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5th International Conference on Communication, Device and networking, ICCDN 2021

作者： PremaLatha, V. Sreedevi, E. Sivakumar, S. Nayak, Soumya Ranjan Bhoi, Akash Kumar Ghosh, Uttam Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation K L University Guntur Vaddeswaram522502 India Department of Computer Applications Faculty of Science and Humanities SRM Institute of Science and Technology Kattankulathur603203 India Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India KIET Group of Institutions Delhi-NCR Ghaziabad201206 India Directorate of Research Sikkim Manipal University Sikkim Gangtok737102 India Department of Computer Science and Data Science Meharry School of Computational Sciences Nashville NashvilleTN United States

ISBN: (纸本)9789811920035

Now a days in EFL procedure of education the ability of reading became as significant belief and personal-efficacy reading as a basic understanding for students. By monitoring the acknowledged participates under the ballpark figure of large studying and methods of understanding, the impact of their observation is premeditated on reading of each one’s personal-efficacy. On a daily routine all these things are comparatively considered which are put into effect by teachers of handful in number. Approach towards exhibiting Extensive reading (ER) is inspected to be "more expensive, difficult, and time-consuming". Method of recognition of elements in a various way for effective impact in putting its efforts to utilize for its empowerment. Paper has been segregated into two contexts: Association with attitude is considered as primary one and attitude is considered as secondary one. Whether knowledge work is understood by student or not is considered as the impact of ER by the first review. Procedure which are convenient is taken as the observations of student and is analysed as second one. The examinations are quantifiable to utilize the observations as information in terms of subjective way taken from students who belong to first academic year of reading course in a systematic way and 603 details of undergraduate students from KLEF of Guntur were chosen as participants for extant examination. In ER programme of includes and excludes "comprehension reading work" is treated as fundamental in the proposal of disclosures. In case of any, "the programme appeared to positively affect contributing students". Techniques of classification like "decision tree and Mixed Model database Miner (MMDBM)" are employed in this paper which leads to improvements of post-test to pre-test in ER group. Observations of students in ER results as optimistic and algorithm of MMDBM which leads to accuracy in higher rate in pre-test and post-test detection. © 2023, The Author(s), under exclusive lice

关键词： Students

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Evaluating the Impact of dataset Size on Univariate Prediction Techniques for Moroccan Agriculture

Evaluating the Impact of Dataset Size on Univariate Predicti...

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International Conference on Artificial Intelligence and Smart Environment, ICAISE 2022

作者： Ed-daoudi, Rachid Alaoui, Altaf Zerouaoui, Jad Ettaki, Badia Zerouaoui, Jamal Laboratory of Engineering Sciences and Modeling Faculty of Sciences Ibn Tofail University Campus Universitaire BP 133 Kenitra Morocco LyRICA: Laboratory of Research in Computer Science Data Sciences and Knowledge Engineering School of Information Sciences Rabat Rabat Morocco

ISBN: (纸本)9783031262531

Learning models used for prediction are mostly developed without taking into account the size of datasets that can produce models of high accuracy and better performance. Although, the general believe is that, large dataset is needed to construct a predictive learning model. To describe a data set as large in size depends on the circumstances and context of prediction. This means that what makes a dataset to be considered as being big or small is controversial. In this paper, the ability of the predictive model to adapt to a particular size of data in training is examined. The study experiments on three different sizes of Moroccan agricultural data using a variety of statistical and Machine Learning techniques, to create predictive models with a view to establishing if the size of data has any effect on the accuracy of a model. The output of each model is measured using the Mean Absolute Error (MAE) and r-squared, and comparisons are made. The results of training the models through the three partitioned dataset show that, the models trained with the smallest and largest size of training data appear to be less accurate, while the models trained with a medium sized dataset delivers a much better results. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Assessing the Improvements Brought by Artificial Intelligence on the Prediction of Aerodynamic Coefficients

Assessing the Improvements Brought by Artificial Intelligenc...

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International Conference on Artificial Intelligence and Smart Environment, ICAISE 2022

作者： Zerouaoui, Jad Alaoui, Altaf Ettaki, Badia Chakir, ElMahjoub Laboratory of Material and Subatomic Physics Faculty of Sciences Ibn Tofail University Campus Universitaire BP 133 Kenitra Morocco LyRICA: Laboratory of Research in Computer Science Data Sciences and Knowledge Engineering School of Information Sciences Rabat Rabat Morocco

ISBN: (纸本)9783031262531

The implementation of Artificial Intelligence in the aerospace field is fairly new and various domains of the aerospace discipline are to be explored. This paper provides a general review on the upbringing and improvements of Artificial Intelligence as a pilar of the next generation of scientific research. Applications and developments brought by Artificial Intelligence and Deep Learning to the aerospace industry generally, mainly the prediction of aerodynamic coefficients and design optimization using intricate neural networks replicating the process of the human brain. Scientific developments participated in improving major areas in the aerospace field. Due to the expensive and time-consuming process related to the complexity of the geometry and the precision of the results required, researchers integrated surrogate models based on Deep Learning in order to improve the calculation process of the aerodynamic coefficients. On one hand, this paper introduces and discusses the general architecture of several networks used to enhance the calculation ability of the machine, while presenting the process of each work along with recounting the different methods employed. On the other hand, a discussion regarding the challenges presented with utilizing Computational Fluid Dynamics while assessing and comparing the results brought by surrogate models based on efficiency and time consumption is also tackled. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Wearable Biosensors: Transformative Innovations and Future Pathways for Continuous Health Monitoring

Wearable Biosensors: Transformative Innovations and Future P...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Trupti Thute Amit Gudadhe Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Wearable biosensors are revolutionizing healthcare by enabling continuous monitoring of health status, transforming it from an event-based process to an ongoing one. When integrated with AI, nanotechnology, and IoMT (Internet of Medical Things), these devices enhance precision care, telediagnosis, and chronic disease management. Advancements in electrochemical, optical, and biochemical sensors, along with innovations in energy harvesting and miniaturized flexible materials, improve both the performance and comfort of these devices. Wearable biosensors are now crucial for everyday fitness, disease prediction, and the seamless exchange of data between m-health apps and EHRs. Despite these advancements, challenges remain, such as data security, system integration, and user incentives. Addressing these concerns through enhanced security features, standardized protocols, and user-focused designs is critical. The future development of wearable biosensors is aimed at leveraging cost-effective energy sources, utilizing AI for data analysis, and improving user convenience, making significant strides in preventive and personalized healthcare.

关键词： Integrated optics Technological innovation System integration Biosensors Optical sensors Energy harvesting Biomedical monitoring Monitoring Nanotechnology Diseases

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A Diversity Inventory Monitoring System of Riparian Vegetation 4

A Diversity Inventory Monitoring System of Riparian Vegetati...

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4th International Conference on Cybernetics and Intelligent System, ICORIS 2022

作者： Putra, Dian Pratama Nugraha, Nanda Satya Suparyanto, Teddy Hidayat, Alam Ahmad Sudigyo, Digdo Pardamean, Bens STIPER Agricultural Institute Faculty of Agricultural Technology Yogyakarta55282 Indonesia STIPER Agricultural Institute Yogyakarta Faculty of Agricultural Technology Faculty of Forestry Yogyakarta55282 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665453950

To strengthen conservation efforts for preserving biodiversity in a conservation area, forest inventory is important to understand the natural succession process in the area and to establish a monitoring strategy. Further, tree inventory aims to monitor the output yielded in the area. More specifically, the tree inventory in the watershed area plays a key role to achieve Sustainable Development Goals (SDG), especially in riparian zones which are also vital parts of green zones in forests. However, the traditional inventory approach is time-consuming and laborious therefore the development of an expert system to assist in inventory monitoring is required. In this study, we develop a monitoring system via a mobile application to collect, analyze and visualize tree inventory data. The application includes algorithms required to compute tree biodiversity, distribution, and richness for the given input of the data of all tree species in a conservation area. For the model validation stage, we compare the traditional inventory approach with our proposed application-based approach to compute diversity inventory in two riparian locations: Klaten Conservation Park and Wonosobo Conservation Park. After the three-day data collection in the areas, we obtain that the accuracy of reading data of our proposed system can achieve more than 90% in comparison with the manual approach. This demonstrates that the system can assist forestry workers to perform more efficient tree inventories in different locations. © 2022 IEEE.

关键词： Biodiversity

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A Trace-Selection Preprocessing Approach of Discovering Structurally Complete ICN-Process Models from Process Logs 3rd

A Trace-Selection Preprocessing Approach of Discovering Str...

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3rd International Conference on Advances in Information and Communication Technology, ICTA 2024

作者： Pham, Dinh-Lam Batzaya, Tsagaantsooj Kim, Kyoung-Sook Chang, Jeong-Hyun Kim, Kwanghoon Pio CCSRI Kyonggi University Gyeonggi Suwon16227 Korea Republic of Department of Public Safety Bigdata Graduate School of Kyonggi University Gyeonggi Suwon16227 Korea Republic of Data Process Engineering Research Laboratory Faculty of AI Computer Science and Engineering Kyonggi University Gyeonggi Suwon16227 Korea Republic of

ISBN: (纸本)9783031809422

In business process life-cycle management and reengineering through process mining, it is crucial for the process mining system to discover structurally safe and complete business process models from process logs. However, most process mining systems typically suffer from discovering inaccurate and unstructured business process models due to various types of anomalous traces hidden in process logs. In this paper, we therefore propose a process log preprocessing approach that purifies a corresponding process log dataset by selecting a group of reasonable process log traces and applying to discover an essential business process model satisfying the structural completeness requirement of matched-pairing and proper-nesting properties. The theoretical basis of the proposed approach is the mathematical graph model of information control net model that is the typical representation of business process models. Finally, we practically demonstrate the conceptual excellence and functional correctness of the proposed preprocessing approach through experimental verifications using real business process enactment log datasets available in the 4TU Center for research data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Reengineering

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Sustainable North-South Africa Collaboration for Disaster and Crisis Management: A Strategic Capacity Development Framework using Open science, Artificial Intelligence and Geoinformatics

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data science Journal 2025年 24卷

作者： Ibrahim, Rania Elsayed Motshegwa, Tshiamo Benouar, Djillali Khodja, Mohamed Ead, Hamed Zhang, Lili Crawley, Francais McHunu, Nokuthula P. Hachigonta, Sepo Hodson, Simon McBride, Vanessa Yohannes, Teketel National Authority for Remote Sensing and Space Sciences Egypt National Research Foundation South Africa Department of Computer Science University of Botswana Botswana Algerian Academy for Science and Technology Algeria Faculty of Science Cairo University Egypt Egyptian Embassy in Morocco Morocco Computer Network Information Center Chinese Academy of Sciences China CODATA International Data Policy Committee Belgium University of KwaZulu Natal South Africa CODATA France International Science Council South Africa

Background: This paper explores strategies to enhance North-South collaboration to foster multi-and trans-disciplinary research in disaster risk reduction (DRR) and crisis management, emphasizing the role of open science, Artificial Intelligence (AI) and geoinformatics in developing a resilient and proactive approach to these challenges, as well as drawing on lessons learned from the COVID-19 pandemic, the paper highlights how collaboration and data sharing can enhance preparedness, response, and recovery in future crises. Methods: The study uses a mixed approach to gather and analyze information. Qualitative methods focus on gathering descriptive data through engaging with experts and a structured review. By investigating literature, a comprehensive strategic framework for minimizing disaster risk, and for crisis management, is developed to provide adequate guidance to policymakers on implementation strategies and the application of how to build the human, institutional, and technological capacity required for effective disaster and crisis management (DCM). The events were used to engage with experts to provide recommendations and opportunities to enhance crisis response, capacity development and policy. Findings: By presenting a comprehensive framework that bridges theoretical knowledge with practical application, our findings indicate that governments must address the challenges of DCM to minimize losses. The practices surrounding disaster recovery can provide opportunities for policymakers to reassess priorities and redirect resources toward building sustainable communities. Originality: There is no strategic framework that provides adequate guidance to policymakers on implementation strategies or the application of how to build the human, institutional and technological capacity required for effective DCM. This article develops a Decision Support Framework for DCM. We outline how open science promotes cross-disciplinary collaboration and the sharing of knowledg

关键词： Risk management

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Beyond the Limit of Weight-Sharing: Pioneering Space-Evolving NAS with Large Language Models

Beyond the Limit of Weight-Sharing: Pioneering Space-Evolvin...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiu Su Shan You Hongyan Xu Xiuxing Li Jun Long Yi Chen Chang Xu Big Data Institute Central South University School of Computer Science Faculty of Engineering The University of Sydney SenseTime Research School of Computer Science and Engineering University of New South Wales Institute of Computing Technology Chinese Academy of Sciences School of Computer Science Hong Kong University of Science and Technology

Large language models (LLMs) offer impressive performance across diverse fields, but their increasing complexity raises both design costs and the need for specialized expertise. These challenges are intensified for Neural Architecture Search (NAS) methods reliant on weight-sharing techniques. This paper introduces GNAS, a new NAS method that boosts the search process with the aid of LLMs for efficient model discovery. With insights from existing architectures, GNAS swiftly identifies superior models that can adapt to changing resource constraints. We provide a mathematical framework to facilitate the transfer of knowledge across different model sizes, thereby improving search efficiency. Our experiments conducted on ImageNet, NAS-Bench-Macro, and ChannelBench-Macro confirm the effectiveness of GNAS across both CNN and Transformer architectures.

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Plant Disease Classification Using VGG-19 Based Faster-RCNN 7th

Plant Disease Classification Using VGG-19 Based Faster-RCNN

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Proceedings of the 7th International Conference on Advances in Computing and data sciences, ICACDS 2023

作者： Nawaz, Marriam Nazir, Tahira Khan, Muhammad Attique Rajinikanth, Venkatesan Kadry, Seifedine Department of Software Engineering University of Engineering and Technology Taxila Taxila47050 Pakistan Faculty of Computing Department of Computer Science Riphah International University Gulberg Green Campus Islamabad Pakistan Department of Computer Science HITEC University Taxila Pakistan Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering SIMATS Chennai602105 India Department of Applied Data Science Noroff University College Kristiansand4612 Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

ISBN: (纸本)9783031379390

Early plant disease diagnosis can help farmers avoid spending money on costly crop pesticides and aid them to boost food production. Scientists have put a lot of effort to classify plant diseases, but it is difficult to quickly locate and identify different crop abnormalities due to the high degree of resemblance between the normal and damaged parts of plant leaves. Additionally, the procedure of detecting plant diseases has been made more challenging due to the extensive color, size, shape, and intensity variations in the background and foreground of the plant images. To address the existing difficulties, we have introduced an effective deep learning (DL) based system called Faster-RCNN o recognize and classify various types of plant diseases. The suggested method consists of 3 basic steps. In order to identify the area of interest in investigated samples, we first create annotations for them which are later used for Faster-RCNN training. The Faster-RCNN model employs the VGG-19 network to extract the relevant keypoints from the given images which are later passed to the regressor and classification units to identify and categorize the various crop diseases using the estimated features. We evaluated our method on a widely used standard plant sample repository called the PlantVillage database, and the findings show that our approach is reliable for classifying plant diseases under a variety of image-capturing scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Crops

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An IoT-based Bearded Dragon Enclosure Integrating YOLO Object Detection for Monitoring Food and Cleanliness

An IoT-based Bearded Dragon Enclosure Integrating YOLO Objec...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Nanmanat Varisthanist Thanapat Tardtong Kittitach Jadee Krittakom Srijiranon Thammasat University Research Unit in Data Innovation and Artificial Intelligence Department of Computer Science Faculty of Science and Technology Thammasat University Pathumthani Thailand

Among exotic reptiles, bearded dragons (Pogona) are popular pets. As ectotherms, bearded dragons require special food and controlled environments, including temperature, humidity, ultraviolet light, and cleanliness. This research applied the Internet of Things (IoT) to control environments within the pet enclosure. In addition, the You Only Look Once (YOLO) deep learning algorithm applied to convolutional neural networks was used to create object detection to count crickets eaten by a dragon and detect feces inside the enclosure. To find the appropriate YOLO structure, three varied experiments were proposed, including different versions YOLO and sizes as well as hyperparameter tuning. Results were that YOLYv5s with 16 batches and 0.01 learning rate was the appropriate structure with an average precision of 0.8246 and 0.6051 for detecting crickets and feces, respectively, at a mean average precision of 0.7149. The proposed enclosure with object detection may improve efficiency and reduce redundancy in breeder tasks.

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