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2024 China Automation Congress, CAC 2024

作者： Zhang, Zhenguo Cao, Yuanzhouhan Zhang, Hui Chen, Naiyue Ren, Chao Li, Yidong Key Laboratory of Big Data & Artificial Intelligence in Transportation Beijing Jiaotong University Ministry of Education China School of Computer Science and Technology Beijing Jiaotong University China College of Electronics and Information Engineering Sichuan University China

ISBN: (纸本)9798350368604

3D object detection is one of the fundamental perception tasks in autonomous driving. In recent years, perception in the Bird’s Eye View (BEV) has attracted tremendous attention due to its outstanding ability in preserving geometric cues. However, like other supervised learning tasks, BEV-based perception suffers from inadequate labeled training data. In this work, we propose the Vision BEV-MAE, a self-supervised learning approach for BEV perception from camera images. Specifically, we design a feature-level masking strategy to mask the mid-level representations, i.e., the encoded BEV features instead of directly masking input multi-view images, and reconstruct BEV features with a light-weight decoder. Our Vision BEV-MAE is independent of any geometric data such as point clouds, or any additional supervision, and can be easily implemented within any state-of-the-art BEV perception frameworks to improve the performance. We conduct experiments on benchmark dataset to validate our approach. © 2024 IEEE.

关键词： Self-supervised learning

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Enhancing Healthcare: Incorporating Blockchain into Electronic Health Records

Enhancing Healthcare: Incorporating Blockchain into Electron...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Saniya Saratkar Meher Langote Aarti Chaudhari Trupti Thute Rohini Raut Gayatri Thakre Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

Handling large amounts of medical data on a blockchain can be challenging, maintaining patient privacy while ensuring data accessibility requires careful design. The healthcare sector is constantly changing, and in order to meet the issues of data security, interoperability, and patient empowerment, it is essential to integrate innovative technology. With its reputation for being decentralized and impervious to tampering, blockchain technology has become a disruptive factor in the redesign of electronic health records (EHR). Blockchain technology has long been a fascinating field of study, and many different businesses have benefited from its applications. Similar to this, the security, privacy, secrecy, and decentralization of blockchain technology present enormous opportunities for the healthcare industry. However, issues with data management, security, and integrity plague Electronic Health Record (EHR) systems. This research study explores how blockchain technology might revolutionize electronic health record systems and perhaps address these problems.

关键词： Industries Electric potential data privacy Technological innovation data security Medical services Transforms

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Ensemble Learning for Detection and Analysis of Depression in Women Using Machine Learning Approaches 4

Ensemble Learning for Detection and Analysis of Depression i...

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

作者： Mahalle, Parikshit N. Bhuvaneshwari, K.S. Khurana, Shivam Geetha, B. Samrat, Bhavuk Rao, Srikrishna Baskar Vishwakarma Institute of Technology Department of Artificial Intelligence & Data Science Pune India Karpagam College of Engineering Department of Artificial Intelligence and Data Science Coimbatore641032 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of S&h Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India Karnataka Bangalore India

ISBN: (纸本)9798331518981

One of the biggest global health issues is cardiovascular disease, which kills a staggering number of people every year. The requirement for early detection pushed the introduction of strong deep learning computational methods, which have enhanced diagnostic accuracy. The goal of this study is to provide a method for detecting cardiovascular disease risk factors using machine learning. This method combines Bagging the classification algorithms (BaC) and boosting basic learners (BoC), using the heart diseases databases given by UCI, to increase prediction accuracy. This kind of classifier takes into account important metrics including maximal heart rate, age, cholesterol levels, and resting pressure in the blood. We beforehand the dataset and run the model via a tenfold cross-validations procedure to ensure its correctness. In order to reach the highest possible level of efficiency, the Bagging Classifier employs a total of 200 estimators, whilst the Boosting Classifier makes use of 100 estimates and a learning rate of 0.134 throughout the training process. In terms of performance criteria such as data delivery rate and data packets loss rate, the suggested system is superior to the current system in terms of its overall performance. When the bagging and boosting classifiers are combined, the total result is superior than the results obtained by either approach alone 98% in data delivery rate. This system's exceptional success may be attributed to the increased reliability it has as a diagnostic instrument for facilitating the early identification of heart illness. It is possible to improve the generalizability of the model by using deeper learning models in a more efficient manner and by using bigger datasets. © 2024 IEEE.

关键词： Adversarial machine learning

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Facial Expression Recognition Model Depending on Optimized Support Vector Machine

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Computers, Materials & Continua 2023年第7期76卷 499-515页

作者： Amel Ali Alhussan Fatma M.Talaat El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Mona Alnaggar Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Machine Learning&Information Retrieval Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Robotics and Intelligent Machines Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt

In computer vision,emotion recognition using facial expression images is considered an important research *** learning advances in recent years have aided in attaining improved results in this *** to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of *** is feasible and useful to convert face photos into collections of visual words and carry out global expression *** main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is *** uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos *** FERM is composed of three main phases:(i)the data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization *** discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously *** search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.

关键词： Facial expression recognition machine learning linear dis-criminant analysis(LDA) support vector machine(SVM) grid search

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Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition

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植物表型组学（英文） 2024年第4期6卷 828-840页

作者： Jianwu Lin Xin Zhang Yongbin Qin Shengxian Yang Xingtian Wen Tomislav Cernava Quirico Migheli Xiaoyulong Chen Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China School of Biological Sciences Faculty of Environmental and Life SciencesUniversity of SouthamptonSouthampton S017 1BJUK Dipartimento di Agraria and NRD-Nucleo di Ricerca sulla Desertificazione Università degli Studi di SassariSassariItaly Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Life Sciences Guizhou UniversityGuiyang 550025China

Accurate identification of plant diseases is important for ensuring the safety of agricultural *** neural networks(CNNs)and visual transformers(VTs)can extract effective representations of images and have been widely used for the intelligent recognition of plant disease ***,CNNs have excellent local perception with poor global perception,and VTs have excellent global perception with poor local *** makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition *** this paper,we propose a local and global feature-aware dual-branch network,named LGNet,for the identification of plant *** specifically,we first design a dual-branch structure based on CNNs and VTs to extract the local and global ***,an adaptive feature fusion(AFF)module is designed to fuse the local and global features,thus driving the model to dynamically perceive the weights of different ***,we design a hierarchical mixed-scale unit-guided feature fusion(HMUFF)module to mine the key information in the features at different levels and fuse the differentiated information among them,thereby enhancing the model's multiscale perception ***,extensive experiments were conducted on the Al Challenger 2018 dataset and the self-collected corn disease(SCD)*** experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the Al Challenger 2018 dataset and the SCD dataset,with accuracies of 88.74％and 99.08％,respectively.

关键词： global plant disease dual-branch feature-aware local networks recognition

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Evolutionary Optimization Methods for High-Dimensional Expensive Problems:A Survey

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1092-1105页

作者： MengChu Zhou Meiji Cui Dian Xu Shuwei Zhu Ziyan Zhao Abdullah Abusorrah Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China School of Intelligent Manufacturing Nanjing University of Science and TechnologyNanjing 210094China Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China School of Artificial Intelligence and Computer Jiangnan UniversityWuxi 214122China School of Information Science and Engineering Northeastern UniversityShenyang 110819China Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia K.A.CARE Energy Research and Innovation Center King Abdulaziz UniversityJeddah 21589Saudi Arabia

Evolutionary computation is a rapidly evolving field and the related algorithms have been successfully used to solve various real-world optimization *** past decade has also witnessed their fast progress to solve a class of challenging optimization problems called high-dimensional expensive problems(HEPs).The evaluation of their objective fitness requires expensive resource due to their use of time-consuming physical experiments or computer ***,it is hard to traverse the huge search space within reasonable resource as problem dimension *** evolutionary algorithms(EAs)tend to fail to solve HEPs competently because they need to conduct many such expensive evaluations before achieving satisfactory *** reduce such evaluations,many novel surrogate-assisted algorithms emerge to cope with HEPs in recent *** there lacks a thorough review of the state of the art in this specific and important *** paper provides a comprehensive survey of these evolutionary algorithms for *** start with a brief introduction to the research status and the basic concepts of ***,we present surrogate-assisted evolutionary algorithms for HEPs from four main *** also give comparative results of some representative algorithms and application ***,we indicate open challenges and several promising directions to advance the progress in evolutionary optimization algorithms for HEPs.

关键词： computer optimization evolutionary

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Efficient Plant Disease Detection using K-Means clustering and DenseNet-based Classification 3

Efficient Plant Disease Detection using K-Means clustering a...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Yashodha, G. Rajakumar, B. Krishnamoorthy, M. Surendran, R. Kg College of Arts and Science Department of Computer Technology Coimbatore641035 India J.N.N. Institute of Engineering Department of Artificial Intelligence and Data Science Chennai601102 India Panimalar Engineering College Department of Computer Science and Engineering Chennai600 123 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Early detection is vital in crop health, yet improvement in productivity faces time-consuming and inefficient challenges due to traditional manual techniques of plant disease detection. Thus, we present a deep learning-based novel framework with a method called k-means clustering with DenseNet Classification denoted by Kmc-Densenet. The approach streamlines this plant disease detection process involving the overall pipeline of extensive preprocessing, segmentation, and classification. In the preprocessing stage, we employ contrast enhancement techniques to improve the visibility of disease symptoms, ensuring clearer and more consistent leaf images for further analysis. For the segmentation task, we leverage a k-means clustering which effectively isolates regions of interest, distinguishing between healthy and diseased areas of the plant leaf. In the classification stage, we use a DenseNet variant of CNN, known for its feature reuse capabilities, to classify the plant diseases with high accuracy. The DenseNet model helps overcome the challenges posed by class imbalance, enhancing the overall detection performance. Finally the parameters of the Densenet model is fine-tuned using the Lyrebird Optimization Algorithm. The proposed Kmc-Densenet method is evaluated on the publicly available Plant village dataset, demonstrating superior performance in both segmentation and classification tasks. This framework offers a more efficient and reliable solution for early plant disease detection in precision agriculture, with potential applications in improving crop yield and quality. © 2025 IEEE.

关键词： K-means clustering

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Runtime-based metaheuristic prediction of the compressive strength of net-zero traditional concrete mixed with BFS, FA, SP considering multiple curing regimes

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Asian Journal of Civil engineering 2024年第2期25卷 1241-1253页

作者： Onyelowe, Kennedy C. Kontoni, Denise-Penelope N. Pilla, Sita Rama Murty Hanandeh, Shadi Ebid, Ahmed M. Razzaghian Ghadikolaee, Mehrdad Stephen, Liberty U. Department of Civil Engineering Michael Okpara University of Agriculture Umudike Umuahia 440109 Nigeria Department of Civil Engineering School of Engineering University of the Peloponnese Patras 26334 Greece School of Science and Technology Hellenic Open University Patras 26335 Greece Department of Artificial Intelligence and Data Science Vishnu Institute of Technology Andhra Pradesh Vishnupur Bhimavaram India Civil Engineering Department Faculty of Engineering Al-Balqa Applied University Salt Jordan Department of Civil Engineering Faculty of Engineering Future University in Egypt New Cairo Egypt School of Civil Engineering Iran University of Science and Technology Tehran Iran Department of Civil Engineering University of Agriculture and Environmental Sciences Umuagwo Nigeria

Toward carbon neutrality concrete technology (TCNCT) is the main topic in the net-zero construction industry, and designing a net-zero concrete optimized to meet the required near-zero carbon footprint specifications is one of the major goals of ecofriendly concrete technology. Several concrete mix-design methods were developed based on optimizing certain factor such as the unit weight of the concrete or the specific surface area of the aggregates. However, these available mix-design methods are limited to certain types of cement and aggregates while the construction industry today is looking for more advanced, sustainable, and ecofriendly (net-zero) materials such as blast furnace slag (BFS), fly ash (FA), and superplasticizer (SP). The aim of this study is to develop a predictive model for the compressive strength of certain net-zero concrete mixes at different ages considering the previously mentioned “towards carbon neutrality” industrial waste materials using the novel superspeed metaheuristic predictive techniques. A database of 1133 records of net-zero concrete mixes was collected from literature and studied to be used in training and testing the predictive models. The performance metrics used in this exercise are mean absolute error (MAE), mean squared error (MSE), and R ² score. The Automl tools were very handy as they presented the fastest models with the best performances also. The AutoSklearn models were presented for both 5 min and 10 min execution time. At the 5-min limit execution time, a performance of R ² equals 91.8%, with MAE of 3.395% and MSE of 23.224% was achieved in 262 s (4.37 min). At the 10-min set execution time, a performance of R ² equals 92.2%, with MAE of 3.197% and MSE of 22.224% was achieved in 597 s (9.95 min). The AutoGluon (TabularPredictor) was also deployed to model the CCS from the global database of nine features with no set time limits. This was executed in 785 s (13.08 min) and produced a mod

关键词： Blast furnace slag (BFS) Fly ash (FA) Metaheuristic modeling Multiple curing Net-zero concrete compressive strength Superplasticizer (SP) Sustainable concrete

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Interference Characterization and Mitigation for Multi-Beam ISAC Systems in Vehicular Networks

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IEEE Transactions on Wireless Communications 2024年第10期23卷 14729-14742页

作者： Wang, Yi Zhang, Qixun Zhang, J. Andrew Wei, Zhiqing Feng, Zhiyong Peng, Jinlin The School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China University of Electronic Science and Technology of China Quzhou324003 China The Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China The Global Big Data Technologies Centre University of Technology Sydney SydneyNSW2007 Australia The Artificial Intelligence Research Center National Innovation Institute of Defense Technology Beijing100071 China

— Millimeter-wave Integrated Sensing and Communications (ISAC) with multi-beam design holds significant promise for vehicular networks, offering multi-target omnidirectional sensing and high-capacity communication services concurrently. Nonetheless, the considerable challenge of potential mutual interference arises due to the high mobility and density of transmitters in such networks. To address this challenge effectively, we propose leveraging inter-vehicle communication to schedule communication and sensing signals for vehicles, thereby enhancing networked sensing capabilities. We first introduce an analytical framework to characterize the mutual interference among multiple vehicles. Subsequently, we evaluate the effectiveness of our proposed interference mitigation method in terms of interference probability, duration, and the achievable detectable density. Additionally, recognizing the different performance requirements of communication and sensing functions, we investigate a joint resource allocation problem catering to both aspects. Simulation results demonstrate a notable enhancement in the proposed ISAC-based interference mitigation, with a 58% reduction in interference probability compared to benchmarking schemes. © 2002-2012 IEEE.

关键词： Resource allocation

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AI based Analysis of Hyperspectral Images with Spectacular Efficacy in Video Analytics

AI based Analysis of Hyperspectral Images with Spectacular E...

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Jadhav, Samarth Yogesh More, Rutuja Rajatam Tingare, Bhagyashree Ashok Lakshmi, G. Prasanna Kolse, Shivam Darwante, N.K. Sanjivani College of Engineering Department of Information Technology MH Kopargaon India D Y Patil College of Engineering Akurdi Department of Artificial Intelligence and Data Science Pune India Sandip University School of Computer Science and Engineering Maharashtra Nashik India Sanjivani College of Engineering Department of Electronics and Computer Engineering MH Kopargaon India

ISBN: (纸本)9798350390049

Hyperspectral imaging and artificial intelligence (AI) have transformed imaging and data processing through their ability to capture and analyze detailed spectral information. This paper explores the integration of hyperspectral imaging with AI, focusing on its impact on video analytics. Hyperspectral imaging provides comprehensive, multi-dimensional data by capturing a wide range of spectral wavelengths, enabling precise material identification and environmental monitoring beyond traditional RGB imaging. The synergy between hyperspectral imaging and AI enhances real-time analysis and decision-making by leveraging deep learning algorithms for pattern recognition and anomaly detection. This study introduces a novel AI-based hyperspectral image analysis approach for video analytics, utilizing convolutional neural networks (CNNs) and hybrid CNN-attention models to improve object recognition and classification. The methodology is validated through experiments that measure classification accuracy, processing speed, and resilience to varying conditions. Results demonstrate significant improvements in accuracy and efficiency over traditional methods, highlighting the potential for advanced applications in surveillance, environmental monitoring, and industrial quality control. © 2024 IEEE.

关键词： Convolutional neural networks

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