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International Conference on computer and Communications Technologies (ICCCT)

作者： Ashok P Bhoomika V Janani A Lavanya S Dept of Computer Science and Engineering Sri Sairam Institute of Technology Chennai India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

The application of machine learning models is increasing rapidly, particularly solving challenges within underwater environments. This research paper introduces a machine learning model designed for the detection of underwater species and garbage waste in underwater *** systems are lacking due to the inadequate dataset real time classification and misclassifications of images. These systems typically identify only a small number of species because of the difficulties associated with varying underwater light conditions. This research proposes a new approach by utilizing the YOLO algorithm for the detection and classification of underwater images. The system functions by capturing underwater images, which are then preprocessed to improve the features. The dataset consists of 600 images, with 7 different classes and splitted into training and testing. The proposed method detects and classifies underwater objects with an overall precision of 95% to 97%. This research helps marine researchers and students in identifying underwater species and helps to improve underwater environments.

关键词： YOLO Training Waste materials Image processing Machine learning Fish Real-time systems Communications technology Classification algorithms Testing

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DevOps Practices Into Machine Learning

DevOps Practices Into Machine Learning

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Intelligent Systems, Smart and Green Technologies (ICISSGT), IEEE International Conference on

作者： Samridhi Jain Puneet Kumar dept. of Computer Science & Engineering Chandigarh University Mohali India

ISBN: (数字)9798350304329

ISBN: (纸本)9798350304336

All industrial machine learning (ML) projects have their ultimate objective to quickly develop and deploy ML solutions. However, a lot of machine learning projects are failing, and never reach production. In order to address these concerns, this paper aims to investigate how manual ML processes can be automated and operationalised so that more ML concept can be put into production. The advantages of DevOps automation and machine learning capabilities are now being combined by industries. MLOps is the name of this combination. Our analysis found that while there are some similar aspects in defining MLOps, still there is a lack of standardisation in the term, which can occasionally lead to misunderstandings and uncertainty. We carried out the research, which includes a review of the literature and an evaluation of the MLOps tools, to fill this gap. These investigations have led us to define MLOps and identify unresolved issues in the field. In addition to this, we offer a workflow-based summary of all essential components. Finally, this paper gives guidance for applying DevOps practises to machine learning for researchers and practitioners.

关键词： Industries DevOps Uncertainty Reviews Pipelines Machine learning Production Delays Intelligent systems Testing

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Machine Learning Techniques in Epileptic Seizure Detection: A Comprehensive Review

Machine Learning Techniques in Epileptic Seizure Detection: ...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Vishal Thakare Rekha Ranawat Dept. of computer Science and Engineering SAGE University Indore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The neurological disorder known as epilepsy is defined by its recurrent, spontaneous episodes of seizures impacting millions of people globally. The timely and accurate detection of epileptic seizures is necessary for a successful administration and ensuring patient safety. In recent times, techniques using machine learning have emerged as promising solutions to automate the detection of seizures. This review paper presents a thorough overview of the latest advancements in the identification of epileptic seizures through the application of machine learning, encompassing data sources, extraction of features, application of classification algorithms, use of performance evaluation metrics, addressing various challenges, and future directions. In addition, we put forth a novel methodology in order to enhance seizure detection accuracy by integrating the models of deep learning. Our approach uses multiple modalities for seizure prediction with cross-model training to mitigate data scarcity. Through a rigorous analysis of existing literature and our proposed methodology, this paper provides insightful information for practitioners and researchers looking into the detection of epileptic seizures.

关键词： Deep learning Training Accuracy Reviews Soft sensors computer architecture Feature extraction Electroencephalography Safety Long short term memory

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Deep Learning for Brain Tumor Classification: CNN Models with Saliency Map Insights

Deep Learning for Brain Tumor Classification: CNN Models wit...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： M Shahriar Mahmud Rafi Aisha Tasnim Aishy Tazrian Alam Mohammed Maruf Hossen Dept. of Computer Science and Engineering East Delta University Chittagong Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

This paper aims to classify brain tumors using Convolutional Neural Networks (CNNs) applied to MRI images. We employed four models: a custom CNN, VGG16, ResNet101, and a hybrid model combining VGG16 and ResNet101, with saliency maps used for model interpretability. The custom CNN achieved the highest accuracy of 98.01%, followed by VGG16 (96.33%), ResNet101 (90%), and the hybrid model (97.10%). Saliency maps provided insights into the features driving predictions, enhancing model transparency. Our findings demonstrate the effectiveness of CNNs for brain tumor classification, with the custom CNN performing the best, and suggest further research into model explainability, data augmentation, and hybrid techniques to improve accuracy and generalizability.

关键词： Deep learning Accuracy Magnetic resonance imaging Brain tumors Transforms Brain modeling Feature extraction Convolutional neural networks Reliability Artificial intelligence

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Using Machine Learning Algorithms in Edge Intelligence for Ultra-Reliable and Low-Latency Communications in the Industrial Metaverse

Using Machine Learning Algorithms in Edge Intelligence for U...

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International CSI computer Conference, CSICC

作者： Reza Mohammadvand Naser Mozayani dept. Computer Engineering Iran University of Science and Technology Tehran Iran

ISBN: (数字)9798331523114

ISBN: (纸本)9798331523121

Given the rapid advancements in technologies such as the metaverse and digital twins, the need for effective integration of communication, computation, and storage in complex systems and edge computing environments is becoming increasingly apparent. This paper proposes an architecture to support an industrial metaverse based on digital twins that optimizes resources by leveraging mobile edge computing and ultra-reliable low-latency communication. This architecture reduces latency by employing task offloading and storage methods on nearby edge servers, meeting the precise requirements of future metaverses in terms of reliability and latency minimization. The proposed model, utilizing reinforcement learning algorithms such as Deep Q-Network (DQN), Double Deep Q-Network (DDQN), and Dueling Double Deep Q-Network (Dueling DDQN), is capable of achieving adaptability in dynamic conditions and making intelligent decisions. The simulation results demonstrate that the proposed method reduces by more than 10% on average compared to the best method available in the literature and improves resource utilization efficiency.

关键词： Metaverse Heuristic algorithms Computational modeling Simulation computer architecture Artificial neural networks Ultra reliable low latency communication Digital twins Delays Optimization

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Image Processing and Deep Learning Based Road Object Detection System for Safe Transportation

Image Processing and Deep Learning Based Road Object Detecti...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Rizvee Hassan Prito Md. Shafayat Hossain Md. Shajibul Islam Mehzabin Meem Md. Nawab Yousuf Ali Computer Science and Engineering Dept East West University Dhaka Bangladesh

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Road object detection, a pivotal task in computer vision and artificial intelligence, is dedicated to the identification and precise localization of a diverse array of elements on roadways, including vehicles, pedestrians, road signs, traffic lights, and potential obstacles. The essence of this task lies in its ability to provide real-time and precise object detection, ultimately serving as a crucial safeguard to prevent accidents and ensure the safety of drivers, passengers, and pedestrians. It also lays the foundation for advanced warning systems and aids in collision avoidance. Several popular models were implemented, encompassing YOLOv7, YOLOv7-Modified, YOLOv7-Tiny, YOLOv7-E6E, Faster R-CNN, and SSD. Among these, YOLOv7 achieved an impressive mean average precision (mAP) of 83.6%, with an inference speed of 15.1 ms, while YOLOv7E6E achieved the highest mAP of 86.2%, but with the cost of a slower inference speed of 30.3 ms. The modified version of YOLOv7 has produced 2.2% higher average precision accuracy (89.1%) than the main version of YOLOv7 due to its double RepVGG layers, skip connection and concatenation layers in the head architecture. To make this research accessible to a wider audience, a user-friendly web application is developed with an intuitive interface.

关键词： Location awareness Pedestrians Head Roads Image processing Object detection Real-time systems Safety Object recognition Vehicles

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Serial vs parallel execution of Principal Component Analysis using Singular Value Decomposition

Serial vs parallel execution of Principal Component Analysis...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Mallegowda M Tanupriya R Vishnupriya C Anita Kanavalli Dept of Computer Science and Engineering Ramaiah Institute of Technology Bangalore India

Principal component analysis (PCA) is a crucial technique in data science and machine learning for reducing dimensionality and identifying crucial characteristics. But when dealing with big datasets, it uses Singular Value Decomposition (SVD), which is computationally costly. To address this issue, this study compares the effectiveness of parallel and serial PCA implementations. Serial execution uses a step-by-step data processing method, which is simple but falls short when dealing with large datasets. The task is divided across many processing units in parallel execution, which is preferred in high-performance computing environments, and thus results in significant speed increases for large datasets. While parallel execution shines with huge datasets and time-sensitive activities, serial execution improves with smaller datasets and simple scenarios. Depending on variables like dataset size and available computer resources, one of these techniques may be preferred over the other. In conclusion, this paper examines the advantages of utilizing parallel computing for PCA via SVD, offering a quick way to speed up calculations for high-dimensional datasets. It emphasizes how flexible it is to choose serial or parallel execution depending on the particular dataset's properties and processing needs.

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Harnessing Transformative Potential of UAV-Based AI-Powered Data Analysis for Emission Monitoring

Harnessing Transformative Potential of UAV-Based AI-Powered ...

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International Conference on Computational Intelligence and Networks (CINE)

作者： Mayank Namdev Jay Dholaria Meet Rathi Dept. of Computer Science Engineering Manipal University Jaipur India

ISBN: (数字)9798331516796

ISBN: (纸本)9798331516802

With the growing pressure to reduce environmental pollution, the combination of Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence (AI) offers a viable path forward for modernizing emission monitoring. This research investigates the revolutionary potential of UAV-based AI-powered data analysis for emissions monitoring, with a particular emphasis on the comparison of XGBoost and CatBoost algorithms. Real-time monitoring, identification, and analysis of pollutants can now be done with remarkable accuracy and efficiency thanks to UAVs outfitted with modern sensors and AI algorithms. This novel methodology has significant advantages over typical monitoring approaches, including increased spatial coverage, lower costs, and higher data quality. Furthermore, AI integration provides predictive analytics and anomaly identification, allowing for proactive environmental risk mitigation interventions. Despite its enormous promise, challenges such as regulatory frameworks, privacy concerns, and technological restrictions must be solved before widespread usage. Overall, the combination of UAVs and AI, as well as the comparison of XGBoost and CatBoost, hold enormous promise for revolutionizing emission monitoring by providing a scalable and versatile solution to protect environmental quality and human health.

关键词： Privacy Data analysis Pollution Prediction algorithms Real-time systems Sensors Artificial intelligence Predictive analytics Monitoring Risk mitigation

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Intrutuion Detection System Using Machine Learning Based IDS

Intrutuion Detection System Using Machine Learning Based IDS

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Bhavani R Veeramalai Sankardass Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

The number of network attacks has increased in today's dynamic environment. Network assaults are unlawful actions taken against virtual assets within a corporate network. Community attacks are typically performed by malicious parties to change, destroy, or gets the private data. Attackers that want access to internal systems usually target network perimeters in their attacks on communities. Network attacks can be categorized into two major types: passive attack and active attack. In passive network attacks, unauthorized users get access to networks, display personal information, and steal it without making any changes. Active network attacks may include material that has been edited, encrypted, or is undesirable. Upon infiltration, malicious events may additionally leverage different hacking activities, inclusive of malware and endpoint assaults, to assault an organizational network. With greater organizations adopting far off operating, networks have end up greater liable to data theft and destruction. As a end result, our proposed device can monitor community visitors for suspicious pastime and come across the presence of any intrusion.

关键词： Passive networks Machine learning algorithms Intrusion detection Organizations Malware Decision trees Cryptography computer crime Random forests Monitoring

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Extensive Benchmarking of Metaheuristics Using Real-World Constrained Multi-Objective Optimization Problems

SSRN

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SSRN 2024年

作者： Mukhopadhyay, Somnath Sarkar, Sunita Das, Devangshu Phukan, Madhurjya Purkyastha, Saptarshi Dept. of Computer Science & Engineering Assam University Silchar India

The current Optimization and Metaheuristics literature is booming with contributions. While this surge fosters advancements in the field, it has also made it into an over-saturated one. Consequently, people are often faced with a plethora of optimization algorithms to choose from. An extensive analysis of the algorithms is essential for better understanding of the abilities of these algorithms. But most of the studies conducted only employ Synthetic Benchmark Problems (SBPs), which can cause overestimation of the algorithms because of their simplified structure, the algorithms also struggle when they are used in real-world contexts. To fill the gap between the theory and the practical, we test several Metaheuristics algorithm using Real World Constrained Multi-Objective Optimization Problems (RWCMOPs) and rank them using various performance measures like Hypervolume Indicator (HV), Feasibilty Rate (FR) and Total Wall-Clock Time. An analysis using Real World problems and multiple performance indicators will help in better understanding of the capabilities of these Metaheuristic Algorithms. © 2024, The Authors. All rights reserved.

关键词： Optimization algorithms

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