Social media such as forums, blogs and microblogs has been increasingly used for public information sharing and opinions exchange nowadays. It has changed the way how online community interacts and somehow has led to a new trend of engagement for online retailers especially on microblogging websites such as Twitter. In this study, we investigated the impact of online retailers' engagement with the online brand communities on users' perception of brand image and service. Firstly, we analysed the overall sentiment trends of different brands and the patterns of engagement between companies and customers using the collected tweets posted on a popular social media platform, Twitter. Then, we studied how different types of engagements affect customer sentiments. Our analysis shows that engagement has an effect on sentiments that associate with brand image, perception and customer service of the online retailers. Our findings indicate that the level, length, type and attitude of retailers' engagement with social media users have a significant impact on their sentiments. Based on our results, we derived several important managerial and practical implications. Better ways to engage and handle customer on microblogs are explored.Level, length, type and retailers' sentiments of tweets affect customer sentiments.Emotional transitions occurred from the beginning to the end of conversations.Effective engagements improve brand image, perceptions and explore market trends.

In Intrusion Detection Systems (IDS), stream data classification faces significant challenges due to concept drifts and feature evolution, where traditional methods struggle to maintain accuracy over time. One critical challenge is feature drift, which refers to changes in the relevance of features over time, directly impacting the model’s classification accuracy. This paper introduces the Incremental Feature Drift-Aware Genetic Programming Combiner (IFDA-GPC), which integrates a Voting Enhanced Deep Q-Network Multi-Agent Feature Selection (VE-DQN-MAFS) mechanism to address these challenges. The framework extends the existing IGPC architecture by incorporating dynamic feature selection and employing a multi-agent system with voting-based aggregation. This approach enhances feature selection decisions, especially in cases where agents provide conflicting assessments of feature relevance. By reconciling these variations, the framework ensures consistency and reliability in real-time classification tasks. The framework was evaluated using benchmark datasets, including KDD Cup ’99, CICIDS-2017, HIKARI-2021, and ISCX2012, under both evolving and non-evolving scenarios. Results demonstrate that GPC-KOS-DFS, derived from IFDA-GPC, significantly outperformed existing methods in accuracy, F1-score, recall, and AUC metrics. Notably, it achieved an accuracy of 93% on the CICIDS-2017 dataset, showcasing its effectiveness in handling feature drifts while maintaining high classification performance. These findings establish IFDA-GPC as a robust solution for managing evolving data streams in intrusion detection systems.

With the advent of Industry 4.0, future workspaces are expected to evolve in tandem with technological advances in industry and education. Industry 4.0 calls for transformation and effective talent development is vital in ensuring national aspirations are achieved while eliminating redundancy and ensuring consistency. As such, this study aims to understand the impact of Industry 4.0 on computer engineering-related workforce and skills development within Multinational Companies (MNCs) to Small Medium Enterprises (SMEs) in Malaysia. In this study, online questionnaires were distributed to evaluate the current and future hiring trends. The study reveals that most of the employees have positive perceptions about the industrial current practice on Industry 4.0 and identified the prospective demands on the professions that will be affected. Five significant areas of required competencies found in this study are adaptability, soft skills, software engineering, data analytics, and technical skills. The findings provide empirical evidence about current and future employment scenarios in Malaysia concerning the possible impact of Industry 4.0 on the companies and issues involved in managing the transition to Industry 4.0. Besides, the emergent skills required by workforces that are previously unaddressed in the literature were identified. Empirical evidence from the analysis contributes to shaping the educational systems of the future and helps to proactively identify specific skills shortages at an early stage.

Precise news recommendations are critical in today’s digital landscape. However, conventional approaches overlook fine-grained sentiment nuances associated with individual entities in news content. This paper presents SentiEntityRec, a novel graph neural network framework that enriches traditional entity embeddings using a global graph-enhanced model by incorporating sentiment vectors. Experiments conducted with MIND datasets have shown that SentiEntityRec surpasses existing models in key performance metrics. For example, the proposed model improves +0.5% AUC over GLoCIM. These results underscore the superior efficacy of incorporating entity-sentiment analysis into graph-based news recommendation systems.