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检索条件"任意字段=7th International Conference on Machine Learning and Soft Computing, ICMLSC 2023"

共 811 条记录，以下是461-470 订阅

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Leveraging machine learning to Identify Synergistic Drug Combinations for Effective Cancer Treatment 7

Leveraging Machine Learning to Identify Synergistic Drug Com...

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7th international conference on Image Information Processing, ICIIP 2023

作者： Sujatha, P. Saravanan, K. Sohail, Mohammed Ali Basi Reddy, A. Dixit, Rohit R. Krishnaiah, Nallam Amrita Vishwa Vidyapeetham Department of AI/ML Guntur Amaravati India R.M.K Engineering College Department of Information Technology Tamil Nadu Tiruvallur District India Jazan University College of Computer Science & Information Technology Department of Computer & Network Engineering Jazan Saudi Arabia Mohan Babu University School of Computing Department of Computer Science And Engineering Andhra Pradesh Tirupati India Siemens Healthineers NH03755 United States St.Martin's Engineering College Department of Ai&ml Secunderabad Telangana Dhulapally500100 India

ISBN: (纸本)9798350371406

the potential of drug combinations to treat and overcome medication resistance complex genetic diseases is evident. Synergistic drug combinations offer a promising way to enhance drug therapy efficacy and reduce the required medication dosage. However, developing effective combination medication therapies with synergistic effects has been challenging, despite numerous ongoing clinical investigations. Current models and approaches to detect medication synergy outlined in the literature lack the expected consistency in outcomes. to better comprehend the impact of particular medication combinations, it is essential to be familiar with the vocabulary used to describe synergy. In this study, a combinational drug screen is utilized to identify useful features for locating synergistic or efficient drug combinations. the feature selection algorithm (Boruta) helps select the most relevant features, and machine learning models are then trained using the selected feature dataset. Performance assessment metrics like sensitivity, accuracy, and specificity are used to compare the trained models, and the Random Forest model stands out for its significantly better performance compared to other models. © 2023 IEEE.

关键词： Drug dosage

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QUASIM: Quantum computing enhanced service ecosystem for simulation in manufacturing 42

QUASIM: Quantum computing enhanced service ecosystem for sim...

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42nd international conference on Conceptual Modeling: ER Forum, 7th Symposium on Conceptual Modeling Education, SCME 2023, Project Exhibitions, Posters and Demos, and Doctoral Consortium, ER-Companion 2023

作者： Agrawal, Ankit Stein, Hannah Xu, Sascha Janzen, Sabine Maass, Wolfgang German Research Center for Artificial Intelligence Stuhlsatzenhausweg 3 Saarbruecken66123 Germany Saarland University Campus A5 4 Saarbruecken66123 Germany

Machining is a key manufacturing technology, representing one of the most significant German economic sectors. To ensure required high-quality assurance and prevent manufacturing errors, process simulations based on digital twins can be applied. However, the current digitization and simulation models face limitations in terms of computational requirements and expert knowledge. As a consequence, important physical effects in industrial practice are either neglected or roughly approximated, resulting in compromised decision-making and economic disadvantages. Since quantum computing (QC) has shown promising benefits in solving numerous algorithmic problems and simulations, the QUASIM research project aims to use QC to improve simulations in manufacturing, reduce modeling efforts and error rates, and develop innovative solutions. © 2023 CEUR-WS. All rights reserved.

关键词： Conceptual Modeling machine learning Quantum computing Quantum computing Applications Simulations in Manufacturing

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Efficient Fetal Health Monitoring and Classification with machine learning

Efficient Fetal Health Monitoring and Classification with Ma...

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international conference on ent computing and Control Systems (ICICCS)

作者： Kabir Singh Prayla Shyry Ramya G Franklin Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

the goal of the research on fetal health categorization using machine learning is to create a model that can precisely predict the condition of a fetus during pregnancy. this is crucial because prompt action following early identification of fetal health issues might enhance the pregnancy’s outcome. this research study suggests classifying fetal health status using machine learning approaches based on ultrasound images and other clinical parameters. To train and test our model, we will gather a sizable dataset of ultrasound images and clinical characteristics from pregnant women. To categorize the state of fetal health, the proposed model will employ machine learning algorithms and image processing techniques. Our approach should categorize fetal health status with a high degree of accuracy, and it will help obstetricians and gynecologists provide better treatment for pregnant patients.

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GUI based Heart using Disease Classification using machine learning

GUI based Heart using Disease Classification using Machine L...

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international conference on computing Methodologies and Communication (ICCMC)

作者： G Revathy P.Muruga Priya K. Senthilnathan P. Mythili S.V. Haridharani School of Computing SASTRA Deemed University Thanjavur India Department of CSE Surya Engineering College Erode India Department of EEE Surya Engineerins College Erode India

Heart disease is a serious health problem that has afflicted a lot of people all over the world. In our work, we have proposed a GUI-based machine learning-based approach that is efficient and accurate for identifying heart illness. An correct diagnosis and the right treatment can save several lives. this testing method not only costs a lot of money, but the results also fail to correctly identify HD patients.

关键词： Heart Deep learning Costs Neural networks Computer architecture Feature extraction Optimization

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An Ameliorate Analysis of Cryptocurrencies to Determine the Trading Business with Deep learning Techniques 7th

An Ameliorate Analysis of Cryptocurrencies to Determine the ...

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7th international conference on Emerging Research in computing, Information, Communication and Applications, ERCICA 2022

作者： Sakure, Neeshad Kumar Manoj Kumar, M.V. Prashanth, B.S. Sneha, H.R. thomas, Likewin Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering PESIT—M Shivamogga India

ISBN: (纸本)9789811954818

As the range of COVID-19 sufferers increased, many nations imposed a complete lockdown. As a result, it caused a devastating international financial disaster everywhere in the world. Technical and essential evaluation are two methods for determining future worth. Different strategies use statistics from outside the market, such as monetary conditions, hobby rates, and geopolitical events, to forecast future charge. We use technical evaluation forecasts potential charge using buying and selling statistics from the market, which includes charge and buying and selling volume, whereas other strategies use statistics from outside the market, such as monetary conditions, hobby rates, and geopolitical events. the objective of this project is to give technical and fundamental analysis using machine learning approaches. In business, AI is broadly used to remedy and optimize various problems, including marketing, credit score card fraud detection, algorithmic trading, patron service, portfolio management, and product advice primarily based totally on patron needs. Furthermore, the technology due used in this finished greater to optimize the proposed set of rules to attain the maximum correct result primarily based totally on the current valuation of cryptocurrency. this project is to use machine learning techniques to provide technical analysis. the incorporation of new technology into financial institutions has the potential to propel cryptocurrency values to time highs. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision making

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Land Use and Land Cover Classification and Changes Detection Using machine learning Approaches 7th

Land Use and Land Cover Classification and Changes Detection...

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7th international conference on Smart Trends in computing and Communications, SmartCom 2023

作者： Ebenezer, P. Adlene Manohar, S. Sakila, V. Sahaya Department of CSE SRM Institute of Science and Technology Vadapalani Campus Chennai India

ISBN: (纸本)9789819907687

Presently, understanding how Land Use and Land Cover (LU/LC) change affects the environment globally is essential. the issues in changes detection in LU/LC has been resolved and adopted widely. this study examines the changes in LU/LC in the Indian state of Tamil Nadu, Javadi Hills region. the USGS provides the Landsat 8 images which collects the data from two science instruments namely the Operational Land Imager (OLI) and the thermal Infrared Sensor (TIRS), the collected image has 11 bands and merged into color images providing various details regarding the circumstances of the land cover. this study compares the approaches for classifying land use and land cover, including the Random Forest Classifier, Support Vector machine, and Maximum Likelihood Classifier. and to evaluate the impact of particular classification methods on analyses of land-use structure in the study area. the land resources management and policy makers will be able to understand how to take action in protecting the environment by observing and analyzing the changes that have taken place in the study region. Support Vector machine produce the best results for overall accuracy 90.23% and kappa value 0.84 and changes analysis for the span of four years is also analyzed. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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Comparison of machine learning Models and NIHSS for Prognosis Prediction in Stroke Patients

Comparison of Machine Learning Models and NIHSS for Prognosi...

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7th international conference on Artificial Intelligence in Information and Communication, ICAIIC 2025

作者： Noh, Yeonwoo Chang, Yun-Young Jeon, Sewon Lee, Minwoo Noh, Wonjong College of Medicine Gachon Univ. Seoul Korea Republic of School of Computing Gachon Univ. Seoul Korea Republic of College of Medicine Hallym University Seoul Korea Republic of College of Information Science Hallym University Chuncheon Korea Republic of

ISBN: (纸本)9798331507022

In this study, we evaluated ten machine learning models to predict stroke prognosis and compared them with the NIHSS scoring system using metrics like AUC, precision, recall, and F1-score. the top performers were DNN, XGBoost, and LightGBM, excelling in AUC, recall, and Fl-score. Given the importance of recall in hospital settings, our findings suggest that machine learning models, particularly DNN, are more effective than NIHSS for predicting the prognosis of stroke patients. © 2025 IEEE.

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Comparative Study on Different Intrusion Detection Datasets Using machine learning and Deep learning Algorithms 7th

Comparative Study on Different Intrusion Detection Datasets ...

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7th international conference on Big Data and Cloud computing Challenges, ICBCC 2022

作者： Aarthi, G. Sharon Priya, S. Aisha Banu, W. Computer Science and Engineering B. S. Abdur Rahman Crescent Institute of Science and Technology Chennai India

ISBN: (纸本)9789819910502

the tremendous growth in the Internet of things (IoT) creates great potential which provides us with incredible productivity and simplified our daily lives. But, due to resource constraints and computation, IoT networks are vulnerable to a variety of malicious activities. thus, protecting the network from hostile attacks should be the top priority. this can be done by planning and implementing effective security measures, one of them is an intrusion detection system. It detects harmful activities on the network and monitors network traffic based on that detection. the aim of the intrusion detection system (IDS) is to afford various approaches for detecting the rapidly growing network attacks, as well as to stop the harmful activities that occur in the IoT devices. Various artificial intelligence methods were evaluated and concluded on various datasets, including BoT-IoT, IoT-23, UNSW-NB15, CSE-CIC-IDS2018, and MQTT-IOT-IDS2020, in search of a suitable algorithm that can easily learn the pattern of network attack activities. the feature extraction and pre-processing data were then fed into IDS as data to train the model for future anomaly detection, prediction, and analysis. this study focuses primarily on the various types of cyber-attacks and machine learning algorithms used to identify cyber-attacks. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Recognition of Respiratory Diseases from X-ray Images Based on machine learning Methods 16th

Recognition of Respiratory Diseases from X-ray Images Based ...

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16th international conference on Application of Fuzzy Systems, soft computing and Artificial Intelligence Tools, ICAFS 2023

作者： Shakhmametova, G.R. Bogdanova, D.R. Ziyazetdinova, L. Yu. Ufa University of Science and Technology Ufa Russia

ISBN: (纸本)9783031725050

the article analyzes the current state of research and existing solutions in the field of recognizing respiratory diseases from functional diagnostic images. the solution of the problem of recognizing respiratory diseases such as cardiomegaly, pleural thickening and pulmonary fibrosis from chest X-rays is presented. the end user of the proposed software is a medical professional - radiologist. the developed software application for recognizing respiratory diseases from X-ray images consists of two interacting parts: user interface and neural network. the information system uses a convolutional neural network to solve the recognition problem. the Python programming language is used to create the convolutional neural network using TensorFlow and Keras libraries. As a dataset for training and testing of the neural network, an open set from the Kaggle website is chosen. the performance analysis of the developed application showed an accuracy of 92% in recognizing respiratory diseases by the trained model. the maximum time of recognizing a chest X-ray image took 1 min and the minimum time 20 s, while a radiologist needs from 10 to 20 min to decipher one X-ray image. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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IoT-Based Mental Health Monitoring System Using machine learning Stress Prediction Algorithm in Real-Time Application 7th

IoT-Based Mental Health Monitoring System Using Machine Lear...

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7th international conference on Big Data and Cloud computing Challenges, ICBCC 2022

作者： Quadir, Md Abdul Bhardwaj, Saumya Verma, Nitika Sivaraman, Arun Kumar Tee, Kong Fah School of Computer Science and Engineering Vellore Institute of Technology Chennai India Digital Engineering Services Photon Inc. Chennai India Department of Civil and Environmental Engineering King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia

ISBN: (纸本)9789819910502

With the primary focus of healthcare technologies being on the physical health of a person, mental health issues sometimes go unattended. Stress, anxiety, and depression are becoming increasingly common problems in our community leading to serious heart-related problems such as high blood pressure, episodes of heart attack and can even lead to chronic illness. Prediction of stress or depression at an earlier stage can prevent serious consequences as sometimes patients suffering from mental illness are not aware of the severity of their condition or do not keep up with counseling for a longer period of time. In this context this paper proposes a stress prediction method using machine learning to detect the development of stress or anxiety problems at an early stage. Our proposed method observes any changes in the human body under stress or depression by monitoring the ECG values and other physiological factors to predict any kind of possible stress or depression. the proposed model provided high accuracy of 98% in predicting stress. On detecting stress, appropriate actions such as informing the patient's guardian and doctor are taken. As compared with other models, our model outperforms the other state of the art models, making it a real-world predication model. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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