Smart Farming – Using weather data to support farmers in tilling their fields most efficiently
using A.I. technology in the agricultural sector; using natural resources efficiently; sustainability
Computers, Mathematics and Engineering as Medium in Creation of Unique and Original Art
The significant contribution of this research is to bridge the gap between techniques found in computing, mathematics and engineering with conventional art techniques to create original art. This is to ensure that human-generate art is tenable in the longterm, as opposed to AI-generated art.
Predictive Analytics for Proactive Email Security Risk Management: A Systematic Review
In recent years, email has become an important communication tool for sharing private messages to crucial business message exchanges. However, its widespread use makes it a major target for cyber-attacks, including phishing, spam, and malware. These growing threats highlight the urgent need to investigate email security risk management to protect against attacks and maintain the integrity of communication systems. The study reviews the literature on the challenges of email security, risk management, and the role of predictive analysis in combating these threats. Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), thirty-five (35) relevant peer-reviewed research articles were identified in various open research databases. This systematic literature review (SLR) also includes relevant case studies. The findings reveal that the integration of machine learning (ML), natural language processing (NLP), and real-time data analytics into email security frameworks improves threat detection and mitigation. Furthermore, these models often lack adaptability across languages and cultures. Additionally, they do not integrate well with human-centric security measures. Therefore, it is important to develop culturally adaptive predictive models, sector-specific solutions for industries such as finance and healthcare and incorporate behavioural analytics to enhance email threat detection and prevention. In other words, a comprehensive approach that combines technical advances with behavioural insights is crucial to strengthening email security and maintaining the integrity of global digital communications amid evolving cyber threats.
Stacking LLM Models’ Predictions for Feature Selection in Anomaly Classification
Large language models (LLMs) are increasingly being integrated into machine learning (ML) pipelines, particularly for tasks like feature selection in supervised classification. With the growing diversity of available LLMs, their predictions often complement one another, making ensembles of LLMs a promising approach for solving various ML challenges. In this paper, we propose using stacking methods to combine the predictions of multiple LLMs. The focus of the ML task is anomaly detection, specifically identifying whether an anomaly has occurred in a system and classifying its type. The ensemble’s base models are built on feature sets selected by six different LLMs. We demonstrate that stacking LLM predictions can enhance the accuracy of individual classifiers and advocate for the use of stacking as a simple yet effective method for integrating traditional classifiers with LLMs. Additionally, we assess the impact of various base classifiers and meta-classifiers on the performance of the proposed approach.
Strengthening Fault Tolerance of Private/Consortium Blockchain with Trusted Execution Environment
Consensus is one of the key components of Blockchain. Common public blockchains use Proof-Of-Work or Proof-Of-Stake as their consensus protocols. In contrast, private or consortium blockchains often use Raft, which is only crash fault tolerant. It means that strong trust on node holders in private or consortium blockchains is required. To relax the strong trust requirement, we propose by taking raft as foundation and modification on raft and leveraging threshold signature and trusted execution environment to improve security. We have implemented and integrated our proposed consensus algorithm with ConsenSys Quorum. Our experiment shows that our work has slight performance degradation on blockchain compared to original Raft.
Leveraging ChatGPT for Sponsored Ad Detection and Keyword Extraction in YouTube Videos
This study is significant for several reasons. First, it provides a scalable and automated solution for detecting and analyzing advertisements within video content, which is typically labor-intensive when done manually. Second, it offers insights into the relationship between advertisements and video content, which can have profound implications for advertisers seeking to improve targeting strategies and for content creators aiming to optimize sponsored ad placements within their videos. Third, the research lays the groundwork for future advancements in content-based advertising, where the alignment between ad messaging and content themes can be refined using advanced natural language processing (NLP).
A Novel Approach to Verification of Neural Cyber-physical Systems
The verification of Cyber-physical System (CPS),
particularly those incorporating neural networks for safety-critical
functions remains an ongoing challenge due to the lack
of advanced verification and validation frameworks. Current
methods are often limited in their scalability and ability to
comprehensively verify system-level and component-level properties,
leading to potential vulnerabilities in these systems. This
issue becomes even more pronounced when dealing with hybrid
systems that integrate physical processes and neural network-based
controllers. We propose a novel verification framework
tailored for complete CPS verification using a decomposition-based
approach to address this challenge. Our method performs
sequential-distributed verification, ensuring each component
adheres to compositional Metric Interval Temporal Logic
(MITL). By applying this framework to a model (CPS), we use
backward induction to verify that component-level and system-level
properties remain within predefined operational ranges,
derived from simulation data. Implemented in MATLAB,
this approach enhances the verification process by identifying
potential failure points across subsystems, providing a scalable
solution for verifying complex hybrid systems. This method
significantly improves verification accuracy and enables precise
identification of faulty components, making it a highly effective
tool for robust system design and analysis.
The Adoption of Internet of Things in Higher Education: Opportunities, Challenges, the Role of vision 2030 in Saudi Arabia
The research highlights the growing yet uneven adoption of IoT in Saudi universities, identifying key benefits like improved pedagogy and decision-making, while also addressing challenges such as infrastructure, financial, and cultural barriers. It provides strategic recommendations to enhance IoT integration in line with Vision 2030.
Quantifying the Effectiveness of Cloud and Edge Servers on Energy-Saving of Mobile Real-time Systems
Our findings provide insights into designing optimized task offloading configurations tailored to specific mobile system characteristics, balancing the benefits of cloud and edge environments.
Monitoring The Rate of Change of Vegetation Growth in Mine Rehabilitation Using Machine Learning
This study contributes to the literature on image processing, clustering, and time series forecasting in environmental monitoring. It successfully applied K-means clustering to segment HSV images, effectively tracking vegetation changes over time with high accuracy, despite challenges from lighting variations. The forecasting component, using Prophet, modelled vegetation growth in relation to mining activities through simulated scenarios, providing insights into the impact of external factors on vegetation. These findings enhance clustering techniques for image segmentation and offer a flexible method for monitoring and forecasting vegetation changes, with potential applications in ecological and environmental management.