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Informatica (Slovenia) 2023年第6期47卷 105-114页

作者： Al-Saadi, Hussain M. Alshawi, Imad S. Department of Computer Science College of Computer Science and Information Technology University of Basrah Basrah Iraq

Lightweight cryptography algorithms have concentrated on key generation's randomness, unpredictable nature, and complexity to improve the resistance of ciphers. Therefore, the key is an essential component of every cryptography algorithm since it affects the algorithm's level of security. Light Encryption Device (LED) is a high-performance, lightweight block encryption solution that works on resource-constrained devices and considers a lighter version of AES. It employs a 64-bit block cipher with two significant instances using 64-bit and 128-bit keys, respectively. A lack of key scheduling in LED heightens security risks, such as key-related attacks. Specifically, now that LED has been hacked and is no longer secure. Therefore, LED must achieve a high diffusion and confusion level to withstand known attacks. Chaos-based encryption provides an exceptionally high level of security because of the unique characteristics of chaotic systems, which are defined by various nonlinear deterministic dynamic equations. Merge LED algorithm and the advantages of chaotic maps randomness provide successful confusion and diffusion property to improve the LED algorithm's shortcomings by increasing its security. This paper presents a lightweight approach to construct a robust, sufficiently using 3-D Lorenz system chaotic map to generate a one-time pseudo-random bit key to avoid being predicted by adversaries, resulting in achieving sound confusion and diffusion and withstand known assaults. A NIST test suite found that the performance of the LED based on the 3-D Lorenz chaotic map approach in terms of data secrecy is nearly 0.3003 higher than that of the LED and keeps the trade-off between computation cost and security. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Lyapunov methods

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Hybrid Variable-Length Spider Monkey Optimization with Good-Point Set Initialization for Data Clustering

Informatica (Slovenia)

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Informatica (Slovenia) 2023年第8期47卷 67-78页

作者： Obaid, Athraa Qays Alabbas, Maytham Department of Computer Science College of Computer Science and Information Technology University of Basrah Basrah Iraq

Data clustering refers to the process of grouping similar data points based on patterns or characteristics. It finds applications in image analysis, pattern recognition, and data mining. The k-means algorithm is commonly used for this purpose, but it has two main limitations. Firstly, it necessitates the user to explicitly specify the number of clusters. Secondly, it is highly sensitive to the initial selection of cluster centroids. To overcome these limitations, this study presents a novel approach that utilizes a variable-length spider monkey optimization algorithm (VLSMO) with a hybrid measure to determine the optimal number of clusters and initial centroids. Experimental results obtained from real-life datasets demonstrate that VLSMO outperforms the standard k-means algorithm and other techniques in terms of accuracy and clustering capacity. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Pattern recognition

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Advanced Machine Learning Approaches for Breast Cancer Detection with Neutrosophic Sets

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Neutrosophic Sets and Systems 2025年 81卷 273-284页

作者： Elbehiery, Hussam Fathi, Hanaa Eassa, Mohamed Abdelhafeez, Ahmed Abdellah, Mohamed Refaat Mahmoud, Hadeer Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt College of Information Technology Amman Arab University Amman Jordan Applied Science Research Center Applied Science Private University Amman Jordan The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt Department of Artificial Intelligence Faculty of Computers and Artificial Intelligence Modern University for Technology & Information Cairo Egypt

Breast Cancer (BC) remains a significant health challenge for women and is one of the leading causes of mortality worldwide. Accurate diagnosis is critical for successful therapy and increased survival rates. Recent advances in medical imaging and computational technologies have enabled more precise methods of detecting and evaluating breast cancer. Accurate analysis and diagnosis utilizing medical imaging have developed as essential research topics, providing important help in clinical decision-making for various illnesses, including breast cancer. Machine learning (ML) can accurately predict breast cancer. But the breast cancer data has vague and uncertainty information. So, the neutrosophic sets (NSs) are used in this study to deal with uncertainty data. We convert the original dataset into neutrosophic data with three components such as truth, indeterminacy, and falsity values. Then we applied four ML models with N-data such as logistic regression, gradient boosting (GB), k-nearest neighbor (KNN), and support vector machines (SVM), to improve diagnostic accuracy. Then we compared the ML models with and without using N-data. The results show the logistic regression has higher accuracy with 98.6% with the N-data and 95.80% without N-data. So, the NSs can improve the accuracy of ML models. © 2025, University of New Mexico. All rights reserved.

关键词： Lung cancer

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Fusion of Hash-Based Hard and Soft Biometrics for Enhancing Face Image Database Search and Retrieval

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computers, Materials & Continua 2023年第12期77卷 3489-3509页

作者： Ameerah Abdullah Alshahrani Emad Sami Jaha Nahed Alowidi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

The utilization of digital picture search and retrieval has grown substantially in numerous fields for different purposes during the last decade,owing to the continuing advances in image processing and computer vision *** multiple real-life applications,for example,social media,content-based face picture retrieval is a well-invested technique for large-scale databases,where there is a significant necessity for reliable retrieval capabilities enabling quick search in a vast number of *** widely employ faces for recognizing and identifying ***,face recognition through formal or personal pictures is increasingly used in various real-life applications,such as helping crime investigators retrieve matching images from face image databases to identify victims and ***,such face image retrieval becomes more challenging in large-scale databases,where traditional vision-based face analysis requires ample additional storage space than the raw face images already occupied to store extracted lengthy feature vectors and takes much longer to process and match thousands of face *** work mainly contributes to enhancing face image retrieval performance in large-scale databases using hash codes inferred by locality-sensitive hashing(LSH)for facial hard and soft biometrics as(Hard BioHash)and(Soft BioHash),respectively,to be used as a search input for retrieving the top-k matching ***,we propose the multi-biometric score-level fusion of both face hard and soft BioHashes(Hard-Soft BioHash Fusion)for further augmented face image *** experimental outcomes applied on the Labeled Faces in the Wild(LFW)dataset and the related attributes dataset(LFW-attributes),demonstrate that the retrieval performance of the suggested fusion approach(Hard-Soft BioHash Fusion)significantly improved the retrieval performance compared to solely using Hard BioHash or Soft BioHash in isolation,where the suggested method provides an augmented

关键词： Face image retrieval soft biometrics similar pictures hashing database search large databases score-level fusion multimodal fusion

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AMachine Learning Approach to Cyberbullying Detection in Arabic Tweets

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computers, Materials & Continua 2024年第7期80卷 1033-1054页

作者： Dhiaa Musleh Atta Rahman Mohammed Abbas Alkherallah Menhal Kamel Al-Bohassan Mustafa Mohammed Alawami Hayder Ali Alsebaa Jawad Ali Alnemer Ghazi Fayez Al-Mutairi May Issa Aldossary Dalal A.Aldowaihi Fahd Alhaidari Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Information Systems College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Networks and Communications College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia

With the rapid growth of internet usage,a new situation has been created that enables practicing *** has increased over the past decade,and it has the same adverse effects as face-to-face bullying,like anger,sadness,anxiety,and *** the anonymity people get on the internet,they tend to bemore aggressive and express their emotions freely without considering the effects,which can be a reason for the increase in cyberbullying and it is the main motive behind the current *** study presents a thorough background of cyberbullying and the techniques used to collect,preprocess,and analyze the ***,a comprehensive review of the literature has been conducted to figure out research gaps and effective techniques and practices in cyberbullying detection in various languages,and it was deduced that there is significant room for improvement in the Arabic *** a result,the current study focuses on the investigation of shortlisted machine learning algorithms in natural language processing(NLP)for the classification of Arabic datasets duly collected from Twitter(also known as X).In this regard,support vector machine(SVM),Naive Bayes(NB),Random Forest(RF),Logistic regression(LR),Bootstrap aggregating(Bagging),Gradient Boosting(GBoost),Light Gradient Boosting Machine(LightGBM),Adaptive Boosting(AdaBoost),and eXtreme Gradient Boosting(XGBoost)were shortlisted and investigated due to their effectiveness in the similar ***,the scheme was evaluated by well-known performance measures like accuracy,precision,Recall,and ***,XGBoost exhibited the best performance with 89.95%accuracy,which is promising compared to the state-of-the-art.

关键词： Supervised machine learning ensemble learning cyberbullying Arabic tweets NLP

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A novel medical steganography technique based on Adversarial Neural Cryptography and digital signature using least significant bit replacement

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International Journal of Cognitive computing in Engineering 2024年第1期5卷 379-397页

作者： Hameed, Mohamed Abdel Hassaballah, M. Abdelazim, Riem Sahu, Aditya Kumar Department of Computer Science Faculty of Computers and Information Luxor University Luxor85951 Egypt Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University AlKharj16278 Saudi Arabia Department of Computer Science Faculty of Computers and Information South Valley University Qena Egypt Department of Information Systems College of Information Technology Misr University for Science and Technology Giza Egypt Amrita School of Computing Amrita Vishwa Vidyapeetham Andhra Pradesh Amaravati522503 India

With recent advances in technology protecting sensitive healthcare data is challenging. Particularly, one of the most serious issues with medical information security is protecting of medical content, such as the privacy of patients. As medical information becomes more widely available, security measures must be established to protect confidentiality, integrity, and availability. Image steganography was recently proposed as an extra data protection mechanism for medical records. This paper describes a data-hiding approach for DICOM medical pictures. To ensure secrecy, we use Adversarial Neural Cryptography with SHA-256 (ANC-SHA-256) to encrypt and conceal the RGB patient picture within the medical image's Region of Non-Interest (RONI). To ensure anonymity, we use ANC-SHA-256 to encrypt the RGB patient image before embedding. We employ a secure hash method with 256bit (SHA-256) to produce a digital signature from the information linked to the DICOM file to validate the authenticity and integrity of medical pictures. Many tests were conducted to assess visual quality using diverse medical datasets, including MRI, CT, X-ray, and ultrasound cover pictures. The LFW dataset was chosen as a patient hidden picture. The proposed method performs well in visual quality measures including the PSNR average of 67.55, the NCC average of 0.9959, the SSIM average of 0.9887, the UQI average of 0.9859, and the APE average of 3.83. It outperforms the most current techniques in these visual quality measures (PSNR, MSE, and SSIM) across six medical assessment categories. Furthermore, the proposed method offers great visual quality while being resilient to physical adjustments, histogram analysis, and other geometrical threats such as cropping, rotation, and scaling. Finally, it is particularly efficient in telemedicine applications with high achieving security with a ratio of 99% during remote transmission of Electronic Patient Records (EPR) over the Internet, which safeguards the patien

关键词： Steganography

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Semantic Document Layout Analysis of Handwritten Manuscripts

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computers, Materials & Continua 2023年第5期75卷 2805-2831页

作者： Emad Sami Jaha Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

A document layout can be more informative than merely a document’s visual and structural ***,document layout analysis(DLA)is considered a necessary prerequisite for advanced processing and detailed document image analysis to be further used in several applications and different *** research extends the traditional approaches of DLA and introduces the concept of semantic document layout analysis(SDLA)by proposing a novel framework for semantic layout analysis and characterization of handwritten *** proposed SDLA approach enables the derivation of implicit information and semantic characteristics,which can be effectively utilized in dozens of practical applications for various purposes,in a way bridging the semantic gap and providingmore understandable high-level document image analysis and more invariant characterization via absolute and relative *** approach is validated and evaluated on a large dataset ofArabic handwrittenmanuscripts comprising complex *** experimental work shows promising results in terms of accurate and effective semantic characteristic-based clustering and retrieval of handwritten *** also indicates the expected efficacy of using the capabilities of the proposed approach in automating and facilitating many functional,reallife tasks such as effort estimation and pricing of transcription or typing of such complex manuscripts.

关键词： Semantic characteristics semantic labeling document layout analysis semantic document layout analysis handwritten manuscripts clustering retrieval image processing computer vision machine learning

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Single-Valued Neutrosophic Graph with Heptapartitioend Structure

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Neutrosophic Sets and Systems 2025年 80卷 728-748页

作者： Fathi, Hanaa Myvizhi, M. Abdelhafeez, Ahmed Abdellah, Mohamed Refaat Eassa, Mohamed Sawah, Mohamed S. Elbehiery, Hussam College of Information Technology Amman Arab University Amman Jordan KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt Department of Computer Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan

This study applies single-valued neutrosophic sets, which extend the frameworks of fuzzy and intuitionistic fuzzy sets, to graph theory. We introduce a new category of graphs called Single-Valued Heptapartitioned Neutrosophic Graphs (SVHNG) and investigate their characteristics through comprehensive proofs and illustrative examples. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioend Neutrosophic Set Neutrosophic Graph Neutrosophic Sets SVHN-Graph

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A Deep Learning Model to Analyse Social-Cyber Psychological Problems in Youth

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computer Systems science & Engineering 2023年第7期46卷 551-562页

作者： Ali Alqazzaz Mohammad Tabrez Quasim Mohammed Mujib Alshahrani Ibrahim Alrashdi Mohammad Ayoub Khan College of Computing and Information Technology University of Bisha67714BishaSaudi Arabia Department of Computer Science College of Computer and Information SciencesJouf UniversitySakakaAljouf72388Saudi Arabia

Facebook,Twitter,Instagram,and other social media have emerged as excellent platforms for interacting with friends and expressing thoughts,posts,comments,images,and videos that express moods,sentiments,and *** this,it has become possible to examine user thoughts and feelings in social network data to better understand their perspectives and ***,the analysis of depression based on social media has gained widespread acceptance worldwide,other verticals still have yet to be *** depression analysis uses Twitter data from a publicly available web source in this *** assess the accuracy of depression detection,long-short-term memory(LSTM)and convolution neural network(CNN)techniques were *** method is both efficient and *** simulation results have shown an accuracy of 86.23%,which is reasonable compared to existing methods.

关键词： Emotions depression social media

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A fully connected polarization-entangled network via integrated spontaneous four-wave mixing engineering

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science China(Physics,Mechanics & Astronomy) 2025年第6期68卷 72-79页

作者： Pingyu Zhu Yan Wang Yuxing Du Miaomiao Yu Kaikai Zhang Kun Wang Ping Xu Institute for Quantum Information&State Key Laboratory of High Performance Computing College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China Hefei National Laboratory Hefei 230088China

Quantum communication is rapidly developing and is gradually being commercialized due to its technological *** dense communication links among multiple users in a scalable and efficient way is of great significance for realizing a large-scale quantum communication ***,we propose a novel scheme to construct a fully connected polarizationentangled network,utilizing the engineering of spontaneous four-wave mixings(SFWMs)and a path-polarization *** does not require active optical switches which limit the communication speed,or trusted nodes which lead to potential security *** required frequency channels in the network grow linearly with the number of *** experimentally demonstrate a six-user fully connected network with on-chip SFWM processes motivated by four *** user in the network receives a frequency channel,and all fifteen connections between the users are implemented *** work opens up a promising scheme to efficiently construct fully connected large-scale networks.

关键词： quantum network quantum entanglement nonlinear optics integrated optics

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