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42nd Chinese Control Conference, CCC 2023

作者： Zhang, Xianlei Zhang, Yan Hu, Qing Yang, Anjie Li, Xuan College of Artificial Intelligence and Data Science Hebei University of Technology Tianjin300401 China Control Engineering Technology Innovation Center of Hebei Province Hebei University of Technology Tianjin300401 China

ISBN: (纸本)9789887581543

This paper studies the problem of trajectory tracking control for the wearable exoskeleton system subject to unknown dynamics. An ultra-local model is adopted to describe the dynamic relationship between control input and output. To achieve high-precision fast tracking, a new fixed-time control method is designed. Not only can the convergence time and tracking accuracy be given in advance, but also the complicated modeling process is avoided. A new performance function is constructed and the specified tracking accuracy is achieved within a given time. Through rigorous theoretical analysis, it can be concluded that all signals of the control system are fixed-time convergent, and the tracking error converges to a small neighborhood of the equilibrium within a fixed-time. Finally, the effectiveness of the control scheme is verified through a practical simulation example. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Exoskeleton (Robotics)

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Deep Learning IoT Malware Analysis: Investigation and Understanding

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Neural Computing and Applications 2025年 1-28页

作者： Abdullah, Muhammed Amin Yu, Yongbin Cai, Jingye Addo, Daniel Bankas, Edem K. Gu, Yeong Hyeon Alqahtani, Ali Al-antari, Mugahed A. School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu 611731 China School of Computing and Information Sciences C.K. Tedam University of Technology and Applied Sciences Navrongo Ghana Department of Artificial Intelligence and Data Science College of AI Convergence Daeyang AI Center Sejong University Seoul 05006 South Korea Center for Artificial Intelligence and Computer Science Department King Khalid University Abha 61421 Saudi Arabia

This empirical study explores the ability of lightweight convolutional neural networks (CNNs) for malware analysis in Internet of Things (IoT) environments, emphasizing the impact of input dimensionality and size on feature extraction quality. By converting malware binaries into 1D and 2D grayscale images, we systematically evaluate how varying input resolutions (e.g., 64 × 64 for 2D, 4096 for 1D) influence model performance, resource efficiency, and interpretability. Experiments on the Microsoft Malware dataset (MMD) and IoT Malware dataset (IMD) reveal that 1D CNN models outperform their 2D counterparts in accuracy, parameter efficiency, and training time, with gated recurrent unit (GRU) classifiers demonstrating superior performance across both dimensions. Post hoc visual interpretability techniques, including saliency maps and activation heatmaps, uncover critical feature patterns driving classification decisions, addressing the "black box" limitations of deep learning in cybersecurity. Our findings challenge the assumption that 2D models inherently excel in image-based tasks and provide actionable insights for designing resource-efficient IoT malware detection systems. By bridging the gap between model complexity and deployment feasibility, this work advances the application of explainable AI to protect IoT ecosystems against evolving cyber threats. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional Neural Networks (CNN) Deep learning Feature selection Internet of things Interpretability saliency maps Malware analysis Malware Images

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Heart disease prediction using machine learning, deep Learning and optimization techniques-A semantic review

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Multimedia Tools and Applications 2024年第39期83卷 86895-86922页

作者： Bhavekar, Girish Shrikrushnarao Das Goswami, Agam Vasantrao, Chafle Pratiksha Gaikwad, Amit K. Zade, Amol V. Vyawahare, Harsha Department of Artificial Intelligence & Data Science CSMSS Chh. Shahu College of Engineering Aurangabad431136 India Department of SENSE VIT-AP University Andhra Pradesh 522237 India Department of AI & DS CSMSS Chh. Shahu College of Engineering Maharashtra Aurangabad431136 India Department of Computer Science & Engineering GH Raisoni University Maharashtra Amravati444701 India Department of Computer Science & Engineering Sipna College of Engineering & Technology Maharashtra Amravati444701 India

Cardiovascular disease holds the position of being the foremost cause of death worldwide. Heart Disease Prediction (HDP) is a difficult task as it needs advanced knowledge with better experience. Moreover, it encounters numerous significant challenges in clinical data analysis. While many researchers have focused on predicting heart disease, the performance metric, namely prediction accuracy, remains suboptimal. The accurate HDP can help the person to prevent himself from life threats and at the same time, inaccurate prediction can prove to be fatal. To solve these issues, in this review work several Deep Learning (DL), Machine Learning (ML) and optimization based HDP techniques are discussed. In recent times, many researchers have been utilizing different DL and ML algorithms to help the professionals and health care industry for the prediction of heart disease. Further, it discussed about various optimization-based algorithms and its performance analysis. Therefore, this review paper suggests that the optimization-based HDP algorithm could assist doctors in predicting the occurrence of heart disease in advance and offering suitable treatment. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

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Indirect adaptive observer control (I-AOC) design for truck–trailer model based on T–S fuzzy system with unknown nonlinear function

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Complex and Intelligent Systems 2024年第5期10卷 7311-7331页

作者： Aslam, Muhammad Shamrooz Bilal, Hazrat Chang, Wer-Jer Yahya, Abid Badruddin, Irfan Anjum Kamangar, Sarfaraz Hussien, Mohamed Artificial Intelligence Research Institute China University of Mining and Technology Department of Automation University of Science and Technology of China Department of Marine Engineering of National Taiwan Ocean University (NTOU) Department of Electrical Computer and Telecommunications Engineering Botswana International University of Science and Technology Department of Mechanical Engineering College of Engineering King Khalid University Department of Chemistry Faculty of Science King Khalid University

Tracking is a crucial problem for nonlinear systems as it ensures stability and enables the system to accurately follow a desired reference signal. Using Takagi–Sugeno (T–S) fuzzy models, this paper addresses the problem of fuzzy observer and control design for a class of nonlinear systems. The Takagi–Sugeno (T–S) fuzzy models can represent nonlinear systems because it is a universal approximation. Firstly, the T–S fuzzy modeling is applied to get the dynamics of an observational system in order to estimate the unmeasurable states of an unknown nonlinear system. There are various kinds of nonlinear systems that can be modeled using T–S fuzzy systems by combining the input state variables linearly. Secondly, the T–S fuzzy systems can handle unknown states as well as parameters known to the indirect adaptive fuzzy observer. A simple feedback method is used to implement the proposed controller. As a result, the feedback linearization method allows for solving the singularity problem without using any additional algorithms. A fuzzy model representation of the observation system comprises parameters and a feedback gain. The Lyapunov function and Lipschitz conditions are used in constructing the adaptive law. This method is then illustrated by an illustrative example to prove its effectiveness with different kinds of nonlinear functions. A well-designed controller is effective and its performance index minimizes network utilization—this factor is particularly significant when applied to wireless communication systems. © The Author(s) 2024.

关键词： Adaptive observer control Feedback control design Indirect method Lyapunov–Krasovskii candidate T–S fuzzy models

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Enhancing Solar Energy Harvesting Efficiency: A Dual-Axis Solar Tracking System 4

Enhancing Solar Energy Harvesting Efficiency: A Dual-Axis So...

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4th International Conference on Innovative Practices in technology and Management, ICIPTM 2024

作者： Ravikumar, S. Eugene Berna, I. Vijay, K. Pradeep, R. Babu, R. R&d Institute of Science and Technology Vel Tech Rangarajan Dr.Sagunthala Department of Computer Science and Engineering Chennai India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Erode India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Faculty of College of Engineering and Technology Department of Computational Intelligence Kattankulathur Chennai India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307757

The photovoltaic effect has permanently transformed the landscape of renewable sources by making it possible to convert solar energy into useable power. This concept is based on solar photovoltaic (PV) cells, but current PV technology limits us to capturing just a small percentage of the sun's energy at any given time. However, productivity can be greatly increased by using solar monitoring equipment. Creating and validating a low-priced active dual-axis solar tracking system is a breeze if you follow the steps outlined in this article. The best orientation for solar panels is perpendicular to the sun because their production is directly related to the amount of light they receive. Conventional solar panels have trouble keeping up with the ever-changing ideal angle for sunlight absorption and energy gathering. By tracing the sun's movement across the sky, solar trackers can triple the effectiveness of solar panels. This technological advancement is expected to lead to an improvement in energy generation and overall efficiency. This state-of-the-art solar tracker is powered by LEDs and an Arduino Uno. These systems work together to follow the sun's motion and maintain the solar panels facing the appropriate way. To ensure that the solar panels receive the most amount of sunshine possible, the system will automatically adjust their angle. data processing and analysis in LabVIEW demonstrated the improved efficiency of the novel architecture. Researchers discovered a remarkable 13.44% boost in energy production compared to conventional fixed solar PV systems. © 2024 IEEE.

关键词： Solar panels

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Next-Generation Diabetes Care: Robust Adaptive Optimal Glucose Control Using Machine Learning, Wearable Integration and Microwave Sensors 4

Next-Generation Diabetes Care: Robust Adaptive Optimal Gluco...

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4th International Conference on Robotics, Automation, and artificial intelligence, RAAI 2024

作者： Mobayen, Saleh Rouhani, Seyed Hossein Ke, Ming-Ta Anvari, Shabnam Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Yunlin Douliou Taiwan National Kaohsiung University of Science and Technology Department of Electrical Engineering Kaohsiung City Taiwan Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Yunlin Douliou640301 Taiwan

ISBN: (纸本)9798331520038

Managing diabetes effectively is vital to prevent complications such as heart disease, nerve damage, and kidney issues. This research shows that weight control and tailored interventions can improve blood sugar and metabolic health. This paper reviews the microwave sensor for continuous, real-time glucose monitoring, designed for wearables like smartwatches. By integrating type-2 fuzzy-logic and machine learning, the sensors can offer accurate glucose readings and monitor additional physiological markers, optimizing personalized interventions for type-1 diabetes. This innovation will aim to advance diabetes management with reliable, user-friendly health monitoring. © 2024 IEEE.

关键词： Microwave sensors

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Genetic Programming with Co-operative Co-evolution for Feature Manipulation in Basal Cell Carcinoma Identification 28th

Genetic Programming with Co-operative Co-evolution for Fea...

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28th European Conference on Applications of Evolutionary Computation, EvoApplications 2025, held as part of EvoStar 2025

作者： John, Taran Cyriac Ul Ain, Qurrat Al-Sahaf, Harith Zhang, Mengjie Centre for Data Science and Artificial Intelligence Victoria University of Wellington P.O. Box 600 Wellington6140 New Zealand College of Information Technology Engineering Al-Zahraa University for Women Karbala Iraq

ISBN: (纸本)9783031900617

As global mortality rates rise alongside an increasing incidence of skin cancer, particularly Basal Cell Carcinoma (BCC), the development of effective automated detection strategies gains urgency. Traditional diagnosis of BCC relies heavily on manual inspection of skin lesions, an approach limited by subjectivity, time constraints, and the invasive nature of biopsy procedures. To address these challenges, this study introduces the two-stage cooperative co-evolution (2SCC)-Criptor, a novel two-stage feature manipulation method that employs genetic programming and co-operative co-evolution for automated BCC identification. The first stage generates an ensemble of models that collaboratively extract discriminative features from decomposed colour channels of skin lesion images, while the second stage constructs additional features to enhance classification performance. The efficacy of the method is evaluated using standard machine learning classifiers, demonstrating statistically significant superiority over traditional and contemporary approaches in the field. Further analysis reveals that the model’s success stems from the synergistic integration of colour channels rather than individual channel contributions, with remarkably uniform utilisation of LAB colour space components. The findings underscore 2SCC-Criptor’s potential in enhancing BCC diagnostic processes while maintaining interpretability through evolved genetic programming individuals. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Genetic programming

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Harnessing QGIS and Random Forest Analysis for Precision Storm Drain Reinforcement

Harnessing QGIS and Random Forest Analysis for Precision Sto...

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Emerging Research in Computational science (ICERCS), International Conference on

作者： Ramya Ramachandran Arthi D Sudha D Abhirami B Anil Aravind Babu Haripriya MD Artificial Intelligence and Data Science KCG College of Technology Chennai India Department of Computer Science and Engineering KCG College of Technology India Artificial Intelligence and Data Science KCG College of Technology Science Chennai India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

Amidst the backdrop of increased urbanization and shifting climate patterns, this research delves into the growing vulnerability of landscapes to floods. The focus is on storm drain placement, emphasizing the identification of suitable locations and the need to reinforce drainage networks. The analysis takes into account parameters linked to flood sensitivity, including total rainfall, decadal growth rate, and soil types in each district within a state. Leveraging Geographic Information Systems (GIS) and relevant Coordinate Reference Systems (CRS), the study integrates a diverse array of datasets to inform precise cartographic projections. Employing a Random Forest Classifier, the study dissects the influence of these parameters on storm drain placement. The findings offer valuable insights for decision-makers to improve stormwater management systems, underscoring the importance of ongoing maintenance. Overcoming the challenge of obtaining official government data for recommendations, the research advocates the adoption of new technologies and data integration methods. By combining various data sources, such as topography, rainfall patterns, historical flood records, land use, and infrastructure data, it lays the groundwork for more advanced urban water management strategies, fortifying cities against flooding.

关键词： Rain Storms Soft sensors Urban areas Government Soil Stormwater Maintenance Floods Random forests

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Deep Learning for Predictive Modeling of Cardiovascular Disease: Optimizing Neural Networks 8th

Deep Learning for Predictive Modeling of Cardiovascular Dise...

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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Hossain, Mohammad Alamgir Amin, Sherif Tawfik Eishah, Hassan Abu Hamdan, Alfadil Ahmed Metwally, Abdelnasser Abdelwakil Limkar, Suresh Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of Artificial Intelligence and Data Science AISSMS Institute of Information Technology Maharashtra Pune India

ISBN: (纸本)9789819765805

Cardiovascular diseases (CVD) are a prominent contributor to illness and death on a global scale, underscoring the need for precise predictive models to facilitate timely intervention. The present study investigates the utilization of deep learning methodologies, specifically Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM), in the context of predictive modeling of cardiovascular diseases. This study examines the efficacy of three well-known optimization techniques, namely Adam Optimization, RMSprop, and Stochastic Gradient Descent (SGD), within the framework of these neural network architectures. Among the various models based on Convolutional Neural Networks (CNNs), Stochastic Gradient Descent (SGD) has been identified as the optimizer that produces the most favorable outcomes for predicting CVD. The utilization of this optimization technique demonstrated exceptional efficacy in the training of the deep neural network, resulting in superior levels of accuracy, sensitivity, and specificity. On the other hand, it was observed that LSTM-based models exhibited the greatest improvement when utilizing RMSprop optimization. The utilization of RMSprop has been found to have a positive impact on the effectiveness of sequence modeling, resulting in enhanced predictive capabilities for assessing the risk of cardiovascular disease. The efficacy of this technique was demonstrated in its ability to capture temporal dependencies within the dataset, consequently enhancing the predictive capability of the model. The results of this study emphasize the importance of carefully choosing neural network architectures and optimization techniques when constructing predictive models for cardiovascular disease. Customizing the selection of neural network architecture and optimization algorithm according to the unique attributes of the dataset can substantially augment the precision and dependability of CVD risk evaluations. This, in turn, can ultimately lead t

关键词： Risk assessment

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Region of Interest Detection in Thermal Image of DC Motor Using Image Processing for Average Temperature Calculation 7th

Region of Interest Detection in Thermal Image of DC Motor Us...

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7th International Conference on Electrical, Control, and Computer engineering, InECCE 2023

作者： Faudzi, Ahmad Afif Mohd Shukri, Tuan Nur Atikah Tuan Ibrahim, Mohd Zamri Shaharum, Syamimi Mardiah Rasid, Mohd Azri Hizami Faculty of Electrical and Electronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pekan26600 Malaysia Centre of Excellence for Artificial Intelligence and Data Science Universiti Malaysia Pahang Al-Sultan Abdullah Pekan26600 Malaysia Faculty of Manufacturing and Mechatronic Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pekan26600 Malaysia

ISBN: (纸本)9789819738502

Anomalies in temperature, such as excessive heat, often indicate potential equipment breakdown or malfunction. Therefore, it is crucial to implement preventive maintenance programs that closely monitor these variations. In motor thermal modelling, calculations involving the convection thermal coefficient are necessary. The coefficient relies on the average temperature of the motor surface, traditionally measured using a thermocouple. Alternatively, thermal imaging technology provides a non-contact and non-intrusive approach. However, using the predefined shapes provided by Testo IRSoft Software may include irrelevant areas, which can affect the accuracy of the average temperature calculation. To address this limitation, this paper proposes thresholding segmentation, an image-processing technique to segment only the relevant area of interest. By utilising this technique, the average temperature can be calculated more accurately. As a result, the average temperature of the segmented Region of Interest (ROI) is higher than the fixed shape ROI (IRSoft Tool). The average temperature difference is between 1.06 and 11.34%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Preventive maintenance

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