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检索条件"任意字段=Proceedings of the 15th International Conference on Distributed Computing Systems"

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Anomaly Detection for Log using AutoM and Auto-Sklearn Classification Algorithm 15

Anomaly Detection for Log using AutoM and Auto-Sklearn Class...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Karthik, G.M. Gupta, Ankur Gupta, Sh Rajesh Jha, Amaresh Sivasangari, A. Mishra, B.P. School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India Department of CSE RIMT University Godindgarh Mandi India Department of Management Sanskriti University Uttar Pradesh Mathura India School of Modern Media University of Petroleum and Energy Studies Dehradun India School of Computing Sathyabama Institute of Science and Technology Chennai India Department of Electronics JSS Academy of Technical Education NoidaSector-62 India

ISBN: (纸本)9798350370249

— In the realm of application development, logs represent a pivotal resource for the identification and comprehension of application performance, as well as the detection of anomalies, all of which are essential for ensuring the overall efficiency of an application. the proliferation of users has resulted in a substantial increase in the volume of logs available for analysis in the field of log analysis research. Typically, these logs exhibit an unstructured format, largely dictated by the developer's design choices. Consequently, there is a need for log parsing techniques to in still structure into these logs, enabling the application of machine learning algorithms for the prediction of anomalies. In the present research, the primary objective is the prediction of anomalies within Hadoop distributed File System (HDFS) logs utilizing an AutoML (Automated Machine Learning) tool known as Auto-Sklearn. the research outcomes illustrate the effectiveness of the proposed model, which attains an impressive accuracy rate of 99% when applied to the dataset. this study underscores the critical role of logs in application development, highlights the importance of log structure transformation, and demonstrates the substantial predictive capabilities of the Auto-Sklearn model in the context of HDFS log analysis. ©2024 IEEE.

关键词： Anomaly detection

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Enhanced Crop Yield Prediction using Machine Learning Techniques 15

Enhanced Crop Yield Prediction using Machine Learning Techni...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Nossam, Sri Chakradhar Katakam, Rishi Anirudh Pulastya, Gopa Venugopalan, Manju Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

In modern agriculture, accurately anticipating crop yield estimation is critical aids of sustainable resource management, efficient decision-making, and food security. this is therefore a process that includes a breakdown of those systems containing data on temperature, rain, yield values, pesticide usage. this process commences with data preprocessing which consist of engineering the features, cleaning, normalization and fusing of discrete data sources. the prediction of agricultural output which is based on the machine learning approach and input interactions is an outcome of this machine learning approach. In the model developed uses several classifiers, that is SVM, Decision Tree, Random Forest, AdaBoost, XGBoost, Naïve Bayes, KNN, MLP, Logistic Regression, Linear Discriminant Analysis, Extra Trees, and Voting Classifiers. the model reported an accuracy of over 99%. this was achieved through the use of Extra Trees technique, which is an ensemble learning method. the reliability of these statistics helps farmers, agricultural policymakers, like the other stakeholders, to make good vital decisions, in turn increasing agricultural production and food security. © 2024 IEEE.

关键词： Clustering Crop Yield Prediction Data Integration Exploratory Data Analysis Feature Engineering Machine Learning Machine Learning Techniques Model Evaluation Model Selection Predictive Model

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Enhancing Oil Spill Detection Using Synthetic Aperture Radar With Dual Attention U-Net Model 15

Enhancing Oil Spill Detection Using Synthetic Aperture Radar...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Shanmukh, M. Phani Priya, S. Baghavathi Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

Oil spills present a critical hazard in oceans, requiring advanced monitoring systems to rapidly detect and contain environmental damage. this work proposes a deep learning approach combining a UNet encoder-decoder architecture with a fully convolutional network in a dual attention model for accurate and efficient oil spill identification from SAR data. the model leverages the strengths of UNet and FCN for robust feature extraction and segmentation, while incorporating a dual attention mechanism to focus on the most salient spatial and channel-wise features. the dual attention model is compared against popular architectures like DeepLabV3, AlexNet, and PSPNet on public SAR datasets capturing oil spills. Results demonstrate the superior performance of the proposed dual attention model, achieving an impressive 97.5% accuracy in detecting oil spills outperforming all benchmark models evaluated. the high accuracy, coupled with the model's computational efficiency, highlights its potential for real-time autonomous oil spill monitoring and disaster management. the study underscores the efficacy of integrating attention mechanisms in deep learning models for SAR-based oil spill detection tasks. © 2024 IEEE.

关键词： Disaster prevention

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Enhancing Fog/Edge computing Education using Extended Network Simulator Omnet plus plus (xFogSim) 15

Enhancing Fog/Edge Computing Education using Extended Networ...

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15th IEEE Global Engineering Education conference (IEEE EDUCON)

作者： Qayyum, Tariq Trabelsi, Zouheir Alomar, Ban Parambil, Medha Mohan Ambali United Arab Emirates Univ Coll Informat Technol Al Ain U Arab Emirates

ISBN: (纸本)9798350394023;9798350394030

Fog computing is a technology that brings computing, storage, and networking services closer to devices and systems, aiming to improve speed, efficiency, and data processing capabilities for various applications. the growing importance of fog and edge computing technologies means we need new and better ways to teach students about these areas. this paper offers a detailed guide on how to use xFogSim, an extended version of the Omnet++ network simulator, for teaching fog and edge computing. We give students a clear path to follow, starting with simple network designs and moving to more complex ones, helping them understand how federated learning works. We tested xFogSim with a group of students and found that it really helps them grasp fog and edge computing ideas better than traditional teaching methods. xFogSim also gives practical information about important performance metrics, helping bridge the gap between what students learn in class and what they need to know in the real world. this paper shows that using xFogSim in classrooms gives students a strong base in distributed computing systems, getting them ready for future tech challenges.

关键词： Omnet plus xFogSim Fog computing Edge computing Experiential Learning Interactive Education Performance Metrics Network Simulation

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Sentiment Analysis using YouTube Comments 15

Sentiment Analysis using YouTube Comments

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Bindhumol, M. Singh, Tripty Patra, Payel Amrita School of Computing Dept. of Computer Science and Engineering Bangalore India University Of L'Aquila Disim Department L'Aquila Italy

ISBN: (纸本)9798350370249

the 'AI-Enhanced Product Recommendation System using YouTube Comments Analysis' leverages advanced deep learning models, specifically the Complex Convolutional Long Short-Term Memory (CLSTM) and Gated Recurrent Unit (CGRU). this system harnesses the rich and diverse user-generated content on YouTube, employing CGRU and CLSTM to analyze and extract valuable sentiments from comments associated with product videos. the CGRU model excels at capturing intricate sequential dependencies, while the CLSTM model excels at recognizing complex linguistic patterns. the synergy between these models enables the system to effectively decipher the subtle nuances and sentiments expressed in online discussions. the process involves collecting and preprocessing YouTube comments, training the models on sentiment analysis tasks, and utilizing the learned representations to enhance product recommendations. the system addresses the challenges posed by the informal and varied nature of user-generated content on YouTube, incorporating robust natural language processing techniques. the implementation of these advanced models contributes to the creation of a sophisticated recommendation system, offering insights derived from user interactions, ultimately empowering content creators, marketers, and platform moderators to make informed decisions in the dynamic digital landscape. © 2024 IEEE.

关键词： Recommender systems

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Reinforcement Empowered Deep Network Enabled Optimal Controlled Environment For Crop Yield 15

Reinforcement Empowered Deep Network Enabled Optimal Control...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Gorantla, Snehitha Veluchamy, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering - Artificial Intelligence Chennai601103 India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electronics and Communication Engineering Chennai601103 India

ISBN: (纸本)9798350370249

this paper focuses on developing a comprehensive system for optimised climate control in a simulated greenhouse setting with complex environment modelling, communication-enabled agents, and hyperparameter optimisation using multiagent reinforcement learning (MARL). the greenhouse environment model gives the agents a richer and more detailed state representation by extending pertinent climate variables, plant health indicators, and resource levels for each zone. In order to stimulate both individual zone performance and overall greenhouse goals, the incentive function has been changed. this involves goals like preserving a stable climate, optimising resource use, or achieving a balanced yield. Based on the communication-based MARL strategy used, information sharing between agents is made possible by the communication protocols included into multi-agent architecture. this enables the agents to work together and make decisions in unison. Neural network designs are used to build both value and policy networks, allowing the agents to understand intricate relationships and decide on the best course of action depending on the observed situation. © 2024 IEEE.

关键词： Crop Yield Greenhouse Multi-Agent systems Neural Network PPO algorithm Reinforcement Learning Resource Management

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Examining the Benefits of AI in Wearable Sensor-based Healthcare Solutions 15

Examining the Benefits of AI in Wearable Sensor-based Health...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Takkalapally, Prashamsh Kasula, Balaram Yadav Patil, Sachin C. Logeshwaran, J. Lewis University Computing and Mathematical Sciences Department of Engineering United States University Of The Cumberlands Dept of Information Technology WilliamsburgKY United States United States Department of Computer Science Karnataka Bengaluru560029 India

ISBN: (纸本)9798350370249

the emergence of the AI generation has introduced adjustments to the manner healthcare solutions are advanced and applied. Wearable sensor-based total healthcare answers were revolutionized by leveraging AI in diverse packages. AI may be used to enhance the accuracy and precision of facts analysis, simplify information collection procedures, and pick out affected person-unique styles from the accrued data. Furthermore, AI can provide both actual-time and predictive analytics abilities, which are particularly useful for devising personalized healthcare offerings. Its provision of scalable systems hurries up the traits of various programs and has enabled personalized healthcare answers to be deployed in a shorter length. In spite of a number of the associated challenges, including data privacy troubles, AI-based wearable sensor-based healthcare answers can revolutionize patient tracking and timely detection of capability health conditions, improve preventive fitness care, and decrease healthcare fees. © 2024 IEEE.

关键词： Adjustments Emergence Generation Healthcare Personalized

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Comparative Deep Learning Approach for Intrusion Detection 15

Comparative Deep Learning Approach for Intrusion Detection

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Sah, Nabin Kumar Kolli, Mahi Dharmaraj, Kadam Prajwal Vishwas, H.N. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

In the modern world, the security of computer networks from harmful activities is very important since cyber threats keep changing from time to time. However, Intrusion Detection systems (IDS) have not been able to match this pace using traditional methods. this research is on checking how well deep learning models which are Convolutional Neural Networks (CNNs), Gated Recurrent Units (GRUs), Long Short-Term Memory (LSTM) networks, and Deep Neural Networks (DNNs) help in enhancing Intrusion Detection capabilities. the NSL-KDD dataset available on Kaggle provides various attack types and normal activities through diverse network traffic data. Rigorous data preprocessing has been done during the study through feature engineering, encoding categorical variables, and normalizing numerical attributes. After that, custom deep learning architectures are created and evaluated with accuracy being one of the standard metrics used for testing their performance among others like recall, precision, or F1-score so as to improve intrusion detection efficiency. © 2024 IEEE.

关键词： CNN cyber threats DNN GRU Intrusion detection LSTM network traffic

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Smart Traffic Flow Optimization with IoT and Machine Learning 15

Smart Traffic Flow Optimization with IoT and Machine Learnin...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Poojitha, D. Darak, Tanmay Samaddar, Ushnish Khanna, Manju Amrita Vishwa Vidyapeetham Amrita School Of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Traffic signal optimization is crucial for the effective traffic management within cities with the primary goals being the reduction of bottleneck occurrences, avoidance of the extension of travelling time, and improvement of overall city street network function. Historically used approaches can hardly be adjusted to fluctuating traffic situation. Hence, a new methodology involving the use of machine learning-based traffic signal control with the assistance of the IR (Infrared) /UV (Ultraviolet) sensors for traffic flow sensing in real-Time environment is introduced in this study. the UV sensors make congestion distance measurements in real time which are assisted by IR sensors for traffic information acquisition. Historic data with data gathered from the traffic sensors are used to train the machine learning model to predict traffic. the strategy employs the use of heuristic search methodologies in the determination of signal timings with reference to congestion information that has been forecasted as well as congestion information that is being experienced. this paper discusses the fact that machine learning-based approaches, supported by sensor technology, can be used for effective traffic management systems that modify with the help of data. the success of this traffic management system is proved by its ability to minimize the MSE (Mean Squared Error) of congestion predictions to an average of 0.69. © 2024 IEEE.

关键词： Information management

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Intrusion Detection System (IDS) in Cloud computing using Machine Learning Algorithms: A Comparative Study 15

Intrusion Detection System (IDS) in Cloud Computing using Ma...

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15th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Rathod, Ganesh Sabnis, Vikrant Jain, Jay Kumar Faculty of Engineering & Technology Mansarovar Global University Bhopal India Indian Institute of Information Technology Madhya Pradesh Bhopal India

ISBN: (纸本)9798331300579

Organizations have witnessed a significant transformation in the realm of data storage and processing owing to the advent of cloud computing. Nonetheless, with its advantages, cloud computing has also brought forth new security concerns that need to be addressed. In this regard, the use of Intrusion Detection systems (IDSs) has become indispensable for identifying and preventing a wide range of attacks that may occur in cloud computing environments, thereby ensuring data security. In modern years, machine learning (ML) algorithms have emerged as a promising approach for IDSs, as they can analyze huge amounts of data and identify patterns that may not be detectable by traditional rule-based IDSs. this review paper presents a comprehensive analysis of ML-based IDSs & Tradition-based IDSs for intrusion detection in cloud computing environments. the literature review covers various Traditional & ML algorithms used for intrusion detection, including decision trees, Neural Networks (NN), Support Vector machines (SVM), random forests, and k-nearest neighbours. the performance evaluation metrics used in this review paper include accuracy and false positive rate. these metrics are generally used to evaluate the performance of IDS and ML algorithms. the results of the analysis indicate that ML-based IDSs outperform traditional IDSs in terms of accuracy and false positive rate. However, ML-based IDSs may also have limitations, such as a high rate of false negatives and the usefulness of huge amounts of training data. Overall, the analysis suggests that ML-based IDSs have the potential to improve the usefulness of intrusion detection in cloud computing environments, but further research is needed to address the limitations of these systems. © Grenze Scientific Society, 2024.

关键词： Network intrusion

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