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PID Control Tuning Technique for Temperature Control in Cotton Fiber Dyeing Using Neural networks and PLC 1

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13th Computer Science Online Conference

作者： Layme, Roguer Contreras Flores, Jorge Galindo Segura, Guillermo Zarate Univ Tecnol Peru Fac Ingn & Sistemas Lima Peru

ISBN: (数字)9783031705182

ISBN: (纸本)9783031705175;9783031705182

This research work focuses on improving temperature control in the cotton fiber treatment process by developing an advanced PID control tuning technique. The proposed methodology uses a Programmable Logic Controller (PLC) and the integration of artificial neural networks to automatically optimize the PID control parameters based on the process conditions. The study explores classical PID tuning, software autotuning, and neural network autotuning. These methods are compared in terms of reference signal tracking, response time and system stability. The results indicate that the implementation of neural networks significantly improves the responsiveness to changes in the set point, achieving a faster settling time and better damping compared to classical and software autotuning methods. Although neural networks present challenges in terms of interpretability, the benefits in control performance suggest their viability in industrial environments for the control of nonlinear or complex systems.

关键词： Temperature control PLC identification techniques neural networks PID control

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A Scoping Review of the Use of Blockchain and Machine Learning in Medical Imaging Applications 12th

A Scoping Review of the Use of Blockchain and Machine Learni...

引用

12th World Conference on Information systems and Technologies (WorldCIST)

作者： Pavao, Joao Bastardo, Rute Rocha, Nelson Pacheco Univ Tras Os Montes & Alto Douro Sci & Technol Sch INESC TEC Vila Real Portugal Univ Tras Os Montes & Alto Douro Sci & Technol Sch UNIDCOM Vila Real Portugal Univ Aveiro IEETA Dept Med Sci Aveiro Portugal

ISBN: (纸本)9783031602177;9783031602184

This scoping review systematizes the current research related to the use of both blockchain and machine learning techniques in medical imaging applications. A systematic electronic search was performed, and twenty-five studies were included in the review. These studies aimed to use blockchain and machine learning techniques to provide (i) efficient security mechanisms to support the communication of medical imaging data, (ii) aggregation of distributed medical imaging data to train machine learning algorithms, and (iii) machine learning algorithms based on federated learning strategies. Among the ten machine learning techniques identified in the included studies, Convolutional Neural Network was the most representative (i.e., 44% of the studies). Moreover, Artificial Neural Network, Capsule Network, Deep Neural Network, Gated Recurrent Units, and Neural Network were machine learning techniques used by more than one study. Although the included studies developed algorithms with potential impact in clinical practice, it must be noted that they did not discuss the generalizability of their algorithms in real-world clinical conditions.

关键词： Medical imaging Blockchain Machine learning Scoping review

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Narrating the Museum: Enhancing Cultural Heritage Through User Profiling and Individualized Content 16th

Narrating the Museum: Enhancing Cultural Heritage Through Us...

引用

16th Learning Ideas Conference

作者： Torre, Eliana Maria Univ Roma La Sapienza I-00185 Rome Italy

ISBN: (纸本)9783031416361;9783031416378

Museums contribute to creating collective memory and identity, provided that their communication strategies reach all social groups. Good stories bring the most disparate and complex content closer to the "feeling" of nonspecialists, staying in people's emotional and cognitive memory. This project aims to promote mental well-being, social inclusion, and active citizenship by stimulating participants' 4 C's (Communication, Collaboration, Critical Thinking, Creativity) and digital skills through individualized paths that prompt effective dialogue between users and museums. The study is developed at the National Roman Museum through three research questions. What digital narrative forms promote the social inclusion of various museum audiences? How do you design educational paths that promote social inclusion and active citizenship by stimulating the 4 C's and digital skills? How do narratives in museums facilitate the participation of different categories of users, especially those at risk of marginalization? The study is based on empirical research and co-participatory design. Facilitated tours and workshops are conducted through inclusive methodologies - i.e., Object-based Learning, Visual Thinking Strategy, and Digital Storytelling. Evaluation measures the levels of the 4 C's and digital skills, mental well-being, and perceived social inclusion through skills assessment grids, the UCL Museum Wellbeing Measures Toolkit, and focus groups. User profiling, carried out through eye-tracking applied to Digital Storytelling products and a brief questionnaire, identifies the characteristics of groups of users to offer individualized pathways. The expected outcomes are a valuable resource for the inclusiveness of museums, the community's participation, and all educators who wish to innovate their educational practices.

关键词： Transverse competences Digital storytelling Social inclusion

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Classification Model for the Detection of Anxiety in University Students: A Case Study at UNMSM 12th

Classification Model for the Detection of Anxiety in Univers...

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12th World Conference on Information systems and Technologies (WorldCIST)

作者： Vera-Leon, Bryan Gozme-Avila, Laura Guzman-Monteza, Yudi Univ Nacl Mayor San Marcos Ave German Amezaga Lima Peru

ISBN: (纸本)9783031602269;9783031602276

Anxiety, a common mental disorder worldwide, affects millions of people, including university students. In Peru, in 2021, a high prevalence of anxiety of 69% was recorded among students at three institutions of the Consortium of Universities. Early detection and treatment of anxiety in academic settings are critical to ensure student well-being. This study proposes a classification model for which Machine Learning algorithms such as Random Forest (RF), Naive Bayes, and Support Vector Machine (SVM) were evaluated using the Universidad Nacional Mayor de San Marcos as a case study. Advanced techniques were implemented, including Adversarial Generative networks data augmentation, and hyperparameter tuning was performed using GridSearch and Random Search. The results highlighted the SVM model, which achieved 85% accuracy and an F1-Score of 0.78, with a prediction time of 0.00100159 s. Notably, the data augmentation technique revealed a steady improvement in all models.

关键词： Supervised Learning Anxiety Detection Classification Model Data Augmentation Techniques

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Agricultural Crop Yield Prediction for Indian Farmers Using Machine Learning 1

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

作者： Narawade, Vaibhav Chaudhari, Akash Mohammad, Muntazir Alam Dubey, Tanmay Jadhav, Bhumika DY Patil Deemed Univ Dept Comp Engn Ramrao Adik Inst Technol Mumbai Maharashtra India

ISBN: (数字)9789819984763

ISBN: (纸本)9789819984756;9789819984763

The growth of our country's economy depends heavily on the sector of agriculture. Agriculture gave rise to civilization. As an agrarian country, India's economy heavily depends on crop output. As a consequence, agriculture may be the cornerstone of every industry in our country. While organizing an agricultural enterprise, each crop must be properly selected. Several variables, the price of crops, the rate of production, and governmental regulations will affect crop selection. One of the challenging issues in the agriculture industry is the estimation of high crop output through the use of machine learning algorithms. Support Vector Regression Algorithm is used for agricultural yield prediction using various parameters. Forecasts are more heavily dependent on West Maharashtra in India. The potential for growth and a variety of production elements, such as climatic factors like rainfall and humidity, soil characteristics like macronutrients, soil type, topography, irrigation, and fertilizer management, all influence crop output. Remote and proximal sensing technologies are being used more often as a result of the requirement for timely and precise sensing of these inputs for big agricultural fields. For a better outcome, algorithms like Bagging, Random Forest, and Support Vector Regression are used to produce the best results.

关键词： Agriculture Crop yield Yield prediction Machine learning Accuracy Support vector regression Ensemble Metrics Prediction

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Image Classification Method Based on Chaos Neural Network

Image Classification Method Based on Chaos Neural Network

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Computing Conference

作者： Arai, Kohei Saga Univ Saga 8408502 Japan

ISBN: (纸本)9783031622793;9783031622779;9783031622762

An image classification method with Chaos Neural Network (CHAOS NN) is proposed. CHAOS NN allows to find closer to a globally optimum solution than Back Propagation Neural Network (BPNN) due to the fact that behavior of the solution from CHAOS NN is chaotic so that the solution may get out from local minima. In order to determine the parameters required for CHAOS NN, training samples are used. The required parameters for the simulation are calculated beforehand using the training samples. Experimental results with simulation and actual satellite imagery data of Landsat TM show that the proposed CHAOS NN based image classification performance are better than BPNN performance by 18.2-54.3% for the simulation data while by 13.4% for Landsat-5 TM data, respectively.

关键词： Neural network Chaos neural network Back propagation learning process Image classification

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Enhancing True Random Number Generation in MRAM Devices Through Response Adjustment

Enhancing True Random Number Generation in MRAM Devices Thro...

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Computing Conference

作者： Jain, Saloni Rios, Manuel Aguilar Cambou, Bertrand No Arizona Univ Flagstaff AZ 86011 USA Intel Corp Santa Clara CA 95054 USA

ISBN: (纸本)9783031622755;9783031622731;9783031622724

Random number generators (RNGs) play a crucial role in cryptographic schemes. If the generated random numbers exhibit patterns or are predictable, it can lead to vulnerabilities and compromise the security of cryptographic protocols, including confidentiality, integrity, and authenticity. However, not all RNGs are suitable for cryptographic applications. Pseudo-random number generators (PRNGs), which are based on mathematical formulas, are highly vulnerable to attacks and can be predictable. Cryptographically Secure Pseudo-random Number Generators (CSPRNGs) offer improved security but require more resources and can still be predictable if the seed is known. True Random Number Generators (TRNGs) extract randomness from physical sources, such as atmospheric noise, thermal noise, or radioactive decay, making them truly unpredictable. Memory Physically Unclonable Functions (PUFs) are promising candidates for TRNGs as they leverage the random silicon fabrication process to generate inherent randomness. The objective of this work is to improve an MRAM-based TRNG by manipulating the analog responses. We propose both hardware and software implementations of TRNG schemes. To evaluate the randomness and quality of the generated sequences, we subject them to the statistical test suite from the National Institute of Standards and Technology (NIST) designed for assessing random and pseudo-random numbers. Additionally, we introduce a post-processing method that involves XORing the generated numbers with pseudo-random numbers to enhance the randomness further and strengthen the overall security of the TRNG.

关键词： Random number generation Magnetoresistive random access memory (MRAM) Cryptographic schemes Physical unclonable function (PUF) Low power Non-volatile Ternary states Exclusive OR logic (XOR) Memory array components Pseudo-random number generation True random number generation

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Practical Approaches to Approximate Dominant Eigenvalues in Large Matrices 15th

Practical Approaches to Approximate Dominant Eigenvalues in ...

引用

15th International Workshop on Self-Organizing Maps, Learning Vector Quantization and Beyond (WSOM)

作者： Schleif, Frank-Michael UAS Wurzburg Schweinfurt Dept Comp Sci & Business Informat Syst Ctr Artificial Intelligence & Robot Wurzburg Germany

ISBN: (纸本)9783031671586;9783031671593

In numerous machine learning algorithms, the computation of eigenvectors and eigenvalues plays a pivotal role in optimizing models. Depending on the specific use case, it is frequently adequate to compute only a subset of dominant eigenvectors or utilize estimations. Handling this task for large matrices poses a challenge, as standard machine learning packages often lack suitable implementations. We explores various techniques for approximating dominant eigenvectors in the context of potentially large symmetric, real-valued matrices and offer an overview of established methods, analyzing their potentials and limitations, including implementation details.

关键词： eigenvalue approximation large scale Nystrom Gershgorin power iteration von Mises Lanczos

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Multiband Antenna Design Using Giuseppe Peano's Fractal with Graphene for THz Applications 4th

Multiband Antenna Design Using Giuseppe Peano's Fractal with...

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

作者： Youssef, Amraoui Halkhams, Imane El Alami, Rachid Jamil, Mohammed Ouazzani Qjidaa, Hassan Sidi Mohamed Ben Abdellah Univ LISAC Lab Fes Morocco Private Univ Fez SIEDD Lab UPF Fes Morocco

ISBN: (纸本)9783031686740;9783031686757

This work presents a flexible multi-band antenna with high gain and sophistication that was created by adding Giuseppe Peano and graphene parasitic components to a standard patch antenna. The patch's borders are decorated with the Giuseppe Peano design. The fractal THz antenna's suggested configuration evolved from a basic rectangular patch antenna with two slots in the ground plane construction through several design revisions. A 50 mu m-thick polyimide flexible substratewith a dielectric constant of Er= 3.5 is used in the design. At 200x200x 50 mu m(3), this antenna is small and resonates in two bands at 0.5 THz and 0.6 THz, with reflection coefficients of -30.2 dB and -33.2 dB, respectively. Furthermore, it achieves 5.02 dBi and 5.57 dBi peak gains in these corresponding frequency regions. Based on the finite integration approach, a CST Microwave Studio is used to confirm the correctness of the recommended antenna. As a result, THz applications in high-resolution radar systems and medical imaging may benefit from this suggested antenna design.

关键词： Fractal antenna Graphene Multiband Giuseppe Peano THz Application

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Proxy Re-encryption Protocol in Off-Road Emergency 9th

Proxy Re-encryption Protocol in Off-Road Emergency

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

作者： Park, Hyun-A Honam Univ 112 Honamdaegil Gwangju South Korea

ISBN: (纸本)9789819733019;9789819733026

We present a secure protocol addressing medical information privacy in off-road accidents, particularly in Flying Ad-Hoc networks (FANETs) using drones and Proxy Re-Encryption. The proposed semi-trusted Cloud Server (CS) model employs proxy re-encryption technology, allowing secure data handling without traditional decryption. The system involves various participants like smartphones, healthcare providers, emergency centers, vehicles, drones, and a Control Center (CC). The protocol ensures encrypted communication and re-encryption for sensitive medical records, optimizing information flow during off-road emergencies.

关键词： Proxy re-encryption FANET Off-road accident

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