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A novel deep learning approach (Bi-xBcNet-96) considering green AI to discover breast cancer using mammography images

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Neural Computing and Applications 2024年第21期36卷 12701-12723页

作者： El-Mawla, Nesma Abd Berbar, Mohamed A. El-Fishawy, Nawal A. El-Rashidy, Mohamed A. Communication and Electronics Engineering Department Nile Higher Institute for Engineering and Technology Mansoura Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Shebeen El-Kom Egypt Department of Computer Arab East Colleges Riyadh Saudi Arabia

Clinical decision support systems (CDSSs) can effectively detect illnesses such as breast cancer (BC) using a variety of medical imaging techniques. BC is a key factor contributing to the rise in the death rate among women worldwide. Early detection will lessen its impact, which may motivate patients to have quick surgical therapy. computer-aided diagnosis (CAD) systems are designed to provide radiologists recommendations to assist them in diagnosing BC. However, it is still restricted and limited, the interpretability cost, time consumption, and complexity of architecture are not considered. These limitations limit their use in healthcare devices. Therefore, we thought of presenting a revolutionary deep learning (DL) architecture based on recurrent and convolutional neural networks called Bi-xBcNet-96. In order to decrease carbon emissions while developing the DL model for medical image analysis and meet the objectives of sustainable artificial intelligence, this study seeks to attain high accuracy at the lowest computing cost. It takes into consideration the various characteristics of the pathological variation of BC disease in mammography images to obtain high detection accuracy. It consists of six stages: identifying the region of interest, detecting spatial features, discovering the effective features of the BC pathological types that have infected nearby cells in a concentrated area, identifying the relationships between distantly infected cells in some BC pathological types, weighing the extracted features, and classifying the mammography image. According to experimental findings, Bi-xBcNet-96 beat other comparable works on the benchmark datasets, attaining a classification accuracy of 98.88% in DDSM dataset, 100% in INbreast dataset with 5.08% and 0.3% improvements over the state-of-the-art methods, respectively. Furthermore, a 95.79% reduction in computing complexity was achieved. © The Author(s) 2024.

关键词： computer aided diagnosis

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Adversarial-Learning-Based Taguchi Convolutional Fuzzy Neural Classifier for Images of Lung Cancer

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IEEE Access 2024年 12卷 72766-72776页

作者： Lin, Cheng-Jian Lin, Xue-Qian Jhang, Jyun-Yu National Chin-Yi University of Technology Department of Computer Science and Information Engineering Taichung41170 Taiwan National Taichung University of Science and Technology Department of Computer Science and Information Engineering Taichung40401 Taiwan

Deep learning technology has extensive application in the classification and recognition of medical images. However, several challenges persist in such application, such as the need for acquiring large-scale labeled data, configuring network parameters, and handling excessive network parameters. To address these challenges, in this study, we developed an adversarial-learning-based Taguchi convolutional fuzzy neural classifier (AL-TCFNC) for classifying malignant and benign lung tumors displayed in computed tomography images. In the framework of the developed AL-TCFNC, a fuzzy neural classifier replaces a conventional fully connected network, thereby reducing the number of network parameters and the training duration. To reduce experimental cost and training time, the Taguchi method was used. This method helps to identify the optimal combination of model parameters through a small number of experiments. The transfer learning of models across databases often results in subpar performance because of the paucity of labeled samples. To resolve this problem, we used a combination of maximum mean discrepancy and cross-entropy for adversarial learning with the proposed model. Two data sets, namely the SPIE-AAPM Lung CT Challenge data set and LIDC-IDRI Lung Imaging Research data set, were used to validate the AL-TCFNC model. When the AL-TCFNC model was used for transfer learning, it exhibited an accuracy rate of 89.55% and outperformed other deep learning models in terms of classification performance. © 2013 IEEE.

关键词： Fuzzy neural networks

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Adaptive neural network control of a 2-DOF helicopter system considering input constraints and global prescribed performance

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science China(Information sciences) 2024年第7期67卷 224-239页

作者： Zhijia ZHAO Jiale WU Zhijie LIU We HE C.L.Philip CHEN School of Mechanical and Electrical Engineering Guangzhou University School of Intelligence Science and Technology and Key Laboratory of Intelligent Bionic Unmanned Systems of Ministry of Education University of Science and Technology Beijing School of Computer Science and Engineering South China University of Technology Pazhou Lab

In this study, an adaptive neural network(NN) control is proposed for nonlinear two-degree-offreedom(2-DOF) helicopter systems considering the input constraints and global prescribed ***, radial basis function NN(RBFNN) is employed to estimate the unknown dynamics of the helicopter system. Second, a smooth nonaffine function is exploited to approximate and address nonlinear constraint functions. Subsequently, a new prescribed function is proposed, and an original constrained error is transformed into an equivalent unconstrained error using the error transformation and barrier function transformation methods. The analysis of the established Lyapunov function proves that the controlled system is globally uniformly bounded. Finally, the simulation and experimental results on a constructed Quanser's test platform verify the rationality and feasibility of the proposed control.

关键词： adaptive NN control 2-DOF helicopter global prescribed performance input constraints

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A Reinforcement Learning Based Approach to Partition Testing

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计算机科学技术学报（英文版） 2025年第1期40卷 99-118页

作者：孙昌爱肖明俊代贺鹏刘淮 School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Department of Computing Technologies Swinburne University of Technology Hawthorn Australia

Partition testing is one of the most fundamental and popularly used software testing *** first di-vides the input domain of the program under test into a set of disjoint partitions,and then creates test cases based on these *** by the theory of software cybernetics,some strategies have been proposed to dynamically se-lect partitions based on the feedback information gained during *** basic intuition of these strategies is to assign higher probabilities to those partitions with higher fault-detection potentials,which are judged and updated mainly ac-cording to the previous test *** a feedback-driven mechanism can be considered as a learning process—it makes decisions based on the observations acquired in the test ***,advanced learning techniques could be leveraged to empower the smart partition selection,with the purpose of further improving the effectiveness and efficiency of partition *** this paper,we particularly leverage reinforcement learning to enhance the state-of-the-art adaptive partition testing *** algorithms,namely RLAPT_Q and RLAPT_S,have been developed to implement the proposed *** studies have been conducted to evaluate the performance of the proposed approach based on seven object programs with 26 *** experimental results show that our approach outperforms the existing partition testing techniques in terms of the fault-detection capability as well as the overall testing *** study demonstrates the applicability and effectiveness of reinforcement learning in advancing the performance of software testing.

关键词： partition testing reinforcement learning temporal-difference learning intelligent software engineering

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Adaptive coupled-sliding-variable-based finite-time control of composite formation for multi-robot systems

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science China(Information sciences) 2024年第10期67卷 244-260页

作者： Xinru MA Hengyu LI Jun LIU Yueying WANG Shaorong XIE Jun LUO School of Mechatronic Engineering and Automation Shanghai University School of Mathematics and Computer Application Technology Jining University School of Computer Science Shanghai University College of Mechanical and Vehicle Engineering Chongqing University

This paper focuses on the finite-time control(FTC) of the composite formation consensus(CFC)problems for multi-robot systems(MRSs). The CFC problems are firstly proposed for MRSs under the complex network topology of cooperative or cooperative-competitive networks. Regarding the problems of FTC and CFC on multiple Lagrange systems(MLSs), coupled sliding variables are introduced to deal with the robustness and consistent convergence. Then, the adaptive finite-time protocols are given based on the displacement approaches. With the premised FTC, tender-tracking methods are further developed for the problems of tracking information disparity. Stability analyses of those MLSs mentioned above are clarified with Lyapunov candidates considering the coupled sliding vectors, which provide new verification for tender-tracking systems. Under the given coupled-sliding-variable-based finite-time protocols, MLSs distributively adjust the local formation error to achieve global CFC and perform uniform convergence in time-varying tracking. Finally, simulation experiments are conducted while providing practical solutions for the theoretical results.

关键词： composite formation time-vary tracking information disparity tender-tracking finite-time control nonlinear robots complex networks symmetry consensus

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SkinMultiNet: Advancements in Skin Cancer Prediction Using Deep Learning with Web Interface

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Biomedical Materials and Devices 2025年第1期3卷 621-637页

作者： Likhon, Md Nur Hosain Rana, Sahab Uddin Akter, Sadeka Ahmed, Md. Shorup Tanha, Khadiza Akter Rahman, Md. Mahbubur Nayeem, Md Emran Hussain Department of Computer Science and Engineering Dhaka International University Dhaka Bangladesh Department of Computer Science and Engineering Dhaka University of Engineering and Technology Gazipur Bangladesh Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh

Cancer remains the leading cause of death worldwide, significantly impacting individuals and healthcare systems alike. In recent decades, skin cancer has surged in prevalence compared to other major cancer types. Various factors such as texture, color, morphological characteristics, and structure are employed in categorizing different forms of skin cancer. However, traditional methods of identification often prove time-consuming and costly. Skin cancer classification predominantly relies on machine learning, with the primary method being convolutional neural networks (CNNs). Our ‘SkinMultiNet’ framework, presented in this study and based on transfer learning principles, integrates the InceptionV3 and Xception CNN models for predicting skin cancer using image data. While other machine learning models such as ResNet50, NasNet, and MobileNet were explored, the 'SkinMultiNet' framework demonstrated the most promising outcomes. Utilizing a publicly available dataset comprising 6086 skin images, we trained, tested, and evaluated our models extensively. Proposed system employed a train generator to feed image data into our deep learning CNN models, followed by implementing a learning rate reducer on the datasets within the model. Through rigorous testing and validation procedures, our models successfully processed a substantial volume of skin image data. In contrast to conventional approaches, our proposed architecture offers the potential for more reliable diagnoses, achieving an optimal accuracy rate of 94% in skin cancer prediction. This advancement holds promise for early detection and improved patient outcomes following therapy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural network Deep learning Django Skin cancer Web interface

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Advances in selective catalytic oxidation of ammonia (NH_(3)-SCO): A review of catalyst structure-activity relationship and design principles

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Chinese Chemical Letters 2024年第1期35卷 169-180页

作者： Zhao Li Chunxue Wang Junjun Qiu Yixing Ma Chi Wang Xin Sun Kai Li Ping Ning Fei Wang Faculty of Environmental Science and Engineering Kunming University of Science and TechnologyKunming 650500China National-Regional Engineering Center for Recovery of Waste Gases from Metallurgical and Chemical Industries Kunming 650500China Faculty of Chemical Engineering Kunming University of Science and TechnologyKunming 650500China

NH_(3) in ambient air directly leads to an increase in the aerosol content in the air. These substances lead to the formation of haze to various environmental problems after atmospheric circulation and diffusion. Controlling NH_(3) emissions caused by ammonia escaping from mobile and industrial sources can effectively reduce the NH_(3) content in ambient air. Among the various NH_(3) removal methods, the selective catalytic oxygen method (NH_(3)-SCO) is committed to oxidizing NH_(3) to environmentally harmless H_(2)O and N_(2);therefore, it is the most valuable and ideal ammonia removal method. In this review, the characteristics of loaded and core-shell catalysts in NH_(3)-SCO have been reviewed in the context of catalyst structure-activity relationships, and the H_(2)O resistance and SO2 resistance of the catalysts are discussed in the context of practical application conditions. Then the effects of the valence state of the active center, oxygen species on the catalyst surface, dispersion of the active center and acidic sites on the catalyst performance are discussed comprehensively. Finally, the shortcomings of the existing catalysts are summarized and the catalyst development is discussed based on the existing studies.

关键词： Ammonia Selective catalytic oxidation Active metals and supports Influence of gas composition Reaction mechanism Catalyst structure-activity relationship

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Traffic Rerouting Optimization Using Scheduling Algorithms

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SN computer science 2024年第6期5卷 1-12页

作者： Garg, Tanya Kaur, Gurjinder Rana, Prashant Singh Department of Computer Science & amp Engineering Sant Longowal Institute of Engineering & amp Technology Computer Science & amp Engineering Department Thapar Institute of Engineering & amp Technology

Traffic rerouting is a technique used to optimize traffic flow and reduce congestion by redirecting vehicles to alternate routes. The work done in this research focuses on a specific case scenario covering a 25 km distance with initially consisting of 25 red lights and the impact of increased number of red lights on average waiting time with the variable number of vehicles over different number of iterations using K-Fold cross-validation and variable speeds of the vehicles have been analyzed. The study compares the results for the average waiting time and shortest path length experienced by vehicles without any scheduling and after scheduling. The distance to be traveled successfully minimized, with values ranging from 734.36 min to 33.68 min for vehicles travelling with fixed speed of 40 km/h and 30.43 min from 500.34 min for variable speed vehicles in range of 30 km/h to 60 km/h using Adaptive Scheduling. In minimizing the distance traveled, the QoS Algorithm performed best by providing the shortest alternate route for both the fixed and variable speed of vehicles. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Average waiting time Delay QoS (Quality of Service) Scheduling algorithms Shortest distance Traffic lights Traffic rerouting

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Self-Learning Robot Manipulator Controller Using Reinforcement Learning

Self-Learning Robot Manipulator Controller Using Reinforceme...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Xhin, Ong Yu Jo, Hudyjaya Siswoyo Swinburne University of Technology Faculty of Engineering Computer & Science Sarawak Malaysia

ISBN: (纸本)9798350350821

Traditionally, robot control has relied on mathematical solutions, particularly inverse kinematics (IK), to determine the joint angles needed for a desired end-effector pose. However, these solutions face significant challenges as robots increase in complexity with higher degrees-of-freedom (DoF) and adaptability requirements. This work introduces an approach for controlling robot manipulators by addressing the IK problem using the artificial intelligence (AI) technique. In this paper, a deep reinforcement learning approach is proposed to solve the IK problem for a 6-DoF industrial robot manipulator. The Deep Deterministic Policy Gradient (DDPG) algorithm is employed to train an agent capable of learning the robot's motion, allowing for random initial and target positions and orientations. Several reward functions are implemented to enhance the accuracy of the robot's movement towards the desired position and orientation. This proposed approach demonstrates a high success rate and low mean error, outperforming other state-of-the-art methods. Furthermore, the approach is validated on a physical robot, confirming its practical applicability and effectiveness. © 2024 IEEE.

关键词： Deep reinforcement learning

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Clustering and Artificial Intelligence-based Prediction of Ecologically Sustainable Species Introductions

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第4期52卷 1159-1168页

作者： Liu, Shuqiao Zhang, Zhao Zhou, Hongyan Chen, Xue-Bo School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

There is a growing interest in sustainable ecosystem development, which includes methods such as scientific modeling, environmental assessment, and development forecasting and planning. However, due to insufficient survey data in many current development areas, development progress is delayed and stagnant. To address this situation, this paper proposes a SWOT-TOPSIS-K-Means (STK) data analysis and evaluation model to analyze ecological factors, which can realize a comprehensive and complete data analysis with fewer samples. Decision tree (DT), random forest (RF), and multilayer perceptron (MLP) neural network models were constructed from the results of this analysis, and statistical tests such as r-squared, mean absolute error, and cross-validation are used to further confirm the performance efficiency of the computational prediction models to provide real-time prediction research solutions. For this purpose, data from research scholars on species introduction in ecosystem development were selected for testing. The results show that the proposed assessment model and modeling results satisfy all accuracy-related acceptance requirements. Among them, MLP is better than DT and RF. In summary, the STK assessment model and the MLP prediction model can provide a basis for the selection and development of ecological factors. © (2025), (International Association of Engineers). All rights reserved.

关键词： Prediction models

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