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Comparative Study of Machine Learning Algorithms for Feature Extraction and Prediction of Bacterial Leaf Blight Disease in Paddy Fields 8

Comparative Study of Machine Learning Algorithms for Feature...

引用

8th International Conference on Inventive Systems and Control, ICISC 202

作者： Krishna, Penugonda Seetha Rama Nagarajan, S. Annamalai University Dept. of Computer Science Engineering Tamilnadu Chidambaram India Government College of Engineering Srirangam Department of Computer Science and Engineering Trichy620012 India

ISBN: (纸本)9798350386578

Paddy farming plays a critical role in global food security, yet diseases like Bacterial Leaf Blight (BLB) pose significant threats to crop yield and quality. Early detection and accurate diagnosis are essential for effective disease management. This research study investigates machine learning methods for feature extraction and prediction of BLB in paddy fields. Utilizing a comprehensive dataset of paddy leaf images, several machine learning models including Support Vector Machines (SVM), Random Forest (RF), k-Nearest Neighbours (kNN), and Gradient Boosting Machine (GBM) were trained and evaluated. Performance metrics such as accuracy, precision, recall, and F1-score were employed to assess the effectiveness of each model. The comparative analysis discusses the strengths and limitations of each method, focusing on those that demonstrate superior capability in early and accurate detection of BLB. These findings provide valuable insights for agricultural scientists and practitioners to enhance disease management strategies using modern machine learning techniques. By leveraging this knowledge, specialists can optimize interventions and improve agricultural practices to mitigate the impact of diseases like BLB on paddy farming and ensure global food security. © 2024 IEEE.

关键词： Adversarial machine learning

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Parametric Cyber Defense: A Sophisticated Machine Learning Architecture for Advanced Intrusion Detection and Threat Classification 5

Parametric Cyber Defense: A Sophisticated Machine Learning A...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Lakshmi, S. Maalan, M.R. Kishore Kumar, R. St. Joseph's College of Engineering Dept. of Computer Science Engineering Chennai India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

In contemporary network environments, effective countermeasures against cybersecurity threats require highly effective Intrusion Detection Systems (IDS). This paper proposes an advanced methodology to enhance IDS by employing parametric analysis on network traffic characteristics. Through careful data selection and preprocessing from a comprehensive dataset, data integrity is maintained, with features such as packet size and transfer duration being leveraged. Machine learning algorithms, including Decision Tree, Extra Tree, and xgBoost, are applied to detect and classify various cyberattack categories. In addition, advanced feature selection and dimensionality reduction techniques are utilized to improve model efficiency. Extensive experimentation demonstrates superior accuracy in identifying and classifying cyber threats while reducing the occurrence of false positives. The proposed framework strengthens cybersecurity infrastructures through the integration of advanced machine learning techniques to address evolving threats. © 2024 IEEE.

关键词： Adversarial machine learning

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A Survey on Blockchain-Based Certificate Authentication System: From Traditional to Digital 4

A Survey on Blockchain-Based Certificate Authentication Syst...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Saraswathi, R Vijaya Vasavi Swathi, Meda Venkata Punwatkar, Sakshi Manoj Konduru, Shreya Sami, Mohammed Abdul Vnr Vignana Jyothi Institute of Engineering and Technology Dept. Of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331529635

This research study pertains to the essential problems of certificate authentication within academic organizations. It concentrates on the major issues of forgery and inefficiencies as well as administrative bottlenecks that arise in conventional systems. The usual ways, which are based on one authority organization, are likely to be compromised, move slowly on verification, and have increased overhead costs. Blockchain technology is a distributed way of doing things without a middleman, and thus it is able to improve security, transparency, and the immutability of educational records. The article identifies the shortcomings of the present blockchain-based implementations with the focus on scalability, interoperability with legacy infrastructure, and high transaction costs. In the paper, an experiment on Ethereum-based smart contracts as well as the Secure Hash Algorithm (SHA-256) and InterPlanetary File System (IPFS) technologies shows the potential of these technologies to a certificate validation system by providing cryptographic integrity and, therefore, data storage that cannot be altered. The proposed work introduces a decentralized architecture that uses smart contracts for certificate issuing and verification to some extent. By integrating cryptographic hashing mechanisms, the system provides real-time, verifiable, and immutable credential records, addressing both fraud prevention and operational inefficiencies. © 2024 IEEE.

关键词： Ethereum

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Career Craft AI: A Personalized Resume Analysis and Job Recommendations System 1

Career Craft AI: A Personalized Resume Analysis and Job Reco...

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1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart Systems, ISTEMS 2024

作者： Rajput, Akshay Dubey, Akanksha Thakur, Rishabh Singh, Deepak Singh, Upendra Pratap Graphic Era Deeemed to Be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350384246

In the today's era of endless career options, the challenge to get that one ideal job that aligns best with individual's skills and interests is absent due to lack of personalized recommendations and lack of guidance about resume making it becomes challenging to find best jobs and get shortlisted for the hiring process of a company. This paper introduces a novel approach to address these challenges by the development of a Career Crafter AI - A Resume analysis and Job Recommendation System. Knowing the limitations of existing recommendations systems that primarily rely on individuals provided interests, our system, implemented on the e-learning platform for skill building, Level Up, goes beyond the traditional approach of recommendation system. Using Vector Machine algorithm and the cosine similarity our system classifies the users based on their skills and profile mentioned on the platform and Natural Language Processing enables to provide the tips and recommendation to improve our resume and get the insights about the user for better recommendation. The recommendation engine utilizes these insights to provide accurate and best fit jobs to the users and as well as provide potential candidate for job roles to the recruiters This research paper aims to provide a one stop destination for learners to improve their resume for getting an edge during shortlisting and interviews and to transform and optimize the job recommendation systems by offering a more personalized and effective approach, empowering individuals to navigate their career paths with confidence and precision. © 2024 IEEE.

关键词： Support vector machines

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Sales Prediction for Imported Fruits using a Linear Regression Model 16

Sales Prediction for Imported Fruits using a Linear Regressi...

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

作者： Bhumireddy, Jayasravani Bose, Ankita Gitam Deemed University Dept. of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798331505264

Forecasting fruit sales is vital for the retail sector to establish an efficient supply chain, control inventory levels effectively, and satisfy customer needs. This research employs a machine learning algorithm to predict upcoming fruit sales by analyzing past data. We aim to create a dependable prediction system by examining various data elements like past month's sales, seasonal patterns, promotions, holidays, and weather conditions. Linear regression, decision trees, and neural networks are being evaluated for their ability to forecast sales volumes. The model is created and coached with sales data, then evaluated for accuracy using different performance measures. Our findings in the fruit retail industry demonstrate that employing advanced forecasting methods decreases wastage and improves decision-making (resulting in increased profits). This research demonstrates that implementing data-driven sales forecasting within the fresh produce industry may result in more effective and environmentally friendly business operations. © 2024 IEEE.

关键词： Prediction models

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Big Data Application in Cancer Classification by Analysis of RNA-seq Gene Expression 3

Big Data Application in Cancer Classification by Analysis of...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Amruth, A. Ramanan, R. Paul, Rhea Vishal, S. Saravanan, S. Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India Amrita School of Computing Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350338607

computer science domain has got massive developments in the past decade, especially in the field of Big Data and Artificial Intelligence. Many industries are adopting these technologies to improve their products and services. Healthcare is one such industry which comes across voluminous data. Big Data platform is used to process the data and subsequently AI models can be used for prediction or classification. This paper focuses on proposing a Spark based pipeline to reduce the time required to process the voluminous data. With the aid of this new pipeline, we classify the type of cancer through analysis of RNA-seq gene expression levels. The Spark-based pipeline is run on default standalone mode and on a cluster. The study recorded that the Spark-based pipeline run on cluster mode improved the performance approximately by 11 times (regarding processing costs) when compared to the performance of Python-based pipeline. © 2023 IEEE.

关键词： Diseases

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A Study on Predicting Ratings System through Sentiment Analysis and Clustering of Coupang Product Reviews 13

A Study on Predicting Ratings System through Sentiment Analy...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Sung, Si-Yoon Jung, Jin-Woo Dongguk Univ. Dept. of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9798350373332

With the expansion of e-commerce platforms, online reviews have become increasingly important. Users often decide on purchases based on reviews and ratings. This study notes that reviews with identical ratings can differ in emotional content and hypothesizes that overall satisfaction can be inferred from these emotions. The aim is to develop a system to quantify satisfaction based on review content, especially when ratings are absent. This approach addresses situations where reviews lack ratings or users struggle to rate products despite providing feedback. We crawled product reviews from Coupang and performed sentiment analysis using a BERT model tailored for Korean. The sentiment analysis results were clustered using six different algorithms, and the outcomes were evaluated. The study explored the best methodology among 30 combinations of clustering algorithms and hyper-parameters. For evaluation, 'accuracy' and 'mean absolute error' were used as external metrics to compare with actual ratings, while the 'silhouette coefficient' was used as an internal metric to assess cluster clarity. Agglomerative Clustering showed the highest performance in external metrics but lower internal metric performance. Spectral Clustering demonstrated well-balanced and excellent performance across all metrics. © 2024 IEEE.

关键词： Sentiment analysis

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A Brief Survey on Feature Extraction Models for Brain Tumor Detection 3

A Brief Survey on Feature Extraction Models for Brain Tumor ...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Janapati, Malathi Akhtar, Shaheda Acharya Nagarjuna University Dept. of Computer Science and Engineering Guntur India Guntur and Research Supervisor Dept. of Computer Science and Engineering Guntur India

ISBN: (纸本)9781665462167

Today, tumours are the second leading cause of cancer deaths. Cancer poses a significant threat to a large population of patients. The medical community needs a quick, automated, efficient, and trustworthy method for detecting tumours like brain tumours. Detection is crucial to effective treatment. If doctors are able to catch a tumour in its earliest stages, they have a better chance of preserving the patient's health. To do this, several distinct image processing methods are used. Through this method, doctors have been able to effectively treat tumours and save the lives of many patients. Tumors are simply abnormal growths of cells that cannot be stopped. As brain tumour cells multiply, they eventually deplete the brain's supply of nutrients. Clinicians currently use MR images (MRI) of the patient's brain to manually pinpoint the location and extent of a brain tumour. Brain tumours can develop at any age in both children and adults. However, this is not the case if detection is timely and accurate. This investigation focuses on three subtypes of brain cancer: gliomas, meningiomas, and pituitary tumours. While there have been numerous publications on the topic of brain tumour classification and prediction, very few have focused on the importance of feature extraction. Manual diagnosis and conventional feature extraction methods have their limitations, and new approaches are needed to overcome them. An automated diagnostic system is necessary for extracting features and making an accurate diagnosis of brain cancer. Although advancements are being made, automatic brain tumour diagnosis continues to struggle with issues like low accuracy and a high proportion of false-positive findings. In this research work, a brief survey is provided on feature extraction for brain tumor detection using machine learning and deep learning techniques. © 2023 IEEE.

关键词： Feature extraction

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Design and Optimization of High-Performance Health Monitoring Systems Using Advanced Sensor Networks 2

Design and Optimization of High-Performance Health Monitorin...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Mehta, Heena Singla, Mukesh Baba Mastnath University Dept. of Computer Science and Engineering Haryana Rohtak India

ISBN: (纸本)9798331519056

This research elucidates the development and optimization of advanced sensor network-based health monitoring systems. In response to the increasing need for accurate health data in real-time, advanced gadgets capable of continuously monitoring essential indicators have been created. Contemporary sensor technologies, such as biosensors, IoT devices, and WSN s, enable these systems to facilitate data collection and processing with little disturbance. Optimization techniques like PSO, including machine learning algorithms and energy conservation procedures, are used to enhance the system's performance, reliability, and battery longevity. The study focuses on developing robust and scalable systems suitable for various healthcare applications, while also investigating data processing, communication protocols, network architecture, and sensor selection. Advanced sensor networks will be crucial for future health monitoring, as research indicates substantial improvements in monitoring accuracy, system efficiency, and patient outcomes. Present approach is supposed to provide scalable and efficient solution. Moreover, it would support privacy in order to provide secure solution. © 2024 IEEE.

关键词： Electronic health record

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Tuberculosis Diagnosis in Chest X-Rays via Transfer Learning Techniques

Tuberculosis Diagnosis in Chest X-Rays via Transfer Learning...

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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Keerthana, T. Jayakumar, K. Iyyappan, V. Shopika, S. Sowmiya, X.L. Ponnusamy, R. Chennai Institute of Technology Dept. of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (数字)9798331535476

ISBN: (纸本)9798331535476

Tuberculosis (TB) remains one of the world's major health problems. Detection with Chest X-rays is a mainstay tool in the early diagnosis of TB. Traditional methods used in the conventional detection of TB are very slow and susceptible to human errors. Thus, there is a need for an automated technique. The paper discusses the possibility of using advanced deep learning techniques, particularly techniques stemming from transfer learning, to enhance the detectability as well as efficiency of TB. Fine-tuned pre-trained convolutional neural networks, such as ResNet and VGG, on a dataset of chest X-ray images extracted high-level features relevant to the detection of TB. Superhuman performance was achieved with limited medical imaging data by transferring learned representations from large-scale image classification tasks. Experimental results illustrate high accuracy, precision, and recall rates, confirming that transfer learning is adequate for automating TB detection. This approach reduces the need for manual intervention and offers a scalable, cost-effective solution to enable rapid TB screening, particularly in low-resource settings. The novelty of the work comes from using transfer learning with fine-tuned pre-trained models (ResNet and VGG) in getting high generalization accuracy for tuberculosis detection from chest X-rays, even with limited data. © 2024 IEEE.

关键词： Convolutional neural networks

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