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Nowadays, computational intelligence-assisted autonomous assessment of education quality has become a more and more general concern in the area of smart education management. As education quality assessment is a complicated process with multiple heterogeneous factors, it remains challenging to make effective assessment using simple information modality and criteria. To deal with this issue, this paper introduces multiscale deep learning, and proposes a novel data-driven autonomous assessment framework for education quality. In particular, four aspects of heterogeneous indicators are selected as the basic indexes and a dilated convolutional neural network structure is formulated to perform multiscale feature extraction. Then, the structural equation is adopted to make multivariate characterization and output the final assessment results. At last, some simulations are carried out on realistic education operation data to evaluate the efficiency of the proposed autonomous assessment framework. Two aspects of findings can be deduced from the results. For one thing, multisource fusion of multiple indicators well makes sense to autonomous education assessment. For another, multiscale deep learning can provide some beneficial promotion to assessment efficiency. [ABSTRACT FROM AUTHOR]

The credit risk assessment acts as an important part in daily affairs for financial institutions. But in the era of big data, the growing business volume makes it an urgent demand to develop digital ways of credit risk assessment. Currently, the machine learning is universally employed to establish various data-driven models for this purpose. However, machine learning models generally suffer from limited ability of feature representation and robustness, and cannot deal with more complex financial security scenarios. To deal with this issue, this work introduces ensemble learning to construct a stronger credit risk prediction model via integration of several basic machine learning models. Thus, an ensemble learning-enhanced smart prediction model for financial credit risk is proposed in this paper. Three classification-based machine learning models (support vector machine, artificial neural network and radial basis function) are selected as the basic classifiers, and "voting" strategy is utilized to integrate them into a novel strong classifier. A real-world financial credit dataset released by a Chinese commercial bank was selected as the experimental scenario. The obtained results show that the proposal has better prediction accuracy compared with basic machine learning models without ensemble learning. [ABSTRACT FROM AUTHOR]

In recent years, cloud computing technologies have been developed rapidly in this computing world to provide suitable on-demand network access all over the world. A cloud service provider offers numerous types of cloud services to the user. But the most significant issue is how to attain optimal virtual machine (VM) allocation for the user and design an efficient big data storage platform thereby satisfying the requirement of both the cloud service provider and the user. Therefore, this paper presents two novel strategies for optimizing VM resource allocation and cloud storage. An optimized cloud cluster storage service is introduced in this paper using a binarization based on modified fuzzy c-means clustering (BMFCM) algorithm to overcome the negative issues caused by the repetitive nature of the big data traffic. The BMFCM algorithm utilized can be implemented transparently and can also address problems associated with massive data storage. The VM selection is optimized in the proposed work using a hybrid COOT-reverse cognitive fruit fly (RCFF) optimization algorithm. The main aim of this algorithm is to improve the massive big data traffic and storage locality. The CPU utilization, VM power, memory dimension and network bandwidth are taken as the fitness function of the hybrid COOT-RCFF algorithm. When implemented in CloudSim and Hadoop, the proposed methodology offers improvements in terms of completion time, overall energy consumption, makespan, user provider satisfaction and load ratio. The results show that the proposed methodology improves the execution time and data retrieval efficiency by up to 32% and 6.3% more than the existing techniques. [ABSTRACT FROM AUTHOR]

In the colloquy concerning human rights, equality, and human health, mental illness and therapy regarding mental health have been condoned. Mental disorder is a behavioral motif that catalyzes the significant anguish or affliction of personal functioning. The symptoms of a mental disorder may be tenacious, degenerative, or transpire as a single episode. Brain sickness is often interpreted as a combination of how a person thinks, perceives, contemplates and reacts. This may be analogous to a specific region or workings of the brain frequently in a social context. Anxiety disorders, psychotic disorders, personality disorders, mood disorders, eating disorders, and many more are examples of mental disorders, while complications include social problems, suicides, and cognitive impairment. These days, mental disorders are quotidian worldwide, and clinically consequential levels of derangement rise adversely. The purpose of this paper is to aid in prognosis of the type of mental disorder by analyzing the brainwaves such as Alpha (α), Beta (β), Gamma (γ), Theta (휃), Delta (δ) with the help of big data analysis and the Internet of Medical Things (IoMT). IoMT helps in gathering the required data and data transmission, while big data analysis helps in predicting the type of disorder. [ABSTRACT FROM AUTHOR]

This paper proposes a point-of-interest (POI) sequence recommendation algorithm based on BERT-ACNN-GRU to address the issues faced by the existing POI recommendation model in social network large data, such as the difficulty in extracting deep feature information and the low recommendation performance. Firstly, the semantic relationship between a word and its context in the text is combined using the bidirectional encoder representation from transformers (BERT) model to effectively eliminate the influence of word distance and obtain the contextualized word vector. Secondly, a convolutional neural network (CNN) utilizing a gated recurrent unit (GRU) is employed to capture the feature information of the text. Lastly, the attention method is utilized to assign weight scores to various terms in order to provide more attention to particular words and boost the precision of recommendations. The experiments demonstrate that the precision, recall rate, F1 score and mean average precision (mAP) of the proposed method are 0.097, 0.26, 0.103, and 0.085 on the Gowalla dataset when the recommendation list has a length of 10, respectively. On the Yelp dataset, the precision is 0.093, the recall rate is 0.26, F1 is 0.099, and MAP is 0.089. Hence, the proposed method can effectively enhance the performance of the POI recommendation system. [ABSTRACT FROM AUTHOR]

The research object of this paper is rumors, mainly focusing on the dissemination characteristics of rumors on Weibo platforms and conducting rumor attribute detection. Aiming at the problems of low accuracy and poor timeliness of current rumor detection methods, a network rumor detection method using attention mechanism and Gated Recurrent Unit (GRU) neural network is proposed. First, pre-processing operations such as data noise cleaning, Chinese word segmentation and stop word removal are performed on the constructed Weibo corpus data, and word vectors are obtained by using the Continuous Bag-of-Words (CBOW) model in the Word2vec neural language model. Then, the Bidirectional GRU neural network (BiGRU) is used to obtain information, combined with the structured attention mechanism to build an GRU rumor detection model. The introduction of attention mechanism makes the network model tend to grasp the text Semantic information. Finally, the Adam algorithm is used to optimize the proposed model, and the loss function of the model is constructed and minimized in combination with the binary cross-entropy loss function. The results show that the detection precision rate, recall rate, F1 value and accuracy rate of the proposed method are the largest, reaching 95.28%, 88.78%, 88.69% and 94.89%, respectively, and the detection performance is better than the other three comparison algorithms. [ABSTRACT FROM AUTHOR]