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9th International Conference on Biosignals, Images, and Instrumentation, ICBSII 2023

作者： Nishitha, R. Sudhakar, T. Bethanney Janney, J. Krishnamoorthy, N.R. Dhanalakshmi, K. Vigneshwaran, S. Sathyabama Institute of Science and Technology Biomedical Engg. Department Chennai India Sathyabama Institute of Science & Technology Dept. of ECE Chennai India Sathyabama Institute of Science and Technology Dept. of Computer Science Engg Chennai India Sathyabama Institute of Science and Technology M.Tech-Medical Instrumentation Chennai India

ISBN: (纸本)9798350338171

Based to statistics, about 80 percent of the population follows incorrect sitting posture. From the year 2020, due to the Covid-19 curfew, the traditional work office of many professionals turned into Work from home and remote office lifestyle. Many of the working professionals has no proper ergonomic workplace setup in their house leading to improper sitting position. Monitoring the sitting posture is needed to avoid chronic complication of MSD, neck, and spine related injuries. Many techniques were emerged for correcting and monitoring the posture. Neural Network based posture detection is being into the field of research as the come with more range of accuracy. This paper discusses about the various Neural Networks used and their accuracies. © 2023 IEEE.

关键词： Convolution

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TeachNet: A Lightweight and User-Friendly Network Visualization and Experimentation Tool

TeachNet: A Lightweight and User-Friendly Network Visualizat...

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Asia-Pacific Conference on Communications

作者： Safiqul Islam Visar Morina Michael Welzl Boning Feng Dept. of Computer Science Oslo Metropolitan University Oslo Norway Dept. of Informatics University of Oslo Oslo Norway

ISBN: (数字)9798331517816

ISBN: (纸本)9798331517823

Network researchers, academics, and students often need to deal with complex scripting in order to design a network topology, configure network nodes, automate experimentation, and visualize network performance metrics. Current simulation or emulation tools lack the ability to provide a comprehensive understanding of the network. This paper introduces TeachNet, a lightweight and user-friendly network tool that provides a graphical user interface for designing, configuring, and automating network experiments. We explain TeachNet’s design and present results to demonstrate its effectiveness and efficiency using our TCP experimentation-focused prototype, and Mininet in the backend. TeachNet creates a repository of log files and offers a framework for future research that is easily extensible to accommodate other tools such as OMNeT++ and ns3.

关键词： Visualization Network topology Emulation Asia Prototypes Routing Reproducibility of results Topology Graphical user interfaces Convergence

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A Study of LSTM Optimisation for Forecasting Volatile Time Series Data

A Study of LSTM Optimisation for Forecasting Volatile Time S...

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computer Communication and Artificial Intelligence (CCAI), International Conference on

作者： Deddy Gunawan Taslim Hendra Tjahyadi I Made Murwantara Graduate Informatics Dept. Faculty of Computer Science Universitas Pelita Harapan Jakarta Indonesia

ISBN: (数字)9798350362763

ISBN: (纸本)9798350362770

Forecasting time series data is an important subject in economics and business where the Autoregressive Integrated Moving Average (ARIMA) has been extensively used despite its weaknesses, from requiring a minimum number of data points to the assumption of linearity of data. With recent advancement, the Long Short-Term Memory (LSTM) shows potential to address such weaknesses. This research is aimed to identify the effect of hyperparameters such as the number of epochs, units, and optimiser on accuracy of prediction on volatile time series data. Performance metric used is the model accuracy measured with RMSE. This research concluded that the accuracy of LSTM can be improved by increasing the number of epochs, where the increase of the number of epochs from 25 to 125 can increase the prediction accuracy by 64%, and 77% when increased from 25 to 300 epochs. SGDM as the optimiser or solver was able to produce the most accurate result (RMSE at 0.51) compared to Adam and RMSProp (RMSE at 0.89 and 0.78, respectively). Meanwhile the number of units showed inconclusive impact to the prediction accuracy.

关键词： Measurement Economics Accuracy Time series analysis Linearity Forecasting Artificial intelligence

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Arabic Sentiment Analysis of Food Delivery Services Reviews

Arabic Sentiment Analysis of Food Delivery Services Reviews

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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Mustafa, Dheya Khabour, Safaa M. Shatnawi, Ahmed S. Taqieddin, Eyad The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Yarmouk University Faculty of Information Technology and Computer Sciences Dept. of Information Systems Irbid21163 Jordan Jordan University of Science & Technology Dept. of Software Engineering Irbid21110 Jordan School of Computing and Informatics Dept. of Cyber Security Al-Hussein Technical University Amman Jordan

ISBN: (纸本)9798350335590

Customer reviews on online platforms have grown to become an important source of insight into a company's performance. Food delivery services (FDS) companies aim to effectively use customers' feedback to identify areas for improvement of customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on FDS-related reviews in Arabic, exploiting natural language processing, and machine learning techniques to extract information that reflects customers' subjective viewpoints, recognize their feelings, and determine polarity in the FDS domain. This work begins with collecting the FDS dataset (Talabat), and then extracting and creating a dialects lexicon for Arabic dialects, followed by walking the reader through detailed steps of cleaning and pre-processing a manually annotated dataset. Finally, we examined classification algorithms including Decision Tree (DT), and Support Vector Machine (SVM). We achieved a maximum accuracy of about 82% using the SVM classifier. © 2023 IEEE.

关键词： Sentiment analysis

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Loan Default Forecasting Using Machine Learning Algorithm in Indonesian Regional Bank

Loan Default Forecasting Using Machine Learning Algorithm in...

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computer Communication and Artificial Intelligence (CCAI), International Conference on

作者： Jimmy Jan Alexander Hendra Tjahyadi Graduate Informatics Dept. Faculty of Computer Science Universitas Pelita Harapan Jakarta Indonesia

ISBN: (数字)9798350362763

ISBN: (纸本)9798350362770

In an effort to address the issues of growing credit risk, this research analyzes the application of machine learning algorithms for loan default prediction in regional banks in Indonesia. This study evaluates the efficacy of four primary algorithms - Decision Tree, Random Forest, Gradient Boosting, and Naïve Bayes - by analyzing a two-year historical loan dataset. In-dept. study indicated that Naïve Bayes is the best algorithm, attaining 99.96% accuracy, 98.45% precision, and 99.63% recall, with a perfect Area Under the Curve (AUC) score of 1,000. These results represent the improved capability of Naïve Bayes in identifying probable defaults with high precision and accuracy, greatly minimizing the probability of false positives. The findings recommend the application of Naïve Bayes as a significant method in local bank credit risk management, providing an effective solution for early default identification and increased financial stability. This study adds to existing literature by presenting actual proof of the efficacy of machine learning technology in predicting credit risk. The suggestions offered can aid regional banks in enhancing their risk management approaches.

关键词： Machine learning algorithms Accuracy Forestry Prediction algorithms Boosting Stability analysis Bayes methods

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Implementing a Robust and Scalable Detect-and-Avoid Algorithm for UAV Navigation in ROS 15

Implementing a Robust and Scalable Detect-and-Avoid Algorith...

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

作者： Meimetis, Dimitrios Daramouskas, Ioannis Lappas, Vaios Perikos, Isidoros Kapoulas, Vaggelis Paraskevas, Michael Nkua Department of Aerospace Science & Technology Athens Greece Computer Technology Institute and Press 'Diophantus' Patras Greece UoP Applied Mechanics Lab Patras Greece UoP Computer Engineering and Informatics Department Patras Greece Computer Technology Institute and Press 'Diophantus' University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350368833

Unmanned aerial vehicles (UAVs) have garnered significant attention from the research community during the last decade, due to their diverse capabilities and potential applications. One of the most critical functions that drones must execute efficiently is navigation in real-world environments. This paper presents a decentralized approach for enabling unmanned aerial vehicles (UAVs) to navigate safely in unknown environments and avoid obstacles. Leveraging the Optimal Reciprocal Collision Avoidance (ORCA) algorithm, implemented in the Robot Operating System (ROS), our method facilitates conflict detection and resolution in 2D environments. Through simulations using ROS, Gazebo, and Iris drones, we validate the effectiveness of our approach in scenarios with initial trajectory conflicts. Our work addresses the pressing need for UAVs to autonomously plan and execute safe flights, laying the groundwork for enhanced UAV capabilities in various real-world applications. The simulation results demonstrate the efficiency and robustness of our approach. © 2024 IEEE.

关键词： Drones

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Land Valuation Using an Innovative Model Combining Machine Learning and Spatial Context

Land Valuation Using an Innovative Model Combining Machine L...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Feride Tanrikulu Timothy L. Haithcoat Grant J. Scott Matthew Foulkes Ilker Ersoy Institute for Data Science and Informatics University of Missouri Dept. Electrical Engineering and Computer Science University of Missouri Dept. of Geography University of Missouri

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Land valuations are used by buyers, sellers, regulators, and authorities to assess fair value. Urbanization demands a modern and efficient land valuation system. An alternative method that integrates geographic information systems and machine learning can produce more reliable, repeatable, and accurate valuations. The land’s value can vary significantly based on the parcel’s neighborhood and context. Geospatial analytics, remote sensing and vector information were integrated to predict land values in Springfield, Missouri, USA. A point geodatabase was assembled using calculated proximities, accessibility, and various index measures. Supervised machine learning models were trained using government-provided appraised land values as ground truth. The database approach integrated spatial context and socioeconomic data. Slight differences in performance between random forest (98%, 0.13) and gradient boosting (98%, 0.15) algorithms were found (Adj. R 2 , STD).

关键词： Regulators Machine learning algorithms Urban areas Spatial databases Vectors Reliability Socioeconomics

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RadioModRec-l: A Large Scale Radio Frequency Dataset for Automatic Modulation Recognition Research 4

RadioModRec-l: A Large Scale Radio Frequency Dataset for Aut...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Adetiba, Emmanuel Olasina, Jamiu R. Obiyemi, Obiseye Covenant University Covenant Applied Informatics and Communication Africa Center of Excellence Dept. of Electrical and Information Engineering Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban4001 South Africa Federal Polytechnic Ilaro Dept. of Computer Engineering Ilaro Nigeria Durban University of Technology Space Science Centre Dept. of Electrical Power Engineering Durban South Africa

ISBN: (纸本)9798350353266

Access to well-curated large datasets remains a significant bottleneck in AI-based research within wireless communication. Rapid advancements in neighbouring fields, such as computer vision and natural language processing, are largely due to the availability of extensive open-access datasets. However, similar progress has not been observed in wireless communication. To address this gap, we curated a comprehensive dataset for fifteen digital modulation schemes, including 4QAM, 16QAM, 64QAM, 256QAM, 8PSK, 16PSK, 32PSK, 64PSK, 128PSK, 256PSK, CPFSK, DBPSK, DQPSK, GFSK, and GMSK. Our dataset considers Rayleigh and Rician channel models under Additive White Gaussian Noise (A WGN) with SNRs ranging from -20dB to + 20dB in 5dB increments. The data samples were converted to constellation signal images and carefully pre-processed. Named RadioModRec, this dataset provides a valuable resource for researchers to train and evaluate AI models. It is freely accessible on Kaggle, promoting further innovation in the wireless communication domain. © 2024 IEEE.

关键词： Signal to noise ratio

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Õ(n+poly(k))-time Algorithm for Bounded Tree Edit Distance 63

Õ(n+poly(k))-time Algorithm for Bounded Tree Edit Distance

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63rd IEEE Annual Symposium on Foundations of computer science, FOCS 2022

作者： Das, Debarati Gilbert, Jacob Hajiaghayi, Mohammad Taghi Kociumaka, Tomasz Saha, Barna Saleh, Hamed Pennsylvania State University Dept. of Computer Science and Engineering State CollegePA United States University of Maryland Dept. of Computer Science College ParkMD United States Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken Germany University of California San Diego Halicioglu Data Science Institute Dept. of Computer Science and Engineering San DiegoCA United States

ISBN: (纸本)9781665455190

Computing the edit distance of two strings is one of the most basic problems in computer science and combinatorial optimization. Tree edit distance is a natural generalization of edit distance in which the task is to compute a measure of dissimilarity between two (unweighted) rooted trees with node labels. Perhaps the most notable recent application of tree edit distance is in NoSQL big databases, such as MongoDB, where each row of the database is a JSON document represented as a labeled rooted tree and finding dissimilarity between two rows is a basic operation. Until recently, the fastest algorithm for tree edit distance ran in cubic time (Demaine, Mozes, Rossman, Weimann;TALG'10);however, Mao (FOCS'21) broke the cubic barrier for the tree edit distance problem using fast matrix *** a parameter k as an upper bound on the distance, an O(n+k2)-time algorithm for edit distance has been known since the 1980s due to works of Myers (Algorithmica'86) and Landau and Vishkin (JCSS'88). The existence of an O(n+poly(k))-time algorithm for tree edit distance has been posed as open question, e.g., by Akmal and Jin (ICALP'21), who give a stateof-the-art O(nk2)-time algorithm. In this paper, we answer this question positively. © 2022 IEEE.

关键词： Trees (mathematics)

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Harmonizing open banking in the European Union: an analysis of PSD2 compliance and interrelation with cybersecurity frameworks and standards

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International Cybersecurity Law Review 2024年第1期5卷 79-120页

作者： Gounari, Marianna Stergiopoulos, George Pipyros, Kosmas Gritzalis, Dimitris Dept. of Information & Communication Systems Engineering University of the Aegean Samos Greece Dept. of Computer Science Philips University Nicosia Cyprus Dept. of Informatics Athens University of Economics & Business Athens Greece

This paper focuses on the security protocols enacted in banking transactions across the European Economic Area (EEA), as stipulated by the Second or Revised Payment Service Directive (commonly referred to as ‘PSD2’ or simply ‘the Directive’). The study aims to comprehensively analyse the implementation and efficacy of these security measures within the specified jurisdiction. The Directive incorporates fundamental rights and obligations that all stakeholders are compelled to adhere to and delineates specific security measures and standards that both traditional banking institutions and third-party providers (TPP) are mandated to implement. In particular, one of the cardinal mandates for banking and financial institutions under PSD2 is the obligation to facilitate third-party access to customer data via open application programming interfaces (API). While this open banking paradigm and the consequent proliferation of data sharing unquestionably bring about various advantages, such as enhanced consumer choice and market competition, they concurrently expose the financial ecosystem to a slew of potential security vulnerabilities and privacy risks. Upon conducting a comprehensive review of the security requirements and measures stipulated under PSD2 and a comparative analysis with essential cybersecurity frameworks and standards (NIS2, Cybersecurity Act, GDPR, ISO 27001:22 and PCI DSS), we have ascertained a discernible lack of harmonisation and clarity concerning the technical security specifications for its effective implementation. This lacuna substantiates the challenges banks face in fully grasping the extensive spectrum of compliance obligations mandated by PSD2. The aim of this research is to offer a valuable contribution to both the comprehension and the pragmatic deployment of security standards in the context of banking transactions, as regulated by the PSD2. The paper serves as a valuable resource for traditional banking institut

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