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A Convolutional Neural Network Model for Wheat Crop Disease Prediction

computers, Materials & Continua 2023年第5期75卷 3867-3882页

作者： Mahmood Ashraf Mohammad Abrar Nauman Qadeer Abdulrahman A.Alshdadi Thabit Sabbah Muhammad Attique Khan Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21577Saudi Arabia Department of Computer Science Bacha Khan University CharsaddaKhyber Pakhtunkhwa24461Pakistan Department of Computer Science Federal Urdu University of ArtsScience&TechnologyIslamabad45570Pakistan Department of Information Systems and Technology College of Computer Science and EngineeringUniversity of JeddahJeddah21577Saudi Arabia Faculty of Technology and Applied Sciences Al-Quds Open UniversityAl-Bireh1804Palestine Department of Computer Science HITEC UniversityTaxila47080Pakistan

Wheat is the most important cereal crop,and its low production incurs import pressure on the *** fulfills a significant portion of the daily energy requirements of the human *** wheat disease is one of the major factors that result in low production and negatively affects the national ***,timely detection of wheat diseases is necessary for improving *** CNN-based architectures showed tremendous achievement in the image-based classification and prediction of crop ***,these models are computationally expensive and need a large amount of training *** this research,a light weighted modified CNN architecture is proposed that uses eight layers particularly,three convolutional layers,three SoftMax layers,and two flattened layers,to detect wheat diseases *** high-resolution images were collected from the fields in Azad Kashmir(Pakistan)and manually annotated by three human *** convolutional layers use 16,32,and 64 *** filter uses a 3×3 kernel *** strides for all convolutional layers are set to *** this research,three different variants of datasets are *** variants S1-70%:15%:15%,S2-75%:15%:10%,and S3-80%:10%:10%(train:validation:test)are used to evaluate the performance of the proposed *** extensive experiments revealed that the S3 performed better than S1 and S2 datasets with 93%*** experiment also concludes that a more extensive training set with high-resolution images can detect wheat diseases more accurately.

关键词： Machine learning wheat crop disease prediction convolutional neural network artificial intelligence

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House Price Prediction System using Machine Learning Algorithms and Visualization 9

House Price Prediction System using Machine Learning Algorit...

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9th IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2023

作者： Supriya, M.S. Vinayak, G.S. Patgar, Vinod Ramesh Mahajan, Vandan Ramaiah University of Applied Sciences Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350334395

This paper introduces a real estate price prediction system that uses machine learning algorithms. The system is intended to forecast the price of a home grounded on its various features such as location, square footage, number of bedrooms and bathrooms, and other related factors. The system uses a variety of machine learning algorithms, including linear regression, decision tree regression, random forest regression, and artificial neural networks to make accurate predictions. Algorithms are trained on a large dataset of house prices and their associated characteristics to learn the relationships between these factors and the corresponding prices. The system is measured against various performance metrics, such as: B. root-mean-square and r-squared score, to measure its accuracy and effectiveness. The results show that the system can predict house prices with great accuracy, making it a valuable tool for real estate agents, home buyers and *** implementation of this system could lead to better informed and more efficient decisions in the housing market. © 2023 IEEE.

关键词： Visualization

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On Campus Student Recruitment Analysis using Machine Learning techniques 9

On Campus Student Recruitment Analysis using Machine Learnin...

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9th IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2023

作者： Varsha, T. Shekar, Yamuna C. Shankar, Sahana P. Bharadwaj, Aryan Varadam, Deepak Supriya, M.S. Ramaiah University of Applied Sciences Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350334395

A nonprofit organization like college, works to assist all student needs and advance science, particularly in the academic sector. Hence, offering a high-quality placement aid service has emerged as a key indicator of a college's overall quality. Nowadays, in this pandemic-affected world, the majority of IT organizations are hiring students online, which makes it difficult for the college and the company to shortlist the students based on the requirements of the companies for various tasks. Due to the large number of students at colleges, the recruitment process requires a lot of human labor. We, therefore, offer a novel method for assessing job applications in online recruitment systems by using several machine learning methods to address the issue. We attempt to put into practice an application in the form of a prototype system, whose functionality is displayed and accessed in a genuine recruitment scenario. It is possible to offer a system where a computer would examine both the company's needs and the student's performance to determine whether the student should be selected for an *** analysis of student recruitment is automated in this paper utilizing machine learning methods by comparing the scores of a test with the benchmark score of the company. © 2023 IEEE.

关键词： Students

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Design of 5G Multi-Frequency Antenna Based on Multi-Objective Sequential Domain Patching

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Progress In Electromagnetics Research C 2024年 145卷 53-61页

作者： Zhu, Wenjian Chen, Jiayi Hu, Panshi Song, Zhi Xue, Yanbing School of Automation and Electrical Engineering Dalian Jiaotong University China School of Computer and Communication Engineering Dalian Jiaotong University Applied Basic Research Project of Liaoning Science and Technology Department Dalian116028 China

This paper introduces a 5G multi-frequency antenna design method based on multi-objective sequential domain patching. By etching helical metamaterials on radiation patches and loading asymmetric electric-inductive-capacitive metamaterials on the transmission line side, the antenna structure is designed and optimized using electromagnetic simulation software, HFSS, and MATLAB. The resulting multi-frequency antenna operates across multiple frequency bands: n1, n41, 3.5G, and 4.9G. To ensure antenna performance and radiation efficiency, the antenna multi-frequency bands are precisely controlled. The experimental results show that the error between each operating frequency band and target frequency bands of the antenna is within 7.2%. Compared with conventional antenna design methods, the use of metamaterials and intelligent algorithms to optimize the structural parameters and loading positions of metamaterials can improve antenna performance while shortening the design cycle. Overall, this research offers novel insights into the design of 5G high-performance antennas. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Metamaterials

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Classification of Cardiac Arrhythmia Using Machine Learning Algorithms 1

Classification of Cardiac Arrhythmia Using Machine Learning ...

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1st IEEE International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Supriya, M.S. Vineet Kushalappa, K. Sanjay, J.R. Patnasetty, Sheetal K Bajpai, Shruti Ojha, Shubhra Ramaiah University of Applied Sciences Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350348057

Irregular and unexpected slow or fast heartbeat is called arrhythmia. The two lower chambers of the heart, known as the ventricles, are where an irregular heart rhythm called a ventricular arrhythmia (VA) originates same in the case of atrial fibrillation which occurs in the two upper chambers called as aorta. An arrhythmia is a dangerous medical condition that needs prompt attention. An electrocardiogram or monitor is typically needed for its diagnosis in order to capture the irregular heart rhythms. If arrhythmia is the cause of cardiac arrest, the electrocardiogram (ECG) can help identify the cause. In this research, to investigate and evaluate a few pre-processing techniques for ECG signals. As the initial stage of pre-processing, this involves a variety of techniques to eliminate noise from the ECG signals and any undesired disturbances like Equi-ripple notch filter and Kaiser Window function. Next, several ways were investigated to turn ECG signals into pictures, segments, or waveforms that can be used as input for various machine learning algorithms like Logistic regression, Random Forest, Decision Tree and KNN classifier to analyze and forecast the likelihood of ventricular arrhythmia. Finally, a comparative analysis is done to compare all the machine learning techniques using multiple pre-processing techniques. © 2023 IEEE.

关键词： Electrocardiograms

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Smart E-Locker System using IoT

Smart E-Locker System using IoT

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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Umadi, Keerti R. Aishwarya, Adoni Narendramath, Jyothi E. Inchara, S.M. Dharishini, P. Padma Priya Ramaiah University of Applied Sciences Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9781665474511

In order to enhance daily life and actualize the Internet-of-Things (IoT) goal, several IoT gadgets have been created. IoT, which is based on the information-sharing capabilities of Radio Frequency Identification (RFID), is developing into one of the key technologies that is being used in a wide range of applications. Security and Privacy are necessities in today's world. Security systems are being used exponentially more frequently in our daily lives and we all know that this is an information era where all transactions are through the exchange of data. The idea behind this smart e-locker system primarily came from the asset security system utilized in bank lockers. This smart e-locker system, triggers an alarm when a user attempts to enter a guarded area by scanning an unregistered token. This system includes unique and personalized tags that allow only the authorized user to enter the secured premises. The most unique feature is that the log of the number of people approach the locker or the secured area is stored in adafruit cloud. If an unauthorized user is trying to enter the secured area by scanning the unregistered token the buzzer will be activated automatically causing the guards to be notified and an alert is send to person in-charge through e-mail. This paper demonstrates how RFID technology improves security and convenience, which will be useful in the future. © 2023 IEEE.

关键词： Internet of things

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Real-Time Facial Recognition System for Secure College Bus Transport using Deep Learning Techniques 3

Real-Time Facial Recognition System for Secure College Bus T...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kavitha, M.N. Saranya, S.S. Pragatheeswari, E. Priya, N. Kaviya Ranjith, S.R. Vanchivel, N. Rahul Kongu Engineering College Department of Computer Science and Design Tamil Nadu India Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Tamil Nadu Coimbatore India Kongu Engineering College Department of Computer Science and Engineering Tamil Nadu Erode India Kongu Engineering College Department of Csd Tamil Nadu India

ISBN: (纸本)9798331509675

The proposed system aims to enhance student transportation security through real-time face detection and recognition. Leveraging the MTCNN framework for accurate face detection and the FaceNet model for reliable face recognition, the system ensures only authorized students can board designated buses by comparing captured faces with a pre-registered database. In case of mismatches, alerts are triggered to notify drivers and authorities. Additionally, parallel implementation of Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers improves authentication accuracy. The system's deep learning-based architecture ensures robust performance under varying environmental conditions and supports continuous learning for long-term reliability. © 2025 IEEE.

关键词： Students

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Ensuring Zero Trust IoT Data Privacy: Differential Privacy in Blockchain using Federated Learning

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 1-1页

作者： Hussain, Altaf Akbar, Wajahat Hussain, Tariq Bashir, Ali Kashif Dabel, Maryam M. Al Ali, Farman Yang, Bailin School of Electronic and Control Engineering Chang’an University Xi’an China School of Computer Science and Technology and School of Mathematics and Statistics Zhejiang Gongshang University Hangzhou China Department of Computing and Mathematics Manchester Metropolitan University UK Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Saudi Arabia Department of Applied AI School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul South Korea

In the increasingly digitized world, the privacy and security of sensitive data shared via IoT devices are paramount. Traditional privacy-preserving methods like k-anonymity and ldiversity are becoming outdated due to technological advancements. In addition, data owners often worry about misuse and unauthorized access to their personal information. To address this, we propose a secure data-sharing framework that uses local differential privacy (LDP) within a permissioned blockchain, enhanced by federated learning (FL) in a zero-trust environment. To further protect sensitive data shared by IoT devices, we use the Interplanetary File System (IPFS) and cryptographic hash functions to create unique digital fingerprints for files. We mainly evaluate our system based on latency, throughput, privacy accuracy, and transaction efficiency, comparing the performance to a benchmark model. The experimental results show that the proposed system outperforms its counterpart in terms of latency, throughput, and transaction efficiency. The proposed model achieved a lower average latency of 4.0 seconds compared to the benchmark model’s 5.3 seconds. In terms of throughput, the proposed model achieved a higher throughput of 10.53 TPS (transactions per second) compared to the benchmark model’s 8 TPS. Furthermore, the proposed system achieves 85% accuracy, whereas the counterpart achieves only 49%. IEEE

关键词： Anonymity

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Prediction Based Reversible Data Hiding for Gray-Scale Images 7

Prediction Based Reversible Data Hiding for Gray-Scale Image...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Uyyala, Ravi Bojjagani, Sriramulu Department of Computer Science And Engineering Hyderabad Gandipet India School of Engineering And Applied Sciences SRM-AP Department of Computer Science And Engineering Andhra Pradesh Amaravathi India

ISBN: (纸本)9798350371406

Predicting the pixel is crucial in prediction error expansion (PEE) based reversible data hiding (RDH). There are a number of methods for predicting pixels that can be found in research papers. The gradients are used to make a prediction about the current pixel. Gradients in the image can be used to make forecasts about the current pixel, and this has been the subject of extensive study. In this work, we suggest a three-by-three grid of surrounding pixels as a basis for prediction. Later, the histogram bin shifting technique was implemented to integrate more information with less distortion. In order to include more information, the suggested technique makes adaptive changes to the histogram based on local complexity. The experimental analysis demonstrates that When compared to the other, the proposed method is superior. © 2023 IEEE.

关键词： Pixels

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Maximizing Resource Efficiency in Cloud Data Centers through Knowledge-Based Flower Pollination Algorithm (KB-FPA)

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computers, Materials & Continua 2024年第6期79卷 3757-3782页

作者： Nidhika Chauhan Navneet Kaur Kamaljit Singh Saini Sahil Verma Kavita Ruba Abu Khurma Pedro A.Castillo University Institute of Computing Department Chandigarh UniversityPunjab140413India Department of Computer Science and Engineering Chandigarh UniversityPunjab140413India Universidade Federal do Piauí TeresinaPiauí64049-550Brazil MEU Research Unit Faculty of Information TechnologyMiddle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan Department of Computer Engineering Automatics and RoboticsUniversity of GranadaGranada18071Spain

Cloud computing is a dynamic and rapidly evolving field,where the demand for resources fluctuates *** paper delves into the imperative need for adaptability in the allocation of resources to applications and services within cloud computing *** motivation stems from the pressing issue of accommodating fluctuating levels of user demand *** adhering to the proposed resource allocation method,we aim to achieve a substantial reduction in energy *** reduction hinges on the precise and efficient allocation of resources to the tasks that require those most,aligning with the broader goal of sustainable and eco-friendly cloud computing *** enhance the resource allocation process,we introduce a novel knowledge-based optimization *** this study,we rigorously evaluate its efficacy by comparing it to existing algorithms,including the Flower Pollination Algorithm(FPA),Spark Lion Whale Optimization(SLWO),and Firefly *** findings reveal that our proposed algorithm,Knowledge Based Flower Pollination Algorithm(KB-FPA),consistently outperforms these conventional methods in both resource allocation efficiency and energy consumption *** paper underscores the profound significance of resource allocation in the realm of cloud *** addressing the critical issue of adaptability and energy efficiency,it lays the groundwork for a more sustainable future in cloud computing *** contribution to the field lies in the introduction of a new resource allocation strategy,offering the potential for significantly improved efficiency and sustainability within cloud computing infrastructures.

关键词： Cloud computing resource allocation energy consumption optimization algorithm flower pollination algorithm

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