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

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Business Processes - The Main Element of the Process that Builds the Business Architecture 13th

Business Processes - The Main Element of the Process that Bu...

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13th Computer Science Online Conference

作者： Ivanova, Boyana Shoilekova, Kamelia Angel Kanchev Univ Ruse 8 Studentska Str Ruse 7000 Bulgaria

ISBN: (纸本)9783031702846;9783031702853

The publication aims to show how important it is to properly design business processes and services. In order to reach the stage of proper design, it is necessary to first consider the strategy of the organization by making a SWOT analysis, identifying the goals of the organization, the strategies, and the means needed to achieve them. All this is a prerequisite for determining the intersection between business transformations, the implementation of these transformations, and technologies.

关键词： Business process service Business architecture strategy End-to end process

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AI Incorporating NLP - to Boldly Go, Where No Algorithms Have Gone Before 1

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11st World Conference on Information systems and Technologies (WorldCIST)

作者： Mohammad, Atif Farid Alagappan, Neela Basit, M. Univ N Carolina Charlotte NC 28223 USA Calif State Univ Long Beach CA 90840 USA

ISBN: (数字)9783031456510

ISBN: (纸本)9783031456503;9783031456510

This document is about understanding Natural Language Processing (NLP) - an area of research and application intended to explore how machines or computers can be used to understand and manipulate natural language text or speech conveyed by humans. In other words, it is discipline that focuses on understanding and interpreting raw or structured data which are in form of speech or text into meaningful interpretation. In recent times, NLP is trending technology and hot spot for researchers and technology experts. With exponentially growing data every day, NLP has already emerged as powerful technique in the history of mankind. NLP breaks down language into shorter tokens and attempts to understand the relationship between tokens. The foundations of NLP lie in several disciplines, namely, computer and information sciences, linguistics, mathematics, artificial intelligence and robotics, electrical and electronic engineering psychology and electrical and electronic engineering. This paper discusses about Techniques, Uses, Mathematics behind NLP and trending research on NLP with established real time examples.

关键词： Artificial Intelligence Natural Language Processing Machine Translation Tokens Causes Algorithms

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Categorizing Philanthropic CSR Activities Through Hybrid Approach of Fuzzy Clustering and Topic Modeling

Categorizing Philanthropic CSR Activities Through Hybrid App...

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Computing Conference

作者： Mangsor, Nik Siti Madihah Nik Md Nasir, Syerina Azlin Abdul-Rahman, Shuzlina Ismail, Zurina Univ Teknol MARA Cawangan Kelantan Coll Comp Informat & Math Sch Comp Sci Kota Baharu Kelantan Malaysia Univ Teknol MARA Coll Comp Informat & Math Sch Comp Sci Shah Alam Selangor Malaysia Univ Teknol MARA Fac Business & Management Dept Business & Management Shah Alam Selangor Malaysia

ISBN: (纸本)9783031622717;9783031622694;9783031622687

This research paper presents a new approach for systematically categorizing philanthropic corporate social responsibility (PCSR) initiatives using document clustering and topic modeling techniques. The primary aim is to efficiently organize and analyze the substantive content within corporate reports. The study leverages a five-year comprehensive dataset, sourced from the annual reports of 19 esteemed CSR-award recipient companies in Malaysia. These reports undergo rigorous transformation into a structured format and followed by meticulous text pre-processing to ensure data integrity. In the pursuit of precision, this study identifies the most suitable Latent Dirichlet Allocation (LDA) topic modeling technique, subsequently integrating it with document clustering using K-Means and Fuzzy C-Means (FCM) clustering. The findings reveal that FCM outperforms K-Means by delineating thirteen distinct clusters, each representing various dimensions of PCSR activities undertaken by the distinguished CSR-award companies in Malaysia. This superiority of FCM is particularly evident in its capability to handle datasets characterized by noise and overlap issues. Additionally, this research enriches comprehension by compiling ten keywords for each cluster, facilitating a more comprehensive and nuanced insights of PCSR topics.

关键词： Annual report Document clustering Philanthropic corporate social responsibility Textual analysis Topic modeling

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3D-Printed Robust Ground Contact Sensors for Haptic Feedback Using Functional Materials 27th

3D-Printed Robust Ground Contact Sensors for Haptic Feedback...

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

作者： Hering, Matthias Hoeving, Simon Schilling, Malte Univ Munster Munster Germany Leibniz Inst Analyt Wissensch ISAS D-44139 Dortmund Germany

ISBN: (纸本)9783031713002;9783031713019

When navigating challenging, uneven terrain, walking robots must rely on reliable sensory feedback. Detection of ground contact has proven sufficient for controlling and coordinating movements. In this paper, we present a novel, cost-effective 3D-printed foot contact sensor designed to enhance proprioceptive capabilities in small legged robots. We introduce a fully 3D-printed design that incorporates the switching mechanism itself, utilizing multiple materials including conductive ones. This approach facilitates easy assembly and produces robust signals without the need for additional processing requirements. We compare various sensor designs to a baseline, highlighting improvements in responsiveness, ease of integration, and cost-effectiveness.

关键词： legged robot sensory-driven control contact sensor

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Automatic Generation of a Portuguese Land Cover Map with Machine Learning 1

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Intelligent systems Conference (IntelliSys)

作者： Esteves, Antonio Valente, Nuno Univ Minho ALGORITMI Res Ctr LASI Braga Portugal Univ Minho Braga Portugal

ISBN: (数字)9783031477218

ISBN: (纸本)9783031477201;9783031477218

The application of machine learning techniques to satellite imagery has been the subject of interest in recent years. The increase in quality and quantity of images, made available by Earth observation programs, such as the Copernicus program, led to the generation of large amounts of data. Among the various applications of this data is the creation of land cover maps. The present work aimed to create machine learning models capable of accurately segmenting and classifying satellite images to automatically generate a land cover map of the Portuguese territory. Several experiments were carried out with the spectral bands of the Sentinel-2 satellite, with vegetation indices, and with several sets of land cover classes. Three machine learning architectures were evaluated, which adopt two different techniques for image classification. One of the classification techniques follows an object-oriented approach, and in this case the architecture adopted in our models was a U-Net artificial neural network. The other classification technique is pixel-oriented, and the machine learning models tested were random forest and support vector machine. The overall accuracy of the results obtained ranged from 68.6% to 94.75%, depending strongly on the number of classes into which the land cover is classified. The result of 94.75% was obtained when classifying the land cover only into five classes. However, a very interesting accuracy of 92.37% was achieved by the model when trained to classify eight classes. These results are superior to those reported in the related bibliography.

关键词： Machine learning Deep learning Remote sensing Land cover map

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A Study About Complexity of Social Network 1

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13th Computer Science Online Conference

作者： Nasution, Mahyuddin K. M. Univ Sumatera Utara Data Sci & Computat Intelligence Res Grp Sumatera Utara 20155 Indonesia

ISBN: (数字)9783031703003

ISBN: (纸本)9783031702990;9783031703003

Artificial intelligence discusses social networks as one of the themes based on unraveling complexity. O(n(2)) is the core problem of the complexity of social networks and continues to increase when the construction of social networks follows the interests of social actors or by recognizing them through keywords (node and edge labels). The study aims to reveal a possible method to avoid that complexity. With the help of computations on several experiments, it shows that the potential is linearization in social networks and that potential from several sides gets a social network.

关键词： Component graph node edge ontology analysis

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Revolutionizing Tomato Farming in Zambia: AI Deep Learning as a Tool for Smart Agriculture 9th

Revolutionizing Tomato Farming in Zambia: AI Deep Learning a...

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

作者： Kunda, Barbara Phiri, Jackson Univ Zambia Dept Comp Sci Lusaka Zambia Sch Nat Sci Lusaka Zambia

ISBN: (纸本)9789819733019;9789819733026

The integration of artificial intelligence (AI) in agriculture represents a pivotal advancement in modern farming practices. AI has introduced a transformative dimension to conventional agriculture, offering innovative solutions to the multifaceted challenges facing global food production. The pursuit of heightened profitability through increased productivity underscores the necessity for agricultural sustainability and efficiency. This imperative calls for a departure from conventional farming methods and urges transition toward data-driven, intelligent systems. CM farms located 35 km southeast of Lusaka central district of Zambia, a country in sub-Saharan region, currently faces a challenge of inefficient disease detection in their tomato field due to inconsistent visits of the agronomist to provide expert guidance. When available, the agronomist uses manual methods of disease detection of row-by-row field inspection, which is both labor-intensive and laborious. Consequently, a Convolutional Neural Network model to facilitate real-time tomato leaf disease detection was built. A Raspberry pi was fitted with a camera for capturing and sending of tomato leaf images to a web-based application which was built with a cron job for automated mail transmission to the user. Three deep learning models, namely VGG 16, ResNet 50, and GoogleNet V2 were trained using the plant village dataset which also incorporated diseased and non- diseased leaf images from CM farms. Of the three models, ResNet 50 had the highest accuracy level of 95%, compared to GoogleNet V2 and VGG 16 with accuracy of 85% and 63%, respectively. Subsequently, ResNet was adopted t integrated in the web application.

关键词： Artificial intelligence Convolutional neural network Deep learning

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Recurrent Neural networks and Machine Learning Models Applied in Sign Language Recognition 9th

Recurrent Neural Networks and Machine Learning Models Applie...

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

作者： Novillo Quinde, Esteban Gustavo Saldana Torres, Juan Pablo Alvarez Valdez, Michael Andres Llivicota Leon, John Santiago Hurtado Ortiz, Remigio Ismael Univ Politecn Salesiana Cuenca Ecuador

ISBN: (纸本)9789819735587;9789819735594

This research is dedicated to promoting the inclusion of individuals with hearing disabilities, addressing their unique communication needs through the development of a sign language translation system. Efficiently predicting the gestures of non-hearing individuals is essential for breaking barriers and facilitating smooth communication in their daily lives. To achieve this goal, we propose a three-phase method that involves data preparation and cleaning. For modeling, we leverage cutting-edge techniques, including random forest with data augmentation, recurrent neural networks (RNNs), and a voting system to combine the best-performing models. Our approach is centered on the 'Australian Sign Language signs' dataset, which offers a valuable resource for sign language recognition. By incorporating these advanced methods, we strive to achieve unparalleled accuracy, precision, recall, and F1-score in predicting signs within the Australian sign language using this dataset. Moreover, our work sets the foundation for future research, encouraging the exploration of advanced supervised modeling techniques to further elevate the obtained results. We envision that the integration of RNN, random forest with data augmentation, and the voting system will enable us to break new ground in sign language translation, empowering individuals with hearing disabilities to engage fully in their personal and professional endeavors with improved accessibility and inclusivity.

关键词： Data science Machine learning Sign language Random forest RNN RF with DataAugm RNN with DataAugm Voting

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The Relationship Between the Mental Ill-Being of Youth and a Question They Wish to Ask the Artificial Intelligence. The Exploratory Study 20th

The Relationship Between the Mental Ill-Being of Youth and a...

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20th International Workshop on Combinatorial Image Analysis (IWCIA)

作者： Modlinski, Artur Pawlak, Ada Florentyna Univ Lodz Lodz Poland Coll Civitas Warsaw Poland

ISBN: (纸本)9783031665936;9783031665943

The emergence of conversational artificial agents facilitates the process of seeking knowledge. Nevertheless, very little is known about what young people want to learn from artificial intelligence in general. Earlier research on the youth's internet behaviour showed that young people suffering from anxiety disorders or generally low mental well-being are looking for the solutions to their problems online. It was suspected that young people may try to obtain such answers also from artificial conversational agents. Using Maslow's concept of needs, a study was conducted on 402 young people aged 9-14, in which they were asked for what question they would like the artificial intelligence responds to. The results show that young people choosing questions related to both psychological and safety needs had lower mental ill-being than those asking for love and belonging as well as esteem. Moreover, the youth trying to find an answer for love and belonging-related issues and those related to esteem had higher mental ill-being than those asking for self-actualization, and personal interests. The findings of exploratory research is used to delineate the contribution to practice, and further research proposal.

关键词： artificial intelligence youth studies mental well-being

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Underwater Image Enhancement by Modified Dark Channel Prior 4th

Underwater Image Enhancement by Modified Dark Channel Prior

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

作者： Noman, Kahttan A. Yaseen, Alauldeen Salah Presidency Council Ministers Iraq Off Prime Ministers Advisor Educ Tourism & Antiqu Baghdad Iraq Univ Technol Baghdad Dept Appl Sci Baghdad Iraq

ISBN: (纸本)9783031686528;9783031686535

The study of underwater image analysis has generated a lot of interest and demonstrated significant improvements. Images taken underwater suffer from unbalanced colors and low contrast. DCP is one of the algorithms used in image enhancement. In this study, an algorithm, Modified Dark Channel Prior (MDCP), was proposed to improve underwater images based on the Dark Channel Prior algorithm and process the illumination component Y using the sigmoid function through the YIQ color space. Using non-reference quality measures Average Gradient (AG), CF, and Natural Image Quality Evaluator (NIQE), the proposed method was compared with other methods (UCM, RSU, RSAP, and MSRCR). The results showed the success of the proposed method in improving underwater images and they have good values.

关键词： DCP Underwater image Contrast enhancement Lightning enhancement

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