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检索条件"机构=Computer Science with Artificial Intelligence and Machine Learning"

共 2743 条记录，以下是481-490 订阅

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Precision Land Use and Land Cover Mapping with a High-Accuracy Time Series LSTM Model

Precision Land Use and Land Cover Mapping with a High-Accura...

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IEEE Students' Conference on Electrical, Electronics and computer science (SCEECS)

作者： Adla Aryan Prakash RV Srinivas Aluvala Venigalla Shashank Chowdary Department of Artificial Intelligence & Machine Learning Ardhaman College of Engineering Hyderabad Telangana India Department of Computer Science & Artificial Intelligence SR University Warangal Telangana India Department of Computer Science and Engineering (Data Science) Rajeev Gandhi Memorial College of Engineering and Technology Nandyal India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

This paper introduces a machine-learning-based LULC classifying model developed with a time series data set, trained on a diverse set of agriculture and non-agricultural classes. It used a strong generalization capability employing an LSTM architecture; that is, it achieved accuracy of 90.04% in training and also validation accuracy of 90.55%. The ultimate accuracy was obtained at the minimal loss of 0.1909 at the accuracy of 95.58%, thereby validating its usability for LULC pattern classification. This model has a wide-range of potential application in environmental monitoring and resource management. Future work involves introducing more variables into the dataset, the inclusion of other environmental factors, and fine-tuning of the model towards more general applications in geospatial applications. Thus, this work advances the current state of remote sensing and LULC classification to present an effective tool in sustainable land management.

关键词： Deep learning Accuracy Image processing Time series analysis Land surface Spatial databases Environmental monitoring Resource management Remote sensing Long short term memory

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Predicting Soil Types and Crop Suitability through Comprehensive Soil Analysis

Predicting Soil Types and Crop Suitability through Comprehen...

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International Conference on Communication, Computing and Internet of Things (IC3IoT)

作者： S Sivakumar U Dharani Dineep Hephzibah Department of Computer Science and Engineering SRM Institute of Science and Technology Tiruchirapalli India Department of Computer Science and Engineering Specialization in Artificial Intelligence and Machine Learning SRM Institute of Science and Technology Tiruchirapalli India

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

The analysis of soil nutrients and water levels holds utmost significance in the realms of agriculture. Determining the soil type and identifying the crop suitable for that specific soil are critical factors in optimizing agricultural yield. These considerations play a pivotal role in precision agriculture, ensuring that the chosen crops align with the soil's characteristics. This strategic alignment enhances the efficiency of cultivation practices, promotes sustainable agricultural methods, and ultimately contributes to increased crop yields. In this research paper, we systematically identify key soil properties, including NPK concentrations, temperature, humidity, and soil pH. The collected dataset, encompassing these crucial soil attributes, is utilized to train machine learning models. The primary goal is to forecast the soil type utilizing the gathered data, and subsequently, undertake further analyses, to ascertain the relevant crop types that are most suitable for the identified soil characteristics. This comprehensive approach involves the integration of data-driven insights and machine learning techniques to enhance our understanding of soil composition and facilitate informed decision-making in agriculture.

关键词： Temperature Computational modeling Crops Soil Predictive models Bayes methods Telecommunication computing

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Heart Disease Prediction Using Logistic Regression

Heart Disease Prediction Using Logistic Regression

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International Conference on computer, Electrical & Communication Engineering (ICCECE)

作者： Dipanjan Saha Soham Guha Kankana Kundu Suchana Das Sajili Chatterjee Pritusna Banik Sohinee Mondal Computer Science & Technology Narula Institute of Technology Kolkata India Computer Science Engineering (Artificial Intelligence & Machine Learning) Institute of Engineering & Management Kolkata India Computer Science & Engineering Narula Institute of Technology Kolkata India

ISBN: (数字)9798331513894

ISBN: (纸本)9798331513900

Cardiovascular diseases (CVDs) are one of the leading causes of death worldwide, and early identification is crucial to improve the patient’s prognosis. Traditional diagnostic methods for heart diseases are highly dependent on clinical expertise and can be time-consuming, making machine learning an attractive alternative for improving accuracy to diagnose and support faster decision making. Previously, research has explored various machine learning algorithms, such as decision trees, support vector machines, and neural networks, to improve prediction accuracy. However, many of these models face challenges related to their interpretability and generalization to unexpected data. This paper presents a logistic regression model to predict CVDs based on a dataset of 303 samples, incorporating key demographic, clinical, and diagnostic features. The model achieved an accuracy of $85.12 \%$ in the training data and 81. $97 \%$ in the testing data, demonstrating its effectiveness for binary classification tasks. The study emphasizes the computational efficiency and interpretability of logistic regression, making it appropriate for real-time applications in clinical settings. In conclusion, the results indicate that logistic regression can serve as a reliable tool for early detection of heart disease, and future work can explore dataset expansion, advanced models, and explainable AI techniques to further improve prediction accuracy and model transparency.

关键词： Heart Logistic regression Accuracy Computational modeling Training data Predictive models Data models Computational efficiency Reliability Testing

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A Review on Web Data Mining to Detect Online Spread of Terrorism Activity 2

A Review on Web Data Mining to Detect Online Spread of Terro...

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2nd International Conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Jha, Saurav Kumar Pacharaney, Utkarsha Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine learning Maharashtra Wardha India

ISBN: (纸本)9798331505745

This research study outlines the significant contribution that web data mining can make in discovering and combating the dissemination of online terrorism. With terrorist groups using the internet more and more to deliver propaganda, recruit, and organize, there is a pressing need for efficient monitoring and analysis of web content. This article discusses different data mining methods that can be used to identify and study patterns suggestive of terrorist activity throughout networked digital environments. Specifically, the embedding of machine learning algorithms, i.e., Support Vector machines (SVM), Random Forest and Gradient Boosting, is investigated with regard to its suitability to differentiate between benign and harmful online content. These algorithms can process large volumes of data, where features are extracted, including URL structures, domain ages, and keyword frequencies, to detect anticipate threats. According to the article, feature extraction plays a crucial role in improving detection system accuracy, enabling real-time surveillance of anomalous online activities. In addition, the review discusses the problems that researchers and law enforcement agencies have been encountering in using web data mining for counter-terrorism purposes. Ethical concerns around privacy and civil liberties are addressed and technical challenges, including the quality of data and the dynamic character of online content, are elaborated. Scenarios in which false positives arising in detection systems may occur are also considered, with emphasis on the need for ongoing algorithm and technique refinement. © 2025 IEEE.

关键词： Crop protection

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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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An Intelligent Deep learning Strategy for Breast Cancer prediction using feature ensemble learning

An Intelligent Deep Learning Strategy for Breast Cancer pred...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, artificial intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Prakash, E.P. Satheshkumar, K. Anandhasilambarasan, D. Prasath, B. Department of Computer Science and Engineering Dr.N.G.P. Institute of Technology Coimbatore India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode India Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India

ISBN: (纸本)9798350305708

One in six women will be diagnosed with breast cancer (BC) by the time they reach their forties, making it the leading cause of death among this demographic. It's the leading killer in the world, yet it's hard to spot and diagnose in its early stages. Among women, BC is a foremost origin of death in third world countries. The prevention and treatment of these tumors can be greatly aided by screening and early diagnosis. At the moment, BC is the leading malignancy in females, even more so than ovarian cancer. When researchers get access to patient medical records, they can uncover hidden patterns in healthcare. The National-Cancer-Institute (NCI) reports that mortality rates from breast cancer can be reduced with early detection. In order to accurately identify patients with cancer, this work presents a voting classifier based on ensemble learning that integrates the logistic-regression(LR) &stochastic-gradient-descent(SGD) with deep-convoluted features. The ensemble voting classifier is fed deeply-convoluted features mined from the microscopic characteristics. This concept offers a refined framework for differentiating amid benign and malignant tumors. The results show that the highest level of classification accuracy, 100%, may be attained when utilizing voting classifiers with complex features. When compared to the traditional methods, the proposed method showed an improvement in accuracy. Thus, the research aids doctors in diagnosing and avoiding breast cancer. © 2023 IEEE.

关键词： breast-cancer classification deep-convoluted-features ensemble learning healthcare prediction

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Lung Cancer Classification Using CNN with Data Augmentation

Lung Cancer Classification Using CNN with Data Augmentation

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： V Karunakaran N Akshaya B Harismita S Atchaya Artificial Intelligence and Machine Learning Sri Eshwar College of Engineering Coimbatore India Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Lung cancer remains a leading cause of cancer-related deaths globally. This study investigates the application of Convolutional Neural Networks (CNNs) for the classification of lung cancer using the IQ-OTH/NCCD lung cancer dataset. The study compares the performance of CNN models with and without data augmentation techniques. Experimental results demonstrate that CNN models with data augmentation exhibit superior performance, achieving higher accuracy, precision, F1-score, and recall compared to models trained without data augmentation. These findings highlight the importance of data augmentation in improving the accuracy and robustness of deep learning models for lung cancer classification, ultimately contributing to early detection and improved patient outcomes.

关键词： Measurement Accuracy Lung cancer Data augmentation Data models Robustness Convolutional neural networks Intelligent systems Standards Medical diagnostic imaging

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Text to Image Generation Using Attentional Generative Adversarial Network 1

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1st International Conference on Advanced Computing, machine learning, Robotics and Internet Technologies, AMRIT 2023

作者： Rao, Supriya B. Shetty, Shailesh S. Singh, Chandra M, Srinivas P. Bekal, Anush Department of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bangalore Karnataka Mangaluru India Department of Computer Science and Business System Srinivas Institute of Technology Karnataka Mangaluru India Department of Electronics & Communication Engineering Sahyadri College of Engineering & Management Karnataka Mangaluru India Department of Computer Science Engineering Sahyadri College of Engineering & Management Karnataka Mangaluru India

ISBN: (数字)9783031472213

ISBN: (纸本)9783031472206

AttnGan – Attentional Generative Adversarial Network (GAN) is a type of GANs that is used for text to image generation. Unlike other art models, it focuses on fine-grained word level information and once the model is trained by mapping the images and the corresponding text in the dataset, the model will generate image part-by-part using the image description given by the user as an input to the model. Current paper, focuses on the conversion of text into images based on the description provided by the user. This enables them to translate their ideas in the form of text into images of their required choices. The model is initially trained by the description of the text and images and when the user provides the text as an input to the system, it is used as a testing data. By analyzing the text word-by-word, the respective images pertaining to the text is generated part-by-part by considering the text description into account. This can heavily help the users in a applications such as in editing aids, documental archives, etc. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Generative adversarial networks

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Enhancing Crop Yield: A Deep learning Approach to Detect Tomato Leaf Diseases

Enhancing Crop Yield: A Deep Learning Approach to Detect Tom...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Shreedevi S Ronihal Sheba Selvam Kusuma P Information Science and Engineering BNM Institute of Technology Bangalore Artificial Intelligence and Machine Learning BNM Institute of Technology Bangalore Computer Science and Engineering BNM Institute of Technology Bangalore

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

Agriculture represents a vital pillar of the global economy, with crop production significantly influenced by a broad variety of Phyto pathological conditions, particularly plant leaf-diseases. The early and accurate detection of these diseases are paramount, as they have a direct impact on crop yield, which consequently influences the economic viability and market profitability of agricultural outputs. However, many farmers and agricultural practitioners often lack the specialized knowledge required for precise diagnosis of leaf diseases, resulting in suboptimal crop production and economic losses. Consequently, the development of robust and efficient disease detection methodologies is imperative for the sustainability and success of agricultural practices. This research investigates the application including sophisticated deep learning algorithms like VGG19 and CNN, to identify and classify diseases in tomato plant leaves. The developed model attains an accuracy of 99.17%, surpassing both VGG19 and CNN, with the goal of enhancing crop management strategies and optimizing overall agricultural productivity.

关键词： Deep learning Accuracy Profitability computer architecture Feature extraction Robustness Convolutional neural networks Sustainable development Crop yield Diseases

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Mediguide:A machine learning based Smart Prescription System for Personalized Healthcare

Mediguide:A Machine Learning based Smart Prescription System...

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artificial intelligence and Data Engineering (AIDE), International Conference on

作者： Jeyaganesh Kumar K Mithilesh G Monisha K M Nandhini S Nithish Kumar M Department of Artificial Intelligence and Data Science M.Kumarasamy College of Engineering India Department Of Artificial Intelligence and Machine Learning M.Kumarasamy College Of Engineering India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

This project uses a Decision Tree Classifier model which gives accurate health predictions based on the symptoms provided by users. The model has been trained at thousands of symptoms against diseases and thus predicts very accurately for the inputted data provided by the user. The system has an easy-to-use interface that helps users input their symptoms with ease and get instant predictions of potential diseases. In addition to predicting ailments, the application provides extensive information on the detected disease. Users are given a comprehensive breakdown of the disease, recommended medications, precautions, and personalized advice on diet and exercise regimes. It provides health insights that are meant to help users better manage their health and make better lifestyle decisions. With feedback loops, prediction accuracy and user experience can always be improved directly without worrying much about each setup. It seeks to use advanced technology to make practical health advice possible. Combining disease prediction in real time with meaningful health advice creates added value for users and encourages informed health choices. It enables users to own their health by providing them with a complete picture of their possible health problems and possible solutions to address them. This Project provides a powerful tool for more proactive users, with its combo of predictive analytics and health education resources.(And it helps us recognise the sport itself the user-friendly and detailed output that makes it easy and helpful for everyone to understand their health and what they can do about it.) It upskill traditionally trained AI learners to be perpendicular over ruinous health data towards an accessibly governed future, furnished with health facts and outcomes to improvement on the go.

关键词： Accuracy Medical services machine learning Predictive models Prediction algorithms Data models User experience Decision trees Medical diagnostic imaging Diseases

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