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1.
  • Aghajary, Mohammad Mahdi, et al. (författare)
  • A novel adaptive control design method for stochastic nonlinear systems using neural network
  • 2021
  • Ingår i: Neural Computing & Applications. - : Springer London. - 0941-0643 .- 1433-3058. ; 33:15, s. 9259-9287
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper presents a novel method for designing an adaptive control system using radial basis function neural network. The method is capable of dealing with nonlinear stochastic systems in strict-feedback form with any unknown dynamics. The proposed neural network allows the method not only to approximate any unknown dynamic of stochastic nonlinear systems, but also to compensate actuator nonlinearity. By employing dynamic surface control method, a common problem that intrinsically exists in the back-stepping design, called "explosion of complexity", is resolved. The proposed method is applied to the control systems comprising various types of the actuator nonlinearities such as Prandtl-Ishlinskii (PI) hysteresis, and dead-zone nonlinearity. The performance of the proposed method is compared to two different baseline methods: a direct form of backstepping method, and an adaptation of the proposed method, named APIC-DSC, in which the neural network is not contributed in compensating the actuator nonlinearity. It is observed that the proposed method improves the failure-free tracking performance in terms of the Integrated Mean Square Error (IMSE) by 25%/11% as compared to the backstepping/APIC-DSC method. This depression in IMSE is further improved by 76%/38% and 32%/49%, when it comes with the actuator nonlinearity of PI hysteresis and dead-zone, respectively. The proposed method also demands shorter adaptation period compared with the baseline methods.
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2.
  • Amirsadri, Shima, et al. (författare)
  • A Levy flight-based grey wolf optimizer combined with back-propagation algorithm for neural network training
  • 2017
  • Ingår i: Neural Computing & Applications. - : Springer Nature. - 0941-0643 .- 1433-3058. ; 30:12, s. 3707-3720
  • Tidskriftsartikel (refereegranskat)abstract
    • In the present study, a new algorithm is developed for neural network training by combining a gradient-based and a meta-heuristic algorithm. The new algorithm benefits from simultaneous local and global search, eliminating the problem of getting stuck in local optimum. For this purpose, first the global search ability of the grey wolf optimizer (GWO) is improved with the Levy flight, a random walk in which the jump size follows the Levy distribution, which results in a more efficient global search in the search space thanks to the long jumps. Then, this improved algorithm is combined with back propagation (BP) to use the advantages of enhanced global search ability of GWO and local search ability of BP algorithm in training neural network. The performance of the proposed algorithm has been evaluated by comparing it against a number of well-known meta-heuristic algorithms using twelve classification and function-approximation datasets.
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3.
  • Bayraktar, Ertugrul, et al. (författare)
  • Object manipulation with a variable-stiffness robotic mechanism using deep neural networks for visual semantics and load estimation
  • 2020
  • Ingår i: Neural Computing and Applications. - : Springer Science and Business Media LLC. - 0941-0643 .- 1433-3058. ; 32:13, s. 9029-9045
  • Tidskriftsartikel (refereegranskat)abstract
    • In recent years, the computer vision applications in the robotics have been improved to approach human-like visual perception and scene/context understanding. Following this aspiration, in this study, we explored the possibility of better object manipulation performance by connecting the visual recognition of objects to their physical attributes, such as weight and center of gravity (CoG). To develop and test this idea, an object manipulation platform is built comprising a robotic arm, a depth camera fixed at the top center of the workspace, embedded encoders in the robotic arm mechanism, and microcontrollers for position and force control. Since both the visual recognition and force estimation algorithms use deep learning principles, the test set-up was named as Deep-Table. The objects in the manipulation tests are selected from everyday life and are common to be seen on modern office desktops. The visual object localization and recognition processes are performed from two distinct branches by deep convolutional neural network architectures. We present five of the possible cases, having different levels of information availability on the object weight and CoG in the experiments. The results confirm that using our algorithm, the robotic arm can move different types of objects successfully varying from several grams (empty bottle) to around 250 g (ceramic cup) without failure or tipping. The proposed method also shows that connecting the object recognition with load estimation and contact point further improves the performance characterized by a smoother motion.
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4.
  • Bayram, Firas, et al. (författare)
  • A domain-region based evaluation of ML performance robustness to covariate shift
  • 2023
  • Ingår i: Neural Computing & Applications. - : Springer. - 0941-0643 .- 1433-3058. ; 35:24, s. 17555-17577
  • Tidskriftsartikel (refereegranskat)abstract
    • Most machine learning methods assume that the input data distribution is the same in the training and testing phases.However, in practice, this stationarity is usually not met and the distribution of inputs differs, leading to unexpectedperformance of the learned model in deployment. The issue in which the training and test data inputs follow differentprobability distributions while the input–output relationship remains unchanged is referred to as covariate shift. In thispaper, the performance of conventional machine learning models was experimentally evaluated in the presence of covariateshift. Furthermore, a region-based evaluation was performed by decomposing the domain of probability density function ofthe input data to assess the classifier’s performance per domain region. Distributional changes were simulated in a twodimensional classification problem. Subsequently, a higher four-dimensional experiments were conducted. Based on theexperimental analysis, the Random Forests algorithm is the most robust classifier in the two-dimensional case, showing thelowest degradation rate for accuracy and F1-score metrics, with a range between 0.1% and 2.08%. Moreover, the resultsreveal that in higher-dimensional experiments, the performance of the models is predominantly influenced by the complexity of the classification function, leading to degradation rates exceeding 25% in most cases. It is also concluded that themodels exhibit high bias toward the region with high density in the input space domain of the training samples.
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5.
  • Borg, Anton, et al. (författare)
  • E-mail classification with machine learning and word embeddings for improved customer support
  • 2021
  • Ingår i: Neural Computing & Applications. - : Springer. - 0941-0643 .- 1433-3058. ; 33:6, s. 1881-1902
  • Tidskriftsartikel (refereegranskat)abstract
    • Classifying e-mails into distinct labels can have a great impact on customer support. By using machine learning to label e-mails, the system can set up queues containing e-mails of a specific category. This enables support personnel to handle request quicker and more easily by selecting a queue that match their expertise. This study aims to improve a manually defined rule-based algorithm, currently implemented at a large telecom company, by using machine learning. The proposed model should have higher F1-score and classification rate. Integrating or migrating from a manually defined rule-based model to a machine learning model should also reduce the administrative and maintenance work. It should also make the model more flexible. By using the frameworks, TensorFlow, Scikit-learn and Gensim, the authors conduct a number of experiments to test the performance of several common machine learning algorithms, text-representations, word embeddings to investigate how they work together. A long short-term memory network showed best classification performance with an F1-score of 0.91. The authors conclude that long short-term memory networks outperform other non-sequential models such as support vector machines and AdaBoost when predicting labels for e-mails. Further, the study also presents a Web-based interface that were implemented around the LSTM network, which can classify e-mails into 33 different labels. © 2020, The Author(s).
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6.
  • Cayir, Sercan, et al. (författare)
  • MITNET : a novel dataset and a two-stage deep learning approach for mitosis recognition in whole slide images of breast cancer tissue
  • 2022
  • Ingår i: Neural Computing & Applications. - : SPRINGER LONDON LTD. - 0941-0643 .- 1433-3058. ; 34:20, s. 17837-17851
  • Tidskriftsartikel (refereegranskat)abstract
    • Mitosis assessment of breast cancer has a strong prognostic importance and is visually evaluated by pathologists. The inter, and intra-observer variability of this assessment is high. In this paper, a two-stage deep learning approach, named MITNET, has been applied to automatically detect nucleus and classify mitoses in whole slide images (WSI) of breast cancer. Moreover, this paper introduces two new datasets. The first dataset is used to detect the nucleus in the WSIs, which contains 139,124 annotated nuclei in 1749 patches extracted from 115 WSIs of breast cancer tissue, and the second dataset consists of 4908 mitotic cells and 4908 non-mitotic cells image samples extracted from 214 WSIs which is used for mitosis classification. The created datasets are used to train the MITNET network, which consists of two deep learning architectures, called MITNET-det and MITNET-rec, respectively, to isolate nuclei cells and identify the mitoses in WSIs. In MITNET-det architecture, to extract features from nucleus images and fuse them, CSPDarknet and Path Aggregation Network (PANet) are used, respectively, and then, a detection strategy using You Look Only Once (scaled-YOLOv4) is employed to detect nucleus at three different scales. In the classification part, the detected isolated nucleus images are passed through proposed MITNET-rec deep learning architecture, to identify the mitosis in the WSIs. Various deep learning classifiers and the proposed classifier are trained with a publicly available mitosis datasets (MIDOG and ATYPIA) and then, validated over our created dataset. The results verify that deep learning-based classifiers trained on MIDOG and ATYPIA have difficulties to recognize mitosis on our dataset which shows that the created mitosis dataset has unique features and characteristics. Besides this, the proposed classifier outperforms the state-of-the-art classifiers significantly and achieves a 68.7% F1-score and 49.0% F1-score on the MIDOG and the created mitosis datasets, respectively. Moreover, the experimental results reveal that the overall proposed MITNET framework detects the nucleus in WSIs with high detection rates and recognizes the mitotic cells in WSI with high F1-score which leads to the improvement of the accuracy of pathologists' decision.
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7.
  • Cheddad, Abbas, et al. (författare)
  • SHIBR-The Swedish Historical Birth Records : a semi-annotated dataset
  • 2021
  • Ingår i: Neural Computing & Applications. - : Springer London. - 0941-0643 .- 1433-3058. ; 33:22, s. 15863-15875
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper presents a digital image dataset of historical handwritten birth records stored in the archives of several parishes across Sweden, together with the corresponding metadata that supports the evaluation of document analysis algorithms' performance. The dataset is called SHIBR (the Swedish Historical Birth Records). The contribution of this paper is twofold. First, we believe it is the first and the largest Swedish dataset of its kind provided as open access (15,000 high-resolution colour images of the era between 1800 and 1840). We also perform some data mining of the dataset to uncover some statistics and facts that might be of interest and use to genealogists. Second, we provide a comprehensive survey of contemporary datasets in the field that are open to the public along with a compact review of word spotting techniques. The word transcription file contains 17 columns of information pertaining to each image (e.g., child's first name, birth date, date of baptism, father's first/last name, mother's first/last name, death records, town, job title of the father/mother, etc.). Moreover, we evaluate some deep learning models, pre-trained on two other renowned datasets, for word spotting in SHIBR. However, our dataset proved challenging due to the unique handwriting style. Therefore, the dataset could also be used for competitions dedicated to a large set of document analysis problems, including word spotting.
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9.
  • Dasari, Siva Krishna, 1988-, et al. (författare)
  • Clustering-based Adaptive Data Augmentation for Class-imbalance in Machine Learning (CADA) : Additive Manufacturing Use-case
  • 2022
  • Ingår i: Neural Computing & Applications. - : Springer London. - 0941-0643 .- 1433-3058.
  • Tidskriftsartikel (refereegranskat)abstract
    • Large amount of data are generated from in-situ monitoring of additive manufacturing (AM) processes which is later used in prediction modelling for defect classification to speed up quality inspection of products. A high volume of this process data is defect-free (majority class) and a lower volume of this data has defects (minority class) which result in the class-imbalance issue. Using imbalanced datasets, classifiers often provide sub-optimal classification results i.e. better performance on the majority class than the minority class. However, it is important for process engineers that models classify defects more accurately than the class with no defects since this is crucial for quality inspection. Hence, we address the class-imbalance issue in manufacturing process data to support in-situ quality control of additive manufactured components.  For this, we propose cluster-based adaptive data augmentation (CADA) for oversampling to address the class-imbalance problem. Quantitative experiments are conducted to evaluate the performance of the proposed method and to compare with other selected oversampling methods using AM datasets from an aerospace industry and a publicly available casting manufacturing dataset. The results show that CADA outperformed random oversampling and the SMOTE method and is similar to random data augmentation and cluster-based oversampling. Furthermore, the results of the statistical significance test show that there is a significant difference between the studied methods.  As such, the CADA method can be considered as an alternative method for oversampling to improve the performance of models on the minority class. 
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10.
  • Dhada, Maharshi, et al. (författare)
  • Weibull recurrent neural networks for failure prognosis using histogram data
  • 2023
  • Ingår i: Neural Computing & Applications. - : Springer Science and Business Media LLC. - 0941-0643 .- 1433-3058. ; 35:4, s. 3011-3024
  • Tidskriftsartikel (refereegranskat)abstract
    • Weibull time-to-event recurrent neural networks (WTTE-RNN) is a simple and versatile prognosis algorithm that works by optimising a Weibull survival function using a recurrent neural network. It offers the combined benefits of the sequential nature of the recurrent neural network, and the ability of the Weibull loss function to incorporate censored data. The goal of this paper is to present the first industrial use case of WTTE-RNN for prognosis. Prognosis of turbocharger conditions in a fleet of heavy-duty trucks is presented here, where the condition data used in the case study were recorded as a time series of sparsely sampled histograms. The experiments include comparison of the prediction models trained using data from the entire fleet of trucks vs data from clustered sub-fleets, where it is concluded that clustering is only beneficial as long as the training dataset is large enough for the model to not overfit. Moreover, the censored data from assets that did not fail are also shown to be incorporated while optimising the Weibull loss function and improve prediction performance. Overall, this paper concludes that WTTE-RNN-based failure predictions enable predictive maintenance policies, which are enhanced by identifying the sub-fleets of similar trucks.
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11.
  • Fouladgar, Nazanin, et al. (författare)
  • CN-waterfall : a deep convolutional neural network for multimodal physiological affect detection
  • 2022
  • Ingår i: Neural Computing & Applications. - : Springer. - 0941-0643 .- 1433-3058. ; 34:3, s. 2157-2176
  • Tidskriftsartikel (refereegranskat)abstract
    • Affective computing solutions, in the literature, mainly rely on machine learning methods designed to accurately detect human affective states. Nevertheless, many of the proposed methods are based on handcrafted features, requiring sufficient expert knowledge in the realm of signal processing. With the advent of deep learning methods, attention has turned toward reduced feature engineering and more end-to-end machine learning. However, most of the proposed models rely on late fusion in a multimodal context. Meanwhile, addressing interrelations between modalities for intermediate-level data representation has been largely neglected. In this paper, we propose a novel deep convolutional neural network, called CN-Waterfall, consisting of two modules: Base and General. While the Base module focuses on the low-level representation of data from each single modality, the General module provides further information, indicating relations between modalities in the intermediate- and high-level data representations. The latter module has been designed based on theoretically grounded concepts in the Explainable AI (XAI) domain, consisting of four different fusions. These fusions are mainly tailored to correlation- and non-correlation-based modalities. To validate our model, we conduct an exhaustive experiment on WESAD and MAHNOB-HCI, two publicly and academically available datasets in the context of multimodal affective computing. We demonstrate that our proposed model significantly improves the performance of physiological-based multimodal affect detection.
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12.
  • Gabi, Danlami, et al. (författare)
  • Cloud customers service selection scheme based on improved conventional cat swarm optimization
  • 2020
  • Ingår i: Neural Computing & Applications. - : Springer. - 0941-0643 .- 1433-3058. ; 32, s. 14817-14838
  • Tidskriftsartikel (refereegranskat)abstract
    • With growing demand on resources situated at the cloud datacenters, the need for customers' resource selection techniques becomes paramount in dealing with the concerns of resource inefficiency. Techniques such as metaheuristics are promising than the heuristics, most especially when handling large scheduling request. However, addressing certain limitations attributed to the metaheuristic such as slow convergence speed and imbalance between its local and global search could enable it become even more promising for customers service selection. In this work, we propose a cloud customers service selection scheme called Dynamic Multi-Objective Orthogonal Taguchi-Cat (DMOOTC). In the proposed scheme, avoidance of local entrapment is achieved by not only increasing its convergence speed, but balancing between its local and global search through the incorporation of Taguchi orthogonal approach. To enable the scheme to meet customers' expectations, Pareto dominant strategy is incorporated providing better options for customers in selecting their service preferences. The implementation of our proposed scheme with that of the benchmarked schemes is carried out on CloudSim simulator tool. With two scheduling scenarios under consideration, simulation results show for the first scenario, our proposed DMOOTC scheme provides better service choices with minimum total execution time and cost (with up to 42.87%, 35.47%, 25.49% and 38.62%, 35.32%, 25.56% reduction) and achieves 21.64%, 18.97% and 13.19% improvement for the second scenario in terms of execution time compared to that of the benchmarked schemes. Similarly, statistical results based on 95% confidence interval for the whole scheduling scheme also show that our proposed scheme can be much more reliable than the benchmarked scheme. This is an indication that the proposed DMOOTC can meet customers' expectations while providing guaranteed performance of the whole cloud computing environment.
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13.
  • Gabi, Danlami, et al. (författare)
  • Dynamic scheduling of heterogeneous resources across mobile edge-cloud continuum using fruit fly-based simulated annealing optimization scheme
  • 2022
  • Ingår i: Neural Computing & Applications. - : Springer Science+Business Media B.V.. - 0941-0643 .- 1433-3058. ; 34, s. 14085-14105
  • Tidskriftsartikel (refereegranskat)abstract
    • Achieving sustainable profit advantage, cost reduction and resource utilization are always a bottleneck for resource providers, especially when trying to meet the computing needs of resource hungry applications in mobile edge-cloud (MEC) continuum. Recent research uses metaheuristic techniques to allocate resources to large-scale applications in MECs. However, some challenges attributed to the metaheuristic techniques include entrapment at the local optima caused by premature convergence and imbalance between the local and global searches. These may affect resource allocation in MECs if continually implemented. To address these concerns and ensure efficient resource allocation in MECs, we propose a fruit fly-based simulated annealing optimization scheme (FSAOS) to serve as a potential solution. In the proposed scheme, the simulated annealing is incorporated to balance between the global and local search and to overcome its premature convergence. We also introduce a trade-off factor to allow application owners to select the best service quality that will minimize their execution cost. Implementation of the FSAOS is carried out on EdgeCloudSim Simulator tool. Simulation results show that the FSAOS can schedule resources effectively based on tasks requirement by returning minimum makespan and execution costs, and achieve better resource utilization compared to the conventional fruit fly optimization algorithm and particle swarm optimization. To further unveil how efficient the FSAOSs, a statistical analysis based on 95% confidential interval is carried out. Numerical results show that FSAOS outperforms the benchmark schemes by achieving higher confidence level. This is an indication that the proposed FSAOS can provide efficient resource allocation in MECs while meeting customers’ aspirations as well as that of the resource providers.
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14.
  • Ghayvat, Hemant, et al. (författare)
  • AI-enabled radiologist in the loop : novel AI-based framework to augment radiologist performance for COVID-19 chest CT medical image annotation and classification from pneumonia
  • 2023
  • Ingår i: Neural Computing & Applications. - : Springer. - 0941-0643 .- 1433-3058. ; 35, s. 14591-14609
  • Tidskriftsartikel (refereegranskat)abstract
    • A SARS-CoV-2 virus-specific reverse transcriptase-polymerase chain reaction (RT-PCR) test is usually used to diagnose COVID-19. However, this test requires up to 2 days for completion. Moreover, to avoid false-negative outcomes, serial testing may be essential. The availability of RT-PCR test kits is currently limited, highlighting the need for alternative approaches for the precise and rapid diagnosis of COVID-19. Patients suspected to be infected with SARS-CoV-2 can be assessed using chest CT scan images. However, CT images alone cannot be used for ruling out SARS-CoV-2 infection because individual patients may exhibit normal radiological results in the primary phases of the disease. A machine learning (ML)-based recognition and segmentation system was developed to spontaneously discover and compute infection areas in CT scans of COVID-19 patients. The computable assessment exhibited suitable performance for automatic infection region allocation. The ML models developed were suitable for the direct detection of COVID-19 (+). ML was confirmed to be a complementary diagnostic technique for diagnosing COVID-19(+) by forefront medical specialists. The complete manual delineation of COVID-19 often requires up to 225.5 min; however, the proposed RILML method decreases the delineation time to 7 min after four iterations of model updating.
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15.
  • Ghayvat, Hemant, et al. (författare)
  • Smart aging monitoring and early dementia recognition (SAMEDR) : uncovering the hidden wellness parameter for preventive well-being monitoring to categorize cognitive impairment and dementia in community-dwelling elderly subjects through AI
  • 2023
  • Ingår i: Neural Computing & Applications. - : Springer. - 0941-0643 .- 1433-3058. ; 35:33, s. 23739-23751
  • Tidskriftsartikel (refereegranskat)abstract
    • Reasoning weakening because of dementia degrades the performance in activities of daily living (ADL). Present research work distinguishes care needs, dangers and monitors the effect of dementia on an individual. This research contrasts in ADL design execution between dementia-affected people and other healthy elderly with heterogeneous sensors. More than 300,000 sensors associated activation data were collected from the dementia patients and healthy controls with wellness sensors networks. Generated ADLs were envisioned and understood through the activity maps, diversity and other wellness parameters to categorize wellness healthy, and dementia affected the elderly. Diversity was significant between diseased and healthy subjects. Heterogeneous unobtrusive sensor data evaluate behavioral patterns associated with ADL, helpful to reveal the impact of cognitive degradation, to measure ADL variation throughout dementia. The primary focus of activity recognition in the current research is to transfer dementia subject occupied homes models to generalized age-matched healthy subject data models to utilize new services, label classified datasets and produce limited datasets due to less training. Current research proposes a novel Smart Aging Monitoring and Early Dementia Recognition system that provides the exchange of data models between dementia subject occupied homes (DSOH) to healthy subject occupied homes (HSOH) in a move to resolve the deficiency of training data. At that point, the key attributes are mapped onto each other utilizing a sensor data fusion that assures to retain the diversities between various HSOH & DSOH by diminishing the divergence between them. Moreover, additional tests have been conducted to quantify the excellence of the offered framework: primary, in contradiction of the precision of feature mapping techniques; next, computing the merit of categorizing data at DSOH; and, the last, the aptitude of the projected structure to function thriving due to noise data. The outcomes show encouraging pointers and highlight the boundaries of the projected approach.
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18.
  • Hashim, Fatma A., et al. (författare)
  • Dimensionality reduction approach based on modified hunger games search: case study on Parkinsons disease phonation
  • 2023
  • Ingår i: Neural Computing & Applications. - : SPRINGER LONDON LTD. - 0941-0643 .- 1433-3058. ; 35:29, s. 21979-22005
  • Tidskriftsartikel (refereegranskat)abstract
    • Hunger Games Search (HGS) is a newly developed swarm-based algorithm inspired by the cooperative behavior of animals and their hunting strategies to find prey. However, HGS has been observed to exhibit slow convergence and may struggle with unbalanced exploration and exploitation phases. To address these issues, this study proposes a modified version of HGS called mHGS, which incorporates five techniques: (1) modified production operator, (2) modified variation control, (3) modified local escaping operator, (4) modified transition factor, and (5) modified foraging behavior. To validate the effectiveness of the mHGS method, 18 different benchmark datasets for dimensionality reduction are utilized, covering a range of sizes (small, medium, and large). Additionally, two Parkinsons disease phonation datasets are employed as real-world applications to demonstrate the superior capabilities of the proposed approach. Experimental and statistical results obtained through the mHGS method indicate its significant performance improvements in terms of Recall, selected attribute count, Precision, F-score, and accuracy when compared to the classical HGS and seven other well-established methods: Gradient-based optimizer (GBO), Grasshopper Optimization Algorithm (GOA), Gray Wolf Optimizer (GWO), Salp Swarm Algorithm (SSA), Whale Optimization Algorithm (WOA), Harris Hawks Optimizer (HHO), and Ant Lion Optimizer (ALO).
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19.
  • Hintze, Arend, Professor, et al. (författare)
  • Neuroevolution gives rise to more focused information transfer compared to backpropagation in recurrent neural networks
  • 2022
  • Ingår i: Neural Computing & Applications. - : Springer Science and Business Media LLC. - 0941-0643 .- 1433-3058.
  • Tidskriftsartikel (refereegranskat)abstract
    • Artificial neural networks (ANNs) are one of the most promising tools in the quest to develop general artificial intelligence. Their design was inspired by how neurons in natural brains connect and process, the only other substrate to harbor intelligence. Compared to biological brains that are sparsely connected and that form sparsely distributed representations, ANNs instead process information by connecting all nodes of one layer to all nodes of the next. In addition, modern ANNs are trained with backpropagation, while their natural counterparts have been optimized by natural evolution over eons. We study whether the training method influences how information propagates through the brain by measuring the transfer entropy, that is, the information that is transferred from one group of neurons to another. We find that while the distribution of connection weights in optimized networks is largely unaffected by the training method, neuroevolution leads to networks in which information transfer is significantly more focused on small groups of neurons (compared to those trained by backpropagation) while also being more robust to perturbations of the weights. We conclude that the specific attributes of a training method (local vs. global) can significantly affect how information is processed and relayed through the brain, even when the overall performance is similar.
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20.
  • Kader, Md. Abdul, et al. (författare)
  • A systematic review on emperor penguin optimizer
  • 2021
  • Ingår i: Neural Computing & Applications. - : Springer. - 0941-0643 .- 1433-3058. ; 33:23, s. 15933-15953
  • Tidskriftsartikel (refereegranskat)abstract
    • Emperor Penguin Optimizer (EPO) is a recently developed metaheuristic algorithm to solve general optimization problems. The main strength of EPO is twofold. Firstly, EPO has low learning curve (i.e., based on the simple analogy of huddling behavior of emperor penguins in nature (i.e., surviving strategy during Antarctic winter). Secondly, EPO offers straightforward implementation. In the EPO, the emperor penguins represent the candidate solution, huddle denotes the search space that comprises a two-dimensional L-shape polygon plane, and randomly positioned of the emperor penguins represents the feasible solution. Among all the emperor penguins, the focus is to locate an effective mover representing the global optimal solution. To-date, EPO has slowly gaining considerable momentum owing to its successful adoption in many broad range of optimization problems, that is, from medical data classification, economic load dispatch problem, engineering design problems, face recognition, multilevel thresholding for color image segmentation, high-dimensional biomedical data analysis for microarray cancer classification, automatic feature selection, event recognition and summarization, smart grid system, and traffic management system to name a few. Reflecting on recent progress, this paper thoroughly presents an in-depth study related to the current EPO's adoption in the scientific literature. In addition to highlighting new potential areas for improvements (and omission), the finding of this study can serve as guidelines for researchers and practitioners to improve the current state-of-the-arts and state-of-practices on general adoption of EPO while highlighting its new emerging areas of applications.
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21.
  • Kleyko, Denis, 1990-, et al. (författare)
  • Autoscaling Bloom filter : controlling trade-off between true and false positives
  • 2020
  • Ingår i: Neural Computing & Applications. - : Springer. - 0941-0643 .- 1433-3058. ; 32:8, s. 3675-3684
  • Tidskriftsartikel (refereegranskat)abstract
    • A Bloom filter is a special case of an artificial neural network with two layers. Traditionally, it is seen as a simple data structure supporting membership queries on a set. The standard Bloom filter does not support the delete operation, and therefore, many applications use a counting Bloom filter to enable deletion. This paper proposes a generalization of the counting Bloom filter approach, called “autoscaling Bloom filters”, which allows adjustment of its capacity with probabilistic bounds on false positives and true positives. Thus, by relaxing the requirement on perfect true positive rate, the proposed autoscaling Bloom filter addresses the major difficulty of Bloom filters with respect to their scalability. In essence, the autoscaling Bloom filter is a binarized counting Bloom filter with an adjustable binarization threshold. We present the mathematical analysis of its performance and provide a procedure for minimizing its false positive rate.
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22.
  • Kusetogullari, Hüseyin, 1981-, et al. (författare)
  • ARDIS : A Swedish Historical Handwritten Digit Dataset
  • 2020
  • Ingår i: Neural Computing & Applications. - : Springer Nature Switzerland. - 0941-0643 .- 1433-3058. ; 32:21, s. 16505-16518
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper introduces a new image-based handwrittenhistorical digit dataset named ARDIS (Arkiv DigitalSweden). The images in ARDIS dataset are extractedfrom 15,000 Swedish church records which were writtenby different priests with various handwriting styles in thenineteenth and twentieth centuries. The constructed datasetconsists of three single digit datasets and one digit stringsdataset. The digit strings dataset includes 10,000 samplesin Red-Green-Blue (RGB) color space, whereas, the otherdatasets contain 7,600 single digit images in different colorspaces. An extensive analysis of machine learning methodson several digit datasets is examined. Additionally, correlationbetween ARDIS and existing digit datasets ModifiedNational Institute of Standards and Technology (MNIST)and United States Postal Service (USPS) is investigated. Experimental results show that machine learning algorithms,including deep learning methods, provide low recognitionaccuracy as they face difficulties when trained on existingdatasets and tested on ARDIS dataset. Accordingly, ConvolutionalNeural Network (CNN) trained on MNIST andUSPS and tested on ARDIS provide the highest accuracies 58.80% and 35.44%, respectively. Consequently, the resultsreveal that machine learning methods trained on existingdatasets can have difficulties to recognize digits effectivelyon our dataset which proves that ARDIS dataset hasunique characteristics. This dataset is publicly available forthe research community to further advance handwritten digitrecognition algorithms.
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23.
  • Makki, Behrooz, 1980, et al. (författare)
  • An evolving neural network to perform dynamic principal component analysis
  • 2009
  • Ingår i: Neural Computing and Applications. - : Springer Science and Business Media LLC. - 0941-0643 .- 1433-3058. ; 19:3, s. 459-463
  • Tidskriftsartikel (refereegranskat)abstract
    • Nonlinear principal component analysis is one of the best dimension reduction techniques developed during the recent years which have been applied in different signal-processing applications. In this paper, an evolving category of auto-associative neural network is presented which is applied to perform dynamic nonlinear principal component analysis. Training strategy of the network implements both constructive and destructive algorithms to extract dynamic principal components of speech database. In addition, the proposed network makes it possible to eliminate some dimensions of sequences that do not play important role in the quality of speech processing. Finally, the network is successfully applied to solve missing data problem.
  •  
24.
  • Makki, Behrooz, 1980, et al. (författare)
  • Some Refinements of the Standard Autoassociative Neural Network
  • 2013
  • Ingår i: Neural Computing and Applications. - : Springer Science and Business Media LLC. - 0941-0643 .- 1433-3058. ; 22:7-8, s. 1461-1475
  • Tidskriftsartikel (refereegranskat)abstract
    • Improving the training algorithm, determining near-optimal number of nonlinear principal components (NLPCs), extracting meaningful NLPCs, and increasing the nonlinear, dynamic, and selective processing capability of the standard autoassociative neural network are the objectives of this article that are achieved independently by some new refinements of the network structure and the training algorithm. In addition, three different topologies of the network are presented, which make it possible to perform local nonlinear principal component analysis. Performances of all methods are evaluated by a stock price database that demonstrates their efficiency in different situations. Finally, as it will be illustrated in the last section, the proposed structures can be easily combined together, which introduce them as efficient tools in a wide range of signal processing applications.
  •  
25.
  • Makki, Behrooz, 1980, et al. (författare)
  • Unaligned Training for Voice Conversion based on a Local-nonlinear Principal Component Analysis Approach
  • 2009
  • Ingår i: Neural Computing and Applications. - : Springer Science and Business Media LLC. - 0941-0643 .- 1433-3058. ; 19:3, s. 437-444
  • Tidskriftsartikel (refereegranskat)abstract
    • During the past years, various principal component analysis algorithms have been developed. In this paper, a new approach for local nonlinear principal component analysis is proposed which is applied to capture voice conversion (VC). A new structure of autoassociative neural network is designed which not only performs data partitioning but also extracts nonlinear principal components of the clusters. Performance of the proposed method is evaluated by means of two experiments that illustrate its efficiency; at first, performance of the network is described by means of an artificial dataset and then, the developed method is applied to perform VC.
  •  
26.
  • Mostafa, Reham R., et al. (författare)
  • An enhanced chameleon swarm algorithm for global optimization and multi-level thresholding medical image segmentation
  • 2024
  • Ingår i: Neural Computing & Applications. - : SPRINGER LONDON LTD. - 0941-0643 .- 1433-3058.
  • Tidskriftsartikel (refereegranskat)abstract
    • Medical image segmentation is crucial in using digital images for disease diagnosis, particularly in post-processing tasks such as analysis and disease identification. Segmentation of magnetic resonance imaging (MRI) and computed tomography images pose distinctive challenges attributed to factors such as inadequate illumination during the image acquisition process. Multilevel thresholding is a widely adopted method for image segmentation due to its effectiveness and ease of implementation. However, the primary challenge lies in selecting the optimal set of thresholds to achieve accurate segmentation. While Otsu's between-class variance and Kapur's entropy assist in identifying optimal thresholds, their application to cases requiring more than two thresholds can be computationally intensive. Meta-heuristic algorithms are commonly employed in literature to calculate the threshold values; however, they have limitations such as a lack of precise convergence and a tendency to become stuck in local optimum solutions. In this paper, we introduce an improved chameleon swarm algorithm (ICSA) to address these limitations. ICSA is designed for image segmentation and global optimization tasks, aiming to improve the precision and efficiency of threshold selection in medical image segmentation. ICSA introduces the concept of the "best random mutation strategy" to enhance the search capabilities of the standard chameleon swarm algorithm (CSA). This strategy leverages three distribution functions-Levy, Gaussian, and Cauchy-for mutating search individuals. These diverse distributions contribute to improved solution quality and help prevent premature convergence. We conduct comprehensive experiments using the IEEE CEC'20 complex optimization benchmark test suite to evaluate ICSA's performance. Additionally, we employ ICSA in image segmentation, utilizing Otsu's approach and Kapur's entropy as fitness functions to determine optimal threshold values for a set of MRI images. Comparative analysis reveals that ICSA outperforms well-known metaheuristic algorithms when applied to the CEC'20 test suite and significantly improves image segmentation performance, proving its ability to avoid local optima and overcome the original algorithm's drawbacks. Medical image segmentation is essential for employing digital images for disease diagnosis, particularly for post-processing activities such as analysis and disease identification. Due to poor illumination and other acquisition-related difficulties, radiologists are especially concerned about the optimal segmentation of brain magnetic resonance imaging (MRI). Multilevel thresholding is the most widely used image segmentation method due to its efficacy and simplicity of implementation. The issue, however, is selecting the optimum set of criteria to effectively segment each image. Although methods like Otsu's between-class variance and Kapur's entropy help locate the optimal thresholds, using them for more than two thresholds requires a significant amount of processing resources. Meta-heuristic algorithms are commonly employed in literature to calculate the threshold values; however, they have limitations such as a lack of precise convergence and a tendency to become stuck in local optimum solutions. Due to the aforementioned, we present an improved chameleon swarm algorithm (ICSA) in this paper for image segmentation and global optimization tasks to be able to address these weaknesses. In the ICSA method, the best random mutation strategy has been introduced to improve the searchability of the standard CSA. The best random strategy utilizes three different types of distribution: Levy, Gaussian, and Cauchy to mutate the search individuals. These distributions have different functions, which help enhance the quality of the solutions and avoid premature convergence. Using the IEEE CEC'20 test suite as a recent complex optimization benchmark, a comprehensive set of experiments is carried out in order to evaluate the ICSA method and demonstrate the impact of combining the best random mutation strategy with the original CSA in improving both the performance of the solutions and the rate at which they converge. Furthermore, utilizing the Otsu approach and Kapur's entropy as a fitness function, ICSA is used as an image segmentation method to select the ideal threshold values for segmenting a set of MRI images. Within the experiments, the ICSA findings are compared with well-known metaheuristic algorithms. The comparative findings showed that ICSA performs better than other competitors in solving the CEC'20 test suite and has a significant performance boost in image segmentation.
  •  
27.
  • Mubashar, Mehreen, et al. (författare)
  • R2U++: a multiscale recurrent residual U-Net with dense skip connections for medical image segmentation
  • 2022
  • Ingår i: Neural Computing & Applications. - : Springer Nature. - 0941-0643 .- 1433-3058. ; 34:20, s. 17723-17739
  • Tidskriftsartikel (refereegranskat)abstract
    • U-Net is a widely adopted neural network in the domain of medical image segmentation. Despite its quick embracement by the medical imaging community, its performance suffers on complicated datasets. The problem can be ascribed to its simple feature extracting blocks: encoder/decoder, and the semantic gap between encoder and decoder. Variants of U-Net (such as R2U-Net) have been proposed to address the problem of simple feature extracting blocks by making the network deeper, but it does not deal with the semantic gap problem. On the other hand, another variant UNET++ deals with the semantic gap problem by introducing dense skip connections but has simple feature extraction blocks. To overcome these issues, we propose a new U-Net based medical image segmentation architecture R2U++. In the proposed architecture, the adapted changes from vanilla U-Net are: (1) the plain convolutional backbone is replaced by a deeper recurrent residual convolution block. The increased field of view with these blocks aids in extracting crucial features for segmentation which is proven by improvement in the overall performance of the network. (2) The semantic gap between encoder and decoder is reduced by dense skip pathways. These pathways accumulate features coming from multiple scales and apply concatenation accordingly. The modified architecture has embedded multi-depth models, and an ensemble of outputs taken from varying depths improves the performance on foreground objects appearing at various scales in the images. The performance of R2U++ is evaluated on four distinct medical imaging modalities: electron microscopy, X-rays, fundus, and computed tomography. The average gain achieved in IoU score is 1.5 ± 0.37% and in dice score is 0.9 ± 0.33% over UNET++, whereas, 4.21 ± 2.72 in IoU and 3.47 ± 1.89 in dice score over R2U-Net across different medical imaging segmentation datasets.
  •  
28.
  • Neggaz, Nabil, et al. (författare)
  • Boosting manta rays foraging optimizer by trigonometry operators: a case study on medical dataset
  • 2024
  • Ingår i: Neural Computing & Applications. - : SPRINGER LONDON LTD. - 0941-0643 .- 1433-3058.
  • Tidskriftsartikel (refereegranskat)abstract
    • The selection of attributes has become a crucial research focus in the domains of pattern recognition, machine learning, and big data analysis. In essence, the contemporary challenge revolves around reducing dimensionality while maintaining both a quick response time and improved classification performance. Metaheuristics algorithms (MAs) have emerged as pivotal tools in addressing this issue. Firstly, the problem of attribute selection was approached using the manta ray foraging optimization (MRFO) approach, but the majority of MAs suffer from a problem of convergence toward local minima. To mitigate this challenge, an enhanced variant of MRFO, known as MRFOSCA, employs trigonometric operators inspired by the sine cosine algorithm (SCA) to tackle the feature selection problem. The k-nearest neighbor (k-NN) technique is employed for feature-set selection. Additionally, the statistical significance of the proposed algorithms is assessed using the nonparametric Wilcoxon's rank-sum test at a 5% significance level. The outcomes are assessed and compared against some well-known MAs, including the original MRFO and SCA, as well as Harris Hawks optimizer, dragonfly algorithm, grasshopper optimizer algorithm, whale optimizer algorithm, salp swarm algorithm, and grey wolf optimizer. The experimental and comparison analyses validate the pretty effective performance of the proposed methods on low- and high-dimensional datasets by providing the highest accuracy in 85% of the feature selection benchmarks.
  •  
29.
  • Persiani, Michele, 1989-, et al. (författare)
  • Policy regularization for legible behavior
  • 2023
  • Ingår i: Neural Computing & Applications. - : Springer. - 0941-0643 .- 1433-3058. ; 35:23, s. 16781-16790
  • Tidskriftsartikel (refereegranskat)abstract
    • In this paper we propose a method to augment a Reinforcement Learning agent with legibility. This method is inspired by the literature in Explainable Planning and allows to regularize the agent’s policy after training, and without requiring to modify its learning algorithm. This is achieved by evaluating how the agent’s optimal policy may produce observations that would make an observer model to infer a wrong policy. In our formulation, the decision boundary introduced by legibility impacts the states in which the agent’s policy returns an action that is non-legible because having high likelihood also in other policies. In these cases, a trade-off between such action, and legible/sub-optimal action is made. We tested our method in a grid-world environment highlighting how legibility impacts the agent’s optimal policy, and gathered both quantitative and qualitative results. In addition, we discuss how the proposed regularization generalizes over methods functioning with goal-driven policies, because applicable to general policies of which goal-driven policies are a special case.
  •  
30.
  • Puchert, Patrik, et al. (författare)
  • Data-driven deep density estimation
  • 2021
  • Ingår i: Neural Computing & Applications. - : Springer Nature. - 0941-0643 .- 1433-3058. ; 33, s. 16773-16807
  • Tidskriftsartikel (refereegranskat)abstract
    • Density estimation plays a crucial role in many data analysis tasks, as it infers a continuous probability density function (PDF) from discrete samples. Thus, it is used in tasks as diverse as analyzing population data, spatial locations in 2D sensor readings, or reconstructing scenes from 3D scans. In this paper, we introduce a learned, data-driven deep density estimation (DDE) to infer PDFs in an accurate and efficient manner, while being independent of domain dimensionality or sample size. Furthermore, we do not require access to the original PDF during estimation, neither in parametric form, nor as priors, or in the form of many samples. This is enabled by training an unstructured convolutional neural network on an infinite stream of synthetic PDFs, as unbound amounts of synthetic training data generalize better across a deck of natural PDFs than any natural finite training data will do. Thus, we hope that our publicly available DDE method will be beneficial in many areas of data analysis, where continuous models are to be estimated from discrete observations.
  •  
31.
  • Rachkovskij, Dmitri A. (författare)
  • Representation of spatial objects by shift-equivariant similarity-preserving hypervectors
  • 2022
  • Ingår i: Neural Computing & Applications. - : Springer Nature. - 0941-0643 .- 1433-3058. ; 34:24, s. 22387-22403
  • Tidskriftsartikel (refereegranskat)abstract
    • Hyperdimensional Computing (HDC), also known as Vector-Symbolic Architectures (VSA), is an approach that has been proposed to combine the advantages of distributed vector representations and symbolic structured data representations in Artificial Intelligence, Machine Learning, and Pattern Recognition problems. HDC/VSA operate with hypervectors, i.e., brain-like distributed representations of large fixed dimension. The key problem of HDC/VSA is how to transform data of various types into hypervectors. In this paper, we propose a novel approach for the formation of hypervectors of spatial objects, such as images, that provides both an equivariance with respect to the shift of objects and preserves the similarity of objects described by similar features at nearby positions. In contrast to known hypervector formation methods, we represent the features by compositional hypervectors and exploit permutations of hypervectors for representing the position of features. We experimentally explored the proposed approach in some tasks that exploit various descriptions of two-dimensional (2D) images. In terms of standard accuracy measures such as error rate or mean average precision, our results are on a par or better than those of other methods and are obtained without feature learning. The proposed techniques were designed for the HDC/VSA model known as Sparse Binary Distributed Representations. However, they can be adapted to hypervectors in formats of other HDC/VSA models, as well as for representing spatial objects other than 2D images.
  •  
32.
  • Rietz, Finn, 1995-, et al. (författare)
  • Hierarchical goals contextualize local reward decomposition explanations
  • 2023
  • Ingår i: Neural Computing & Applications. - : Springer. - 0941-0643 .- 1433-3058. ; 35:23, s. 16693-16704
  • Tidskriftsartikel (refereegranskat)abstract
    • One-step reinforcement learning explanation methods account for individual actions but fail to consider the agent's future behavior, which can make their interpretation ambiguous. We propose to address this limitation by providing hierarchical goals as context for one-step explanations. By considering the current hierarchical goal as a context, one-step explanations can be interpreted with higher certainty, as the agent's future behavior is more predictable. We combine reward decomposition with hierarchical reinforcement learning into a novel explainable reinforcement learning framework, which yields more interpretable, goal-contextualized one-step explanations. With a qualitative analysis of one-step reward decomposition explanations, we first show that their interpretability is indeed limited in scenarios with multiple, different optimal policies-a characteristic shared by other one-step explanation methods. Then, we show that our framework retains high interpretability in such cases, as the hierarchical goal can be considered as context for the explanation. To the best of our knowledge, our work is the first to investigate hierarchical goals not as an explanation directly but as additional context for one-step reinforcement learning explanations.
  •  
33.
  • Smrekar, J., et al. (författare)
  • Prediction of power output of a coal-fired power plant by artificial neural network
  • 2010
  • Ingår i: Neural Computing & Applications. - : Springer Science and Business Media LLC. - 0941-0643 .- 1433-3058. ; 19:5, s. 725-740
  • Tidskriftsartikel (refereegranskat)abstract
    • Accurate modeling of thermal power plant is very useful as well as difficult. Conventional simulation programs based on heat and mass balances represent plant processes with mathematical equations. These are good for understanding the processes but usually complicated and at times limited with large number of parameters needed. On the other hand, artificial neural network (ANN) models could be developed using real plant data, which are already measured and stored. These models are fast in response and easy to be updated with new plant data. Usually, in ANN modeling, energy systems can also be simulated with fewer numbers of parameters compared to mathematical ones. Step-by-step method of the ANN model development of a coal-fired power plant for its base line operation is discussed in this paper. The ultimate objective of the work was to predict power output from a coal-fired plant by using the least number of controllable parameters as inputs. The paper describes two ANN models, one for boiler and one for turbine, which are eventually integrated into a single ANN model representing the real power plant. The two models are connected through main steam properties, which are the predicted parameters from boiler ANN model. Detailed procedure of ANN model development has been discussed along with the expected prediction accuracies and validation of models with real plant data. The interpolation and extrapolation capability of ANN models for the plant has also been studied, and observed results are reported.
  •  
34.
  • Sodhro, Ali Hassan, et al. (författare)
  • An adaptive QoS computation for medical data processing in intelligent healthcare applications
  • 2020
  • Ingår i: Neural Computing and Applications. - : Springer. - 0941-0643 .- 1433-3058. ; 32:2020, s. 723-734
  • Tidskriftsartikel (refereegranskat)abstract
    • Efficient computation of quality of service (QoS) during medical data processing through intelligent measurement methods is one of the mandatory requirements of the medial healthcare world. However, emergency medical services often involve transmission of critical data, thus having stringent requirements for network quality of service (QoS). This paper contributes in three distinct ways. First, it proposes the novel adaptive QoS computation algorithm (AQCA) for fair and efficient monitoring of the performance indicators, i.e., transmission power, duty cycle and route selection during medical data processing in healthcare applications. Second, framework of QoS computation in medical applications is proposed at physical, medium access control (MAC) and network layers. Third, QoS computation mechanism with proposed AQCA and quality of experience (QoE) is developed. Besides, proper examination of QoS computation for medical healthcare application is evaluated with 4–10 inches large-screen user terminal (UT) devices (for example, LCD panel size, resolution, etc.). These devices are based on high visualization, battery lifetime and power optimization for ECG service in emergency condition. These UT devices are used to achieve highest level of satisfaction in terms, i.e., less power drain, extended battery lifetime and optimal route selection. QoS parameters with estimation of QoE perception identify the degree of influence of each QoS parameters on the medical data processing is analyzed. The experimental results indicate that QoS is computed at physical, MAC and network layers with transmission power (− 15 dBm), delay (100 ms), jitter (40 ms), throughput (200 Bytes), duty cycle (10%) and route selection (optimal). Thus it can be said that proposed AQCA is the potential candidate for QoS computation than Baseline for medical healthcare applications.
  •  
35.
  • Solans, Virginie, et al. (författare)
  • Optimisation of used nuclear fuel canister loading using a neural network and genetic algorithm
  • 2021
  • Ingår i: Neural Computing & Applications. - : Springer Nature. - 0941-0643 .- 1433-3058. ; 33:23, s. 16627-16639
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper presents an approach for the optimisation of geological disposal canister loadings, combining high resolution simulations of used nuclear fuel characteristics with an articial neural network and a genetic algorithm. The used nuclear fuels (produced in an open fuel cycle without reprocessing) considered in this work come from a Swiss Pressurised Water Reactor, taking into account their realistic lifetime in the reactor core and cooling periods, up to their disposal in the final geological repository. The case of 212 representative used nuclear fuel assemblies is analysed, assuming a loading of 4 fuel assemblies per canister, and optimizing two safety parameters: the fuel decay heat (DH) and the canister effective neutron multiplication factor keff. In the present approach, a neural network is trained as a surrogate model to evaluate the keff value to substitute the time-consuming-code Monte Carlo transport & depletion SERPENT for specific canister loading calculations. A genetic algorithm is then developed to optimise simultaneously the canister keff and DH values. The keff computed during the optimisation algorithm is using the previously developed artificial neural network. The optimisation algorithm allows (1) to minimize the number of canisters, given assumed limits for both DH and keff quantities and (2) to minimize DH and keff differences among canisters. This study represents a proof-of-principle of the neural network and genetic algorithm capabilities, and will be applied in the future to a larger number of cases.
  •  
36.
  • Soltanali, Hamzeh, et al. (författare)
  • An improved fuzzy inference system-based risk analysis approach with application to automotive production line
  • 2020
  • Ingår i: Neural Computing & Applications. - : Springer. - 0941-0643 .- 1433-3058. ; 32:14, s. 10573-10591
  • Tidskriftsartikel (refereegranskat)abstract
    • Reliability and safety in the process industries like automotive industry are important key success factors for upgrading availability and preventing catastrophic failures. In this context, failure mode and effect analysis (FMEA) technique is a proactive diagnostic tool for evaluating all failure modes which reduces the highest risk priority failures. However, it still suffers from subjective uncertainty and ambiguity which are important factors in risk analysis procedures. Hence, this paper provides a comprehensive survey to overcome the drawbacks of the traditional FMEA through improved FMEA, incorporating the fuzzy inference system (FIS) environment. For this purpose, the effective attributes, such as; various scales and rules, various membership functions, different defuzzification algorithms and their impacts on fuzzy RPN (FRPN) have been investigated. Moreover, three types of sensitivity analysis were performed to identify the effect and authority control of risk parameters, i.e., severity, occurrence and detection on FRPN. To demonstrate the feasibility of the proposed framework, as a practical example, the method was implemented in complex equipment in an automotive production line. The result of FIS-FMEA model revealed that the proposed framework could be useful in recognizing the failure modes with critical risk values compared to the traditional FMEA. Given the potential applications of this approach, suitable maintenance actions can be recommended to improve the reliability and safety of process industry, such as automotive production line.
  •  
37.
  • Tarasov, Vladimir, 1966-, et al. (författare)
  • Fuzzy logic-based modelling of yield strength of as-cast A356 alloy
  • 2020
  • Ingår i: Neural Computing & Applications. - : Springer. - 0941-0643 .- 1433-3058. ; 32:10, s. 5833-5844
  • Tidskriftsartikel (refereegranskat)abstract
    • Uncertain and imprecise data are inherent to many domains, e.g. casting lightweight components. Fuzzy logic offers a way to handle such data, which makes it possible to create predictive models even with small and imprecise data sets. Modelling of cast components under fatigue load leads to understanding of material behaviour on component level. Such understanding is important for the design for minimum warranty risk and maximum weight reduction of lightweight cast components. This paper contributes with a fuzzy logic-based approach to model fatigue-related mechanical properties of as-cast components, which has not been fully addressed by the current research. Two fuzzy logic models are constructed to map yield strength to the chemical composition and the rate of solidification of castings for two A356 alloys. Artificial neural networks are created for the same data sets and then compared to the fuzzy logic approach. The comparison shows that although the neural networks yield similar prediction accuracy, they are less suitable for the domain because they are opaque models. The prediction errors exhibited by the fuzzy logic models are 3.53% for the model and 3.19% for the second, which is the same error level as reported in related work. An examination of prediction errors indicated that these are affected by parameters of the membership functions of the fuzzy logic model.
  •  
38.
  • Tatar, Kivanc, 1988, et al. (författare)
  • Latent Timbre Synthesis: Audio-based Variational Auto-Encoders for Music Composition Applications
  • 2020
  • Ingår i: Neural Computing and Applications. - : Springer Science and Business Media LLC. - 0941-0643 .- 1433-3058. ; 33:The Special Issue of Neural Computing and Applications: “Networks in Art, Sound and Design.”, s. 67-84
  • Tidskriftsartikel (refereegranskat)abstract
    • We present the Latent Timbre Synthesis, a new audio synthesis method using deep learning. The synthesis method allows composers and sound designers to interpolate and extrapolate between the timbre of multiple sounds using the latent space of audio frames. We provide the details of two Variational Autoencoder architectures for the Latent Timbre Synthesis and compare their advantages and drawbacks. The implementation includes a fully working application with a graphical user interface, called interpolate_two, which enables practitioners to generate timbres between two audio excerpts of their selection using interpolation and extrapolation in the latent space of audio frames. Our implementation is open source, and we aim to improve the accessibility of this technology by providing a guide for users with any technical background. Our study includes a qualitative analysis where nine composers evaluated the Latent Timbre Synthesis and the interpolate_two application within their practices. 2.14.0.0
  •  
39.
  • Thomas, Bertil, et al. (författare)
  • Artificial Neural Network Models for Indoor Temperature Prediction : investigations in two buildings
  • 2007
  • Ingår i: Neural Computing & Applications. - London : Springer London. - 0941-0643 .- 1433-3058. ; 16:1, s. 81-89
  • Tidskriftsartikel (refereegranskat)abstract
    • The problem how to identify prediction models of the indoor climate in buildings is discussed. Identification experiments have been carried out in two buildings and different models, such as linear ARX-, ARMAX- and BJ-models as well as non-linear artificial neural network models (ANN-models) of different orders, have been identified based on these experiments. In the models, many different input signals have been used, such as the outdoor and indoor temperature, heating power, wall temperatures, ventilation flow rate, time of day and sun radiation. For both buildings, it is shown that ANN-models give more accurate temperature predictions than linear models. For the first building, it is shown that a non-linear combination of sun radiation and time of day is important when predicting the indoor temperature. For the second building, it is shown that the indoor temperature is non-linearly dependent on the ventilation flow rate. © Springer-Verlag London Limited 2006.
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40.
  • Tran, Khanh-Tung, et al. (författare)
  • NeuProNet: neural profiling networks for sound classification
  • 2024
  • Ingår i: Neural Computing & Applications. - : Springer Nature. - 0941-0643 .- 1433-3058. ; 36:11, s. 5873-5887
  • Tidskriftsartikel (refereegranskat)abstract
    • Real-world sound signals exhibit various aspects of grouping and profiling behaviors, such as being recorded from identical sources, having similar environmental settings, or encountering related background noises. In this work, we propose novel neural profiling networks (NeuProNet) capable of learning and extracting high-level unique profile representations from sounds. An end-to-end framework is developed so that any backbone architectures can be plugged in and trained, achieving better performance in any downstream sound classification tasks. We introduce an in-batch profile grouping mechanism based on profile awareness and attention pooling to produce reliable and robust features with contrastive learning. Furthermore, extensive experiments are conducted on multiple benchmark datasets and tasks to show that neural computing models under the guidance of our framework gain significant performance gaps across all evaluation tasks. Particularly, the integration of NeuProNet surpasses recent state-of-the-art (SoTA) approaches on UrbanSound8K and VocalSound datasets with statistically significant improvements in benchmarking metrics, up to 5.92% in accuracy compared to the previous SoTA method and up to 20.19% compared to baselines. Our work provides a strong foundation for utilizing neural profiling for machine learning tasks.
  •  
41.
  • Truica, Ciprian-Octavian, et al. (författare)
  • SimpLex : a lexical text simplification architecture
  • 2023
  • Ingår i: Neural Computing & Applications. - : Springer Nature. - 0941-0643 .- 1433-3058. ; 35:8, s. 6265-6280
  • Tidskriftsartikel (refereegranskat)abstract
    • Text simplification (TS) is the process of generating easy-to-understand sentences from a given sentence or piece of text. The aim of TS is to reduce both the lexical (which refers to vocabulary complexity and meaning) and syntactic (which refers to the sentence structure) complexity of a given text or sentence without the loss of meaning or nuance. In this paper, we present SimpLex, a novel simplification architecture for generating simplified English sentences. To generate a simplified sentence, the proposed architecture uses either word embeddings (i.e., Word2Vec) and perplexity, or sentence transformers (i.e., BERT, RoBERTa, and GPT2) and cosine similarity. The solution is incorporated into a user-friendly and simple-to-use software. We evaluate our system using two metrics, i.e., SARI and Perplexity Decrease. Experimentally, we observe that the transformer models outperform the other models in terms of the SARI score. However, in terms of perplexity, the word embedding-based models achieve the biggest decrease. Thus, the main contributions of this paper are: (1) We propose a new word embedding and transformer-based algorithm for text simplification; (2) we design SimpLex-a modular novel text simplification system-that can provide a baseline for further research; and (3) we perform an in-depth analysis of our solution and compare our results with two state-of-the-art models, i.e., LightLS as reported by Glavas and Stajner (in: Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing, 2015) and NTS-w2v as reported by Nisioi et al. (in: Proceedings of the 55th annual meeting of the association for computational linguistics, 2017). We also make the code publicly available online.
  •  
42.
  • Van Calster, Ben, et al. (författare)
  • Using Bayesian neural networks with ARD input selection to detect malignant ovarian masses prior to surgery
  • 2008
  • Ingår i: NEURAL COMPUTING & APPLICATIONS. - : Springer Science and Business Media LLC. - 0941-0643 .- 1433-3058. ; 17:5-6, s. 489-500
  • Konferensbidrag (refereegranskat)abstract
    • In this paper, we applied Bayesian multi-layer perceptrons (MLP) using the evidence procedure to predict malignancy of ovarian masses in a large (n = 1,066) multi-centre data set. Automatic relevance determination (ARD) was used to select the most relevant inputs. Fivefold cross-validation (5CV) and repeated 5CV was used to select the optimal combination of input set and number of hidden neurons. Results indicate good performance of the models with area under the receiver operating characteristic curve values of 0.93-0.94 on independent data. Comparison with a linear benchmark model and a previously developed logistic regression model shows that the present problem is very well linearly separable. A resampling analysis further shows that the number of hidden neurons specified in the ARD analyses for input selection may influence model performance. This paper shows that Bayesian MLPs, although not frequently used, are a useful tool for detecting malignant ovarian tumours.
  •  
43.
  • Verikas, Antanas, 1951-, et al. (författare)
  • Colour classification by neural networks in graphic arts
  • 1998
  • Ingår i: Neural Computing & Applications. - London : Springer London. - 0941-0643 .- 1433-3058. ; 7:1, s. 52-64
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper presents a hierarchical modular neural network for colour classification in graphic arts, capable of distinguishing among very Similar colour classes. The network performs analysis in a rough to fine fashion, and is able to achieve a high average classification speed and a low classification error. In the rough stage of the analysis, clusters of highly overlapping colour classes are detected Discrimination between such colour classes is performed in the next stage by using additional colour information from the surroundings of the pixel being classified. Committees of networks make decisions in the next stage. Outputs of members of the committees are adaptively fused through the BADD defuzzification strategy or the discrete Choquet fuzzy integral. The structure of the network is automatically established during the training process. Experimental investigations show the capability of the network to distinguish among very similar colour classes that can occur in multicoloured printed pictures. The classification accuracy obtained is sufficient for the network to be used for inspecting the quality of multicoloured prints.
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44.
  • Verikas, Antanas, et al. (författare)
  • Estimating the amount of cyan, magenta, yellow, and black inks in arbitrary colour pictures
  • 2007
  • Ingår i: Neural Computing & Applications. - London : Springer London. - 0941-0643 .- 1433-3058. ; 16:2, s. 187-195
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper is concerned with the offset lithographic colour printing. To obtain high quality colour prints, given proportions of cyan (C), magenta (M), yellow (Y), and black (K) inks (four primary inks used in the printing process) should be accurately maintained in any area of the printed picture. To accomplish the task, the press operator needs to measure the printed result for assessing the proportions and use the measurement results to reduce the colour deviations. Specially designed colour bars are usually printed to enable the measurements. This paper presents an approach to estimate the proportions directly in colour pictures without using any dedicated areas. The proportions—the average amount of C, M, Y, and K inks in the area of interest—are estimated from the CCD colour camera RGB (L*a*b*) values recorded from that area. The local kernel ridge regression and the support vector regression are combined for obtaining the desired mapping L*a*b* ⇒ CMYK, which can be multi-valued.
  •  
45.
  • Verikas, Antanas, 1951-, et al. (författare)
  • Learning an Adaptive Dissimilarity Measure for Nearest Neighbour Classification
  • 2003
  • Ingår i: Neural Computing & Applications. - London : Springer. - 0941-0643 .- 1433-3058. ; 11:3-4, s. 203-209
  • Tidskriftsartikel (refereegranskat)abstract
    • In this paper, an approach to weighting features for classification based on the nearest-neighbour rules is proposed. The weights are adaptive in the sense that the weight values are different in various regions of the feature space. The values of the weights are found by performing a random search in the weight space. A correct classification rate is the criterion maximised during the search. Experimentally, we have shown that the proposed approach is useful for classification. The weight values obtained during the experiments show that the importance of features may be different in different regions of the feature space
  •  
46.
  • Verikas, Antanas, 1951-, et al. (författare)
  • Monitoring the de-inking process through neural network-based colour image analysis
  • 2000
  • Ingår i: Neural Computing & Applications. - New York, USA : Springer-Verlag New York. - 0941-0643 .- 1433-3058. ; 9:2, s. 142-151
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper presents an approach to determining the colours of specks in an image of a pulp being recycled. The task is solved through colour classification by an artificial neural network. The network is trained using fuzzy possibilistic target values. The number of colour classes found in the images is determined through the self-organising process in the two-dimensional self-organising map. The experiments performed have shown that the colour classification results correspond well with human perception of the colours of the specks.
  •  
47.
  • Verikas, Antanas, 1951-, et al. (författare)
  • Neural networks based colour measuring for process monitoring and control in multicoloured newspaper printing
  • 2000
  • Ingår i: Neural Computing & Applications. - London : Springer. - 0941-0643 .- 1433-3058. ; 9:3, s. 227-242
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper presents a neural networks based method and a system for colour measurements on printed halftone multicoloured pictures and halftone multicoloured bars in newspapers. The measured values, called a colour vector, are used by the operator controlling the printing process to make appropriate ink feed adjustments to compensate for colour deviations of the picture being measured from the desired print. By the colour vector concept, we mean the CMY or CMYK (cyan, magenta, yellow and black) vector, which lives in the three- or four-dimensional space of printing inks. Two factors contribute to values of the vector components, namely the percentage of the area covered by cyan, magenta, yellow and black inks (tonal values) and ink densities. Values of the colour vector components increase if tonal values or ink densities rise, and vice versa. If some reference values of the colour vector components are set from a desired print, then after an appropriate calibration, the colour vector measured on an actual halftone multicoloured area directly shows how much the operator needs to raise or lower the cyan, magenta, yellow and black ink densities to compensate for colour deviation from the desired print. The 18 months experience of the use of the system in the printing shop witnesses its usefulness through the improved quality of multicoloured pictures, the reduced consumption of inks and, therefore, less severe problems of smearing and printing through.
  •  
48.
  • Vettoruzzo, Anna, 1998-, et al. (författare)
  • Multimodal meta-learning through meta-learned task representations
  • 2024
  • Ingår i: Neural Computing & Applications. - London : Springer. - 0941-0643 .- 1433-3058. ; 36:15, s. 8519-8529
  • Tidskriftsartikel (refereegranskat)abstract
    • Few-shot meta-learning involves training a model on multiple tasks to enable it to efficiently adapt to new, previously unseen tasks with only a limited number of samples. However, current meta-learning methods assume that all tasks are closely related and belong to a common domain, whereas in practice, tasks can be highly diverse and originate from multiple domains, resulting in a multimodal task distribution. This poses a challenge for existing methods as they struggle to learn a shared representation that can be easily adapted to all tasks within the distribution. To address this challenge, we propose a meta-learning framework that can handle multimodal task distributions by conditioning the model on the current task, resulting in a faster adaptation. Our proposed method learns to encode each task and generate task embeddings that modulate the model’s activations. The resulting modulated model becomes specialized for the current task and leads to more effective adaptation. Our framework is designed to work in a realistic setting where the mode from which a task is sampled is unknown. Nonetheless, we also explore the possibility of incorporating auxiliary information, such as the task-mode-label, to further enhance the performance of our method if such information is available. We evaluate our proposed framework on various few-shot regression and image classification tasks, demonstrating its superiority over other state-of-the-art meta-learning methods. The results highlight the benefits of learning to embed task-specific information in the model to guide the adaptation when tasks are sampled from a multimodal distribution. © The Author(s) 2024.
  •  
49.
  • Vyas, Aditi Haresh, et al. (författare)
  • Tear film breakup time-based dry eye disease detection using convolutional neural network
  • 2024
  • Ingår i: Neural Computing & Applications. - : Springer. - 0941-0643 .- 1433-3058. ; 36, s. 143-161
  • Tidskriftsartikel (refereegranskat)abstract
    • Dry eye disease (DED) is a chronic eye disease and a common complication among the world's population. Evaporation of moisture from tear film or a decrease in tear production leads to an unstable tear film which causes DED. The tear film breakup time (TBUT) test is a common clinical test used to diagnose DED. In this test, DED is diagnosed by measuring the time at which the first breakup pattern appears on the tear film. TBUT test is subjective, labour-intensive and time-consuming. These weaknesses make a computer-aided diagnosis of DED highly desirable. The existing computer-aided DED detection techniques use expensive instruments for image acquisition which may not be available in all eye clinics. Moreover, among these techniques, TBUT-based DED detection techniques are limited to finding only tear film breakup area/time and do not identify the severity of DED, which can essentially be helpful to ophthalmologists in prescribing the right treatment. Additionally, a few challenges in developing a DED detection approach are less illuminated video, constant blinking of eyes in the videos, blurred video, and lack of public datasets. This paper presents a novel TBUT-based DED detection approach that detects the presence/absence of DED from TBUT video. In addition, the proposed approach accurately identifies the severity level of DED and further categorizes it as normal, moderate or severe based on the TBUT. The proposed approach exhibits high performance in classifying TBUT frames, detecting DED, and severity grading of TBUT video with an accuracy of 83%. Also, the correlation computed between the proposed approach and the Ophthalmologist's opinion is 90%, which reflects the noteworthy contribution of our proposed approach.
  •  
50.
  • Zamli, K. Z., et al. (författare)
  • Hybrid Henry gas solubility optimization algorithm with dynamic cluster-to-algorithm mapping
  • 2021
  • Ingår i: Neural Computing & Applications. - : Springer Science+Business Media B.V.. - 0941-0643 .- 1433-3058. ; 33, s. 8389-8416
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper discusses a new variant of Henry Gas Solubility Optimization (HGSO) Algorithm, called Hybrid HGSO (HHGSO). Unlike its predecessor, HHGSO allows multiple clusters serving different individual meta-heuristic algorithms (i.e., with its own defined parameters and local best) to coexist within the same population. Exploiting the dynamic cluster-to-algorithm mapping via penalized and reward model with adaptive switching factor, HHGSO offers a novel approach for meta-heuristic hybridization consisting of Jaya Algorithm, Sooty Tern Optimization Algorithm, Butterfly Optimization Algorithm, and Owl Search Algorithm, respectively. The acquired results from the selected two case studies (i.e., involving team formation problem and combinatorial test suite generation) indicate that the hybridization has notably improved the performance of HGSO and gives superior performance against other competing meta-heuristic and hyper-heuristic algorithms.
  •  
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