Difficulties In Classification Research Articles

AI-Enhanced Medicinal Plant Identification System with Multilingual Social Media Integration

Sri Lanka is a country with a Ayurvedic culture which cannot be experienced anywhere in the world. This cultural system is based on a series of knowledge passed on from generations over 3000 years that could treat a variety of diseases. This traditional ayurvedic system consist of a vast herbal plant collection. Most of the information about this ayurvedic system is written in manuscripts for thousands of years. Sri Lanka lacks a proper system which is specific to ayurvedic sector is a major concern that should be addressed at present. Absence of a system has lead to problems and difficulties in identification and classification of herbal plants, to transfer knowledge about herbal plants and to conserve these ayurvedic plants for the future generation. Another concern is that Ayurvedic undergraduate students face many difficulties when gathering knowledge of these herbal plants and medicinal practices. Sri Lanka does not comprise with a full ayurvedic plant inventory system is another major concern that identified in the country. By considering all the problems an intelligent system has been recognized as a solution. The system will be based on Deep Learning, CNN, GIS, Artificial Intelligence and Machine Learning based principals to cater all the identified problems. The system will be able to identify ayurvedic plant with an image of a leave, flower, or fruit as input. And also, system will be able to classify and provide a detailed description about the identified plant including medicinal value and the distribution of the plant in the island. System will provide a crowdsourcing social media facility with both English and Sinhala languages to share information with fellow herbalist in the country.

Pedestrian Pose Recognition Based on Frequency-Modulated Continuous-Wave Radar with Meta-Learning.

With the continuous advancement of autonomous driving and monitoring technologies, there is increasing attention on non-intrusive target monitoring and recognition. This paper proposes an ArcFace SE-attention model-agnostic meta-learning approach (AS-MAML) by integrating attention mechanisms into residual networks for pedestrian gait recognition using frequency-modulated continuous-wave (FMCW) millimeter-wave radar through meta-learning. We enhance the feature extraction capability of the base network using channel attention mechanisms and integrate the additive angular margin loss function (ArcFace loss) into the inner loop of MAML to constrain inner loop optimization and improve radar discrimination. Then, this network is used to classify small-sample micro-Doppler images obtained from millimeter-wave radar as the data source for pose recognition. Experimental tests were conducted on pose estimation and image classification tasks. The results demonstrate significant detection and recognition performance, with an accuracy of 94.5%, accompanied by a 95% confidence interval. Additionally, on the open-source dataset DIAT-μRadHAR, which is specially processed to increase classification difficulty, the network achieves a classification accuracy of 85.9%.

Comparison between parameter-efficient techniques and full fine-tuning: A case study on multilingual news article classification.

Adapters and Low-Rank Adaptation (LoRA) are parameter-efficient fine-tuning techniques designed to make the training of language models more efficient. Previous results demonstrated that these methods can even improve performance on some classification tasks. This paper complements existing research by investigating how these techniques influence classification performance and computation costs compared to full fine-tuning. We focus specifically on multilingual text classification tasks (genre, framing, and persuasion techniques detection; with different input lengths, number of predicted classes and classification difficulty), some of which have limited training data. In addition, we conduct in-depth analyses of their efficacy across different training scenarios (training on the original multilingual data; on the translations into English; and on a subset of English-only data) and different languages. Our findings provide valuable insights into the applicability of parameter-efficient fine-tuning techniques, particularly for multilabel classification and non-parallel multilingual tasks which are aimed at analysing input texts of varying length.

Improving Oriented Object Detection by Scene Classification and Task-Aligned Focal Loss

Oriented object detection (OOD) can precisely detect objects with arbitrary direction in remote sensing images (RSIs). Up to now, the two-stage OOD methods have attracted more attention because of their high detection accuracy. However, the two-stage methods only rely on the features of each proposal for object recognition, which leads to the misclassification problem because of the intra-class diversity, inter-class similarity and clutter backgrounds in RSIs. To address the above problem, an OOD model combining scene classification is proposed. Considering the fact that each foreground object has a strong contextual relationship with the scene of the RSI, a scene classification branch is added to the baseline OOD model, and the scene classification result of input RSI is used to exclude the impossible categories. To focus on the hard instances and enhance the consistency between classification and regression, a task-aligned focal loss (TFL) which combines the classification difficulty with the regression loss is proposed, and TFL assigns lager weights to the hard instances and optimizes the classification and regression branches simultaneously. The ablation study proves the effectiveness of scene classification branch, TFL and their combination. The comparisons with 15 and 14 OOD methods on the DOTA and DIOR-R datasets validate the superiority of our method.

Imbalance data: The application of RUS FCM K-RBFNN Smote with XGBoost in the elderly well-being identification

Imbalanced data is a serious binary classification difficulty in forecasting the well-being of the elderly. This paper improves the Smote algorithm from the algorithm and sample dimensions to tackle the issue of imbalanced distribution of questionnaire data. The k-means Smote is combined with RBFNN as K-RBFNN Smote in the algorithm dimension and add FCM link to resample the minority set in the sample dimension as FCM K-RBFNN Smote. In order to improve the generalization of models, the RUS module is added to the algorithm. Experiments are carried out on four improved Smote technologies and two existing Smote technologies combined with XGBoost, which is superior than the other five conventional classification models. The experimental results indicate that the performance order is RUS FCM K-RBFNN Smote > K-RBFNN Smote > FCM K-RBFNN Smote > RUS K-RBFNN Smote > K-Means Smote > FCM Smote. The RUS FCM K-RBFNN method has been identified as the optimal approach for enhancing performance, resulting in a 98.58% accuracy rate. In conclusion, Smote algorithm undergoes the implementation of K-RBFNN shows greater performance and the enhancement of FCM and RUS relies on the structure of sampling.

Pol-SAR image classification using multifarious stratification stratagem in machine learning

Image classification using polarimetric synthetic aperture radar (Pol-SAR) is becoming more important in image processing for remote sensing applications. However, in the existing techniques, during the feature extraction process, there exist some limitations including laborious endeavour for Pol-SAR image classification, identifying intrinsic features for target recognition is difficult in feature selection, and pixel-level Pol-SAR image classification is difficult for obtaining more precise and coherent interpretation consequences. Hence to overcome these issues, a novel Multifarious Stratification Stratagem in machine learning is proposed to achieve pixel-level Pol-SAR classification. In this proposed model, a novel Scrumptious Integrant Wrenching method is used for efficient feature extraction. It is compatible with the orientation-sensitive of the Pol-SAR image which increases the variety of intra-layer features. To remove the difficulty in feature selection, a novel Episodicical Proximity Selection method is proposed in which a Split-level parallel feature selection strategy is used to select the best qualities from the extracted features. To tackle the difficulty in classification, an Elastic Net Classifier (ENC) is used that find the coefficient vector for the linear combination of the training sets. This efficiently classified the best features in the Pol-SAR images and improved the proposed system’s accuracy. As a result, the performance measures of the proposed system demonstrate that the accuracy is increased by 99.69%, precision is increased by 98.99%, recall is increased by 98.99%, sensitivity is increased by 98.99%, and F1-score is increased by 98.99% as a response.

A Comparative Analysis of Combination of CNN-Based Models with Ensemble Learning on Imbalanced Data

This study investigates the usefulness of the Synthetic Minority Oversampling Technique (SMOTE) in conjunction with convolutional neural network (CNN) models, which include both single and ensemble classifiers. The objective of this research is to handle the difficulty of multi-class imbalanced image classification. The application of SMOTE in imbalanced picture datasets is still underexplored, even though CNNs have been shown to be successful in image classification and that ensemble learning approaches have improved their performance. To investigate whether or not SMOTE can increase classification accuracy and other performance measures when combined with CNN-based classifiers, our research makes use of a CIFAR-10 dataset that has been artificially step-imbalanced and has varying imbalanced ratios. We conducted experiments using five distinct models, namely AdaBoost, XGBoost, standalone CNN, CNN-AdaBoost, and CNN-XGBoost, on datasets that were either imbalanced or SMOTE-balanced. Metrics such as accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC) were included in the evaluation process. The findings indicate that SMOTE dramatically improves the accuracy of minority classes, and that the combination of ensemble classifiers with CNNs and oversampling techniques significantly improves overall classification performance, particularly in situations when there is a high-class imbalance. When it comes to enhancing imbalanced classification tasks, this study demonstrates the potential of merging oversampling techniques with CNN-based ensemble classifiers to minimize the impacts of class imbalance in picture datasets. This suggests a promising direction for future research in this area.

Update on olfactory neuroblastoma.

Olfactory neuroblastomas are uncommon malignancies that arise from olfactory receptor cells located high in the nasal cavity. Accurate diagnosis plays a crucial role in determining clinical results and guiding treatment decisions. Diagnosis can be a major challenge for pathologists, especially when dealing with tumours with poor differentiation. The discovery of several molecular and immunohistochemical markers would help to overcome classification difficulties. Due to the paucity of large-scale studies, standardisation of diagnosis, treatment and prediction of outcome remains a challenge. Surgical resection by endoscopic techniques with the addition of postoperative irradiation is the treatment of choice. In addition, it is advisable to consider elective neck irradiation to minimise the risk of nodal recurrence. Molecular characterisation will help not only to make more accurate diagnoses but also to identify specific molecular targets that can be used to develop personalised treatment options tailored to each patient. The present review aims to summarise the current state of knowledge on histopathological diagnosis, the molecular biology and management of this disease.

Multi-domain aspect-oriented sentiment analysis for movie recommendations using feature extraction

<span>Sentiment analysis is a well-recognized research field that has acknowledged significant attention in recent years. Researchers have made extensive efforts in employing various methodologies to explore these domains. Sentiment classification plays a fundamental role in natural language processing (NLP). However, studies have shown that sentiment classification models heavily depend on the specific domain. In the context of movie industry, where the demand for reliable movie reviews is high and not all movies are of exceptional quality and worthy of viewers time. Therefore, people depend on movie reviews before watching a movie. This explores the use of data from various domains to improve classification performance within each domain, addressing the difficulty of multi-domain sentiment classification in natural language processing. Therefore, it is crucial to effectively utilize shared sentiment knowledge across different domains for real-world applications. To solve these issues, a multi-domain aspect-oriented sentiment analysis for movie recommendation using feature extraction techniques. The main contribution of this work is to eliminate the time for users to go through a lengthy list of movies to make their decision. The novelty of this work is analysis of different movie genres, TV shows genres with accurate results. The presented approach's performance is validated by evaluating various metrics, including precision, recall, mean square error (MSE) and F1-score.</span>

The Image Data and Backbone in Weakly Supervised Fine-Grained Visual Categorization: A Revisit and Further Thinking

Weakly-supervised fine-grained visual categorization (FGVC) aims to achieve subclass classification within the same large class using only label information. Compared to general images, fine-grained images have similar appearances and features, and are often affected by disturbances such as viewpoint, lighting, and occlusion during data collection, resulting in significant intra-class variance and small inter-class variance. To achieve FGVC, carefully designed models are often needed to explore the locally discriminative regions of the image. This paper revisits high-quality FGVC publications based on deep learning and analyzes from two new perspective: fine-grained image data and backbone. We address two ignored but interesting problems in FGVC. First, we argue that the reasons for exacerbating intra-class variance are not the same in data of animal, plant, and commodity types, and it is necessary to consider the effects of posture, covariate shift, and structural changes. Additionally, the “soft boundary” between subclasses intensifies the difficulty of classification. Second, we highlight that convolutional networks and self-attention networks have different receptive fields and shape biases, leading to performance differences when processing different types of fine-grained data. Overall, our analysis provides new insights into recent advances, challenges, and future directions for FGVC based on deep learning, which can help researchers develop more effective models for FGVC.

A Hybrid Deep Learning GRU based Approach for Text Classification using Word Embedding

Text categorization has become an increasingly important issue for businesses that handle massive volumes of data generated online, and it has found substantial use in the field of NLP. The capacity to group texts into separate categories is crucial for users to effectively retain and utilize important information. Our goal is to improve upon existing recurrent neural network (RNN) techniques for text classification by creating a deep learning strategy through our study. Raising the quality of the classifications made is the main difficulty in text classification, nevertheless, as the overall efficacy of text classification is often hampered by the data semantics' inadequate context sensitivity. Our study presents a unified approach to examine the effects of word embedding and the GRU on text classification to address this difficulty. In this study, we use the TREC standard dataset. RCNN has four convolution layers, four LSTM levels, and two GRU layers. RNN, on the other hand, has four GRU layers and four LSTM levels. One kind of recurrent neural network (RNN) that is well-known for its comprehension of sequential data is the gated recurrent unit (GRU). We found in our tests that words with comparable meanings are typically found near each other in embedding spaces. The trials' findings demonstrate that our hybrid GRU model is capable of efficiently picking up word usage patterns from the provided training set. Remember that the depth and breadth of the training data greatly influence the model's effectiveness. Our suggested method performs remarkably well when compared to other well-known recurrent algorithms such as RNN, MV-RNN, and LSTM on a single benchmark dataset. In comparison to the hybrid GRU's F-measure 0.952, the proposed model's F-measure is 0.982%. We compared the performance of the proposed method to that of the three most popular recurrent neural network designs at the moment RNNs, MV-RNNs, and LSTMs, and found that the new method achieved better results on two benchmark datasets, both in terms of accuracy and error rate.

Parasitic Egg Detection and Classification in Low-Cost Microscopic Images Using Transfer Learning

Intestinal parasitic infection leads to several morbidities in humans worldwide, especially in tropical countries. The traditional diagnosis usually relies on manual analysis from microscopic images which is prone to human error due to morphological similarity of different parasitic eggs and abundance of impurities in a sample. Many studies have developed automatic systems for parasite egg detection to reduce human workload. However, they work with high-quality microscopes, which unfortunately remain unaffordable in some rural areas. Our work thus exploits a benefit of a low-cost USB microscope. This instrument however provides poor quality images due to the limitation of magnification (10×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document}), causing difficulty in parasite detection and species classification. In this paper, we propose a CNN-based technique using transfer learning strategy to enhance the efficiency of automatic parasite classification in poor-quality microscopic images. The patch-based technique with a sliding window is employed to search for the location of the eggs. Two networks, AlexNet and ResNet50, are examined with a trade-off between architecture size and classification performance. The results show that our proposed framework outperforms the state-of-the-art object recognition methods. Our system combined with the final decision from an expert may improve the real faecal examination with low-cost microscopes.

LoCoExNet: Low-Cost Early Exit Network for Energy Efficient CNN Accelerator Design

Early exit techniques, where the inference process of convolutional neural networks (CNNs) is early terminated by using auxiliary classifiers (called branches) to reduce data processing energy for easy inputs, have been actively researched. However, the conventional early exit works have suffered from the large size of branches, whose memory access energy is significantly large compared to that of the main network. In this paper, we propose a low-cost early exit network (LoCoExNet), which significantly improves energy efficiencies by reducing the parameters used in inference with efficient branch structures. To further reduce the energy consumption of branches, based on the observation that the classification difficulties of the images can be predicted using the magnitudes of input activations, we also propose hardware-friendly dynamic branch pruning (DBP). Given the network and target dataset, we can construct an energy-efficient early exit network through the branch structure determination algorithm and DBP algorithm. Finally, we develop several architectural supporting modules to improve the energy efficiency of the proposed LoCoExNet with DBP. The experimental results show that the LoCoExNet uses 28.7% of the total network parameters on average for Tiny-ImageNet dataset using VGG-16 without accuracy loss. The CNN accelerator that implements LoCoExNet with DBP has been implemented using the 65nm CMOS process. The implementation results show that the LoCoExNet accelerator achieves up to 91% of energy savings and up to 4.46× of speedups without accuracy loss for CIFAR-10 dataset using VGG-16 compared to the state-of-the-art CNN accelerators.

Morphologic and Genetic Features of Unclassifiable Chronic Myeloid Neoplasms: Five-Year Experience of a Canadian Provincial Referral Center

Introduction: The challenge of diagnosing and definitively classifying myeloproliferative neoplasms (MPNs) and myelodysplastic/myeloproliferative neoplasms (MDS/MPNs) remains a conundrum in the field of hematology. While the World Health Organization has outlined diagnostic criteria for various categories, there remain cases that elude specific classification, falling under the umbrella of MPN, unclassifiable (MPN-U) and MDS/MPN, unclassifiable (MDS/MPN-U). In this study, we aimed to compare the morphological and genetic profiles of cases classified as MPN-U or MDS/MPN-U against more definitively classified cases (including chronic myelomonocytic leukemia (CMML), essential thrombocythemia (ET), MDS/MPN with ring sideroblasts and thrombocytosis (MDS/MPN-RS-T), and primary myelofibrosis (PMF), including prefibrotic PMF (pre-PMF)). Methods: Morphologically unclassifiable myeloid neoplasm cases referred for pathologist review to our laboratory in the largest tertiary care center in British Columbia, Canada were identified through pathology database query over a five-year period. Bone marrow materials were reviewed and reanalyzed with standardized collection of qualitative and semiquantitative data by two hematopathologists. Morphological parameters studied included the myeloid to erythroid ratio, bone marrow cellularity, lineage dysplasia, megakaryocyte histotopography, bone marrow blast frequency, fibrotic changes, and bone marrow vascularity, among others. Clinical data, cytogenetic findings, and results of a targeted sequencing panel assessing a set of approximately 50 genes recurrently mutated in myeloid malignancies were collected by electronic medical record review. A cohort of contemporarily collected myeloid neoplasms with definitive pathologic diagnoses was reviewed for comparison. Statistical analyses were performed using python3. Results: The cohort comprised 29 cases classified as MPN-U, 12 cases of MDS/MPN-U, and 5 cases each of ET, PMF, pre-PMF, CMML, and MDS/MPN-RS-T. MPN-U cases were further subcategorized based on the apparent reason for difficulty in classification, including: overlapping features with another MPN; late stage/overt fibrosis; early stage without fully-developed morphologic features; and coexisting atypical morphologic findings. MPN-U with overlapping features had highly variable reticulin fibrosis, while, as expected, late stage entities had a high degree of reticulin fibrosis and early stage entities showed less overt fibrosis (Figure, Left, demonstrating select morphologic features of evaluated chronic myeloid neoplasms). MPN-U as a whole appeared to show less prominent fibrosis-associated findings than MDS/MPN-U and to, in general, show a greater degree of neovascularization, similar to changes observed in cases with PMF. Splicing factor and epigenetic modifier mutations appeared to be overrepresented in MDS/MPN-U relative to MPN-U, notably excluding MPN-U with atypical morphologic features (Figure, Right, demonstrating the distribution of pathogenic variants identified in evaluated myeloid neoplasms; results are indicated as presence (1) or absence (0) of a variant for each gene). Notably, no CALR mutations were identified in the MDS/MPN-U category or early stage / atypical morphology MPN-U, though they were enriched in overtly fibrotic MPN-U entities. Discussion/Conclusion: In this cohort we observed, as expected, significant overlap in cases classified as MPN-U and MDS/MPN-U with better delineated entities, though patterns of morphologic change and mutational spectra suggest potential directions for further investigation of additional criteria that may guide accurate classification of these neoplasms. These findings reflect the heterogeneity of MPNs described in the literature and the complexity of their genetic underpinnings, particularly in the context of concurrent dyshematopoiesis; suggest enhanced bone marrow vascularization may be an informative differential diagnostic criterion in evaluation of these neoplasms, though further study is required; and emphasize the need for broader genetic testing and perhaps further subtyping of MPN-U cases for a more nuanced analytical approaches for these disorders.

Scleroderma: Difficulties in classification, diagnosis, treatment

Localized scleroderma (LoS) is an autoimmune connective tissue disease with a variety of clinical manifestations. The dermatologist faces the difficulties of establishing a diagnosis and choosing the most appropriate therapy due to the relatively rare prevalence. This review article explores the challenges of classification, diagnosis, and treatment, as well as the factors that contribute to their occurrence. The greatest difficulties in diagnosing LoS that affected the prognosis and progression of the illness are: a delay in diagnosis, including early detection; omission of the active stage of LoS; difficulties in differential diagnosis; lack of association with Lyme borreliosis; low diagnostic value of laboratory and histological tests; limited use of sensitive instrumental methods for diagnosis; and monitoring the activity of LoS in a doctor’s practice. The main difficulties in treatment are the delay in starting treatment; the use of therapy methods with low evidence-based effectiveness; the rare prescription of highly effective approaches; the insufficient effectiveness of systemic drugs and phototherapy; difficulties in determining the volume, timing, and regimen of therapy for each subtype of LoS; the difficulty of achieving a remission; dependence of treatment approach on the doctor’s specialization; the lack of targeted drugs with evidence-based effectiveness in LoS; the lack of high-level evidence-based effectiveness and safety of therapy in LoS; difficulties in the correction of complications of Lo S.

Comparison of Machine Learning Algorithms for Species Family Classification using DNA Barcode

Classifying plant species within the Liliaceae and Amaryllidaceae families presents inherent challenges due to the complex genetic diversity and overlapping morphological traits among species. This study explores the difficulties in accurate classification by comparing 11 supervised learning algorithms applied to DNA barcode data, aiming to enhance the precision of species family classification in these taxonomically intricate plant families. The ribulose-1,5-bisphosphate carboxylase-oxygenase large sub-unit (rbcL) gene, selected as a DNA barcode locus for plants, is used to represent species within the Amaryllidaceae and Liliaceae families. The experimental results demonstrate that nearly all tested models achieve accurate species classification into the appropriate families, with an accuracy rate exceeding 97%, except for the Naïve Bayes model. Regarding computational time, the Random Forest model requires significantly more time for training than other models. Regarding memory usage, the Least Squares Support Vector Machine with a polynomial kernel, and Regularized Logistic Regression consume more memory than other models. These machine learning models exhibit strong concordance with NCBI's classifications when predicting families using the test dataset, effectively categorizing species into the Amaryllidaceae and Liliaceae families.

Multilabel Convolutional Network With Feature Denoising and Details Supplement.

In multilabel images, the changeable size, posture, and position of objects in the image will increase the difficulty of classification. Moreover, a large amount of irrelevant information interferes with the recognition of objects. Therefore, how to remove irrelevant information from the image to improve the performance of label recognition is an important problem. In this article, we propose a convolutional network based on feature denoising and details supplement (FDDS) to address this issue. In FDDS, we first design a cascade convolution module (CCM) to collect spatial details of upper features, in order to enhance the information expression of features. Second, the feature denoising module (FDM) is further put forward to reallocate the weight of the feature semantic area, in order to enrich the effective semantic information of the current feature and perform denoising operations on object-irrelevant information. Experimental results show that the proposed FDDS outperforms the existing state-of-the-art models on several benchmark datasets, especially for complex scenes.

Clinical boundaries in adult cases of large B cell lymphoma with IRF4 rearrangement.

Large B-cell lymphoma with IRF4 rearrangement (LBCL-IRF4) is a recently described entity included in the revised 4th edition of the WHO Classification of Haematolymphoid Tumours (2017). Here we highlight the difficulties in classification of those cases which arise in adult patients with unusual clinical features. We present three cases with morphological and immunohistochemical features consistent with large B-cell lymphoma arising in adult patients, which were found to have isolated IRF4 rearrangements on FISH analysis. Each patient presented with advanced-stage disease and had a history of immunosuppression; clinical features that are not typical of LBCL-IRF4 and which make the distinction from DLBCL, not otherwise specified (NOS) challenging. We propose that the clinical boundaries of LBCL-IRF4 arising in adult patients need further delineation to allow distinction from true cases of DLBCL, NOS.

Descriptive assay of the clinico-morphological characteristics of dermatoses presenting with reticulate pigmentation

Background: Reticulate pigmentation is characterized by freckle-like lesions configured to form a net-like or chicken-wire configuration with varying degrees of pigmentation. It intermingles with similar terms such as mottled pigmentation and dyschromia and poses difficulty in classification. Both genetic and acquired dermatoses may present with reticulate patterns yet may vary in morphology as well as the age of onset and presentation. Materials and methods: This was a hospital-based, descriptive, observational study conducted on thirty-two patients over a period of fifteen months at the dermatology OPD. Patients presented with reticulate pigmentary dermatoses were enrolled in the study after giving written informed consent. A detailed history and clinical examination were performed, and findings were recorded on a standard predesigned proforma. The data was analyzed with appropriate statistical tests. Results: In our study, a total of thirty-two patients with reticulate pigmentary dermatoses were enrolled, fourteen males and eighteen females, yielding a male-to-female ratio of 1:1.28. The most common age group affected was 41–50 years (28.12%). The onset of lesions was in young to middle adulthood (56.25%), followed by childhood and teenage years (34.37%). The most common disorder found was erythema ab igne (15.62%), while livedoid vasculopathy, Dowling–Degos disease, and confluent reticulate papillomatosis each constituted 9.37% of cases. Conclusion: This study assisted to incorporate the spectrum of RPD and to assess its frequency, morphological patterns, and prognosis. Owing to the paucity of research studies on RPD, the present study will be helpful in exploring future treatment modalities, thereby decreasing the enigma and concerns associated with RPD. Key words: Reticulate pigmentary dermatoses, Net-like lesions, Dyschromia

Assessing the Performance of Video Classification Models in Identifying Violent Actions

Abstract: Manual video surveillance takes time and is prone to human error. Thanks to machine learning and deep learning, video analytics automates these processes. Video classification is an important aspect of video analytics. It is critical to detect violent behavior in recordings for surveillance, crime prevention, and public safety. Video classification algorithms are capable of detecting violent acts in video data. This paper introduces readers to the many approaches to video classification and describes the underlying network structure of the 3DCNN, ConvLSTM, and LRCN models, which are commonly used for video classification. Additionally, the models' implementation results were compared in order to conduct a comparative performance analysis of the models for the task of violent action classification. When it comes to classification, F1-Score, AUC score, and accuracy are useful metrics for evaluating models, and they were compared. We discuss some of the difficulties in violent action classification, as well as some potential future opportunities and new perspectives on how to address them and improve the system.