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Informatica (Slovenia) 2025年第1期49卷 81-92页

作者： Abdullah, Lazim Awang, Noor Azzah Qiyas, Muhammad Special Interest Group of Modelling and Data Analytics Faculty of Computer Science and Mathematics Universiti Malaysia Terengganu Kuala Nerus21030 Malaysia College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Department of Mathematics Riphah International University Failsabad Campus Pakistan

Failure Modes and Effects Analysis (FMEA) is a widely used tool for risk analysis, primarily to identify risk factors affecting system quality. Due to the limitations of the traditional FMEA model, several recent models incorporating advanced fuzzy set extensions have been developed to enhance the reliability of risk assessment outcomes. However, most of these models limit expert flexibility in expressing preferences and often overlook the impact of unequal expert weights and the stability of risk ranking results. This study introduces a new FMEA model based on Q-Rung Orthopair Fuzzy Sets (Q-ROFSs), termed Q-ROFSs-FMEA. Q-ROFSs, an extension of intuitionistic fuzzy sets, introduce a new linguistic term. The Q-ROFSs-FMEA model considers the unequal weights of experts, enabling a dynamic representation of expert preferences. These weights and the linguistic evaluation of risk factors are integrated through an aggregation operator, facilitating consensus among experts. The model is applied to a case study on COVID-19 risk factors, revealing that ‘older age’ (risk priority number 0.000012) is the highest risk factor, while ‘gender’ (risk priority number -0.0037) is the lowest. It is found that the ranking of risk factors determined by the Q-ROFSs-FMEA model is obtained as FM1 FM3 FM6 FM4 FM5 FM7 FM8 FM2 . Furthermore, a comparative analysis indicates consistent ranking results across different models, demonstrating the reliability of the proposed model. The case study and comparative analysis validate the effectiveness and applicability of the Q-ROFSsbased risk assessment model. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Failure modes

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Single Qubit Quantum Logistic-Sine XYZ-Rotation Maps:An Ultra-Wide Range Dynamics for Image Encryption

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computers, Materials & Continua 2025年第5期83卷 2161-2188页

作者： De Rosal Ignatius Moses Setiadi T.Sutojo Supriadi Rustad Muhamad Akrom Sudipta Kr Ghosal Minh T.Nguyen Arnold Adimabua Ojugo Research Center for Quantum Computing and Materials Informatics Faculty of Computer ScienceDian Nuswantoro UniversitySemarang50131Indonesia Department of Cyber Forensics and Information Security Behala Government Polytechnic756Upendra Nath Banerjee RoadParnasreeBehalaKolkata700060India Thai Nguyen University of Technology Thai Nguyen UniversityThai Nguyen240000Vietnam Department of Computer Science Federal University of Petroleum ResourcesEffurun330102Nigeria

Data security has become a growing priority due to the increasing frequency of cyber-attacks,necessitating the development of more advanced encryption *** paper introduces Single Qubit Quantum Logistic-Sine XYZ-Rotation Maps(SQQLSR),a quantum-based chaos map designed to generate one-dimensional chaotic sequences with an ultra-wide parameter *** proposed model leverages quantum superposition using Hadamard gates and quantum rotations along the X,Y,and Z axes to enhance *** numerical experiments validate the effectiveness of *** proposed method achieves a maximum Lyapunov exponent(LE)of≈55.265,surpassing traditional chaotic maps in *** bifurcation analysis confirms a uniform chaotic distribution,eliminating periodic windows and ensuring higher *** system also generates an expanded key space exceeding 10^(40),enhancing security against brute-force ***,SQQLSR is applied to image encryption using a simple three-layer encryption scheme combining permutation and substitution *** approach is intentionally designed to highlight the impact of SQQLSR-generated chaotic sequences rather than relying on a complex encryption *** method achieves an average entropy of 7.9994,NPCR above 99.6%,and UACI within 32.8%–33.8%,confirming its strong randomness and sensitivity to minor *** robustness tests against noise,cropping,and JPEG compression demonstrate its resistance to statistical and differential ***,the decryption process ensures perfect image reconstruction with an infinite PSNR value,proving the algorithm’s *** results highlight SQQLSR’s potential as a lightweight yet highly secure encryption mechanism suitable for quantum cryptography and secure communications.

关键词： Single qubit quantumchaotic quantumchaotic map quantumimage encryption quantumlogistic map quantum sine map

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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

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Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Integration, Cataloguing and Management of Biobanking and Clinical Data Using FAIR Genomes Metadata Schema

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Data Intelligence 2025年第1期7卷 163-184页

作者： Radoslava Kacová TomášHoufek Ondřej Horký Jan Kuráň Radovan Tomášik Michal Růžička Roman Hrstka Vít Nováček Zdenka Dudová Masaryk Memorial Cancer Institute ŽlutýKopec 765653 BrnoCzech Republic Institute of Computer Science Šumavská416/1560200 Brno-Královo PoleCzech Republic Faculty of Informatics Masaryk UniversityBotanická554/68a60200 BrnoCzech Republic

In the dynamic environment of hospitals, valuable real-world data often remain underutilised despite their potential to revolutionize cancer research and personalised medicine. This study explores the challenges and opportunities in managing hospital-generated data, particularly within the Masaryk Memorial Cancer Institute (MMCI) in Brno, Czech Republic. Utilizing Next-Generation Sequencing (NGS) technology, MMCI generates substantial volumes of genomic data. Due to inadequate curation, these data remain difficult to integrate with clinical records for secondary use (such as personalised treatment outcome prediction and patient stratification based on their genomic profiles). This paper proposes solutions based on the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) to enhance data sharing and reuse. The primary output of our work is the development of an automated pipeline that continuously processes and integrates NGS data with clinical and biobank information upon their creation. It stores the data in a special secured repository for sensitive data in a structured form to ensure smooth retrieval.

关键词： FAIR data point FAIR principles Metadata Interoperability Secondary use of healthcare data Hospital-generated data Genomic data Data sharing

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Knowledge Augmented Significant Language Model-Based Chatbot for Explainable Diabetes Mellitus Prediction

Knowledge Augmented Significant Language Model-Based Chatbot...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Mazen Elfayoumi Mohamed AbouElazm Omar Mohamed Tamer Abuhmed Shaker El-Sappagh Faculty of Computer Science and Engineering Galala University Egypt Faculty of Computer Science Goldsmith UOL United Kingdom College of Computing and Informatics Sungkyunkwan University South Korea

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

This study proposes an innovative diabetes prediction chatbot that utilizes large language models (LLMs) to determine the likelihood of diabetes based on specific patient inputs. Unlike conventional machine learning models and in addition to providing precise, individualized, robust prediction of diabetes augmented by the percentage of its confidence, this chatbot provides detailed text-based explanations for its individual predictions and enhances user's understanding by retrieving and presenting similar cases using case-based reasoning techniques. Utilizing key health indicators such as hypertension status, heart disease presence, smoking history, BMI, HbA1c level, and blood glucose levels, the chatbot not only predicts the presence of diabetes but also educates users about the underlying reasons for each prediction. The system's explanation module promotes transparency and trust in the predictive process. The proposed architecture integrates the retrieval augmented generation (RAG) technique with prompt engineering across multiple LLMs, including Llama 3.1, GPT- 3.5, and Gemma2. This approach augments the chatbot's responses with contextually relevant data from similar past cases, thereby enhancing both relevance and accuracy. Furthermore, RAG minimizes LLM hallucinations while enhancing them with up-to-date, precise, personalized medical information. The objective of this chatbot is to offer healthcare professionals and patients a dependable, educational instrument for early detection of diabetes supported by transparent AI principles. This will have a positive effect on preventative healthcare measures.

关键词： Technological innovation Accuracy Large language models Computational modeling Retrieval augmented generation Medical services Predictive models Chatbots Diabetes Medical diagnostic imaging

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Multilingual Hate Speech Detection: Innovations in Optimized Deep Learning for English and Arabic Hate Speech Detection

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SN computer science 2025年第3期6卷 1-16页

作者： AL-Sukhani, Hassan Bsoul, Qusay Elhawary, Abdelrahman H. Nasr, Ziad M. Mansour, Ahmed E. Batyha, Radwan M. Alqadi, Basma S. Alqurni, Jehad Saad Alfagham, Hayat Madbouly, Magda M. Cybersecurity Department Faculty of Science and Information Technology Jadara University Irbid Jordan Cybersecurity Department College of Computer Sciences and Informatics Amman Arab University Amman Jordan Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Computer Science Misr International University Cairo Egypt Faculty of Information Technology Applied Science Private University Amman 11931 Jordan Computer Science Department College of Computer and Information Sciences Al Imam Mohammad Ibn Saud Islamic University Riyadh Saudi Arabia Department of Educational Technologies College of Education Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Department of MIS College of Applied Studies and Community Service Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Institute of Graduate Studies & Research Alexandria University Alexandria Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt

This paper presents the development of a multilingual hate speech detection model that effectively processes and classifies content in both Arabic and English. The study leverages both traditional machine learning models, such as K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Machines (SVM), as well as advanced deep learning models, specifically Bi-directional Long Short-Term Memory (Bi-LSTM) networks. A key challenge addressed is the classification of mixed-language content, which is common on social media platforms in the MENA region. To enhance detection performance, preprocessing techniques were applied to the text data, and the Synthetic Minority Over-sampling Technique (SMOTE) was used to balance the dataset. The results show that the Bi-LSTM model outperformed traditional machine learning approaches, particularly in identifying hate speech across multiple languages. The proposed model demonstrates superior accuracy and robustness in handling mixed-language input, providing a more effective solution for real-world hate speech detection tasks. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Class imbalance Deep learning Hate speech detection Machine learning Mixed Arabic and English training Multilingual NLP Natural language processing (NLP) Sentiment analysis

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Bidirectional LSTM-Based Energy Consumption Forecasting:Advancing AI-Driven Cloud Integration for Cognitive City Energy Management

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computers, Materials & Continua 2025年第5期83卷 2907-2926页

作者： Sheik Mohideen Shah Meganathan Selvamani Mahesh Thyluru Ramakrishna Surbhi Bhatia Khan Shakila Basheer Wajdan Al Malwi Mohammad Tabrez Quasim Department of Computer Science and Engineering Srinivasa Ramanujan CentreSASTRA Deemed UniversityKumbakonam612001India Department of Computer Science and Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bangalore562112India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK University Centre for Research and Development Chandigarh UniversityMohali140413PunjabIndia Centre for Research Impact and Outcome Chitkara University Institute of Engineering and TechnologyChitkara UniversityRajpura140401PunjabIndia Department of Information Systems College ofComputer and Information SciencePrincessNourah bintAbdulrahmanUniversityP.O.Box 84428Riyadh11671Saudi Arabia College of Computer Science Informatics and Computer Systems Department King Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy *** this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and *** study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory(LSTM)*** a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear *** bidirectional LSTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LSTMs consider only a single temporal *** design,combined with dropout regularization,leads to a 20.6%reduction in RMSE and an 18.8%improvement in MAE over conventional unidirectional LSTMs,demonstrating a substantial enhancement in prediction accuracy and *** to existing models—including SVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMSE of 0.2213 during testing,significantly outperforming these *** results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive *** integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.

关键词： Deep learning bidirectional LSTM energy consumption forecasting time-series analysis predictive modeling machine learning in energy management

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CrazyKhoreia: A Robotic Perception System for MAV Path Planning From Digital Images

SN Computer Science

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SN computer science 2025年第5期6卷 1-20页

作者： Restrepo-Garcia, Santiago Romero-Cano, Victor Faculty of Engineering Universidad Autónoma de Occidente Cali Colombia School of Computer Science and Informatics Cardiff University Cardiff UK

Micro Air Vehicles (MAVs) can be used for a wide range of applications, such as drone-based light shows for cultural or marketing purposes, or as a replacement for fireworks. However, more widespread usage of this technology is precluded by the usability gap between choreography design and deployment on target drones. Seamless deployment of MAV choreographies requires the building of computational interfaces that can obtain drone trajectories from conventional media such as digital images. In this paper, we propose CrazyKhoreia, a low-cost, computationally efficient approach to MAV choreography design. CrazyKhoreia is a robotic perception system that obtains a safe, traversable, and accurate waypoint matrix from a digital image. We validate our trajectory generation system through two distinct modes of operation: light painting, where an MAV flies through all waypoints, and multi-drone formation, in which multiple MAVs are arranged to emulate a given image. The utility of each mode is evaluated differently, using the full resolution CrazyKhoreia output as a reference for comparison. For the light painting mode, we logged the MAV’s pose at a frequency of 10 Hz and compared it with the reference for different levels of detail in the reproduced path. The Root Mean Squared Error (RMSE) ranges from 0.1287 to 0.3190 m. For multi-drone formation, where the swarm remains stationary, we computed the RMSE by comparing observed positions with the reference across multiple tests, resulting in a mean RMSE value of 0.1360 m.

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Blocked Bloom Filters with Choices

arXiv

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arXiv 2025年

作者： Schmitz, Johanna Elena Zentgraf, Jens Rahmann, Sven Faculty of Mathematics and Computer Science Saarland University Saarbrücken Graduate School of Computer Science Center for Bioinformatics Saar Saarland Informatics Campus Saarbrücken Germany Faculty of Mathematics and Computer Science Saarland University Center for Bioinformatics Saar Saarland Informatics Campus Saarbrücken Germany

Probabilistic filters are approximate set membership data structures that represent a set of keys in small space, and answer set membership queries without false negative answers, but with a certain allowed false positive probability. Such filters are widely used in database systems, networks, storage systems and in biological sequence analysis because of their fast query times and low space requirements. Starting with Bloom filters in the 1970s, many filter data structures have been developed, each with its own advantages and disadvantages, e.g., Blocked Bloom filters, Cuckoo filters, XOR filters, Ribbon filters, and more. We introduce Blocked Bloom filters with choices that work similarly to Blocked Bloom filters, except that for each key there are two (or more) alternative choices of blocks where the key’s information may be stored. The result is a filter that partially inherits the advantages of a Blocked Bloom filter, such as the ability to insert keys rapidly online or the ability to slightly overload the filter with only a small penalty to the false positive rate. At the same time, it avoids the major disadvantage of a Blocked Bloom filter, namely the larger space consumption. Our new data structure uses less space at the same false positive rate, or has a lower false positive rate at the same space consumption as a Blocked Bloom filter. We discuss the methodology, engineered implementation, a detailed performance evaluation and use cases in bioinformatics of Blocked Bloom filters with choices, showing that they can be of practical value. The implementation of the evaluated filters and the workflows used are provided via Gitlab at https://***/rahmannlab/blowchoc-filters. © 2025, CC BY.

关键词： Bandpass filters

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Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

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International Journal of Advanced computer science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (science and Information Organization). All rights reserved.

关键词： Quantum cryptography

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