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12th International Conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

作者： Saravanan, D. Kaushik, S. Handaragall, Vijayalaxmi C. Raja Vikram, G. Sudha, C. Kalaivani, J. Hyderabad Hyderabad India ICFAI Foundation for Higher Education Hyderabad Hyderabad India School of Computing and Information Technology REVA University Bengaluru Bengaluru India Department of Computer Science & Engineering Vignan Institute of Technology and Science Hyderabad India Hyderabad India Department of Computing Technologies SRM Institute of Science and Technologies Kattankulathur Chennai India

ISBN: (纸本)9789819601387

Extracting the necessary information from the stored database is one of the most demanded research areas today, especially in the field of subject-based information extraction. This application is a crucial area of focus for many researchers. The existing technique in this area relies on user observation-based responses. In this approach, each time a user submits a query, they need to select the most relevant content from the query extraction and then rank the items. The search is then refined, and the process continues in the next iteration. This method is time-consuming and requires domain knowledge about the system. To overcome these issues in subject-based information extraction, creating an index of datasets can improve search time and enhance information extraction efficiency. Initially, this process may take some time to train the input datasets, but the extraction will yield the most relevant data. Experimental results and practical applications indicate that the proposed technique performs well overall. The outputs confirm that even if the query is designed by an unprofessional user or lacks specific query points, the technique produces effective results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Creation of evidence Evidence-based search Identify the patterns Response Subject-based content User Assessment

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Advanced Persistent Threat Identification with Boosting and Explainable AI

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SN computer Science 2023年第3期4卷 271页

作者： Hasan, Md. Mahadi Islam, Muhammad Usama Uddin, Jasim Department of Computer Science and Engineering Asian University of Bangladesh Ashulia Dhaka 1341 Bangladesh School of Computing and Informatics University of Louisiana at Lafayette 104 E University Ave Lafayette 70503 LA United States Department of Applied Computing and Engineering Cardiff School of Technologies Cardiff Metropolitan University Western Avenue Wales Cardiff CF5 2YB United Kingdom

Advanced persistent threat (APT) is a serious concern in cyber-security that has matured and grown over the years with the advent of technology. The main aim of this study is to establish an effective identification model for APT attacks to prevent and reduce their influence. Machine learning has the potential as well as substantial background to detect and predict cyber-security threats including APT. This study utilized several boosting-based machine learning methods to predict various types of APTs that are consistent in cyber-security domain. Furthermore, Explainable Artificial Intelligence (XAI) was coupled with the predictions to provide actionable insights to the domain stakeholders as well as practitioners in this domain. The results, particularly XGBoost with weighted F1 score of 0.97 and SHapley Additive exPlanations (SHAP)-based explanation, prove that boosting methods as well as machine learning models paired with XAI are indeed promising in handling cyber-security-related dataset problems which can be extrapolated towards new avenues of challenging research by effectively deploying boosting-based XAI models. © 2023, The Author(s).

关键词： APT Boosting Cyber-security Explainability Machine learning XAI

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Design of a Secured and Trusted E-Voting System Using Blockchain Technology

Design of a Secured and Trusted E-Voting System Using Blockc...

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SmartBlock4Africa, IEEE

作者： Elegba Jeremiah. O. Agbelusi O. Olutayo Victor A. Akintola Kolawole G. Department of Software Engineering School of Computing The Federal University of Technology Akure Nigeria Department of Computer Science School of Computing The Federal University of Technology Akure Nigeria

ISBN: (数字)9798350376838

ISBN: (纸本)9798350376845

The state of election in Nigeria is worrisome. Electoral malpractices have been a major challenge to the Nigerian government in recent times. Government has made frantic efforts to tackle these electoral challenges but the recent event in year 2023 presidential election is worrisome suggests that the solution is not near. Recently, the percentage of eligible voters who vote on election days is declining. Voters turnout went up from 52.3% in 1999 - the first general election since 1993 - to 69% in 2003. But, it has been on the decline nearly ever since − 57.5% in 2007, 53.7% in 2011, 43.7% in 2015 and 34.8% in 2019. In 2023, it is 28.63%. With the way things are going, if urgent action is not taken, this could signal the end of democracy in Nigeria. This research presents the design and implementation of a trusted and secured e-voting based on blockchain technology that will increase voters confidence and trust. The system utilizes *** for the frontend and Solidity on the Polygon blockchain for the backend, ensuring transparency, tamper-resistance, and heightened security. This innovative approach addresses trust and reliability concerns in traditional voting systems, offering a modernized and trustworthy electoral process.

关键词： Smart contracts Authentication Blockchains Reliability Security Electronic voting

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HoP: Homeomorphic Polar Learning for Hard Constrained Optimization

arXiv

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arXiv 2025年

作者： Deng, Ke Zhang, Hanwen Lu, Jin Sun, Haijian School of Electrical & Computer Engineering University of Georgia Athens United States School of Computing University of Georgia Athens United States

Constrained optimization demands highly efficient solvers which promotes the development of learn-to-optimize (L2O) approaches. As a data-driven method, L2O leverages neural networks to efficiently produce approximate solutions. However, a significant challenge remains in ensuring both optimality and feasibility of neural networks’ output. To tackle this issue, we introduce Homeomorphic Polar Learning (HoP) to solve the star-convex hard-constrained optimization by embedding homeomorphic mapping in neural networks. The bijective structure enables end-to-end training without extra penalty or correction. For performance evaluation, we evaluate HoP’s performance across a variety of synthetic optimization tasks and real-world applications in wireless communications. In all cases, HoP achieves solutions closer to the optimum than existing L2O methods while strictly maintaining feasibility. © 2025, CC BY.

关键词： Constrained optimization

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Detection of anomalies in networks by implementing and analyzing deep learning-based classifiers

Detection of anomalies in networks by implementing and analy...

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2024 International Conference on Communication, computing and Internet of Things, IC3IoT 2024

作者： Anitha, Cuddapah Aswal, Upendra Singh Chaithra, M.H. Dange, Jyoti Barve, Amit Maranan, Ramya Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College School Of Computing Department Of Computer Science And Engineering Andhra Pradesh Tirupati517102 India Graphic Era Deemed To Be University Department Of Computer Science & Engineering Uttarakhand Dehradun248002 India Reva University Department Of Computer Science And Engineering Karnataka Bangalore560064 India Atharva College Of Engineering Department Of Information Technology Malad West Maharashtra Mumbai400095 India Parul University Parul Institute Of Technology Faculty Of Engineering And Technology Department Of Computer Science And Engineering P.O.Limda Gujarat 391760 India Saveetha School Of Engineering Simats Department Of Research And Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350352689

With the increasing complexity and sophistication of cyber threats, the demand for effective anomaly detection mechanisms in network security has grown substantially. This research focuses on the utilization of deep learning (DL) to detect anomalies in networks, implementing and analyzing three distinct models of deep learning-based classifiers. The classifiers include a binary classification model, a multiclass classification model with proportionality classification, and a multiclass classification model with exclusion strategies. The efficacy of these models is evaluated through cross-validation runs and testing on data from a database parallel to that of its training. The binary classification model serves as the baseline, distinguishing between normal and anomalous network behavior. The multiclass classification model with proportionality classification extends the binary approach by categorizing anomalies into multiple classes with consideration for their proportional representation. On the other hand, the multiclass classification model with exclusion strategies refines anomaly detection by explicitly excluding certain patterns from classification, enhancing the model's specificity. The research methodology involves the development of deep learning-based classifiers, the collection of network data for training, and rigorous evaluation through cross-validation techniques. Additionally, the models are tested on a separate dataset to assess their generalization capabilities in real-world scenarios. The outcomes of this research contribute to the understanding of DL applications in network anomaly detection, providing insights into the strengths and limitations of different classifier strategies. © 2024 IEEE.

关键词： Classification (of information)

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Ortho Vision: Autoencoder-CNN Fusion Approach for Fracture Detection

Ortho Vision: Autoencoder-CNN Fusion Approach for Fracture D...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： S Abhiram Amal Krishna K Vaishnav G M Anjali T Abhishek S Department of Computer Science and Engineering Amrita School of Computing Amritapuri India

We propose an advanced framework for automated fracture detection in medical X-ray images., harnessing the power of hybrid deep learning methodologies. Through the fusion of autoencoders” adeptness in feature extraction and a convolutional neural networks (CNN) efficiency in recognizing the complex patterns., the proposed model achieves remarkable accuracy in identifying fractures. By integrating data augmentation and preprocessing strategies., the system demonstrates enhanced robustness in handling variations in image quality and presentation. This innovation holds substantial promise in revolutionizing the field of medical diagnostics by offering a reliable and efficient tool for clinicians to expedite fracture diagnosis., facilitate treatment decisions., and ultimately improve patient care. The amalgamation of diverse techniques not only establishes a robust fracture detection mechanism but also showcases the potential of interdisciplinary approaches. The proposed approach demonstrated excellent accuracy in fracture detection with a 92% percentage., which can greatly improve medical diagnosis and treatment planning.

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A Novel Internet of Medical Thing Cryptosystem Based on Jigsaw Transformation and Ikeda Chaotic Map

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computer Systems Science & engineering 2023年第9期46卷 3017-3036页

作者： Sultan Almakdi Mohammed SAlshehri Yousef Asiri Mimonah Al Qathrady Anas Ibrar Jawad Ahmad Department of Computer Science Najran UniversityNajran61441Saudi Arabia Department of Information Systems Najran UniversityNajran61441Saudi Arabia Department of Electrical Engineering Wah Engineering CollegeUniversity of WahWah Cantt47040Pakistan School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK

Image encryption has attracted much interest as a robust security solution for preventing unauthorized access to critical image *** picture encryption is a crucial step in many cloud-based and healthcare *** this study,a strong cryptosystem based on a 2D chaotic map and Jigsaw transformation is presented for the encryption of medical photos in private Internet of Medical Things(IoMT)and cloud storage.A disorganized three-dimensional map is the foundation of the proposed *** dispersion of pixel values and the permutation of their places in this map are accomplished using a nonlinear encoding *** suggested cryptosystem enhances the security of the delivered medical images by performing many *** validate the efficiency of the recommended cryptosystem,various medical image kinds are used,each with its unique *** measures are used to evaluate the proposed cryptosystem,which all support its robust *** simulation results confirm the supplied cryptosystem’s ***,it provides strong robustness and suggested protection standards for cloud service applications,healthcare,and *** is seen that the proposed 3D chaotic cryptosystem obtains an average entropy of 7.9998,which is near its most excellent value of 8,and a typical NPCR value of 99.62%,which is also near its extreme value of 99.60%.Moreover,the recommended cryptosystem outperforms conventional security systems across the test assessment criteria.

关键词： Jigsaw transformation cryptosystem image encryption medical images Ikeda map chaotic system

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Preserving medical information from doctor's prescription ensuring relation among the terminology

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computers in Biology and Medicine 2025年 188卷 109812-109812页

作者： Datta, Apurba Hasan, Md. Mehedi Mahmud, Niaz Ferdosi, Bilkis Jamal Islam, Rafiqul Rahman, Ziaur Mehedi, Sk. Tanzir Department of Computer Science and Engineering University of Asia Pacific Dhaka1205 Bangladesh School of Computing Mathematics & Engineering Charles Sturt University Albury2640 Australia School of Computer Science Queensland University of Technology Brisbane4000 Australia

Healthcare is a fundamental human right, yet accessing proper healthcare remains a significant challenge. Many patients still rely on physical documents, requiring them to carry all relevant medical records during consultations. Existing methods for extracting data from medical prescriptions have primarily focused on recognizing medication names using manual image annotation or binarization techniques. These approaches often fail to capture detailed prescription information, struggle with multilingual text, and lack the ability to structure medicine-related data comprehensively. To address these limitations, we propose an advanced Electronic Health Record (EHR) system that provides a secure and accessible digital platform for storing and managing patients’ medical histories. Our study implements the best Region of Interest (ROI) detection model using the You Only Look Once (YOLO) framework, achieving 99.6% detection accuracy. The extracted ROI is processed using the best Optical Character Recognition (OCR) technique to digitize prescription data, which is then organized into a structured format within a central database. Furthermore, the system integrates a spell correction algorithm with a 96% accuracy rate to rectify misspelled medication names. Beyond text extraction, the system links corrected medication names with dosages, instructions, and manufacturer information, combining generic and brand details to ensure precise and comprehensive healthcare data management. This integrated solution enhances medication data organization, facilitates better healthcare delivery, and improves patient outcomes by streamlining prescription handling and ensuring accurate medication administration. Our EHR system bridges the gap between physical and digital records, advancing an efficient and reliable healthcare ecosystem. © 2025 The Authors

关键词： Electronic health record

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SAHA: An SDN Enabled Fog Architecture for Healthcare Applications 24

SAHA: An SDN Enabled Fog Architecture for Healthcare Applica...

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25th International Conference on Distributed computing and Networking, ICDCN 2024

作者： Sarkar, Joy Lal Peng, Sheng-Lung Deb, Subhrajyoti Muthaiah, U. Mondal, Ayan Gopal, Gaddam Venu Techno College of Engineering Agartala India National Taipei University of Business Taiwan Icfai University Tripura India Amrita School of Computing Amrita Vishwa Vidyapeetham India Department of Computer Science and Engineering Iit Indore India Koneru Lakshmaiah Education Foundation India

ISBN: (纸本)9798400716737

In this paper, a software-defined Fog architecture is suggested to initiate an immediate course of treatment for a patient. Sensors on a patient outfitted with various sensors can read physiological data. This work uses two types of queries such as strict and flexible queries. Strict inquiries are used when an emergency is recognised due to a patient's criticality. Non-emergency queries are referred to as flexible inquiry. The strict queries are executed at a nearby edge node, and the data is sent to the fog node via a switch or SDN controller. Instead, a patient can move for treatment depending on the proximity of medical professionals, the quality of the facilities, and the travel time. The task of determining availability falls to a Local Medical Team (LMT). It is also the responsibility of a Global Medical Team (GMT) to keep track of the patient's health and provide the LMT with any information that is required. We can observe from the outcomes that the suggested algorithm outperforms the baseline algorithms. © 2024 ACM.

关键词： Travel time

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Ensemble Nonlinear Support Vector Machine Approach for Predicting Chronic Kidney Diseases

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computer Systems Science & engineering 2022年第9期42卷 1273-1287页

作者： S.Prakash P.Vishnu Raja A.Baseera D.Mansoor Hussain V.R.Balaji K.Venkatachalam Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and TechnologyCoimbatore641062India Department of Computer Science and Engineering Kongu Engineering CollegePerundurai638060India School of Computing Science and Engineering VIT Bhopal UniversityBhopal466114India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of ECE Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic

Urban living in large modern cities exerts considerable adverse effectson health and thus increases the risk of contracting several chronic kidney diseases (CKD). The prediction of CKDs has become a major task in urbanizedcountries. The primary objective of this work is to introduce and develop predictive analytics for predicting CKDs. However, prediction of huge samples isbecoming increasingly difficult. Meanwhile, MapReduce provides a feasible framework for programming predictive algorithms with map and reduce *** relatively simple programming interface helps solve problems in the scalability and efficiency of predictive learning algorithms. In the proposed work, theiterative weighted map reduce framework is introduced for the effective management of large dataset samples. A binary classification problem is formulated usingensemble nonlinear support vector machines and random forests. Thus, instead ofusing the normal linear combination of kernel activations, the proposed work creates nonlinear combinations of kernel activations in prototype examples. Furthermore, different descriptors are combined in an ensemble of deep support vectormachines, where the product rule is used to combine probability estimates ofdifferent classifiers. Performance is evaluated in terms of the prediction accuracyand interpretability of the model and the results.

关键词： Chronic disease classification iterative weighted map reduce machine learning methods ensemble nonlinear support vector machines random forests

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