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Multilevel Chaotic Maps with Differential Encoding-based Encryption Method for DICOM images 5th

Multilevel Chaotic Maps with Differential Encoding-Based En...

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5th international conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Sharma, Suvita Rani Singh, Birmohan Kaur, Manpreet Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Longowal Punjab Sangrur148106 India Department of Electrical and Instrumentation Engineering Sant Longowal Institute of Engineering and Technology Longowal Punjab Sangrur148106 India

ISBN: (纸本)9789819773701

This paper proposes an encryption method based on multiple chaotic maps and differential encoding method to secure the medical images. The proposed method uses a permutation-based image scrambling method in the confusion phase. In the diffusion phase, Piecewise Linear Chaotic Map (PWLCM), Piecewise Chaotic Map (PWCM), and tent map with differential encoding method have been employed at different levels of the phase to secure the medical images. The model has been tested on Digital Imaging and Communications in Medicine (DICOM) images. The proposed encryption method demonstrates robust security against statistical and entropy attacks, with encrypted images exhibiting a correlation close to zero and achieving a maximum entropy value of 7.9993. The low SSIM value between the original and encrypted images confirms their dissimilarity. Higher Mean Square Error (MSE) values and lower Peak signal-to-Noise Ratio (PSNR) values for encrypted images prove a negligible presence of original pixel values. Additionally, the encryption approach showcases resilience against differential attacks, with a Number of Pixel Change Rate (NPCR) value of 99.6% and a Unified Averaged Changed Intensity (UACI) value of 33.4%, both within acceptable standard ranges. The effectiveness of the proposed method demonstrates that these encrypted DICOM images are safe against various forms of attacks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Digital Imaging and Communications in Medicine (DICOM)

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A Method for Removing Reflections from Water Surface images based on Pre-trained image Restoration

A Method for Removing Reflections from Water Surface Images ...

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Minghua Zhao Rui Zhi Shuangli Du Jing Hu Cheng Shi Lin Wang Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Reflections on the water surface hinder the extraction of valuable information from water surface images. To remove reflections from water surface images, we construct a synthetic dataset and propose a multi-task network for water surface reflection detection and removal. Specifically, we first use a U-Net-based reflection detection module to generate a reflection mask, followed by a GAN-based network to remove the reflection. To extract multi-level features from the images, we design a color feature extraction network and a detail feature extraction network. Finally, to enhance the model's ability to remove large-area reflections, we pre-train the reflection removal network on an image restoration dataset. Experimental results on the proposed synthetic dataset and real water surface reflection images from the internet show that our method significantly outperforms other methods in water surface reflection detection and removal.

关键词： image color analysis signal processing Feature extraction Multitasking Reflection image restoration Speech processing Surface treatment Synthetic data Sports

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Developing and Deploying Open-Access Web-based Educational Tools for Teaching Data Preprocessing and Classification in Bioinformatics 5th

Developing and Deploying Open-Access Web-Based Educational T...

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5th international conference on Computing and Network Communications, CoCoNet 2023

作者： Ottappurakkal, Bhavya Kumar, Dhanush Vijayan, Asha Radhamani, Rakhi Achuthan, Krishnashree Diwakar, Shyam Amrita Mind Brain Center Amrita Vishwa Vidyapeetham Amritapuri Campus Clappana P.O. Kerala Kollam690525 India Center for Cybersecurity Systems and Networks Amrita Vishwa Vidyapeetham Amritapuri Campus Clappana P.O. Kerala Kollam690525 India Department of Electronics and Communication Engineering School of Engineering Amrita Vishwa Vidyapeetham Amritapuri Campus Clappana P.O. Kerala Kollam690525 India

ISBN: (纸本)9789819747108

Machine learning and data mining techniques have become indispensable in the field of bioinformatics with the advancements in big data analysis such as omics, biomedical imaging, and signal processing. Toward developing a pedagogical solution for data preprocessing, this paper explores design architecture of easily accessible, free-cost novel open-source web-based virtual laboratory to simplify the data preprocessing and visualization steps for big data analysis. A user-friendly interface combines computational capabilities with HTML and JavaScript as the underlying technology. This virtual laboratory architecture was designed to be compatible for access using mobile phones, tablets, and desktop computers having an active internet connection, making the education resource usage independent of end user dependencies such as specific operating systems, installed apps or other not so easily available applications. The software implementation was created to allow student learners to preprocess and classify data, incorporating techniques like variable type conversion, normalization, standardization. With this web-based virtual laboratory, large datasets can also be preprocessed and classified that may help as education resources for students, data analysts and researchers from different domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Personal computers

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Assessing signal Noise Effects on Machine Learning Models for ECG-based Cardiac Diagnosis

Assessing Signal Noise Effects on Machine Learning Models fo...

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18th international Joint conference on Biomedical Engineering systems and Technologies, BIOSTEC 2025

作者： Guglielmi, Emanuela Russo, Davide Donato Trinchese, Pasquale Laudato, Gennaro Scalabrino, Simone Testa, Gianluca Oliveto, Rocco Department of Bioscience and Territory University of Molise Italy Department of Medicine and Health Sciences University of Molise Italy

The internet of Medical Things (IoMT) plays a vital role in healthcare by enhancing preventive careand chronic disease management through continuous monitoring using smart sensors and wearable devices. However, the reliability of IoMT systems can be compromised by noise in the acquired vital signals, which can negatively impact the accuracy of Machine Learning (ML) models used for anomaly detection. This study evaluates the impact of various disturbances on the performance of ML models in predicting cardiac conditions, with a focus on assessing the reliability and effectiveness of these systems in real-world applications. We investigated the effects of three types of noise—baseline wander, muscle artifact noise, and electrode motion artifact—on the performance of two advanced ML models designed to predict cardiac conditions, specifically atrial fibrillation (AF) and ventricular tachycardia (VT). Our analysis centered on how different noise intensities (i.e., the "loudness" of the noise) and durations (i.e., the length of time the noise persists) impacted the classification performance of these models. The VT detection model showed robust performance, with minimal impact even under intense and prolonged noise conditions. In contrast, AF detection was affected by all types of noise, with classification accuracy decreasing by up to ∼59% in the most challenging scenarios. © 2025 by SCITEPRESS – Science and technology Publications, Lda.

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Face Recognition based Vehicle Theft Detection

Face Recognition Based Vehicle Theft Detection

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Multi-Agent systems for Collaborative Intelligence (ICMSCI), international conference on

作者： J. Prasad Chakravarthi Ctr Daniel K Deepak R Dheshnamoorthi M Department of ECE KPR Institute of Engineering & Technology Coimbatore India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Adding a vehicle theft security system and improving vehicle technology systems are becoming more and more popular study topics in an effort to prevent car theft in parking lots and occasionally when driving in unsafe locations. This work objective is to develop a prototype model to detect vehicle theft using a facial recognition algorithm. The image is initially captured using a camera that is angled differently, and it is subsequently trained using the cascade Haar classifier technique. Furthermore, a linear binary pattern algorithm in a Python environment is used to extract the captured images. If the extracted image and the training image differ, theft is detected, and the vehicle owner is contacted via the mobile app. The developed prototype model offers a superior security alarm for vehicle theft and is tested with a real-time application.

关键词： Training Face recognition Prototypes Real-time systems Classification algorithms Mobile applications internet of Things Protection Driver behavior Collaborative intelligence

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Enhancing image Search Engine Accuracy Using Psychological Intention Diagrams

Enhancing Image Search Engine Accuracy Using Psychological I...

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Integrated Circuits and Communication systems (ICICACS), IEEE international conference on

作者： Aswathi KP Sunita Yadwad D. Nirmal Raj Shailesh Rastogi Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Department of Computer science and Engineering Faculty of Engineering and Technology Parul University Vadodara India Balaji Institute of Management and Human Resource Development (BIMHRD) Pune India Symbiosis Institute of Business Management Nagpur Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

As the internet becomes increasingly image-centric, users face substantial challenges in efficiently locating desired images due to the inherent limitations of image search engines in interpreting visual content and keyword ambiguity. This paper introduces an innovative solution employing psychological intention diagrams to enhance search accuracy. These diagrams harness historical data to depict the search intentions associated with specific keywords, reflecting both user and web designer perspectives. By integrating these diagrams, our proposed search engine significantly refines its ability to predict and retrieve the most relevant images. The implementation of this system involves constructing two types of intention diagrams: one based on past user queries and another derived from the intentions of web designers for cases without historical user data. This dual approach not only captures the semantic essence of images more effectively but also addresses the multi-faceted meanings of search keywords. Our experimental results confirm that incorporating psychological dimensions into image search mechanisms substantially improves precision, facilitating more accurate and user-aligned image retrieval. This pioneering approach is the first to apply psychological profiling in enhancing image search engine functionalities, marking a significant advancement in digital image retrieval technologies.

关键词： Visualization Accuracy Digital images Semantics image retrieval Psychology Machine learning Search engines internet Standards

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Power internet of Things Access System based on Low Power Communication technology

Power Internet of Things Access System Based on Low Power Co...

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international conference on Communication systems and Network Technologies (CSNT)

作者： Zhigang Jiang Liming Ren Bing Zhu Jiafeng Jiang School of Computer Science and Technology University of Science and Technology of China Hefei China School of Department of Electronic Information and Electrical Engineering Hefei University Hefei China School of Computer and Information Hefei University of Technology Hefei China School of Information Engineering Inner Mongolia University of Science and Technology Baotou China

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

In response to the issues of data transmission energy efficiency and network coverage in the power internet of Things system, this study introduces an access scheme based on low-power communication technology. By optimizing the network architecture and communication protocol, the aim is to improve the energy efficiency of sensor nodes in the power internet of Things and reduce communication latency. Firstly, an access scheme based on low power wide area network (LPWAN) technology is designed. LoRa (Long Range Radio) or NB-IoT (Narrow Band internet of Thing) is selected as the communication protocol. By optimizing the working mode of sensor nodes, periodic transmission and event-driven mechanism are adopted. Secondly, an adaptive data transmission mechanism is designed to dynamically adjust the transmission frequency and rate according to environmental changes to ensure reliability under different network conditions. Furthermore, deep sleep mode and power consumption scheduling algorithm are adopted, and the network architecture adopts star topology to ensure the efficiency of signal coverage and data forwarding, and the secure transmission of data information is guaranteed by encryption and authentication mechanism. Finally, through simulation and actual deployment, the performance of the scheme in the actual environment is verified, and its stability and data transmission efficiency under different network scales are evaluated. The battery life of periodic wake-up mode is significantly lower than that of deep sleep mode. Under low-frequency transmission (once an hour), the battery life is 320 days. In periodic wake-up mode, the average power consumption of sensor nodes is significantly higher than that of deep sleep mode. At low frequency transmission, average power consumption is 3.5 mW and battery life drops to 75 days. The power internet of Things access system based on low-power communication technology proposed in this work effectively solves the bottleneck pr

关键词： Power demand Protocols Network architecture Communications technology Energy efficiency Stability analysis Delays Batteries internet of Things Thermal stability

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IoT based Line of Control Monitoring System

IoT based Line of Control Monitoring System

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Machine Learning and Autonomous systems (ICMLAS), international conference on

作者： Taniya Petkar Department of Computer Science And Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

This paper presents a novel line-of-control (LoC) monitoring system that leverages the internet of Things (IoT) to improve border security. The system creates a strong infrastructure for real-time monitoring throughout critical border regions, especially the Line of Control, by utilizing IoT devices, sensors, and communication technologies. The coverage, timeliness, and data quality of conventional border surveillance systems are frequently compromised. As opposed to this, the suggested system makes use of a wirelessly networked network of intelligent sensors, such as infrared, acoustic, and video cameras. By utilizing sophisticated image processing algorithms and machine learning to analyze the gathered data, the system is able to differentiate between regular activities and any security risks. To help security staff make decisions quickly, real-time updates are transmitted to a centralized control center. Integration with cloud computing also makes data analysis, retrieval, and storage simpler and guarantees data accessibility for forensic investigations and trend research.

关键词： Data analysis Surveillance Scalability Machine learning Cameras Real-time systems Sensor systems Security internet of Things Intelligent sensors

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Enhanced Data Hiding based on First, Middle, and Last Order Pixel in Spatial Domain images

Enhanced Data Hiding Based on First, Middle, and Last Order ...

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international conference on signal Processing and Communication (ICSC)

作者： Rahmad Hidayat Adifa Widyadhani Chanda D'Layla Ntivuguruzwa Jean De La Croix Tohari Ahmad Department of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia STAIN Sultan Abdurrahman Kepulauan Riau Indonesia Department of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia University of Rwanda Kigali Rwanda Department of Informatics Intitut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

The rapid advancement of internet technology has revolutionized global information sharing, enabling faster and more efficient digital communication. However, the increased reliance on online platforms for exchanging sensitive information raises significant concerns about data security, as unauthorized access and misuse of transmitted data pose severe risks, including identity theft and financial fraud. Steganography has emerged as a promising technique for securing data by embedding it imperceptibly into digital media. This study introduces a novel steganographic method that utilizes the difference between pixels in the cover image to embed secret messages while maintaining high image quality. The proposed scheme divides image pixels into blocks of 9. It calculates the difference between pixels with each block's first, middle, or last pixel to ensure minimal distortion and superior visual quality post-embedding. Experimental results validate the effectiveness of the method, achieving consistently higher PSNR (Peak signal-to-Noise Ratio) values compared to a baseline approach, with a PSNR of 64.9205 dB for a 10 KB payload and similarly superior results for payloads of 20 KB and 40 KB.

关键词： image quality Steganography Visualization PSNR Scalability Media Distortion Robustness internet Payloads

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Adaptive KOA: Leaf disease classification using hyperspectral images for internet of Things (IoT)-based sustainable agriculture

Adaptive KOA: Leaf disease classification using hyperspectra...

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Emerging systems and Intelligent Computing (ESIC), international conference on

作者： Pradeep Kumar Mallick Annapareddy V N Reddy Manas Ranjan Mohanty Biswajit Sahoo Alok Kumar Jagadev School of Computer Engineering Kalinga Institute of Industrial Technology(KIIT) DU Bhubaneswar Odisha Department of Information Technology Lakireddy Bali Reddy College of Engg. Mylavaram Andhra Pradesh

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

In recent times, the crops as well as the agriculture management is one of the important field to watch out, therefore the image processing business provide more benefit to the crops along with support precaution needs. In this research, an adaptive Kookaburra Optimization Algorithm (Adaptive KOA) is implemented for leaf disease classification using hyperspectral images for Sustainable Agriculture in *** first, the IoT modelis replicated and the routing is conductedto transmit the data from source to the Base Station (BS). Moreover, in BS the following steps are conducted. The input hyperspectral image (HSI)is subjected to a band selection module. The band selection is done using the implemented Adaptive Kookaburra Optimization Algorithm (Adaptive KOA). The developed Adaptive KOA is devised by incorporating Adaptive concept with Kookaburra Optimization Algorithm (KOA). The Selected bands are fed into the leaf segmentation module, where the Deep Embedded Clustering (DEC) technique is utilized for segmentation. Fusion of segmented image from different bands is carried out by the majority voting method. Finally, Leaf disease classification is carried out based on the 3D-Convolutional Neural Network (3DCNN) into normal and abnormal cases. The developed Adaptive KOA-3DCNN method has the maximum value for accuracy as 0.917%, highest value for True negative rate(TNR)as 0.927%, highest value for True positive rate (TPR) as 0.907%.

关键词： image segmentation Plant diseases Adaptation models Clustering algorithms Routing Agriculture Classification algorithms internet of Things Optimization Hyperspectral imaging

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