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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341-350 订阅

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Prediction of Road Traffic Accident Severity Using machine learning Techniques in the Case of Addis Ababa 7th

Prediction of Road Traffic Accident Severity Using Machine L...

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7th EAI international conference on Robotics and Networks, ROSENET 2023

作者： Wubineh, Betelhem Zewdu Asamenew, Yigezu Agonafir Kassa, Semachew Molla Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wroclaw Poland College of Engineering and Technology Wachemo University Hosaena Ethiopia Department of Information Technology College of Informatics University of Gondar Gondar Ethiopia Bahir Dar Institute of Technology Faculty of Civil and Water Resources Engineering Bahir Dar University Bahir Dar Ethiopia

ISBN: (纸本)9783031644948

Road traffic accidents (RTAs) are vehicle accidents that occur on a route, involving collisions between vehicles and various moving objects, such as fixed obstructions, pedestrians, other vehicles, or animals. RTAs rank as the sixth leading cause of death in Ethiopia, with approximately 31,564 lives lost to road accidents every year. the objective of this study is to analyze road accident data to predict the severity of accidents using a machine learning technique. We used 7744 records with six attributes, including the final class, to develop the model. To balance the data, the author uses SMOTE analysis. the data were split into two parts for model training and testing: 80% for training and 20% for testing. the machine learning algorithms used in this study include artificial neural network (ANN), random forest (RF), decision tree (DT), and support vector machine (SVM). the accuracy achieved by these algorithms is as follows: ANN 90%, RF 89%, DT 87%, and SVM 89%. Consequently, ANN slightly outperforms the other machine learning algorithms in predicting accident severity using traffic record data. In conclusion, machine learning algorithms demonstrate promising results in predicting the severity of accidents. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Random forests

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Predictive Maintenance of Lead-Acid Batteries Using machine learning Algorithms 7th

Predictive Maintenance of Lead-Acid Batteries Using Machine ...

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

作者： Sridevi, H.R. Bothra, Shrey Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (纸本)9789811954818

the industry is currently undergoing what analysts have dubbed "the Fourth Industrial Revolution," often known as Industry 4.0. this is closely linked to the integration of physical and digital systems. the integration of these ecosystems allows for the acquisition of a significant amount of data from many sources. the most prevalent type of energy storage option for electrical systems that provide backup power are batteries. It is vital to ensure that these batteries are in proper operating condition. A thorough discussion of this important subject is provided in this paper, which includes data-driven approaches for predictive maintenance of batteries with the help of statistical tools and machine learning algorithms. Predictive maintenance, in its most general sense, is the practise of discovering early symptoms of system failure in order to make maintenance efforts more fruitful. It also strives to rectify any problems, even if there is no imminent danger of failure, in order to maintain proper functioning and save resource expenditure. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Artificial Intelligence and machine learning for Foundry Industry—A Case Study of Belagavi Foundry Industry 7th

Artificial Intelligence and Machine Learning for Foundry Ind...

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

作者： Kulkarni, Praveen M. Gokhale, Prayag Appasaba, L.V. Lakshminarayana, K. Tigadi, Basavaraj S. Department of MBA K.L.S. Institute of Management Education and Research Karnataka Belagavi590006 India Department of MBA KLE Technological University Dr. M. S. Sheshgiri College of Engineering and Technology Udyambag Belgaum590008 India Department of Management Studies Visvesvaraya Technological University Belagavi India Visvesvaraya Technological University Belagavi India

ISBN: (纸本)9789811954818

Technology in manufacturing is evolving at a rapid pace and has provided opportunity to increase the productivity of the manufacturing in the foundry industry. Technology such as artificial intelligence and machine learning provides greater information on the manufacturing process and supports in reducing the wastage in the production. this is indicated in the study as the study results indicate that artificial intelligence and machine learning are a powerful tool for foundry industry for improving production efficiency and enhancing customer satisfaction. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Foundries

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7th international conference on soft computing and Signal Processing, ICSCSP 2024

7th International Conference on Soft Computing and Signal Pr...

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7th international conference on soft computing and Signal Processing, ICSCSP 2024

ISBN: (纸本)9789819609239

the proceedings contain 54 papers. the special focus in this conference is on soft computing and Signal Processing. the topics include: An Analysis on the Capacity of the Convolutional Neural Network and the Current Neural Network to Identify Vocal Problems;recent Advances in Diabetic Monitoring Devices: Present to Future Directions;navigating the Digital Realm: E-Visual Online Examination for the Visually Challenged;the Evaluation of Data Aggregation and Compression in WSN Routing Networks Using the Best Clustering Methods;Improved AODV Routing Protocol Development and Analysis for Enhanced Network Performance in MANETs Based on machine learning Model;comprehensive Analysis of Cognitive Radio Technologies and Its Applications;Classification of Brain Tumor from MRI Images Using Supervised machine learning;the Augmentation of Social Media and Its Notable Impact on Sentiment Research Review Systems for Public Movie theaters Using Deep learning;whatsApp Chat Analysis and Spam Discovery Using machine learning Models;Prediction of Distinctive Human Behavioral Activity Using PIFP and Big Data Methods;a Signals–Systems Perspective on Electronic Structure theory;Evaluation of Customized Pre-trained Models to Predict Percentage of Being COVID-19 Cases on Chest X-Ray and COVID Radiography Images;statistical Analysis of Success Rate of the Movie Based on a Combination of Actor, Director;machine learning Methodologies in Social Platform: An Analytical Survey;AI-Driven Cattle Skin Disease Diagnosis: Enhancing Precision through Advanced Deep learning and Interactive Chatbot Technologies;revolutionizing Autism Spectrum Prognostication via Ensemble learning;rose Doctor: Identification of Fungal Diseases in Rose Plants Using Convolutional Neural Networks;recommending Emergency Medication Using machine learning Approaches.

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Deep learning Approach for Arrhythmia Detection Using STFT-Based Spectrogram 15th

Deep Learning Approach for Arrhythmia Detection Using STFT-B...

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15th international conference on soft computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Chopra, Shreya Sambyal, Nitigya Bajaj, Anu Thapar Institute of Engineering and Technology Patiala India

ISBN: (纸本)9783031810794

Arrhythmia is a medical condition where a patient experiences an irregular heartbeat. the presence of arrhythmia indicates a much more serious heart condition, which, if not treated, may worsen and even lead to death. Hence, there is a requirement for a system that can detect the presence of such a condition. the patient’s ECG recordings were first converted into a spectrogram using short-time Fourier transform. these resultant spectrogram images were given as input to VGGNet models for arrhythmia classification. the VGG16 model exhibited 98.87% accuracy, 99.18% precision, 99.14% recall, and 97.94% specificity. On the other hand, VGG19 achieved 98.83% accuracy, 99.18% precision, 99.18% recall, and 97.95% specificity. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electrocardiography

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7th international conference on soft computing and Signal Processing, ICSCSP 2024

7th International Conference on Soft Computing and Signal Pr...

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7th international conference on soft computing and Signal Processing, ICSCSP 2024

ISBN: (纸本)9789819799251

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Detection of Misinformation Related to Pandemic Diseases Using machine learning 7th

Detection of Misinformation Related to Pandemic Diseases Usi...

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7th EAI international conference on Robotics and Networks, ROSENET 2023

作者： Naeem, Javaria Gül, Ömer Melih Parlak, Ismail Burak Karpouzis, Kostas Kadry, Seifedine Nimer Salman, Yücel Batu Department of Computer Engineering Bahçeşehir University İstanbul Turkey Department of Computer Engineering Galatasaray University İstanbul Turkey Panteion Uninversity of Social and Political Sciences Kallithea Greece Department of Applied Data Science Noroff College Kristiansand Norway Ajman University Ajman United Arab Emirates Department of Software Engineering Bahcesehir University İstanbul Turkey

ISBN: (纸本)9783031644948

the advent of the COVID-19 pandemic has brought with it not only a global health crisis but also an infodemic characterized by the rampant spread of misinformation on social media platforms. In response to the urgent need for effective misinformation detection, this study presents a comprehensive approach harnessing machine learning and deep learning techniques, culminating in ensemble methods, to combat the proliferation of COVID-19 misinformation on Facebook, Twitter, Instagram, and YouTube. Drawing from a rich dataset comprising user comments on these platforms, encompassing diverse COVID-19-related discussions, our research applies SVM, decision tree, logistic regression, and neural networks to perform in-depth analysis and classification of comments into two categories: positive and negative information. the innovation of our approach lies in the final phase, where we employ ensemble methods to consolidate the strengths of various machine learning and deep learning algorithms. After applying ensemble learning, accuracy reached 91% for Facebook content, 79% for Instagram, 80% for Twitter, and 95% for YouTube. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： COVID-19

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Survey on Irrigation Scheduling with machine learning 7th

Survey on Irrigation Scheduling with Machine Learning

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

作者： Dutta, Ankan Pal, Surbhi Banerjee, Aishwarya Karmakar, Pratap Mukherjee, Arpita Mukherjee, Debaprasad Sahu, Prabal Kumar Dr. B. C. Roy Engineering College Durgapur India AcSIR Ghaziabad201002 India CSIR-Central Mechanical Engineering Research Institute Durgapur713209 India

ISBN: (纸本)9789819908370

In our paper, a review of several techniques for irrigation scheduling like Soil Moisture Level Measurement, Plant-Based Monitoring, Climatological Approach (Irrigation water/Cumulative pan evaporation ratio or IW/CPE Ratio), and Water Balance Approach has been briefly discussed. Various technologies can be applied to design smart irrigation systems for optimal use of water and reduce wastage of water. thus, optimal irrigation decision management is a very important issue in today’s scenario. A sustainable irrigation management system can be maintained by the application of machine learning algorithms. State-of-the-art studies on different machine-learning approaches for smart real-time irrigation systems are presented in this paper. the different issues in machine learning Models like Unnecessary Overfitting and Underfitting, Accessibility of Web Infrastructure, and Initial Cost of Deployment are also briefly discussed. this study provides an introduction to the field of research, covering irrigation scheduling in smart agriculture, summaries of previous studies, irrigation scheduling techniques, and areas for improvement. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Analysis of Digital Twins Implementation in Smart City using Big Data and Deep learning 7

Analysis of Digital Twins Implementation in Smart City using...

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7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Nayak, Chinmaya Kumar Karunakaran, S. Yamunaa, P. Kayalvili, S. Tiwari, Mohit Unni, Manu Vasudevan Sri Sri University Faculty of Emerging Technologies Sri Sri Vihar Ward No - 3 Godi Sahi Odisha Barang754006 India Computer Technology Kongu Engineering College Erode Tamilnadu Perundurai India Peri Institue of Technology Department of EEE India Computer Science & Engineering Velalar College of Engineering and Technology Erode Tamil Nadu Thindal638012 India Computer Science and Engineering Bharati Vidy Ap Eeth's College of Engineering A-4 Rohtak Road Paschim Vihar Delhi110063 India St. Claret College P. O. Box 1355 MES Ring Rd Sharadhamba Nagar Jalahalli Karnataka Bengaluru560013 India

ISBN: (纸本)9798350397253

Digital twins are becoming more relevant for business and academic users due to advances in IoT, AI, and Big Data. Due to global urbanization, pollution, public safety, traffic congestion, and other challenges have arisen. New technologies make cities smarter to keep up with growth. In the Internet of things (IoT) age, many sensing devices acquire and/or produce a broad range of sensory data over long periods of time for a variety of businesses and applications. the use case determines the device's data stream volume and speed. the efficacy of the analytics process used to analyze these streams of data to learn, predict, and act determines IoT's worth as a business paradigm changer and quality-of-life technology. this study introduces Deep learning (DL), a family of advanced machine learning techniques, to enhance IoT analytics and teaching. Introducing new results, challenges, and research opportunities. this study may assist academics and newbies comprehend how to use DL to smart cities. Analyzing and summarizing major IoT DL research initiatives. Check out smart IoT devices with DL embedded into their AI. Ultimately, the study will identify issues and suggest additional research. Each chapter concludes with experimental findings and the newest literature review. © 2023 IEEE.

关键词： Smart city

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Early Detection of Colorectal Cancer from Polyps Images Using Deep learning 7th

Early Detection of Colorectal Cancer from Polyps Images Usin...

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7th international conference on soft computing: theories and Applications, SoCTA 2023

作者： Kumar, Ravi Singh, Amritpal Khamparia, Aditya Department of Computer Science Engineering Lovely Professional University Punjab Phagwara India Department of Computer Science Engineering Jawaharlal Nehru Government Engineering College Himachal Pradesh Sundernagar Mandi India Department of Computer Science Babasaheb Bhimrao Ambedkar University Amethi India

ISBN: (纸本)9789819720309

Detecting colorectal cancer in early stages and predicting associated risk is very important as it has the highest mortality rate after lung cancer. the polyps present in the colon and rectum are one of the main causes of colorectal cancer. In this study, we propose different deep learning methods to classify images as normal or containing polyps. the performance of these models was evaluated via implementation on public and private datasets. On the WCE dataset, the OEM model implemented with transfer learning achieved a classification accuracy of 99.5%, 96.37%, and 96% on training, validation, and test set, respectively. On the Kvasir dataset, the OEM model obtained a classification accuracy of 97.80%, 96.40%, and 96.03% on training, validation, and test set, respectively. On SCPolyps dataset, the OEM model achieved a classification accuracy of 93.80%. Hence, the utilization of deep learning techniques can be viewed as an efficient approach to creating tools for assisting in the diagnosis of CRC. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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