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An optimized ensemble model based on meta-heuristic algorithms for effective detection and classification of breast tumors

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Neural Computing and Applications 2025年第6期37卷 4881-4894页

作者： Saber, Abeer Elbedwehy, Samar Awad, Wael A. Hassan, Esraa Information Technology Department Faculty of Computers and Artificial Intelligence Damietta University Damietta34517 Egypt Department of Data Science Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33511 Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Damietta University Damietta34517 Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33511 Egypt

One of the most common cancers among women worldwide is breast cancer (BC), and early diagnosis can save lives. Early detection of BC increases the likelihood of a successful outcome by enabling treatment to start sooner. Even in areas without access to a specialist physician, machine learning (ML) aids in early BC detection. The medical imaging community is becoming more interested in using ML, and deep learning (DL) to increase the accuracy of cancer screening. Many disease-related data are sparse. However, for DL models to perform well, a large amount of data is required. Because of this, the DL models that are currently in use on medical images are not as effective as they could be. Convolutional neural network (CNN) models have recently gained popularity in the medical industry, and they perform admirably in terms of high performance and robustness at image classification. The proposed method classifies data using ensemble pre-trained models such as the dense convolutional network (DenseNet)-121 and EfficientNet-B5 feature extractor networks, as well as the support vector machine for classification. Using a modified meta-heuristic optimizer, the selected pre-trained CNN hyperparameters were optimized to improve the performance. The experimental results for the presented model on the INbreast dataset show that the EfficientNet-B5 model is effective for BC classification, with overall accuracy, sensitivity, specificity, precision, and area under the ROC curve (AUC) values of 99.9%, 99.9%, 99.8%, 99.1%, 1.0, respectively. © The Author(s) 2024.

关键词： Adversarial machine learning

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Shrinkage Estimation for Location and Scale Parameters of Logistic Distribution Under Record Values

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Annals of data science 2024年第4期11卷 1209-1224页

作者： Gupta, Shubham Vishwakarma, Gajendra K. Elsawah, A.M. Department of Mathematics & amp Computing Indian Institute of Technology Dhanbad826004 India Department of Statistics and Data Science Faculty of Science and Technology Beijing Normal University-Hong Kong Baptist University United International College Zhuhai519087 China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College Zhuhai519087 China Department of Mathematics Faculty of Science Zagazig University Zagazig44519 Egypt

Logistic distribution (LogDis) is frequently used in many different applications, such as logistic regression, logit models, classification, neural networks, physical sciences, sports modeling, finance and health and disease studies. For instance, the distribution function of the LogDis has the same functional form as the derivative of the Fermi function that can be used to set the relative weight of various electron energies in their contributions to electron transport. The LogDis has wider tails than a normal distribution (NorDis), so it is more consistent with the underlying data and provides better insight into the likelihood of extreme events. For this reason the United States Chess Federation has switched its formula for calculating chess ratings from the NorDis to the LogDis. The outcomes of many real-life experiments are sequences of record-breaking data sets, where only observations that exceed (or only those that fall below) the current extreme value are recorded. The practice demonstrated that the widely used estimators of the scale and location parameters of logistic record values, such as the best linear unbiased estimators (BLUEs), have some defects. This paper investigates the shrinkage estimators of the location and scale parameters for logistic record values using prior information about their BLUEs. Theoretical and computational justifications for the accuracy and precision of the proposed shrinkage estimators are investigated via their bias and mean square error (MSE), which provide sufficient conditions for improving the proposed shrinkage estimators to get unbiased estimators with minimum MSE. The performance of the proposed shrinkage estimators is compared with the performances of the BLUEs. The results demonstrate that the resulting shrinkage estimators are shown to be remarkably efficient. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： Mean square error

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Digital Forensics Meets Blockchain: Enhancing Evidence Authenticity and Traceability 2

Digital Forensics Meets Blockchain: Enhancing Evidence Authe...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Al-E'mari, Salam Sanjalawe, Yousef Makhadmeh, Sharif Alqudah, Hamzah University Of Petra Faculty Of Information Technology Department Of Information Security Amman Jordan American University Of Madaba Faculty Of Information Technology Department Of Cybersecurity Amman Jordan University Of Petra Faculty Of Information Technology Department Of Data Science And Artificial Intelligence Amman Jordan American University Of Madaba Faculty Of Information Technology Department Of Data Science And Artificial Intelligence Amman Jordan

ISBN: (纸本)9798350394962

In the rapidly evolving landscape of cybercrime, ensuring the authenticity and traceability of digital evidence has become paramount. This paper explores the integration of blockchain technology with digital forensics as a novel approach to address these concerns. Blockchain, with its inherent characteristics of immutability and decentralization, can offer significant advancements in ensuring tamper-proof storage and chronological verification of forensic artifacts. By merging these two domains, we introduce a framework that not only bolsters the authenticity of digital evidence but also facilitates a robust traceability mechanism. Through a detailed case study, we demonstrate the practicality and effectiveness of this integrated approach, highlighting its potential to revolutionize current forensic methodologies. However, we also delve into the challenges posed, such as scalability, privacy, and legal implications, and suggest future research avenues. Leveraging blockchain technology in the realm of digital forensics has the potential to redefine evidence integrity and the forensic investigation landscape. © 2024 IEEE.

关键词： Digital forensics

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Customer Segmentation Based on Recency, Frequency, Monetary, Variety and Duration (RFMVD) 2

Customer Segmentation Based on Recency, Frequency, Monetary,...

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2nd International Conference on technology Innovation and Its Applications, ICTIIA 2024

作者： Sundari, Agus Lydia, Maya Silvi Muchtar, Muhammad Anggia Universitas Sumatera Utara Faculty of Computer Science and Information Technology Department of Data Science and Artificial Intelligence Medan Indonesia Universitas Sumatera Utara Faculty of Computer Science and Information Technology Department of Computer Science Medan Indonesia

ISBN: (纸本)9798350351613

In banking, maintaining customer retention and customer satisfaction are important. Effective customer segmentation can be a strategic tool to improve customer loyalty and business performance. This research can assist banks in developing marketing strategies to retain customers and improve services based on bill payment transactions. The data that will be used in this study is bill payment transaction data at regional banks in 2023, as for the types of bill payments made by customers, namely electricity, water, telephone payments, and internet bills. The amount of data used is 702,174 rows. This research uses K-Means clustering to group customers based on recency (the last time the customer made a transaction), frequency (the number of customers making transactions), monetary (the nominal amount of money spent by the customer), variety (the number of types of bills paid) and duration (the average duration of time the customer makes the previous transaction until the next transaction) (RFMVD). The results of customer segmentation based on recency, frequency, monetary, variety and duration using K-Means clustering produce 3 customer groups, namely passive customers, loyal customers, and VIP customers. The silhouette score value on customer segmentation using K-Means clustering is 0.7303, which indicates that the grouping is quite good and the number of clusters is correct. © 2024 IEEE.

关键词： bill payments Customer segmentation K-Means clustering RFMVD Model silhouette score

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Improved DeepFM-Based Model with LSTM on Click-Through-Rate 24

Improved DeepFM-Based Model with LSTM on Click-Through-Rate

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5th International Conference on Computer Information and Big data Applications, CIBDA 2024

作者： Li, Yu Chu, Chiawei Liu, Xingyu Faculty of Data Science City University of Macau China School of Finance and Commerce Zhuhai College of Science and Technology Zhuhai China

ISBN: (纸本)9798400718106

In recent years, the integration of deep learning into recommendation systems has significantly improved their ability to make accurate suggestions by capturing complex user interaction patterns. However, conventional deep learning models often overlook sequential patterns and prioritise high and low order features. To address this limitation, our work proposes a novel model called lstmDeepFM, which combines DeepFM with Long Short-Term Memory (LSTM) networks to optimise click-through rate (CTR) prediction tasks. Our approach involves transforming word features into dense, low-dimensional vectors using an embedding layer, and enriching article features with context using Doc2Vec. These features are then fused and fed into the FM, DNN and LSTM networks, with particular emphasis on integrating a self-attention mechanism into the DNN component to effectively capture essential information. Finally, a novel output function is applied to refine the predictions. Evaluation on real datasets, Criteo and Avazu, using mAUC as the metric, shows significant performance improvements, with our model achieving improvements of 1.31% and 3.28% on the respective datasets. © 2024 ACM.

关键词： Recommender systems

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System for IoT Agriculture Using LoRaWAN 1

System for IoT Agriculture Using LoRaWAN

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2023 1st DMIHER International Conference on Artificial Intelligence in Education and Industry 4.0, IDICAIEI 2023

作者： Langote, Meher Chandankhade, Arpit Waghade, Munabber Zade, Shizhan Lokhande, Priyanshu Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Sawangi India

ISBN: (纸本)9798350338423

The Internet of Things (IoT) has acquired increasing significance in agriculture and food production in recent years, expanding its reach beyond smart city applications. In this comprehensive study, we present a pioneering, exceptionally scalable IoT system specifically tailored for agricultural purposes. This innovative system leverages the Low-Power Wide-Area Network (LoRaWAN) for long-distance, energy-efficient data transmission from sensor nodes to cloud-based services. This IoT solution is underpinned by a highly extensible cloud computing framework that effectively harnesses data streams for in-depth analytics. In the study, we introduce an innovative, highly scalable Internet of Things agricultural system. For long-distance and low-power data transfer from the sensor nodes to the cloud services, this system is built on the LoRaWAn network. Our highly scalable cloud computing solution makes use of data streams for analytics. In this case study, we display some early findings for a grape vineyard. © 2023 IEEE.

关键词： Internet of things

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Systematic Assessment of Factual Knowledge in Large Language Models

Systematic Assessment of Factual Knowledge in Large Language...

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2023 Findings of the Association for Computational Linguistics: EMNLP 2023

作者： Luo, Linhao Vu, Thuy-Trang Phung, Dinh Haffari, Gholamreza Department of Data Science and AI Faculty of Information Technology Monash University Australia

ISBN: (纸本)9798891760615

Previous studies have relied on existing question-answering benchmarks to evaluate the knowledge stored in large language models (LLMs). However, this approach has limitations regarding factual knowledge coverage, as it mostly focuses on generic domains which may overlap with the pretraining data. This paper proposes a framework to systematically assess the factual knowledge of LLMs by leveraging knowledge graphs (KGs). Our framework automatically generates a set of questions and expected answers from the facts stored in a given KG, and then evaluates the accuracy of LLMs in answering these questions. We systematically evaluate the state-of-the-art LLMs with KGs in generic and specific domains. The experiment shows that ChatGPT is consistently the top performer across all domains. We also find that LLMs performance depends on the instruction finetuning, domain and question complexity and is prone to adversarial context. © 2023 Association for Computational Linguistics.

关键词： Computational linguistics

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GPMBC-FZ: A Query Optimization Method in Distributed database 6

GPMBC-FZ: A Query Optimization Method in Distributed Databas...

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6th International Conference on Next Generation data-Driven Networks, NGDN 2024

作者： Du, Yan Ding, Zhiming Faculty of Information Technology Beijing University of Technology Beijing China Center of Spatial-Temporal Data Management and Data Sciecne Institute of Software Chinese Academy of Science Beijing China

ISBN: (纸本)9798350388374

The rapid growth in the scale of distributed databases has led to a deeper impact of the multi-join query optimization problem on data query efficiency. The increase in the number of relations causes a significant increase in the search space complexity of distributed database multi-join optimization, which greatly affects the efficiency of finding the optimal execution plan, thus hindering the further optimization of multi-join queries. In this regard, a genetic multiple bee colony algorithm combined with fuzzy logic is proposed to solve the problem of further optimizing multi-join queries of distributed databases. Firstly, the fast convergence property of the genetic algorithm is utilized to obtain a set of relatively optimal solutions in order to overcome the blindness of the artificial bee algorithm's pre-search, and then the parallel multiple bee algorithm is introduced, which makes use of the information interaction between multiple bee colonies to better prevent the algorithm from falling into the local optimal situation as well as the parallelization of the execution of the multiple bee colonies to improve the search efficiency and convergence speed of the algorithm. Secondly, the fuzzy logic system is also used to optimize the parameters of the algorithm and set the corresponding algorithmic control parameters for different dimensional queries. Finally, a comparison experiment is conducted to test the average execution cost and algorithm execution time of the Top-K query plans output by the algorithm in the case of six query statements involving different lengths, and the experimental results show that the K optimal query plans generated by the method proposed in this paper have lower query excution cost and algorithm execution time, and can effectively improve the query efficiency. © 2024 IEEE.

关键词： Genetic algorithms

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Deep Learning Approaches for Battery Capacity and State of Charge Estimation with the NASA B0005 dataset

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Computers, Materials & Continua 2025年第6期83卷 4795-4813页

作者： Zeyang Zhou Zachary James Ryan Utkarsh Sharma Tran Tien Anh Shashi Mehrotra Angelo Greco Jason West Mukesh Prasad School of Computer Science Faculty of Engineering and Information TechnologyUniversity of Technology SydneyUltimoNSW 2007Australia School of Data Science and Engineering Indian Institute of Science Education and ResearchBhopal462066India Research Institute of Marine Systems Engineering Seoul National UniversitySeoul08826Republic of Korea Department of Computer Science and Engineering Faculty of Engineering and TechnologySRM Institute of Science and TechnologyDelhi NCR CampusGhaziabad201204India Battery System Modelling Department CLARIOS VARTA Hannover GmbHHanover30419Germany Science and Innovation Group Bureau of MeteorologyBrisbane4000Australia

Accurate capacity and State of Charge(SOC)estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric *** study examines ten machine learning architectures,Including Deep Belief Network(DBN),Bidirectional Recurrent Neural Network(BiDirRNN),Gated Recurrent Unit(GRU),and others using the NASA B0005 dataset of 591,458 *** indicate that DBN excels in capacity estimation,achieving orders-of-magnitude lower error values and explaining over 99.97%of the predicted variable’s *** computational efficiency is paramount,the Deep Neural Network(DNN)offers a strong alternative,delivering near-competitive accuracy with significantly reduced prediction *** GRU achieves the best overall performance for SOC estimation,attaining an R^(2) of 0.9999,while the BiDirRNN provides a marginally lower error at a slightly higher computational *** contrast,Convolutional Neural Networks(CNN)and Radial Basis Function Networks(RBFN)exhibit relatively high error rates,making them less viable for real-world battery *** of error distributions reveal that the top-performing models cluster most predictions within tight bounds,limiting the risk of overcharging or deep *** findings highlight the trade-off between accuracy and computational overhead,offering valuable guidance for battery management system(BMS)designers seeking optimal performance under constrained *** work may further explore advanced data augmentation and domain adaptation techniques to enhance these models’robustness in diverse operating conditions.

关键词： Battery capacity estimation state of charge deep learning prediction efficiency energy storage systems

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A Review on Web data Mining to Detect Online Spread of Terrorism Activity 2

A Review on Web Data Mining to Detect Online Spread of Terro...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Jha, Saurav Kumar Pacharaney, Utkarsha Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine learning Maharashtra Wardha India

ISBN: (纸本)9798331505745

This research study outlines the significant contribution that web data mining can make in discovering and combating the dissemination of online terrorism. With terrorist groups using the internet more and more to deliver propaganda, recruit, and organize, there is a pressing need for efficient monitoring and analysis of web content. This article discusses different data mining methods that can be used to identify and study patterns suggestive of terrorist activity throughout networked digital environments. Specifically, the embedding of machine learning algorithms, i.e., Support Vector Machines (SVM), Random Forest and Gradient Boosting, is investigated with regard to its suitability to differentiate between benign and harmful online content. These algorithms can process large volumes of data, where features are extracted, including URL structures, domain ages, and keyword frequencies, to detect anticipate threats. According to the article, feature extraction plays a crucial role in improving detection system accuracy, enabling real-time surveillance of anomalous online activities. In addition, the review discusses the problems that researchers and law enforcement agencies have been encountering in using web data mining for counter-terrorism purposes. Ethical concerns around privacy and civil liberties are addressed and technical challenges, including the quality of data and the dynamic character of online content, are elaborated. Scenarios in which false positives arising in detection systems may occur are also considered, with emphasis on the need for ongoing algorithm and technique refinement. © 2025 IEEE.

关键词： Crop protection

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