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Leveraging Adaptive Algorithms for Real-Time Data Analysis 15

Leveraging Adaptive Algorithms for Real-Time Data Analysis

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

作者： Sahoo, Girija Shankar Hannah Jessie Rani, R. Goyal, Manish Kumar Mohammed, Vazeer Ali Jana, D Little Femilin Wasatkar, Namrata N. Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Lewis University Illinois Department of Computer & Information System United States Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350370249

This paper proposes the use of adaptive algorithms for real-time data analysis. Adaptive algorithms are a set of practical methods used for acting information-driven optimization and studying. They have a wide variety of applications and may be implemented to a selection of facts analysis obligations. This paper offers an overview of existing adaptive algorithms and descriptions of several new programs in the area of actual-time facts analysis. In particular, the paper discusses the notion of adaptive thresholding, which allows for practical real-time evaluation of large datasets. The paper also provides a novel technique for making use of these algorithms in an online learning environment. Results from simulations illustrate the ability of the proposed method in real-international packages. Sooner or later, the paper presents numerous key lessons discovered and affords recommendations for future research and packages in this area. © 2024 IEEE.

关键词： Adaptive algorithms

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Analysis of Performance of YOLOv8 Algorithm for Pedestrian Detection 9

Analysis of Performance of YOLOv8 Algorithm for Pedestrian D...

引用

9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Dixit, Ishita Amol Bhoite, Sachin MKSSS's K.B. Joshi Institute of Information Technology Pune India Dr. Vishwanath Karad Mit World Peace University Department of Computer Science and Application Pune India

ISBN: (纸本)9798350377972

Pedestrian detection is widely used nowadays in several applications like Robotics, Security systems and autonomous driving systems. Pedestrian detection is a rapidly advancing field within computer vision. Many algorithms have been developed for pedestrian detection using various deep learning approaches. Despite this development, the task of pedestrian detection from a mixed object dataset containing pedestrian and non-pedestrian images still poses a big challenge. After going through latest papers, it has been found that there is a need for enhancement in the correctness and speed of pedestrian detection for use in real world scenarios like autonomous driving systems. This paper proposes to analyze the execution of the Yolo V8algorithm with an aim of pedestrian detection. To achieve this objective, we have used machine learning pipeline from data collection to evaluation with the help of Yolo V8. The objectives of this paper are to observe and analyse the performance of the Yolo V8 model for pedestrian detection, to compare the time required by the model for two different datasets and to fine tune the model as per requirement. The Yolo v8 algorithm has demonstrated great success in the domain of computer vision for real time object detection. Initially, this algorithm was applied on the Roboflow Pedestrian Yolo V5 dataset and has shown to effectively improve pedestrian detection accuracy as the number of epochs increases. Later the algorithm was applied on the Yolo dataset Sindhi Label containing pedestrian as well as non-pedestrian images. This was done to avoid the chances of overfitting the model. © 2024 IEEE.

关键词： Machine vision

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Efficient opportunistic routing with social context awareness for distributed mobile social networks

Efficient opportunistic routing with social context awarenes...

引用

作者： Xu, Fang Xiao, Nan Deng, Min Xie, Yong Xiong, Zenggang Xu, Qiong School of Computer and Information Science Hubei Engineering University Xiaogan China Department of Computer Technology and Application Qinghai University Xining China

Mobile social networks (MSNs) are developed from mobile ad hoc networks. Nodes in such networks usually have social characteristics. In recent years, researchers are trying to use the social characteristics of the network to propose new data forwarding metrics, so as to design more efficient routing algorithms. However, most of the proposed algorithms only consider local context information, which leads to the performance of the routing is not optimized enough. In this paper, we introduce two key metrics, namely, social relationship and social activity. The metrics will be used to search the best data forwarding nodes to improve the probability of data delivery. We propose a prediction-based social-aware opportunistic routing (PSOR). In the proposed method, node's social profiles are used to search relay candidates set, and the discrete-time semi-Markov prediction model is used to find the probability distribution of node transition between communities. Many simulation experiments based on real traces show that the proposed PSOR algorithm is more efficient to maximize the packet delivery probability than other state-of-the-art algorithms. © 2019 John Wiley & Sons, Ltd.

关键词： Forecasting

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Research RSA Accumulator-Based Stateless Blockchain Optimisation for Smart Contracts

Research RSA Accumulator-Based Stateless Blockchain Optimisa...

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2023 International Conference on Cyber Security, Artificial Intelligence, and Digital Economy, CSAIDE 2023

作者： Zhuang, Jinyu Fan, Hongbo Li, Xiang Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming650500 China Yunnan Key Laboratory of computer technology application Kunming University of Science and Technology Yunnan Kunming650500 China

ISBN: (纸本)9781510666597

Scalability has grown more important as blockchain technology is adopted. This study introduces RSA-accumulator-based stateless blockchain performance optimization. Verifiable computing technology and distributed design compress node state data and prove smart contract validity, while a new smart contract execution logic and transaction verification scheme solve the problem of transaction verification and execution under stateless conditions, increasing system throughput. The technique reduces consensus node storage and improves throughput compared to Ethereum blockchains, according to experiments. This study examines the scheme's concept, implementation, and blockchain scalability potential. © 2023 SPIE.

关键词： Smart contract

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Assessment and Prediction of Urban Pollutants and its Influence on Human Health using Deep Learning Algorithm 9

Assessment and Prediction of Urban Pollutants and its Influe...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Kavathekar, Vaishali Tripathy, Amiya Kumar Chettri, Sarat Kumar Bhanage, Vinayak Zaidansari, Mohammad Babu, Lavena Joshua, Allen Assam Don Bosco University Computer Science And Engineering Assam India Don Bosco Institute of Technology Mumbai India Don Bosco Institute of Technology Computer Engineering Mumbai India Assam Don Bosco University Computer Application Assam India Hiroshima University Advance Science And Engineering Hiroshima Japan Don Bosco Institute of Technology Information Technology Mumbai India

ISBN: (纸本)9798350394474

Mumbai, the financial center of India, is a thriving city where the urban environment is constantly changing. Degradation of the environment is a result of these changes. Recent research reveals a recurring problem, wherein pollutant levels regularly exceed advised thresholds, thus having an adverse effect on human health. The research makes use of meteorological data from four weather stations (Powai, Sion, Colaba and Borivali) that span the years 2000-2023. The chosen contaminants for study are PM2.5, PM10, and air temperature. All of the contaminants are growing exponentially, according to the study. For future prediction, deep learning techniques are used. In comparison to the other algorithms, the results show that Bi-LSTM is more accurate. Based on this model, it appears that regular places will soon become hotspots and maybe dangerous. With the use of this model, Mumbai metropolitan planners and authorities may make timely and well-informed decisions that would promote the city's sustainable growth and citizens well-being. © 2024 IEEE.

关键词： Data visualization

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Embedding of Q-Learning in Sine Co-Sine Algorithm for Optimal Multi Robot Path Planning 1

引用

International Conference on Advances and applications of Artificial Intelligence and Machine Learning, ICAAAIML 2021

作者： Paikray, H.K. Das, P.K. Panda, S. Department of Computer Application VSSUT Odisha Burla India Department of Information Technology VSSUT Odisha Burla India

ISBN: (数字)9789811948312

ISBN: (纸本)9789811948305

In recent days extensive use of mobile robots has been felt in most of the human inaccessible area for its intelligent behavior. The paper introduces a new approach for solving the path planning problem of multiple mobile robots in clutter environment. The combine benefits of two existing algorithms such as modified Q-learning and sine cosine algorithm (SCA) has been availed by hybridizing MQL and SCA. The Modified Q-learning algorithm (MQL) has been implemented to compute the next position through its automatic reinforment learning power. The SCA algorithm used to apply the greedy selection approach for further refinement of computing the next position. The proposed hybrid algorithm is implemented to address the path planning of mobile robots in both the static and dynamic environment. The algorithm has been validated through simulation environment and real robot platform to justify its superiority as compared to some existing approaches as described in the literature. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mobile robots

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A Novel Approach with Fusion Edge Prediction Strategy for Table Structure Recognition 11

A Novel Approach with Fusion Edge Prediction Strategy for Ta...

引用

11th International Conference on information Systems and Computing technology, ISCTech 2023

作者： Liu, Yingli Zhang, Guangtao Shen, Tao Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650093 China Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China

ISBN: (纸本)9798350342406

Table, as an information carrier with high information density and direct display form, are widely used in scientific literature and social production documents. Accurately identifying tables from various documents will significantly promote the intelligent application of documents. The table's structural information is crucial for comprehending how the text content in the table relates to one another, but teaching a computer to fully understand a table's structure is still a very difficult undertaking. In this research work, we provided a unique edge-based method to recognize table structures. Specifically, we define the inter-cell relationships as an edge prediction problem and leverage graph convolutional networks to learn representations of the cells. These learned representations are then utilized to directly predict the connections between the cells. The experimental results conducted on three publicly available datasets for table structure recognition provide compelling evidence supporting the feasibility and usefulness of our presented methodology. © 2023 IEEE.

关键词： Forecasting

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Multi-objective performance prediction based on a self-constructed Al-Si alloy dataset 11

Multi-objective performance prediction based on a self-const...

引用

11th International Conference on information Systems and Computing technology, ISCTech 2023

作者： Hu, Dan Liu, Yingli Shen, Tao Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650093 China Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China

ISBN: (纸本)9798350342406

The combination of data-driven approaches and machine learning techniques has changed the design approach for new materials, propelling materials science research into the realm of the fourth paradigm. Researchers in the field of materials science have increasingly focused on utilizing machine learning methods to uncover the underlying nonlinear relationships within material data. However, there is currently limited research in the field of aluminum-silicon alloys regarding the prediction of alloy performance based on the composition-process-structure relationship. Additionally, there is a lack of data sharing in the aluminum-silicon alloy domain. Therefore, this study proposes a methodology. Firstly, an aluminum-silicon alloy dataset is constructed by mining relevant information from materials literature. Then, high-dimensional features in the dataset are subjected to feature selection techniques for dimensionality reduction. Subsequently, a multilayer perceptron model is applied to investigate the relationship between input features such as alloy composition, preparation process, and microstructural parameters, and output features such as yield strength, ultimate tensile strength, and elongation, enabling performance prediction. © 2023 IEEE.

关键词： Feature Selection

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An Automated System for Bone Invasion Detection Occurring at Stage T4 in Oral Squamous Carcinoma using Machine Learning Algorithms 2

An Automated System for Bone Invasion Detection Occurring at...

引用

2nd IEEE International Conference on Intelligent Technologies, CONIT 2022

作者： Agarwal, Pinky Yadav, Anju Mathur, Pratistha Manipal University Jaipur Computer Application Jaipur India Manipal University Jaipur Information Technology Jaipur India

ISBN: (数字)9781665484077

ISBN: (纸本)9781665484077

Bone invasion is the condition where a cancer tumor invades nearby bones. Detection of bone invasion in oral squamous carcinoma is important for treatment planning and surgery of patients. This bone invasion can be a subtle erosion or deep erosion. It is challenging for radiologists to identify the subtle erosion in ct-scan images of oral cancer patients. This paper presents three machine learning (ML) classifiers: Support Vector Machine (SVM), Random Forest (RF), and XG Boost classifiers for automatic detection of bone invasion in CT images of oral cancer patients. The proposed framework classifies CT images as images without invasion and images with invasion. Due to the unavailability of the public dataset for this work, a dataset of 37 patients is collected from S.M.S. hospital, Jaipur. All the three classifiers SVM, RF, and XG Boost have given outstanding results with 0.94, 0.93, and 0.95 accuracies respectively. For this, both time and spatial domain features are extracted using Grey Level Co-occurrence Matrix (GLCM), Gray Level Difference Method (GLDM), Wavelet Transform (WT), and Fast Fourier Transform (FFT) methods, and extracted features are concatenated and a feature pool is created. Among all XG Boost has outperformed others. © 2022 IEEE.

关键词： Decision trees

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NOFTrack: Efficient Multi-Object Tracking with a Speed Advantage

NOFTrack: Efficient Multi-Object Tracking with a Speed Advan...

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2024 International Symposium on Digital Home, ISDH 2024

作者： Jiang, Ming Ke, Jiehao Guo, Biao Metaverse Application Engineering Center Guilin University of Electronic Technology Guilin China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software Guilin China

ISBN: (纸本)9798331509873

In the field of multi-object tracking (MOT), accurately tracking the position and characteristics of targets is crucial. However, traditional methods often overlook detection results with low confidence, which can lead to the omission of targets and the fragmentation of trajectories. To overcome these challenges, we have introduced a novel NOF algorithm. This algorithm efficiently handles detection boxes through a tiered strategy, initially using an adaptive Kalman filter for preliminary matching, followed by a secondary screening of detection boxes with lower confidence, and a final stage for processing dubious detection results. This approach not only enhances the speed and accuracy of tracking but also performs exceptionally well when dealing with complex scenes. Moreover, it is capable of achieving high efficiency, with a rate of over 300 frames per second (FPS), ensuring the efficiency and precision of the tracking process. © 2024 IEEE.

关键词： Target tracking

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