The field of self-driving cars is one that is rapidly growing in popularity. The goal of autonomous vehicles has always been to avoid accidents. It has long been argued that human errors while driving are the primary cause of traffic accidents, and autonomous cars have the potential to remove this. An intelligent transportation system based on the Internet of Things (IoT) is required at some point for the vehicle to make an instant choice to evade accidents, regardless of the competence of a decent driver Mishaps on the road and in the weather are those that occur due to unfavourable weather circumstances such as fog, gusts, snow, rain, slick pavement, sleet, etc. There are many factors that might cause a vehicle to lose control, including speed, weight, momentum, poor fleet maintenance. It has the potential to lessen the number of collisions caused by poor weather and deteriorating road circumstances. An IoT-based intelligent accident escaping system for poor weather and traffic circumstances is presented here. A variety of sensors are used to check the health of the vehicle. Data from sensors is processed by a microcontroller and displayed on the dashboard of a car after it has been received. The proposed model combines both an IoT system that monitors weather and road conditions and an intelligent system based on deep learning that learns the adverse variables that impact an accident in order to anticipate and prescribe a harmless speed to the driver. The experimental results show that the proposed deep learning technique achieved 94% of accuracy, where the existing LeNet model achieved 80% of accuracy for the prediction process. The proposed ResNet is more effective than LeNet, because identity mapping is used to solve the vanishing gradient problems.

In 2020, the entire world was confronted with a pandemic that would inevitably cause profound changes in all spheres of life, from social to economic. The severe economic crisis that followed the pandemic outbreak has compelled organisations to reconsider the necessary changes and new challenges for ensuring continuity and performance. Researchers have produced a body of knowledge discussing the challenges faced by organisations due to the Pandemic; the trends and innovations emerged to ensure sustainability in this unprecedented situation. However, there are limited studies providing insights into the Malaysian context. Hence, this paper presents a case study of a Malaysian government agency, drawing insights on their approaches for sustaining performance whilst navigating the Covid-19 Pandemic. This agency was chosen because it is one of the government departments mentioned in the frontline category and must continue functioning during the pandemic. Due to limitations and restrictions during the lockdown, the case study was done by collecting information verbally, which was done entirely through tele-conversation. Respondents were chosen based on the hierarchy and ranking, as job duties differ according to rank. According to the study, employees are more motivated to achieve, be recognised, take responsibility, and grow. We found that re-designing the existing jobs into job enrichment and job simplification would help the agency unit to run more smoothly during pandemic. Decisionmaking can be improved by involving more people, as each person brings a distinct perspective to the table. A performance management system should be established to track remote workers' performance.