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

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Vortex Search Optimizer based Hybrid Watermarking Scheme for Securing the Medical Images 3

Vortex Search Optimizer based Hybrid Watermarking Scheme for...

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3rd IEEE Mysore Sub Section International Conference, MysuruCon 2023

作者： Murthy, G N Keshava Nalini, B.M. Madhura, G.K. Nayana, S.N. Siddaganga Institute of Technology Department of Electronics and Instrumentation Engineering Karnataka Tumakuru India East West Institute of Technology Department of Computer Science and Engineering Bengaluru560091 India Nitte Meenakshi Institute of Technology Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798350340358

Preventing the duplication, modification, and claim of unauthorised ownership of digital content is one of the major issues caused by the growing use of computer capabilities. Watermarking is one way to put a visible or invisible watermark on a digital document in order to later show that the document is legitimate or belongs to its owner. concentrating on security issues in the storage and broadcast of images in the medical info scheme, as well as the special requirements for medical images for the protection of lesion regions. In this study, a hybrid watermarking strategy was presented utilising the Slantlet transform, randomised singular value decomposition, and optimisation methods (Vortex Search algorithm (VSA)) that are modelled after natural processes. The XOR encryption method is used to encrypt the watermark image. Extensive testing demonstrates that our novel approach works better than the current approaches based on the NC, SSIM, and PSNR. The PSNR of the suggested technique is between 58 and 59 dB, suggesting increased performance, and at a scale factor of 0.06, the SSIM and NC standards of the watermarked picture and the extracted watermark are nearly equal to. The recommended method outperforms past studies in the field in terms of usability, robustness, security, and invisibility, as well as its ability to repel local nonlinear geometric attacks. © 2023 IEEE.

关键词： Singular value decomposition

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Vehicle Type Detection and Classification Using Improved Relief Feature Selection Algorithm and Bidirectional Gated Recurrent Unit 2

Vehicle Type Detection and Classification Using Improved Rel...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Monica, K.M. Al-Farouni, Mohammed Madhura, G.K. Ghamya, K. Geethalakshmi, M. Vellore Institute Of Technology School Of Computer Science And Engineering Chennai India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of Artificial Intelligence And Machine Learning Bengaluru India Tirupati India Kongunadu College Of Engineering And Technology Department Of Electronics And Communication Engineering Trichy India

ISBN: (纸本)9798350381665

Nowadays, Traffic density estimation can be utilized to control traffic light signals for managing the traffic effectively. Vehicle Type Classification (VTC) is used in this traffic density. Because of the factors like minimal interclass variance and maximum intraclass variation, the VTC is challenging which is handled by proposed Improved Relief Feature Selection Algorithm and Bi-directional Gated Recurrent Unit (IRFSA-Bi-GRU) method. Bi-directional Gated Recurrent Unit (Bi-GRU) model is proposed for enhancing Vehicle Type Classification (VTC) within Intelligent Transport Systems. Multiview In Out Traffic Complexity Database (MIOTCD) is used for data collection then a camera response model is used for enhancing visual data quality. Kalman filtering and Gaussian mixture model techniques are utilized for vehicle recognition and tracking. Local Binary Pattern (LBP), Steerable Pyramid Transform (SPT), and Dual Tree Complex Wavelet Transform (DTCWT) are used for feature extraction and Improved Relief Feature Selection Algorithm (IRFSA) is used for feature selection. Finally, the Bi-directional Gated Recurrent Unit (Bi-GRU) model is used for classifying 11 vehicle categories on MIO-TCD. The obtained results exposed that the IRFSA-BiGRU method outperformed the existing methods by achieving an accuracy of 99.02%, 98.34% F-score, 98.78% MCC,99.93% specificity, 99.32% NPV, 98.75% precision, 98.14% recall on the MIO-TCD database. © 2024 IEEE.

关键词： Classification (of information)

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Facial Expression Recognition Using Grid Wise Attention and Distilled Swin Transformer 2

Facial Expression Recognition Using Grid Wise Attention and ...

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

作者： Anitha, K. Al-Jawahry, Hassan M. Shankar, Smitha Sharath Lande, Jayapal Revathi, R. Gsss Institute of Engineering & Technology for Women Dept of Ece Mysuru India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India Sr University School of Computer Science & Artificial Intelligence Warangal India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India

ISBN: (纸本)9798350372892

The attention in Facial Expression Recognition (FER) is rise in interaction between human - computer that is to enhance ability of the machine to predict emotions of users. Many previous research works for automatic FER based on using Convolutional Neural Networks (CNN) and other Deep Learning (DL) models. However, the CNN and the existing models fails to acquire long-range learning among various facial areas in maximum layers. To resolve this, a Grid Wise Attention and the Distilled Swin Transformer (GWA-DST) model is proposed to enhance recognition of facial expressions of the image. The images from benchmark CK+ dataset is separated in the form of grids and features from different facial areas of image are extracted. Features are fused by ResNet-50 network and then recognized by proposed Swin transformer. The evaluation results of the proposed approach using performance metrics are Accuracy, F1-score, Precision and Recall. The presented GWA-DST model achieved high accuracy of 96.3 %, recall of 96.1 %, precision of 96.4 % and F1-score of 9 6. 2 % is greater than previous DL models such as Resnet-50, FERDERnet, MERsiamC3D, Inception-Resnet+ Long Short Term Memory (LSTM). © 2024 IEEE.

关键词： Convolutional neural networks

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Load Balancing in Fog Nodes Involves in Dynamic Resource Allocation Method Utilizing the Bat Optimization Algorithm 3

Load Balancing in Fog Nodes Involves in Dynamic Resource All...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Adnan, Myasar Mundher Shaikh, Yasmeen Ramachandra, A.C. Babu, D. Raja Selvi, V. College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq S G Balekundri Institute Of Technology Department Of Computer Science And Engineering Belagavi India Nitte Meenakshi Institute Of Technology Department Of Ece Bengaluru India Sr University Department Of Electrical And Electronics Engineering Warangal India Mother Teresa Women's University Department Of Computer Science Kodaikanal India

ISBN: (纸本)9798350318609

Efficient load balancing in fog nodes is essential for optimal resource utilization in fog computing environments. Despite advancements, existing methods lack efficiency, prompting the introduction of an optimization-based dynamic allocation approach in this paper. This paper introduces an approach, Task Classification and Buffering (TCB), designed for effective task management by categorizing tasks and optimizing buffer utilization. Simultaneously, Task Offloading and Optimal Resources Allocation (TOORA), empowered by the Bat Optimization Algorithm (BOA), ensures optimal resource allocation for offloaded tasks, thereby boosting overall system performance. The proposed methodology is evaluated using performance metrics, including Energy Consumption (6.00 W), Cost ($14.53), Make-span (0.32 ms), and Latency(1000s), demonstrating its efficacy. Comparative analysis with existing methods like Dynamic Energy Efficient Resource Allocation (DEER), accelerated Particle Swarm Optimization (APSO) algorithm, and Cat Swarm Optimization (CSO) with Support Vector Machines (SVM) underscores the proposed TCB and TOORA approach in achieving optimal load balancing in fog computing environments. © 2024 IEEE.

关键词： Resource allocation

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Anomaly Detection in Social Networks using Improved Local Outlier Factor 1

Anomaly Detection in Social Networks using Improved Local Ou...

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

作者： Rana Veer Samara Sihman Bharattej, R. Mhsnhasan, Muntader Sowmya, M. Prabhakaran, M.V. Ghamya, K. Saint Leo University Department of Computer Science FL United States The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India Mamallapuram India Tirupati India

ISBN: (纸本)9798331529246

In the present era, Anomaly Detection (AD) aims to identify malicious activities in social networks. Even though previous researchers suggest effective methods for AD still there are limitations such as a lack of labeled data and struggled to handle high dimensional data. In this research, Improved Local Outlier Factor (ILOF) is proposed to detect anomalies and effectively handles the high dimensional data by using distance metric to calculate local density. Primarily, the data is collected from CERT insider threat and then preprocessed with truncation to remove extreme values for effective feature extraction. After that, the Principal Component Analysis (PCA) is incorporated to extract most informative features such as principal components to identify anomalies as data points. Finally, the extracted features are fed into proposed ILOF uses density-based approach to calculate the local density effectively for detecting anomalies in social networks. From the results, proposed ILOF model attained better outcomes compared to existing Bidirectional Long Short-Term Memory (BiLSTM) in terms of accuracy, precision, recall and F1-score as 98.50%, 97.08%, 98.25% and 97.66% respectively. © 2025 IEEE.

关键词： Principal component analysis

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Military Vehicle Object Detection Based on Feature Representation and Refined Localization Using Inception Recurrent Convolutional Neural Network 2

Military Vehicle Object Detection Based on Feature Represent...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Almusawi, Muntather Yadav, S. G. Shivaprasad Rahim, Abdul Aluvala, Srinivas Ramachandra, A.C. The Islamic University Najaf Iraq Electronics and Telecommunication Engineering Ramaiah Institute of Technology Bangalore India Electronics and Communication Engineering Department Telangana Hyderabad India School of Computer Science and Artificial Intelligence Sr University Warangal India Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India

ISBN: (纸本)9798350382952

Advanced reconnaissance technologies for military vehicle components form basis for weapons and equipment reconnaissance and tracking operations in complex environments, which is crucial to allow intelligent warfare using information. Addressing limitations of conventional detection algorithms in this domain, a novel approach termed as Military Vehicle Object Detection Method based on Inception Recurrent Convolutional Neural Network (MVODM-IRCNN) and Hierarchical Feature Representation is proposed for detecting military vehicle object. first is a comprehensive data set specially designed for military vehicles. Then it is two system improvements: hierarchical feature representation and state-of-the-art localization, using Inception Recurrent Convolutional Neural Network (IRCNN), aimed at improving detection performance. Hierarchical feature representation techniques make it easier to find good feature representation levels for different object shapes, improving accuracy of these techniques boxes. combination of materials provides a significant gain in detector performance. Finally, an exploratory study conducted on own dataset shows that MVODM-IRCNN has exceptional recognition capabilities, which proves its usefulness in military vehicle recognition applications. © 2024 IEEE.

关键词： Object detection

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Deep Transfer Learning Model-Based Automated Detection of COVID-19 from X-ray Images and Interpretation of COVID-19 Images Using GLCM Texture Features 6th

Deep Transfer Learning Model-Based Automated Detection of CO...

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6th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020

作者： Ankalaki, Shilpa Shorya, Kartikeya Majumdar, Jharna Department of M.Tech CSE Nitte Meenakshi Institute of Technology Bangalore560064 India Department of CSE Nitte Meenakshi Institute of Technology Bangalore560064 India Department of M. Tech Computer Science & Engineering Head Centre for Robotics Research Head Centre for Space Research Nitte Meenakshi Institute of Technology Bangalore560064 India

ISBN: (纸本)9789811613418

The novel coronavirus 2019 (COVID-2019), which initially proved its existence in Wuhan city of China in December 2019, spread quickly around the globe and turned into a pandemic. It has caused a staggering impact on all the sectors of the world like public health, global economy and daily lives. COVID-19 positive cases and death due to COVID-19 are rapidly increasing day by day. It is crucial and essential for fast and accurate automatic detection of COVID-19 infection to make better decisions and to provide appropriate treatment for the patients that can hopefully save their lives. The current has employed the VGG19, RESNET50 and DesNet121 deep learning convolutional neural network with transfer learning to identify and classifies the X-ray images into COVID-19 and non-COVID-19 classes. This study has been extended to analyse the factors which distinguishes COVID-19 and non-COVID-19 images. To accomplish this task, we have employed GLCM features and determined that variance is the best feature for this purpose. GRAD-CAM algorithm has been used to interpret the decision of CNN architecture. In this study, VGG19 and DenseNet121 achieved the classification accuracy of 98.80%, and ResNet50 achieved the accuracy of 97.65% for binary classes (COVID-19 and non-COVID-19 classes). © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Coronavirus

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Applying a Hybrid Deep Learning Framework to Efficient Stock Price Prediction 4

Applying a Hybrid Deep Learning Framework to Efficient Stock...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Khatri, Eti Ramesh Babu, Ch. Nandhini, I. Reddy, Sowmya Kethi Yoges, G. Reddy, Pundru Chandra Shaker Nitte Meenakshi Institute of Technology Department of Management Studies Bengaluru India Department of Computer Science and Engineering Hyderabad India V.S.B.Engineering College Department of Information Technology Karur India School of Management Studies Chaitanya Bharathi Institute of Technology Hyderabad India Maharaja Engineering College Avinashi India School of Computer Science and Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798350352931

It is very difficult to make reliable financial predictions using stock market data due to its inherent volatility and non-linearity, especially in the period following a pandemic. In response to these difficulties, this study builds a hybrid framework using deep learning to forecast technology stock prices, financial trends, and risk quantification. An increasing number of individuals have started investing in the stock market due to the fast growth of the economy in the past few years. A novel stock market prediction based on deep learning (SMP-DL) is presented in this research as a method for predicting stock market values. This study forecasted the stock market closing price using a bidirectional gated recurrent unit (BiGRU) in conjunction with long short-term memory (LSTM). When tested against other systems currently in use, the results demonstrated that the suggested system performed admirably. Since the values of R2, RMSE, MDA, MAE, and 0.8, respectively, are 3.4, 6.7, and 3.8. © 2024 IEEE.

关键词： Long short-term memory

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A Beetle Antennae Search-Based Sparrow Search Algorithm for Network Intrusion Detection

A Beetle Antennae Search-Based Sparrow Search Algorithm for ...

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

作者： Baig, Ayub Reddy, R. Archana Muhsin Hassan, Muntather Subramanya, S.G. Manu, Hajari Department of Infornation Technology 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 Nitte Meenakshi Institute of Technology Department of Cse Bengaluru India Gokaraju Rangaraju Institute of Engineering and Technology Department of Ai&ml Hyderabad India

ISBN: (纸本)9798350375442

Nowadays, the network Intrusion Detection System (IDS) is a crucial cybersecurity technique that predicts unauthorized access, and exploits malfunction of computer networks. Traditional IDS struggle with classifying multiple, varied attack patterns which often lead to poor performance in detecting critical threats. In order to improve detection accuracy, identify numerous attacks, developing an efficient system becomes crucial which handles imbalanced data. Thus, this paper presents an Adaptive network IDS that employs a hybrid approach by combining the Improved Beetle Antenna Search (BAS) Algorithm and the Sparrow Search Algorithm (SSA) to enhance detection accuracy and adaptability in identifying cyber threats on NSL-KDD and CSE-CIC-IDS2018 datasets. Additionally, a Bi-directional Long Short-Term Memory (Bi-LSTM) network is utilized to reduce the False Positive Rate (FPR) and increase the entire efficiency of the system by effectively overcoming the challenges such as imbalanced data and low detection rate. Experimental evaluations on benchmark intrusion datasets such as NSL-KDD and CSE-CIC-IDS2018 using proposed IBAS-SSA + Bi-LSTM demonstrate higher detection results with accuracy of 98.12% and 98.64% respectively, when compared to other traditional IDS methods. © 2024 IEEE.

关键词： Network intrusion

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Quantum Machine Learning in Healthcare: Developments and Challenges

Quantum Machine Learning in Healthcare: Developments and Cha...

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

作者： Rani, Sita Kumar Pareek, Piyush Kaur, Jaskiran Chauhan, Meetali Bhambri, Pankaj Guru Nanak Dev Engineering College Department of Computer Science and Engineering Ludhiana India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Ipr Cell Bengaluru India Guru Nanak Dev Engineering College Department of Information Technology Ludhiana India

ISBN: (纸本)9798350398465

Machine learning is playing a very significant role to process voluminous data and its classification in a variety of domains. Due to better performance and rapid development in the last decade, quantum computing is also benefiting many areas. With the amalgamation of these two technologies, a new domain for processing big data more efficiently and accurately has evolved, known as quantum machine learning. Healthcare is one of the prominent domains where a huge volume of data is produced from several processes. Efficient processing of healthcare data and records is very important to facilitate many biological and medical processes to provide better treatments to patients. The fundamental aim of this paper is to present a state-of-the-art review of quantum computing concepts, quantum machine learning framework, and the various applications of quantum machine learning in the domain of healthcare. The comparison of QML healthcare models with ML-based healthcare applications is discussed in this work. The authors also present the various challenges faced in the deployment of quantum machine learning algorithms in the domain of healthcare and possible future research directions. © 2023 IEEE.

关键词： Health care

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