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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Sudar, K. Muthamil Nagaraj, P. Raghavendra, V. Jaashvitha, V. Jayasri, Y. Reddy, Yerva Dasaradha Ramanjaneya Mepco Schlenk Engineering College Department of Computer Science and Engineering Virudhnagar India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil Virudhunagar India

ISBN: (纸本)9798350361186

It has been proposed that physicians use machine learning-based lung cancer prediction algorithms to treat indeterminate pulmonary nodules that are discovered by accident or during screening. These methods might improve decisionmaking, decrease variability in nodule classification, and decrease the proportion of benign nodules that require unnecessary monitoring or investigation. In this work, we summarise the principal lung cancer prediction methods that have been put out so far and discuss some of the advantages and disadvantages of each. We discuss some of the challenges in creating and verifying such methods and outline the path to clinical acceptance. To ensure the best possible quality of input data for the deep learning models, the preprocessing step involves anatomical area segmentation, noise reduction, and image enhancement. The process of feature extraction involves using convolutional neural networks (CNNs) to extract intricate patterns and features from the lung pictures, which are then utilized to diagnose benign and malignant tumors. Typically, a deep neural network or an ensemble of models is used as the classification network to process this information and determine the final diagnosis. A faster and better outcome for patients may result from the recommended pulmonary cancer detection system's ability to assist medical professionals in accurately identifying malignant tumors in lung imaging. Deep learning is projected to be used in lung cancer detection, which will eventually result in earlier and more effective treatment, hence reducing the burden of this fatal condition. © 2024 IEEE.

关键词： Classification (of information)

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An Efficient Approach for Crop Disease Detection using Deep Learning 2

An Efficient Approach for Crop Disease Detection using Deep ...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Lunawat, Sonali Pawar, Vaidehi Deore, Rajat Bile, Aditya Gawade, Abhishek Nikam, Nandita Department of Computer Engineering Pimpri Chinchwad College of Engineering and Research Ravet India

ISBN: (纸本)9798350340235

Recognizing plant diseases is crucial for agricultural sustainability and high-quality produce. The conventional manual method of monitoring plant diseases is labor-intensive, requires expertise, and involves lengthy processing times. Despite advancements in machine learning (ML) and predictive analysis, the efficiency of classifying diseased plants remains limited, causing delays in report generation due to dataset constraints. Many researchers have developed crop disease detection models and soil testing models using various deep learning and machine learning approaches but encountered drawbacks such as poor robustness, high training time, and inaccurate accuracy. This research employs a deep-learning classification algorithm to distinguish healthy crops from diseased ones, aiming to provide a comprehensive report on the percentage of diseased and healthy crops, the specific type of disease, and corresponding remedies. The YOLOv5 model is chosen for its superior performance, achieving an accuracy of 0.90, surpassing manual sorting in identifying crop damage. The developed system includes a sensor-based device measuring soil pH, moisture, temperature, and TDS, ensuring optimal conditions for plant growth. Implementation of this method holds the potential to significantly increase crop yield and reduce losses. Notably, the YOLOv5 model stands out among other models due to its speed, accuracy, and proficiency in real-time applications. This model excels in detecting and classifying multiple objects within an image, making it particularly suitable for our plant disease classification task. © 2023 IEEE.

关键词： Machine learning pH sensors TDS YOLOv5

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Prediction of Bitcoin Price using Optimized Genetic ARIMA Model and Analysis in Post and Pre Covid Eras* 3

Prediction of Bitcoin Price using Optimized Genetic ARIMA Mo...

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3rd International Conference on Smart Data Intelligence, ICSMDI 2023

作者： Srivastava, Vibha Kumar Dwivedi, Vijay Kumar Singh, Ashutosh United College of Engineering & Research Department of Computer Science and Engineering Prayagraj India

ISBN: (纸本)9781665464871

Predicting Bitcoin price is a universal research area as it attains significance in predicting the market way of its rate so that, investors could procure profits. Concurrently, with the evolution of Machine Learning (ML), researchers attempted to use ML based algorithms for forecasting the Bitcoin price. However, these researches have resulted in inefficient prediction due to error rate. For alleviating such pitfalls, this study intends to forecast the Bitcoin price by comparing its deviations pre and post Covid using suitable ML algorithms. To achieve this, the study proposes Auto Regressive Integrated Moving Average (ARIMA) with Optimized Genetic Algorithm (OGA). In this case, ARIMA model is considered as it possess the innate ability in capturing standard temporal reliances which is distinct to time-series data. Further, hyperparameters are selected by GA based on the fitness function. Based on this, hyperparameter tuning is performed which assist to improvise the model performance. For determining if there exists any deviations in Bitcoin price (pre and post Covid), Augmented Dickey Fuller (ADF) test is considered. Further, comparative analysis is regarded in accordance with performance metrics to validate the performance of the proposed system which proves its effectiveness in predicting Bitcoin price. © 2023 IEEE.

关键词： Genetic algorithms

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An Approach Towards Decentralized E-Voting 11

An Approach Towards Decentralized E-Voting

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11th International Conference on Emerging Trends in engineering and Technology - Signal and Information Processing, ICETET - SIP 2023

作者： Vaidya, Chandu Kirnapure, Chinmay Rithe, Jitesh Sonkusare, Devashish Khade, Prathmesh Kharche, Kartik Rajiv Gandhi College of Engineering and Research Computer Science Engineering Nagpur India

ISBN: (纸本)9798350348422

Voting is an essential component of democratic regimes because it allows every member of a community to express their opinions. Voter turnout has decreased recently, while worries about the reliability, security, and accessibility of the current voting technologies have grown. Voting was introduced to address those concerns, however, it still requires full supervision by a central authority. The blockchain is an emerging, decentralized, and distributed technology. A shared, distributed record of all finished transactions or digital activities between all parties involved. Each vote is viewed as a separate transaction in this instance. We store voting transactions on a private blockchain that is built using a peer-to-peer network. Voting specifics are abstracted from the user by programming of this application. While system is running, users will have adequate time to cast their votes. This study is about (decentralized voting system) that protects voters privacy while maintaining transparency and security. The system has three implementation layers: a user interface for voters to register and vote a smart contract and MetaMask wallet for automatic vote transactions on the Ethereum blockchain, and an administrator function for managing the election. The smart contract is programmed to check for registration and validity, and once deployed on the blockchain, it cannot be tampered with. The administrator adds candidate information and manages the election. © 2023 IEEE.

关键词： Blockchain

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Quantum Inspired Image Classification: A Hybrid SVM Framework 6

Quantum Inspired Image Classification: A Hybrid SVM Framewor...

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Patel, Heet Kamthekar, Sneha Prajapati, Dinesh Agarwal, Richa Trinity College of Engineering and Research Savitribai Phule Pune University Dept. of Information Technology Pune India

ISBN: (纸本)9798350306613

We propose a Hybrid Quantum Classical Algorithm (HQCA) for image classification using a Quantum Kernel which is efficient to produce than a traditional kernel for performing the kernel trick required in Support Vector Classification. Since classical data cannot be directly provided to a quantum circuit, amplitude encoding is performed on the image matrix. Using the ZZFeatureMap of Qiskit framework we store the extracted feature of the images. ZZFeatureMap is chosen because of its promising behaviour towards providing quantum advantage. Using this feature map we create a quantum kernel which can be fine-tuned to a ground level for a particular data set by the programmers. We use FidelityQuantumKernel class from qiskit library to create the kernel. To reduce the size of the dataset required to train the model we use a image augmentation layer for data expansion. Python library called Augmentor is used for creating this layer. This model can prove to be more accurate and light weight than other quantum counter parts as quantum convolutional neural network. Iterative approach is adopted for the minimization of the loss after each optimization the model is checked for accuracy and efficiency until the threshold is met. With advancement in quantum hardware the precision of such model can be increased. © 2024 IEEE.

关键词： Augmentor Hybrid Quantum Classical Algorithm Quantum Kernel Support Vector Classification ZZFeatureMap

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Sarcasm Detection Analysis - Comparative Analysis

Sarcasm Detection Analysis - Comparative Analysis

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2023 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2023

作者： Vaidya, Chandu Gupta, Ajit Shastrakar, Aditya Kathane, Anil Atmande, Kunal Iyer, Krishnan Rajiv Gandhi College of Engineering and Research Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350398748

Sarcasm is a unique class of figurative language, that is in particular utilized in on-line social media. Such messages are used for sarcastic feedback the usage of contemptuous, taunt, and mock-associated phrases or phrases. Normally, the term 'rule-primarily based totally method is implemented to structures concerning human-crafted or curated rule sets. There are many algorithms for figuring out whether or not a sentence is sarcastic or now no longer. This paper complies with the Rule-primarily based totally method this is evolved to address the trouble of sarcasm detection and additionally describes numerous datasets. However we can in particular cognizance on Random Forest, Decision Tree, Support Vector Machine. In our project we are going to compare the accuracies from the model used and find the most accurate model giving high accuracy. © 2023 IEEE.

关键词： Sentiment analysis

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Secured Cyber Security Algorithm for Healthcare System Using Blockchain Technology

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Intelligent Automation & Soft Computing 2023年第2期35卷 1889-1906页

作者： D.Doreen Hephzibah Miriam Deepak Dahiya Nitin C.R.Rene Robin Computational Intelligence Research Foundation(CIRF) Chennai600023TamilnaduIndia College of Computer and Information Sciences Majmaah UniversityMajmaah11952Saudi Arabia Department of Electrical Engineering and Computer Science College of Engineering and Applied ScienceUniversity of CincinnatiCincinnati45221OHUnited States Department of Computer Science and Engineering Sri Sairam Engineering CollegeChennai600044TamilnaduIndia

Blockchain technology is critical in cyber *** most recent cryptographic strategies may be hacked as efforts are made to build massive elec-tronic *** of the ethical and legal implications of a patient’s medical data,cyber security is a critical and challenging problem in *** image secrecy is highly vulnerable to various types of *** a result,designing a cyber security model for healthcare applications necessitates extra caution in terms of data *** resolve this issue,this paper proposes a Lionized Golden Eagle based Homomorphic Elapid Security(LGE-HES)algorithm for the cybersecurity of blockchain in healthcare *** blockchain algorithm preserves the security of the medical image by performing hash *** execution of this research is carried out by MATLAB *** suggested fra-mework was tested utilizing Computed Tumor(CT)pictures and MRI image data-sets,and the simulation results revealed the proposed model’s profound *** the simulation,94.9%of malicious communications were recognized and identiﬁed effectively,according to the total outcomes *** suggested model’s performance is also compared to that of standard approaches in terms of Root Mean Square Error(RMSE),Peak Signal to Noise Ratio(PSNR),Mean Square Error(MSE),time complexity,and other factors.

关键词： Healthcare system cybersecurity security data sharing blockchain

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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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Traffic Control Based on Integrated Kalman Filtering and Adaptive Quantized Q-Learning Framework for Internet of Vehicles

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computer Modeling in engineering & Sciences 2024年第3期138卷 2103-2127页

作者： Othman S.Al-Heety Zahriladha Zakaria Ahmed Abu-Khadrah Mahamod Ismail Sarmad Nozad Mahmood Mohammed Mudhafar Shakir Hussein Alsariera Faculty of Electronic and Computer Engineering Universiti Teknikal Malaysia Melaka(UTeM)Durian TunggalMalaysia College of Computing&Informatics Saudi Electronic UniversityRiyadhSaudi Arabia Department of Electrical Electronic and System EngineeringUniversiti Kebangsaan MalaysiaUKM BangiSelangorMalaysia Computer Technology Engineering College of Engineering TechnologyAl-Kitab UniversityKirkukIraq Faculty of Engineering and Electronic Engineering Universiti Tun Hussein Onn Malaysia(UTHM)Batu PahatMalaysia 6Research Center DepartmentThe University of MashreqBaghdadIraq

Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the *** this article,these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things *** framework integrates Kalman filtering and *** smoothing Kalman filtering,our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction *** traditional Q-learning,our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road according to the maximum number of vehicles in the junction *** evaluation,the model has been simulated on a single intersection consisting of four roads:east,west,north,and south.A comparison of the developed adaptive quantized Q-learning(AQQL)framework with state-of-the-art and greedy approaches shows the superiority of AQQL with an improvement percentage in terms of the released number of vehicles of AQQL is 5%over the greedy approach and 340%over the state-of-the-art ***,AQQL provides an effective traffic control that can be applied in today’s intelligent traffic system.

关键词： Q-learning intelligent transportation system(ITS) traffic control vehicular communication kalman filtering smart city Internet of Things

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Environmental water quality prediction based on COOT-CSO-LSTM deep learning

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Environmental Science and Pollution research 2024年第42期31卷 54525-54533页

作者： Rajagopal, Sankarasubbu Ganesh, Sundaram Sankar Karthick, Alagar Sampradeepraj, Thangavel Department of Information Technology National Engineering College Tamilnadu Kovilpatti628503 India Department of Computer Science and Engineering R.M.K. College of Engineering and Technology Puduvoyal Tamilnadu Thiruvallur601206 India Renewable Energy Lab Department of Electrical and Electronics Engineering KPR Institute of Engineering and Technology Tamilnadu Coimbatore641407 India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu Virudhunagar626126 India

Water resource management relies heavily on reliable water quality predictions. Predicting water quality metrics in the watershed system, including dissolved oxygen (DO), is the main emphasis of this work. The enhanced long short-term memory (LSTM) model was suggested to improve the model’s performance. Additionally, a hybrid model was employed to calculate the ideal parameter values for the LSTM model, which helped overcome the nonstationarity, unpredictability, and nonlinearity of the data about the water quality parameters. This model recruited the COOT method. The original weekly water quality values at the Vaigai River, Madurai, Tamil Nadu, India, were tested using the suggested hybrid model. An independent LSTM, the hybrid optimisation method takes its cues from the cuckoo bird’s reproductive strategy and a novel meta-heuristic optimisation technique dubbed COOT, which is based on the behaviour of a flock of coot birds. If implemented, the suggested hybrid model might serve as an alternate framework for water quality prediction, laying the groundwork for basin-wide efforts to manage water quality and control pollutants. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Macroinvertebrates

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