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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Hermanus, Davy Ronald Supangkat, Suhono Harso Hidayat, Fadhil Bina Nusantara University Research Interest Group in Educational Technology Computer Science Department Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9798350389654

With regard to Agriculture 5.0, the goal of this research is to create an intelligent platform for multi-drone collaboration that will improve situational awareness. This study examines the crucial phases in the Sensing, Understanding, and Action processes by fusing cutting-edge drone technology with creative coordination methods. Creating the architecture and foundation for drone collaboration, utilizing artificial intelligence for real-time data processing, and creating user interfaces that work are all part of the study technique. Area coverage, data quality, processing speed, and decision-making effectiveness are some of the performance evaluation measures. The goal of the research is to identify strategies for enhancing large-scale farming methods' resource management and operational effectiveness. The results will greatly enhance Agriculture 5.0 technologies and create avenues for the deployment of multi-drone collaboration platforms in a variety of high-situational awareness businesses. © 2024 IEEE.

关键词： Data quality

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Cutting-edge approaches in intrusion detection systems: a systematic review of deep learning, reinforcement learning, and ensemble techniques

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Iran Journal of Computer science 2025年 1-31页

作者： Kalpani, Nethma Rodrigo, Nureka Seneviratne, Dilmi Ariyadasa, Subhash Senanayake, Janaka Department of Computer Science and Informatics Uva Wellassa University Badulla Sri Lanka School of Computing Engineering and Technology Robert Gordon University Aberdeen AB10 7GJ United Kingdom

The growing number of networked devices and complex network infrastructures necessitates robust network security measures. Network intrusion detection systems are crucial for identifying and mitigating malicious activities within network environments. Traditional intrusion detection systems (IDS) often struggle to adapt to new and evolving threats. To address these limitations, researchers are increasingly turning to advanced methodologies such as deep learning (DL), reinforcement learning (RL), and ensemble learning (EL), which offer enhanced capabilities in detecting sophisticated and previously unseen attacks. This systematic literature review (SLR) evaluates 33 technical studies from 2020 to 2024 on the application of DL, RL, and EL in IDS. The study reviews the DL and RL methods used in IDS, along with detection methods that employ EL techniques, to combine the strengths of these approaches. It highlights the advantages, disadvantages, and applicability of the proposed techniques, while also suggesting potential improvements. The aim of this SLR is to provide researchers and practitioners with a comprehensive understanding of current IDS methodologies leveraging DL, RL, and EL, enabling the development of more effective and resilient network security solutions. In addition, this review identifies gaps in the existing literature and outlines potential future research directions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Deep learning Ensemble learning Intrusion detection systems Network security Reinforcement learning

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SP-DSTS-MIMO Scheme-Aided H.266 for Reliable High Data Rate Mobile Video Communication

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Computers, Materials & Continua 2023年第1期74卷 995-1010页

作者： Khadem Ullah Nasru Minallah Durre Nayab Ishtiaque Ahmed Jaroslav Frnda Jan Nedoma Department of Computer Systems Engineering University of Engineering and Technology PeshawarPeshawar25000Pakistan National Centre in Big Data and Cloud Computing University of Engineering and Technology Peshawar(NCBC-UETP)Peshawar25000Pakistan Department of Quantitative Methods and Economic Informatics Faculty of Operation and Economics of Transport and CommunicationsUniversity of Zilina01026ZilinaSlovakia Department of Telecommunications Faculty of Electrical Engineering and Computer ScienceVSB-Technical University of OstravaOstrava-PorubaCzech Republic

With the ever growth of Internet users,video applications,and massive data traffic across the network,there is a higher need for reliable bandwidth-efficient multimedia *** Video Coding(VVC/H.266)is finalized in September 2020 providing significantly greater compression efficiency compared to Highest Efficient Video Coding(HEVC)while providing versatile effective use for Ultra-High Definition(HD)*** article analyzes the quality performance of convolutional codes,turbo codes and self-concatenated convolutional(SCC)codes based on performance metrics for reliable future video *** advent of turbo codes was a significant achievement ever in the era of wireless communication approaching nearly the Shannon *** codes are operated by the deployment of an interleaver between two Recursive Systematic Convolutional(RSC)encoders in a parallel *** RSC encoders may be operating on the same or different architectures and code *** proposed work utilizes the latest source compression standards H.266 and H.265 encoded standards and Sphere Packing modulation aided differential Space Time Spreading(SP-DSTS)for video transmission in order to provide bandwidth-efficient wireless video ***,simulation results show that turbo codes defeat convolutional codes with an averaged E_(b)/N_(0) gain of 1.5 dB while convolutional codes outperformcompared to SCC codes with an E_(b)/N_(0) gain of 3.5 dBatBit ErrorRate(BER)of 10−*** Peak Signal to Noise Ratio(PSNR)results of convolutional codes with the latest source coding standard of H.266 is plotted against convolutional codes with H.265 and it was concluded H.266 outperform with about 6 dB PSNR gain at E_(b)/N_(0) value of 4.5 dB.

关键词： H.265 RSC turbo codes SCC SP-DSTS BP-CNN BER PSNR

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The Calibration of Single Beam Distance Sensors based on Machine Learning Methods

The Calibration of Single Beam Distance Sensors based on Mac...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Biernacki, Piotr Ziebinski, Adam Syu, Jia-Hao Lin, Jerry Chun-Wei Silesian University of Technology Institute of Theoretical and Applied Informatics Polish Academy of Sciences Institute of Informatics Gliwice Poland Silesian University of Technology Institute of Informatics Gliwice Poland National Taiwan University Department of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350324457

Smart cities require the use of many different types of sensors to make the communication, and distance sensors are one of the most commonly used elements in transportation systems and related infrastructures. The introduction of increasingly advanced autonomous systems in many areas of smart cities requires high measurement precision of the sensors used. High precision is essential for proper operation, long-term use, and safety in machine-to-machine or machine-to-human interactions. This paper presents a comparison of the accuracy of distance measurements for two commercially available single-beam LiDARs and two ultrasonic sensors. The aim of the research was to develop a calibration method in order to improve the accuracy of distance sensors. Based on the collected distance measurements, the sensors were calibrated using selected machine learning algorithms. The results of the experiments show the effectiveness of the proposed calibration methods, which yield an average mean absolute error (MAE) of 1.76 E-05 meters (m) and a root mean square error (RMSE) of 1.33 E-04 m for the tested sensors. © 2023 IEEE.

关键词： machine learning sensors calibration single-beam distance sensors

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Comparative Analysis of Movie Recommendation Systems Using Filtering Techniques on IMDB and Rotten Tomatoes 15

Comparative Analysis of Movie Recommendation Systems Using F...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Majhi, Santosh Kumar Sahani, Preetipadma Pradhan, Rosy Acharya, Biswaranjan Grivokostopoulou, Foteini Kanavos, Andreas Gerogiannis, Vassilis C. Department of Computer Science and Information Technology Bilaspur India Veer Surendra Sai University of Technology Department of Computer Science and Engineering Odisha Burla India Veer Surendra Sai University of Technology Department of Electrical Engineering Odisha Burla India Marwadi University Department of Computer Engineering Ai Rajkot India Computer Technology Institute and Press 'Diophantus' Patras Greece Ionian University Department of Informatics Corfu Greece University of Thessaly Department of Digital Systems Larissa Greece

ISBN: (纸本)9798350368833

Recommendation systems are pivotal in industries like entertainment, where competitiveness is intense. This study enhances movie recommendations by utilizing rich datasets from IMDB and Rotten Tomatoes, incorporating movie ratings, user profiles, audience scores, and critics' reviews. These elements were subjected to sentiment analysis to refine the data quality. We explored three filtering techniques: content-based, which analyzes movie features;collaborative, which processes user behavior patterns;and hybrid, combining both to leverage their strengths. The effectiveness of these methods was assessed using Mean Average Precision at k (MAP-k) and Root Mean Square Error (RMSE), with our results showing notable improvements in recommendation accuracy. For instance, the hybrid method achieved a MAP ©k of 0.271 and an RMSE of 0.634, indicating enhanced precision and reliability of the recommendations. These findings suggest significant potential for improving user satisfaction and retention in digital platforms, demonstrating the benefit of integrating diverse data sources and analytical techniques in recommendation systems.

关键词： Collaborative filtering

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Plant-derived nanovesicles: Current understanding and applications for cancer therapy

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Bioactive Materials 2023年第4期22卷 365-383页

作者： Ngoc Phung Ly Hwa Seung Han Myungsuk Kim Jae Hyung Park Ki Young Choi Natural Product Informatics Research Center Korea Institute of Science and Technology(KIST)Gangneung25451Republic of Korea Division of Bio-Medical Science and Technology KIST SchoolUniversity of Science and Technology(UST)Seoul02792Republic of Korea School of Chemical Engineering College of EngineeringSungkyunkwan UniversitySuwon16419Republic of Korea Department of Health Sciences and Technology SAIHSTSungkyunkwan UniversitySeoul06351Republic of Korea Biomedical Institute for Convergence at SKKU Sungkyunkwan UniversitySuwon16419Republic of Korea

Plant-derived vesicles(PDVs)are membranous structures that originate from plant cells and are responsible for multiple physiological and pathological *** the last decade,PDVs have gained much attention for their involvement in different biological processes,including intercellular communication and defense response,and recent scientific evidence has opened a new avenue for their applications in cancer ***,much remains unknown about these vesicles,and current research remains *** review aims to provide a comprehensive introduction to PDVs,from their biological characteristics to purification methods,and to summarize the status of their potential development for cancer therapy.

关键词： Plant-derived vesicles Extracellular vesicles Cancer therapy Anti-cancer Drug delivery

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Traffic Sign Recognition and Auditory Alert System for Sri Lankan Drivers Using Deep-Learning

Traffic Sign Recognition and Auditory Alert System for Sri L...

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2024 International Research Conference on Smart Computing and Systems engineering, SCSE 2024

作者： Vimalambikaipakan, Geerthana Amarasinghe, Chinthaka Rajapaksha, Tharangani Thayananthan, Thevathayarajh Mariyathas, Janotheepan Uva Wellassa University Department of Science and Technology Badulla Sri Lanka Mississippi State University Department of Agricultural and Biological Engineering Mississippi State United States Uva Wellassa University Department of Computer Science and Informatics Badulla Sri Lanka

ISBN: (纸本)9798350375688

Traffic signs are essential for road safety, guiding and informing drivers about rules and conditions. Ignoring signs or traffic rules can lead to accidents, causing property damage, and even deaths. To ensure safety, drivers must remain aware of traffic signs, obey their instructions, and drive responsibly. Notably, Sri Lanka has unique traffic signs compared to other countries. This study aims to develop a deep learning-based traffic sign alert system with good accuracy and efficiency in traffic sign recognition and enhance driver awareness by integrating an auditory alert system. This alert system was implemented under two main sections: object detection system and auditory alert system. The traffic signs dataset images were labeled and pre-processed using the Roboflow software tool. The You Only Look Once version 5 (YOLOv5) model was used to train the system, which allowed it to understand complicated patterns and attributes associated with 15 different sign classes. To improve driver awareness, an auditory alert system was developed using the PyDub library to generate real-time alerts in the form of voice messages. These alerts are triggered whenever a traffic sign is detected, giving an additional layer of information to the driver. On the test dataset, the integrated and fine-tuned YOLOv5 model achieved the F1 score of 90.09% and mean average precision (mAP) of 87.55% resulting in good evaluation metrics values for detection. This YOLOv5 model-based traffic sign auditory alert system was highly effective and efficient in increasing driver awareness and also helped to reduce road accidents. © 2024 IEEE.

关键词： Traffic signs

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Evaluation of large language models for the classification of medical device software

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Bio-Design and Manufacturing 2024年第5期7卷 819-822页

作者： Yu Han Aaron Ceross Florence Bourgeois Paulo Savaget Jeroen H.M.Bergmann Department of Engineering Science University of OxfordOld Road CampusOxford OX37QDUK Harvard-MIT Center for Regulatory Science Harvard Medical SchoolBostonMAUSA Computational Health Informatics Program(CHIP) Boston Children’s HospitalBostonMAUSA Department of Technology and Innovation The University of Southern DenmarkOdenseDenmark

Amidst the rapidly expanding integration of large language models(LLMs)across various sectors(ranging from everyday applications to specialized fields demanding stringent regulatory adherence),our investigation seeks to determine how well these models can support medical device software *** device classification functions to systematically categorize devices according to their designated use,associated risk levels,and requisite regulatory oversight,thereby providing a structured framework for ensuring safety and efficacy as mandated by regulatory authorities.

关键词： everyday demanding thereby

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Bounded and Uniform Energy-based Out-of-distribution Detection for Graphs 41

Bounded and Uniform Energy-based Out-of-distribution Detecti...

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41st International Conference on Machine Learning, ICML 2024

作者： Yang, Shenzhi Liang, Bin Liu, An Gui, Lin Yao, Xingkai Zhang, Xiaofang School of Computer Science and Technology Soochow University Suzhou China Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Department of Informatics King's College London London United Kingdom

Given the critical role of graphs in real-world applications and their high-security requirements, improving the ability of graph neural networks (GNNs) to detect out-of-distribution (OOD) data is an urgent research problem. The recent work GNNSAFE (Wu et al., 2023) proposes a framework based on the aggregation of negative energy scores that significantly improves the performance of GNNs to detect node-level OOD data. However, our study finds that score aggregation among nodes is susceptible to extreme values due to the unboundedness of the negative energy scores and logit shifts, which severely limits the accuracy of GNNs in detecting node-level OOD data. In this paper, we propose NODESAFE: reducing the generation of extreme scores of nodes by adding two optimization terms that make the negative energy scores bounded and mitigate the logit shift. Experimental results show that our approach dramatically improves the ability of GNNs to detect OOD data at the node level, e.g., in detecting OOD data induced by Structure Manipulation, the metric of FPR95 (lower is better) in scenarios without (with) OOD data exposure are reduced from the current SOTA by 28.4% (22.7%). The code is available via https://***/ShenzhiYang2000/NODESAFE. Copyright 2024 by the author(s)

关键词： Graph neural networks

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Enhanced Phonocardiogram Analysis Through Troponin Test for the Myocardial Injury Detection

Enhanced Phonocardiogram Analysis Through Troponin Test for ...

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2024 International Conference on TVET Excellence and Development, ICTeD 2024

作者： Puspasari, Ira Mengko, Tati L. R. Setiawan, Agung W. Pramudyo, Miftah Watanabe, Nobuo Adiono, Trio Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia Padjajaran University Department of Cardiology and Vascular Bandung Indonesia Graduate School of Engineering and Science Shibaura Institute of Technology Saitama Japan

ISBN: (纸本)9798331516413

Myocardial Infarction (MI) is a condition that necessitates prompt medical intervention. Current examinations using ECG and Troponin-test have the disadvantage of diagnosing MI. Phonocardiograms (PCG) represent the physiological and pathological cardiac, with the data acquisition each cycle being 0.8 s. This research aims to develop a non-parametric correlation model between PCG-MI signal feature results and the troponin tests. The signals were obtained using an electronic stethoscope. The acquired signals in (.wav) were extracted into each cycle, time, and statistical domain features. Twelve selected features were tested for normality of distribution using SaphiroWilk. The Principal component regression was used to model the relationship between the feature and troponin test. Then, we found that 12 features were categorized into non-normal distributions. The correlation test between STEMI and NSTEMI features resulted in insignificant correlations, suggesting the model's feasibility. The proposed principal component modeling achieved an accuracy of 91.07%. Multivariate analysis, aligning with earlier studies on the features of PCG signals, has been represented by transfer functions. This study can serve as a basis for clarifying patients exhibiting pathological symptoms for MI detection. © 2024 IEEE.

关键词： Phonocardiography

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