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QLAW: An Improved Quantization-Based Local Audio Watermarking Scheme Using Inter-Frame Correlation

IEEE Access 2025年 13卷 93359-93371页

作者： Li, Qiutong Xing, Zheng Wang, Ju Huang, Guoheng Yuan, Xiaochen Macao Polytechnic University Faculty of Applied Sciences China Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China

With the rapid development of the Internet, audio distribution has become more convenient with increasing copyright infringement. To address this problem, this paper proposes a quantization-based local audio watermarking scheme using inter-frame correlation, integrating machine learning techniques and traditional methods. To obtain the time-frequency spectrogram of the audio signal, a Short-Time Fourier Transform (STFT) is first applied to the audio signal. Then, Main Energy Region Extractor (MERE) is proposed to extract the main energy region of the spectrogram. Based on the main energy region, the Stable Frequency and Energy Region Extractor is conducted to find the local feature region for embedding. After segmenting the local feature embedding region into several frames, Adjacent Frame Extraction Process (AFEP) is conducted to select the adjacent frame. Then, Discrete Cosine Transform (DCT) is applied to each embedding frame and its adjacent frame to extract their corresponding frequency domain coefficients. To improve robustness, mid-frequency DCT coefficients are alternately selected to embed the watermark. By adjusting the difference between the embedding frame and its corresponding adjacent frame in a predefined range, the local watermark is embedded. Experimental results show that the proposed scheme outperforms existing schemes in inaudibility and robustness, achieving an average Signal-to-Noise Ratio (SNR) above 25 dB and a lower Bit Error Rate (BER) under various attacks. © 2013 IEEE.

关键词： Audio watermarking

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Optimization and evaluation of a mobile IMU-based ballistocardiography system

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Current Directions in Biomedical Engineering 2023年第1期9卷 678-681页

作者： Klauth, Lucas Mühlen, Andreas Kitzig, Andreas Naroska, Edwin Stockmanns, Gudrun Faculty of Electrical Engineering and Computer Science Niederrhein University of Applied Sciences Krefeld Germany

In recent decades ballistocardiography (BCG) has regained popularity as a way to measure the mechanical activ- ity of the heart. In this paper we present and evaluate a new iteration of our mobile BCG measurement system. The pro- posed system uses an inertial measurement unit (IMU) placed on the carotid artery to derive the BCG. We conducted a mea- surement series to evaluate the system and to do an initial in- vestigation into whether more complex heart parameters can be derived from BCG data. The evaluation shows that heart rate (HR) and heart rate variability (HRV) calculated using BCG data, agrees well with the reference measurement and amplitudes calculated are mostly comparable in range to other papers. In conclusion, the system can reliably derive features from BCG data that can be used in further research. © 2023 the author(s), published by Walter de Gruyter Berlin/Boston.

关键词： Heart

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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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An efficient feature selection and classification system for microarray cancer data using genetic algorithm and deep belief networks

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Multimedia Tools and Applications 2025年第8期84卷 4393-4434页

作者： Lawrence, Morolake Oladayo Jimoh, Rasheed Gbenga Yahya, Waheed Babatunde Department of Computer Science Faculty of Computing & Applied Sciences Baze University Abuja Nigeria Department of Computer Science Faculty of Communication & Information Sciences University of Ilorin Kwara State Ilorin Nigeria Department of Statistics Faculty of Physical Sciences University of Ilorin Kwara State Ilorin Nigeria

Cancer is one of the most devastating health conditions in the world. In the diagnosis and treatment of the various forms of cancer illness, studies have shown that early detection of the cancer by clinical methods usually takes a considerable lengthy time. This informs searching for an alternative non-clinical diagnosis of cancer cases using microarray technology. Therefore, this study develops an efficient feature selection and classification method for high-dimensional microarray cancer data by combining Genetic Algorithms (GA) and Deep Belief Networks (DBN). The study employed a GA for selecting the most informative gene biomarker and DBN for the classification of biological samples. In a Monte Carlo experiment, the simulated and real-life microarray datasets were partitioned into 95% training and 5% test samples at 100 to 1000 epochs. The classifier was constructed using the training datasets while its efficiency was assessed on the test sample using the Misclassification Error Rate, Sensitivity, Specificity, and Receiver Operating Characteristic Analysis. The GA and DBN were implemented using Caret and Deepnet (R statistical packages). The proposed GADBN method was implemented using simulated and real-life datasets, which yielded out-of-bag average classification accuracy of 98.8% and 93.1% respectively. The proposed GADBN outperformed the other existing classifiers;and the GADBN method was more efficient with a smaller Misclassification Error Rate (MER) for Leukaemia 1 (0.18), Prostate 1 (0.35) and Prostate 3 (0.09) datasets than some of the existing methods under various performance indices considered. The proposed model is a powerful and effective instrument for identifying useful features in microarray cancer data and classifying the cancer types accordingly. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Genetic algorithms

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Survey on the Adoption of Blockchain Technology in Internet of Things Environments: Techniques, Challenges and Future Research Directions

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第1期52卷 59-89页

作者： Elhadari, Zouhair Zougagh, Hicham Idboufker, Noureddine Ech-chebaby, Mohamed Department of Computer Science Faculty of Sciences and Techniques Moulay Slimane University Beni Mellal Morocco Department of Computer Science Faculty of Sciences and Techniques Moulay Slimane University Beni Mellal Morocco Department of Telecommunications and Computer Sciences National School of Applied Sciences Cady Ayyad University Marrakech Morocco Department of Computer Science Faculty of Sciences and Techniques Moulay Slimane University Beni Mellal Morocco

Blockchain has recently garnered considerable attention in academic research across various fields. In the Internet of Things (IoT) domain, blockchain is viewed as a tool for establishing a decentralized, reliable, and secure environment. However, its application in IoT, especially for smaller-scale systems, is still nascent. Consequently, the future direction of blockchain in IoT remains somewhat uncertain, with numerous challenges and unresolved questions. Although many articles have explored the intersection of blockchain technology and IoT, most offer only surface-level discussions on the technological potential, with few addressing the complexities of implementing blockchain in IoT environments. This paper aims to provide a coherent and comprehensive overview of current leading efforts in this area. Specifically, we survey the integration of IoT and blockchain by examining key research themes and trends in blockchain-related methodologies and technologies applied to IoT. In this study, we reviewed articles published between 2021 and 2023 on blockchain-based IoT solutions, categorizing them into distinct research areas. We aim to explore the technologies, benefits, issues, and challenges related to the integration of IoT and blockchain, while also investigating the efforts made to address these challenges. © (2025), (International Association of Engineers). All rights reserved.

关键词： adoption of blockchain in IoT Blockchain Blockchain challenges Integration of blockchain with IoT Internet of things IoT Survey

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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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IMFlow: Inverse Modeling with Conditional Normalizing Flows for Data-Driven Model Predictive Control 63

IMFlow: Inverse Modeling with Conditional Normalizing Flows ...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Zhang, Yi Mikelsons, Lars University of Augsburg Chair of Mechatronics Faculty of Applied Computer Science Germany

ISBN: (纸本)9798350316339

Inverse modeling is the process uncovering the relationships from the system observations to its inputs. It is essential in various fields such as control, robotics, and signal processing. We propose an inverse modeling method using amortized variational inference based on conditional normalizing flows (IMFlow). IMFlow is data-driven and can therefore be applied to black-box environments with limited observability and unknown complexity. Besides, the probabilistic modeling characteristics of conditional normalizing flows allow IMFlow to cope with unknown system uncertainties. We deploy IMFlow as a probabilistic model predictive controller, which estimates the control inputs as stochastic processes based on reference signals and system responses. In addition, we also adjust IMFlow to an online model-free reinforcement learning setting. We demonstrate our proposed method achieves the same accuracy in comparison to the standard model predictive control method using white-box models. © 2024 IEEE.

关键词： Predictive control systems

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Supervised machine learning algorithms for predicting student dropout and academic success: a comparative study

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Discover Artificial Intelligence 2024年第1期4卷 2页

作者： Villar, Alice de Andrade, Carolina Robledo Velini Faculty of Computer Science University of Essex Essex Colchester United Kingdom Faculty of Applied Mathematics University of Sao Paulo Sao Paulo Sao Paulo Brazil

Utilizing a dataset sourced from a higher education institution, this study aims to assess the efficacy of diverse machine learning algorithms in predicting student dropout and academic success. Our focus was on algorithms capable of effectively handling imbalanced data. To tackle class imbalance, we employed the SMOTE resampling technique. We applied a range of algorithms, including Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), as well as boosting algorithms such as Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), CatBoost (CB), and Light Gradient Boosting Machine (LB). To enhance the models' performance, we conducted hyperparameter tuning using Optuna. Additionally, we employed the Isolation Forest (IF) method to identify outliers or anomalies within the dataset. Notably, our findings indicate that boosting algorithms, particularly LightGBM and CatBoost with Optuna, outperformed traditional classification methods. Our study's generalizability to other contexts is constrained due to its reliance on a single dataset, with inherent limitations. Nevertheless, this research provides valuable insights into the effectiveness of various machine learning algorithms for predicting student dropout and academic success. By benchmarking these algorithms, our project offers guidance to both researchers and practitioners in their choice of suitable approaches for similar predictive tasks. © The Author(s) 2024.

关键词： Forecasting

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Modified Feature Selection with BPSO to Apply PSO for Solving Handwritten Digits 12

Modified Feature Selection with BPSO to Apply PSO for Solvin...

引用

12th International Electrical Engineering Congress, iEECON 2024

作者： Ratanavilisagul, Chiabwoot Faculty Of Applied Science Department Of Computer And Information Sciences Bangkok Thailand

ISBN: (纸本)9798350383591

The task of Handwriting Digits Recognition represents a classification challenge involving the interpretation of handwritten digits sourced from diverse mediums such as paper, photos, touch screens, and other devices. This challenge is very intricate due to the distinctiveness of each individual's handwriting, and several essential traits that influence the interpretation process. Numerous researchers have devoted efforts to solve this problem. Recent research has improved preprocessing and feature extraction techniques by using particle swarm optimization to address the handwritten digit recognition problem. The outcomes of these experiments have shown obtain good results. Nevertheless, outcomes from this technique can be further improved. The feature selection technique popularly applies pattern recognition to improve outcomes. Thus, this study proposes a modified feature selection technique that developed from binary particle swarm optimization to optimize particle swarm optimization for handwritten digit recognition. This proposed feature selection technique has demonstrated the potential to yield higher Recognition Rates significantly in both training and testing phases. Comparative analysis against the original technique, devoid of the feature selection technique, reveals that the proposed method exhibits superior Recognition Rates when tested on both the original MNIST datasets and the individual's each person handwritten digit datasets. © 2024 IEEE.

关键词： Feature Selection

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Application of inhomogeneous QMIX in various architectures to solve dynamic scheduling in manufacturing environments 22

Application of inhomogeneous QMIX in various architectures t...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Heik, David Böhm, Alexander Bahrpeyma, Fouad Reichelt, Dirk Faculty of Computer Science HTWD – University of Applied Sciences Dresden Germany

ISBN: (纸本)9798331527471

In light of the growth in data availability, the manufacturing industry is experiencing a growing change in its needs and shape, which necessitates the use of more efficient data-driven methodologies in real-time production scheduling. Conventional dynamic scheduling approaches, designed for non-dynamic problem sizes, struggle in dealing with the inherent volatility and complexity of contemporary production scheduling scenarios. As an attempt to meet the demands of near real-time decision-making mechanisms on the shop floor, this study explores variations of QMIX, including local QMIX (LQMIX), where separate instances are employed for specific tasks, and gated QMIX (GQMIX), which utilizes specific agents for tasks while employing a central mixing network. Scheduling systems may not satisfy the recent requirements, emphasizing the need for more adaptive systems. Utilizing QMIX, manufacturing operations can be streamlined by integrating the collaborative synergy of multiple agents. Reinforcement learning models are trained using QMIX and benchmarked against heuristic dispatch strategies such as Shortest Path First as one of the most popular method used in the community. The experimental findings highlight the effectiveness of QMIX, in particular the original version, in tackling the challenges of dynamic scheduling within the manufacturing domain. QMIX exhibits superior performance compared to alternative algorithms and heuristic dispatching methods in specific contexts. Nonetheless, the study underscores the imperative of striking a balance between adaptability and specialization. © 2024 IEEE.

关键词： Reinforcement learning

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