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检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

共 6956 条记录，以下是4591-4600 订阅

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A Real-Time Video Surveillance-Based Framework for Early Plant Disease Detection Using Jetson TX1 and Novel LeafNet-104 Algorithm 4th

A Real-Time Video Surveillance-Based Framework for Early Pla...

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4th international conference on Frontiers in computing and Systems, COMSYS 2023

作者： Murthy, Jamuna S. Dhanashekar, K. Siddesh, G.M. Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru India The Ohio State University Columbus United States

ISBN: (纸本)9789819726134

The precise and timely identification of plant diseases plays a pivotal role in safeguarding crop health and optimizing agricultural yields. This paper introduces an innovative framework designed for plant disease detection using video surveillance-based cameras strategically deployed on the Nvidia Jetson TX1 hardware platform within agricultural farms. Leveraging the capabilities of deep learning and the novel neural network architecture known as LeafNet-104, this framework empowers real-time disease detection on plants. Through the deployment of video surveillance cameras, continuous monitoring and early disease detection become achievable, enabling farmers to take prompt corrective measures and mitigate further crop damage. The Nvidia Jetson TX1, with its robust high-performance computing capabilities, enables on-device processing, rendering it ideal for real-time applications. To assess the efficacy of the proposed framework, comprehensive experiments were conducted on an actual agricultural farm. The dataset encompassed various plant species and disease types. Evaluation metrics such as accuracy, precision, recall, and the F1-score were employed to gauge the performance of the LeafNet-104 algorithm. The experimental findings underscored the framework's capacity to accurately and swiftly detect and classify plant diseases in real time, attaining an impressive accuracy rate of 97.5%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Crops

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An Analysis of Cancer data Sets Utilizing data Mining

An Analysis of Cancer Data Sets Utilizing Data Mining

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Advances in computing, Communication and Applied Informatics (ACCAI), international conference on

作者： Bhaludra R Nadh Singh Sripuram Sai Keerthi Vantari Sai Nikitha Sama Sai Sradha Neelagiri Shiva Rithika Shivani Dornala Department of Computer Science and Engineering Bhoj Reddy Engineering College for Women Hyderabad-59 Telangana India Bhoj Reddy Engineering College for Women Hyderabad-59 Telangana India

This research aims to ensure precision in medical outcomes by comparing and contrasting classification approaches using various (Lucamia) cancer knowledge sets using data processing technologies. Many researchers have investigated this question, and their findings have lent credence to using specialised knowledge bases and classifiers. They have compared and contrasted various knowledge sets, including those found online. Here, we compare the results of a neural network classification with those obtained without one.

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Forecasting Stock Prices using Gated Recurrent Unit with the Help of Feature Engineering

Forecasting Stock Prices using Gated Recurrent Unit with the...

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Sustainable computing and Smart Systems (ICSCSS), international conference on

作者： Challa Vijay Seshachala Sarma Bezawada Sri Bharath Krishna Buyyanapragada Phani Siva Baradwaz Yedupati Sai Madhav Sunitha Pachala Vuyyuru Lakshmi Lalitha Artificial Intelligence and Data Science KL University Vijayawada India AI&DS department Koneru Lakshmaiah Education Foundation Vaddeswaram AP India

Forecasting the stock prices is one of the hardest tasks because of its volatility and uncertainty, where additional features such as foreign exchange impacts the stock markets. Introducing AI in forecasting has helped in prediction tasks, especially the time series algorithms. This study attempts to implement various regression models such as the ARIMA model, which gives us improved results for data of linear nature but it suffers from conditions such as non-linearity, unable to cope up with dynamic changes in prices, and overdependence on historical data. This study attempts to implement ML and DL mechanisms to predict stock prices. As most stock markets deal with probabilistic functions and mathematical computations, usage of technologies such include algorithms for machine learning and deep learning. The proposed objective is to forecast the stock prices to enable the users to make decisions and undertake profitable trades within certain intervals by using GRU (Gated Recurrent Unit).

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The 5G Edge computing using Deep Reinforcement Learning for data processing

The 5G Edge Computing using Deep Reinforcement Learning for ...

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Smart Generation computing, Communication and Networking (SMART GENCON), international conference on

作者： Manish Nagpal Centre for Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Due to the absence of methods and dedicated equipment for underground 5G applications, data processing latency is significant in coal mines and transmission efficiency is low. To combat this problem, scientists are investigating how to improve underground 5G communication through edge caching and automatically allocating resources based on the actual situation in coal mines, using a technology called mobile edge processing (MEC), which is built on 5G wireless base stations. Based on experimental data, it can be concluded that the median delay of all local execution techniques is 30 ms, whereas the mean delay of completed jobs is 15 ms, provided no modifications are made to the present base station's reported power or transmission power. Compared to other MEC execution techniques, this one has the lowest average time, by 37.5%. A single base stations has the potential to achieve throughputs of 1 Gbps on the uplink and 1.5 Gbps on the downlink simultaneously. Our approach has the potential to considerably enhance the efficiency with which resources are allocated in mining 5G communication networks.

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Consumer Buying Behavior Analysis During COVID-19 Phase Using Statistical data Mining and Multi-verse Stochastic Algorithm 5th

Consumer Buying Behavior Analysis During COVID-19 Phase Usin...

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5th international conference on Recent Innovations in computing , ICRIC 2022

作者： Sinha, Anurag Bhargavi, Mopuru Singh, N.K. Narayan, Devendra Garg, Namit Pal, Siddhartha Department of Computer Science IGNOU New Delhi India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Vaddeswaram India Department of Computer Science BIT Mesra Jharkhand Ranchi India Department of Biotechnology Amity University Jharkhand Ranchi India Department of Mathematics and Statistics IIT Kanpur Uttar Pradesh Kanpur India Department of Physics IIT Kanpur Uttar Pradesh Kanpur India

ISBN: (纸本)9789819906000

COVID-19 has changed particularly the marketing and retail sectors, the epidemic has changed peoples’ habits. Analyzing client purchase patterns and flows is a common use for consumer behavior analysis. For the purpose of analyzing consumer behavior and purchase trends across the COVID-19 era, this work combined a statistical strategy with a data mining approach. Furthermore, a survey-based (online) data analysis was conducted for the evaluation by retailers and customers, filling out a questionnaire that comprised demographic information and product associations obtained during the epidemic. In order to test the association rule of data mining, the information for this study was gathered from a nearby grocery. Additionally, main data was converted to secondary data using the meta-verse algorithm (MOA) (balanced). One of the newest meta-heuristic optimization algorithms, multi-verse optimization (MVO), imitates the multiversity hypothesis of physics and simulates the interaction of many universes. This MOA is based on natural phenomena that employed a stochastic method to accomplish its objective. Finally, statistical analysis has been carried out to look into the purchasing and selling trends of retailers within the dataset. In this method, association rules are generated via pincher search. It counts the supports of the candidate in each run using a bottom-up approach in addition to the supports of chosen item sets using a top-down approach. The Maximum Frequent Candidate Set refers to these. The proposed binary versions transfer a continuous version of the MVO algorithm to its binary counterparts using the idea of transformation functions. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Association rules

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TeraHeap: Reducing Memory Pressure in Managed Big data Frameworks 2023

TeraHeap: Reducing Memory Pressure in Managed Big Data Frame...

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28th ACM international conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2023

作者： Kolokasis, Iacovos G. Evdorou, Giannos Akram, Shoaib Kozanitis, Christos Papagiannis, Anastasios Zakkak, Foivos S. Pratikakis, Polyvios Bilas, Angelos Department of Computer Science University of Crete Greece Greece Australian National University Australia Isovelent Inc. United States Red Hat Inc. United Kingdom

ISBN: (纸本)9781450399180

Big data analytics frameworks, such as Spark and Giraph, need to process and cache massive amounts of data that do not always fit on the managed heap. Therefore, frameworks temporarily move long-lived objects outside the managed heap (off-heap) on a fast storage device. However, this practice results in (1) high serialization/deserialization (S/D) cost and (2) high memory pressure when off-heap objects are moved back to the heap for processing. In this paper, we propose TeraHeap, a system that eliminates S/D overhead and expensive GC scans for a large portion of the objects in big data frameworks. TeraHeap relies on three concepts. (1) It eliminates S/D cost by extending the managed runtime (JVM) to use a second high-capacity heap (H2) over a fast storage device. (2) It offers a simple hint-based interface, allowing big data analytics frameworks to leverage knowledge about objects to populate H2. (3) It reduces GC cost by fencing the garbage collector from scanning H2 objects while maintaining the illusion of a single managed heap. We implement TeraHeap in OpenJDK and evaluate it with 15 widely used applications in two real-world big data frameworks, Spark and Giraph. Our evaluation shows that for the same DRAM size, TeraHeap improves performance by up to 73% and 28% compared to native Spark and Giraph, respectively. Also, it provides better performance by consuming up to 4.6× and 1.2× less DRAM capacity than native Spark and Giraph, respectively. Finally, it outperforms Panthera, a state-of-the-art garbage collector for hybrid memories, by up to 69%. © 2023 ACM.

关键词： data Analytics

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Research on the design of a target control platform for engineering projects under big data

Research on the design of a target control platform for engi...

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Electrical Engineering, Big data and algorithms (EEBDA), IEEE international conference on

作者： Jieru Song Tianjin University of Technology Tianjin China

Big data has great advantages in scientific management, and this technical means also provides new space for the improvement of engineering project management. From the perspective of system analysis and design, the core architecture and big data application framework of the engineering project target control platform under the environment of big data are studied. Through identifying project risk factors and analyzing and processing project data, the target control platform of big data in three aspects of project progress, quality and cost is constructed. The engineering project target control platform based on big data can effectively warn the problems such as delayed progress, hidden quality and excessive cost, and provide project managers with decision-making ideas, so as to promote the reasonable realization of the basic project goals.

关键词： Training Costs Process control Quality control Big data Big data applications Real-time systems

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Medical data Clustering Based on Multi-objective Clustering Algorithm 4th

Medical Data Clustering Based on Multi-objective Clustering ...

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4th international conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Chen, Shilian Tan, Yingsi Guo, Junkai He, Yuqin Geng, Shuang College of Management Shenzhen University Shenzhen518060 China

ISBN: (纸本)9783031201011

With the development of massive medical data, clustering algorithm becomes an effective way for medical data processing and data mining. On the one hand, it helps medical learners find effective information patterns from massive data;on the other hand, it promotes the development of medical technology and increase productivity. For traditional clustering algorithm, a single clustering index is difficult to meet people's needs of diversity and comprehensiveness. In contrast, multi-objective clustering (MOC) considers multiple objectives at the same time, and comprehensively deals with various clustering problems and standards, such as compactness, diversity of feature selection and high data dimension. Artificial bee colony algorithm (ABC) has a faster speed and embodies the idea of swarm intelligence. It imitates the optimization process of bees, and finally obtains the global optimal value. On this basis, this paper proposed a multi-objective artificial bee colony clustering algorithm (MOC-NABC) that is combined with current better-performed clustering algorithm. It takes normalized mutual information (NMI), Calinski-Harabasz (CH), Fowlkes-Mallows index (FMI) and silhouette coefficient (SC) of clustering as the final evaluation indexes. The experiment on UCI mouse protein gene dataset shows that the overall performance effect is greatly improved, e.g. compact clustering and the effective utilization of data features. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Clustering algorithms

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A study of base station establishment site selection based on cluster analysis

A study of base station establishment site selection based o...

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Electrical Engineering, Big data and algorithms (EEBDA), IEEE international conference on

作者： Runxi Luo School of Electronic Information Engineering Wuhan Donghu University Wuhan China

In this paper, to address the site planning and area clustering problems of mobile communication networks, the K-mean clustering algorithm, linear programming, K-mean clustering model, single-objective planning model, and simulation model are established, and python is used to program the solution to obtain the optimal site coordinates of new base stations and base station types, the optimal main direction angle of base stations and the total business volume of weak coverage points covered by base stations, how to cut and divide the area so that The paper features a series of conclusions such as how to cut and divide the area to minimize the total time complexity of clustering. This paper features the analytical processing of data and the establishment and application of K-mean clustering model.

关键词： Base stations Solid modeling Analytical models Clustering algorithms Programming Mobile communication data models

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Construction of Network data processing Model Based on Mathematical Statistics and Wavelet Decomposition Algorithm

Construction of Network Data Processing Model Based on Mathe...

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Networking, Informatics and computing (ICNETIC), international conference on

作者： Wenting Guo Changjiang Institute of Technology Wuhan China

With the rapid growth of people's demand for network, the scale of telecommunication network is becoming larger and larger, and the network technology is becoming more and more complex. However, the difference of equipment from different manufacturers and equipment management means makes network management more complex. Effective maintenance of the network and ensuring the operation quality of the network have become a huge challenge. The information age puts forward higher requirements for data security. Therefore, this paper proposes a network operation anomaly data mining model based on mathematical statistics and wavelet decomposition algorithm, including data extraction, data feature extraction, and anomaly data mining. Through the experiment, it is concluded that the model designed in this paper can basically detect abnormal data by more than 98.6%. Therefore, it proves that the network operation anomaly data mining model based on mathematical statistics designed in this paper is highly effective.

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