Dependencies In Sequential Data Research Articles

Research on prediction and evaluation algorithm of sports athletes performance based on neural network.

The Ultimate Fighting Championship (UFC) stands as a prominent global platform for professional mixed martial arts, captivating audiences worldwide. With its continuous growth and globalization efforts, UFC events have garnered significant attention and achieved commendable results. However, as the scale of development expands, the operational demands on UFC events intensify. At its core, UFC thrives on the exceptional performances of its athletes, which serve as the primary allure for audiences. This study aims to enhance the allure of UFC matches and cultivate exceptional athletes by predicting athlete performance on the field. To achieve this, a recurrent neural network prediction model based on Bidirectional Long Short-Term Memory (BiLSTM) is proposed. The model seeks to leverage athlete portraits and characteristics for performance prediction. The proposed methodology involves constructing athlete portraits and analyzing athlete characteristics to develop the prediction model. The BiLSTM-based recurrent neural network is utilized for its ability to capture temporal dependencies in sequential data. The model's performance is assessed through experimental analysis. Experimental results demonstrate that the athlete performance prediction model achieved an overall accuracy of 0.7524. Comparative analysis reveals that the proposed BiLSTM model outperforms traditional methods such as Linear Regression and Multilayer Perceptron (MLP), showcasing superior prediction accuracy. This study introduces a novel approach to predicting athlete performance in UFC matches using a BiLSTM-based recurrent neural network. By leveraging athlete portraits and characteristics, the proposed model offers improved accuracy compared to classical methods. Enhancing the predictive capabilities in UFC not only enriches the viewing experience but also contributes to the development of exceptional athletes in the sport.

Biomechanical and machine learning approaches to automating the identification of musical styles and emotions through human motion analysis

This study explores the intricate relationship between biomechanical movements and musical expression, focusing on the identification of musical styles and emotions. Violin performance is characterized by complex interactions between physical actions—such as bowing techniques, finger placements, and posture—and the resulting acoustic output. Recent advances in motion capture technology and sound analysis have enabled a more objective examination of these processes. However, the current literature frequently addresses biomechanics and acoustic features in isolation, lacking an integrated understanding of how physical movements translate into specific musical expressions. Machine Learning (ML), particularly Long Short-Term Memory (LSTM) networks, provides a promising avenue for bridging this gap. LSTM models are adept at capturing temporal dependencies in sequential data, making them suitable for analyzing the dynamic nature of violin performance. In this work, they have proposed a comprehensive model that combines biomechanical analysis with Mel-spectrogram-based LSTM modeling to automate the identification of musical styles and emotions in violin performances. Using motion capture systems, Inertial Measurement Units (IMUs), and high-fidelity audio recordings, we collected synchronized biomechanical and acoustic data from violinists performing various musical excerpts. The LSTM model was trained on this dataset to learn the intricate connections between physical movements and the acoustic features of each performance. Key findings from the study demonstrate the effectiveness of this integrated approach. The LSTM model achieved a validation accuracy of 92.5% in classifying musical styles and emotions, with precision, recall, and F1-score reaching 94.3%, 92.6%, and 93.4%, respectively, by the 100th epoch. The analysis also revealed strong correlations between specific biomechanical parameters, such as shoulder joint angle and bowing velocity, and acoustic features, like sound intensity and vibrato amplitude.

Human Pose Classification Based on Pose Long Short-Term Memory

Objective: At present, pose classification research is a hot topic in the field of computer vision, playing a crucial role in surveillance security, and motion data mining. The research serves as a crucial follow-up to three dimensional (3D) pose estimation and makes a significant contribution to the advancement of artificial intelligence. Pose classification is a complex problem involving a large amount of complicated data, making its digital modeling and further processing challenging. Methods: This article proposes the Pose long short - term memory (LSTM) method to classify 3D poses reconstructed from 2D image sequences. Result: Compared with traditional methods, the experimental tests show that the performance of the Pose LSTM is more superior in all aspects. Conclusion: Pose LSTM is particularly effective for recognizing continuous action poses because they can capture temporal dependencies in sequential data. In the context of pose recognition, it can analyze the sequence of poses over time and understand how these poses transition from one to another, making it possible to accurately classify or predict actions that unfold over a series of frames.

Transforming Mexico’s Electric Load Infrastructure: A Quantile Transformer Network Deep Learning Approach, 2019-2020

To transform Mexico's electric load infrastructure, accurate electric load forecasts are required that are crucial to efficiently allocate resources, maintain system stability, and manage energy. The purpose of this study is to use the Quantile Transformer Network (QTN) as a novel approach for a deep learning framework for load forecasting, emphasizing its potential and practical consequences in enhancing the accuracy of load forecasting in real-world energy systems. Moreover, it is shown that QTN efficiently captures complex patterns, temporal relationships and interconnections among factors that influence electric load. The dataset utilized consists of past records of energy consumption in the Baja California System in Mexico. It includes several factors such as electricity demand, marginal prices, temporal characteristics, temperature-related variables, seasonal patterns, and holidays. Additionally, QTN is combined with the Rainbow Technique (RT) to manage categorical variables, resulting in the creation of a unified feature called category. In this case, RT examines the connections between descriptive phrases that reflect distinct combinations of categorical factors depending on the load values. Moreover, this research shows that the common Quantile Regression (QR) outperforms QTN in capturing dependencies in sequential data. Finally, several recommendations for promoting Mexico's Electric Load Infrastructure are provided.

LSTM-BIGRU based Spectrum Sensing for Cognitive Radio

There is a shortage of wireless spectrum due to developments in the area of wireless communications as well as the number of users that are using resources. Spectrum sensing is a method that solves the issue of shortage. Deep learning surpasses classical methods in spectrum sensing by enabling autonomous feature learning, which enables the adaptive identification of complicated patterns in radio frequency data for cognitive radio in wireless sensor networks. This innovation increases the system's capacity to manage dynamic, real-time circumstances, resulting in increased accuracy over traditional approaches. Spectrum sensing (SS) using LSTM-BIGRU with gaussian noise has been suggested in this article. Long-term dependencies in sequential data are well- preserved by LSTM due to its dedicated memory cells. In addressing and man- aging long-term dependencies in sequential data, BIGRU's integration enhances the efficacy of the model as a whole. To conduct the investigation, RadioML2016.04C.multisnr open-source dataset was utilized. Whereas, by using RadioML2016.10b open-source dataset, QAM64, QPSK and QAM16 performance evaluation has been investigated. The experimental findings demonstrate that the suggested Spectrum Sensing has better accuracy on the dataset particularly at lower SNRs. The improved spectrum sensing (SS) performance of our suggested model is shown by the evaluation of performance indicators, such as the F1 Score, CKC and Matthew's correlation coefficient, highlighting its potency in the field of spectrum sensing applications.

Indonesian disaster named entity recognition from multi source information using bidirectional LSTM (BiLSTM)

Precise logistic support is essential after a disaster occurs. It must be timely, accurate, targeted, and based on existing needs. However, obtaining sufficient and accurate information related to logistic distribution locations remains a key problem. Therefore, implementing Named Entity Recognition (NER) can address this issue. In recent years, news coverage through Indonesian digital news media and social media accounts has emerged as a promising source for building a disaster data corpus. This study implemented NER to extract and identify named entities from text-based information, particularly from Indonesian digital news media. In addition to using regular entities from the NER standard, this study introduced new entities specialized for disaster-related information, including DISASTER, SCALE, SUPPLIES, CASUALTIES, and OUTSIDE. The new disaster corpus in the Indonesian language for the NER model was obtained with an imbalanced dataset composition. To overcome this problem, random oversampling was applied. This study also utilized the BiLSTM model to recognize each entity in new textual information, evaluating its performance when the proposed Indonesian disaster corpus was used as a training reference in the deep learning model. Several optimization algorithms applied in BiLSTM were evaluated. The results showed improved BiLSTM performance using Adam optimization and a balanced corpus. Performance indicators achieved were 93.4 %, 82.4 %, and 87.5 % for precision, recall, and F1-score, respectively. The BiLSTM network captured long-range dependencies in sequential data provided by NER. Oversampling ensured that the proposed NER model could precisely recognize all entities and reduce biased results. Thus, the BiLSTM method can better identify entities in the textual corpus of Indonesian disaster-related online news.

AI Assisted Deceptive Content Analysis System Using Bi-LSTM

Abstract: In an age of widespread misinformation, creating accurate machine learning algorithms is vital for ensuring the integrity of information sharing. This project aims to develop a machine learning model capable of distinguishing between fake and true news articles. [1]The model utilizes a Bidirectional Long Short-Term Memory (Bi-LSTM) neural network architecture. Exploratory data analysis techniques are employed to gain in-sights into the characteristics and distributions within the dataset. Data visualization techniques aid in understanding patterns and relationships within the dataset. Additionally, unigram analysis is conducted to extract meaningful features from the text data. The datasets are then prepared for model training, involving preprocessing steps such as tokenization and vectorization. Fi-nally, the Bi-LSTM model is constructed, leveraging its ability to capture long-range dependencies in sequential data. The model is trained on the prepared datasets, optimized using appropri-ate techniques, and evaluated using metrics such as accuracy, precision, recall, and F1-score. The Bi-LSTM architecture offers the advantage of capturing long-range dependencies in sequential data, thereby enhancing the model’s ability to discern nuanced patterns in news articles. The primary objective of this project is to develop a robust and accurate system for automatically detecting fake news, thus playing a pivotal role in enhancing the dissemination of reliable information in the digital age.

AK-MADDPG-Based Antijamming Strategy Design Method for Frequency Agile Radar.

Frequency agility refers to the rapid variation of the carrier frequency of adjacent pulses, which is an effective radar active antijamming method against frequency spot jamming. Variation patterns of traditional pseudo-random frequency hopping methods are susceptible to analysis and decryption, rendering them ineffective against increasingly sophisticated jamming strategies. Although existing reinforcement learning-based methods can adaptively optimize frequency hopping strategies, they are limited in adapting to the diversity and dynamics of jamming strategies, resulting in poor performance in the face of complex unknown jamming strategies. This paper proposes an AK-MADDPG (Adaptive K-th order history-based Multi-Agent Deep Deterministic Policy Gradient) method for designing frequency hopping strategies in frequency agile radar. Signal pulses within a coherent processing interval are treated as agents, learning to optimize their hopping strategies in the case of unknown jamming strategies. Agents dynamically adjust their carrier frequencies to evade jamming and collaborate with others to enhance antijamming efficacy. This approach exploits cooperative relationships among the pulses, providing additional information for optimized frequency hopping strategies. In addition, an adaptive K-th order history method has been introduced into the algorithm to capture long-term dependencies in sequential data. Simulation results demonstrate the superior performance of the proposed method.

A Deep Learning Approach for Real-Time Sign Language Detection and Response through Avatar Model

Abstract: This paper focuses on the evolution of a real-time sign language detection model using computer vision, machine learning, and deep learning. Its goal is to narrow the poor communication for the speaking and hearing-impaired community by using recurrent neural networks and long short-term memory models. The proposed system utilizes a dataset comprising sign language gestures captured in various contexts and by different signers. Preprocessing techniques are applied to draw out fitting features from the video frames, including hand movements, facial expressions, and body postures. The LSTM neural network architecture is chosen to grab temporal dependencies in sequential data, making it suitable for the robust nature of sign language. The training process involves optimizing the LSTM network on the labeled dataset, incorporating techniques such as transfer learning and data augmentation to enhance model generalization. The resulting model is capable of recognizing a divergent set of sign language actions in real-time

A novel machine learning-based framework for the water quality parameters prediction using hybrid long short-term memory and locally weighted scatterplot smoothing methods

ABSTRACT Water quality prediction is crucial for effective river stream management. Dissolved oxygen, conductivity and chemical oxygen demand are vital chemical parameters for water quality. Development of machine learning (ML) and deep learning (DL) methods made them widely used in this domain. Sophisticated DL techniques, especially long short-term memory (LSTM) networks, are required for accurate, real-time multistep prediction. LSTM networks are effective in predicting water quality due to their ability to handle long-term dependencies in sequential data. We propose a novel hybrid approach for water quality parameters prediction combining DL with data smoothing method. The Sava river at the Jamena hydrological station serves as a case study. Our workflow uses LSTM networks alongside LOcally WEighted Scatterplot Smoothing (LOWESS) technique for data filtering. For comparison, Support Vector Regressor (SVR) is used as the baseline method. Performance is evaluated using Root Mean Squared Error (RMSE) and Coefficient of Determination R2 metrics. Results demonstrate that LSTM outperforms the baseline method, with an R2 up to 0.9998 and RMSE of 0.0230 on the test set for dissolved oxygen. Over a 5-day prediction period, our approach achieves R2 of 0.9912 and RMSE of 0.1610 confirming it as a reliable method for water quality multistep parameters prediction.

Utilizing BiLSTM For Fine-Grained Aspect-Based Travel Recommendations Using Travel Reviews In Low Resourced Language

Recommender systems have become an essential tool for enhancing user experiences by providing personalized recommendations. In this study, we present a novel approach to constructing a recommender system specifically tailored for Malayalam travel reviews. Our objective was to extract relevant features from these reviews and employ a bidirectional Long Short-Term Memory (BiLSTM) architecture to construct a robust and accurate recommendation model. We focused on four key features extracted from the travel reviews: travel mode, travel type, location climate, and location type. The travel mode feature encompassed the mode of transport opted for the travel such as bus, car, train, etc., while the travel type captured the nature of the trip, including family, friends, or solo travel. Additionally, we considered the climate of the location, including rainy, snowy, hot, and dry, among others, and the location type, such as beach, hilly, or forest destinations. To construct our recommender system, we implemented a BiLSTM architecture, a powerful deep-learning model known for effectively capturing temporal dependencies in sequential data. This architecture allowed us to process the extracted features and learn the underlying patterns within the Malayalam travel reviews. Our experiments were conducted on a comprehensive dataset of Malayalam travel reviews, carefully curated for this study. The dataset encompassed a diverse range of travel experiences, enabling our model to learn from a wide variety of user preferences and recommendations. The performance evaluation of our recommender system yielded promising results. With an accuracy of 83.65 percent, our model showcased its ability to accurately predict and recommend travel options based on the extracted features from the reviews. The high accuracy achieved by our model underscores the effectiveness of the BiLSTM architecture in capturing the nuances of the Malayalam language and understanding the subtle preferences expressed in travel reviews. The practical implications of our work are significant, as it offers a valuable tool for travelers seeking personalized recommendations based on their travel preferences. The use of the Malayalam language in this context expands the reach of recommender systems to a wider audience, catering specifically to individuals who prefer to consume content and make decisions in their native language.

Securing web applications against XSS and SQLi attacks using a novel deep learning approach

Modern web application development involves handling enormous amounts of sensitive and consequential data. Security is, therefore, a crucial component of developing web applications. A web application's security is concerned with safeguarding the data it processes. The web application framework must have safeguards to stop and find application vulnerabilities. Among all web application attacks, SQL injection and XSS attacks are common, which may lead to severe damage to Web application data or web functionalities. Currently, there are many solutions provided by various study for SQLi and XSS attack detection, but most of the work shown have used either SQL/XSS payload-based detection or HTTP request-based detection. Few solutions available can detect SQLi and XSS attacks, but these methods provide very high false positive rates, and the accuracy of these models can further be improved. We proposed a novel approach for securing web applications from both cross-site scripting attacks and SQL injection attacks using decoding and standardization of SQL and XSS payloads and HTTP requests and trained our model using hybrid deep learning networks in this paper. The proposed hybrid DL model combines the strengths of CNNs in extracting features from input data and LSTMs in capturing temporal dependencies in sequential data. The soundness of our approach lies in the use of deep learning techniques that can identify subtle patterns in the data that traditional machine learning-based methods might miss. We have created a testbed dataset of Normal and SQLi/XSS HTTP requests and evaluated the performance of our model on this dataset. We have also trained and evaluated the proposed model on the Benchmark dataset HTTP CSIC 2010 and another SQL/XSS payload dataset. The experimental findings show that our proposed approach effectively identifies these attacks with high accuracy and a low percentage of false positives. Additionally, our model performed better than traditional machine learning-based methods. This soundness approach can be applied to various network security applications such as intrusion detection systems and web application firewalls. Using our model, we achieved an accuracy of 99.84%, 99.23% and 99.77% on the SQL-XSS Payload dataset, Testbed dataset and HTTP CSIC 2010 dataset, respectively.

Federated Learning Framework for Human Activity Recognition Using Smartphones

Human activity recognition plays a vital role in various applications like healthcare, sports, and smart environments. … With the increase in the use of smart wearables and sensors, the concern for privacy has increased drastically in recognizing the activities of individuals. The legacy techniques send the data from millions of users to a central server where the processing and model generation happens. This increases the load on the server. Federated Learning is a promising method to handle sensitive and personal data in an efficient manner as well as use distributed techniques to reduce challenges that are faced in legacy approaches. Edge devices like smartphones and embedded devices extract the raw data, preprocess it, and generate a personalized model. These models are then aggregated at a central server using federated learning methods and their aggregate model is sent back to clients. Long Short-Term Memory (LSTM) is used for model generation as it can effectively capture temporal dependencies in sequential data. In order to show a real-world scenario, an AWS EC2 instance is used as a server, and laptops and smartphones are used as clients. Our results demonstrate that such methods can be adopted and can be used on a much larger scale. The performance of the global and local models is comparable to those attained by conventional methods. The utilization of federated learning shows a significant step forward in the development of human activity recognition. This approach allows for collaborative learning across multiple devices while also ensuring the privacy of users is maintained.

Prediction and Recognition of Epileptic Seizures Based on Artificial Intelligence

Repetitive seizures caused by abnormal brain activity are the hallmark of the common neurological illness epilepsy, which has a serious impact on the health of those affected. The introduction of artificial intelligence (AI) in recent years has ushered in novel ways of predicting and recognizing epileptic seizures, transforming the discipline of neurology. In particular, Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), Support Vector Machines (SVMs), and Extreme Learning Machines (ELMs) are examined in this research as potential AI approaches for the prediction and identification of epileptic episodes. The underlying working logic of these AI approaches is investigated, emphasizing their distinct strengths and capacities. RNNs and LSTMs are well-known for their ability to describe temporal dependencies in sequential data, whereas SVMs excel in robust classification and ELMs excel at rapid training and real-time prediction. Real-world case studies show how these AI models may be used to analyze electroencephalogram (EEG) data, extract relevant features, and distinguish between seizure and non-seizure states. Furthermore, the essay explores the ramifications of AI in the medical profession, emphasizing the possibility of prompt intervention, improved patient care, and resource optimization. The difficulties and ethical concerns regarding AI in healthcare are also discussed. Finally, this piece highlights AI's promising role in predicting and identifying epileptic seizures, providing a glimpse into the future of personalized, data-driven healthcare. The combination of AI approaches opens the door to more accurate and timely seizure forecasts, increasing the quality of life for those living with epilepsy and revolutionizing the landscape of neurological healthcare.

Machine Learning Algorithm for Stock Market Prediction – A Comparison

The prediction about the stock market serves as an effort to forecast the value of the market, an individual stock, or a particular industrial sector. The prediction or forecast is usually done by using several approaches and analyzing the fundamental or technical details of an industry, an economy, or both. Predicting stock markets is very essential, as successful prediction can help in proper decision-making as well as in increasing the profit of the business. As prediction of the stock market is a bit complicated and challenging, conventional methods do not consistently forecast the changes with absolute certainty. For this reason, the proposed study has come up with a comparison of the machine learning models for forecasting the market changes. The aim of the study is to compare two machine learning models: the decision tree (DT) algorithm, which excels at handling nonlinear relationships and feature interaction, and the long short-term memory (LSTM) algorithm, which could efficiently capture long-range dependencies in sequential data. The Yahoo Finance dataset was used for performing the comparison. The results observed will be utilized to analyze the stock expenses and their predictions in the future.

RNN-BIRNN-LSTM based spectrum sensing for proficient data transmission in cognitive radio

With the advancements that are taking place in the wireless communications field, the number of users who are utilizing resources is also increasing; as a result, the wireless spectrum is scarce. In this article, RNN-BIRNN-LSTM with Gaussian noise (RBRLG)-based spectrum sensing (SS) for QAM16, CPFSK, QPSK, and BPSK modulation schemes has been proposed. Recurrent Neural Networks for sequential data use recurrent connections to capture temporal dependencies; BIRNN extends RNN by processing input in both forward and backward directions, capturing past and future context; and finally, LSTM, using specialized memory cells, efficiently manages long-term dependencies in sequential data. In order to create a spectrum sensing model, RNN units, BIRNN units, and LSTM units were cascaded in this paper. Open-source dataset RadioML2016.10B has been used for the investigation. The experimental results show that the proposed RBRLG-based SS has higher accuracy on the dataset especially at -20 dB, a lower probability of miss detection percentage of 7.19 %, and a lower sensing error (SE) percentage of 10.80 % for QAM16. The evaluation of performance indicators for our suggested model, such as the F1 Score, Jaccard Index, and Matthew's correlation coefficient, demonstrates that the proposed model provides improved SS performance.

Stock Price Prediction Using Machine Learning

Abstract—As the need for precise and fast stock price projections is growing as financial markets continue to become more complicated. The use of Long Short-Term Memory (LSTM) neural networks for stock price forecasting is investigated in this paper. LSTMs are especially important for time series prediction tasks because they are good at capturing temporal dependencies in sequential data. In this study, LSTM models are trained and evaluated using historical stock price data while taking into account a variety of input features and hyperparameters. By contrasting the performance of LSTM models with conventional machine learning techniques, the advantages and disadvantages of each methodology are brought to light. The findings showed that LSTM models offer greater predictive capabilities, highlighting its potential in improving investment decisions and furthermore, the analysis of IBM Company Stock Price Values and model performance configurations add to the expanding body of knowledge about using deep learning approaches for financial forecasting. Keywords—Machine Learning, Neural Networks, RNN, Linear Regression, LSTM, Supervised Learning.

Analysis of spectrum sensing using deep learning algorithms: CNNs and RNNs

Spectrum sensing is a critical component of cognitive radio systems, enabling the detection and utilization of underutilized frequency bands. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs), in particular, have showed promise in recent years for enhancing the precision and effectiveness of spectrum sensing. This paper presents an overview of spectrum sensing using CNNs and RNNs and their performance in cognitive radio systems. Furthermore, the paper delves into the spectrum sensing performance of RNNs, particularly for processing time-series data. RNNs are capable of capturing temporal dependencies in sequential data, which is essential for spectrum sensing tasks where signals evolve over time. Further, the parakeets such as probability of detection (Pd), Probability of false alarm (PFA), Bite Error rate (BER) and Power spectral density (PSD) are analysed for spectrum sensing algorithms. RNNs and CNNs, two examples of deep learning methods (DLM), performed better than more traditional approaches.

Implementation of Efficient Intrusion Detection of Imbalanced Network Traffic using Deep Learning

A crucial component of network security is intrusion detection, but this task can be difficult when dealing with statistics on the distribution of network traffic, where benign cases exceed malicious ones by a large margin. The goal of this project is to create a powerful intrusion detection system using a recurrent neural network (RNN) to address this problem. RNNs are excellent at identifying temporal dependencies in sequential data, which makes them useful for examining network traffic patterns. The suggested methodology calls for preprocessing the data, balancing the dataset, feature extraction, choosing an RNN model, training, assessing, adjusting, and monitoring in real time. The network traffic data is cleaned and formatted during the preprocessing stage, and balancing algorithms make sure the RNN is trained on a representative dataset. It then extracts pertinent aspects, including behavioral patterns and packet header data. Effective intrusion detection requires the use of an appropriate RNN architecture, such as LSTM or GRU. The model is subsequently trained, assessed using a range of criteria, and refined through iterations. The optimized RNN model is then implemented for real-time intrusion detection, monitoring network traffic continuously and producing alerts for probable intrusions. This research seeks to improve the precision and efficacy of network security systems, maintaining the integrity of crucial information in the linked world of today, by utilizing RNNs for intrusion detection in imbalanced network trafficking. Keywords IDS, Imbalanced Network Traffic, Deep Learning, Recurrent Neural Network.

DEEP LEARNING ALGORITHMS FOR DETECTION AND CLASSIFICATION OF CONGENITAL BRAIN ANOMALY

Congenital brain anomalies are structural abnormalities that occur during fetal development and can have a significant impact on an individual neurological function. Detecting and classifying these anomalies accurately and efficiently is crucial for early diagnosis, intervention, and treatment planning. In recent years, recurrent neural networks (RNNs) have emerged as powerful tools for analyzing sequential and time-series data in various domains, including medical imaging. This research presents an overview of RNN-based algorithms for the detection and classification of congenital brain anomalies. Specifically, Long Short-Term Memory (LSTM) networks and Convolutional LSTM networks have demonstrated great potential in this domain. LSTMs excel at capturing long-range dependencies in sequential data and mitigating the vanishing gradient problem, making them well-suited for analyzing brain scans or other medical imaging sequences. Convolutional LSTM networks combine the strengths of convolutional neural networks (CNNs) and LSTMs, enabling them to extract spatial features from brain images while preserving temporal dependencies. The application of RNN algorithms in the detection and classification of congenital brain anomalies shows promising results, enabling accurate and timely identification of these abnormalities. However, further research is needed to validate and refine these algorithms, improve their interpretability, and enhance their clinical utility in real-world scenarios.