Employee attrition, which causes a significant loss for an organization, is the term used to describe the natural decline in the number of employees in an organization as a result of numerous unavoidable events. If a company can predict the likelihood of an employee leaving, it can take proactive steps to address the issue. In this study, we introduce a deep learning framework based on a Bidirectional Temporal Convolutional Network (Bi-TCN) to predict employee attrition. We conduct extensive experiments on two publicly available datasets, including IBM and Kaggle, comparing our model’s performance against classical machine learning, deep learning models, and state-of-the-art approaches across multiple evaluation metrics. The proposed model yields promising results in predicting employee attrition, achieving accuracy rates of 89.65% on the IBM dataset and 97.83% on the Kaggle dataset. We also apply a fully connected GAN-based data augmentation technique and three oversampling methods to augment and balance the IBM dataset. The results show that our proposed model, combined with the GAN-based approach, improves accuracy to 92.17%. We also applied the SHAP method to identify the key features that most significantly influence employee attrition. These findings demonstrate the efficacy of our model, showcasing its potential for use in various industries and organizations.

Driver behavior is a determining factor in more than 90% of road accidents. Previous research regarding the relationship between speeding behavior and crashes suggests that drivers who engage in frequent and extreme speeding behavior are overinvolved in crashes. Consequently, there is a significant benefit in identifying drivers who engage in unsafe driving practices to enhance road safety. The proposed method uses continuously logged driving data to collect vehicle operation information, including vehicle speed, engine revolutions per minute (RPM), throttle position, and calculated engine load via the on-board diagnostics (OBD) interface. Then the proposed method makes use of severity stratification of acceleration to create a driving behavior classification model to determine whether the current driving behavior belongs to safe driving or not. The safe driving behavior is characterized by an acceleration value that ranges from about ±2 m/s2. The risk of collision starts from ±4 m/s2, which represents in this study the aggressive drivers. By measuring the in-vehicle accelerations, it is possible to categorize the driving behavior into four main classes based on real-time experiments: safe drivers, normal, aggressive, and dangerous drivers. Subsequently, the driver’s characteristics derived from the driver model are embedded into the advanced driver assistance systems. When the vehicle is in a risk situation, the system based on nRF24L01 + power amplifier/low noise amplifier PA/LNA, global positioning system GPS, and OBD-II passes a signal to the driver using a dedicated liquid-crystal display LCD and light signal. Experimental results show the correctness of the proposed driving behavior analysis method can achieve an average of 90% accuracy rate in various driving scenarios.

This project is aimed to develop an indoor locating system (ILS) using Bluetooth low energy (BLE) that able to track personal asset in indoor venues, in order to aid a user to trace for lost or misplaced items where the GPS signal strength is uncovered. The system design provides the user with the Internet of Things (IoT) proof-of-concept (PoC) for indoor locating based systems using Bluetooth connectivity topology. Furthermore, the performance of the system is evaluated in term of signal speed, energy consumption, and sufficient distance of deploying both transmitter and receiver. For the system development, ESP32 WiFi and BLE supported modules are used as the front-end support devices to receive nearby reporting Bluetooth Beacons signal, while a local computer runs as backend computing server to allow trilateration processing based on the reported Bluetooth RSSI values. MQTT that support IoT technology is installed on the local computer with web socket enable-feature allows the plugin to run on the internet browser. Besides that, React Application is used to create user interface on the internet browser which converts the received raw RSSI data into an animated reactive web interface. Using this IoT application, users can monitor or track the current location of the reported Bluetooth signal devices. In summary, the ILS is capable of showing the tracking device using a 2D map on the browser. Hence, it can save consumers time and effectively useful.

This research proposes a study on two-way communication between deaf/mute and normal people using an Android application. Despite advancements in technology, there is still a lack of mobile applications that facilitate two-way communication between deaf/mute and normal people, especially by using Bahasa Isyarat Malaysia (BIM). This project consists of three parts: First, we use BIM letters, which enables the recognition of BIM letters and BIM combined letters to form a word. In this part, a MobileNet pre-trained model is implemented to train the model with a total of 87,000 images for 29 classes, with a 10% test size and a 90% training size. The second part is BIM word hand gestures, which consists of five classes that are trained with the SSD-MobileNet-V2 FPNLite 320 × 320 pre-trained model with a speed of 22 s/frame rate and COCO mAP of 22.2, with a total of 500 images for all five classes and first-time training set to 2000 steps, while the second- and third-time training are set to 2500 steps. The third part is Android application development using Android Studio, which contains the features of the BIM letters and BIM word hand gestures, with the trained models converted into TensorFlow Lite. This feature also includes the conversion of speech to text, whereby this feature allows converting speech to text through the Android application. Thus, BIM letters obtain 99.75% accuracy after training the models, while BIM word hand gestures obtain 61.60% accuracy. The suggested system is validated as a result of these simulations and tests.

Online services, ambient services, and recommendation systems take user preferences into data processing so that the services can be tailored to the customer’s preferences. Associative rules have been used to capture combinations of frequently preferred items. However, for some item sets X and Y, only the frequency of occurrences is taken into consideration, and most of the rules have weak correlations between item sets. In this paper, we proposed a method to extract associative rules with a high correlation between multivariate attributes based on intuitive preference settings, process mining, and correlation distance. The main contribution of this paper is the intuitive preference that is optimized to extract newly discovered preferences, i.e., implicit preferences. As a result, the rules output from the methods has around 70% of improvement in correlation value even if customers do not specify their preference at all.

This paper focuses on the cooperation between humans and AI programmers. We propose a kind of method named Human-AI pair programming by data stream in the form of a white box. The method can create a data stream programming showing its intermediate process in the IoT system. Using a dataset of machines from a chocolate production line, we present the usage of our method and compare two programmings for the predictive maintenance of a chocolate factory. We show that the prediction accuracy rate has increased by around 40% by Human-AI pair programming with the cooperation between human and AI programmers.