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Bottom-Up Cracks Detection in Road Pavement in artificial Neural Network 1

Bottom-Up Cracks Detection in Road Pavement in Artificial Ne...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Sunil Kumar, V. Alzubaidi, Laith H. Ramesh, G.P. Sreenath, M.V. Gayathri, T. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India The Islamic University of Babylon Department of Computers Techniques Babylon Iraq Department of Electronics and Communication Engineering Chennai India Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Bangalore India Dayananda Sagar University School of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350352931

In recent years, road crack detection is a significant task for detecting surface defects, road safety and road maintenance. But there is lack of approach that able to detect the concealed cracks especially bottom-up cracks, and this is challenging due to poor continuity of cracks, various crack's widths, and low contrast among cracks and the surrounding pavement. The artificial Neural Network (ANN) algorithm is proposed for detecting and classifying the structural health status of bottom-up crack in road pavements, which allows detecting surface defects and failures. The approach aims at collection of these signatures by utilizing acoustic-sensors, located at roadside, and also classification of SHS in pavements over the ANN approaches. The various machine learning (ML) classifiers such as Multilayer Perceptron (MLP), 2-Dimensional Convolutional Neural Network (2D-CNN), Support Vector Classifier (SVC) and Random Forest Classifier (RFC) are utilized as well as compared for the analysis of crack in road pavement. The experimental results illustrate the probability of associating with great accuracy 92.7 %of MLP, 96.7 %of 2D-CNN, 92.5 %of RFC and 9 9. 3 %of SVC respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Unveiling Customer Segmentation Patterns in Credit Card Data using K-Means Clustering: A machine learning Approach

Unveiling Customer Segmentation Patterns in Credit Card Data...

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2023 IEEE International Conference on Modelling, Simulation and Intelligent Computing, MoSICom 2023

作者： Priyadarshni, Sakshi Fathima, Rakshan Urolagin, Siddhaling Bongale, Anupkumar M. Dharrao, Deepak Sudhakar Birla Institute of Technology and Science Dept. of Computer Science Pilani Dubai United Arab Emirates Symbiosis Institute of Technology Dept. of Artificial Intelligence and Machine Learning Pune India Symbiosis Institute of Technology Dept. of Computer Science & Engineering Pune India

ISBN: (纸本)9798350393415

Customer segmentation plays a crucial role in business development by providing deep insights into customer behaviors and preferences, facilitating effective and personalized marketing strategies. However, traditional segmentation methods often fall short of accurately analyzing large and complex datasets. In recent years, the application of machine learning techniques has transformed the field of customer segmentation, enabling automated analysis, enhanced accuracy, and the discovery of intricate patterns and key trends. This research paper provides a detailed review of the application of K-Means clustering in credit card companies for effective customer segmentation. It explores the benefits, challenges, and practical considerations of utilizing K-Means clustering, along with real-world case studies highlighting trends and potential advancements for using K-Means clustering in credit card customer segmentation. © 2023 IEEE.

关键词： K-means clustering

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Lung Nodule Segmentation and Classification Using Conv-Unet Based Deep learning

Lung Nodule Segmentation and Classification Using Conv-Unet ...

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2023 IEEE International Conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Akash, V. Monish Sai, R. Amith, K.B. Jayanthi, M.G. Kannadaguli, Prashanth Cambridge Institute of Technology Dept. of Artificial Intelligence and Machine Learning Bangalore India Cambridge Institute of Technology Dept. of Information Science and Engineering Bangalore India Cambridge Institute of Technology Dept. of Computer Science and Engineering Bangalore India Dhaarini Academy of Technical Education Bangalore India

ISBN: (纸本)9798350300826

This paper proposes a Convolutional U-Net architecture, a variation of the standard U-Net architecture for the segmentation of lung nodules and classification using Deep learning on Computerized Tomography (CT) scans. The Primary steps employed are Preprocessing, Segmentation and Classification of nodules. In the preprocessing step, the lung region is segmented using techniques such as normalization, median filtering, Kmeans clustering, morphological and thresholding operations to extract lung Region of Interest (ROI) and nodule masks. The Conv-Unet design adds more convolutional layers to the standard U -Net architecture to help capture complicated patterns and boundaries of lung nodules for more accurate segmentation. Categorization of the segmented lung nodules is done using a CNN network on the LIDC-IDRI, and LUNA16 dataset. Overall, this model achieves a dice score of 62% and classification accuracy of 82% displaying appropriate performance in comparison with other variations of the U-Net architecture. © 2023 IEEE.

关键词： Convolution

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Detection of Weed plant in Farm and Removing using Unmanned Ground Vehicle 7

Detection of Weed plant in Farm and Removing using Unmanned ...

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Sandeep Telkar, R. Yakkundimath, Rajesh Thomas, Likewin Kl, Thara Shivamogga and Visvesveraya Technological University Pes Institute of Technology and Management Department of Artificial Intelligence & Machine Learning Karnataka Belagavi590018 India Hubbali and Visvesveraya Technological University Kle Institute of Technology Department of Computer Science & Engineering Karnataka Belagavi590018 India

ISBN: (纸本)9798350341485

India's agriculture sector has contributed greatly to the country's economic expansion. Therefore, reducing expenses and raising crop productivity are essential. In a country like India, where there are hundreds of fields, weeds are always growing there. Weeds and crops fight for space, water, food, nutrients, and other resources, which reduces agricultural output. Currently, one of agriculture's most difficult duties is weed control. Herbicide hand spraying is one of the most popular weed management techniques. However, this strategy has a number of shortcomings. Because manual labor is costly, an automated weed management system may be economically viable. There is currently no technology available that can be utilized in the field in real time to autonomously control in-row weeds, despite repeated attempts. Weed control has become more important as food grain production has increased per person. The suggested system makes an effort to provide a robotic, computer vision-based Unmanned Ground Vehicle (UGV) for real-time weed management in row crops in order to address the aforementioned issues. This tool can recognize weeds and remove them gently without endangering the environment. It would be ideal to achieve crop contact without the involvement of humans using IoT, machine learning, and picture analysis. © 2023 IEEE.

关键词： Ground vehicles

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Exploratory Data Analysis and Classification of Employee Retention based on Logistic Regression Model 9

Exploratory Data Analysis and Classification of Employee Ret...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Bongale, Anupkumar M Dharrao, Deepak Urolagin, Siddhaling University Symbiosis Institute of Technology Department of Artificial Intelligence and Machine Learning Pune India University Symbiosis Institute of Technology Department of Computer Science and Engineering Pune India Dubai International Academic City Birla Institute of Technology & Science Department of Computer Science and Engineering Dubai United Arab Emirates

ISBN: (纸本)9798350397376

Nowadays, a huge number of companies are using employee data records to understand the employee churn rate in the company. Companies use predictive analytics to predict the chances of an employee leaving the company. Organizations are effectively using Human Resource (HR) predictive analytics to increase revenue and turnover. This paper is about building a predictive model on the employee dataset to predict if an employee is likely to leave or continue service in the employed company. The steps carried out for visualization and Exploratory Data Analysis (EDA) has been highlighted in this paper by bringing out valuable insights from the employee dataset. Observations were made to the features that determine the chances of an employee leaving the company. For the prediction and classification of employee retention, a Logistic Regression (LR) model is applied to the dataset. The article focuses on the analysis of data and understanding the working of the LR model for predicting categorical features. The performance of the applied model was evaluated using a confusion matrix. The accuracy of the model was calculated from values in the confusion matrix. © 2023 IEEE.

关键词： Predictive analytics

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Indian Food Segmentation and Calorie Estimation Using CatBoost and Masked Convolutional Neural Networks

Indian Food Segmentation and Calorie Estimation Using CatBoo...

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2023 IEEE International Conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Acharya, Nabin Singh, Ayush Kumar Dwivedi, Anshuman Kumar Jayanthi, M.G. Kannadaguli, Prashant Cambridge Institute of Technology Dept. of Information Science and Engineering Bengaluru India Cambridge Institute of Technology Dept. of Artificial Intelligence and Machine Learning Bengaluru India Cambridge Institute of Technology Dept. of Computer Science and Engineering Bengaluru India Dhaarini Academy of Technical Education Bengaluru India

ISBN: (纸本)9798350300826

Accurate food segmentation and calorie estimation play a pivotal role in effective dietary monitoring and health management. In this paper, after extensive experimentation on a diverse Indian food dataset of 20 classes collected by us, we present two separate innovative models designed to address these tasks with exceptional precision. The first model employs a machine learning approach by incorporating the CatBoost algorithm, while the second model leverages a deep learning technique utilizing the U-Net architecture for high-quality image segmentation. Additionally, we merge the U-Net outcomes with a convolutional neural network (CNN) to enhance the deep learning-based classification. Our models effectively handle categorical features and address imbalanced data, resulting in significantly improved accuracy in food item delineation within images. This advancement enables a more reliable and comprehensive analysis of dietary patterns. We demonstrate the effectiveness and robustness of our proposed models. Comparative evaluations against state-of-the-art methods verify exceptional performance in terms of segmentation accuracy, precision in food classification, and estimation of calorie intake. Notably, our models are specifically tailored for food images and do not take ingredients or any other information into consideration. In comparison CNN model is performs better than CatBoost, achieving an accuracy of 78%. © 2023 IEEE.

关键词： Convolutional neural networks

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An Optimal Planning of Energy Storage System in Radial Distribution Network Using Aquila Optimization Algorithm 2

An Optimal Planning of Energy Storage System in Radial Distr...

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2nd International Conference on Data Science and Information System, ICDSIS 2024

作者： Adnan, Myasar Mundher Sudhakar, A.V.V. Kumar, S. Arun Madhura, G.K. Kaliappan, S. The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Sr University Department Of Electrical And Electronics Engineering Telangana Warangal India Sona College Of Technology Department Of Electrical And Electronics Engineering Salem India Nitte Meenakshi Institute Of Technology Department Of Artificial Intelligence And Machine Learning Bengaluru India Amity University Department Of Mechanical Engineering Dubai United Arab Emirates

ISBN: (纸本)9798350381665

Nowadays smart grids are emerging rapidly for their efficiency in operating modern distribution systems. Energy storage systems (ESSs) play a key role in achieving the objectives of smart grids, facilitating the combination of renewable sources and enhancing system reliability. Aquila Optimization Algorithm (AOA) is proposed for optimal placement and sizing of ESS within the IEEE Radial Distribution System (RDS) to minimize power losses while maintaining voltage magnitude. This method is used to achieve improvements in loss reduction and voltage profile compared to conventional Analytical Index method and Whale Optimization Algorithm (WOA). The experimental results indicate that if ESS are sized and installed correctly, the overall losses in the radial system are decreased. The suggested AOA achieved a loss reduction rate of 62.58% and a voltage profile of 79.87 kW in enhancing the performance of distribution systems within smart grid frameworks. © 2024 IEEE.

关键词： Energy storage

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A Practical IoT-Based PM2.5 Air Contaminant Tracking Mechanism with Suitable machine learning Support 1

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19th Control Instrumentation System Conference, CISCON 2022

作者： Gunasekar, S. Kumar, G. Joselin Retna Agbulu, G. Pius Kumar, Y. Dileep Department of Artificial Intelligence and Machine Learning New Horizon College of Engineering Karnataka Bengaluru India Department of Electronics and Instrumentation Engineering SRM Institute of Science and Technology Kattankulatur Tamil Nadu Chennai India Department of of Mechatronics and Industrial Electronics Engineering Chinhoyi University of Technology Chinhoyi Zimbabwe Department of Electronics and Instrumentation Engineering Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati517102 India

ISBN: (数字)9789819995547

ISBN: (纸本)9789819995530

The air quality of any environment is of crucial concern. With developments in (CPS) cyber-physical systems and data analytics, air contaminants can be tracked employing mechanisms of Internet of Things (IoT) and machine learning (ML). Therefore, this paper proposes a practical IoT-based PM 2.5 air contaminant tracking mechanism with suitable machine learning support. With the suggested mechanism, readings on particulate matter concentration can be tracked and revealed in real times. The proposed mechanism applies benefit of IoT-enabled gas sensors and a decision tree ML-model to track and analyze fine particle (PM2.5) levels with time trends. The IoT nodes incorporates MQ-135 and DHT-11 sensors with ESP32 chip. The nodes exploit power-saving modes of the chip to assure optimal duty cycling and sustained operation. The tracked data amassed at the devices are sent to a gateway that finally forward the data to a server for recovery at the user end. Ultimately, the analysis result recommends that the presented IoT-based tracking mechanism can support practical air quality tracking and forecasting in various pollutant concentrated atmospheres while guaranteeing minimal power consummation at the devices. The proposed scheme can be adopted in diverse pollution concentrated zones to provide sustained real-time information on PM2.5 concentrations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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Liver Disorder Diagnosis Using machine learning Technique

Liver Disorder Diagnosis Using Machine Learning Technique

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Science, technology, engineering and Management (ICSTEM), International Conference on

作者： Aneesh Nemalikanti Ebenezer Jangam Computer Science and Engineering Karunya Institute Of Technology and Sciences Coimbatore India Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

The utilization of machine learning techniques in diagnosing liver disorders has garnered considerable attention for its potential to enhance accuracy and efficiency. However, in this study, we pivot our focus towards developing a predictive model for detecting liver tumors, leveraging patient information encompassing demographic details, medical history, and laboratory test results. By employing advanced algorithms such as Efficient-Net and LeNet, we aim to classify whether the input data indicates the presence of a liver tumor. This novel approach has demonstrated improved accuracy compared to traditional methods, marking a significant advancement in liver tumor diagnosis. The outcomes demonstrate how well our suggested methodology works to reliably identify liver tumors. thereby potentially aiding healthcare professionals in making timely and precise decisions, ultimately leading to enhanced patient care.

关键词： Accuracy Liver Medical services machine learning Predictive models Prediction algorithms User experience

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Hybrid CNN Classifier for Accurate Detection of Oral Cysts in Panoramic Dental Radiograph 9

Hybrid CNN Classifier for Accurate Detection of Oral Cysts i...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Sivagami, S. Senthilvadivu, S. Geetha, P. Yukthavarshika, J. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Artificial Intelligence and Machine Learning Tamil Nadu Chennai India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377972

In dentistry, machine learning approaches play a significant role in the early detection and diagnosis of oral cysts. When planning accurate medical treatment, oral and maxillofacial surgeons can benefit from preoperative detection of these cysts. Surgical planning is directly affected by classifying oral cysts as dentigerous, radicular, ameloblastoma, odontogenic carcinoma, and odontogenic keratocyte. In dentistry, the outcomes of differential diagnosis based solely on imaging are unacceptable. This research proposes a three-step framework for autonomous cyst classification, segmentation, and detection based on a Convolutional Neural Network (CNN). To begin, DENT-CNN, a modified CNN architecture is proposed to classify the oral cyst as normal or abnormal. Second, UNet is a CNN variant used to segment the categorized cyst regions on panoramic dental images. Third, the Gray Level Co-occurrence Matrix (GLCM) is combined with CNN to identify segmented cysts as benign or malignant. The proposed work is extensively evaluated using 1500 real-time dental panoramic radiographic images of the oral cyst collected from a dental hospital. To alleviate overfitting, the data augmentation approach is used. The performance of the proposed work is analyzed in terms of a cyst classification rate of 96.8 %, a Dice Score of 96.3 % for cyst segmentation, and an accuracy for the Identification phase of 96.5%. © 2024 IEEE.

关键词： Dentistry

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