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Topology, Size, and Shape Optimization in Civil engineering Structures: A Review

computer Modeling in engineering & sciences 2025年第2期142卷 933-971页

作者： Ahmed Manguri Hogr Hassan Najmadeen Saeed Robert Jankowski Faculty of Civil and Environmental Engineering Gdansk University of TechnologyGdansk80-223Poland Department of Computer Science ModelingElectronics and Systems EngineeringUniversity of CalabriaCosenza87036Italy Civil Engineering Department University of RaparinRania46012Iraq Civil Engineering Department Tishk International UniversityErbil44001Iraq

The optimization of civil engineering structures is critical for enhancing structural performance and material efficiency in engineering *** optimization approaches seek to determine the optimal design,by considering material performance,cost,and structural *** design approaches aim to reduce the built environment’s energy use and carbon *** comprehensive review examines optimization techniques,including size,shape,topology,and multi-objective approaches,by integrating these *** trends and advancements that contribute to developing more efficient,cost-effective,and reliable structural designs were *** review also discusses emerging technologies,such as machine learning applications with different optimization *** of truss,frame,tensegrity,reinforced concrete,origami,pantographic,and adaptive structures are covered and *** techniques are explained,including metaheuristics,genetic algorithm,particle swarm,ant-colony,harmony search algorithm,and their applications with mentioned structure *** and non-linear structures,including geometric and material nonlinearity,are *** role of optimization in active structures,structural design,seismic design,form-finding,and structural control is taken into account,and the most recent techniques and advancements are mentioned.

关键词： Structural optimization topology optimization size optimization shape optimization multi-objective optimization

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Cross-Lingual Speaker Identification for Indian Languages

Cross-Lingual Speaker Identification for Indian Languages

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2023 International Conference Recent Advances in Natural Language Processing: Large Language Models for Natural Language Processing, RANLP 2023

作者： Rizvi, Amaan Jamatia, Anupam Rudrapal, Dwijen Chakma, Kunal Gambäck, Björn Department of Computer Science and Engineering National Institute of Technology Agartala Tripura India Department of Computer Science Norwegian University of Science and Technology Trondheim Norway

ISBN: (纸本)9789544520922

The paper introduces a cross-lingual speaker identification system for Indian languages, utilising a Long Short-Term Memory dense neural network (LSTM-DNN). The system was trained on audio recordings in English and evaluated on data from Hindi, Kannada, Malayalam, Tamil, and Telugu, with a view to how factors such as phonetic similarity and native accent affect performance. The model was fed with MFCC (mel-frequency cepstral coefficient) features extracted from the audio file. For comparison, the corresponding melspectrogram images were also used as input to a ResNet-50 model, while the raw audio was used to train a Siamese network. The LSTM-DNN model outperformed the other two models as well as two more traditional baseline speaker identification models, showing that deep learning models are superior to probabilistic models for capturing low-level speech features and learning speaker characteristics. © 2023 Incoma Ltd. All rights reserved.

关键词： Long short-term memory

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An Important and Successful Multi-image Stereography-Based Secret Communication System for Military Applications 12th

An Important and Successful Multi-image Stereography-Based S...

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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Ravi, C.N. Suhasini, T.S. Narasimharao, Jonnadula Mahendar, A. Amruthraj, B. Venkateswara Reddy, R. Department of Computer Science and Engineering CMR Engineering College Telangana Hyderabad India CMR Engineering College Telangana Hyderabad India Department of Computer Science and Engineering CMR Technical Campus Telangana Hyderabad India CMR Technical Campus Telangana Hyderabad India Department of Computer Science and Engineering CMR College of Engineering and Technology Telangana Hyderabad India CMR College of Engineering and Technology Telangana Hyderabad India

ISBN: (纸本)9789819601387

A significant factor in data transfer is computer science. Security considerations make it difficult to transmit sensitive data or have conversations via the internet. Typically, cryptography is used to transmit text-based underground messages and conceal in sequence. These days, there are numerous methods for concealing information on any media. A technique like this is steganography. The creation of a covert communication system that uses multi-image steganography will enable safe communication among the dispatcher and the recipient free from hacker intervention. Image steganography is the main technique for information hiding. It involves integrating the cipher text within a picture called the cover picture that is very difficult for strangers to view through an unassisted eye. Within a cover image, the concealed data may take the shape of any kind of text, picture, music, or even video. The secret code is split up into several pieces and etched into several cover images, according to the conceptual notion of multi-image steganography. Thus, in order to make it extremely difficult for the hackers to hide the data, we suggested two image steganography concepts. In order to create a safe and protected system, this learning suggest by means of the Advanced Encryption Standard (AES) approach of cryptography with the Least Significant Bit (LSB) method of steganography. Here, a symmetric key—which is used by both the sender and the recipient to encrypt and decode data—is used by both parties. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Stereo image processing

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GAN-Enhanced Brain Slice Images for Detecting Alzheimer's Disease 11

GAN-Enhanced Brain Slice Images for Detecting Alzheimer's Di...

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11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Vincent, Albin Ancy, C.A. Rajagiri School of Engineering & Technology Department of Information Technology Kakkanad India Rajagiri School of Engineering & Technology Department of Computer Science and Business Systems Kakkanad India

ISBN: (纸本)9798350379136

Alzheimer's Disease (AD) is a progressive neurological disorder marked by cognitive decline, memory loss, and impaired reasoning, affecting millions globally. Early and accurate diagnosis of AD is crucial for managing the disease, yet traditional diagnostic methods such as clinical assessments and Magnetic Resonance Imaging (MRI) face significant challenges. This research proposes a novel approach to enhance the diagnostic process by using Generative Adversarial Networks (GANs) to improve brain MRI image quality, thereby aiding automated detection of AD. It involves preprocessing brain MRI scans, designing a GAN for image enhancement, and utilizing these enhanced images in a Convolutional Neural Network (CNN) for detecting AD. The effectiveness of method is evaluated using key metrics such as accuracy, sensitivity, and specificity, comparing its performance to baseline methods using non-enhanced images. Cross-validation and testing with external datasets are conducted to ensure the model's robustness and generalizability. The integrated GAN-CNN framework demonstrated a remarkable accuracy of 99.9 % in predicting AD using MRI images from the OASIS dataset. © 2024 IEEE.

关键词： Generative adversarial networks

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Dragonfly Algorithm and Variants for Feature Selection: A Review

Dragonfly Algorithm and Variants for Feature Selection: A Re...

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2023 IEEE International Conference on Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security, iQ-CCHESS 2023

作者： Prinson, P.T. Geetha, Angelina Hindustan Institute of Technology and Science Department of Computer Applications Chennai India Hindustan Institute of Technology and Science Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350314946

Ultimately, the key to successfully analysing real-world problems is to be diligent and thorough. It is important to carefully consider all available data, remove any redundant or unnecessary information, and approach the analysis with an open mind. By doing so, it is possible to gain valuable insights and make informed decisions that can help solve complex real-world problems. By removing redundant, irrelevant, and noisy data, feature selection (FS), a difficult machine learning problem, attempts to lessen the number of features while maintaining a decent degree of classification accuracy. Feature selection is intended to reduce features. This immediately reduces the search space and aids machine learning algorithms in using only the most significant features. A review of Dragonfly algorithms and variants is presented in this paper, emphasizing their main characteristics. Eventually, the conclusion concentrates on the present research on Dragonfly Algorithm, emphasizing its limitations with suggested solutions that lead to future directions © 2023 IEEE.

关键词： Swarm intelligence

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Intelligent Drug Release System Development and Evaluation via Support Vector Machine Algorithm

Intelligent Drug Release System Development and Evaluation v...

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2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2024

作者： Kalyani, G. Jayaraman, Venkatesh Sathish Kumar, P.J. Surendran, R. Balamurugan, K.S. Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India

ISBN: (纸本)9798331533137

This study proposes a unique sensor plus machine learning system designed to enhance patient care and targeted medicine administration, with a specific focus on controlling Cryptococcosis, in the search for more precise and proactive healthcare solutions. This research had contributed to the conceptualization, design and implementation of system which integrates constant sensor data monitoring and machine learning algorithms to anticipate and prepare for any health problems. Over the course of a day, Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Random Forest (RF) were used to analyse sensor data from 10 patients with Cryptococcosis. The sensor data gathered from these people included information on their body temperature, blood pressure, oxygen saturation, heart rate, blood sugar levels, and ECG measurements. The models executed flawlessly, achieving F1 scores for accuracy, recall, and precision. SVM won with a precision score of 97.6%, followed by ANN with a score of 95.3% and RF with a score of 92.1%. The models' capacity to get rid of misclassifications and provide precise predictions needed for right away action was demonstrated by the confusion matrices that went along with them. This discovery, which might revolutionise how patients are treated for difficult-to-treat illnesses like cryptococcosis, is an encouraging first step in the direction of proactive healthcare management. The sensor and machine learning system offers a patient-centered, data-driven approach that keeps up with the changing landscape of healthcare. ©2024 IEEE.

关键词： Support vector machines

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General Optimal Trajectory Planning:Enabling Autonomous Vehicles with the Principle of Least Action

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engineering 2024年第2期33卷 63-76页

作者： Heye Huang Yicong Liu Jinxin Liu Qisong Yang Jianqiang Wang David Abbink Arkady Zgonnikov School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China Department of Cognitive Robotics Faculty of MechanicalMaritime and Materials Engineering(3mE)Delft University of TechnologyDelft 2628Netherlands Department of Software Technology Faculty of Electrical EngineeringMathematics and Computer ScienceDelft University of TechnologyDelft 2628Netherlands Xi’an Institute of High-Technology Xi’an 710051China

This study presents a general optimal trajectory planning(GOTP)framework for autonomous vehicles(AVs)that can effectively avoid obstacles and guide AVs to complete driving tasks safely and ***,we employ the fifth-order Bezier curve to generate and smooth the reference path along the road *** coordinates are then transformed to achieve the curvature continuity of the generated *** the road constraints and vehicle dynamics,limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate ***,in selecting the optimal trajectory,we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers’behavior and summarizing their manipulation characteristics of“seeking benefits and avoiding losses.”Finally,by integrating the idea of receding-horizon optimization,the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility,optimality,and *** simulations and experiments are performed,and the results demonstrate the framework’s feasibility and effectiveness,which avoids both dynamic and static obstacles and applies to various scenarios with multi-source interactive traffic ***,we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers’manipulation.

关键词： Autonomous vehicle Trajectory planning Multi-performance objectives Principle of least action

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Application and Effectiveness Evaluation of BP Neural Network Model Based on Rafflesia Optimization Algorithm Optimization in Daily Average Temperature Prediction 15th

Application and Effectiveness Evaluation of BP Neural Netw...

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15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Zhang, Zhen Chu, Shu-Chuan Pan, Jeng-Shyang Li, Wei Yang, Hongmei College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Computer Science and Technology Harbin Engineering University Harbin China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan

ISBN: (纸本)9789819700677

With economic and social development, the impact of climate change on human social life is constantly deepening. The ability to accurately predict trends in meteorological data has become an important area of research at present. The main focus of this research is to explore the application and evaluation of the effectiveness of a BP neural network model based on the optimization of the Rafflesia Optimization Algorithm(ROA) for daily average temperature prediction. The data for this study uses a large amount of historical meteorological data as the training set, including date, time and the corresponding daily mean temperature. The prediction model was constructed using a BP neural network-based temperature prediction model, and the ROA was used to optimise the weights and biases of the BP neural network to improve the accuracy and reliability of the prediction model. The experimental results show that the optimized BP neural network model has higher accuracy and stability in predicting the daily average temperature compared with the traditional BP neural network model. The optimised model is also able to better capture the non-linear relationships and time-series features in the meteorological data, thus improving the accuracy and reliability of the prediction. This research can provide a reliable tool for meteorological prediction and climate research, which helps to accurately predict future climate change trends and provide scientific basis and decision support for future economic and social development and human life progress. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Climate change

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A Delay-Tolerant low-duty cycle scheme in wireless sensor networks for IoT applications

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International Journal of Cognitive Computing in engineering 2023年第1期4卷 194-204页

作者： Singh, Shashank Anand, Veena Bera, Pallav Kumar Department of CSE National Institute of Technology Raipur India School of Engineering and Computer Science University of Evansville IN United States

Energy efficiency is an existing research challenge in wireless sensor networks (WSNs);to make WSNs energy efficient, many researchers moved to low-duty cycle WSNs with different data forwarding schemes. Low-duty cycle is considered to be a promising approach to design routing protocols for resource-constrained WSNs. Many applications have real-time constraints, which requires an event must be reported to the sink before deadline. Furthermore, wireless links between low-power radios are highly unreliable, and end-to-end latency requirements are challenging due to multiple transmissions for a single packet delivery. By considering probabilistic delay (i.e., delay bounded data delivery with reliability constraints) and energy efficiency issues, little research has been done using different data forwarding schemes, but still, the problem exists. In this work, two strategies are proposed for data forwarding: an energy-optimal path and a delay-optimal path. The early-arrived packet follows an optimal energy route and a diverse path to achieve a delay guarantee with minimum transmission cost. The proposed scheme improves the performance of low-duty cycle WSNs in terms of delay, reliability, stability, and transmission cost. It is observed that under different low-duty cycles, the proposed scheme achieves up to 25% energy conservation compared with both MinEEC and MinEED. At last, undirected spanning trees are considered to balance communication costs. The simulation results of the proposed scheme are compared with the existing methods. © 2023 The Authors

关键词： Wireless sensor networks

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Tracking and monitoring the safety of the child in the stroller 4

Tracking and monitoring the safety of the child in the strol...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Pandiaraja, P. Rajesh Kanna, P. Sumathi, K. Karthik, K. Muthumanickam, K. Radha, S. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai600 062 India Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamilnadu Erode638401 India Builders Engineering College Department of Computer Science and Engineering Tamilnadu Kangeyam638108 India M.Kumarasamy College of Engineering Department of Computer Science and Engineering Tamilnadu Karur639113 India Kongunadu College of Engineering and Technology Department of Information Technology Tamilnadu Tiruchirappalli621215 India Department of Information Technology Tiruchengode TamilNadu Elayampalayam637205 India

ISBN: (纸本)9798331508845

The safety of the child in the stroller is at risk when there is a deviation in the parent's focus. The automatic movement in the baby strollers have led to many accidents and have been fatal to the baby. To overcome this, a stroller integrated with the touch sensor in the handle and movement sensor in wheels using IoT is planned in the project. The touchsensor is implemented using Capacitive or resistive sensing technology and works when an individual gets in physical contact with it. If a wheel movement is detected without touch in the handle, the customized voice alarm present in the stroller will go off to alert the parent. In addition to this, to locate the stroller through a device like mobile or tablet, a Global Positioning System (GPS) tracker will be placed in the stroller. When the stroller is left for about certain meters from the connected device, the parent will be notified through the connected device and can monitor the location of stroller in the device all the time using Blynk application. © 2024 IEEE.

关键词： Global positioning system

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