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Hybrid Image Encryption for IoT Applications: Integrating Cellular Automata and Henon Map to Improve Security and Performance

Hybrid Image Encryption for IoT Applications: Integrating Ce...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Biswarup Yogi Ajoy Kumar Khan Satyabrata Roy Dept. of Computer Engineering Mizoram University Aizawl Mizoram India Dept. of Computer Science and Engineering Manipal University Jaipur Rajasthan India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Securing the vast volumes of data exchanged over networks is crucial in IoT (Internet of Things) applications. This study introduces an innovative image encryption algorithm that combines the interactive properties of Cellular Automata (CA) and the Henon map to improve both security and performance. CA, renowned for its simplicity and parallelism, are utilized to produce intricate pseudorandom sequences. These sequences are then used as the keystream for encryption purposes. Whereas, the Henon map is employed to incorporate further levels of unpredictability and intricacy into the encryption procedure. The proposed method is thoroughly assessed through considerable experimentation and is compared with existing methodologies. The findings demonstrate substantial enhancements with respect to various standard metrics such as “Mean Squared Error (MSE)”., “Peak Signal-to-Noise Ratio (PSNR)”., “Unified Average Changing Intensity (UACI)”., and “Number of Pixels Change Rate (NPCR)” measurements., underscoring the resilience and efficacy of the proposed methodology. The results emphasize the capability of this hybrid method to offer a secure and robust performance for image encryption in IoT applications. This method guarantees the integrity and confidentiality of data while outperforming current encryption techniques in performance.

关键词： Image quality PSNR Measurement uncertainty Automata Parallel processing Encryption Internet of Things Standards Resilience Random sequences

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Recognition of Identifying Malicious Cyber Attack Intention in Cyber Physical System

Recognition of Identifying Malicious Cyber Attack Intention ...

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International Conference on computer and Communications Technologies (ICCCT)

作者： P Vasanthan S. Srinivasan Dept. of Information Technology Sri Sairam Engineering College Dept. of Computer Science and Engineering R.M.D Engineering College

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

As the threat over the cyber-attack is predominantly increasing in every aspects such as based on network traffic, spoofing, phishing attacks the intrusion detection system is used. The network layer is maliciously attacked by the intruder. The intruder tries to capture network packets of the connection between client and server. Based upon the encryption standard, the intruder tries to comprise the user system. Intrusion cannot be easily detected since it is a manual process. In this novel approach, deep learning reinforcement technique is implemented. The bidirectional end to end communication along with the Bayesian inverse reinforcement learning is used to detect the malicious cyber intention in cyber physical system. Inverse Bayesian reinforcement learning technique is initiated to identify the behavior of the pattern recognition and to resample the dataset. This deals with the risk factor and determines whether the connection is comprised. In this work, the novel application is introduced with deep reinforcement learning to identify the intruder. The intrusion detection is labeled in dataset. These dataset implements reward functions to detect the intrusion over the cps.

关键词： Uncertainty Phishing Intrusion detection Telecommunication traffic Probability distribution Bayes methods Pattern recognition Servers Cyberattack Standards

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Leveraging Smart Video-Based Learning to Enhance Student Engagement and Collaboration 6

Leveraging Smart Video-Based Learning to Enhance Student Eng...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Serasinghe, Chanukya Perera, Nashali Samarasinghe, Devindu Muthusinghe, Madusha Wijesundara, Malitha Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Humanities and Sciences Malabe Sri Lanka

ISBN: (纸本)9798331517878

The COVID-19 pandemic has expedited the shift in education to online learning, which has exposed shortcomings in virtual learning environments' personalization and engagement. This research addresses these issues by developing a web application designed to enhance student engagement and learning efficiency. The system has gamified collaboration features, video chapter segmentation, and personalized video recommendation system. The methodology comprises models for generating chapter titles and for precise video transcription, and machine learning algorithms for tailored suggestions depending on user input. Interactive Questions and Answers modules and real-time synchronized video playback are included. The results demonstrate how the system's personalized content, interactive tests, and teamwork tools increase user engagement and comprehension. The efficacy of personalized material and real-time synchronization in enhancing learning results is discussed. This innovative approach offers a comprehensive solution to the challenges of online education, creating a more immersive and interactive learning experience. © 2024 IEEE.

关键词： Federated learning

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A Innovative Network Security Regulations Dependent on Improved Support Vector Machine from the Outlook of Modern Cities

A Innovative Network Security Regulations Dependent on Impro...

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International Conference on computer and Communications Technologies (ICCCT)

作者： Vasanthan P S. Srinivasan Dept. of Information Technology Sri Sairam Engineering College Dept. of Computer Science and Engineering R.M.D Engineering College

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

The concerns about the security created by the PC have gotten more advanced and complicated. Interruption detection is a pragmatic subject in the area of PC security whose essential target is to identify uncommon assault or attacks and to guarantee the safety of inside frameworks. This paper likewise suggests a semi-class interruption recognition strategy that joins various classifiers to mastermind exemptions and regular activities in a PC framework. In the consideration preference tree learning-iterative dichotomy 3, the maltreatment recognition method is developed and is gathered by using the cumulative knowledge based on the peculiarity detection system performed by one class- uphold vector machine. As of late, individuals have paid more thoughtfulness regarding ID/interruption avoidance framework, which is firmly identified with the insurance and use of framework the executives. A couple of AI principles including neural framework, genetic programming, and progressed uphold vector machines, Bayesian framework, multivariate adaptable backslide splines, feathery deduction systems and other analogical frameworks has been scrutinized for the layout of interruption identification framework. In this article, we suggest a combination strategy dependent on DTL-ID3 and OC-SVM assess the presentation of the extended procedure by utilizing a particular dataset and a hybrid technique to upgrade the precision of IDS/IPS when stood out from a solitary help vector machine.

关键词： Surveys Support vector machines Urban areas Vectors data models Regulation Safety Registers Splines (mathematics) Pragmatics

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PV-Power Forecasting using Machine Learning Techniques

PV-Power Forecasting using Machine Learning Techniques

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IEEE International Conference on Electro-Information Technology

作者： Kazi Abdullah Al Arafat Kode Creer Anjan Debnath Temitayo O. Olowu Imtiaz Parvez Dept. of Computer Sci and Eng Atish Dipankar University of Science and Technology Dhaka Bangladesh Dept. of Computer Science Utah Valley University Orem UT Dept. of Electrical Engineering and Computer Science OR Idaho National Laboratory Idaho Falls ID

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

Solar energy forecasting plays a pivotal role in the efficient utilization of renewable energy resources for sustainable power generation. This study delves into the domain of solar-power forecasting, employing a comprehensive analysis of machine learning models. The primary objective is to evaluate and compare the performance of Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), Multi-Layer Perceptron (MLP), and Linear Regression (LR) models in predicting solar energy production. Through a comprehensive evaluation of individual model performance, the study provides nuanced insights into the strengths and limitations of each forecasting approach. Results indicate that the Multy-Layer Perceptron (MLP) model excels in accuracy, exhibiting low root mean square error (RMSE) and high correlation among the parameters. The Gated Recurrent Unit (GRU) model demonstrates competitive performance, while the Recurrent Neural Network model showcases strengths in multiple metrics. Additionally, MLP and GRU models display superior predictive accuracy, emphasizing their efficacy in solar energy forecasting.

关键词： Renewable energy sources Recurrent neural networks Correlation Accuracy Machine learning algorithms Solar energy Machine learning

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Highly Accurate Two Channel Single-Cycle EOG Classification for Smart Wearable Technologies

Highly Accurate Two Channel Single-Cycle EOG Classification ...

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IEEE International Conference on Biomedical engineering, computer and Information Technology for Health

作者： Wakim Sajjad Sakib Abdullah Bin Shams Raisa Tasrin Ridi Md. Mohsin Sarker Raihan Raihani Jannat Shapnil Dept. of Electronics & Communication Engineering Institute Of Science and Technology Dhaka Bangladesh Dept. of Electrical & Computer Engineering University of Toronto Toronto Ontario Canada Dept. of Computer Science & Engineering American International University - Bangladesh Dhaka Bangladesh Dept. of Electrical & Electronic Engineering University of Science and Technology Chittagong Chattogram Bangladesh

ISBN: (数字)9798331534356

ISBN: (纸本)9798331534363

Motion of the eye generates an electrical potential difference between the cornea and retina. This signal, known as the Electrooculogram (EOG), is unique and characteristic of eye movements. This can be leveraged for noninvasive eye-tracking. Although the accuracy of this procedure decreases as the number of ocular motions increases, it is crucial for enabling more complex tasks in the Human-computer Interaction (HCI) field. The performance further deteriorates when the motion classification is constrained within a single EOG cycle. This is imperative and significant for real-time practical applications. In this experimental study, (i) we have addressed the complexities in working with real-time single cycle EOG signals and provided necessary solutions (ii) worked with nine distinct eye movements, and (iii) proposed a cascaded Artificial Neural Network (ANN) approach to classify nine different eye movements based on single-cycle EOG signal data. Leveraging statistical properties and dominant frequency analysis of the EOG signals, our approach achieved an accuracy of 99.26%, precision of 99.26%, recall of 99.32%, and a F1-score of 99.25%. This is a significant improvement over past studies conducted by various researchers for similar purposes and to the knowledge of the authors, such high-performance metrics over this wide range of eye movements based on single-cycle EOG signals have not been previously reported.

关键词： Measurement Electrooculography Accuracy Low-pass filters Artificial neural networks computer architecture Real-time systems Complexity theory Wearable devices Robots

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Designing and Implementing an IoT-Based Long-Distance Fire-Extinguishment System 'Stationary 3-Axis Fire-Emergency Response Base' 8

Designing and Implementing an IoT-Based Long-Distance Fire-E...

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8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Kundu, Shyantan Dey, Swarajit Abdullha, Md. Mustafa Sen, Gobinda Gangopadhyay, Malay Institute of Engineering & Management Dept. of Computer Science & Engineering Kolkata India Institute of Engineering & Management Dept. of Electronics & Communication Engineering Kolkata India

ISBN: (纸本)9798331520762

This paper presents the design and implementation of an Internet of Thing (IOT)-based long-distance fire-extinguishment system named 'Stationary 3-Axis Fire-Emergency Response Base' (STAFERB). The system aims to provide immediate response to fire hazards in various environments, including residential areas, building complexes, commercial areas, small-scale industries, large factories, dense slum areas, and remotely located forest regions. The proposed STAFERB is designed to receive fire location coordinates, fire intensity, and probable cause information. It then calculates the path of a projectile based on considering obstacles and self-detection of a clear line of sight to reach the t1arget location. The system uses this information to select the appropriate speed and type of fire retardant. The designed STAFERB features robust communication, self-path detection, auto-orientation, and reduced response time, making it a significant advancement in fire-fighting technology. Experimental results demonstrate the effectiveness of the proposed STAFERB in mitigating fire hazards and hence can be well suited for real-time disaster management. © 2025 IEEE.

关键词： Fire hazards

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CA-CR: Context-Aware Congestion Resolution Method for Automated Guided Vehicles

CA-CR: Context-Aware Congestion Resolution Method for Automa...

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Applied Artificial Intelligence (ICAPAI), International Conference on

作者： Yigit Can Dundar Bernt Arild Bremdal Anne Håkansson Randi Karlsen Dept. of Computer Science UiT - The Arctic University of Norway Tromsø Norway Dept. of Computer Science and Computational Engineering UiT - The Arctic University of Norway Narvik Norway

ISBN: (数字)9798350349764

ISBN: (纸本)9798350349771

Modern logistics solutions use Automated Guided Vehicles (AGVs) in order to streamline logistics tasks in various work environments. While handling logistics tasks, AGVs can encounter congestions in the form of traffic or blockages which can cause slow-downs and deadlocks. Congestions can occur randomly and unexpectedly in work environments that are unknown to the AGVs. In unknown environments, access to environmental information is limited. Therefore, avoiding or resolving a congestion is a difficult task for AGVs. In order to compensate for the lack of environmental information, the contextual information of the congestion can be used. This paper presents the Context-Aware Congestion Resolution (CACR) method for AGVs in unknown work environments. The method uses contextual information in the form of number of agents, the vehicles’ current battery levels, task priorities and agent positioning to resolve congestions through a multi-agent negotiation. The negotiation uses the contextual information to find various solutions to random and unpredictable congestion scenarios. The negotiations are aided with reinforcement learning to train AGVs to move towards a collective utility maximizing behavior. To test the AGVs performance, the trained behavior is compared to a baseline, non-trained behavior for evaluation of reinforcement learning’s impact on optimizing the resolution of congestions. Findings indicate that the trained behavior shows better scaling with number of agents, better battery utilization and collective utility compared to baseline.

关键词： Training Remotely guided vehicles Reinforcement learning System recovery Batteries Resource management Proposals

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A Systematic Review of Computational Intelligence Approaches for Prediction of Chronic Kidney Disease

A Systematic Review of Computational Intelligence Approaches...

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International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)

作者： M.B.L. Prasanna G. Lavanya Devi Dept. of Computer Science and Systems Engineering Andhra University College of Engineering Visakhapatnam India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

CKD is a global health issue with rising prevalence and severe morbidity and mortality. Detecting and addressing diseases early is crucial to minimize disease progression and enhance patient outcomes. In the field of chronic kidney disease (CKD) prediction, computational intelligence (CI) algorithms have shown significant potential as effective tools, with the assurance of non-invasive and precise diagnosis. The use of CI methodologies, such as machine learning and deep learning techniques, for the prediction of chronic kidney disease (CKD) is examined in this systematic review. The advancement of CKD is linked to a number of significant effects such as an increased incidence of various ailments, Anemia, and death. There is a risk of hyperlipidaemia, nerve damage, pregnancy difficulties, and possibly renal failure. It kills millions of individuals each year. Diagnosis of CKD is difficult because there are no obvious symptoms to diagnose the illness. However, some results may be overlooked when the diagnosis is chronic. This study investigated CI approaches for CKD prediction using clinical data, biomarkers, or imaging aspects to diagnose CKD. Age, glucose level, number of red blood cells, and other symptoms were included when training the algorithm with 500 participants. The experimental results show that the suggested model excels at categorization. The objective is to develop computational intelligence (CI) algorithms for learning from attribute reports in datasets and effectively detect CKD that is more accessible. Eligible studies used CI to predict CKD based on clinical data, biomarkers, or imaging characteristics. This systematic investigation contributes to a better understanding of the landscape of CI applications in predicting CKD and provides light on potential future advances in the field.

关键词： Deep learning Machine learning algorithms Computational modeling Signal processing algorithms Imaging Prediction algorithms Chronic kidney disease Computational intelligence Diseases Systematic literature review

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Comfortable long term ECG monitoring system by the filtering electrodes

Comfortable long term ECG monitoring system by the filtering...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Yang, Chang-Ming Wu, Shu-Cing Huang, Ji-Jer Chen, Chi-Chun Ming Young Biomedical Corp. Miaoli Taiwan National Chin-Yi University of Technology Prospective Technology of Electrical Engineering and Computer Science Taichung Taiwan Southern Taiwan University of Science and Technology Department of Electrical Engineering Tainan Taiwan National Chin-Yi University of Technology Dept. of Electronic Engineering Taichung Taiwan

ISBN: (纸本)9798350324136

This paper introduces a novel standard 3lead ECG monitoring system, designed for enhanced comfort in daily wear. The system utilizes innovative dry electrodes, seamlessly integrated into both underwear and shirts. These electrodes, made of non-filtering silver material, are specifically engineered to maximize wearer comfort while reliably capturing standard 3-lead ECG signals. This design allows for accurate ECG signal acquisition in both stationary and ambulatory states. The system uniquely records life behavior patterns by analyzing the noise components within the 3-lead ECG signals. For physical activities, the system switches to a filtering silver electrode, ensuring the capture of high-fidelity ECG signals without compromising comfort. The primary goal of this research is to develop a versatile standard 3-lead ECG monitoring system suitable for various daily activities, including exercise, Holter monitoring, and sleep tracking. © 2024 IEEE.

关键词： exercise and sleep ECG Holter monitoring technology Innovative dry electrode non-filtering and filtering silver electrodes versatile standard 3-lead ECG monitoring

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