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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Ramijaanaki, S. Niveditha, V.R. Sree, G.V.N. Bhagya Rajababu, D. Maranan, R. Kaliappan, S. R.M.K College of Engineering and Technology Artificial Intelligence and Data Science Puduvoyal Tiruvallur district India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Semmancheri Chennai600119 India Aditya University Department of Computer Science and Engineering Andhra Pradesh Surampalem India Sr University Department of Electrical and Electronics Engineering Telangana Warangal506371 India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai602105 India Lovely Professional University Division of Research and Development Jalandhar - Delhi G.T.Road Punjab Phagwara144411 India

ISBN: (纸本)9798331518981

This research proposes a novel implementation of Long Short-Term Memory with Encoder-Decoder and Skill Optimization Algorithm (LSTM-ED-SOA) seeking to forecast Weather Radar Echoes. It presents a model that seeks to strengthen 'the weak' by introducing an optimization algorithm that combines the advantages of LSTM in handling sequential data and improves the performance of the predictions over time. Temporal dependencies are appropriately modeled with the encoder-decoder structure which is coupled with a skill optimization technique that enables the model to mitigate against capricious weather conditions. Forecasting models such as EPD, TrajGRU, and ConvLSTM as well as, and PredRNN put together show weaknesses when compared to LSTM-ED-SOA. Thus, it comes out convincingly that the model performs exceptionally achieving an accuracy of 99.8%, precision of 99.6%, and an F1 score of 99.7% way ahead of the best conventional models which were achieved accuracy of 85% at the very best. These results underscore the stability and effectiveness of the LSTM-ED-SOA system, for more advanced radars, wherein, it will greatly enhance the accuracy and timeliness of weather forecasting. © 2024 IEEE.

关键词： Weather forecasting

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Application of Remote Sensing Image Processing for Classification and Recognition 15

Application of Remote Sensing Image Processing for Classific...

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15th IEEE International Conference on Advanced Infocomm Technology, ICAIT 2023

作者： Shi, Xiaolong Huang, Dan Li, Wenjie Wang, Xianjie College of Computer Science and Engineering Chongqing University of Technology Chongqing China China Research and Development Academy of Machinery Equipment Beijing China Chongqing High-tech Zone Pegasus Innovation Institute Chongqing University of Technology Chongqing China

ISBN: (纸本)9798350314120

Aiming at the difficulties in object detection and recognition in remote sensing images caused by high background complexity, large scale variations of targets, and the presence of numerous small objects, an improved method for remote sensing image object detection based on YOLOv7-tiny is proposed. This method combines the loss function based on normalized Gaussian Wasserstein distance (NWD) with the CIoU loss function to address the problem of sensitivity to positional deviation of small objects by IoU-Loss. The addition of a global attention mechanism (GAM) in the backbone network reduces information diffusion and enhances the interaction at the global dimension to mitigate the interference of complex backgrounds in remote sensing images on the model, enabling the model to focus on the feature extraction of the desired targets. Finally, the coupled detection head (Coupled Head) of the model is replaced with a decoupled detection head (Decoupled Head), allowing the classification and regression tasks to output from different branches to achieve decoupling and avoid the decrease in detection accuracy caused by conflicts between classification and regression. The experimental results of this method on the public dataset DIOR achieved 88.73% accuracy, which is an improvement of 1.78% compared to the unimproved method's accuracy of 86.95%. Furthermore, compared to other researchers' methods tested on DIOR, the proposed method also shows improvement, thus validating its effectiveness. © 2023 IEEE.

关键词： Image classification

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Bilateral-Head Region-Based Convolutional Neural Networks: A Unified Approach for Incremental Few-Shot Object Detection

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4376-4390页

作者： Li, Yiting Zhu, Haiyue Tian, Sichao Ma, Jun Xiang, Cheng Vadakkepat, Prahlad National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore Singapore Institute of Manufacturing Technology Agency for Science Technology and Research 138634 Singapore Peking Union Medical College Institute of Medicinal Plant Development Chinese Academy of Medical Sciences Beijing100193 China Robotics and Autonomous Systems Thrust Guangzhou511453 China The Hong Kong University of Science and Technology Division of Emerging Interdisciplinary Areas Hong Kong SAR Hong Kong Hkust Shenzhen-Hong Kong Collaborative Innovation Research Institute Shenzhen Futian518045 China

Practical object detection systems are highly desired to be open-ended for learning on frequently evolved datasets. Moreover, learning with little supervision further adds flexibility for real-world applications such as autonomous driving and robotics, where large-scale datasets could be prohibitive or expensive to obtain. However, continual adaption with small training examples often results in catastrophic forgetting and dramatic overfitting. To address such issues, a compositional learning system is proposed to enable effective incremental object detection from nonstationary and few-shot data streams. First of all, a novel bilateral-head framework is proposed to decouple the representation learning of base (pretrained) and novel (few-shot) classes into separate embedding spaces, which takes care of novel concept integration and base knowledge retention simultaneously. Moreover, to enhance learning stability, a robust parameter updating rule, i.e., recall and progress mechanism, is carried out to constrain the optimization trajectory of sequential model adaption. Beyond that, to enforce intertask class discrimination with little memory burden, we present a between-class regularization method that expands the decision space of few-shot classes for constructing unbiased feature representation. Final, we deeply investigate the incomplete annotation issue considering the realistic scenario of incremental few-shot object detection (iFSOD) and propose a semisupervised object labeling mechanism to accurately recover the missing annotations for previously encountered classes, which further enhances the robustness of the target detector to counteract catastrophic forgetting. Extensive experiments conducted on both Pascal visual object classes dataset (VOC) and microsoft common objects in context dataset (MS-COCO) datasets demonstrate the effectiveness of our method. © 2020 IEEE.

关键词： Feature extraction

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Free Opensource Mobile GIS Applications for Land Survey 8

Free Opensource Mobile GIS Applications for Land Survey

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Joint 8th International Conference on Digital Arts, Media and Technology and 6th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications engineering, ECTI DAMT and NCON 2023

作者： Jeefoo, Phaisarn Onnom, Worawej Rojanavasu, Pornthep School of Information and Communication Technology Research Unit of Spatial Innovation Development Geographic Information Science University of Phayao Phayao Thailand Human Capital Development Centre Suan Dusit University Bangkok10300 Thailand School of Information and Communication Technology Computer Engineering University of Phayao Phayao Thailand

ISBN: (纸本)9798350396546

Geography location and object report is a key aspect in field survey, nevertheless of the spatial level. This research shown the request of land survey by using no code software. The study process collects data in the first part, mainly using forms and brief descriptions. Later on technologies are applied with increasing the availability of smartphone and improvement of features of the smartphones. Detection expose that smartphone GIS technique has a high adeptness, precision and consistency as a data collection technique for land survey development. Additional the study can be developed to use more advanced equipment. © 2023 IEEE.

关键词： Smartphones

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Analysis and optimization of Dual secondary fuel injector for Thrust vector control

Analysis and optimization of Dual secondary fuel injector fo...

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2022 International Conference on Design, Manufacturing and Materials engineering, ICDMME 2021

作者： Sharma, Kuldeep Manohar, P.M.S.S. Mishra, Ankit Kumar Department of Aeronautical Engineering School of Aeronautics Rajasthan 301705 India Department of Aerospace Engineering Hindustan Institute of Technology and Science Chennai 603103 India Department of Research and Development LIPS Research Foundation Kerala 673528 India

The motive of this analysis is to compare the performance parameters of a single secondary fuel injector and performance of Dual Secondary fuel injector system for Thrust Vector Control (SITVC) in a supersonic nozzle for different distances between the two ports. With the help of Computational Fluid Dynamics (CFD) the performance characteristics of SITVC has been analyzed. Experimental and numerical investigations have been done with various circumstances to examine the performance augmentations of the 2-D nozzle. © 2022 Published under licence by IOP Publishing Ltd.

关键词： Computational fluid dynamics

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Parallel Acceleration of Real-time Feature Extraction Based on SURF Algorithm 15

Parallel Acceleration of Real-time Feature Extraction Based ...

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15th International Conference on Computer research and development, ICCRD 2023

作者： Yu, Jiyang Huang, Dan Li, Jinyang Li, Wenjie Wang, Xianjie Shi, Xiaolong China Academy of Space Technology Beijing Institute of Spacecraft System Engineering Beijing China China Research and Development Academy of Machinery Equipment Chongqing University of Technology Chongqing Beijing China Chongqing Feima Innovation Research Institute Intelligent Equipment Research Department Chongqing China College of Computer Science and Engineering Chongqing University of Technology Chongqing China

ISBN: (纸本)9781665487504

Visual feature extraction is the key to target registration calculation. Compared with SIFT, SURF feature extraction algorithm has less computational complexity and better applicability when image signal-to-noise ratio is low. In this paper, an FPGA-based SURF algorithm for real-time feature extraction and parallel acceleration is designed for large-field scene registration applications of space targets. Firstly, the application scenario of space target is analyzed, and the grayscale features of the data containing the target used for registration are counted. Then, the five main parts of SURF algorithm were analyzed, and the algorithm was simplified based on the gray feature of the image. Finally, according to the simplified results, the parallel pipeline architecture of each module is designed based on FPGA. SURF computing modules are also given in this paper the design of the architecture and data flow of information, in XC7K325 completed the whole design of experiment, the results show that the design for 1024 × 1024 pixel image, single frame image processing time need only 51 us, resource usage is not more than 18.6%, power consumption is not more than 7.5 W, The computational efficiency is 87% higher than the previous design. Low signal-to-noise ratio (SNR) was used to simulate space target scene data for simulation test calculation, and the SURF descriptor error was less than 3%, ensuring the system's calculation error while effectively utilizing resources. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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CNN-Based Deep Learning Model for Early Identification and Categorization of Melanoma Skin Cancer Using Medical Imaging

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SN Computer science 2024年第7期5卷 911页

作者： Pandimurugan, V. Ahmad, Sultan Prabu, A.V. Rahmani, Mohammad Khalid Imam Abdeljaber, Hikmat A. M. Eswaran, Malathi Nazeer, Jabeen Computer Science and Engineering Acharya Institute of Technology Bangalore India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur 522502 India College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Computer Technology Kongu Engineering College Perundurai Tamil Nadu Erode India

In human life, skin cancer is a curse. If not appropriately diagnosed, it spreads around all body parts in the earlier stage. The melanoma skin cancer death rate is 75% all over the world. There is an urgent need for a cure to be in place. Swarm and evolutionary computation for healthcare deals with real-world healthcare applications. One of the best ways to do this is to find the genes that need to be turned off such that only cancer cells can die. The current therapies used to cure cancer focus on turning off cancer cells but, at times, affect healthy tissues. Using the power of Artificial Intelligence, one can easily mark out the cancer-affected area and provide treatment accordingly. Segmentation techniques can be used to mark cancer lesions, and advanced deep learning algorithms in computer vision can help classify which class of cancer lesions. Automatic detection of melanoma by using a dermoscopic sample is tricky to find the stage and percentage of lesions affected by using deep convolutional neural networks with the help of machine vision tools. To predict skin lesions, we developed a model with three layers, each having output channels of 16, 32, and 64, respectively. For this research, different samples were collected from the international skin imaging collaboration database (ISIC2019, ISIC2020, ISIC2021). The study computed the most important parameters for classifying and identifying the lesion with accuracy, precision, recall, and specificity values. The proposed DCNN model classifier attained an accuracy of 88.82%, 93.45%, and 95.15% with the respective datasets of ISIC2019, ISIC2020, and ISIC2021, indicating high performance compared with the other state-of-the-art networks. The proposed model is a better framework for automating early detection and classification of melanoma skin cancer to protect many lives and avoid mishaps. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification Convolution neural network Deep learning Medical imaging Melanoma Skin cancer Swarm and evolutionary computation

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Substitutability of urban sustainability assessment indicators:A semantic similarity analysis

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Chinese Journal of Population,Resources and Environment 2024年第4期22卷 436-442页

作者： Yunlin He Tianshu Yu Jiangming Ma China Architecture Design&Research Group Beijing 100044China China National Engineering Research Center for Human Settlements Beijing 100044China Guangxi Key Laboratory of Landscape Resources Conservation and Sustainable Utilization in Lijiang River Basin Guilin 541006China Institute for Sustainable Development and Innovation Guangxi Normal UniversityGuilin 541006China College of Environmental Science and Engineering Nankai UniversityTianjin 300350China

Urban sustainability assessment is an effective method for objectively presenting the current state of sustainable urban development and diagnosing sustainability-related *** the global community intensifies its efforts to implement the sustainable development goals(SDGs),the demand for assessing progress in urban sustainable development has *** has led to the emergence of numerous indicator systems with varying scales and themes published by different *** participating in these evaluations often encounter difficulties in matching indicators or the absence of certain *** this context,urban decision makers and planners urgently need to identify substitute indicators that can express the semantic meaning of the original indicators and consider the availability of indicators for participating ***,this study explores the relationships of substitution between indicators and constructs a collection of substitute indicators to serve as a reference for sustainable urban development ***,building on a review of international and Chinese indicators related to urban sustainability assessment,this study employs natural semantic analysis methods based on the Word2Vec model and cosine similarity algorithm to calculate the similarity between indicators related to sustainable urban *** results show that the Skip-gram algorithm with a word vector dimensionality of 600 has the best performance in terms of calculating the similarity between sustainable urban development assessment *** findings provide valuable insights into selecting substitute indicators for future sustainable urban development assessment,particularly in China.

关键词： Urban sustainability assessment Sustainable development Semantic analysis Word2Vec Cosine similarity

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Dissipative observer design for singular neutral systems 4

Dissipative observer design for singular neutral systems

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4th International Conference on Computer science and Communication Technology, ICCSCT 2023

作者： Wang, Jichun Xiao, Dong Li, Feifei College of Science Shenyang Jianzhu University Liaoning Shenyang110168 China School of Information Science and Engineering Northeastern University Liaoning Shenyang110167 China Sci-tech Novelty Search Department Liaoning Province Important Technology Innovation and Research Development Base Construction Engineering Center Liaoning Shenyang110015 China

ISBN: (纸本)9781510671232

The dissipative observer design for a singular neutral system is studied in the article. The rigorous dissipative analysis of a singular neutral system is carried out. And the observer-based dissipative control problem is discussed through the use of linear matrix inequality (LMI). The method of observer design is given. Finally, the proposed method's effectiveness and universality are demonstrated by a final example. © 2023 SPIE.

关键词： Linear matrix inequalities

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Cuckoo Search-Optimized Deep CNN for Enhanced Cyber Security in IoT Networks

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Computers, Materials & Continua 2024年第12期81卷 4109-4124页

作者： Brij B.Gupta Akshat Gaurav Varsha Arya Razaz Waheeb Attar Shavi Bansal Ahmed Alhomoud Kwok Tai Chui Department of Computer Science and Information Engineering Asia UniversityTaichung413TaiwanChina Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057MaharashtraIndia Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India University Centre for Research and Development(UCRD) Chandigarh UniversityChandigarh140413India Computer Engineering Ronin InstituteMontclairNJ 07043USA Department of Business Administration Asia UniversityTaichung413TaiwanChina Department of Electrical and Computer Engineering Lebanese American UniversityBeirut1102Lebanon Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Research and Innovation Insights2TechinfoJaipur302001India Department of Computer Science Faculty of ScienceNorthern Border UniversityArar91431Saudi Arabia Department of Electronic Engineering and Computer Science Hong Kong Metropolitan University(HKMU)Hong Kong518031China

Phishing attacks seriously threaten information privacy and security within the Internet of Things(IoT)*** phishing attack detection solutions have been developed for IoT;however,many of these are either not optimally efficient or lack the lightweight characteristics needed for practical *** paper proposes and optimizes a lightweight deep-learning model for phishing attack *** model employs a two-fold optimization approach:first,it utilizes the analysis of the variance(ANOVA)F-test to select the optimal features for phishing detection,and second,it applies the Cuckoo Search algorithm to tune the hyperparameters(learning rate and dropout rate)of the deep learning ***,our model is trained in only five epochs,making it more lightweight than other deep learning(DL)and machine learning(ML)*** proposed model achieved a phishing detection accuracy of 91%,with a precision of 92%for the’normal’class and 91%for the‘attack’***,the model’s recall and F1-score are 91%for both *** also compared our approach with traditional DL/ML models and past literature,demonstrating that our model is more *** study enhances the security of sensitive information and IoT devices by offering a novel and effective approach to phishing detection.

关键词： Deep learning phishing Cuckoo Search cable news network(CNN) IoT ANOVA F-test

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