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Image Cryptography Using Fibonacci Bit-plane Decomposition and Quantum Chaotic Permutation

Image Cryptography Using Fibonacci Bit-plane Decomposition a...

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International Conference on Computing, Intelligence and Data Analytics, ICCIDA 2022

作者： Ravi, Renjith V. Goyal, S.B. Djeddi, Chawki Kustov, Vladimir Department of Electronics and Communication Engineering M.E.A Engineering College Kerala Malappuram India Faculty of Information Technology City University Petaling Jaya46100 Malaysia Department of Mathematics and Computer Science Larbi Tebessi University Tebessa Algeria LITIS Lab Rouen University Rouen France Saint Petersburg Railway Transport University of Emperor Alexander I Saint Petersburg Russia

ISBN: (纸本)9783031270987

The use of image encryption to safeguard the privacy and security of images is a good idea. This study presents a unique image encryption technique that encrypts images using Fibonacci Bitplanes as security key images. The Fibonacci numbers are used in this research to present a new encryption technique. Furthermore, a unique bit-plane decomposition for Fibonacci weights is explored, which has cryptographic advantages. The novel lossless image encryption technique provided may encrypt an image for privacy protection utilizing this new decomposition approach. It also offers two layers of encryption, allowing it to handle both pay-preview apps and encrypted communications simultaneously. The algorithm’s performance in image encryption is confirmed by modeling and analysis. Finally, the suggested algorithm’s encryption performance is shown using results obtained and security assessments. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Number theory

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Optimizing Waveform Power Allocation in Cognitive DFRC Systems: An Individual User AoI Preference-Based Approach

Optimizing Waveform Power Allocation in Cognitive DFRC Syste...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Zhuofei Li Fengye Hu Qihao Li Zheng Chang Timo Hämäläinen College of Communication Engineering Jilin University Changchun China Faculty of Information Technology University of Jyväskvlä Jyväskylä Finland School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

In this paper, we propose a novel orthogonal frequency division multiplexing (OFDM) waveform power allocation approach in the spectrum-sharing dual-functional radar-communication (DFRC) systems, with a particular emphasis on improving radar recognition performance while considering the specific communication requirements of individual users. Specifically, the radar mutual information (RMI) is maximized in terms of allocating the radar power to the OFDM subcarrier within the limits of the power constraints and meeting the age of information (AoI) expectation requirements. By considering the impact of radar interference, we measure the AoI performance of individual users using the transmission outage probability. Then, an iterative constraints-splitting (ICS) method is developed to find the optimal radar power allocation results from the formulated non-convex problem by transforming it into an equivalent convex problem using an iteratively determined factor. Simulation results demonstrate that the proposed individual user AoI preference-based approach can improve RMI performance while meeting the AoI communication requirements of individual users. Additionally, higher RMI and more stable AoI performance can be achieved while maintaining total communication performance by allocating more sub-carriers to fewer users.

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High-Resolution Optical Imaging Incorporated Non-Destructive Morphological Boundary Detection of Fruit Disorders: A Pilot Study

High-Resolution Optical Imaging Incorporated Non-Destructive...

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Advancements in Computing (ICAC), International Conference on

作者： Nethmi Tharushi Hakmana Nipun Shantha Kahatapitiya Sasmitha Weerasinghe Deshan Kalupahana Bhagya Nathali Silva Naresh Kumar Ravichandran Daewoon Seong Sangyeob Han Seung Yeol Lee Hee-Young June Mansik Jeon Jeehyun Kim Udaya Wijenayake Ruchire Eranga Wijesinghe Department of Mechanical Engineering Faculty of Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Engineering Faculty of Engineering University of Sri Jayewardenepura Nugegoda Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Center for Excellence in Informatics Electronics & Transmission (CIET) Sri Lanka Institute of Information Technology Malabe Sri Lanka Center for Scientific Instrumentation Korea Basic Science Institute Daejeon Republic of Korea School of Electronic and Electrical Engineering College of IT Engineering Kyungpook National University Daegu Republic of Korea ICT Convergence Research Center Kyungpook National University Daegu Republic of Korea Department of Plant Medicine Kyungpook National University Daegu Republic of Korea Institute of Plant Medicine Kyungpook National University Daegu Republic of Korea Department of Electrical and Electronic Engineering Faculty of Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

Plant diseases caused by various pathogens, such as fungi, bacteria, and viruses, lead to significant reductions in crop yield and quality. Early detection of these diseases is critical for preventing their spread and optimizing crop yield. Traditional methods of disease detection, while effective, often involve invasive or time-consuming procedures that may not provide real-time insights. This study investigates the use of Swept-Source Optical Coherence Tomography (SS-OCT) for non-destructive, high-resolution imaging to identify morphological abnormalities in fruit specimens affected by diseases. The research focuses on differentiating healthy regions, healthy boundaries, infected boundaries, and fully infected regions in apples. Using a 1310 nm center wavelength laser, the SS-OCT system captured detailed cross-sectional images of fruit tissue, revealing microstructural changes associated with disease progression. A qualitative assessment of the two-dimensional (2D) OCT images highlighted distinct layers in healthy tissues, noticeable structural deformities, and the merging of microstructural layers in the boundary and infected regions. Quantitative depth-profile analysis of axial-direction A-scan profiles further demonstrated the reduction and merging of intensity peaks correlating with tissue abnormalities in infected fruit specimens. These findings underscore the potential of SS-OCT as an effective tool for early disease detection in agricultural products, providing a means for timely intervention and optimized postharvest management.

关键词： Plant diseases Pathogens Statistical analysis Two-dimensional displays Merging High-resolution imaging Optical imaging Real-time systems Crop yield Diseases

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Analyzing the Potential of Deep Learning for Enhancing Data Science Practices

Analyzing the Potential of Deep Learning for Enhancing Data ...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Mercilin Raajini X Gopal B. Deshmukh Savita Prabha Jagtej Singh Ankita Agarwal Manu S E Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune INDIA Department of Computer Science & Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Civil Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar Karnataka India

Deep learning is a progressive new field of artificial intelligence that has drastically changed the landscape of technology over the last few years. It has enabled the creation of clever packages, including self-using motors, facial recognition, natural language processing, and others. It has also affected information science, with deep getting to know presenting a new set of tools to system and examine information. This paper discusses the capacity of deep gaining knowledge for enhancing records technological know-how practices, consisting of predictive analytics, visible insights, and self-studying structures. For each subject, the paper provides a short evaluation accompanied by an evaluation and discussion. Subsequently, it provides an outline of cutting-edge developments and guidelines for how records scientists can OK use their abilities and revel to capitalize on the capacity of deep learning.

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Traffic Surveillance System Through Capsule Gated Graph Attention Network for IoT/IIoT Cyberthreat Detection and Mitigation

Traffic Surveillance System Through Capsule Gated Graph Atte...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Mahesh Prasanna K Devashree Marotka Muralidharan J Rachit Adhvaryu Vani V Ramya Maranan Department of Electronics and Communication Engineering Vivekananda College of Engineering and Technology Puttur Department of Electronics and Telecommunication Engineering G H Raisoni College of Engineering and Management Nagpur Maharashtra Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore Tamil Nadu Computer science and Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Gujarat Department of Information Science and Engineering Bangalore Institute of Technology Bangalore Karnataka Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Internet of Things and Industrial Internet of Things have seen rapid adoption and have transformed industries through smart automation, constant monitoring and better connected systems. The progressive inclusion of such solutions has contributed to the growth in exposure to complex and changing global threats. The primary challenge lies in accurately detecting and classifying cyber threats from high-volume, unstructured network traffic data in real-time. Existing methods are not very accurate and cannot be easily scaled due to constraints in computational resources and the nature of IIoT network systems. This paper introduces Traffic Surveillance System through Capsule Gated Graph Attention Network for IoT/IIoT Cyberthreat Detection and Mitigation (Cyber-CapG). The system uses raw traffic data from Edge-IIoTset and uses the Tunable Transformer for feature extraction. The extracted features are passed through the Capsule Gated Graph Attention Network to model the traffic as a graph based on communication hierarchy and being selective to essential traffic patterns. Furthermore, weights and loss functions are tuned based on the Progressive Crested Porcupine Optimization so as to achieve maximum optimization. The proposed system exhibits outstanding performance with 99.21 % detection rate, 99.07% precision, 99.15% recall, 99.18% F1 measure, and it provides an effective way of identifying threats like DDoS, SQL Injection, and Ransomware. In conclusion, Cyber-CapG provides a solid solution for traffic surveillance in IoT/IIoT, providing the high detection and prevention of various cyber threats, as well as optimizing the cybersecurity and reliability of industrial networks.

关键词： Surveillance Prevention and mitigation Telecommunication traffic Traffic control Logic gates SQL injection Feature extraction Real-time systems Optimization Industrial Internet of Things

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Synthesis of Non-Integer Order Uncertain Thau-Luenberger Observer for Non-Integer Order Multiple Model with Unknown Inputs

Synthesis of Non-Integer Order Uncertain Thau-Luenberger Obs...

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2024 International Conference on Control, Automation and Diagnosis, ICCAD 2024

作者： Djeddi, Abdelghani Azar, Ahmad Taher Fekik, Arezki Kamal, Nashwa Ahmad Njima, Chakib Ben Echahid Cheikh Larbi Tebessi University Faculty of Sciences and Technology Tebessa12002 Algeria Prince Sultan University College of Computer & Information Sciences Riyadh11586 Saudi Arabia Riyadh Saudi Arabia Benha University Faculty of Computers and Artificial Intelligence Benha Egypt University Akli Mohand Oulhadj-Bouria Department of Electrical Engineering Rue Drissi Yahia Bouira 10000 Algeria Nantes Université École Centrale Nantes Cnrs LS2N Umr 6004 NantesF-44000 France Cairo University Faculty of Engineering Giza Egypt University of Sousse Tunisia

ISBN: (纸本)9798350361025

The present work introduces a novel method for state estimation of physical systems represented by an uncertain Non-Integer Order Multiple Model (NIO-MM) model with unknown inputs. The key concept underlying this approach involves formulating a non-integer order Thau-Luenberger Observer (NIO-TLO) designed specifically for the NIO-MM. This observer is developed to handle situations where both known and unknown inputs are subject to uncertainties, and the model itself incorporates bounded unknown inputs. The analysis of the convergence of the error of the state estimation is presented through the formulation of linear matrix inequalities (LMIs). To validate the proposed approach, an academic example is examined. © 2024 IEEE.

关键词： Linear matrix inequalities

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Study of Factors Influencing on Mental Health and Their Relationships, of IT Sector Employees in Colombo District, Sri Lanka

Study of Factors Influencing on Mental Health and Their Rela...

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Advancements in Computing (ICAC), International Conference on

作者： S.P.L. Dias S. Lokuliyana Department of Physical Sciences and Technology Faculty of Applied Sciences University of Sabaragamuwa Belihuloya Sri Lanka Department of Computer Systems and Engineering Faculty of Graduate Studies and Research Sri Lanka Institute Information Technology Malabe Sri Lanka

This study focused on the mental health of IT sector employees in Colombo, using quantitative research methods and data from 300 participants (162 males and 138 females) using the DASS 21 questionnaire and self-administered surveys through convenience sampling. Correlation analysis, chi-square tests, and regression analysis were utilized to examine the data. It found significant correlations between mental health outcomes (stress, anxiety, and depression) and factors like work-life balance, workload, working conditions, and physical fitness. Work-life balance, working environment, and workload were identified as the most significant predictors of mental health outcomes. Physical fitness was associated with stress, depression, and anxiety but had a limited overall impact on mental health. Differences in depression, stress, and anxiety levels were observed based on marital status, age group, work experience, salary, and gender. These findings emphasize the importance of addressing work-life balance and working conditions to enhance the mental health of IT sector employees in Colombo. They provide valuable insights for organizations and policymakers to develop strategies and interventions that promote employee well-being and productivity, underscoring the need to prioritize mental health and create a supportive work environment.

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Aspect Extraction in Sentiment Analysis Based on Emotional Affect Using Supervised Approach 4

Aspect Extraction in Sentiment Analysis Based on Emotional A...

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4th International Conference on Artificial Intelligence and Big Data, ICAIBD 2021

作者： Maitama, Jaafar Zubairu Idris, Norisma Abdi, Asad Bimba, Andrew Thomas Universiti Malaya Faculty of Computer Science and Information Technology Department of Artificial Intelligence Kuala Lumpur50603 Malaysia University of Twente Department of Industrial Engineering and Business Information System Netherlands

ISBN: (纸本)9780738131702

Aspect based sentiment analysis (ABSA) is regards as the most demanding area of research in the field of text mining and natural language processing. In the last decade, people tend to be more interested in consulting online media outlets to make decisions concerning products or events. However, the existing ABSA approaches alone are not sufficient in providing comprehensive information required to make an informed decision. Most of the early studies focused on the binary classification of 'positive' and 'negative' to predict an overall sentiment of a review, which reduced the effectiveness in decision making towards the entities. Even though earlier studies on SA are centered on aspect extraction, there are limited works that effectively combine aspect extraction with emotional affects such as surprise, sadness and happiness, which consequently leads to incomplete meaning of a content. In this paper, we investigate a problem involving aspects in relation to emotional affects. To propose a novel supervised approach for the aspect extraction with their associated emotion classes in sentiment analysis (SA), we conducted an analysis of some prominent ABSA approaches. This research work will lead to a more accurate results of SA and could assist both novice and prominent researchers in comprehending the relationship between aspects and emotions in SA considering its sematic role in business intelligence. © 2021 IEEE.

关键词： Sentiment analysis

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An Embedded System for Non-Invasive Glucose Monitoring

An Embedded System for Non-Invasive Glucose Monitoring

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International Conference on Multimedia Analysis and Pattern Recognition (MAPR)

作者： Quoc-Hung Pham Thanh-Nhan Nguyen Anh-Quan Huynh Ba Hoang-Huy Ngo Huy-Hoang Vo Nhut-Thanh Tran Faculty of Computer Engineering University of Information Technology Ho Chi Minh City Vietnam Vietnam National University Ho Chi Minh City Vietnam Faculty of Automation Engineering Can Tho University Can Tho Vietnam

Diabetes is a persistent condition characterized by the body’s inability to properly utilize insulin. Therefore, noninvasive methods have been proposed to mitigate the pain and risk of infection due to non-required blood extraction compared to the methods for glucose measurement. The invasive methods are done by finger-pricking blood samples from the body. However, this method increases the risk of blood-related infections and pains. Hence, non-invasive Glucose in the blood is an essential method to measure glucose levels. This study successfully implemented a portable embedded system device based on a multi-spectral sensor and Raspberry Pi 4 microprocessor for noninvasive glucose measurement. This study used machine learning, including Multiple Linear Regression and Support Vector Machine, to predict the glucose level with over 90% accuracy. Moreover, the Clarke error grid analysis was used to analyze the accuracy results of this study. It also compared with invasive measurements and previous studies.

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Comparative Analysis of Machine Learning Models in Online Payment Fraud Prediction 2

Comparative Analysis of Machine Learning Models in Online Pa...

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2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Shdefat, Ahmed Younes Mohamed, Mennatallah Khaled, Shahd Hany, Farrah Fathi, Hanaa Abdelminaam, Diaa Salama College of Engineering and Technology American University of the Middle East Egaila54200 Kuwait Misr International University Faculty of Computer Science Cairo Egypt Applied Science Private University Applied Science Research Center Amman Jordan Jadara University Jadara Research Center Irbid21110 Jordan Benha University Faculty of Computers and Artificial Intelligence Information Systems department Benha Egypt MEU Research Unit Middle East University Amman11831 Jordan

ISBN: (纸本)9798350362633

With the substantial increase in Cybercrime, the pressing need for effective solutions in the light of the rising amount of corporate financial losses, is a concern f or many researchers. The widespread adoption of ecommerce and diverse online payment methods has contributed to the surge in a major form of cybercrime - online payment fraud. This further highlights the critical need to control and reduce the widespread danger of fraudulent behaviour on the internet. However, predicting online payment fraud is difficult as fraudsters are constantly changing their tactics, transactions happen quickly and in large volumes, transactions involve different regions and countries, stolen data is often used and other factors. Our goal is to improve the accuracy and usability of online payment fraud prediction through the use of machine learning. A well-established and widely recognized dataset specifically designed for studying online payment fraud was used. The six proposed algorithms trained and tested were (SVM), Decision Tree, Naïve Bayes, Random Forest, (KNN) and Logistic Regression. After rigorous testing, Decision Tree presented the highest accuracy in performance of 98.58%. However, as ten-fold cross validation was implemented on the dataset, Decision Tree suffered a loss while Random Forest had an increase in accuracy making it the best model with accuracy of 98.47%. As shown in this paper, machine learning has proven to be a reliable and ever-evolving approach for the prediction of Online Payment Fraud and results can be further improved in collaboration with financial and cybercrime specialists. © 2024 IEEE.

关键词： Contrastive Learning

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