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computer Vision, Image and Deep Learning (CVIDL), International Conference on

作者： Yongxiang Wei Xin Pan School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin China School of Computer Technology and Engineering Changchun Institute of Technology Changchun China School of Computer Technology and Engineering Changchun Institute of Technology Changchun Jilin China

ISBN: (数字)9798350373820

ISBN: (纸本)9798350373837

Agriculture is one of the main economic pillars in many countries and regions, contributing significantly to the stable development of the national economy. However, the growth rate of agriculture is currently declining rapidly, and plant diseases and pests are increasingly prevalent due to various factors such as climate and pollution. In response to the detection and recognition of plant diseases and pests, we propose introducing a multi-channel BiFormer module into the basic YOLOv8 target detection algorithm, which can effectively improve recognition accuracy. In experiments, we tested our method using a cashew dataset, and the results showed that our method outperforms traditional methods in terms of precision (P), recall rate (R), and mean average precision (mAP), indicating its high effectiveness.

关键词： Deep learning YOLO Plant diseases Accuracy Target recognition Heuristic algorithms Crops

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Results From a Large-Scale IoT-based Intervention for Energy Saving and Sustainability Awareness via Behavior Change in 25 K-12 Schools in Europe

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IEEE Internet of Things Journal 2025年第12期12卷 19308-19325页

作者： Mylonas, Georgios Amaxilatis, Dimitrios Paganelli, Federica Chatzigiannakis, Ioannis Koulouris, Pavlos Falkouskaya, Yelizaveta Markopoulos, Panos Athena Research Center Industrial Systems Institute Patras26504 Greece Comp. Tech. Institute and Press "Diophantus" 26504 Greece Spark Works Ltd GalwayH91 AHR1 Ireland Univ. of Pisa Dpt. of Computer Science Pisa56127 Italy CNIT Florence50134 Italy Sapienza University of Rome Dpt. of Computer Control and Management Engineering 00185 Italy Ellinogermaniki Agogi Athens Greece Eindhoven University of Technology AI Systems Institute Industrial Design Noord-Brabant Eindhoven5612 AE Netherlands

The unfolding climate crisis has resulted in a rising interest for increasing sustainability awareness and achieving energy savings worldwide. Several interventions within educational environments have been aimed at mobilizing younger audiences towards such goals. However, most of the interventions carried out so far are based on a subset of possible tools (e.g., IoT monitoring, gamification) and offer "all or nothing"approaches, which do not cope well with the uniqueness of schools and the need to adapt interventions to specific contexts (e.g. school size, building, curriculum, teacher involvement). We present a large-scale intervention in 25 school buildings in Europe over the course of two school years. Our intervention was based on the installation of a relatively low-cost IoT infrastructure in schools, that produced real-time energy-related data from the school buildings involved, and the application of a strategy combining, in a flexible and customizable way, educational activities, monitoring tools, gamification, and competition to motivate behavior change and achieve energy savings. Overall, 2983 students and 204 educators were directly involved to a variable degree of participation. Our results indicate a positive overall result in terms of short-term energy savings in most schools involved, with an average reduction of 20-25% in the energy that could be affected by end-users, as well as a 32.9% increase after the intervention in students self-reporting good or very good sustainability awareness. These findings suggest that such interventions can be a valuable step towards educating young students regarding sustainability and towards sustainable schools, if designed and implemented properly. © 2014 IEEE.

关键词： Sustainable development Buildings Energy conservation Europe Meteorology Internet of Things Energy consumption Real-time systems Energy efficiency Psychology

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Optimizing Machine Learning Algorithms to Enhance Blood Donation for Liver Patients: A Case Study 7

Optimizing Machine Learning Algorithms to Enhance Blood Dona...

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7th IET Smart Cities Symposium, SCS 2023

作者： Mijwil, Maad M. El-Kenawy, El-Sayed M. Bala, Indu Aljanabi, Mohammad Abotaleb, Mostafa Ibrahim, Abdelhameed Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt School of Electrical and Electronics Engineering Lovely Professional University Punjab India Department of Computer Aliraqia University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

Blood is vital for transporting oxygen, nutrients, and hormones to all body parts as it circulates through arteries and veins. It removes carbon dioxide, regulates body temperature, and maintains the body's immune system. Individuals constantly need blood and its derivatives to save their lives and improve their health through medical treatments and surgical operations. Liver diseases are one of the diseases that affects the health of individuals and requires blood to continue living. These diseases cause significant damage to people's health, and early diagnosis plays a crucial role in saving lives. In this paper, machine learning algorithms (support vector machine and random forest) are involved in detecting liver diseases and determining whether donors are suitable to donate blood from blood values. This paper is applied research that found that the performance measures of the random forest algorithm achieved excellent performance in identifying suitable people to donate blood. © The Institution of engineering & Technology 2023.

关键词： Support vector machines

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BFuse-Net: Bonferroni Mean Operator-Aided Fusion of Neural Networks for Medical Image Classification 5th

BFuse-Net: Bonferroni Mean Operator-Aided Fusion of Neural ...

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5th International Conference on Medical Imaging and computer Aided Diagnosis, MICAD 2024

作者： Ghosh, Triyas Chakraborty, Soham Kaplun, Dmitrii Gulvanskii, Vyacheslav Sarkar, Ram Department of Computer Science and Engineering Jadavpur University Kolkata700032 India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

ISBN: (纸本)9789819638628

In this paper, we have proposed a novel model, called Bonferroni Mean Operator-aided Fusion of Neural Networks (BFuse-Net). Here, we have taken advantage of the capabilities of four deep learning models as the base learners and then applied a customized attention method to prioritize different fine-grained features by passing their outputs via four parallel pipelines. Next, we have employed a novel aggregation method, a modified Bonferroni Mean operator, to merge the decision scores acquired from every pipeline, prioritizing their individual contributions above their interactions with others in the decision scores. Three medical image datasets— LC25000 (colon cancer), Oral Squamous Cell Carcinoma, and Malaria Cell Images have been used for experimentation. The model yields state-of-the-art outcomes for the three datasets. The source codes and additional results can be found at GitHub repository. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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An AI-Driven Framework for Personalized Diet Generation and Nutrition Suggestions Using Machine Learning, computer Vision and NLP

An AI-Driven Framework for Personalized Diet Generation and ...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Jyoti Kanjalkar Pramod Kanjalkar Raj Khanke Rushikesh Mane Kunal Kharat Kanad Kolhe Department of Computer Science & Engineering (AIML) Vishwakarma Institute of Technology Pune India Department of Instrumentation & Control Engineering Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

To counteract unhealthy eating habits among the younger generation, a novel and economical AI-powered framework has been developed to serve as a virtual nutritionist and nutrition counsellor. The system uses machine learning, computer vision, and natural language processing to provide personalized nutrition plans via an easy-to-use online application. It generates tailored meal plans that consider user preferences, allergies, and meal times by calculating Body Mass Index (BMI) based on user input. In addition to blog links and tailored training regimens (enhanced by the LLM3 model trained on a Kaggle gym dataset), a feedback mechanism allows for continuous optimization. Real-time food classification uses VGG16 and ResNet deep learning models to detect damaged or harmful foods and send out alerts for unsuitable foods. Additionally, integration with Google Maps and KNN helps users locate nearby dietitians, fostering better access to professional guidance. These comprehensive improvements significantly contribute to maintaining a good diet.

关键词： Photography computer vision Wearable Health Monitoring Systems Visualization Web services Real-time systems Natural language processing User experience Satellite images Allergies

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Mixed Reality Robot Programming Using Hand Gestures

Mixed Reality Robot Programming Using Hand Gestures

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International Conference on Advanced Robotics and Intelligent Systems (ARIS)

作者： Kai-Tai Song Shih-Wei Chiu Institute of Electrical and Control Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

ISBN: (数字)9798350362572

ISBN: (纸本)9798350362589

In this paper, we present a hand gesture-based robot path generation system using mixed reality (MR) for interactive robot programming. A hand-gesture recognition scheme is proposed to recognize specific gestures for path generation and modification. A user can intuitively plan a virtual path by hand gestures in the actual working space and send the planned path to the robot controller to execute through the MR glasses. A human-machine interface is designed to allow a user to assign the required task and send control commands to the robot. To ensure the robot can accurately move along the planned path, a calibration procedure is designed to establish the relationship between the MR system and robot navigation system. The proposed method has been realized and verified using a TM5 cobot and HoloLens II. The experimental results show that the robot navigation error is within 2mm using the developed MR-based robot programming system.

关键词： Presses Navigation Mixed reality Virtual environments Glass Manipulators Path planning

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Modeling and Simulation of Two Axes Gimbal Using Fuzzy control

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computers, Materials & Continua 2022年第7期72卷 93-107页

作者： Ayman A.Aly Mohamed O.Elhabib Bassem F.Felemban B.Saleh Dac-Nhuong Le Department of Mechanical Engineering College of EngineeringTaif UniversityTaif21944Saudi Arabia Control and Mechatronics Division School of Electrical EngineeringUTMJohor Bahru81300Malaysia Institute of Research and Development Duy Tan UniversityDanang550000Vietnam School of Computer Science Duy Tan UniversityDanang550000Vietnam

The application of the guided missile seeker is to provide stability to the sensor’s line of sight toward a target by isolating it from the missile motion and *** main objective of this paper is not only to present the physical modeling of two axes gimbal system but also to improve its performance through using fuzzy logic controlling *** paper is started by deriving the mathematical model for gimbals motion using Newton’s second law,followed by designing the mechanical parts of model using SOLIDWORKS and converted to xml file to connect dc motors and sensors using MATLAB/***,a Mamdani-type fuzzy and a Proportional-Integral-Derivative(PID)controllers were designed using MATLAB *** performance of both controllers was evaluated and tested for different types of input *** simulation results showed that self-tuning fuzzy controller provides better performance,since no overshoot,small steady-state error and small settling time compared to PID controller.

关键词： Gimbal system self-tuning fuzzy proportional-integral-derivative(PID)control cross coupling

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Adversarial Training with Maximal Coding Rate Reduction

Adversarial Training with Maximal Coding Rate Reduction

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Asilomar Conference on Signals, Systems & computers

作者： Hsiang-Yu Chu Hongbo Zhao Markus Flierl Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Control Science and Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

Deep convolutional networks can solve various complex tasks in the field of image processing. However, adversarial attacks have been shown to have the ability of fooling deep learning models. Adversarial training is one commonly used strategy to improve the robustness of deep learning models against adversarial examples, which is performed by incorporating adversarial examples into the training process. Traditionally, during this process, cross-entropy loss is used as the loss function. In order to improve the robustness of deep learning models against adversarial examples, we propose in this paper two new methods of adversarial training by applying the principle of Maximal Coding Rate Reduction (MCR 2 ). We evaluate the performance of different adversarial training methods by comparing the clean accuracy and adversarial accuracy. It is shown that adversarial training with the MCR 2 loss function yields a more robust network than the traditional adversarial training method. In our experiments, adversarial accuracies are improved by up to 10%. The two loss functions are discussed by using a model.

关键词： Training Deep learning Hands Accuracy Image coding Computational modeling Robustness Data models Encoding Convolutional neural networks

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Predictive Cruise control for Connected Autonomous Vehicles Considering Communication Delay in Mixed Traffic

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IFAC-PapersOnLine 2024年第29期58卷 172-177页

作者： Xinyu Li Jin Zhang Jiaqi Xue Lin Qi Wenbin Tang Xiaohong Jiao Institute of Electrical Engineering Yanshan University Qinhuangdao China Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao 066004 Hebei Province China

The stochastic behaviors of human-driven vehicles (HDVs) and the communication delay of Vehicle-to-Everything (V2X) technique present challenges to the advancement of connected autonomous vehicles (CAVs) in mixed traffic comprising CAVs, uncertain numbers of HDVs and connected HDVs (CHDVs). Consequently, a predictive cruise control strategy based on V2X communication is proposed for CAVs in a mixed platoon. Firstly, a long-short-term memory (LSTM) network combined with a conditional linear Gaussian (CLG) model is adopted for speed prediction of the preceding vehicle considering the communication delay. Subsequently, the CAV's speed planning is conducted for passing continuous signalized intersections without stopping. Then, the planned speed is tracked in energy-efficient, safe, and comfortable manners under a stochastic model predictive control (SMPC) framework. The simulation results validate the effectiveness and advantages of the proposed strategy.

关键词： Connected autonomous vehicles Mixed traffic Communication delay Predictive cruise control

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Multi-view stereo based on adaptive expansion propagation

Multi-view stereo based on adaptive expansion propagation

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computer Information Science and Application Technology (CISAT), International Conference on

作者： Zixuan Nie Haohai Fu School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin China School of Computer Technology and Engineering Changchun Institute of Technology Changchun China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

Through computer vision and image processing techniques, a set of images from a scene can be reconstructed in 3D to recover a 3D model of the scene, in which dense reconstruction is a crucial part, and most existing algorithms cannot effectively recover weak texture regions in dense reconstruction. In response to this issue, this article proposes a hypothesis propagation strategy based on adaptive extension. In the hypothesis propagation stage of PatchMatch MVS, an adaptive mechanism for region propagation is introduced, and an update counter is set to regulate the expansion of the sampling area. This ensures that there are enough excellent hypotheses for sampling in this algorithm iteration, and the quality of sampling hypotheses is not limited by fixed sampling pixel positions, thereby improving the probability of excellent hypotheses being sampled in the sampling space. It is demonstrated in the ETH3D dataset that our method reconstructs models with higher integrity, and fewer voids and smoother surfaces in the reconstruction of weakly textured regions.

关键词： Point cloud compression Solid modeling Adaptation models Surface reconstruction Information science Three-dimensional displays Computational modeling Surface texture Image reconstruction Optimization

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