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An empirical intelligent water irrigation system using soft computing and IoT

International Journal of Information Technology (Singapore) 2024年第6期16卷 3983-3989页

作者： Patel, Warish Sangeetha, Y. Gopagoni, Deepa Rani Mallik, D. M. Arvind Agrawal, Rakshal Mishra, Saket Sunil, M.P. Kolandaisamy, Raenu Department of Computer Science and Engineering Faculty of Engineering and Technology Parul University Gujarat India Department of Information Technology VR Siddhartha Engineering College Andhra Pradesh Vijayawada India Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India PES Institute of Technology and Management Karnataka Shivamogga India Department of Industrial Engineering Shri Ramdeobaba College of Engineering and Management Maharashtra Nagpur India Centre of Interdisciplinary Research in Business and Technology Chitkara Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of Electronics and Communication Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Karnataka Bengaluru India Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia

Water loss from fruit and vegetable production is mostly caused by leakage in the transportation system and handling of land, as well as by using traditional processing techniques. In order to increase the wellness of plants, save power, and optimize consumption of water, information should be widely applied in designing intelligent irrigation systems, which is possible with the help of the Internet of Things (IoT). In the given paper, an IoT-based soft computing recommendation framework is proposed for performing irrigation in a smarter way. The system works in such a way that IoT devices are positioned in the field to gather information about crops and groundwater levels. The acquired data is then fed to a cloud repository, which makes use of soft computing techniques such as neural networks for analysing and providing effective recommendations to farmers on their smartphones. In this way, the framework is tuned and the farmers are also updated from time to time regarding judicious irrigation processes. Lastly, the performance of the proposed system is validated against traditional techniques by taking crop datasets into consideration. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Efficient water management Intelligent devices IoT Smart irrigation Soft computing

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Convergence of Blockchain, AI and IoT for Effective Monitoring and Securing Smart City Infrastructure 2

Convergence of Blockchain, AI and IoT for Effective Monitori...

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2nd International Conference on Multidisciplinary engineering and Applied Science, ICMEAS 2023

作者： Agajo, James Imoru, Odunayo Aigbavboa, Clinton Mishra, Amit Onah, Fidelis Ijala, Abdullahi Danjuma Federal University of Technology Department of Computer Engineering Niger State Minna Nigeria University of Johannesburg Department of Electrical and Computer Engineering South Africa University of Johannesburg Faculty of Engineering and the Built Environment South Africa Department of Computer Science School of Computing and Applied Science Baze University Abuja Nigeria Veritas University Faculty of Engineering Abuja Nigeria Nigeria Communication Commission Abuja Nigeria

ISBN: (纸本)9798350358834

The convergence of block chain, artificial intelligence, and the internet of things (IoT) for effective monitoring and securing smart city infrastructure combines these three hot trends in technology and promises to advance the development of smart cities. Blockchain, AI, and IoT represent one of the most notable technological revolutions since the combination of computing and transaction processing systems, which is now having a positive impact. Due to the actions of cyber assault, the applications of these technologies will soon have such a significant impact on our society that it will be difficult to picture life without them. The Internet of Things Infrastructure is vulnerable to assaults since it is hosted on the cloud. Modern methods are thoroughly examined to talk about the advantages and difficulties in each domain. The combination of these three will change technology and advance a number of fields that will benefit academics and experts in industry, fostering improved service delivery and enhancing corporate structures. The convergence will change the game since it will address security and data privacy concerns. © 2023 IEEE.

关键词： Internet of things

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Attacks Detection in 6G Wireless Networks using Machine Learning 9

Attacks Detection in 6G Wireless Networks using Machine Lear...

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9th International Conference on computer and communication engineering, ICCCE 2023

作者： Saeed, Mamoon M. Saeed, Rashid A. Gaid, Abdulguddoos S. A. Mokhtar, Rania A. Khalifa, Othman O. Ahmed, Zeinab E. Faculty of Engineering Department of Communications and Electronics Engineering Yemen Taif University College of Computers and Information Technology Department of Computer Engineering P.O. Box 11099 Taif21944 Saudi Arabia Taiz University Faculty of Engineering & Information Technology Department of Communication & Computer Engineering Taiz Yemen International Islamic University Malaysia Department of Electrical and Computer Engineering Malaysia Libyan Center for Engineering Research and Information Technology Bani Waleed Libya International Islamic University Malaysia Electrical and Computer Engineering Dept Malaysia University of Gezira Computer Engineering Dept Sudan

ISBN: (纸本)9798350325218

Unlike the fifth generation (5G), which is well recognized for network cloudification with micro-service-oriented design, the sixth generation (6G) of networks is directly tied to intelligent network orchestration and management. The Attacks Detection in 6G (AD6Gs) wireless networks created by this research uses a Machine Learning (ML) algorithm. The pre-processing stage of the ML-AD6Gs process is the initial step. The second stage involves the feature selection approach. Correlation Feature Selection algorithm (CFS) is used to implement the suggested hybrid strategy. It selects the best subset feature and reduces dimensionality for each independent analyses of the dataset CICDDOS2019. The voting average method is used as an aggregation step, and two classifiers - Random Forest (RF) and Support Vector Machine (SVM) - are modified to be used as ML Algorithms. The proposed method shown an outperformed the existing classification method. The accuracy was 99.9%% for CICDDOS2019 dataset with a false alarm rate of 0.00102 © 2023 IEEE.

关键词： Support vector machines

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Advancing Agricultural Health: Hybrid CNN-SVM Framework for Classifying Tomato Diseases 9

Advancing Agricultural Health: Hybrid CNN-SVM Framework for ...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Mahajan, Manish Upadhyay, Deepak Aeri, Manisha Kukreja, Vinay Sharma, Rishabh Military College of Telecommunication Engineering Faculty of Communication Engineering Madhya Pradesh Mhow India Graphic Era Hill University Computer Science And Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed To Be University Computer Science And Engineering Uttarakhand Dehradun India Chitkara University Institute of Engineering And Technology Chitkara University Punjab India Chitkara University Institute of Engineering And Technology Punjab India

ISBN: (纸本)9798350394474

The farm tomato disease diagnosis has a key function in the improvement of agricultural productivity and food security. Historically, the diagnostic methods of diseases have mostly resulted from staining of the affected part followed by inspection and expertise of the doctor. These approaches are, therefore, often slow and prone to human errors. This work constitutes a novel model that merges Convolutional Neural Networks (CNN) syntaxes in feature extraction and Support Vector Machines (SVM) characteristics of classification that address tomato turmoil automatically into five categories. Implementing a complete dataset of tomato leaf images along with transfer learning for CNN facilitates the improvement of feature extraction and using SVM connects its strong classification capacity, which becomes even more prominent in high-dimensional spaces. The hybrid model CNN-SVM was trained and evaluated in depth with classifying 96.72% accuracy showing a huge difference from that of standalone CNN and SVM models. This research demonstrates the capacity of hybrid AI systems for fulfilling the purpose of early discovery of diseases in tomato plantations, being a versatile, correct, and scalable solution. As the results show, combining deep learning with machine learning methods must be some way of optimizing prediction accuracy and making an instrument that helps agriculturists and farmers have better crop health and bigger harvesting treaties. Future projects will be directed at enhancing the versatility of the model to accommodate not only other crop species but also the inclusion of extra data streams for highly tailored disease diagnosis. © 2024 IEEE.

关键词： Support vector machines

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Transfer Learning for Efficient Node Placement in Dynamic Wireless Sensor Networks 16

Transfer Learning for Efficient Node Placement in Dynamic Wi...

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16th IEEE International Conference on Computational Intelligence and communication Networks, CICN 2024

作者： Priyadarshi, Rahul Bagri, Shiva Singh Ranjan, Rakesh Sundaram Faculty of Engineering and Technology Bhubaneswar India National Institute of Technology Patna Department of Mathematics Bihar 800005 India School of Computer Science Upes Uttarakhand Dehradun248007 India Government Engineering College Sheikhpura Department of Electronics and Communication Engineering Bihar 811105 India

ISBN: (纸本)9798331505264

Transfer learning is employed in this paper to introduce a novel method for node allocation in dynamic Wireless Sensor Networks (WSNs). In dynamic situations, traditional node placement techniques typically face challenges in terms of flexibility and efficiency. We propose a transfer learning approach that utilizes information from past network deployments to improve the design of node placement techniques in novel situations. In comparison to traditional approaches, the suggested method exhibits substantial enhancements in coverage ratio, energy efficiency, and execution time. The findings of our study suggest that transfer learning has the potential to significantly enhance network performance, thereby presenting a viable approach for dynamic WSNs. Improved network management and flexibility in different settings are among the ramifications of this study. © 2024 IEEE.

关键词： Network Management Network Performance Node Allocation Transfer learning Wireless Sensor Networks

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Satellite-Derived NO2 Data to Predict Hospital Admissions: A Machine Learning-Based Approach

Satellite-Derived NO2 Data to Predict Hospital Admissions: A...

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Emerging Technologies and Innovation for Sustainability (EmergIN), International Conference on

作者： Bhushan Pawar Lalit Garg Vijay Prakash Ryan Stevenson Matthew Amaira Daria Soloducha Adam Kraus Computer Information Systems Faculty of Information & Communication Technology University of Malta Msida Malta Communications & Computer Engineering Faculty of Information & Communication Technology University of Malta Msida Malta

ISBN: (数字)9798350391268

ISBN: (纸本)9798350391275

Rising Levels of air pollution and changing environmental conditions are the global concern. There is substantial evidence linking short-term and long-term exposure to NO 2 (nitrogen dioxide) with multiple disorders such as asthma, Bronchitis, Chronic Obstructive Pulmonary Disease (COPD), and Respiratory Infections in humans. However, multiple researchers have mainly depended on time-based exposure data, possibly overlooking fluctuations in NO 2 levels across different locations. We aim to analyze the relationship between NO 2 fluctuations and emergency department hospital admissions. We adopted machine learning (ML) based regression approaches to predict hospital admissions, we obtained emergency department hospital admissions from 2018–2019, while NO 2 tropospheric concentration was collected from the Sentinel 5P satellite. The results are evaluated using various evaluation matrices such as MSE (Mean Square Error), RMSE (Root Mean Square Error), R2 score, etc. Our study demonstrated that Random Forest and ExtraTreesRegressor models gained substantial predictive accuracy compared to other applied regression models. ExtraTreesRegressor proves its significance with R2 score of 67.11. These results deliver significant insights for hospitals and healthcare authorities by offering resource management through anticipating fluctuating NO 2 concentrations. Moreover, the findings of our study highlight the potential of extending this research to include several air pollutants and meteorological factors.

关键词： Technological innovation Hospitals Atmospheric modeling Predictive models Air pollution Resource management Nitrogen Forecasting Regression tree analysis Random forests

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Growing Greener: Exploring the Creative Potential of Deep Learning in Agricultural Disease Control

Growing Greener: Exploring the Creative Potential of Deep Le...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Kiran, Ajmeera Rahman, Syed Ziaur Sreelatha, P. Dhanasekaran, S. Ayyasamy, Ramesh Kumar Swetha, Devulapally MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India Majan University College Faculty of Information Technology Oman A.P Tirupati India Kalasalingam Academy of Research and Education Deemed to be University Department of Information Technology Tamilnadu India Faculty of Information and Communication Technology Kampar Malaysia Vaagdevi Engineering College Department of Computer Science and Engineering Telangana Bolli Kunta India

ISBN: (纸本)9798331540685

According to estimates, by mid-2030, there will be 8.6 billion people on the planet. The human population has been growing exponentially. There will be a rise in food production and consumption due to the massive population growth. Crop disease is the primary danger to food security, which damages agricultural goods while growing. Due to the limited availability of infrastructure, it remains challenging to recognize crop-damaging diseases in various parts of the world. Deep learning methods and computer vision technologies are used in this work to help identify agricultural illnesses. The train set is used to train the model, and the data split is used for validation. The farmer can use the app (Identifying and Resolving Crop Diseases using Deep Learning) to take pictures of the crops and examine whether or not there are any infections, proving the solution's viability. © 2024 IEEE.

关键词： Plant diseases

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Swallowing Assessment using High-Resolution Cervical Auscultations and Transformer-based Neural Networks 46

Swallowing Assessment using High-Resolution Cervical Auscult...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Anwar, Ayman Khalifa, Yassin Lucatorto, Erin Coyle, James L. Sejdic, Ervin University of Toronto Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada University of Pittsburgh Center for Research Computing and Information Technology Analytics PittsburghPA United States Cairo University Faculty of Engineering Systems and Biomedical Engineering Department Giza Egypt University of Pittsburgh School of Health and Rehabilitation Sciences Department of Communication Science and Disorders PittsburghPA United States University of Toronto North York General Hospital Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada

ISBN: (纸本)9798350371499

Swallowing assessment is a crucial task to reveal swallowing abnormalities. There are multiple modalities to analyze swallowing kinematics, such as videofluoroscopic swallow studies (VFSS), which is the gold standard method, and high-resolution cervical auscultation (HRCA), which is a noninvasive technique that uses a triaxial accelerometer attached to the patient's neck. Deep learning models play an essential role in data driven analysis of swallowing landmarks using VFSS and/or HRCA as input data. Most of these models utilize convolutional and recurrent neural networks. Here, we investigate the ability of transformers to analyze swallowing kinematics;specifically upper esophageal sphincter opening and laryngeal vestibule closure using HRCA signals. We tested the model using an independent test dataset to assess the generalizability of the proposed network. The proposed network achieved an average detection accuracy higher than 90% and 85% for both segmentation tasks, which outperform the hybrid neural networks from the literature, and the model obtained high-performance measures for the independent dataset, showing the transformers' ability to generalize on unseen data. © 2024 IEEE.

关键词： Recurrent neural networks

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Wireless Liberty: Sensors Measuring Pressure and Temperature

Wireless Liberty: Sensors Measuring Pressure and Temperature

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2023 International Conference on Advances in Computation, communication and Information Technology, ICAICCIT 2023

作者： Chaudhary, Pavan Vanithalakshmi, M. Raja Praveen, K.N. Maharishi University of Information Technology School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India

ISBN: (纸本)9798350344387

The continuous collection of physiological data, especially temperature and pressure, throughout the human body, using wireless, skin-adherent detectors based on NFC (near-field communication) technology, is presented in this research study. To better understand human health, forecast illnesses, and avoid problems like pressure ulcers, the article stresses the significance of monitoring skin temperature as well as pressure in diverse anatomical locations. This study proposes a novel NFC-based method for continuous physiological monitoring. It provides hitherto unattainable tracking capabilities for pressure and temperature throughout the human body and has the potential to significantly enhance healthcare results. In order to increase the radius and sensitivities of these sensors, future research will examine enlarged clinical investigations and developments in wireless technology. © 2023 IEEE.

关键词： Continuous monitoring and Health outcomes Healthcare NFC technology Physiological monitoring Pressure sensing element Pressure ulcers Skin temperature Temperature measurement Wireless sensors

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Utilization of ECC in Smart Grids: A Review

Utilization of ECC in Smart Grids: A Review

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Gopala Krishna, K. Shekhawat, Jyoti Agarwal, Trapty Faculty of Engineering and Technology Department of Electronics and Communication Engineering Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Maharishi University of Information Technology Engineering Department Uttar Pradesh India

ISBN: (纸本)9798350357769

Within the realm of smart grids, a power service provider plays a crucial function in coordinating predicted power production with actual power demand in order to support power system stability. Insufficient protection means that, even with these advancements, data sent inside smart grids is still vulnerable to a variety of security risks and assaults. To enhance the security of electronic grid networks, a robust and effective authentication procedure must be put in place. The trouble of ensuring the security of communications among substations and electronic machines stays, even in shrewd lattice settings where SCADA (supervisory controls and data acquisition) frameworks assume a part in shielding associations between the administration place and substations. In this present circumstance, predominant security strategies frequently neglect to accomplish an unpretentious harmony among security and execution. With the expansion of an safe part on the smart machine side, this work incorporates security insurance into a moderation verification framework in view of elliptic bend cryptography. This clever convention handles the requirement for a finely tuned balance between the security and execution parts of shrewd lattices. Key arrangement, common verification, and character security are significant parts of the proposed convention. The convention's viability is upheld by a complete assessment utilizing the Gong-Needham-Yahalom rationale. This procedure is a critical improvement in reinforcing the security position of smart grids, particularly taking into account the continually changing climate of cyber threats and weaknesses. © 2023 IEEE.

关键词： Authentication Elliptic Curve Cryptography Privacy Protection SCADA Systems Security Smart Grids

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