International Journal of Computational Intelligence and Applications

In the research of deep learning, an Unsupervised deep convolution-generated Generated Adversarial Network (UGAN) usually needs a large number of data samples to train. However, when faced with some small samples, the performance of the algorithm is often degraded due to over-fitting. Combined with specially designed data enhancement methods, a generated adversarial network optimization algorithm based on adaptive data augmentation (AdauGAN) is proposed. The adaptive data augmentation module is added before the discriminant network, and a spatial transformation is carried out simultaneously at the probability distribution level of generated data and real data. To alleviate the over-fitting phenomenon in the training process, the current enhancement intensity is adjusted adaptively after the over-fitting occurs. The proposed algorithm is verified on SVHN, CelebA and CIFAR-10 data sets. The Frechet Inception Distance (FID) values of AdauGAN achieve 22.10, 23.94, 34.87, respectively, which is close to or even higher than the training results of Deep Convolution Generated Adversarial Network (DCGAN) under all data. Extensive experiment results show that the proposed Adaugan has an excellent performance in small samples. Besides, in some cases, it can catch up with the large sample results of existing algorithms.

In response to the security management issues of student apartments, a study is conducted on a student apartment access control system based on multitasking cascaded convolutional networks and FaceNet. Firstly, a face detection model is built based on an improved multi-task cascaded convolutional network, and then a face recognition model is built using FaceNet. The results showed that the detection accuracy of the multi-task cascaded convolutional network using the improved non-maximum suppression algorithm was 98.7%, which was higher than the traditional multi-task cascaded convolutional network and effectively improved the detection performance of the multi-task cascaded convolutional network. The face detection model based on the improved multi-task cascaded convolutional network had the shortest average detection time of 361s, the highest average detection accuracy of 90.3%, an accuracy of 99%, a recall rate of 98.5%, and an F1 value of 99%. While maintaining high detection efficiency, it also ensured the accuracy of detection. The average accuracy of the mask detection method based on the MobileNet V2 network was relatively high, at 98.96%. The facial recognition model based on FaceNet achieved a recognition accuracy of 99.15% for faces without masks and 92.04% for faces with masks, with the highest accuracy and recall rates of 99.3% and 99.6%. The model constructed in the study has good application effects in face detection, which helps to improve the security of the student apartment access control system.

In pattern mining, high-utility itemset mining (HUIM) is useful for discovering high-utility patterns. The study of HUIM using heuristic techniques reflects issues in producing better offspring. It is ineffective in terms of search space organization, population diversity, and utility calculation, which impact runtime and accuracy. It is observed that very few researchers have experimented with genetic algorithm (GA) and are still facing the same issues as mentioned before. To overcome these problems, a novel approach is proposed for HUIM using modified GA and optimized local search (HUIM-MGALS) with six potential contributions. First is linking the utility with the Bitmap dataset to reduce utility access time, leading to effective search space organization. Second, HUIM-MGALS employs a fitness scaling strategy to avoid redundancy. Third, a high-utility itemset (HUI) revision strategy is employed to explore significant HUIs. Modified population diversity maintenance strategy and iterative crossover help to preserve significant HUIs and improve search capability as fourth and fifth contributions. Sixth, the use of multiple mutations refines the wasted individuals to boost accuracy. Extensive experimentation showed that HUIM-MGALS significantly outperforms the presented algorithms, up to 8.6 times faster. It also demonstrates superior HUI discovery capabilities for both sparse and dense datasets. This is supported by the modified population diversity maintenance strategy, which is proved to be the most impactful modification for HUI discovery in HUIM-MGALS.

In this paper, a bi-population competition adaptive interior search algorithm (BCAISA) based on a reinforcement learning strategy is proposed for the classical flexible job shop scheduling problem (FJSP) to optimize the makespan. First, the scheduling solution is represented using a machine-job-based two-segment integer encoding method, and various heuristic rules are then applied to generate the initial population. Secondly, a bi-population mechanism is introduced to partition the population into two distinct sub-populations. These sub-populations are specifically tailored for machine assignment and operation permutation, employing different search strategies respectively, aiming to facilitate an efficient implementation of parallel search. A competition mechanism is introduced to facilitate the information exchange between the two sub-populations. Thirdly, the ISA is adapted for the discrete scheduling problem by discretizing a series of search operators, which include composition optimization, mirror search, and random walk. A Q-learning-based approach is proposed to dynamically adjust a key parameter, aiming to strike a balance between the capacity for global exploration and local exploitation. Finally, extensive experiments are conducted based on 10 well-known benchmark instances of the FJSP. The design of the experiment (DOE) method is employed to determine the algorithm’s parameters. Based on the computational results, the effectiveness of four improvement strategies is first validated. The BCAISA is then compared with fifteen published algorithms. The comparative data demonstrate that our algorithm outperforms other algorithms in 50% of benchmark instances. Additionally, according to the relative percentage deviation (RPD) from the state-of-the-art results, the BCAISA also exhibits superior performance. This highlights the effectiveness of our algorithm for solving the classical FJSP. To enhance the practical application, the scope of the ISA will be broadened in future work to more complex problems in real-world scenarios.

Music composition, an intricate blend of human creativity and emotion, presents substantial challenges when generating melodies from lyrics which hinders effective learning in neural networks and the inadequate depiction of harmonic structure that fails to encapsulate the complex relationships between lyrics and melodies. The existing methods often struggle to balance emotional depth and structural coherence, leading to compositions that lack both the intended emotional resonance and musical consistency. To overcome these issues, this research introduces a novel approach named Dual Interactive Wasserstein Fourier Acquisitive Generative Adversarial Network (DIWFA-GAN), which integrates innovative techniques like swish activation functions and the Giant Trevally Optimizer (GTO) for parameter optimization. Meanwhile, the GTO, inspired by the movement patterns of the Giant Trevally fish, provides efficient and effective parameter optimization, improving the model’s convergence speed and accuracy. Comparative analysis against recent existing models reveals superior performance for both the LMD-full MIDI and Reddit MIDI datasets, with impressive metrics including inception scores of 9.36 and 2.98, Fréchet inception distances of 35.29 and 135.54 and accuracies of 99.98% and 99.95%, respectively. The DIWFA-GAN significantly outperforms existing models in generating high-fidelity melodies, as evidenced by superior inception scores, Fréchet inception distances, and accuracies on both datasets.

With the rapid growth of the rural tourism industry, traditional tourism recommendation technologies can no longer meet the necessary requirements. To address the issue of rural tourist attraction recommendations, a rural tourist attraction recommendation model is constructed based on a multi-feature fusion graph neural network. First, construct a feature map based on the relationship between tourists’ preferences and tourist attractions, and incorporate the attention mechanism to enhance the model’s learning capabilities. Second, utilize a two-part graph model to extract positive and negative preference features of tourists, and a conversation graph model to extract tourists’ transfer preference features. Finally, various features are utilized to generate suggested content by computing scores for tourists’ travel preferences. To address the problem of recommending tourist groups, suitable features for random group matching are collected and the cosine function is employed to identify users with similar random group features. Finally, the multi-features are merged, and the tourists’ interest preferences are scored to arrive at content recommendations. In the experiment on individualized attraction recommendations, data from the Chengdu area were used to test the proposed model. The accuracy of the model’s recommendations was 0.822 for five recommendations which outperformed the other models. In the experiment for group-based attraction recommendations, this experiment tested the Chengdu dataset. The proposed model achieved the highest accuracy of 0.972 when the group size was 70, outperforming the other two models. Additionally, with regards to different numbers of recommendations, the proposed model’s accuracy was 0.5241, which was the best performance among the three models when the number of recommendations was set to five. The proposed recommendation model performs optimally in suggesting tourist attractions and meets the needs of rural tourism. The research content provides crucial technical references for tourist traveling and rural tourism development.

With the increasing awareness of health, using wearable sensors to monitor individual activities and accurately estimate energy expenditure has become a current research focus. However, existing research encounters challenges including low estimation accuracy, a deficiency of frequency domain features, and difficulty in integrating time domain and frequency domain features. To address these issues, we propose an innovative framework called the Dual-Stream Fusion Network (DSFN). This framework combines the Time Domain Encoding (TDE) module, the Frequency Domain Hierarchical-Split Encoding (FDHSE) module, and a Two-Stage Feature Fusion (TSF) module. Specifically, the temporal stream of the framework employs the TDE module to capture deep temporal features that reflect the complex dynamic variations in time-series data. The frequency domain stream introduces the FDHSE module, which extracts frequency domain features using a multi-level, multi-scale approach, ensuring a comprehensive and diverse representation of frequency information. Through this dual-stream architecture, our model effectively learns both time and frequency domain features, addressing the limitations of frequency domain features observed in prior studies. Additionally, we propose the TSF module to fully integrate time and frequency domain features, effectively overcoming the challenge of fusing these two types of features. We conducted experiments on two public datasets, namely the GOTOV dataset (elderly people) and the JSI dataset (young people). Experimental results demonstrate that our method achieves excellent performance across different age groups. Compared to the baseline models, the proposed DSFN significantly improves the accuracy of human energy expenditure estimation.

Visual aesthetics has always been an important area of computational vision, and researchers have continued exploring it. To further improve the performance of the image aesthetic evaluation task, we introduce a Transformer into the image aesthetic evaluation task. This paper pioneers a novel self-supervised image aesthetic evaluation model founded upon Transformers. Meanwhile, we expand the pretext task to capture rich visual representations, adding a branch for inpainting the masked images in parallel with the tasks related to aesthetic quality degradation operations. Our model’s refinement employs the innovative uncertainty weighting method, seamlessly amalgamating three distinct losses into a unified objective. On the AVA dataset, our approach surpasses the efficacy of prevailing self-supervised image aesthetic assessment methods. Remarkably, we attain results approaching those of supervised methods, even while operating with a limited dataset. On the AADB dataset, our approach improves the aesthetic binary classification accuracy by roughly 16% compared to other self-supervised image aesthetic assessment methods and improves the prediction of aesthetic attributes.

Epilepsy is referred to as a neurological disorder, which is detected via examination and manual comprehension of Electroencephalogram (EEG) signals. In deep learning schemes, various enhancements have emerged to efficiently address complex issues by end-to-end learning. The major objective of this research is to propose a new seizure detection approach from EEG signals using a deep learning-based classification technique. The pre-processing is the initial stage, where denoising is performed using a Short-Time Fourier Transform (STFT). Subsequently, the statistical features, time-domain features and spectral features are extracted from the pre-processed signal. Finally, an efficient optimization approach, named Adadelta-Chameleon Swarm Algorithm (Adadelta-CSA), is proposed and employed to train Deep Neural Network (DNN) to carry out the precise seizure prediction. Here, the integration of the Adadelta concept in the Chameleon Swarm Algorithm (CSA) has resulted in Adadelta-CSA. At last, the performance of the Adadelta-CSA scheme-based DNN is compared with the existing techniques by considering accuracy, sensitivity and specificity, and it is found to produce better values of 0.951, 0.966, and 0.935, respectively.

Biotechnological analysis of DNA microarray genes provides valuable insights into the discovery and treatment of diseases such as cancer. It may also be crucial for the prevention and treatment of other genetic diseases. However, due to the large number of features and dimensions in a DNA microarray, the “curse of dimensions” problem is very common. Many machine learning methods require an effective subset of input genes to achieve high accuracy. Unfortunately, extracting features (genes) is an inherently NP-hard problem. Recently, the use of metaheuristics to overcome the NP-hardness of the feature extraction problem has attracted the attention of many researchers. In this paper, we use the combination of fuzzy entropy and Giza Pyramid Construction (GPC) for feature selection. First, redundant features in the microarray dataset are removed using the fuzzy entropy approach. GPC is then used to reduce the execution time. This results in the selection of a near-optimal subset of genes for cancer detection. Dimensionality reduction with GPC followed by classification with Convolutional Neural Network (CNN) creates a synergy to increase efficiency. The proposed method is tested on five well-known cancer patient datasets: leukemia, lymphoma, MLL, ovarian, and SRBCT. The performance of CNN was also measured with four well-known classifiers, including K-nearest neighbor, naïve Bayesian, decision tree, and logistic regression. Our results show that, on average, CNN has the highest accuracy, recall, precision, and F-measure in all datasets.

Generative adversarial networks (GANs) have been widely applied in the field of meteorological research, particularly in the downscaling of images due to their ability to generate super-resolution images. In recent years, numerous researchers have combined GANs with recurrent neural networks (RNNs) to address the issue of meteorological super-resolution. However, these models do not take into account the spatial variations of meteorological sequences. In this paper, we propose a super-resolution method named RN-STLSTM-GAN, which combines GANs with RN-STLSTM and ESA networks to learn the spatiotemporal features of meteorological sequences. Specifically, we first apply the RN-STLSTM at the initialization of the generator and discriminator to learn the spatiotemporal relationships between sequential images. Second, an ESA network is combined with the RN-STLSTM structure to enhance the learning of spatial features. Thirdly, LeakyReLU is used as the activation function for both the generator and discriminator to minimize the loss of image data during model training. Experiments conducted on the NJU-CPOL datasets demonstrate that our proposed method outperforms other existing methods and can generate realistic and temporally consistent super-resolution sequences for datasets at different heights.

In the medical field, Internet of Things (IoT) applications allow for real-time diagnosis and remote patient monitoring, commonly called Internet of Health Things (IoHT). However, cybersecurity attacks may interrupt hospital operations and threaten patients’ health and well-being due to this integration. Hence, developing an Intrusion Detection System (IDS) suited explicitly for healthcare systems is essential to ensure efficiency and accuracy. Nevertheless, it is challenging to integrate anomaly-based IDS frameworks in healthcare systems as they necessitate additional processing time, temporal feature retention, and increased complexity. Therefore, a deep learning system based on SqueezeNet and NasNet is presented in this paper to detect intrusions in a healthcare setting. In this, SqueezeNet is employed to extract more significant features. On the other hand, network breaches while data transmission across distinct locations are detected by the NasNet-based classifier. In addition, the Rider Optimization Algorithm (ROA) is applied to adjust the classifier’s hyperparameters, guaranteeing that it would accurately detect attacks. Moreover, the Auxiliary Classifier Generative Adversarial Network (ACGAN) approach is integrated into the proposed framework to avoid data imbalance. Applying different performance constraints, the proposed approach is thoroughly assessed on three publicly available datasets (TON-IoT, ECU-IoHT, and WUSTL-EHMS). The results show that the proposed deep learning-based cybersecurity model outperforms traditional methods and produces better outcomes.