Cyberattacks can trigger power outages, military equipment problems, and breaches of confidential information, i.e., medical records could be stolen if they get into the wrong hands. Due to the great monetary worth of the data it holds, the banking industry is particularly at risk. As the number of digital footprints of banks grows, so does the attack surface that hackers can exploit. This paper aims to detect distributed denial-of-service (DDOS) attacks on financial organizations using the Banking Dataset. In this research, we have used multiple classification models for the prediction of DDOS attacks. We have added some complexity to the architecture of generic models to enable them to perform well. We have further applied a support vector machine (SVM), K-Nearest Neighbors (KNN) and random forest algorithms (RF). The SVM shows an accuracy of 99.5%, while KNN and RF scored an accuracy of 97.5% and 98.74%, respectively, for the detection of (DDoS) attacks. Upon comparison, it has been concluded that the SVM is more robust as compared to KNN, RF and existing machine learning (ML) and deep learning (DL) approaches.

The construction industry is known for poor performance, low productivity, high waste generation, and for lagging in the adoption of new technology. A high rate of material wastage in construction projects has economic and environmental implications for concerned stakeholders. Construction waste quantification is an essential requirement for formulating waste management strategies. Technological advancements like building information modeling (BIM) and artificial intelligence (AI) provide effective solutions to the construction industry to deal with these prevalent issues. This literature review-based study observes the scarcity of research on the application of BIM in construction waste quantification. The limited number of studies found in the literature confirm the ability of BIM-aided waste quantification models to forecast waste generation. Moreover, the application of these models can also assist in reducing waste to a considerable extent. This study recommends that further studies should be conducted on technology-assisted waste quantification in building and infrastructure projects to evaluate their effectiveness for subsequent implementation in the industry.

Technologically advanced countries are accruing benefits from the adoption of Building Information Modelling (BIM) in the Architecture, Engineering, and Construction (AEC) industry after decades of struggling for productivity enhancement using innovation and automation. Most of the developing countries have not been able to embrace technology in the AEC Sector and, consequently, are unable to ameliorate prevalent performance-related issues in construction projects. This review article identifies hurdles to BIM implementation in selected developing countries via an examination of the latest studies. The most significant challenges observed in this study are Lack of Training for Professionals, Lack of Awareness, Huge Capital Cost, Resistance to Change, and Complexity of BIM Software. This study is an update on previous studies conducted with the aim of assisting the implementation of BIM in developing countries.

Quality Function Deployment (QFD) is a powerful approach for establishing a strong relationship between a product or service and customer needs. It focuses on capturing the voice of the customer to enhance their satisfaction. In today’s competitive landscape, organizations must meet customer needs to thrive. This research focuses on utility-imparting organizations and aims to enhance customer satisfaction and service efficiency through the development of a correlation matrix. The study proposes an inductive framework and evaluates it using typical QFD techniques. Specifically, the research utilizes the House of Quality (HOQ) to improve organizational efficiency and customer satisfaction. The HOQ identifies key quality indicators by analyzing the relationship between customer needs (WHATs), and technical solutions (HOWs), as well as between different HOWs. The results of the HOQ analysis highlight the most crucial factors for improving service efficiency: effective customer service, a competitive and focused organization, accurate billing meters and methods, precise meter readings and billing, and prioritization of emergency response. The findings provide valuable guidelines for improving the efficiency of utility energy services.

The construction industry is struggling to resolve the issue of the enormous quantity of waste produced during construction processes, which impacts the performance and sustainability of projects. Causes of waste generation have been studied by researchers to formulate waste minimization strategies for these projects. The research on waste in highway infrastructure projects and waste causes specific to the roles and competencies of project team members is inadequate. This quantitative study addresses this gap by evaluating the influence of project managers (PMs) in minimizing CW through a structured questionnaire survey administered to 300 professionals, yielding 129 valid responses (43% response rate). The results indicate that 8.5% of construction materials are wasted in highway projects. Among four key project stakeholders (PM, quantity surveyor, designer, and client), PMs were rated as having the most significant impact on waste minimization (mean Likert score: 4.5/5). Using the Relative Importance Index (RII), the study identified the top five waste causes linked to PM competencies: faulty work requiring the work to be carried out again (RII = 0.742), wrong construction methods (0.734), lack of awareness (0.731), poor supervision (0.721), and poor material planning (0.706). A waste minimization framework is proposed, linking each of these causes to specific PM competencies and actionable strategies. These findings provide empirical support for targeting PM training and resource planning to reduce material waste in highway construction projects.