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Heterogeneous Transfer learning Optimization For Greenhouse Gas Emissions Prediction Using Quantum Annealing 11

Heterogeneous Transfer Learning Optimization For Greenhouse ...

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11th international conference on Advanced Informatics: Concept, theory and Application, ICAICTA 2024

作者： Hidayat, Wahyu Maulidevi, Nur Ulfa Surendro, Kridanto School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia School of Applied Science Telkom University Bandung Indonesia

ISBN: (纸本)9798331520311

Despite its urgency, the prediction of greenhouse gas (GHG) emissions at the city level is hampered by limited quality and quantity of training data so most predictions of GHG emissions are carried out at the country level, using different feature spaces. Heterogeneous Transfer learning (HeTL) is considered capable of getting around this limitation due to its ability to facilitate the transfer of knowledge between domains that have different feature spaces and distributions. However, the implementation of HeTL is still haunted by the potential of negative transfer in the knowledge transfer process. Current studies on mitigating negative transfer in HeTL still rely heavily on classical optimization techniques and focus solely on either feature-level or instance-level optimization. In this paper, a method is proposed to optimize the knowledge transfer process in HeTL using quantum annealing. the proposed optimization is carried out in three stages: (1) feature alignment, (2) common feature optimization, and (3) data instance optimization. the proposed method seeks to optimize the knowledge transfer process at both the feature and instance levels. It utilizes a combination of classical computing and quantum computing, thereby combining the advantages of the classical approach and the quantum approach to obtain optimal results. © 2024 IEEE.

关键词： Greenhouse gas emissions

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A Spatial-Aware Representation learning Model for Link Completion in GeoKG: A Case Study on Wikidata and OpenStreetMap 11

A Spatial-Aware Representation Learning Model for Link Compl...

引用

11th international conference on Agro-Geoinformatics, Agro-Geoinformatics 2023

作者： Zhang, Rui Li, Hao Yue, Peng Wuhan University School of Remote Sensing and Information Engineering Wuhan China

ISBN: (纸本)9798350303513

Link completion of knowledge graphs related to geographic entities has become crucial for geospatial knowledge graphs (GeoKG). Pre-trained models have shown promise to support downstream tasks. this paper proposes a POI Entity Linking (PEL) method that demonstrates good generalization for this problem. the geographic entities in Wikidata and the node entities in OpenStreetMap (OSM) are used as experimental cases to fine-tune and test a multilingual pretraining model. data argumentation and location encoding strategies are introduced. Finally, good experimental results are obtained using POIs from Italy, Germany, and China, validating the effectiveness of the proposed strategies. Compared with the traditional method based on feature engineering, our method has obtained similar results on the Italian dataset and is superior to this method on the other two datasets. © 2023 IEEE.

关键词： Knowledge graph

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Adaptive Step-Size Sparse Millimeter Wave Imaging Algorithm Based on Approximate Observations 11

Adaptive Step-Size Sparse Millimeter Wave Imaging Algorithm ...

引用

11th IEEE international conference on Computer Science and Network Technology, ICCSNT 2023

作者： Wang, Dianhao Huang, Xinqi Yu, Canping Mao, Qi Li, Yingsong College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (纸本)9798350311594

the currently constructed millimeter wave imaging system has the problems of long sampling time and more sampling points of antenna units, and the use of compressed perception algorithm can improve the imaging quality when the number of sampling points are much smaller than the Nyquist distance sampling. the traditional compressed perception algorithm can achieve better sparse recovery than the matched filter imaging algorithm, but there is a large dimension of the measurement matrix and high computational complexity. For the problems of difficult data processing and large dimensions of measurement matrix, a sparse imaging regularization model is constructed based on approximate observation, and an improved soft threshold iterative algorithm is used with adaptive step size, which improves the convergence performance, reduces the computational complexity of the sparse recovery dramatically and achieves a better quality of sparse imaging than the traditional compressed perception algorithm. © 2023 IEEE.

关键词： Computational complexity

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Proceedings - 2023 international conference on Information Technology: IoT and Smart City, ITIoTSC 2023

Proceedings - 2023 International Conference on Information T...

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11th international conference on Information Technology: IoT and Smart City, ITIoTSC 2023

ISBN: (纸本)9798350328349

the proceedings contain 50 papers. the topics discussed include: trading strategy optimization with a multi-objective genetic algorithm;quality of cigarette cooperative production process based on AHP establishment and application of digital management and control mode;research on accounting information distortion recognition model of listed companies based on machine learning;a non-invasive load identification method based on data association learning;cross-modal traceability method of unstructured multi-source data based on knowledge map technology;construction of a predictive model of college students' satisfaction with campus life based on intelligent algorithm;short-term industrial load forecasting based on Bi LSTM optimized by SSA and dropout;Bayesian algorithm in library information social services research;and application practice of AI intelligent technology in customer service of China Telecom.

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Improving Deep learning Classifiers Performance using Preprocessing and Cycle Scheduling Approaches in a Plant Disease Detection 11

Improving Deep Learning Classifiers Performance using Prepro...

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11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Dhiman, Poonam Choudhary, Aryan Wadhwa, Shivani Kaur, Amandeep Government Pg College Ambala Cantt133001 India Nurdsoft.co Los Angeles United States Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350350357

the impact of plant diseases on the growth of their corresponding species enhances the critical importance of early identification. Farmers currently grapple with significant challenges posed by these diseases, jeopardizing entire seasonal yields. To address this pressing issue, this paper introduces a ResNet-based Convolutional Neural Network model designed to detect various early-stage plant diseases. the dataset employed comprises approximately 46,000 RGB images of both healthy and diseased crop leaves, categorized into six distinct classes sourced from Plant Village. the dataset is strategically divided into an 80/20 ratio for training and validation sets while maintaining the directory structure. Various preprocessing and data augmentation techniques are applied to the input image samples. Subsequently, a Resnet9 model is employed to extract features. the developed model exhibits an impressive accuracy of 99.37 % and a minimal loss of 0.0018. this proposed model demonstrates its capability to achieve promising accuracy in distinguishing between healthy and diseased plants, showcasing the effectiveness of the developed approach. © 2024 IEEE.

关键词： Neural network models

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Soil Monitoring and Crop Recommendation System via IoT and Machine learning 11

Soil Monitoring and Crop Recommendation System via IoT and M...

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11th IEEE international conference on Advances in Computing and Communications, ICACC 2024

作者： Bennila thangammal, C. Poorna Saai, M. Muhammad Taufeeq Umar, N. Nithissh, S.S. Murugesh, K. tharun Kumar, S. R. M. D. Engineering College Electronics and Communication Engineering Kavaraipettai India R. M. D. Engineering College Artificial Intelligence and Machine Learning Engineering Kavaraipettai India

ISBN: (纸本)9798350379136

Traditional practices in agriculture lack the proper guidance in regard to crop production and yields, making the processes inefficient with Poor use of resources. To address the above challenges, we proposed an IoT and machine learning-based system targeting on the improvement of precision farming. the technology incorporated into our system is a microcontroller, numerous soil sensors and a GSM for transmitting the data. the main focus of our study is accurate crop recommendations that depend on which crops are suitable for the farmer and his environment. this is done via, evaluating some of the key factors of the environment in terms of Nitrogen, Phosphorous, and potassium Content in the soil, its Ph Level, the temperature, humidity and TDS. the way it works is that an image or a video of the soil is taken while, at the same time, it monitors the real-time data of the soil parameters using the sensors. then it sends this data to a GSM module with IoT technology which connects to a model website on the cloud. the information is then processed by enhanced machine learning formulas in order to determine the fertility level of the soils and the type of crops to be grown on the land. the processed data and recommendations are sent back to the device with enabled user interface to see the analyzed results on a display. Here our use of IoT with machine learning enables the system give accurate and recommendable crop data, making the most of the resources and maximizing crop production. It bypasses the shortcomings of conventional ways of agricultural methods offering a scalable and efficient solution for modern agriculture. © 2024 IEEE.

关键词： Smart agriculture

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DISTRIBUTED DIFFERENTIAL PRIVACY IN MULTI-ARMED BANDITS 11

DISTRIBUTED DIFFERENTIAL PRIVACY IN MULTI-ARMED BANDITS

引用

11th international conference on learning Representations, ICLR 2023

作者： Chowdhury, Sayak Ray Zhou, Xingyu Microsoft Research Karnataka Bengaluru India Department of Electrical and Computer Engineering Wayne State University Detroit United States

We consider the standard K-armed bandit problem under a distributed trust model of differential privacy (DP), which enables to guarantee privacy without a trustworthy server. Under this trust model, previous work on private bandits largely focus on achieving privacy using a shuffle protocol, where a batch of users data are randomly permuted before sending to a central server. this protocol achieves (Ε, δ) or approximate-DP guarantee by sacrificing an additive O (equation presented) Ε factor in T-step cumulative regret. In contrast, the optimal privacy cost to achieve a stronger (Ε, 0) or pure-DP guarantee under the widely used central trust model is only (equation presented), where, however, a trusted server is required. In this work, we aim to obtain a pure-DP guarantee under distributed trust model while sacrificing no more regret than that under the central trust model. We achieve this by designing a generic bandit algorithm based on successive arm elimination, where privacy is guaranteed by corrupting rewards with an equivalent discrete Laplace noise ensured by a secure computation protocol. We also show that our algorithm, when instantiated with Skellam noise and the secure protocol, ensures Rényi differential privacy - a stronger notion than approximate DP - under distributed trust model with a privacy cost of (equation presented). Our theoretical findings are corroborated by numerical evaluations on both synthetic and real-world data. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

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Scalability and Performance Evaluation of Machine learning Techniques in High-Volume Social Media data Analysis 11

Scalability and Performance Evaluation of Machine Learning T...

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11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Yogi, Kottala Sri Dankan Gowda, V. Mouna, K.M. Sujithra, L.R. Prasad, K.D.V. Midhun, P. Symbiosis Institute of Business Management Hyderabad Symbiosis International University Department of Operations Pune India Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Bgs Institute of Technology/ACU B G Nagara Department of Electronics & Communication Engineering Karnataka Mandya India Dr.N.G.P.Institute of Technology Department of Computer Science and Engineering Tamilnadu Coimbatore India Hyderabad Pune India Department of Electronics & Communication Engineering Andhra Pradesh Tirupati India

ISBN: (纸本)9798350350357

In the modern era of digital communication, social media platforms are data factories and hence provide a new set of opportunities as well as challenges before the analysts. the objective of this paper is to evaluate the scalability and performance of various ML algorithms for social media data analysis in practice with high-volume datasets. the objective is to design ML algorithms that are scalable and achieve high accuracy when used with big datasets. In a literature survey, observed gaps in the present research advancements toward scalability and performance analysis for various ML techniques used for this intent. To make sure of diversity and size, we collected a big source from different social networks. the approach we did involve assessing a number of ML methods that are widely known like natural language processing (NLP), sentiment analysis (SA) and predictive analytics on scalability factors such as time to process and resource utilization while also considering performance metrics such as accuracy precision recall rate f1 score. the results demonstrate significant differences in the scalability of investigated approaches and their quality, such as some algorithms being more efficient or accurate at a large scale. this research adds value to the existing works in this area, by performing a detailed comparative study of ML techniques for large-scale social media data processing considering scalability vs performance tradeoffs. the findings are informative, for the research and practice community, on selection of suitable ML techniques that will be applicable to social media analytics;scalability is an important factor in this era dominated by big data. In future research, more advanced techniques and hybrids should be taken into account to enhance the scalability and performance of social media data analysis. © 2024 IEEE.

关键词： Scalability

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intelligent Multi-agent Distributed System for Improving and Secure Travel Procedures: Al-Karama-King Hussein Bridge Study Case 11th

Intelligent Multi-agent Distributed System for Improving and...

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10th international conference on Innovation and New Trends in Information Technology, INTIS 2022 and 11th international conference on Innovation and New Trends in Information Technology, INTIS 2023

作者： Rattrout, Amjad Younis, Hussein Department of Natural Engineering and Technology Sciences Arab American University Jenin Palestine

ISBN: (纸本)9783031473654

Passports and other types of identification can take advantage of advanced technologies. Many national and international organizations are developing machine-readable solutions for biometric data. However, the selection of technologies remains questionable due to privacy and security concerns. the State of Palestine has recently started issuing biometric passports, and therefore such techniques had to be highlighted in depth to be employed at border bridges, especially et al.-Karama-King Hussein Bridge. this paper proposes an intelligent multi-agent distributed system for improving and securing travel procedures based on RFID technology and blockchain, replacing manual procedures et al.-Karama-King Hussein Bridge, and making travel mobility more secure. We implement agent-based and discrete event simulation for the proposed system and design an experimentation framework where we can reliably vary several parameters. the result from the experiments shows that the number of agents involved in the travel procedures, particularly in the early and last stages, and the optimal situation to decrease passenger overcrowding at the bridge and travel time demand a rapid reaction in the number of agents corresponding with the crossing’s capacity and available resources. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Biometrics

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EFFICIENT CONDITIONALLY INVARIANT REPRESENTATION learning 11

EFFICIENT CONDITIONALLY INVARIANT REPRESENTATION LEARNING

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11th international conference on learning Representations, ICLR 2023

作者： Pogodin, Roman Deka, Namrata Li, Yazhe Sutherland, Danica J. Veitch, Victor Gretton, Arthur Gatsby Unit UCL United States UBC Canada DeepMind Gatsby Unit UCL United States UBC Amii Canada UChicago Google Brain United States

We introduce the Conditional Independence Regression CovariancE (CIRCE), a measure of conditional independence for multivariate continuous-valued variables. CIRCE applies as a regularizer in settings where we wish to learn neural features φ(X) of data X to estimate a target Y, while being conditionally independent of a distractor Z given Y. Both Z and Y are assumed to be continuous-valued but relatively low dimensional, whereas X and its features may be complex and high dimensional. Relevant settings include domain-invariant learning, fairness, and causal learning. the procedure requires just a single ridge regression from Y to kernelized features of Z, which can be done in advance. It is then only necessary to enforce independence of φ(X) from residuals of this regression, which is possible with attractive estimation properties and consistency guarantees. By contrast, earlier measures of conditional feature dependence require multiple regressions for each step of feature learning, resulting in more severe bias and variance, and greater computational cost. When sufficiently rich features are used, we establish that CIRCE is zero if and only if φ(X) Z | Y. In experiments, we show superior performance to previous methods on challenging benchmarks, including learning conditionally invariant image features. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Benchmarking

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