Abstract Submission Deadline
September 14,2023
Webinar
GREETINGS!!!
We warmly welcome everyone to attend the "International Conference on Machine Learning Applications in Healthcare" which will be held in Berlin, Germany, on September 14–16, 2023.
We are delighted to gather experts, researchers, and professionals from around the world in this prestigious event dedicated to exploring the remarkable intersection of machine learning and healthcare."Machine Learning in Medicine: Revolutionizing Diagnosis, Treatment, and Patient Outcomes"
Set against the backdrop of constant advancements and transformative innovations, this conference aims to be a platform for sharing groundbreaking research, fostering collaboration, and shaping the future of healthcare.
Machine learning has emerged as a powerful tool, revolutionizing the way we approach healthcare challenges. From early disease detection to personalized treatment plans, from predictive analytics to optimizing healthcare systems, the potential of machine learning in healthcare is vast. This conference serves as a catalyst for knowledge exchange, as we come together to explore the latest trends, breakthroughs, and practical applications in this rapidly evolving field.
Throughout the conference, you can expect an exceptional lineup of keynote speakers, thought-provoking panel discussions, insightful presentations, and interactive workshops. Our esteemed speakers will provide deep insights into the current state of machine learning applications in healthcare, share success stories, and discuss emerging trends and future directions.
Moreover, this conference offers an exceptional opportunity to network with like-minded professionals, establish new collaborations, and engage in vibrant discussions. You will have the chance to connect with leading researchers, industry experts, and practitioners, fostering a sense of community and enabling the exchange of ideas that will shape the future of healthcare.
I would also like to thank the speakers and send out my best wishes in advance to the entire team for a successful conference. Maintain contact with us throughout the conference.
We are forward to have you in Berlin, Germany.
BEST REGARDS
Organising committee
Machine Learning 2023
The Machine learning 2023 is going to bring together academics, researchers, and others from various fields related to Machine learning. conferences 2023 and Data Science. We are going to discuss the topics such as artificial intelligence and Machine learning 2023, data structures and algorithms, bioinformatics, and scientific computing.
Set against the backdrop of constant advancements and transformative innovations, this conference aims to be a platform for sharing groundbreaking research, fostering collaboration, and shaping the future of healthcare.We hope that the International Conference on Machine Learning Applications in Healthcare will be an enriching and transformative experience for all attendees. Let us harness the power of machine learning to revolutionize healthcare and improve the lives of people around the world.
In combination with a keynote forum, workshops meet organizational, a young researcher’s symposium, poster presentations, and panel discussions, the Machine learning 2023 will also include a presentation discussion board. Mode of participation will be speaker, delegate, exhibitor, sponsor . On September 14-16, 2023 the Machine learning 2023 conference will take place. We respectfully ask that each of you support the success of our event by attending.
Together, we can pave the way for a future where technology and healthcare intersect to create a healthier, more equitable world.
Benefits of attending the conference:
Target Audience:
Advantages of Participating in our Conference
Benefits of Participation for Speaker:
The benefit of Participation for the Sponsor:
Session 1: Deep Learning for Medical Image Analysis: Exploring the use of deep Learning algorithms, such as convolutional neural networks (CNNs), for automated analysis and interpretation of medical images
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Session 2: Clinical Decision Support Systems using Machine Learning: Discussing the development and implementation of machine Learningmodels to assist healthcare professionals in making accurate and timely clinical decisions.
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Session 3: Predictive Analytics for Disease Progression: Addressing the use machine Learning techniques to predict disease progression and patient outcomes based on clinical data, genetics, and other relevant factors.
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Session 4: Machine Learning in Electronic Health Records (EHR): Exploring the application of machine learning algorithms to extract insights, identify patterns, and improve decision-making from large-scale electronic health record data
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Session 5: Explainable AI in Medicine: Discussing the interpretability and transparency of machine Learning models in medicine to ensure trust, ethical considerations, and regulatory compliance
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Session 6: Drug Discovery and Development using Machine Learning: Exploring the use of machine learning in drug discovery pipelines, virtual screening, and predicting drug-target interactions for accelerating the development of new medications.
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Session 7: Machine Learningfor Personalized Medicine: Addressing the application of machine learning algorithms to analyze patient data and create personalized treatment plans based on individual characteristics, genomics, and medical history.
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Session8: Natural Language Processing (NLP) in Healthcare: Discussing the use of NLP techniques to extract information, classify medical documents, and enable semantic understanding for applications like clinical coding and adverse event detection Machine Learning
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Session 9: Real-time Health Monitoring with Machine Learning: Exploring the integration of machine learning models with wearable devices and sensors for continuous monitoring of vital signs, early detection of anomalies, and personalized health feedback
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Session 10: Transfer Learning in Medical Data Analysis: Addressing the utilization of transfer learning techniques to leverage pre-trained models and limited annotated medical datasets for various medical tasks, such as classification and segmentation in Machine Learning
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Special Interest Group on Management of Data (SIGMOD) | Web Analytics Association (WAA)| Special Interest Group on Knowledge Discovery in Data and Data Mining (SIGKDD) | SF Bay ACM Data Mining SIG | European Knowledge Discovery Network of Excellence (KDNet) | IEEE International Conference on Data Mining (ICDM) | Data Mining Section of INFORMS | Association of Data Scientists (ADASci)
Session 11: Machine Learning for Cancer Diagnosis and Prognosis: Discussing the advancements in machine learning algorithms for accurate and early detection of cancer, prognosis prediction, and treatment optimization
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Special Interest Group on Management of Data (SIGMOD) | Web Analytics Association (WAA)| Special Interest Group on Knowledge Discovery in Data and Data Mining (SIGKDD) | SF Bay ACM Data Mining SIG | European Knowledge Discovery Network of Excellence (KDNet) | IEEE International Conference on Data Mining (ICDM) | Data Mining Section of INFORMS | Association of Data Scientists (ADASci)
Session 12: Ethical Considerations in Medical Machine Learning: Examining the ethical challenges, bias mitigation, privacy concerns, and regulatory frameworks associated with the deployment of machine learning in healthcare
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Session 13: Machine Learning in Remote Patient Monitoring: Exploring the use of machine learning algorithms for analyzing data from remote patient monitoring systems, identifying health trends, and enabling proactive interventions.
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Session 14: Machine Learning for Precision Radiology: Discussing the integration of machine learning models in radiology workflows to enhance image interpretation, automate detection of abnormalities, and improve diagnostic accuracy
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Session 15: Federated Learning in Healthcare: Addressing the use of federated learning approaches to train Machine Learning models across multiple healthcare institutions while preserving data privacy and security.
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Session 16: Data Warehousing and Cybersecurity
Cyber security will always be a top priority for businesses. Cyber security is largely concerned with dataprivacy protection. For diverse purposes, they employ data mining techniques. Database analysis, text analysis, and other techniques are among them. We also offer a number of online tools, like Rapid Miner, orange, NTLK, and others. Malware detection and fraud detection are two examples of applications. Come to Machine Learning with us.
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Session 17: Information Science
Information science (also known as information studies) is a branch of study that focuses on the analysis, gathering, categorization, modification, storage, retrieval, transportation, distribution, and protection of data.
Practitioners in and out of the field research the application and use of knowledge in organisations, as well as the interaction between people, organisations, and any existing information systems, with the goal of developing, replacing, enhancing, Machine Learning or understanding information systems.
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Special Interest Group on Management of Data (SIGMOD) | Web Analytics Association (WAA)| Special Interest Group on Knowledge Discovery in Data and Data Mining (SIGKDD) | SF Bay ACM Data Mining SIG | European Knowledge Discovery Network of Excellence (KDNet) | IEEE International Conference on Data Mining (ICDM) | Data Mining Section of INFORMS | Association of Data Scientists (ADASci)
Session 18: Data mining and Statistical Analysis
The true Data mining work is a self-loader or programmed examination of massive volumes of data to separate out ambiguous, fascinating information, such as meetings of information records (group research), odd records (asymmetry discovery), and constraints (affiliation rule mining, consecutive example mining). Machine Learning This usually entails using database techniques like dimensional files. These samples might then be used as a kind of information overview for subsequent research or, for example, in Artificial Intelligence and prognostic analysis. For example, the information mining process may identify diverse groups of data, which could subsequently be used by a choice emotionally supporting network to provide increasingly precise forecast findings.
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Session 19: Neurocomputing
Neurocomputing is the branch of science and engineering, which is based on human like intelligent behaviors of machines. It is a vast discipline of research that mainly includes neuroscience, Machine Learning searching and knowledge representation. The traditional rule-based learning is now appears to be inadequate for various engineering applications because it is incompetent to serve increasing demand of Machine Learning when dealing with large amount of data.
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Session 20: Data Visualization and Presentation
Data visualisation and presentation are both a science and an art. Some regard it as part of explaining measurements, while others see it as a tool for improving grounded theory. The term "big information" or "Internet of things" refers to expanded measurements of information created by Internet movement and an increasing number of detectors in the earth. Information visualisation has moral and logical challenges when it comes to preparing, breaking down, and conveying this data. This test is addressed by the discipline of Machine Learning and individuals known as information examiners.
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Machine learning 2023 Market Analysis
The increased use of technology advancements in a variety of industries, including healthcare, automotive, retail, and manufacturing, is expected to drive growth in the global machine learning market over the course of the projected period. This data was provided in a study by Fortune Business InsightsTM titled "Machine learning 2023 (ML) Market, 2022-2029." The report estimates that the machine learning (ML) industry will be worth USD 15.44 billion in 2021. The market is anticipated to increase during the projected period at a CAGR of 38.8%, rising from USD 21.17 billion in 2022 to USD 209.91 billion in 2029.
A data analysis technique called Machine learning 2023automates the development of analytical models. The market is anticipated to be driven by growing use of artificial intelligence and Machine learning during the forecast period. As learning skills advance, a branch of artificial intelligence called deep learning is anticipated to dominate the market in the next years.
Data Science 2023 Market Analysis The market for Data science conferences 2023platforms is anticipated to grow at a compound annual growth rate (CAGR) of 27.7% from USD 95.3 billion in 2021 to USD 322.9 billion in 2026. The astounding growth of big data, as well as the rising adoption of cloud-based solutions, the expanding use of Data science conferences 2023platforms in various industries, and the growing need to extract in-depth insights from massive amounts of data in order to gain a competitive advantage, all drive the market for Machine learning 2023 platforms.
The amount of data that businesses collect is constantly growing as a result of the emergence of social media, IoT, and multimedia, which have produced an overwhelming flow of data in both structured and unstructured forms. For instance, 90% of the world's data was produced in the past two years alone. Data that is produced by both humans and Machine learning 2023 is expanding ten times more quickly than conventional commercial data. For instance, machine data is expanding 50 times more quickly than human data. Consumer-driven and -oriented data is the norm. The vast bulk of data produced globally is produced by consumers in Machine learning 2023, who are becoming more and more "always-on." Nowadays, the majority of individuals use various devices and (social) applications for 4-6 hours each day to consume and produce content. Every click, swipe, and communication adds new information to a database located somewhere in the world. Since everyone now carries a smartphone, a remarkable amount of data is being produced.
Top Universities around the globe: -
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The Pulsus Group is one of the most renowned scientific conference organizers in the world, with a unique perspective on all areas of science, technology, and business. How well a conference series touches the online community in particular Machine learning 2023 determines a portion of its overall performance. An internet audience is essential for every worldwide conference, whether it be for research, academia, or business.
According to the metrics provided below, data scientists, academicians, research institutions on data, software design institutes, and students are the main attendees of Pulsus conferences on Machine learning 2023. The number of unique visitors and page views at conferences with a focus on big data, data analytics, data mining, artificial intelligence, Machine learning 2023, and other Machine learning 2023 related tracks is astronomically high.
Machine Learning Metrics
Pulsus Conferences Metrics:
