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International Symposium on Computational Intelligence and Design, ISCID

作者： Qiuchan BAI Jiahao Wu Chunxia JIN Faculty of Automation Huaiyin Institute of Technology Huaian China Faculty of Computer & Software Engineering Huaiyin Institute of Technology Huaian China

ISBN: (数字)9781728184463

ISBN: (纸本)9781728184470

Due to the poor real-time performance of water quality monitoring, secondary pollution, high cost and other issues, this paper proposes to use wireless sensor network technology to design a water quality monitoring system. The system has strong real-time, online monitoring functions, and acquires multiple parameter data that affect water quality, timely and accurately monitor water quality information, prevent water environmental pollution, reduce the impact of water pollution, and meet the requirements of efficient and intelligent water quality.

关键词： Monitoring Wireless sensor networks Zigbee Water quality GSM Temperature sensors Protocols

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Study on Text Classification of Traditional Chinese Medicine Based on LD and ANN-SoftMaxRegressor 14

Study on Text Classification of Traditional Chinese Medicine...

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14th IEEE International Conference on Intelligent Systems and Knowledge engineering, ISKE 2019

作者： Zhang, Fa Zhang, Hui Jiang, Xiaoling Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huai'an China

ISBN: (纸本)9781728123486

In the long-term accumulation of traditional Chinese medicine, there are many synonymous and different words because of different expressions and descriptions, or the same disease is divided into different diseases because of different clinical manifestations. This phenomenon makes the dimension of the disease data set larger and sparse. In this study, two data sets (one is the uci-breast Cancer data set, the other is the Chinese medicine data crawled by yaozhi network) were used for algorithm experiment. It has been proved by experiments that after the establishment of the disease dictionary through the string editing distance (Levenshtein Distance), the bi-gram smoothing process is performed, and then the PCA dimension reduction is lower. After the ANN-SoftMax Regressor training, the Chinese medicine data fuzzy symptom classification is more accurate, achieving an accuracy of 90.95%. © 2019 IEEE.

关键词： Data mining

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Cloud Computing Methods for Leaking Detection of Halogen Pipeline Based On Distributed Terminals

Cloud Computing Methods for Leaking Detection of Halogen Pip...

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International Conference on Robots & Intelligent System (ICRIS)

作者： Min Xu Jianyang Zhao Qiwei Ye Weihong Ding Xiaoqi Zhu Qin Zhang Faculty of Automation Huaiyin Institute of Technology Huaian China Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian China

In order to ensure the safe transmission of the brine pipeline, this paper designs a leakage detection terminal based on STM32F7. This terminal uses GPS to added time stamps to the collected data. And each distributed terminal uses second pulse signal of GPS to realize the function of synchronously collecting signals. Finally, the data is transmitted to the cloud computing platform through the 4G wireless transmission module system. This paper uses the S transform algorithm to analyze the characteristics of these transmitted data. S transform algorithm fully extracts the characteristics of the data in the time-frequency-mode domain. The simulation results show that the data generates high-frequency signals at default point. Through the whole system, it can effectively realize real-time monitoring.

关键词： Wireless communication Cloud computing Simulation Pipelines Transforms Real-time systems Timing

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Enhancing Sovereign Allocation of Resources in Cloud Milieus Using a Causal Dilated Geometric Algebra Approach for Dynamic Scalability 2

Enhancing Sovereign Allocation of Resources in Cloud Milieus...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Katru, Chandrasekhar Rao Srinivasan, Sathish Kumar, Mohan Yadwad, Sunita Satish, G. Maranan, Ramya Lead Software Engineer Indian LandSC29707 United States Machine Learning & AI Development Oracle Cloud Infrastructure Division San FranciscoCA United States Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Karnataka Bengaluru India Parul institute of Engineering and Technology Parul University Computer science and Engineering Faculty of Engineering and Technology Gujarat India Sree Dattha Institute of Engineering and Science Department of Electrical and Electronics Engineering Telangana Sheriguda India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai India

ISBN: (纸本)9798331505745

Cloud environments utilize availability and usage of resources as crucial components in order to produce scalability, reliability and low cost. Such resource capabilities require autonomous systems that can handle these resources dynamically in spite of fluctuating workloads and service delivery requirements. Static models of resource management suffer from scalability and inability to react on the fly and accurately predict the demand thus causing Service Level Agreement (SLA) breaches and affecting the Quality of Service (QoS) as well as energy consumption profile. Advanced and elaborative cloud workloads require an intelligent system that can analyse the temporal and spatial patterns without impacting the overall system's performance significantly. Inspired by these challenges, this research proposes a Causal Dilated Geometric Algebra Convolutional Transformer Network (CATNet) along with the Prairie-Dog Optimizer (P-DogO), referred to as CAT-DogNet to learn and predict the dynamic scalability. CATNet integrates Causal Dilated Convolutional Networks in time domain for temporal structure capturing and Geometric Algebra Transformer for spatial resource coding. P-DogO adjusts weight parameters and losses for resource allocation, enhancing them depending on a work environment. A set of comprehensive assessments demonstrate a 0.85% SLA violation rate, average response time of less than 10ms, and overall 12.3% less energy consumption than baseline methods. The system had favourable predictability rates over 99.4% for the resource demand estimate. CAT-DogN et presents a sound and agnostic architecture for autonomous resource allocation in cloud environment, which indeed extend the state of art by considering flexibility, power consumption and QoS compliance. © 2025 IEEE.

关键词： Infrastructure as a service (IaaS)

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Application of soft computing algorithms for hybrid modular multilevel inverters

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Measurement: Sensors 2024年 31卷

作者： Gireesh Kumar, Devineni Venkata Sireesha, Nagineni Bhoopal, Neerudi Kumar Gatla, Ranjith Kotb, Hossam AboRas, Kareem M. Elrashidi, Ali Alqarni, Mohammed Yasin Ghadi, Yazeed Oubelaid, Adel Department of Electrical & Electronics Engineering B V Raju Institute of Technology Narsapur502313 India Department of Information Technology Institute of Aeronautical Engineering Hyderabad500043 India Department of Electrical & Electronics Engineering Institute of Aeronautical Engineering Hyderabad500043 India Department of Electrical Power and Machines Faculty of Engineering Alexandria University Alexandria21544 Egypt Electrical Engineering Department University of Business and Technology Ar Rawdah Jeddah23435 Saudi Arabia Engineering Mathematics Department Faculty of Engineering Alexandria University Alexandria21544 Egypt Department of Computer Science and Software Engineering Al Ain University Abu Dhabi15322 United Arab Emirates Laboratoire de Technologie Industrielle et de L'Information Faculté de Technologie Université de Bejaia Bejaia06000 Algeria

This study utilized Harris Hawk optimization to analyze the harmonic distortions of a symmetric capacitor based multilevel converter. The proposed multilevel converter uses identical and symmetric DC sources in its input. The proposed topology for a multilevel converter uses fewer switches than typical cascaded designs. It can output 9-level outputs and can be scaled up to higher levels. The proposed multilevel converter can be improved by implementing a low frequency pulse width modulation technique to reduce the stress on the switches. The optimization of the switching angles using Harris Hawk was able to solve the non-linear aspects of the Selective Harmonic Elimination Pulse Width Modulation. The output voltage's THD was compared with that of other optimization methods, such as ant colony and particle swarm. The comparison shows that the output THD of the Harris Hawk optimizer is lower than that of other methods, which is about 5 %, according to the standards of IEEE-519. © 2023

关键词： Pulse width modulation

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The Monitoring System of Aquaculture Environment

The Monitoring System of Aquaculture Environment

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International Symposium on Computational Intelligence and Design, ISCID

作者： Chunxia JIN Qiuchan BAI Faculty of Computer & Software Engineering Huaiyin Institute of Technology Huaian China Faculty of Automation Huaiyin Institute of Technology Huaian China

ISBN: (数字)9781728184463

ISBN: (纸本)9781728184470

According to the needs of the aquaculture industry, this article proposes to design an aquaculture environment monitoring system for collecting temperature, PH, and dissolved oxygen parameters that affect fishery water quality environment, real-time tracking and monitoring, remote transmission and abnormal handling, etc. The system has played an important role in the application of fish breeding industry, providing technical support for the development of the breeding industry, and bringing greater economic profits to farmers.

关键词： Aquaculture Monitoring Temperature sensors Water quality Temperature measurement Fish Temperature control

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Enhancing Federated Learning by One-Shot Transferring of Intermediate Features from Clients

Enhancing Federated Learning by One-Shot Transferring of Int...

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International Conference on Data Science and Advanced Analytics (DSAA)

作者： Youxingzhu Deng Yipeng Zhou Gang Liu Jessie Hui Wang Yu Shui College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computing Faculty of Science and Engineering Macquarie University Sydney Australia Institute for Network Sciences and Cyberspace Tsinghua University Peking China School of Computer Science University of Technology Sydney Sydney Australia

Federated learning (FL) is an emerging paradigm using a parameter server (PS) to coordinate multiple decentralized clients for training a common model without exposing their raw data. Despite its amazing capability in preserving data privacy, FL confronts two significant challenges that have not been sufficiently addressed by existing works, which are: 1) heterogeneous data distributed on clients given that the PS cannot alter data locations. 2) limited computation resources of FL clients who may conduct model training with mobile devices. To tackle these challenges, we propose a novel federated one-shot transferring of intermediate features (FedOTF) algorithm. More specifically, FedOTF consists of two stages: feature extraction and model reconstruction. To overcome challenge 1, clients in stage 1 only collaboratively train a small model in a federated manner, with the objective to extract features. In stage 2, intermediate features (generated by the small model trained by stage 1) are transferred from clients to the PS so that a large model can be trained to overcome challenge 2. In addition, the design of FedOTF is robust, which can flexibly diminish the number of communication rounds of stage 1 when network capacity is limited, and reduce the amount of exposed features when privacy is concerned. To verify the superiority of FedOTF, we conduct comprehensive experiments with real datasets. The experiment results demonstrate that FedOTF can significantly improve the model utility of FL because a better model can be finally obtained by the PS without incurring heavy computational load on clients. Besides, we conduct robustness evaluation of FedOTF which can achieve stable performance when varying network capacity and privacy requirement.

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A New Rhythm in A1: Convolutional Neural Networks for Music Genre Classification

A New Rhythm in A1: Convolutional Neural Networks for Music ...

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International Conference on Emerging Technologies, ICET

作者： Huzaifa Javed Usama Arshad Zain ul Abideen Raja Hashim Ali Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

Despite the significance of music genre classification in audio identification, it remains under-explored within AI research. This tool is crucial for personalized music recommendations and similar music detection. We have developed an efficient AI model that leverages Convolutional Neural Networks (CNNs), offering high-precision genre identification when integrated into a graphical user interface. The model effectively extracts audio features like Mel Frequency Cepstral Coefficients (MFCCs), zero-crossing rate, and tempo. Testing results reveal strong performance in genre prediction across diverse tracks, affirming the model's ability to discern unique characteristics of various music genres. This performance not only attests to the model's capability in discerning the unique characteristics inherent to different music genres but also suggests that it can effectively generalize to novel, unseen data. Our model lays the groundwork for future enhancements and demonstrates the potential of AI in transforming the music industry - from personalized music playlists to exploratory recommendation systems. The success of this model paves the way for more intricate applications of AI within music analysis.

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An Enhanced Genetic Algorithm Framework for Efficient Solutions to Capacitated Vehicle Routing Problems

An Enhanced Genetic Algorithm Framework for Efficient Soluti...

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International Conference on Emerging Technologies, ICET

作者： Usama Arshad Hamza Hasan Ellahie Zain ul Abideen Raja Hashim Ali Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

Capacitated Vehicle Routing Problems (CVRPs), a widely acknowledged NP-hard issue, pertains to the optimal routing of a limited-capacity vehicle fleet to fulfill customer demand, aiming for the least possible travel distance or cost. Despite the presence of numerous heuristic and exact approaches, the combinatorial characteristic of CVRP renders it challenging, especially for large-scale instances. This research provides an in-depth exploration of utilizing Genetic Algorithms (GAs) to address Capacitated Vehicle Routing Problems (CVRPs), a recognized and intricate optimization issue in the realm of logistics and supply chain management. Our paper concentrates on the innovative usage of GAs, a category of stochastic search methodologies inspired by natural selection and genetics, to grapple with CVRP. We put forth a fresh framework grounded in GA that infuses unique crossover and mutation operations tailor-made for CVRP. Our comprehensive computational trials on benchmark datasets suggest that our GA-centric method is proficient in deriving high-standard solutions within acceptable computational durations, surpassing multiple contemporary techniques concerning solution quality and resilience. Our results also underscore the scalability of our proposed approach, marking it as a viable choice for tackling extensive, real-world CVRPs. This paper enriches the current knowledge bank by demonstrating the prowess of GAs in deciphering complicated combinatorial optimization issues, thus offering a novel viewpoint for future advancements in crafting more robust and efficient CVRP resolutions.

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Client Segmentation and Customization in E-Commerce: Applications of Machine Learning from a Management Perspective

Client Segmentation and Customization in E-Commerce: Applica...

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Communication, computer Sciences and engineering (IC3SE), International Conference on

作者： Sriprasadh K Shamik Palit B Pravallika Manjunatha Reena Lenka Atul Singla Department of Computer Science and Applications Faculty of Science and Humanities SRM Institute of Science and Technology Vadapalani Chennai Department of Computing Science and Software Engineering RAK Campus University of Stirling UAE Institute of Aeronautical Engineering Hyderabad Department of Mechanical Engineering New Horizon College of Engineering Bangalore Symbiosis Institute of Management Studies Symbiosis International (Deemed) University Pune Lovely Professional University Phagwara India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

A machine learning-based e-commerce personalised recommendation system helps address the issue of information overload that inevitably arises when consumers have more and more options in e-commerce, leading to an increasingly complex structure. Customer segmentation is a crucial component of contemporary marketing strategy since it enables companies to effectively adjust their advertising campaigns and customise their communications with customers. Through the analysis of enormous quantities of consumer behaviour data and the discovery of trends that can be utilised for dividing customers into segments, ML algorithms provide a potent tool for automating the procedure of customer segmentation. We report our research and results for identifying a customer's segment. In order to perform machine learning, we integrate the Python Tensorflow library with Pandas for manipulating data frames. To address any discrepancies or gaps in the collected client data, it is refined. Key features are found, and to find hidden patterns and relationships, an exploratory analysis of the data is done. After that, we decide on a suitable machine learning technique to divide the clientele into various groups. We also experiment with several clustering models, including conventional machine learning techniques.

关键词： Consumer behavior Social networking (online) Machine learning Companies Market research Real-time systems Libraries

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