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Research on Forecasting Flowering Phase of Pear Tree Based on Neural Network

Computers, Materials & Continua 2021年第9期68卷 3431-3446页

作者： Zhenzhou Wang Yinuo Ma Pingping Yu Ning Cao Heiner Dintera School of Information Science and Engineering Hebei University of Science and TechnologyShijiazhuang050000China School of Internet of Things and Software Technology Wuxi Vocational College of Science and TechnologyWuxi214028China German-Russian Institute of Advanced Technologies Karan420126Russia

Predicting the blooming season of ornamental plants is significant for guiding adjustments in production decisions and providing viewing periods and *** current strategies for observation of ornamental plant booming periods are mainly based on manpower and experience,which have problems such as inaccurate recognition time,time-consuming and energy ***,this paper proposes a neural network-based method for predicting the flowering phase of pear ***,based on the meteorological observation data of Shijiazhuang Meteorological Station from 2000 to 2019,three principal components(the temperature factor,weather factor,and humidity factor)with high correlation coefficient with the flowering phase of pear tree are obtained by using the principal component analysis ***,the three components are used as input factors for the BP neural network.A BP neural network prediction model is constructed based on genetic algorithm *** crossover operator and mutation operator in the adaptive genetic algorithm are ***,the meteorological sample data from 2013 to 2019 are used to test and verify the algorithm in this *** results demonstrate that,the model can solve the local optimization problem of the BP neural network *** prediction results of the flowering phase of pear tree are evaluated in terms of relevance and prediction *** are superior to the traditional effective accumulated temperature and the prediction results of the stepwise regression *** method can provide more reliable forecast information for the blooming period,which can provide decision-making reference for improving the development of tourism industry.

关键词： Pear flower flowering phase principal component analysis BP neural network prediction model

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Towards personalized control of things using Arabic voice commands for elderly and with disabilities people

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International Journal of Information technology (Singapore) 2023年 1-22页

作者： Salah, Abdelrahman Adel, Ghada Mohamed, Hussein Baghdady, Youssef Moussa, Sherin M. Department of Software Engineering and Information Technology Faculty of Engineering and Technology Egyptian Chinese University Cairo 11351 Egypt Department of Information Systems Faculty of Computer and Information Sciences Ain Shams University Cairo 11566 Egypt Laboratoire Interdisciplinaire de l’Université Française d’Égypte (UFEID Lab) Université Française d’Égypte Cairo 11837 Egypt

Considering recent smart home solutions that automate home appliances through voice commands, elderly people and/or with special needs prefer using their mother-tongue. However, most of current systems adopt the English language, making it harder for non-native English speakers, while personalization is not heavily considered. In this paper, we propose the Arabic Personalized Control of Things (APCoT) system that achieves inhabitant-tailored home automation through Arabic voice commands based on the user’s preferences to control a home device. A new dataset is created for a wider range of Arabic voice commands to interact with home appliances rather than just turning them on/off. Internet of Things, voice and speech recognition are utilized to attain personalization according to the inhabitants' voiceprints, while infrared is adopted to control non-smart devices supporting infrared signals. As a Proof-of-Concept, three home appliances are adopted: TV, Lights and Bluetooth speaker. Machine learning algorithms are customized for Arabic voice recognition, integrating face and hand gestures recognition to provide an additional facility for inhabitant’s identification and sending control commands respectively. The experimental results indicate that the best average accuracies obtained for Arabic voice recognition were 99.63% for a single speaker and 95.4% for multi-speakers, with average response times 0.141s and 14.29s respectively. For speech recognition, Google online API and a customized Vosk offline pretrained model achieved average accuracies of 95% and 90%, average response times 0.7553s and 0.3933s respectively. The face and hand gestures recognition achieved average accuracies of 90% and 98%, average response times 0.5934s and 15.62049s respectively. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Arabic commands Elderly people Home automation People with disabilities Smart home Speech and voice recognition

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Research on Face Anti-Spoofing Algorithm Based on Image Fusion

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Computers, Materials & Continua 2021年第9期68卷 3861-3876页

作者： Pingping Yu Jiayu Wang Ning Cao Heiner Dintera School of Information Science and Engineering Hebei University of Science and TechnologyShijiazhuang050000China School of Internet of Things and Software Technology Wuxi Vocational College of Science and TechnologyWuxi214028China German-Russian Institute of Advanced Technologies Karan420126Russia

Along with the rapid development of biometric authentication technology,face recognition has been commercially used in many industries in recent ***,it cannot be ignored that face recognition-based authentication techniques can be easily spoofed using various types of attacks such photographs,videos or forged 3D *** order to solve this problem,this work proposed a face anti-fraud algorithm based on the fusion of thermal infrared images and visible light *** normal temperature distribution of the human face is stable and characteristic,and the important physiological information of the human body can be observed by the infrared thermal ***,based on the thermal infrared image,the pixel value of the pulse sensitive area of the human face is collected,and the human heart rate signal is detected to distinguish between real faces and spoofing *** order to better obtain the texture features of the face,an image fusion algorithm based on DTCWT and the improved Roberts algorithm is ***,DTCWT is used to decompose the thermal infrared image and visible light image of the face to obtain high-and low-frequency ***,the method based on region energy and the improved Roberts algorithm are then used to fuse the coefficients of the high-and low-frequency ***,the DTCWT inverse transform is used to obtain the fused image containing the facial texture *** recognition is carried out on the fused image to realize identity *** results show that this algorithm can effectively resist attacks from photos,videos or *** with the use of visible light images alone for face recognition,this algorithm has higher recognition accuracy and better robustness.

关键词： Anti-spoofing infrared thermal images image fusion heart rate detection

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Bluetooth Vulnerabilities and Security

Bluetooth Vulnerabilities and Security

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Mareeswari V Vijayan R Ashish Shukla Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India Department of Information Technology School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India Master of Computer Applications School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Bluetooth technology, which facilitates wireless communication between Billions of devices including smartphones, tablets, laptops, and Internet of Thing (IoT) devices, is a cornerstone of modern connectivity. Its importance lies in its ability to enable seamless data exchange and interaction across a wide range of applications, from personal gadgets to complex industrial systems. Despite its widespread adoption, Bluetooth is not immune to critical security vulnerabilities. Issues like Bluetooth Low Energy (BLE) vulnerabilities and denial-of-service (DoS) attacks can overwhelm devices with excessive traffic, while BLE Forced Connection can lead to unauthorized access, and eavesdropping exposes sensitive data being exchanged. We have discussed these vulnerabilities in detail, examining their mechanisms, potential impacts, and various implementation aspects. Additionally, we have added demonstrations to illustrate how these attacks can be executed and the potential consequences. To prevent these threats, we explored essential measures, including regular firmware updates, secure pairing protocols, and careful management of Bluetooth settings. Adopting best practices like disabling Bluetooth when not in use and monitoring connected devices can further enhance security, ensuring the reliable and safe operation of the vast and growing ecosystem of Bluetooth-enabled devices.

关键词： Wireless communication Ethics Protocols Ecosystems Denial-of-service attack Security Internet of Things Bluetooth Low Energy Microprogramming Guidelines

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Family of Mutually Uncorrelated Codes for DNA Storage Address Design

IEEE Transactions on Nanobioscience

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IEEE Transactions on Nanobioscience 2025年 PP卷 PP页

作者： Liu, Zhenlu Cao, Ben Shao, Qi Zheng, Yanfen Wang, Bin Zhou, Shihua Zheng, Pan Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China University of Canterbury Upper Riccarton Department of Accounting and Information Systems Christchurch8140 New Zealand

Deoxyribonucleic acid (DNA) has become an ideal medium for long-term storage and retrieval due to its extremely high storage density and long-term stability. But access efficiency is an existing bottleneck in DNA storage, especially the lack of high-quality random access address sequences. Therefore, in this paper, we report a series of approaches based on k-weakly mutually uncorrelated (k-WMU) codes to design the address sequence to improve the access efficiency of DNA storage. To address the problem of DNA sequences that are poorly scalable at the base level, we propose a 0-m-ruling coding scheme combined with k-WMU codes that can make address sequences avoid generating secondary structure with stem lengths ranging from 3 to 9. Based on the decoupled structure, We further extend the k-WMU codes with error correction function while satisfying combinatorial biological constraints. In order to investigate the performance of the designed address sequences for real-world applications, we perform simulation experiments based on thermodynamic properties and error correction capability as well as compared the minimum free energy (MFE), melting temperature (TM), and average decoding success rate (ADSR) with previous work. The results show that designed address sequences have a high MFE value and ADSR and a substantial reduction in TM-variance while satisfying the combinatorial biological constraints. As the quality of address sequences improves, this will help to achieve accurate random access as well as enhance the robustness of the DNA storage system. © 2002-2011 IEEE.

关键词： DNA sequences

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A Comparison of Deep Learning and Machine Learning Methods for Water Quality Prediction

A Comparison of Deep Learning and Machine Learning Methods f...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： N. Srinivasa Gupta B. Valarmathi Ishaan Hiwanj Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamil Nadu India Department of Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamil Nadu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Predicting water quality is essential to preserving human health and environmental sustain ability. Traditional water quality assessment methods often face scalability and real-time monitoring limitations. With accuracies of 62%, 72 %, 83 %, 69%, 63 %, 66%, 71 %, 63 %, and 64%, respectively, the current techniques utilized were Logistic Regression, Decision Trees, Random Forest Regressor, Extreme Gradient Boosting, Naive Bayes, K-nearest neighbors, Support Vector Machine, AdaBoost, and Bagging [9]. This study addresses these challenges by leveraging Adaptive Synthetic Sampling (ADASYN) to balance the dataset and evaluating model performance on datasets of 5,000 and 10,000 entries per class. A robust dataset obtained from Kaggle was used, with five models - Long Short-Term Memory (LSTM), Feed Forward Neural Network (FFNN), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Random Forest - evaluated and compared. The proposed methods demonstrate significant improvements in accuracy, with XGBoost achieving the highest accuracy of 95.53%, followed by Random Forest at 93.98%. This work underscores the importance of advanced machine learning techniques in addressing the limitations of traditional methods, enhancing accuracy, scalability, and adaptability in water quality prediction. These findings contribute to advancing environmental monitoring and management practices with reliable, data-driven insights.

关键词： Support vector machines Adaptation models Accuracy Scalability Water quality Boosting Reliability Random forests Long short term memory Regression tree analysis

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SSpose: Self-Supervised Spatial-Aware Model for Human Pose Estimation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5403-5417页

作者： Yu, Linfang Qin, Zhen Xu, Liqun Qin, Zhiguang Choo, Kim-Kwang Raymond University of Electronic Science and Technology of China Network and Data Security Key Laboratory of Sichuan Province Chengdu610054 China Industrial Research Institute Chengdu610213 China University of Electronic Science and Technology of China Network and Data Security Key Laboratory of Sichuan Province School of Information and Software Engineering Chengdu610054 China Industrial Research Institute Chengdu610213 China University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX78249 United States

Human pose estimation (HPE) relies on the anatomical relationships among different body parts to locate keypoints. Despite the significant progress achieved by convolutional neural networks (CNN)-based models in HPE, they typically fail to explicitly learn the global dependencies among various body parts. To overcome this limitation, we propose a spatial-aware HPE model called SSpose that explicitly captures the spatial dependencies between specific key points and different locations in an image. The proposed SSpose model adopts a hybrid CNN-Transformer encoder to simultaneously capture local features and global dependencies. To better preserve image details, a multiscale fusion module is introduced to integrate coarse- and fine-grained image information. By establishing a connection with the activation maximization (AM) principle, the final attention layer of the Transformer aggregates contributions (i.e., attention scores) from all image positions and forms the maximum position in the heatmap, thereby achieving keypoint localization in the head structure. Additionally, to address the issue of visible information leakage in convolutional reconstruction, we have devised a self-supervised training framework for the SSpose model. This framework incorporates mask autoencoder (MAE) technology into SSpose models by utilizing masked convolution and hierarchical masking strategy, thereby facilitating efficient self-supervised learning. Extensive experiments demonstrate that SSpose performs exceptionally well in the pose estimation task. On the COCO val set, it achieves an AP and AR of 77.3% and 82.1%, respectively, while on the COCO test-dev set, the AP and AR are 76.4% and 81.5%. Moreover, the model exhibits strong generalization capabilities on MPII. © 2024 IEEE.

关键词： Supervised learning

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Speed Control of Motor Based on Improved Glowworm Swarm Optimization

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Computers, Materials & Continua 2021年第10期69卷 503-519页

作者： Zhenzhou Wang Yan Zhang Pingping Yu Ning Cao Heiner Dintera School of Information Science and Engineering Hebei University of Science and TechnologyShijiazhuang050018China School of Internet of Things and Software Technology Wuxi Vocational College of Science and TechnologyWuxi214028China German-Russian Institute of Advanced Technologies Karan420126Russia

To better regulate the speed of brushless DC motors,an improved algorithm based on the original Glowworm Swarm Optimization is *** proposed algorithm solves the problems of poor robustness,slow convergence,and low accuracy exhibited by traditional PID *** selecting the glowworm neighborhood set,an optimization scheme based on the growth and competition behavior of weeds is applied to a single glowworm to prevent falling into a local optimal *** the glowworm’s position is updated,the league selection operator is introduced to search for the global optimal *** the local search ability of the invasive weed optimization with the global search ability of the league selection operator enhances the robustness of the algorithm and also accelerates the convergence speed of the *** mathematical model of the brushless DC motor is established,the PID parameters are tuned and optimized using improved Glowworm Swarm Optimization algorithm,and the speed of the brushless DC motor is *** a Simulink environment,a double closed-loop speed control model was established to simulate the speed control of a brushless DC motor,and this simulation was compared with a traditional PID *** simulation results show that the model based on the improved Glowworm Swarm Optimization algorithm has good robustness and a steady-state response speed for motor speed control.

关键词： PID speed control improved Glowworm Swarm Optimization brushless DC motor

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Research on Single Bottleneck Optimization Based on Intelligent Manufacturing Mode

Research on Single Bottleneck Optimization Based on Intellig...

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2023 IEEE International Conference on Control, Electronics and Computer technology, ICCECT 2023

作者： Hu, Han Cao, Jun Hui, Guan Wuhan Vocational College of Software and Engineering Wuhan China Huazhong University of Science and Technology State Key Lab of Digital Manufacturing Equipment and Technology Hust-Sany Joint Lab of Advanced Manufacturing School of Mechanical Science and Engineering Wuhan China Wuhan Railway Vocation College of Technology Wuhan China Northwest A&F University College of Languages and Culture Xianyang China

ISBN: (纸本)9798350399028

More and more manufacturing companies are adopting a multi-variety, low-volume production model, and there are bottlenecks in the workshop due to the different demands on machine capacity for different products. The presence of the bottleneck in the workshop will severely limit the daily output of the workshop, reducing productivity and profits. In this paper, an improved Jaya algorithm based on problem characteristics is proposed to solve the single bottleneck optimization problem within intelligent manufacturing mode. Firstly, under the single bottleneck of intelligent manufacturing mode, the flexible factor of the manufacturing machine is considered, which effectively solves the problem of machine load distribution in the workshop. Secondly, based on the basic JAYA algorithm, a two-layer encoding mechanism and decoding mechanism based on problem characteristics are proposed, as well as the improvement of the similarity method. Finally, the effectiveness of the improved algorithm in this problem is demonstrated by comparing it with other algorithms. © 2023 IEEE.

关键词： Scheduling algorithms

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Unveiling Public Perception of ChatGPT Through Twitter Data and Machine Learning 4

Unveiling Public Perception of ChatGPT Through Twitter Data ...

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4th International Conference on Data Analytics for Business and Industry, ICDABI 2023

作者： Abishethvarman, Vadivel Banujan, Kuhaneswaran Wijeratne, Ashansa Nirubikaa, Ravikumar Kumara, Samantha Prasanth, Senthan Sabaragamuwa University of Sri Lanka Department of Computing and Information Systems Belihuloya Sri Lanka Sabaragamuwa University of Sri Lanka Department of Software Engineering Belihuloya Sri Lanka Sabaragamuwa University of Sri Lanka Department of Physical Sciences and Technology Belihuloya Sri Lanka

ISBN: (纸本)9798350369786

The study comprehensively analysed public sentiment towards ChatGPT, a language processing AI tool, using approximately 135,000 tweets from an initial dataset of 236,275 entries. After cleaning and pre-processing the data using basic NLP techniques, Machine Learning (ML) models - Long Short-Term Memory (LSTM), Decision Tree (DT), and Support Vector Machine (SVM) - were applied to classify the tweets into positive, negative, and neutral categories. The LSTM model demonstrated superior performance across all evaluation metrics - accuracy, precision, recall, and F1 score - for all tweet categories. Word clouds generated for each category revealed that the most frequent words in positive tweets included 'student,' 'education,' 'research,' and 'teacher,' indicating a strong association of ChatGPT with educational and research contexts. Similarly, 'AI,' 'student,' 'research,' 'course,' and 'education' dominated neutral and negative tweets, highlighting prevalent discussion topics. However, the study had limitations, including dependency on a single dataset, potential loss of contextual information during pre-processing, and a focus on English tweets only. Despite these limitations, the study provides valuable insights into the public's perception of ChatGPT, underscoring its significant association with education and research and the effectiveness of the LSTM model in classifying tweets. The findings suggest that future research should incorporate diverse data sources and languages better to understand global sentiment towards ChatGPT and similar AI tools. © 2023 IEEE.

关键词： Tweets

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