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Intelligent Systems for Data Driven Agriculture: Enhancing Farmer Productivity Through Automation and Artificial Intelligence

Intelligent Systems for Data Driven Agriculture: Enhancing F...

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Smart Systems for Electrical, Electronics, Communication and computer engineering (ICSSEECC), International Conference on

作者： S. Lokesh Arvind Madhavan RM Prakash Ramanathan Krteen Anand Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Tamilnadu India

ISBN: (数字)9798350378177

ISBN: (纸本)9798350378184

Numerous obstacles endanger the livelihoods of smallholder farmers and global food security. Crop output losses due to plant diseases alone are estimated to be between 15% to 25% every year. However, farmers' capacity to recognize and treat illnesses in a timely manner is hampered by things like a lack of agronomic expertise, limited access to actionable insights, and information gaps. The work proposes a comprehensive end-to-end web application that uses machine learning models to help farmers with configurable expert advice, smart farm management, and automatic disease detection. Thus, enabling farmers to identify the infection using their smart phones and obtain crop - fertilizer recommendations for the crops to be treated in the farmer's area based on historical data analysis of yield-influencing elements such as geography, weather, and soil. The proposed system will employ Generative Artificial Intelligence, such as ChatGPT's natural language processing and generation capabilities, to engage in conversational interactions with farmers. The work achieves close to 95% accuracy after utilizing 30 epochs in the area of disease detection and 97% accurate models are achieved for fertilizer outputs. These features along with it's high accuracy can assist farmers in making informed decisions, utilising the data obtained to reduce crop output losses.

关键词： Smart agriculture Productivity Plant diseases Accuracy Neural networks Crops Soil

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Robust asymmetric Cherenkov radiation in tilted anisotropic medium

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Science China(Physics,Mechanics & Astronomy) 2024年第1期67卷 46-51页

作者： Shuwen Xue Yadong Xu Linkang Han Zhi Ning Chen Qiaoliang Bao Shan Zhu Huanyang Chen Institute of Electromagnetics and Acoustics and Department of Physics College of Physical Science and TechnologyJiujiang Research InstituteXiamen UniversityXiamen 361005China School of Physical Science and Technology Institute of Theoretical and Applied PhysicsSoochow UniversitySuzhou 215006China Department of Electrical and Computer Engineering National University of SingaporeSingapore 8117583Singapore Institute of Energy Materials Science(IEMS) University of Shanghai for Science and TechnologyShanghai 200093China

Cherenkov radiation(CR)is available for a wide variety of terahertz(THz)radiation sources,but its efficiency is deeply affected by intrinsic *** find that if the tilted angle(α)of anisotropic material and radiation angle(θ)meet the condition ofθ+α=π/2,the intensity of radiation fields for the charged particle bunch(CPB)moving from left to right cannot be influenced by intrinsic losses,which means long-distance radiation can be ***,we observe an asymmetric CR when the CPB moves from the opposite *** addition,we select natural van der Waals(vd W)materialα-MoO3as an example,further confirming that the radiation field can reach the far field and the asymmetric CR radiation can also be *** wonderful properties with long-distance radiation will extend the application of CR to a certain extent for future design and fabrication.

关键词： asymmetric Cherenkov radiation intrinsic losses long-distance radiation

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A Way to Integrate ML Algorithm with the NN for the Purpose of Crop Production Through Continuous Monitoring 4

A Way to Integrate ML Algorithm with the NN for the Purpose ...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Varshney, Neeraj Rajeswari, A. Mounika Pramanik, Atreyi Alabdeli, Haider Kumar, Pranav Prashanth Kumar, K. Gla University Department of Computer Engineering and Applications Mathura India Cmr College of Engineering & Technology Department of Cse India Uttaranchal University School of Applied and Life Sciences Division of Research and Innovation Uttarakhand Dehradun India The Islamic University Najaf Iraq Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India Department of Civil Engineering Karnataka Karkala India

ISBN: (纸本)9798350360165

This check examines the evolution of machine studying (ML) strategies in forecasting hydrological droughts, focusing at the Standardized Runoff Index (SRI) in the Han River basin, South Korea. Given the dramatic climatic shifts witnessed throughout this era, our research evaluates the general performance of six ML models - Decision Trees (DT), Support Vector Machines (SVM), Deep Learning Neural Networks (DLNN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Artificial Neural Networks (ANN), and Fuzzy Rule-Based Systems (FRBS) - in predicting drought severity and frequency. Emphasis is placed on the model and refinement of these fashions to account for converting climatic styles and their impact on drought characteristics. The DT version, renowned for its predictive accuracy and computational performance, is identified as specifically promising. This has a look at targets to manual water resource manage and disaster preparedness via advanced drought forecasting methodologies. © 2024 IEEE.

关键词： Adversarial machine learning

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Secure Data Transmission in IoT Networks: A Machine Learning-Based Approach 9

Secure Data Transmission in IoT Networks: A Machine Learning...

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9th International Conference on Science, Technology, engineering and mathematics, ICONSTEM 2024

作者： Subathra, K. Vignesh, G.R. Babu, S. B. G. Tilak Mendhe, Dinesh Yada, Ramesh Kumar Maranan, Ramya Sri Sairam Engineering College Department of Mathematics Tamil Nadu Chennai44 India Ssn College of Engineering Department of Computer Science Tamil Nadu India Aditya Engineering College Department of Ece Surampalem India Office of Research Computing Rutgers The State University of New Jersey New Brunswick United States Icfai University Fst Department Chhattisgarh Raipur India Saveetha School of Engineering Simats Department of Research And Innovation Chennai India

ISBN: (纸本)9798350365092

This research studies the application of machine learning to enhance the security of data transport for the. of Things (IoT). Conventional encryption methods might not be adequate in Internet of Things scenarios because of the fluidity and limited resources presented by these contexts. In the course of our research, we make use of fictional Internet of Things data in order to evaluate the effectiveness of machine learning models for anomaly detection, intrusion detection, and data encryption. When it comes to resolving difficulties related to the security of the internet of things (IoT), the findings suggest that machine learning is better to traditional cryptography methodologies. The Internet of Things (IoT) security is going to be strengthened by the introduction of new strategies, dynamic security frameworks, privacy-preserving methodologies, and practical implementations in the near future. It is possible that the implementation of machine learning will make the ecosystem of the Internet of Things (IoT) more safe and efficient, which would, in turn, foster innovation across a wide range of industries. © 2024 IEEE.

关键词： Internet of things

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Certifying IoT Data with Verifiable Credentials

Certifying IoT Data with Verifiable Credentials

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IEEE International Symposium on Network Computing and Applications

作者： Carlo Mazzocca Stefano Allevi Rebecca Montanari Department of Information and Electrical Engineering and Applied Mathematics University of Salerno Salerno Italy Department of Computer Science and Engineering University of Bologna Bologna Italy

ISBN: (数字)9798331510183

ISBN: (纸本)9798331510190

The Internet of Things (IoT) is a major contributor to the vast amount of data generated worldwide, significantly impacting the big data market. However, this data holds value only when utilized for insights and applications. Many organizations hesitate to use third-party data due to concerns about accuracy, reliability, and integrity. Enhancing trustworthiness in data is crucial to unlock their full potential, especially in critical domains such as healthcare where erroneous data can have severe consequences. The Identity of Things (IDoT) paradigm addresses this need by identifying trustworthy devices using Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs), offering promising solutions for digital identification. Although DIDs and VCs have primarily been used for mutual trust and access control, their potential for data certification in IoT remains underexplored. This paper is the first to investigate the feasibility of IoT devices to use VCs for certifying data. We evaluated devices with varying capabilities, focusing on the latency, computing, and storage requirements for issuing VCs. Our findings demonstrate that IoT devices can issue VCs with up to 100 claims in less than 90 ms, with storage requirements growing linearly and remaining below 4 KB. Using VCs for data certification does not introduce significant computational overhead, suggesting its practicality for IoT environments.

关键词： Accuracy Focusing Organizations Medical services Big Data Internet of Things Object recognition Stakeholders Reliability Certification

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An Innovative Method for Fraud Detection in E-Commerce Using DCNN-Multiclass SVM Model

An Innovative Method for Fraud Detection in E-Commerce Using...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Banu, Rafiya Ashok, Abbarapu Dwivedi, Vijay Kumar Amaranadha Reddy, Kotte Thulasimani, T. Nishant, Neerav B. S. Abdur Rahman Crescent Institute of Science and Technology Department of Commerce Vandalur India School of Advanced Sciences Vit Department of Mathematics Andrapradesh India Ambikapur India Kalasalingam Academy of Research and Education Department of Mathematics Krishnankoil India Bannari Amman Institute of Technology Department of Mathematics Sathyamangalam India Babu Banarasi Das University Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798350360660

A growing number of monetary transactions are taking place through different E-commerce platforms in this age of big data. Theft of personal information for fraudulent reasons is one of the risks that could arise from these opportunities. Credit cards are a prime target for cybercriminals seeking to steal personal data or execute fraudulent transactions due to their extensive use for online shopping. Intelligent fraud detection systems have come a long way, yet the system still haven't solved the notorious problems brought on by data imbalances. Following this important sequence will ensure that the data is properly prepared for feature extraction and model training. Prior to ATF processing raw log files pertaining to online e-commerce activity, a preprocessor removes data noise, such as click records created by customers who have not registered. Data mining for features an evolving metaheuristic algorithm called the multi-verse optimizer imitates the laws of the popular theory called multi-verse. While training the model, the proposed approach utilized DCNN-Multiclass SVM. Two state-of-the-art approaches, CNN and SVM, are beaten by the suggested approach. After using the strategy, accuracy improved by 95.65%. © 2024 IEEE.

关键词： Convolutional neural networks

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Child Tracking and Prediction of Violence on Children In Social Media Using Natural Language Processing and Machine Learning 1

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22nd International Conference on Artificial Intelligence and Soft Computing, ICAISC 2023

作者： Nallakaruppan, M.K. Srivastava, Gautam Gadekallu, Thippa Reddy Reddy, Praveen Kumar Krishnan, Sivarama Polap, Dawid School of Information Technology and Engineering Vellore Institute of Technology Vellore India Department of Mathematics and Computer Science Brandon University Brandon Canada School of Information Technology and Engineering Vellore Institute of Technology Department of Electrical and Computer Engineering Lebanese American University Lebanon Zhongda Group Zhejiang Jiaxing314312 China Faculty of Applied Mathematics Silesian University of Technology Kaszubska 23 Gliwice44-100 Poland

ISBN: (数字)9783031425059

ISBN: (纸本)9783031425042

Crimes against children are a direct threat to the world of tomorrow. The future of the world is in the hands of children who are the real wealth of mankind. The challenge for society is to address and handle challenges against children such as sexual abuse, trafficking, child labour, and harassment. Even though measures are taken by several governments globally, violence against children is an evident day-to-day process. This becomes the most challenging issue in the post-Internet era, where organized crime against children takes place around the world through a targeted group of people through virtual private networks and the dark web. What comes next is the emergence of social media, which provides an opportunity for these criminals to harm children globally through powerful social networks [15]. The existing methods focus on classifiers that do not work across multiple keywords [6] to look for sentiment analysis regarding a single classifier. There are only a few works that address the issue and these methods do not employ the multi-classification model [2]. In this paper, we propose a model which includes the Twitter API, the APIFY web scraper framework, as well as the VADER semantic analyzer in Python which possesses Decision tree analysis of sentiment prediction. The proposed work provides 99.6904% accuracy and predicts the classification of sentiment analysis. Our work has novelty in attribute selection, child trade, and violence analysis in social media and prediction of classification accuracy using the Decision Tree algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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AGV indoor localization: a high fidelity positioning and map building solution based on drawstring displacement sensors

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Journal of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2277-2293页

作者： Wang, Shih-Yuan Li, Che-Ming Liong, Sze-Teng Sheng, Yu-Ting Huang, Yen-Chang Liong, Gen-Bing Gan, Yee-Siang Graduate Institute of Architecture National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electronic Engineering Feng Chia University Taichung40724 Taiwan School of Architecture Feng Chia University Taichung40724 Taiwan Department of Applied Mathematics National University of Tainan Tainan Taiwan Faculty of Computer Science & amp Information Technology University of Malaya Kuala Lumpur50603 Malaysia

The rapid evolution of artificial intelligence (AI) and robotics technologies are bringing drastic changes to society and industry in these recent years. The impressive progress in facilitating smart manufacturing in this era of Industry 4.0 has made our lives much more convenient than ever before. Despite the greater reliability and stability of the robotic system, there are several challenges to overcome such as the restrictions of scenes, obstacles, and hardware specifications. Since a high precision positioning algorithm is of paramount importance in devising a mobile robot, the research in developing simultaneous localization and mapping has been garnered immerse attention especially from domains of the computer vision and autonomous robots. In this paper, a novel method is presented to significantly enhance the positioning precision of indoor unmanned guided vehicles. The approach involves several steps, including setting up hardware configurations and collecting relevant data by installing necessary devices and system packages within the robot operating system (ROS). Trilateration is employed to determine the relative position of the mobile robot using distance measurements. Coordinate transformation is then conducted to convert the collected input data of relative distances and orientations. Trajectory paths are obtained, and occupancy maps are constructed to estimate the resulting trajectory and generate a 2D grid map. Indoor localization and mapping are achieved using three drawstring displacement sensors along with orientation information from an Inertial Measurement Unit (IMU). The proposed method is extensively evaluated through experimentation on predefined navigation paths, and its performance is compared to state-of-the-art methods such as RealSense T265, Hector SLAM, and wheel odometry. The results show that the proposed method exhibits compelling performance in both mean error and occupancy map construction. Ultimately, the findings reported herein

关键词： Mobile robots

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An Optimized Algorithm for CR-MIMO Wireless Networks

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computers, Materials & Continua 2022年第4期71卷 697-715页

作者： Imran Khan Fahd N.Al-Wesabi Marwa Obayya Anwer Mustafa Hilal Manar Ahmed Hamza Mohammed Rizwanullah Fahad Ahmed Al-Zahrani Hirofumi Amano Samih M.Mostafa Department of Electrical Engineering University of Engineering&TechnologyPeshawarPakistan Department of Computer Science King Khalid UniversityMuhayel AseerKSA Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Computer Engineering Department Umm Al-Qura UniversityMecca24381Saudi Arabia Research Institute for Information Technology Kyushu UniversityFukuoka819-0395Japan Computer Science-Mathematics Department Faculty of ScienceSouth Valley UniversityQena83523Egypt

With the rapid development of wireless communication technology,the spectrum resources are increasingly strained which needs optimal *** radio(CR)is one of the key technologies to solve this *** sensing not only includes the precise detection of the communication signal of the primary user(PU),but also the precise identification of its modulation type,which can then determine the a priori information such as the PU’service category,so as to use this information to make the cognitive user(CU)aware to discover and use the idle spectrum more effectively,and improve the spectrum *** sensing is the primary feature and core part of *** sensing algorithms includes energy detection,cyclostationary feature detection,matched filter detection,and so *** energy detection algorithm has a simple structure and does not require prior knowledge of the PU transmitter signal,but it is easily affected by noise and the threshold is not easy to *** combination of multiple-input multiple-output(MIMO)with CR improves the spectral efficiency and multipath fading *** best utilize the PU spectrum while minimizing the overall transmit power,an iterative technique based on semidefinite programming(SDP)and minimum mean squared error(MMSE)is ***,this article proposed a new method for max-min fairness *** compared to existing algorithms,the simulation results show that the proposed algorithms perform better in terms of total transmitted power and signal-tointerference plus noise ratio(SINR).Furthermore,the proposed algorithm effectively improved the system performance in terms of number of iterations,interference temperature threshold and balance SINR level which makes it superior over the conventional schemes.

关键词： Cognitive radio spectrum utilization bandwidth primary user

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Asset Management Strategies for District Heating Networks

Asset Management Strategies for District Heating Networks

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International Conference on System Reliability and Science (ICSRS)

作者： Jens Grønberg Maryamsadat Tahavori Peder Bacher DTU Engineering Technology Ballerup Denmark Department of Applied Mathematics and Computer Science DTU Kongens Lyngby Denmark

ISBN: (数字)9798350354508

ISBN: (纸本)9798350354515

District Heating (DH) became widely adopted in European cities during the 20th century to meet demand for heating from a growing urban population. Today, the desire to decarbonize Europe's heat consumption has lead to an renewed interest in increasing the adoption of DH. To meet this end, DH has to be a price competitive alternative to other heating technologies. Over time the underground heat distribution network that DH companies operate suffers from faults that require replacement of pipes. On-demand replacement is more expensive than planned large scale replacement projects. The only question is, when is the optimal time to renew the pipes? In this article this question will be formulated using Reliability Theory. Our approach is to use historical records of time to failure and a bounding result to estimate the optimal time for replacement. The method can be used by DH companies with historical fault records to form an Asset Management strategy on the question of when to renew which pipes.

关键词： Maximum likelihood estimation Costs District heating Urban areas Europe Companies Maintenance Asset management Steel Weibull distribution

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