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检索条件"机构=Artificial Intelligence and Machine Learning Engineering"

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Clustering Based Segmentation with 1D-CNN Model for Grape Fruit Disease Detection

Clustering Based Segmentation with 1D-CNN Model for Grape Fr...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Pareek, Piyush Kumar Ramya, I.M. Jagadeesh, N.B. LeenaShruthi, M.H. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India East West Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350398465

There has been a lot of focus on agricultural image dispensation research in the previous years. Computers employ image processing procedures to do picture analysis. Recent technological advances in the fields of picture imprisonment and data dispensation have greatly facilitated the solution of several agricultural issues. Identification and categorization of crop diseases are essential to the agricultural sector's technical and economic health. The first step in agricultural image processing is acquiring a digital colour photograph of a sick leaf. Grapes have the highest global per capita fruit consumption. It is unacceptable to let the spread of grape leaf contamination in the grapefruit crop to go unchecked, since this would be detrimental to the industry's growth. Farmers in rural regions sometimes lack the skills to spot these illnesses early on, which leads to poor crop yields. In order to detect diseases in grapefruit leaves, the suggested system employs a deep learning-based categorization method. K-means clustering is first used to segregate the backgrounds of the input datasets. The Convolutional Neural Network (CNN) is then fed the segmented regions to get a disease classification. The suggested work utilises the hyperparameter tuning operation with the assistance of Firefly with cyclic randomization to enhance the classification accuracy (FCR). The accuracy, f-measure, recall, and precision of the proposed model are evaluated in light of the experimental data. Finally, compared to the current models, which obtained accuracies of 88% to 90% for grape fruit disease detection, the suggested CNN model achieved 95% accuracy and 92% precision. © 2023 IEEE.

关键词： K-means clustering

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Prediction of Rainfall in Karnataka Region using optimised MVC-LSTM Model

Prediction of Rainfall in Karnataka Region using optimised M...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Pareek, Piyush Kumar Achyutha Prasad, N. Srinivas, Chetana Jagadeesh, N.B. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India East West Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350398465

Forecasting precipitation is a prominent topic of study in meteorology. Predictions of precipitation using statistical analysis, learning methods are only a few of the methods that have been offered in the past. Organizations tasked with preventing natural catastrophes might benefit from using meteorological time series data prediction in their decision-making processes. The volume, dimension, and frequency of updates to Time Series data are quite high. The study of period sequence data for forecasting is a crucial part of the practical application. Time series data analysis allows for more precise rainfall forecasts, which is useful for assessing the severity of potential droughts and floods. Precipitation forecasting publications have employed a wide range of approaches. This improved forecast accuracy may be attributed to these methods. In this research, we apply a deep learning technique to examine the precipitation records from the Karnataka Division. This article presents a network consisting of a generator and a predictor for predicting spatial-temporal rainfall data. To imprisonment spatial correlations and build high-resolution data from sparse comments, it uses a multi-layer perceptron (MLP) in its generative module. A Multivariate Convolutional (MVC-LSTM) network is used to create the prediction unit;this network is able to capture the interplay between many variables and their temporal and spatial correlations. The Honey Badger approach finds the appropriate weight for the LSTM, which improves the model's ability to classify data. Additionally, this report suggests several avenues for further study in the fields of rainfall prediction and time series data analysis. © 2023 IEEE.

关键词： Decision making

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Fake Social Media Detection using machine learning

Fake Social Media Detection using Machine Learning

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International Conference on Computing and Networking Technology (ICCNT)

作者： R Thangamani M. Vimaladevi J Mohammed Rifath G Harini S Hari Department of Artificial Intelligence Kongu Engineering College Tamil Nadu India Artificial Intelligence And Machine Learning Kongu Engineering College Perundurai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Social media platforms such as Facebook, Twitter, Instagram and others have become an integral part of our lives, facilitating global connectivity. But the proliferation of fake profiles on these platforms poses serious risks, including the spread of false information, phishing attacks and identity theft. This research project focuses on identifying fake Twitter profiles using machine learning techniques to distinguish them from genuine accounts. In the dataset used in this study, Twitter profiles are classified into genuine accounts (TFP and E13) and fake accounts (INT, TWT and FSF).. The main objective is to evaluate the effectiveness of the ensemble learning technique Random Forest algorithm in classifying social media profiles. In-depth discussions include algorithm architecture, hyperparameters, and selection of critical features such as follower and friend counts, status changes, and other relevant features. Additionally, alternative classifiers including neural networks, LSTM, XG Boost and others are briefly discussed in the summary. The Random Forest algorithm has a commendable 95.54% accuracy after rigorous training. This high accuracy underlines its ability to effectively distinguish between real and fake Twitter profiles. By assigning a value of 0 to genuine profiles and 1 to fake profiles, the algorithm provides a practical solution to detect and mitigate fake accounts, thereby solving the cyber security issues associated with social media. This research contributes to ongoing efforts to create a safer online environment by identifying and removing fake profiles.

关键词： Training Accuracy machine learning algorithms Social networking (online) Blogs Real-time systems Classification algorithms Web sites Multimedia communication Random forests

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LDWPSO based Bi-LSTM Model for Predicting the Missing Data in PHRs

LDWPSO based Bi-LSTM Model for Predicting the Missing Data i...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Pareek, Piyush Kumar Prasad, Anoop N Gangambika, G. Nayana, P.C. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India East West Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350398465

In medical and health datasets, it's common for some information to be missing. When you look at datasets that are missing some information, you might get skewed results or less statistical power. Because of this, some data may be missing, which hurts the analysis of the data. These missing data should be replaced with appropriate values. This article suggests a way to estimate missing data using an optimised Bi-Directional Long Short-Term Memory (OBi-LSTM) applied to big data in healthcare. A memory gate is used in the suggested method to estimate missing data that comes up when collecting and processing data. In this study, information from the Korean National Health Nutrition Examination Survey (KNHNES) by the Korea Centers for Disease Control and Prevention (KCDC) is used. As typical South Korean health care records, they have a lot in common with personal health records (PHRs). On the basis of this, models can be made to estimate the missing data in PHRs. PHRs are also multimodal, which means that data can be collected from many different sources for a single object. The Bi-LSTM model is used in a multimodal setting because of this. By preprocessing, a KNHNES data set is made that has no missing values. In data set-based learning, a label is set as the first piece of data. The parameters of Bi-computational LSTM affect how fast the model works. Linearly decreasing weight particle swarm optimization is used in the research to deal with these problems (LDWPSO). There is a big difference in the number of parameters between the two models, but there isn't much difference in how accurate they are. A multi-modal approach can also cut down on the time it takes to process large amounts of data in places like hospitals and institutions. © 2023 IEEE.

关键词： Nutrition

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Prevent Data in Embedded Based Customized Wireless Message Transmitting System Using AES Algorithm with artificial Bee Colony Optimisation Techniques 1

Prevent Data in Embedded Based Customized Wireless Message T...

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1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Sathia Priya, M. Bhagavathi Priya, M. Manojkumar, B. Vijayakumar, M. Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Dr.Mahalingam College of Engineering & Technology Department of Artificial Intelligence & Machine Learning Pollachi India Dr. Mahalingam College of Engineering & Technology Department of Artificial Intelligence & Data Science Pollachi India

ISBN: (纸本)9798350359756

The sender encrypts a message before transferring it to the recipient over a channel in a unidirectional, linear process, according to the transmission model of communication. Hardware and software components known as "message transmission systems"are in charge of managing the transmission path and the communication process. It is possible to study transmission data with the traditional information theory, which is based on "emitters,""messages,"and "receivers". The execution of control tasks such data collecting, control algorithm execution, system identification, and actuation frequently takes place in several system nodes. The effectiveness of a status monitoring system where a sensor delivers sporadic status updates to a remote monitor across a network is observed. Additionally, wireless protocol is utilized in embedded devices to convey messages because it is made for lightweight devices and unstable network conditions. The core concept is to develop a new class of applications through direct interaction amongst intelligent techniques, without the requirement for human involvement. Because it embeds user personal data and activates the aims of information, data prevention is crucial for information sharing. This article has covered the issues of using wireless data, messages, or information with an encryption system, as well as a summary of previous studies in the field. A suggested plan of action has also been offered. To protect user data and privacy safer than any other existing approach, the proposed system also uses artificial Bee Colony Optimisation (ABC) to optimise the key used for encrypting the sensor data with the aid of the AES Encryption algorithm. Even so, the suggested solution uses less time to encrypt and decode patent data before saving it to the server. © 2023 IEEE.

关键词： Information theory

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Pharmatrack Innovations Advanced Pharmacy App Enhancing Efficiency, Compliance, and Management with AI & ML

Pharmatrack Innovations Advanced Pharmacy App Enhancing Effi...

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International Conference on Computer and Communications Technologies (ICCCT)

作者： N.Mirudhula Devi Architha Rajeswaran G. Hema S.Rajesh Kumar CSE-Artificial Intelligence and Machine Learning Sri Sairam Engineering College Chennai-044 India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

Pharmacies play a vital role in healthcare by managing drug storage, distribution, and administration, but operational inefficiencies hinder productivity. Manual processes, dispensing errors, outdated medications, and inventory mismanagement create significant challenges. This project leverages AI-driven tools and machine learning (ML) algorithms to modernize pharmacy operations, ensuring regulatory compliance, streamlined inventory management, and improved collaboration. The core AI-powered application predicts seasonal pharmaceutical demand using weather data and analyzes inventory patterns to prevent stock shortages and overstocking. It features automated dosage control, restricting high-dosage medication sales without a doctor's approval, and news extraction to keep pharmacists updated on medical trends. Additionally, it supports pharmacy collaboration, allowing chains to share resources and automates inventory updates, ensuring real-time availability of new drugs. Other key features include billing automation, secure client data management, and expiration tracking to prevent outdated medications from being dispensed. By integrating AI, ML, and automation, Pharmatrack enhances efficiency, patient safety, and pharmacy profitability. It transforms traditional pharmacy operations into a cost-effective, technology driven system, ensuring seamless management, regulatory adherence, and improved patient satisfaction.

关键词： Drugs Accuracy Automation artificial neural networks Medical services Inventory management Predictive models Feature extraction Real-time systems Meteorology

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Unveiling the Potential of machine learning: Harnessing machine learning for Enhanced Coronary Heart Disease Detection and Intervention

Unveiling the Potential of Machine Learning: Harnessing Mach...

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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Harshali Hemant Vadher Adla Aryan Kuruva Vamshi Ramojula Rajashekar Degavath Ashish Gagan Deep Arora Department of Artificial Intelligence & Machine Learning Vardhaman College of Engineering Hyderabad Telangana India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

Coronary heart disease (CHD) remains a critical global health issue, necessitating effective predictive models to enhance early diagnosis and intervention. This study explores the application of various machine learning algorithms to CHD data to identify and predict the risk of developing the disease based on health indicators. We employed several algorithms, including Logistic Regression, Random Forest Classifier, Support Vector machine (SVM), and Recurrent Neural Network (RNN), each with its unique strengths and limitations. The Logistic Regression model achieved an accuracy of approximately 86%, proving effective in binary classification tasks but revealing potential class imbalance issues. The Random Forest Classifier demonstrated robustness with an accuracy of around 85.24%, adeptly handling complex datasets. While specific accuracy metrics for the SVM were not detailed, it was noted for its classification capabilities. The RNN achieved an accuracy of approximately 85.96%, effectively capturing temporal dependencies within the data. Results indicate that although all models performed well, challenges such as class imbalance impacted performance metrics, especially in predicting CHD cases. This highlights the need for further refinement in predictive models to improve precision, recall, and F1-scores. Later research will examine the integration of advanced deep learning methodologies to enhance the precision and dependability of CHD forecasts.

关键词： Support vector machines Heart Logistic regression Recurrent neural networks machine learning algorithms Accuracy Predictive models Prediction algorithms Random forests Diseases

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OptiNLP: Leveraging Natural Language Processing for Optimal Feature Selection in Dimensionality Reduction

OptiNLP: Leveraging Natural Language Processing for Optimal ...

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Intelligent Technologies (ACOIT), Asian Conference on

作者： Nadimpallli Madana Kailash Varma Lingampalli Anjali Macharla Vishnu Vardhan Babu Shaik Ismail Mohammed Sharfuddin Sahel Gagandeep Arora Department of Artificial Intelligence &Machine Learning Vardhaman College of Engineering Hyderabad Telangana India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

In today’s data-driven world, the exponential growth of data has led to the emergence of high-dimensional datasets, often causing complexity and overload in machine learning models. To address these challenges, dimensionality reduction techniques such as feature selection and feature extraction have become pivotal in enhancing model performance and interpretability. This paper focuses on exploring various feature selection methods to assist researchers in identifying the most suitable technique for sentiment analysis tasks. Utilizing Natural Language Processing (NLP), we conduct a comprehensive evaluation of different feature selection models. Our experimental findings show that, in the context of sentiment analysis, filter method consistently tends to be more effective than other evaluated feature selection techniques. Filter performs better than other methods in terms of precision, effectiveness, and model interpretability, which makes them ideal compared to other feature selection techniques. By leveraging insights from our comparative analysis, researchers can make informed decisions on feature selection techniques.

关键词： Dimensionality reduction Sentiment analysis Feature extraction Transformers Data models Complexity theory

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Data-Driven Urban Safety: A CNN-Based Predictive Model for Manhole Hazard Detection

Data-Driven Urban Safety: A CNN-Based Predictive Model for M...

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Students Conference on engineering and Systems (SCES)

作者： Sudabathula Vijay Sai Kumar Jagini Naga Padmaja Sri Harsh Mattaparty Shaik Ismail Nadimpalli Madana Kailash Varma Polasani Vaishnavi Department of Artificial Intelligence & Machine Learning Vardhaman College of Engineering Hyderabad Telangana India

ISBN: (数字)9798350374711

ISBN: (纸本)9798350374728

The safety of urban environments is a paramount concern, with manholes serving as critical components of infrastructure by regulating sewages but also susceptible to pose hazard when improperly maintained or obscured. The passing years have shown the level of threat an open manhole poses. In this research paper, we try to introduce a novel approach to address these concerns by leveraging Convolutional Neural Networks (CNNs) for real-time manhole status prediction. This research offers a practical and effective tool for authorities to promptly address hazardous conditions and ultimately minimizing the risks to the public.

关键词： Deep learning Training Smart cities Urban planning Predictive models Hazards Convolutional neural networks

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Enhancing E-commerce Fashion Sales through Personalized Recommendation Systems

Enhancing E-commerce Fashion Sales through Personalized Reco...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： T Subaranjani Sathiyapriya Kannaiyan S Parvathy P R Shwetha Dept. of Artificial Intelligence and Machine Learning The Oxford College of Engineering Bangalore India

Personalized recommendation systems are pivotal in the thriving e-commerce fashion sector, enriching customer experiences and driving sales. This paper introduces an innovative method to enhance e-commerce fashion sales through personalized recommendations. The model merges Singular Value Decomposition (SVD) Reranking with customer grouping, yielding tailored product suggestions for distinct customer segments. The study comprehensively explores multiple recommendation techniques, assessing their performance using Mean Average Precision (MAP) scores. Results demonstrate the superiority of the proposed model, achieving a validation score of 0.032007. This success is attributed to the model’s ability to capture latent features and offer personalized suggestions based on customer group traits. This research underscores the significance of personalized recommendations in boosting ecommerce fashion sales. The proposed approach, SVD Reranking with customer grouping, excels in providing personalized suggestions across diverse customer segments. These findings contribute to the advancement of recommendation systems in fashion, fostering customer satisfaction, minimizing returns, and bolstering sustainability.

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