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

作者： Kumari, Ankita Gupta, Deepali Uppal, Mudita Kumar, Komuravelly Sudheer Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Science & Artificial Intelligence SR University Warangal India

ISBN: (纸本)9798331505134

Ransomware attacks which use system flaws to lock private data and mess with important financial operations threaten the banking industry more and more. Significant financial losses, harm of image, and falling client trust follow from these strikes. Conventional protection solutions are not always able to match the smart and changing character of ransom-ware. This work offers a new way of banking protection by use of deep learning techniques to successfully address malware risks. Deep learning algorithms are used to quickly find and stop malware activity as they can study huge amounts of data and spot detailed trends. This paper uses a proactive and adaptable approach to safeguard financial institutions from the rising risk of ransom-ware attacks, therefore demonstrating the potential of deep learning to alter banking cybersecurity. Examining coming and leaving data packets helps the system to see any dangers and stop them cold. An artificial intelligence-based technique helps it to more exactly spot emerging risks. In this view, in addition to stopping ransom-ware from getting access to private e-commerce data, the effectiveness of the warning system has dropped financial loss and brand damage. Moreover, in the tests this model showed an 89% accuracy rate, therefore reducing ransom-ware-related risks. Including an artificial intelligence-enabled smart chip into an e-commerce system helps businesses to improve their security stance. This means that they are fewer likely targets of ransom-ware attacks. It therefore became a necessary tool for properly addressing cyber security issues and lowering the risk ransom-ware brought to companies. © 2024 IEEE.

关键词： Cyber attacks

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Reversible Data Hiding in Shared Images with Separate Cover Image Reconstruction and Secret Extraction

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IEEE Transactions on Cloud Computing 2024年第1期12卷 186-199页

作者： Xiong, Lizhi Han, Xiao Yang, Ching-Nung Shi, Yun-Qing Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Engineering Research Center of Digital Forensics Ministry of Education Nanjing210044 China Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China National Dong Hwa University Department of Computer Science & Information Engineering Hualien974301 Taiwan New Jersey Institute of Technology ECE Department NewarkNJ07102 United States

Reversible data hiding is widely utilized for secure communication and copyright protection. Recently, to improve embedding capacity and visual quality of stego-images, some Partial Reversible Data Hiding (PRDH) schemes are proposed. But these schemes are over the plaintext domain. To protect the privacy of the cover image, Reversible Data Hiding in Encrypted Images (RDHEI) techniques are preferred. In addition, the full separability of cover image reconstruction and data restoration is also an important characteristic that cannot be achieved by most RDHEI schemes. To solve the issues, a partial and a complete Reversible Data Hiding in Shared Images with Separate Cover Image Reconstruction and Secret Extraction (RDHSI-SRE) are proposed in this paper. In the proposed schemes, the secret data is divided by Secret Sharing (SS). Then, the marked shared images are generated based on the proposed modify-and-recalculate strategy. The receiver can extract embedded data and reconstruct the image separably using k-out-of-n marked shared images. In the embedding phase of partial RDHSI-SRE (PRDHSI-SRE), the pixel values are modified according to the proposed Minimizing-Square-Errors Strategy to achieve high visual quality, and the complete RDHSI-SRE (CRDHSI-SRE) embeds data by modifying random coefficients to achieve reversibility. The experimental results and theoretical analyses demonstrate that the proposed schemes have a high embedding performance. Most importantly, the proposed schemes are fault-tolerant and completely separable. © 2013 IEEE.

关键词： Image reconstruction

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Literature Survey on Design and Analysis of Microstrip Patch Antenna for Wireless Application

Literature Survey on Design and Analysis of Microstrip Patch...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Pushpanjali, J. Ezhilarasan, K. Satheesha, T.Y. CMR University India Bangalore Institute of Technology India CMR University School of Engineering and Technology Dept. of ECE Bengaluru India REVA University School of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350361537

The rapid evolution of wireless communication technologies necessitates the continuous enhancement of antenna systems to meet the growing demands for compactness, flexibility, and multifunctionality. Among the myriad antenna technologies, microstrip patch antennas (MPAs) have emerged as a dominant and versatile solution for wireless applications. This literature survey reviews recent advancements in the design and analysis of microstrip patch antennas, focusing on their application in wireless communication. The survey explores various design techniques, analytical and numerical methods, emerging applications, and the challenges faced in the field. By synthesizing the latest research findings, this survey aims to provide a comprehensive overview of the state-of-the-art in microstrip patch antenna technology for wireless applications. © 2024 IEEE.

关键词： Slot antennas

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Study on Sales Forecast and Impact of New Energy Electric Vehicles Based on BP Neural Network 2

Study on Sales Forecast and Impact of New Energy Electric Ve...

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2nd IEEE International Conference on Sensors, Electronics and computer engineering, ICSECE 2024

作者： Li, Xiaoyang Tian, Changhao Liu, Zhixuan Zeng, Xiangrui International Engineering College Xi'an University of Technology Xi'an China School of Computer Science and Technology Herzen University St. Petersburg Russia School of Computer and Communication Engineering Nanjing Tech University Pujiang Institute Nanjing China School of Petrochemical Engineering Liaoning Petrochemical University Fushun China

ISBN: (纸本)9798350373646

This paper presents a study on sales forecasts and influencing factors of new energy electric vehicles based on the BP neural network. In order to predict the development of China's new energy-electric vehicle industry, a predictive model is established using the BP neural network, which undergoes training at each stage. The error distribution histogram of the training results demonstrates the model's strong fitting effect, providing a reliable basis for future predictive analysis. Additionally, unit root testing (ADF testing) is conducted to assess the stationarity of the global new conventional energy vehicle sales time series from 2013 to 2023. Through impulse response analysis, the impact of new energy vehicle sales on traditional vehicle sales is observed, revealing early technological inadequacies and the gradual increase in market awareness. Furthermore, variance analysis quantifies the gradual growth impact of new energy vehicles on traditional vehicle sales. © 2024 IEEE.

关键词： ADF testing BP neural network New energy electric vehicle Variance analysis

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RGB-guided hyperspectral image super-resolution with deep progressive learning

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CAAI Transactions on Intelligence Technology 2024年第3期9卷 679-694页

作者： Tao Zhang Ying Fu Liwei Huang Siyuan Li Shaodi You Chenggang Yan School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina Satellite Information Intelligent Processing and Application Research Laboratory Beijing Institute of Remote SensingBeijingChina Key Laboratory of Spectral Imaging Technology Xi'an Institute of Optics and Precision Mechanics of Chinese Academy of SciencesXi'anChina Institute of Informatics University of AmsterdamAmsterdamNetherlands School of Communication Engineering Hangzhou Dianzi UniversityHangzhouChina

Due to hardware limitations,existing hyperspectral(HS)camera often suffer from low spatial/temporal ***,it has been prevalent to super-resolve a low reso-lution(LR)HS image into a high resolution(HR)HS image with a HR RGB(or mul-tispectral)image *** approaches for this guided super-resolution task often model the intrinsic characteristic of the desired HR HS image using hand-crafted ***,researchers pay more attention to deep learning methods with direct supervised or unsupervised learning,which exploit deep prior only from training dataset or testing *** this article,an efficient convolutional neural network-based method is presented to progressively super-resolve HS image with RGB image ***-ically,a progressive HS image super-resolution network is proposed,which progressively super-resolve the LR HS image with pixel shuffled HR RGB image ***,the super-resolution network is progressively trained with supervised pre-training and un-supervised adaption,where supervised pre-training learns the general prior on training data and unsupervised adaptation generalises the general prior to specific prior for variant testing *** proposed method can effectively exploit prior from training dataset and testing HS and RGB images with spectral-spatial *** has a good general-isation capability,especially for blind HS image *** experimental results show that the proposed deep progressive learning method out-performs the existing state-of-the-art methods for HS image super-resolution in non-blind and blind cases.

关键词： computer vision deep neural networks image processing image resolution unsupervised learning

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Early Prediction and Diagnosis Cardiovascular Disease Using Deep Learning Models

Early Prediction and Diagnosis Cardiovascular Disease Using ...

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2024 IEEE International Conference on Emerging Technologies in computer science for Interdisciplinary Applications, ICETCS 2024

作者： Rani, Rudra Yamini Prasad, Mudarakola Lakshmi Reddy, Induru Sunnihith Tayubi, Iftikhar Aslam Sultan, Ghazala Reddy, Pundru Chandra Shaker VNR Vignana Jyothi Institute of Engineering and Technology Department of Information Technology Telangana Hyderabad India Computer Science and Engineering Institute of Aeronautical Engineering Telangana Dundigal India Computer Science and Business Systems RMK Engineering College Tamilnadu Tiruvallur India King Abdulaziz University Faculty of Computing and Information Technology Rabigh Jeddah Saudi Arabia Aligarh Muslim University Department of Computer Science Uttar Pradesh Aligarh India SR University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India

ISBN: (纸本)9798350372502

Cardiovascular disease (CVD) risk assessment and prognosis in otherwise healthy people is an important part of disease management. Early detection and diagnosis of CVD, supported by the extensive health data on the condition already stored in hospital systems, has the ability to greatly improve disease outcomes. Clinical practice for the management of Cardiovascular Diseases (CVDs) stands to benefit greatly from the use of machine learning approaches. Patients and the healthcare system can save money thanks to these methods, which can be used to create evidence-based clinical guidelines and management algorithms. This will cut down on expensive and time-consuming clinical and laboratory examinations. This study suggests creating new deep learning algorithms that can automatically identify important variables and detect early-stage heart disease;these algorithms should be resilient, effective, efficient, and unique in order to improve early anticipation and interference for CVDs. An F1-score of around 89% is produced by the suggested Catboost model. Hence, it outperformed numerous other state-of-the-art methods by maximizing classification efficiency with better correctness and precision. © 2024 IEEE.

关键词： Hospitals

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Forecasting EV Market Trends in India: A Deep Learning Approach for Two/Three-Wheelers 12

Forecasting EV Market Trends in India: A Deep Learning Appro...

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12th International Electrical engineering Congress, iEECON 2024

作者： Mishra, Rishav Dev Kumar Dash, Santanu Chudjuarjeen, Saichol Korkua, Suratsavadee School Of Computer Science And Engineering Vellore Institute Of Technology Vellore India Vellore Institute Of Technology TIFAC-CORE Vellore India Rajamangala University Of Technology Krungthep Faculty Of Engineering Dept. Electrical Engineering Bangkok Thailand School Of Engineering And Technology Walailak University Thailand

ISBN: (纸本)9798350383591

This research paper presents a comprehensive analysis of machine learning models for predicting future sales of electric vehicles (EVs) in the Indian Market. With a specific focus on 2 and 3 wheeler sales. The study compares different machine learning approaches, including Linear Regression, Support Vector Regression (SVR), Long Short-Term Memory (LSTM) Neural Network, Through an evaluation of their respective errors, LSTM Neural Network method demonstrates superior prediction accuracy, making it the preferred model for forecasting EV sales, specifically 2 wheeler and 3 wheeler sales over the next five years. The paper aims to present the forecasted sales figures, in hopes of providing valuable insights into the future trajectory of EV sales and serving as a vital resource for stakeholders in the electric vehicle industry. © 2024 IEEE.

关键词： Long short-term memory

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Novel blockchain-based framework to publish, verify, and store digital academic credentials of universities

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International Journal of Information Technology (Singapore) 2024年第5期16卷 3273-3281页

作者： Chandra, Tara Kaur, Mandeep Rakesh, Nitin Gulhane, Monali Maurya, Sudhanshu Department of Computer Science and Engineering School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India

Blockchain is the technology of choice to create cryptocurrency and bitcoins through the maintenance of immutable distributed ledgers in multiple nodes. Analysis indicates that less emphasis is being placed on the implementation of Blockchain in the educational sector. Most of the academic applications, apply the weak proof of work or proof of stack as their consensus mechanism. This research proposes a model based on blockchain technology that will publish, verify, and store academic credentials of university stakeholders through the trustworthy proof of authority (PoA) as its consensus mechanism. The web-based model also introduces smart contract for issuing the certificate to the blockchain network and file listing mechanism for revocation of issued digital certificate. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Academic certificate verification Blockchain Consensus mechanism Proof of authority (PoA) Smart contract

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Mitigation for cloud computing security risks and governance

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International Journal of Cloud Computing 2022年第5-6期11卷 560-567页

作者： Jabez, J. Narmadha, R. Porkodi, S. Devi, L. School of Computing Sathyabama Institute of Science and Technology Chennai India School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Chennai India College of Engineering and Computer Science Lebanese French University Erbil Iraq PGCS Department Muthayammal College of Arts and Science Rasipuram India

The recent prosperous of cloud computing has become the biggest revenue for originalities with the issues of intricate servers that need regular maintenance, security, power and cooling systems throughout. Hence, it is extremely significant for any cloud service earner is to offer dense security models and policies to all users involves. Although creating best suited security strategies might necessitate enormous amount of reserve and endurance, it will bounce the casual for end user to relay on a computerised security system throughout. The cloud environs supplementary strongly secure, qualifying security hazards, appropriate controls must be imposed. Major contest is to consider cloud security and governance explanation together. In this segment, the emphasis will be in the contests involved in preserving a dependable security policy computerisation in at the application layer, network layer and host level of cloud computing. Moreover, securities related to models will be discussed to recruit for those security applications which are more composite and active. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Security systems

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Cocoon morphological Features Based Silk Quality Prediction Using XG Boost Algorithm

Cocoon morphological Features Based Silk Quality Prediction ...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Nivetha, M. Sudha, I. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350348460

Silk quality is a critical determinant in the silk industry, significantly influencing the market value of silk products. This study proposes an innovative method for forecasting silk quality by leveraging cocoon morphological features through the application of the XG Boost algorithm. The morphological characteristics of the cocoon, including dimensions, shape, and colour, are identified as key indicators of silk quality. The XG Boost algorithm, known for its effectiveness in handling complex datasets, is employed for building a predictive model. A comprehensive dataset is assembled, encompassing a diverse set of cocoon morphological features obtained from a representative sample of silkworms. These features comprise cocoon size, length, width, shape parameters, and colour attributes. The dataset is then utilized to train and validate the XG Boost model, fine-tuning its parameters to ensure accurate prediction of silk quality. The outcomes of this study showcase the effectiveness of the proposed approach, demonstrating high predictive accuracy in determining silk quality based on cocoon morphological features. Moreover, the XG Boost algorithm facilitates the identification of crucial features that significantly contribute to the prediction, offering valuable insights into the factors influencing silk quality. In conclusion, the integration of cocoon morphological features and the XG Boost algorithm presents a promising avenue for accurate silk quality prediction. This research contributes to the advancement of silk production methodologies, introducing innovative technologies to the silk industry and fostering a pathway for the adoption of cutting-edge practices. © 2024 IEEE.

关键词： Data Security Deep Learning Machine Learning Quality Prediction Silk Quality XGBoost Algorithm

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