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Machine Learning Techniques for Identifying Child Abusive Texts in Online Platforms 15

Machine Learning Techniques for Identifying Child Abusive Te...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mahmud, Tanjim Akter, Tahmina Uddin, Mohammad Kamal Aziz, Mohammad Tarek Hossain, Mohammad Shahadat Andersson, Karl Rangamati Science and Technology University Department of Computer Science and Engineering Rangamati4500 Bangladesh Port City International University Dept. of Computer Science and Engineering Chittagong Bangladesh Chittagong University of Engineering & Technology Department of Computer Science and Engineering Chittagong Bangladesh University of Chittagong Dept. of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Cybersecurity Laboratory Luleå97187 Sweden

ISBN: (纸本)9798350370249

This study addresses the critical issue of child abuse in digital communications by developing advanced machine learning and deep learning models for detecting child abusive texts in the Bengali language on online platforms. The primary goal of this research is to contribute to child abuse prevention by creating an effective tool for accurately identifying abusive content. Utilizing a combination of natural language processing (NLP) techniques and deep learning algorithms, this model distinguishes between abusive and non-abusive text. The dataset, comprising manually collected and labeled comments and posts from social media sites, is categorized into Positive, Neutral, and Negative classes. For model development, a variety of machine learning algorithms including RF, DT, and SVM, alongside deep learning approaches such as LSTM and CNN, were tested. The performance of the proposed model was assessed using precision, recall, and F1 score metrics. The findings reveal that the model performs with high accuracy in classifying abusive text in Bengali, offering significant potential as a tool for effective child abuse identification and prevention. © 2024 IEEE.

关键词： Deep Learning Machine Learning Natural Language Processing Sentiment Analysis Supervised Model

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Design and Development of a Low-Cost Communication-Based Protection System for Fishermen 1

Design and Development of a Low-Cost Communication-Based Pro...

引用

1st International Conference on Optimization Techniques for Learning, ICOTL 2023

作者： Sivakumar, S. Arthi, Ts. Premkumar, S. Thillaieaswaran, B. Srm Institute of Science and Technology Dept of Computer Science and Engineering India Galgotia College of Engineering and Technology Dept of Computer Science and Engineering Greater Noida India Galgotias University School of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350328042

Safety and wellbeing of fishermen in maritime environments is of utmost importance. This research seeks to evaluate the feasibility and practicability of an innovative communication-based system designed specifically to protect fisherman. Its main goal is providing an easy, cost-effective solution that improves both safety and communication abilities of these professionals. The system proposed here aims to promote fishers' welfare and safety at its core. It seeks to reduce risks when fishing by employing an infrared-sound wave communication infrastructure that reduces risks while simultaneously increasing efficiency compared to standalone systems. This research investigates scenarios where this combination proves superior. This communication-based system's comprehensive sensor network features both aerial and terrestrial elements. The system seamlessly transfers sensor information, so fishermen have access to vital information that will enhance their situational awareness and facilitate sound decision-making processes. This system employs small sound waves that travel along the surface of the ocean to transmit digital information in binary format as 1s and 0s. They are created by an underwater transmitter emitting ultrasonic signal, effectively creating a communication link between underwater elements and surface ones. This research seeks to address a critical gap in maritime security by proposing and testing a communication-based protection system specifically tailored to fishermen. This system uses infrared, sound wave and other technologies to facilitate data exchange among various sensors;ultimately resulting in a robust tool with low costs for improving communication and security capabilities for fishermen. © 2023 IEEE.

关键词： Transmitters

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Arrhythmia Classification Using Graph Neural Networks Based on Correlation Matrix

Arrhythmia Classification Using Graph Neural Networks Based ...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Han, Seungwoo Tokyo University of Agriculture and Technology Dept. of Electrical Engineering and Computer Science Japan

ISBN: (纸本)9798350386226

With the advancements in graph neural network (GNN), there has been increasing interest in applying GNN to electrocardiogram (ECG) analysis. In this study, we generated an adjacency matrix using correlation matrix of extracted ECG features and applied a GNN to classify arrhythmias. Our work was compared with existing approaches from the literature. The results demonstrated that precision and recall for all arrhythmia classes exceeded 50%, suggesting that proposed method can be considered an approach for arrhythmia classification. © 2024 IEEE.

关键词： Graph neural networks

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Employing Convolutional Neural Network for Real-time Detection and Localization of Physical Contact in a Redundant Manipulator Body 12

Employing Convolutional Neural Network for Real-time Detecti...

引用

12th RSI International Conference on Robotics and Mechatronics, ICRoM 2024

作者： Jafari, Amirhossein Tafti, Ali Abolhasani Karami, Abbas Isfahan University of Technology Dept. of Electrical and Computer Engineering Isfahan Iran Isfahan University of Technology Dept. of Mechanical Engineering Isfahan Iran Shiraz University of Technology Dept. of Mechanical Engineering Shiraz Iran

ISBN: (纸本)9798331529734

In the developing field of human-robot collaboration, robots increasingly share their workspace with humans, which becomes necessary to achieve seamless and safe interactions for manual guidance of robots in physical cooperation scenarios. The present study proposes a new method based on a convolutional neural network (CNN) for safe human-robot cooperation in the presence of intentional physical interactions, which is able to detect the occurrence and localization of contact on the robot arm in real-time. In this approach, contact detection and its localization are done only using the online external torque vector obtained by the residual-based observer of the robot, which eliminates any need to use additional force and vision sensors. Unlike traditional methods that depend on predefined thresholds or model-based techniques, our presented architecture with one-dimensional CNN can automatically learn the patterns from collected raw torque data and, by analyzing them, can properly predict the contact position. The performance of the proposed approach during manipulation has been validated through multiple online experiments in static and dynamic states on a KUKA LBR iiwa robot. © 2024 IEEE.

关键词： Redundant manipulators

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Network Intrusion Detection System Using Decision Tree and KNN Algorithm 3

Network Intrusion Detection System Using Decision Tree and K...

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3rd International Conference on Power Electronics and IoT Applications in Renewable Energy and its Control, PARC 2024

作者： Singh, Km Shivani Singh, Khundrapam Johnson National Institute of Technology Dept. of Computer Science and Engineering Manipur Imphal India

ISBN: (纸本)9798350309409

The complexity of attacks is increasing, making it increasingly difficult to effectively discover breaches. A network intrusion detection system is needed to handle the aforementioned problem. An automated software program called a network intrusion detection system alerts administrators when someone attempts to compromise the system by participating in hazardous behaviors. A system or network is protected from harmful invasions by hardware and software firewalls. They function like filters, removing any data that could put the system or network at risk. We aimed to design the best intrusion detection system possible so that it could distinguish between 'normal' and 'attacked' categories of network data with high accuracy. The best accuracy can be obtained by using soft computing techniques like Decision Trees and KNN. We tested a variety of techniques, such as preprocessing the data, feature selection, principal component analysis (PCA) reduction, standardization, and normalization, in order to increase our model's accuracy scores. We also assess the two approaches' results according to their accuracy in order to decide which way is more effective. © 2024 IEEE.

关键词： Decision trees

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Dynamic Landslide Prediction, Monitoring, and Early Warning with Explainable AI: A Comprehensive Approach 3

Dynamic Landslide Prediction, Monitoring, and Early Warning ...

引用

3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Binu, Kristin Elizabeth Anoopkumar, Lakshmi Thampi Sunil, Maria Jose, Merin Preetha, K.G. Rajagiri School of Engineering & Technology Dept. of Computer Science and Engineering Rajagiri Valley PO Kochi India

ISBN: (纸本)9798350375190

Landslides are a major threat to infrastructure, human lives, and the environment. Therefore, early warning systems and precise forecasting are essential for reducing their effects. This study investigates how to enhance landslide prediction models using Explainable Artificial Intelligence (XAI) methodologies. Intending to improve complex machine learning models' interpretability and transparency, XAI seeks to improve comprehension and confidence in the model's predictions. XAI methodologies used are SHapley Additive exPlanations (SHAP), LIME (Local Interpretable Model-agnostic Explanations), and feature importance analysis. These techniques produce comprehensible explanations for each forecast and assist in determining the most significant variables in landslide prediction. The proposed XAI-enhanced landslide prediction model demonstrates promising results in terms of accuracy and interpretability. The research reported here helps to design more potent early warning systems for landslides by advancing explainable AI applications in geoscience and disaster risk reduction. © 2024 IEEE.

关键词： Landslides

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Advancements in Flood Mapping Methodologies: A Comprehensive Review 6

Advancements in Flood Mapping Methodologies: A Comprehensive...

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Chandran, Divya V Anitha, J. Chandran, Divya V Vijayakumar, Amrutha Raveendran, Anjana Karunya Institute of Technology and Sciences Dept.of Computer Science Engineering Coimbatore India Adi Shankara Institute of Engineering and Technology Dept. of Electronics and Communication Engineering Kalady India

ISBN: (纸本)9798350381689

This review aims to comprehensively assess the recent advancements in flood mapping methodologies and technologies. By analyzing diverse techniques and approaches employed in flood mapping, this review seeks to present an in-depth overview of the current landscape of this field. The primary focus is on evaluating the strengths and limitations inherent in various flood mapping methods. Special attention will be given to exploring the integration of remote sensing technologies and geographic information systems (GIS) in flood mapping. Additionally, the review will delve into the transformative impact of machine learning and artificial intelligence on enhancing the precision and efficiency of flood mapping processes. Recent developments in flood mapping have significantly elevated the accuracy and reliability of the techniques employed. The integration of AI technologies with remote sensing methods has notably contributed to this advancement. Conventional approaches to flood mapping have traditionally relied on multiple methodologies to comprehend potential flood impacts. However, these methods often prove laborious, resource-intensive, and may lack access to real-time data, posing limitations for precise flood mapping and prediction. These identified limitations underscore the critical need for the development of more robust flood mapping techniques. Addressing these challenges is essential for enhancing flood risk assessment, improving management strategies, and ensuring better preparedness for future flood events. © 2024 IEEE.

关键词： Floods

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Land Use Land Cover Classification Using Machine Learning and Remote Sensing Data: A Case Study of Karnaprayag, Uttarakhand, India 1

Land Use Land Cover Classification Using Machine Learning an...

引用

1st International Conference on Electronics, Communication and Signal Processing, ICECSP 2024

作者： Saini, Rashmi Singh, Suraj Govind Ballabh Pant Institute of Engineering and Technology Dept. of Computer Science and Engineering Pauri Garhwal India

ISBN: (纸本)9798350364590

Land use and land cover (LULC) classification is crucial for planning and sustainable development, especially in mountainous regions. This study focuses on the Tehsil Karnaprayag, situated in the vicinity of the Greater Himalaya. With a total area of 1858.903 km2 and elevations ranging from 647m to 7641m. Using multispectral Sentinel-2 imagery and Machine Learning techniques i.e. Extreme gradient boosting (XGBoost) and K-nearest neighbor (KNN). This study aims to classify seven distinct land cover classes: snow, forest, water bodies, grassland, agricultural land, fallow land, and built-up areas. The classification results demonstrate the effectiveness of XGBoost classifier achieving an accuracy of 90.22% and a kappa value of 0.882, while KNN achieved an accuracy of 88.59% with a kappa value of 0.862. The analysis reveals that forest and fallow land are the predominant land cover types in the study area, constituting nearly 60% and 15% of the total area respectively. Notably, XGBoost exhibits the highest class-specific accuracy of 97.60% for the snow class, demonstrating its utility in discriminating between challenging land cover types. This research highlights the importance of LULC classification in informing land management strategies and facilitating sustainable development initiatives in mountainous regions. The findings provide valuable insights into the spatial distribution and composition of land cover in Tehsil Karnaprayag, essential for effective planning and conservation efforts in the Himalayan ecosystem. © 2024 IEEE.

关键词： Sustainable development

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Deep Learning-Based Flood Detection System Using Semantic Segmentation 7

Deep Learning-Based Flood Detection System Using Semantic Se...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Chandran, Divya V Anitha, J. Anusree, A.R. Nair, Gowri Karunya Institute of Technology and Sciences Dept.of Computer Science Engineering Coimbatore India Adi Shankara Institute of Engineering and Technology Dept. Electronics and Communication Engineering Ernakulam India

ISBN: (纸本)9798350372816

Floods cause immense damage worldwide, resulting in significant financial losses and loss of life. Existing flood detection methods often face limitations, including delays and errors due to manual data interpretation and limited coverage. In this study, we propose a novel flood detection system that leverages deep learning techniques for semantic segmentation. Our system employs a U-Net architecture to analyze aerial and satellite images, identifying flood-affected areas at a pixel level. Trained on a diverse dataset of labeled flood images from various regions, our model is both robust and adaptable. Notably, our system achieves an Intersection over Union (IoU) score of 0.85, surpassing traditional methods in accuracy. This advanced system offers the potential to provide real-time flood information, enhancing disaster response and management. With real-time flood mapping, emergency services can better understand the disaster's scope and plan rescue operations more effectively. Improved emergency response and resource management become possible with timely, precise data on flooded areas, allowing for informed decisions regarding evacuation, relief distribution, and infrastructure maintenance. Future work will focus on integrating additional data sources, such as weather conditions and water flow rates, to further enhance system performance. © 2024 IEEE.

关键词： Semantic Segmentation

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Performance Analysis of Modern Garbage Collectors using Big Data Benchmarks in the JDK 20 Environment 5

Performance Analysis of Modern Garbage Collectors using Big ...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Islam, Md. Jahidul Rahman, A.T.M Mizanur Rana, Sohel Chandpur Science and Technology University Dept of Computer Science and Engineering Bangladesh Brac University Dept of Computer Science and Engineering Bangladesh Chandpur Science and Technology University Dept of Information and Communication Technology Bangladesh

ISBN: (纸本)9798350394290

Garbage collection is a fundamental aspect of Java Virtual Machine (JVM) memory management, and choosing the optimal garbage collector is essential for attaining optimal application performance. In this work, we conduct experiments with the big data benchmarks from DaCapo and Renaissance benchmark suites in both fixed and variable heap environments to determine the efficacy of JDK 20's garbage collectors. The ZGC algorithm has the highest throughput and the shortest pause periods, whereas the Serial GC algorithm has the lowest throughput and the longest pause intervals. Additionally, it is discovered that the G1 algorithm manages the previous generation heap and metaspace less efficiently. Our work offers valuable insights into the efficacy of garbage collection algorithms and can aid application developers in selecting the optimal garbage collection algorithm. Our investigation can be expanded by analyzing additional benchmark suites and garbage collection algorithms across a broader range of heap sizes in future work. © 2023 IEEE.

关键词： Big data

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