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检索条件"任意字段=4th International Multi-Conference on Computing in the Global Information Technology"

共 11813 条记录，以下是61-70 订阅

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A New Indoor Occupancy Detection Model by Integrating the Efficient multi-scale Attention Mechanism into the EfficientDet Model 5th

A New Indoor Occupancy Detection Model by Integrating the Ef...

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5th international conference on Neural computing for Advanced Applications (NCAA)

作者： Yan, Qichao Zhang, Hanyuan Zhao, Hao Shandong Jianzhu Univ Sch Informat & Elect Engn Jinan 250101 Peoples R China

ISBN: (纸本)9789819770007;9789819770014

the occupancy information is essential for ensuring indoor comfort and enhancing building energy efficiency. through image-based object detection, the indoor occupancy information can be obtained. However, small targets of the human always reveal the weak feature expression ability in indoor scenes, which increases the difficulty of detection. In addition, due to the complexity of the indoor environment, the possibility of false detection of objects similar to human characteristics is increased. Aim at the above problems, a new indoor occupancy detection model by integrating the efficient multi-scale attention mechanism into the EfficientDet model is proposed herein. Firstly, to solve the problem of indoor human small target detection, the EfficientDet model is innovatively applied to indoor human detection. Secondly, for the purpose of reducing the interference from background objects and focus on human key information, this paper infuses the EMA mechanism with the EfficientDet model. Finally, a self-built classroom dataset has been constructed for validating the occupancy detection performance of the model. the experimental results show that the average recognition accuracy of this method on public dataset SCUT-HEAD-Dataset Part B is 92.85%, and that on self-built classroom dataset is 96.15%. Compared with other mainstream algorithms, EMA-EfficientDet also shows good performance.

关键词： Occupancy information Image-based object detection EfficientDet efficient multi-scale attention

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Enhanced Speech Emotion and Gender Recognition Using Hybrid CNN-LSTM Models 4

Enhanced Speech Emotion and Gender Recognition Using Hybrid ...

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

作者： Priyadharshini, S. Sundar, G. Naveen Narmadha, D. thangavel, Senthil Kumar Ajibesin, Adeyemi Abel Kolandapalayam Shanmugam, Selvanayaki Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Cape Peninsula University of Technology Faculty of Informatics and Design Department of Information Technology South Africa Ashland University Department of Mathematics and Computer Science United States

ISBN: (纸本)9798331523923

the current paper proposes a new approach for peripheral speech emotion analysis and gender estimation incorporating the best machine learning architectures such as CNNs and LSTMs. Its correct depiction of emotions and the variation of the gendered speech to indicate manifold signal-to-noise ratio make the approach accommodate the difficulty inherent in recognizing emotions from the recording of voices as well as predicting the gender of a speaker. It is then able to ally with preprocessing and collection of a vast range of different speech samples, feature extraction of pitch, tone and spectral data. these features serve as input to the CNNs and LSTMs: LSTMs work well at temporal regular features and the CNNs work well in feature extraction by location (spatio feature). the approach is tested on the RAVDESS and IEMOCAP datasets and establishes a near perfect match to prior approaches with reference to recognition accuracy of emotion analysis and gender prediction. thus, applying the identified dual method approach, which could be described by such characteristics as highly accurate data pre-preprocessing, clear indication of the features of interest, a different CNN-LSTM model joint training, it offers a reasonably good platform to gain certain insights into such characteristics of the speech as our experiment demonstrates. this work offers satisfactory configurations on concerns such as human computer relation, customer relation and mental health surveillance to suggest that other applications could be achieved in future. © 2025 IEEE.

关键词： Convolutional neural networks

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Efficient cloud-assisted revocable identity-based broadcast encryption in IoT 4

Efficient cloud-assisted revocable identity-based broadcast ...

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4th international conference on Network Communication and information Security, ICNCIS 2024

作者： Chen, Zhou Han, Lidong School of Information Science and Technology Hangzhou Normal University Hangzhou China

ISBN: (数字)9781510688261

ISBN: (纸本)9781510688254

While the Internet of things (IoT) has greatly facilitated our daily lives, this trend has encountered numerous security challenges in data storing and sharing especially for sensitive personal data. Identity-Based Broadcast Encryption (IBBE) is an efficient cryptographic technique to securely share messages among multiple users. Nevertheless, to prevent access by users who were previously authorized but now lack permission, user revocation should be integrated into IBBE. Despite several previous Revocable Identity-Based Broadcast Encryption (RIBBE) schemes were proposed, they are not practical for resource-constrained IoT devices due to their high computational cost during encryption and decryption, as well as the significant storage needs for the revocation list. To tackle these issues, we propose an outsourced RIBBE scheme that is appropriate for resource-constrained IoT devices. It not only effectively implements user revocation but also leverages a cloud server to handle complex calculations. Additionally, our scheme is designed to resist collusion attacks, enhancing overall security. Performance evaluation describes the great practicality and efficiency of our scheme. © 2025 SPIE.

关键词： Internet of things Clouds Computer security Cloud computing Cryptography Network security Systems modeling

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Grading of wheat powdery mildew based on pixel point 4

Grading of wheat powdery mildew based on pixel point

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4th international conference on Computer Vision, Application, and Algorithm, CVAA 2024

作者： Zang, Hecang Wang, Congsheng Zhao, Qing Chen, Dandan Zhang, Jie Li, Guoqiang Institute of Agricultural Information Technology Henan Academy of Agricultural Sciences Zhengzhou450002 China Huanghuaihai Key Laboratory of Intelligent Agricultural Technology Ministry of Agriculture and Rural Areas Zhengzhou450002 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Powdery mildew is an important factor affecting wheat yield and global food security, and precise grading of wheat powdery mildew images is an important means of computer-aided precision control. this article is based on the improved Swin Unet method for segmenting images of wheat powdery mildew and calculating the areas of wheat leaf lesions and healthy regions;By using the principle of pixel statistics to calculate the ratio of lesion area to leaf area, and utilizing the segmentation results of lesions for pixel statistics, the severity of wheat powdery mildew can be accurately evaluated. this research method can accurately segment wheat powdery mildew in complex backgrounds, providing scientific basis for automatic grading and early prevention of wheat powdery mildew in complex field environments. © 2025 SPIE.

关键词： Pixels

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Automated Aeroponics System: IoT-Based Monitoring and Control for Optimized Plant Growth 4

Automated Aeroponics System: IoT-Based Monitoring and Contro...

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4th international conference on Power, Control and computing Technologies, ICPC2T 2025

作者： Tanna, Rajesh Santhosh, Talada Gollavilli, Sanjay Kumar Demudu, Lagudu Suri Naik, Ratala Koteswara Raghuram, Cheepurupalli Department of Electrical and Electronics Engineering Vignan's Institute of Information Technology Visakhapatnam530049 India Department of Electrical and Electronics Engineering Visakha Institute of Engineering & Technology Visakhapatnam530027 India Department of Electrical and Electronics Engineering Vignan's Institute of Engineering for Women Visakhapatnam530046 India Department of Electrical and Electronics Engineering A. M. Reddy Memorial College of Engineering and Technology Narasaraopet522601 India Department of Computer Science and Engineering Sagi Rama Krishnam Raju Engineering College Bhimavaram534204 India

ISBN: (纸本)9798331530402

Challenges of farming are getting worse due to pests, other environmental issues, and changes in the climate. Several novel technologies have been developed to improve plant growth and productivity, yet there are still numerous issues in this field. this suggests that daily improvements are needed. this paper provides an innovative automated system that uses a variety of environmental sensors, including as pH, TDS, and humidity, for hydroponics and aeroponics. these sensors continuously monitor and modify crucial components to provide the perfect growth environment. In addition, the system has autonomous nutrient dosing, which reduces the need for human intervention and boosts overall effectiveness. Better control over the farming process is made possible by the system's ability to scale and monitor in real-time thanks to the integration of IoT technologies. the study also looks at how machine learning (ML) and artificial intelligence (AI) might improve crop health monitoring and growth forecasts. Additionally, an LED-lit indoor farming setup is included in the system. © 2025 IEEE.

关键词： Agricultural robots

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Generative AI in Agri: Sustainability in Smart Precision Farming Yield Prediction Mapping System Based on GIS Using Deep Learning and GPS 4

Generative AI in Agri: Sustainability in Smart Precision Far...

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4th international conference on Evolutionary computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Senthil, G.A. Suganthi, S.U. Prinslin, L. Selvi, R. Prabha, R. Department of Information Technology Agni College of Technology Chennai India Department of Artificial Intelligence and Data Science Sri Sai Ram Institute of Technology Chennai India Department of Computer Science and Engineering Agni College of Technology Chennai India Assistant Professor Department of Computing Technologies SRM Institute of Science and Technology kattankulathur Chennai India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India

Agriculture faces many challenges of precision farming, such as the need for sustainable practices, improving yields, ensuring high yields. In resolution to these challenges, the present research provides an AI-based system that enables the use of deep learning, global Positioning System (GPS), and Geographic information System (GIS) technologies to create a highly intelligent smart agricultural precision farming system. Its goal is to monitoring crop health and reduce disease risk, which will lead to improved resource utilization and environmentally sustainability techniques. the proposed framework addresses the urgent need for consistency in agricultural practices, especially as global agriculture deals with pressures from climate change, resource shortages, and increasing demand for food. Traditional agricultural methods for predicting and optimizing crop yields due to increasing factors affecting crop performance Not enough generative AI, especially the use of deep learning models, supports agricultural research in many cases, allowing patterns to be identified and future results to be predicted accurately. the integration of GPS and GIS allows for more accurate mapping, real-time analysis, and effective decision-making. Weather forecasting variability, resource constraints, and demand for more food are isolated from environmental influences using deep learning models, especially Artificial Neural Networks (ANN). By using large data sets, including historical crop yield performance, soil properties, and weather conditions, the system provides highly accurate crop forecasts. Generative Adversarial Networks (GANs) and You Only Look Once (YOLO) hybrid model is playing a key role in generating crop yield and growth potential under different conditions, adjusting model accuracy over time, and this combination of ANN, GANs and YOLO optimization algorithms ensures that the system continuously enhances its predictive accuracy and overall effectiveness. the proposed gene

关键词： Generative adversarial networks

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Fusion of data dimensionality reduction algorithms baced on category representation theory 4

Fusion of data dimensionality reduction algorithms baced on ...

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4th international conference on Computer Vision, Application, and Algorithm, CVAA 2024

作者： Xu, Xiaoxiang Li, Fanzhang Zhang, Li School of Computer Science and Technology Joint International Research Laboratory of Machine Learning and Neuromorphic Computing Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China

ISBN: (纸本)9781510687615

For a long time, people have believed that representation problems are one of the bottlenecks in the field of machine learning. therefore, it is a long-term and exploratory work to study machine learning representation methods. Due to this, we use category theory to study the fusion representation of data dimensionality reduction. We propose the basic concept of category representation for data dimensionality reduction and provide a data dimensionality reduction fusion representation framework. We have conducted research and analysis on algorithms such as PCA, KPCA, and LDA, identified the essential connection between PCA, KPCA, and LDA and proposed a data dimensionality reduction fusion representation algorithm based on a data dimensionality reduction fusion representation framework. Finally, we demonstrated the feasibility of the proposed method through experiments. © 2025 SPIE.

关键词： Dimensionality reduction

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Implementation of multi-Label Fuzzy Classification System using Topic Detection Data set 4

Implementation of Multi-Label Fuzzy Classification System us...

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4th international conference on Evolutionary computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Kanagaraj, R. Krishnaraj, N. Selvakumar, J. Ramprasath, J. School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore641022 India Department of Information Technology Dr.Mahalingam College of Engineering and Technology Pollachi642003 India

multiclass Classification can be implemented by using consequent approaches to translate the multiclass problem into binary class classification problems and fuzzy classification methods. this work proposes a predictive analysis of the multiclass fuzzy Classification integrated with time series historical data and topic detection. the fuzzy classification techniques can be successfully applied to Topic detection and sub-topic detection. Text databases' manual topic detection method must be more feasible, uncontrollable and effective. thus, initiating the huge amount of data implemented by manual methods is idealistic. Fuzzy historical data is more significant for data analysis in different models to make predictions. Innumerable fuzzy logic on time series methods has been implemented for data prediction. A multiclass Fuzzy Time Series Classification Algorithm has been implemented to analyze and predict the topic detection database. the outcomes of the Fuzzy classification technique have been implemented for the need for an extensive pattern of topic detection. An enhanced multiclass Fuzzy Time Series Classification Algorithm has been applied to achieve the efficient de-fuzzification operation of the topic detection data set. To illuminate the forecasting method, the historical data of multi-labeled has been used for the predictive model. the investigation result illustrates that the MHTSC algorithm generates mode fuzzy classification and irregular rules, efficiently reducing the error rate from multi-labeled data. © 2025 the Authors. Published by Elsevier B.V.

关键词： Fuzzy Classification Fuzzy Time Series MHTSC Topic Detection

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Pneumonia Detection in X-Ray Chest Images Based on Convolutional Neural Networks and Data Augmentation Methods 11

Pneumonia Detection in X-Ray Chest Images Based on Convoluti...

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11th international conference on information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2025

作者： Dardouri, Samia Department of Computer Science College of Computing and Information Technology Shaqra University Saudi Arabia InnoV'COM Laboratory-Sup'Com University of Carthage Tunisia

ISBN: (纸本)9789897587436

Pneumonia, a widespread lung ailment, stands as a leading global cause of mortality, particularly affecting vulnerable demographics such as children under five, the elderly, and individuals with underlying health conditions. Accounting for a significant portion of childhood fatalities, at 18%, pneumonia remains a critical health concern. Despite advancements in imaging diagnostic methods, chest radiographs remain pivotal due to their cost-effectiveness and rapid results. the proposed model, trained on data sourced from a readily available Kaggle database, consists of two primary stages: image preprocessing and feature extraction/image classification. Utilizing a CNN model, the framework achieves remarkable performance metrics, with precision, recall, F1-score, and accuracy reaching 93%, 96%, 94%, and 96%, respectively. these results underscore the CNN model's effectiveness in pneumonia detection, showcasing superior consistency and accuracy compared to other pretrained deep learning models. Copyright © 2025 by SCITEPRESS - Science and technology Publications, Lda.

关键词： Adam Optimizer CNN Deep Learning Early Detection Feature Extraction Image Data Augmentation Pneumonia Infection

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Hybrid genetic algorithm with rank sorting for obtaining bijective S-boxes with good cryptographic properties 4

Hybrid genetic algorithm with rank sorting for obtaining bij...

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4th international conference on Network Communication and information Security, ICNCIS 2024

作者： Li, Xin Liu, Zhenghong Zhao, Haixia School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin541004 China Guilin541004 China Guangxi Colleges Universities Key Laboratory of Data Analysis and Computation Guilin541004 China School of Information and Communication Guilin University of Electronic Technology Guilin541004 China

ISBN: (数字)9781510688261

ISBN: (纸本)9781510688254

An S-box is an essential component in block ciphers, and its cryptography properties play a significant role in the security of the cipher. A good S-box must have high nonlinearity (NLF ) and low differential uniformity (χF ). In this paper, a rank-sorting hybrid genetic algorithm is designed to find S-boxes that possess high nonlinearity and low difference uniformity. Compared with the traditional genetic algorithm, the rank-sorting hybrid genetic algorithm designed in this work was improved in the following aspects. First, the crossover protection strategy was designed and used in the crossover phase, which can assure the bijectivity of offspring. Second, an elite selection strategy based on the value of the fitness function was utilized in the selection step to prevent the loss of excellent individuals. In particular, a sorting method named the rank-sorting algorithm was proposed and implemented on the selected offspring before the next iteration began. the result of using the rank-sorting algorithm is that the differences between individuals for the next crossover increase, the possibility of generating excellent offspring increases, and the risk of the whole algorithm becoming stuck in local optimal solutions decreases. the simulation results show that when the number of variables is even (8 to 12), the bijective S-boxes with almost high nonlinearity and low differential uniformity can be searched by using the rank-sorting hybrid genetic algorithm. © 2025 SPIE.

关键词： Genetic algorithms

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