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检索条件"丛书名=Lecture Notes in Networks and Systems"

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Statistical Analysis of Stakeholders Perception on Adoption of AI/ML in Sustainable Agricultural Practices in Rural Development 9th

Statistical Analysis of Stakeholders Perception on Adoption ...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Taiwo, Gbadegesin Adetayo Akinwole, Tinuade Oluwatosin Ogundepo, Olumide Benjamin Univ Salford Sch Sci Engn & Environm Manchester Lancs England Univ Ilorin Dept Stat Ilorin Kwara States Nigeria

ISBN: (纸本)9789819733019;9789819733026

The integration of artificial intelligence (AI) and machine-learning (ML) techniques is swiftly revolutionizing global agriculture. These advanced technologies find applications across various domains, including automated monitoring of crops and livestock, as well as predictive analytics for precision agriculture. However, the perspective and assessment of stakeholders on the adaption of these technologies to agriculture have not been evaluated. Hence, a survey with 145 stakeholders from Shonga farms in Nigeria to gather their perspectives on the implementation of AI and ML was conducted. By analyzing their responses across 11 factors, it was found that there is positive support for adopting AI/ML in areas such as training, infrastructure, productivity and economic outcomes. However, some shortcomings in terms of resources, policy frameworks, sustainability measures, and equitable access were identified. The variation in responses highlights the challenges related to training and infrastructure that need to be addressed. To fully unlock the potential of these technologies in improving productivity, sustainability, economic viability, and community well-being at Shonga farms targeted investments and policy improvements are necessary. This empirical evidence provides insights, for applying emerging technologies to promote sustainable rural development while ensuring inclusivity.

关键词： Artificial intelligence Sustainable agriculture Rural development

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Real-Time Live Insult Analysis on Twitter-X Social Media Platform 2nd

Real-Time Live Insult Analysis on Twitter-X Social Media Pla...

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2nd International Conference on Forthcoming networks and Sustainability in the AIoT Era (FoNeS-AIoT)

作者： Sahin, Fatih Istanbul Topkapi Univ Software Engn Dept TR-34662 Istanbul Turkiye

ISBN: (纸本)9783031628801;9783031628818

Real-time insult analysis could help platforms identify and filter out harmful content promptly, promoting a healthier online environment and reducing the spread of negativity. Identifying and addressing insults in real-time, platforms can enhance user experience by fostering more civil and respectful interactions among users. It requires robust algorithms capable of accurately detecting insults across various languages and contexts while ensuring user privacy and minimizing false positives. In this study, sentiment analysis was conducted using Turkish Twitter data. The data was analyzed by applying data analysis methods and Naive Bayes (NB), K-Nearest Neighbor (KNN), SupportVector Machine (SVM), Logistic Regression (LR), Random Forest, and Decision Tree. They were classified as positive and negative using classification algorithms. The performance of these classifiers was evaluated with the F1 score. According to the results, the Logistic Regression classifier performed with the highest F1 score (85%). Among other classifiers, SVM(84%), Random Forest (83%), Naive Bayes (83%), and Decision Tree (80%) achieved F1 scores. This study aimed to compare classifiers when performing sentiment analysis from Turkish Twitter data.

关键词： Twitter Tweet Analysis Data Mining Text Mining Sentiment Analysis

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Advantages of Active and Passive Suspension systems in Obstacle Negotiation for Planetary Rovers 27th

Advantages of Active and Passive Suspension Systems in Obsta...

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27th International Conference on Climbing and Walking Robots and Mobile Machine Support Technologies (CLAWAR)

作者： Wirkus, Malte Babel, Jonathan Backe, Christian Deutsch Forschungszentrum Kunstliche Intelligenz Robot Innovat Ctr Bremen Germany

ISBN: (纸本)9783031713002;9783031713019

Hybrid locomotion systems with actively controlled wheeled-legged suspension offer high off-road mobility, feature ground adaptation and are capable of overcoming large obstacles. Rovers with passive suspension also have remarkable climbing abilities and are much easier to control. In this paper, experiments on overcoming obstacles with two robots with different suspension systems, one with an active wheeled-legged suspension and one with a passive triple-bogie suspension, are analysed and the advantages and disadvantages for different applications are shown. The control effort of the active suspension significantly slows down the rover in situations that can also be handled with a passive suspension. However, the unique ability of the active suspension to overcome obstacles without contact is a promising feature that requires further research and development to realise its full technological potential for autonomous operation.

关键词： hybrid locomotion wheeled-legged robots bogie suspension planetary rovers obstacle avoidance

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Towards an Adaptive Approach to a Personalised Design of Intelligent Learning Assistants (ILAs)

Towards an Adaptive Approach to a Personalised Design of Int...

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Intelligent systems Conference

作者： Schelter, Nicole Veiel, Dirk Fernuniv Hagen Germany

ISBN: (纸本)9783031664274;9783031664281

This paper presents an innovative approach to designing Intelligent Learning Assistants (ILAs) named GITTE (Great Individual TuTor Embodiment), focusing on their visual representation and personalisation. The significance of visual representations for ILAs is explored, underlining their impact on learning outcomes and student perceptions. The presented approach introduces a novel, exploratory, and participatory method for identifying influential design features in ILAs using the Pedagogical Agents-Levels of Design (PALD) model and generative AI models like DALL-E and ChatGPT. This method allows for the exploration of design features not previously considered, capturing unconscious student preferences. The paper documents the initial test involving a manual approach to evaluate the applicability of generative AI models in designing GITTE, emphasizing the interaction between students and the generative model. The results indicate the potential of this approach in enhancing the personalisation and effectiveness of ILAs, suggesting new directions for research in this emerging field. The concept presented is poised to make significant contributions to the development of more engaging and personalized digital learning aids.

关键词： Intelligent learning assistants Personalization Generative AI

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Use of Fuzzy Sets in Process Capability Analysis: A Comparative Literature Analysis

Use of Fuzzy Sets in Process Capability Analysis: A Comparat...

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International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Gundogdu, Fatma Kutlu Ilbahar, Esra Yazir, Kubra Karasan, Ali Kaya, Ihsan Natl Def Univ Turkish Air Force Acad Dept Ind Engn Istanbul Turkiye Yildiz Tech Univ Dept Ind Engn Istanbul Turkiye Presidency Republ Turkiye Undersecretariat Def Ind Ankara Turkiye

ISBN: (纸本)9783031671944;9783031671951

Process capability analysis (PCA) is the assessment of a process's ability to meet customer requirements specified by constraints. It is a crucial aspect of statistical process control used to evaluate process variability. PCA provides data on both conforming and nonconforming production rates, which represent the quantity of items that meet and do not meet SLs, respectively. Processes can be categorized as "capable", or "incapable" based on their process capability indices (PCIs). The fuzzy set theory can effectively address ambiguity and enhance flexibility and sensitivity in classical PCIs. To achieve this goal, upper and lower specification limitations can be represented using linguistic variables. Fuzzy process capability indexes (FPCIs) are generated by utilizing fuzzy mean and fuzzy variance. This study aims to provide an extensive literature evaluation on publications about FPCIs. The research was assessed based on many parameters. The study presented classifications such as FPCI, application area, fuzzy parameters, and type of fuzzy sets. We aimed to create an outline for researchers in this subject and discuss recent advancements in FPCIs and fuzzy PCA.

关键词： Fuzzy set theory Linguistic variables Process capability indices

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Experimental Demonstration for Learning Analytics and Data Mining on e-assessment

Experimental Demonstration for Learning Analytics and Data M...

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2024 International Conference on Computing, Communication, Cybersecurity and AI

作者： Bhurre, Shraddha Prajapat, Shaligram Raikwar, Sunny Goswami, Prakshep Kothari, Soham Khanuja, Gurpreet Yadav, Kartikey Choure, Purvi DAVV Int Inst Profess Studies Indore MP India Dr APJ Kalam Univ Indore India

ISBN: (纸本)9783031744426;9783031744433

Every subject and course has a learning goal that we work towards during the teaching-learning process, whether it be online or offline. Under the current system, assignments, project analysis, semester exams, and internal exams are used to evaluate students' learning. Several factors, including participation in class, prior knowledge, engagement time, activity logs, forum posts, understanding level, etc., influence how well students learn throughout a course or session. All these factors will be difficult to analyze in offline mode. All of these activities determine whether or not the learning objective is met. This study examines an experiment that provided blended learning data on students' academic details, learning behaviors, and evaluation modules to determine the success of the two subjects. It discussed the details of each phase involved in the experiment. Students enrolled in integrated courses, MTech(PG-1) and MCA(PG-2) were analyzed in this study procedure through 4 weeks of offline classes. Moodle Gnomio was used for the online processes related to the Registration process, Quizzes, and discussion forums. A total of 6 quizzes for PG-1(M-Tech) and PG-2(MCA) were organized, and learning resources were provided before the exam. This study discusses detailed experiment setup, and Statistical analysis involving difficulty level of quizzes. This study proposed a framework for the dashboard that utilize this dataset and involves analysis related to student performance, grade distribution, inferring learning patterns, and course success.

关键词： Online Learning Moodle Blended Learning Learning Analytics

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A Voice-Based Emotion Recognition System Using Deep Learning Techniques 4th

A Voice-Based Emotion Recognition System Using Deep Learning...

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4th International Research and Innovation Congress - Smart Technologies and systems (CI3)

作者： Pantoja, Carlos Guerron Maya-Olalla, Edgar Dominguez-Limaico, Hernan M. Zambrano, Marcelo Ayala, Carlos Vasquez Pasquel, Marco Gordillo Univ Tecn Norte Ave 17 Julio 5-21 & Gen Maria Cordoba Ibarra 100105 Ecuador Inst Super Tecnol Ruminahui Ave Atahualpa 1701 & 8 Febrero Sangolqui 171103 Ecuador

ISBN: (纸本)9783031634338;9783031634345

The project aims to create an emotion recognition system based on voice using deep learning techniques. The system is based on supervised learning with artificial neural networks, enabling it to accurately predict emotions. The system's potential usage in detecting depression pathologies in the psychological area gives rise to its development. The system is designed using the KDD (Knowledge Discovery in Database) methodology and utilizes an existing database containing audio with various emotions. These audios are subjected to Multilevel Wavelet transform, decomposing the original signal into sub-signals with specific characteristics for each audio to form a training data set that is subsequently normalized, followed by the generation of the LSTM neural network architecture. Performance tests are eventually conducted on patients with depressive pathology, involving the application of the "Beck test", which indicates the severity of depression experienced by the patient. As a result, the individual reads a text that is recorded, followed by the process of feature extraction and emotion recognition performed with the pre-trained neural network. The outcome indicates that 50% of the patients exhibit severe depression, while the remainder displays milder symptoms, which is supported by the emotions detected by the system alongside the administered test.

关键词： Emotion Recognition Wavelet Transform deep learning emotion recognition

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Balancing Immersion and Simplicity: Unlocking Extended Reality's Potential for Dynamic Learning in Industries 14th

Balancing Immersion and Simplicity: Unlocking Extended Reali...

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14th International Conference on Learning Factories (CLF)

作者： Koenigs, Lisa Chloe Hoehne, Benjamin Longmuss, Jorg Sustainum Inst Sustainable Econ D-10965 Berlin Germany Berlin Univ Appl Sci & Technol Inst Distance Learning D-13353 Berlin Germany

ISBN: (纸本)9783031653995;9783031654008

Extended Reality (XR - comprising virtual and augmented reality) holds promise for dynamic learning, particularly in learning factories and other on-the-job training arrangements. It fits the need for flexible learning materials, e.g. substituting costly or hazardous real life processes. Despite its potential, adoption is still limited in workplace settings and vocational training. Two recent explorative case studies show the obstacles in more detail. These include the costly programming, the low level of software development competencies in SMEs and therefore the hurdle of creating and editing XR applications. Promising solution strategies are also offered. A comparison of XR and paper instructions (first case study) for an industrial assembly task showed that XR enhances work performance but lacks intuitiveness, leading users to rely only on embedded media. This indicates an unbalanced cost-benefit-ratio. More low-threshold (using open-source software programs and already available devices) digital instructions with intuitive XR features (easy to create and use) could bridge experience gaps while benefiting users, SMEs, and trainers with low digital expertise. This need for low-threshold creation and adaptation has been met in a second case study with a digital learning template with XR-features. It facilitates a modular compilation of existing images, videos, 360-degree views and 3-D models. It allows easy realization, manageable also for teachers with limited IT knowledge. In a first test case, trainers and participants rated this approach as very promising.

关键词： XR Dynamic Learning On-the-job Training Immersion and Simplicity Digital Competency

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Identifying Fake Twitter Trends with Deep Learning 1

引用

International Conference on Artificial Intelligence: Theory and Applications (AITA)

作者： AlBuhairi, Thahab M. Alhakbani, Haya A. Imam Mohammad Ibn Saud Islamic Univ IMSIU Riyadh Saudi Arabia

ISBN: (数字)9789819984794

ISBN: (纸本)9789819984787;9789819984794

This paper describes a model for detecting and distinguishing fake tweets from real tweets. The model uses one of the most commonly used deep learning algorithms in natural language processing, the Bidirectional Long Short-Term Memory (Bi-LSTM) algorithm. The model was trained on two datasets, the PolitiFact dataset from the FakeNewsNet repository and the PHEME dataset. The datasets are divided into 80% for training and 20% for testing. The model contains two merged models (hybrid model), one for text attributes and the other model for numeric/categorical attributes. To achieve the best result, the model balanced the datasets using four balancing techniques: RandomOverSampler, SMOTE, RandomUnderSampler, and NearMiss. In addition, there are two combination strategies that combine two strategies with certain percentages: combineOverUnderSampling and combineSMOTEUnderSampling balancing strategies. This work is one of the first works to use these combinations and obtain very good results, more than most related studies. For the PHEME dataset, the model was trained with a variety of epochs (10, 20, 50, and 100) to obtain the best result. For the PolitiFact dataset, only 10 epochs were trained at a time. The best result obtained with the PolitiFact dataset is 0.96 for accuracy and precision, 0.95 for recall, F1-score, and ROC-AUC. The best result obtained with the PHEME dataset is 0.87 for accuracy and 0.86 for precision and 0.85 for recall, F1-score, and ROC-AUC.

关键词： Fake Tweets Twitter Trends Deep learning NLP AI Bi-LSTM

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Satellite Imagery and Deep Learning Combined for Wheat Yield Forecasting 4th

Satellite Imagery and Deep Learning Combined for Wheat Yield...

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4th International Conference on Digital Technologies and Applications (ICDTA)

作者： Boukhris, Abdelouafi Antari, Jilali Sadiq, Abderrahmane Ibnou Zohr Univ Polydisciplinary Fac Taroudant Lab Comp Syst Engn Math & Applicat ISIMA BP 8106 Agadir Morocco

ISBN: (纸本)9783031686740;9783031686757

In the context of effective resource management and ensuring nutritional stability, precise forecasting of crop yields becomes essential. The development of artificial intelligence methodologies, coupled with satellite imagery, has emerged as a powerful strategy for predicting crop yields in modern times. In This study, deep learning algorithms based on LSTM (Long Short-Term Memory) were developed to efficiently optimize and extract from Sentinel-2 data spatiotemporal information of wheat yield. To estimate accurately wheat yield in Morocco, several machine learning and deep learning techniques such as Random Forest, LSTM, Bi-LSTM (Bidirectional LSTM), stacked LSTM, etc. were used and compared. The optimized Bi-LSTM model accurately estimates wheat yield based on NDVI (normalized difference vegetation index) data and weather data (temperature, precipitation). Three datasets gathered from satellite imagery were used which are temperature data, precipitation data and NDVI data combined for training and testing the proposed model. After data processing, different machine learning and deep learning algorithms were compared, and the result showed that Bi-LSTM estimates wheat yield accurately. The proposed and optimized Bi-LSTM model reached a satisfactory accuracy at the sizable regional scale. The obtained result demonstrates that the RMSE (Root Mean Square Error) score was 6.22 and the loss was 6.61 center dot 10(-4) after 20 epochs of training the proposed model, which overcomes most of the existing methods.

关键词： Bi-LSTM KNN (K-Nearest Neighbor) Vegetation Indices Machine Learning Deep Learning

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