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A Novel Approach for Medical Image Encryption Using Multiple Chaotic Maps and DNA Diffusion Operations

Pattern Recognition and Image Analysis 2025年第2期35卷 94-105页

作者： Biradar, Sumangala Akkasaligar, Prema T. Biradar, Sunanda Department of CSE(Artificial Intelligence and Machine Learning) BLDEA’s V.P. Dr. P. G. Halakatti College of Engineering and Technology Vijayapur India Department of Computer Science and Engineering KLE Technological University’s Dr. M.S. Sheshgiri College of Engineering and Technology Belagavi Campus Belgaum India

Due to advancement in electronic interaction, the broadcast of a medical image is very common. The electronic interaction is open to very one and hence, it is risk for medical image communication. The communication of medical image through opensource network adds uncertain risk of undesirable effect and causes important disease related facts in the image being lost or corrupted. For medical practitioner, diagnosing the correct disease from the lost or corrupted medical image. Hence high-level security and verifying integrity is very essential. A new cryptosystem based on DNA cryptography and multiple high dimensional chaotic maps and SHA-256 is proposed to deliver high-level security and verify integrity. The medical image is divided into two subimages. The subimages are converted into DNA synthesis using dynamic DNA coding rules. The hash keys are generated for both DNA synthesis using SHA-256. The Chen’s chaotic sequences are utilized to permute the pixels of DNA synthesis of subimage one. Another DNA synthesis of subimage two are permuted using Lorenz chaotic sequences. The hash keys are XORed with chaotic sequences. The DNA diffusion operation diffuses the pixels of the DNA synthesis. The dynamic DNA decoding rules are used to get cipher image. The simulation outcomes prove that the proposed cryptosystem is suitable to provide high-level security and integrity. The cryptanalysis confirms that proposed system is invulnerable to various types of attacks.

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Active learning of Robot Vision Using Adaptive Path Planning

arXiv

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arXiv 2024年

作者： Rückin, Julius Magistri, Federico Stachniss, Cyrill Popović, Marija Institute of Geodesy and Geoinformation Cluster of Excellence PhenoRob University of Bonn Germany MAVLab Faculty of Aerospace Engineering TU Delft Netherlands University of Oxford United Kingdom Lamarr Institute for Machine Learning and Artificial Intelligence Germany

Robots need robust and flexible vision systems to perceive and reason about their environments beyond geometry. Most of such systems build upon deep learning approaches. As autonomous robots are commonly deployed in initially unknown environments, pre-training on static datasets cannot always capture the variety of domains and limits the robot’s vision performance during missions. Recently, self-supervised as well as fully supervised active learning methods emerged to improve robotic vision. These approaches rely on large in-domain pre-training datasets or require substantial human labelling effort. To address these issues, we present a recent adaptive planning framework for efficient training data collection to substantially reduce human labelling requirements in semantic terrain monitoring missions. To this end, we combine high-quality human labels with automatically generated pseudo labels. Experimental results show that the framework reaches segmentation performance close to fully supervised approaches with drastically reduced human labelling effort while outperforming purely self-supervised approaches. We discuss the advantages and limitations of current methods and outline valuable future research avenues towards more robust and flexible robotic vision systems in unknown environments. © 2024, CC BY.

关键词： machine vision

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Algorithmic Crypto Trading using EMA Strategy

Algorithmic Crypto Trading using EMA Strategy

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Revathi S Shobha Meesala Sudha V Priyanka R Manikandakumar M Vigneshwaran T Computer Science & Engineering (AIML) CMR Institute of Technology Bengaluru India Computer Science & Engineering REVA University Bangalore India Artificial Intelligence & Machine Learning Cambridge Institute of Technology Bangalore India AI ML and Data Science Christ University Bangalore India

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

Algorithmic trading has transformed financial markets by enabling data-driven strategies that enhance efficiency and decision-making. This paper presents a web-based crypto currency trading platform that employs the Exponential Moving Average (EMA) strategy for automated trade execution, market trend analysis, and portfolio tracking. The platform integrates key performance metrics, including win rate, average profit per trade, risk-reward ratio, and profit factor to assess trading effectiveness. Notably, EMA-based trading achieves the highest profit factor of 3.5 which outperformed deep learning and manual trading by 9.37% and 133%, respectively. Additionally, EMA exhibits a strong win rate of 60%, compared to 65% for deep learning and 40% for manual trading, while maintaining a balanced risk-reward ratio of 2.2. The system features live data visualization, customizable watchlists, and automated trading workflows, providing traders with actionable insights with minimized human error. Performance evaluation indicates that EMA offers a superior trade-off between profitability and risk management, making it a robust and adaptable solution for navigating cryptocurrency markets. This work bridges the gap between manual trading and advanced algorithmic strategies, delivering a user-friendly and efficient trading framework.

关键词： Deep learning Pervasive computing Social networking (online) Profitability Navigation Data visualization Manuals Cryptocurrency Risk management Portfolios

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machine learning Induced Selective Identification of Dopamine Using Doped Carbon Quantum Dots

Machine Learning Induced Selective Identification of Dopamin...

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International Conference on Electronics, Materials engineering and Nano-technology (IEMENTech)

作者： Ruma Das Abhirup Paria P. K. Giri Department of Applied Science (Physics) Haldia Institute of Technology Haldia India Department of Computer Science & Engineering (Artificial Intelligence & Machine Learning) Haldia Institute of Technology Haldia India Department of Physics & Centre for Nanotechnology Indian Institute of Technology Guwahati Guwahati India

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

The present work highlights the selective identification of dopamine (DA) with machine learning (ML) classification. Dopamine is an essential organic neurotransmitter that acts a dynamic role. With the inhouse experimental da-ta, the ML models are trained with more than thirty classification algorithms for the efficient selection of DA among various metal ions and biomolecules that exist in human body fluids. The experimental data was collected by measuring the turn-off photoluminescence intensity of nitrogen-doped car-bon quantum dots in the presence of various metal ions and biomolecules. A promising agreement between predicted result and experimental consequences endorses the effectiveness of the proposed ML prediction. Additionally, confusion matrices and receiver operating characteristic (ROC) are introduced to validate the outcomes by visual analysis.

关键词： Visualization Biological system modeling Quantum dots Metals Molecular biophysics machine learning Photoluminescence Ions Classification algorithms Sensors

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Code Smell Detection Using Whale Optimization Algorithm

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Computers, Materials & Continua 2021年第8期68卷 1919-1935页

作者： Moatasem M.Draz Marwa S.Farhan Sarah N.Abdulkader M.G.Gafar Department of Software Engineering Faculty of Computers and InformationKafrelsheikh UniversityKafr ElsheikhEgypt Faculty of Informatics and Computer Science British University in EgyptCairoEgypt Department of Information Systems Faculty of Computers and Artificial IntelligenceHelwan UniversityCairoEgypt Department of Computer Science Faculty of Computers and Artificial IntelligenceHelwan UniversityCairoEgypt Faculty of Computer Studies Arab Open UniversityCairoEgypt Department of Computer Science College of Science and Humanities in Al-SulailPrince Sattam bin Abdulaziz UniversityKharjSaudi Arabia Department of Machine Learning and Information Retrieval Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr ElsheikhEgypt

Software systems have been employed in many fields as a means to reduce human efforts;consequently,stakeholders are interested in more updates of their *** smells arise as one of the obstacles in the software *** are characteristics of software source code that indicate a deeper problem in *** smells appear not only in the design but also in software *** smells introduce bugs,affect software maintainability,and lead to higher maintenance *** code smells can be formulated as an optimization problem of finding the best detection *** researchers have recommended different techniques to improve the accuracy of code smell detection,these methods are still unstable and need to be *** research has sought only to discover a few at a time(three or five types)and did not set rules for detecting their *** research improves code smell detection by applying a search-based technique;we use the Whale Optimization Algorithm as a classifier to find ideal detection *** this algorithm,the Fisher criterion is utilized as a fitness function to maximize the between-class distance over the withinclass *** proposed framework adopts if-then detection rules during the software development life *** rules identify the types for both medium and large *** are conducted on five open-source software projects to discover nine smell types that mostly appear in *** proposed detection framework has an average of 94.24%precision and 93.4%*** accurate values are better than other search-based algorithms of the same *** proposed framework improves code smell detection,which increases software quality while minimizing maintenance effort,time,and ***,the resulting classification rules are analyzed to find the software metrics that differentiate the nine code smells.

关键词： Software engineering intelligence search-based software engineering code smell detection software metrics whale optimization algorithm fisher criterion

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Human Factors engineering Simulated Analysis in Administrative, Operational and Maintenance Loops of Nuclear Reactor Control Unit Using artificial intelligence and machine learning Techniques

SSRN

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SSRN 2024年

作者： Khamaj, Abdulrahman Ali, Abdulelah M. Saminathan, Rajasekaran Shanmugasundaram, M. Industrial Engineering Department College of Engineering Jazan University Jazan Saudi Arabia Mechanical Engineering Department College of Engineering Jazan University Jazan Saudi Arabia Department of Artificial Intelligence and Machine Learning BMS Institute of Technology and Management Bangalore India

The nuclear reactor control unit employs human factor engineering to ensure efficient operations and prevent any catastrophic incidents. This sector is of utmost importance for public safety. This study focuses on simulated analysis of specific areas of nuclear reactor control, specifically administration, operation, and maintenance, using artificial intelligence software. The investigation yields effective artificial intelligence algorithms that capture the essential and non-essential components of numerous parameters to be monitored in nuclear reactor control. The investigation further examines the interdependencies between various parameters and validates the statistical outputs of the model through attribution analysis. Furthermore, a Multivariant ANOVA analysis is conducted to identify the interactive plots and mean plots of crucial parameters interactions. An AI method uncovers the relationships between the operator failing rate (OFR), operator processed errors (OEE), and operations at limited time frames (OLC). The AI algorithm reveals the interdependence between equipment in the out of service (EOS), progressive maintenance schedule (PRMR), and preventive maintenance schedules (PMRC). Effective neural network models for machine learning program are suggested for administrative, operational and maintenance loops of nuclear reactor control unit. © 2024, The Authors. All rights reserved.

关键词： machine learning

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One-Shot Vision, AI for Effortless Identification of Individuals with and Without Glasses

One-Shot Vision, AI for Effortless Identification of Individ...

引用

Global Conference for Advancement in technology (GCAT)

作者： Gangadhar Tejesh Rashmi Karchi Sanjeevakumar Hatture Asharani Sankanagoudra Rashmi Saini Department of Artificial Intelligence and Machine Learning Nagarjuna College of Engineering and Technology Bengaluru Karnataka India Department of Information Science and Engineering Nagarjuna College of Engineering and Technology Bengaluru Karnataka India Department of Computer Science G. B. Pant Institute of Engineering and Technology Uttarakhand India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

Facial recognition is a challenging task, especially in the wild where there exist a substantial amount of variation in lighting, pose, and expression. One-shot knowledge acquisition is a form of machine learning that allows a model to learn to recognize a new class Upon observing just a single instance of that class. the proposed work of a one-shot learning strategy for facial recognition system that is specifically designed to detect persons with or without spectacles. The proposed work approach has the capability to achieve high accuracy on a limited dataset of labeled images, and possesses the capacity to adjust to unfamiliar images that were not encountered in prior training stages. In this work, One-shot learning aims to recognize individuals based on a limited number of examples with and without specs, making it suitable for scenarios where acquiring a large dataset is challenging.

关键词： Training Accuracy Face recognition Surveillance Knowledge acquisition Neural networks Lighting One shot learning Medical services Reliability

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Vortex Search Optimizer based Hybrid Watermarking Scheme for Securing the Medical Images

Vortex Search Optimizer based Hybrid Watermarking Scheme for...

引用

Mysore Sub Section International Conference (MysuruCon), IEEE

作者： G N Keshava Murthy Nalini B M Madhura G K Nayana S Department of Electronics and Instrumentation Engineering Siddaganga Institute of Technology Tumakuru Karnataka Department of Computer Science and Engineering East West Institute of Technology Bengaluru Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru

Preventing the duplication, modification, and claim of unauthorised ownership of digital content is one of the major issues caused by the growing use of computer capabilities. Watermarking is one way to put a visible or invisible watermark on a digital document in order to later show that the document is legitimate or belongs to its owner. concentrating on security issues in the storage and broadcast of images in the medical info scheme, as well as the special requirements for medical images for the protection of lesion regions. In this study, a hybrid watermarking strategy was presented utilising the Slantlet transform, randomised singular value decomposition, and optimisation methods (Vortex Search algorithm (VSA)) that are modelled after natural processes. The XOR encryption method is used to encrypt the watermark image. Extensive testing demonstrates that our novel approach works better than the current approaches based on the NC, SSIM, and PSNR. The PSNR of the suggested technique is between 58 and 59 dB, suggesting increased performance, and at a scale factor of 0.06, the SSIM and NC standards of the watermarked picture and the extracted watermark are nearly equal to. The recommended method outperforms past studies in the field in terms of usability, robustness, security, and invisibility, as well as its ability to repel local nonlinear geometric attacks.

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Support Vector machines for Soil Quality Assessment in Sustainable Farming 7

Support Vector Machines for Soil Quality Assessment in Susta...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Nainwal, Ankita Alabdeli, Haider Varshney, Neeraj Deepak, A. Reddy, N. V. Uma Reddy, K. Jyothsna Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Gla University Department of Computer Engineering and Applications Mathura India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Tamilnadu Chennai India New Horizon College of Engineering Department of Artificial Intelligence & Machine Learning Bangalore India Mlr Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798350350067

In order to understand the soil's health, fertility, and crop appropriateness, soil quality assessments are an important part of sustainable agricultural techniques. Using their accuracy and resilience in dealing with complicated, nonlinear datasets, Support Vector machines (SVMs) are investigated in this research as a potential tool for soil quality evaluation. Our study presents a comprehensive methodology for using SVM to analyze soil properties, including pH, nutrient content, moisture levels, and organic matter. We employ a dataset encompassing diverse soil samples from different geographical regions and farming conditions. The proposed SVM-based model is trained and validated using this dataset, demonstrating high predictive accuracy and reliability in classifying soil quality. Additionally, we compare the performance of SVM with other machine learning techniques such as Random Forest and k-Nearest Neighbors, highlighting the advantages of SVM in terms of precision and computational efficiency. The results indicate that SVM can serve as a powerful tool for farmers and agronomists, facilitating informed decision-making and promoting sustainable agricultural practices. In conclusion, this research underscores the potential of SVM in soil quality assessment, contributing to the advancement of precision agriculture. Future work will focus on integrating SVM with remote sensing technologies and expanding the dataset to include more diverse soil types and environmental conditions. © 2024 IEEE.

关键词： Support vector machines

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Abnormal Load Patterns Detection in Smart Grids Using Temporal Convolutional Neural Network Based Gated Recurrent Units Networks with Multi-Head Temporal Attention

Abnormal Load Patterns Detection in Smart Grids Using Tempor...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Zayd Balassem Ramya Srikanteswara Esakki Madura E Madugula Anjaneyulu M Devi Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

In recent era, the advancement in smart grid technologies and the integration of renewable energy sources have modernized the power distribution landscape. The efficiency and stability of overall system is ensured by the controlling of power converters. In existing, Support Vector machine (SVM) is utilized but it assumes the normal data follows Gaussian distribution which is not be the same for smart grid data. In this research, Temporal Convolutional Neural Network based Gated Recurrent Units Networks with Multi-Head Temporal Attention (TCN-GRU-MTA) is proposed. Initially, the input signals are considered from Smart Grid Stability (SGS) dataset. Then, the signals are further preprocessed with autoencoders to reduce noise and to improve quality of signals. The Siamese Neural Networks (SNN) feature extractor efficiently learns relevant features from smart grid data which are indicative of abnormal behavior. After that, the SNN is introduced for extracting features from preprocessed data. Next, the proposed TCN-GRU-MTA is utilized to detect the abnormal load patterns in smart grid networks. From the results, the proposed_established better results including accuracy (99.10%), precision (89.98%), recall (99.05%) and F1-score (95.20%) when compared to existing Quadratic SVM.

关键词： Support vector machines Accuracy Neural networks Noise Power system stability Logic gates Feature extraction Smart grids Data mining Convolutional neural networks

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