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Dynamics analysis and cryptographic implementation of a fractional-order memristive cellular neural network model

Chinese Physics B 2024年第4期33卷 418-433页

作者：周新卫蒋东华 Jean De Dieu Nkapkop Musheer Ahmad Jules Tagne Fossi Nestor Tsafack 吴建华 Department of Information Engineering Gongqing CollegeNanchang UniversityJiujiang 332020China School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 511400China Department of Electrical Engineering and Industrial Computing University Institute of TechnologyDoualaCameroon Department of Computer Engineering Jamia Millia IslamiaNew Delhi 110025India Department of Physics Faculty of ScienceUniversity of YaoundeCameroon Electrical Engineering Department and Industrial Computing of ISTAMA University of DoualaDoualaCameroon

Due to the fact that a memristor with memory properties is an ideal electronic component for implementation of the artificial neural synaptic function,a brand-new tristable locally active memristor model is first proposed in this ***,a novel four-dimensional fractional-order memristive cellular neural network(FO-MCNN)model with hidden attractors is constructed to enhance the engineering feasibility of the original CNN model and its ***,its hardware circuit implementation and complicated dynamic properties are investigated on multi-simulation ***,it is used toward secure communication application *** it as the pseudo-random number generator(PRNG),a new privacy image security scheme is designed based on the adaptive sampling rate compressive sensing(ASR-CS)***,the simulation analysis and comparative experiments manifest that the proposed data encryption scheme possesses strong immunity against various security attack models and satisfactory compression performance.

关键词： cellular neural network memristor hardware circuit compressive sensing privacy data protection

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YOLOv8 for Pedestrian Detection: A Comparative Study for Pedestrian Detection 3

YOLOv8 for Pedestrian Detection: A Comparative Study for Ped...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Sivaraman, G. Sophiya, E. Diviya, M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369083

This research analyzes the usefulness of YOLOv8 for robust pedestrian recognition in demanding real world situations. A custom-trained YOLOv8 model is evaluated against SSD models, displaying better accuracy in detecting and localizing human classes within images characterized by occlusions and variable scales. We analyze the model's performance across confidence thresholds, demonstrating a positive balance between precision and recall. While noting computational considerations, our findings demonstrate YOLOv8's potential for advancing pedestrian detection systems, opening the way for enhanced safety and efficiency in applications such as autonomous driving, surveillance, and smart city efforts. © 2024 IEEE.

关键词： Deep learning

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Does the Order Matter? Curriculum Learning over Languages 30

Does the Order Matter? Curriculum Learning over Languages

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Ranaldi, Leonardo Pucci, Giulia Freitas, Andrè Idiap Research Institute Martigny Switzerland Department of Computer Science University of Manchester United Kingdom Department of Computing Science University of Aberdeen United Kingdom

ISBN: (纸本)9782493814104

Curriculum Learning (CL) has been emerged as an effective technique for improving the performances and reducing the cost of pre-training Large Language Models (LLMs). The efficacy of CL demonstrated in different scenarios is in the training LLMs by organizing examples from the simplest to the most complex. Although improvements have been shown extensively, this approach was used for pre-training, leaving novel fine-tuning approaches such as instruction-tuning unexplored. In this paper, we propose a novel complexity measure to empower the instruction-tuning method using the CL paradigm. To complement previous works, we propose cognitively motivated measures to determine the complexity of training demonstrations used in the instruction-tuning paradigm. Hence, we experiment with the proposed heuristics first in English and then in other languages. The downstream results show that delivering training examples by complexity ranking is also effective for instruction tuning, as it improves downstream performance while reducing costs. Furthermore, the technique can be easily transferred to languages other than English, e.g., Italian and French, without any adaptation, maintaining functionality and effectiveness. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Curricula

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Deep Learning-based Intelligent Algorithms for Effective Transmission Authentication and Anomaly Identification in Vehicular Networks

Deep Learning-based Intelligent Algorithms for Effective Tra...

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2023 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Marappan, Sibi Marappan, Harshini Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai600119 India SRM Institute of Science and Technology Department of Computing Technologies Kattankulathur603203 India

ISBN: (纸本)9798350319200

Vehicular networks enable effective traffic control and security on the roads. Sustaining security and network performance depends on authentic transmission and anomaly detection. The inherent delays in conventional approaches to transmission authentication and anomaly detection limit their utility in actual-time vehicular network conditions. In this paper, we present the Deep Attentive Optimized Bidirectional Long/Short Term Memory (DABLSTM) method. The method combines the advantages of deep learning with those of attention mechanisms and bidirectional LSTM. The dataset was gathered from the Hacking and Countermeasure Research Lab (HCRL). Min-max normalization is used for data pre-processing, and then Principal Component Analysis (PCA) is used for efficient feature extraction, which optimizes the source data for our method. The DA-BLSTM method examines the approach's efficiency by evaluating its performance achieves the accuracy (91.2%), recall rate (92.8%), precision (92.3%), and F1-score (93.4%). The DA-BLSTM prevails over traditional techniques by displaying lower latency and considerably higher accuracy in detecting proper transmissions and anomalies within vehicular network data. We present a promising approach for real-time, safe, reliable, and effective authentication and anomaly identification in vehicle networks, establishing an innovative standard for vehicular communication safety. © 2023 IEEE.

关键词： Anomaly identification deep attentive optimized Bidirectional long/short term memory (DA-BLSTM) Principal component analysis (PCA) Transmission authentication Vehicular network

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Novel Lightweight Encryption Scheme Based on Transformation Matrix and ChaCha20 Algorithm for Secure Snap Transmission

Novel Lightweight Encryption Scheme Based on Transformation ...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Sellam, V. Kumar, M. Ranjith Sudeesh Kumar, V. Mahendira Kumar, S. Tharun Kaarthik, G.K. Vellore Institute of Technology School of Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engg. Chennai India

ISBN: (纸本)9798331539948

A revolution for ultra-security in image transmission, due to the innovation and upgrade of cryptographic algorithms occurred during recent years. Proposed image encryption algorithm with ChaCha20 Our matrix-based shuffling not only improves image security but also adds to the complexity of data stealth. The scheme is made up of four main operations: Rearranging pixels in the image with a transform matrix, encryption using ChaCha20, decryption to get back the original image and finally descrambling it by applying inverse transformation changes. In this way, integrity is maintained and image privacy becomes secure. The provided transformation matrix makes scrambling with reversible operations and speed possible against all of the known standard attacks to which ChaCha20 encryption is subjected. The suggested method, led to a Peak Signal Noise Ratio of 361.20 and zero Mean Squared Error indicating a top-quality reconstructed image concerning the original image. The study is raising the security alerts provided by ChaCha20, a relatively new but efficient and high-fidelity encryption solution that makes it an attractive option in today's increasingly digitalized environment where data security has become everyone's top priority. © 2024 IEEE.

关键词： Signal to noise ratio

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Big Data Analytics and Weather Insights for Improved Crop Planning in India 15th

Big Data Analytics and Weather Insights for Improved Crop Pl...

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15th International Conference on Soft computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Bikku, Thulasi Anusha, Mupparaju Sreelekha, Pavuluri Jhansi Mamatha, Choppara Sri, Vinnakota Phani Computer Science and Engineering School of Computing Amaravati Amrita Vishwa Vidyapeetham Andhra Pradesh Amaravati522503 India Department of Computer Science and Engineering Vignan’s Nirula Institute of Technology and Science for Women Guntur India

ISBN: (纸本)9783031889912

The purpose of this research is to gather and analyze a wide range of data related to weather and crop production in specific regions of India. The data includes information on temperature, rainfall, sand, grain, crop production, moisture content, and wind velocity, among others. The aim is to use this information to help farmers improve their crop yields and make more informed decisions about planting and harvesting crops. To achieve this goal, the study employs a method known as big data analytics for weather-based crop prediction. This approach utilizes historical weather data, crop yield, and soil moisture information to forecast future crop yields. Machine learning algorithms are applied to analyze large datasets and identify patterns and trends that can be used to make predictions about future crop yields. The research involves several stages. First, the data is processed using Python to remove any unwanted or noisy data. Next, the MapReduce framework is used to analyze and process the large amount of data collected. Then, k-means clustering is applied to the MapReduce outcomes to group similar data points together and obtain an expected average for the data, which helps in assessing its accuracy. The research also involves the development of a customized approach for predicting and displaying harvest forecasts through a user-friendly interface based on Flask. This approach allows users to make informed decisions about farming practices based on information such as temperature, rainfall, moisture content, and wind velocity. Furthermore, the study recommends three crops for farming and is configurable, allowing for the prediction of the best farms for each region. The research is essential for policymakers and farmers in India as it provides valuable insights into the potential impact of climate change on agricultural production. The study also offers a method for using data analytics to improve crop production, which can help increase food security in India an

关键词： Global warming

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WNet: Multi-Task Artistic Portrait Generation Method 14

WNet: Multi-Task Artistic Portrait Generation Method

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14th International Conference on Information Technology in Medicine and Education, ITME 2024

作者： Li, Juan Wu, Binlong Lin, Yuhui Zhang, Huang Huang, Xiangping Fuzhou Institute of Technology College of Computing and Information Science Fuzhou350506 China Fuzhou University College of Computer and Data Science Fuzhou350108 China

ISBN: (纸本)9798350356670

Artistic portrait usually uses a set of sparse continuous lines to capture a person's unique appearance characteristics based on real people or photos. This paper addresses the issues of strong background noise and the loss of key details of human faces in artistic portraiture, leading to the inability to produce realistic and high quality portraits. A multi-task portrait painting model with hierarchical loss called WNet is proposed based on U2Net used in salient object detection. This model can generate highly abstract portrait painting that do not depend on the background. Compared to existing popular portrait painting generation methods, the images generated by our method have a better visual effect and retain the key details of the facial features better. The experimental results indicate that the proposed WNet is optimal in terms of PSNR, SSIM and MAE performance. Finally, The software communicating robotic arm can quickly spot-paint the fused art portraits. © 2024 IEEE.

关键词： Robotic arms

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An Extended Agglomerative Hierarchical Clustering Techniques 2

An Extended Agglomerative Hierarchical Clustering Techniques

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2nd IEEE International Conference on Advances in computing, Communication and Applied Informatics, ACCAI 2023

作者： Maravarman, M. Babu, S. Pitchai, R. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Tamil Nadu Kattangulathur India B v Raju Institute of Technology Department of Computer Science and Engineering Telangana Narsapur India

ISBN: (纸本)9798350315905

Clustering is a significant method of data analytics in real world environments since human labelling of the data is often costly. Clustering was developed as an alternative to manual tagging. In the field of data analytics, hierarchical clustering is of critical significance, particularly in light of the exponential rise of data derived from the normal world. You may derive a variety of hierarchical agglomerative clustering algorithms from this architecture by providing an inter-cluster semantic similarity, an expression patterns of the -similarity graph, and a cover procedure. These three pieces of information are required. According to the findings of our experiments, our approaches are not only more efficient than conventional hierarchical algorithms, but they also produce smaller agglomerative hierarchical clustering while maintaining the same level of clustering effectiveness. It is generally agreed that topology management is an effective strategy for addressing these challenges. This method groups nodes together for the purpose of managing them and/or carrying out a variety of duties in a dispersed way, such as resource management. There are many quality-driven goals that may be accomplished by clustering, despite the fact that approaches for clustering are mostly renowned for their ability to reduce energy usage. The purpose of this study is to provide a comprehensive explanation on various enhanced agglomerative hierarchical clustering techniques. In addition to this, the authors have provided certain criteria, on the basis of which one may also assess which of these previously described algorithms is the most effective. © 2023 IEEE.

关键词： Data Analytics

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Enhancing Malicious QR Code Link Detection with Swarm-Intelligent Squirrel-Search Optimized ANN

Enhancing Malicious QR Code Link Detection with Swarm-Intell...

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2023 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

ISBN: (纸本)9798350319200

Recently, hackers have made plans to replicate harmful links by tricking people in a variety of ways. By acquiring access to sensitive information they use online, they attempt to remotely manipulate the victims' machines or obtain their data. QR codes are two-dimensional barcodes that can encode different types of data and are viewable by digital devices like smart phones. However, there is no recognized procedure for creating QR codes;therefore they could be vulnerable to a number of dubious assaults. The goal of this study is to use the Swarm-Intelligent Squirrel-Search Optimized Artificial Neural Network (SISSO-ANN) to identify harmful URLs concealed behind 2D QR codes. On the basis of barcode forgery, a cyber attack that may be carried out online has been proposed. Through a number of sites, a dataset of harmful and helpful Uniform Resource Locators (URLs) was produced, and their lexical properties were gathered. The experimental finding demonstrates that SISSO-ANN works better than other current techniques and achieves detection precision value of 93.80%,and accuracy of 93.50% which is much greater than the present secure QR code scanners. Additionally, the application with the least privilege authorization, SISSO-ANN, is the most privacy-friendly. © 2023 IEEE.

关键词： Malicious Links Quick Response (QR) Swarm-Intelligent Squirrel-Search Optimized Artificial Neural Network (SISSO-ANN) Uniform Resource Locators (URLs)

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Secured Normalized Tagged Response for Patient Critical Care Monitoring System: A Unified approach

Secured Normalized Tagged Response for Patient Critical Care...

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2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Sivakumar, Soubraylu Vinay, B. Sai Arulmurugan, A. Prakash, U.M. Kumar, T. Rajesh Sridevi, S. Srm Institute of Science and Technology Computer Technologies School of Computing Chennai Kattankulathur India Saveetha Institute of Medical and Technical Sciences Computer Science and Engineering Saveetha School of Engineering Chennai India Vels Institute of Science Technology and Advanced Studies Computer Science and Engineering Chennai India

ISBN: (纸本)9798350344387

In the rapidly advancing realm of healthcare, ensuring the well-being of critical care patients stands as a paramount mission. Various cutting-edge sensors are available in the market, proficient in monitoring vital health. A substantial number of healthcare facilities still rely on manual processes, a practice that is not only repetitive and labor-intensive but also fraught with the potential for human errors. This pressing need for immediate, accurate responses in the context of critical care has driven the development of a comprehensive solution. Our proposed system brings together security, data normalization, and efficient data exchange, underpinned by a unified approach. Security, a cornerstone of healthcare, finds its realization in the implementation of JSON Web Tokens (JWT). This technology ensures the privacy and integrity of communication between healthcare entities. Crucial patient data, including personal particulars, lifeline parameters, and historical records, is securely housed in a scalable and non-redundant Postgres SQL database. In emergencies, time is of the essence. To expedite responses, an innovative tagged QR code technique is introduced. The unified approach doesn't simply prioritize security;it underscores the significance of data standardization and efficient data exchange. A robust security framework has been meticulously designed to protect sensitive patient information, minimize the risk of data breaches, and prevent unauthorized access. Furthermore, data normalization ensures that patient data is consistently structured and formatted, facilitating compatibility with diverse healthcare platforms and devices. The normalization additionally enhances interoperability, enabling seamless information exchange between different systems and healthcare providers. In summary, our unified approach, marked by state-of-the-art security, data normalization, and efficient data tagging, sets a new standard for patient care in critical settings, ensurin

关键词： Critical Care Emergency Health Care JSON Web Token Patient monitoring Postgres SQL QR Code

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