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2024 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2024

作者： Su, Te-Jen Cheng, Jui-Chuan Lin, Wei-Hong Zhuang, Qian-Yi Huang, Yao-Tzeng Wang, Shih-Ming Horng, Mong-Fong Department of Electronic Engineering National Kaohsiung University of Sciences and Technology Kaohsiung Taiwan Department of Computer Science and Information Engineering Cheng Shiu University Kaohsiung Taiwan

ISBN: (纸本)9798350389210

With the development and application of internet and information communication technology, countries around the world have invested heavily in the space industry in recent years, with particular emphasis on the deployment of satellite communication systems. However, the complexity of satellite communication and the uncertainty of environmental changes often affect communication quality. In order to enhance the fault diagnosis and maintenance capabilities of technicians for satellite communication systems, this study applies fuzzy theory to fault diagnosis in satellite communication ground station. The research work introduces the basic architecture and equipment functions of satellite communication systems and combines the years of maintenance experience of experienced personnel to analyze possible types of faults and their impact on system performance. It proposes a fault diagnosis method based on fuzzy theory and establishes a database of possible fault causes. After maintenance or operation personnel input the quality parameters of satellite modem Eb/No(energy per bit to noise power spectral density ratio) and RSL (Received Signal Level) into the system, fuzzy logic inference and data analysis comparison are used to quickly and accurately determine possible fault causes and improvement methods in satellite communication ground station. Finally, through simulation experiments and actual maintenance, the accuracy and reliability of this system are verified. Both maintenance and operation personnel can use this diagnostic method to clarify possible fault causes in satellite communication systems. New staff can quickly enter the independent working stage, thereby effectively improving the work efficiency of the entire maintenance team, reducing time costs, and ensuring the reliability and stability of satellite communication systems. © 2024 IEEE.

关键词： Satellite communication systems

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Cloud-Enabled Neural Networks for Intelligent Vehicle Emissions Tracking and Analysis

Cloud-Enabled Neural Networks for Intelligent Vehicle Emissi...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Anusha, N. Jeslin, J Gnana Srividhya, V. Gupta, Nukala Sujata Meenakshi, R. Srinivasan, C. Vellore Institute of Technology School of Computer Science and Engineering Department of IoT Tamil Nadu Vellore India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Meenakshi College of Engineering Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Chaitanya Bharathi Institute of Technology Department of Computer Engineering and Technology Hyderabad Gandipet India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350382723

A system for smart vehicle emissions monitoring and analysis using cloud computing and neural networks is presented in this paper. As global environmental concerns develop, monitoring and reducing vehicle emissions is crucial. Scalability and real-time data processing are issues for traditional approaches. Thus, cloud-enabled neural network architecture for scalable and efficient vehicle emissions monitoring using distributed computing is developed. A neural network model trained on various vehicle emission datasets, allowing for precise pattern analysis and prediction of emissions. The scalability of the system is guaranteed by using cloud resources, which can accommodate the rising amount of data created by an increasing number of vehicles. By incorporating cloud-based data processing seamlessly, real-time monitoring is accomplished, enabling immediate analysis and prompt emission alerts. The suggested emissions tracking platform is robust and scalable to intelligent transportation systems by revealing vehicle emissions' environmental effect. Future smart city and sustainable urban transportation developments are built on cloud computing and neural networks. The proposed system shows how cloud-enabled neural networks may solve complicated vehicle emissions problems. © 2024 IEEE.

关键词： Scalability

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Public Decision-Making Analysis in Urban Heat Islands Based on Socioeconomic Modelling by Deep Learning Techniques

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Remote Sensing in Earth Systems sciences 2024年第1期8卷 108-117页

作者： Prakash, S. Wilson Kiruthigha, R. Yadav, Ajay Kumar Kanna, S. K. Rajesh Shaik, Khasimbee Krishna, B. Murali Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai Kattankulathur India Department of Information Technology SRM Institute of Science and Technology Chennai Ramapuram India Department of Computer Application United Institute of Management UP Prayagraj 211010 India Department of Mechanical Engineering Rajalakshmi Institute of Technology Chennai India Department of Computer Science and Engineering Aditya University Andhra Pradesh Surampalem India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Urban heat island (UHI) effects, especially in highly urbanised areas, and greenhouse gas emissions from human activity are two elements that accelerate global climate change (GCC). Sustainable city planning and modification may benefit from the quantification of the surface UHI (SUHI) effect utilising land surface temperature (LST), local climate zones (LCZ), and deep learning methods like convolutional neural networks (CNN) and pix2pix. The findings show that socioeconomic variables account for 12–20% of variability in UHI intensity. In general, the contribution rate of urban economic scale is higher than that of population and industrial structure variables. The increase in urban economic expansion increased summertime heat discomfort. This research propose novel method in socioeconomic analysis based on urban heat pattern analysis with public healthcare system using decision-making with DL (deep learning) model. Input is collected as urban heat pattern dataset and processed for noise removal and normalisation. This data has been features are extracted and classified using convolutional fuzzy Q-Gaussian neural network and variational K-C-means regressive learning. The simulation analysis has been carried out based on training accuracy, average precision, recall, and F-measure. The proposed model attained average precision was 94%, recall was 90%, training accuracy was 97%, and F1-score was 92%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Decision-making Deep learning model Public healthcare system Socioeconomic analysis Urban heat pattern

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An Unequal Cluster-Based Routing Mechanism Using Lyrebird Optimization Algorithm to Achieve Network Longevity in WSNs 12th

An Unequal Cluster-Based Routing Mechanism Using Lyrebird Op...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Sengathir, J. Deva Priya, M. Christy Jeba Malar, A. Department of Information Technology CVR College of Engineering Mangalpally Vastunagar Telangana Hyderabad India Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India Department of Information Technology Sri Krishna College of Technology Kovaipudur Tamilnadu Coimbatore India

ISBN: (纸本)9789819788354

Energy constrained feature of sensor nodes, hostile environment over which it is deployed for monitoring and the necessity to make reactive decisions introduce crucial challenges in Wireless Sensor Networks (WSNs). They demand energy utilization to be optimized for extending network lifetime. Unequal cluster-based routing is identified to be the most significant methodology, which when adopted in WSNs helps in reducing the degree of energy utilization without impacting the longevity of the network. In this paper, an Unequal Cluster-based Routing Mechanism using Lyrebird Optimization Algorithm (UCRMLOA) is proposed for improving energy efficiency which helps in preventing the formation of energy holes and improving network longevity. This UCRMLOA approach is proposed by mimicking the natural behaviour of Lyrebird in the event of danger which is analogical to the process of exploration and exploitation introduced during clustering and Cluster Head (CH) selection. It specifically uses Lyrebird Optimization Algorithm (LOA) for forming clusters that effectively balances energy in the network. It uses a fitness function that uses parameters such as number of sensor nodes selected as relay, distance between the selected CHs and the sink, Residual Energy (RE) for CH selection and subsequent optimal route determination. It is also proposed as a potential approach that balances the network traffic load efficiently such that formation of energy holes is completely prevented for maximizing network lifetime. The results of UCRMLOA for varying number of sensor nodes confirm 22.1% better sustenance of alive nodes, 24.76% minimized energy consumption and 20.42% reduced packet delay in contrast to baseline approaches considered for evaluation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sensor nodes

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An Effective Cyber Security Threat Detection in Smart Cities Using Dueling Deep Q Networks 4

An Effective Cyber Security Threat Detection in Smart Cities...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Stanly Jayaprakash, J. Kodati, Sarangam Kanchana, A. Al-Farouni, Mohammed Ramachandra, A.C. Mahendra Institute of Technology Department of Computer Science and Engineering Namakkal India Cvr College of Engineering Department of Information Technology Hyderabad India Mahendra Engineering College for Women Department of Computer Science and Engineering Namakkal India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350352931

In recent days, smart city technologies are developing rapidly with the incorporation of IoT devices and AI-driven systems has modernized and thus facilitated for enhancing productivity, sustainability, and connectivity. The motive is to mitigate the rising cyber threats which are affecting the interrelated organizations of smart cities. Despite advancements in threat detection technologies, challenges persist, including real-time anomaly detection in vast data streams, managing resource constraints on IoT devices, and ensuring adaptability against evolving attack vectors. Therefore, this research suggests a novel approach by using Dueling Deep Q Networks (DDQN) method. Initially, the data is collected from the dataset and then Inter Quartile Range (IQR) is imposed at the preprocessing stage for detection of outliers. Then, extraction of optimal features is done by using Term Frequency-Inverse Document Frequency (TF-IDF) and Recursive Feature Elimination (RFE) which transforms the data into statistical vectors and eliminates the least significance feature respectively. Finally, the obtained optimal features are detected using DDQN method as it has the ability to detect effectively with respect to the state of action. The proposed DDQN has resulted with accuracy of 99.32% when compared with Improved Bacterial Foraging Optimization using optimum deep learning for Anomaly detection (IBFO-ODLAD). © 2024 IEEE.

关键词： Cyber attacks

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Tomato Leaves Disease Detection Using Gray Level Co-Occurrence Matrix (GLCM) and Image Processing Techniques

Tomato Leaves Disease Detection Using Gray Level Co-Occurren...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Arikaran, N. Prabu, G. Ejoumalai, Arya Bhuvanesh, A. Kamalesh, S. Sathishkumar, R. Manakula Vinayagar Institute of Technology Department of Computer Science and Engineering Puducherry India Sri Manakula Vinayagar Engineering College Department of Information Technology Puducherry India

ISBN: (纸本)9798331510022

Crop diseases pose a serious danger to agricultural productivity and food security, making accurate detection techniques urgently needed. Convolutional Neural Networks (CNN) are used in this project's automated approach to better detect damaged tomato leaves using image processing. First, a sizable dataset with tagged photos of both healthy and diseased leaves is gathered from sources such as the Plant Village collection. To increase the diversity and quality of data, preprocessing techniques like data augmentation, scaling, and standardization are used. To guarantee accuracy and robustness, the CNN model is trained on well-labeled datasets after extracting complex features from the images. Techniques like batch normalization and dropout help reduce overfitting. Metrics including accuracy, precision, recall, and F1-score are used to evaluate the model's performance, showing significant improvements over conventional methods. With potential for wider uses in precision farming, the findings show that CNNs are a viable tool for the early and precise diagnosis of tomato leaf diseases. In order to enhance performance across a range of crops and diseases, future research will concentrate on transfer learning and growing datasets. The proposed model achieved 97.2% ACC, 95.7% Spec, 94.5% Recall, 98% Prec, and 97.5% F1-Score. © 2024 IEEE.

关键词： Fruits

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A Study on Dynamic Adjustment of Radiator Fan in Automotive Cooling System Based on Fuzzy Algorithm

A Study on Dynamic Adjustment of Radiator Fan in Automotive ...

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2024 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2024

作者： Su, Te-Jen Zhuang, Qian-Yi Teng, Ya-Mao Cheng, Jui-Chuan Wang, Shih-Ming Department of Electronic Engineering National Kaohsiung University of Sciences and Technology Kaohsiung Taiwan Department of Computer Science and Information Engineering Cheng Shiu University Kaohsiung Taiwan

ISBN: (纸本)9798350389210

This work explores the application of fuzzy theory to shorten the development timeline and enhance the flexibility of fan speed control logic for automotive radiators, using vehicle speed and engine temperature as input variables. With the variability of vehicle operating conditions, traditional fan speed control methods often require consideration of multiple parameters such as air conditioning pressure, the amount and temperature of coolant, engine rotational temperature, and various driving conditions, leading to extended development times for fan controllers. To address this, we adopted a fuzzy logic controller (FLC) to design a novel fan speed regulation mechanism that dynamically adjusts fan speed based on real-time changes in vehicle speed and engine temperature. Our research method includes establishing a fuzzy control system model, defining the fuzzy sets for input and output variables, and their corresponding fuzzy rules. Through simulation testing, we evaluated the performance of the fuzzy control strategy under different operating conditions. The results indicate that compared to traditional control methods, the fuzzy control approach provides greater flexibility in fan speed development, thereby ensuring that the engine temperature remains within an ideal range. This study demonstrates the potential of fuzzy theory in controlling complex systems and provides a new approach to enhancing the flexibility of fan development and optimizing vehicle cooling systems. Future work will explore input variables and control strategies to improve system performance and adaptability. © 2024 IEEE.

关键词： Fuzzy control

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Detection of malaria parasite in thick blood smears using deep learning

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Materials Today: Proceedings 2022年 64卷 511-516页

作者： Balaram, Allam Silparaj, Manda Gajula, Rajender Department of Information Technology MLR Institute of Technology Hyderabad 500043 India Department of Computer Science and Engineering Vignan Institute of Technology and Science Hyderabad 508284 India Department of Computer Science and Engineering Vignana Bharathi Engineering College Hyderabad 501510 India

We're working on a deep learning algorithm which can distinguish between malaria parasites in blood smears in a video format which consists of several images. There are two processing steps in our method. Initially, we locate parasites by quickly inspecting a thick smear image and remove noise from that parasite. Then, using a customized Convolutional Neural Network (CNN), each image in a video is classified as one of two ways, whether it consists of parasite or not. We took a datafile of 1817 smear photographs from 148 victims from an openly accessible institute. This datafile was used to assess and trial our algorithm. © 2022

关键词： Iterative methods

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AI-Driven Innovations: Transforming Data Support and Environmental Management for the Future

AI-Driven Innovations: Transforming Data Support and Environ...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Mahfuri, Mahmoud Ibrahim, Amer International University for Science and Technology Computer and Information Technology Department Syria United Arab Emirates University Department of Computer Science and Software Engineering United Arab Emirates

ISBN: (纸本)9798350369106

This paper presents the evolving role of artificial intelligence (AI) in improving internal control and management processes. AI-driven technologies, including Generative Adversarial Networks (GANs) and ontologies, in addition to enabling data-supported decision-making, and empowering local governments to execute administrative tasks more efficiently and effectively. The study explores the wide range of opportunities that AI offers, highlighting the potential for optimizing internal workflows. Besides, it delves into key challenges, models, and methods for fully integrating AI into organizational processes, providing actionable insights for achieving streamlined, data-driven management systems. © 2024 IEEE.

关键词： Generative adversarial networks

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A Framework for Securing Saudi Arabian Hospital Industry: Vision-2030 Perspective

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Intelligent Automation & Soft Computing 2023年第6期36卷 2773-2786页

作者： Hosam Alhakami Abdullah Baz Mohammad Al-shareef Rajeev Kumar Alka Agrawal Raees Ahmad Khan Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955SaudiArabia Department of Computer Engineering College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Security Forces Hospital Makkah21955Saudi Arabia Department of Computer Science and Engineering Babu Banarasi Das UniversityLucknow226028Uttar PradeshIndia Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknow226025Uttar PradeshIndia

Recent transformation of Saudi Arabian healthcare sector into a reven-ue producing one has signaled several advancements in healthcare in the *** healthcare management into Smart hospital systems is one of *** hospital management systems which are breach-proof only can be termed as effective smart hospital *** the perspective of Saudi Vision-2030,many practitioners are trying to achieve a cost-effective hospital management sys-tem by using smart *** this row,the proposed framework posits the main objectives for creating smart hospital management systems that can only be acknowledged by managing the security of healthcare data and medical ***,the proposed framework will also be helpful in gaining satisfactory rev-enue from the healthcare sector by reducing the cost and time involved in mana-ging the smart hospital *** framework is based on a hybrid approach of three key methods which include:employing the Internet of Medical Things(IoMT)and blockchain methodologies for maintaining the security and privacy of healthcare data and medical practices,and using big data analytics methodol-ogy for raising the funds and revenue by managing the bulk volume of healthcare ***,the framework will also be helpful for both the patients and the doctors,thus enabling the Kingdom of Saudi Arabia(KSA)to meet its goals of Vision-2030 by ensuring low cost,yet credible,healthcare services.

关键词： Smart healthcare healthcare industry data security

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