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4th IEEE International Conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Ahmed, Marzia Mohamad, Ahmad Johari Hassan, Md Maruf Bhuiyan, Touhid Department of Software Engineering Dhaka Bangladesh Faculty of Electrical & Electronics Engineering Technology Malaysia Southeast University Department of Computer Science and Engineering Dhaka Bangladesh School of IT VA United States

ISBN: (纸本)9798331518882

Air quality forecasting is critical for environmental monitoring and public health, and in this study, we propose a hybrid approach utilizing Gooseneck Barnacle Optimization (GBO) and Artificial Neural Networks (ANN) to enhance predictive accuracy. The air quality dataset, comprising pollutant concentrations (CO, NOx, benzene) and meteorological factors (temperature, humidity), is sourced from the UCI Machine Learning Repository. GBO is employed to fine-tune the ANN's hyperparameters (especially the weight and bias), as well as the number of layers, neurons, and activation functions, to improve regression-based forecasting. The GBOANN model achieves a Mean Squared Error (MSE) of 0.562 and a Root Mean Squared Percentage Error (RMSPE) of 5.63%, outperforming other metaheuristic methods like BMO, SSA, DA, MVO, and EMA. This study demonstrates the GBO-ANN model's potential for accurate real-time air quality forecasting, offering a robust, sustainable solution. © 2025 IEEE.

关键词： Air Quality GBO Metaheuristic Neural Network Time series forecasting

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A Blended Learning Strategy for English-Based Programming Courses in China's Higher Education 13

A Blended Learning Strategy for English-Based Programming Co...

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13th International Conference on Educational and Information technology, ICEIT 2024

作者： Wu, Mei Wang, Xining Lu, Tao O'Hare, Gregory M.P School of Computer Science and Engineering School of Artificial Intelligence Wuhan Institute of Technology China School of Education School of Computer Science and Statistics Trinity College Dublin Dublin Ireland School of Computer Science and Statistics Trinity College Dublin Dublin Ireland

ISBN: (纸本)9798350372663

As English becomes the mainstream language in communication and education worldwide, more and more Chinese universities emphasize the importance of adding English content to the higher education curriculum. This paper explores the integration of English content into computer science education in Chinese universities, with a focus on bilingual programming courses. It proposes a blended learning strategy that combines face-to-face classroom interaction with platform-based self-directed learning. It designed the experiment and the number of participants is 84. The experimental results show that the adoption of this blended teaching strategy can enhance students' enthusiasm for bilingual programming courses and facilitate students in achieving the desired learning effect. © 2024 IEEE.

关键词： Students

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Machine Learning-Based Lifespan Prediction Modelling for Electric Submersible Pumps in Oil Wells 2

Machine Learning-Based Lifespan Prediction Modelling for Ele...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Rajakala, J. Gupta, Satyaveer Naidu, Pothala Mutyalu Basi Reddy, A. Revathi, S. Selvan, R. Senthamil Sai Spurthi Institute of Technology Department of Computer Science and Engineering Gangaram India Delhi Technical Campus Department of Electrical Engineering Greater Noida India Vignan Institute of Technology And Science Department of Cse Hyderabad India Mohan Babu University School of Computing Department of Computer Science and Engineering Tirupati India Sri Ramakrishna Institute of Technology Department of Computer Science and Engineering Coimbatore India Annamacharya Institute of Technology and Sciences Department of Electronics and Communication Engineering Tirupati India

ISBN: (纸本)9798350364699

To maintain the output flow rate and prolong the life of oil wells, artificial lift is crucial to the petroleum industry. Electric Submersible Pumps (ESP) are a widely used artificial lift technique due to their ability to generate large flow rates in very deep wells. ESPs have a much shorter lifetime than anticipated, even though they are designed to function in harsh environments including corrosion, high temperatures, and high pressure. Because ESP intervention work is far more expensive than other artificial lift techniques, particularly for offshore wells, ESP failures result in production loss and raise replacement costs. Knowing when an ESP may fail is thus highly helpful in the oil production industry and affects how oil wells are designed, built, and managed. Using three machine learning algorithms - Decision Tree, Random Forest, and Gradient Boosting Machine - this study contributes to the construction of prediction models for ESP lifespan that take into account both dynamic and static ESP features. The model that produced the best results for ESP life cycle prediction was determined by comparing the output of various models. To predict the most influential characteristics on the length of ESP, this research also assessed the influence factor of different operational parameters. By knowing more about ESP behavior, early efforts to avert possible ESP failures may be realized thanks to the study's findings. © 2024 IEEE.

关键词： Submersible pumps

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Research on Elevator Group Control Algorithms 6

Research on Elevator Group Control Algorithms

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6th International Conference on Internet of Things, Automation and Artificial Intelligence, IoTAAI 2024

作者： Zheng, Fanghao Yao, Haobin Liang, Ruishi Tu, Mingjun University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China University of Electronic Science and Technology of China Zhongshan Institute Zhongshan China University of Electronic Science and Technology of China Zhongshan Institute School of Computer Science Zhongshan China

ISBN: (纸本)9798350386974

Elevator group control algorithms constitute a vital component of intelligent building transportation management systems. This paper delves into the multidimensional challenges inherent in such algorithms: the uncertainty of passenger demand, striking a balance between energy efficiency and service quality, and the intricacy of the algorithms necessitating real-time execution. It provides a comprehensive review of the evolution and core challenges faced in elevator group control algorithms, with an emphasis on analyzing the characteristics, advantages, and limitations of various methodologies, including expert systems, fuzzy logic control, genetic algorithms, particle swarm optimization, neural networks, and reinforcement learning. The study concludes by casting a forward glance at the prospective trajectories of elevator group control technology, notably the promising prospects of group control algorithms that integrate reinforcement learning with deep neural networks. These advanced algorithms hold significant potential in enhancing transportation efficiency, facilitating energy conservation and emission reduction, as well as optimizing passengers' psychological waiting experience in elevators. © 2024 IEEE.

关键词： Energy efficiency

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FedHZone: Federated Learning Based Hazardous Zone Detection Using Internet of Drone Things

FedHZone: Federated Learning Based Hazardous Zone Detection ...

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2024 IEEE International Conference on Communications, Computing, Cybersecurity and Informatics, CCCI 2024

作者： Biswas, Payel De, Debashis Obaidat, Mohammad S. Hazra, Arnab Sadoun, Balqies Jogesh Chandra Chaudhuri College Department of Computer Science Kolkata India Maulana Abul Kalam Azad University of Technology Centre of Mobile Cloud Computing Department of Computer Science and Engineering West Bengal WB Nadia India School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China School of Computing SRM University Department of Computational Intelligence SRM Nagar TN Kattankulathur603203 India School of Engineering The Amity University UP Noida201301 India Dr. Sudhir Chandra sur Institute of Technology & Sports Complex Department of Computer Science and Engineering Kolkata India College of Engineering Al Balqa ' Applied University Al-Salt19117 Jordan

ISBN: (纸本)9798350349832

Air pollution can affect human health, so it is necessary to predict the air quality index (AQI) in advance. In this work, air quality data collected by the Internet of Drone Things (IoDT) is predicted and analyzed to detect highly polluted zones using a distributed privacy-preserving federated learning (FL) approach. Air quality data for a particular zone is collected and then fed into a Long short-Term memory (LSTM) based model to predict the pollution level in local training. Global aggregation is done by an IoDT-mounted central server to aggregate the received models from the local IoDT level. Privacy-preserving FedHZone predicts and analyses the AQI values from the past time series dataset in a multi-drone environment. This work detects the most hazardous zone in a larger region of Jaipur, Rajasthan using real-Time data. The result shows FedHZone with stacked LSTM having ''2%-7% better accuracy than the vanilla LSTM-based model. © 2024 IEEE.

关键词： Federated learning

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Evaluation of Supply Chain Design Decisions under Uncertainty - A Case Study Analysis

Evaluation of Supply Chain Design Decisions under Uncertaint...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Joe Prathap, P.M. Beevi, L. Sherin Dani, W. Vinil Vivek, Panguluri Venkata Jubilson, E. Ajith R.M.D. Engineering College Department Of Computer Science And Engineering Kavaraipettai India St. Peter's College Of Engineering And Technology Department Of Eee Chennai India School Of Computer Science And Engineering VIT-AP University Andhrapradesh India

ISBN: (纸本)9798350371567

This study digs into the strategic supply chain decision-making process of Bridgestone Tires, a global tyre manufacturing leader, demonstrating the effectiveness of decision tree analysis in dealing with complex uncertainty. Bridgestone Tires has a variety of problems, including fluctuating exchange rates and uncertain demand patterns, demanding strategic plant placement across its global network. Using decision tree methodology, this study examines optimal choices under various current and future situations, providing light on the importance of addressing demand unpredictability and economic variables in financial supply chain design decisions. The findings emphasise the importance of thoroughly integrating financial data, identifying risks, calculating cash flows, and considering alternative scenarios. While decision tree tools are cost-effective, simulation models enable nuanced assessments of complex scenarios, but at a greater setup and operating cost. Finally, this study explains how firms can use decision tree analysis and simulation modelling to effectively manage uncertainties, allowing for more informed and optimised supply chain design decisions in dynamic global environments. © 2024 IEEE.

关键词： Decision trees

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Early-Stage Disease Prediction in Chilli Plant Using YOLO Models 2

Early-Stage Disease Prediction in Chilli Plant Using YOLO Mo...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Manoj, R. Girish, Sandra Pushpa, B.R. Rani, N. Shobha Sangamesha, D. Department of Computer Science School of Computing Amrita Vishwa Vidyapeetham Mysuru India Department of Computer Science and Engineering Gitam School of Technology Gitam University Bengaluru India Department of Chemistry The National Institute of Engineering Mysore India

ISBN: (纸本)9798350383867

Agriculture is an important component of every country's economy, supplying the necessary resources to farmers and their families. The livelihoods of farmers are greatly threatened by crop diseases, which highlights the importance of early identification of diseases in chilli plants at the right stage of growth is crucial for timely fertilizer recommendation. This paper presents an intelligent transfer learning technique to detect thrips and viruses in chilli plants in the early stages of development. Stage-by-stage datasets are collected from chilli plant orchards around Mysore district, Karnataka, to carry out the training using the YOLO model. The dataset consists of three growth stages of chilli plants (15, 25, and 35 days) that are captured in diverse environmental settings with multiple-resolution smart phones. The annotations are created for the diseases healthy, viruses, and thrips at leaf level from whole plants for all three stages. Hyperparameters such as learning rate, the learning optimization algorithm, batch size, epochs, and loss functions are employed to fine-tune the YOLO v5, v6, and v7 models. To achieve high generalisation and reduce overfitting, a weight decay (weight_decay) of 0.0005 is employed. From the evaluation, it was noticed that YOLOv7 outperformed other YOLO models. A mAP score of 0.80 is achieved in stage 1, 0.82 in stage 2, and 0.73 in stage 3 with minimal validation loss and remarkable inference speed. © 2024 IEEE.

关键词： Plant diseases

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Multimodal Emotion Recognition Using computer Vision: A Comprehensive Approach 15

Multimodal Emotion Recognition Using Computer Vision: A Comp...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mohan, G. Bharathi Sarrvesh, N. Hrishikeasan, N.P. Boopalamani, J. Doss, Srinath Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Faculty of Engineering and Technology Botho university Botswana

ISBN: (纸本)9798350370249

Real-time understanding and response to human emotions have become critical in today’s connected world of prevalent human-computer interaction. This study presents a novel approach to real-time emotion detection using a camera-based system that analyzes both voice and facial expressions. Our method advances the state-of-the-art by integrating deep learning techniques with parallel models for audio and visual inputs through a weighted fusion approach, a strategy not commonly employed in existing systems. Utilizing datasets such as CK+48 and the Ryerson Audio-Visual Database of Emotional Speech and Song (Ravdess), our system achieves superior performance metrics, including an accuracy of up to 92.92% and an F1 score of 92.94%. Encapsulated within a user-friendly microservice with an intuitive online interface, our solution enables seamless real-time interaction using only a webcam and microphone. Ongoing efforts focus on refining the system’s ability to recognize diverse voices and expressions, aiming to create technology that empathizes with human emotions and enhances interpersonal interactions. ©2024 IEEE.

关键词： Emotion Recognition

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Automatic skin lesion classification using a novel densely connected convolutional network integrated with an attention module

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 8943-8956页

作者： Shan, Pufang Chen, Jialei Fu, Chong Cao, Lin Tie, Ming Sham, Chiu-Wing School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China Science and Technology on Space Physics Laboratory Beijing100076 China School of Computer Science The University of Auckland Auckland New Zealand

Automated skin lesion classification in dermoscopy images remains challenging due to the existence of artefacts and intrinsic cutaneous features, diversity of lesion morphology, insufficiency of training data, and class imbalance problem. To address these challenges, we propose a new densely connected convolutional network termed AttDenseNet-121, which is obtained by integrating the convolutional block attention module (CBAM) into DenseNet-121. CBAM is a simple yet efficient attention module, which is further improved by adding a novel pooling layer (multiscale-pooling) to effectively enhance its attention capability. The optimized CBAM strengthens the representation power of DenseNet-121 by emphasizing meaningful features and suppressing unnecessary ones, thus significantly enhancing the classification performance. Besides, to handle the imbalanced dataset, we employ an improved focal loss function rather than the traditional cross-entropy loss function to train AttDenseNet-121. The improved focal loss is calculated from a different perspective compared with the original focal loss. It makes the distribution of positive samples and negative samples in each batch the same as that in the original data, successfully mitigating the negative influence of class imbalance on this multi-class classification task. We conduct extensive experiments on the public benchmark dataset (HAM10000) and the results indicate that our proposed method achieves superior performance compared with that of the baselines and state-of-the-art algorithms. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolution

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DSP-Based Industrial Defect Detection for Intelligent Manufacturing 26

DSP-Based Industrial Defect Detection for Intelligent Manufa...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Yue, Han Wang, Rucen Gao, Yi Xia, Ailing Zhang, Jianhua Tianjin University of Technology School of Computer Science and Engineering Tianjin China Zhejiang University of Technology College of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798350331684

Internet of Things (IoT) based industrial defect detection has attracted more and more attention. As a key component of intelligent manufacturing, defect detection is very important. Although deep learning (DL) can reduce the cost of traditional manual inspection and improve accuracy and efficiency, it requires huge computing resources and cannot be simply deployed on IoT devices. Digital signal processor (DSP) is an important IoT device with the characteristics of small size, strong performance and low energy consumption, and has been widely used in intelligent manufacturing. In order to achieve accurate defect detection on DSP, we proposed a variety of optimization strategies, and then extended the model to run on multi-core using a parallel scheme, and further quantified the implementation of the model. We evaluated it on three datasets, i.e. NEUSDD, MTDD and RSDD. Experimental results show that our method achieves a faster speed than running the same CNN model on a mainstream desktop CPU, with slightly accuracy loss. © 2023 IEEE.

关键词： Internet of things

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