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7th International Conference on Information System and Data Mining, ICISDM 2023

作者： Belachew, Ermiyas Birihanu Bekele, Mebiratu Beyene Birawo, Birtukan Adamu Elte Data Science and Engineering Department Faculty of Informatics Eötvös Loránd University Hungary Department of Software Engineering Wolkite University Ethiopia Bahir Dar Institute of Technology Ict 4D Research Centre Bahir Dar University Ethiopia Applied Informatics Silesian University of Technology Poland

ISBN: (纸本)9798400700637

In recent years, wireless sensor networks (WSN) have received considerable attention in environmental, industrial monitoring, and control applications. It is used to monitor, collect, maintain, and analyse environmental data from a remote place with the help of remote sensing and monitoring technologies. Identification of suitable algorithms and protocol WSNs for water quality monitoring is a challenge due to the limitation of energy, computation capability, and storage resources of the sensor nodes. In this paper, we designed a model for a WSN-based water quality monitoring system that employs a machine learning technique to analyse water data and facilitates decision-making. The suggested model can gather water quality parameters from a water body and send them to a distant data centre, where they will be processed using a machine learning (ML) algorithm. The experiment shown that by adopting WSN and ML approach, we can obtain state-of-the-art 0.0723 RMSE and 0.0052 MAE, indicating that the proposed methodology is appropriate for usage in intelligence water quality monitoring. © 2023 Owner/Author.

关键词： Water quality

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Improved Transportation Model with Internet of Things Using Artificial Intelligence Algorithm

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Computers, Materials & Continua 2023年第8期76卷 2261-2279页

作者： Ayman Khallel Al-Ani Shams Ul Arfeen Laghari Hariprasath Manoharan Shitharth Selvarajan Mueen Uddin Cybersecurity Research Group Faculty of Computing and InformaticsUniversity Malaysia SabahJalan UMSKota KinabaluSabah88400Malaysia National Advanced IPv6 Centre(NAv6) Universiti Sains MalaysiaGelugorPenang11800Malaysia Department of Electronics and Communication Engineering Panimalar Engineering CollegePoonamalleeChennai600123TamilnaduIndia Department of Computer Science&Engineering Kebri Dehar UniversityKebri Dehar250Ethiopia College of Computing and Information Technology University of Doha for Science and TechnologyDoha24449Qatar

In this paper,the application of transportation systems in realtime traffic conditions is evaluated with data handling *** proposed method is designed in such a way as to detect the number of loads that are present in a vehicle where functionality tasks are computed in the *** to the existing approach,the design model in the proposed method is made by dividing the computing areas into several cluster regions,thereby reducing the complex monitoring system where control errors are ***,a route management technique is combined with Artificial Intelligence(AI)algorithm to transmit the data to appropriate central ***,the combined objective case studies are examined as minimization and maximization criteria,thus increasing the efficiency of the proposed ***,four scenarios are chosen to investigate the projected design’s *** all simulated metrics,the proposed approach provides better operational outcomes for an average percentage of 97,thereby reducing the amount of traffic in real-time conditions.

关键词： Transportation Artificial Intelligence(AI) data-driven Internet of Things(IoT)

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Influence of charge transport layer on the crystallinity and charge extraction of pure tin-based halide perovskite film

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Journal of Energy Chemistry 2022年第6期31卷 612-615,I0017页

作者： Yaohong Zhang Muhammad Akmal Kamarudin Qiao Li Chao Ding Yong Zhou Yingfang Yao Zhigang Zou Satoshi Iikubo Takashi Minemoto Kenji Yoshino Shuzi Hayase Qing Shen School of Physics Northwest UniversityXi'an 710127China Faculty of Informatics and Engineering The University of Electro-CommunicationsTokyo 182-8585Japan Eco-materials and Renewable Energy Research Center Jiangsu Key Laboratory for Nano TechnologyNanjing UniversityNanjing 210093China Department of Advanced Materials Science and Engineering Faculty of Engineering SciencesKyushu UniversityFukuoka 819-0395Japan Department of Electrical and Electronic Engineering Faculty of Science and EngineeringRitsumeikan UniversityShiga 525-8577Japan Department of Electrical and Electronic Engineering Miyazaki UniversityMiyazaki 889-2192Japan

As one of the most compelling photovoltaic devices, halide perovskite (PVK) solar cells have achieved a new surprising record power conversion efficiency (PCE) of 25.8%in 2021 [1]. This demonstrates the great potential of halide PVK solar cells as a highly competitive substitute to replace silicon-based solar cells in the photovoltaic market [2–6].

关键词： Tin-based halide perovskite Charge transport layers Crystallinity Charge extraction Photoexcited carrier dynamics

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Video Streaming QoS Analysis with TIPHON Standard NDNts 9

Video Streaming QoS Analysis with TIPHON Standard NDNts

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9th International Conference on Wireless and Telematics, ICWT 2023

作者： J.d, I Made Aditya Putra Alifa, Lisdatul Fawwaz, Muhammad Hilmi Mauren Sibayak, Nissha Kinaya Mayasari, Ratna Arifin, Hasan Nur Ahdan, Syaiful Negara, Ridha Muldina Syambas, Nana Rachmana Bandung Institute of Technology Dept. of Telecommunication Engineering Bandung40132 Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung40132 Indonesia School of Electrical Engineering Telkom University Bandung Indonesia Universitas Teknokrat Indonesia Faculty of Engineering & Computer Science Lampung35132 Indonesia

ISBN: (纸本)9798350305029

Named data networks are a new concept in network architecture that can process data transmission by name. This concept can be utilized in the case of video streaming. TIPHON is a standard used to support streaming multimedia activities. According to TIPHON, three factors-delay, packet loss, and throughput-can influence how a stream behaves. This study's goal is to evaluate the quality of service (QoS) of video streaming services using an NDN network that adheres to the TIPHON NDN standard. Several things can affect throughput, such as bottlenecks on the computer or network device used. © 2023 IEEE.

关键词： Quality control

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Comparison of Mechanical Properties of PLA-Based Biocomposites Filled with Different Agricultural By-Products 7

Comparison of Mechanical Properties of PLA-Based Biocomposit...

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7th International Conference on Material Strength and Applied Mechanics, MSAM 2024

作者： Jakab, Sándor Kálmán Singh, Tej Lendvai, László Department of Materials Science and Engineering Széchenyi István University GyorH-9026 Hungary Savaria Institute of Technology Faculty of Informatics ELTE Eötvös Loránd University BudapestH-1117 Hungary

ISBN: (纸本)9781643685472

In this study, biopolymer composites were developed using poly(lactic acid) (PLA) as a polymer matrix. Various agricultural by-products, including flax seed meal, rapeseed straw, and mustard seed meal, were added as a reinforcement. The research aimed to provide insight into the valorization of cheap, readily available residues generated in the agricultural industry and assess the mechanical properties of composites prepared using them. The experimental fabrication was conducted by compounding PLA with agro-waste particles in 10 and 20 wt% concentrations. These components were melt mixed with a twin-screw extruder and injection molded into standardized forms. The resulting fabricated composites were tested for tensile and flexural mechanical properties and hardness. Through scanning electron microscopy, images of the natural particles were taken to better understand their structure, geometry, and possible ways of interaction between them and the PLA matrix. The results of quasi-static mechanical tests suggest that using agricultural by-products can effectively improve Young's modulus and flexural modulus of PLA but at the cost of tensile and flexural strength, which decreased with the by-products' introduction. Of the three agro-waste options, rapeseed straw emerged as the superior choice because it only marginally reduced the mechanical strength of the PLA and enhanced its stiffness the most. Hardness was the least affected property, test results showed that the added fillers did not substantially change the polymer matrix's hardness. © The Author(s), 2024.

关键词： Straw

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SEMU5G: an Emulator for Improving Model Accuracy and Performance Evaluation of 5G Network Core Architectures based on SDN 10

SEMU5G: an Emulator for Improving Model Accuracy and Perform...

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10th International Conference on Wireless and Telematics, ICWT 2024

作者： Zaki Hamidi, Eki Ahmad Lazuardi, Arga Valen Rahmansyah, Muhammad Litfan Novel Bajri, Salman Alfarizi Tulloh, Rohmat Negara, Ridha Muldina Ahdan, Syaiful Jupriyadi UIN Sunan Gunung Djati Bandung Electrical Engineering Department Bandung Indonesia Telkom University Telecommunication Technology School of Applied Science Bandung Indonesia The University Center of Excellence Intelligent Sensing-IoT Telkom University Indonesia Telkom University School of Electrical Engineering Bandung Indonesia Universitas Teknokrat Indonesia Faculty of Engineering & Computer Science Indonesia Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandarlampung Indonesia

ISBN: (纸本)9798350377057

The rapid advancement of 5G Radio Access Network (RAN) architecture is facilitating the construction of 5G networks, marking a significant milestone in telecommunications evolution. Given the complexity of the 5G core architecture, traditional simulation methods are insufficient, necessitating novel approaches. Emulator systems are crucial for creating dynamic, controlled environments that enable exploration of real-world scenarios without physical constraints. SEMU5G, a 5G core emulator, SDN controller, and RAN simulator, utilizes Open5GS implemented in Docker Container for test system flexibility and isolation. Additionally, an SDN controller is integrated to monitor data flows in the User Plane Function (UPF) and gNB simulated by UERANSIM in Mininet-WiFi. This comprehensive integration facilitates effective and flexible real-world exploration, providing a dynamic and controlled test environment for 5G core research. Scenario testing comprises two stages: firstly, a fixed network topology is employed to compare resource usage and confirm successful SEMU5G integration. Secondly, a mobile network topology is utilized to implement a mobile device scenario and compare the Quality of Service (QoS) of SEMU5G with other available emulators. These stages ensure thorough evaluation of SEMU5G's performance and its comparative advantage over existing solutions. © 2024 IEEE.

关键词： Wi-Fi

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Oil Palm Leaf Disease Detection on Natural Background Using Convolutional Neural Networks 11

Oil Palm Leaf Disease Detection on Natural Background Using ...

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11th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2022

作者： Septiarini, Anindita Hamdani, Hamdani Junirianto, Eko Thayf, Mohammad Sofyan S. Triyono, Gandung Henderi Mulawarman University Faculty of Engineering Department of Informatics Samarinda Indonesia Polytechnic of Agriculture Department of Information Technology Samarinda Indonesia Stmik Kharisma Makassar Department of Information System Makassar Indonesia Universitas Budi Luhur Faculty of Information Technology Jakarta Indonesia University of Raharja Informatics Engineering Faculty of Science and Technology Tangerang Indonesia

ISBN: (纸本)9781665460309

Oil palm plant diseases typically manifest themselves on the leaves, resulting in reduced crop quality. It is necessary to solve this issue as the need for premium-quality palm oil keeps growing. Despite the fact that various automatic detection models for oil palm leaf disease have been developed, their performance was frequently inadequate due to the similarity of class characteristics. This work proposes a method that automatically detects the oil palm leaf disease on a natural background to distinguish between infected and healthy leaf classes. The method was developed using deep learning based on Convolution Neural Network (CNN) model. The private dataset consists of 600 oil palm leaf images (300 healthy and 300 infected) on a natural background. In order to decrease the computation time, pre-processing was carried out, which consists of resizing and normalizing the image, followed by augmentation. Augmentation was applied by rotation, flip, shear, and zooming techniques. Furthermore, the CNN model was employed to detect oil palm leaf disease using Tensorflow 2.5.0 framework with 224 × 224 input data. The proposed method successfully achieved the highest performance, revealed by the accuracy value of 1. © 2022 IEEE.

关键词： Neural network models

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Dynamic anaerobic digestion-based biorefineries for on-demand renewable energy and bioproducts in a circular bioeconomy

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Trends in Biotechnology 2025年第5期43卷 1140-1165页

作者： Shinde, Rajas Hackula, Anga Marycz, Milena Bose, Archishman O'Shea, Richard Barth, Susanne Murphy, Jerry D. Wall, David M. SFI MaREI Centre for Energy Climate and Marine Environmental Research Institute University College Cork Cork Ireland Civil Structural and Environmental Engineering School of Engineering and Architecture University College Cork Cork Ireland Crops Environment and Land Use Programme Crop Science Department Teagasc Oak Park Ireland Faculty of Electronics Telecommunications and Informatics and Advanced Materials Centre Gdansk University of Technology Gdańsk Poland Process and Chemical Engineering School of Engineering and Architecture University College Cork Cork Ireland

Anaerobic digestion (AD) is an important biotechnology for treating biodegradable residues and producing bioenergy, yet its full potential remains untapped. We investigate a two-phase AD system for biorefinery applications, producing valuable bioproducts, such as volatile fatty acids (VFAs) and biogas, from grass feedstock. We introduce a demand-driven operational approach to match market conditions, while minimising water use by reusing the process effluent. The proposed biorefinery model yields ~23 kg of VFAs and 75 kWh of biogas, with a potential gross revenue of €84 per tonne of grass. However, a preliminary economic analysis indicates that this biorefinery model is currently unprofitable. A sensitivity analysis suggests that reducing operating costs through technology advancements and policy support are vital to ensure economic viability. Such biorefineries offer opportunities for the diversification of farmers' incomes and the transition away from fossil resources. Our work exemplifies the role of AD as a key biotechnology in the circular bioeconomy. © 2025 The Author(s)

关键词： Sensitivity analysis

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Artificial Intelligence in Next-Generation Networking: Energy Efficiency Optimization in IoT Networks Using Hybrid LEACH Protocol

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SN Computer science 2024年第5期5卷 546页

作者： Khan, Surbhi Bhatia Kumar, Ankit Mashat, Arwa Pruthviraja, Dayananda Imam Rahmani, Mohammad Khalid Mathew, Jimson School of Science Engineering and Environment University of Salford Salford United Kingdom Department of Information Technology Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur India Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Rabigh 21911 Saudi Arabia Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal 576104 India Department of Computer Science College of Computing and Informatics Saudi Electronic University Riyadh 11673 Saudi Arabia Department of Computer Science and Engineering Indian Institute of Technology Patna Bihar Patna India

The convergence of the Internet of Things (IoT) and Artificial Intelligence (AI) is significantly transforming the landscape of future networking. The Internet of Things (IoT) is a technological paradigm that encompasses embedded systems, wireless sensors, and automation, facilitating the integration of various applications ranging from smart homes to wearable devices. In addition, the advent of artificial intelligence (AI) amplifies this influence by providing data-driven analytics, optimising processes, and presenting novel opportunities for growth. Nevertheless, the widespread adoption of devices within Internet of Things (IoT) networks gives rise to apprehensions regarding increased energy consumption. In order to ensure the longevity of network operations, it is imperative to employ energy-efficient protocols for sensor nodes that possess limited power resources. One example of a protocol that demonstrates this concept is the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. This protocol effectively divides networks into clusters and dynamically adjusts the cluster heads to optimise the transmission of data to the base stations. Our study enhances the LEACH protocol by incorporating digital twin simulation, thereby enhancing the efficiency of IoT systems. Virtual network models and AI analytics are employed to assess energy consumption and performance. Cache nodes play a crucial role within this framework as they collect data from cluster heads in order to transmit it to the base station. By leveraging artificial intelligence (AI) and simulation techniques, we are able to improve the energy efficiency and reliability of the Internet of Things (IoT) systems. The findings indicate a significant reduction of 83% in non-functioning nodes and a notable increase of 1.66 times in energy levels of nodes compared to conventional approaches. This study highlights a potential direction for energy-efficient, AI-enhanced Internet of Things (IoT) networking through

关键词： Cluster head Clustering Data aggregation Gateway Internet of things LEACH Lifetime

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Vehicle type recognition based on audio data 58

Vehicle type recognition based on audio data

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58th Hawaii International Conference on System sciences, HICSS 2025

作者： Kobiela, Dariusz Hajdasz, Michal Erezman, Mateusz Nurzyńska, Karolina Zaporowski, Szymon Kurowski, Adam Weichbroth, Pawel Department of Software Engineering Faculty of Electronics Telecommunication and Informatics Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk80-233 Poland Department of Multimedia Systems Faculty of Electronics Telecommunication and Informatics Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk80-233 Poland Department of Algorithmics and Software Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

ISBN: (纸本)9780998133188

Identifying different vehicle types can help manage traffic more efficiently, reduce congestion, and improve public safety. This study aims to create a classification model that can recognize vehicle types based on the sound of passing vehicles. To achieve this, a database of raw audio files containing 1763 samples from two sources was assembled. The time-domain signals were converted to a time-frequency representation using the short-time Fourier transform to generate Mel Spectrograms. Mel-frequency Cepstral Coefficients (MFCCs) were also generated using the discrete cosine transform. In our experiments we compared these approaches. Since the data was imbalanced we applied online augmentation. Based on the literature review, we chose a Convolutional Neural Network (CNN) classifier because it is particularly well suited for analyzing large datasets due to its automatic feature extraction, parameter sharing and sparsity. The results showed that Mel Spectrograms were more effective for audio data preprocessing in this particular use case, achieving the highest accuracy of 0.875 and the highest f1-score of 0.877 compared to MFCCs. © 2025 IEEE Computer Society. All rights reserved.

关键词： Image coding

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