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Anonymous and robust biometric authentication scheme for secure social IoT healthcare applications

Journal of Engineering and Applied science 2024年第1期71卷 8页

作者： Arpitha, T. Chouhan, Dharamendra Shreyas, J. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

In the era of rapid technological advancement, the Internet of Things (IoT) has revolutionised healthcare through systems like the Telecare Medicine information system (TMIS), designed to streamline patient-doctor interactions and enhance medical treatment. However, the transmission of sensitive patient data over inherently insecure Internet channels exposes it to a spectrum of security risks. Protecting patient medical privacy and ensuring system reliability necessitate mutual authentication between both patients and medical servers. TMIS relies on robust authentication mechanisms, and combining passwords and smart cards has been a recognised approach for mutual authentication. This research introduces an innovative three-factor authentication technique with perfect forward secrecy by leveraging the power of Elliptic Curve Cryptography (ECC) in tandem with smart cards. Additionally, we have incorporated biometric authentication with a Fuzzy Extractor technology to enhance the security and reliability of the system, setting a new standard for user authentication within the realm of Social IoT healthcare. The use of ECC in the method is justified due to its compact key size and robust security measures, making the solution both efficient and secure. The proposed method safeguards user privacy by permitting registered users to change their passwords without divulging their identity to the server. The Burrows–Abadi–Needham logic (also known as the BAN logic) serves as a proof-of-concept for the proposed scheme’s security. Our system provides privacy protection along with mutual authentication and session key negotiation at a considerably low computation cost and communication cost of up to 71.03% compared to the other four relevant techniques, making it more useful in real-world scenarios. © 2023, The Author(s).

关键词： Smart cards

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Enhanced Agricultural Decision-Making: Merging Climatic Data with Machine Learning Algorithms 5

Enhanced Agricultural Decision-Making: Merging Climatic Data...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Rathi, Snehal Wamanacharya, Shubham Shah, Samkit Khedkar, Vijayshri BTACT'S Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Symbiosis Institute of Technology Department of Computer Science & Engineering Pune India

ISBN: (纸本)9798350389609

Climate change still implies risks for economic security, such as for agriculture and its productivity and, therefore, national economic potential. Although similar approaches have been used in publications concerning individual crops or soil types, the application of climatology parameters in agriculture and it limited indices have not been optimal. Owing to the recognized limitations of traditional linear productivity models, this thesis subjects' agricultural productivity to a Cobb-Douglas multi-factorial modeling approach through intensive levels of data decomposition. The proposed approach enabled the researchers to adjust the impact of climate parameters during students' evaluations in the secondary education curricula. Additionally, frameworks which relate temperature variations and crop growth do exist, and therefore the temperature and agriculture performance model effects determinants were determined using complex analysis of temperature relations and crop outputs. This highlights the various factors affecting production, its structure, and the relative importance of some conditions in agriculture and explains their dynamics depending on the changes in growth factors. Hence, a set of factors employed in the modeling resulted mainly from economic conditions, production system structures and advanced agricultural technology applications in the economy as prediction © 2024 IEEE.

关键词： Agricultural Data Analysis Climate Impact on Agriculture Crop Growth Days Growth Degree Days (GDD) R Programming

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Leveraging Adaptive Algorithms for Real-Time Data Analysis 15

Leveraging Adaptive Algorithms for Real-Time Data Analysis

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sahoo, Girija Shankar Hannah Jessie Rani, R. Goyal, Manish Kumar Mohammed, Vazeer Ali Jana, D Little Femilin Wasatkar, Namrata N. Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Lewis University Illinois Department of Computer & Information System United States Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350370249

This paper proposes the use of adaptive algorithms for real-time data analysis. Adaptive algorithms are a set of practical methods used for acting information-driven optimization and studying. They have a wide variety of applications and may be implemented to a selection of facts analysis obligations. This paper offers an overview of existing adaptive algorithms and descriptions of several new programs in the area of actual-time facts analysis. In particular, the paper discusses the notion of adaptive thresholding, which allows for practical real-time evaluation of large datasets. The paper also provides a novel technique for making use of these algorithms in an online learning environment. Results from simulations illustrate the ability of the proposed method in real-international packages. Sooner or later, the paper presents numerous key lessons discovered and affords recommendations for future research and packages in this area. © 2024 IEEE.

关键词： Adaptive algorithms

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Comparative Analysis of Ensemble Learning Techniques for Brain Tumor Classification

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Informatica (Slovenia) 2024年第20期48卷 41-50页

作者： Sharif, Rehman Azam, Muhammad Ali, Arshad Hashmi, Muhammad Usman Uzair, Muhammad Department of Mathematics & Physics The Superior University Lahore54000 Pakistan Department of Computer Science & Information Technology The Superior University Lahore54000 Pakistan Faculty of Computer and Information Systems Islamic University of Madinah Al Madinah Al Munawarah Saudi Arabia Faculty of Engineering Islamic University of Madinah Al Madinah Al Munawarah Saudi Arabia

For the evaluation of our work, we used a brain tumor MRI dataset obtained from Kaggle for the experimental analysis. Our study investigates the performance of ensemble learning techniques for brain tumor classification. This study focuses on comparing the efficacy of homogenous ensemble classifiers, exemplified by the Random Forest (RF) algorithm, against heterogeneous ensemble classifiers like Voting and Stacking, this study embarks on a thorough evaluation journey. Our evaluation is not limited to accuracy measures only;instead, it surrounds recall, ROC AUC, precision, and F1-score, for better assessment of classifiers’ performance. Our findings demonstrate that the Random Forest classifier achieved an accuracy of 99%, an F1 score of 0.99%, and AUC of 0.99%, outperforming other ensemble techniques like Voting and Stacking classifiers. Building upon an observed assessment performed on an appropriately selected brain tumor dataset, we provide solid empirical support demonstrating that RF not only performs better than base classifiers but also outperforms the heterogeneous ensemble methods in terms of many different performance measures. Furthermore, we discuss the specific reason that makes RF outperform other algorithms in this dataset and discuss the robustness and flexibility of this method. By unscrambling these insights, this paper addresses to fill gaps in the existing knowledge regarding the ensemble learning techniques in medical imaging, particularly for brain tumor classification diagnostics. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Random forests

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MQTT based monitoring and control system for remote stations in automotive industry

MQTT based monitoring and control system for remote stations...

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2023 IEEE Zooming Innovation in Consumer Technologies Conference, ZINC 2023

作者： Ivanovic, Denis Job, Josip Puntaric, Marko Herceg, Marijan TTTech-Auto CX Osijek Croatia Faculty of Electrical Engineering Computer Science and Information Technology Department of Software Engineering Osijek Croatia Faculty of Electrical Engineering Computer Science and Information Technology Department of Communications Osijek Croatia

ISBN: (纸本)9798350347722

In this paper is presented implementation of a lightweight supervisory control and data acquisition system for remote test stations as a part of a larger test house in the automotive industry. It is necessary to allow remote set up and monitoring of the test stations, which means defining the structure of a system with numerous test stations as endpoints. The test stations are meant to be virtual machines but can be both virtual and physical machines. A communication protocol must be used that enables this within the organization's network, regardless of actual distance and location. The parameters of the test stations to be monitored can be defined according to the needs and purpose. Even when creating your own tool, this function is not unique, i.e. on one test station parameters of operating system resources such as memory usage can be monitored, while on another test station logged-in users or running programs, reported system errors and others must be monitored. Data storage is one of the mandatory functions of this system. This process allows viewing data throughout history and more detailed analysis of events. There are numerous benefits that can be drawn and used from the data stored in the database. It is necessary to pay attention to the data stored as well as the scope and retention throughout history. The correct and secure setup of a database is one of the prerequisites for any serious system that processes data. Appropriate views should be created for users involved in the data monitoring process using all the elements mentioned so far. Creating a visually acceptable and easy-To-use interface that serves the main purpose is the final presentation of the entire system to users. A complete system leads to higher productivity, profitability and better organization of the company. It also allows project managers to better plan the use and occupancy of test stations. At any time, they have insight into the status of individual test stations, their activitie

关键词： Automotive industry

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Intelligent Deep Convolutional Neural Network Based Object DetectionModel for Visually Challenged People

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computer systems science & Engineering 2023年第9期46卷 3191-3207页

作者： S.Kiruthika Devi Amani Abdulrahman Albraikan Fahd N.Al-Wesabi Mohamed K.Nour Ahmed Ashour Anwer Mustafa Hilal Department of Computing Technology SRM Institute of Science and TechnologyIndia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Engineering Mathematics and Physics Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Artificial Intelligence(AI)and computer Vision(CV)advancements have led to many useful methodologies in recent years,particularly to help visually-challenged *** detection includes a variety of challenges,for example,handlingmultiple class images,images that get augmented when captured by a camera and so *** test images include all these variants as *** detection models alert them about their surroundings when they want to walk *** study compares four CNN-based pre-trainedmodels:ResidualNetwork(ResNet-50),Inception v3,DenseConvolutional Network(DenseNet-121),and SqueezeNet,predominantly used in image recognition *** on the analysis performed on these test images,the study infers that Inception V3 outperformed other pre-trained models in terms of accuracy and *** further improve the performance of the Inception v3 model,the thermal exchange optimization(TEO)algorithm is applied to tune the hyperparameters(number of epochs,batch size,and learning rate)showing the novelty of the *** accuracy was achieved owing to the inclusion of an auxiliary classifier as a regularizer,hyperparameter optimizer,and factorization ***,Inception V3 can handle images of different *** makes Inception V3 the optimum model for assisting visually challenged people in real-world communication when integrated with Internet of Things(IoT)-based devices.

关键词： Pre-trained models object detection visually challenged people deep learning Inception V3 DenseNet-121

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Vision based intelligent traffic light management system using Faster R‐CNN

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CAAI Transactions on Intelligence technology 2024年第4期9卷 932-947页

作者： Syed Konain Abbas Muhammad Usman Ghani Khan Jia Zhu Raheem Sarwar Naif R.Aljohani Ibrahim A.Hameed Muhammad Umair Hassan Department of Computer Science and Engineering University of Engineering and TechnologyLahorePakistan Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal UniversityJinhuaChina OTHEM Manchester Metropolitan UniversityManchesterUK Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of ICT and Natural Sciences Norwegian University of Science and Technology(NTNU)ÅlesundNorway

Transportation systems primarily depend on vehicular flow on roads. Developed coun-tries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly governed by manual traffic light systems. These existing manual systems lead to numerous issues, wasting substantial resources such as time, energy, and fuel, as they cannot make real‐time decisions. In this work, we propose an algorithm to determine traffic signal durations based on real‐time vehicle density, obtained from live closed circuit television camera feeds adjacent to traffic signals. The algorithm automates the traffic light system, making decisions based on vehicle density and employing Faster R‐CNN for vehicle detection. Additionally, we have created a local dataset from live streams of Punjab Safe City cameras in collaboration with the local police authority. The proposed algorithm achieves a class accuracy of 96.6% and a vehicle detection accuracy of 95.7%. Across both day and night modes, our proposed method maintains an average precision, recall, F1 score, and vehicle detection accuracy of 0.94, 0.98, 0.96 and 0.95, respectively. Our proposed work surpasses all evaluation metrics compared to state‐of‐the‐art methodologies.

关键词： access control artificial intelligence computer vision intelligent control

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Enhancing handwritten text feature extraction through key point detection and graph representation

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Soft Computing 2024年第21期28卷 12977-12990页

作者： Rahman, Atta Ur Alahmadi, Tahani Jaser Alsenani, Yousef S. Ali, Sania Faculty of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technologies P.O. Box 23460 Topi Pakistan P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science University of Science and Technology Bannu P.O. Box 28100 Khyber Pakhtunkhwa Bannu Pakistan

Handwriting typically consists of a wide range of writing forms with substantial differences in the placements and size of those writing shapes. The arrangement, organization, and spatial association of individual letters, words, and phrases on a written page are all examples of handwritten text structure. This study proposed a novel approach in which a handwritten text structure is primarily transformed into a Cartesian XY-coordinate system while maintaining its shape characteristics. The points on the contour of handwritten text with local maximum bending values are considered hypothetical breakdown points called Key Points (KPs). From the skeletal representation of handwriting, this approach first identifies KPs and their coordinate positions. After that, KPs are transformed into a graph-based representation with vertices and edges. With the use of geometric graphs, this representation attempts to capture the spatial and temporal organization of handwriting. Previous techniques focused on statistical methods, which offer fixed-size descriptions;however, graph-based representations are flexible in size and reveal the relationship between text structure. Graph representations enable the incorporation of contextual information by integrating extra features which improve text accuracy and understanding. The proposed approach is tested for writer identification and verification tasks on four benchmark datasets (CERUG-EN, CVL, Firemaker, and IAM) and one custom-built dataset. The findings demonstrated that the proposed approach obtained state-of-the-art accuracies across all the mentioned datasets. Particularly, it achieved the highest accuracy of 99.75% for identification and 99.81% for verification on the Firemaker dataset. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Feature extraction

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Analysis of Bio-Inspired approaches and Social Media Analytics in Twitter datasets for Misinformation Detection 1

Analysis of Bio-Inspired approaches and Social Media Analyti...

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1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Nicholas, Neomi Nelin Nirmalrani, V. Sathyabama Institute of Science and Technology Faculty of Computer Science and Engineering Chennai India Sathyabama Institute of Science and Technology Department of Information Technology Chennai India

ISBN: (纸本)9798350342796

Bio-inspired algorithms, such as those with regard to collective intelligence and evolutionary algorithms have been increasingly used in social media analytics in recent years. These algorithms have been shown to be effective in many areas, such as sentiment analysis, community identification, and misinformation detection. This paper explains the underlying principles and features of the most recent bio-inspired techniques. The effectiveness of bio-inspired algorithms for the sentiment analysis categorization on Twitter datasets is studied. In this paper, several social media spam detection techniques that are currently in use are tabulated, and detailed study on the use of social media analytics and bio-inspired algorithms to detect misinformation in social media datasets is performed. The findings of the proposed analysis throws light into the efficiency of these algorithms and gaps for more research, which is inevitable to further improve their performance and robustness for misinformation detection. © 2023 IEEE.

关键词： bio inspired algorithms misinformation detection optimization social media analytics twitter datasets

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Security to text (S2T): multi-layered based security approaches for secret text content

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Multimedia Tools and Applications 2025年第15期84卷 15151-15171页

作者： Kashid, Shamal Awasthi, Lalit K. Berwal, Krishan Department of Computer Science and Engineering National Institute of Technology Uttarakhand IN Srinagar India Faculty of Communication Engineering Military College of Telecommunication Engineering IN Mhow India

In the digital world, text data is produced in an unstructured manner across various communication channels. Extracting valuable information from such data with security is crucial and requires the development of techniques in text mining, information retrieval, and natural language processing (NLP). To solve this issue, we introduce two novel approaches: keyword extraction (KE) and a multi-layered secret sharing scheme (MLSS) to provide security to extracted keywords rather than overall text documents. The KE approach encompasses a sequence of text pre-processing procedures, including tokenization, stopword removal, stemming, and bag of words representation, followed by indexing. This methodology aims to efficiently extract keywords from text datasets. For this research work, we have proposed three datasets, including text messages, whatsapp messages, and electronic mail. MLSS enhances the security of extracted textual data by leveraging text pre-processing steps. This scheme ensures better confidentiality and the non-revealment of sensitive information. Additionally, we evaluate our KE model on our dataset as well as on standard datasets. Experimental results demonstrate the effectiveness of our proposed security to text (S2T) model, which outperform existing state-of-the-art approaches. The model obtains a 100% correlation between the reconstructed text and the original text. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Extraction

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