Classification Method Research Articles

Breast Cancer Prediction Web Model

In this era cancer is biggest problem for human resource and there is multiple type of cancer like blood cancer, breast cancer etc. but here we are talk about only breast cancer. Breast cancer is generally faced by women. Every year the cases of breast cancer increase rapidly and as well as death rate also. The breast cancer is the cause of death in women worldwide. To make breast cancer prediction model is a very challenging task because high accuracy in prediction is very important to know the actual condition or results in the form of chances of survival. Through Machine Learning the prediction and early diagnosis can be easy by using different techniques and algorithms of Machine Learning. But here we use classification method to predict the breast cancer.

Research on Customer Group Division and Precision Marketing Based on the DWKCN Algorithm

Classifying customers according to their characteristics can effectively meet the genuine needs of different customer groups. It also helps enterprises formulate reasonable marketing strategies and obtain considerable profits. Currently, there are many ways to classify customers. However, the procedures involved are complicated and cannot comprehensively and objectively reflect customer characteristics. Therefore, a customer group classification model is designed based on the deep cross network (DCN). The DCN algorithm can automatically learn simple data features, achieving data clustering. For the defects in this model, the deep weighted k-means clustering network (DWKCN) customer group classification method is constructed, improving the DCN algorithm. From the results, the algorithm has a high accuracy of 99.5%. Therefore, the proposed DWKCN algorithm can realize the customer group’s precise division and the marketing plan design, providing the references for different types of customers to formulate personalized needs.

Visualising and quantifying the usefulness of new predictors stratified by outcome class: The U-smile method.

Binary classification methods encompass various algorithms to categorize data points into two distinct classes. Binary prediction, in contrast, estimates the likelihood of a binary event occurring. We introduce a novel graphical and quantitative approach, the U-smile method, for assessing prediction improvement stratified by binary outcome class. The U-smile method utilizes a smile-like plot and novel coefficients to measure the relative and absolute change in prediction compared with the reference method. The likelihood-ratio test was used to assess the significance of the change in prediction. Logistic regression models using the Heart Disease dataset and generated random variables were employed to validate the U-smile method. The receiver operating characteristic (ROC) curve was used to compare the results of the U-smile method. The likelihood-ratio test demonstrated that the proposed coefficients consistently generated smile-shaped U-smile plots for the most informative predictors. The U-smile plot proved more effective than the ROC curve in comparing the effects of adding new predictors to the reference method. It effectively highlighted differences in model performance for both non-events and events. Visual analysis of the U-smile plots provided an immediate impression of the usefulness of different predictors at a glance. The U-smile method can guide the selection of the most valuable predictors. It can also be helpful in applications beyond prediction.

Res2Net-based multi-scale and multi-attention model for traffic scene image classification.

With the increasing applications of traffic scene image classification in intelligent transportation systems, there is a growing demand for improved accuracy and robustness in this classification task. However, due to weather conditions, time, lighting variations, and annotation costs, traditional deep learning methods still have limitations in extracting complex traffic scene features and achieving higher recognition accuracy. The previous classification methods for traffic scene images had gaps in multi-scale feature extraction and the combination of frequency domain, spatial, and channel attention. To address these issues, this paper proposes a multi-scale and multi-attention model based on Res2Net. Our proposed framework introduces an Adaptive Feature Refinement Pyramid Module (AFRPM) to enhance multi-scale feature extraction, thus improving the accuracy of traffic scene image classification. Additionally, we integrate frequency domain and spatial-channel attention mechanisms to develop recognition capabilities for complex backgrounds, objects of different scales, and local details in traffic scene images. The paper conducts the task of classifying traffic scene images using the Traffic-Net dataset. The experimental results demonstrate that our model achieves an accuracy of 96.88% on this dataset, which is an improvement of approximately 2% compared to the baseline Res2Net network. Furthermore, we validate the effectiveness of the proposed modules through ablation experiments.

Получение и обработка акустических откликов волн Лэмба в датчиках водных растворов базовых вкусов

In this work, the total acoustic wave losses in 128°Y-LiNbO3 wafers during liquid application due to radiation emission of waves into the liquid medium, as well as its viscosity, conductivity, density and dielectric constant of the liquid analyte are measured. Solutions with 5 basic flavors, widely used for calibration and reference creation in liquid media testing, were selected as test liquids. The measurements were performed in the range of 30...60 MHz. After the measurements, the experimental data were presented in the form of polar histograms, from the comparison of areas and shapes of which the flavor of a particular liquid was determined. Then, a principal component analysis (PCA) method was applied to the same experimental results to describe linear elastic wave deformation processes based on experimental data containing known values of physical wave measurements for different types of liquid media, which was previously used to identify gases and predict the properties of various substances from the linear response of a normal acoustic-electronic wave. In this paper, a comparative analysis of the results obtained using polar histograms and the standard PCA classification method is performed. The advantages of the latter are demonstrated. The results of this study are useful both for the development of acoustic sensors without sensor layers and for the application of machine learning methods to data in other types of sensors.

Deep learning‐based seizure prediction using EEG signals: A comparative analysis of classification methods on the CHB‐MIT dataset

AbstractEpilepsy is a brain disorder that causes patients to have multiple seizures. About 30% of patients with epilepsy are not treated with medication or surgery. The abnormal activity of brain before occurring of a seizure (about a few minutes before a seizure occurs) are known as the preictal area. Therefore, if we can predict this state, we can control possible seizures by using appropriate medications. In this study, we present a method for predicting epileptic seizures using electroencephalogram (EEG) signals. The method can identify the preictal region that occurs before the onset of seizures. In our proposed method, first the noise removal of EEG signals is performed, and then the necessary features are extracted using a convolution neural network. Finally, we use the feature vectors in order to train multiple classifiers, fully connected layer, random forest, and support vector machines with linear kernel. Additionally, we apply maximum voting, which is an ensemble method, to classify preictal segments from interictal ones. In this study, using EEG signals of patients from CHB‐MIT dataset, we were able to achieve sensitivity of 90.76%.

Automated Scoring of Alzheimer’s Disease Atrophy Scale with Subtype Classification Using Deep Learning-Based T1-Weighted Magnetic Resonance Image Segmentation

Background: Application of visual scoring scales for regional atrophy in Alzheimer’s disease (AD) in clinical settings is limited by their high time cost and low intra/inter-rater agreement. Objective: To provide automated atrophy scoring using objective volume driven from deep-learning segmentation methods for AD subtype classification using magnetic resonance imaging (MRI). Methods: We enrolled 3,959 participants (1,732 cognitively normal [CN], 1594 with mild cognitive impairment [MCI], and 633 with AD). The occupancy indices for each regional volume were calculated by dividing each volume by the size of the lateral and inferior ventricular volumes. MR images from 355 participants (119 CN, 119 MCI, and 117 AD) from three different centers were used for validation. Two neuroradiologists performed visual assessments of the medial temporal, posterior, and global cortical atrophy scores in the frontal lobe using T1-weighted MR images. Images were also analyzed using the deep learning-based segmentation software, Neurophet AQUA. Cutoff values for the three scores were determined using the data distribution according to age. The scoring results were compared for consistency and reliability. Results: Four volumetric-driven scoring results showed a high correlation with the visual scoring results for AD, MCI, and CN. The overall agreement with human raters was weak-to-moderate for atrophy scoring in CN participants, and good-to-almost perfect in AD and MCI participants. AD subtyping by automated scores also showed usefulness as a research tool. Conclusions: Determining AD subtypes using automated atrophy scoring for late-MCI and AD could be useful in clinical settings or multicenter studies with large datasets.

Adap-BDCM: Adaptive Bilinear Dynamic Cascade Model for Classification Tasks on CNV Datasets.

Copy number variation (CNV) is an essential genetic driving factor of cancer formation and progression, making intelligent classification based on CNV feasible. However, there are a few challenges in the current machine learning and deep learning methods, such as the design of base classifier combination schemes in ensemble methods and the selection of layers of neural networks, which often result in low accuracy. Therefore, an adaptive bilinear dynamic cascade model (Adap-BDCM) is developed to further enhance the accuracy and applicability of these methods for intelligent classification on CNV datasets. In this model, a feature selection module is introduced to mitigate the interference of redundant information, and a bilinear model based on the gated attention mechanism is proposed to extract more beneficial deep fusion features. Furthermore, an adaptive base classifier selection scheme is designed to overcome the difficulty of manually designing base classifier combinations and enhance the applicability of the model. Lastly, a novel feature fusion scheme with an attribute recall submoduleis constructed, effectively avoiding getting stuck in local solutions and missing some valuable information. Numerous experiments have demonstrated that our Adap-BDCM model exhibits optimal performance in cancer classification, stage prediction, and recurrence on CNV datasets. This study can assist physicians in making diagnoses faster and better.

Classification of Benign-Malignant Thyroid Nodules Based on Hyperspectral Technology.

In recent years, the incidence of thyroid cancer has rapidly increased. To address the issue of the inefficient diagnosis of thyroid cancer during surgery, we propose a rapid method for the diagnosis of benign and malignant thyroid nodules based on hyperspectral technology. Firstly, using our self-developed thyroid nodule hyperspectral acquisition system, data for a large number of diverse thyroid nodule samples were obtained, providing a foundation for subsequent diagnosis. Secondly, to better meet clinical practical needs, we address the current situation of medical hyperspectral image classification research being mainly focused on pixel-based region segmentation, by proposing a method for nodule classification as benign or malignant based on thyroid nodule hyperspectral data blocks. Using 3D CNN and VGG16 networks as a basis, we designed a neural network algorithm (V3Dnet) for classification based on three-dimensional hyperspectral data blocks. In the case of a dataset with a block size of 50 × 50 × 196, the classification accuracy for benign and malignant samples reaches 84.63%. We also investigated the impact of data block size on the classification performance and constructed a classification model that includes thyroid nodule sample acquisition, hyperspectral data preprocessing, and an algorithm for thyroid nodule classification as benign and malignant based on hyperspectral data blocks. The proposed model for thyroid nodule classification is expected to be applied in thyroid surgery, thereby improving surgical accuracy and providing strong support for scientific research in related fields.

Improved tomato leaf disease classification through adaptive ensemble models with exponential moving average fusion and enhanced weighted gradient optimization

Tomato is one of the most popular and most important food crops consumed globally. The quality and quantity of yield by tomato plants are affected by the impact made by various kinds of diseases. Therefore, it is essential to identify these diseases early so that it is possible to reduce the occurrences and effect of the diseases on tomato plants to improve the overall crop yield and to support the farmers. In the past, many research works have been carried out by applying the machine learning techniques to segment and classify the tomato leaf images. However, the existing machine learning-based classifiers are not able to detect the new types of diseases more accurately. On the other hand, deep learning-based classifiers with the support of swarm intelligence-based optimization techniques are able to enhance the classification accuracy, leading to the more effective and accurate detection of leaf diseases. This research paper proposes a new method for the accurate classification of tomato leaf diseases by harnessing the power of an ensemble model in a sample dataset of tomato plants, containing images pertaining to nine different types of leaf diseases. This research introduces an ensemble model with an exponential moving average function with temporal constraints and an enhanced weighted gradient optimizer that is integrated into fine-tuned Visual Geometry Group-16 (VGG-16) and Neural Architecture Search Network (NASNet) mobile training methods for providing improved learning and classification accuracy. The dataset used for the research consists of 10,000 tomato leaf images categorized into nine classes for training and validating the model and an additional 1,000 images reserved for testing the model. The results have been analyzed thoroughly and benchmarked with existing performance metrics, thus proving that the proposed approach gives better performance in terms of accuracy, loss, precision, recall, receiver operating characteristic curve, and F1-score with values of 98.7%, 4%, 97.9%, 98.6%, 99.97%, and 98.7%, respectively.

Self-supervised motor imagery EEG recognition model based on 1-D MTCNN-LSTM network

Objective. Aiming for the research on the brain–computer interface (BCI), it is crucial to design a MI-EEG recognition model, possessing a high classification accuracy and strong generalization ability, and not relying on a large number of labeled training samples. Approach. In this paper, we propose a self-supervised MI-EEG recognition method based on self-supervised learning with one-dimensional multi-task convolutional neural networks and long short-term memory (1-D MTCNN-LSTM). The model is divided into two stages: signal transform identification stage and pattern recognition stage. In the signal transform recognition phase, the signal transform dataset is recognized by the upstream 1-D MTCNN-LSTM network model. Subsequently, the backbone network from the signal transform identification phase is transferred to the pattern recognition phase. Then, it is fine-tuned using a trace amount of labeled data to finally obtain the motion recognition model. Main results. The upstream stage of this study achieves more than 95% recognition accuracy for EEG signal transforms, up to 100%. For MI-EEG pattern recognition, the model obtained recognition accuracies of 82.04% and 87.14% with F1 scores of 0.7856 and 0.839 on the datasets of BCIC-IV-2b and BCIC-IV-2a. Significance. The improved accuracy proves the superiority of the proposed method. It is prospected to be a method for accurate classification of MI-EEG in the BCI system.

Various tomato infection discrimination using spectroscopy

AbstractDiagnosing plant diseases is a difficult task, but it could be made easier with the use of advanced instrumentation and the latest machine learning techniques. This paper is a further development of a previous authors study by the authors, which has been extended to provide the classification method for tomato diseases and to indicate the spectral ranges of greatest importance for this process. As tomatoes are one of the most popular and consumed vegetables, and diseases of this crop even reduce yields by up to 80% every year, their detection is a vague topic. This manuscript describes research in which spectroscopy was used to develop methods for discriminating between selected tomato diseases. The following, frequently occurring diseases were investigated for this research: anthracnose, bacterial speck, early blight, late blight, and Septoria Leaf Spot. The study used a dataset consisting of 3877 measurements taken with the ASD FieldSpec 4 Hi-Res spectroradiometer in the 350–2500 nm range from 2019/09/10 to 2019/12/20. The highest classification efficiency ($$F_1$$ F 1 score) of 0.896 was obtained for the logistic regression based model which was evaluated on Septoria Leaf Spot disease records.

SEMG-based automatic characterization of swallowed materials

Monitoring of ingestive activities is critically important for managing the health and wellness of individuals with various health conditions, including the elderly, diabetics, and individuals seeking better weight control. Monitoring swallowing events can be an ideal surrogate for developing streamlined methods for effective monitoring and quantification of eating or drinking events. Swallowing is an essential process for maintaining life. This seemingly simple process is the result of coordinated actions of several muscles and nerves in a complex fashion. In this study, we introduce automated methods for the detection and quantification of various eating and drinking activities. Wireless surface electromyography (sEMG) was used to detect chewing and swallowing from sEMG signals obtained from the sternocleidomastoid muscle, in addition to signals obtained from a wrist-mounted IMU sensor. A total of 4675 swallows were collected from 55 participants in the study. Multiple methods were employed to estimate bolus volumes in the case of fluid intake, including regression and classification models. Among the tested models, neural networks-based regression achieved an R2 of 0.88 and a root mean squared error of 0.2 (minimum bolus volume was 10 ml). Convolutional neural networks-based classification (when considering each bolus volume as a separate class) achieved an accuracy of over 99% using random cross-validation and around 66% using cross-subject validation. Multiple classification methods were also used for solid bolus type detection, including SVM and decision trees (DT), which achieved an accuracy above 99% with random validation and above 94% in cross-subject validation. Finally, regression models with both random and cross-subject validation were used for estimating the solid bolus volume with an R2 value that approached 1 and root mean squared error values as low as 0.00037 (minimum solid bolus weight was 3 gm). These reported results lay the foundation for a cost-effective and non-invasive method for monitoring swallowing activities which can be extremely beneficial in managing various chronic health conditions, such as diabetes and obesity.

The Influence of Spatial Heterogeneity of Urban Green Space on Surface Temperature

Urban green space (UGS) has been recognized as a key factor in enhancing the urban ecosystem balance, particularly in arid areas. It is often considered an effective means to mitigate the urban heat island (UHI) effect. In this study, the reference comparison method was utilized to optimize the process of nighttime lighting data; the random forest classification method was employed to extract UGS data; and the radiative transfer method was applied in land surface temperature (LST) inversion. Additionally, moving window analysis was conducted to assess the robustness of the results. The objective of this research was to analyze the spatial distribution characteristics of UGS and LST and to explore their bivariate local spatial autocorrelations by calculating four landscape metrics, including the aggregation index (AI), edge density (ED), patch density (PD), and area-weighted mean shape index (Shape_am). It was found that the distribution of UGS in the study area was uneven, with higher temperatures in the eastern and western regions and lower temperatures in the central and southern regions. The results also revealed that ED, PD, and Shape_am were negatively correlated with LST, with correlation coefficients being −0.469, −0.388, and −0.411, respectively, indicating that UGS in these regions were more effective in terms of cooling effect. Conversely, AI was found to be positively correlated with LST (Moran’ I index of 0.449), indicating that surface temperatures were relatively higher in regions of high aggregation. In essence, the fragmented, complex, and evenly distributed green patches in the study area provided a better cooling effect. These findings should persuade decision makers and municipal planners to allocate more UGS in cities for UHI alleviation to improve quality of life and enhance recreational opportunities.

RQ-OSPTrans: A Semantic Classification Method Based on Transformer That Combines Overall Semantic Perception and “Repeated Questioning” Learning Mechanism

The pre-trained language model based on Transformers possesses exceptional general text-understanding capabilities, empowering it to adeptly manage a variety of tasks. However, the topic classification ability of the pre-trained language model will be seriously affected in the face of long colloquial texts, expressions with similar semantics but completely different expressions, and text errors caused by partial speech recognition. We propose a long-text topic classification method called RQ-OSPTrans to effectively address these challenges. To this end, two parallel learning modules are proposed to learn long texts, namely, the repeat question module and the overall semantic perception module. The overall semantic perception module will conduct average pooling on the semantic embeddings produced by BERT, in addition to multi-layer perceptron learning. The repeat question module will learn the text-embedding matrix, extracting detailed clues for classification based on words as fundamental elements. Comprehensive experiments demonstrate that RQ-OSPTrans can achieve a generalization performance of 98.5% on the Chinese dataset THUCNews. Moreover, RQ-OSPTrans can achieve state-of-the-art performance on the arXiv-10 dataset (84.4%) and has a comparable performance with other state-of-the-art pre-trained models on the AG’s News dataset. Finally, the results indicate that our method exhibits a superior performance compared with the baseline methods on small-scale domain-specific datasets by validating RQ-OSPTrans on a specific task scenario by using our custom-built dataset CCIPC.

Development of a Phishing Detection System Using Support Vector Machine

Phishing represents a significant and escalating threat within the cyber domain, inflicting substantial financial losses on internet users annually. This illicit practice leverages both social engineering tactics and technological means to unlawfully obtain sensitive information from individuals online. Despite numerous studies and publications exploring various methodologies to combat phishing, the number of victims continues to surge due to the inefficiencies of current security measures. The inherently anonymous and unregulated nature of the internet further compounds its susceptibility to phishing attacks. While it's commonly believed that successful phishing endeavours involve the creation of replica messages or websites to deceive users, this notion has not undergone systematic examination to identify potential vulnerabilities. This paper endeavours to fill this gap by conducting a comprehensive evaluation of phishing, synthesizing diverse research perspectives and methodologies. It introduces an innovative classification method utilizing Support Vector Machine (SVM), achieving an impressive accuracy rate of 96.4% in detecting phishing attempts. By implementing this model to distinguish between phishing and legitimate URLs, the proposed solution offers a valuable tool for individuals and organizations to promptly identify and mitigate phishing threats. The findings of this study hold significant implications for bolstering internet security measures and enhancing user awareness in navigating potentially malicious online content.

Pine Wilt Disease Segmentation with Deep Metric Learning Species Classification for Early-Stage Disease and Potential False Positive Identification

Pine Wilt Disease poses a significant global threat to forests, necessitating swift detection methods. Conventional approaches are resource-intensive but utilizing deep learning on ortho-mapped images obtained from Unmanned Aerial Vehicles offers cost-effective and scalable solutions. This study presents a novel method for Pine Wilt Disease detection and classification using YOLOv8 for segmenting diseased areas, followed by cropping the diseased regions from the original image and applying Deep Metric Learning for classification. We trained a ResNet50 model using semi-hard triplet loss to obtain embeddings, and subsequently trained a Random Forest classifier tasked with identifying tree species and distinguishing false positives. Segmentation was favored over object detection due to its ability to provide pixel-level information, enabling the flexible extension of subsequent bounding boxes. Deep Metric Learning-based classification after segmentation was chosen for its effectiveness in handling visually similar images. The results indicate a mean Intersection over Union of 83.12% for segmentation, with classification accuracies of 98.7% and 90.7% on the validation and test sets, respectively.

Tracking states and transitions in semiarid rangelands: A spatiotemporal archetypal analysis of productivity dynamics using wavelets

Climate change poses challenges in classifying ecosystem dynamics, as they are influenced by shifting dynamics resulting from changes in climate forces and meteorological variables, including temperature and water availability. To address this, our study presents a novel approach using Continuous Wavelet Transform (CWT) and power spectrum analysis to classify vegetation dynamics, considering the time-dependent variability of ecosystem frequencies. We applied our method to centred and standardized MODIS NDVI time series for the period 2000–2021, using an experimental field station in northern Patagonia as a case study. By performing a continuous wavelet transform on the data for each pixel, we obtained instantaneous power spectra, capturing variability across different dates and pixels. These spectrums were then consolidated into a comprehensive database, and subsequently classified using archetypal analysis. We identified a convex combination of archetypal spectrums that best represented the entire power spectrum database. Mapping the resulting archetypes and their weights in both space and time allowed us to explore pixels' variations in archetype weights in relation to factors such as time, topography, and climate. In addition, to examine the potential relationship between the NDVI time series and climate drivers, we computed the Average Cross-Wavelet Power Spectrum (ACWPS) to different climatic indices. Three archetypes were sufficient to explain the majority of power spectrum variability in the study area. These archetypes exhibited distinctive characteristics: 1) medium-frequency variability (2–4 years), 2) low-frequency variability (>4 years), and 3) an annual (i.e. seasonal) cycle with low-frequency variability. Spatially, the first two archetypes were predominantly observed in highland steppes, while the third archetype prevailed in lowland areas associated with meadows. At the beginning of the studied period, Archetypes 1 and 3 dominated, but after the Puyehue-Cordón Caulle Volcanic Complex eruption in 2011 their prominence diminished, and Archetype 2 became more prevalent in the whole study area. Finally, all three NDVI series representative of archetypes showed a relative peak at approximately four years, which could be linked to the Indian Ocean Dipole variability. These results highlight an abrupt shift in the system's behaviour, primarily related to changes in variability distribution rather than mean values. This disturbance-induced transition aligns with the theory of state and transitions in ecological system dynamics. We propose that the states in this model are not fixed but represent alternative dynamic behaviours, akin to different types of limit cycles. Consequently, employing a wavelet analysis-based classification method provides a robust means of studying and understanding such variability and transitions, thereby offering clarity and comprehension of ecosystem states. Notably, this methodology proves particularly effective for large databases of detailed time series.

The Theory of Quantity in Measures and Scales a Comparative Jurisprudential Study

Every body has a size and mass that distinguishes it from others and makes it different from others. Some of these bodies are huge and large in size, and some are small and light in weight. Among these masses and bodies are some that are dealt with by their size and weight, each according to its quantity, weight, and cheapness. This is why they created quantities by which these weights and quantities could be estimated, so they used measures and weights for that. Objectives: The research aims to know some measures and weights, such as the wife’s maintenance, the amount of zakat, etc.I found it to be a widely spread topic, and widely used in the folds of jurisprudence. During my reading of jurisprudence books, I found jurists using many quantities, of multiple types, and different forms, according to the type of each quantity. methodology: I begin by mentioning the title and the definition in its language and terminology, and the basis of the issue from a verse or hadith, then I arrange the opinions, starting with the first, then the next, then the next, and I make the statement that seems to me closest to the truth - according to what I have arrived at - in the last. I have attributed the Qur’anic verses to their surahs, and the noble hadiths to their original sources as much as I can. If I do not find it, I indicate the place from which I took it. Results: Conclusion: Jurisprudential theories are a new method of writing, and a new method of classification, created by contemporary scholars. They composed several theories, with multiple topics, according to each theory and its details. The theory of magnitude is a theory that includes the origins of the particulars of quantities, in its spread within the various sections of jurisprudence.

Assessing Residency and Site Fidelity in Bottlenose Dolphins: A Literature Review and Bibliometric Analysis

Residency and site fidelity are important parameters in population ecology for many species as they indicate the temporal and spatial use for individuals. Although both terms are clearly different, they are used interchangeably due to lack of clarity in their definitions and the ways to assess them, especially in bottlenose dolphins (Tursiops spp.). Individuals with well-defined patterns of residency and site fidelity may be more vulnerable to anthropogenic disturbances; thus, the study of these parameters may help to better assess such impacts. However, interspecific differences and logistical restrictions for the study of dolphins have prevented consensus. We set out to investigate the factors that have influenced measurements of residency and site fidelity in bottlenose and a set of other dolphin species through a retrospective (~30 y: 1990 to 2019) literature review. We found 117 scientific papers with a two-fold increasing trend approximately every 15 years, with 50% published in only four journals. Most of the studies were developed in the United States, followed by Australia and Europe, likely due to strict policies for marine mammal conservation. Author collaboration network analysis showed highly atomized national groups in which North American organizations formed the biggest cluster, followed by Australia, Africa, and Europe. However, large worldwide gaps still exist, possibly because of the heterogeneity in marine mammal sampling efforts and limited information availability in the North Pacific, Central and South America, Africa, Asia, and the Indian Ocean. We found 30 different classification methods in which 12 were based on defined categories. We provide conceptual aid to distinguish between the use of both terms, as well as guidance to tackle the challenge of defining patterns of temporal use due to the variability of thresholds for classifying individuals. A paradigm shift is needed to create comprehensive, standardized, and generalized assessments of residency and site fidelity, which can be compared based on their performance across different populations of bottlenose dolphins around the world.