International Journal on Artificial Intelligence Tools

Smart Educational Learning (SEL) has recently opened its ways in various changes in scientific discoveries, informatics, globalization, astronautics production, robotics, and artificial intelligence in the Higher Education System. In such an educational system, managing resources to increase education quality with an effective interactive environment has been considered a significant challenging factor for the Students and Teachers. Hence, in this paper, the internet of things assisted Interactive system (IoT-IS) for Smart Learning is used to measure the teachers’ and students’ performance analysis in the SEL platform. The psychometric processes with standards for effective teaching using smart educational learning tools have been discussed based on the higher education system requirements. Furthermore, an active learning strategy with an attention scoring method promotes students’ performance assessment in the higher education system using the interactive system. Facial expression detection and analysis are used and applied in online classroom videos in the SEL. Based on this detection and analysis, the attention of the students is observed. The experimental results show that the method enhances the student performance ratio of 98.5%, an accuracy ratio of 95.3%, an efficiency ratio of 96.7%, a reliability ratio of 93.2%, and a probability ratio of 94.5% compared to other existing methods.

Worldwide, epidemics continue to be a concern on public health. Even with the technological advances, there are still barriers present in predicting the outbreaks. We propose a new methodology known as FREEDOM (Effective Surveillance and Investigation of Water-borne Diseases from data-centric networking using Machine Learning) to perform effective surveillance and investigation of water-borne diseases from social media with next-generation data. In the proposed model, we collected the data from the Twitter media, preprocessed the tweet content, performed hierarchical spectral clustering, and generated the frequent word set from each cluster through the apriori algorithm. At last, the inferences are extracted from the frequent word set through human intervention. From the experimental results, the support and confidence value of the outcome derived from the Apriori algorithm has exhibited the different water-borne diseases that are not listed in the WHO (World Health Organization), and the surveillance of those diseases with percentage ranking and has been achieved using the data-centric networking. They get aligned with precise results portraying real statistics. This type of analysis will empower doctors and health organizations (Government sector) to keep track of the water-borne diseases, their symptoms for early detection, and safe recovery thereby sufficiently reducing the death tolls.

Sleep is a fundamental restorative process for human mental and physical health. Considering the risks that sleep disorders can present, sleep analysis is considered as a primordial task to identify the different abnormalities. Sleep scoring is the gold standard for human sleep analysis. The manual sleep scoring task is considered exhausting, subjective, time-consuming and error prone. Moreover, sleep scoring is based on fixed epoch lengths usually of 30 seconds, which leads to an information loss problem. In this paper, we propose an automatic unsupervised sleep scoring model. The aim of our work is to consider different epoch’s durations to classify sleep stages. Therefore, we developed a model based on Hierarchical Multi-Agent Systems (HMASs) that presents different layers where each layer contains a number of adaptive agents working with a specific time epoch. The effectiveness of our approach was investigated using real electroencephalography (EEG) data. Good results were reached according to a comparative study realized with the often used machine learning techniques for sleep stages classification problems.

Mobile robot path planning (MRPP) plays an irreplaceable role in the process of intelligent robots and practical artificial intelligence. The traditional global path planning methods have some shortcomings, such as difficulty in digging into environmental information and finding the optimal path effectively. To solve the above problems, this paper proposes a negative gradient differential biogeography-based optimization (NG-DBBO), which has strong local search ability and global optimization ability. Firstly, we present a differential migration approach to increase the population diversity in the iterative process of NG-DBBO, which can realize the information sharing between feature solutions effectively. Then a negative gradient descent strategy based on negative gradient descent is introduced to improve the learning rate, which not only enhances initial global search ability, but also avoids premature convergence. Noteworthily, the convergence of the algorithm is analyzed for single-peak and multi-peak problems respectively. After that, NG-DBBO is combined with the cubic spline interpolation to realize MRPP by the defined coding method and fitness function. The simulation experiments are used to demonstrate the availability of our method, which consist of two parts. In the first part, we select 23 benchmark functions to verify the accuracy and convergence speed of the NG-DBBO algorithm. The practicability of path planning in different environments is demonstrated in the second part.

Training has great significance and should be an integral part of the daily routines of all elite athletes. Training allows the body to gradually develop strength and endurance, increase skill levels, and build trust, motivation, and ambition. The risk factors in sports training sessions include fragile or low self-confidence, Breakdowns in self-assurance, and high expectations are considered an important factors. In this paper, Dynamic Activity Adaptive Physical Fuzzy Model (DAAPFM) is proposed to become better athletes and meet their mental challenges. Mel does time-frequency analysis, and they tend to experience anxiety about performance. Fuzzy wrapping optimization analysis is integrated with DAAPFM to build confidence in the belief in one’s ability to execute a task or win an event. The experimental results show that the data fusion approach can analyze the activities of athletes effectively. The simulation results give the average accuracy of the classification of sports activity 99.40% framework’s efficiency to gradually develop strength and endurance, increase skill levels, and build trust, motivation, and ambition.

This paper constructs a neural machine translation model for Chinese and English using encoder feature extraction with migration learning, allowing the machine to automatically perform feature migration learning, transforming Chinese and English data into word vectors using a centralized representation, and using neural networks to achieve a direct mapping between Chinese and English languages. In this paper, we study the construction of a neural machine translation model by the neural network structure. Firstly, the neural machine translation model is constructed using the neural network structure, which is simplified based on the neural network structure and achieves good performance while reducing the training complexity. To address the problem that neural machine translation cannot make good use of linguistic knowledge, a neural machine translation model with lexical sequence information is proposed. Based on the translation model of a bidirectional neural network incorporating attention mechanism, syntactic analysis is performed to obtain lexical sequence information, and then it is incorporated into the encoder part of the translation model in the form of bidirectional encoding, and the background vector is formed together by vector splicing. To address the problem that the Chinese-English language sequences of any length in the encoder part are encoded into a fixed dimensional background vector, an attention mechanism is introduced to dynamically adjust the degree of influence of the context at the source language end on the target language sequences to improve the translation model’s ability to deal with long-distance dependencies. To better reflect the contextual information, this paper further proposes a machine translation model based on migration learning and neural network and conducts a comparative analysis for various translation models to verify the effectiveness of the model performance improvement.

The vast amount of data available on social media and microblogs can be a valuable resource for mining opinions or for analyzing the overall mood of the public. This helps in identifying potential customers, exploring market trends and predicting events. Analyzing twitter data is comparatively difficult due to the large amount of irregularities present in tweets. Many approaches that use sentiment dictionaries and machine learning have been proposed until now. In this paper, we present a new feature that is extracted using dependency parsing and an emotion lexicon. This feature, along with n-grams, syntactic n-grams and lexicon-based features, is used to classify the tweets. We also use custom dictionaries to identify slang words, SMS short forms, emoticons and word contractions. The performance of various classification algorithms and ensemble techniques is compared. Our results show that the new feature along with the ensemble framework improves sentiment classification.

Optical networks are one of the fastest data communication networks, which use optical fiber cables for transmitting data in the form of light pulses between sender and receiver nodes. As the quality of optical signal deteriorates when increasing the distance among terminal nodes, expensive electronic devices (regenerators) must be deployed in the network to recover the signal quality. The objective of Generalized Regenerator Location Problem (GRLP) is to minimize the number of installed regenerators, while weighted variant (WGRLP) assumes different installation costs (weights) of potential regenerator locations and minimizes the sum of regenerator installation costs. In this study, an improved variant of Basic Variable Neighborhood Search (iBVNS) was developed as a metaheuristic solution approach to both GRLP and WGRLP. Computational results provided by the proposed iBVNS on all available data sets from the literature are compared with the results of existing solution methods for GRLP and WGRLP. The obtained results show that the proposed iBVNS quickly reaches optimal or near-optimal solutions and in many cases improves upper bounds provided by exact method. In addition, the conducted non-parametric statistical tests indicate that iBVNS outperforms all heuristic approaches proposed earlier in the literature for the considered problems.

This paper focuses on the popular problem of short text clustering. Since the short text documents typically exhibit high degrees of data sparseness and dimensionality, the problem in question is generally considered more challenging than the traditional clustering scenarios. Our proposed solution, named VEPH, is based on a novel algorithm that was published recently with the aim of optimally clustering short text documents. VEPH includes two stages: During the first stage, the original text vectors are projected on a lower dimensional space and the documents with projection vectors lying on the same dimensional space are grouped in the same cluster. The second stage is a refinement process which attempts to improve the quality of the clusters that were generated during the previous stage. The quality of a cluster is determined by its homogeneity and completeness and these are the two primary design criteria of this stage. Initially VEPH cleanses the clusters by removing all dissimilar elements, and then, it iteratively merges the similar clusters in a hierarchical agglomerative manner. The proposed algorithm has been experimentally evaluated in terms of F1 and NMI, by employing three datasets with diverse attributes. The results demonstrated its superiority over other state-of-the-art works of the relevant literature.

Embracing generative models to the inpainting realm has resulted in its overwhelming acceptability as an image editing technique. However, these generative inpainting techniques tend to be cumbersome in view of the large memory footprint and resources consumed during the operation. This accounts for why images above 2k resolution are far from being considered as input for inpainting operations. In the present era of super-resolution, where human eyes are accustomed to viewing images beyond 6k and 8k, inpainting algorithms were not at par with the resolution benchmark. This paper proposes a high-resolution inpainting algorithm that addresses these concerns. To improve the computational overhead, the proposed architecture follows a hierarchically scaled 2-stage generative inpainting network. The proposed framework showcases a novel approach of injecting a blind Super Resolution Generative Adversarial Network network in an inpainting scenario. The resolution lost in the first phase is regained by the embedded pre-trained Real-ESRGAN model employed in the final phase. Qualitative and quantitative evaluation of the proposed method was performed using various datasets. Results obtained on comparing with the state-of-the-art inpainting methods were promising and ought to be a big leap towards super-resolution inpainting methods.