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Evolutionary Algorithms for Optimizing Wind Turbine Blade Design 2

Evolutionary Algorithms for Optimizing Wind Turbine Blade De...

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2nd International Conference on IT Innovations and Knowledge Discovery, ITIKD 2024

作者： Nair, Rekha R Babu, Tina Kishore, S. Kumar, K. Nithin Vardhan, R. Guna Alliance School of Advanced Computing Department of Computer Science and Engineering Alliance University Bengaluru India Nitte Institute of Professional Education Department of Computer Applications Nitte Deemed to Be University Mangalore India

ISBN: (纸本)9798350355468

Wind turbine blade design is a complex multi-objective optimization challenge, requiring a balance between aerodynamic efficiency, structural integrity, and cost minimization. This study proposes a hybrid optimization framework combining Genetic Algorithms (GA) for global exploration and Differential Evolution (DE) for local refinement. The hybrid GA-DE approach accelerates convergence and enhances solution quality by efficiently navigating the large design space. Experimental results show up to a 20% reduction in structural loads while maintaining high energy output, with a 15% faster convergence rate compared to traditional single-algorithm methods. The optimized blade designs achieved improved aerodynamic performance and reduced material costs, offering a more practical solution for next-generation wind energy systems. This research demonstrates the potential of hybrid evolutionary algorithms to drive innovation in renewable energy by delivering cost-effective and high-performance turbine blades, particularly for larger turbines requiring enhanced durability and power output. © 2025 IEEE.

关键词： Aero-dynamic Efficiency Differential Evolution (DE) Evolutionary Algorithms Genetic Algorithm (GA) Hybrid Optimization Multi-Objective Optimization Wind Turbine Blade Optimization

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Automated Detection of Diabetic Retinopathy using Deep Learning in Retinal Fundus Images: Analysis 3

Automated Detection of Diabetic Retinopathy using Deep Learn...

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3rd International Conference on Communication, Computing and Industry 4.0, C2I4 2022

作者： Manjushree, R. Bhoomika, D. Nair, Rekha R Babu, Tina School of Engineering Department of Computer Applications Dayananda Sagar University Bengaluru India School of Engineering Department of Computer Science and Engineering Dayananda Sagar University Bengaluru India

ISBN: (纸本)9781665474979

Diabetes frequently results in diabetic retinopathy (DR), which develops retinal lesions that impair vision. It can cause blindness if not caught in time. Treatments simply maintain vision because DR is an irreversible process. The risk of blindness can be considerably decreased with early DR detection and treat- ment. The traditional diagnosis of DR retinal fundus pictures by an ophthalmologist is time-consuming, labor-intensive, expensive, and prone to error in comparison to computer-assisted diagnostic techniques Recent advances in deep learning have propelled them to the top of the list of the most widely used methods. Deep learning is particularly effective at classifying and analysing medical *** neural networks, a more common and effective deep learning technique, handle medical images very effectively. The proposed model makes use of Inception V3which is Convolutional Neural Network that provides accuracy of 93% which is the highest accuracy when compared to AlexNet, DenseNet121, RestNet50 and EfficientnetBO in detecting diabetic retinopathy. © 2022 IEEE.

关键词： AlexNet DenseN et121 EfficientNetB0 InceptionV3 RestN et50

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Deep Learning Technique for computer Vision-Based Pose Estimation for Augmented Reality 3rd

Deep Learning Technique for Computer Vision-Based Pose Estim...

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3rd International Conference on Intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

作者： Palanimeera, J. Ponmozhi, K. Jyothi, Kanagaraj Dargar, Shashi Kant Birla, Shilpi Department of Computer Applications Sathyabama Institute of Science and Technology Chennai India Department of Computer Applications Kalasalingam Academy of Research and Education Krishnan Kovil Tamil Nadu Virudhunagar India Department of Biotechnology Kalasalingam Academy of Research and Education Krishnan Kovil Tamil Nadu Virudhunagar India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnan Kovil Tamil Nadu Virudhunagar India Department of Electronics and Communication Engineering Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9789819784288

Pose estimation approaches of open pose were suggested. Pose estimation in real-time is a difficult research subject with many computer vision applications. Pose estimation applications are Human Activity Estimation, Motion Transfer and Augmented Reality, Training Robots, Motion Tracking for Consoles, and Human Fall Detection. This paper describes the Augmented Reality (AR) of pose estimation. AR is an enhanced version of the real physical world that is achieved through the use of digital visual elements, sound, or other sensory stimuli delivered via technology. One interesting use for obtaining the Open-Pose discovered important locations in each pose is in the field of augmented reality. A technique for 3D model overlays on an image using pose key point information that is presented in this study. To extract crucial information from the data, the fully convolutional networks deep learning technique is used. The technique used in this work comprises the real-time intersection of the picture plane with the intended 3D model and accurate model orientation based on the key point data. The performance will greatly depend on the Graphics Processing Unit specifications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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Implementing Machine Learning for Early Detection and Prognostic Modeling of Chronic Diseases

Implementing Machine Learning for Early Detection and Progno...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Iqbal, J. L. Mazher Gurrapu, Omprakash Saritha, P.S. Rajesh Kanna, R. Jayanthi, R. Tejaskumar, M.B. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi India Volvo Trucks NC United States Dhanalakshmi Srinivasan College of Engineering Department of Artificial intelligence and data science Coimbatore India Christ University Department of Computer Science Bangalore India Dayananda Sagar College of Engineering Department of Master of Computer Applications Bengaluru India Don Bosco Institute of Technology Bangalore India

ISBN: (纸本)9798331538538

The employ of deep learning methods for the diagnosis and prognosis model of chronic diseases is an important discovery to change the healthcare service. Some of the chronic diseases which prevalence and incidence rates remain high globally include diabetes, cardiovascular diseases, chronic kidney diseases, and cancers. There is nothing more critical than early diagnosis and accurate prediction of the patients' condition and the best course of action that has to be taken. This paper aims at examining the possibility of utilizing ANN, Random Forest, XGBoost, and CNN to forecast the occurrence of the. Due to integration of big and varied data which involve clinical characteristics, biochemical parameters and medical images among others, ML models have the ability recognize complex relations not easily recognizable by conventional diagnostic procedures. These illustrations prove that deep learning models or more specifically the convolutional neural networks for image diagnosis outperform other traditional methods in performance and prognosis. Nevertheless, some issues, such as data quality, model's interpretability, and its implementation into clinical practice, are still present. The challenges appeared in this paper are key to understanding the future of ML in healthcare as they can pave the way to the integration of such models into practice, therefore leading to early detection, better prognosis, and effective management of chronic diseases. This paper aims at exploring on how ML can be of significance in transformation of the health care sector and orderly improve patients care. © 2025 IEEE.

关键词： Diseases

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Efficient Software Vulnerability Detection with Minimal Data Size in 5G-IoT 1

Efficient Software Vulnerability Detection with Minimal Data...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Kushwaha, Sumit Asha, V. Kumar, B Santhosh Singh, Navdeep Prabagar, S. Supriya, B Yamini University Institute of Computing Chandigarh University Department of Computer Applications Punjab India New Horizon College of Engineering Department of Computer Applications Bangalore India Institute of Aeronautical Engineering Hyderabad India Lovely Professional University Phagwara India Alliance University Alliance College of Engineering and Design Department of Computer Science and Engineering Karnataka Bangalore562106 India Koneru Lakshmaiah Education Foundation Department of Cse AP Vaddeswaram India

ISBN: (纸本)9798350359756

This research addresses the security challenges arising from the widespread implementation of Internet of Things (IoT) networks in the industry, a situation exacerbated by the integration of artificial intelligence (AI) and 5G mobile communication technologies. While these IoT networks enhance industrial efficiency and convenience, their vulnerability to security breaches is heightened due to nonstandard protocol stacks and open-source software that lacks thorough vetting. Traditional AI-based vulnerability detection methods, with their high memory and computational demands, are unsuitable for IoT applications. In response, this study introduces a novel approach utilizing the Sampling Threshold Optimization Strategy. This method maximizes the training data size while preserving learning accuracy, enabling swift identification of software vulnerabilities in IoT systems. Experiments conducted on a software vulnerability classification dataset reveal that the effectiveness of the models is not significantly impacted by variations in ideal data sizes. Notably, the research establishes the minimum amount of data required to train a model without compromising performance. For instance, employing the random forest model with the sampling threshold optimization technique resulted in a 97.9% improvement in latency and conserved 85.2% of memory, 1% of the data while still achieving learning accuracy that is on par with 100% of the data. This innovative method provides a practical means to enhance the security of IoT networks without the need for extensive processing power. © 2023 IEEE.

关键词： Internet of things

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A Detailed Investigation into the Brain Cancer Detection Capabilities of CNN

A Detailed Investigation into the Brain Cancer Detection Cap...

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International Health Informatics Conference, IHIC 2023

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Choudhury, Tanupriya Kotecha, Ketan School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering and Applications GLA University UP Mathura India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun248007 India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International University Lavale Campus Pune India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune411045 India

ISBN: (纸本)9789819771899

Brain tumour detection is an accepted testing task in the beginning phases of life. Yet, presently it has progressed with different AI calculations. Along these lines, to identify the brain tumour of a patient we consider the data of patients like MRI pictures of a patient’s brain. Here our concern is to spot if the tumour is available in persisting’s brain. It’s crucial to identify the tumour at the beginning level for a sound lifetime of a patient. During this task, we gauge the seriousness utilizing Convolutional Neural Network calculationwhich gives us precise outcomes. Tumour division is a pivotal and burdensome assignment inside the territory of clinical picture preparation as a human-helped manual characterization may bring about erroneous expectations and findings. We suggested using convolutional neural organization to distinguish tumours from 2D appealing reverberation brain images (MRI). The exploratory analysis was performed on a dataset that included tumours of various sizes, regions, forms, and picture forces. We used convolutional neural networks, which are implemented using Keras and Tensorflow because they respect a much better display than standard ones. CNN achieved an accuracy of 83.54% in our research. The recently developed CNN design may be used as a productive decisionaid device for radiologists in clinical diagnostics because of its high speculation capacity and quickness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Neuroimaging

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Humanoid Robots that Move on Two Feet and Are Led by Vision Systems 3

Humanoid Robots that Move on Two Feet and Are Led by Vision ...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Kumar, Santosh Yadav, Satya Prakash School of Computing Graphic Era Hill University Dehradun India Gla University Department of Computer Engineering & Applications UP Mathura India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Jaipuria Institute of Management Department of Management Jaipur302033 India Glbitm Department of Computer Science and Engineering UP Greater Noida India

ISBN: (纸本)9798350342338

At the convergence of robotics and computer vision lies a crucial research field that focuses on the development of vision-guided walking for humanoid robots. The ability of humanoid robots to walk independently while simultaneously seeing and comprehending their surroundings via the use of visual inputs offers tremendous potential. Humanoid robots are developed to reproduce human-like qualities and skills. This abstract discusses current developments in the area of vision-guided walking for humanoid robots as well as the approaches that have been used. Through the use of computer vision methods in conjunction with walking algorithms, humanoid robots are given the capacity to traverse situations that are both complex and dynamic while exhibiting increased flexibility. For this, real-time sensing of the environment, object identification and avoidance of obstacles, as well as analysis of the terrain, are required in order to arrive at intelligent judgements while moving. Vision-guided walking not only makes robotic locomotion more secure and effective, but it also paves the way for a wide range of potential applications, including exploration, human-robot interaction, search and rescue, and many more. Visual sensing systems, sensor fusion procedures, gait generation methods, motion planning, and control strategies. These topics are discussed in relation to vision-guided walking. Different types of sensors, such as cameras, depth sensors, and LiDAR, are being investigated to see how they can best provide a holistic understanding of the surrounding environment. In addition, the abstract draws attention to the difficulties caused by sensor noise, occlusions, fluctuations in illumination, and the need for real-time processing. In places an emphasis on the role that methods of machine learning play in the improvement of vision-guided walking. The use of deep learning algorithms gives robots the ability to learn from data, hence enhancing their capacity to understand and r

关键词： Head movement in robot Humanoid robots Planning and control Proprioceptive sensors Sensors Vision guided

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Graph Encoding-Enhanced Transformer for Drug Recommendation

Graph Encoding-Enhanced Transformer for Drug Recommendation

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Cai, Xunsheng Thamrin, Syauki Aulia Chen, Arbee L.P. Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Asia University Department of Computer Science and Information Engineering Taichung Taiwan

ISBN: (纸本)9798350324457

Doctors prescribe drugs for the patient with the objective of curing the patient. Some drugs cannot be consumed together since doing so may cause negative effects. This can be avoided by knowing the effects caused by consuming combinations of drugs. However, for complex cases of a patient, it can be difficult to decide the best combination of drugs. Therefore, automatic drug recommendation method was used to recommend drugs with minimal negative effects. It is performed by using a deep learning model which is trained on drug data. A graph called drug-drug interaction (DDI) is used to represent the drugs and effects of consuming one drug with other drugs. Additionally, information about the combination of drugs prescribed in the past for a patient is also important for drug recommendation. It can also be represented as a graph called drug concurrence relation (DCR). The DDI and DCR graphs can be input to the deep learning model through an encoding process. In this paper, we propose a graph encoding-enhanced transformer (GEET) to recommend drugs. The DDI and DCR graphs are encoded by using Graph Attention Network (GAT). The graph encoding model has multi-head attention, which makes the GEET model aware of the most important DDI and DCR from the graphs. Additionally, the encoding outputs are combined, and activation function and normalization methods are used to improve the performance. The model has been evaluated on the publicly available MIMIC-III dataset and has the best results on F1, Jaccard and PRAUC scores compared to the models proposed by the existing related research papers. © 2023 IEEE.

关键词： drug concurrence relation drug graph encoding Drug recommendation drug-drug interaction graph attention network transformer

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A Comparative Study of Deep Learning Methods for Short-Term Solar Radiation Forecasting 3rd

A Comparative Study of Deep Learning Methods for Short-Term ...

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3rd International Conference on Flexible Electronics for Electrical Vehicle, FlexEV 2022

作者： Singh, Praveen Kumar Saraswat, Amit Gupta, Yogesh Goyal, Sunil Kumar Gupta, Yeshpal Department of Computer Applications Manipal University Jaipur Rajasthan Jaipur India Department of Electrical Engineering Manipal University Jaipur Rajasthan Jaipur India Department of Computer Science BML Munjal University Gurugram India Lincus Inc. TempeAZ United States

ISBN: (纸本)9789819947942

Energy and climate challenges have led to significant growth in solar power generation, and because of this, smart grids are increasingly utilizing solar power. The solar power is intermittent because solar energy is mainly dependent on radiation and other atmospheric factors. To ensure the reliable economic operation of micro grids and smart grids, precise forecasting of solar radiation is crucial. Deep learning techniques are proposed in this study to model the solar radiation production. Four artificial intelligence and deep learning-based forecasting models, the fully connected artificial neural network (ANN), the convolutional neural network (CNN), the long short-term memory network (LSTM), and the bidirectional neural network (Bi-LSTM) were examined for this study. To compare the prediction accuracy of all models, three performance evaluation metrics RSME, MAE, and R2 are used. The results obtained indicate that all four methods produce reasonable estimates of solar radiation generation. As a result of RMSE, MSE, and R2, the Bi-LSTM forecasting model offers the best estimate of forecasting accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024

关键词： Convolutional neural network Fully connected Artificial Neural Network Bi-Directional Neural Network Long short-term memory Solar power Solar radiation

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The Efficient Traffic Sign Detection and Recognition for Taiwan Road Using YOLO Model with Hybrid Dataset 9

The Efficient Traffic Sign Detection and Recognition for Tai...

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9th International Conference on Applied System Innovation, ICASI 2023

作者： Suwattanapunkul, Taweelap Wang, Lung-Jen National Pingtung University International Master Program in Information Technology and Applications Taiwan National Pingtung University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350398380

According to the trend of worldwide car sales have grown up, this cause may increase accidents on the road due to human error. The self-driverless car has been developed to solve this problem. One task of the self-driverless car is traffic sign detection and recognition (TSDR), which will help drivers notify the traffic sign installed on the road in advance. Taiwan roads have specific traffic signs, and no Taiwan traffic sign public dataset is available. In this paper, our proposed object detection method was experimentally performed using YOLOv5s6 and YOLOv8s models on three different datasets, as Tsinghua-Tencent 100K (TT100k), the self-created Taiwan traffic sign (TWTS), and the hybrid dataset, which combine the traffic scenes between TT100k and TWTS dataset. The output results from each dataset and each model, which is trained on the same parameter, will be compared to validate the proposed method. The experiment results' comparison of the hybrid dataset between YOLOv5s6 and YOLOv8s models display the results of the mAP@.5 is about 65% and 76.2%, respectively, which means the performance of the YOLOv8s is higher than the YOLOv5s6 when using hybrid dataset. © 2023 IEEE.

关键词： Object detection

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