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10th International Conference on Web Research, ICWR 2024

作者： Talaeepoor, Maryam Soleimani Neysiani, Behzad Jezi, Mohammad Davarpanah Naghshe Jahan Institute of Higher Education Faculty of Computer Engineering Department of Computer Engineering Isfahan Iran Department of Software Engineering Isfahan Iran Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan Iran

ISBN: (纸本)9798350394986

As long as a computer system is connected to the Internet, it is susceptible to attack as a victim. In computer networks, it becomes important to manage the network based on parameters such as network size and network data. Firewalls are devices that help network administrators in this case to establish security in the network, and can be based on the rules that the firewall is based on. It is configured to control incoming and outgoing network traffic. Firewalls can be considered the most vital components of the network in establishing security. Firat University introduced a dataset containing firewall logs with multiple classes for firewall decisions. This study uses data mining techniques to improve the validation performance of classification using various machine learning algorithms like neural networks, deep learning, and kNN. The experimental results show more than 10% improvement according to precision and recall rates among various folding scenarios used in related works with minor improvement in accuracy, too. The decision tree algorithm is fast and explainable versus other algorithms. © 2024 IEEE.

关键词： Classification (of information)

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Intelligent wearable vision systems for the visually impaired in Saudi Arabia

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Neural Computing and Applications 2025年第14期37卷 8571-8591页

作者： Talaat, Fatma M. El-Shafai, Walid Soliman, Naglaa F. Algarni, Abeer D. El-Samie, Fathi E. Abd Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Navigating the world with visual impairments presents unique challenges, often limiting independence and safety. This research introduces SafeStride, a novel algorithm designed to empower visually impaired individuals through real-time obstacle detection and navigation assistance. SafeStride functions through five interconnected phases: sensor data acquisition, data preprocessing, obstacle detection, path planning, and feedback and guidance. A combination of ultrasonic sensors, cameras, and inertial measurement units (IMUs) feed comprehensive environmental data into the system, enabling accurate obstacle detection and safe path planning. Evaluating SafeStride's performance involved the MNIST dataset for image recognition and a dedicated indoor object detection dataset. Additionally, comparisons were made against three deep learning models—convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU). The results showcased SafeStride's high accuracy and effectiveness in both obstacle detection and classification. Beyond its impressive performance, SafeStride represents a significant leap forward in navigation aids for the visually impaired. By offering a comprehensive solution that enhances safety and independence in diverse environments, it holds immense potential to improve the lives of countless individuals. Future efforts will focus on further optimizing the algorithm and testing its real-world capabilities, paving the way for a future where safe and independent navigation becomes a reality for all. All three deep learning models (CNN, LSTM, GRU) excelled in the experiment, achieving high accuracy above 0.98, strong recall and F1-scores near 0.99, and exceptional AUC-ROC scores exceeding 0.9998. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Motion planning

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Safeguarding Fuel Resources for BTS Generator Sets: An Arduino-Based Monitoring System with Fuel Theft Detection Prototype 2

Safeguarding Fuel Resources for BTS Generator Sets: An Ardui...

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2nd International Conference on Information technology Research and Innovation, ICITRI 2023

作者： Erlina, Tati Aisuwarya, Ratna Alwizar Universitas Andalas Computer Engineering Faculty of Information Technology Padang Indonesia

ISBN: (纸本)9798350324945

Fuel theft from Base Transceiver Station (BTS) generator sets (Gensets) is a prevalent issue that poses financial losses and operational disruptions for telecommunication companies. Traditional security measures have limitations in preventing fuel theft effectively, necessitating innovative monitoring systems. This paper presents the design and testing of a monitoring system with a fuel theft detection prototype for Gensets at BTS. The results obtained demonstrate promising findings. The fuel sensor (float sensor) accurately detects fuel volume reductions with an average error of 1.16% compared to manual and system measurements and reliably detects fuel volume increases with an average error of 1.62%. The SCT-103 current sensor successfully detects the operational status of the Gensets with a 100% success rate by measuring the current flowing to the load. Using internet connectivity, the NodeMCU ESP32 enables seamless data transmission and storage to databases like Firebase and Google Sheets. The developed smartphone application provides real-time information on available fuel volume and user-friendly access to historical fuel usage data. These findings highlight the potential and feasibility of the prototype system as an effective and efficient solution for monitoring fuel volume in Gensets at BTS, with potential applications in various industries. Further research and implementation can be pursued to optimize and expand the system's capabilities for practical use in real-world scenarios. © 2023 IEEE.

关键词： Digital storage

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Analysis of energy consumption for SPI and I2C communications in ultra-low power embedded systems 46

Analysis of energy consumption for SPI and I2C communication...

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46th ICT and Electronics Convention, MIPRO 2023

作者： Zidar, Josip Aleksi, Ivan Matic, Tomislav Faculty of Electrical Engineering Computer Science and Information Technology Osijek Croatia

ISBN: (纸本)9789532331042

Developing ultra-low-power devices requires low-power components, algorithms, and communication protocols. For environmental monitoring along the supply chain, products may travel a long way from the distributor to the customer. In addition, extreme climatic circumstances, such as temperatures below 0°C, affect battery capacity. In order to monitor package's environment, it is crucial to ensure that all embedded system parts use as little energy as possible. This paper addresses how much power the SPI and I2C communication protocols use. These protocols are widely used in low-power embedded devices for sensor communication. Based on its results, this research article presented recommendations for developing ultra-low power embedded devices that use the SPI/I2C protocol. © 2023 MIPRO Croatian Society.

关键词： Supply chains

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A Study on the Potency of Hybrid Models: Detecting Diseases in Cucumber Leaves with Pre-trained CNNs and SVM 12

A Study on the Potency of Hybrid Models: Detecting Diseases ...

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12th International Electrical engineering Congress, iEECON 2024

作者： Yodrot, Teerapon Sutacha, Chalermkiat Orachon, Teerapong Jangjongdee, Nattapon Boonyasuwanno, Sompol Rajamangala University Of Technology Rattanakosin Faculty Of Industry And Technology Department Of Electrical Engineering Technology Prachuap Khiri Khan Thailand Ubon Ratchathani Rajabhat University Faculty Of Industrial Technology Department Of Computer Network Engineering Ubon Ratchathani Thailand Kosen Of King Mongkut's Institute Of Technology Ladkrabang Department Of Computer Engineering Bangkok Thailand

ISBN: (纸本)9798350383591

This study investigates a hybrid machine learning approach for the classification of cucumber leaf diseases, combining the strengths of pre-trained Convolutional Neural Networks (CNNs) and Support Vector Machines (SVM). Focusing on a publicly available dataset, we evaluated four CNN architectures - VGG16, ResNet50, AlexNet, and EfficientNetV2 - for feature extraction, followed by SVM for the final classification task. The performance of each model was assessed using F1 scores and confusion matrices, with ResNet50 yielding the highest F1 score, indicative of a strong balance between precision and recall. VGG16 and AlexNet showed potential for improvement, which could be realized through model tuning or integration into an ensemble framework. EfficientNetV2, despite a slightly lower F1 score, highlighted the importance of further training to enhance class distinction. The research underlines the importance of a hybrid approach, which harnesses deep learning's feature extraction capabilities with SVM's classification strength, to improve the accuracy of disease detection in cucumber plants. © 2024 IEEE.

关键词： Convolutional neural networks

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Improving VANET Localization Performance with Data Verification: Challenges and Solutions

Improving VANET Localization Performance with Data Verificat...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Kadhim, Aqeel Kamil Najim, Ali Hamza Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisa Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

In VANETs, the important and effective applications of vehicle localization include safety and communication applications. Thus, it is difficult to get very accurate localization in the dynamic and often very fluctuating environment of VANET. This research responds to this challenge with a keen thrust on improving vehicle localization using the Mobility Data Verification process. The primary goal of this work is to achieve high accuracy of vehicle positioning within VANET by checking the coherence of mobility data of moving vehicles. Hence, we present a concept that builds upon Mobility Data Verification to provide real-time location details from vehicles. Both GPS and onboard sensors feed this method, which uses a multi-level verification to flag out or eliminate the errors of deliberately spoofed data points. We use simulations to evaluate and validate the effectiveness of the proposed verification approach. As the proposed system, our previously developed and fine-tuned weighted localization (OWL) strategy does so. The performance evaluation results suggest that our proposed solution is feasible to distinguish automobiles that are not trustworthy and improve the performance of the localization algorithm. © 2024 IEEE.

关键词： Vane pumps

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Helmet Detection Recognition Algorithm Based on Improved Yolov5

Helmet Detection Recognition Algorithm Based on Improved Yol...

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2023 International Conference on Image Processing and computer Vision, IPCV 2023

作者： Lin, Shuo Li, Zongmei Xiamen University of Technology Faculty of Computer and Information Engineering Xiamen China

ISBN: (纸本)9781665473941

The existing helmet detection algorithm is mainly based on a single-stage object detection algorithm, which has high detection speed and can achieve the requirement of real-time detection. Still, the accuracy of detecting small objects and objects with obstacles is no high. Based on this, this paper proposes a helmet detection recognition algorithm based on improved YOLOv5. The SE attention mechanism has the advantages of low complexity, few parameters and low computational effort. Firstly, the SE attention module is added to the Yolov5 backbone network to weigh the features of different channels and improve the model's attention to important features. Secondly, using softnms to replace nms in the original network can retain more small target prediction frames, improve the detection accuracy, and improve the model's ability to detect small targets. The experiments show that the average accuracy, precision, and recall of the proposed method reach 96.51%, 95.20%, and 90.08%, respectively, and the real-time performance meets the need for helmet detection in practical work. © 2023 IEEE.

关键词： Object detection

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Convolutional neural network based data interpretable framework for Alzheimer’s treatment planning

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 375-386页

作者： Sazia Parvin Sonia Farhana Nimmy Md Sarwar Kamal Information Technology Melbourne PolytechnicMelbourne VIC 3072Australia Faculty of Economics and Business University of New South WalesSydney ACT 2612Australia School of Computer Science Faculty of Engineering and ITUniversity of Technology SydneySydney NSW 2007Australia

Alzheimer’s disease(AD)is a neurological disorder that predominantly affects the *** the coming years,it is expected to spread rapidly,with limited progress in diagnostic *** machine learning(ML)and artificial intelligence(AI)algorithms have been employed to detect AD using single-modality ***,recent developments in ML have enabled the application of these methods to multiple data sources and input modalities for AD *** this study,we developed a framework that utilizes multimodal data(tabular data,magnetic resonance imaging(MRI)images,and genetic information)to classify *** part of the pre-processing phase,we generated a knowledge graph from the tabular data and MRI *** employed graph neural networks for knowledge graph creation,and region-based convolutional neural network approach for image-to-knowledge graph ***,we integrated various explainable AI(XAI)techniques to interpret and elucidate the prediction outcomes derived from multimodal ***-wise relevance propagation was used to explain the layer-wise outcomes in the MRI *** also incorporated submodular pick local interpretable model-agnostic explanations to interpret the decision-making process based on the tabular data *** expression values play a crucial role in AD *** used a graphical gene tree to identify genes associated with the ***,a dashboard was designed to display XAI outcomes,enabling experts and medical professionals to easily comprehend the predic-tion results.

关键词： Multimodal Region-based convolutional neural network Layer-wise relevance propagation Submodular pick local interpretable model-agnostic explanations Graphical genes tree Alzheimer’s disease

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Comment Analyzer by Sentimental Analysis through Natural Language Processing 10

Comment Analyzer by Sentimental Analysis through Natural Lan...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Chinnasamy, P. Ayyasamy, Ramesh Kumar Madhukar, Gadde Karthik, Gaddamidi Bhargav, Tanneru Nayak, Dungavath Yuva Kiran Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Faculty of Information and Communication Technology Kampar Malaysia

ISBN: (纸本)9798350353068

Comment analyzers were widely employed across industries for sentiment analysis, social media monitoring, and customer feedback evaluation. These tools facilitated insight into public opinions and sentiments expressed in comments on various platforms. Sentiment analysis is a natural language processing technique that determines the emotional tone of the text, helping to categorize it as positive, negative, or neutral, making it valuable for understanding public opinion and customer feedback. With the explosive increase of comments, it is difficult to analyze them manually. The issue arises because online content is huge and complicated, making it tough to grasp the subtle emotions and attitudes people have in their comments. This mystery surrounding digital conversations needs a solution: a Comment Analyzer with Sentiment Analysis. Text categorization in the form of sentiment analysis incorporates NLP, machine learning, data mining, information retrieval, Power BI for data analysis, and other study areas. These limitations highlight the need for more sophisticated sentiment analysis techniques, such as the Vader sentiment analysis provided in this study, to overcome existing system shortcomings and increase sentiment classification accuracy. The Comment Analyzer using Sentiment Analysis represents a pioneering approach to unraveling the cryptic sentiments embedded within the myriad comments and discussions that saturate the digital sphere. We are using data sets such as YouTube video comments and Ecommerce Product comments. © 2024 IEEE.

关键词： Sentiment analysis

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Smart Water-Powered Renewable Energy System with IoT Integration

Smart Water-Powered Renewable Energy System with IoT Integra...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Najim, Ali Hamza Ibraheem, Ahmed Salman Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq Department of Computer Technical Engineering Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

An advanced hybrid renewable energy collecting and storage system prototype that utilizes water flow as its primary power source. Additionally, the system incorporates an energy monitoring system based on the Internet of Things (IoT) technology. The device effectively harnesses energy from the movement of water, storing it for future use in low-voltage applications. It demonstrates a maximum daily energy capture capacity of about 100Wh. The energy monitoring system, based on the Internet of Things (IoT), enables the real-time and remote monitoring of energy harvesting and usage. We thoroughly assessed the energy harvesting component across different situations to identify the crucial parameters that impact energy output. The results of our study demonstrate that several elements, including the spatial separation 'between the energy harvester and the water source, Water flow' speed, sensor orientation, and sensor density, all play significant roles in determining the quantity of energy collected. This research shows that it is possible to integrate IoT technology with hydropower renewable energy systems. It provides significant perspectives on enhancing energy extraction efficiency in practical scenarios. © 2024 IEEE.

关键词： harvesting renewable energy IoT storage energy water flow energy

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