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A Comprehensive but Simple Method Decision Making in Z-Environment 1

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12th World Conference on Intelligent systems for Industrial Automation, WCIS 2022

作者： Aliyev, Rafig R. Azerbaijan State Oil and Industry University Azadlyg Ave. 34 BakuAZ1010 Azerbaijan

ISBN: (数字)9783031515217

ISBN: (纸本)9783031515200

This paper introduces comprehensive and theoretically-driven approach to decision making under bimodal uncertainty described by Z-sets, that has relatively low complexity. The proposed decision making approach is based on synergy of consistency-driven preferences construction method, eigenvector determination method of Z-number valued matrixes, proposed by author and his colleagues and modified WSM model. Correctness and feasibility of suggested method is illustrated by example. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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A Checklist of the Bryophytes of the Sidi Boughaba Wetland (Kénitra, Morocco) 1

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International Conference on Advanced Intelligent systems for Sustainable Development, AI2SD 2023

作者： Bouhssini, Ahmed Achoual, Khalid Saadi, Kamal Dahmani, Jamila Belahbib, Nadia Laboratory of Plants Animals Productions and Agro-Industry Faculty of Sciences Ibn Tofail University BP 133 Kenitra14000 Morocco

ISBN: (数字)9783031543180

ISBN: (纸本)9783031543173

The Sidi Boughaba Biological Reserve is a nature reserve known for its lake, which is a protected site for the preservation of its biodiversity, particularly that of migratory birds. The aim of this study of the bryophytes of this wetland on the north-west coast of Morocco is to establish as exhaustive a list as possible, while at the same time contributing to the completion of the Moroccan bryophytic catalog. The study of the diversity and ecology of the reserve’s bryophytic species is based on the analysis of 20 surveys carried out during different seasons, from January 2022 to May 2023. A total of 61 species and subspecies were recorded, including 6 liverworts (5 thalloids, 1 leafy) and 55 mosses (43 acrocarps and 12 pleurocarps). These taxa are divided into 12 families and 29 genera, with around 75% of families represented by one to three species and/or subspecies, with Pottiaceae, Brachytheciaceae and Bryaceae dominating. Substrate type analyses show that terricolous taxa dominate with 24 species (39.3%), followed by corticolous with 24 species (27.9%) and saxicolous with 7 species (11.5%). In terms of water requirement, mesophyllous taxa (62.3%) are more dominant than xerophils (36.1%). Selon l’indice de signification écologique (IES), nous avons noté 34 espèces très rares, 15 espèces rares, 10 modérément abondantes et seulement deux espèces sont abondantes, il s’agit de Funaria hygrometrica (IES = 142) et Scorpiurium circinatum (IES = 113). This study represents the first fieldwork on the bryophytic flora of the Sidi Boughaba nature reserve, with the aim of better understanding the ecology and role of these small plants in the local biodiversity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Wetlands

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Breast Density Prediction from Mammograms: A Comprehensive Review 6th

Breast Density Prediction from Mammograms: A Comprehensive...

引用

6th conference on Computing systems and Applications, CSA 2024

作者： Dif, Nassima Abdelali, Mohamed Amine Boudinar, Mohamed El Amine Benchouk, Jesia Asma Benslimane, Sidi Mohammed LabRI-SBA Lab Ecole Superieure en Informatique Sidi Bel Abbes Algeria Ecole Superieure en Informatique Sidi Bel Abbes Algeria Breast and Pelvic Surgery Department Cancer Center Tidjini Haddam Sidi Bel Abbes Algeria

ISBN: (纸本)9783031718472

Predictive medicine aims to improve the stratification and management of individuals according to their probability of developing a pathology in the future. The risk of breast cancer is influenced by multiple factors, such as lifestyle, age, breast density, and family history. Nevertheless, radiologists face challenges in accurately classifying breast density from mammogram images due to the inter-observer subjectivity problem. To address this issue, artificial intelligence systems, especially deep learning techniques, have been exploited. The purpose of this research paper is to review the current state of research on the use of machine learning and deep learning based approaches for breast density classification. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep learning

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Growth and Development of Spring Triticale Plants at Different Sowing Dates 1

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International Scientific Conference on Fundamental and Applied Scientific Research in the Development of Agriculture in the Far East, AFE 2022

作者： Muratov, Aleksey Epifantsev, Viktor Far Eastern State Agrarian University 86 Politehnicheskaja Street Blagoveshchensk675005 Russia

ISBN: (数字)9783031379789

ISBN: (纸本)9783031379772

The basis for the work was: triticale is a new grain forage crop, there is no data on the study of the timing of sowing of its varieties in the Amur Region. Purpose—to study the effect of sowing time on the growth, development and productivity of various varieties of spring triticale in the region. The experiment was carried out in 2014–2016 on the experimental field of the Far Eastern State Agrarian University. The soil is meadow-chernozem-like. A two-factor field experiment included factor A sowing dates: April 15 (control), April 22 (control), April 29 and May 5, and factor B varieties: Yarilo—standard, Ukro (St) and Carmen. At the end of April, the most favorable thermal and water regimes of the soil for the germination of grain and seedlings of triticale develop. The highest field germination rate is 87%, the leaf area of the Yarilo variety is 30.4 thousand m2/ha, Ukro—37.2 and Carmen—33.0 m2/ha, and plant safety of 80% by the time of harvest of all varieties is ensured by the sowing date—April 29. The correlation between leaf area and yield is strong positive rxy = 0.833. The processes of plant growth and development that make up productivity are interdependent. The yield is determined by meteorological conditions during the growing season of triticale varieties and the choice of the optimal sowing time. The high grain yield is provided by the triticale varieties Yarilo—2.26 t/ha, Ukro—2.61 and Karmen—2.23 t/ha when sown on April 29. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Germination

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Implementation of Individual Learning Trajectories in LMS Moodle 1

引用

7th Computational Methods in systems and Software, CoMeSySo 2023

作者： Bensalah, Faycal Daniel, Marjorie P. Patra, Indrajit García, Darío Salguero Irgasheva, Shokhida Tsarev, Roman Lab LERSEM ENCG University Chouaib Doukkali El Jadida Morocco Zayed University Dubai United Arab Emirates Mediterranea International Centre for Human Rights Research Mediterranea University of Reggio Calabria Reggio Calabria Italy Almería University Almería Spain Tashkent Institute of Finance Tashkent Uzbekistan Moscow Russia Bauman Moscow State Technical University Moscow Russia

ISBN: (数字)9783031548208

ISBN: (纸本)9783031548192

Informatization of education has entailed significant changes in the issue of organization, content, use of methods and means of learning. Along with these changes there is a need for personality-oriented, individual learning. The merging of e-learning and individual learning allows to automate the process of developing individual educational trajectories, which implies a personal way to realize the personal potential of each student in joint activities with the teacher. Thanks to the LMS Moodle and the creation of e-courses on its basis, the teacher can easily organize learning on individual trajectories and optimize the learning process, taking into account the intellectual, mental and physical characteristics of students. In this paper, the authors formulated the concept of reverse programming, designed to implement the logic of learning by individual trajectory in LMS Moodle. The concept of reverse programming allowed to implement in practice the logic of passing through individual learning trajectories, using the settings of LMS Moodle. In this study, learning along individual trajectories is driven by test results and the mode of access to learning material determined by the logic functionality of LMS Moodle. #COMESYSO1120. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Trajectories

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Analysis of the Impact of Smartphone on the Environment Using the LCA Method 1

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32nd International Conference on Organization and Technology of Maintenance, OTO 2023

作者： Marijić, Jure Vilić, Marko Grgić, Ivan Karakašić, Mirko Ivandić, Željko Komarčić, Domagoj Mechanical Engineering Faculty in Slavonski Brod University of Slavonski Brod Trg I. B. Mažuranić 2 Slavonski Brod35000 Croatia

ISBN: (数字)9783031514944

ISBN: (纸本)9783031514937

Today’s industrial production has a great impact on the environment all over the world. The constant increase in the world’s population has a great impact on the expanding industries in the world. Maintenance, eco-design and life cycle analysis are important concepts related to sus-tainability and environmental protection. Life cycle refers to everything associated with the product at all stages of its life, from the extraction of raw materials to the disposal or recycling of the product. The mobile phone was chosen as a representative in accordance with the global development of the relevant industry and the desire to show its impact on the environment using the LCA (Life cycle assessment) method. The concepts of sustainable development, recyclability and eco-indicators were explained and some concepts and tools were presented to show how can mobile phones industry reduce the impact and con-tribute to the environment and sustainability. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Life cycle

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Fake News Detector Using Machine Learning 1

引用

5th International Conference on Computer and Communication Technologies, IC3T 2023

作者： Barhate, Madhuri Yadav, Harshita Bhadke, Harshwardhan Kothawade, Harshali Hatedikar, Tejas Havaldar, Aamod Department of Engineering Sciences and Humanities Vishwakarma Institute of Technology Pune411037 India

ISBN: (数字)9789819997077

ISBN: (纸本)9789819997060

In this contemporary era, technology is considered to be the best ally of human beings. It indeed is an indifferentiable element of human lives, but it is also the cause of some of the issues around us. One problem of this kind, which requires immediate attention, is spreading of fake news. The fake news is able to spread throughout the population with ease because of advancement in technology. With the increase in technology, people are able to access various kind of information through their cell phones itself, making it quite simple to trick them by providing rumors or unverified information. In this paper, the authors have tried to come up with an effective algorithm in order to tackle the raised issue by designing a fake news detector with the help of machine learning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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Improving Digital Marketing Using Sentiment Analysis with Deep LSTM 1

引用

International Conference on Data Analytics and Management, ICDAM 2023

作者： Lasi, Masri bin Abdul Hamid, Abu Bakar bin Abdul Jantan, Amer Hamzah bin Goyal, S.B. Tarmidzi, Nurun Najah binti City University Petaing Jaya Malaysia Putra Business School University Putra Malaysia Serdang Malaysia

ISBN: (数字)9789819965441

ISBN: (纸本)9789819965434

With As digital channels continue to grow, digital marketing has become a crucial area for businesses. Customers share their experiences with products on social media and e-commerce platforms, providing businesses with valuable feedback. Sentiment analysis techniques are used to analyze customer feedback and improve business decisions. Deep learning techniques, such as Long Short-Term Memory (LSTM), have the potential to extract knowledge from large volumes of data with greater accuracy than manual approaches. In this study, we propose using Deep LSTM to enhance the accuracy of sentiment analysis. Our simulation results show that the proposed model improves upon conventional schemes in terms of accuracy, precision, recall, and F-measure. The proposed model achieved an accuracy rate of over 90%, which is significantly higher than the accuracy rate achieved by other sentiment analysis models. Additionally, the proposed model outperformed other state-of-the-art sentiment analysis techniques in our empirical evaluation using a large dataset. Furthermore, we tested the proposed model in a real-world scenario, where it was used to analyze customer sentiment toward a newly launched product. The proposed model accurately identified positive and negative sentiments expressed by customers toward the product. The marketing team used this information to make informed decisions regarding product improvements and marketing strategies, demonstrating the practical applications of the proposed model. Our study highlights the effectiveness of deep learning techniques, specifically deep LSTM, in improving the accuracy and reliability of sentiment analysis. Our findings have important implications for businesses seeking to leverage customer feedback to improve their products and services. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Minimization of Task Offloading Latency for COVID-19 IoT Devices 1

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International Conference on Intelligent systems and Smart Technologies, I2ST 2023

作者： Amzil, Abdellah Hanini, Mohamed Zaaloul, Abdellah Abid, Mohamed El Kafhali, Said Faculty of Sciences and Techniques Hassan First University of Settat Settat26000 Morocco Faculty of Sciences Ibn Zohr University of Agadir Agadir Morocco

ISBN: (数字)9783031476723

ISBN: (纸本)9783031476716

The tasks offloaded by COVID-19 IoT (Internet of Things) devices are very important when they are new. Therefore, latency is a significant problem in time-sensitive IoT applications. On the other hand, wireless sensor devices are better suited for computationally intensive applications based in urban environments. However, these wireless sensor devices are limited in energy. In this paper, we consider an Edge Computing scenario consisting of two subsets of users: COVID-19 devices and sensor devices, while they use Multi-access Edge Computing (MEC) for computation offloading (CO). Network slicing seeks to suit their diverse needs, whereas the deployment of an edge server seeks to achieve reduced communication latency under the orthogonal multiple access (OMA) approach. Then, an optimization problem is built to reduce the MEC offloading latency for COVID-19 IoT devices subject to the energy consumption requirement of sensor devices. The numerical results show that our proposed method for task offloading on COVID-19 devices has much lower latency than other proposals. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： COVID-19

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An Automated Smart Plastic Waste Recycling Management systems 1

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6th International Conference on Soft Computing and Signal Processing, ICSCSP 2023

作者： Sai, Vamaraju Hari Hara Nadha Bhaskar, Nuthanakanti Dharmireddi, Srinivasarao Srujan Raju, K. Divya, G. Narasimharao, Jonnadula Department of CSE CMR Technical Campus Telangana Hyderabad India Department of Cyber Security MasterCard St. LouisMO United States Department of IT CMR Technical Campus Telangana Hyderabad India

ISBN: (数字)9789819984510

ISBN: (纸本)9789819984503

This work presents an automated smart plastic waste recycling management system is presented. In this analysis, Waste Classification data set from Kaggle is used. Firstly, the images of various years will be cleaned and merged. Using Time series algorithm, waste generation, recycling generation and recycling rate of various years will be calculated. In addition, it generates the time series graphs for waste material generation, recycled waste and recycle rate of different materials. Compared to previous system this system will achieve better recycling rate. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Time series

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