Algorithm News and Research

RSS

Harnessing Machine Learning for Predicting School Dropout

Researchers utilized various machine learning algorithms to develop predictive models for identifying students at risk of dropping out of secondary and higher education in Mexico. Leveraging demographic, socioeconomic, and educational data, the study demonstrated the effectiveness of artificial neural networks (ANN) in achieving high reliability (99%) in predicting school dropout, highlighting key variables such as school attendance, type, location, occupation, income, and marital status.

22 Feb 2024

A Novel Approach for Edge Unlearning in Graph Neural Networks

Researchers introduced Unlink to Unlearn (UtU), a novel method designed to simplify edge unlearning in graph neural networks (GNNs) and address the issue of over-forgetting. UtU facilitates unlearning by unlinking forget edges from the original graph structure, effectively preserving privacy protection capabilities and link prediction accuracy while minimizing computational overhead. Experimental evaluation across real-world datasets demonstrated UtU's efficacy in unlearning, maintaining high accuracy in downstream tasks, and effectively preventing over-forgetting, making it a practical and lightweight solution for edge unlearning in GNNs.

21 Feb 2024

Optimizing Content Caching in Vehicular Networks: A Gale–Shapley Approach

Researchers proposed a novel content caching scheme for fog nodes in 5G-enabled vehicular networks, utilizing a modified Gale–Shapley algorithm to optimize content placement based on the preferences of content providers and vehicles. Their approach effectively reduced content downloading time, improved quality of service, and ensured fair and efficient content placement by considering factors such as content popularity, vehicle connectivity, and channel quality.

21 Feb 2024

NLP-based Quantification of Low-Carbon Policies in China's Manufacturing Sector

This article presents a novel method for quantifying low-carbon policies in China's manufacturing industries, addressing previous deficiencies in direct measurement. By constructing a comprehensive low-carbon policy intensity index and utilizing innovative natural language processing techniques, researchers provided valuable insights into policy quantification and its impact. The resulting dataset, comprising 7282 policies, offers multidisciplinary researchers a robust foundation for analyzing the effectiveness of low-carbon policies in China's manufacturing sector.

20 Feb 2024

Lightweight Enhancements in YOLOv5 for Vehicle Detection

Researchers introduce a lightweight enhancement to the YOLOv5 algorithm for vehicle detection, integrating integrated perceptual attention (IPA) and multiscale spatial channel reconstruction (MSCCR) modules. The method reduces model parameters while boosting accuracy, making it optimal for intelligent traffic management systems. Experimental results showcase superior performance compared to existing algorithms, promising advancements in efficiency and functionality for vehicle detection in diverse traffic environments.

18 Feb 2024

Optimizing Electric Vehicle Power: A Novel Energy Management Strategy

Researchers present an innovative energy management strategy (EMS) for electric vehicles (EVs), combining slap swarm optimization and differential flatness control to enhance performance and reliability. The proposed EMS efficiently coordinates power flow among battery, supercapacitor, and motor components, stabilizing voltage, reducing harmonics, and meeting power demand, promising improved EV efficiency and driving range while reducing greenhouse gas emissions.

18 Feb 2024

AI's Deep Impact: Exploring Facial Expressions in Job Interviews

Researchers harnessed AI technology to create deepfake videos portraying various facial expressions, investigating their influence on observer perceptions in job interviews. The study highlights how deepfake facilitates controlled experimentation in studying nonverbal behavior, shedding light on its crucial role in social interactions and offering insights for job interview training and beyond.

18 Feb 2024

Empowering Legal Education: AI's Role in International Law

This article outlines a pioneering AI-integrated model for international legal education, aiming to revolutionize traditional teaching methods by leveraging AI's capabilities. The model, employing correlation analysis, AI knowledge mapping, and neural algorithms, promises personalized learning experiences, efficient assessment, and enhanced career prospects for students, as evidenced by its impressive performance in practical teaching evaluations.

18 Feb 2024

Dynamic Node Selection for Federated Learning in Space-Air-Ground Information Networks

Researchers addressed challenges in Federated Learning (FL) within Space-Air-Ground Information Networks (SAGIN) by introducing the LCNSFL algorithm. LCNSFL, based on a Double Deep Q Network (DDQN), strategically selects nodes to minimize time and energy costs. Simulation results demonstrate LCNSFL's superiority over traditional methods, offering efficient convergence and resource utilization in dynamic network environments, essential for practical applications in SAGIN.

16 Feb 2024

Solar Irradiance Prediction in Central Africa with MLP Neural Network

Researchers proposed a novel approach utilizing a multilayer perceptron (MLP) neural network to forecast solar irradiance in Central Africa, crucial for sustainable energy development. By training the MLP model with meteorological data, including atmospheric pressure, humidity, temperature, wind speed, hour, and day, the study achieved a strong correlation (98.83%) between observed and predicted solar irradiance levels.

16 Feb 2024

Robust Path-Following Control for Autonomous Ground Vehicles Using SNFTSM

Researchers introduced a novel control strategy for autonomous ground vehicles (AGVs) utilizing a self-tuning nonsingular fast terminal sliding manifold (SNFTSM) to enhance convergence and tracking accuracy. Integrated with a high-gain disturbance observer (HGDO) and a super-twisting algorithm (STW), the approach effectively addressed uncertainties and reduced chattering in control signals, demonstrating superior performance compared to existing methods in numerical simulations.

16 Feb 2024

E-commerce Data and ML for Poverty Estimation in Indonesia

This study explored the use of e-commerce data and machine learning (ML) algorithms to expedite poverty assessment in Indonesia. By employing statistical-based feature selection and comparing three ML algorithms, the research demonstrated the potential of this approach to provide timely and nuanced poverty predictions, offering valuable insights for policymakers. Despite challenges and limitations, such as data accessibility constraints, the study highlighted the promise of integrating e-commerce data, feature selection, and ML for effective poverty estimation, suggesting avenues for future research.

16 Feb 2024

Graph-Based Machine Learning for Advanced Cyber Threat Detection

Researchers introduce a novel approach to cybersecurity by extracting graph-based features from network traffic data and employing machine learning for early detection of cyber threats. Through experimentation and validation on the CIC-IDS2017 dataset, the method showcases superior performance compared to traditional connection analysis methods, indicating its potential for enhancing cybersecurity measures.

15 Feb 2024

Low-Carbon Transformation in Resource-Based Cities by Integrating ChatGPT and ABC Algorithms

Researchers propose a novel approach utilizing ChatGPT and artificial bee colony (ABC) algorithms to advance low-carbon transformation in resource-based cities. Their study demonstrates significant improvements in energy efficiency, carbon emissions reduction, and traffic congestion alleviation, highlighting the potential of these methods in promoting green development and sustainable urban planning.

15 Feb 2024

Intelligent Upper-Limb Exoskeleton with DL-Augmented Strength

Researchers present an innovative upper-limb exoskeleton system leveraging deep learning (DL) to predict and enhance human strength. Integrating soft wearable sensors and cloud-based DL, the system achieves a remarkable 96.2% accuracy in real-time motion prediction, significantly reducing muscle activities by 3.7 times on average. This user-friendly solution addresses age and stroke-related strength decline, marking a transformative leap in robotic exoskeleton technology for assisting individuals with neuromotor disorders in daily tasks.

15 Feb 2024

SmartMuraDetection: Small-Sample DL Approach for OLED Defect Detection

Researchers propose SmartMuraDetection, a novel organic light emitting diode (OLED) defect detection method based on small-sample deep learning (DL), targeting mura defects. Utilizing gradient edge linear stretching for preprocessing and a TinyDetection model for small-scale target detection, the method achieves a high accuracy of 96% in point mura defect detection, surpassing previous approaches. While effective for point mura defects, further research is needed to address limitations in detecting other types of mura defects.

15 Feb 2024

Advancing Multi-Vehicle Dial-a-Ride Problems with Enhanced Deterministic Annealing Algorithm

This paper introduces an innovative Linearly Decreasing Deterministic Annealing (LD-DA) algorithm tailored for large-scale Multi-Vehicle Dial-a-Ride Problems (DARPs). By incorporating advanced local search techniques and a linearly decreasing annealing schedule, LD-DA ensures efficient exploration of feasible solution spaces, yielding competitive solutions at accelerated convergence rates. Extensive computational experiments demonstrate LD-DA's superiority over existing algorithms, highlighting its potential for real-world applications in transportation optimization.

15 Feb 2024

Improved Analysis of Highway Construction Safety Incidents Using AI

This study in the journal Applied Sciences utilizes large language models (LLMs) and artificial intelligence (AI) to analyze textual narratives from the Occupational Safety and Health Administration (OSHA) severe injury reports (SIR) database related to highway construction accidents. By employing LLMs such as GPT-3.5, along with natural language processing (NLP) techniques and clustering algorithms, the researchers identified major accident causes and types, providing valuable insights for improving accident prevention and intervention strategies in the industry.

14 Feb 2024

Leveraging Successor Representation for Behavior Learning in Artificial Agents

This paper explores the applications of successor representation (SR) and its generalizations in advancing artificial intelligence (AI) agents' learning and transfer of behaviors. By reviewing various AI applications, including exploring Atari games without initial rewards, enhancing exploration in sparse-reward environments, and enabling transfer learning to novel tasks, the authors demonstrate the effectiveness of SR-based approaches in efficient decision-making, adaptation to reward changes, and behavior transfer. SR and its variants offer promising avenues for developing adaptable and transferable AI agents across diverse domains.

14 Feb 2024

EU Artificial Intelligence Act: Implications for DeepFake Detection

This article explores the ramifications of the European Union (EU) Artificial Intelligence Act (AIA) on high-risk AI systems, focusing on decision-support systems with human control, particularly in the context of DeepFake detection. By delving into requirements under the AIA and proposing an adapted evaluation scheme, the paper contributes to the design and evaluation of high-risk AI systems. It emphasizes the critical role of human oversight, qualitative feedback, and explainability in ensuring the efficacy and ethicality of AI applications, especially in forensic scenarios.

14 Feb 2024