Human resource management is a crucial component for the smooth operation of business organizations. However, changing perspectives and values among different age groups present significant challenges for organizations. Resignation and quick job changes can lead to huge budgetary and manpower issues. This research aims to develop and compare machine learning algorithms for predicting employees' turnover decisions using a public dataset on Kaggle.com The dataset includes performance data for 15,000 employees. The eight features and five algorithms, including Random Forest, Support vector machine, Logistic Regression, K-Nearest Neighbor, and Gaussian Naïve Bayes classifier, were employed to develop the model. The results showed that the Random Forest Classifier was the most efficient model with Geometric Mean, F1-score and AUC which are 0.995, 0.995, and 0.999, respectively when evaluated with stratified K-fold cross-validation. Moreover, using the SHapley Additive exPlanations method, five important factors for predicting employee turnover were identified. These factors include satisfaction level, average working hours in a month, number of responsibility projects, time spent with the company and salary level. These features enable the Human Resources department to gain valuable insights, facilitating strategic planning and proactive measures to prevent employee resignations. By doing so, businesses can achieve smoother and more efficient operations.
A user story is commonly applied in requirement elicitation, particularly in agile software development. User story is typically composed in semi-formal natural language, and often follow a predefined template. The user story is used to elicit requirements from the users' perspective, emphasizing who requires the system, what they expect from it, and why it is important. This study aims to acquire a comprehensive understanding of user stories in requirement elicitation. To achieve this aim, this systematic review merged an electronic search of four databases related to computer science. 40 papers were chosen and examined. The majority of selected papers were published through conference channels which comprising 75% of total publications. This study identified 24 problems in user stories related to requirements elicitation, with ambiguity or vagueness being the most frequently occurring problem reported 18 times, followed by incompleteness reported 11 times. Finally, the model approach was the most popular approach reported in the research paper, accounting for 30% of the total approaches reported.
Improper disposal of e-waste poses global environmental and health risks, raising serious concerns. The accurate classification of e-waste images is critical for efficient management and recycling. In this paper, we have presented a comprehensive dataset comprised of eight different classes of images of electronic devices named the E-Waste Vision Dataset. We have also presented EWasteNet, a novel two-stream approach for precise e-waste image classification based on a data-efficient image transformer (DeiT). The first stream of EWasteNet passes through a sobel operator that detects the edges while the second stream is directed through an Atrous Spatial Pyramid Pooling and attention block where multi-scale contextual information is captured. We train both of the streams simultaneously and their features are merged at the decision level. The DeiT is used as the backbone of both streams. Extensive analysis of the e-waste dataset indicates the usefulness of our method, providing 96% accuracy in e-waste classification. The proposed approach demonstrates significant usefulness in addressing the global concern of e-waste management. It facilitates efficient waste management and recycling by accurately classifying e-waste images, reducing health and safety hazards associated with improper disposal.
The Emergency Intercom System (EIS) is a communication and video conferencing system designed to enhance emergency response in areas, such as malfunctioning lifts. This paper presents a feasibility analysis of EIS as internet of things device, focusing on the utilization of WebRTC over WebSocket, Socket.io, and LAN device hostname assignment. The study evaluates performance metrics including latency, throughput, dropped calls, lost packets, audio and video quality, resource utilization, and network bandwidth. The results indicate that EIS offers acceptable latency, sufficient throughput for simultaneous calls, minimal dropped calls and lost packets, and satisfactory audio and video quality. The system demonstrates effective communication and video conferencing capabilities, making it a promising solution for improving emergency response in areas. The findings highlight the potential for further research to optimize performance, address security considerations, and extend the system's applicability in diverse emergency scenarios.
Sarcasm detection is an imperative undertaking within the realm of natural language processing, albeit one that poses considerable challenges when confronted with mash-up languages, characterized by the amalgamation of multiple distinct languages. In response to the intricacies of sarcasm detection in mash-up languages, with a specific focus on the Indonesian-English language mash-up, this study introduces the Hybrid Pretrained Word Embedding approach as a means to enhance sarcasm detection. The primary objective of this research is to augment the precision of sarcasm detection in mash-up languages by amalgamating suitable word embeddings tailored to the employed terms. The present study combines two prevalent pretrained word embeddings, i.e Glove and Fasttext, wherein Glove is utilized to extract semantic context vectors for English words, while Fasttext is employed to extract semantic context vectors for Indonesian words. The classification process in this research leverages the deep learning methodology known as Bidirectional Gated Recurrent Unit (BiGRU). To assess the efficacy of the proposed approach, an extensive dataset comprising sarcastic and non-sarcastic tweets, written in a hybrid language of Indonesian and English, is acquired from the Twitter platform. The results unequivocally demonstrate that the Hybrid Pretrained Word Embedding approach significantly enhances sarcasm detection in mash-up languages, attaining a commendable classification accuracy of 93.57% and an F-measure of 97.94%. By offering an effective methodology to identify sarcasm in mash-up languages, this study yields a substantive contribution to the field of natural language processing.
