EAI Endorsed Transactions on Scalable Information Systems

Grasshopper-Based Detection of Fake Social Media Profiles

2024-09-01T17:13:43+00:00

The proliferation of fake profiles on social media platforms presents a growing challenge for digital ecosystems, where the detection of such profiles is critical to maintaining the integrity of online environments. This paper introduces a hybrid approach that integrates the Grasshopper Optimization Algorithm with various Machine Learning classifiers, including Support Vector Machine, Naive Bayes, and Random Forest. The nature-inspired metaheurisitic used is employed to optimize key hyperparameters of these classifiers, thereby enhancing their performance in detecting fake profiles. The proposed method is evaluated on a well defined balanced dataset, demonstrating significant improvements in classification performance, particularly in terms of accuracy, precision, recall, and F1-score. The results suggest that the proposed hybrid approach can effectively address the challenges associated with balanced and imbalanced datasets in fake profile detection. Furthermore, the study discusses potential directions for improving scalability and applying the approach to larger and more dynamic datasets in the future.

Predicting product sales performance using various types of customer review data

2024-09-08T09:43:45+00:00

Today, in the e-commerce world, product reviews are a critical part of influencing consumer buying decisions and offer valuable insight to determine sales quality. But many current methods do not make efficient use of heterogeneous user-generated content (UGC) and those they predict with a unified model may ignore the different nature between various review types. In light of these limitations, this study introduces an integrated algorithmic framework that combines cutting-edge sentiment analyses and machine learning (ML) algorithms for sales quality prediction through automatic analysis of product reviews over the internet. The approach proposed will collect structured data from different sources during a systematic process and then consider the path of normalization, and sentiment analysis followed by feature selection to construct advanced prognosis models. The model proved highly effective, achieving an 88% accuracy rate in predicting sales quality. This strong performance indicates a significant correlation between sales performance and sentiment reviews. This new framework shows good promise that sentiment analysis in UGC can be used and deployed in e-commerce product evaluation and recommendation systems. Further research should investigate the integration of regional and temporal dynamics to improve model accuracy.

Adopting Open-Source SD-WAN: A Comprehensive Analysis of Performance, Cost, and Security Benefits Over Traditional WAN Architectures

2024-09-08T16:01:32+00:00

Many enterprises are using cloud computing innovation and remote services to the maximum. Working from home is becoming the norm. Favored legacy Wide Area Networks (WANs) are not up to the tasks, as they are suffering due to lack of scalability with their traditional non-virtualized form as it still requires a lot of physical components. Update and maintenance of fickle hardware costs a lot. There is a need for more flexible and scalable networking solutions. Many enterprise solutions offer proprietary form of SD-WANs (Software-Defined Wide Area Networks), but they are costly and inflexible, which means they are not practical for all applications. This paper proposes an Open-source SD-WAN with OpenDaylight platform as core that we have tested in a simulated environment along with Mininet and Oracle Virtual Box to study various scenarios. Test results show that it provides a 35% increase in throughput, decreases 40% in latency, and reduces packet loss by 50%, compared to traditional WANs. Additionally with Open-Source nature, it has a 20% lower operational coupled with the problem mitigation factors listed above, which makes it a more potential solution for the current woes of businesses.

Multimodal-Driven Emotion-Controlled Facial Animation Generation Model

2024-10-21T05:32:45+00:00

INTRODUCTION: In recent years, the generation of facial animation technology has emerged as a prominent area of focus within computer vision, achieving varying degrees of progress in lip-synchronization quality and emotion control.

OBJECTIVES: However, existing research often compromises lip movements during facial expression generation, thereby diminishing lip synchronisation accuracy. This study proposes a multimodal, emotion-controlled facial animation generation model to address this challenge.

METHODS: The proposed model comprises two custom deep-learning networks arranged sequentially. By inputting an expressionless target portrait image, the model generates high-quality, lip-synchronized, and emotion-controlled facial videos driven by three modalities: audio, text, and emotional portrait images.

RESULTS: In this framework, text features serve a critical supplementary function in predicting lip movements from audio input, thereby enhancing lip-synchronization quality.

CONCLUSION: Experimental findings indicate that the proposed model achieves a reduction in lip feature coordinate distance (L-LD) of 5.93% and 33.52% compared to established facial animation generation methods, such as MakeItTalk and the Emotion-Aware Motion Model (EAMM), and a decrease in facial feature coordinate distance (F-LD) of 7.00% and 8.79%. These results substantiate the efficacy of the proposed model in generating high-quality, lip-synchronized, and emotion-controlled facial animations.

NOMA Assisted Energy-Efficient MEC for Environmental Severity Monitoring in Power IoT Networks

2025-03-28T03:28:29+00:00

This paper proposes an energy-efficient mobile edge computing (MEC) scheme that utilizes non-orthogonal multiple access (NOMA) for environmental severity monitoring in Power Internet of Things (IoT) networks. The primary objective of the proposed approach is to optimize energy consumption while ensuring tasks are completed within their respective deadlines and meet reliability constraints. The scheme integrates NOMA's superposition coding with mobile edge computing to improve task offloading efficiency and reduce computational delays. To achieve this, an iterative water-filling (IWF) algorithm is applied to dynamically adjust the power allocation for each task based on varying channel conditions and latency requirements. The optimization problem is formulated to minimize energy consumption while respecting the given constraints, including outage probability and transmission rate. Simulation results demonstrate that the proposed IWF-based method significantly outperforms traditional schemes. For instance, under a stringent delay threshold of 10 ms, the IWF method reduces energy consumption by approximately 30\% compared to conventional approaches. Furthermore, even as the delay threshold increases, the IWF method consistently maintains a noticeable advantage, achieving up to 20\% lower energy consumption compared to other schemes.