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5th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2021

作者： Poonkodi, M. Vadivu, G. SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India Big Data Analytics School of Computing SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9781665435246

computer Vision is playing aremarkable role right from essentials to entertainment and thus trying to turn computer as a 'seeing' machine. Having widespread applications in most of the real world domain like healthcare, surveillance., space and social media, yet it is still challenging to match human intelligence in understanding the underlying context. Recognition of a task performed by human is important to learn the happenings, but predicting it, from the activities' initial subset becomes an aid to overcome adversities in near future. Numerous algorithms and techniques are present, yet a gap exists due to the environment in which it is recorded, illumination condition,overlapping of men and materials on captured scenes *** lead to the requirement of high power computing devices for recognition, which is considerably *** this work, Dense OPnet (Dense optical flow and Convolution neural Net) is proposed which takes in the RGB input video, converted to dense optical flow frames and then fed to three layered CNN for recognition and early detection of an activity using N/2 frames of the total input video. This method is evaluated on two benchmark datasetlike J-HMDB21 and UCF50 giving an accuracy of 93.4% and 74.3% on recognition, 68.2% and 50.24% on early action detection respectively. © 2021 IEEE.

关键词： computer vision

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Adaptive and non-adaptive fusion algorithms analysis for digital surface model generated using census and convolutional neural networks 24

Adaptive and non-adaptive fusion algorithms analysis for dig...

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2021 24th ISPRS Congress Commission II: Imaging Today, Foreseeing Tomorrow

作者： Albanwan, Hessah Qin, Rongjun Geospatial Data Analytics Laboratory Department of Civil Environmental and Geodetic Engineering The Ohio State University ColumbusOH United States Department of Electrical and Computer Engineering The Ohio State University United States Translational Data Analytics Institute The Ohio State University United States

The digital surface models (DSM) fusion algorithms are one of the ongoing challenging problems to enhance the quality of 3D models, especially for complex regions with variable radiometric and geometric distortions like satellite datasets. DSM generation using Multiview stereo analysis (MVS) is the most common cost-efficient approach to recover elevations. Algorithms like Census-semi global matching (SGM) and Convolutional Neural Networks (MC-CNN) have been successfully implemented to generate the disparity and recover DSMs;however, their performances are limited when matching stereo pair images with ill-posed regions, low texture, dense texture, occluded, or noisy, which can yield missing or incorrect elevation values, in additions to fuzzy boundaries. DSM fusion algorithms have proven to tackle such problems, but their performance may vary based on the quality of the input and the type of fusion which can be classified into adaptive and non-adaptive. In this paper, we evaluate the performance of the adaptive and nonadaptive fusion methods using median filter, adaptive median filter, K-median clustering fusion, weighted average fusion, and adaptive spatiotemporal fusion for DSM generated using Census and MC-CNN. We perform our evaluation on 9 testing regions using stereo pair images from Worldview-3 satellite to generate DSMs using Census and MC-CNN. Our results show that adaptive fusion algorithms are more accurate than non-adaptive algorithms in predicting elevations due to their ability to learn from temporal and contextual information. Our results also show that MC-CNN produces better fusion results with a lower overall average RMSE than Census. © 2021 International Archives of the Photogrammetry, Remote Sensing and Spatial Information sciences - ISPRS Archives. All rights reserved.

关键词： Textures

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eCeLLM: generalizing large language models for E-commerce from large-scale, high-quality instruction data 24

eCeLLM: generalizing large language models for E-commerce fr...

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Proceedings of the 41st International Conference on Machine Learning

作者： Bo Peng Xinyi Ling Ziru Chen Huan Sun Xia Ning Department of Computer Science and Engineering The Ohio State University Department of Computer Science and Engineering and Translational Data Analytics Institute The Ohio State University Department of Computer Science and Engineering and Translational Data Analytics Institute and Department of Biomedical Informatics The Ohio State University

With tremendous efforts in developing effective e-commerce models, conventional e-commerce models show limited success in generalist e-commerce modeling, and suffer from unsatisfactory performance on new users and new products - a typical out-of-domain generalization challenge. Meanwhile, large language models (LLMs) demonstrate outstanding performance in generalist modeling and out-of-domain generalizability in many fields. Toward fully unleashing their power for e-commerce, in this paper, we construct ECInstruct, the first open-sourced, large-scale, and high-quality benchmark instruction dataset for e-commerce. Leveraging ECInstruct, we develop eCeLLM, a series of e-commerce LLMs, by instruction-tuning general-purpose LLMs. Our comprehensive experiments and evaluation demonstrate that eCeLLM models substantially outperform baseline models, including the most advanced GPT-4, and the state-of-the-art task-specific models in in-domain evaluation. Moreover, eCeLLM exhibits excellent generalizability to out-of-domain settings, including unseen products and unseen instructions, highlighting its superiority as a generalist e-commerce model. Both the ECInstruct dataset and the eCeLLM models show great potential in empowering versatile and effective LLMs for e-commerce. ECInstruct and eCeLLM models are publicly accessible through https://***/eCeLLM/.

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Automated Detection of Type 2 Diabetes with Imbalanced and Machine Learning Methods 3rd

Automated Detection of Type 2 Diabetes with Imbalanced and M...

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3rd International Conference on Machine Learning, Image Processing, Network Security and data sciences, MIND 2021

作者： Anirudh, G. Talukdar, Upasana Department of Data Science and Analytics Central University of Rajasthan Rajasthan Ajmer India Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati Guwahati India

ISBN: (纸本)9789811958670

Diabetes mellitus is an abominable disease, if left untreated, may turn fatal. This paper presents rigorous experimentation to ascertain a machine learning model that can implement an accurate diagnosis of diabetes mellitus. While many research efforts have been publicized till date to detect diabetes, none of them addresses the class imbalance problem. This paper carries out the experiments on Pima Indian Diabetes in three categories a. experiments with traditional machine learning algorithms, b. experiments with ensemble machine learning methods, and c. experiments with machine learning algorithms after imbalanced data pre-processing techniques. The performance of the solutions has been evaluated using F1-score, Precision, Recall, Area Under Precision-Recall curve, Area Under Receiver Operating Characteristic curve, and Classification Accuracy. The experimental results show improved performance with the proposed approach as compared to the state-of-the-art methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Urban Sensing Based on Mobile Phone data:Approaches,Applications,and Challenges

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IEEE/CAA Journal of Automatica Sinica 2020年第3期7卷 627-637页

作者： Mohammadhossein Ghahramani MengChu Zhou Gang Wang Insight Centre for Data Analytics University College DublinDublin 4Ireland IEEE Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyMacao 999078China Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China China Telecom at Macao Macao 999078China

data volume grows explosively with the proliferation of powerful smartphones and innovative mobile *** ability to accurately and extensively monitor and analyze these data is *** concern in cellular data analysis is related to human beings and their *** to the potential value that lies behind these massive data,there have been different proposed approaches for understanding corresponding *** that end,analyzing people's activities,e.g.,counting them at fixed locations and tracking them by generating origindestination matrices is *** former can be used to determine the utilization of assets like roads and city *** latter is valuable when planning transport *** insights allow a government to predict the adoption of new roads,new public transport routes,modification of existing infrastructure,and detection of congestion zones,resulting in more efficient designs and *** data exploration can help research in various fields,e.g.,urban planning,transportation,health care,and business *** can also help organizations in decision making,policy implementation,monitoring,and evaluation at all *** work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone *** classify these existing methods and present a taxonomy of the related work by discussing their pros and cons.

关键词： Big data analysis human mobility origin destination matrices smart infrastructure urban planning

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Swarm-LSTM: Condition Monitoring of Gearbox Fault Diagnosis Based on Hybrid LSTM Deep Neural Network Optimized by Swarm Intelligence Algorithms

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computers, Materials & Continua 2021年第2期66卷 2041-2059页

作者： Gopi Krishna Durbhaka Barani Selvaraj Mamta Mittal Tanzila Saba Amjad Rehman Lalit Mohan Goyal School of Electrical and Electronics Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering G.B.Pant Government Engineering CollegeNew Delhi110020India Artificial Intelligence and Data Analytics(AIDA)Laboratory CCISPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Engineering J.C.Bose University of Science and TechnologyFaridabad121006India

Nowadays,renewable energy has been emerging as the major source of energy and is driven by its aggressive expansion and falling *** of the renewable energy sources involve turbines and their operation and maintenance are vital and a difficult *** monitoring and fault diagnosis have seen remarkable and revolutionary up-gradation in approaches,practices and technology during the last *** mostly do use a rotating type of machinery and analysis of those signals has been challenging to localize the *** paper proposes a new hybrid model wherein multiple swarm intelligence models have been evaluated to optimize the conventional Long Short-Term Memory(LSTM)model in classifying the faults from the vibration signals data acquired from the *** helps to analyze the performance and behavioral patterns of the system more effectively and efficiently which helps to suggest for replacement of the unit with higher *** results have demonstrated that the proposed hybrid modeling approach is effective in classifying the faults of the gearbox from the time series data and achieve higher diagnostic accuracy in comparison to the conventional LSTM methods.

关键词： Gearbox long short term memory fault classification swarm intelligence optimization condition monitoring

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LSTM based Soft-Sensor for Estimating Nitrate Concentration in Aquaponics pond 3

LSTM based Soft-Sensor for Estimating Nitrate Concentration ...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Dharshan, A. Kumar, Purushottam Ravimaran, S. Srinivasulu Reddy, U. Saranathan College of Engineering Department of Artificial Intelligence and Data Science Trichy India National Institute of Technology Artificial Intelligence Machine Learning & Data Analytics Lab Trichy India National Institute of Technology Department of Computer Applications Machine Learning & Data Analytics Lab Trichy India

ISBN: (纸本)9798350381931

In the field of aquaponics, where fish and plants coexist in a symbiotic environment, closely monitoring nitrate levels in the water is crucial due to their profound impact on aquatic and plant well-being. Traditional nitrate measurement methods are often time-consuming and costly. Various approaches, including first principles, IoT-based sensors, and machine learning-based soft sensors, have been attempted to address this challenge. However, these efforts face challenges such as expensive sensors, infrequent data collection, multistage data processing using limited sensor types, and the need for regular maintenance like cleaning and calibration. Additionally, varied environmental conditions affect sensor suitability for different water environments, and even some machine learning-based soft sensors have proven inaccurate. In response, soft sensors, especially deep learning-based ones, have gained prominence in industrial applications for their adaptability and accuracy. These sensors provide real-time insights into complex processes without requiring expensive hardware. In this study, an innovative solution was introduced using Long Short-Term Memory (LSTM) technology, a neural network architecture in deep learning known for capturing complex temporal patterns. LSTM is well-suited for modeling and predicting nitrate concentration changes in aquaponics, trained with extensive data collected from various aquaponic ponds. Through rigorous evaluation, a remarkable MSE value of 0.00074 and an impressive R-squared score of 0.98 were achieved, holding potential for scaling up to commercial applications, benefiting aquaponics operations, supporting researchers, and enhancing sustainability and productivity in aquaponic systems. © 2024 IEEE.

关键词： Nitrates

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An Auto Encoder-Decoder Approach to Classify the Bird Sounds Using Deep Learning Techniques

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SN computer science 2023年第3期4卷 1-13页

作者： Vamsi, Bandi Mahanty, Mohan Doppala, Bhanu Prakash Department of Artificial Intelligence and Data Science Madanapalle Institute of Technology and Science Madanapalle 517326 India Department of Computer Science and Engineering Vignan’s Institute of Information Technology (A) Visakhapatnam 530046 India Data Analytics Generation Australia 88 Phillip St Sydney 2000 NSW Australia

Nowadays identification of bird species is the main problem for ornithologists and ecologists. The increase in volumes of data labelling, still there is a scope to implement an automated bird classification through deep learning techniques. Identifying the nature of a bird and its flock aids in analyzing different other species in the natural environment. This is considered as the main role in the problem of ecology. The species of birds act accordingly to the changes in ecology and this became a challenging task to identify the growth of the bird species with advanced technologies. In this work, our aim is to classify the different bird species based on voice recordings across various locations using autoencoders and decoders. This proposed work is a four-step process: data preprocessing of bird acoustic, feature extraction using a deep network, classification by random forest (RF) and support vector machine (SVM) and identifying the pitch intensities of a bird in low, medium and high ranges. In data pre-processing, the recorded audio is converted into spectrograms through frequency waves. The Mel Spectrogram filter is used to remove the noise from the recorded bird audio. For extracting the features in the spectrogram image, encoders and decoder convolutions are used to construct a fully connected vector. These features are given to RF and SVM for classifying the bird species to obtain standard prediction accuracy. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Bird classification CNN Decoder Encoder Mel spectrum

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An Adaptive and Image-guided Fusion for Stereo Satellite Image Derived Digital Surface Models

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Journal of Geodesy and Geoinformation science 2022年第4期5卷 1-9页

作者： Hessah ALBANWAN Rongjun QIN Department of Civil Engineering Kuwait UniversityKuwait 72504Kuwait Geospatial Data Analytics Lab The Ohio State UniversityColumbus 43210USA Department of Civil Environmental and Geodetic EngineeringThe Ohio State UniversityColumbus 43210USA Department of Electrical and Computer Engineering The Ohio State UniversityColumbus 43210USA Translational Data Analytics Institute The Ohio State UniversityColumbus 43210USA

The accuracy of Digital Surface Models(DSMs)generated using stereo matching methods varies due to the varying acquisition conditions and configuration parameters of stereo *** has been a good practice to fuse these DSMs generated from various stereo pairs to achieve enhanced,in which multiple DSMs are combined through computational approaches into a single,more accurate,and complete ***,accurately characterizing detailed objects and their boundaries still present a challenge since most boundary-ware fusion methods still struggle to achieve sharpened depth discontinuities due to the averaging effects of different ***,we propose a simple and efficient adaptive image-guided DSM fusion method that applies k-means clustering on small patches of the orthophoto to guide the pixel-level fusion adapted to the most consistent and relevant elevation *** experiment results show that our proposed method has outperformed comparing methods in accuracy and the ability to preserve sharpened depth edges.

关键词： Digital Surface Model(DSM) DSM Fusion adaptive fusion satellite stereo images

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Estimating fish weight growth in aquaponic farming through machine learning techniques 3

Estimating fish weight growth in aquaponic farming through m...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Kumar, Purushottam Tiwari, Pranav Srinivasulu Reddy, U. Machine Learning & Data Analytics Lab CoE in Artificial Intelligence National Institute of Technology Trichy India Indian Institute of Information Technology Tiruchirappalli Computer Science and Engineering Trichy India National Institute of Technology Machine Learning & Data Analytics Lab Department of Computer Applications Trichy India

ISBN: (纸本)9798350338607

Due to the ever-growing population, rapid urbanization, unusual environmental change, and dwindling water supply, the food production from conventional farming techniques won't be able to keep up with increasing food demand for long, leading to severe food scarcity. Aquaponics is emerging as a viable alternative to food cultivation techniques but lacks efficiency, hindering its adoption on a large-scale production basis. The adoption of Artificial Intelligence (AI) techniques in smart aquaponic farming can further contribute to increasing the yield in aquaponics. In the study, both linear and non-linear regression algorithms such as linear regression, lasso regression, ridge regression, KNN regression, SVR, and decision tree regression were trained on aquaponic fish pond dataset, evaluated on the basis of multiple metrics like MAE, MSE, RMSE, and RMSLE, and a competitive analysis was done to determine the best algorithm for predicting the harvest weight of the fish in aquaponic ponds. The results showed that KNN regression and decision tree regression algorithms have lowest error and highest accuracy of 99.15% and 99.1% respectively, for predicting the harvest weight of the fish. The results obtained could be scaled up to commercial levels and used in regulating nutrient concentration and aquatic environmental conditions © 2023 IEEE.

关键词： Fish

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