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Classification of Stakeholders of an Information System using Fuzzy based Adjusted Cosine Similarity Measure during Requirements Elicitation Process

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Procedia computer Science 2023年 230卷 620-628页

作者： Faiz Akram Tanvir Ahmad Mohd. Sadiq Department of Computer Engineering Faculty of Engineering and Technology Jamia Millia Islamia New Delhi-25 India Software Engineering Laboratory Computer Engineering Section UPFET Jamia Millia Islamia New Delhi-25 India

Requirements elicitation process is employed for the identification of stakeholders of an information system. A large number of stakeholders from different domains across the globe participate during the requirements elicitation process. Based on our review, we found that existing methods for the analysis of stakeholders do not support how to classify the stakeholders based on the similarity measures. These measures play an important role for the recommendation of an item/user in a recommendation system. Thus, the objective of this paper is to classify the stakeholders of an information system using fuzzy-based adjusted cosine similarity measure. We have identified the stakeholders of library information system and their opinions for different requirements are recorded. The identified stakeholders have been classified and analysed based on the following similarity measures under fuzzy environment, i.e., Cosine similarity measure, Euclidean distance, Pearson coefficient correlation etc., so that stakeholders having similar requirements can be recommended to strengthen the requirements elicitation process.

关键词： Software Requirements Elicitation Information System Similarity Measures Recommendation System Stakeholders

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USRP-Based Single Anchor Positioning: AoA with 5G Uplink Signals, and UWB Ranging 11

USRP-Based Single Anchor Positioning: AoA with 5G Uplink Sig...

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11th Workshop on Satellite Navigation Technology, NAVITEC 2024

作者： Spanos, Thodoris Fabra, Fran Lopez-Salcedo, Jose A. Seco-Granados, Gonzalo Kanistras, Nikos Lapin, Ivan Paliouras, Vassilis University of Patras Dept. of Electrical and Computer Engineering Greece Loctio P.C Greece Dept. of Telecommunication and Systems Eng Spain Radio Navigation Systems and Techniques Section European Space Agency Netherlands

ISBN: (纸本)9798350356854

This paper presents a novel testbed designed for 5th-Generation (5G) positioning using Universal Software Radio Peripherals (USRPs). The testbed integrates multiple units: an Operation Unit for test management, a User Unit equipped with an Ettus E312 USRP, and a Station Unit featuring an Ettus N310 USRP equipped with a three-element Uniform Linear Array for Angle of Arrival estimation. Alongside ultra wide-band ranging, the testbed estimates the user's position relative to the base station. Signal processing algorithms are executed in a dedicated processing unit. Key challenges addressed include phase misalignment between RX channel pairs due to different Local Oscillators in the Ettus N310, necessitating real-time calibration for precise signal alignment. High sampling rates (up to 61.44 MSps) result in large IQ sample files, managed efficiently using a snapshot technique to optimize storage without compromising testbed positioning capabilities. The testbed synchronizes angular measurements with ranging estimates allowing consistent performance evaluation for real-life cases of dynamic users (e.g. pedestrian). Experimental results demonstrate the testbed's effectiveness in achieving accurate pedestrian user localization. © 2024 IEEE.

关键词： Testbeds

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Simulated Data Enhances Three-dimensional Segmentation-based Characterization of Real Apple Trees

Simulated Data Enhances Three-dimensional Segmentation-based...

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2024 American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2024

作者： Du, Ruiming Qiu, Tian Xu, Kenong Jiang, Yu Department of Biological and Environmental Engineering Cornell University IthacaNY14850 United States School of Electrical and Computer Engineering Cornell University IthacaNY14850 United States Horticulture Section School of Integrative Plant Science Cornell AgriTech GenevaNY14456 United States

ISBN: (纸本)9798331302214

Tree morphology plays an important role in both crop training system design and crop load management, which are essential to tree fruit crops such as apples. Accurate and rapid characterization of tree morphology patterns would facilitate the reach of crop ideotypes, the implementation of data-driven management such as precision pruning, and the scientific discovery of genetic mechanisms for controlling tree architecture. While 3D imaging has been intensively investigated for tree characterization in recent studies, segmentation of orchard trees into topological organs and acquiring enough annotated 3D data for network training are two main bottlenecks hindering the successive characterization processes. In this study, we propose a pipeline using simulated apple trees generated from real tree information for training a deep neural segmentation network. Subsequently, we designed 9 experiments to fine-tune the network with a different number of real trees and benchmarked the phenotyping performance on 21 real trees based on the segmentation results across several experiments to demonstrate the capabilities of introducing simulated trees as a source substitute for reliable crop tree phenotyping. Results indicated that the phenotyping performance from six-shot (Mix) segmentation results with a more balanced tree composition in the fine-tuning set are highly correlated with the ground truth, showing similar accuracy to those obtained using a substantial amount of real training data. Therefore, to optimize the use of simulated data for real-world applications of tree morphology phenotyping, it’s critical to make the feature distribution of the simulated data used for fine-tuning as balanced as possible. This approach helps to alleviate issues like uneven feature distribution and ensures more robust and unbiased phenotyping results. © 2024 ASABE Annual International Meeting. All rights reserved.

关键词： Fruits

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An efficient computational procedure to solve the biological nonlinear Leptospirosis model using the genetic algorithms

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Soft Computing 2024年第1期28卷 607-625页

作者： Sabir, Zulqurnain Ali, Mohamed R. Raja, Muhammad Asif Zahoor Sadat, R. Department of Mathematics and Statistics Hazara University Mansehra Pakistan Faculty of Engineering and Technology Future University in Egypt New Cairo11835 Egypt Basic Engineering Science Department Benha Faculty of Engineering Benha University Banha Egypt Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Department of Mathematics Zagazig Faculty of Engineering Zagazig University Zagazig Egypt Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon

The purpose of this work is to present the numerical solutions of the nonlinear mathematical Leptospirosis disease (LD) model using the computational performances of the artificial neural networks (ANNs) along with the optimization procedures based on the global search genetic algorithm (GA) and local search active-set algorithm (ASA) scheme, i.e., ANNs-GA-ASA. LD is a zoonotic disease that is occurring in the whole world, obtained by rodents that become the reason of death in the people. The LD model based on the susceptible-infected-recovered, i.e., SIR, is used to solve the mechanisms of disease spread. The optimization of an error-based fitness function, which is constructed through the differential mathematical system using the hybrid computing proficiencies of the ANNs-GA-ASA for solving the LD model. The stochastic ANNs-GA-ASA procedures are applied to the LD model to authenticate the precision, exactness, reliability and competence of the ANNs-GA-ASA. The obtained results using the ANNs-GA-ASA of the LD model will be compared using the Runge–Kutta scheme, which authenticate the importance of the ANNs-GA-ASA. Furthermore, statistical analysis through different actions for the LD model approve the convergence and precision of the ANNs-GA-ASA. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Neural networks

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Attribute Selection, Outliers Impact Study and Visualization within Breast Cancer Detection 13

Attribute Selection, Outliers Impact Study and Visualization...

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13th IEEE International Conference on Electronics and Information Technologies, ELIT 2023

作者： Chuiko, Gennady Dvornik, Olga Darnapuk, Yeugen Honcharov, Denis Krainyk, Yaroslav Yaremchuk, Olga Computer Engineering Department Petro Mohyla Black Sea National University Mykolayiv Ukraine Ukraine Section Computer Engineering Department Petro Mohyla Black Sea National University Mykolayiv Ukraine Information computer center Petro Mohyla Black Sea National University Mykolayiv Ukraine Medical Institute Petro Mohyla Black Sea National University Mykolayiv Ukraine

ISBN: (纸本)9798350383096

Classification of mammography data into two types of breast tumors, benign or malignant, is an effective screening tool and the primary way of diagnosis and decision-making. This report aims to opt for the most relevant attributes of the well-known Wisconsin Breast Cancer Diagnostic Data Set to reduce its size at first. The reduction was performed initially by ranking attributes and finally by "decision tree" analysis. The clipped data set had only six attributes, against 31 in the initial one. The five most relevant attributes were the following: "perimeter_worst," "area_worst," "concave points_worst," "texture_mean," and "concave points_mean." If possible, It should be done without losing classification potency. Over and above, our extra goal was to find classifiers that provide acceptable performance while allowing visualization of the results in a way accessible to clinicians. Here, we mean various visualization tools in the Machine learning framework: "decision trees," association rules, attribute ranking, and so forth, to improve breast cancer diagnosis. © 2023 IEEE.

关键词： Decision trees

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A Robust Bio-inspired Color Detection Algorithm Under Low/High Light and Shadow for UAV Applications

A Robust Bio-inspired Color Detection Algorithm Under Low/Hi...

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IEEE Latin American Robotics Symposium, LARS

作者： Viviane S. S. Freitas Fabio Suim Chagas Thiago J. M. Baldivieso Daniel Morais Marcos P. S. Campanha Andre Rotava Paulo F. F. Rosa Defense Engineering Section Military Institute of Engineering Rio de Janeiro Brazil Computer Engineering Section Military Institute of Engineering Rio de Janeiro Brazil Electrical Engineering Section Military Institute of Engineering Rio de Janeiro Brazil

This project presents a bio-inspired color detection algorithm designed to keep good performance in scenarios where the light conditions are variable and challenging. It is based on the theory of opposite colors, studied in the context of human vision, where the ability to detect colors is associated with a neural response to contrast. To validate the algorithm, we have performed three experiments under adverse luminosity conditions in an indoor environment. Then, we compared the processed images analyzing them in two color spaces - YCrCb and CIEXYZ. As a result, we identified that the YCrCb space provided superior responses and allowed the detection of all eight colors chosen for the experiments.

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Enhancing Public Transportation Detection using YOLOv5 9

Enhancing Public Transportation Detection using YOLOv5

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9th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2023

作者： Shukhair, Nur Liyana Ameera Arbi Mansor, Hasmah Gunawan, Teddy Surya Kadir, Kushsairy Abdul International Islamic University Malaysia Electrical and Computer Engineering Department Kuala Lumpur53100 Malaysia Universiti Kuala Lumpur-British Malaysian Institute Electrical Section Kuala Lumpur53100 Malaysia

ISBN: (纸本)9798350343380

In the current context of urbanization and transportation expansion, the need for accurate and efficient detection systems for public transportation has become of the utmost importance. The paper presents a novel strategy to establish new standards in transportation detection systems. Using the power of the YOLOv5 deep learning algorithm, the dataset is divided into training, testing, and validation segments to ensure a thorough evaluation. With a training dataset size of 75% and a test-validation split of 25%, our methodology showcases a compelling mean Average Precision (mAP) value of 0.973. Our findings highlight a precision of 0.971, pointing to accurate predictions in approximately 97.1% of cases, and a recall of 0.953, underscoring the model's efficiency in capturing around 95.3% of relevant objects. Such results, particularly the distinguishable taxi class among similar objects, represent significant improvements over previous benchmarks. The model's prowess is evident in its ability to distinguish even in situations involving entities that closely resemble one another, such as taxis and police cars. Our proposed system excels with increased accuracy, precision, and F1 scores compared to a standard study. This paper concludes that with the strategic application of YOLOv5, the future of public transportation detection systems is bright and on the cusp of a new era of efficiency and precision. © 2023 IEEE.

关键词： Object detection

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Localizable Instruments for Navigated Treatment of Ischemic Stroke

Localizable Instruments for Navigated Treatment of Ischemic ...

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Bildverarbeitung für die Medizin Workshop, BVM 2023

作者： Stevanovic, Lena Bodanowitz, Ramona Mittmann, Benjamin J. Greiner-Perth, Ann-Kathrin Marschall, Eva Kannberg, Tobias Baumgärtner, Timo Braun, Michael Schmitz, Bernd Franz, Alfred M. Institute for Medical Engineering and Mechatronics Ulm University of Applied Sciences Germany Institute for Computer Science Ulm University of Applied Sciences Germany Neuroradiology Section Günzburg District Hospital Germany

ISBN: (纸本)9783658416560

Mechanical thrombectomy as a therapeutic option for ischemic stroke can possibly be supported by localizing the used catheters and guidewires during the intervention. In this study we equipped a probing catheter and a guidewire with electromagnetic (EM) sensors that can be localized in real time by means of an EM field generator. These instruments were tested regarding tracking accuracy and handling in laboratory and clinics. While accuracy was better than 0.4mm in all cases and the probing catheter showed promising handling and robustness, we observed handling problems and a sensor defect in case of the guidewire. We further demonstrated 3D navigation of the tracked catheter by probing 10 targets inside a vascular phantom with an average error of 1.8 mm. © 2023 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature.

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Tomato Disease Classification Using CNN 4th

Tomato Disease Classification Using CNN

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4th International Conference on Smart Systems: Innovations in Computing, SSIC 2023

作者： Archanaa, N. Daniel, V. Vifert Jenuben Divya, S. Raja, Kalpana Oviya, I.R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Sciences Amrita School of Engineering Amrita Vishwa Vidyapeetham Chennai India Section for Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT United States

ISBN: (纸本)9789819736898

Plant disease detection using various machine learning techniques is a very popular research area. Many promising results have been obtained, but only a few specific applications can still make farmers’ lives easier. This research focuses on the important task of identifying tomato diseases at an early stage to prevent their spread and protect crop yields more broadly, emphasizing the idea that disease prevention will more important than treatment. Leveraging the capabilities of convolutional neural networks (CNN), we developed a model that diagnoses eight distinct tomato diseases using only their images. Diseases listed include bacterial spot, early blight, late blight, leaf mold, septoria leaf spot, two-spotted spider mite, target spot, and tomato yellow leaf curl virus. An integral part of our approach involves sophisticated image prepossessing and enhancement techniques, ensuring data quality and model robustness. Feature extraction plays a key role in transforming raw image data into recognizable patterns. The architecture of the CNN has been meticulously designed, consisting of multiple layers with a notable focus on the ReLU activation function and other relevant hyper-parameters. Our main dataset comes from PlantVillage, known for synthesizing rich and diverse plant images. After rigorous training and validation, the CNN model showed exceptional accuracy of 96.88% in tomato disease identification. However, challenges such as overfitting, image quality variability, and class imbalances were encountered but were resolved through strategic data augmentation, careful CNN overlay. In summary, our research presents a practical solution for farmers and agricultural entities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fruits

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Research on the Current Situation and Development Strategies of Large-Scale K12 Online Education: A Case Study of a Province in Western China 12

Research on the Current Situation and Development Strategies...

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12th International Conference of Educational Innovation through Technology, EITT 2023

作者： Zhang, Le Li, Yun Yang, ShuRan Yuan, XiaoFan School of Computer Science and Engineering North Minzu University Yinchuan China Technical Service Section Ningxia Education Information Management Center Yinchuan China School of Education Ningxia University Yinchuan China

ISBN: (纸本)9798350383362

With the rise of modern information technologies such as computer technology, internet technology (especially 5G), artificial intelligence, big data, cloud computing, and mobile devices, the connotation, manifestation, and functional role of online teaching have undergone certain changes in the field of education. This study focuses on 1,744 primary and secondary school teachers in a province in western China. Through a questionnaire survey, it analyzes and studies the current situation of scaled K12 online teaching, teaching effectiveness, existing problems, and teaching needs. Based on this, it puts forward strategies for the deepening development of scaled K12 online teaching to promote the improvement of teaching quality. © 2023 IEEE.

关键词： E-learning

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