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

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Development of Healthcare Model Using AR-DVAE with Mayfly-MLP-BPN for Parkinson's Disease Detection

Development of Healthcare Model Using AR-DVAE with Mayfly-ML...

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Data science and Network Security (ICDSNS), International Conference on

作者： G N Keshava Murthy Piyush Kumar Pareek Manjunath T N Shwetha N Deepak R Rashmi P Department of Electronics and Instrumentation Engineering Siddaganga Institute of Technology Tumakuru India Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering East West Institute of Technology Bengaluru India Department of Electronics and Communication Engineering BGS Institute of Technology Adichunchanagiri University B G Nagara India Inventuriz Labs Private Limited Bengaluru India

Research on reliable biomarkers that permit the creation of medical-decision support technologies is currently a major focus in healthcare biometrics. These gadgets aid in the analysis and nursing of conditions like Parkinson's disease (PD). If PD patients' vocal alterations could be detected, this would allow for early diagnosis and treatment, thereby preventing the onset of physically disabling symptoms. More recent research has centred on the suggestion of new automatic and objective tools to aid in diagnosis and severity assessment. In this study, we provide a dual approach to detecting PD. The first section extracts features mechanically with the help of an AR-DVAE model (autoregressive deep variational autoencoder). Lastly, the Mayfly Algorithm is used to maximise the bias weight in a modified multilayer perceptron utilising a backpropagation network (MLP-BPN). To conduct this research, we used the publicly available PD Using Digitized Graphics Tablet dataset. In comparison to other models, the suggested model performed better across the board, as shown by the validation analysis.

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Enhancing Segmentation of Abdominal Aortic Aneurysms in CT Images

Enhancing Segmentation of Abdominal Aortic Aneurysms in CT I...

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Data science and Network Security (ICDSNS), International Conference on

作者： Abdul Saleem Javeed Veeresh K M Ganesh V N Chunduri Lavanya Parameshachari B D Dept. of CSE GST GITAM University Bengaluru Campus Bengaluru India Dept. of CSE SJBIT Bengaluru India Department of ECE Mangalore Institute of Technology & Engineering Moodabidri India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaadeswaram India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

This study concentrates on a potential strategy for improving Abdominal aortic aneurysms (AAAs) segmentation in CT imaging. The study's dataset included 19 CT images of healthy AAAs acquired from different patients. Ground-truth segmentations that matched the training data were also included. For enhanced accuracy while segmenting AAA, the suggested technique integrates pre-processing, deep learning segmentation, and post-processing into a cohesive workflow. Convolutional neural networks (CNNs) are trained for deep learning segmentation using a U-Net architecture, and noise in input pictures is removed during pre-processing. This differs from the conventional habit of employing linear regression for image segmentation. The results were measured using the dice similarity coefficient, sensitivity, specificity, accuracy, recall, and F1 score. With a DSC of 0.81, sensitivity of 0.89, specificity of 0.97, accuracy of 0.86, recall of 0.89, and F1 score of 0.86, the suggested approach was proven to be successful in segmenting AAAs in CT images. The technique has shown potential in clinical settings for improving AAA diagnosis and treatment planning.

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A Hybrid Data Sensing in Intra cluster Communication by using Mac Protocols in Wireless Communication

A Hybrid Data Sensing in Intra cluster Communication by usin...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： C B Vinutha Geetha N Viriyala Sri Anima Padmini Sanjib Kumar Nayak Chaithra K N Department of Electronics and Communication Engineering Sri Siddhartha Institute of Technology SSAHE Tumakuru India Department of Computer Science & Engineering H. K. E. Society's S. L. N. College of Engineering Raichur India Department of Computer Science & Engineering K L University Vadeeshwaram Vijayawada India Department of CSE Hyderabad India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

Wireless Sensor Networks (WSNs) have become increasingly popular due to their numerous beneficial features, such as low power consumption, adaptability, fast response time, reliability, dynamic nature, affordability, and ease of installation, making them applicable in various fields for different purposes. However, effective data sensing in WSNs requires the use of proper Medium Access Control (MAC) protocols. In order to conserve energy and minimize redundant sensing, cluster members transmit their data to the leader node, which is facilitated by appropriate clustering methods. In this study, we propose the Enhanced Energy-efficient Real-time MAC (EERMAC) protocol. The EERMAC protocol uses a combination of two different access methods: CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance) for real-time data and vTDMA (variable Time Division Multiple Access) for periodic data. The proposed EERMAC protocol outperforms existing protocols in terms of packet delivery ratio (PDR), throughput, jitter, and packet drop rates. The queuing model implemented by EERMAC effectively prevents congestion, leading to reduced packet delay. This study presents an innovative solution for addressing the challenges of data sensing in WSNs and showcases the potential of EERMAC to enhance the performance of WSNs for various applications.

关键词： Wireless communication Wireless sensor networks Time division multiple access Protocols Media Access Protocol Real-time systems Energy efficiency

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A Fault Diagnosis of Sound and Vibration Signals Using Statistical Features and Machine Learning Algorithm

A Fault Diagnosis of Sound and Vibration Signals Using Stati...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Inakollu Aswani Naresh Kumar Kar Tanaya Ganguly G.P. Ramesh Tejaswini N P Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaadeswaram India Department of CSE GITAM Deemed to be University Hyderabad India Department of Computer Science & Engineering K L University Vadeeshwaram Vijayawada India Department of Electronics and Communication Engineering St. Peter's Institute of Higher Education and Research Chennai India Department of ISE Nitte Meenakshi Institute of Technology Bengaluru India

The sounds and vibrations that are given off by a rotating machine can provide insight into a wide variety of distinct problem situations. By conducting statistical analysis on the recorded sound and vibration signals and subjecting them to a variety of fault conditions, it is possible to generate statistical parameters. In order to obtain information regarding the numerous flaws, the signals in the temporal domain are analyzed using these characteristics. In order to locate flaws in the system, we make use of these values, also known as “statistical features.” In this work, dimensionality reduction was achieved through the utilisation of a decision tree, principal component analysis (PCA), and independent component analysis (ICA). In order to classify the various fault states, the various classifiers were given these streamlined features to work on. During this inquiry, different kinds of classifiers were utilized, such as decision trees, support vector machines, clonal selection classification algorithms, and proximal support vector machines. This paper presents the findings of two sets of research involving statistical features extracted from sound and vibration signals and used in conjunction with the aforementioned feature reduction techniques and classifiers to detect twelve different fault conditions involving shaft, rotor, and bearing failures. The research was conducted by using sound and vibration signals as the primary source of data. According to this Proposed Approach, the Mean Classification Efficiency% of Vibration Signals for 24 Classes is 94.27%, while the Mean Classification Efficiency”% of Sound Signals for 24 Classes is 95.41%.

关键词： Vibrations Support vector machines Fault diagnosis Machine learning algorithms Rotating machines Feature extraction Classification algorithms

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Advancements in Multi-Cloud Applications for Enhanced E-Healthcare Services

Advancements in Multi-Cloud Applications for Enhanced E-Heal...

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Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), International Conference on

作者： P Ramesh Naidu Dankan Gowda Parismita Sarma Dinesh Arora Sampathirao Suneetha Sandip Ramesh Patil Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore Karnataka India Department of Electronics & Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka India Department of Information Technology Gauhati University Assam Guwahati Assam India Department of Electronics and Communication Chandigarh Engineering College Mohali Punjab India Department of Computer Science & Systems Engineering Andhra University Visakhapatnam Andhra Pradesh India Department of Communication Skill Bharati Vidyapeeth College of Engineering Navi Mumbai India

E-Health Record Security Research on a Cloud-Based Multi-Layer Framework reaches its climax in a string of noteworthy discoveries, demonstrating how the framework may transform cloud health data security. The framework's multi-tiered design proved to be an effective barrier against a wide range of cyber threats, protecting the privacy and security of patients' medical records. An important factor in the varied healthcare industry is the fact that it can be easily scaled and adjusted to meet the needs of healthcare providers of varied sizes and capabilities. Nevertheless, there are obstacles that need to be addressed, according to the report. These include the difficulty of implementation and the ongoing need for upgrades and modifications to address changing cyber threats and technical advances. Healthcare firms must continuously implement educational programs due to the reliance on user compliance and training. With an eye toward the future, this structure lays the groundwork for more sophisticated studies in the subject. Potential research directions include improving cross-platform compatibility, optimizing resource utilization to reduce performance implications, and integrating AI and ML for automated threat response and predictive analytics.

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An Efficient Novel Approach for Iris Recognition and Segmentation Based on the Utilization of Deep Learning

An Efficient Novel Approach for Iris Recognition and Segment...

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： Syed Nawaz Pasha Anitha D R Anbunathan Gobinath C Madhura G K School of Computer Science & Artificial Intelligence SR University Warangal India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur India Department of Mathematics SA Engineering College Chennai India School of Computing and Information Technology Reva University Bangalore India Department of AI and ML Nitte Meenakshi Institute of Technology Bengaluru India

Iris recognition stands out as one of the most stable and accurate biometric identification systems such as facial, voice, fingerprint, handwriting, and iris recognition itself. Deep Learning (DL) plays a pivotal role in enhancing security by leveraging Convolutional Neural Networks (CNNs) for iris recognition. The acquisition of iris images and real-time processing have become focal points demanding heightened attention in the realm of DL. Iris recognition is prized for its high level of protection, making it challenging to reconstruct iris data easily from the database. The CASIA-Iris Dataset serves as a benchmark to verify the uniqueness of iris patterns and evaluate the performance of iris recognition. However, due to limitations in iris image access, the application of CNNs in the segmentation approach remains critical for iris localization and pupil boundary identification, ensuring both efficiency and specificity in iris images. The results obtained from this study reveal that the proposed CNN, utilizing DL models, achieves an impressive accuracy of 99.89% on the CASIA-Iris dataset, better performance of other existing models, such as U-Net and VGG-Net, and ensuring accurate classification.

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Whale Optimization Algorithm with Convolutional Neural Network for Object Detection

Whale Optimization Algorithm with Convolutional Neural Netwo...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Nagendar Yamsani Hassan M. Al-Jawahry Karunakara Rai B Bollipelly PruthviRaj Goud Ghazi Mohamad Ramadan School of Computer Science and Artificial Intelligence SR University Warangal India The Islamic university Najaf Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Anurag University Hyderabad India College of MLT Ahl Al Bayt University Karbala Iraq

The Object detection is method of computer vision for locate instances of objects in videos or images. Algorithms for object detection generally control Machine (ML) or Deep Learning (DL) for producing results. Several ML methods are used for detecting objects from image acquired through video. Huge incoming volume information is high for ML methods to manage. DL methods are used for recognizing objects and individuals from videos in accordance with handle high quantity of information as input. Several DL methods are used like Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and etc., to handle huge quantity of input data and also human and object recognition. Thus, Hybrid Whale optimization Algorithm (WOA) and Convolutional Neural Network (CNN) is proposed to detect object from video frames. WOA is used to enhance the method parameter of CNN architecture. Initially, the pre-processing technique is used to delete noise in image and quality of image is improved. Gaussian filter is utilized to subtract background in images. The proposed method improved accuracy of object detection and identifies the small targets in videos. The proposed algorithm attained high accuracy of 98.23%, precision of 96.78%, recall of 96.02% and f1-score of 96.45% which is higher than other existing methods.

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Beam Selection Approach in Millimeter Wave Communication using Multi-Objective Prairie Dog Optimization

Beam Selection Approach in Millimeter Wave Communication usi...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： R Archana Reddy Laith H. Alzubaidi Abbas Hameed Abdul Hussein S Pradeep Kumar J Rajaram School of Computer Science and Artificial Intelligence SR University Warangal India The Islamic university Najaf Iraq College of Pharmacy Ahl Al Bayt University Karbala Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Malla Reddy Engineering College for Women Hyderabad India

The selection of beam relies as a major conflict in wireless communication systems. The beam selection has been widely utilized in millimeter wave bands which helps to mitigate the path loss and minimize the energy consumption. The existing beam selection approaches results in high computational and communication costs and hinders the widespread deployment of mm Wave communications. Moreover, the existing approaches faced issues related to select the users for multiple radio frequencies and this suffers the users to face computational burden and results in beam optimization problem. So, this research utilized a Multi-Objective Prairie Dog Optimization (MOPDO) algorithm to perform an effective beam selection for multiple users in a single iteration. MOPDO considers the multiobjective functionalities such as beam power, energy efficiency, spectral efficiency and computational time. The experimental evaluation of proposed approach is evaluated with Alternative Grey Wolf with Beetle Swarm Optimization (AGW-BSO). For example, computational time of the proposed MOPDO is 320.87 seconds whereas the existing AGW-BSO is 355.72 seconds.

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Hybrid Optimization Based Convolutional Neural Network for Intrusion Detection System

Hybrid Optimization Based Convolutional Neural Network for I...

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： Sureka N B. Girirajan Komuravelly Sudheer Kumar B. S. Deepa Priya Sowmya M Department of Biomedical Engineering Agni College of Technology Chennai India Department of ECE SR University Warangal India School of Computer Science & Artificial Intelligence SR University Warangal India Department of CSE Bannari Amman Institute of Technology Sathyamangalam Erode India Department of AI and DS Nitte Meenakshi Institute of Technology Bengaluru India

Intrusion Detection System (IDS) is one of the common deep learning (DL) techniques that are used to find and identify outliers to prevent adversarial attacks, fraud, and network intrusions. This paper proposed a hybrid Particle Swarm Optimization and Grey Wolf Optimization (HPSOGWO) based Convolutional Neural Network (CNN) model for intrusion detection systems. The proposed HPSOGWO is evaluated on the NSL-KDD dataset which contains 5 different classes and 41 features. The PSO is good for global exploration and GWO is for local exploitation. The hybrid PSO and GWO algorithm achieves a better balance between exploration and exploitation and enhances convergence speed. The CNN is utilized to enhance the system's capability to identify and classify intrusion accurately and effectively. The proposed HPSOGWO-based CNN model attains better results by utilizing evaluation metrics like accuracy, precision, recall, specificity, and f1-score values about 0.9918, 0.9852, 0.9908, 0.9879 and 0.9767 correspondingly which is comparatively higher than existing techniques like Chicken Swarm Optimization based Deep Long Short-Term Memory (ChCSO based Deep LSTM), Deep Neural Network (DNN) and LSTM.

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A Smart Governance Architecture for Integrating Heterogeneous IoT Solutions within the Framework of Smart Cities

A Smart Governance Architecture for Integrating Heterogeneou...

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Data science and Network Security (ICDSNS), International Conference on

作者： Madhurya J A Manoj Kumar Padhi Kondala Rao P Mohan Awasthy Piyush Kumar Pareek Department of CSE GITAM School of Technology GITAM University Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaadeswaram India Department of ECE Hyderabad Institute of Technology and Management Hyderabad India Department of Engineering and Technology Bharati Vidyapeeth Peeth Deemed to be University Navi Mumbai India Department of AI and ML Nitte Meenakshi Institute of Technology Bengaluru India

The Internet of Things (IoT) is a network of networked devices that links commonplace things to the Internet. The present IT infrastructure for managing IoT technologies lacks the necessary enterprise networking approach, which necessitates municipal collaboration. This is due to the compartmentalization of smart city app functionality. To integrate heterogeneous Internet of Things solutions within the framework of smart cities, we offer a smart governance architecture that integrates power needs, security constraints, and scalability components. This will promote cross-sector contacts at all levels and contribute to the accessibility of smart cities. This research proposal suggests a multi-level governance model for the distributed administration of IoT subsystems as a possible way to solve the problem of IoT systems in smart city applications that don't work together. As an important part of smart governance, the proposed architecture includes things like a one-of-a-kind model, open-source software, and digital objects. The goal of this research is to run a proof-of-concept simulation to test the suggested infrastructure. A lot of thought will be put into the solution for multi-level governance, as well as security and the resolution of digital objects with security constraints. The proposed infrastructure is meant to make it possible for different Internet of Things technologies and agents to work well together by providing security, repeatability, scalability, and interoperability.

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