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Presence of globally-affecting issues, such as the recent COVID-19 pandemic is a major factor impacting the operation of services provided by high-stake companies. These factors create huge hindrances in the regular and proper operations of companies in staying relevant in market while catering to the services they provide. In such cases, in order to maintain and achieve their internal goals should any possible losses that the grave situation might incur, relevant experts within these firms must arrive at optimal decisions taking into account human cognition as well as all possibilities of risk and regrets. A suitable regret theory based linguistic decision-making model called THREAD which computes with inherent hesitancy using interval type-2 fuzzy sets (IT2 FS) and hesitant fuzzy linguistic term sets-based techniques is introduced in this paper.

Accurate forecasting of sea surface temperature (SST) is pivotal for a wide range of applications ranging from climate modelling to marine ecosystem management. This study introduces an ingenious multi-dilated model that employs dilated Convolutional Long Short-Term Memory (ConvLSTM) networks alongside a U-Net architecture which aims to enhance the precision of monthly SST predictions at lead periods of 3, 6 and 12 months. Our approach innovatively combines dilated convolutions within ConvLSTM with the segmentation capabilities of U-Net to adeptly capture the complex Spatio-temporal dynamics as well as relevant attributes of SST. The study utilizes a high-resolution MPI-ESM1-2-HR SST dataset for a series of rigorous tests, achieving a Mean Square Error of 0.01, indicating a high accuracy level in SST predictions. The model at a lead time of 12 months also showed a Sea Surface Microwave Index of 0.036, illustrating its effectiveness in reflecting SST’s microwave emissivity characteristics, and an Earth Mover’s Distance score of 0.97, highlighting its ability to closely match the predicted SST distribution with the actual one. Furthermore, a cosine similarity score of 0.99 suggests a significant alignment between the predicted and actual SST patterns. By merging multi-dilated convolutions with segmentation, our model tackles the intricate challenge of simultaneously capturing the spatial and temporal dependencies of SST, setting a new standard for forecasting accuracy in the field.

The Indian Ocean Dipole (IOD) is a critical coupled ocean-atmosphere oscillation system associated with significant weather anomalies in the global climate, particularly in the Indian Ocean rim countries. The paper presents a novel deep learning (DL) model, which we call the “spatio-temporal dilated ConvLSTM (STDNet) model”, for forecasting the Dipole Mode Index (DMI) using global sea surface temperature (SST) and heat content (HC) data. The model combines the techniques of dilation and fine-tuning to learn efficiently from the training data. CMIP6 historical simulation data from 5 modeling centres for 1861–2014 is used to train the model. Furthermore, the model is fine-tuned on reanalysis data from 1871–1973. During the testing period (1982–2019), the dipole correlation coefficient (DCC) was the highest when compared with state-of-the-art dynamical North American Multi-Model Ensemble (NMME) models, a convolutional neural network (CNN) and a dilated CNN. On a lead of 12 months, the DCC is 0.40 for the CNN, 0.44 for the dilated CNN, and 0.51 for the STDNet, and all the NMME models have negative correlations. The results show that the STDNet efficiently forecasts the DMI at leads of up to 12 months. The STDNet shows results to overcome the winter predictability barrier.

In this paper, the probabilistic version of intuitionistic fuzzy decision making methods is introduced to show the duality present within probabilistic distance. Here, two types of methods, namely, probabilistic intuitionistic fuzzy TOPSIS (PI-TOPSIS) algorithm and probabilistic intuitionistic fuzzy face identification (PIFI) algorithm are proposed. The PI-TOPSIS is utilizing probabilistic distance as the separation measure; for its justification rankings and reduced information loss of the multi criteria decision making problems are compared. Our other proposed decision making method called PIFI algorithm handles Face Identification Problem. It is exemplified by better benchmark indexes of PIFI in comparison to support vector machine, naive bais classifier, fuzzy support vector machine algorithms, that, the probabilistic distance can be used as a similarity measure. The two well-known feature extraction techniques, called Local binary pattern (LBP) and Angular radial transformation (ART) are employed to extract the features in the face images. Further, it is concluded from the experimental findings, that, the proposed algorithms are adaptive in nature.

An explainable artificial intelligence (XAI) agent is an autonomous agent that uses a fundamental XAI model at its core to perceive its environment and suggests actions to be performed. One of the significant challenges for these XAI agents is performing their operation efficiently, which is governed by the underlying inference and optimization system. Along similar lines, an Explainable Fuzzy AI Challenge (XFC 2022) competition was launched, whose principal objective was to develop a fully autonomous and optimized XAI algorithm that could play the Python arcade game “Asteroid Smasher”. This research first investigates inference models to implement an efficient (XAI) agent using rule-based fuzzy systems. We also discuss the proposed approach (which won the competition) to attain efficiency in the XAI algorithm. We have explored the potential of the widely used Mamdani- and TSK-based fuzzy inference systems and investigated which model might have a more optimized implementation. Even though the TSK-based model outperforms Mamdani in several applications, no empirical evidence suggests this will also be applicable in implementing an XAI agent. The experimentations are then performed to find a better-performing inference system in a fast-paced environment. The thorough analysis recommends more robust and efficient TSK-based XAI agents than Mamdani-based fuzzy inference systems.

Similarity measure, also called information measure, is a concept used to distinguish different objects. It has been studied from different contexts by employing mathematical, psychological, and fuzzy approaches. Image steganography is the art of hiding secret data into an image in such a way that it cannot be detected by an intruder. In image steganography, hiding secret data in the plain or non-edge regions of the image is significant due to the high similarity and redundancy of the pixels in their neighborhood. However, the similarity measure of the neighboring pixels, i.e., their proximity in color space, is perceptual rather than mathematical. Thus, this paper proposes an interval type-2 fuzzy logic system (IT2 FLS) to determine the similarity between the neighboring pixels by involving an instinctive human perception through a rule-based approach. The pixels of the image having high similarity values, calculated using the proposed IT2 FLS similarity measure, are selected for embedding via the least significant bit (LSB) method. We term the proposed procedure of steganography as ‘IT2 FLS-LSB method’. Moreover, we have developed two more methods, namely, type-1 fuzzy logic system based least significant bits (T1FLS-LSB) and Euclidean distance based similarity measures for least significant bit (SM-LSB) steganographic methods. Experimental simulations were conducted for a collection of images and quality index metrics, such as PSNR (peak signal-to-noise ratio), UQI (universal quality index), and SSIM (structural similarity measure) are used. All the three steganographic methods are applied on dataset and the quality metrics are calculated. The obtained stego images and results are shown and thoroughly compared to determine the efficacy of the IT2 FLS-LSB method. We have also demonstrated the high payload capacity of our proposed method. Finally, we have done a comparative analysis of the proposed approach with the existing well-known steganographic methods to show the effectiveness of our proposed steganographic method.

The international journal of neurocomputing (NC) is considered to be one of the most sought out journals in the computer science research fraternity. In this paper, an extensive bibliometric overview of this journal is performed. The bibliometric data is extracted from the Web of Science (WoS) repository. The main objective of this study is to reveal internal structures and hidden inferences, such as highly productive and influential authors, most contributing countries, top institutions, collaborating authors, and so on. The CiteSpace and VOS viewer is used to visualize the graphical mapping of the bibliometric data. Further, the document co-citations network, cluster detection and references with strong citation burst is analyzed to reveal the intellectual base of NC publications.