Cognitive Computation最新文献

With the burgeoning global demand for seafood, potential solutions like aquaculture are increasingly significant, provided they address issues like pollution and food security challenges in a sustainable manner. However, significant obstacles such as disease outbreaks and inaccurate biomass estimation underscore the need for optimized solutions. This paper proposes “Fish-Sense”, a deep learning-based pipeline inspired by the human visual system’s ability to recognize and classify objects, developed in conjunction with fish farms, aiming to enhance disease detection and biomass estimation in the aquaculture industry. Our automated framework is two-pronged: one module for biomass estimation using deep learning algorithms to segment fish, classify species, and estimate biomass; and another for disease symptom detection symptoms, employing deep learning algorithms to classify fish into healthy and unhealthy categories, and subsequently identifying symptoms and locations of bacterial infections if a fish is classified as unhealthy. To overcome data scarcity in this field, we have created four novel real-world datasets for fish segmentation, health classification, species classification, and fish part segmentation. Our biomass estimation algorithms demonstrated substantial accuracy across five species, and the health classification. These algorithms provide a foundation for the development of industrial software solutions to improve fish health monitoring in aquaculture farms. Our integrated pipeline facilitates the transition from research to real-world applications, potentially encouraging responsible aquaculture practices. Nevertheless, these advancements must be seen as part of a comprehensive strategy aimed at improving the aquaculture industry’s sustainability and efficiency, in line with the United Nations’ Sustainable Development Goals’ evolving interpretations. The code, trained models, and the data for this project can be obtained from the following GitHub repository: https://github.com/Vision-At-SEECS/Fish-Sense.

Emotion analysis, an integral aspect of human–machine interactions, has witnessed significant advancements in recent years. With the rise of multimodal data sources such as speech, text, and images, there is a profound need for a comprehensive review of pivotal elements within this domain. Our paper delves deep into the realm of emotion analysis, examining multimodal data sources encompassing speech, text, images, and physiological signals. We provide a curated overview of relevant literature, academic forums, and competitions. Emphasis is laid on dissecting unimodal processing methods, including preprocessing, feature extraction, and tools across speech, text, images, and physiological signals. We further discuss the nuances of multimodal data fusion techniques, spotlighting early, late, model, and hybrid fusion strategies. Key findings indicate the essentiality of analyzing emotions across multiple modalities. Detailed discussions on emotion elicitation, expression, and representation models are presented. Moreover, we uncover challenges such as dataset creation, modality synchronization, model efficiency, limited data scenarios, cross-domain applicability, and the handling of missing modalities. Practical solutions and suggestions are provided to address these challenges. The realm of multimodal emotion analysis is vast, with numerous applications ranging from driver sentiment detection to medical evaluations. Our comprehensive review serves as a valuable resource for both scholars and industry professionals. It not only sheds light on the current state of research but also highlights potential directions for future innovations. The insights garnered from this paper are expected to pave the way for subsequent advancements in deep multimodal emotion analysis tailored for real-world deployments.

Ransomware is a significant security threat that poses a serious risk to the security of smartphones, and its impact on portable devices has been extensively discussed in a number of research papers. In recent times, this threat has witnessed a significant increase, causing substantial losses for both individuals and organizations. The emergence and widespread occurrence of diverse forms of ransomware present a significant impediment to the pursuit of reliable security measures that can effectively combat them. This constitutes a formidable challenge due to the dynamic nature of ransomware, which renders traditional security protocols inadequate, as they might have a high false alarm rate and exert significant processing demands on mobile devices that are restricted by limited battery life, CPU, and memory. This paper proposes a novel intelligent method for detecting ransomware that is based on a hybrid multi-solution binary JAYA algorithm with a single-solution simulated annealing (SA). The primary objective is to leverage the exploitation power of SA in supporting the exploration power of the binary JAYA algorithm. This approach results in a better balance between global and local search milestones. The empirical results of our research demonstrate the superiority of the proposed SMO-BJAYA-SA-SVM method over other algorithms based on the evaluation measures used. The proposed method achieved an accuracy rate of 98.7%, a precision of 98.6%, a recall of 98.7%, and an F1 score of 98.6%. Therefore, we believe that our approach is an effective method for detecting ransomware on portable devices. It has the potential to provide a more reliable and efficient solution to this growing security threat.

The precise segmentation of skin lesion in dermoscopic images is essential for the early detection of skin cancer. However, the irregular shapes of the lesions, the absence of sharp edges, the existence of artifacts like hair follicles, and marker color make this task difficult. Currently, fully connected networks (FCNs) and U-Nets are the most commonly used techniques for melanoma segmentation. However, as the depth of these neural network models increases, they become prone to various challenges. The most pertinent of these challenges are the vanishing gradient problem and the parameter redundancy problem. These can result in a decline in Jaccard index of the segmentation model. This study introduces a novel end-to-end trainable network designed for skin lesion segmentation. The proposed methodology consists of an encoder-decoder, a region-aware attention approach, and guided loss function. The trainable parameters are reduced using depth-wise separable convolution, and the attention features are refined using a guided loss, resulting in a high Jaccard index. We assessed the effectiveness of our proposed RA-Net on four frequently utilized benchmark datasets for skin lesion segmentation: ISIC 2016, ISIC 2017, ISIC 2018, and PH2. The empirical results validate that our method achieves state-of-the-art performance, as indicated by a notably high Jaccard index.

Differences in data size per class, also known as imbalanced data distribution, have become a common problem affecting data quality. Big Data scenarios pose a new challenge to traditional imbalanced classification algorithms, since they are not prepared to work with such amount of data. Split data strategies and lack of data in the minority class due to the use of MapReduce paradigm have posed new challenges for tackling the imbalance between classes in Big Data scenarios. Ensembles have been shown to be able to successfully address imbalanced data problems. Smart Data refers to data of enough quality to achieve high-performance models. The combination of ensembles and Smart Data, achieved through Big Data preprocessing, should be a great synergy. In this paper, we propose a novel Smart Data driven Decision Trees Ensemble methodology for addressing the imbalanced classification problem in Big Data domains, namely SD_DeTE methodology. This methodology is based on the learning of different decision trees using distributed quality data for the ensemble process. This quality data is achieved by fusing random discretization, principal components analysis, and clustering-based random oversampling for obtaining different Smart Data versions of the original data. Experiments carried out in 21 binary adapted datasets have shown that our methodology outperforms random forest.

Attention-deficit/hyperactivity disorder (ADHD) is a behavioral disorder in children that can persist into adulthood if not treated. Early diagnosis of this condition is crucial for effective treatment. The database includes 61 children with attention-deficit/hyperactivity disorder and 60 healthy children as a control group. To diagnose children with ADHD, features were first extracted from EEG signals. Next, a convolutional neural network model was trained, and a new residual network was introduced. The two proposed models were evaluated using tenfold cross-validation on the test data. The average accuracy and F1 score were 92.52% and 93.6%, respectively, for the convolutional model and 96.8% and 97.1% for the ResNet model on the epoch data, respectively. On the other hand, accuracy for subject-based prediction was 96.5% for the convolution model and 98.6% for the modified ResNet model. Accuracy, precision, recall, and F1 score for the proposed ResNet model are better than the convolution model proposed in previous studies and better than the proposed model in the literature. This work presents a paradigm shift in the cognitive-inspired domain by introducing a novel ResNet model for ADHD diagnosis. The model’s exceptional accuracy, exceeding conventional methods, showcases its potential as a biologically inspired tool. This opens avenues for exploring the neurological underpinnings of ADHD because the model can be used for the manifold learning of EEG signals. Analyzing the proposed network can lead to a deeper understanding of EEG, bridging the gap between artificial intelligence and cognitive neuroscience. The paper’s innovative approach has far-reaching implications, offering a concrete application of cognitive principles to improve mental health diagnostics in children. It is important to note that the data were augmented and the classification model is based on a single experiment containing a very small number of children but the results, and accuracy of classification, are based on classifying augmented data samples that compose the EEG signals of this small number of individuals. It is prudent to undertake a comprehensive investigation into the efficacy of these models across a broad cohort of subjects.

Gaining clinicians’ trust will unleash the full potential of artificial intelligence (AI) in medicine, and explaining AI decisions is seen as the way to build trustworthy systems. However, explainable artificial intelligence (XAI) methods in medicine often lack a proper evaluation. In this paper, we present our evaluation methodology for XAI methods using forward simulatability. We define the Forward Simulatability Score (FSS) and analyze its limitations in the context of clinical predictors. Then, we applied FSS to our XAI approach defined over an ML-RO, a machine learning clinical predictor based on random optimization over a multiple kernel support vector machine (SVM) algorithm. To Compare FSS values before and after the explanation phase, we test our evaluation methodology for XAI methods on three clinical datasets, namely breast cancer, VTE, and migraine. The ML-RO system is a good model on which to test our XAI evaluation strategy based on the FSS. Indeed, ML-RO outperforms two other base models—a decision tree (DT) and a plain SVM—in the three datasets and gives the possibility of defining different XAI models: TOPK, MIGF, and F4G. The FSS evaluation score suggests that the explanation method F4G for the ML-RO is the most effective in two datasets out of the three tested, and it shows the limits of the learned model for one dataset. Our study aims to introduce a standard practice for evaluating XAI methods in medicine. By establishing a rigorous evaluation framework, we seek to provide healthcare professionals with reliable tools for assessing the performance of XAI methods to enhance the adoption of AI systems in clinical practice.

In this study, we propose a pioneering framework for generating multi-objective counterfactual explanations in job-shop scheduling contexts, combining predictive process monitoring with advanced mathematical optimization techniques. Using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for multi-objective optimization, our approach enhances the generation of counterfactual explanations that illuminate potential enhancements at both the operational and systemic levels. Validated with real-world data, our methodology underscores the superiority of NSGA-II in crafting pertinent and actionable counterfactual explanations, surpassing traditional methods in both efficiency and practical relevance. This work advances the domains of explainable artificial intelligence (XAI), predictive process monitoring, and combinatorial optimization, providing an effective tool for improving automated scheduling systems’ clarity, and decision-making capabilities.

Cardiovascular diseases are the leading contributor of mortality worldwide. Accurate cardiovascular disease prediction is crucial, and the application of machine learning and data mining techniques could facilitate decision-making and improve predictive capabilities. This study aimed to present a model for accurate prediction of cardiovascular diseases and identifying key contributing factors with the greatest impact. The Cleveland dataset besides the locally collected dataset, called the Noor dataset, was used in this study. Accordingly, various data mining techniques besides four ensemble learning-based models were implemented on both datasets. Moreover, a novel model for combining individual classifiers in ensemble learning, wherein weights were assigned to each classifier (using a genetic algorithm), was developed. The predictive strength of each feature was also investigated to ensure the generalizability of the outcomes. The ultimate ensemble-based model achieved a precision rate of 88.05% and 90.12% on the Cleveland and Noor datasets, respectively, demonstrating its reliability and suitability for future research in predicting the likelihood of cardiovascular diseases. Not only the proposed model introduces an innovative approach for specifying cardiovascular diseases by unraveling the intricate relationships between various biological variables but also facilitates early detection of cardiovascular diseases.

The rise in power consumption (PC) is caused by several factors such as the growing global population, urbanization, technological advances, economic development, and growth of businesses and commercial sectors. In these days, intermittent renewable energy sources (RESs) are widely utilized in electric grids to meet the need for power. Data-driven techniques are essential to assuring the steady operation of the electric grid and accurate power consumption and generation forecasting. Conversely, the available datasets for time series electric power forecasting in the energy industry are not as large as those for other domains such as in computer vision. Thus, a deep learning (DL) framework for predicting PC in residential and commercial buildings as well as the power generation (PG) from RESs is introduced. The raw power data obtained from buildings and RESs-based power plants are conceded by the purging process where absent values are filled in and noise and outliers are eliminated. Next, the proposed generative adversarial network (GAN) uses a portion of the cleaned data to generate synthetic parallel data, which is combined with the actual data to make a hybrid dataset. Subsequently, the stacked gated recurrent unit (GRU) model, which is optimized for power forecasting, is trained using the hybrid dataset. Six existent power data are used to train and test sixteen linear and nonlinear models for energy forecasting. The best-performing network is selected as the proposed method for forecasting tasks. For Korea Yeongam solar power (KYSP), individual household electric power consumption (IHEPC), and advanced institute of convergence technology (AICT) datasets, the proposed model obtains mean absolute error (MAE) values of 0.0716, 0.0819, and 0.0877, respectively. Similarly, its MAE values are 0.1215, 0.5093, and 0.5751, for Australia Alice Springs solar power (AASSP), Korea south east wind power (KSEWP), and, Korea south east solar power (KSESP) datasets, respectively.