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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Rajvanshi, Anita Mahajan, Prerna Singh, Alka Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Bangalore India Noida Institute of Engineering and Technology Computer Applications Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350357769

This essay delves into the concept of 'Smart Cities,' outlining its numerous benefits. It elucidates the diverse applications and services essential for transforming a city into a Smart City, underscoring the pivotal role of Information and Communication Technology (ICT) in their realization. Furthermore, it scrutinizes the challenges posed by the deployment of Smart Cities for contemporary ICT systems. The study investigates the promised attributes of 5G technology and its potential as a linchpin for the efficient implementation of urban smart cities. Additionally, it provides a catalog of 5G-enabled Smart City use cases, emphasizing how 5G will serve as the bedrock for the Internet of Things (IoT), fostering the evolution of smart cities. © 2023 IEEE.

关键词： 5G technology ICT infrastructure Information and Communication Technology Internet of Things Quality of Life Smart Cities Smart ICT infrastructure Sustainable City Urban Transit and City Governance

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A Novel Approach to Optimize EV's Dealing and Charging

A Novel Approach to Optimize EV's Dealing and Charging

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Vijay, Sandip Gobi, N. Sharma, Ambrish Kumar Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Bangalore India Computer Applications Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350357769

It's an interesting idea to investigate the energy market as a potential means of reducing the power costs related to charging electric cars at public charging stations. Through intraday energy markets., several nations allow electricity trading until just before the delivery time. This research utilizes the market as an illustration to investigate the possibility of lowering power prices via intraday market trading. After conducting extensive simulations., the results show that charging schedule optimization combined with intraday trading in the markets may result in cost savings of around 9% when compared to obtaining all needed energy from the energy provider. A further 2% or more of cost savings may be realized by giving the charging station manager the ability to resell energy to the everyday electricity market. The research explores how the intraday electricity market's lead time and trading unit affect total expenses in addition to any possible cost savings. Lead time is shown to have a major impact on feasible power prices., whereas each trading unit has a negligible impact on the total cost dynamics. © 2023 IEEE.

关键词： charging schedule Electric vehicles (EV's) electricity costs mixed-integer linear programming trading

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Secure Communication over IoT Networks using Mutual Authentication

Secure Communication over IoT Networks using Mutual Authenti...

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2024 International Conference on Computational Intelligence and Computing applications, ICCICA 2024

作者： Verma, Dinesh Chander Chopra, Amit Kanika Kaushik, Vinita Kaur, Jaspreet Panipat Institute Of Engineering & Technology Department Of Computer Applications Panipat India Maharaja Agrasen University Department Of Computer Science & Engineering H.P Baddi India Thompson Rivers University KamloopsBC Canada

ISBN: (纸本)9798350306446

Internet of Things (IoT) network transmit the data in an open environment and it can be intercepted by unauthorized users. Different sensors share their data with each other without authentication and it becomes a major security concern. Data privacy and integrity are other security issues. In this paper, a secure communication using mutual authentication model will be introduced to secure the transmisssion over IoT networks. It will isolate the communication between filed sensors and gateways as well as it will also protect the data exchange between gateways and server. Its performance will be analyzed using. Long Range Low powered technology (LoRa) under the constraints of intruder density that varies from 5 to 25 nodes with the legitimate sensors density (100-600) © 2024 IEEE.

关键词： Authentication

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Retinal Scan Denoising Using Generative Adversarial Networks: A Deep Convolutional Approach 5

Retinal Scan Denoising Using Generative Adversarial Networks...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Gowthami, G. Mano Shankari, J. Babu, Tina St. Francis de Sales College Department of Computer Applications Bengaluru India Alliance School of Advanced Computing Department of Computer Science and Engineering Alliance University Bengaluru India

ISBN: (纸本)9798331518097

Image denoising is needed if there is a need to remove noise while preserving details or structures in images in computing vision and image processing. Recent advancements in generative adversarial networks (GANs) and convolutional neural networks (CNNs) have only improved the application of denoising techniques in varying ways. These methods give clearer and more accurate images since these approaches are able to separate noise from image substance. One of the main use of application of the image denoising is that they enhance the images in media, movies and also in medical field where high quality of images are required for diagnosing the diseases. This paper examines the significance of image denoising and introduces a novel GAN-based retinal scan denoising technique that outperforms both CNN-based denoising and conventional Gaussian filtering, achieving higher performance (PSNR: 27). It outperforms other approaches with SCD of 10 dB, SSIM of 0.866 and MSE of 18.99. In this paper, we propose a GAN-based solution for denoising images and leveraging deep learning-based techniques for evaluating the quality of retinal scans and we demonstrate the potential of our approach for enhancing the quality of the images. © 2024 IEEE.

关键词： Generative adversarial networks

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Reversible Data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

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IEEE Internet of Things Journal 2025年第13期12卷 23933-23945页

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： Cryptography Encryption Security Data mining Adaptive coding Correlation Cloud computing Polynomials Image coding Streaming media

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Deep Learning-Based Approach for Detecting Copy-Move Forgery in Digital Images 27

Deep Learning-Based Approach for Detecting Copy-Move Forgery...

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27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024

作者： Dar, Navid Ahmad Atsya, Rani Kumar, Anuj Sharda University dept. of Computer Science and Engineering UP Greater Noida India Greater Noida Institute of Technology dept. of computer applications UP Greater Noida India

ISBN: (纸本)9798350392319

Copying and pasting a portion of the same image to hide another portion is the most common method of fraudulent image enhancement. Cloning a restricted portion of an image and pasting it once or more inside a comparable picture is known as copy-move image forgery (CMF). The procedure aims to highlight or hide a certain aspect in the finished picture, usually a person or an object. The harm brought about by the horrible deed frequently takes unexpected forms. For the automated identification of CMF, the paper proposes a Generative Adversarial Network (GAN) and Convolutional Neural Network (CNN) approach. The CASIA 2.0 dataset's, which includes both real and fake photos, was used in the study. The proposed model is simple to use, and its application shows that it is faster than previous approaches that are now regarded as state of the art. The results show that, when compared to the other currently available methods, the recommended approach achieves the highest level of accuracy (90.56%). © 2024 IEEE.

关键词： Generative adversarial networks

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Oral Cancer Classification Using GLRLM Combined with Fuzzy Cognitive Map and Support Vector Machines from Dental Radiograph Images 1

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1st International Conference on Intelligent Computing for Sustainable Development, ICICSD 2023

作者： Anuradha, K. Fathima, H. Kavithamani, K. Uma, K.P. School of Computer Applications Karpagam College of Engineering Tamil Nadu Othakkal Mandapam India Department of Science and Humanities Hindusthan College of Engineering and Technology Tamil Nadu Coimbatore India

ISBN: (数字)9783031612985

ISBN: (纸本)9783031612978

In recent years, Fuzzy Cognitive Maps (FCM) are widely used in medical decision schemes. According to data, oral cancer is the fifth most common cancer in India, with a higher occurrence among men than women. In order to save lives, correct diagnosis and prompt treatment is required. When oral cancer is identified early, the 5-year survival rate exceeds 80%, but advanced stages of the illness have 5-year survival rates that are less than 20–30%. But, the majority of cases are detected in advanced stages where treatment becomes unsuccessful. To achieve a better classification of oral cancers, Gray Level Run Length Matrix (GLRLM) is combined with Fuzzy Cognitive Map and Support Vector Machines. In this work, fifty Dental Radiograph images are used for cancer classification. Initially, Linear contrast stretching algorithm is employed to remove the noise present in the image. The filtered picture is split to create a Region of Interest (ROI). The ROI picture is used to extract the GLRLM features. The classification of cancer is based on the features extracted. From the extracted features, a fuzzy cognitive map is employed. Cancer categorization is carried through Machine Learning approaches. Measures like sensitivity, specificity, precision, accuracy, and F-score have been used to describe the performance of the SVM classifier. The study’s findings demonstrate that GLRLM-based features are strong discriminating features for statistical analysis of Dental Radiograph images and can be helpful in the identification of cancer. By combining the texture features based on GLRLM and FCM, the accuracy (94%) as a whole is improved. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Support vector machines

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Intelligent analysis to detect phishing websites using machine learning ensemble techniques

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Human-Intelligent Systems Integration 2024年第1期6卷 39-47页

作者： Pandey, Mithilesh Kumar Pal, Rekha Pal, Saurabh Kumar, Alok Shukla, Arvind Kumar Yadav, Dhyan Chandra Department of Computer Applications VBS Purvanchal University Jaunpur India Department of Computer Applications Vivek College of Education Bijnor India School of Computer Science & Applications IFTM University Moradabad India Department of Computer Science and Engineering MUIT Lucknow India

The Internet has grown to be a vital part of our everyday existence. Web browsing is the most popular Internet service. A lot of people use their browser for banking, online shopping, bill paying, and mobile phone recharging. Due to the extensive use of this service, users are exposed to many security risks, including cybercrime. One kind of online danger that lures consumers into connecting with a phoney website is cyber phishing. This study paper’s primary objective is to safeguard sensitive user data. The suggested model is created in three stages. In the first phase, we select a dataset to train on and subsequently use the dataset to test classifiers. After applying the three classifiers in step 2 and finishing all of the predictions in step 3, we found that XGBoost performed better than the machine learning techniques AdaBoost and Gradient boosting.

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Stacked Bi-LSTM for Advanced Toxicity Detection in Comment Classification 6

Stacked Bi-LSTM for Advanced Toxicity Detection in Comment C...

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6th IEEE International Conference on Artificial Intelligence in engineering and Technology, IICAIET 2024

作者： Kumar, N. Naveen Abraham, Prabhakaran Kaliba, Syed Ibrahim Diniesh, V.C. Maruthamuthu, R. Balamurugan Seshu Kumar, P. Vekkudu, Naveen Madanapalle Institute of Technology & Science Department of Computer Applications Andhra Pradesh Madanapalle India Mai-nefhi College of Engineering and Technology Department of Computer Science and Engineering Mainefhi Eritrea Kongu Engineering College Department of Electronics & Communication Engineering Tamil Nadu Erode India Jain University Department of Information Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350389692

The surge in Internet usage has revolutionized online forums, providing dynamic forums for active participation and meaningful debates. However, this has also exposed users to the risk of harassment. Efforts to address toxic comments in online forums have faced challenges, with current solutions unveiling unreliability. Advances in hardware, cloud computing, and natural language processing (NLP) enable the development of more robust approaches, predominantly NLP-based deep learning models. Pre-trained transformer models are gradually utilized for accurate toxic comment classification, bigger traditional methods. Stacked LSTM models, emphasizing hierarchical feature learning and improved contextual understanding, outperform single layer counterparts, offering an effective solution for online content moderation and sentiment analysis. This research indicates a significant stride towards more dependable and sophisticated toxic comment detection, addressing the challenges of social media effectively. © 2024 IEEE.

关键词： Sentiment analysis

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Deep Convolutional Network Based Machine Intelligence Model for Satellite Cloud Image Classification

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Big Data Mining and Analytics 2023年第1期6卷 32-43页

作者： Kalyan Kumar Jena Sourav Kumar Bhoi Soumya Ranjan Nayak Ranjit Panigrahi Akash Kumar Bhoi Department of Computer Science and Engineering Parala Maharaja Engineering CollegeBerhampur 761003India Amity School of Engineering and Technology Amity UniversityUttar Pradesh 201303India Department of Computer Applications Sikkim Manipal Institute of TechnologySikkim Manipal UniversitySikkim 737102India Directorate of Research Sikkim Manipal UniversityGangtokSikkim 737102India

As a huge number of satellites revolve around the earth,a great probability exists to observe and determine the change phenomena on the earth through the analysis of satellite images on a real-time ***,classifying satellite images plays strong assistance in remote sensing communities for predicting tropical *** this article,a classification approach is proposed using Deep Convolutional Neural Network(DCNN),comprising numerous layers,which extract the features through a downsampling process for classifying satellite cloud *** is trained marvelously on cloud images with an impressive amount of prediction *** time decreases for testing images,whereas prediction accuracy increases using an appropriate deep convolutional network with a huge number of training dataset *** satellite images are taken from the Meteorological&Oceanographic Satellite Data Archival Centre,the organization is responsible for availing satellite cloud images of India and its *** proposed cloud image classification shows 94% prediction accuracy with the DCNN framework.

关键词： satellite images satellite image classification cyclone prediction Deep Convolutional Neural Network(DCNN) features layers down-sampling process

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