EAD: effortless anomalies detection, a deep learning based approach for detecting outliers in English textual data.

Anomalies are the existential abnormalities in data, the identification of which is known as anomaly detection. The absence of timely detection of anomalies may affect the key processes of decision-making, fraud detection, and automated classification. Most of the existing models of anomaly detection utilize the traditional way of tokenizing and are computationally costlier, mainly if the outliers are to be extracted from a large script.

Examining the drivers of the imbalance price: Insights from the balancing mechanism in the United Kingdom.

The increasing integration of renewable energy sources in the UK electricity sector has posed challenges to the stability of the system, leading to a sharp rise in the costs of balancing services. This study analyses half-hourly data from January 2017 to December 2023 to examine the factors determining the imbalance price in the UK's balancing mechanism, with particular focus on the sharp price increases in 2021-2022. Employing a Generalised Additive Model (GAM) to account for non-linear relationships, the analysis finds that the imbalance price is positively affected by the net imbalance volume (demand-side factor) and negatively impacted by the de-rated margin (supply-side factor).

Development and validation of a higher-order thinking skills (HOTS) scale for major students in the interior design discipline for blended learning.

Assessing and cultivating students' HOTS are crucial for interior design education in a blended learning environment. However, current research has focused primarily on the impact of blended learning instructional strategies, learning tasks, and activities on the development of HOTS, whereas few studies have specifically addressed the assessment of these skills through dedicated scales in the context of blended learning. This study aimed to develop a comprehensive scale for assessing HOTS in interior design major students within the context of blended learning.

Corrigendum to "Do maternal socioeconomic status influence child overweight? [10(2) 30 January 2024 e24630].

[This corrects the article DOI: 10.1016/j.heliyon.

Do maternal socioeconomic status influence child overweight?

The prevalence of overweight among Chinese children under 5 years of age has been increasing steadily. Using data from China Health and Nutrition Survey (CHNS) spanning from 1991 to 2015, this study investigates the relationship between maternal employment status, maternal education level, and the prevalence of child overweight among Chinese children under 5 years old. The findings indicate that having mothers with low middle school education significantly reduces their children's body mass index z-scores (BMIZ) (p < 0.

Social determinants of child malnutrition outcomes: Evidence from CHNS in China.

China is committed to reduce child malnutrition outcomes (CMO) rates to less than 5 % by 2030 in order to meet the Sustainable Development Goal (SDG). Yet, this is still an enormous challenge for China, particularly in disadvantaged areas, due to regional and urban-rural disparities. Using China Health and Nutrition Survey (CHNS) data from 1991 to 2015 and fixed-effect models, this study investigates the social determinants of stunting (n = 4012) measured by height-for-age z score (HAZ) and wasting (n = 4229) measured by weight-for-height z score (WHZ) in children under the age of five.

Innovation, advertising, personal selling, and sustainability in the industries massively benefited from major public health emergencies: paradoxical evidence from China.

Background: While major public health emergencies have severe socio-economic impacts, they may also present many opportunities for certain industries. For these industries that have benefited significantly (e.g.

A Deep Learning-Based TE Method for MSs' Mental Health Analysis.

Teaching evaluation (TE) is of great significance in education and can judge the value and appropriateness of the curriculum, which is a distinguished part of the educational work. Compared with other courses focusing on imparting knowledge, mental health not only imparts psychological knowledge but also cultivates Marxism students' (MSs) ability to adjust psychology and maintain mental health. Therefore, the evaluation of this course has a special character.

Beliefs and Practice Evaluation Based on Artificial Intelligence Models under the IP Environment.

The digitization of thought theory is not yet sufficient to meet the needs of the students. It is very necessary to strengthen the construction of ideological and political (IP) courses, strengthen the education of mainstream ideology, and occupy the initiative of discourse. There are effective ways and means to study the deep integration of information technologies into the new age of philosophy and philosophy education of students, which can greatly improve the quality of teaching and the effectiveness of humanities courses.

Short-Term Demand Forecast of E-Commerce Platform Based on ConvLSTM Network.

Based on real sales data, this article constructed LGBM and LSTM sales prediction models to compare and verify the performance of the proposed models. In this article, we forecast the product sales of stores in the future  + 3 days and use MAPE as the evaluation index. The experiment shows that the product sales prediction model based on the convolutional LSTM (ConvLSTM) network has better prediction accuracy.

Analysis of the Intelligent Tourism Route Planning Scheme Based on the Cluster Analysis Algorithm.

In view of the problems of the traditional cluster analysis algorithm such as strong dependence on the initial cluster center, the traditional -means cluster analysis algorithm is improved and the experiment proves that the improved algorithm has a better clustering effect; in view of the problems of the traditional tourism route planning, the improved -means cluster analysis algorithm is applied to the intelligent tourism route planning scheme design and an intelligent tourism planning scheme based on the cluster analysis algorithm is proposed; the tourists' preference metric is fully considered, and the experimental results show that the scheme has certain reasonableness and reference value.

Machine Learning Methods to Detect Voltage Glitch Attacks on IoT/IIoT Infrastructures.

A majority of modern IoT/IIoT digital systems rely on cryptographic implementations to provide satisfactory levels of security. Hardware attacks such as side-channel analysis attacks or fault injection attacks can significantly degrade and even eliminate the desired level of security of the infrastructure in question. One of the most dangerous attacks of this type is voltage glitch attacks (VGAs), which can change the intended behavior of a system.

A Machine Vision Anomaly Detection System to Industry 4.0 Based on Variational Fuzzy Autoencoder.

From a technological point of view, Industry 4.0 evolves and operates in a smart environment in which the real and virtual worlds come together through smart cyber-physical systems. These devices that control each other autonomously activate innovative functions that enhance the production process.