around delays observed in renewable projects in India, especially during the pursuit of transitioning to 100% renewable energy, which is an overarching goal worldwide. The study aims to investigate the effectiveness of Risk Management Adoption, specifically looking at knowledge and implementation, in Public Sector Solar Projects in Kerala. The research targets the awareness, information dissemination, and data collection practices of middle and lower management, aiming to bridge a crucial gap in understanding risk management in this sector. Using a deductive approach and cross-sectional survey strategy, the study collects insights from professionals involved in solar construction projects. By analysing structured survey data from supervisors and engineers with SPSS, the findings emphasise the urgency of addressing delays and the need for improved Risk Management practices. The results and recommendations aim to enhance project management in the renewable energy sector worldwide