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Reconstructing low-dose CT imaging deals with handling the inherent noise within the data, which makes it a complex mathematical problem known as an ill-posed inverse problem. Recent attention has shifted towards deep learning-based techniques in CT image reconstruction. However, these approaches encounter limitations due to extensive data requirements for training and validation. We propose an unsupervised CT reconstruction technique that leverages the power of Deep convolutional neural networks (Deep CNNs), demonstrating that a randomly initialized neural network can serve as a prior. We have proposed a completely unsupervised deep learning technique called Loss-construct unsupervised network (LUNA) adjustment for low-dose CT image reconstruction. Our approach combines the traditional reconstruction technique, i.e., simultaneous algebraic reconstruction technique (SART), and integrates the image prior i.e., weighted total variation (WTV) regularization within the Deep CNN model. The overall reconstruction process is optimized using the alternating direction method of multipliers (ADMM) framework, that balances the neural network’s internal representation with the observed data, yielding a more consistent and accurate final image. The proposed method uses various loss functions to update the Deep CNN. The optimal update of the network depends on the various loss functions. Different loss functions, including perceptual loss, SSIM loss, WL2 loss, WTV loss, and sinogram loss, are used to guide the overall reconstruction. This approach effectively handles the constraints of data limitation of deep learning-based techniques, offering a robust and unsupervised solution for low-dose CT image reconstruction.

Anomaly detection is an important application in factory environments. The sounds emitted by a manufacturing machine during runtime can be indicative of either normal performance or of mechanical failure. Traditionally, cosine losses are frequently utilized in anomalous sound detection (ASD) algorithms. In this work, we evaluate cosine losses within the context and scope of Deep Metric Learning (DML), and we investigate various ways to modify and potentially boost their performance. The impact of each modification on the performance of ASD systems is studied under extensive experimental settings and ablations utilizing both the DCASE 2022 and DCASE 2023 machine ASD datasets. Additionally, under the framework of DML, we develop a key new insight into the inner workings of cosine losses, and we verify it with an ablation.

This research work introduces two novel loss functions, pattern-loss (POL) and label similarity-based instance modeling (LSIM), for improving the performance of multi-label classification using artificial neural network-based techniques. These loss functions incorporate additional optimization constraints based on the distribution of multi-label class patterns and the similarity of data instances. By integrating these patterns during the network training process, the trained model is tuned to align with the existing patterns in the training data. The proposed approach decomposes the loss function into two components: the cross entropy loss and the pattern loss derived from the distribution of class-label patterns. Experimental evaluations were conducted on eight standard datasets, comparing the proposed methods with three existing techniques.The results demonstrate the effectiveness of the proposed approach, with POL and LSIM consistently achieving superior accuracy performance compared to the benchmark methods.

Manually checking for defects on industrial parts such as steel surfaces is ineffective, error-prone, and can damage a company’s reputation. Current automated methods often lack accuracy or real-time detection capabilities. Early detection allows for timely corrective action, such as removing defective parts or adjusting production parameters which can streamline the manufacturing process and improve brand reputation, customer satisfaction, and compliance with industry standards. This paper presents MoL-YOLOv7 (MobileNet integrated with attention and loss function to You Only Look Once version 7), a deep learning model for accurate and real-time detection of steel defects. MoL-YOLOv7 modifies the YOLOv7 model by inserting a MobileNet block that reduces computational complexity while maintaining accuracy, allowing for faster detection. Adding the SimAm (Simple Parameter free Attention module) attention mechanism to the MobileNet block refines feature representations for complex tasks such as steel defect detection. Finally, replacing loss functions with EIoU (Effective IoU), WIoU (Wise-IoU), and SIoU (Scylla-IoU) improves the localization accuracy and addresses the class imbalance in the data. The modified model achieves high accuracy and real-time detection, enabling a streamlined defect detection process. Experimental results show that the modified model involving different loss functions used in this work achieves high accuracy, i.e. 0.5% to 3.5% higher than the original model YOLOv7. The superiority and validity of our modified model are demonstrated by comparison with other attention mechanisms and loss functions integrated into YOLOv7, and also on different texture datasets, putting forward a modified method to detect surface defects on steel strips in daily operations.

Classification applications in order to obtain the desired information from satellite images are one of the increasing areas of study. Remote sensing satellite images are very difficult to obtain, but nevertheless, they can be used in many different areas. In this context, satellite images have become an important data source for land use and land cover analysis. In this study, the public EuroSAT dataset, which is compatible with land cover and land use classification, has been used. There are a total of 27000 images in this dataset, with ten different class labels, such as industry, forest, and lake, and approximately 3000 images in each. In order to perform a more precise and efficient classification study, the images have first been subjected to data augmentation using various techniques and have been divided into a training, test, and validation set. In terms of classification, six different convolutional neural network (CNN) architectures and two different vision transformer architectures have been used. Three experiments have been carried out for the proposed system, and the selected models have been trained with three different loss functions, and the results have been evaluated. The first of the loss functions used is cross-entropy, which is one of the most well-known and basic functions; the second is label smoothing cross-entropy; and the third is a new custom loss function that has been improved and consists of a special combination of both loss functions. The eight proposed models have been trained for all three loss functions; performance evaluations have been made by analyzing metrics such as accuracy, precision, and recall; and comparisons have been realized. After the proposed models have been evaluated in terms of these metrics, a Fuzzy Cognitive Map (FCM) has been created to integrate the model outputs to obtain the final classification result. For this construction, the accuracy of each model has been used in the neighborhood matrix of FCM. The accuracy of the proposed FCM method has been calculated as 99.97%. When the results of the proposed different models have been examined, it has been clearly seen that the new loss function improved in this study has been more successful than the other two loss functions in all models. In addition, the simulation results of FCM-based integration indicate that our approach has very high accuracy.

Enhancing the crop yield predictability in intercropping systems is important for optimizing agricultural productivity. However, accurately predicting yield in such systems is quite challenging due to complex interactions between crops. This study introduces an advanced methodology using integrated loss functions within an optimally driven Feedback Neural Network (FNN) approach to improve yield prediction in a pea-cucumber intercropping systems. Traditional models relying only on Mean Square Error (MSE) loss function often unable to capture the complexity of models, leading to suboptimal performance. To address this limitation, the advanced loss functions are introduced like Dynamic Margin Loss (DML), Risk-Adjusted Loss (RAL), Quantile Loss (QL), and Hybrid Agronomic Efficiency Loss (HAEL) along with three optimizers such as Adaptive Momentum (Adam), Root Mean Square Propagation (RMSprop), and Adaptive delta (Adadelta). These loss functions incorporate risk, uncertainty, and agronomic efficiency into the model training process, enhances predictive capabilities and robustness. This proposed framework is able to capture the complexity of yield prediction by incorporating agricultural factors. While Gradient Boost Machines (GBM) and Long Short Term Memory (LSTM) have some potential, they are not able to capture these dynamics. The sensitivity and weight analysis also focuses that HAEL targets important agronomic factors such as nitrogen uptake and residue biomass, which provide a holistic view of yield prediction. The proposed approach improves the predictive performance compared to traditional models and helps to identify the importance of features, which makes it an effective tool for decision making in sustainable agriculture. Selecting appropriate loss functions is essential to improve the accuracy and robustness of crops yield prediction models. Thus, study provides a strong foundation for enhancing yield prediction in intricate intercropping systems, which all significantly enhance the advancement of precision agriculture.

Deep learning-based models generalize better to unknown data samples after being guided “where to look” by incorporating human perception into training strategies. We made an observation that the entropy of the model’s salience trained in that way is lower when compared to salience entropy computed for models training without human perceptual intelligence. The research problem addressed by this paper is whether lowering the entropy of model’s class activation map helps in further increasing the performance, on top of the performance increase we observe for human saliency-based model’s training. In this paper we propose and evaluate four new entropy-based loss functions controlling the model’s focus, covering the full range of the level of such control, from none to its “aggresive” minimization. We show, using a problem of synthetic face detection, that improving the model’s focus, through lowering entropy by the proposed loss components, leads to models that perform better in an open-set scenario (in which the test samples are synthesized by unknown generative models): the obtained average Area Under the ROC curve (AUROC) ranges from 0.72 to 0.78, compared to AUROC = 0.64 observed for a state-of-the-art human-salience-only-based control of the model’s focus. We also show that optimal performance is obtained when the model’s loss function blends three aspects: regular classification performance, low-entropy of the model’s focus, and closeness of the model’s focus to human saliency. The major conclusion from this work is that maximization of the model’s focus is an important regularizer allowing the models to generalize better in an open set scenario. Future work directions include methods of blending classification-, human salience-, and model’s salience entropy-based loss components to achieve optimal performance in other domains than the synthetic face detection.

Atanassov intuitionistic fuzzy set (AIFS) has the capability to deal with various uncertain situations, so its popularity among researchers is quite high. It has been observed that Euclidean distance measure based AIFS clustering algorithms perform well on imprecise datasets. The performance of Euclidean measure based clustering algorithms deteriorates due to the presence of outliers/noise within a dataset. In the paper, an extension of the algorithm given by Leski, Jacek M. [Fuzzy Sets and Systems, 286 (2016): 114-133] is proposed as intuitionistic fuzzy $c$ -ordered means clustering algorithm. This paper analyses the functionality of clustering algorithms over outliers/noises based datasets. In IFCOM, an alternate of Euclidean distance known as Loss function is used. Moreover, IFCOM uses intuitionistic fuzzy OWA to combat the ill effects of the noises and outliers. The proposed algorithm exploits a typicality function based weighing ordering approach. The approach assigins lower weights to the outliers. Hence, the catastrophic behavior is not observed in IFCOM while dealing outliers possessing synthetic and UCI machine learning datasets.

This article contains all of the information needed to conduct a study on monocular facial depth estimation problems. A brief literature review and applications on facial depth map research were offered first, followed by a comprehensive evaluation of publicly available facial depth datasets and widely used loss functions. The key properties and characteristics of each facial depth map dataset are described and evaluated. Furthermore, facial depth maps loss functions are briefly discussed, which will make it easier to train neural facial depth models on a variety of datasets for both short- and long-range depth maps. The network’s design and components are essential, but its effectiveness is largely determined by how it is trained, which necessitates a large dataset and a suitable loss function. Implementation details of how neural depth networks work and their corresponding evaluation matrices are presented and explained. In addition, an SoA neural model for facial depth estimation is proposed, along with a detailed comparison evaluation and, where feasible, direct comparison of facial depth estimation methods to serve as a foundation for a proposed model that is utilized. The model employed shows better performance compared with current state-of-the-art methods when tested across four datasets. The new loss function used in the proposed method helps the network to learn the facial regions resulting in an accurate depth prediction. The network is trained on synthetic human facial depth datasets whereas for validation purposes real as well as synthetic facial images are used. The results prove that the trained network outperforms current state-of-the-art networks performances, thus setting up a new baseline method for facial depth estimations.

Knowledge graphs (KGs) are widely used for modeling scholarly communication, performing scientometric analyses, and supporting a variety of intelligent services to explore the literature and predict research dynamics. However, they often suffer from incompleteness (e.g., missing affiliations, references, research topics), leading to a reduced scope and quality of the resulting analyses. This issue is usually tackled by computing knowledge graph embeddings (KGEs) and applying link prediction techniques. However, only a few KGE models are capable of taking weights of facts in the knowledge graph into account. Such weights can have different meanings, e.g. describe the degree of association or the degree of truth of a certain triple. In this paper, we propose the Weighted Triple Loss, a new loss function for KGE models that takes full advantage of the additional numerical weights on facts and it is even tolerant to incorrect weights. We also extend the Rule Loss, a loss function that is able to exploit a set of logical rules, in order to work with weighted triples. The evaluation of our solutions on several knowledge graphs indicates significant performance improvements with respect to the state of the art. Our main use case is the large-scale AIDA knowledge graph, which describes 21 million research articles. Our approach enables to complete information about affiliation types, countries, and research topics, greatly improving the scope of the resulting scientometrics analyses and providing better support to systems for monitoring and predicting research dynamics.

Mixture modelling has stunning applications to explain the composite problems in simple way. Bayesian demonstration of 3-Component mixture model of Exponentiated Pareto distribution in right-type-I censoring scheme is presented in this article. The posterior densities of the parameter(s) are attained supposing the non-informative (uniform, Jeffreys) priors. The symmetric and asymmetric Loss Functions (Squared Error, Precautionary, Quadratic and DeGroot Loss Function) are assumed to get the Bayes estimator(s) and posterior risk(s). The presentation of the Bayes estimator(s) over posterior risk(s) in the studied loss functions is examined over simulation practice. Two real-data sets, wind speed and tensile strength of carbon fiber, are also analyzed for mixture to complete the performance of Bayes estimator(s). To enhance the study, the limiting forms are also derived for Bayes estimator(s) and posterior risk(s). The results reveal that for the component parameter(s), the Bayes Estimator(s) have their risks accordingly: DeGroot Loss Function