The traditional methods of searching for flats are often inefficient and lack necessary features, making it difficult for both landlords and tenants to find what they are looking for. The “Sakanat” mobile application is designed to bridge this gap by offering a user-friendly platform for property listing, searching, and management. Landlords can easily upload details, photos, and set rental terms, while tenants can efficiently find suitable flats based on various criteria using advanced search filters. Rent payments can be made directly through the app using a secure payment gateway, and tenants can also access a variety of additional services such as maintenance companies and flat transfer services. Sakanat leverages Flutter for cross-platform app development and Firebase for backend services and data storage. Development will prioritize user-friendliness, performance, and security. By facilitating connections between landlords and tenants, simplifying rent collection, and offering valuable additional services, Sakanat has the potential to revolutionize the flat rental experience.
This study employs the Theory of Reasoned Action (TRA) to elucidate the relationship between key factors and the purchase intentions of Malaysian Generation Z consumers during online flash sales promotions. The research framework posits utilitarian value (UV) and hedonic value (HV) as components shaping consumer attitudes, while informational influence (II) and normative influence (NI) contribute to subjective norms. Additionally, perceived perishability (PP) and perceived scarcity (PS) are considered to capture the effects of flash sales strategies. Subsequently, six hypotheses are formulated. A quantitative approach is adopted, involving the distribution of questionnaires to 280 respondents within the 1625 age range, representative of Generation Z in Malaysia. Preliminary findings from this study indicate the viability of the research, with data analysis conducted using SmartPLS software. Results reveal significant impacts of UV, HV, II, PP and PS on the purchase intentions (PI) of Malaysian Generation Z during online flash sales promotions. However, NI demonstrates no significant effect. This study offers valuable insights for both researchers and marketers, informing future endeavors aimed at optimizing online flash sales promotions.
Road transport, utilizing roadways, is important for the movement of goods and people, posing a significant maintenance challenge due to the deterioration caused by poor materials and natural disasters. Cracks in asphalt roads are a major concern, impacting transportation quality, safety, and comfort. Identifying these cracks is crucial for maintaining high-quality roads. Nonetheless, the subjective nature of crack detection, time limitations, inaccuracies, and expenses restrict the extent of manual assessment by professionals. To address these issues, this study proposes YOLOv4, a deep learning solution adept at detecting road cracks. Utilizing Unmanned Aerial Vehicles (UAVs) or drones, the road pavement was captured in Perlis, Malaysia, at an altitude of 10 meters for optimal image acquisition. These images are then labelled and input into the YOLOv4 model for training, resulting in accurate crack detection and validation. Comparing the results with the YOLOv3, Faster RCNN, and SSD model, found that YOLOv4 significantly improves crack detection accuracy, especially when using UAV images, thus enhancing previous research outcomes.
The study focuses on Artificial Intelligence’s importance in developing green innovation. Artificial intelligence significantly enhances green innovation by enhancing productivity, accelerating environmentally friendly technological advancements, and facilitating better decision-making through energy optimisation, waste reduction, and smart infrastructure support. This research adopted a scoping review approach utilising the Scopus database as a source of documents. The study highlights several aspects, such as publication trend by years, publication source and context analysis. The reviews included documents published since 2021. The method section is derived from the PRISM-ScR checklist table. The highest publisher was the Business Strategy and The Environment journal, and the country with the highest publishing was China. In addition, the study recommends that more efforts should be exerted to increase companies’ awareness of the importance of green innovation and the feasibility of developing green products.
Cardiovascular disease (CVD) has become a significant concern, being a leading cause of illness and death in many parts of the world. India, being a developing nation, plays a significant role in the burden of cardiovascular disease due to its unique combination of factors such as demography, lifestyle, pollution, genetic tendency, and other influences. Traditional cardiovascular risk calculators may not always provide precise estimates for the Indian population due to variations in risk factors. Hence, there arose a necessity to incorporate machine learning algorithms in this domain. This research suggests an effective cardiovascular disease prediction system tailored specifically for the Indian population, utilizing a novel feature selection approach. This new feature selection method (NFS) is the integration of three different existing methods-Mean Gini decrease (MDG), Recursive feature elimination (RFE) and Mutual Information (MI) based selection. The novelty in the method is that the importance score of each feature was obtained from each traditional feature selection method. These scores were then normalized and added. The top 10 features were selected on the basis of total score. After selecting these 10 features Statistical technique of Pearson Correlation coefficient was used to check the correlation between these features which resulted in selection of optimal feature set of 8 features. Three different machine learning techniques were used to compare the performance of this NFS namely Random Forest (RF), Naïve Bayes (NB) and Logistic Regression (LR). Out of all, RF was found to be the best algorithm with accuracy of 97.5% and sensitivity of 98.2%. The preprocessing techniques used were missing value imputation by Proximity Matrix and Normalization by Min/Max method. K fold cross validation was used with value of K as 10 to avoid any overfitting.
