Template-Type: ReDIF-Article 1.0 Author-Name: Jiwei Tu Author-X-Name-First: Jiwei Author-X-Name-Last: Tu Author-Name: Hong Li Author-X-Name-First: Hong Author-X-Name-Last: Li Author-Name: Yuanlong Hu Author-X-Name-First: Yuanlong Author-X-Name-Last: Hu Author-Name: Shaojin Geng Author-X-Name-First: Shaojin Author-X-Name-Last: Geng Author-Name: Dongyang Li Author-X-Name-First: Dongyang Author-X-Name-Last: Li Author-Name: Lei Wang Author-X-Name-First: Lei Author-X-Name-Last: Wang Title: A constrained multi-objective evolutionary algorithm based on dynamic clustering strategy Abstract: The dual-population co-evolution strategy is a class of methods that can efficiently solve constrained multi-objective optimisation problems. However, the auxiliary population does not contribute effective individuals to the main population at all stages of population evolution. Considering the utilisation of auxiliary population at later evolutionary stage, a constrained multi-objective evolutionary algorithm based on the dynamic clustering co-evolutionary strategy is proposed. This paper proposes a dynamic clustering strategy that dynamically divides the population into active and inactive populations based on the auxiliary population status, where only the active population participates in generating the offspring, so as to reasonably allocate the computational resources and enhance the convergence of the population. In addition, the feasible solutions found by the auxiliary population are retained using an additional archived population to improve the diversity of the main population. Experimental results demonstrate the effectiveness of the algorithm. Journal: Int. J. of Complexity in Applied Science and Technology Pages: 253-280 Issue: 3 Volume: 1 Year: 2025 Keywords: constrained optimisation; evolutionary algorithm; dynamic clustering; multiple population. File-URL: http://www.inderscience.com/link.php?id=145832 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcast:v:1:y:2025:i:3:p:253-280 Template-Type: ReDIF-Article 1.0 Author-Name: Emir Oncu Author-X-Name-First: Emir Author-X-Name-Last: Oncu Title: Integrating CNNs and ANNs: a comprehensive AI framework for enhanced breast cancer detection and diagnosis Abstract: Among women globally, breast cancer is a major cause of cancer-related death. Accurate and timely diagnosis is essential, and results can be significantly improved. A new era in image analysis has been brought about by the emergence of artificial intelligence (AI), which has made significant progress in the diagnosis and customisation of treatment plans for breast cancer possible. This study aimed to develop a comprehensive AI framework for breast cancer detection by integrating convolutional neural networks (CNNs) for image analysis with an artificial neural networks (ANNs) for clinical data. Using a dataset of ultrasound and pathology images, along with clinical features from 569 patients, we trained CNN models to classify breast tissue as benign or malignant, and the ANN to process clinical data for the same task. The results demonstrate that the fusion of CNNs and ANNs enhances diagnostic accuracy and offers a promising tool for early breast cancer detection. Journal: Int. J. of Complexity in Applied Science and Technology Pages: 281-299 Issue: 3 Volume: 1 Year: 2025 Keywords: breast cancer; convolutional neural network; CNN; imaging; machine learning; prediction. File-URL: http://www.inderscience.com/link.php?id=145833 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcast:v:1:y:2025:i:3:p:281-299 Template-Type: ReDIF-Article 1.0 Author-Name: Bingting Li Author-X-Name-First: Bingting Author-X-Name-Last: Li Author-Name: Yijing Wu Author-X-Name-First: Yijing Author-X-Name-Last: Wu Author-Name: Tianhao Zhao Author-X-Name-First: Tianhao Author-X-Name-Last: Zhao Author-Name: Zhihua Cui Author-X-Name-First: Zhihua Author-X-Name-Last: Cui Title: Three-way decision-making based on index threshold acquisition Abstract: Currently, the fact that the equilibrium between diversity and convergence deteriorates as the count of objectives rises is now a significant barrier in the multi-objective optimisation problem. Additionally, conventional techniques for determining three-way decision thresholds rely on real-valued state-based cost loss functions that cannot handle intricate real-world scenarios. The three decisions of the index threshold are used to design a new environment-selecting technique and a multi-objective optimisation algorithm (MaOEA-TWD-IT) in this paper to address the aforementioned issues. In more detail, the convergence threshold and diversity threshold are first obtained using the index thresholds acquisition method, and then the solutions owning better diversity and convergence are chosen based on three decision-making techniques. By assessing the smallest distance between individuals and other solutions, the diversity of solutions is revealed. The solutions' convergence is quantified with the distance between the solutions and the ideal solution. Finally, the MaOEA-TWD-IT method suggested in our study also performs excellently and is able to successfully equilibrium population diversity and convergence, according to experimental results. Journal: Int. J. of Complexity in Applied Science and Technology Pages: 233-252 Issue: 3 Volume: 1 Year: 2025 Keywords: many-objective optimisation; threshold acquisition; three-way decision; index threshold; evolutionary algorithm. File-URL: http://www.inderscience.com/link.php?id=145844 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcast:v:1:y:2025:i:3:p:233-252 Template-Type: ReDIF-Article 1.0 Author-Name: Jiaxiu Lin Author-X-Name-First: Jiaxiu Author-X-Name-Last: Lin Author-Name: Ying Sun Author-X-Name-First: Ying Author-X-Name-Last: Sun Author-Name: Yuelin Gao Author-X-Name-First: Yuelin Author-X-Name-Last: Gao Title: A solution to portfolio optimisation based on random forest and long short-term memory networks Abstract: The randomness of the stock market presents a significant challenge in utilising known information to construct investment portfolios that maximise returns while minimising risks. This paper employs the entropy weight method combined with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to assign weights to indicators and subsequently ranks and selects stocks from the CSI 300 Index constituents in the Chinese market and the NASDAQ-100 Index constituents in the US. market. Using random forest (RF) and long short-term memory (LSTM) to predict stock closing prices, the experimental results show that the LSTM model achieves higher predictive accuracy and more stable errors. By selecting the top 7 stocks based on monthly returns to construct a portfolio, the study analyses the returns of investment strategies under different prediction models. The results demonstrate that the portfolio constructed using the LSTM prediction model outperforms other portfolios in terms of cumulative return, annualised return, and Sharpe ratio. Journal: Int. J. of Complexity in Applied Science and Technology Pages: 211-232 Issue: 3 Volume: 1 Year: 2025 Keywords: stock market; investment portfolio; entropy weight-TOPSIS; random forest; RF; long short-term memory; LSTM. File-URL: http://www.inderscience.com/link.php?id=145845 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcast:v:1:y:2025:i:3:p:211-232 Template-Type: ReDIF-Article 1.0 Author-Name: Minchong Chen Author-X-Name-First: Minchong Author-X-Name-Last: Chen Author-Name: Hong Li Author-X-Name-First: Hong Author-X-Name-Last: Li Author-Name: Qi Yu Author-X-Name-First: Qi Author-X-Name-Last: Yu Author-Name: Xuejing Hou Author-X-Name-First: Xuejing Author-X-Name-Last: Hou Title: Particle swarm optimisation with modified global search and local search exemplars for large-scale optimisation Abstract: Canonical particle swarm optimisation (cPSO) has been criticised for its premature convergence when tackling large-scale optimisation problems (LSOPs). During optimisation, the swarm diversity of cPSO rapidly decays, leading to its poor global search performance. To improve the global search ability of cPSO, a particle swarm optimisation with modified global search and local search exemplars (PSO-MGLE) is proposed. In PSO-MGLE, two novel exemplar selection strategies are designed to diversify the selection of global search and local search exemplars for updated particles, thereby preserving high swarm diversity. Second, a dynamic adjustment strategy for the acceleration coefficient is designed to encourage the swarm to prioritise the global search at the early stage while emphasising the local search at the later stage. PSO-MGLE is tested on the 2022 benchmark suite, scaled to 500, 1,000, and 2,000 dimensions. Experimental results demonstrate the competitive performance and good scalability of PSO-MGLE in comparison with seven state-of-the-art approaches. Journal: Int. J. of Complexity in Applied Science and Technology Pages: 301-329 Issue: 4 Volume: 1 Year: 2025 Keywords: particle swarm optimisation; PSO; large-scale optimisation; global search; local search; swarm diversity. File-URL: http://www.inderscience.com/link.php?id=147066 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcast:v:1:y:2025:i:4:p:301-329 Template-Type: ReDIF-Article 1.0 Author-Name: Emir Oncu Author-X-Name-First: Emir Author-X-Name-Last: Oncu Title: Combining CNNs and symptom data for monkeypox virus detection Abstract: The zoonotic disease monkeypox, related to smallpox, presents diagnostic challenges due to its resemblance to other illnesses with similar symptoms. In this study, we propose a robust method for monkeypox detection utilising convolutional neural networks (CNNs). Our approach integrates lesion images and symptom analysis to enhance diagnostic reliability. A dataset comprising high-definition lesion images and nine significant symptoms was employed to train the CNN model. The model classifies cases based on a probabilistic score, while symptom-based analysis is used as a secondary measure when lesion analysis is inconclusive. Built with convolutional, pooling, and fully connected layers, the model demonstrates strong predictive capabilities, effectively distinguishing monkeypox from other conditions. The study highlights the CNN model's ability to assess monkeypox risk with high confidence, even with limited high-resolution imaging data, underscoring its potential in medical diagnostics. Tables summarise the model's predictions based on symptom combinations, showcasing its practical applicability. This research emphasises the integration of CNNs with clinical symptom data as a promising tool for accurate and early monkeypox diagnosis. Future work could further refine the model by incorporating larger datasets and advanced methodologies to improve its generalisability and effectiveness in outbreak scenarios. Journal: Int. J. of Complexity in Applied Science and Technology Pages: 330-349 Issue: 4 Volume: 1 Year: 2025 Keywords: monkeypox; convolutional neural networks; CNNs; virus; machine learning; prediction. File-URL: http://www.inderscience.com/link.php?id=147069 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcast:v:1:y:2025:i:4:p:330-349 Template-Type: ReDIF-Article 1.0 Author-Name: Bo-Yi Lin Author-X-Name-First: Bo-Yi Author-X-Name-Last: Lin Author-Name: Kai Chun Lin Author-X-Name-First: Kai Chun Author-X-Name-Last: Lin Title: Analysis of centrifugal clutches in two-speed automatic transmissions with multilayer perceptron neural network-based engagement prediction Abstract: Numerical analysis of centrifugal clutch systems integrated with a two-speed automatic transmission is shown in this paper. Various clutch configurations and their effects on the dynamics of the considered transmission have been examined. Based on these configurations, torque transfer, upshifting, and downshifting behaviours under various conditions are discussed. This paper presents a multilayer perceptron neural network (MLPNN) model for clutch engagements, whose parameters are spring preload and shoe mass. In this paper, a computationally efficient alternative to the complex simulations for the modelling is presented. MLPNN and numerical modelling further help in the critical insights required for improvement in the design parameters, performance, and efficiency of the clutch-transmission system. Journal: Int. J. of Complexity in Applied Science and Technology Pages: 350-363 Issue: 4 Volume: 1 Year: 2025 Keywords: centrifugal clutch; automatic transmission; numerical modelling; multilayer perceptron neural network; MLPNN; vehicle dynamics. File-URL: http://www.inderscience.com/link.php?id=147074 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcast:v:1:y:2025:i:4:p:350-363 Template-Type: ReDIF-Article 1.0 Author-Name: Roberto Rodríguez Author-X-Name-First: Roberto Author-X-Name-Last: Rodríguez Author-Name: Laura Brito Author-X-Name-First: Laura Author-X-Name-Last: Brito Author-Name: Anthony León Author-X-Name-First: Anthony Author-X-Name-Last: León Author-Name: Esley Torres Author-X-Name-First: Esley Author-X-Name-Last: Torres Title: Super resolution in microscopic images of SARS-CoV-2 through deep learning Abstract: In this work, we carried out a study on the importance of super-resolution in SARS-CoV-2 microscopic images. We analysed the impossibility of realising super-resolution in SARS-CoV-2 microscopic images, through deep learning, without a database of real images that allows training of convolutional neural networks. In this sense, we proposed an intelligent strategy that made it possible to select, by means of deep learning, the most appropriate algorithm from several previously developed ones. In other words, the strategy consisted in analysing, via deep learning, the characteristics of the microscopic images, classifying them and recommending the most appropriate algorithm to carry out the super-resolution task. In order to evaluate the effectiveness of the obtained results, we realised a quantitative comparison of the selected algorithm through our strategy with the one proposed by experts in computer vision. The efficiency of our smart strategy was over 97%. Journal: Int. J. of Complexity in Applied Science and Technology Pages: 364-381 Issue: 4 Volume: 1 Year: 2025 Keywords: super-resolution; SR; SARS-CoV-2 coronavirus; smart system; deep learning; DL; hybrid algorithm. File-URL: http://www.inderscience.com/link.php?id=147080 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcast:v:1:y:2025:i:4:p:364-381 Template-Type: ReDIF-Article 1.0 Author-Name: Md. Mahadi Hassan Author-X-Name-First: Md. Mahadi Author-X-Name-Last: Hassan Author-Name: Noushin Nohor Author-X-Name-First: Noushin Author-X-Name-Last: Nohor Title: Evaluating the effectiveness of large language models in medicine education: a comparison of current medicine knowledge Abstract: Recent advancements in artificial intelligence have led to the development of powerful large language models (LLMs) like ChatGPT-4-turbo, Gemini 2.0 Flash, DeepSeek-R1, and Qwen2.5-Max. This study evaluates their medical knowledge proficiency using multiple-choice questions (MCQs) sourced from a reputable medical textbook, with answers verified by experts. Each model was tested on its ability to select correct answers, and performance was analysed using ANOVA and Tukey's HSD tests. Results showed that while all models exhibited some proficiency, ChatGPT-4-turbo significantly outperformed Gemini 2.0 Flash and Qwen2.5-Max, with no notable difference between ChatGPT-4-turbo and DeepSeek-R1. Despite their capabilities, these models remain unreliable for medical education and assistance. Enhancing their accuracy and reliability is crucial for their effective application in healthcare, enabling medical students and professionals to utilise AI for learning and clinical decision-making. Further development is needed to improve their utility in medical practice. Journal: Int. J. of Complexity in Applied Science and Technology Pages: 382-396 Issue: 4 Volume: 1 Year: 2025 Keywords: large language models; LLMs; artificial intelligence; ChatGPT; Gemini; DeepSeek; Qwen. File-URL: http://www.inderscience.com/link.php?id=147091 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcast:v:1:y:2025:i:4:p:382-396