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Comparison of AI Concepts, machine learning Hybrid Approach and IoT in Monitoring Smart Home Appliances in IDS 4

Comparison of AI Concepts, Machine Learning Hybrid Approach ...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Vanipriya, B. Janarthanan, Vignesh Bhargavi, M. Rajesh, V. Boopathiraja, K.P. Satyanarayana Tallapragada, V.V. Panimalar Engineering College Department of Computer and Communication Engineering Tamil Nadu Chennai600123 India Malla Reddy Institute of Technology and Science Department of Information Technology Telangana Secunderabad500100 India CMR Engineering College Department of Computer Science and Engineering Telangana Hyderabad501505 India Koneru Lakshmaiah Education Foundation Department of Electronics and Communication Engineering Andhra Pradesh Vaddeswaram522302 India Bannari Amman Institute of Technology Department of Mechanical Engineering Tamil Nadu Sathyamangalam638401 India Mohan Babu University Department of Electronics and Communication Engineering Andhra Pradesh Tirupati517102 India

ISBN: (纸本)9798350300888

Even though smart homes have automated the day-to-day activities;it also introduces some security-related challenges. With the help pf IoT technology, people can operate various types of gadgets without their physical presence at home. By using the framework described in this manuscript, all household equipment and electrical devices can be readily monitored and managed via a website. this service can also be used to monitor a smart home surveillance system. the proposed method can also be used for generating an online invoice. Intrusion detection is a well-known method for detecting anomalies in network data. the proposed Intrusion Detection System (IDS) module identifies abnormalities in a smart home network by utilizing Artificial Immune System, Extreme learning machine, and IoT. Furthermore, the proposed model is integrated into a smart home network interface and combines it with a text warning system to fix problems without using any external assistance. © 2023 IEEE.

关键词： Home networks

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Cutting-Edge Deep learning Techniques in Plant Disease Detection: A Systematic Review 4

Cutting-Edge Deep Learning Techniques in Plant Disease Detec...

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4th international conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Vinodh Kannan, T. Shanmuga Priya, S. Srm Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Trichy India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529635

the developments in deep learning have greatly influenced the processes of the identification of plant diseases, producing outstanding enhancements in terms of precision and functionality. the aims of this survey are to provide a comprehensive literature review of deep learning techniques in plant disease diagnosis and prognosis with special emphasis on recent development and future developments for further study. It provides critical analysis of the most popular models including CNNs, Transfer learning, Few-Shot learning and shares insights on how these technologies are applied in crops including rice, wheat and maize. these challenges include the lack of quality data, and the problem of transferring models from one environment to another, as well as the computational complexity that makes the application of these models in agriculture. Moreover, the investigation identifies the IoT and Edge Computing as a potential for the early disease diagnosis and surveillance. the paper also points out that the significance of Explainable AI or XAI is taking firm root;this is an innovation that seeks to explain the workings of models;thus, making it popular among stakeholders in agriculture. this literature from the last decade is analyzed to outline the research priorities and possibilities for future development in the design of new models, ways of sharing data, and ongoing field evaluation. this survey is to be used by the researchers and practitioners who are involved in developing sustainable and scalable approaches of managing plant diseases using deep learning technology. © 2024 IEEE.

关键词： Convolutional neural networks

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WINS - CVMLH 2023 - Joint proceedings of the Workshop on Intelligent Systems, WINS 2023 and the Workshop on computer Vision and machine learning for Healthcare, CVMLH 2023, Co-located with CODD 100 - 7th international conference on Computing Sciences, ICCS 2023

WINS - CVMLH 2023 - Joint Proceedings of the Workshop on Int...

引用

2023 Joint of the Workshop on Intelligent Systems, WINS 2023 and the Workshop on computer Vision and machine learning for Healthcare, CVMLH 2023

the proceedings contain 10 papers. the topics discussed include: comparative analysis on enhanced image compression algorithms;military detection during close combat situations and border monitoring using adaptive customized convolutional neural network;machine learning-based rainfall prediction for various regions;methodology for the registration of images and their inclusion in medica image management systems, based on the DICOM protocol;mobile application success prediction using machine learning;a secure mobile application for speech to text conversion using artificial intelligence techniques;a comparative study of heart disease prediction using machine learning;method for teaching medical imaging diagnosis, applying virtual reality techniques;heart disease prediction using machine learning method – a review article;and web system for the management of clinical laboratory test, applied to health facilities in rural areas.

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Research on Intelligent Big Data Information Monitoring System Based on computer machine learning Technology 4

Research on Intelligent Big Data Information Monitoring Syst...

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4th international conference on computers, Information Processing and Advanced Education, CIPAE 2023

作者： Liu, Yitian Zhejiang University School of Earth Sciences Hangzhou310058 China

ISBN: (纸本)9798350342710

the communication information acquisition system has the problem of high packet loss rate. this paper designs an intelligent communication big data information monitoring system based on genetic algorithm. through the built-in Transmission Control protocol/Internet Protocol (TCP/IP) stack, additional pins are selected as the carrier of the register and connected to the circuit. through the design of environmental information communication software architecture, coordination node program and terminal node program, the environmental communication software system is designed. the wireless communication network is selected as the network module of the communication information monitoring system, and combined with the transmission link balanced configuration method, the communication information monitoring data is sent and received and the communication route is designed. the simulation results show that the overall stability of the system is superior. the output results are accurate and reliable. © 2023 IEEE.

关键词： Big data

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A BigData/machine learning system for Monitoring Road Surface Condition 20

A BigData/Machine Learning system for Monitoring Road Surfac...

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20th ACS/IEEE international conference on computer Systems and applications (AICCSA)

作者： Patel, Preet Ojo, Tiwaloluwa Mohsin, Faraaz Jeyabalan, Janajan El Alawi, Waleed Daoud, George El-Darieby, Mohamed Ontario Tech Univ Fac Engn & Appl Sci Oshawa ON L1G 0C5 Canada

ISBN: (纸本)9798350319439

Road surface condition and deterioration are crucial factors to monitor as it affects the safety and comfort of travellers. Currently, road surface inspection is conducted mostly with manual methods that require expensive machinery. Many municipalities lack the resources to conduct such inspections thoroughly and frequently. Collection and maintenance of updated data of road surface damage and conditions is crucial in making decisions regarding road maintenance. this paper leverages machine learning to create a cloud-based application that can detect different road conditions and damages. We describe the functional and non-functional requirements and discuss the detailed design of the software system developed and method of implementation for each component of the system. the paper also covers the integration and unit tests designed to test each component of the system and provides the results of these tests. the literature also provides the results for the acceptance tests and validates if the product developed meets the defined business requirements. Lastly, the paper discusses ethical and safety considerations.

关键词： Road Surface YOLO Kafka Containerization Neo4j

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Design and application of Language Translation System Resource Platform on Basis of Artificial Intelligence 4

Design and Application of Language Translation System Resour...

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4th international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Chen, Ruicai Li, Caiyun General Educational School Jinan Vocational College Shandong Jinan250103 China

In daily lives, people often use translators for cross-language translation, but the current translation system has the problem of translation accuracy, and its translation speed also has some limitations. Artificial intelligence has begun to develop, and the language translation system resource platform based on artificial intelligence has gradually attracted attention. the design and application of this platform are of great significance for providing high-quality and efficient language translation services. this research proposes and demonstrates a language translation system resource platform based on artificial intelligence technology. the platform is based on artificial intelligence technology for hardware resources, network resources, and the use of artificial intelligence algorithms to achieve language translation that is accurate and smooth. through experimental testing, the results showed that the translation accuracy of the system platform reached 95-99%. the platform provides quality and efficient language translation services which can promote and facilitate human cross-language communication and understanding. © 2024 the Authors.

关键词： Neural machine translation

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A Comparative Analysis in Using Deep learning Models Which Results in Efficient Image Data Augmentation 4

A Comparative Analysis in Using Deep Learning Models Which R...

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4th international conference on Computation, Automation and Knowledge Management, ICCAKM 2023

作者： Kumar Nassa, Vinay Kumar Satpathy, Susanta Pathak, Mrunal K. Takale, Dattatray G. Rawat, Swati Rana, Samir Rajarambapu Institute of Technology Rajaramnagar Sakharale Tel Walwa Dist. Maharshtra Sangli415414 India Vignan Foundation of Science Technology and Research Vadlamudi Computer Science and Engineering Andhra Pradesh Guntur India Aissms Institute of Information Technology Savitribai Phule Pune University Department of Information Technology Pune India Vishwakarma Institute of Information Technology Sppu Computer Engineering Pune India Ambala Mullana India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350393248

'Deep learning' is now the speediest field in the 'machine learning (ML)' and 'Deep Neural Networks fields (DNN)'. 'Convolution Neural Networks (CNN)' are probably the most popular used technique for image classification and prediction among many DNN architectures. 'Deep neural networks' as well as their accompanying machine learning, despite their achievements and promises, face a number of serious challenges. CNNs have achieved attempting to cut results in a variety of multiclass classification, and they still face a number of challenges, despite their broad perspectives. they are driven primarily by the vast scale of the models, which can exceed millions of features, as well as a lack of valid training data sets, and they have issues with overfitting and generalization abilities. this research study focuses on the most commonly discussed difficulty in the area of 'machine learning', which is the absence of adequate 'training data' or an 'unequal class imbalance' within databases. the use of such data augmentation is one method of addressing this issue. Moreover, researchers have evaluated and analysed several data augmentation approaches throughout this research. Secondary method is used for this paper to gather in depth information about deep learning methods to get results in Image data augmentation. © 2023 IEEE.

关键词： Deep neural networks

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application for Integration of Haar Cascade and Local Binary Pattern Histogram 23

Application for Integration of Haar Cascade and Local Binary...

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4th international conference on Computing, Networks and Internet of things, CNIOT 2023

作者： Paglinawan, Charmaine C. Mactal, therese Marie M. Bantegui, John Allan Kurt M. School of Electrical Electronics Computer Engineering Mapua University Intramuros Manila Philippines

ISBN: (纸本)9798400700705

the use of machine learning in AI solutions can speed up the delivery of results, allowing these tools to achieve even more impressive feats. the present study presented a developed system that can be applied and used in many fields, such as a smart attendance system in a classroom setting and an intruder detection system. the two fused algorithms-Haar Cascade and Local Binary Pattern Histogram are used to train and utilize a system to recognize a registered face. Amid health crises, multiple face detection and face mask detection systems were also taken into consideration in the developed system. © 2023 ACM.

关键词： Local binary pattern

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MLCDT: Fine-Grained Multi-Task learning for enhanced cognitive assessment in the clock drawing test 4

MLCDT: Fine-Grained Multi-Task Learning for enhanced cogniti...

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4th international conference on computer Vision, application, and Algorithm, CVAA 2024

作者： Yao, Lei Ma, Haomiao Sun, Haoran Zhang, Yingwei Liang, Shili Zhang, Lei Shang, Pan Sun, Shiwen The School of Physics Northeast Normal University Changchun130000 China Beijing Key Laboratory of Mobile Computing and Pervasive Device Institue of Computing Technology Chinese Academy of Sciences Beijing100190 China First People’s Hospital of Foshan Guangdong Foshan528010 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Artificial Intelligence (AI) is increasingly used in cognitive health assessments, with the Clock Drawing Test (CDT) being an effective cognitive evaluation tool. However, the complexity of CDT image structures, high subjectivity, and the lack of specialized cognitive health assessment datasets for specific populations pose significant challenges for feature learning and model construction using this method. To address these issues, we propose a fine-grained multi-task learning approach (MLCDT) for AI-assisted diagnosis of cognitive health using CDT. MLCDT integrates image pre-training models with a multi-task learning framework to capture fine-grained features of CDT images and constructs a final diagnostic support model through scientifically designed tasks. Experiments using real data from cognitive health assessments in a neurology department at a hospital validate the effectiveness of MLCDT in handling fine-grained tasks and aiding cognitive disorder assessments. © 2025 SPIE.

关键词： Electronic health record

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SkyStrokes: Bridging Artistic Expression and Communication Barriers through Aerial Digital Canvas 6

SkyStrokes: Bridging Artistic Expression and Communication B...

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6th IEEE international conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Bommala, Harikrishna Suvekha, Raman Reddy, Orra Venkata Srikanth Kumar, Neerati Shiva Rokati, Sai Kiran KG Reddy College of Engineering and Technology Department of Computer Science and Engineering Telanagana Moinabad India KG Reddy College of Engineering and Technology 4th Year Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350306613

this endeavors to address pressing societal concerns by introducing innovative solutions. It confronts the challenges of communication barriers encountered by the hearing-impaired, the excessive reliance on smartphones, and the environmental impact of paper waste. through the implementation of 'on-Air writing,' an application is proposed that tracks object movements, enabling users to draw on a computer screen by simply waving their hands. By merging technology and social responsibility, this initiative strives to empower the deaf and mute community while advocating for eco-conscious practices. In doing so, it not only revolutionizes communication but also encourages a more inclusive, tech-savvy, and environmentally aware world. © 2024 IEEE.

关键词： computer Vision Gesture recognition Hand tracking Human-computer Interaction machine learning Social Issues

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