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

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Modified Whale Optimization Algorithm for Task Scheduling in Cloud Computing 2

Modified Whale Optimization Algorithm for Task Scheduling in...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Girirajan, B. Nijaguna, G.S. Pramodhini, R. Adnan, Myasar Mundher Shivakanth, Gandla SR University Department of ECE Telangana Warangal India S.E.A. College of Engineering and Technology Department of Information Science and Engineering Bangalore India Nitte Meenakshi Institute of Technology Yelahanka Department of Electronics and Communication Engineering Bengaluru India The Islamic University Najaf Iraq Koneru Laksmaiah Education Foundation Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350317558

In recent years, an efficiency of task scheduling is evolved as a major challenge in cloud platforms. Especially, identifying the optimal resources for input tasks is the major challenges faced by the task scheduler. So, in this research, a Modified Whale Optimization Algorithm (MWOA) is proposed to improve the behaviour in task scheduling by applying the parameters such as resource allocation and load balancing. Capacity criteria based MWOA algorithm determines the effective Virtual Machine (VM) for execution of tasks in queue. An effectiveness of proposed WOA algorithm is analysed by the utilization of performance measures such as memory storage, execution time, cost and makespan. The attained results from the proposed WOA algorithm are outperformed in various predictable optimization algorithms such as Storm, Spark, Flink and Kafka. The results of proposed WOA algorithms demonstrates the better performances in minimum execution time of 612ms, and memory storage of 309Kb on Kafka platform. © 2024 IEEE.

关键词： Load Balancing Modified Whale Optimization Algorithm Optimal resources Resource Allocation Task Scheduling

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A Novel Transfer Learning Technique for Rice Leaf Disease Detection 2

A Novel Transfer Learning Technique for Rice Leaf Disease De...

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2nd IEEE International Conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication engineering, ICATIECE 2022

作者： Kumar, Naveen Nikshepa, T. Pareek, Piyush Kumar Parameshachari, B.D. Madhumala, R.B. Sowmya, S. Bengaluru India Dayananda Sagar Academy of Technology and Management Department of Information Science and Engineering Bangalore India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Dayananda Sagar Academy of Technology and Management Department of ISE Bangalore India

ISBN: (纸本)9781665493963

Image processing has become more sophisticated over the course of the last several years. In this study, we are presenting a model that, based on a convolution network, can identify the status of the rice leaf, namely whether it is a healthy leaf or one that has been afflicted by any illness. Where rice is widely regarded as the primary source of sustenance not just in India but also in other areas of the globe. The agriculture may get infected with the illness and the crops may be affected by the disease for some of the causes listed above. When it may be more difficult to discover the kind of illness, and where farmers may not have the information to find the specific type of disease that an infected leaf was afflicted with. In order to do this, we are use the VGG16 network, which is an architecture based on a convolution neural network (CNN). This is done so that the crop disease can be readily identified, and so that correct findings can be obtained. © 2022 IEEE.

关键词： Convolutional neural networks

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Design of a Chatbot for People under Distress using Transformer Model. 2

Design of a Chatbot for People under Distress using Transfor...

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2nd Global Conference for Advancement in technology, GCAT 2021

作者： Konapur, Sushruth P Krishna, Tejas Varun, G. Uma, R. Sarojadevi, H. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9780738132150

As indicated by the WHO, the variety of mental issues incorporates dementia, discouragement, schizophrenia and other psychoses, intellectual disabilities and developmental disorders. Sadness alone influences in excess of 300 million individuals. Indeed, even with admittance to proficient medical care and social administrations, those tormented with emotional well-being conditions wonder whether or not to profit from the therapy because of the stigma encompassing the disease. Accordingly, to this we would like to build an artificial intelligent agent that responds and helps the patient using an interactive chat bot. With the recent changes in Deep learning technology and specifically in NLP with models such as BERT and Open-AI GPT getting efficient contextual responses is not a task too hard. The chatbot will be able to identify, address and help relieve the mental stress of the user through soothing message responses similar to a therapist's conversation. Also, other relaxation techniques are provided on the same platform for further support to the distressed user. © 2021 IEEE.

关键词： Natural language processing systems

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A Quantum Variational Classifier for Predictive Maintenance and Monitoring of Battery Health in Electric Vehicles

A Quantum Variational Classifier for Predictive Maintenance ...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Grandhi, Sri Harsha Al-Jawahry, Hassan M. Divya, S. Kumar, Bura Vijay Padhi, Manoj Kumar Intel FolsomCA United States The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Eee Bengaluru India Sr University School of Computer Science and Artificial Intelligence Warangal India Centurion University of Technology and Management School of Engineering and Technology Paralakhemundi India

ISBN: (纸本)9798350360660

In order to ensure the dependability and durability of Electric Vehicles (EVs), there is an increasing requirement for improved maintenance systems. This research combines optical and quantum approaches to present a unique methodology for the predictive maintenance of EV components. High-resolution, real-time data on vital components including the battery, electric motor, and power electronics are captured by the Fiber Bragg Grating (FBG) sensors. These sensors are useful because of their high sensitivity, ability to multiplex, and resilience to electromagnetic interference. A Quantum Variational Classifier (QVC) with quantum computing is proposed to manage massive datasets and improve prediction accuracy. The AI model is trained to anticipate possible failures and identify early indicators of component degradation, allowing for proactive maintenance and reducing downtime. The experimental results prove the efficacy of proposed method in providing accurate, appropriate predictions and improving the durability and performance of EV components. The proposed method achieved 0.0035 of MAE and 0.0097 of RMSE when compared with the existing methods. © 2024 IEEE.

关键词： Quantum computers

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Leveraging Deep Autoencoders for Security in Big Data Framework: An Unsupervised Cloud Computing Approach 2

Leveraging Deep Autoencoders for Security in Big Data Framew...

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

作者： Roy, Sourav Nijaguna, G.S. Shankar, Smitha Sharath Adnan, Myasar Mundher Umaeswari, P. Department of Electronics and Communication Engineering Bangalore India S.E.A. College of Engineering and Technology Department of Information Science and Engineering Bangalore India Nitte Meenakshi Institute of Technology Dept of Ece Bengaluru India The Islamic University Najaf Iraq R.M.K. Engineering College Department of Computer Science and Business Systems Tamil Nadu Kavaraipettai India

ISBN: (纸本)9798350382952

Abnormalities recognition in bank transaction big data is the number one issue for stability of financial security system. Due to the rate digital transactions are increasing it is vital to have effective ways. Encryption with deep autoencoder model should be explored as it involves trained neural networks that learn such patterns from the complex transaction data. The following paper demonstrates application of anomaly detection using deep autoencoders in the banking big data transactions. It focuses on the theoretical bases, network design, preparedness and the testing measures for deep autoencoders. On the other hand, it solves problems such as high dimensionality and imbalanced dataset. This research paper shows deep autoencoders effectiveness in deep learning and how the network identifies different fraudulent big data transactions, money laundry and unauthorized access. It also encompasses recent developments of cloud environments and future methods using deep autoencoders including the fact that constant search for new possible solutions is a must. The insights delivered contribute to the discourse in financial security community, which incorporates researchers, practitioners, and policymakers involved in anomaly detection in cloud. © 2024 IEEE.

关键词： Cryptography

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Deep Belief Network Based Intrusion Detection in Vehicle Ad Hoc Network 2

Deep Belief Network Based Intrusion Detection in Vehicle Ad ...

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

作者： Al-Jawahry, Hassan M. Mamatha Bai, B.G. Veena, T. Ramesh, Vankudoth Indupalli, Tabitha The Islamic University Najaf Iraq Department of CSE Nitte Meenakshi Institute of Technology Karnataka Bengaluru India Department of Information Technology S.A. Engineering College Chennai India Department of CSE-DS CVR College of Engineering Andhra Pradesh India Department of Computer Science and Engineering GRIET Telangana Hyderabad India

ISBN: (纸本)9798350382952

Vehicular Ad Hoc Networks (VANET) have garnered a great deal of attention and development in recent years. These networks are essential to the development of fully and partially autonomous vehicles. But there are a lot of obstacles because of security risks that are either exclusive to VANET or inherent in ad hoc networks. Enhancing driving efficiency, comfort, and safety is VANET's primary goal. These vehicles rely significantly on external communication through data control and cooperative awareness message exchange with the surrounding environment. This study presents the implementation of a Deep Belief Network (DBN) model for VANET intrusion detection. Initially, the DBN is employed for intrusion detection and the dataset is pre-processed for class balance. The DBN model that was put into practice performs better than expected;it achieves 96.32% accuracy, 96.21% recall, 96.87% F1-Score, and 92.95% precision. The suggested DBN system is distinct from other models such as the Optimal Weighted Extreme Learning Machine (OWELM), Gated Recurrent Unit (GRU), and Technique for Oder Preference by Similarity to Ideal (TOPSIS). © 2024 IEEE.

关键词： Network intrusion

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Road Accident Detection using MobileNetv2 with Gradient Boosting Algorithm 1

Road Accident Detection using MobileNetv2 with Gradient Boos...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Priyanka, K. Obaid, Saif Anguraj, K. Thangam, V. Thirumani Sahana, R. Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Bengaluru India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Sona College of Technology Department of Electronics and Communication Engineering Salem India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India Vel Tech Multi Tech Dr. Rangarajan Dr.Sakunthala Engineering College Avadi Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350352931

An increased number of vehicles on the roads increases the risk of accidents and eventually resulted huge loss of lives and property. Accident detection utilizing computer vision in much consideration as of late. The existing methods for accident detection gave inaccurate detection due to the errors in sensors and cameras may not capture all angles or all areas. Therefore, this paper proposes MobileNetV2-Gradient Boosting (MNGB) for detection of road accidents which effectively gave accurate detections. Firstly, all videos are split up into smaller shots according to scene changes and then key frames are extracted from each shot based on consecutive frames. This images are augmented by using Kalman filter which reduced the noise and artifacts in images, accurately tracks the objects across frames. Then, features are extracted by using Multi-Scale Network (MSNet) where it extracts the features at various scales by capturing both local and global information. After that, these features were classified by using proposed MNGB which enables the real time processing and reduces the computational resources and gave accurate detection results. The proposed MNGB for road accident detection achieved better results in terms of accuracy 95 %, precision 9 8 %and F1-Score of 9 6 %when compared with existing YOLOv3. © 2024 IEEE.

关键词： Kalman filters

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Canonical Huffman Coding (CHC) - Discrete Wavelet Transform (DWT) Method for Image Steganography 2

Canonical Huffman Coding (CHC) - Discrete Wavelet Transform ...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Babu, A. Ramesh Al-Fatlawy, Ramy Riad Veeranjaneyulu, K. Divya, G. Kumar, Komuravelly Sudheer Anil Neerukonda Institute of Technology and Sciences Department of Engineering Mathematics Visakhapatnam India The Islamic University Najaf Iraq Cvr College of Engineering Dept of It Telangana India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Sr University School of Computer Science and Artificial Intelligence Warangal India

ISBN: (纸本)9798350317558

Steganography is a tool that allowed an information to transmit through revealing secret data in enormously growing network. The existing methods has limitations like performance and security problems. In this research, proposed a Canonical Huffman Coding (CHC) - Discrete Wavelet Transform (DWT) for image steganography. The images used in the research are Lena, pepper, barbarra and airplane. The images are pre-processed through image acquisition, filtering and histogram shifting. Then, the images are steganographic by proposed CHC-DWT method. Performance of proposed technique is analysed by performance by Peak Signal-to-Noise Ratio (PSNR), Normalized Coefficient (NC) and Structural Similarity Index Measure (SSIM). Proposed algorithm obtained PSNR 54.09 dB in Lena image, PSNR 53.67 dB in Pepper image, PSNR 54.37 dB in Barbarra image and PSNR 53.72 dB in Airplane image which is effecient than existing algorithms like Quick Response Method (QRM), Ballot Transform (Bat) and hybrid Lempel Ziv Welch (LZW) and Genetic Algorithms (GA) © 2024 IEEE.

关键词： Canonical Huffman Coding Discrete Wavelet Transform Peak Signal-to-Noise Ratio Structural Similarity Index Measure and Steganography

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An IoT-Based CNN Model for Patients in ICU Beds During the COVID-19 Outburst

An IoT-Based CNN Model for Patients in ICU Beds During the C...

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2023 International Conference on Data science and Network Security, ICDSNS 2023

作者： Parameshachari, B.D. Srinivas, Chethana Rekha, H. Harshitha, P. Deepak, R. Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India East West Institute of Technology Department of Computer Science and Engineering Bengaluru India Shridevi Institute of Engineering and Technology Department of Information Science and Engineering Tumkur India Bgs Institute of Technology Adichunchanagiri University Department of Electronics and Communication Engineering B G Nagara India

ISBN: (纸本)9798350301595

The rising number of hospital admissions is a worldwide cause for alarm, with major contributors being an ageing population, the prevalence of diseases, and the current COVID-19 epidemic. IoT provides the scalability necessary for this determination, enabling incessant and reliable health monitoring on a worldwide scale, and therefore easing this difficulty. The COVID-19 virus has quickly disseminated all across the globe. Healthcare practitioners around the world face a number of difficulties during the epidemic, one of the most significant being the management of available resources. Since disease, approximately patients will need to be self-confessed to the hospital with an immediate need for oxygen acute medical care, making it crucial to properly allocate the beds in the (ICU). Many nations' efforts to stop the spread of the deadly COVID-19 virus relied heavily on technology, especially Artificial Intelligence (AI). In this research, we modify a (CNN) to better detect the requirement for the ICU and the predicted length of stay among COVID-19 patients. A 895 patients and evaluate the CNN with Sun flower Optimization (SFO). The results of the studies disclose that the projected perfect for predicting ICU Length of Stay (LoS) achieves the maximum accuracy. It is believed that this contribution would pave the way for better resource management in healthcare institutions, ultimately leading to the saving of lives. © 2023 IEEE.

关键词： COVID-19

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An Efficient Biomedical Solicitation in Liver Cancer Classification by Deep Learning Approach

An Efficient Biomedical Solicitation in Liver Cancer Classif...

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

作者： Vasundhara, N. Nandan, Archana S Hemanth, S.V. MacHerla, Sivudu Madhura, G.K. Cmr Technical Campus Department of Cse Hyderabad India Gitam School of Technology Gitam University Department of Computer Science & Engineering India Hyderabad Institute of Technology and Management Department of Computer Science and Engineering Telangana India Pace Institute of Technology & Science Andhra Pradesh India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798350398465

The liver, a significant physical organ, is located in the upper right and left abdominal cavities just below the diaphragm. It produces several different chemicals that the body needs to function properly. Researchers can benefit from converting images into data to more easily exchange and generate precise results. Since the process of conversion relies on technology and an algorithm, it eliminates the possibility of human error. Liver cancer has the highest fatality rate of any cancer since its symptoms don't present until late in the disease's progression, making early detection difficult. Skimming, sifting, segmenting, feature abstraction, and presentation via ANN (Artificial Neural Network) are the primary topics of the first stage of the debate. Real-time data sets use Feed-Forward Neural Network (FFNN) for identifying liver cancer and classifying its severity. Filtering has two primary applications: noise suppression and edge rounding. Then, segmentation is employed to isolate the relevant area, allowing for more compact data storage. The Gray Level and Co-occurrence Matrix (GLCM) is used to extract features, and the resulting matrix can have many different forms. This criterion helps classify tumors as benign or malignant. Metrics such as accuracy, sensitivity, positive and negative predictive values, and precision are used to assess the rate. The experimental method for identifying liver tumors uses CT liver pictures to achieve an average accuracy of 99.45% for malignant images. © 2023 IEEE.

关键词： computerized tomography

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