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2nd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2022

作者： Murali, N. Kumar, A. Sasi Karunamurthy, A. Suseendra, R. Manikandan, S. Department of Computer Science E.G.S. Pillay Engineering College Tamil Nadu Nagapattinam India Institute of Engineering and Technology Department of Cloud Technology and Data Science Srinivas University Surathkal Mangalore Mukka India Department of Computer Science and Engineering Chirst College of Engineering and Technology Pudhucherry Mulakulam India Panimalar Engineering College Department of Information Technology Tamil Nadu Chennai India Department of Information Technology E.G.S. Pillay Engineering College Tamil Nadu Nagapattinam India

ISBN: (纸本)9781665491112

Agriculture is backbone of India. In the emerge of human civilization, agriculture has been an essential part of every human society due to the basic fact that the sustenance of any civilization directly depends on agriculture. Agriculture has the great impact on the economy of the country. This paper mainly concentrates on increasing crop production in large scale agricultural area. Due to changing climatic conditions, insufficient water level, insufficient or excessive use of fertilizers etc. the crop production may be affected. Astonishingly, agriculture has not been blessed with the latest advancements in the high-tech space unlike other areas like transport, education, finance, etc. Advancement in agriculture is necessary to balance the demands of people needs as the population grows day by day. The detection of outliers in large-scale area is most challenging tasks. Hence, we explore machine learning methodologies namely Deep Neural Network and Tucker Decomposition is used to detect those anomalies in large scale agriculture area to increase crop production. In this smart farming, the system automatically learns the soil features from IoT sensor data then provides anomaly detection accuracy which helps the farmer to understand the parameters affect the crop growth. This will enhance the crop productivity and economy of farmer. Our system automatically learns the data, analyzes the data and predicts the future. © 2022 IEEE.

关键词： Anomaly detection

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Evolutionary Multitasking With Adaptive Knowledge Transfer for Expensive Multiobjective Optimization

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IEEE Transactions on Evolutionary Computation 2024年

作者： Wu, Xunfeng Liu, Songbai Lin, Qiuzhen Tan, Kay Chen Leung, Victor C. M. The Hong Kong Polytechnic University Department of Data Science and Artificial Intelligence Hong Kong Hong Kong Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Surrogate-assisted evolutionary algorithms (SAEAs) have shown promising performance in tackling expensive multiobjective optimization problems (EMOPs). However, existing SAEAs solve EMOPs separately, which ignore their optimization experiences earned before. Inspired by multitasking optimization paradigm for multitasking multiobjective optimization problems (MTMOPs), this article designs the first SAEA for tackling expensive MTMOPs (EMTMOPs) with adaptive knowledge transfer. First, a competitive surrogate selection is proposed to improve the generalization ability of approximating various EMOP tasks, where two types of surrogate models are trained and then compete for use to replace real expensive evaluations. Then, an adaptive solution selection is designed, which identifies promising transfer solutions to accelerate the solving of target task and selects promising infill solutions for real expensive evaluations to refine the surrogate models. The performance of our algorithm is validated on three commonly used benchmark suites and some real-world EMTMOPs. The experiments vali-date our superiority over several state-of-the-art SAEAs on most test cases. © 1997-2012 IEEE.

关键词： Multiobjective optimization

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Human Activity Recognition Using 1-Dimensional CNN and Comparison with LSTM

Human Activity Recognition Using 1-Dimensional CNN and Compa...

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International Conference on Sustainable Technology and Advanced Computing in Electrical engineering, ICSTACE 2021

作者： Kashyap, Suryakant Kumar Mahalle, Parikshit Narendra Shinde, Gitanjali Rahul Computer Engineering Smt. Kashibai Navale College of Engineering Savitribai Phule Pune University Pune India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Pune India Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9789811943638

The practice of recognizing different forms of human activities is known as human activity recognition (HAR). We are using two deep learning architectures LSTM and CNN for recognizing activities using smartphones sensors data and then, comparing them with each other because they are best suited for time series data which is generated from sensors. Our proposed architecture, a lightweight CNN model, achieved an accuracy of 96.06%, outperforming more complex and cumbersome algorithms like fast and robust deep convolutional neural network structure (FR-DCNN) and residual LSTM (Res-LSTM). Further, we compared our model to the LSTM model, in terms of accuracy, time taken for training, f1-scores of each activity, precision, recall values, and resources consumed. Our CNN architecture performed better than the LSTM architecture in all the mentioned terms. Hence, it is concluded that for real-world applications, CNN models should be adopted for this purpose. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Reliable Vertical Ground Reaction Force Estimation with Smart Insole During Walking

arXiv

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arXiv 2025年

作者： Olugbon, Femi Ghoreishi, Nozhan Huang, Ming-Chun Xu, Wenyao Chen, Diliang Department of Electrical and Computer Engineering University of New Hampshire DurhamNH03824 United States Department of Data and Computational Science Duke Kunshan University Suzhou China Department of Computer Science & Engineering University at Buffalo The State University of New York BuffaloNY14260 United States

The vertical ground reaction force (vGRF) and its characteristic weight acceptance and push-off peaks measured during walking are important for gait and biomechanical analysis. Current wearable vGRF estimation methods suffer from drifting errors or low generalization performances, limiting their practical application. This paper proposes a novel method for reliably estimating vGRF and its characteristic peaks using data collected from the smart insole, including inertial measurement unit data and the newly introduced center of the pressed sensor data. These data were fused with machine learning algorithms including artificial neural networks, random forest regression, and bi-directional long-short-term memory. The proposed method outperformed the state-of-the-art methods with the root mean squared error, normalized root mean squared error, and correlation coefficient of 0.024 body weight (BW), 1.79% BW, and 0.997 in intra-participant testing, and 0.044 BW, 3.22% BW, and 0.991 in inter-participant testing, respectively. The difference between the reference and estimated weight acceptance and push-off peak values are 0.022 BW and 0.017 BW with a delay of 1.4% and 1.8% of the gait cycle for the intra-participant testing and 0.044 BW and 0.025 BW with a delay of 1.5% and 2.3% of the gait cycle for the inter-participant testing. The results indicate that the proposed vGRF estimation method has the potential to achieve accurate vGRF measurement during walking in free-living environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Mean square error

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ML_Recognition: A Robust Model for Handwritten Digit Recognition Using Machine Learning Classifiers 3

ML_Recognition: A Robust Model for Handwritten Digit Recogni...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Abdelminaam, Diaa Salama Madbouly, Magda M. Zeid, Magdy Abd-Elghany Abualigah, Laith Misr International University Faculty of Computer Science Cairo Egypt Benha University Faculty of Computers and Artificial Intelligence Benha Egypt Alexandria University Faculty of Computer and Data Science Alexandria Egypt Alobour High Institute for Computer and Informatics Ibrahim Abd Elatif Gomaa Computer Science Department Cairo Egypt Lebanese American University Department of Electrical and Computer Engineering Byblos13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Middle East University Meu Research Unit Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia

ISBN: (纸本)9798350306231

Handwritten digit recognition is a branch of machine learning in which a computer is taught to recognize hand-written numbers. Classification and regression are applied using deep learning and machine learning algorithms. In this paper, we discussed the efficiency of different algorithms: Random Forest, KNN, Naive Bayes, SVM, CNN, and Decision Tree Algorithm. Those algorithms were applied in the handwritten digit recognition process using the MINIST data set. By comparing each algorithm by training every one of them on the data set, then by testing, we got the best algorithm to get the optimal results with the highest accuracy. After comparing the results of each algorithm, it has shown that the neural network has the best results with 97.3% classification accuracy followed by K- the nearest neighbor with 97.2%. © 2023 IEEE.

关键词： Support vector machines

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An Amalgamated CNN-Transformer Network for Lightweight Image Super-Resolution

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1376-1387页

作者： Fang, Jinsheng Lin, Hanjiang Zeng, Kun School of Computer Science and Engineering Minnan Normal University Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Fujian Province University Fujian Zhangzhou363000 China School of Computer and Control engineering Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China

Recently, Transformer-based methods for single image super-resolution (SISR) have achieved better performance advantages than the methods based on convolutional neural network (CNN). Exploiting self-attention mechanism to model global context definitely improves the SR results. However, the neglect of local information will bring inevitable reduction of the network performance. In this work, we propose an Amalgamated CNN-Transformer network for lightweight SR, namely ACTSR. Specifically, an amalgamated CNN-Transformer block (ACTB) is developed to extract the useful information of both local and global features. By employing stacked ACTBs, our ACTSR extracts more informative deep features beneficially for super-resolution reconstruction to improve network performance while keeps lightweight and flexible enough. Extensive experiments on commonly used benchmark datasets validate our ACTSR outperforms the advanced competitors. Our codes are available at: https://***/ginsengf/ACTSR. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Convolutional neural networks

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Security Performance Analysis for MIMO Systems: From Artificial Noise's Power and Dimension Perspective

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IEEE Transactions on Vehicular Technology 2025年

作者： Xu, Panpan Li, Zhenling Li, Chunguo Tan, Weijie Guizhou University School of Mathematics and Statistics Guiyang550025 China Southeast University School of Information Science and Engineering Nanjing210096 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China

This paper analyzes the influence of power and dimension of artificial noise (AN) on security performance of multiple-input multiple-output (MIMO) system with multiple randomly located eavesdroppers. We derive the closed-form expressions of the secrecy outage probability (SOP) and the optimal secrecy capacity by utilizing the irrelevance among transmission channels, and the orthogonality between AN and the basis vectors of the legitimate channel matrix's null space. We use the Poisson point process to determine the number and location of eavesdroppers, then analyze the security performance of the communication system under the 3D channel condition of probability-based line-of-sight (LoS) or non-line-of-sight (NLoS) channel. The simulation results show the following conclusions. When the AN dimension reaches 1/4 highest dimension (i.e., one quarter of the number of transmitting antennae less than one) and the power splitter factor (PSF) falls in the open interval (0.6, 0.9), we can achieve a trade-off between secrecy capacity and design complexity. © 1967-2012 IEEE.

关键词： Vector spaces

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Joint Location and Power Optimization for Secure Communications in Multi-UAVs Enabled Cellular Networks

Joint Location and Power Optimization for Secure Communicati...

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2023 International Conference on Networking and Network Applications, NaNA 2023

作者： Deng, Fei Qian, Kaiguo Shen, Shikai Hong, Sunyan Yang, Bin Li, Xuanxuan He, Jun College of Information Engineering Kunming University Kunming650214 China Key Laboratory of Data Governance and Intelligent Decision Universities of Yunnan Kunming650214 China School of Computer and Information Engineering Chuzhou University Chuzhou239000 China Kunming University Office of Science and Technology Kunming650214 China

ISBN: (纸本)9798350327380

This paper investigates joint location and power optimization for secure communication in a multi-unmanned acrial vehicles (U A V s) enabled cellular network consisting of interference link, eavesdropping link, cellular link and D2D link. To this end, we first derive the expression of secure rate. Then, we formulate the maximization of secure rate as a constrained optimization problem. Finally, we solve it using the method of exhaustion. © 2023 IEEE.

关键词： Wireless networks

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Rethinking Local and Global Feature Representation for Semantic Segmentation 32

Rethinking Local and Global Feature Representation for Seman...

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32nd British Machine Vision Conference, BMVC 2021

作者： Chen, Mohan Zhao, Xinxuan Fu, Bingfei Zhang, Li Xue, Xiangyang Academy for Engineering & Technology Fudan University China School of Data Science Fudan University China School of Computer Science Fudan University China

Although fully convolution networks (FCN) have dominated semantic segmentation since the birth of [24], they are inherently limited in capturing long-range structured relationship with the layers of local kernels. While recent Transformer-based models have proven extremely successful in computer vision tasks by capturing global representation, they would deteriorate semantic segmentation by over-smoothing the regions contain fine details (e.g., boundaries and small objects). To this end, we propose a Dual-Stream Convolution-Transformer segmentation framework, called DSCT, by taking advantage of both the convolution and Transformer to learn a rich feature representation for semantic segmentation. Specifically, DSCT extracts high resolution local feature information from convolution layers and global feature representation across the Transformer layers. Moreover, a feature fusion module is plugged to exchange information between spatial stream and context stream at each stage. With the local and global context modeled explicitly in every layer, the two streams can be combined with a simple decoder to provide a powerful segmentation model. Extensive experiments show that our model builds a new state of the art on Cityscapes dataset (83.31% mIoU) with only 80K training iterations and appealing performance (49.27% mIoU) on ADE20K, outperforming most of the alternatives with a new perspective. © 2021. The copyright of this document resides with its authors.

关键词： Convolution

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Image Caption Generation using CNN and Audio Conversion

Image Caption Generation using CNN and Audio Conversion

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： K. Priya R.Vijay Babu M.Muralidhar Reddy T.H. Mohan Reddy M. Maanesh Department of Artificial Intelligence & Data Science Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Data Science Arizona state university Tempe United states

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

This research focuses on developing a system that can generate descriptive captions for images and convert them into audio output. The system employs a Convolutional Neural Network (CNN) to extract visual features from images, which are then fed into a Gated Recurrent Unit (GRU) to generate textual descriptions. The generated captions are subsequently converted into audio using text-to-speech techniques. By training the model on a large dataset of image-caption pairs, the system learns to associate visual information with textual descriptions, enabling accurate and coherent caption generation. This research contributes to the advancement of image understanding and generation, with potential applications in various fields such as image search, accessibility, and content creation.

关键词： Training Visualization Multimedia systems Logic gates Speech enhancement Feature extraction Robustness Text to speech Internet Convolutional neural networks

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