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AVirtual Cloud Storage Architecture for Enhanced Data Security

computer Systems science & engineering 2023年第2期44卷 1735-1747页

作者： M.Antony Joans Kumar C.Christopher Columbus E.Ben George T.Ajith Bosco Raj Department of Information Technology St.Xavier’s Catholic College of EngineeringNagercoil629003Tamil NaduIndia Department of Computer Science Engineering PSN College of Engineering and TechnologyTirunelveli627152India Department of Computer Science Engineering University of Technology and Applied SciencesMuscat112Oman Department of Electronics and Communications Engineering PSN College of Engineering and TechnologyTirunelveli627152India

The sensitive data stored in the public cloud by privileged users,such as corporate companies and government agencies are highly vulnerable in the hands of cloud providers and *** proposed Virtual Cloud Storage Archi-tecture is primarily concerned with data integrity and conﬁdentiality,as well as *** provide conﬁdentiality and availability,theﬁle to be stored in cloud storage should be encrypted using an auto-generated key and then encoded into distinct *** the encoded chunks ensured theﬁle integrity,and a newly proposed Circular Shift Chunk Allocation technique was used to determine the order of chunk ***ﬁle could be retrieved by performing the opera-tions in *** the regenerating code,the model could regenerate the missing and corrupted chunks from the *** proposed architecture adds an extra layer of security while maintaining a reasonable response time and sto-rage *** results analysis show that the proposed model has been tested with storage space and response time for storage and *** VCSA model consumes 1.5x(150%)storage *** was found that total storage required for the VCSA model is very low when compared with 2x Replication and completely satisﬁes the CIA *** response time VCSA model was tested with different sizedﬁles starting from 2 to 16 *** response time for storing and retrieving a 2 MBﬁle is 4.96 and 3.77 s respectively,and for a 16 MBﬁle,the response times are 11.06 s for storage and 5.6 s for retrieval.

关键词： Erasure code regenerating code cryptography virtual cloud storage architecture circular shift chunk allocation algorithm

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CNN and SSD-based Real-Time Motorcycle Ignition Control based on Helmet Detection 8

CNN and SSD-based Real-Time Motorcycle Ignition Control base...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Daniel, E. Akilan, L.H. Infant, P. Alwin Jainish, G.R. Panimalar Engineering College Department of Computer Science & Engineering Chennai City Campus Chennai India Loyola Icam College of Engineering and Technology Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350376425

The alarming rise in motorcycle accidents worldwide necessitates the development of effective safety measures. This research introduces a novel helmet identification system utilizing Single Shot Detection (SSD) algorithm and Convolutional Neural Network (CNN) technology. The proposed system combines feature extraction and object identification techniques to accurately determine whether riders are wearing helmets in real-time. By integrating helmet detection with ignition control, this system actively prevents motorcycles from starting without a helmet, ensuring enhanced rider safety. Experimental results demonstrate the effectiveness of the CNN-SSD based helmet detection system, achieving minimal false positives and high detection accuracy, making it suitable for real-world deployment. This innovative approach contributes to road safety and reduces the risk of injuries associated with motorcycle accidents. © 2024 IEEE.

关键词： Motorcycles

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Sensors Based Assistive Technology for Corona Healthcare Insights 1

Sensors Based Assistive Technology for Corona Healthcare Ins...

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1st International Conference on Computational Intelligence for Security, Communication and Sustainable Development, CISCSD 2024

作者： Rajendra Prasad, K. Narasimhulu, K. N Santhosh Kumar, C.H. Hyderabad India Rajeev Gandhi College of Engineering and Technology Department of Computer Science and Engineering Nandyal India Anurag Engineering College Department of Computer Science and Engineering Telengana Kodad India

ISBN: (数字)9798350365405

ISBN: (纸本)9798350365405

Nano-assistive technology is essential to scientific study in the medical sciences. It offers more substantial options for examining macro-ailments, such as the coronavirus illness (COVID-19). Nanotechnology can sense intrusive sensing of the macro-molecule size of nano-sensors and analyze antibody systems for COVID-19 patients during a pandemic. Traditional big data clustering methods, such as single-pass k-means (spkm), and mini-batch k-means (mmbk), can classify gene data based on molecule similarity structures. However, they suffered with the initial assessment of gene cluster tendency. This paper presents the novel similarity method, Multiview modeling cosine similarities (MMCS), for a better assessment of cluster tendency, combined with the visual technique of visual assessment of cluster tendency (VAT) for the clustering results. The invasive sensors collect data on the internal gene structure of the patient, and the proposed MMCS-VAT makes it easy to make visual classifications. There was a big change in the area under the curve (AUC) of the performance parameters in the experiment with COVID datasets. MMCS-VAT made the AUC better than mbkm by 22-23% and better than spkm and mbkm by 10-16% for all five performance parameters. It also proved that MMCS-VAT is 8 to 10 times and 10 to 15 times faster than spkm, and mbkm methods respectively for the big COVID 19 datasets. © 2024 IEEE.

关键词： COVID-19

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From Past to Present: A Comprehensive Review of Antenna Technology in Modern Wireless Communication

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Journal of engineering science and Technology Review 2024年第3期17卷 179-200页

作者： Mishra, Brijesh Singh, Aditya Kumar Satheesha, T.Y. Verma, Ramesh Kumar Singh, Vivek Department of Electronics and Communication Engineering School of Engineering and Technology CMR University Karnataka Bengaluru India Department of Electronics and Communication Engineering Noida Institute of Engineering and Technology U.P. Greater Noida India School of Computer Science Engineering REVA University Karnataka Bengaluru India Department of Computer Science and Engineering IMS Engineering College Uttar Pradesh Ghaziabad India Department of Electronics and Communication Engineering Nagarjuna College of Engineering and Technology Karnataka Bengaluru India

This research looks at microwave devices, specifically, patch antenna along with electromagnetic spectrum, shape, mechanism, analytical methods, simulation tools, and feeding procedures. Patch antennas are distinguished by their rectangular form, with a patch on one side and a ground plane on the other. Patch antennas work by exciting electromagnetic waves inside the patch, which are subsequently transmitted into the surrounding environment. The report also outlines numerous ways for evaluating the performance of microstrip patch antennas. The electromagnetic characteristics of the antenna are analyzed using the transmission line model, cavity model, and multiport network model (MNM). The integral equations that regulate the behavior of the antenna are solved using the method of moments (MoM) and the finite element method (FEM). The spectral domain technique (SDT) is used to analyze the antenna’s frequency response, while the finite difference time domain (FDTD) approach is used to analyze the antenna’s time-domain behavior. Overall, these methodologies give a thorough understanding of microstrip patch antenna performance and may be utilized to optimize their design. Furthermore, several patch antenna feeding methods, such as probe feed, microstrip line feeding, aperture coupling, proximity coupling, and CPW feed, are investigated. Attaching a microstrip line to the patch, which is subsequently linked to the RF source, is what microstrip line feeding entails. Aperture coupling entails making a hole in the ground plane that allows the RF source to feed the patch directly. Proximity coupling is accomplished by placing a probe near the patch, which creates an electromagnetic field on the patch. Patch antenna simulation software includes programmers such as HFSS, CST, and FEKO. These tools simulate the patch antenna’s performance, including its radiation pattern, gain, and input impedance. These simulations may be used to optimize the patch antenna design for specific a

关键词： Microstrip antennas

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Compact 1D Self-Operational Neural Networks with Feature Injection for Global ECG Classification 4

Compact 1D Self-Operational Neural Networks with Feature Inj...

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Sengottaiyan, N. Sathish Kumar, S. Sharma R, Rajesh Sungheetha, Akey Chinnaiyan, R. Hamsanandini, S. Alliance University Department of Computer Science and Engineering Bangalore India Adiyaman College of Engineering Department of It Hosur India Kongu Engineering College Department of Computer Science and Engineering Perundurai India

ISBN: (纸本)9798350307757

for solving problems in ECG classification readings for rhythm detection, a novel deep learning approach is presented in this paper. To capture important information about heart cycles like morphology and timing, a proposed technique integrates feature injection with a compact 1D Self-Operational Neural Network (Self-ONN). This allows for fluent and engaging data recording. With superior performance, the network surpasses current models in classification while maintaining minimal computational complexity. Its outstanding results are achieved through training with the MIT-BIH arrhythmias benchmark database. The network displays exceptional levels of accuracy, recall, and F1-score for normal segments (99%), supraventricular ectopic beats (82%), and ventricular ectopic beats (94%), demonstrating its remarkable capabilities. Here study demonstrates the applicability of the approach in wearable ECG sensors for self-monitoring and early detection of cardiovascular diseases. The paper offers comprehensive insights into network architecture, data processing stages, as well as comparisons with current algorithms. The suggested approach outperforms others in terms of F1-score, accuracy, sensitivity, specificity, and positive predictive value when applied to various beat types. The report contains a computational complex analysis that emphasizes the compactness of the model and its importance in reducing the difference in performance between patient-specific and global ECG classification techniques. © 2024 IEEE.

关键词： Electrocardiograms

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Analysis and Performance Evaluation of various Artificial Intelligence techniques for Disease Prediction 7

Analysis and Performance Evaluation of various Artificial In...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Asha, P. Refonaa, J. Jany Shabu, S.L. Rishi, S. Padma Suresh, L. Praveen, S. Sathyabama Institute of Science and Technology Computer Science and Engineering Chennai India Baselios Mathews II College of Engineering Computer Science and Engineering Kollam India

ISBN: (纸本)9798350372816

The framework of the proposed system utilizes AI, machine learning procedures and data handling methodologies to analyze, gather data and reports from clinics or patient facilities, and prognosticate reports that assist the outcomes used to foresee various kinds of infection and disease. The most recurrently occurring disease and its infection is the dominant source for the human's death. Clinical endlessly reports from different documents have been taken out to analyze and diagnose breast malignant growth or cancer, coronary or heart illness, and diabetes which are very frequent in present day people, and their wellbeing is significant in the finding. Today, propels in clinical innovation are assisting individuals with improving on the manner in which they give medical clinics and doctors' the most straightforward method for treating, oblige, and treat any infection they need. We have brought this sort of framework into our application, which can give positive and negative outcomes to make individuals' lives simpler to anticipate the sickness by bringing explicit data into their reports, and backing the theory of the infection with the choice of looking for exhortation good clinics and great specialists close to clients or guardians in our future enactments. © 2024 IEEE.

关键词： Logistic regression

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Small Intestine Cancer Prediction using Deep Learning: A Comparative Study of CNNs and RNNs 3

Small Intestine Cancer Prediction using Deep Learning: A Com...

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3rd International Conference on Distributed Computing and High Performance Computing, DCHPC 2024

作者： Sherin Beevi, L. Joe Prathap, P.M. Reddy, Pulimi Hanith Sai Kumar Priya, Gomasani Bhanu Sai R.M.D. Engineering College Department of Computer Science and Engineering Kavaraipettai India R.M.D. Engineering College Department of Computer Science and Engineering India

ISBN: (纸本)9798350381580

Small bowel cancer is one of the rare tumors that often stays untreated until it has spread to a more advanced stage. Cancer is the leading cause of death in the globe. Early detection is critical for successful treatment, and machine learning algorithms have showed promise in detecting cancer at its early stages. In this article, we offer a deep learning-based technique to predicting small intestinal cancer that employs Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). We used a dataset of clinical characteristics and biomarkers from small intestinal cancer patients to train and evaluate our models. Our findings reveal that the proposed models were highly accurate in predicting small intestinal cancer, with the CNN-based model slightly outperforming the RNN-based model. We also compared our approach to other cutting-edge methodologies, and discovered that the proposed models surpassed them in terms of accuracy. Our study indicates the potential of deep learning-based approaches for predicting small intestine cancer and provides information on the relative efficacy of CNNs and RNNs. Our findings can help design more accurate and efficient cancer prediction models for small intestinal cancer. © 2024 IEEE.

关键词： Recurrent neural networks

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Attention-based convolutional dual sparse autoencoder for analyzing air, water and heat stress in poultry farms for egg production

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Multimedia Tools and Applications 2025年第14期84卷 14105-14137页

作者： Deepika Nagarathna Channegowda Department of Computer Science and Engineering P E S College of Engineering Karnataka Mandya India P E S College of Engineering Karnataka Mandya India Zeus Biotech Pvt Limited Karnataka Mysore570016 India

Heat, water, and air maintenance are essential for effective environmental management in laying hen poultry farms. An efficient automated prediction of an egg production model based on a deep learning model is proposed to overcome the existing drawbacks. The air, water and heat parameter details are collected in real time and then pre-processed using data normalization and a one-hot encoding process. The most significant features are selected using an Improved Gannet Optimization assisted Linear Discriminant Analysis (IGO-LDA) algorithm to minimize computational complexity. The proposed Attention-based Convolutional Dual Sparse Autoencoder (AtN_Conv-DSAE) model predicts parameters like air, heat and water quality. To optimize the loss function in the network model, the hyperparameters are effectively tuned using the Chaotic Mud Ring (CMR) algorithm. The proposed classifier predicts the quality of parameters as low, medium and high for the development of egg production in poultry farms. The proposed methodologies are implemented for better prediction using the Python simulation platform. An accuracy of 98.18% is attained for the air dataset, 98.22% for the water dataset and 99.33% for the heat dataset. The proposed work promotes improved outcomes compared with several existing methodologies. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Environmental management

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On the Complexity Aspects of Various Sub-classes of Linear Languages 9

On the Complexity Aspects of Various Sub-classes of Linear L...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Praveen, G.L. Sunil, K.S. Mar Baselios College of Engineering And Technology Department of Computer Science & Engineering Kerala Thiruvananthapuram India Government Engineering College Idukki Department of Computer Science & Engineering Kerala Painavu India

ISBN: (纸本)9798350394474

The non-terminals in the underlying grammar play an important role in determining different characteristics of the language generated. The languages generated by the grammars are determined by a unique non-terminal known as the start symbol. Even without altering any of the production rules, the grammar produces a distinct language if we swap the start symbol for any other non-terminal. Occasionally, we purposefully include extra non-terminals to attain specific characteristics in both the grammar and parse trees. One such scenario is when we convert a language to Chomsky's Normal Form (CNF), in which case we add additional non-terminals to convert the parse tree into a binary tree. In this paper, we consider different sub classes of CFLs by restricting the number of nonterminals. We study different characteristics like the generative power of the grammar, the stack height, and the number of turns of the pda which accept the languages produced by the grammars. We show that these characteristics are influenced by the number of nonterminals in the grammar. © 2024 IEEE.

关键词： Binary trees

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A Comprehensive Analysis of Human Action Recognition for Noisy Videos Using CNN and LSTM

A Comprehensive Analysis of Human Action Recognition for Noi...

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2024 IEEE International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Suma, S. Patel, Punyaban Tiwari, Rajesh Gundu, Lavanya Devi, Guntoju Kalpana Raju, K. Srujan Department of Computer Science and Engineering CMR Technical Campus Telangana Hyderabad India Department of Computer Science and Engineering CMR College of Engineering & Technology Hyderabad India Department of Computer Science and Engineering CMR Engineering College Hyderabad India Department of Computer Science and Engineering CMR Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9798331505134

Human action identification has advanced significantly as a result of the development of deep learning algorithms. Convolutional Neural Networks (CNNs) are known for their adeptness at extracting crucial information within video frames, especially shapes and postures. However, understanding movements necessitates not just individual frames as well as the temporal flow of the movement;this is wherein LSTM (long short-term memory) variants of Recurrent Neural Networks (RNNs) come into play. By analyzing frame sequences, LSTMs are particularly effective in gathering temporal connections, recalling prior motions, and overcoming the gap amongst static features as well as dynamic actions. Even with CNNs and LSTMs' remarkable accuracy in human action recognition (HAR), challenges remain. © 2024 IEEE.

关键词： Convolutional neural networks

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