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Generative AI in Agri: Sustainability in Smart Precision Farming Yield Prediction Mapping System Based on GIS Using Deep Learning and GPS

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Procedia computer science 2025年 252卷 365-380页

作者： Senthil G. A S.U. Suganthi L. Prinslin R. Selvi R. Prabha Associate Professor Department of Information Technology Agni College of Technology Chennai India Associate Professor Department of Artificial Intelligence and Data Science Sri Sai Ram Institute of Technology Chennai India Assistant Professor Department of Computer Science and Engineering Agni College of Technology Chennai India Assistant Professor Department of Computing Technologies SRM Institute of Science and Technology kattankulathur Chennai India Associate Professor Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India

Agriculture faces many challenges of precision farming, such as the need for sustainable practices, improving yields, ensuring high yields. In resolution to these challenges, the present research provides an AI-based system that enables the use of deep learning, Global Positioning System (GPS), and Geographic Information System (GIS) technologies to create a highly intelligent smart agricultural precision farming system. Its goal is to monitoring crop health and reduce disease risk, which will lead to improved resource utilization and environmentally sustainability techniques. The proposed framework addresses the urgent need for consistency in agricultural practices, especially as global agriculture deals with pressures from climate change, resource shortages, and increasing demand for food. Traditional agricultural methods for predicting and optimizing crop yields due to increasing factors affecting crop performance Not enough generative AI, especially the use of deep learning models, supports agricultural research in many cases, allowing patterns to be identified and future results to be predicted accurately. The integration of GPS and GIS allows for more accurate mapping, real-time analysis, and effective decision-making. Weather forecasting variability, resource constraints, and demand for more food are isolated from environmental influences using deep learning models, especially Artificial Neural Networks (ANN). By using large data sets, including historical crop yield performance, soil properties, and weather conditions, the system provides highly accurate crop forecasts. Generative Adversarial Networks (GANs) and You Only Look Once (YOLO) hybrid model is playing a key role in generating crop yield and growth potential under different conditions, adjusting model accuracy over time, and this combination of ANN, GANs and YOLO optimization algorithms ensures that the system continuously enhances its predictive accuracy and overall effectiveness. The proposed gene

关键词： Generative AI Deep Learning Agriculture Smart Farming Crop Health Yield Prediction Global Positioning System Geographic Information System Artificial Neural Networks Generative Adversarial Networks Sustainability You Only Look Once (YOLO)

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Characterization and Micro Structural Analysis of Concrete Using Industrial By-Products and Incinerated Ash

Characterization and Micro Structural Analysis of Concrete U...

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International Conference on Recent Trends in Infrastructural Development and Sustainable Materials, IC-RTIDSM 2023

作者： Dambhare, Pranita S. Dhale, S.A. Das, A. Tajne, K.M. Yadav, A. Ph.D Research Scholar Department of Electronics and Computer Science R T M Nagpur University Nagpur India Principal & Professor Priyadarshini College of Engineering Nagpur India Assistant Professor Priyadarshini College of Engineering Nagpur India Associate Professor Government College of Engineering Nagpur India Assistant Professor Yashwantrao Chavan College of Engineering Nagpur440033 India

ISBN: (纸本)9783031821325

For the last vicennial, scientists have been focusing attention on mitigating the risks of environmental contamination brought on by the manufacturing of industrial waste and cementitious materials. Finding efficient waste to effectively incorporate into concrete production is the most feasible approach. Incinerated ash, an industrial by-product, offers unique qualities and long-term advantages that make it suitable as a building material. As a result, the characteristics of waste material are thoroughly covered in this paper to assess how effective it is as filler or cement substitute in concrete. This study examines the impact of replacing silica fume in concrete with incinerated ash on the material's strength and penetration capabilities. To achieve this, four concrete mixes containing four different percentages of silica fume (6, 8, 10, and 20%) and incinerated ash (4, 6, 8, and 10%) were prepared, along with a control concrete mix. It was noted that the control-mix concrete was easier to work than the waste-mix concrete. These investigations verified that 4% incinerated ash replacement combined with 8% silica fume results in improved mechanical properties and elevated pozzolanic reactivity. In the meanwhile, further study is required, particularly in the area of durability details, to assess the comprehensive performance of concrete made from industrial and incinerated ash. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Concrete mixtures

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Artificial intelligence in the detection and classification of dental caries

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Journal of Prosthetic Dentistry 2025年第5期133卷 1326-1332页

作者： Ahmed, Walaa Magdy Azhari, Amr Ahmed Fawaz, Khaled Ahmed Ahmed, Hani M. Alsadah, Zainab M. Majumdar, Aritra Carvalho, Ricardo Marins Assistant Professor Department of Restorative Dentistry Faculty of Dentistry King Abdulaziz University Jeddah Saudi Arabia Associate Professor Department of Orthopedic Surgery Faculty of Medicine Cairo University Cairo Egypt Assistant Professor Department of Civil Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia Consultant in Restorative Dentistry Dental Department East Jeddah General Hospital Ministry of Health Jeddah Saudi Arabia Graduate student Department of Computer Science Computer Science and Applications Virginia Polytechnic Institute and State University Blacksburg VA United States Professor Department of Oral Biological and Medical Sciences Faculty of Dentistry University of British Columbia Vancouver BC Canada

Statement of problem: Automated detection of dental caries could enhance early detection, save clinician time, and enrich treatment decisions. However, a reliable system is lacking. Purpose: The purpose of this study was to train a deep learning model and to assess its ability to detect and classify dental caries. Material and methods: Bitewings radiographs with a 1876×1402-pixel resolution were collected, segmented, and anonymized with a radiographic image analysis software program and were identified and classified according to the modified King Abdulaziz University (KAU) classification for dental caries. The method was based on supervised learning algorithms trained on semantic segmentation tasks. Results: The mean score for the intersection-over-union of the model was 0.55 for proximal carious lesions on a 5-category segmentation assignment and a mean F1 score of 0.535 using 554 training samples. Conclusions: The study validated the high potential for developing an accurate caries detection model that will expedite caries identification, assess clinician decision-making, and improve the quality of patient care. © 2025 Editorial Council for The Journal of Prosthetic Dentistry

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Decision Making and Recognition of String Characters using NN-Fuzzy Implication System

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Procedia computer science 2025年 258卷 893-902页

作者： Santosh Kumar Henge Avinash Bhagat Sanjeev Kumar Mandal Nikhil Reddy Viraati Ravleen Singh K. Rushi Kedhar Associate Professor Department of Computer Science and Engineering School of Computer Science and Artificial Intelligence SR University Warangal 506371 India Associate Professor School of Computer Applications Lovely Professional University Phagwara Punjab India Assistant Professor Department of CS and IT Jain (Deemed-to-be University) Bangalore India Department of Data Insights Crisp Shared Services 7160 Columbia Gateway Dr Columbia United States of America Assistant Professor Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddewaram AP India BE-CSE RV Institute of Technology and Management Bengaluru India

This research proposes the neural network (NN)-fuzzy logic control (FLC)-based methodology, which is designed with two stages of execution. Stage-1 is composed with the NN approach; it takes the input from the scanned image, the input from the classified clustered group, processes it with hidden layers, and generates the final output, which is input to the FLC in stage 2. The input of stage 2 converts the input into fuzzified data (0s and 1s) and trains the FLC system with fuzzified set values. The trained data has been simulated for further stages in the FLC-based decision-making approach and integrated to identify the character patterns based on the trained dataset. FLC-based planting and normalization techniques are integrated for re-training the data. The dataset has been framed with more than 6200 handwritten image characters, which were written by 235 writers. It aligned with the pre-processing, classification, and filtering stages and achieved a 99.8% recognition rate.

关键词： Fuzzy Logic Control (FLC) Artificial Neural Network (ANN) Multiple Layered Perceptron (MLP) Convolutional Neural Network (CNN) Feed Forward Neural Network (FNN) Optical Character Recognition (OCR) Handwritten (HW)

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A Novel Approach to Prevent Hijacking of Accounts in the Cloud

A Novel Approach to Prevent Hijacking of Accounts in the Clo...

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computer science and Emerging Technologies (CSET), International Conference on

作者： Ananthoju Vijay Kumar Yeshitla Woinshet Mihret Nesru Kemal Nephtalem Woldeyohanes Ahmad Shah Dagemawi Ameha Computer Science and Engineering Associate Professor Jain University Bangalore India Computer science and engineering student Jain University Bangalore India

According to studies, majority of enterprises globally are now adopting cloud services, and the number of migrants to cloud environments in the industrialized nations is expanding ever since. Moving business data from a centrally hosted server to cloud storage has a lot of merits. However, it increases the chance of data security breach. An aaccount hijacking is the most frequent type of attack that happens in cloud system, it is a type of attack where hackers will get the account holder's credentials and control over the account. Considering the fact that cloud system is vulnerable to attacks, it is crucial to have a remediation mechanism like a secured authentication system. Authentication system which uses textual password can be used in cloud system, but it is vulnerable to regular attacks like brute-force attack, spyware attack, and so many more. The present research mainly focuses on creating a unique authentication system that prevents account hijacking in cloud by mitigating common attacks. To achieve the key objective a graphical password authentication system has been developed with OTP and colouring scheme in order to specifically minimize brute force and shoulder surfing attacks. It is a simple and easy to remember authentication system for customers while minimizing account hijacking in cloud at the same time. It is proved that the present approach is successful authentication system that is less vulnerable to shoulder surfing and brute force attack while involving easy way for customers to remember their passwords without hassle.

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Reading Pixels Parallel and Processing Pixels Parallel (RPP-PPP): An Approach to Detecting and Tracking Objects in Static and Dynamic camera

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Procedia computer science 2023年 218卷 1475-1484页

作者： K. Sivakumar P. Shanmugapriya Research Scholar Dept. of Computer Scienc and Engineering SCSVMV Kanchipuram Tamilnadu India Associate Professor Department of Computer Science and Engineering SCSVMV Kanchipuram Tamilnadu India

Object detection and Tracking are important tasks in both moving and static camera applications like unmanned vehicles, human-assisted vehicles, product parts inspection, video surveillance, and many more. These applications are required to detect moving objects and track static and dynamic cameras. It also needed to process these tasks with rate and lesser time. Many computer vision applications are always used to read the image/frame pixels one by one from row by column or column by row pattern. Reading pixels one by one from the image/frame took more time in real-time applications. In this proposed method we used to read the pixels from the entire image/frame in parallel using Parallel. For utilities and process these pixels in parallel. In the proposed work we used the background subtraction model for object detection and tracking and noted the difference in both sequential and parallel processes and compared the output with the existing background subtraction model. In this work analyzed the time taken to read the entire image using various tools and languages. The work was implemented using C#.NET parallel. For other thread-based functions to minimize the pixel reading time and processing time for object detection and tracking. Finally, the proposed work gave a considerable performance and compared with the existing system.

关键词： Background subtraction parallel Computing Thread Task Pixels processing static dynamic camera

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Proficient Detection of Flash Attacks Using a Predictive Strategy 6th

Proficient Detection of Flash Attacks Using a Predictive Str...

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6th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020

作者： Om Kumar, C.U. Sathia Bhama, Ponsy R. K. Department of Computer Science & Engineering SRM Easwari Engineering College Chennai600 044 India Associate Professor Department of Computer Technology MIT Campus-Anna University Chennai600 044 India

ISBN: (纸本)9789811613371

The availability of service through cloud computing upon the emergence of IoT and wireless networking has exposed the security of information to an even greater risk of abuse. These security threats are caused by cyber criminals through Malware, Cracker, Insider and Zombie. Among the strategies attacking the network DoS or DDoS is disastrous as its objective is to paralyse the complete network system. The bursty or volatile nature of traffic is indicative of a surge in traffic flow. Such an event also implies the incidence of a DDoS attack. If left unchecked this attack will cause a disruption in service. The study focuses on deriving a statistical model for discriminating malign traffic by devising a predictive model using error optimization technique and Hurst Correlogram analysis for effectively detecting and controlling the spread of bot-induced DDoS attack in a cloud environment. This work proposes ARIMA model that detects DDoS with an accuracy of 92% which is further assessed through MLR with an accuracy of 84%. The Hurst estimation of the univariate lies very close to the Hurst of raw data needs mention and proves the maturity of the prediction strategy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Malware

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A Review on Vulnerabilities to Modern Processors and its Mitigation for Various Variants

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Procedia computer science 2022年 215卷 91-97页

作者： E Praveen Kumar S Priyanka T Sudhakar Associate Professor School of Computer Science and Engineering VIT-AP University Amaravathi India

Recently, security researchers have found two hardware security vulnerabilities namely Spectre and Meltdown, related to computer memory. They are not singular, many variants of these two vulnerabilities are being a head ache for secure hardware designers. These attacks affect the modern CPUs which use speculative and out-of-order execution for optimization. This attack may affect the computing device which using the processor like Intel, AMD, ARM, and other processors like ARM processors that may use by mobile devices. By exploiting these attack that may allow the adversary to gain the control of a device to capture all the secrete data available on the system or a memory. These vulnerabilities need the better solution at the hardware level, but the problem is hardware updates is not possible at the real-time. In the face, the software-based solution is not addressed by the long run to avoid this attack, this solution is only for time constrain. This paper presents the solution for different variants of Spectre and Meltdown vulnerabilities and how they are dangerous to the modern processors like Intel, AMD, and so on to avoid the damage of the hardware using Hardware Performance Counters (HPCs). Finally, some of the mitigation techniques against these vulnerabilities are suggested based on the running application of a device.

关键词： Spectre Meltdown Processors Vulnerability attacks AMD INTEL Mitigation Technique HPCs

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Revolutionizing COVID-19 Management: Block chain-Enabled Prediction and Secure Storage using Deep Learning Techniques

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Procedia computer science 2023年 230卷 853-863页

作者： B. Arulmozhi Dr. J I Sheeba S. Pradeep Devaneyan Research Scholar Department of Computer Science and Engineering Puducherry Technological University Puducherry-605014 India Associate Professor Department of Computer Science and Engineering Puducherry Technological University Puducherry-605014 India Professor Department of Mechanical Engineering Sri Venkateshwaraa College of Engineering and Technology Puducherry-605012 India

The proposed research presents a blockchain-based healthcare system that seeks to address the limitations of current data-sharing methods by providing secure and efficient data transfer, enhancing data privacy and security, enabling effective data sharing interoperability, and promoting data-driven healthcare decision-making. A blockchain platform, IPFS, and rule-based access control are among the technologies suggested for use in the proposed system. Implementing this system could result in more personalized and effective medication for patients, as well as lower healthcare costs and improved care quality. The suggested approach, known as the Homomorphic Zero-Knowledge Blockchain Algorithm (HZBA), employs a block chain technique linked with Proxy Re Encryption with Homomorphic with Zero proof knowledge. Finally, the Covid19-dataset has collected from Kaggle repository and HCDNN method has used to complete the classification. The proposed blockchain-based healthcare system has the potential to revolutionize healthcare by resolving data privacy and security concerns, increasing data accuracy, fostering more interoperability in data sharing, and enhancing data-driven healthcare decision-making. The proposed blockchain-based healthcare system provides potential answers to data sharing difficulties in the healthcare business. One potential disadvantage is the system's complexity and cost of installation. The experimental results and classification performance parameters have been thoroughly demonstrated

关键词： Blockchain healthcare data sharing deep learning COVID-19 security prediction privacy data transfer interoperability

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Intelligent Methods for Early Prediction of Heart DiseaseBehaviors

Intelligent Methods for Early Prediction of Heart DiseaseBeh...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Hamdi A. Al-Jamimi Hamdi A. Al-Jamimi is an associate professor specializing in Computer Science & Engineering at the Research Institute KFUPM

The early diagnosis of heart disease relies on accurately assessing symptoms that manifest at different stages of the disease. The health care sector holds extensive patient records containing invaluable data that necessitate in-depth investigation for uncovering hidden patterns crucial to early disease diagnosis. Timely detection of cardiovascular disease has the potential to save lives. Consequently, the application of intelligent methods in biomedical research has garnered significant interest, leading to the exploration of various artificial intelligence techniques at different levels within the medical domain. These techniques aim to utilize the existing data to improve early detection and mitigate the risks of numerous diseases, including heart disease. This study highlights the importance of leveraging machine learning (ML) methods for accurate heart disease prediction. By utilizing a diverse range of ML algorithms and a carefully collected dataset, we have successfully developed and evaluated multiple classifiers for heart disease diagnosis. The performance of these classifiers is evaluated using a newly published dataset that was collected from the Medical Help Center, a private hospital and heart center in Erbil, Iraq. This dataset comprises 333 cases and 21 attributes, which are utilized for training and validating the developed ML models. Experimental results demonstrate that the employed techniques achieve an accuracy level of over 92%. Notably, the gradient-boosting classifier exhibits the highest performance, achieving an accuracy level of 98.51 % during the testing phase. The promising results obtained demonstrated the potential of ML techniques in enhancing early detection and improving patient outcomes.

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