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An Improved Artificial Rabbits Optimization Algorithm with Chaotic Local Search and Opposition-Based Learning for engineering Problems and Its Applications in Breast Cancer Problem

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computer Modeling in engineering & Sciences 2024年第11期141卷 1067-1110页

作者： Feyza AltunbeyÖzbay ErdalÖzbay Farhad Soleimanian Gharehchopogh Department of Software Engineering Firat UniversityElazig23119Turkey Department of Computer Engineering Firat UniversityElazig23119Turkey Department of Computer Engineering Urmia BranchIslamic Azad UniversityUrmia44867-57159Iran

Artificial rabbits optimization(ARO)is a recently proposed biology-based optimization algorithm inspired by the detour foraging and random hiding behavior of rabbits in ***,for solving optimization problems,the ARO algorithm shows slow convergence speed and can fall into local *** overcome these drawbacks,this paper proposes chaotic opposition-based learning ARO(COARO),an improved version of the ARO algorithm that incorporates opposition-based learning(OBL)and chaotic local search(CLS)*** adding OBL to ARO,the convergence speed of the algorithm increases and it explores the search space *** maps in CLS provide rapid convergence by scanning the search space efficiently,since their ergodicity and non-repetitive *** proposed COARO algorithm has been tested using thirty-three distinct benchmark *** outcomes have been compared with the most recent optimization ***,the COARO algorithm’s problem-solving capabilities have been evaluated using six different engineering design problems and compared with various other *** study also introduces a binary variant of the continuous COARO algorithm,named *** performance of BCOARO was evaluated on the breast cancer *** effectiveness of BCOARO has been compared with different feature selection *** proposed BCOARO outperforms alternative algorithms,according to the findings obtained for real applications in terms of accuracy performance,and fitness *** experiments show that the COARO and BCOARO algorithms achieve promising results compared to other metaheuristic algorithms.

关键词： Artificial rabbit optimization binary optimization breast cancer chaotic local search engineering design problem opposition-based learning

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A novel binary Grey Wolf Optimizer algorithm with a new dynamic position update mechanism for feature selection problem: A novel binary Grey Wolf Optimizer algorithm with a new dynamic position…: F. Erdoğan et al.

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Soft Computing 2024年第21期28卷 12623-12654页

作者： Erdoğan, Feyza Karakoyun, Murat Gülcü, Şaban Department of Software Engineering Konya Technical University Konya Turkey Department of Computer Engineering Necmettin Erbakan University Konya Turkey

Feature selection (FS) is one of the basic preprocessing steps in data mining and is a challenging binary optimization problem. FS is the process of determining the subset that can best represent the dataset by removing features that have little impact from a given dataset without affecting performance and accuracy. In this paper, Binary Dynamic Grey Wolf Optimization Algorithm (binDGWO) is proposed for the solution of binary optimization problems. To binaryize the Grey Wolf Optimization Algorithm (GWO), the original position update equation was binaryized using the logical XOR operator to achieve a balance between local search and global search. In addition, a simple and effective innovation has been introduced to the position update equation with the dynamic coefficient method. This method has been developed to make the affected solution better by determining and applying the effects of the wolves in the lead team on the position according to the solution quality. The performance of binDGWO over FS is compared to the performance of over 20 different algorithms, including binary variants of GWO and different binary metaheuristics. 41 datasets with different numbers of samples and features were used in the experiments. Various performance metrics were used to determine the superiority of the methods over each other. In addition, the Friedman test was performed to statistically evaluate the results of the methods. According to the performance metrics and the Friedman test results, it was seen that the proposed algorithm has better results than other binary variants of GWO, and when the comparison results with other metaheuristic algorithms are examined, it is generally more successful and effective. In conclusion, it can be said that binDGWO is a simple, effective, and efficient binary method and it achieves its purpose. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Optimization algorithms

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Stability Prediction in Smart Grid Using PSO Optimized XGBoost Algorithm with Dynamic Inertia Weight Updation

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computer Modeling in engineering & Sciences 2025年第1期142卷 909-931页

作者： Adel Binbusayyis Mohemmed Sha Department of Software Engineering College of Computer Engineering and Sciences in Al-Kharj Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia

Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid *** the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the *** also possesses a better impact on averting overloading and permitting effective energy *** though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized *** overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning *** accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data ***,the pre-processed data are taken for training and *** that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in *** PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed *** hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on *** results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.

关键词： Smart Grid machine learning particle swarm optimization XGBoost dynamic inertia weight update

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A graph neural network technique for the prediction of cerebral stroke using an unbalanced medical dataset

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Multimedia Tools and Applications 2025年 1-37页

作者： Sha, Mohemmed Department of Software Engineering College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia

Cerebral stroke is a major health problem, and if not recognized and treated immediately, it can result in considerable morbidity and fatality. Predicting the possibility of a stroke can help with intervention, resulting in improved patient outcomes. However, constructing reliable stroke prediction algorithms can be difficult, especially when working with unbalanced medical data. Timely detection of the various warning signs of a stroke can significantly reduce its severity. To deal with unbalanced data, this research proposed the hybridization of two techniques Noise Reduction and oversampling technique (Noise Reduction-Border Line- Synthetic Minority Oversampling Technique (NR-BL-SMOTE) which only oversamples or strengthens the borderline minority class. It aims at eliminating noise from minority groups while preventing the making of noisy samples and BL-SMOTE is used to create synthetic data to balance the dataset. After that, feature extraction is done to extract features from the cerebral stroke prediction dataset using fine-tuned VGG19 architecture to improve the performance of the proposed model. The fine-tuned model uses an already trained network and re-trains the part of that by the new data set. Finally, classification is done using a graph neural network (GNN) to classify the prediction of normal or cerebral stroke (ischemic and hemorrhagic). Further, to optimize the accuracy of the classification performance;Improved Golden Jackal Optimization (IGJO) is done by repeating their iterations on the GNN hidden layer until we get the final optimal value. The proposed model will be simulated using the PYTHON tool. Then in the simulation results, the proposed approach achieves more powerful classification accuracy than established technologies, demonstrating its efficiency in stroke prediction and classification. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Graph neural networks

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MI-BMPI motor imagery brain–mobile phone dataset and performance evaluation of voting ensembles utilizing QPDM

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Neural Computing and Applications 2025年第6期37卷 4679-4696页

作者： Yilmaz, Cagatay Murat Yilmaz, Bahar Hatipoglu Kose, Cemal Department of Software Engineering Karadeniz Technical University Trabzon61080 Turkey Department of Computer Engineering Karadeniz Technical University Trabzon61080 Turkey

EEG-based interfaces are an active research area with great potential. We, therefore, focused on classifying motor imaging (MI) tasks from various problem areas. Because of that, we applied MI patterns to voting ensembles differently and constructed voters. They employ quasi-probabilistic distribution models based on sub-classifiers of different frequency bands and time segments. Much previous work focused on just a few MI tasks for BCIs. To that end, we constructed a new mobile EEG dataset, abbreviated as MI-BMPI, containing two significant gestures for mobile phone interfaces. The research experiments used a consumer market EEG system, the mobile wireless Emotiv EPOC Flex neuroheadset. Experiments were carried out on the BCI Competition IV Dataset 2a and MI-BMPI. On the BCI and BMPI datasets, promising results were obtained in classifying various MI tasks. In conclusion, new solutions were introduced for tougher EEG-based interfaces, which have the potential to classify MI tasks and develop EEG-based interfaces. In addition to the average performance improvements, more stable results were achieved for subject and task variations. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Electroencephalography

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A novel multimodal EEG-image fusion approach for emotion recognition: introducing a multimodal KMED dataset

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Neural Computing and Applications 2025年第6期37卷 5187-5202页

作者： Hatipoglu Yilmaz, Bahar Kose, Cemal Yilmaz, Cagatay Murat Department of Computer Engineering Karadeniz Technical University Trabzon61080 Turkey Department of Software Engineering Karadeniz Technical University Trabzon61080 Turkey

Nowadays, bio-signal-based emotion recognition have become a popular research topic. However, there are some problems that must be solved before emotion-based systems can be realized. We therefore aimed to propose a feature-level fusion (FLF) method for multimodal emotion recognition (MER). In this method, first, EEG signals are transformed to signal images named angle amplitude graphs (AAG). Second, facial images are recorded simultaneously with EEG signals, and then peak frames are selected among all the recorded facial images. After that, these modalities are fused at the feature level. Finally, all feature extraction and classification experiments are evaluated on these final features. In this work, we also introduce a new multimodal benchmark dataset, KMED, which includes EEG signals and facial videos from 14 participants. Experiments were carried out on the newly introduced KMED and publicly available DEAP datasets. For the KMED dataset, we achieved the highest classification accuracy of 89.95% with k-Nearest Neighbor algorithm in the (3-disgusting and 4-relaxing) class pair. For the DEAP dataset, we got the highest accuracy of 92.44% with support vector machines in arousal compared to the results of previous works. These results demonstrate that the proposed feature-level fusion approach have considerable potential for MER systems. Additionally, the introduced KMED benchmark dataset will facilitate future studies of multimodal emotion recognition. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image fusion

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From light sensing to adaptive learning:hafnium diselenide reconfigurable memcapacitive devices in neuromorphic computing

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Light(Science & Applications) 2025年第1期14卷 215-226页

作者： Bashayr Alqahtani Hanrui Li Abdul Momin Syed Nazek El-Atab Electrical and Computer Engineering Program Computer Electrical Mathematical Science and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)ThuwalSaudi Arabia Electrical Engineering Department College of EngineeringPrincess Nourah Bint Abdulrahman University(PNU)RiyadhSaudi Arabia

Advancements in neuromorphic computing have given an impetus to the development of systems with adaptive behavior,dynamic responses,and energy efficiency *** charge-based or emerging memory technologies such as memristors have been developed to emulate synaptic plasticity,replicating the key functionality of neurons—integrating diverse presynaptic inputs to fire electrical impulses—has remained *** this study,we developed reconfigurable metal-oxide-semiconductor capacitors(MOSCaps)based on hafnium diselenide(HfSe2).The proposed devices exhibit(1)optoelectronic synaptic features and perform separate stimulus-associated learning,indicating considerable adaptive neuron emulation,(2)dual light-enabled charge-trapping and memcapacitive behavior within the same MOSCap device,whose threshold voltage and capacitance vary based on the light intensity across the visible spectrum,(3)memcapacitor volatility tuning based on the biasing conditions,enabling the transition from volatile light sensing to non-volatile optical data *** reconfigurability and multifunctionality of MOSCap were used to integrate the device into a leaky integrate-and-fire neuron model within a spiking neural network to dynamically adjust firing patterns based on light stimuli and detect exoplanets through variations in light intensity.

关键词： integrate volatile integrating

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A new chaotic system and its practical applications in substitution box and random number generator

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Multimedia Tools and Applications 2024年第42期83卷 90053-90067页

作者： Artuğer, Fırat Özkaynak, Fatih Department of Computer Engineering Munzur University Tunceli62200 Turkey Department of Software Engineering Fırat University Elazig23119 Turkey

One of the successful practical applications of chaos theory and nonlinear dynamics is chaos-based cryptology studies. In this study, a new chaotic system is proposed. The proposed chaotic system generator model has a simple algorithmic structure. In addition to, generated chaotic systems have rich dynamics. It has been examined whether this system has potential advantages, especially for information security applications. As a result of the analysis and experimental studies, it is seen that the study makes many contributions to the literature. The simple mathematical generator structure has the potential to reduce computational complexity, which is an important problem for cryptology applications. One of the most comprehensive studies has been realized for chaos-based substitution box designs. 70,000 different substitution box structures have been generated. It has been known that the highest nonlinearity value that can be achieved for designs that transform chaotic system outputs into substitution box structures is 106.75 today. It has been shown that this value may be higher. The new highest nonlinearity value is calculated as 107. Side channel analysis has been examined for substitution box structures with the highest nonlinearity value among the substitution boxes generated in the study. Analysis results show that the proposed new substitution box structures may have an advantage for application-oriented attacks. Among the generated substitution box structures, 289 substitution box structures with a nonlinearity value of 106 and above are presented as a public dataset for researchers to use. In addition, 3 different random sequences with a length of 1 million-bit were produced with the proposed map, and the NIST SP 800–22 test was applied. Test results for all datasets were successful. In this way, the proposed map can also be used as a random number generator. © The Author(s) 2024.

关键词： Chaotic systems

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A reliable and secure video steganography method based on 4d-logistic mapping

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Multimedia Tools and Applications 2025年 1-31页

作者： Sha, Mohemmed Department of Software Engineering College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj16278 Saudi Arabia

Data security is becoming more and more crucial due to developments in communication and information technology, particularly when it comes to video transmission. This research provides a unique approach that combines 4D Logistic Mapping (4DLM) with MASK cryptography technology to improve video data security. The five steps of the suggested approach are as follows: gathering video, extracting frames, compressing data using the LZW technique, encrypting it using MASK, and embedding it using 4DLM. To protect the video frames, MASK encryption uses a single matrix and four arrays, advancing through matrix initialization, key scheduling, and substitution/diffusion. 4DLM is used after encryption to improve the system's overall security and effectiveness. This model's major goal is to keep performance high while safeguarding video data from unwanted access. Data confidentiality is significantly improved by the suggested strategy, as shown by simulations performed with the Python program. For the example video sequences, the Structural Similarity Index (SSIM) was close to 99%, which indicates better performance than current methods. MASK and 4D Logistic Mapping together provide a powerful answer for today's data security issues by providing an effective and safe method of video data protection. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Steganography

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Unraveling the intricacies of EEG seizure detection: A comprehensive exploration of machine learning model performance, interpretability, and clinical insights

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Multimedia Tools and Applications 2024年第41期83卷 89217-89247页

作者： Mridha, Krishna Priyok, Masrur Ahsan Shukla, Madhu Department of Computer Engineering Marwadi University Rajkot India Department of Software Engineering Tianjin University Tianjin China Department of Computer Engineering Artificial Intelligence and Big Data Analytics Marwadi University Gujarat Rajkot India

In neurology, it is critical to promptly and precisely identify epileptic episodes using EEG data. Interpretability and thorough model evaluation are still crucial to guarantee reliability, even though machine learning provides sophisticated tools for automated EEG analysis. We undertook a thorough investigation of EEG seizure classification in this work, with equal emphasis on statistical validation, interpretability, and model performance. Following thorough preprocessing, we used statistical significance tests in conjunction with Chernoff and Bhattacharyya Bounds to select features. Top performers were found to be the Random Forest which had accuracies of 96%. A paired t-test and bootstrapped 95% confidence intervals were used to statistically validate the performance robustness, demonstrating the large variation in model performances. Going beyond simple performance measures, we set out to decipher the nuances of model choices by utilizing methods devoted to model interpretability and shedding light on the importance of distinct aspects. This helped neurologists grasp the primary EEG indicators predictive of seizures and opened the door for future clinical insights. It also provided transparency in diagnostic predictions. Our efforts highlight the mutually reinforcing importance of predictability and model interpretability in the context of medical machine-learning applications, hence intensifying the demand for the creation and use of trustworthy and transparent neurology diagnostic instruments. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Neurology

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