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共 20108 条记录，以下是91-100 订阅

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Detection of Medicinal Plants Using Machine Learning 12th

Detection of Medicinal Plants Using Machine Learning

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12th international conference on Recent Trends in Computing, ICRTC 2024

作者： Ravikumar, S. Eugene Berna, I. Babu, R. Arockia Raj, Y. Vijay, K. Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9789819789450

An exciting new direction in plant categorization and identification is the use of machine learning techniques for medicinal plant detection. Using a variety of photo datasets containing medicinal plants, this technique trains models using ANNs. the ML model learns the complex traits that distinguish various plant species by using data from pre-trained models or by developing bespoke architectures. thanks to the trained model’s precision in visually recognizing therapeutic plants, everyone from researchers to ecologists can reap the benefits. Methods for medicinal plant identification that are both accurate and objective are crucial to biodiversity research and attempts to safeguard resources. Considerations of ethics, model calibration, and data curation should be incorporated into the construction of such systems. Gather any and all images of plants used for medical purposes. Various viewpoints, lighting scenarios, and plant types will enhance the set. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Plant diseases

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Development of EEG-Based Optimal Biomarkers for Decoding Fatigue during Motor Imagery Tasks 13

Development of EEG-Based Optimal Biomarkers for Decoding Fat...

引用

13th international Winter conference on Brain-computer Interface, BCI 2025

作者： Lee, Jun-Seok Lee, Seung-Won Kim, Sang-Rok Hwang, Han-Jeong Dept. Electronics and Information Engineering Korea University Sejong30019 Korea Republic of Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong30019 Korea Republic of

ISBN: (纸本)9798331521929

this study aimed to identify electroencephalography (EEG)-based neurophysiological biomarkers for monitoring mental fatigue during motor imagery (MI) tasks. We used EEG data recorded from 29 healthy adults during MI tasks. For the preprocessing of the EEG data, we sequentially performed down-sampling into 200 Hz, independent component analysis (ICA), band-pass filtering between 0.5 and 50 Hz, and common average reference (CAR). the preprocessed EEG data were analyzed using a fast Fourier transform (FFT) to calculate spectral power in the δ (0.5–3 Hz), θ (4–7 Hz), α (8–13 Hz), and β (13–30 Hz) frequency bands. Subsequently, seventeen fatigue-related metrics were calculated using individual spectral powers and their combinations. these fatigue-related metrics were averaged for the frontal lobe (F3 and F4), central lobe (FCC3h and FCC4h), parietal lobe (P3 and P4), and occipital lobe (POO1 and POO2). the statistical significance of the average fatigue metrics was evaluated through Pearson correlation analysis with the sequence of trials. As a result, we observed strong positive correlations (r > 0.9, p © 2025 ieee.

关键词： Fast Fourier transforms

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ElephantGuardNet: A Comprehensive Solution for Elephant Conservation and Human-Elephant Conflict Reduction 4

ElephantGuardNet: A Comprehensive Solution for Elephant Cons...

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Naveen, S. Dhamotharan, R. Sahebzathi, S. Krishnan, R. Santhana Kumar, V. Vinoth Sundaravadivel, P. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Mechanical Engineering Chennai India Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331523923

the ElephantGuardNet system is an advanced technological framework designed to monitor and protect elephants while minimizing human-elephant conflict. the system leverages a combination of real-time data collection, AI-driven analysis, and IoT sensors to track elephant movements across vast territories. By integrating wildlife camera traps, drones, satellite imagery, and IoT sensors such as infrared motion detectors and seismic sensors, the system provides continuous monitoring of elephant activity, enabling proactive management of potential risks. A custom dataset, ElephantMovementTracker, compiled from diverse data sources, forms the foundation for the model's training. Preprocessing techniques such as data normalization, augmentation, and noise filtering enhance the dataset's quality and ensure its suitability for deep learning applications. EfficientNet B7 and YOLO are employed for image and real-time detection tasks, respectively, while 3D CNNs are used for video analytics, enabling accurate tracking of dynamic movement patterns. GRU-based models predict future elephant movements, helping assess the likelihood of conflict. XGBoost is utilized for classification tasks, evaluating the risk of human-elephant conflict. the system is deployed on AWS and incorporates NVIDIA Jetson Nano for real-time detection and automated alerting via SMS and email. IoT sensors and automated deterrent systems, including sound emitters and drones, further mitigate conflict risk. the system's effectiveness in reducing human-elephant conflicts and providing a scalable solution for elephant conservation demonstrates the potential of AI and IoT integration in wildlife protection efforts, offering a comprehensive approach to both conservation and human-elephant coexistence. © 2025 ieee.

关键词： Drones

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Optimizing Intelligent Transportation systems with Multi-agent Reinforcement Learning: A Socio-economic Impact Assessment 8th

Optimizing Intelligent Transportation Systems with Multi-ag...

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8th international conference on Intelligent Transport systems, INTSYS 2024

作者： Cao, Qian Li, Jing Trucco, Paolo Department of Management Economics and Industrial Engineering Politecnico di Milano Milan Italy School of Economics and Management Tsinghua University Beijing China Mogo Co. Beijing China

ISBN: (纸本)9783031863691

Rapid urbanization has exacerbated traffic congestion, presenting significant socio-economic and environmental challenges globally. this paper evaluates the socio-economic impact of implementing Intelligent Transportation systems (ITS) enhanced by a novel Socio-Economic Reinforcement Learning (SERL) framework. We aim to minimize congestion and enhance overall transportation efficiency. the proposed method employs a hierarchical reinforcement learning algorithm specifically designed for complex multi-intersection urban traffic networks, considering socio-economic and environmental factors. Extensive simulations utilizing real-world traffic data assess the impact on travel time, fuel consumption, and emission levels. Experimental results indicate that our approach reduces average travel time by up to 26.67% compared to fixed-time control methods, decreases fuel consumption by 13.99%, and lowers COx/NOx emissions by 20.82% in specific scenarios. these significant improvements over traditional and existing RL-based methods underscore the potential of SERL-powered ITS in promoting sustainable urban development and improving socio-economic outcomes. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Travel time

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Scientific Documents Recommendation Based on Graph Convolutional Network

Scientific Documents Recommendation Based on Graph Convolut...

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PhD Symposium, Posters, Demos, and A Web for more inclusive, sustainable and prosperous societies, WEB-for-GOOD 2024 and 1st international Workshop on AI and Web Data Analytics, AIWDA 2024 form the 25th international conference on Web Information systems engineering, WISE 2024

作者： Li, Jianmin School of Cyber Security and Computer Hebei University Baoding071002 China Hebei Machine Vision Engineering Research Center Hebei University Baoding071002 China

ISBN: (纸本)9789819614820

the rapid growth in the number of scientific documents makes it increasingly necessary to provide researchers with efficient paper recommendation services. Since scientific documents contain many mathematical expressions and rich text information, effectively mining this information to improve recommendation quality is a challenging and necessary task. therefore, this paper proposes a method that fuses content-based and Graph Convolutional Network (GCN) introducing the multi-head attention mechanism for scientific document recommendation. the method employs Hesitant Fuzzy Set (HFS) and Bidirectional Encoder Representations from Transformers (BERT) to calculate the similarity between mathematical expressions and their context, respectively, and utilizes GCN to learn the complex relationships between nodes and mine the potential similarity between nodes. then, the multi-head attention mechanism is introduced to dynamically assign importance weights to neighbor nodes to enhance the expression of embedding vectors, thereby generating more accurate and personalized recommendation lists. Experimental results show that the method performs well on both Ntcir and extended Chinese datasets, achieving maximum F1_Score vales of 0.817 and 0.837 at Top-50 and Top-30, respectively. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolution

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Leveraging Optimization Techniques for Effective Arabic Query Expansion

Leveraging Optimization Techniques for Effective Arabic Que...

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作者： Allahim, Azzah Cherif, Asma College of Computer and Information Sciences Jouf University Sakaka Saudi Arabia IT Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia The Center of Excellence in Smart Environment Research King Abdulaziz University Jeddah21589 Saudi Arabia

ISBN: (纸本)9789819614820

the primary goal of query expansion is to gather terms that are closely associated with the original query terms. To accomplish this goal, similarity measurements are used to evaluate the similarity between the query terms and the collected terms. Unfortunately, existing Arabic query expansion frameworks focus on expanding the query words individually. this approach can affect the expansion process for a complex language such as Arabic. In this paper, we investigate the effectiveness of applying an AI optimization algorithm to Arabic query expansion. the study aims to produce the optimal combination of terms by measuring the similarity of the collected terms against all query terms. We tested two intelligent swarm algorithms: the Cuckoo Search algorithm and Harmony search algorithm. Both algorithms were successful in enhancing the model’s precision and recall score with Cuckoo Search algorithm achieving the best performance of (97.5%) precision and (93%) recall. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Structured Query Language

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Quantum Convolutional Neural Network for Bone Fracture Classification from X-Ray Images 16

Quantum Convolutional Neural Network for Bone Fracture Class...

引用

16th international conference on ENTERprise Information systems, CENTERIS 2024 - 12th international conference on Project MANagement, ProjMAN 2024 - 14th international conference on Health and Social Care Information systems and Technologies, HCist 2024

作者： Mewada, Hiren Pires, Ivan Miguel Rahevar, Mrugendrasinh Khatri, Narendra Electrical Engineering Department Prince Mohammad Bin Fahd University P.O. Box 1664 Al Khobar31952 Saudi Arabia Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal Changa Gujarat Anand388421 India Department of Mechatronics Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

Accurate and efficient classification of bone fractures from X-ray images is crucial for timely diagnosis, effective treatment planning, and improved patient outcomes in orthopedic medicine. Convolutional neural networks (CNNs) have demonstrated their ability to automatically extract relevant features from X-ray images, making them a powerful tool for the classification of complex bone fracture patterns. However, as the complexity of the classification task increases, the computational requirements of classical CNNs also grow rapidly. this poses a challenge in high-stakes medical applications, where the accuracy and efficiency of the classification system are paramount. In this work, we present a quantum convolutional neural network (QCNN) for the classification of bone fractures from X-ray images. A quantum convolution layer is integrated as the first layer of the network, which leverages the unique properties of quantum mechanics to extract more informative features from the input X-ray images. A traditional CNN network follows this quantum-based feature extraction step to perform the final bone fracture classification. We tested the proposed QCNN model on the Bone Fracture Multi-Region X-ray Data, a benchmark dataset for evaluating bone fracture classification systems. the QCNN architecture achieved an impressive 96% accuracy and 95.81% F1-score, demonstrating its superior performance compared to state-of-the-Art classical CNN models. © 2025 the Author(s).

关键词： Multilayer neural networks

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Enhancing TrUStAPIS Methodology in the Web of things with LLM-Generated IoT Trust Semantics 26th

Enhancing TrUStAPIS Methodology in the Web of Things with LL...

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26th international conference on Information and Communications Security, ICICS 2024

作者： Ferraris, Davide Kotis, Konstantinos Kalloniatis, Christos Network Information and Computer Security Lab University of Malaga Malaga Spain Department of Cultural Technology and Communication University of the Aegean Mytilene Greece

ISBN: (纸本)9789819787975

In the Internet of things (IoT) there are ecosystems where their physical’smart’ entities virtually interact with each other. Often, this interaction occurs among unknown entities, making trust an essential requirement to overcome uncertainty in several aspects of this interaction. However, trust is a complex concept, and incorporating it in IoT is still a challenging topic. For this reason, it is highly significant to specify and model trust in early stages of the System Development Life Cycle (SDLC) of IoT-integrated systems, thus enhancing the aforementioned task. TrUStAPIS is a requirements engineering methodology recently introduced for incorporating trust requirements during IoT-based system design. the scope of this paper is to provide an extension of TrUStAPIS by introducing IoT trust semantics compatible with the W3C Web of things (WoT) recommendations generated with the assistance of Large Language Models (LLMs). Taking advantage of LLMs as a tool for integrating and refining existing methodologies, in this paper we present our work towards a revision of the TrUStAPIS methodology. In this work, we contribute a new conceptual model and a refined JSON-LD ontology that takes into account IoT trust semantics, providing eventually a valuable tool for software engineers to design and model IoT-based systems and services. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Requirements engineering

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CSI-Based Signal Reconstruction for WiFi Localization 9th

CSI-Based Signal Reconstruction for WiFi Localization

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9th international conference on Intelligent IoT as a Service

作者： Hu, Yunbing Peng, Ao Li, Shenghong Xiamen Univ Sch Informat Xiamen 361001 Peoples R China Chongqing Coll Elect Engn Chongqing 401331 Peoples R China Commonwealth Sci & Ind Res Org Marsfield Canberra NSW 2122 Australia

ISBN: (纸本)9783031705069;9783031705076

With the advent of the 5G era, the demand for high-precision wireless positioning continues to grow. However, traditional ranging-based positioning systems are highly susceptible to interferences caused by multipath and none-line-of-sight (LOS) propagation, which can significantly degrade the accuracy of the estimated time-of-arrival (TOA) values. To address this challenge, this paper proposes a Deep neural networks (DNN)-based approach for accurate TOA estimation in indoor environments. Using a complex-values neural network model, the proposed method predicts TOA directly from the frequency domain channel state information (CSI) of wideband WiFi receivers. We also propose an input normalization method based on peak search in the channel impulse response, which improves both the accuracy of TOA estimation and the efficiency of model training. the proposed method was verified experimentally both in an outdoor area of 900 m(2) with 6 anchors and an indoor area of 700 m(2). It is shown that the proposed approach significantly outperforms conventional methods, with 77% of the positioning errors within 0.5m in the outdoor test and 95% within 1m. In the indoor test, about 64% of the positioning errors were within 0.5m, and approximately 80% were within 1m.

关键词： Channel state information ieee 802.11 signals complex value deep neural network

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Federated Learning for Edge Heterogeneous Object Detection Algorithm 18th

Federated Learning for Edge Heterogeneous Object Detection ...

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18th international conference on Wireless Artificial Intelligent Computing systems and Applications, WASA 2024

作者： Hu, Yanxin Liu, Gang Cheng, Chao School of Computer Science and Engineering Changchun University of Technology Changchun130102 China Jilin Province Data Service Industry Public Technology Research Centre Changchun130102 China

ISBN: (纸本)9783031714665

the combination of federated learning and object detection techniques allows for the use of extensive datasets. this approach creates target detection models that are robust and adaptable. It also upholds strict privacy and security protocols. However, the computational resource variance among edge nodes poses significant constraints on the scalability of detection algorithms, thereby impinging on their accuracy and operational efficiency. this discrepancy compels clients, who might otherwise engage complex models, to resort to more simplified algorithms. To navigate this challenge, we introduce a federated learning framework enriched with a knowledge distillation strategy, specifically engineered for the object detection realm. this framework intelligently selects the largest model from the pool of client-contributed models and facilitates model upload and dissemination through a knowledge transfer mechanism orchestrated at the server side. Extensive experimental validations underscore the method’s proficiency in addressing the heterogeneity of detection algorithms within a varied environment, showcasing its viability for real-world application. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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