Target Classification Research Articles

Associations among attentional state, retrieval quality, and mnemonic discrimination

Memory specificity is shown when participants reject lures that are similar to studied objects. Lure rejections may reflect hippocampal pattern separation that encodes objects distinctively. However, lure features shared with studied objects may evoke pattern completion of varying quality. This was shown when self-reported attention during study promoted lure rejections and false alarms. We used an experimental and individual differences approach to examine the roles of attentive encoding and retrieval quality in lure classifications. An object-based mnemonic discrimination task included thought probes during study and subjective retrieval reports after recognition responses. On-task reports reflecting attentive encoding were associated with lure rejections and false alarms within-and between-subjects. Additionally, accurate lure and target classifications were more strongly associated with subjective recollection following on- than off-task reports. Collectively, these results suggest that attention during study was associated with recollection of criterial features that differentiated existing memories from perceptual inputs.

Radar Target Classification Using Enhanced Doppler Spectrograms with ResNet34_CA in Ubiquitous Radar

Ubiquitous Radar has become an essential tool for preventing bird strikes at airports, where accurate target classification is of paramount importance. The working mode of Ubiquitous Radar, which operates in track-then-identify (TTI) mode, provides both tracking information and Doppler information for the classification and recognition module. Moreover, the main features of the target’s Doppler information are concentrated around the Doppler main spectrum. This study innovatively used tracking information to generate a feature enhancement layer that can indicate the area where the main spectrum is located and combines it with the RGB three-channel Doppler spectrogram to form an RGBA four-channel Doppler spectrogram. Compared with the RGB three-channel Doppler spectrogram, this method increases the classification accuracy for four types of targets (ships, birds, flapping birds, and bird flocks) from 93.13% to 97.13%, an improvement of 4%. On this basis, this study integrated the coordinate attention (CA) module into the building block of the 34-layer residual network (ResNet34), forming ResNet34_CA. This integration enables the network to focus more on the main spectrum information of the target, thereby further improving the classification accuracy from 97.13% to 97.22%.

Raman spectroscopy-based authentication of powder goat milk adulteration with cow milk

This study introduces, for the first time, the utilization of a handheld Raman spectrometer in conjunction with Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) for the authentication of powdered goat milk (target class) against adulteration with powdered cow milk. Since the representativeness of the target class is the first critical step to construct a model without overfitting or underfitting, especially when dealing with a few numbers of target samples, three distinct validation strategies (approaches 1, 2, and 3, respectively), incorporating no partitioning of the target samples, Procrustes cross-validation, and Kennard-Stone-based partitioning, were assessed. Moreover, the spectral data underwent preprocessing techniques including offset correction (OFF), multiplicative scatter correction (MSC), standard normal variate transformation (SNV), and Savitzky-Golay derivative (SGD). Achieving a correct classification rate of 100% for all studied samples using MSC for approaches 1 and 2, and SGD for approach 3, underscores the robustness of Raman spectroscopy in furnishing pertinent analytical insights for the authentication of pure powdered goat milk, with a significance level of 0.01. This methodology furnishes distinct spectral signatures associated with the macromolecular composition, particularly fats, proteins, carbohydrates, and carotenoids, facilitating the detection of adulterations as low as 1% w/w of cow milk added to powdered goat milk. Thus, the proposed method offers a rapid and reliable on-site screening tool, consonant with the principles of green food analysis, especially advantageous in situations where identifying the type and/or nature of the adulterants is not feasible.

Efficient multi-level cross-modal fusion and detection network for infrared and visible image

With the rapid development of uncrewed aerial vehicle (UAV) technology, detecting aerial images has found significant applications across various domains. However, existing algorithms overlook the impact of illumination on target detection, resulting in less satisfactory detection performance under low-light conditions. We propose EfficientFuseDet, a visible and infrared image fusion detection network to overcome this issue. First, an effective multilevel cross-modal fusion network called EfficientFuse is presented to combine complementary information from both modalities better. EfficientFuse captures local dependencies and global contextual information in shallow and deep layers, seamlessly combining complimentary local and global features throughout the network. The generated fused images can exhibit clear target contours and abundant texture information. Second, we propose a detection network called AFI-YOLO, which employs an inverted residual vision transformer backbone (IRViT) to effectively address the challenges associated with background interference in fused images. We design an efficient feature pyramid network (EFPN) that efficiently integrates multiscale information, enhancing multiscale detection capability using aerial images. A reparameterization decoupling head (RepHead) is proposed to further improve target classification and localization precision. Finally, experiments on the DroneVehicle dataset indicate that the detection accuracy using fused images can reach 47.2 %, which is higher than that observed with visible light images of 45 %. Compared to state-of-the-art detection algorithms, EfficientFuseDet exhibits a slight decrease in speed. However, it demonstrates superior detection capabilities and effectively enhances the detection accuracy using aerial images under low-light conditions.

A Clean-Label Graph Backdoor Attack Method in Node Classification Task

Backdoor attacks in the traditional graph neural networks (GNNs) field are easily detectable due to the dilemma of confusing labels. To explore the backdoor vulnerability of GNNs and create a more stealthy backdoor attack method, a clean-label graph backdoor attack method(CGBA) in the node classification task is proposed in this paper. Differently from existing backdoor attack methods, CGBA requires neither modification of node labels nor graph structure. Specifically, to solve the problem of inconsistency between the contents and labels of the samples, CGBA selects poisoning samples in a specific target class and uses the samples’ own label as the target label (i.e., clean-label) after injecting triggers into the target samples. To guarantee the similarity of neighboring nodes, the raw features of the nodes are elaborately picked as triggers to further improve the concealment of the triggers. Extensive experiments results show the effectiveness of our method. When the poisoning rate is 0.04, CGBA can achieve an average attack success rate of 87.8%, 98.9%, 89.1%, and 98.5%, respectively.

Cropping intensity mapping in Sentinel-2 and Landsat-8/9 remote sensing data using temporal transfer of a stacked ensemble machine learning model within google earth engine

This article aimed to map Cropping Intensity Patterns (CIPs) in the southwest region of Iran using Google Earth Engine and monthly composites of Sentinel-2 and Landsat-8/9 data. To detect CIPs with high inter- and intra-class variability of crops, a heterogeneous Stack ensemble of machine learning model was developed. The model incorporated the Minimum Distance (MD) approach as a meta-classifier, combining multiple base models, including Support Vector Machines (SVM), Random Forest (RF), Classification and Regression Trees (CART), and Gradient Boosted Trees (GBT). In 2021, the Stack model was trained and evaluated using Ground Truth (GT) samples from the same year, achieving an Overall Accuracy (OA) of 94.24%. This performance surpassed the base models by about 4% in OA and was also reflected in the detection accuracies, including User’s Accuracy (UA), Producer’s Accuracy (PA), and F1-score, of the target classes. Subsequently, the trained stack model was temporally transferred to generate CIP maps for other years. The model achieved high OAs of 91.82% and 90.97% based on GT samples from 2020 and 2022, respectively. Finally, the time series of CIP maps (2019-2023) were utilized by the Cellular Automata-Markov model to forecast the map for 2024.

Intelligent Data Processing for Alzheimer's Disease Using Deep Learning

Background: With the advancement in Alzheimer's disease (AD) brain, cells start to deteriorate, which eventually creates physical dependency and mental instability that interferes with daily living. Presently, this disease is immedicable. Therefore, the only suitable treatment is early detection and prevention. Although many studies have investigated the usefulness of deep learning in AD detection, relatively few have focused on the necessary image preprocessing processes, which are essential to any computer-aided diagnostic system. Furthermore, an optimal classification strategy that takes into account a diverse handful of prominent features is required. Method: This paper focuses on improving MRI-based AD detection by incorporating image enhancement approaches and deep hybrid learning into a fused framework to harness the power of multiple Deep Learning (DL) architectures and Machine Learning (ML) classifiers. The deep features extracted from three heterogeneous CNN architectures, namely, VGG16, DensetNet169, and MobileNetV1, are fused to produce a more informative and discriminative hybrid feature. Furthermore, the mRMR approach was used to optimise the acquired features, followed by classification via a stack of multiple ML classifiers to predict the target class. Results: The proposed architecture based on feature fusion strategy and ensemble learning resulted in 99.53%(Accuracy), 99.73%(Precision),99.70%(Recall), and 99.72% (F1 score). The presented model outperformed individual deep CNN architectures. Conclusion: Lastly, we present a sobol-based sensitivity analysis that illustrates the concentration of the presented technique upon significant regions of the image and can assist medical professionals in decoding the decisions. The presented technique exeplifies the potency and constancy of categorizing Alzheimer's disease.

DESI Early Data Release Milky Way Survey value-added catalogue

ABSTRACT We present the stellar value-added catalogue based on the Dark Energy Spectroscopic Instrument (DESI) Early Data Release. The catalogue contains radial velocity and stellar parameter measurements for $\simeq$ 400 000 unique stars observed during commissioning and survey validation by DESI. These observations were made under conditions similar to the Milky Way Survey (MWS) currently carried out by DESI but also include multiple specially targeted fields, such as those containing well-studied dwarf galaxies and stellar streams. The majority of observed stars have $16\lt r\lt 20$ with a median signal-to-noise ratio in the spectra of $\sim$ 20. In the paper, we describe the structure of the catalogue, give an overview of different target classes observed, as well as provide recipes for selecting clean stellar samples. We validate the catalogue using external high-resolution measurements and show that radial velocities, surface gravities, and iron abundances determined by DESI are accurate to 1 km s−1, 0.3 dex, and $\sim$ 0.15 dex respectively. We also demonstrate possible uses of the catalogue for chemo-dynamical studies of the Milky Way stellar halo and Draco dwarf spheroidal. The value-added catalogue described in this paper is the very first DESI MWS catalogue. The next DESI data release, expected in less than a year, will add the data from the first year of DESI survey operations and will contain approximately 4 million stars, along with significant processing improvements.

MRNA and circRNA mislocalization to synapses are key features of Alzheimer's disease.

Proper transport of RNAs to synapses is essential for localized translation of proteins in response to synaptic signals and synaptic plasticity. Alzheimer's disease (AD) is a neurodegenerative disease characterized by accumulation of amyloid aggregates and hyperphosphorylated tau neurofibrillary tangles followed by widespread synapse loss. To understand whether RNA synaptic localization is impacted in AD, we performed RNA sequencing on synaptosomes and brain homogenates from AD patients and cognitively healthy controls. This resulted in the discovery of hundreds of mislocalized mRNAs in AD among frontal and temporal brain regions. Similar observations were found in an APPswe/PSEN1dE9 mouse model. Furthermore, major differences were observed among circular RNAs (circRNAs) localized to synapses in AD including two overlapping isoforms of circGSK3β, one upregulated, and one downregulated. Expression of these distinct isoforms affected tau phosphorylation in neuronal cells substantiating the importance of circRNAs in the brain and pointing to a new class of therapeutic targets.

Design and Application of an Onboard Particle Identification Platform Based on Convolutional Neural Networks

Space radiation particle detection plays a crucial role in scientific research and engineering practice, especially in particle species identification. Currently, commonly used in-orbit particle identification techniques include telescope methods, electrostatic analysis time of flight (ESA × TOF), time-of-flight energy (TOF × E), and pulse shape discrimination (PSD). However, these methods usually fail to utilize the full waveform information containing rich features, and their particle identification results may be affected by the random rise and fall of particle deposition and noise interference. In this study, a low-latency and lightweight onboard FPGA real-time particle identification platform based on full waveform information was developed utilizing the superior target classification, robustness, and generalization capabilities of convolutional neural networks (CNNs). The platform constructs diversified input datasets based on the physical features of waveforms and uses Optuna and Pytorch software architectures for model training. The hardware platform is responsible for the real-time inference of waveform data and the dynamic expansion of the dataset. The platform was utilized for deep learning training and the testing of the historical waveform data of neutron and gamma rays, and the inference time of a single waveform takes 4.9 microseconds, with an accuracy rate of over 97%. The classification expectation FOM (figure-of-merit) value of this CNN model is 133, which is better than the traditional pulse shape discrimination (PSD) algorithm’s FOM value of 0.8. The development of this platform not only improves the accuracy and efficiency of space particle discrimination but also provides an advanced tool for future space environment monitoring, which is of great value for engineering applications.

Convergence of hybrid slice sampling via spectral gap

Abstract It is known that the simple slice sampler has robust convergence properties; however, the class of problems where it can be implemented is limited. In contrast, we consider hybrid slice samplers which are easily implementable and where another Markov chain approximately samples the uniform distribution on each slice. Under appropriate assumptions on the Markov chain on the slice, we give a lower bound and an upper bound of the spectral gap of the hybrid slice sampler in terms of the spectral gap of the simple slice sampler. An immediate consequence of this is that the spectral gap and geometric ergodicity of the hybrid slice sampler can be concluded from the spectral gap and geometric ergodicity of the simple version, which is very well understood. These results indicate that robustness properties of the simple slice sampler are inherited by (appropriately designed) easily implementable hybrid versions. We apply the developed theory and analyze a number of specific algorithms, such as the stepping-out shrinkage slice sampling, hit-and-run slice sampling on a class of multivariate targets, and an easily implementable combination of both procedures on multidimensional bimodal densities.

An underwater wake sensing method based on Micro Thermoelectric Generator for enhancing recognition accuracy

The accidental entry and poaching of foreign boats and autonomous underwater vehicles (AUVs) have a detrimental impact on the ecological environment of marine ranching. Currently, there is a need for a monitoring method specifically designed to monitor underwater moving objects (UMOs), such as foreign boats and AUVs, in real-time within marine ranching. Therefore, this paper creatively proposes a method for monitoring UMOs based on thermal tactile of Micro Thermoelectric Generator (MTEG). A thermal tactile perceptual model for propeller wake identification is established and the wake sensing sensor (WSS) is designed. Furthermore, the performance verification experiment of wake sensing and the target classification experiment based on machine learning are carried out to prove the feasibility of the method. The experimental results demonstrate that the propeller wakes are identified with an accuracy of more than 90 % within 30 s in the monitoring terminal. Besides, the method can accurately distinguish between propeller wakes and fish wakes, thereby avoiding false alarms. The reported underwater wake sensing method is a new solution to real-time monitoring and safety early warning of marine ranching.

Decoupled graph knowledge distillation: A general logits-based method for learning MLPs on graphs

While Graph Neural Networks (GNNs) have demonstrated their effectiveness in processing non-Euclidean structured data, the neighborhood fetching of GNNs is time-consuming and computationally intensive, making them difficult to deploy in low-latency industrial applications. To address the issue, a feasible solution is graph knowledge distillation (KD), which can learn high-performance student Multi-layer Perceptrons (MLPs) to replace GNNs by mimicking the superior output of teacher GNNs. However, state-of-the-art graph knowledge distillation methods are mainly based on distilling deep features from intermediate hidden layers, this leads to the significance of logit layer distillation being greatly overlooked. To provide a novel viewpoint for studying logits-based KD methods, we introduce the idea of decoupling into graph knowledge distillation. Specifically, we first reformulate the classical graph knowledge distillation loss into two parts, i.e., the target class graph distillation (TCGD) loss and the non-target class graph distillation (NCGD) loss. Next, we decouple the negative correlation between GNN’s prediction confidence and NCGD loss, as well as eliminate the fixed weight between TCGD and NCGD. We named this logits-based method Decoupled Graph Knowledge Distillation (DGKD). It can flexibly adjust the weights of TCGD and NCGD for different data samples, thereby improving the prediction accuracy of the student MLP. Extensive experiments conducted on public benchmark datasets show the effectiveness of our method. Additionally, DGKD can be incorporated into any existing graph knowledge distillation framework as a plug-and-play loss function, further improving distillation performance. The code is available at https://github.com/xsk160/DGKD.

Active sonar target recognition method based on multi‐domain transformations and attention‐based fusion network

AbstractThe classification and recognition of underwater targets by an active sonar system remain challenging and complex. Traditional methods have limited classification performance in time and spatially varying ocean channels. An active sonar target recognition method is proposed based on multi‐domain transformations and an attention‐based fusion network. Initially, the active target echo undergoes time‐frequency analysis, auditory signal processing, and matched filtering to represent target attributes in joint spatial‐time‐frequency domains. Subsequently, multiple attention‐based fusion models fuse the multi‐domain transformations either early or late in the processing stages. An attention module further enhances significant feature channels through adaptive weight assignment. Experiment results demonstrate that the recognition accuracy of active sonar echoes using multi‐domain transformations improves significantly compared to that of single‐domain methods, with an increase of up to 10.5%. The incorporation of multiple transformation domains provides complementary information about the target, thereby enhancing the network's representation ability, especially with limited data samples. Furthermore, the findings indicate that feature fusion of multiple transformations in a high‐level feature space yields more informative and effective results for active sonar echoes compared to low‐level feature spaces.

Toward a Responsible Fairness Analysis: From Binary to Multiclass and Multigroup Assessment in Graph Neural Network-Based User Modeling Tasks

User modeling is a key topic in many applications, mainly social networks and information retrieval systems. To assess the effectiveness of a user modeling approach, its capability to classify personal characteristics (e.g., the gender, age, or consumption grade of the users) is evaluated. Due to the fact that some of the attributes to predict are multiclass (e.g., age usually encompasses multiple ranges), assessing fairness in user modeling becomes a challenge since most of the related metrics work with binary attributes. As a workaround, the original multiclass attributes are usually binarized to meet standard fairness metrics definitions where both the target class and sensitive attribute (such as gender or age) are binary. However, this alters the original conditions, and fairness is evaluated on classes that differ from those used in the classification. In this article, we extend the definitions of four existing fairness metrics (related to disparate impact and disparate mistreatment) from binary to multiclass scenarios, considering different settings where either the target class or the sensitive attribute includes more than two groups. Our work endeavors to bridge the gap between formal definitions and real use cases in bias detection. The results of the experiments, conducted on four real-world datasets by leveraging two state-of-the-art graph neural network-based models for user modeling, show that the proposed generalization of fairness metrics can lead to a more effective and fine-grained comprehension of disadvantaged sensitive groups and, in some cases, to a better analysis of machine learning models originally deemed to be fair. The source code and the preprocessed datasets are available at the following link: https://github.com/erasmopurif/toward-responsible-fairness-analysis.

Evaluation of adulteration in soy-based beverages by water addition using chemometrics applied to ATR-FTIR spectroscopy

The adulteration of soy-based beverages (SBBs) by adding water to increase profitability is a fraudulent practice that requires urgent solutions to ensure product integrity and consumer trust. Therefore, the use of infrared spectroscopy (ATR-FTIR) associated with chemometrics methods can be a quick and advantageous alternative to this problem. In this study, the one-class and multiclass methods applied to ATR-FTIR data to classify a set of 80 SBBs samples were used. The unequal dispersed classes (UNEQ), soft independent modeling of class analogy (SIMCA), data driven SIMCA (DD-SIMCA), and one-class random forest (OC-RF) methods were used for one-class modeling. Models were constructed using the non-adulterated samples as target class (TA) and the adulterated samples as non-target class (NT). The k-nearest neighbors (k-NN), partial least squares discriminant analysis (PLS-DA), dual class random forest (DC-RF), and dual class random forest with Monte Carlo sampling (DC-RF-MC) methods were used for multiclass modeling. For k-NN and PLS-DA, samples were organized into four classes (non-adulterated samples, adulterated with 5% v∙v−1, 10% v∙v−1, and 20% v∙v−1 of water). DC-RF models used the same class settings as one-class models. DD-SIMCA, PLS-DA, and DC-RF-MC showed accuracy of 100%. The results show the feasibility of ATR-FTIR and chemometrics models to identify adulterations by adding water.

Generative Adversarial Networks for SAR Automatic Target Recognition and Classification Models Enhanced Explainability: Perspectives and Challenges

Generative adversarial networks (or GANs) are a specific deep learning architecture often used for different usages, such as data generation or image-to-image translation. In recent years, this structure has gained increased popularity and has been used in different fields. One area of expertise currently in vogue is the use of GANs to produce synthetic aperture radar (SAR) data, and especially expand training datasets for SAR automatic target recognition (ATR). In effect, the complex SAR image formation makes these kind of data rich in information, leading to the use of deep networks in deep learning-based methods. Yet, deep networks also require sufficient data for training. However, contrary to optical images, we generally do not have a substantial number of available SAR images because of their acquisition and labelling cost; GANs are then an interesting tool. Concurrently, how to improve explainability for SAR ATR deep neural networks and how to make their reasoning more transparent have been increasingly explored as model opacity deteriorates trust of users. This paper aims at reviewing how GANs are used with SAR images, but also giving perspectives on how GANs could be used to improve interpretability and explainability of SAR classifiers.

The potential role of CD8+ cytotoxic T lymphocytes and one branch connected with tissue-resident memory in non-luminal breast cancer.

Advances in understanding the pathological mechanisms of breast cancer have resulted in the emergence of novel therapeutic strategies. However, triple-negative breast cancer (TNBC), a molecular subtype of breast cancer with a poor prognosis, lacks classical and general therapeutic targets, hindering the clinical application of several therapies to breast cancer. As insights into the unique immunity and molecular mechanisms of TNBC have become more extensive, immunotherapy has gradually become a valuable complementary approach to classical radiotherapy and chemotherapy. CD8+ cells are significant actors in the tumor immunity cycle; thus, research on TNBC immunotherapy is increasingly focused in this direction. Recently, CD8+ tissue-resident memory (TRM) cells, a subpopulation of CD8+ cells, have been explored in relation to breast cancer and found to seemingly play an undeniably important role in tumor surveillance and lymphocytic infiltration. In this review, we summarize the recent advances in the mechanisms and relative targets of CD8+ T cells, and discuss the features and potential applications of CD8+ TRM cells in non-luminal breast cancer immunotherapy.

Prototype as query for few shot semantic segmentation

Few-shot Semantic Segmentation (FSS) was proposed to segment unseen classes in a query image, referring to only a few annotated examples named support images. One of the characteristics of FSS is spatial inconsistency between query and support targets, e.g., texture or appearance. This greatly challenges the generalization ability of methods for FSS, which requires to effectively exploit the dependency of the query image and the support examples. Most existing methods abstracted support features into prototype vectors and implemented the interaction with query features using cosine similarity or feature concatenation. However, this simple interaction may not capture spatial details in query features. To address this limitation, some methods utilized pixel-level support information by computing pixel-level correlations between paired query and support features implemented with the attention mechanism of Transformer. Nevertheless, these approaches suffer from heavy computation due to dot-product attention between all pixels of support and query features. In this paper, we propose a novel framework, termed ProtoFormer, built upon the Transformer architecture, to fully capture spatial details in query features. ProtoFormer treats the abstracted prototype of the target class in support features as the Query and the query features as Key and Value embeddings, which are input to the Transformer decoder. This approach enables better capture of spatial details and focuses on the semantic features of the target class in the query image. The output of the Transformer-based module can be interpreted as semantic-aware dynamic kernels that filter the segmentation mask from the enriched query features. Extensive experiments conducted on PASCAL-5i\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$5^{i}$$\\end{document} and COCO-20i\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$20^{i}$$\\end{document} datasets demonstrate that ProtoFormer significantly outperforms the state-of-the-art methods in FSS.

Evaluating and classifying market alternatives using the CAPMA technique to assess potential export volume: an application for Turkey’s fig exports

PurposeAppropriate target market selection and classification are complex, time-consuming, and strategically important management decisions. The current study proposes and implements a new systematic approach that evaluates and ranks alternative markets’ potential to increase the export volume of Turkey’s fig exports.Design/methodology/approachThe classification approach of potential market alternatives (CAPMA) method, which was developed to evaluate, compare, and classify alternative markets according to their potential, was used in this study. CAPMA, an objective decision-making technique, is based on calculating the classification coefficient and classifying the alternatives into four dimensions, taking this coefficient into consideration. In the current study, 25 alternative markets were analysed on the basis of four evaluation criteria and classified into four dimensions according to their potential to increase export volume.FindingsAccording to the application results, alternative markets with the greatest potential to increase export volume were identified as the United States of America (USA), Netherlands, China, and Spain. The USA has the greatest potential for expansion of export volume. By implementing correct and effective marketing strategies, fig export volume has the potential to reach very high levels.Originality/valueThe study’s results on alternative markets for fig exports were categorized, countries with greater potential to increase export volume were identified, and findings that could contribute to the development of the sector were obtained. The approach proposed in the current research can be used to determine the target market of many other products, especially agricultural products, whose production is concentrated within a specific geography.