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Machine Learning Techniques Using Deep Instinctive Encoder-Based Feature Extraction for Optimized Breast Cancer Detection

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computers, Materials & Continua 2024年第2期78卷 2441-2468页

作者： Vaishnawi Priyadarshni Sanjay Kumar Sharma Mohammad Khalid Imam Rahmani Baijnath Kaushik Rania Almajalid Department of Computer Science Gautam Buddha UniversityGreater NoidaUttar PradeshIndia College of Computing and Informatics Saudi Electronic UniversityRiyadh11673Saudi Arabia School of Computer Science Shri Vaishno Devi UniversityKatraIndia

Breast cancer(BC)is one of the leading causes of death among women worldwide,as it has emerged as the most commonly diagnosed malignancy in *** detection and effective treatment of BC can help save women’s *** an efficient technology-based detection system can lead to non-destructive and preliminary cancer detection *** paper proposes a comprehensive framework that can effectively diagnose cancerous cells from benign cells using the Curated Breast Imaging Subset of the Digital Database for Screening Mammography(CBIS-DDSM)data *** novelty of the proposed framework lies in the integration of various techniques,where the fusion of deep learning(DL),traditional machine learning(ML)techniques,and enhanced classification models have been deployed using the curated *** analysis outcome proves that the proposed enhanced RF(ERF),enhanced DT(EDT)and enhanced LR(ELR)models for BC detection outperformed most of the existing models with impressive results.

关键词： Autoencoder breast cancer deep neural network convolutional neural network image processing machine learning deep learning

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Enhancing Pretrained Multilingual Machine Translation Model with Code-Switching: A Study on Chinese, English and Malay Language 24

Enhancing Pretrained Multilingual Machine Translation Model ...

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13th International Conference on computing and Pattern Recognition, ICCPR 2024

作者： Liu, Haijing Seman, Noraini Department of Computer Engineering Taiyuan Institute of Technology Shanxi Taiyuan China College of Computing Informatics and Mathematics University Technology MARA Shah Alam Selangor Malaysia

ISBN: (纸本)9798400717482

In the field of multilingual machine translation, many pretrained language models have achieved the inspiring results. However, the results based on pretrained models are not yet very satisfactory for low-resource languages. This paper investigates how to leverage code-switching data to fine-tune pretrained multilingual machine translation model, in order to boost the performance of few-shot low resource machine translation. By utilizing a multilingual mixed corpus, the code-switching method can enhance the cross-linguistic generalization ability of the model and improve its overall understanding of the language. In this paper, on the smaller size model of FLORES-101 benchmark, we use the code-switching data augmentation method to achieve the results of benchmark's larger model on six direction pairs of three languages, Chinese, English and Malay. This paper studied various corpus mixture mechanisms to construct the data in code-switching, and the experimental findings show that the results using the code-switching fine-tuning model improve the spBLEU score by an average of 2 to 3 points over the results without code-switching. © 2024 Copyright held by the owner/author(s).

关键词： Machine translation

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GAProtoNet: A Multi-head Graph Attention-based Prototypical Network for Interpretable Text Classification 31

GAProtoNet: A Multi-head Graph Attention-based Prototypical ...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wen, Ximing Tan, Wenjuan Weber, Rosina O. College of Computing and Informatics Drexel University Philadelphia United States Department of Computer Science Tsinghua University Beijing China

ISBN: (纸本)9798891761964

Pretrained transformer-based Language Models (LMs) are well-known for their ability to achieve significant improvement on text classification tasks with their powerful word embeddings, but their black-box nature, which leads to a lack of interpretability, has been a major concern. In this work, we introduce GAProtoNet, a novel white-box Multi-head Graph Attention-based Prototypical Network designed to explain the decisions of text classification models built with LM encoders. In our approach, the input vector and prototypes are regarded as nodes within a graph, and we utilize multi-head graph attention to selectively construct edges between the input node and prototype nodes to learn an interpretable prototypical representation. During inference, the model makes decisions based on a linear combination of activated prototypes weighted by the attention score assigned for each prototype, allowing its choices to be transparently explained by the attention weights and the prototypes. Experiments on multiple public datasets show our approach achieves superior results without sacrificing the accuracy of the original black-box LMs. We also compare with four alternative prototypical network variations and our approach achieves the best accuracy and F1 among all. Our case study and visualization of prototype clusters also demonstrate the efficiency in explaining the decisions of black-box models built with LMs. Our codes are available at https://***/ximingwen/GAProtoNet. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Multiclass Classification for Cyber Threats Detection on Twitter

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computers, Materials & Continua 2023年第12期77卷 3853-3866页

作者： Adnan Hussein Abdulwahab Ali Almazroi College of Computer Science and Engineering Department of Computer ScienceAL-Ahgaff UniversityMukallaYemen College of Computing and Information Technology at Khulais Department of Information TechnologyUniversity of JeddahJeddahSaudi Arabia

The advances in technology increase the number of internet systems *** a result,cybersecurity issues have become more *** threats are one of the main problems in the area of ***,detecting cybersecurity threats is not a trivial task and thus is the center of focus for many researchers due to its *** study aims to analyze Twitter data to detect cyber threats using a multiclass classification *** data is passed through different tasks to prepare it for the *** Frequency and Inverse Document Frequency(TFIDF)features are extracted to vectorize the cleaned data and several machine learning algorithms are used to classify the Twitter posts into multiple classes of cyber *** results are evaluated using different metrics including precision,recall,F-score,and *** work contributes to the cyber security research *** experiments revealed the promised results of the analysis using the Random Forest(RF)algorithm with(F-score=81%).This result outperformed the existing studies in the field of cyber threat detection and showed the importance of detecting cyber threats in social media *** is a need for more investigation in the field of multiclass classification to achieve more accurate *** the future,this study suggests applying different data representations for the feature extraction other than TF-IDF such as Word2Vec,and adding a new phase for feature selection to select the optimum features subset to achieve higher accuracy of the detection process.

关键词： Cybersecurity cyber threat detection artificial intelligence machine learning Twitter

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NeurDB: an AI-powered autonomous data system

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Science China(Information Sciences) 2024年第10期67卷 129-150页

作者： Beng Chin OOI Shaofeng CAI Gang CHEN Yanyan SHEN Kian-Lee TAN Yuncheng WU Xiaokui XIAO Naili XING Cong YUE Lingze ZENG Meihui ZHANG Zhanhao ZHAO School of Computing National University of Singapore College of Computer Science and Technology Zhejiang University Department of Computer Science and Engineering Shanghai Jiao Tong University School of Information Renmin University of China School of Computer Science and Technology Beijing Institute of Technology

In the wake of rapid advancements in artificial intelligence(AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB(AI×DB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, and selfdriving capabilities for improved system performance. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan.

关键词： AI$\times$DB in-database AI intelligent data system

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Automated face recognition using deep learning technique and center symmetric multivariant local binary pattern

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Neural computing and Applications 2025年第1期37卷 263-281页

作者： Sekhar, J.C. Josephson, P. Joel Chinnasamy, A. Maheswari, M. Sankar, S. Kalangi, Ruth Ramya Department of Computer Science and Engineering NRI Institute of Technology Andhra Pradesh Guntur India Department of Electronics and Communication Engineering Malla Reddy Engineering College Telangana Hyderabad India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vijayawada India

Researchers have recently created several deep learning strategies for various tasks, and facial recognition has made remarkable progress in employing these techniques. Face recognition is a noncontact, nonobligatory, acceptable, and harmonious biometric recognition method with a promising national and social security future. The purpose of this paper is to improve the existing face recognition algorithm, investigate extensive data-driven face recognition methods, and propose a unique automated face recognition methodology based on generative adversarial networks (GANs) and the center symmetric multivariable local binary pattern (CS-MLBP). To begin, this paper employs the center symmetric multivariant local binary pattern (CS-MLBP) algorithm to extract the texture features of the face, addressing the issue that C2DPCA (column-based two-dimensional principle component analysis) does an excellent job of removing the global characteristics of the face but struggles to process the local features of the face under large samples. The extracted texture features are combined with the international features retrieved using C2DPCA to generate a multifeatured face. The proposed method, GAN-CS-MLBP, syndicates the power of GAN with the robustness of CS-MLBP, resulting in an accurate and efficient face recognition system. Deep learning algorithms, mainly neural networks, automatically extract discriminative properties from facial images. The learned features capture low-level information and high-level meanings, permitting the model to distinguish among dissimilar persons more successfully. To assess the proposed technique’s GAN-CS-MLBP performance, extensive experiments are performed on benchmark face recognition datasets such as LFW, YTF, and CASIA-WebFace. Giving to the findings, our method exceeds state-of-the-art facial recognition systems in terms of recognition accuracy and resilience. The proposed automatic face recognition system GAN-CS-MLBP provides a solid basis for a

关键词： Principal component analysis

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Path-Planning and Obstacle Avoidance Algorithms for UAVs: A Systematic Literature Review 25

Path-Planning and Obstacle Avoidance Algorithms for UAVs: A ...

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Unmanned Systems Technology XXV 2023

作者： Almazrouei, Khawla Saif Nassif, Ali Bou AlShabi, Mohammad Department of Computer Engineering College of Computing and Informatics University of Sharjah United Arab Emirates Department of Mechanical & Nuclear Engineering College of Engineering University of Sharjah United Arab Emirates

ISBN: (纸本)9781510662148

Recent interest in unmanned aerial vehicles (UAVs) has grown due to the wide range of possible civilian uses for these aircraft. However, present robot navigation technologies still need to be improved in various situations. Researchers are particularly interested in the 'Sense and Avoid' capacity as a critical issue. UAVs operating in civilian areas must have this functionality to do so safely. Numerous path planning and navigation algorithms have been developed for autonomous decision-making and control of UAVs. These path-planning algorithms are divided into either heuristic and non-heuristic or accurate methods. Both existing UAV route planning algorithms for the first and second techniques will be thoroughly compared in this work. Each algorithm is put through its paces in three diverse obstacle scenarios. Each method has been evaluated under various global and local obstacle information availability conditions while comparing the computational time and solution optimality. © 2023 SPIE.

关键词： Motion planning

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Evaluating the Utilization of Grammatical Structures of Arabic Language in Hiding Bits

Evaluating the Utilization of Grammatical Structures of Arab...

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2024 Advances in Science and engineering Technology International Conferences, ASET 2024

作者： Rihan, Yazen Altaweel, Ala Khattab, Khattab Kamel, Ibrahim University of Sharjah College of Computing and Informatics Department of Computer Engineering Sharjah United Arab Emirates Ondokuz Mayis University Faculty of Ilahiyat Samsun Turkey

ISBN: (纸本)9798350344134

Nowadays, there is massive volume of data transmitted constantly through digital mediums. This data faces the risks of various cyber-attacks like eavesdropping, traffic analysis, packet injection, and packet dropping. There has been several security mechanisms to secure sensitive data and ensure their confidentiality, integrity, and availability. Indeed, Encryption, which is the process of converting the plain-text data into a non-understandable form known as the cipher-text, can secure the sensitive data. However, Encryption techniques are costly in terms of computational resources. Steganography, on the other hand, attempts to exploit the redundancy in data representation and structure to hide secret information within the data. Text steganography refers to the art of hiding secret information inside long passages of text. This paper proposes a new text steganography technique that exploits grammatical structures of Arabic language. Specifically, the paper exploits the redundancy in the language structure of Arabic grammar. The paper also presents the results of applying the proposed technique on a large dataset of articles from different categories such as politics, sports, and religion. The conducted experiments measure the number of secret bits that can be hidden in 1000 words for each category. The results show that some steganography rules can perform differently when annlied to different categories of text. © 2024 IEEE.

关键词： Data integrity

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Early fire danger monitoring system in smart cities using optimization-based deep learning techniques with artificial intelligence

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Journal of Reliable Intelligent Environments 2024年第2期10卷 197-210页

作者： Reddy, P. Dileep Kumar Margala, Martin Shankar, S. Siva Chakrabarti, Prasun Telangana Secunderabad India School of Computing and Informatics University of Louisiana at Lafayette Lafayette United States Department of CSE KG Reddy College of Engineering and Technology Telangana Hyderabad501504 India Department of Computer Science and Engineering Sir Padampat Singhania University Rajasthan Udaipur313601 India

One primary safety concern for smart cities is fire. Traditional techniques are not appropriate because of their high false alarm rates, delayed characteristics, and susceptibility in situations with heritage buildings. Smart cities must develop sophisticated methods to mitigate the severe effects of fires and achieve early fire detection in real time. An artificial intelligence-based recurrent neural network with a whale optimization framework (AI-RNN-WO) was introduced to estimate the risk of fire hazards early on. IoT sensor devices are first deployed in smart cities to continuously monitor environmental parameters such as temperature, smoke, flame, relative humidity, fuel moisture, and duff moisture code. These sensed data are then saved in the cloud storage system Firebase. Then, the sensed dataset is updated to the designed model, which pre-processes the data and extracts relevant features from the dataset. The RNN parameters are tuned using whale optimization, which improves the prediction results and attains better accuracy. The performance of the proposed AI-RNN-WO model is validated using a MATLAB tool, and the performance is compared with existing models. The produced model has demonstrated its effectiveness by attaining the highest accuracy (99.5%) and lowest error rate (0.1%). © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Recurrent neural networks

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Design of Rule Translator for Temporal Rules Execution of BM-DEVS Model 15th

Design of Rule Translator for Temporal Rules Execution of BM...

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15th International Conference on computer Science and its Applications, CSA 2023 and 17th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2023

作者： Ahn, Jungsub Cho, Taeho Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon16419 Korea Republic of

ISBN: (纸本)9789819724468

With the increasing complexity of systems, various studies are being conducted to accurately express and solve problems. Discrete Event System Specification (DEVS), one of the simulation theories, expresses a problem as a hierarchical model. The Behavior Monitoring-DEVS (BM-DEVS) theory is an extension of DEVS. It monitors the behavior of interest by expressing it as temporal rules in the model. In BM-DEVS, the defined temporal rules need to be converted into Büchi automata and applied to the model to evaluate the behavior expressed by the rules. To achieve this, modelers need to translate temporal rules into automata and verify these. This consumes a substantial amount of time. To solve this problem, this paper proposes a method to automatically translate temporal rules into automata for rule evaluation. In this proposed method, when temporal rules are input, structuring work is performed to match the rule structure and model structure. Moreover, a more convenient modeling environment is provided to modelers through the rule expansion function. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Automata theory

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