Nafees Zaman’s research while affiliated with University of Zagreb and other places

Predictive maintenance has gained importance across various industries, including the automotive sector. It is very challenging to detect vehicle failures in advance due to the intricate composition of various components and sensors. The vehicle’s reliability is of utmost importance for ensuring the absence of fatalities or malfunctions to foster economic development. This study introduces an innovative method for developing a predictive framework for vehicle engines with faster and higher decision accuracy. The framework is specifically designed to recognize patterns and abnormalities that may suggest prospective engine problems in real-time and allow proactive maintenance. We assessed the performance of the developed vehicular engine health monitoring systems using a deep learning model based on essential measures like root mean square error, root mean square deviation, mean absolute error, accuracy, confusion matrix, and area under the curve. In this case, the deep learning models are developed by following ensemble techniques using the most prominently used machine learning techniques. Significantly, Stacked Model 1 outperformed other stacked models (Models 2 and 3) and achieved an impressive AUC value of 0.9702 with a low root mean square error (RMSE) of 0.3355, a high accuracy rate of 0.9470, and a precision of 0.9486. It happens due to the effective incorporation of different approaches into Stacked Model 1 , which signifies a significant advancement in predicting vehicular engine failures. The model can be used in real-time monitoring systems to continuously monitor the health of vehicular engines and provide early warnings of potential failures, thereby reducing maintenance costs and improving safety.

A considerable portion of the world frequently experiences flooding during the monsoon season. As a result of this catastrophic event, hundreds of individuals have become homeless. In addition, rescuers are not usually effective enough to rescue the majority of victims. This is due to inadequate rescue operations infrastructure, a severe flaw in today’s technologically advanced society. This manuscript proposes a microservice-dependent secure rescue framework that uses geographic information system mapping with a K-Means clustering algorithm to identify flood-prone regions. Numerous microservices, such as fleet management, cloud computing, and data security, integrate and execute the framework in pre- and post-flood situations. Labeling data from the proposed framework generates a support vector machine-based classifier for predicting flood risk. Furthermore, a hybrid A* algorithm is developed to find an optimal route for the rescue operation. Based on the K-means clustering results, which reduced the variance by 89.2 percent overall, dividing the data into six clusters was the best option for this study. The smoothness of the suggested hybrid algorithm is also used to verify its superiority.

The unwanted vehicular engine irregularities diminish vehicular competence, hinder productivity, waste time, and sluggish personal/national economic growth. Transportation sectors are essential infrastructures that require practical vulnerability assessment to avoid unexpected consequences through risk severity assessment. Artificial intelligence would be vital in the Industry 4.0 era to eliminate these issues for seamless activity and ultimate productivity. This article presents a risk management framework that includes an efficient decision model for monitoring and diagnosing vehicular engine health and condition in real-time using vulnerable components information and advanced techniques. To do this, we used the vulnerability identification frame to identify the vulnerable objects. We created a decision model that used an infrastructure vulnerability assessment model and sensor-actuator data to diagnose and categorise engine conditions as good, minor, moderate, or critical. We used machine learning and deep learning algorithms to assess the effectiveness of the risk management system’s decision model. The stacked ensemble of the deep learning algorithm outperformed other standard machine learning and deep learning algorithms in providing 80.3% decision accuracy for the 80% training data and efficiently managing large amounts of data. Anticipating the proposed framework might assist the automotive sector in advancing with cutting-edge facilities that are up to date.

A far-reaching expansion of advanced information technology enables ease and seamless communications over online social networks, which have been a de facto premium correspondents in the current cyber world. The ever-growing social network data has gained attention in recent years and can be handy for industrial revolution 4.0. With the integration of social networks with the Internet of Things being noticed in different industries to enhance human involvement and increase their productivity, security in such networks is increasingly alarming. Vulnerabilities can be characterized in the form of privacy invasion, leading to hazardous contents, which can be detrimental to social media actors and in turn impact the processes of the overall Social Network-Integrated Industrial Internet of Things (SN-IIoT) ecosystem. Despite this prevalence, the current platforms do not have any significant level of functionality to capture, process, and reveal unhealthy content among the social media actors. To address those challenges by detecting hazardous contents and create a stable social internet environment within IIoT, a statistical learning-enabled trustworthy analytic tool for human behaviors has been developed in this paper. More specifically, this paper proposes a machine learning (ML)-enabled scheme SPY-BOT, which incorporates a hybrid data extraction algorithm to perform post-filtering that arbitrates the users’ behavior polarity. The scheme creates class labels based on the featured keywords from the decision user and classifies suspicious contacts through the aid of ML. The results suggest the potential of the proposed approach to classify the users’ behavior in SN-IIoT.

Smart campus is a recent idea in the development of information and communication technology, being a combination of cloud computing, Internet of Things (IoT) and other emerging technologies. This paper demonstrates the result of our research efforts using IoT technology for the development of a smart campus, which also assures the improvement of vocabulary knowledge of the students. Our proposed system is mainly based on e-learning which provides definition of words and how to use that word in a sentence. However, students can take part in it and add more vocabularies in this glossary system under supervision through cloud computing. The quantitative research method is applied to validate the proposed system that provides the positive outcome. Therefore, it is an important research finding for vocabulary learning that can contribute to the building of smart campus exploiting the e-learning technologies.