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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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Leveraging Machine Learning for Pattern-Based Detection of Respiratory Illnesses

Leveraging Machine Learning for Pattern-Based Detection of R...

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2025 International Conference on Artificial Intelligence and Data engineering, AIDE 2025

作者： Shetty, Anusha Prashanth Deepthi, L. Puthran, Raksha Shetty, Surendra Hegde, Pavan Kumar, Shravan NMAM Insitute of Technology Department of Master of Computer Applications Karkala Nitte India Sahyadri College of Engineering and Management Department of Computer Science and Engineering Mangalore India Father Muller Medical College and Hospital Department of Paediatrics Mangalore India

ISBN: (纸本)9798331527518

Lung sound analysis presents a promising, noninvasive, and potentially cost-effective approach for detecting respiratory diseases. This research aims to assess the feasibility of using lung sounds for pattern recognition and detection of various respiratory disorders, specifically by conducting an analysis of cough sounds. We review a range of recent studies that explore the use of deep learning models, machine learning algorithms, and feature extraction techniques in this context. These studies are categorized based on their methodologies, and their performance in accurately classifying lung sounds is discussed. Research findings highlight the significant diagnostic potential of lung sound analysis, demonstrating improved classification accuracies across various disease types. However, challenges such as data inconsistency, noise interference, and limited model generalizability remain. It captures the current state of lung sound analysis research in respiratory disease detection, offering insights that could drive future advancements in this critical field. © 2025 IEEE.

关键词： Lung cancer

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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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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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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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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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Blood Cell Classification and Counting in Hematology through Deep Learning 4

Blood Cell Classification and Counting in Hematology through...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Ananthi, P. Devi, K. Nirmala Ramya, T.E. Shanmugapriya, P. Aradhya, T. Kongu Engineering College Department of Computer Technology Perundurai Tamil Nadu Erode India Madanapalle Institute of Technology & Science Department of Computer Applications India Kongu Engineering College Perundurai Tamil Nadu Erode India

ISBN: (纸本)9798331540364

Medical diagnosis is one of the areas that has been greatly influenced by the progress of computer vision technologies. This study presents a unique approach for the detection and counting of blood cells in hematology: You Only Look One algorithm version 7 (YOLOv7). YOLO is designed for real-time object detection, making it ideal for applications such as blood vessel detection where speed is critical. The architecture allows the model to process images faster than other recognition models, such as R-CNN or SSD, which is very important for situations that require fast results, such as analysis automatic blood at clinical sites. The purpose of the proposed method is to overcome the shortcomings of conventional methods and to accurately and quickly identify blood cells in mechanical images. The YOLOv7 model has been utilizedbecause it can detect objects in real-time with highprecision and speed. The methodology for bloodcell counting involves the use of YOLOv7 togenerate bounding boxes around each detectedblood cell in an image, where the count of these bounding boxes directly corresponds to the number of cells. This advancement in hematology not onlyimproves blood cell analysis efficiency but also expedites the process of diagnosing and planningtreatment in various medical situations. This paper details how YOLOv7 was modified and adjusted to meet the specific requirements of hematologicalimage analysis, including training models, preparing datasets, and evaluating performance. The study also addresses the potential impact of this technology on clinical workflows, highlightinghow it might help medical practitioners make decisions more quickly and intelligently. Hematology analysis systems including YOLOv7 are able to improve laboratory diagnostics and help patients by providing better care and results. This workconcludes by demonstrating the revolutionary potential of YOLOv7 in blood cell identificationand counting in hematology, paving the way for accurate and better

关键词： Diagnosis

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Harnessing the Potential of the Internet of Things: Navigating Choices Between Fog and Cloud Infrastructure in Agriculture 1

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Singh, Malikhan Arora, Kavita Pathak, Sonal Quraishi, Suhail Javed Singh, Anupam Ather, Danish Computer Science and Engineering Modern Institute of Technology and Research Centre Rajasthan Alwar India School of Computer Applications Manav Rachna International Institute of Research and Studies Haryana Faridabad India Department of Computer Science and Engineering Graphic era Hill University Dehradun India Amity University in Tashkent Tashkent Uzbekistan

ISBN: (数字)9789819776030

ISBN: (纸本)9789819776023

In recent years, the Internet of Things (IoT) has become a pivotal force in transforming various sectors, with agriculture being a prominent beneficiary. The integration of smart devices, sensors, and data-driven decision-making tools has led to the emergence of a more efficient and sustainable farming landscape. Central to this transformation is the decision of where and how to process and store the vast amounts of data generated by these devices. This paper delves into the practical implications of fog and cloud computing systems in the agricultural domain taking Delhi NCR and region as a sample area by considering factors like efficiency, scalability, and real-world challenges faced by farmers. The aim here is to provide a comprehensive guide to stakeholders in making an informed choice between fog and cloud infrastructure for their IoT-enabled agricultural endeavors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart agriculture

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