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Enhanced multilevel autism classification for children using eye-tracking and hybrid CNN-RNN deep learning models

Neural Computing and applications 2024年 1-24页

作者： Cheekaty, Suresh Muneeswari, G. School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India

This article aims to improve the detection of ASD in children using ET data and advanced ML techniques. ASD, a neurodevelopmental disorder characterized by impairments in social communication, interaction, and repetitive behaviors, typically manifests before the age of three. Early diagnosis is crucial for timely and effective interventions. ET data have revealed distinct gaze patterns in children with ASD, such as diminished attention to social cues and increased fixation on repetitive stimuli. However, current methods of ET data analysis are largely manual and lack scalability for widespread clinical screening. To address these limitations, we propose a novel, scanpath-based ASD detection method that identifies atypical gaze behaviors through dynamic changes in visual attention. We extract four sequential features from scanpaths and analyze variations in feature space across different ASD severity levels low, mild, moderate, and severe using the MultiMatch and DTW similarity metrics. Our analysis reveals that children with ASD exhibit unique and highly individualized gaze patterns when compared to TD children. Notable differences are observed in attention duration and vertical gaze distribution, providing key insights into ASD-related visual behaviors. For classification, we employ a hybrid CNN-RNN model, which significantly outperforms traditional ML methods. The CNN-RNN model achieves an accuracy of 97%, recall of 98.24%, and an F1-score of 97.04% using the feature set (x, d, y). In comparison, models based on GRU and 2-LSTM networks show competitive accuracies of 92% and 90%, respectively. However, RFC and XGBoost models underperform, with accuracies ranging between 70.25 and 80.80%. These findings demonstrate the efficacy of DL approaches, particularly the CNN-RNN hybrid model, in accurately classifying ASD based on ET data, emphasizing their potential to enhance diagnostic accuracy. The proposed scalable method for ASD detection holds promise for improving ea

关键词： Diseases

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Fetal Health Classification using LightGBM with Grid Search Based Hyper Parameter Tuning

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Recent Patents on engineering 2025年第1期19卷 50-63页

作者： Nagabotu, Vimala Namburu, Anupama School of Computer Science Engineering VIT-AP University Guntur Andhra Pradesh Amaravati522337 India

Background: Fetal health monitoring throughout pregnancy is challenging and complex. Complications in the fetal health not identified at the right time lead to mortality of the fetus as well the pregnant women. Hence, obstetricians check the fetal health state by monitoring the fetal heart rate (FHR). Cardiotocography (CTG) is a technique used by obstetricians to access the physical well-being of fetal during pregnancy. It provides information on the fetal heart rate and uterine respiration, which can assist in determining whether the fetus is normal or suspect or pathology. CTG data has typically been evaluated using machine learning (ML) algorithms in predicting the wellness of the fetal and speeding up the detection process. Methods: In this work, we developed LightGBM with a Grid search-based hyperparameter tuning model to predict fetal health classification. The classification results are analysed quantitatively using the performance measures, namely, precision, Recall, F1-Score, and Accuracy Comparisons were made between different classification models like Logistic Regression, Decision Tree, Random Forest, k-nearest neighbors, Bagging, ADA boosting, XG boosting, and LightGBM, which were trained with the CTG Dataset obtained by the patented fetal monitoring system of 2,216 data points from pregnantwomen in their third trimester available in the Kaggle dataset. The dataset contains three classes: normal, suspect, and pathology. Our proposed model will give better results in predicting fetal health classification. Results: In this paper, the performance of the proposed algorithm LightGBM is compared and experimented with various Machine learning Techniques namely LR, DT, RF, KNN, Boosting, Ada boosting, and XG Boost and the classification accuracy of the respective algorithms are 84%, 94%, 93%, 88%, 94%, 89%, 96%. The LightGBM achieved a performance of 97% and outperforms the former models. Conclusion: The LightGBM-based fetal health classification has been pres

关键词： Fetal monitoring

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Deep Convolutional Neural Networks for South Indian Mango Leaf Disease Detection and Classification

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computers, Materials & Continua 2023年第12期77卷 3593-3618页

作者： Shaik Thaseentaj S.Sudhakar Ilango School of Computer Science and Engineering VIT-AP UniversityAmaravatiVijayawada522237India

The South Indian mango industry is confronting severe threats due to various leaf diseases,which significantly impact the yield and quality of the *** management and prevention of these diseases depend mainly on their early identification and accurate *** central objective of this research is to propose and examine the application of Deep Convolutional Neural Networks(CNNs)as a potential solution for the precise detection and categorization of diseases impacting the leaves of South Indian mango *** study collected a rich dataset of leaf images representing different disease classes,including Anthracnose,Powdery Mildew,and Leaf *** maintain image quality and consistency,pre-processing techniques were *** then used a customized deep CNN architecture to analyze the accuracy of South Indian mango leaf disease detection and *** proposed CNN model was trained and evaluated using our collected *** customized deep CNN model demonstrated high performance in experiments,achieving an impressive 93.34%classification *** result outperformed traditional CNN algorithms,indicating the potential of customized deep CNN as a dependable tool for disease *** proposed model showed superior accuracy and computational efficiency performance compared to other basic CNN *** research underscores the practical benefits of customized deep CNNs for automated leaf disease detection and classification in South Indian mango *** findings support deep CNN as a valuable tool for real-time interventions and improving crop management practices,thereby mitigating the issues currently facing the South Indian mango industry.

关键词： South Indian mango tree customized deep CNN mango leaf disease classification accuracy

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LuNet-LightGBM:An Effective Hybrid approach for Lesion Segmentation and DR Grading

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computer Systems science & engineering 2023年第7期46卷 597-617页

作者： Sesikala Bapatla J.Harikiran School of Computer Science and Engineering VIT-AP UniversityAmaravatiAndhra Pradesh522237India School of Computer Science and Engineering VIT-AP UniversityAmaravathi522237India

Diabetes problems can lead to an eye disease called Diabetic Retinopathy(DR),which permanently damages the blood vessels in the *** not treated early,DR becomes a significant reason for *** identify the DR and determine the stages,medical tests are very labor-intensive,expensive,and *** address the issue,a hybrid deep and machine learning techniquebased autonomous diagnostic system is provided in this *** proposal is based on lesion segmentation of the fundus images based on the LuNet *** a Refined Attention Pyramid Network(RapNet)is used for extracting global and local *** increase the performance of the classifier,the unique features are selected from the extracted feature set using Aquila Optimizer(AO)***,the LightGBM model is applied to classify the input image based on the *** investigations have been done to analyze the performance of the proposed framework on three publically available datasets(MESSIDOR,apTOS,and IDRiD)using several performance metrics such as accuracy,precision,recall,and *** proposed classifier achieves 99.29%,99.35%,and 99.31%accuracy for these three datasets *** outcomes of the experiments demonstrate that the suggested technique is effective for disease identification and reliable DR grading.

关键词： Deep learning diabetic retinopathy DR grading lightGBM LuNet lesion segmentation

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Smart Fraud Detection in E-Transactions Using Synthetic Minority Oversampling and Binary Harris Hawks Optimization

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computers, Materials & Continua 2023年第5期75卷 3171-3187页

作者： Chandana Gouri Tekkali Karthika Natarajan School of Computer Science and Engineering VIT-AP University522237Andhra PradeshIndia

Fraud Transactions are haunting the economy of many individuals with several factors across the *** research focuses on developing a mechanism by integrating various optimized machine-learning algorithms to ensure the security and integrity of digital *** research proposes a novel methodology through three ***,Synthetic Minority Oversampling Technique(SMOTE)is applied to get balanced ***,SMOTE is fed to the nature-inspired Meta Heuristic(MH)algorithm,namely Binary Harris Hawks Optimization(BinHHO),Binary Aquila Optimization(BAO),and Binary Grey Wolf Optimization(BGWO),for feature *** has performed well when compared with the other ***,features from BinHHO are fed to the supervised learning algorithms to classify the transactions such as fraud and *** efficiency of BinHHO is analyzed with other popular MH *** BinHHO has achieved the highest accuracy of 99.95%and demonstrates amore significant positive effect on the performance of the proposed model.

关键词： Metaheuristic algorithms K-nearest-neighbour binary aquila optimization binary grey wolf optimization BinHHO optimization support vector machine

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Dynamic Behavior-Based Churn Forecasts in the Insurance Sector

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computers, Materials & Continua 2023年第4期75卷 977-997页

作者： Nagaraju Jajam Nagendra Panini Challa School of Computer Science and Engineering(SCOPE) VIT-AP UniversityAmaravati522237India

In the insurance sector, a massive volume of data is being generatedon a daily basis due to a vast client base. Decision makers and businessanalysts emphasized that attaining new customers is costlier than retainingexisting ones. The success of retention initiatives is determined not only bythe accuracy of forecasting churners but also by the timing of the *** works on churn forecast presented models for anticipating churnquarterly or monthly with an emphasis on customers’ static behavior. Thispaper’s objective is to calculate daily churn based on dynamic variations inclient behavior. Training excellent models to further identify potential churningcustomers helps insurance companies make decisions to retain customerswhile also identifying areas for improvement. Thus, it is possible to identifyand analyse clients who are likely to churn, allowing for a reduction in thecost of support and maintenance. Binary Golden Eagle Optimizer (BGEO)is used to select optimal features from the datasets in a preprocessing *** a result, this research characterized the customer’s daily behavior usingvarious models such as RFM (Recency, Frequency, Monetary), MultivariateTime Series (MTS), Statistics-based Model (SM), Survival analysis (SA),Deep learning (DL) based methodologies such as Recurrent Neural Network(RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU),and Customized Extreme Learning Machine (CELM) are framed the problemof daily forecasting using this description. It can be concluded that all modelsproduced better overall outcomes with only slight variations in performancemeasures. The proposed CELM outperforms all other models in terms ofaccuracy (96.4).

关键词： Customer churn customized extreme learning machine deep learning survival analysis RFM MTS SM BGEO

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Xception-Fractalnet:Hybrid Deep Learning Based Multi-Class Classification of Alzheimer’s Disease

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computers, Materials & Continua 2023年第3期74卷 6909-6932页

作者： Mudiyala aparna Battula Srinivasa Rao School of Computer Science and Engineering VIT-AP UniversityAndhra Pradesh522237India

Neurological disorders such as Alzheimer’s disease(AD)are very challenging to treat due to their sensitivity,technical challenges during surgery,and high *** complexity of the brain structures makes it difficult to distinguish between the various brain tissues and categorize AD using conventional classification ***,conventional approaches take a lot of time and might not always be ***,a suitable classification framework with brain imaging may produce more accurate findings for early diagnosis of *** in this paper,an effective hybrid Xception and Fractalnet-based deep learning framework are implemented to classify the stages of AD into five ***,a network based on Unet++is built to segment the tissues of the ***,using the segmented tissue components as input,the Xception-based deep learning technique is employed to extract high-level ***,the optimized Fractalnet framework is used to categorize the disease condition using the acquired *** proposed strategy is tested on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset that accurately segments brain tissues with a 98.45%of dice similarity coefficient(DSC).Additionally,for themulticlass classification of AD,the suggested technique obtains an accuracy of 99.06%.Moreover,ANOVA statistical analysis is also used to evaluate if the groups are significant or *** findings show that the suggested model outperforms various stateof-the-art methods in terms of several performance metrics.

关键词： Fractalnet deep learning xception unet++ alzheimer’s disease magnetic resonance imaging

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Transfer Learning Based Yogic Posture Recognition System Using Deep Pre-trained Features

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SN computer science 2024年第6期5卷 746页

作者： Rajendran, Arun Kumar Sethuraman, Sibi Chakkaravarthy School of Computer Science and Engineering VIT-AP University School of Computer Science & amp Engineering and Center of Excellence Artificial Intelligence and Robotics (AIR) VIT-AP University

Yoga is a healthy exercise that focuses on physical, psychological, and divine connections. However, engaging in yoga while adopting poor postures might result in health issues like muscle discomfort and sprains. Discovering the specific posture amid an activity is still a very challenging and complicated task in artificial intelligence. With the aid of transfer learning, a model which can identify the yogic posture from images has been constructed in this work. In this approach, sixteen postures of yogic data are collected, and then pre-processing techniques are used to resize the images. This work employs the transfer learning method to extract the features from popular convolutional neural network architectures such as VGG16, VGG19, InceptionV3, Xception, MobileNet, and MobileNetV2;all are pre-trained on the ImageNet database. Extracted features are fed to machine learning models like Naive Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) for classification. The linear SVM-based classifier, trained on features extracted by MobileNet, performs better than its rivals with an accuracy of 99.92%. In total, six transfer learning models are exploited in this yogic posture classification approach and yield encouraging results in terms of prediction accuracy. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： InceptionV3 MobileNet MobileNetV2 NB RF SVM Transfer learning VGG16 VGG19 Xception XGBoost Yoga posture classification

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Fusion of Feature Ranking Methods for an Effective Intrusion Detection System

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computers, Materials & Continua 2023年第8期76卷 1721-1744页

作者： Seshu Bhavani Mallampati Seetha Hari School of Computer Science and Engineering VIT-AP UniversityAndhra PradeshIndia Center of Excellence AIand RoboticsVIT-AP UniversityAndhra PradeshIndia

Expanding internet-connected services has increased cyberattacks,many of which have grave and disastrous *** Intrusion Detection System(IDS)plays an essential role in network security since it helps to protect the network from vulnerabilities and *** extensive research was reported in IDS,detecting novel intrusions with optimal features and reducing false alarm rates are still ***,we developed a novel fusion-based feature importance method to reduce the high dimensional feature space,which helps to identify attacks accurately with less false alarm ***,to improve training data quality,various preprocessing techniques are *** Adaptive Synthetic oversampling technique generates synthetic samples for minority *** the proposed fusion-based feature importance,we use different approaches from the filter,wrapper,and embedded methods like mutual information,random forest importance,permutation importance,Shapley Additive exPlanations(SHap)-based feature importance,and statistical feature importance methods like the difference of mean and median and standard deviation to rank each feature according to its *** by simple plurality voting,the most optimal features are *** the optimal features are fed to various models like Extra Tree(ET),Logistic Regression(LR),Support vector Machine(SVM),Decision Tree(DT),and Extreme Gradient Boosting Machine(XGBM).Then the hyperparameters of classification models are tuned with Halving Random Search cross-validation to enhance the *** experiments were carried out on the original imbalanced data and balanced *** outcomes demonstrate that the balanced data scenario knocked out the imbalanced ***,the experimental analysis proved that our proposed fusionbased feature importance performed well with XGBM giving an accuracy of 99.86%,99.68%,and 92.4%,with 9,7 and 8 features by training time of 1.5,4.5 and 5.5 s on Network Security Laborat

关键词： Cyber security feature ranking imbalance preprocessing IDS SHap

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Improved Monarchy Butterfly Optimization Algorithm (IMBO): Intrusion Detection Using Mapreduce Framework Based Optimized ANU-Net

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computers, Materials & Continua 2023年第6期75卷 5887-5909页

作者： Kunda Suresh Babu Yamarthi Narasimha Rao School of Computer Science and Engineering VIT-AP UniversityAmaravathi522237Andhra PradeshIndia

The demand for cybersecurity is rising recently due to the rapid improvement of network *** a primary defense mechanism,an intrusion detection system(IDS)was anticipated to adapt and secure com-puting infrastructures from the constantly evolving,sophisticated threat ***,various deep learning methods have been put forth;however,these methods struggle to recognize all forms of assaults,especially infrequent attacks,because of network traffic imbalances and a shortage of aberrant traffic samples for model *** work introduces deep learning(DL)based Attention based Nested U-Net(ANU-Net)for intrusion detection to address these issues and enhance detection *** this IDS model,the first data preprocessing is carried out in three stages:duplication elimi-nation,label transformation,and data *** the features are extracted and selected based on the Improved Flower Pollination Algorithm(IFPA).The Improved Monarchy Butterfly Optimization Algorithm(IMBO),a new metaheuristic,is used to modify the hyper-parameters in ANU-Net,effectively increasing the learning rate for spatial-temporal information and resolving the imbalance *** the use of parallel programming,the MapReduce architecture reduces computation complexity while signifi-cantly accelerating *** publicly available data sets were used to evaluate and test the *** investigational outcomes suggest that the proposed technique can more efficiently boost the performances of IDS under the scenario of unbalanced *** proposed method achieves above 98%accuracy and classifies various attacks significantly well compared to other classifiers.

关键词： Intrusion detection system(IDS) attention based nested U-Net

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