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Hybrid Deep Maxout-VGG-16 model for brain tumour detection and classification using MRI images

Journal of Biotechnology 2025年 405卷 124-138页

作者： Loganayagi, T. Sravani, Meesala Maram, Balajee Rao, Telu Venkata Madhusudhana Department of Electronics and Communication Engineering Paavai Engineering College Pachal Tamilnadu Namakkal India Department of Computer Science and Engineering GMR Institute of Technology Andhra Pradesh Rajam India School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India Department of AI&DS Vignan's Institute of Information Technology Visakhapatnam530049 India

Brain tumor detection is essential to identify tumors at an early stage, allowing for more effective treatment. The patient's chances of recovery and survival can be improved by early detection. The existing methods for detecting brain tumour have several limitations, including limited accessibility, exposure to radiation, high costs and potential for false negatives. To overcome the issues, a Deep Maxout-Visual Geometry Group-16 (DM-VGG-16) model is devised for detecting tumour in brain from Magnetic Resonance Imaging (MRI). Initially, MRI image is sent for pre-processing as input. Here, Non-Local Mean (NLM) filter performs pre-processing. The pre-processed image is subjected to segmentation stage, which is accomplished by Template–based K-means and improved Fuzzy C Means algorithm (TKFCM). Moreover, in feature extraction stage, various features, like area, cluster prominence, Hybrid PCA- Normalized GIST (NGIST) and Improved Median binary Pattern (IMBP) are extracted. Lastly, proposed DM-VGG-16 model is utilized for detection of brain tumors from extracted features. The DM-VGG-16 is the integration of Deep Maxout Network (DMN) and Visual Geometry Group-16 (VGG-16). The DM-VGG-16 outperformed superior results than conventional techniques with performance metrics, including accuracy, True Negative Rate (TNR) and True Positive Rate (TPR) of 90.76 %, 90.65 % and 90.75 % correspondingly. © 2025 Elsevier B.V.

关键词： K-means clustering

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MIGRATE: Cross-Lingual Adaptation of Domain-Specific LLMs through Code-Switching and Embedding Transfer 31

MIGRATE: Cross-Lingual Adaptation of Domain-Specific LLMs th...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Hong, Seongtae Lee, Seungyoon Moon, Hyeonseok Lim, Heuiseok Department of Computer Science and Engineering Korea University Korea Republic of Human-inspired AI Research Korea Republic of

ISBN: (纸本)9798891761964

Large Language Models (LLMs) have rapidly advanced, with domain-specific expert models emerging to handle specialized tasks across various fields. However, the predominant focus on English-centric models demands extensive data, making it challenging to develop comparable models for middle and low-resource languages. To address this limitation, we introduce MIGRATE, a novel method that leverages open-source static embedding models and up to 3 million tokens of code-switching data to facilitate the seamless transfer of embeddings to target languages. MIGRATE enables effective cross-lingual adaptation without requiring large-scale domain-specific corpora in the target language, promoting the accessibility of expert LLMs to a diverse range of linguistic communities. Our experimental results demonstrate that MIGRATE significantly enhances model performance in target languages, outperforming baseline and existing cross-lingual transfer methods. This approach provides a practical and efficient solution for extending the capabilities of domain-specific expert models. © 2025 Association for Computational Linguistics.

关键词： Embeddings

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An Intelligent Smart Dynamic Feature Analysis Based Approach by Utilizing Deep Learning to Improve the Breast Cancer Detection

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IEEE Access 2025年 13卷 81643-81665页

作者： Shim, Seong-O Hussain, Lal Alsolami, Eesa Alkinani, Monagi H. University of Jeddah College of Computer Science and Engineering Department of Computer and Network Engineering Jeddah23890 Saudi Arabia The University of Azad Jammu and Kashmir Department of Computer Science and Information Technology King Abdullah Campus Chatter Kalas Muzaffarabad13100 Pakistan The University of Azad Jammu and Kashmir Neelum Campus Department of Computer Science Athmuqam13230 Pakistan University of Jeddah College of Computer Science and Engineering Department of Cybersecurity Jeddah23890 Saudi Arabia University of Jeddah College of Computer Science and Engineering Department of Computer Science and AI Jeddah23890 Saudi Arabia

Breast cancer remains one of the important global health concerns with high rates of mortality, highlighting the significance of more sophisticated diagnostic methods. Conventional methods, generally comprised of costly imaging and invasive biopsies, are of high burdens. Motivated by the limitations, the present study comes up with an innovative automated solution for the identification of breast cancer using deep learning analysis of mammograms. Moving away from the traditional approaches with inherent pre-processing and feature extraction constraints, this research focuses on a two-pronged improvement strategy: improved mammogram quality and highly optimized deep learning architecture. Specifically, we present a new Optimized InceptionResNetV2 model significantly optimized through the thoughtful addition of large data augmentation to increase robustness, LeakyReLU activation to facilitate gradient flow and accelerate learning, and MeanDropout regularization to mitigate overfitting and improve generalization. The model was also trained using Quantization aware training (QAT) to enable efficient deployment on low-resource devices without significant performance degradation. The performance on our proposed approach for the massive mammogram dataset reflects an evident improvement in detection performance over traditional techniques. Our InceptionResNetV2 optimized achieved state-of-the-art accuracy with outstanding measures of 98.06% sensitivity, 97.05%, positive predictive value (PPV) and specificity of 99.60%, negative predictive value (NPV) of 86.83%, 97.94% accuracy, F1-score of 96.90%, Matthew’s correlation coefficient (MCC) of 90.67%, and AUC of 0.9939. The benefits of proposed system are that it can deliver a more efficient, precise, and possibly cost-effective diagnostic tool for breast cancer. Through synergistic integration of architectural optimization, sophisticated regularization methods, and deployment-aware training, our proposed system enables earlier

关键词： Diseases

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Disease Detection in Finger Millet Plant Using Fine-Tuned Transfer Learning Models

Disease Detection in Finger Millet Plant Using Fine-Tuned Tr...

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Computing for Sustainability and Intelligent Future (COMP-SIF), International Conference on

作者： Prajwal B Subhash Mondal Raghu M Shashank N Computer Science & Engineering (AI & ML) Dayananda Sagar University Bengaluru India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

Plant diseases are becoming one of the major threats to global food security, particularly in carbohydrate rich staple crops like finger millet (Eleusine coracana) which requires minimal water and can be grown in diverse climate conditions, highlighting the need for early disease detection to minimize yield losses and promote sustainable agriculture. We employed web scraping for data collection from different open source, data augmentation techniques like flip, rotation, zoom to enhance the model's performance and enhance generalization capabilities. This research focuses on developing an effective disease detection system for finger millet using Convolutional Neural Networks (CNNs) and transfer learning methodologies. We analyzed with pretrained CNN architectures, such as DenseNet121, VGG16, and VGG19, using a dataset of more than thousands of labelled images collected from diverse agricultural settings. Fine tuning these models ensures optimising model's performance and achieving better accuracy. Among the tested models, DenseNet121 achieved the highest accuracy, reaching 96.88%. Additionally, we developed a user-friendly real-time application to disease detection using Streamlit library helping the farmers to detect the disease in mobile for remote access. This study demonstrates the significant use of deep learning techniques like CNN architectures, particularly transfer learning, for developing better crop disease detection systems. By detecting the disease early and prevention at earlier stage will save tonnes of crops. This solves the major issues like food security and also malnutrition.

关键词： Plant diseases Accuracy Transfer learning Fingers Food security Crops computer architecture Convolutional neural networks Sustainable development Tuning

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Towards intelligent and trustworthy task assignments for 5G-enabled industrial communication systems

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Digital Communications and Networks 2025年第1期11卷 246-255页

作者： Mingfeng Huang Anfeng Liu Neal N.Xiong Athanasios V.Vasilakos School of Computer and Communication Engineering Changsha University of Science&TechnologyChangsha410114China School of Electronic Information Central South UniversityChangsha410083China Department of Computer Science and Mathematics Sul Ross State UniversityAlpineTX 79830USA College of Mathematics and Computer Science Fuzhou UniversityFuzhou350116China Center for AI Research University of AgderGrimstad 999026Norway

With the unprecedented prevalence of Industrial Internet of Things(IIoT)and 5G technology,various applications supported by industrial communication systems have generated exponentially increased processing tasks,which makes task assignment inefficient due to insufficient *** this paper,an Intelligent and Trustworthy task assignment method based on Trust and Social relations(ITTS)is proposed for scenarios with many tasks and few ***,ITTS first makes initial assignments based on trust and social influences,thereby transforming the complex large-scale industrial task assignment of the platform into the small-scale task assignment for each ***,an intelligent Q-decision mechanism based on workers'social relation is proposed,which adopts the first-exploration-then-utilization principle to allocate *** when a worker cannot cope with the assigned tasks,it initiates dynamic worker recruitment,thus effectively solving the worker shortage problem as well as the cold start *** importantly,we consider trust and security issues,and evaluate the trust and social circles of workers by accumulating task feedback,to provide the platform a reference for worker recruitment,thereby creating a high-quality worker ***,extensive simulations demonstrate ITTS outperforms two benchmark methods by increasing task completion rates by 56.49%-61.53%and profit by 42.34%-47.19%.

关键词： Industrial Internet of Things Insufficient workers Trust evaluation Social relation Task assignment

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A Novel Architecture for a Medical Image Recognition System Using Deep Learning-Based Multiple Regression Evaluation 4th

A Novel Architecture for a Medical Image Recognition System ...

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4th International Conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Parashar, Jyoti Upreti, Kamal Fatima, Iram Gangwar, Divya Saini, Ashok Kumar Vats, Prashant Department of Computer Science & Engineering Dr. Akhilesh Das Gupta Institute of Technology & Management New Delhi India Department of Computer Science Christ University Delhi NCR Campus Ghaziabad India Department of AI & ML Dr. Akhilesh Das Gupta Institute of Technology & Management New Delhi India Department of Management Babu Banarasi Das Institute of Technology & Management New Delhi India Department of Computer Science & Engineering School of Computer Science & Engineering Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9789819751990

Models based on machine learning are optimization models that collect data, assess it, and deliver the reports required by specialists and management to make the best decisions. The application of contemporary machine learning allows the organization to quickly analyze photographs, differentiate voices assist in providing customer service, assess the information that is at hand, and uncover connections to aid in decision-making processes. The results of this investigation use quantitative methodologies to collect data and analyze it using mathematical procedures such as regression modeling as well as analysis of variance. Deep learning techniques applied to digital imaging, particularly in medical treatment, can increase picture quality, aid in modeling, aid in making the best possible diagnosis, and successfully address demands from patients. To analyze the hypothesis, investigators intend to utilize statistical approaches such as descriptive data analysis, regression evaluation, and analysis of variance (ANOVA). The authors employ the purposive sample approach to choose respondents from the healthcare industry. Purpose sampling is a non-probability sampling approach. Researchers collected data from 193 respondents working at hospitals that are privately owned in Southern Asia. As stated by the study, all factors, including efficiently meeting patient needs, have a probability value of under 0.05, indicating that they are statistically noteworthy. Following the study, the coefficient of variance (R squared) is 0.744, or 74.4%. According to the study, there is a high association between better image quality and ml-based digital picture identification systems. The recognition of patterns and the application of artificial intelligence to computerized recognition of pictures also have a close link. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical imaging

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Nonlinear Dynamic Inversion-based Motion Planning of a Floating Satellite Platform

Nonlinear Dynamic Inversion-based Motion Planning of a Float...

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aiAA science and Technology Forum and Exposition, aiAA SciTech Forum 2025

作者： Banerjee, Avijit Tevetzidis, Ilias Satpute, Sumeet Gajanan Nikolakopoulos, George Robotics and AI research group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This article presents a nonlinear dynamic inversion-based motion plan for a levitating robotic satellite emulation platform. The frictionless motion of such a levitating platform has dynamic equivalency with a satellite’s realistic movement in space, thus evolving as an efficient demonstration tool as a hardware-in-loop satellite motion simulator. A brief design description of the emulation platform unified with various sensor and actuator units is presented, and an associated nonlinear mathematical model representing its coupled dynamic motion is described. The proposed control design architecture manipulates the associated nonlinear dynamics, which appear due to the coupling of translational and rotational movement of the robotic platform, thereby establishing an efficient trajectory tracking capability for various operating regions without the additional requirement of gain scheduling. In order to realize the control requirement through a set of ON-OFF thruster-based actuation units, which closely resembles the reaction control system of realistic satellite design, an optimal thrust allocation logic is proposed that accounts for various physical limitations of thrusters such as thrusters as its magnitude constraint and minimum on time. The efficacy of the proposed modelling and control scheme is experimentally validated in different realistic motion planning scenarios, such as point-to-point manoeuvres with terminal objectives and a superior path-following capability. The experimental studies are supported with related results. © 2025, American Institute of Aeronautics and Astronautics Inc, aiAA. All rights reserved.

关键词： Motion planning

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Ecology, Spatial Structure, and Selection Pressure Induce Strong Signatures in Phylogenetic Structure

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Artificial life 2025年第2期31卷 129-152页

作者： Moreno, Matthew Andres Rodriguez-Papa, Santiago Dolson, Emily University of Michigan Department of Ecology and Evolutionary Biology Center for the Study of Complex Systems Michigan Institute for Data and AI in Society Michigan State University Department of Computer Science and Engineering Michigan State University Department of Computer Science and Engineering Program in Ecology Evolution Behavior

Evolutionary dynamics are shaped by a variety of fundamental, generic drivers, including spatial structure, ecology, and selection pressure. These drivers impact the trajectory of evolution and have been hypothesized to influence phylogenetic structure. For instance, they can help explain natural history, steer behavior of contemporary evolving populations, and influence the efficacy of application-oriented evolutionary optimization. Likewise, in inquiry-oriented Artificial Life systems, these drivers constitute key building blocks for open-ended evolution. Here we set out to assess (a) if spatial structure, ecology, and selection pressure leave detectable signatures in phylogenetic structure;(b) the extent, in particular, to which ecology can be detected and discerned in the presence of spatial structure;and (c) the extent to which these phylogenetic signatures generalize across evolutionary systems. To this end, we analyze phylogenies generated by manipulating spatial structure, ecology, and selection pressure within three computational models of varied scope and sophistication. We find that selection pressure, spatial structure, and ecology have characteristic effects on phylogenetic metrics, although these effects are complex and not always intuitive. Signatures have some consistency across systems when using equivalent taxonomic unit definitions (e.g., individual, genotype, species). Furthermore, we find that sufficiently strong ecology can be detected in the presence of spatial structure. We also find that, while low-resolution phylogenetic reconstructions can bias some phylogenetic metrics, high-resolution reconstructions recapitulate them faithfully. Although our results suggest a potential for evolutionary inference of spatial structure, ecology, and selection pressure through phylogenetic analysis, further methods development is needed to distinguish these drivers' phylometric signatures from each other and to appropriately normalize phylogenetic metrics.

关键词： agent-based simulation Artificial Life Ecology evolution evolutionary dynamics phylogenetic analysis phylogeny metrics

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Quadtree-based Free-space Cell-decomposition for Pathplanning with RRT* Implementation

Quadtree-based Free-space Cell-decomposition for Pathplannin...

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aiAA science and Technology Forum and Exposition, aiAA SciTech Forum 2025

作者： Otte, Noah Satpute, Sumeet Gajanan Banerjee, Avijit Nikolakopoulos, George Robotics and AI research group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper introduces a novel, computationally efficient, random-search based path-planning algorithm specifically designed to enhance the dexterous accessibility of autonomous lunar rovers operating on highly cluttered obstacle-dense planetary surfaces. The proposed pathplanning framework employs a Quadtree-based cell decomposition to enhance the performance of sampling-based path-planning algorithms, specifically those in the category of Rapidlyexploring Random Trees (RRT) and its optimized variant, RRT*. In the process of generating the path through an occupancy grid map, the obstacle-free cells are utilized as discrete sampling points for the RRT* algorithm. A unique non-uniform sampling approach is employed to favour larger cells with a higher degree of safety, thereby enabling seamless navigation through the obstacle-dense map. Additionally, the non-uniform sampling approach enhances the overall computational efficiency of the framework, making it suitable for fast, lightweight onboard implementation. The efficacy of the proposed planning framework is demonstrated through numerous simulation scenarios. © 2025, American Institute of Aeronautics and Astronautics Inc, aiAA. All rights reserved.

关键词： Decision trees

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Multi-orthogonal-oppositional enhanced African vultures optimization for combined heat and power economic dispatch under uncertainty

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Neural Computing and Applications 2025年第8期37卷 6097-6123页

作者： Rizk-Allah, Rizk M. Snášel, Václav Hassanien, Aboul Ella Basic Engineering Science Department Faculty of Engineering Menoufia University Shebin El-Kom32511 Egypt Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Poruba-Ostrava70800 Czech Republic Faculty of Computers and AI Cairo University Giza Egypt

This paper attempts to conceptualize a potent methodology by combining the African vultures optimization algorithm (AVOA) with a multi-orthogonal-oppositional strategy (M2OS), named AVO-M2OS, to address the nonconvexity and multidimensional nature of the combined heat and power economic dispatch (CHPED) problem under both crisp and uncertainty aspects. The AVO-M2OS uses the M2OS to simultaneously explore the search region, improving solutions’ diversity as well as solution quality. Therefore, AVO-M2OS can perform deeper exploration and exploitation features and thus mitigate the trapping at local optima, especially when tackling the more complicated nature of the CHPED problem. A three-stage analysis is conducted to assess the effectiveness of the proposed AVO-M2OS algorithm. During the first stage, the algorithm’s performance is evaluated on benchmark problems such as CEC 2005 and CEC 2019, employing statistical verifications and convergence characteristics. In the second stage, the significance of the results is evaluated using the nonparametric Friedman test to demonstrate that the results did not occur by chance. The results indicate that the AVO-M2OS algorithm outperforms the best existing algorithm (AVOA) by an average rank of the Friedman test exceeding 26% for the CEC 2005 suite while outperforming the gray wolf optimization (GWO) by 60% for the CEC 2019 suite. Moreover, the AVO-M2OS demonstrates exceptional performance compared to existing state-of-the-art algorithms, surpassing the best algorithm available by an average rank of the Friedman test that exceeds 41%. Finally, the AVO-M2OS’s applicability is achieved by minimizing the operational costs by finding the optimal power and heat generation scheduling for the CHPED problem. The recorded results realize that the AVO-M2OS algorithm offers accurate performance compared to competing optimizers, where it saves the operational cost of the 48-unit system by 24% on the original AVO variant. Furthermore, the u

关键词： Quality control

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