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E-Goyama -A Modern Solution for Profitable Paddy Cultivation 5

E-Goyama -A Modern Solution for Profitable Paddy Cultivation

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5th international conference on Advancements in Computing, ICAC 2023

作者： Ranasinghe, R.M.R.S.B. Kareshaan, L. Karunanayake, K.M.S.M. Ranasinghe, R.A.S.U.K. Tissera, W. Rupasinghe, S. Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350358131

this research employs cutting-edge machine learning and image processing techniques to transform critical aspects of paddy cultivation, discovering and bridging a research scope of substantial research gap in the Central Province of Sri Lanka. through the augmentation of advanced technologies like machine learning, convolutional neural networks, and image processing, it revolutionizes four key domains within paddy cultivation. Firstly, it deploys machine learning for precise rice variety identification by analyzing soil nutrition data, thereby optimizing crop selection. Secondly, it implements image processing for the early detection of three major leaf-based diseases, significantly improving disease management and ensuring crop protection. thirdly, it employs machine learning for forecasting farmers' costs, enhancing economic planning and resource allocation. Finally, the study accurately identifies various paddy growth stages through image processing, facilitating the timing of essential agricultural activities. these innovations promise to markedly enhance paddy cultivation efficiency, deliver valuable insights to stakeholders, and promote sustainable agricultural practices, reinforcing food security and safety. the high accuracy models developed, cater towards promoting the expected efficiency and effectiveness in advancing the paddy cultivation to newer heights. © 2023 IEEE.

关键词： Food supply

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A Framework to Boost Team Effectiveness through AI based Personality and Skill-Based Assessment 6

A Framework to Boost Team Effectiveness Through AI based Per...

引用

6th international conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Suganya, G. Kamath, Varun Ramadasan, Manigandan Titiya, Prasham Naik, Ronit Nair, Rishi Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350394702

Choosing the right team members in a project is important as it determines how successful they can get together and productivity of the team as choosing the wrong ones can lead to delays, lack of motivation and other scenarios. One of the important factors that help determine good team members would be their alignment with the project's goals and needs. However, these do not entirely help understand a candidate's personality, which is key to their performance and productivity. this research focuses on implementing Deep Learning to extract the "big 5 Ocean Traits"of user behavior from carefully designed questions and create groups based on OCEAN trait scores after shortlisting based on their skills. BERT (Bidirectional Encoder Representations from Transformers) was used to determine the personality scores of the person using the answers that they provided for the given questions. An optimized algorithm was developed to form teams based on their OCEAN trait score that will improve the overall efficiency © 2023 IEEE.

关键词： Behavioral research

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Research on Development of Simulation Models Based on Civil Aircraft engineering Simulator

Research on Development of Simulation Models Based on Civil ...

引用

international conference on Machine Learning, big data and Business Intelligence (MLBDBI)

作者： Shuai Huang State Key Laboratory of Airliner Integration Technology and Flight Simulation Shanghai China

the engineering simulator is one of the most important test equipments in the development of civil aircraft. Simulation models are the core component of engineering simulator. Based on the engineering simulator, the development process and idea of the simulation models are proposed, including the requirements capture, model development, model verification and validation. For the simulation model, the ultimate goal is to achieve its fidelity. In order to meet the needs of engineering simulators for airworthiness compliance testing (MOC8 test), the requirements, airworthiness compliance assessment and model configuration management are analyzed and explained in this paper, which is based on the development experience of a certain type of aircraft.

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Implementation of Power Meter System Security in Smart Grids with QRNG Method 5

Implementation of Power Meter System Security in Smart Grids...

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5th IEEE international conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Patel, Aum Shrivastava, Simran Sujatha, G. Srm Institute of Science and Technology School of Computing College of Engineering and Technology Faculty of Engineering and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350383522

Smart grids face emerging threats as connectivity expands attack surfaces. To strengthen smart meter data encryption through optimized integration of a cloud-based quantum random number generator (QRNG) service. QRNGs leverage intrinsic quantum properties to produce true randomness for cryptographic keys. An architecture is presented utilizing a QRNG service on the IBM quantum computing platform. Quantum entropy is remotely sampled and delivered on-demand via APIs to generate quantum random AES encryption keys for smart meter communications. Homomorphic encryption secures credential transmission. Compared to mathematical pseudorandom number generators, the quantum origin provides stronger security against brute force attacks and future quantum computing decryption. Tailoring QRNG generation and integration focuses on the performance, cost, and deployment constraints of grid-wide encryption hardening. the optimized approach aims to maximize randomness while minimizing complexity. By hardening cryptography via readily available quantum key generation, the QRNG integration delivers indispensable security enhancement. © 2024 IEEE.

关键词： Cryptography cryptography protocols cybersecurity encryption privacy QKD QRNG quantum entropy quantum key distribution quantum random number generator quantum uncertainty security smart grid smart meter true randomness

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Development of a Construction Type Classification Model for Construction Using the KLUE-BERT

Development of a Construction Type Classification Model for ...

引用

2025 IEEE international conference on big data and Smart Computing, bigComp 2025

作者： Shin, Seung-Hyeon Won, Jeong-Hun Kim, Moon Gyu Shin, Doo Hwan Chungbuk National University Department of Big Data Cheongju-si Korea Republic of Chungbuk National University Department of Safety Engineering Cheongju-si Korea Republic of Occupational Accident Prevention Guidance Division Ministry of Employment and Labor Cheongju-si Korea Republic of Gangdong University Division of Industrial Safety Eumseong-gun Korea Republic of

the construction industry experiences elevated occupational accident rates due to the variability of sites, diverse work types, and non-repetitive nature of work. this study addresses the necessity of a refined classification model capable of categorizing construction types within occupational accident data, thereby supporting effective prevention strategies. We propose an automated model utilizing KLUE-BERT, a Korean language processing architecture, to enhance the classification performance by leveraging contextual learning techniques. Our model was trained on occupational accident data from 2018 to 2023, with data augmentation expanding the labeled dataset to 2 4, 692 samples for robust training and validation. three model configurations were developed. the first model was a base KLUE-BERT model, second was an MLM-enhanced model employing Masked Language Modeling, and third was a model incorporating both MLM and a Gated Recurrent Unit (GRU) layer. the evaluation results demonstrated that the MLM-enhanced model yielded the highest accuracy, precision, recall, and F1 score, surpassing both the base and GRU models, indicating that contextual learning via MLM significantly improves classification accuracy. However, the GRU layer did not further enhance performance, suggesting that sequential learning provided limited benefits in this context. the findings highlight the efficacy of the MLM-enhanced KLUE-BERT model in categorizing diverse construction types, thereby offering a robust tool for analyzing occupational accident data. Future research could refine the data augmentation techniques and extend the scope of the model to adapt to evolving construction categories, further supporting targeted accident prevention efforts. © 2025 IEEE.

关键词： Construction industry

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Individual Health Profiling Using Multimodal Sensors on Smart Health Care 17

Individual Health Profiling Using Multimodal Sensors on Smar...

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17th international conference on Telecommunication Systems, Services, and Applications, TSSA 2023

作者： Ikawati, Vidya Saepudin, Didin Susanto, Bambang Munandar, Ahmad Nurba, Hardy Purnama Prihatmanto, Ary Setijadi Doctoral Program of School of Electrical and Informatics Engineering Institute of Technology Bandung Bandung Indonesia Universitas Sangga Buana Bandung Indonesia Universitas Sangga Buana Faculty of Engineering Department of Electrical Engineering Bandung Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9798350309164

Health Service, also referred to as Health Care System, plays a pivotal role in maintaining and enhancing health by preventing illnesses and injuries. In this context, electronic health records are essential for both inpatients and outpatients, serving as a crucial tool for healthcare providers and policymakers. However, the potential of these records extends beyond individual care to support broader health policy and research initiatives. Personal Smart Health Care, an individual-based digital health technology, bridges the gap between personal health management and medical records, enabling comprehensive healthcare improvements. Smart healthcare, driven by technologies like IoT, mobile applications, cloud computing, big data, 5G, artificial intelligence, and biotechnology, offers a holistic approach to healthcare, emphasizing diagnosis, treatment, disease prevention, risk monitoring, and virtual assistance. An intelligent framework for smart healthcare includes four layers: the instrumentation layer, information system layer, business intelligence layer, and gamification layer, each contributing to enhanced patient outcomes, streamlined processes, and improved patient care. the gamification layer incentivizes healthy behaviors and fosters patient engagement, especially in healthcare scenarios. this comprehensive framework underscores the transformative potential of smart healthcare, where technology-driven solutions can revolutionize the healthcare landscape, improving patient care, health management, and the overall well-being of individuals and populations. © 2023 IEEE.

关键词： big data

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A review of trajectory data preprocessing and mining technology research 4

A review of trajectory data preprocessing and mining technol...

引用

4th international conference on big data, Artificial Intelligence and Risk Management, ICBAR 2024

作者： Sheng, Honghao Wang, Tianqi Luo, Yi Liang, Hongxiang School of Safety Engineering Shenyang Aerospace University Liaoning Shenyang China

ISBN: (纸本)9798400709753

Under the background of wireless communication, Internet, and bigdata becoming increasingly perfect, trajectory data of a large number of movable objects are being generated. the trajectory data contains a large amount of spatiotemporal feature information, and by mining it, we can get the information implied in it, such as personnel activity characteristics, interests and hobbies, as well as public transportation movement trajectory and stay characteristics. this paper takes the trajectory data mining processing process as a framework and summarizes the data processing techniques involved in each process. Firstly, it introduces the definition, characteristics and research value of trajectory data;secondly, it describes the preprocessing techniques of trajectory data, including data cleaning, compression, segmentation and matching;thirdly, it summarizes and categorizes the trajectory datamining techniques in the past 5 to 10 years, including support vector machine, artificial neural network, Bayesian network and other methods;finally, its future research directions are prospected. © 2024 Copyright held by the owner/author(s).

关键词： data mining overview preprocessing trajectory data

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A Hybrid LSTM-DBN Approach for Automatic Epileptic Seizure Detection compared with Normal Human Activity using EEG Signals 5

A Hybrid LSTM-DBN Approach for Automatic Epileptic Seizure D...

引用

5th international conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Cherukuvada, Srikanth Kayalvizhi, R. School of Computing Srm Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350321425

More than 2% of people throughout the globe suffer from the neurodegenerative condition known as epilepsy. A sudden onset of convulsive seizures characterizes epilepsy, a condition of the neurological system. these seizures are brought on by an imbalance in the electrical processes of the brain. Neurostimulation devices possess the capability to pre-emptively intervene and prevent the onset of seizures. Its effectiveness can be improved by using techniques like advanced seizure forecast. the prompt detection of the preictal phase would facilitate timely activation of the neurostimulation device. In this work, a Deep Learning (DL) approach was utilized to detect the Epileptic Seizure (ES) before onset by utilizing Electroencephalogram (EEG) signals. Long Short-Term Memory (LSTM) model work focuses on evaluating the efficacy of several machine learning (ML) algorithms such as Logistic Regression (LR), Support Vector Machine (SVM) by applying them to Seizure recorded EEG data. Additionally, a Deep Belief Network (DBN) was employed to choose and interpret the captured dataset's most useful and efficient characteristics. the short name for this proposed methodology is LSTM-DBN. Ultimately, our objective is to identify the most efficient method for constructing a hybrid learning model. In this work, a public dataset was used to evaluate the ES such as CHB-MIT. these data are were collected from EEG signals. Comparing with conventional approaches of ML compared with DL techniques, LSTM-DBN had significantly got better performance with an accuracy of 99.07% © 2023 IEEE.

关键词： Electroencephalography

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Large-scale 3D Mesh data Semantic Segmentation: A Survey 9

Large-scale 3D Mesh Data Semantic Segmentation: A Survey

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9th international conference on big data and Information Analytics, bigDIA 2023

作者： Gao, Lin Liu, Yu Liu, YuXiang Cheng, XiaoYa Zhu, JueLin Wang, JingYi University of Defense Technology College of Systems Engineering ChangSha China

ISBN: (纸本)9798350330076

Utilizing 3D mesh data presents myriad advantages for representing actual environments, encompassing the capability to exhibit geometric information and high-quality textures. Within the domain of geographical information systems, there exists a substantial need for 3D data to precisely depict extensive remote sensing scenes. Meshes are commonly employed for representing geographical terrains, structures, and urban models. Effective visualization and representation of meshes are imperative for managing expansive geographical datasets. the procedure of 3D mesh semantic segmentation entails assigning distinct semantic labels to each triangle, thus dividing the entire scene into various categories. Nevertheless, owing to the irregular structure of 3D mesh data, the progress in developing pertinent semantic segmentation techniques is still in its nascent stages. Many conventional methods rely on multi-view approaches. Given the growing popularity of deep learning for segmenting remote sensing data, end-to-end 3D mesh semantic segmentation techniques have become a focal point of current research. this article firstly introduces existing 3D mesh datasets and classifies them based on scale and application, as these datasets serve as the groundwork for algorithmic research. Subsequently, existing 3D mesh semantic segmentation techniques are presented, categorized into traditional methods and deep learning methods. Lastly, the challenges confronted by 3D mesh semantic segmentation at present are deliberated. © 2023 IEEE.

关键词： Remote sensing

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Larger receptive Field and Context Information for Pose estimation:Larger Gaussian kernel 5

Larger receptive Field and Context Information for Pose esti...

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5th international conference on High Performance big data and Intelligent Systems, HDIS 2023

作者： Ma, Junxiao Yang, Kai Ke, Zunwang Wang, Gang Zhang, Yugui Cao, Fengcai Zhou, Qi Fei, Yang School of Information Beijing Forestry University Beijing100083 China CGNPC Uranium Industry Development Co. Ltd. Beijing100020 China School of Software Xinjiang University Urumqi830046 China School of Computing and Data Engineering NingboTech University Ningbo315100 China AnnLab Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing100083 China School of Integrated Circuits University of Chinese Academy of Sciences Beijing100049 China Co. Ltd Beijing100176 China School of Semiconductor Science and Technology South China Normal University GuangZhou510631 China

ISBN: (纸本)9798350394160

the field of pose estimation has a wide range of application prospects in various industries in the current era. With the continuous development of deep learning techniques, the effects in the field of human pose estimation are constantly being optimized. However, due to the influence of factors such as occlusion and human posture variability, it is easy to lead to inaccurate identification of keypoints and even keypoints’ missing and matching errors, there is still a lot of optimization space for the effect of pose estimation. In this paper, we propose an improvement to generate heatmaps in the training process: increase the radius of the Gaussian kernel and adjust the smoothness of Gaussian distribution to get a larger receptive field, encourage the model to use more context information in prediction, and control other parameters unchanged on the premise of using heatmap regression. Improve keypoint detection performance by optimizing this item. In the experiment, for the COCO-Keypoint data set, we selected different methods published in recent years, and after our improvement, we obtained better accuracy and performance than the original method, which proves the validity of our method. ©2023 IEEE.

关键词： Gaussian distribution

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