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Classifying white blood cells using combining different convolutional neural networks

Multimedia Tools and Applications 2025年 1-24页

作者： Toptaş, Murat Toptaş, Buket Hanbay, Davut Software Eng. Dept. Engineering and Natural Science Faculty Bandırma Onyedi Eylül University Balıkesir Turkey Computer Eng. Dept. Engineering Faculty Inonu University Malatya44280 Turkey

White blood cells are warrior cells that protect the human body against external factors. Each of these warrior cells performs a distinct task, making every piece of information about them highly valuable in the medical field. In this article, a classification framework for the four known types of white blood cells is proposed. It is hoped that the classification of these types will contribute to the prediction of diseases such as AIDS, malaria, leukemia, and many others. In the proposed method, images of white blood cells from the Blood Cell Classification and Detection dataset were used as input to Convolutional Neural Networks. The feature vectors extracted using these Convolutional Neural Network architectures were combined into a single vector. A Minimum Redundancy Maximum Relevance algorithm was then employed to identify the most effective features within the feature vector. Experiments were conducted using these selected features, and the analysis of each experiment was reported in detail. The Support Vector Machines classifier achieved an accuracy of 98.63% in classifying white blood cell types by combining features from multiple deep learning architectures. The experimental results demonstrated that the features obtained from different layers of the Convolutional Neural Networks had varying impacts on the classification performance. © The Author(s) 2025.

关键词： Convolutional neural networks

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Construction of PCOS Prediction Model based on BP Neural Network 6

Construction of PCOS Prediction Model based on BP Neural Net...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Hu, Xianqing Yadav, Amit Khan, Asif Sah, Abhishek Pratap Azam, Sami School of Computer and Software Chengdu Neusoft University Chengdu China Faculty of Science and Technology Charles Darwin University NT Australia Department of Computer Application Integral University Lucknow India School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331522667

Polycystic ovary syndrome (PCOS), a common endocrine-metabolic disorder affecting about 10-13% of women during reproductive age worldwide, often leads to irregular menstruation, infertility, obesity, and long-term health risks such as diabetes and cardiovascular disease. In this paper, clinical data from PCOS patients and healthy control group were collected from ten hospitals containing 37 key indicators such as age, weight, BMI, menstrual cycle, etc. BP (Back Propagation) Neural Network prediction models were constructed and compared using the collected data and the cross-validation method was used for parameter tuning. It was found that BP Neural Network performed particularly well on the test set, and both demonstrated high prediction accuracy and generalization ability which provided strong evidence for the early identification and early valuable intervention opportunities for PCOS patients. © 2025 IEEE.

关键词： Prediction models

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ATM: Adaptive transformer model for reconstruction of remote sensing multi-temporal non-equispaced sequence imagery

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Journal of King Saud University - computer and Information sciences 2025年第4期37.0卷 1-13页

作者： Qi, Zhi-Peng Zhao, Zi-Yi Jin, Xing Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo 315211 China

Due to the influence of the imaging characteristics of the solar orbiting satellite and atmospheric conditions, the multi-spectral observation data often have the missing of phase image, which brings difficulties to the efficient and automatic processing of regional remote sensing data. However, the observation sequence contain complex and diverse information, it is difficult for the conventional method to characterize this information. To address this issue, we perform an adaptive transformer model (ATM) for reconstruction of remote sensing multi-temporal non-equispaced sequence imagery. To realize efficient information processing by dynamically adjusting the weight distribution of the internal relations of data, the core workflow of ATM is divided into three steps: feature coding, relationship evaluation and adaptive focusing. Firstly, the input data is encoded as a sequence of vectors, each representing a local feature of the data. Then, the model generates an attention score by calculating the similarity between the query and the key, quantifying the strength of the association between the different features. Finally, a context-aware feature representation is formed by using the normalized attention score weighted aggregation vector. Our experiments, conducted utilizing unmanned aerial vehicle (UAV) datasets and the Landsat-8 datasets, demonstrate that the proposed methodology achieves promising performance in terms of both precision (RMSE decreases by about 2.5 points, and SSIM increases by about 0.05 points) and effectiveness (time cost savings of about 50 %), and the proposed model has wide application prospects in intelligent agriculture, water color inversion and vegetation phenology detection. © The Author(s) 2025.

关键词： Adaptive transformer model Missing of phase image Multi-spectral observation data Non-equispaced sequence imagery

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DDT-Net:Deep Detail Tracking Network for Image Tampering Detection

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computers, Materials & Continua 2025年第5期83卷 3451-3469页

作者： Jim Wong Zhaoxiang Zang Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang443002China College of Computer and Information Technology ChinaThree Gorges UniversityYichang443002China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650504China

In the field of image forensics,image tampering detection is a critical and challenging *** methods based on manually designed feature extraction typically focus on a specific type of tampering operation,which limits their effectiveness in complex scenarios involving multiple forms of *** deep learningbasedmethods offer the advantage of automatic feature learning,current approaches still require further improvements in terms of detection accuracy and computational *** address these challenges,this study applies the UNet 3+model to image tampering detection and proposes a hybrid framework,referred to as DDT-Net(Deep Detail Tracking Network),which integrates deep learning with traditional detection *** contrast to traditional additive methods,this approach innovatively applies amultiplicative fusion technique during downsampling,effectively combining the deep learning feature maps at each layer with those generated by the Bayar noise *** design enables noise residual features to guide the learning of semantic features more precisely and efficiently,thus facilitating comprehensive feature-level ***,by leveraging the complementary strengths of deep networks in capturing large-scale semantic manipulations and traditional algorithms’proficiency in detecting fine-grained local traces,the method significantly enhances the accuracy and robustness of tampered region *** with other approaches,the proposed method achieves an F1 score improvement exceeding 30% on the DEFACTO and DIS25k *** addition,it has been extensively validated on other datasets,including CASIA and *** results demonstrate that this method achieves outstanding performance across various types of image tampering detection tasks.

关键词： Image forensics image tampering detection image manipulation detection noise flow Bayar

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Load Balancing in Cloud Computing based on Ant Colony Optimization and Crow Search Algorithm 27

Load Balancing in Cloud Computing based on Ant Colony Optimi...

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27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Alsheavi, Amar N. Wang, Xingfu Zhao, Wei Hawbani, Ammar Alhusaini, Naji Ahmed, Ibrahim Abdulrab Ismail, A.S. School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230027 China School of Computer Science and Technology Anhui University of Technology Anhui China School of Computer Science Shenyang Aerospace University Shenyang110136 China School of Computer and Information Engineering Chuzhou University Anhui Chuzhou239000 China Computer Department Applied College Najran University Najran66462 Saudi Arabia College of Life Sciences and Oceanography Shenzhen University Shenzhen518060 China Faculty of Science Zagazig University Zagazig44519 Egypt

ISBN: (纸本)9791188428137

With the rapid increase in cloud services and the increasing shift toward them, balancing the cloud load has become a critical research issue. The increasing demand from customers for technology services worldwide is largely due to its direct impact on performance quality. Therefore, to provide better service quality, it is essential to consider reducing response time and cost in load balancing. This paper addresses the load-balancing problem by optimizing response time and computing cost in cloud computing systems. Specifically, we first formulated the load balancing problem in cloud computing mathematically by designing an objective function that optimizes response time and cost, including constraints to ensure task assignment to a single virtual machine (VM) and resource limits are not exceeded. To solve this optimization problem, we proposed a hybrid algorithm, ACOCSA, that combines Ant Colony Optimization (ACO) and Crow Search Algorithm (CSA). We implemented the ACOCSA algorithm, evaluated the response time, cost, and load balancing metrics, and found that our algorithm performed better in terms of response time and computational cost, and showed good fairness among VMs. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Ant colony optimization

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Dysarthric speech recognition: an investigation on using depthwise separable convolutions and residual connections

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Neural Computing and Applications 2025年第12期37卷 7991-8005页

作者： Shahamiri, Seyed Reza Mandal, Krishnendu Sarkar, Sudeshna DeepNet Discovery Network Faculty of Engineering and Design University of Auckland Private Bag 92019 Building 405 Level 6 Room 669 3 Garfton Road Auckland1142 New Zealand Computer Science and Engineering Department Indian Institute of Technology Kharagpur India

Dysarthria is a neurological condition resulting from impairments affecting muscle control involved in speech articulation, leading to reduced intelligibility or unintelligible speech, which affects communication abilities. Although Automatic Speech Recognition (ASR) technologies hold the potential to improve the lives of people with dysarthria significantly, ASR systems designed for normal speech have shown limited effectiveness when presented with impaired speech. Consequently, researchers have focused on developing ASR systems specifically tailored for dysarthria. However, progress in this area has been gradual due to the scarcity of dysarthric speech for training and the increased variability of speech among dysarthric individuals, necessitating a larger dataset of dysarthric utterances. One potential solution to enhance the robustness of dysarthric ASR is to deepen the architecture of the acoustic model, which maps the speech signal to words or phonetic units. However, deeper architectures require more training data and pose challenges in dealing with issues such as the vanishing gradient problem and representational bottlenecks in deep learning models. In this study, we expanded on our previous findings and investigated the applications of Depthwise Separable Convolution neurons and the inclusion of Residual Connections to propose a deep dysarthric acoustic model, tackling both vanishing gradients and representational bottleneck issues in dysarthric ASR. Multiple speaker-adaptive dysarthric ASRs were trained and evaluated for 15 UA-Speech dysarthric subjects, then benchmarked against the state-of-the-art and our previous dysarthric ASRs. Our proposed architectures have delivered up to 22.58% word recognition rate (WRR) improvements over the reference models. We observed an average of 10.81% better WRRs over the base traditional dysarthric ASR for all speakers. Likewise, the proposed acoustic model outperformed the state-of-the-art Transformer-based dysarthric

关键词： Convolution

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Machine Learning Model for Wind Power Forecasting Using Enhanced Multilayer Perceptron

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computers, Materials & Continua 2025年第5期83卷 2287-2303页

作者： Ahmed A.Ewees Mohammed A.A.Al-Qaness Ali Alshahrani Mohamed Abd Elaziz Computer Science and Artificial Intelligence Department College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia Department of Computer Damietta UniversityDamietta34517Egypt College of Physics and Electronic Information Engineering Zhejiang Normal UniversityJinhua321004China Department of Mathematics Faculty of ScienceZagazig UniversityZagazig44519Egypt

Wind power forecasting plays a crucial role in optimizing the integration of wind energy into the grid by predicting wind patterns and energy *** enhances the efficiency and reliability of renewable energy *** approaches inform energy management strategies,reduce reliance on fossil fuels,and support the broader transition to sustainable energy *** primary goal of this study is to introduce an effective methodology for estimating wind power through temporal data *** research advances an optimized Multilayer Perceptron(MLP)model using recently proposedmetaheuristic optimization algorithms,namely the FireHawk Optimizer(FHO)and the Non-Monopolize Search(NO).A modified version of FHO,termed FHONO,is developed by integrating NO as a local search mechanism to enhance the exploration capability and address the shortcomings of the original *** developed FHONO is then employed to optimize the MLP for enhanced wind power *** effectiveness of the proposed FHONO-MLP model is validated using renowned datasets from wind turbines in *** results of the comparative analysis between FHONO-MLP,conventionalMLP,and other optimized versions of MLP show that FHONO-MLP outperforms the others,achieving an average RootMean Square Error(RMSE)of 0.105,Mean Absolute Error(MAE)of 0.082,and Coefficient of Determination(R^(2))of 0.967 across all *** findings underscore the significant enhancement in predictive accuracy provided by FHONO and demonstrate its effectiveness in improving wind power forecasting.

关键词： Wind power forecasting multilayer perceptron fire hawk optimizer non-monopolize search

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Real-World Implementation of Offline Model-Free Reinforcement Learning for Thermostat Control

Real-World Implementation of Offline Model-Free Reinforcemen...

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2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Ghane, Sara Elmaz, Furkan Jacobs, Stef Huybrechts, Thomas Verhaert, Ivan Mercelis, Siegfried Mannens, Erik University of Antwerp - imec IDLab - Faculty of Applied Engineering Antwerp Belgium University of Antwerp EMIB Faculty of Applied Engineering Antwerp Belgium University of Antwerp - imec IDLab Department of Computer Science Antwerp Belgium

ISBN: (纸本)9798331533892

This study introduces a model-free, offline Reinforcement Learning (RL) approach for optimizing the thermostat control in heating systems. Specifically, historical data from a real-world building was used to train the RL agent that is based on the Dueling Double Deep Q-Network (DQN) algorithm. The RL agent's objective is to balance thermal comfort with energy savings, by controlling indoor temperature set points. Traditional rule-based thermostat control methods often depend on fixed schedules and lack adaptability to varying conditions, resulting in energy inefficiencies. Training the RL model offline eliminates the requirement for live interaction, thereby preventing potential disruptions to occupant comfort. During evaluation, the RL model achieved an 18.66% reduction in energy use compared to a rule-based controller, while maintaining recommended indoor temperature levels. © 2025 IEEE.

关键词： Reinforcement learning

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Secure Chat System: Harnessing the Power of Hybrid Encryption for Enhanced Security and Confidentiality 12th

Secure Chat System: Harnessing the Power of Hybrid Encryptio...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Jerlin, M. Asha Shrivastav, Rochak Anusha, K. Devarajan, Ganesh Gopal Nagarajan, Senthil Murugan School of Computer Science and Engineering Vellore Institute of Technology Kelambakkam—Vandalur Rd Rajan Nagar Tamil Nadu Chennai600127 India Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Delhi NCR Campus Delhi - Meerut Road Modinagar Uttar Pradesh Ghaziabad201204 India Department of Mathematics Faculty of Science Technology and Medicine University of Luxembourg Esch-Sur-Alzette Luxembourg

ISBN: (纸本)9789819788354

Strong and secure communication solutions are essential in a time when data breaches and privacy violations are becoming more common. The extensive design for a secure chat system is presented in this research study with the goal of protecting user privacy and guaranteeing the accuracy of data shared. The fundamental idea of this design is hybrid encryption, which carefully combines the strength of Rivest–Shamir–Adleman (RSA) asymmetric encryption with the effectiveness of Advanced Encryption Standard (AES) symmetric encryption. The system also includes advanced cryptographic features like Diffie–Hellman Key Exchange for symmetric key exchange and secure communication channels, Elliptic Curve Digital Signature Algorithm (ECDSA) for message authenticity, and HMAC-SHA-512 for message authentication. The secure distribution of RSA public keys is facilitated by Public Key Infrastructure (PKI), which enhances the confidentiality and authenticity of communication channels. JSON Web Tokens (JWT) are used to provide authentication, offering a scalable and secure method for user verification throughout login procedures. Furthermore, the system incorporates a bot element that proactively alerts users to occurrences related to entry and leave from chat rooms, hence enhancing transparency and privacy consciousness among users. The suggested architecture provides a solid answer to the problems of secure communication in modern digital settings by fusing these cryptographic primitives and features. This architecture has undergone extensive testing and validation, proving its effectiveness in protecting sensitive data and providing users with secure, frictionless communication experiences. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Authentication

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Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

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International Journal of Advanced computer science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (science and Information Organization). All rights reserved.

关键词： Quantum cryptography

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