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Performance of LORA Gateway MQTT Protocol Communication on Motor Cycle IoT-Based: A QoS Analysis

Performance of LORA Gateway MQTT Protocol Communication on M...

引用

International Conference on Information and Communications Technology (ICOIACT)

作者： Iik Muhamad Malik Matin Susana Dwi Yulianti Rifka Nur Cahyani Nisrina Tsany Sulthanah Dinda Kadarwati Noorlela Marcheta Computer and Informatics Engineering Politeknik Negeri Jakarta Depok Indonesia

ISBN: (数字)9798331536206

ISBN: (纸本)9798331536213

In IoT-based systems that utilize platforms for data exchange, reliable and efficient communication methods are essential to enable users to search for and monitor system data. Similarly, in IoT -based motorcycle security protection systems, dependable communication methods are crucial. LoRa and the MQTT protocol serve as solutions for data communication in the IoT system implemented in this research. To evaluate the reliability of the system's communication, tests were conducted under LOS (Line of Sight) and NLOS (Non-Line of Sight) conditions, with optimal transmission ranges of 500 meters for LOS and 300–325 meters for NLOS. The analysis was based on test parameters, including a delay of 122.25 ms, a throughput of 66.75 bps, and a packet loss rate of 1.5%. The LoRa testing across different ranges yielded RSSI values of -115 to -71 dBm under LOS conditions and -122 to -52 dBm under NLOS conditions. These test parameter results for LoRa and MQTT communication fall into the excellent category with a high SF 12 value. Additionally, the Telegram Bot platform was successfully integrated into the motorcycle security protection system, enabling remote control and monitoring. Notifications and data from sensor nodes are received by users through the Telegram Bot, demonstrating the system's effectiveness.

关键词： Meters Protocols LoRa Packet loss Motorcycles Logic gates Throughput Chatbots Delays Security

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Adaptive Vehicle Headlights Direction System based on Road Lane Detection

Adaptive Vehicle Headlights Direction System based on Road L...

引用

2023 International Conference on informatics engineering, Science and Technology, INCITEST 2023

作者： Nizar, Taufiq Nuzwir Hartono, Rodi Jatmiko, Didit Andri Universitas Komputer Indonesia Department of Computer Engineering Bandung Indonesia Universitas Komputer Indonesia Department of Electrical Engineering Bandung Indonesia Universitas Komputer Indonesia Department of Informatics Engineering Bandung Indonesia

ISBN: (纸本)9798350317862

Almost half of the vehicle accident fatalities occur at night. Driving at night is riskier. Even the most familiar roads can look distant in the dark. It becomes difficult to see other vehicles, pedestrians, and other objects. One of the problems is the lack of light, especially at road bends, because the vehicle headlights are always straight. The purpose of the research is to design the adaptive vehicle headlights direction that is always facing the road even if it is on the curving road. There are several processes to implement the system are detecting roads, processing detected road conditions and controlling the direction of the headlights. To detect the road using the camera sensor and image processing based the road lane and recognizing it using Hough Transform algorithm. the results of road detection are processed to drive the servo motor which adjust the direction of the lights. The implemented system works with the success rate 76.63% with accuracy error rate 1.28 degrees for the left bend and 80% with accuracy error rate 1.45 degrees to the right bend. © 2023 IEEE.

关键词： Headlights

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Breast Cancer Prediction Using Random Forest and Gaussian Naïve Bayes Algorithms 1

Breast Cancer Prediction Using Random Forest and Gaussian Na...

引用

1st International Conference on Information System and Information Technology, ICISIT 2022

作者： Sulistiani, Ita Wulandari, Windu Astuti, Fathia Dwi Widodo State University of Jakarta Dept. of Informatics and Computer Engineering Education Jakarta Indonesia

ISBN: (数字)9781665402002

ISBN: (纸本)9781665402002

Breast cancer is the second deadliest cancer after lung cancer. In 2021, ASCO-American Society of Clinical Oncology states that female invasive breast cancer increased by half a percent from 2008 to 2017. Breast cancer is induced by a misspelling of a cell, which causes the cell to become uncontrollable. If the problem is not treated soon within a few months, a large number of cells containing the wrong instructions can be detected as cancer. Machine learning has been widely used for developing breast cancer prediction models. Unfortunately, the problem of imbalanced datasets tends to have little to no attention in previous research using machine learning. This research aimed to develop breast cancer prediction models using Random Forest and Gaussian Naïve Bayes Classifier. Borderline Synthetic Minority Oversampling Technique (BSM) is applied to handle the imbalanced dataset problem;meanwhile, machine learning algorithms such as Random Forest and Gaussian Naïve Bayes algorithms were used to build the prediction models. Using UCI Machine Learning Wisconsin Breast Cancer Dataset (WBCD), the combination of BSM and Random Forest algorithm showed the highest recall score, approximately around 99.8%. Meanwhile, the BSM and Gaussian Naïve Bayes Classifier combination provided the lowest recall score among generated models, 78.2%. © 2022 IEEE.

关键词： Decision trees

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Data Augmentation of Morphological Brain Network Using Multi-variate Normal Distribution 7

Data Augmentation of Morphological Brain Network Using Multi...

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7th International Conference on computer Science and engineering, UBMK 2022

作者： Celik, Mustafa Rekik, Islem Istanbul Technical University Basira Lab Faculty of Computer and Informatics Engineering Istanbul Turkey

ISBN: (数字)9781665470100

ISBN: (纸本)9781665470100

Multi-view Brain Networks (MBNs) have made a significant contribution to the diagnosis of neurological disorders using computers. MBNs can boost the classification accuracy of neurological disorders such as Alzheimer's disease (AD) by capturing diverse characteristics from multiple perspectives of a population. MBNs provide rich data diversity and better generalization for the classifier models, but the classifiers require large-sized dataset to avoid being over-trained. Moreover, it is challenging to collect clinical data because it is scarce and expensive. In this work, we present a method that augments fake (i.e., synthetic) data samples to boost the neurological disorder classification models. It has been shown that the classifiers trained on our synthetic samples achieve better results than those trained on synthetic samples of traditional data augmentation techniques. © 2022 IEEE.

关键词： Classification (of information)

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Predictive World Models from Real-World Partial Observations 1

Predictive World Models from Real-World Partial Observations

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1st IEEE International Conference on Mobility, Operations, Services and Technologies, MOST 2023

作者： Karlsson, Robin Carballo, Alexander Fujii, Keisuke Ohtani, Kento Takeda, Kazuya Nagoya University Graduate School of Informatics Nagoya Japan Gifu University Department of Electrical Electronic and Computer Engineering Gifu Japan Nagoya University Tier Iv Graduate School of Informatics Nagoya Japan

ISBN: (纸本)9798350319958

Cognitive scientists believe adaptable intelligent agents like humans perform reasoning through learned causal mental simulations of agents and environments. The problem of learning such simulations is called predictive world modeling. Recently, reinforcement learning (RL) agents leveraging world models have achieved SOTA performance in game environments. However, understanding how to apply the world modeling approach in complex real-world environments relevant to mobile robots remains an open question. In this paper, we present a framework for learning a probabilistic predictive world model for real-world road environments. We implement the model using a hierarchical VAE (HVAE) capable of predicting a diverse set of fully observed plausible worlds from accumulated sensor observations. While prior HVAE methods require complete states as ground truth for learning, we present a novel sequential training method to allow HVAEs to learn to predict complete states from partially observed states only. We experimentally demonstrate accurate spatial structure prediction of deterministic regions achieving 96.21 IoU, and close the gap to perfect prediction by 62 % for stochastic regions using the best prediction. By extending HVAEs to cases where complete ground truth states do not exist, we facilitate continual learning of spatial prediction as a step towards realizing explainable and comprehensive predictive world models for real-world mobile robotics applications. Code is available at https://***/robin-karlsson0/predictive-world-models. © 2023 IEEE.

关键词： Mobile robots

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AutoNLP: A Framework for Automated Model Selection in Natural Language Processing 18

AutoNLP: A Framework for Automated Model Selection in Natura...

引用

18th Iberian Conference on Information Systems and Technologies, CISTI 2023

作者： Saleem, Shehan Kumarapathirage, Sapna University of Westminster Computer Science and Engineering London United Kingdom Informatics Institute of Technology School of Computing Colombo06 Sri Lanka

ISBN: (纸本)9789893347928

Although numerous open-source tools exist for machine learning with tabular data, there is a scarcity of comparable resources tailored specifically for NLP. The lack of transparency in the inner workings of existing AutoNLP tools is a significant obstacle in scientific research. AutoML tools are known for their ability to perform model selection with little human intervention, improving the accuracy and reliability of the results. However, conducting a model space search among pre-trained NLP models can be computationally infeasible, making it challenging to determine the optimal NLP model for a given dataset. This research aims to enhance the performance of NLP model selection. Our approach has resulted in higher accuracy than existing methods on the dataset that was created. In our future work, we plan to benchmark our algorithm against datasets created by other researchers to validate its effectiveness. Additionally, we intend to use the same system to perform model selection among popular large language Transformer models. © 2023 ITMA.

关键词： Machine learning

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Human Gender Detection from Facial Image using Global and Local Feature

Human Gender Detection from Facial Image using Global and Lo...

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2022 International Conference on Electrical and Information Technology, IEIT 2022

作者： Ningrum, Lusiana Diyan Dewantara, Bima Sena Bayu Sari, Dewi Mutiara Politeknik Elektronika Negeri Surabaya Dept. of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (数字)9781665453035

ISBN: (纸本)9781665453035

Identifying gender is frequently difficult when physical characteristics show different facts, which can lead to public controversy. This paper describes a method for determining a person's gender solely based on physical characteristics, particularly facial shape and features. The method used combines global and local facial features. Face landmark detection is a global feature that displays a face's shape or contour in 2D. While the Histogram of Oriented Gradient (HOG) is a local feature that shows the detailed shape of facial components like eyebrows, eyes, nose, and mouth. The combined information from global and local features is fed into the Support Vector Machine (SVM) and classified as male or female. Based on the experimental results, our system was able to identify gender with an accuracy of 80% with an average F-measure is 0.8. © 2022 IEEE.

关键词： Support vector machines

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Personalized User Experience and Engagement in Interactive Mobile Augmented Reality Software Through Fuzzy Weights

Personalized User Experience and Engagement in Interactive M...

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19th International Conference on Web Information Systems and Technologies, WEBIST 2023

作者： Papakostas, Christos Troussas, Christos Krouska, Akrivi Mylonas, Phivos Sgouropoulou, Cleo Department of Informatics and Computer Engineering University of West Attica Egaleo Greece

ISBN: (纸本)9783031896200

The integration of digital content into the real world through mobile augmented reality (AR) systems presents captivating prospects, yet user engagement and experience within such environments are largely contingent upon individual computer skills, which can vary significantly among users, thereby influencing their proficiency in utilizing this technology. This paper aims to bridge this gap by examining the impact of users’ computer skills on interactions within mobile AR settings. To this end, a novel fuzzy logic-based model is introduced to evaluate and refine users’ computer skills within the realm of mobile AR systems. By modeling users’ computer aptitude, the system is capable of delivering tailored assistive prompts and feedback. These prompts are strategically aligned with established fuzzy weights, thereby augmenting users’ interactions within the mobile AR sphere. The proposed methodology is seamlessly integrated into a personalized mobile AR platform designed for spatial ability training. Evaluation findings reveal promising outcomes, underscoring the substantial potential of fuzzy logic modeling to elevate user experiences and propel advancements in mobile augmented reality technology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Teaching

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Partition Strategies for Vertex-Centric Historical Graph Systems 25

Partition Strategies for Vertex-Centric Historical Graph Sys...

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Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing

作者： Alexandros Spitalas Georgios Tsolas Kostas Tsichlas Computer Engineering and Informatics Department University of Patras Rio Achaia Greece

ISBN: (纸本)9798400706295

This paper explores partitioning strategies for vertex-centric historical graph systems within distributed environments, focusing on efficient data management and query execution. Historical graphs, which capture the dynamic evolution of vertices and edges over time, present unique challenges for storage and computation due to their constantly changing nature. We investigate two partitioning approaches for offline and online environments. Both offline and online algorithms aim to minimize an appropriately defined notion of edge cuts within the historical graph setting; the former algorithm is based on BFS, while the latter is based on a greedy partitioning approach. Both algorithms are compared qualitatively and quantitatively to hash-based methods that are mainly used in this setting. They are evaluated on real-world datasets, using metrics such as weighted edge cut score ratio and load balance ratio. Our experiments reveal that our online method consistently reduces edge cuts with minimal overhead, making it suitable for real-time distributed processing systems for historical graphs. Similarly, the offline algorithm achieves much better results for weighted edge cuts, but it requires severely more computational resources to achieve this performance. Additionally, the study highlights how varying the number of workers and partitioning thresholds impacts system performance across different datasets. All our experiments have been carried out in a simulation environment. The results provide valuable insights into optimizing partition strategies for historical graphs, paving the way for more efficient graph analytics in distributed systems.

关键词： partitioning strategies

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IoT-Based Anti-Theft and Violation System with Keyless Ignition for Motorcycle

IoT-Based Anti-Theft and Violation System with Keyless Ignit...

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International Conference on Cyber and IT Service Management (CITSM)

ISBN: (数字)9798331528799

ISBN: (纸本)9798331528805

Motorcycles are one of the most flexible means of transportation, especially in urban areas. Motorcycles are prone to theft. This is because access to ignition is easier than other vehicles such as cars. For this reason, a motorcycle security system was developed that has two main systems, namely a tracking system (vehicle tracker) and a keyless ignition system using a LoRa-based Smart SIM (e-driver License) card. The results of functionality and integration tests show that the entire system can be interconnected and work according to the system plan. Performance testing shows that the GPS module performs better when in open space conditions. The average time required by the system to start the vehicle using Smart SIM is 2.83 seconds, start the engine for 6.46 seconds, and turn off the vehicle for 5.18 seconds. The vibration sensor has a presentation error rate of 28% in detecting vibrations. The LoRa module has a greater transmission range in LOS (Line of Sight) conditions.

关键词： Vibrations Meters Motorcycles SIM card LoRa Ignition Security Engines Global Positioning System Radiofrequency identification

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