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7th International Conference on Control Science and Systems engineering, ICCSSE 2021

作者： Ren, Jing Huang, Xishi Ontario Tech U Dept. of Electrical Computer and Software Engineering OshawaON Canada Rs Opto Tech Ltd. Suzhou China

ISBN: (纸本)9781665444057

Using mobile robots for transportation in a warehouse is becoming more and more common. Compared with human staff, these robots can handle the goods more accurately and more efficiently. Using robots can greatly reduce the operation cost of a warehouse. Optimal path planning for these robots can reduce the transportation time, guarantee the safety of the people in the warehouse, and reduce the goods delivery time and increase daily output. In this paper, we propose an optimal path planning algorithm for the mobile robot using deep reinforcement learning (DRL). Potential fields are employed to guide to collect better quality training data to improve data efficiency. The simulation results have shown that DRL can successfully reach the goal position and avoid collision with the obstacles using the potential fields guided trail-and-error method. © 2021 IEEE.

关键词： Motion planning

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Rain Predictive Model using Machine learning Techniques

Rain Predictive Model using Machine learning Techniques

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Multi-disciplinary Conference in Emerging Research Trends (IMCERT), International

作者： Muhammad Shahbaz Muneer Syed Muhammad Nabeel Mustafa Syeda Sundus Zehra Haniya Maqsood Dpertment of Computational Finance NED University of Engineering and Technology Karachi Pakistan Dept. of Computer Science & I.T NED University of Engineering and Technology Karachi Pakistan Dept. of Software Engineering Sir Syed Uuniversity of Engineering and Technology Karachi Pakistan Department of Computer and Information Systems Engineering NED University of Engineering and Technology Karachi Pakistan

Climate is rapidly changing around the world. Over time, there have been significant changes in the weather. Rainfall is now erratic due to climate change. The frequency of extreme weather events like droughts and floods has increased due to climate change, necessitating the need for more precise and timely rainfall forecasts. For strategic reasons including agriculture, water resource management, and architectural design, rain forecasting is crucial. The naturally occurring non-stationary component in the rainfall time series impairs model performance for practical hydrologists and drought risk assessors. We present a rain predicting model based on machine learning to address the forecasting issue. In our work, we predict the possibility of rain the next day on the basis of last 10 years' data. The variables that were calculated during the experiments were humidity, pressure, evaporation, sunshine, rainfall, and so on. Random Forest gave the 90% accuracy with 0.904 Area under Curve, highest out of all the algorithms. The model's performance will significantly aid in the rain forecast.

关键词： Climate change Rain Machine learning algorithms Droughts Weather forecasting Predictive models Prediction algorithms

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Waypoint-based global optimal path planning algorithm for mobile robots

Waypoint-based global optimal path planning algorithm for mo...

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2021 IEEE International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2021

作者： Ren, Jing Huang, Xishi Huang, Raymond N. Ontario Tech U Dept. of Electrical Computer and Software Engineering OshawaON Canada RS Opto Tech Ltd. Jiangsu Suzhou China University of Toronto Dept. of Mechanical and Industrial Engineering TorontoON Canada

ISBN: (纸本)9781665412629

Dynamic programming has long been used for optimal path generation. Different from the most research works in this area which discretize the workspace and use cells for path planning, we propose a global optimal path planning method using waypoints. Although the waypoints of a simple environment with a few rectangular obstacles can be pre-defined manually, it is almost impossible to manually define the global optimal waypoints for a complex environment with many obstacles of different shapes. In this paper, we propose a global optimal path planning algorithm using automatically generated waypoints based on dynamic programming. We have shown that the proposed algorithm can find an exact optimal solution or the shortest path to the goal position from any starting point in environments with multiple convex polygon obstacles. © 2021 IEEE.

关键词： Dynamic programming

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AL-MUSACTRA: A Unified Digital Platform for Inclusive Access to the Cultural Heritage of Andalusia

AL-MUSACTRA: A Unified Digital Platform for Inclusive Access...

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International Symposium on computers in Education (SIIE)

作者： Gabriel Guerrero-Contreras Sara Balderas-Díaz Silvia Toribio-Camuñas Maria Luísa Rodríguez-Almendros María Visitación Hurtado-Torres Dept. of Computer Science and Engineering University of Cadiz Av. Universidad de Cádiz 10 Cadiz Spain Dept. of Translation and Interpreting University of Granada C. Puentezuelas n°55 Granada Spain Dept. of Software Engineering University of Granada C. Periodista Daniel Saucedo Aranda s/n Granada Spain

ISBN: (数字)9798350376616

ISBN: (纸本)9798350376623

This paper introduces the AL-MUSACTRA platform, an initiative designed to democratize access to cultural heritage through digital innovation, specifically catering to the diverse needs of individuals with disabilities. Grounded in the universal right to leisure and culture, the platform is a direct response to the mandates of international conventions and the Sustainable Development Goals, with a focus on inclusivity, safety, resilience, and sustainable access to cultural and natural heritage. Despite the progress in accessible audiovisual translation, the challenge of ensuring universal access to cultural heritage persists, particularly for those with visual, hearing, and cognitive disabilities. The AL-MUSACTRA platform leverages the Drupal Content Management System to overcome these barriers, offering features such as audio descriptions, sign language videos, and simplified texts. This paper details the platform's development, highlighting its emphasis on web accessibility standards and its potential to serve as a model for similar initiatives globally.

关键词： Visualization Technological innovation Sign language Education Safety Cultural differences Sustainable development

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A Novel Energy-Efficient Cross-Layer Design for Scheduling and Routing in 6TiSCH Networks

arXiv

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arXiv 2024年

作者： Hannachi, Ahlam Jaafar, Wael Bitam, Salim Ouazene, Nabil University of Biskra Dept. Computer Science BP 145 RP Biskra Biskra07000 Algeria Dept. Software and IT Engineering 1100 R. Notre Dame W. MontrealQCH3C 1K3 Canada University of Batna Dept. Computer Science Univ. Batna Batna05000 Algeria

The 6TiSCH protocol stack plays a vital role in enabling reliable and energy-efficient communications for the Industrial Internet of Things (IIoT). However, it faces challenges, including prolonged network formation, inefficient parent switching, high control packet overhead, and suboptimal resource utilization. To tackle these issues, we propose in this paper a novel cross-layer optimization framework aiming to enhance the coordination between the Scheduling Function (SF), the Routing Protocol for Low-Power and Lossy Networks (RPL), and queue management. Our solution introduces a slot-aware parent switching mechanism, early slot reservation to mitigate queue overflow, and a refined slot locking strategy to improve slot availability. To reduce control overhead, the proposed method merges 6P cell reservation information into RPL control packets (DIO/DAO), thus minimizing control exchanges during parent switching and node joining. Optimized slot selection further reduces latency and jitter. Through extensive simulations on the 6TiSCH simulator and under varying network densities and traffic loads, we demonstrate significant improvements over the standard 6TiSCH benchmark in terms of traffic load, joining time, latency, and energy efficiency. These enhancements make the proposed solution suitable for time-sensitive IIoT applications. © 2024, CC BY.

关键词： Queueing networks

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Federated Learning with Clustering-Based Participant Selection for IoT Applications

Federated Learning with Clustering-Based Participant Selecti...

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IEEE International Conference on Big Data

作者： Kevin I-Kai Wang Xiaozhou Ye Kouichi Sakurai Dept. of Electrical Computer and Software Engineering The University of Auckland Auckland New Zealand Dept. of Informatics Kyushu University Fukuoka Japan

ISBN: (纸本)9781665480468

Modern Internet of Things (IoT) systems are highly complex due to its mobile, ad-hoc and geographically distributed nature. Very often, an edge-cloud infrastructure is established to offer intelligent services in modern IoT systems. However, IoT edge devices are typically resource-constrained and can not perform sophisticated machine learning algorithm on board. Data sharing with a central server is a common approach of crowdsourcing, but also brings privacy and security concerns. The emerging federated learning offers a promising pathway to achieve an accurate model through distributed machine learning while ensuring data privacy. The existing federated learning process is not tailored to the mobile and adhoc nature of IoT systems where devices are of varying data and system qualities and may not be able to participate the entire training process. Therefore, in this paper, a new federated learning framework is proposed to support asynchronous model fusion with clustering-based participant selection. The proposed framework aims to accommodate the ad-hoc nature of IoT devices, and at the same time avoiding low quality or even malicious data from its participants to ensure model convergence and performance.

关键词： Performance evaluation Training Data privacy Machine learning algorithms Federated learning Big Data Data models

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Custom Hidden Markov Models for Effective Part-of-Speech Tagging

Custom Hidden Markov Models for Effective Part-of-Speech Tag...

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International Conference on Emerging Technologies, ICET

作者： Hassan Bin Khalid Abu Bakar Siddique Raja Hashim Ali Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

Hidden Markov Models have proved to be a very significant tool for various time-series related problems, especially where context is important. One such problem is Part-of-speech tagging. The work uses a customized HMM to propose an effective and advanced solution to POS tagging. With a precision rate of 0.9657, recall of 0.9656, and F1-score of 0.9655, this proposed HMM-based model achieves an exceptional level of accuracy, exhibiting its accurate identification of the POS of words in a sentence. The statistical model employed by the HMM-based method predicts the most likely POS tags while taking into account the probabilities of transition between various POS tags. The model's dependability and resilience were demonstrated when it was tested on a different dataset after being trained on a extensive collection of text data. The study's findings demonstrate that the HMM-based strategy outperforms current POS tagging techniques, making it a significant contribution to the field of natural language processing. In addition, this research has significant implications for a number of NLP applications, including sentiment analysis, machine translation, and text categorization, paving the way for additional innovation and exploration in this domain.

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Heuristic approaches for hiding sensitive frequent itemsets in uncertain databases

Heuristic approaches for hiding sensitive frequent itemsets ...

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International Conference on Pattern Analysis and Intelligent Systems (PAIS)

作者： Mira Lefkir Farid Nouioua Phillipe Fournier-Viger Dept. of Computer Science University Mohamed El Bachir El Ibrahimi of Bordj Bou Arreridj BBA Algeria College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Pattern mining is a core objective within data mining, involving the detection of frequent itemsets (collections of values) within databases. This process serves to extract valuable insights from the data, facilitating informed decision-making. In recent times, a range of algorithms has emerged to adapt this task to the realm of uncertain *** has a wide range of applications, such as analyzing medical data to identify correlated symptoms based on their existential probabilities. However, pattern mining algorithms also pose the risk of revealing sensitive information. As far as we know, there is no existing solution that addresses the inadvertent disclosure of sensitive itemsets when pattern mining algorithms are employed on uncertain databases. This paper addresses this problem by proposing three heuristic approaches, which ensure that sensitive expected frequent itemsets are not discovered while minimizing undesirable side effects for other itemsets. The first heuristic approach (called aggregate) deletes some transactions from an uncertain database. The second approach (named disaggregate) removes some selected items from each transaction. And the third approach (called the hybrid approach) combines the two previous approaches, and deletes some selected items from some selected transactions. An experimental evaluation is presented to assess and compare the effectiveness of the three approaches.

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Revolutionizing Campus Exploration with GikiLenS: A Deep Learning-Powered Object Detection App

Revolutionizing Campus Exploration with GikiLenS: A Deep Lea...

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International Conference on Emerging Technologies, ICET

作者： Iqra Mueed Usama Arshad Raja Hashim Ali Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

For newcomers and tourists, navigating university campuses can be difficult, resulting in aggravation and lost time. We respond by introducing “GikiLenS”, an object identification application driven by deep learning that revolutionizes campus exploration by accurately identifying buildings and landmarks while enhancing user experience. GikiLenS is a comprehensive and user-friendly smartphone application that precisely caters to the demands of newcomers and visitors, unlike previous studies in campus navigation. Our study intends to close this gap and develop a reliable method for identifying campus buildings, optimizing navigation, and enhancing user experience. Through rigorous testing, GikiLenS has proven to have remarkable real-time building detection accuracy, highlighting its potential as an important tool for campus exploration. The app's outcomes and conclusions demonstrate how well it works to give users specific building information, promoting a more knowledgeable and enjoyable campus experience. The importance of our findings lies in the development of a unique, user-centered app that offers a cutting-edge method of campus mobility. Our contribution consists of a game-changing method for building detection that improves campus exploration and user pleasure.

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Diving into Brain Complexity: Exploring Functional and Effective Connectivity Networks

Diving into Brain Complexity: Exploring Functional and Effec...

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International Conference on Emerging Technologies, ICET

作者： Abu Bakar Siddique Hassan Bin Khalid Raja Hashim Ali Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

The human brain's intricate functions are under-pinned by a vast network of synapses that enable chemical impulses between neurons. Neuroscientists employ two key approaches, functional and effective connectivity, to understand the brain's complexity, focusing on its operations, cognition, and behavior. While both methods utilize graph theory for network analysis, functional connectivity, which investigates associated brain activity, has seen more substantial research efforts than effective connectivity, which examines information processing's inter-regional impacts. In this research paper, we aim to present an extensive examination of the emerging discipline that combines graph theory with the analysis of brain characteristics, shedding light on how these characteristics arise from the interactions among different groups of neurons. Our primary focus lies in exploring the diverse cognitive and neurological applications that leverage functional Magnetic Resonance Imaging (fMRI) as a tool. Additionally, we offer a comprehensive overview of the methods employed to construct brain networks based on functional and efficient connections. Throughout the discussion, we emphasize the advantages and limitations associated with these approaches.

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