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Comparative Study of Deep Learning Techniques in Cattle Identification 2

Comparative Study of Deep Learning Techniques in Cattle Iden...

引用

2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Luthra, Meghna Sharma, Meghna Chaudhary, Poonam Department of Computer Science & Engineering TheNorthCap University Gurugram India

ISBN: (纸本)9798331541217

Accurate identification of individual livestock is extremely important for proper care and herd disease management. Livestock identification contributes to breed management, productivity monitoring and reduce labour cost through automation. Cattle identification with deep learning using biometric and facial recognition is more accurate, consistent than traditional branding or ear tags which may go missing or get damaged. Due to the enormous amount of data that is sent through quickly, an electromagnetic technique like RFID may be able to marginally speed up the process over hand sorting, but it will never be able to attain the same levels of analysis as Deep Learning. This adds up for behavioural analysis, continuous health monitoring and automated decision-making that will eventually lead to increased yields and herd management. This study compares the capabilities of state-of-the-art deep learning systems and looks ahead at future research in this area. In this study, opencows2020, dataset is used, which comprises of 4243 images of Holestian frestian cattle, that are divided into 46 categories are used to compare the performance of the deep learning models AlexNet and ResNet-50 with the DeiT transformer. The DeiT transformer performed better than the other two versions, according to the study. © 2024 IEEE.

关键词： Decision making

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Predicting Code Runtime Complexity Using ML Techniques 8th

Predicting Code Runtime Complexity Using ML Techniques

引用

8th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2023

作者： Deepa Shree, C.V. Kotian, Jaaswin D. Gupta, Nidhi Adyapak, Nikhil M. Ananthanagu, U. Department of Computer Science Engineering PES University Bengaluru India

ISBN: (纸本)9789819976218

There are several approaches to solving every coding algorithm in computer science. To achieve the same result, these methods may use various techniques and reasoning. The difficulty is that as the number of inputs increases, certain algorithms tend to perform poorly. Several metrics may be used to assess the quality of any code. The code runtime complexity is one of these measurements. To determine this runtime complexity, substantial study and a thorough understanding of algorithms are necessary, which is a challenging manual undertaking. In this study, the worst-case runtime complexity of codes in programming languages C, Java and Python are calculated as Big-O notations utilising code features like Abstract Syntax Trees, ML approaches and static code analysis. The novelty of the research is our labelled runtime complexity dataset which was constructed manually, implementing Deep Learning Algorithms like Bi-LSTM and calculating Code Runtime Complexity for the worst-case scenario as Big-O notations for codes in three languages, C, Java and Python. To predict the runtime complexity for a given code more accurately than the traditional legacy methods such as manually asserting code runtime complexity or running code for different amounts of input, we have presented a more effective manner for the same. The results portray that the XGBoost classifier outperforms the other models with an accuracy of 96%. The current study can also be extended to other high-level programming languages, including more training samples and making use of graph neural networks. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Abstract Syntax Tree AST Bi-LSTM graph2vec LSTM Runtime complexity Static analysis

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Machine Learning Method for Optimal Route Calculation

Machine Learning Method for Optimal Route Calculation

引用

2024 International Conference on computer, Electronics, Electrical engineering and their Applications, IC2E3 2024

作者： Singh, Nongmeikapam Thoiba Satwik, Vishwaroju Mani Wadhwa, Manoj Tirukala, Gowtham Pandey, Shradha Saxena, Smriti Srivastava, Hitanya Chandigarh University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350388534

This study unveils an innovative approach to optimizing routes by merging the capabilities of the BellmanFord algorithm with the adaptive nature of Q-Learning, addressing the dynamic obstacles encountered in route planning. By integrating the Bellman-Ford algorithm, renowned for its shortest path calculation even with negative weights, with the dynamic adaptability offered by Q-Learning, we introduce a method that surpasses the limitations of traditional routing strategies. Through a series of simulations, our proposed methodology showcases enhanced adaptability, efficiency, and precision in navigating dynamic environments, outperforming both conventional routing strategies and standalone machine learning approaches. The collaboration between Bellman-Ford and Q-Learning yields a robust solution for real-time routing challenges, making it indispensable for applications in logistics, autonomous vehicle navigation, and efficient traffic management. This contribution signifies a groundbreaking step in the domain of route optimization, offering a model that proficiently adjusts to evolving conditions, thus leading the way for future research and applications in this field. © 2024 IEEE.

关键词： Dynamic routing algorithms

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Covid-19 Detection From CT Scan Images Using Deep Learning Models 6

Covid-19 Detection From CT Scan Images Using Deep Learning M...

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6th International Conference on Intelligent Systems and computer Vision, ISCV 2024

作者： Alam, Syeda Sadia Habib Munna, Kawsar Shahriyar Rafee, Md. Mushtaq Metropolitan University Department of Computer Science & Engineering Sylhet Bangladesh

ISBN: (纸本)9798350350180

Since 2020, the whole world has been dealing with a pandemic called coronavirus (COVID-19). Compared to automated methods, detecting the presence of the virus in the human body by manual methods is more expensive and time-consuming. Therefore detection of the virus by automated method has further stimulated our research interest. One of the most popular and important methods for automated detection of any disease is CT scanning of the lungs. Our study presents a novel technique that uses a state-of-the-art computer analysis to accurately classify and isolate lung infections by meticulously analyzing lung CT scan images. Our research uncovers minor differences among classes. So, it indicates the need for ongoing development through collaboration with medical specialists to address the minor differences. This study presents a comprehensive technique that uses sophisticated computer analysis to accurately classify and isolate lung infections from Lung CT scan images. The technique, which is a complex combination of some classical CNN architectures, performs admirably in terms of accuracy and is especially good at identifying spots of infection in lung pictures. Our study integrates advanced techniques such as EfficientNetB4, VGG19, VGG16, NASNetMobile, Xception and proposed a new model. The latters obtain an in-depth understanding of complex lung architecture and infection patterns by using several masks. We get 99.96% accuracy on our proposed model compared to other evaluation. This work highly benefits the field of COVID-19 image analysis and offers a thorough framework for real-world use in healthcare sectors. We also find that this proposed model is appropriate for the used dataset. © 2024 IEEE.

关键词： COVID-19

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Carrying-Mode-Free Stair Ascent and Descent Estimation using Smartphones

Carrying-Mode-Free Stair Ascent and Descent Estimation using...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Kajimoto, Dai Saeki, Etsushi Bao, Siya Togawa, Nozomu Waseda University Department of Computer Science and Communications Engineering Japan

ISBN: (纸本)9798350324136

This paper proposes a smartphone-based pedestrian dead reckoning to track stair ascent and descent. By using accelerometer and barometer information, pedestrians' gait patterns are collected under different carrying modes to detect the stair ascent and descent motions. Also, weather influences are considered to eliminate the barometer measurement errors. We evaluate the proposed method in a seven-floor environment, and we compare our method with a conventional method without multiple carrying modes and weather considerations. The minimal error rate of the proposed method is only 4.2% when ascending or descending seven floors, and at most 14.4% error are reduced compared with the conventional method. © 2024 IEEE.

关键词： indoor localization PDR smartphone stair ascent/descent

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XSTART: XApp Simulated Evaluation Environment for Developers 7

XSTART: XApp Simulated Evaluation Environment for Developers

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7th IEEE International Workshop on Metrology for Industry 4.0 and IoT, MetroInd4.0 and IoT 2024

作者： Herrera Gonzalez, Juan Luis Montebugnoli, Sofia Scotece, Domenico Foschini, Luca University of Bologna Department of Computer Science and Engineering Bologna Italy

ISBN: (纸本)9798350385823

The advent of the Open Radio Access Network (Open RAN) in 5G, as delineated in the standards introduced by 3GPP and O-RAN Alliance, posed a pivotal shift in revolutionizing the telecommunications landscape. Central to this transformation is the Open RAN control-plane architecture. Control plane encompasses containerized network functions, operating as intelligent controllers for RAN nodes and resources with non-Real-Time and near-Real-Time constraints. These functions, called Near Real-Time RAN Intelligent Controller (Near-RT RIC) and Non Real-Time RAN Intelligent Controller (Non-RT RIC), include operators-defined applications, respectively named xApps and rApps, serving as common cloud-native applications controlling and optimizing Open RAN elements. Understanding the entire development process and runtime behaviour for these applications becomes an essential prerequisite for providing support for next-generation networks. However, the lack of ready-to-use tools that allow developers to test and evaluate their applications in simulated and real environments makes it difficult to study and experiment with xApps. In this work, we propose xSTART, a ready-to-deploy environment based on Docker technology that allows xApp developers to quickly deploy and incorporate their xApps in a simulated ns-3 environment to test and evaluate their functionalities. Then, we evaluate an IIoT Network use-case scenario showing a machine learning (ML)-based xApp applied to the O-RAN environment. The results compare the use of different ML techniques and show the correct behavior of the simulation. Finally, we make xSTART available to the community to ease the development and evaluation of future xApps. © 2024 IEEE.

关键词： Internet of things

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Overlap-Free Pattern Formation Method Using Particle Swarm Optimization 28th

Overlap-Free Pattern Formation Method Using Particle Swarm O...

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28th International Conference on Knowledge Based and Intelligent information and engineering Systems, KES 2024

作者： Yamada, Kotaro Sugawara, Toshiharu Department of Computer Science and Communications Engineering Waseda University Japan

We propose a method based on particle swarm optimization (PSO) for the multi-agent pattern formation problem (MAPFP), which allows agents to form formation patterns with a high completion rate without overlaps. MAPFP is a problem in which a large number of agents autonomously move to appropriate locations to fill in a target pattern, such as line drawings, letters, and geometric figure, in a cooperative manner. They are used in several applications such as route planning and object exploration. Although some studies consider the pattern-formation problem using swarm intelligence (SI), including PSO, they do not often consider conflicts such as collisions and overlaps of particles, and thus cannot be used in real-world applications. Our proposed method, based on PSO, an SI algorithm, allows agents to act independently, while avoiding overlaps, to form a pattern by introducing a local field of view to each particle and secreting pheromones to their neighbor points to attract other agents that have not yet found places to stay. Through experimental evaluation, we show that the proposed method not only eliminates overlaps during movement and unwanted overlaps on a target pattern but also improves the pattern completion rate compared to the baseline. © 2024 The Authors.

关键词： Particle swarm optimization (PSO)

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Optimizing Cloud Computing Energy Efficiency with a Grasshopper-Inspired Technique for Virtual Machine Migration

Optimizing Cloud Computing Energy Efficiency with a Grasshop...

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2024 International Conference on Advancements in Power, Communication and Intelligent Systems, APCI 2024

作者： Singh, Jaspreet Walia, Navpreet Kaur Chandigarh University Department of Computer Science & Engineering Mohali India

ISBN: (纸本)9798350363289

This research paper presents a novel approach using the grasshopper algorithm to improve the energy efficiency of virtual machine (VM) migration in cloud computing. This methodology reframes resource allocation techniques to address the complexity of VM selection from heavily utilized physical machines (PMs). Its effectiveness is evaluated in comparison to traditional VM migration techniques and optimization algorithms such as Firefly and Artificial Bee Colony (ABC). Significant metrics that act as evaluation criteria for algorithm include the reduction of energy usage, mitigation of service level agreement violations (SLA-V), and overall resource optimization. The research paper not only presents a novel approach but also provides significant perspectives for the advancement of dynamic resource allocation in cloud systems in the future. It creates opportunities for enhancing energy efficiency in the constantly changing field of cloud computing by exploring computational approaches inspired by nature. © 2024 IEEE.

关键词： Cloud platforms

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Dynamic Prototype Weighting for Multi-label Classification Problems 20

Dynamic Prototype Weighting for Multi-label Classification P...

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20th CSI International Symposium on Artificial Intelligence and Signal Processing, AISP 2024

作者： Shahsavandi, Ali Taheri, Mohammad Ziarati, Koorush Shiraz University Department of Computer Science and Engineering Shiraz Iran

ISBN: (纸本)9798350383942

Multi-label classification involves the assignment of several labels to each data instance simultaneously. Binary relevance (BR) is widely acknowledged as the most straightforward approach for handling multi-label scenarios. The process involves breaking down the multi-label learning task into a number of independent binary learning tasks. Using Nearest Neighbor (NN) as the binary classifier in BR (BRNN) is a simple, descriptive, and powerful approach. However, the NN has drawbacks, including its dependence on the distance criterion. To address this, we propose a method that tunes a parametric distance using a prototype weighting approach, minimizing complete cross-validation (CCV) classification error on training data. This improves the classifier's generalization and reduces randomness. Nevertheless, it is well established that when dealing with imbalanced data, other performance evaluation metrics, such as the F-measure, offer more relevant insights than classification error. The second contribution of this paper is extending this method to improve the F-measure. Due to the imbalanced nature of multi-label data, it is expected that performance will improve. Our method is compared to state-of-the-art NN-based approaches, demonstrating comparable or superior performance in experiments. © 2024 IEEE.

关键词： Classification (of information)

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Optimizing Block Propagation in Bitcoin Network with Region-based Neighbor Selection Using Reinforcement Learning 6

Optimizing Block Propagation in Bitcoin Network with Region-...

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6th IEEE International Conference on Blockchain and Cryptocurrency, ICBC 2024

作者： Choi, Wonseok Jeong, Euidong Woo, Jongsoo Hong, James Won-Ki Postech Department of Computer Science and Engineering Pohang Korea Republic of

ISBN: (纸本)9798350316742

Bitcoin proved the potential of blockchain technology through decentralized, transparent, and immutable transactions. However, there are still challenges for fast and stable transitions. Optimizing block propagation times within the network is one of them. Prolonged propagation times can restrict efficiency, scalability, and security. This paper presents a novel approach to reducing block propagation time through leveraging reinforce-ment learning (RL) for the node's neighbor selection strategies. We implemented a Deep Q-Network (DQN) model in minimizing block receive times at each node, thereby impacting overall block propagation time. We used a model that defines node states based on latencies of outbound connections, which present the node's region. By evaluating this model through simulations using SimBlock, a robust Bitcoin network simulator, we observed a significant reduction in block propagation time - approximately 30% for smaller networks and 20% for larger ones. Our analysis extended to node connections generated by our model and comparative evaluation against existing methodologies. © 2024 IEEE.

关键词： Deep reinforcement learning

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