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

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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Ancient Cultural Models from Informal Learning Structures That Can Transform Developments in Africa: This Ndịgbo Discuss

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Journal of Entrepreneurship and Innovation in Emerging Economies 2025年第1期11卷 49-67页

作者： Ikpo, C.V. Akowuah, E.K. Boateng, K.O. Kponyo, J.J. Nunoo-Mensah, H. Appiah, Y.S. Department of Computer Engineering Kwame Nkrumah University of Science and Technology Kumasi Ghana Department of Telecommunication Engineering Kwame Nkrumah University of Science and Technology Kumasi Ghana

The innovation for entrepreneurial systems and the advocacy to enact policies that institutionalise it had recently flooded the literature. Every system has fundamental principles responsible for their state, progress and change;such is the impact of the Igbo apprenticeship scheme on the entrepreneurial system. This has recently resulted in many scholars calling for government involvement through enacting policies that will make the scheme all-inclusive for sustainable economic growth in developing economies. Amongst the huge body of literature in its advocacy, none has addressed the question of the underlying principles for reproducing the relations in terms of the mathematical phenomena that the system embodies. This work shows that the success of the Ndịgbo apprenticeship hinges on certain defined underlying metaphorical mathematical functional patterns responsible for the strategic informal management behavioural model. The symbolic characteristics derived from the informal discipleship learning mode are activated for commerce, by young males and their hands-on in trading, with persistent cooperation, integrity, communication and collective purposeful attempts to succeed. The evidence of the model described is extracted from the explorative and exploitative trajectories that emerged from their goal-pursuing dedications to satisfy customer needs, with optimal methods for managing resources and building economies of scale, emerge. This is known as Ịgba-ọsọ-ahịa. This work reveals that Ịgba-ọsọ-ahịa embodies attributes of models that have shaped world-class productive systems such as (a) the geometrical Mandelbrot model, (b) Markov Chain variant with a backoff process, (c) stochastic nonlinear models, (d) network spatial model and (e) system computational model. Finally, a simplistic novel stochastic model of the socio-economic processes is developed to solve complex engineering decisions and hyperparameter optimisation problems in this work. © 2024 SAGE Publicatio

关键词： behaviour discipleship Entrepreneurial minimisation socio-economic and cultural Ịgba-ọsọ-ahịa

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An efficient extraction of the maximum power in a photovoltaic system based on enhanced P&O-Fuzzy logic controller

An efficient extraction of the maximum power in a photovolta...

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International Conference on Innovation and Technological Advances for Sustainability, ITAS 2023

作者： Elzein, I.M. Kurdi, M. Department of Telecommunication and Networking Engineering University of Doha for Science and Technology Doha Qatar Department of Computer Science City University Lebanon

ISBN: (纸本)9781032803722

The concept of solar cell is changing the world in obtaining a clean energy that is an environment user friendly source of energy. Solar arrays process and convert the irradiance which is representing the quantity of light that comes from sun to a usable energy. The maximum power point which is denoted by Maximum power (Pmax) can be efficiently extracted from solar panels through the implementation of a tracking system denoted by the maximum power point tracking algorithm (MPPT). Such a tracking system is well known and utilized in detecting the ultimate maximum power that can be taken from the PV solar arrays under the circumstances of the various changes in both the temperature and irradiance. Through this analysis study, the Perturb and Observe (P&O) algorithm will be proposed to adjust the perturbation steps that will be controlled by an enhanced fuzzy logic controller. It will utilize the fuzzy concepts to extract the maximum power. In this research paper an analysis will be conducted to find the unknown parameters of the PV module along in selecting the appropriate set of fuzzy rules/fuzzy table as well as introducing a high gain dc-dc boost converter to improve the efficiency of power, response time, and reducing the oscillation around the Pmax in the proposed photovoltaic system. © 2025 The Author(s).

关键词： Solar irradiance

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A Comprehensive Ensemble-Based Approach for Heart Disease Prediction with Feature Selection and Model Analysis 5

A Comprehensive Ensemble-Based Approach for Heart Disease Pr...

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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Tahosin, Mst. Sazia Hossain Pranto, Ismail Uddin Chowdhury, Mohamamd Muid Barua Emon, Sonet Alam, Md Ashraful Khan, Chonchal Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh American International University-Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Chittagong University of Engineering and Technology Department of Electronics and Telecommunication Engineering Chattogram Bangladesh Noakhali Science and Technology University Department of Computer Science and Telecommunication Engineering Noakhali Bangladesh Colorado State University Department of Computer Science CO United States

ISBN: (纸本)9798331530983

Heart disease is a leading global cause of mortality, emphasizing the urgent need for early and accurate diagnostic methods. However, traditional diagnostic tools often lack the scalability and precision to meet diverse healthcare demands. We propose a robust ensemble-based machine learning framework for heart disease prediction that combines advanced data preprocessing, feature selection, and model optimization techniques to address this. This study utilized a consolidated dataset of 1,888 records from five heart health databases. First, systematic preprocessing addressed missing values and standardized features. Next, a hybrid feature selection strategy, integrating Univariate Selection, Correlation Coefficient Analysis, and Mutual Information, reduced the feature set from 14 to 11 key indicators. Subsequently, we evaluated 12 machine learning models on all and reduced feature sets. Finally, our ensemble model, combining Random Forest, Decision Trees, XGBoost, CatBoost, and Extra Trees, achieved an outstanding accuracy of 98.12±1.20. Thus, our results demonstrate that integrating multiple classifiers with optimized feature selection significantly enhances predictive accuracy and model reliability. This scalable framework can transform early heart disease detection, improving outcomes across diverse healthcare settings. © 2025 IEEE.

关键词： Cardiology

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Ultrasound-based AI for COVID-19 detection: a comprehensive review of public and private lung ultrasound datasets and studies

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Multimedia Tools and Applications 2025年 1-55页

作者： Morshed, Abrar Al Shihab, Abdulla Jahin, Md Abrar Al Nahian, Md Jaber Hossain Sarker, Md Murad Ibne Wadud, Md Sharjis Uddin, Mohammad Istiaq Imtiaz Siraji, Muntequa Anjum, Nafisa Shristy, Sumiya Rajjab Rahman, Tanvin Khatun, Mahmuda Rubel Dewan, Md Hossain, Mosaddeq Sultana, Razia Chakma, Ripel Emon, Sonet Barua Islam, Towhidul Hussain, Mohammad Arafat Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka1208 Bangladesh Department of Electrical and Telecommunication Engineering Daffodil International University Dhaka1216 Bangladesh Khulna9203 Bangladesh Okinawa904-0412 Japan Department of Information and Communication Technology Bangladesh University of Professionals Dhaka1216 Bangladesh Department of Information and Communication Technology Comilla University Cumilla3506 Bangladesh Department of Biomedical Physics and Technology University of Dhaka Dhaka1000 Bangladesh Department of Computer Science and Engineering International Islamic University Chittagong Chittagong4318 Bangladesh Department of Electrical and Electronic Engineering Islamic University of Technology Gazipur1704 Bangladesh Department of Electronics and Telecommunication Engineering Chittagong University of Engineering and Technology Chittagong4349 Bangladesh Department of Computer Science and Engineering Notre Dame University Bangladesh Dhaka1000 Bangladesh Department of Electrical and Electronic Engineering Khulna University of Engineering & Technology Khulna9203 Bangladesh Department of Computer Science and Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Electronics and Communication Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Mathematics Jahangirnagar University Dhaka1342 Bangladesh Department of Electrical and Electronic Engineering Daffodil International University Dhaka1216 Bangladesh Department of Electrical and Electronic Engineering Dhaka University of Engineering and Technology Gazipur1707 Bangladesh Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Noakhali3814 Bangladesh Department of Computer Science and Engineering Northern University Bangladesh Dhaka1230 Bangladesh Boston Children’s Hospital Harvard Medical School BostonMA02

The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings for detecting COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as a low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on AI-driven studies utilizing lung ultrasound (LUS) for COVID-19 detection and analysis. We provide a detailed overview of both publicly available and private LUS datasets and categorize the AI studies according to the dataset they used. Additionally, we systematically analyzed and tabulated the studies across various dimensions, including data preprocessing methods, AI models, cross-validation techniques, and evaluation metrics. In total, we reviewed 60 articles, 41 of which utilized public datasets, while the remaining employed private data. Our findings suggest that ultrasound-based AI studies for COVID-19 detection have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： COVID-19

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A Tolerant and Energy Optimization Approach for Internet of Things to Enhance the QoS Using Adaptive Blended Marine Predators Algorithm

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computers, Materials & Continua 2025年第5期83卷 2449-2479页

作者： Vijaya Krishna Akula Tan Kuan Tak Pravin Ramdas Kshirsagar Shrikant Vijayrao Sonekar Gopichand Ginnela Department of Information Technology G.Narayanamma Institute of Technology and Science for WomenHyderabad500104India Engineering Cluster Singapore Institute of Technology10 Dover DriveSingapore138683Singapore Department of Electronics and Telecommunication Engineering J.D.College of Engineering&ManagementNagpur441501India Department of Computer Science and Engineering J.D.College of Engineering&ManagementNagpur441501India School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014TamilnaduIndia

The rapid expansion of Internet of Things(IoT)networks has introduced challenges in network management,primarily in maintaining energy efficiency and robust connectivity across an increasing array of *** paper introduces the Adaptive Blended Marine Predators Algorithm(AB-MPA),a novel optimization technique designed to enhance Quality of Service(QoS)in IoT systems by dynamically optimizing network configurations for improved energy efficiency and *** results represent significant improvements in network performance metrics such as energy consumption,throughput,and operational stability,indicating that AB-MPA effectively addresses the pressing needs ofmodern IoT *** are initiated with 100 J of stored energy,and energy is consumed at 0.01 J per square meter in each node to emphasize energy-efficient *** algorithm also provides sufficient network lifetime extension to a resourceful 7000 cycles for up to 200 nodes with a maximum Packet Delivery Ratio(PDR)of 99% and a robust network throughput of up to 1800 kbps in more compact node *** study proposes a viable solution to a critical problem and opens avenues for further research into scalable network management for diverse applications.

关键词： Internet of things trust energy marine predators algorithm(MPA) differential evolution(DE) nodes throughput lifetime

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Comparison of Machine Learning Algorithms for Detection of Stuttering in Speech

Comparison of Machine Learning Algorithms for Detection of S...

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International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2023

作者： Gudlavalleti, Sarvagna Devi, P. Sunitha Lakka, Ramyasri Kuchanpally, Rithika Dudekula, Sai Sonali Department of Computer Science and Engineering G Narayanamma Institute of Technology and Science Shaikpet Telangana Hyderabad India Department of Electronics and Telecommunication Engineering G Narayanamma Institute of Technology and Science Shaikpet Telangana Hyderabad India

ISBN: (纸本)9783031513374

Stuttering is a speech disorder characterized by the repetition of sounds, syllables, or words and the prolongation of sounds. An individual who stutters exactly knows what he or she would like to say but has trouble producing a normal flow of speech. This project aims to use machine learning algorithms to detect stuttering behavior in Telugu language speech samples. Stuttering is a speech disorder that affects the flow of speech, making it difficult to communicate with others. The study will collect voice samples from individuals reading the same Telugu script and generate 8- and 16-kHz samples in .wav files. The data will be annotated and coded on a 0–8 scale and will be generated as a dataset. Machine learning algorithms will be used to build a model for detecting stuttering patterns in the audio files. Finally, the model will be integrated with a user interface. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： User interfaces

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Deciphering Diabetes: Illuminating Prognostic Insights with an Interpretable Machine Learning Framework 3rd

Deciphering Diabetes: Illuminating Prognostic Insights with ...

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3rd International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Rony, Mohammad Abu Tareq Islam, Mohammad Shariful Saha, Pritom Johora, Fatama Tuz Ahammad, Mejbah Alam, Shah Md Nazmul Rahman, Md Saifur Nag, Anindya Department of Statistics Noakhali Science and Technology University Noakhali3814 Bangladesh Computer Science and Telecommunication Engineering Noakhali Science and Technology University NoakhaliNoakhali-3814 Bangladesh Computer Science and Engineering Daffodil International University Dhaka Bangladesh Computer Science and Engineering University of Chittagong Chittagong Bangladesh Computer Science and Engineering American International University Bangladesh Dhaka Bangladesh Masters of Computer Science University Arizona State University Tempe United States Strategic Management University UniversityoftheCumberlands Williamsburg United States Computer Science and Engineering Khulna University Khulna9208 Bangladesh

ISBN: (纸本)9789819783540

Diabetes is a chronic disease that impacts millions of people globally. Early detection and management of diabetes can help mitigate potential complications and enhance patient outcomes. Machine learning (ML) algorithms have become more prevalent for predicting diabetes in recent years. The research paper proposed a machine learning-based approach for predicting diabetes utilizing the Diabetes Health indicator dataset and compared the comprehensive performance of multiple ML algorithms. The study used the LIME (Local Interpretable Model-Agnostic Explanations) package to provide interpretable predictions and acquire insights into the factors contributing to the model’s predictions. Although the ML models were interpretable, they also delivered the highest accuracy 86% demonstrated by the XGBoost classifier in prediction. The LIME produces local explanations for individual predictions, emphasizing the most significant features contributing to the prediction. The study found that, for the XGB model, BMI has a negative influence, while Genhlth positively impacts the outcome. Moreover, Age and Income have a positive impact on the outcome of diabetes. The findings of this research can potentially improve patient outcomes and alleviate the global burden of diabetes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Desynchronized Wake-Up Algorithm for Energy Efficient EPON-Based 5G Backhaul

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Journal of Lightwave Technology 2025年第10期43卷 4516-4529页

作者： Dutta, Sourav Roy, Dibbendu Das, Goutam Indian Institute of Technology Kharagpur G. S. Sanyal School of Telecommunication Kharagpur721302 India KTH Royal Institute of Technology Department of School of Electrical Engineering and Computer Science Stockholm114 28 Sweden

This paper focuses on enhancing the energy savings of EPON-based backhaul for 5G and beyond networks. Here, we introduce a novel technique of the desynchronization of wake-up cycles among Optical Network Units (ONUs) in Low-Power Mode (LPM). In this technique, the Optical Line Terminal (OLT) decides the wake-up slots of all ONUs such that they are spread out and staggered, maximizing the likelihood of fewer ONUs waking up simultaneously. We have shown that this technique can be implemented in addition to any of the existing protocols to improve the energy savings of the protocol. We formulate an optimization problem to achieve wake-up desynchronization while ensuring latency requirements. To address this problem, we present a polynomial-time 1-approximation algorithm. The convergence and complexity of the algorithm are analyzed. Through simulations, we illustrate a significant increase in energy savings and a reduction in the probability of delay violation by implementing our proposed method. © 1983-2012 IEEE.

关键词： Approximation algorithms

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Deep-EERA:DRL-Based Energy-Efficient Resource Allocation in UAV-Empowered Beyond 5G Networks

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Tsinghua science and Technology 2025年第1期30卷 418-432页

作者： Shabeer Ahmad Jinling Zhang Ali Nauman Adil Khan Khizar Abbas Babar Hayat School of Electronic Engineering Beijing University of Posts and TelecommunicationBeijing 1000876China Department of Information and Communication Engineering Yeungnam UniversityGyeongsan 38541Republic of Korea School of Information Engineering Xi’an Eurasia UniversityXi’an 710065China Department of Computer Science Hanyang UniversitySeoul 04763Republic of Korea

The rise of innovative applications,like online gaming,smart healthcare,and Internet of Things(IoT)services,has increased demand for high data rates and seamless connectivity,posing challenges for Beyond 5G(B5G)*** is a need for cost-effective solutions to enhance spectral efficiency in densely populated areas,ensuring higher data rates and uninterrupted connectivity while minimizing *** Aerial Vehicles(UAVs)as Aerial Base Stations(ABSs)offer a promising and cost-effective solution to boost network capacity,especially during emergencies and high-data-rate ***,integrating UAVs into the B5G networks presents new challenges,including resource scarcity,energy efficiency,resource allocation,optimal power transmission control,and maximizing overall *** paper presents a UAV-assisted B5G communication system where UAVs act as ABSs,and introduces the Deep Reinforcement Learning(DRL)based Energy Efficient Resource Allocation(Deep-EERA)*** efficient DRL-based Deep Deterministic Policy Gradient(DDPG)mechanism is introduced for optimal resource allocation with the twin goals of energy efficiency and average throughput *** proposed Deep-EERA method learns optimal policies to conserve energy and enhance throughput within the dynamic and complex UAV-empowered B5G *** extensive simulations,we validate the performance of the proposed approach,demonstrating that it outperforms other baseline methods in energy efficiency and throughput maximization.

关键词： Deep Reinforcement Learning(DRL) Unmanned Aerial Vehicles(UAVs) resource allocation energy efficiency 5G and beyond network

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