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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Veerabomma, Supraja Sika, K. Rajeswari, G. Murthy, B Satyanarayana Selvan, R. Senthamil G Pullaiah College Of Engineering and Technology Department of Ece Kurnool India Nehru Institute of Engineering And Technology Department of Artificial Intelligence and Data Science Coimbatore India India Department of Cse Odalarevu India Department of Electronics and Communication Engineering Tirupati India

ISBN: (纸本)9798350364699

Internet of Things (IoT) technologies allow real-time patient monitoring and data collection, changing healthcare. Deep learning has the potential to improve IoT-enabled medical facilities by combining sensor data, making predictions, and developing techniques to assist. The integration of sensor data is crucial for amalgamating medical, intelligent, and electronic medical record data. CNNs and RNNs excel at processing various data streams. This provides a complete patient health picture. Combining physical indications, activity levels, and outside elements may provide doctors with an accurate assessment of a patient's health. Based on historical data, predictive modelling employs deep learning to predict future health outcomes. Using LSTM networks and attention processes, IoT healthcare systems may predict illness progression, identify risk factors, and recommend early treatment. These prediction models enable fast therapies, preventative care, and resource optimization, improving patient outcomes and lowering healthcare expenditures. Deep learning helps to create patient-specific smart management approaches. Using real-time monitoring data and previous patient information, machine learning algorithms may create customized treatment regimens. These therapies may involve lifestyle modifications, pharmaceutical adjustments, or medical worker advice. These enlighten patients and healthcare providers to help them manage chronic diseases and make better decisions. When coupled, IoT and deep learning may transform healthcare. IoT-enabled healthcare systems may enhance patient surveillance, analysis, and treatment with improved information from sensor fusion, forecasting algorithms, and intelligent actions. This improves treatment and health outcomes. © 2024 IEEE.

关键词： Electronic health record

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Dynamic Resource Allocation for IoT-Fog Architectures Leveraging Differential Services and Adaptive QoS Models 5

Dynamic Resource Allocation for IoT-Fog Architectures Levera...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Mahavaishnavi, V. Padmavathi, V. Saminathan, R. Panimalar Engineering College Department of Artificial Intelligence and Data Science Chennai600123 India A.V.C. Engineering College Department of Information Technology Mayiladuthurai609305 India Annamalai University Department of Computer Science and Engineering Annamalainagar608002 India

ISBN: (纸本)9798331518097

The constant changes in IoT-Fog architectures, allocating resources plays a vital role in overall resource management, which includes several different requests for applications. This paper proposes a dynamic resource allocation framework based on adaptive QoS and differential service models for IoT-Fog networks. Consequently, the proposed model sorts IoT applications according to the necessary QoS for their implementation, thus improving resource use and satisfying end-users. In addition, the framework complements machine learning algorithms to forecast the resources' needs and dynamically allocate the resources in response to the changing requirements, thereby increasing responsivity and decreasing resource rivalry. In this paper, to conduct several simulations and real-world evaluations of this adaptive QoS-based approach, showing that it improves service delivery, especially for latency-sensitive and high-priority applications. Thus, this research presents a method for large-scale optimization of resource allocation in the IoT-fog environment and supports reliable and efficient service deployment in distributed and often limited-resource settings. © 2024 IEEE.

关键词： Quality of service

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An Auto Encoder-Decoder Approach to Classify the Bird Sounds Using Deep Learning Techniques

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SN Computer science 2023年第3期4卷 1-13页

作者： Vamsi, Bandi Mahanty, Mohan Doppala, Bhanu Prakash Department of Artificial Intelligence and Data Science Madanapalle Institute of Technology and Science Madanapalle 517326 India Department of Computer Science and Engineering Vignan’s Institute of Information Technology (A) Visakhapatnam 530046 India Data Analytics Generation Australia 88 Phillip St Sydney 2000 NSW Australia

Nowadays identification of bird species is the main problem for ornithologists and ecologists. The increase in volumes of data labelling, still there is a scope to implement an automated bird classification through deep learning techniques. Identifying the nature of a bird and its flock aids in analyzing different other species in the natural environment. This is considered as the main role in the problem of ecology. The species of birds act accordingly to the changes in ecology and this became a challenging task to identify the growth of the bird species with advanced technologies. In this work, our aim is to classify the different bird species based on voice recordings across various locations using autoencoders and decoders. This proposed work is a four-step process: data preprocessing of bird acoustic, feature extraction using a deep network, classification by random forest (RF) and support vector machine (SVM) and identifying the pitch intensities of a bird in low, medium and high ranges. In data pre-processing, the recorded audio is converted into spectrograms through frequency waves. The Mel Spectrogram filter is used to remove the noise from the recorded bird audio. For extracting the features in the spectrogram image, encoders and decoder convolutions are used to construct a fully connected vector. These features are given to RF and SVM for classifying the bird species to obtain standard prediction accuracy. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Bird classification CNN Decoder Encoder Mel spectrum

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Robust graph‐based localization for industrial Internet of things in the presence of flipping ambiguities

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CAAI Transactions on intelligence technology 2023年第4期8卷 1140-1149页

作者： Mian Imtiaz ul Haq Ruhul Amin Khalil Muhannad Almutiry Ahmad Sawalmeh Tanveer Ahmad Nasir Saeed Faculty of Electrical Engineering Swedish College of Engineering and TechnologyWah CanttPakistan Department of Electrical Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawarPakistan Department of Electrical Engineering Remote Sensing UnitNorthern Border UniversityArarSaudi Arabia Data Science and Artificial Intelligence Department‐College of Science and Information Technology Irbid National UniversityIrbadJordan Innovation Education and Research Center for On‐Device AI Software(Bk21) Department of Computer Science and EngineeringChungnam National UniversityDaejeonRepublic of Korea Department of Electrical and Communication Engineering United Arab Emirates University(UAEU)Al AinUnited Arab Emirates

Localisation of machines in harsh Industrial Internet of Things(IIoT)environment is necessary for various ***,a novel localisation algorithm is proposed for noisy range measurements in IIoT *** position of an unknown machine device in the network is estimated using the relative distances between blind machines(BMs)and anchor machines(AMs).Moreover,a more practical and challenging scenario with the erroneous position of AM is considered,which brings additional uncertainty to the final position ***,the AMs selection algorithm for the localisation of BMs in the IIoT network is *** those AMs will participate in the localisation process,which increases the accuracy of the final location ***,the closed‐form expression of the proposed greedy successive anchorization process is derived,which prevents possible local convergence,reduces computation,and achieves Cramér‐Rao lower bound accuracy for white Gaussian measurement *** results are compared with the state‐of‐the‐art and verified through numerous simulations.

关键词： Cramér‐Rao lower bound greedy successive anchorization industrial internet of things localization

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A Framework for Democratizing Open-Source Decision-Making using Decentralized Autonomous Organization 6

A Framework for Democratizing Open-Source Decision-Making us...

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6th International Conference on Computing and Informatics, ICCI 2024

作者： Afifi, Khalifa H. Fouad, Khaled M. Asian, Heba K. Faculty of Information Technology and Computer Science Nile University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Egypt Faculty of Computers and Engineering New Mansoura University Egypt Cairo Egypt Center of Informatics Science Faculty of Information Technology and Computer Science Nile University Cairo Egypt

ISBN: (纸本)9798350373875

The open Source Software (OSS) became the backbone of the most heavily used technologies, including operating systems, cloud computing, AI, Blockchain, Bigdata Systems, IoT, and many more. Although the OSS individual contributors are the primary power for developing the OSS projects, they do not contribute to the OSS project's decisionmaking as much as their contributions in the OSS Projects development. This paper proposes a framework to democratize the OSS Project's decision-making using a blockchain-related technology called Decentralized Autonomous Organization (DAO). Using DAO, contributors get incentive tokens as a reward in return for contributions. The earned tokens can be used in the process of decision-making governance. This governance model tends to allow OSS individual contributors to have a public voice in the OSS project's development roadmap in particular, and the OSS decision making governance in general. © 2024 IEEE.

关键词： Open source software

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Research on Wireless Sensor Network Localization Method for Real-Time System

Research on Wireless Sensor Network Localization Method for ...

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2023 China Automation Congress, CAC 2023

作者： Liu, Bowen Lei, Chenghao School of Artificial Intelligence Hebei Oriental University Langfang China Macau University of Science and Technology Faculty of Innovation Engineering Macau China

ISBN: (纸本)9798350303759

Wireless sensor networks (WSNs) inherently face constraints arising from limited computing power, data storage, and communication bandwidth. These inherent limitations necessitate innovative solutions. This study underscores the importance of ultrawideband technology, media access control, and routing protocols in the domain of WSNs. A key application of WSNs is real-time mine safety monitoring, which plays a critical role in preventing serious accidents by detecting hazardous areas. This research adopts a system-centric approach to real-time WSNs, rooted in an exhaustive literature review. It provides a comprehensive evaluation of the current WSN technology landscape, with a specific focus on unraveling the complexities of real-time system-oriented wireless sensor network technology. The investigation covers ultrawideband technology and medium access control within the real-time system paradigm, all supported by simulation verification. The simulations unequivocally confirm the effectiveness of the proposed method, making a significant contribution to the advancement of wireless sensor network technology, especially in real-time applications. This highlights the practical significance of the research. © 2023 IEEE.

关键词： Wireless sensor networks

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Pickit be Safe With Us 1

Pickit be Safe With Us

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1st International Conference on Computational science and technology, ICCST 2022

作者： Renukadevi, B. Ponmalar, A. Deepa, R. Dinesh, P. Rusitha, U. Hemanth, P. Department of Information Technology Sri Sai Ram Engineering College India Department of Artificial Intelligence and Data Science Sri Sai Ram Institute of Technology India Department of Artificial Intelligence and Data Science SRM Valliammai Engineering College India

ISBN: (纸本)9781665476553

In essence, our application is a Smartphone app that facilitates ordering and purchasing of groceries, produce, and other goods. Our app introduces the concept of preserving social distance in tiny supermarkets. Our main goal is to make grocery shopping quick and safe. Through our app, customers can place orders from anywhere, including their homes and offices, and pick up their orders whenever they need to. The Shop Owners can view the orders placed by the consumers through our app, pack those items, and then send the customer a notification. Customers only need to pay the whole cost to select their items from the store. We gain safety and save time waiting in line for purchases thanks to this software. We anticipate that this software will be more helpful for shop owners and customers during the pandemic condition. © 2022 IEEE.

关键词： Sales

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Multi-Blockchain Based data Trading Markets With Novel Pricing Mechanisms

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IEEE/CAA Journal of Automatica Sinica 2023年第12期10卷 2222-2232页

作者： Juanjuan Li Junqing Li Xiao Wang Rui Qin Yong Yuan Fei-Yue Wang IEEE the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590China the School of Artificial Intelligence Anhui UniversityHefei 266114 the Qingdao Academy of Intelligent Industries Qingdao 230031China the School of Mathematics Renmin University of ChinaBeijing 100872China the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 the Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China

In the era of big data,there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive *** security and data pricing,however,are still widely regarded as major challenges in this respect,which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing *** this context,data recording and trading are conducted separately within two separate blockchains:the data blockchain(DChain) and the value blockchain(VChain).This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary ***,pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and ***,in regular data trading on VChain-S2D,two auction models are employed according to the demand scale,for dealing with users’ strategic *** incentive-compatible Vickrey-Clarke-Groves(VCG)model is deployed to the low-demand trading scenario,while the nearly incentive-compatible monopolistic price(MP) model is utilized for the high-demand trading *** temporary data trading on VChain-D2S,a reverse auction mechanism namely two-stage obscure selection(TSOS) is designed to regulate both suppliers’ quoting and users’ valuation ***,experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.

关键词： Auction data trading markets multi-blockchain pricing mechanisms

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Diagnosis of Plant Infection for Optimised Authomatic Health Monitoring System Using Vision Transformer Models 5

Diagnosis of Plant Infection for Optimised Authomatic Health...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Thapliyal, Nitin Aeri, Manisha Satyarthi, Abhishek Kukreja, Vinay Sharma, Rishabh Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Artificial Intelligence & Data Science Iimt College of Engineering Uttar Pradesh Greater Noida India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350383522

Mosaic Virus is highly responsible for the crop loss, especially the pepper, which is considered part of food crop, an essential aspect of world's agriculture. This work aims at automating the classification of disease severity level-related the Pepper Leaf Mosaic Virus Disease threshold through vision transformer (ViT) model to ensure a critical job. The classification of the severity is more often accompanied by a fairly significant amount of workload and arbitrariness, resulting in equipment innovation in most of the locations. The work applies ViT-based approaches with sufficient precision to appropriately classify a dataset, which contains 10,800 meticulously selected photographs. The present model obtained a commendable average accuracy of 97.25%, which shows that the model was able to detect even smaller differences between the classes of severity. The operational criteria that include the tale of the precision, recall, and F1-score highlight the model's ability to minimize to Feggsor false Positives with its ability to discriminate cases of viruses at different levels of the spectrum. The more advanced comparison charts made hand methodologies stand at the pinnacle in plant pathology studies. This study is not only beneficial in terms of illuminating the understanding about the Pepper Leaf Mosaic Virus but also gives farmers and researchers a transformative tool that could change the way disease control methods are used. Technologies AI that power ViT model are helping the agriculture industries throughout the world to implement sustainability and resiliency of the systems by providing responses in the right time and place in addressing viral infections. © 2024 IEEE.

关键词： Agricultural Automation Agricultural technology artificial intelligence Automated Diagnosis Crop Health Disease Management Global Food Security Image Classification Machine Learning Pepper Leaf Mosaic Virus Plant Pathology Precision Agriculture Severity Classification Vision Transformer ViT Model

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Machine Learning Driven Precise Automobile Insurance Claim Predictions 15th

Machine Learning Driven Precise Automobile Insurance Claim P...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Logeswaran, K. Savitha, S. Sree Harshan, A.S. Suganraj, S. Dinesh Kumar, M. Prasanna Kumar, K.R. Gunasekar, M. Rajdevi, R. Department of Artificial Intelligence and Data Science Kongu Engineering College Perundurai India Department of Computer Science and Engineering K.S.R College of Engineering Tiruchengode India Department of Information Technology Kongu Engineering College Perundurai India

ISBN: (纸本)9783031810855

New techniques are required to effectively manage vehicle insurance claims due to the rising trend in claim severity and volume. One approach to resolving this issue is machine learning (ML).Car insurance firms have started implementing Machine Learning to boost the efficient interpretation of client data, greater knowledge of client demands to enhance customer service. With the help of Machine Learning algorithm, one can predict the car insurance claim. This is done in an effort to better serve their customers. This study examines how auto insurance firms use machine learning within their organizations and investigates how ML models may be used with large-scale insurance data. To forecast the recurrence of a claim, a variety of ML techniques is used, including Naive Bayes, KNN, Decision Tree, Random Forest, logistic regression, and XGBoost. The performances of these models are also assessed and contrasted. The outcomes demonstrated that Random Forest had superior accuracy than other approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Logistic regression

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