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检索条件"机构=Computer Science and Engineering Nitte Meenakshi Intitute of Technology"

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A Gaussian Multivariate Mixture Model and VGG19 Model for Classification and Detection of Motor Vehicle 2

A Gaussian Multivariate Mixture Model and VGG19 Model for Cl...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Deepa, N. Kumar, Bura Vijay Sowmya, M. Al-Jawahry, Hassan M. Bhushan, Yannam Bharath Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Sr University School of Computer Science and Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of Ai & Ds Bengaluru India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Gokaraju Rangaraju Institute of Engineering and Technology Department of Ai&ml Bachupally Hyderabad India

ISBN: (纸本)9798350372892

In daily life, the moving vehicle plays a significant part in effective system of transportation and traffic visual surveillance systems. Most critical task is to detect the vehicle image and most of the existing method, concentrated on maximizing the prediction percentage only. Also it lacks in classifying the imbalance data. To overcome this kind of problem, the Gaussian Multivariate Mixture Model (G-MMM) and VGG19 model is implemented to detect and classify the vehicle types. The proposed method contained four process namely, preprocessing performed by Flexible Histogram Equalization method (FHE), vehicle detection using G-MMM, feature extraction using Steerable Pyramid Transform (SPT) and Weber Local Descriptor (WLD) and classification using VGG19 ensemble deep learning model. G-MMM significantly improves detection sensitivity and it transformed the correlation between attributes through the training process. VGG19 is pretrained model, so it allows the fast learning recognition on new images of vehicle. The proposed method tested on Beijing Institute of technology (BIT) dataset and MIO vision traffic camera dataset. When compared to the others methods like modified AlexNet, Deep Convolution Neural Network (DCNN), this proposed G-MMM-VGG19 gives 99.47 % of output rate in classification phase. © 2024 IEEE.

关键词： Equalizers

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Levy Flight based Gannet Optimization Algorithm with Euclidean Distance-Support Vector Machine for Predictive Analysis of Mental Health Crisis using Social Media Data 2

Levy Flight based Gannet Optimization Algorithm with Euclide...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Shaikh, Anwar Ahamed Al-Farouni, Mohammed Manjula, B.M. Sivagami, S. Devi, S. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Chemistry New Prince Shri Bhavani College of Engineering and Technology Chennai India Erode India

ISBN: (纸本)9798331504960

The analysis of mental health crisis helps to know the mindset of the social media users so that the applications are modified based on the user requirements. The existing algorithms failed perform the predictive analysis of mental health crisis using social media data due to lack of dimensionality handling ability, that reduced the performance the model. The LF-GOA with ED-SVM is proposed to overcome the problem of existing algorithms for the mental health crisis predictive analysis. The twitter data is used as input for the predictive analysis model. The data was pre-processed using data cleaning, stemming, tokenization and stop word removal techniques. The n-gram and Term Frequency-Inverse Document Frequency (TF-IDF) were used to get the feature vectors and the Levy Flight based Gannet Optimization Algorithm (LF-GOA) was used for the selection of significant features from the data. At last, the Euclidean Distance -Support Vector Machine (ED-SVM) classifier was used for the prediction of mental health crisis. The performance of the proposed LF-GOA with ED-SVM model is evaluated and obtained better results of 98.79% accuracy, 97.81% precision, 97.45% recall and 97.62% f1-score for sentiment 140 dataset compared to the existing Gated Attention Recurrent Network (GARN) method. © 2024 IEEE.

关键词： Vectors

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Chaotic Water Wave Optimization and Support Vector Machine of Role-Based Access Control Policies in Enterprise IAM

Chaotic Water Wave Optimization and Support Vector Machine o...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Priyanka, K. Harshini, Chikka Aluvala, Srinivas Abbas, Haider Mohammed Ram Kumar, Ramaswamy Padmanathan Nitte Meenakshi Institute of Technology Department of Ise Bengaluru India Department of Cse Hyderabad India Sr University Department of Computer Science and Engineering Warangal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Gokaraju Rangaraju Institute of Engineering and Technology Department of Ai&ml Bachupally Hyderabad India

ISBN: (纸本)9798350375442

The access control state of a system is complex, and Machine Learning (ML) based access control decision engines have demonstrated advantages of policies. However, struggle to effectively implement Role-Based Access Control (RBAC) policies because require large enterprises Identity and Access Management (IAM) system with a high volume of users and access logs. This paper, proposed Chaotic Water Wave Optimization (CWWO) algorithm combined with a Support Vector Machine (SVM) to classify users based on selected features, leading to more accurate and secure role to assignment. The dataset used is obtained from amazon and pre-processing is done using Min-max normalization, which scales the feature to a specific range, ensuring that different features are on same scale. Feature selection is performed using the CWWO algorithm, which helps in selecting relevant feature and reduce dimensionality. The classification using SVM for binary classification, which assigns relevant feature and handle large amounts of data, achieving better accuracy. The proposed ensemble model technique for ship motion detection is achieving better outcomes such as high accuracy of 0.98, precision of 0.97, F1-score of 0.96 and recall of 0.98 and respectively. The existing technique such Random Forest (RF) and Decision Tree (DT) are evaluated of proposed method. © 2024 IEEE.

关键词： Random forests

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Land Cover Classification in High-Resolution Satellite Images Using Vision Transformer and ConvNeXt Approach 2

Land Cover Classification in High-Resolution Satellite Image...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Gadiparthi, Manjunath Al-Fatlawy, Ramy Riad Kulkarni, Megha Malathy, V. Anbunathan, R. Malla Reddy College of Engineering Department of Computer Science and Engineering Hyderabad Secunderabad India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Civil Engineering Bengaluru India Sr University Department of Ece Warangal India Sa Engineering College Department of Mathematics Chennai India

ISBN: (纸本)9798350373110

Nowadays, the urbanization has been increasing where planning of land, monitoring environment and managing land is a critical task. However, the exposure to noise, low generalization ability and complexity in geographical distributions has led to low accuracy in classification. The proposed Vision Transformer and ConvNeXt (ViT-ConvNeXt) method provides a robust framework for efficient classification of land cover. Initially, the high-resolution satellite data with different values is normalized by min-max normalization technique to prevent imbalance of the model and transformed to a common scale. The normalized data is further analyzed for spatial information by Vision Transformer (ViT) method that provides strong global feature extraction. The hyperparameters of the classification model are tuned by a computationally efficient Bayesian Optimization Algorithm (BOA) that offers balance between exploration and exploitation and provides better solutions. The ConvNeXt is a simple, accurate and efficient image classification model that comprises of various normalization and attention mechanisms that enables the model to capture complex patterns, prevents overfitting and provides accurate classification. The proposed model is evaluated on EuroSAT dataset and achieved 99.38% accuracy when compared against existing Morphologically Augmented Finetuned DenseNet-121(MAFDN), Shapley additive explanations (SHAPs) approaches. © 2024 IEEE.

关键词： Normal distribution

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Real-Time Blood Pressure Monitoring and Hypertension Prediction based on Multitask Temporal Convolutional Network 1

Real-Time Blood Pressure Monitoring and Hypertension Predict...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Musnmusawi, Muntather Deepa, R. Shankar, Smitha Sharath Nirupashri, G. Khanam, P. Katheeja The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq St.Joseph's Institute of Technology Department of Information Technology Chennai India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Bio Medical Engineering Mamallapuram India Mohan Babu University Department of Computer Science and Engineering Tirupati India

ISBN: (纸本)9798331529246

In recent days, blood pressure monitoring and hypertension prediction have become essential for effective measurement and management. Continuously tracking blood pressure helps in making informed decisions like changing medication or dosage. However, some challenges are raised while monitoring such as blood pressure readings being prone to errors, variability in measurements if a patient moves and environmental noise. Therefore, this research proposes Multitask Temporal Convolutional Network (MTCN) for realtime blood pressure monitoring and hypertension prediction. Initially, data is collected from Medical Information Mart for Intensive Care III (MIMIC-III) and preprocessed by using Min- Max Normalization which rescales numeric data to definite range. After that, features like time frequency coefficients, power spectral density, Scalogram features are extracted by using Continuous Wavelet Transform (CWT). Finally, blood pressure monitoring and hypertension prediction is done by using proposed MTCN which provides accurate hypertension prediction and personalized risk assessments. The proposed MTCN achieved better results in terms of Mean Absolute Error (MAE) of 2.01, Root Mean Square Error (RMSE) of 3.26 when compared with existing Random Forest Regression model. © 2025 IEEE.

关键词： Wavelet transforms

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Multi-Scale Feature Fusion Module for Change Detection in Remote Sensing Images 2

Multi-Scale Feature Fusion Module for Change Detection in Re...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Parameshachari, B.D. Alzubaidi, Laith H. Patil, Sujata N. Hemalatha, K.L. Pradhan, Manaswini Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India The Islamic University of Babylon Babylon Iraq Department of Radiation Oncology Thomas Jefferson University Philadelphia United States Department of Information Science and Engineering Sri Krishna Institute of Technology Bangalore India Department of Computer Science Fakir Mohan University Odisha India

ISBN: (纸本)9798331504960

Change detection refers to an identification of pixels that have undergone semantic changes among two remote sensing images captured at different times in a same area. The problem statement is that remote sensing images has high-resolution causes a challenge for current deep learning-based change and missed detection of small targets. It is overcome by the proposed method with Multi-scale Feature Fusion (MSFF) for feature fusion;semantic variation data is extracted from the Global Semantic Aggregation Module (GSAM);during a decoding phase, Attention Fusion Module (AFM) is applied to extract features;and Cross-scale Fusion Module (CFM) is used to enhance accuracy by removing background noise and the Levir-CD dataset is used. The numerical validation of this approach has a precision of 95.43%, a recall of 97.80%, an IoU of 93.49%, an OA of 99.67%, and a Kappa of 96.59%. The Attention-Guided Siamese Network (SAGNet), Multi-Stage Progressive Change Detection Network (MSP-CD), Teacher-Student Framework (TSF), and Global Attention Module (GAM) are the existing models;the limitations of these methods are overcome by the proposed method. © 2024 IEEE.

关键词： Change detection

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Sentiment Analysis in Social Media using Bidirectional Long Short Term Memory with Focus Loss function

Sentiment Analysis in Social Media using Bidirectional Long ...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Sivakumaran, A.R. Shnain, Ammar H. Lande, Jayapal Sindgi, Anupama Sruthi, Patlolla Department of Information Technology Hyderabad India College of Technical Engineering the Islamic University Department of Computers Techniques Engineering Najaf Iraq Sr University Department of Computer Science & Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India Gokaraju Rangaraju Institute of Engineering and Technology Department of Ai&ml Hyderabad India

ISBN: (纸本)9798350375442

Sentiment Analysis (SA), is often known as "Opinion Mining"which seeks to analyze sentiment polarities in opinion-based texts. Several Deep Learning (DL) approaches are developed to effectively solves the complex SA challenges. However, existing methods failed to identify the sentiments correctly due to contextual embedding's and different meaning for same word in a sentence or document. To overcome this, a Bidirectional - Long Short Term Memory with Focal Loss (Bi-LSTM-FL) function for SA in social media. The proposed Bi-LSTM-FL model enhanced the SA process by capturing the context from both past and future words in a sentence which lead to understand complex sentence structures. The twitter dataset is utilized and preprocessed by to enhance the text format for classification. The preprocessed texts are fed to Principal Component Analysis (PCA) for selecting optimal set of features and to reduce dimensionality of features. Finally, with the help of selected features the tweets are categorized as positive, negative or neutral effectively. The experimental results of proposed Bi-LSTM-FL achieved accuracy of 98.2% which is greater when compared to previous method such as Gated Attention Recurrent Network (GARN). © 2024 IEEE.

关键词： Long short-term memory

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Cardio-Vascular Disease Prediction using Machine Learning Techniques

Cardio-Vascular Disease Prediction using Machine Learning Te...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Konda, Srinivas Kar, Naresh Kumar Pulicherla, Padmaja Shivakanth, Gandla Ramachandra, C.A. Cmr Technical Campus Department of Data Science Hyderabad India Gitam Deemed to Be University Department of Cse Hyderabad India Hyderabad Institute of Technology and Management Department of Computer Science and Engineering Telangana India K L University Vadeeshwaram Department of Computer Science & Engineering Vijayawada India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350398465

The main goal of this study is to use Data Mining Method and Artificial Neural Network to develop a system that can automatically and rapidly predict the risk of coronary heart disease (ANN). The IRT Perundurai Medical College and Hospital's master health checkup data on occupational drivers were used to test this idea (PMCH). Analysis for risk identification is performed in the first stage of the hybrid approach suggested in this study, and level prediction is performed in the second. The sensitivity, specificity, precision, receiver operating curve, area under curve, 10-fold cross validation technique, and the F-measure are used for this investigation. The initial step of the study involves thinking about the most common and changeable dangers. Systolic blood pressure, diastolic blood pressure, and body mass index (BMI) are three biophysical variables, whereas fasting blood sugar, postprandial blood sugar, and triglyceride levels are three blood chemical factors (TG). All of these characteristics have a predetermined margin value that is based on WHO guidelines. Support Vector Machine (SVM), Naive Bayes (NB), and the C4.5 algorithm in Decision Tree are the three approaches used to categorize these variables and forecast the risk (DT). The C4.5 algorithm fared best in forecasting CHD risk when the three approaches were compared using the performance metrics, as discovered by the investigation. The decision tree C4.5 method outperformed the other two classifiers with an improved 99.5% accuracy and 99.67% sensitivity. The increased percentage demonstrates that the Decision tree method delivered consistent results that were better to those produced by the Naive Bayes and SVM models. © 2023 IEEE.

关键词： Decision trees

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An Efficient SFLA and CUCKOO Search Hybridization for Source Distribution in Cloud Computing

An Efficient SFLA and CUCKOO Search Hybridization for Source...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Malsoru, Vankudothu Patil, Amareshwari Kusuma, S.M. Shivakanth, Gandla Samanvita, N. Cmr Technical Campus Department of It Hyderabad India Pda College of Engineering Department of Computer Science & Engineering Kalaburagi India Ramaiah Institute of Technology Department of Electronics and Telecommunication Engineering Bengaluru India K L University Vadeeshwaram Department of Computer Science & Engineering Vijayawada India Nitte Meenakshi Institute of Technology Department of Electrical & Electronics Engineering Bengaluru India

ISBN: (纸本)9798350398465

Cloud calculating is a computing prototype that offers inexpensive, scalable computing resources like CPU, storage, and network bandwidth. It provides a pay-as-you-go model for gaining instantaneous online access to a shared pool of resources. This strategy is proposed to cut down on the required amount of computing time. In the most recent paper, authors employ a hybrid approach to resource allocation by combining two powerful optimization algorithms: the Shuffled Frog Leaping Algorithm (SFLA) and the Cuckoo Search (CS) Algorithm. With this algorithm, we can get around the computational time constraints of the HABCCS algorithm and the krill herd algorithm. It's easily scalable, so even niche uses can benefit. The SFLA has the potential to generate a global optimization solution and comes with the added benefits of faster convergence and simpler implementation. Algorithms from the field of computer science are used in difficult situations because of their advantageous simplicity in evaluation. The knapsack problems that normally crop up during the phase of allocating resources are also addressed. The efficacy of the third strategy is evaluated. The investigational consequences demonstrate the greater presentation of the projected method equated to the state-of-the-art methods. © 2023 IEEE.

关键词： Cloud computing

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Crash Injury Prediction using Bidirectional Gated Recurrent Unit with Multi Head Attention Network 2

Crash Injury Prediction using Bidirectional Gated Recurrent ...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Rana Veer Samara Sihman Bharattej, R. Shnain, Ammar H. Al-Fatlawy, Ramy Riad Prabhakar, P.B. Edwin Chakraborty, Subhra Department of Computer Science Saint Leo University St Leo United States Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Computer Science and Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (纸本)9798331504960

In this era, Crash injury prediction is a crucial area of research aimed at reducing road traffic mortalities and injuries, which remain a leading global cause of death. Previous researchers have suggested various methods but still there is need to improve the accuracy and reliability of these predictions by analyzing large, multi-dimensional datasets from various sources, road conditions, and driver data. Therefore, this paper suggests an approach to predict the severity of injury occurred during the vehicle crash. Firstly, the data is collected from the Ministry of transport and logistics services. Then, the accumulated data is processed through a sampling technique namely Adaptive Synthetic (ADASYN). The sampled data is embedded into the model combined with correlation based selection with Recursive Feature Elimination (RFE) techniques for extraction of suitable features and selection of optimal attributes based on ranking method. Then based on the selected optimal features, injury prediction is initiated by employing Bidirectional Gated Recurrent Unit (BiGRU) with Multi-head Attention Network (MAN) technique. Finally the overall interpretation of the model is developed by SHapley Additive exPlanations (SHAP). The proposed model namely BiGRU-MAN-SHAP resulted with 93.63% accuracy. © 2024 IEEE.

关键词： Highway accidents

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