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Design of intelligent System for Puzzle Games Based on Machine Learning 3

Design of Intelligent System for Puzzle Games Based on Machi...

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3rd ieee international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Jiang, Xinli Fu, Peng Center Of Well-being Training Changchun Humanities And Sciences College Changchun China

ISBN: (纸本)9798350318609

In terms of data processing in puzzle games, this research paper studies and implements a set of data table workflows, with the aim of improving the flexibility of data configuration and increasing the convenience of data export, runtime loading, and data usage. This article implements a puzzle game framework that encapsulates the underlying modules and provides support for the upper logic modules. The three most important and commonly used modules in game frameworks are: state machine module, resource management module, and log module. The research results indicate that based on the puzzle game framework, the development efficiency of logical functions has been greatly improved, and the portability of the code has been greatly enhanced. Through overall testing, it can be seen from the overall performance that the game client is running well, and all parts meet the expected requirements. It also meets the requirements in non-functional aspects such as performance. © 2024 ieee.

关键词： intelligent systems

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Inventory Big data Management for Internet of Things based on Privacy Preserving 9

Inventory Big Data Management for Internet of Things based o...

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9th ieee international conference on Big data Security on Cloud (BigdataSecurity) / 9th ieee international conference on High Performance and Smart computing (ieee HPSC) / 9th ieee international conference on intelligent data and Security (ieee IDS)

作者： Li, Chaoliang Zhou, HongJing Liu, Yao Huang, Hua Liu, Shukun Hunan Univ Technol & Business Sch Comp Sci Changsha Peoples R China

ISBN: (纸本)9798350312935

RFID and cloud computing technologies have been widely applied in warehouses to improve the efficiency and accuracy of inventory management based on the above excellent features. Besides the detection of missing RFID tags, a number of efficient schemes are proposed in recent studies, in which the security and privacy aspects are less concerned. In real -world applications, the integrity of tag data is an important factor. To address these problems, we propose an inventory data integrity detection scheme for cloud -based Internet of things warehouse systems. For the purpose of evaluation, we conduct experiments to compare the proposed detection protocol and tag data update protocols with existing schemes in simulations. The results show that the proposed scheme has better performance, as it not only identifies the missing tags in a secure and efficient way but also enables dynamic tag data integrity management quickly.

关键词： IoT Integrity Detection Bilinear Pairings' Privacy

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Overview of Machine Learning for Bioengineering EEG Signal Processing 2

Overview of Machine Learning for Bioengineering EEG Signal P...

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2nd international conference on intelligent Cyber Physical systems and Internet of Things (ICoICI)

作者： Dhande, Siddhi Kamble, Aahash Gundewar, Swapnil Babu, Naathi Naresh Kumar, Praveen Datta Meghe Inst Higher Educ & Res Dept Comp Sci & Med Engn Wardha India Datta Meghe Inst Higher Educ & Res DU Dept Artificial Intelligence & Data Sci Wardha Maharashtra India Datta Meghe Inst Higher Educ & Res Dept Artificial Intelligence & Machine Learning Wardha India

ISBN: (纸本)9798331540661;9798331540678

Electroencephalography (EEG) is a well-established method in neuroscience and bioengineering that offers valuable information about brain function. Recent technological advancements have led to more complex EEG recordings, which necessitate more advanced analysis techniques. Machine learning has emerged as an asset in EEG signal processing, enabling novel approaches to comprehension and utilization of brain activity. This article provides an in-depth analysis of the intersection of machine learning and EEG signal processing in bioengineering applications. This text presents a comprehensive overview of EEG data analysis techniques, focusing on the key steps of acquisition, preprocessing, and feature extraction. It highlights the challenges and strategies used to extract valuable information from raw EEG recordings. Additionally, it surveys various machine learning algorithms, including classic and modern deep learning methods, demonstrating their effectiveness in analyzing EEG signals and opening new frontiers in the field of bioengineering. This paper explores the growing connection between machine learning and EEG signal processing, examining how they work together to enhance healthcare, neurotechnology, and our knowledge of the brain. By studying existing research and cutting-edge developments, this study intends to focus on this synergistic relationship and its importance in these fields.

关键词： EEG signal processing Machine learning algorithms Bioengineering applications Neurotechnology

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Integrating Machine Learning and Epidemiological data to Forecast Zoonotic Disease Outbreaks from Avian Influenza and Other Animal

Integrating Machine Learning and Epidemiological Data to For...

引用

2024 international conference on Innovative computing, intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Priya Dharshini, B. Saranya, A. Senthilnayaki, B. St. Joseph's Institute of Technology Department of Information Technology OMR Chennai India

ISBN: (纸本)9798331543617

There are growing concerns about zoonotic diseases, such as avian influenza and other zoonotic pathogens, that increasingly threaten global health security. advanced methods for predicting outbreaks are thus needed. This article pools epidemiological data with machine learning in order to develop a time series forecasting model capable of predicting the potential of new outbreaks using historical information. Analyses of past outbreaks, pathogen strains, geographic patterns, and temporal tr capture these relations and allow for early warnings of future zoonotic spill overs. Yet another important advantage for using machine learning in time-series analysis is that it can more effectively process complex high-dimensional data, and it might just be the key to finding some of these hidden patterns. In this way, the tools for dynamic and accurate outbreak prediction will be met, given how unpredictable and ever more frequent zoonotic diseases have become. It would be able to empower public health authorities better to foresee the outbreak potential, allocate appropriate resources beforehand, prepare relevant preventive measures, and mitigate more significant risks of transmission among the whole population. Second, this framework can be used to predict all other zoonotic diseases, including further enhancement of global public health surveillance and pandemic preparedness efforts © 2024 ieee.

关键词： Adversarial machine learning

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Research on Internet of Things Signals acquisition and Processing based on Edge Computation 5

Research on Internet of Things Signals Acquisition and Proce...

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5th international conference on Computer Engineering and intelligent Control, ICCEIC 2024

作者： Zhang, Xiaolong Qiu, Wenheng Yang, Huaping Hou, Aohua College of Electrical Engineering and New Energy China Three Gorges University Yichang Hubei443002 China Hubei University of Arts and Sciences Faculty of Automotive and Traffic Engineering Xiangyang Hubei441053 China

ISBN: (纸本)9798331507992

The proliferation of Internet of Things (IoT) devices has led to the generation of vast quantities of data, placing considerable strain on conventional cloud computing infrastructure. Edge computing has emerged as a viable solution to address this challenge. In this study, we propose an Internet of Things (IoT) signal processing framework that is combined with distributed edge nodes. The objective is to improve data transmission efficiency and real-time performance. Firstly, in consideration of the constrained resources of edge devices, we have devised lightweight preprocessing algorithms, including frequency domain filtering based on Fourier transform and a denoising algorithm based on wavelet transform, with the objective of reducing the noise and redundant information present in the original signal. Subsequently, the adaptive compressed sensing technology was employed to compress the preprocessed signal, which resulted in a notable reduction in the bandwidth requirement for data transmission. Subsequently, in order to fully utilise the computing capabilities of edge nodes, we introduce a multi-task learning collaborative computing model. The model permits the sharing of computing tasks between multiple edge nodes, facilitates data collaboration between edge nodes through federated learning, and reduces computing redundancy and communication overhead. The experimental phase employs the Google Edge TPU dataset to evaluate the dataset pertinent to the Internet of Things. The results indicate that the proposed methodology exhibits a notable enhancement in data processing latency and transmission efficacy in comparison to the conventional approach. © 2024 ieee.

关键词： Collaborative computing Edge Computation Internet of Things Signal Processing

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FPGA-Based Multi-Channel Image acquisition and Processing System 2

FPGA-Based Multi-Channel Image Acquisition and Processing Sy...

引用

2nd international conference on Signal Processing and intelligent computing, SPIC 2024

作者： Chu, Xiaohui Jiang, Qiang Zhang, Zhichao Chen, Tong Wang, Xin Yuan, Yi Shenyang Ligong University School of Automation and Electrical Engineering Shenyang110159 China Shenyang Ligong University Shenyang110159 China

ISBN: (纸本)9798350368888

The deep integration of new-generation information technology and manufacturing is triggering far-reaching industrial changes. Machine vision inspection is widely used in large-scale repetitive industrial production processes. In order to solve the problem of the limited field of view of the machine vision inspection system and the obstruction of the camera view during the inspection process, this article takes the glue dispenser vision inspection system as the project background, and designs a high-performance and low-cost FPGA-based multi-channel image acquisition and transmission system scheme. The system can realize parallel acquisition, decoding, storage, display, and transmission of data from 4 image sensors. The OV5640 is used as the video sensor for video capture, and video data is cached using DDR3 SDRAM and transmitted to the host computer through Gigabit Ethernet communication technology. This thesis utilizes the advantages of FPGA in the field of image processing to perform real-time pipeline operations on video data, solving the limitation of video capture by a single camera and improving the system's real-time performance. Implementing multi-channel image grayscale conversion, filtering, edge detection, and stitching fusion in FPGA. © 2024 ieee.

关键词： Inspection

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intelligent Energy Management for Electric Vehicles: Fuzzy Logic and Neural Network Integration 10

Intelligent Energy Management for Electric Vehicles: Fuzzy L...

引用

10th international conference on advanced computing and Communication systems, ICACCS 2024

作者： Thirumalaikumari, T. Princy, D. Lita Uma, S. Sadaieswaran, R. Malathi, P. Legapriyadharshini, N. Saveetha College of Liberal Arts and Sciences Simats Deemed to be University Department of Computer Science Tamil Nadu Chennai India Meenakshi College of Engineering Department of Master of Computer Applications Tamil Nadu Chennai India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science Engineering Tamil Nadu Mahabalipuram India Tagore College of arts and science Department of Computer Applications Chennai Chromepet India Saveetha College of Liberal Arts and Sciences Simats Deemed to be University Department of Data Analytics Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Simats Deemed to be University Department of Computer Application Tamil Nadu Chennai India

ISBN: (纸本)9798350384369

This study suggests a novel methodology for intelligent energy management in electric vehicles (EVs) through the integration of neural networks and fuzzy logic. Achieving enhanced energy efficiency for electric vehicles is a critical concern that is tackled in this research through the integration of neural networks' decision-making and pattern recognition functionalities with fuzzy logic's adaptability and learning capabilities. The proposed method seeks to optimize energy consumption, enhance overall performance, and extend driving range. The integrated system is subjected to rigorous testing using a meticulously planned experimental setup and simulation environment, which enables it to surpass the performance of more conventional approaches to energy management. The results emphasize the potential of the novel methodology to improve environmentally sustainable transportation options and propel the development of electric vehicle technology. This study not only offers valuable insights into efficient energy management but also establishes the groundwork for subsequent developments in the domain. © 2024 ieee.

关键词： Electric Mobility Electric Vehicles Energy Management intelligent systems Neural Networks Smart Grid

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The Sugeno integral used for federated learning with uncertainty for unbalanced data

The Sugeno integral used for federated learning with uncerta...

引用

ieee international conference on Fuzzy systems (FUZZ-ieee)

作者： Wilbik, Anna Pekala, Barbara Szkola, Jaroslaw Dyczkowski, Krzysztof Maastricht Univ Maastricht Netherlands Univ Rzeszow Univ Informat Technol & Management Rzeszow Poland Univ Rzeszow Rzeszow Poland Adam Mickiewicz Univ Poznan Poland

ISBN: (纸本)9798350332285

data is crucial in the digital economy. Many businesses collect and use their data to enhance their performance. However, limited data or low data quality can hinder model development, particularly in dynamic environments. To overcome this, companies collecting similar data may opt to exchange knowledge without sharing their data, due to privacy or legal issues. This is where federated learning comes in. In horizontal federated learning, each client (organization) iteratively improves its model, so that it can be regularly aggregated and shared with all clients participating in the federation for further improvements. In federated averaging, the aggregation mechanism is based on the weighted average and the weights depend on the amount of data available to each client. In this paper, we propose to use a more advanced aggregation mechanism, namely the Sugeno integral. The initial results are promising.

关键词： data acquisition

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Enhancing data Security in Mobile Traffic Networks Through Reverse Engineering 4

Enhancing Data Security in Mobile Traffic Networks Through R...

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4th international conference on Ubiquitous computing and intelligent Information systems, ICUIS 2024

作者： Islam, Syed Arif Mohankumar, M. Khatuna Jannat, Umma Karpagam Academy of Higher Education Dept. of Computer Science Coimbatore India Sns College of Technology Dept. of Computer Application Coimbatore India

ISBN: (纸本)9798331529635

This research enhances mobile traffic network security using reverse engineering and encryption. It analyzes vulnerabilities in evolving mobile protocols (GSM, 4G, 5G) and applies encryption schemes like AES and RSA to secure data transmission. The study also explores secure key exchange mechanisms such as Diffie-Hellman (DH) and Elliptic Curve Cryptography (ECC) and evaluates Intrusion Detection systems (IDS) for threat detection. Tests in a Python-simulated environment confirm the effectiveness of these solutions, providing insights into balancing security and performance in dynamic mobile networks. © 2024 ieee.

关键词： data Security Encryption Mobile Traffic Networks Reverse Engineering Vulnerability Assessment

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Synergistic Multi-Sensor Fusion for Enhanced Vehicle Safety and Security-Integrating advanced Technologies and intelligent systems 5

Synergistic Multi-Sensor Fusion for Enhanced Vehicle Safety ...

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5th ieee international conference on Advances in computing, Communication Control and Networking, ICAC3N 2023

作者： Akilan, T. Manikandan, P. Pushpavanam, B. Swedheetha, C. Naveen, P. Sinha, Jaya Amity University Dept. Of Iqac Kolkata India Kalasalingam Academy Of Research And Education Dept. Of Ece Krishnankoil India Psna College Of Engineering And Technology Dept. Of Eee Dindigul India Velammal College Of Engineering And Technology Dept. Of Cse Madurai India Kpr Institute Of Engineering And Technology Dept. Of Eee Coimbatore India Galgotias College Of Engineering And Technology Computer Science And Engineering Greater Noida India

ISBN: (纸本)9798350330861

The integration of multiple sensors and advanced technologies in the field of vehicle safety and security has emerged as a promising approach to enhance overall road safety and protect vehicles from potential threats. This paper presents a comprehensive study on the concept of synergistic multi-sensor fusion for advanced vehicle safety and security, focusing on the integration of cutting-edge technologies and intelligent systems. The proposed framework involves fusing data from various sensors to obtain a comprehensive and accurate perception of the vehicle's surroundings and safety of the driver. By combining the strengths of each sensor, the system overcomes individual limitations and provides robust detection and recognition capabilities. Firstly, it enables accurate and timely detection of potential hazardous gas, infront nearest obstacle detection, accident identification this allowing the vehicle to take proactive measures to avoid collisions. Secondly, it enhances security by detecting and mitigating threats, such as fingerprint-based security system, theft alert. Additionally, the integration of intelligent systems enables driver safety measure based on driver heart rate monitoring, fire detection, current location detection and prone zone vehicle control. This paper highlights the significance of synergistic multi-sensor fusion in advancing vehicle safety and security. The integration of advanced technologies and intelligent systems enables robust detection, recognition, and decision-making capabilities, contributing to a safer and more secure driving experience. © 2023 ieee.

关键词： intelligent systems

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