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QRCrop: A QR Code-Based Edge-Cloud Framework for Crop Yield Prediction 3rd

QRCrop: A QR Code-Based Edge-Cloud Framework for Crop Yield ...

引用

3rd International Conference on Network Security and Blockchain Technology

作者： Dey, Tanushree Bera, Somnath Mukherjee, Anwesha Roy, Samarjit De, Debashis Maulana Abul Kalam Azad Univ Technol Ctr Mobile Cloud Comp Dept Comp Sci & Engn BengalNH-12SimhatHaringhata Nadia 741249 W Bengal India Mahishadal Raj Coll Dept Comp Sci Mahishadal 721628 W Bengal India Eastern Int Univ Sch Comp & Informat Technol Nam Ky Khoi Nghia StHoa Phu Ward Thu Dau Mot City Vietnam

ISBN: (纸本)9789819780501;9789819780518

In this paper, a crop yield prediction system is proposed based on QR code and edge-cloud framework. In traditional systems, the cloud is used for IoT data analysis and storage. The concerns over the cloud-only system include security, latency, energy consumption, etc. The use of edge-cloud computing deals with the problem of latency and energy consumption. Nevertheless, the security is still amajor concern. In the proposed system, the user uploads soil and environmental parameters' data to the edge server using QR code to achieve secure data transmission. The data are analyzed using gated recurrent unit and long short-term memory network, and the result is sent to the cloud, from which the user can access the result anytime, anywhere. From the results, we observe that the proposed framework reduces the latency and energy consumption by similar to 28% than the cloud-only framework. The results also demonstrate that the proposed framework has an accuracy of similar to 99% in crop yield prediction.

关键词： QR code Deep learning Crop yield prediction Energy Latency

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Human-Oriented Fuzzy-Based Assessments of Knowledge Graph Embeddings for Fake News Detection 20th

Human-Oriented Fuzzy-Based Assessments of Knowledge Graph Em...

引用

20th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based systems

作者： Gutierrez-Batista, Karel Rincon-Yanez, Diego Senatore, Sabrina Univ Granada Dept Comp Sci & AI Granada Spain Univ Salerno Dept Informat & Elect Engn & Appl Math I-84084 Fisciano Italy Univ Santander Fac & Ingn Tecnol Cucuta Colombia

ISBN: (纸本)9783031739965;9783031739972

In the era of information overload, distinguishing between real and fake news is a critical challenge, particularly in public social networking domains. For this purpose, an approach based on the synergy accomplished by a Neurosymbolic AI system, powered with Fuzzy Logic techniques, is introduced to achieve understandable fact-checking classification results. The work proposes a fact-checking approach based on Knowledge Graph Embedding (KGE) techniques. It extracts the involved entities from textual data in the form of triples that, projected in a vector space, form graphs that effectively highlight contextual information. The classification results are interpreted by exploiting fuzzy set modelling, which aims to improve the presentation of the final results. Specifically, we use the Hits@N metric to design fuzzy variables whose linguistic terms reflect news distribution. Then, by exploiting fuzzy rule design, human-like classification performance evaluation is provided. Through experimental evaluation of the benchmark dataset, our approach shows its effectiveness in discriminating between real and fake news, enhanced by straightforward explanations driven by the fuzzy rule design.

关键词： Fuzzy logic Knowledge graph embeddings NeuroSymbolic AI Fake news detection Human-oriented approach

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Dynamic Programming for Training DNN in Distributed Computing 21st

Dynamic Programming for Training DNN in Distributed Computin...

引用

21st International Symposium on Distributed Computing and Artificial Intelligence

作者： Cabello, Julia Garcia Carbo-Garcia, S. Univ Granada Andalusian Res Inst Data Sci & Computat Intellige Dept Appl Math Granada Spain Univ Granada Andalusian Res Inst Data Sci & Computat Intellige Dept Comp Sci & Artificial Intelligence Granada Spain

ISBN: (纸本)9783031820724;9783031820731

In recent years, the architecture and structure of Deep Neural networks (DNNs) have become progressively more complex in order to respond to the increasing complexity of real problems. A strategy to deal with this complexity when it affects training would be to partition DNN training in some way: for example, by distributing it among different components of a computer network. For this, training (which is in essence the minimization of the loss function) should be performed through separated "smaller pieces". This paper offers an alternative to the gradient-based DNN training from a Dynamic Programming (DP) point of view (DP is an optimisation methodology supported by the division of a complex problem into many problems of lower complexity). To do so, conditions which enable the DNN minimization algorithm to be solved under a DP perspective are studied here. In this line, in this work is proved that any artificial neural network ANN (and thus also DNNs) with monotonic activation is separable. Furthermore, whenever ANNs are considered as a dynamical system in the form of a network (known as coupled cell networks CCNs), we show that the transmission function is a separable function assuming that the activation is non-decreasing.

关键词： Dynamic Programming Separable function DNN training

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Design and Implementation of a Customized Myoelectric Prosthesis for a Transradial Amputation Level with Interactive Theming to Measure the User's Psychological Well-Being 9th

Design and Implementation of a Customized Myoelectric Prosth...

引用

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

作者： Urquizo, Gladys Llerena, Angel Rivera, Alexander Paguay, Estefania Vaca, Alexis Pazmino, Alexandra Escuela Super Politecn Chimborazo Riobamba Ecuador Univ Nacl Chimborazo Riobamba Ecuador

ISBN: (纸本)9789819754403;9789819754410

This article describes a customized myoelectric prosthesis for a transradial amputation level with an interactive theme to measure psychological well-being in an 8-year-old child aged 8 and 4months. The clinical assessment is fundamental to determine the type of prosthesis that best suits the patient's needs. At the same time, factors such as age, profession, and daily activities, among others, significantly influence the design of the customized prosthesis. The methodology used in this study includes scanning the patient's dummy, designing or modeling the prosthesis using 360 degrees fusion software, 3D printing of the prosthesis model, post-processing, assembly, and application. The results obtained show that the customized prosthesis designed with a theme and successfully fabricated allowed the child patient to recover the mobility and functionality of his upper limb. The prosthesis was controlled by myoelectric signals detected with the MyoWare sensor, which allowed the user to emit signals to execute the opening and closing action of the hand. This study highlights the potential of electronic engineering and 3D printing to improve the quality of life of a child with a missing upper limb.

关键词： Myoelectrical prostheses Friendly theme 3d printer

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VirtualSign: Revolutionizing Accessibility and Inclusivity in Education Through Automatic Sign Language Translation 9th

VirtualSign: Revolutionizing Accessibility and Inclusivity i...

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

作者： Escudeiro, Paula Gouveia, Marcia Campos Escudeiro, Francisca Escudeiro, Nuno GILT ISEP Rua Dr Antonio Bernardino Almeida 431 P-4249015 Porto Portugal UP ICBAS R Jorge Viterbo Ferreira 228 P-4050313 Porto Portugal

ISBN: (纸本)9789819750344;9789819750351

Promoting accessibility in education is crucial to ensure equal opportunities for all students. Assistive technologies, including an automatic bidirectional translator between sign and written languages, play a significant role in incentivizing inclusiveness and improving communication between the deaf and non-deaf communities. The translator utilizes advanced devices like the Microsoft Kinect and 5DT Sensor Gloves to capture hand motion and shape data, enabling accurate interpretation of sign language. Through machine learning and natural language processing, the translator processes and translates the data in real time. Known as VirtualSign, this technology promotes accessibility, social inclusion, and educational integration by facilitating communication and access to information while raising awareness of sign language in schools. Embracing these advancements supports equal access to education and empowers individuals with disabilities.

关键词： Accessibility in education Equal opportunities Assistive technologies Automatic sign language translator

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An Architectural Proposal for Injury Assessment in Occupational Medicine 21st

An Architectural Proposal for Injury Assessment in Occupatio...

引用

21st International Symposium on Distributed Computing and Artificial Intelligence

作者： Fernandes, Mariana Peixoto, Hugo Machado, Jose Univ Minho LASI Algoritmi Braga Portugal

ISBN: (纸本)9783031820724;9783031820731

Occupational medicine focuses on protecting workers' health, through the prevention of work-related illnesses and diseases, ensuring safety and well-being to perform their functions. This reflects in more productivity and resilience that comes with healthy workers, who experience fewer sick days and have more job satisfaction. Ultimately, this benefits both employees and the companies they work for, contributing to the overall performance and good functioning of the workplace. However, there is a gap in terms of existing software for corporal damage assessment of workplace injuries. Therefore, it is proposed the development of a web application designed to help professionals in the occupational medicine field with the evaluation of diseases and injuries sustained in the workplace. This solution aims to be more precise, effective, and timesaving, thereby enhancing the efficiency of assessments in this field. Moreover, the data collection of injury types and disability coefficients aims to establish a dataset for future machine learning applications. This initiative holds promise for advancing occupational health practices and promoting safer work environments for all.

关键词： Web Application Occupational Medicine Injury Assessment Machine Learning

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Acoustic Anomaly Detection on Weather Radar Machine Sounds Using Augmented Spectrograms 9th

Acoustic Anomaly Detection on Weather Radar Machine Sounds U...

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

作者： Loufakis, Michail Styanidis, Aristotelis Symeonidis, Panagiotis Ioannidis, Dimosthenis Tzovaras, Dimitrios Oikonomou, George Kourmpetis, Ioannis Papagianni, Panagiota Agoudimos, Ilias Informat Technol Inst Ctr Res & Technol Hellas Thessaloniki 57001 Greece Austriacard SA Thessaloniki 54636 Greece Hellen Air Force Telecommun & Elect Depot Appl Res & Technol Dev Directorate Attiki 16562 Greece

ISBN: (纸本)9789819750344;9789819750351

Weather radars play a crucial role in monitoring atmospheric conditions, especially in regions prone to adverse weather phenomena and near critical infrastructure hubs like airports. Ensuring their continuous and reliable operation is essential for accurate weather forecasting and early warning systems. This paper addresses the challenge of acoustic anomaly detection in weather radar systems through a novel approach using augmented spectrograms and autoencoders. We detail the development of a model capable of distinguishing between normal and abnormal operational sounds, an essential step toward effective predictive maintenance. Our method demonstrates outstanding performance, particularly on a dataset provided by the Hellenic Air Force, achieving an AUC of 1.00, signifying flawless anomaly detection. The study's outcomes highlight the potential of machine learning in reducing the need for manual inspections and pave the way for further exploration in applying these techniques in real-world scenarios for enhanced radar system reliability.

关键词： Acoustic anomaly detection Weather radar Autoencoder Spectrograms Augmentation Predictive maintenance Sound processing Machine learning Unsupervised learning

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An Explainable AI-Based Demand Response Optimization Framework for Smart Buildings 21st

An Explainable AI-Based Demand Response Optimization Framewo...

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21st International Symposium on Distributed Computing and Artificial Intelligence

作者： Ibrar, Muhammad Nahom, Hayla Mohammed, Abegaz Marquez-Sanchez, Sergio Fernandez, Javier Hernandez Corchado, Juan Manuel Erbad, Aiman Hamad Bin Khalifa Univ Ar Rayyan Qatar Univ Salamanca BISITE Res Grp Calle Espejo Multiusos S-N Salamanca 37007 Spain Iberdrola Innovat Middle East Doha 210177 Qatar Qatar Univ Coll Engn Doha 2713 Qatar

ISBN: (纸本)9783031820724;9783031820731

Smart building energy demand response (DR) plays a paramount role in the decarbonization of energy use by leveraging different techniques that adjust energy consumption in response to fluctuating prices and demands. In this paper, we present an explainable AI-integrated DR optimization framework for smart buildings. This adaptive XAI-based DR approach intelligently forecasts energy demands and schedules the energy consumption of the buildings based on the forecasted demand patterns, grid conditions, and energy prices. This approach allows the stakeholders to make informed and optimal DR decisions based on the forecasted demand to maximize energy efficiency and user comfort while minimizing electricity costs. The simulation results demonstrate that the proposed approach promotes energy efficiency and user comfort.

关键词： Demand response optimization Energy management Explainable artificial intelligence K-means Random forest regressor

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Fuzzy Linguistic Summaries for Hidden Markov Models 20th

Fuzzy Linguistic Summaries for Hidden Markov Models

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20th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based systems

作者： Kaczmarek-Majer, Katarzyna Baczynski, Michal Hryniewicz, Olgierd Mis, Katarzyna Mucha, Weronika Wichrowski, Filip Polish Acad Sci Syst Res Inst Newelska 6 PL-01147 Warsaw Poland Univ Silesia Katowice Fac Sci & Technol Bankowa 14 PL-40007 Katowice Poland Warsaw Univ Technol Fac Math & Informat Sci Koszykowa 75 PL-00662 Warsaw Poland

ISBN: (纸本)9783031739965;9783031739972

Linguistic summaries are an intuitive tool for obtaining analysis and data mining results that are easy to use, even for novice users. Until now, linguistic summarization has been used primarily to describe and facilitate the interpretation of large data sets. This work aims to develop methods enabling the construction of linguistically quantified sentences reflecting both the sequence of observations of a time series as well as the estimated parameters of hidden Markov models. The resulting fuzzy linguistic summaries with hidden Markov models (HMMs) may be exemplified as follows: "For most observations around 1.1, we have a high exact match rate". Preliminary results illustrate the effectiveness of the proposed approach using simulation methods.

关键词： Fuzzy Linguistic Summaries Hidden Markov Modelling Explainability Data Stream Analysis

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ESMDP: Encrypted ML Framework for Secure Medical Data Processing 3rd

ESMDP: Encrypted ML Framework for Secure Medical Data Proces...

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3rd International Conference on Network Security and Blockchain Technology

作者： Reddy, B. Pradeep Kumar Chatterjee, Ayantika Indian Instutite Technol Kharagpur Kharagpur W Bengal India

ISBN: (纸本)9789819780501;9789819780518

In this age of Internet of Things (IoT), remote health monitoring and out-of-hospital treatment is not a distant dream. Wearable medical sensors and smartphones in the current IoT-driven environment allow for real-time monitoring of important health markers, with data quickly sent to distant specialists for analysis. Moreover, due to the onset of technological advancements like Electronic Health Records (EHR), IoT-enabled devices, Public Open health Data (POD), Linked Opened Data (LOD), remote health monitoring, vast quantities of information created in this domain as big data in health care. Intelligent storage and analysis of this big data may lead to generate critical insights for future predictions. The storage challenge of big data can be mitigated by the use of cloud computing. However, processing of Personal Health Information (PHI) on remote medical servers or cloud raises serious concern about the security of critical PHI. One immediate solution is for the information proprietors to transmit and store their delicate information in encrypted form. However, traditional encryption schemes do not support processing on that encrypted form of data. To take full advantage of cloud computing and outof-hospital treatment, there should be a solution to process on that encrypted form of data directly. In this paper, we propose a framework which supports secure data transmission from medical sensors to distant cloud server or remote medical organizations, secure processing on that data to provide distant medical treatments and perform secure machine learning-based analytics on such sensor-generated datasets. For this secure processing, we use Fully Homomorphic Encryption(FHE) scheme, which supports direct processing on encrypted data. Our implementation shows the proposed secure framework is capable of disease detection remotely in an encrypted domain confirming PHI data confidentiality. The framework also supports performing encrypted machine learning algorithm on clo

关键词： IoT Security eHealth Fully homomorphic encryption (FHE)

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