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检索条件"主题词=Machine Learning Algorithms"

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Revolutionizing Healthcare: Synergistic Integration of Genomics, Clinical Decision Support Systems, and Advanced machine learning for Precision Medicine

Revolutionizing Healthcare: Synergistic Integration of Genom...

引用

AI-Driven Smart Healthcare for Society 5.0

作者： Samarendra Das Ranjali Roy Anusreya Sarkar Rajesh Bose Indranil Sarkar Sandip Roy Dept. of CSE JIS University Kolkata India Dept. of CSE Guru Nanak Institute of Technology Kolkata India

ISBN: (数字)9798331536336

ISBN: (纸本)9798331536343

Precision medicine is a groundbreaking paradigm in healthcare, aiming to customize medical treatment by integrating unique genetic, clinical, and lifestyle attributes of individual patients. This research explores the synergistic application of genomics, clinical decision support systems (CDSS), machine learning, and deep learning in precision medicine. This amalgamation of cutting-edge technologies presents an unprecedented opportunity to provide highly accurate, personalized, and efficient medical interventions. By leveraging genomics, CDSS, and advanced machine learning, this transformative approach has the potential to revolutionize medical practices, allowing healthcare providers to deliver meticulously tailored treatments. The result is enhanced patient outcomes with minimized adverse effects. The innovative use of these tools signifies a profound paradigm shift in healthcare, fostering more effective and patient-centered care. This research holds the promise of significantly improving the overall quality of healthcare delivery, marking a crucial advancement in the pursuit of precision medicine's potential to reshape the landscape of personalized healthcare.

关键词： Decision support systems Deep learning Accuracy machine learning algorithms Precision medicine Genomics Medical services Prediction algorithms Bioinformatics Interoperability

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Comparative Study of machine learning Models for Diabetes Prediction

Comparative Study of Machine Learning Models for Diabetes Pr...

引用

International Conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech)

作者： Amit Kumar Das Saptarshee Basak Shounak Choudhury Dept. of ECE Institute of Engineering & Management University of Engineering & Management Kolkata India

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

Diabetes is one of the most widespread and critical health issues globally, attributed to factors such as obesity, sedentary lifestyles, and elevated blood sugar levels. These factors disrupt insulin function, resulting in metabolic irregularities and increased glucose concentrations in the bloodstream. This research aims to address the rising prevalence of diabetes by leveraging machine learning techniques to predict its onset. By providing early warnings, this approach seeks to motivate individuals to adopt healthier dietary and lifestyle choices, potentially reducing the risk of developing diabetes. The performance of several machine learning algorithms, such as classification and ensemble learning techniques, is investigated in this paper. K-Nearest Neighbours (KNN), Random Forest, Decision Tree, Support Vector machine (SVM), Logistic Regression, XGBoost algorithm, Neural Network, and Ensemble learning voting classifier are some of the important methods that were assessed. Complementary preprocessing techniques, such as Label Encoding, Feature Engineering, replacing missing values, train-test split methods, are used to prepare and analyze the data. These methodologies are applied to large healthcare datasets, enabling the extraction of significant features, validation of results, and the generation of accurate predictions. The primary purpose of the research is to examine the effectiveness of different models to discover the most robust and reliable technique for predicting diabetes. The focus lies on developing models that can effectively differentiate between diabetic and non-diabetic individuals while achieving high validation scores. By uncovering hidden patterns and trends in the data, the study provides valuable insights for medical professionals, enhancing their ability to make early diagnoses and informed decisions.

关键词： Support vector machines Logistic regression machine learning algorithms Accuracy Neural networks Predictive models Prediction algorithms Diabetes Ensemble learning Random forests

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Leveraging machine learning for Dynamic Resource Allocation in Cloud Automation

Leveraging Machine Learning for Dynamic Resource Allocation ...

引用

Automation and Computation (AUTOCOM), International Conference on

作者： Gunveen Ahluwalia Vaibhav Goutham Suresh M D Anto Praveena Kalyan Acharjya Trushna Paridaname Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India School of Mangement - PG Management JAIN (Deemed to be University) Bangalore Karnataka Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Cloud automation enables the dynamic management and allocation of resources in a computing environment and is one of the most common uses available. Yet despite their power when cloud systems were simple and small, traditional approaches to allocating resources have proven far less feasible lately. This scenario has also created the need for new evolution methods that will adapt to dynamic cloud environments and improve the system's overall performance. A solution to this challenge comes from machine learning, which has been increasingly used recently. ML algorithms can process vast amounts of data and identify patterns and trends to predict resource use and user needs. Deep learning helps cloud-enabled automation systems adapt in real-time to changing conditions, distribute resources efficiently and improve user experience. For example, reinforcement learning and deep learning are ML techniques that can be applied to resource allocation algorithms or demand prediction models to increase accuracy. Integrating cloud automation and machine learning brings effectiveness in dynamic resource allocation, boosting the overall performance of a system, which leads to savings, better experience, and greater flexibility in using cloud resources. Reinforcement and deep learning are machine learning techniques to optimize order allocation algorithms and enhance demand estimation models.

关键词： Deep learning Cloud computing Automation machine learning algorithms Heuristic algorithms Estimation Dynamic scheduling Prediction algorithms Real-time systems Resource management

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Diagnostic Analysis of Skin Disease Using Deep learning

Diagnostic Analysis of Skin Disease Using Deep Learning

引用

Disruptive Technologies (ICDT), International Conference on

作者： Sonam Sirohi Dipak Kumar Ghosh Usha Chauhan Anil Kumar Dubey Department of Electronics and Communication Engineering Galgotia University Greater Noida U.P. India Greater Noida Institute of Technology Greater Noida U.P. India Department of Electrical Electronics and Communications Galgotias University Greater Noida U.P. India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Skin diseases are common disease mostly ignored but sometimes it becomes dangerous for human being. Ignoring this is not a good sign, but most of the time it's normal to see any skin disease on human body, and its color shows the depth of the seriousness. So it is clear that the image of the skin is the core factor of diagnosis of the disease related with it. Technologies of image processing based on machine learning and ANN provide optimum approaches to find the skin disease which will help in diagnosis of the disease. Over decades of research, edge detection techniques have evolved from simple gradient-based methods to sophisticated deep learning approaches. In this paper edge detection techniques are used to diagnosis of skin disease by finding the color difference of skin from neighboring body cell and comparing it by using machine learning algorithmsand the performances are evaluated on HAM10000 skin cancer dataset. Different machine learning algorithms are used for finding the best image analysis tool for skin disease detection in medical field.

关键词： Deep learning machine learning algorithms Accuracy Image color analysis Reviews Image edge detection Neural networks Skin Diseases Skin cancer

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Advancing Phishing Detection with Random Forest: A machine learning Approach for Enhanced Cybersecurity

Advancing Phishing Detection with Random Forest: A Machine L...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Sucheta Chandra Priyanka Das Ankita Mandal Sriparno Chakroborty Ishika Chowdhury Kathamrita Ghosh Dept. of Computer Application and Science Institute of Engineering & Management University of Engineering & Management Kolkata India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Phishing attacks remain a critical threat to cybersecurity, leveraging deception to steal sensitive information from individuals and organizations. Traditional detection methods often fail to keep up with evolving phishing tactics, highlighting the need for advanced solutions. This paper investigates the use of the Random Forest algorithm, a powerful machine learning technique, to enhance phishing detection systems. By leveraging the ensemble learning approach of Random Forest, we improve accuracy and efficiency in classifying phishing attempts, while reducing false positives. The methodology includes data preprocessing, feature extraction, and model implementation, showcasing its effectiveness in real-time detection. The study also emphasizes the importance of continuous learning, allowing the model to adapt to new phishing strategies, thereby offering a robust defense against emerging threats. This research contributes to advancing cybersecurity by providing a dynamic, scalable approach to phishing detection.

关键词： Support vector machines Uniform resource locators Accuracy machine learning algorithms Phishing Feature extraction Ensemble learning Noise measurement Random forests Overfitting

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Personalized Dementia Prediction Using machine learning Techniques

Personalized Dementia Prediction Using Machine Learning Tech...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Jayashree S Livedha S T Ragupathi Kumaran Hariharan R. Sanjana Dr Ginni Arora Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai India Manav Rachna International Institute of Research and Studies

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Early-stage dementia detection is a significant challenge in the healthcare field. Many individuals remain unaware of their cognitive decline until the condition progresses. This research aims to develop a personalized approach for predicting dementia. Multiple machine learning algorithms were applied to the dataset. Impressively, Random Forest model achieved a perfect accuracy of 98.75%.

关键词： Support vector machines Logistic regression Accuracy machine learning algorithms Training data Medical services Data models Random forests Dementia Overfitting

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Artificial Intelligence and machine learning in Cardiovascular Risk Prediction: A Comprehensive Review of Models and Validation and Multimodal Imaging Approaches

Artificial Intelligence and Machine Learning in Cardiovascul...

引用

Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Jesmin Mofi S Madhavan K Pavithra S Department of Computer Science and Engineering Chennai Institute of Technology Chennai

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

cardiovascular disease is an area where more advanced diagnostic, detection, and prediction models are necessary. Advances in machine learning, deep learning and artificial intelligence hold great potential for detecting at-risk individuals, yet validation and application remain challenging. This study examines AI models for estimating cardiovascular disease, with an emphasis on advanced technology, limits, and validation requirements. To improve diagnosis accuracy, consistent validation techniques must be developed, replicability assessed, and many datasets integrated. The importance of multimodal integration, external validation, and ethical considerations such as data privacy and biases that may affect specific populations is underlined. This analysis emphasizes the need of continuing to develop AI and machine learning algorithms in order to overcome these limitations and broaden their applications in cardiovascular healthcare for better prediction and treatment results.

关键词： machine learning algorithms Reviews Imaging Medical services Predictive models Genetics Regulation Multisensory integration Cardiovascular diseases Replicability

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Advanced machine learning Approaches for Liver Disease Prediction: A Focus on Feature Selection and Hybrid Ensemble Model

Advanced Machine Learning Approaches for Liver Disease Predi...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Pasupuleti Pavani P Sravanthi Chalasani Suneetha Mohammad Azhar L Hema supriya Reddy Tejaswi Maddineni Department of CSE MLR Institute of Technology Hyderabad India Department of CSE CMR College of Engineering and Technology Hyderabad India Department of ECE Vignana Bharathi Institute of Technology Hyderabad India Department of Computational Intelligence (CSE-AIML) Malla Reddy college of Engineering and Technology Secunderabad India Data Engineer Independent Researcher USA

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

This paper classifies liver disease using advanced techniques of authentication such as comprehensive datasets upon which the advance machine learning methods are based. In the methodology, careful data preprocessing included data cleaning and treatment of class imbalances through Random Oversampling, outlier detection by the Local Outlier Factor method, and Min-Max Scaling for feature standardization to ensure uniformity in the presence of such features across the overall dataset. However, in the case of LARS feature selection for LASSO-ANOVA Recursive Selection approach, the hybrid combination of LASSO algorithm and Recursive Feature Elimination and ANOVA-a that successfully picked five major features is involved, namely, Direct Bilirubin, Aspartate Aminotransferase, Alkaline Phosphatase, Age, and Albumin. These features enhance quite a margin above the predictability of the model. During the model-building phase, an ensemble fusion strategy was employed using Random Forest, AdaBoost, and CatBoost algorithms that utilize the overall strength of the algorithm. Outstanding performances were delivered with 96.67% accuracy in the test dataset, a weighted F1 score of 90.62%, precision of 90.67%, and a root mean square error of 0.3055. This scientific work brings to the fore the potential application of advanced algorithms of machine learning in the diagnosis of liver disease, providing health professionals with information crucial to enrich their clinical decision-making and the ultimate outcome for patients suffering from these diseases.

关键词： machine learning algorithms Liver diseases Standardization Predictive models Prediction algorithms Feature extraction Root mean square Informatics Random forests Mobile computing

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Big Mart Sales Prediction Using machine learning

Big Mart Sales Prediction Using Machine Learning

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International Conference on Communications and Signal Processing

作者： Sagar Mondal Anindita Debbarma B Prakash Department Of Computer Science & Engineering SRM Institute Of Science & Technology Chennai India Department Of Computing Technologies SRM Institute Of Science & Technology Chennai India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

In today’s competitive retail landscape, supermarkets like Big Marts meticulously track the sales data of each product to anticipate consumer demand and optimize inventory management. By analyzing this data, including identifying anomalies and trends through data mining techniques, retailers can develop predictive models using advanced machine learning algorithms. Techniques such as Xgboost, Linear Regression, Polynomial Regression, and Ridge Regression are employed to forecast sales volumes with greater accuracy than traditional methods. Through the application of these models, businesses like Big Mart can make informed decisions to enhance their operational efficiency and strategic planning, ensuring they stay ahead in the dynamic market environment.

关键词： machine learning algorithms Linear regression Time series analysis machine learning Predictive models Inventory management Strategic planning

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machine learning based Wild-Animal Detection near Roads using IoT Sensor and Optimization

Machine Learning based Wild-Animal Detection near Roads usin...

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Intelligent Systems and Advanced Applications (ICISAA), International Conference on

作者： S. Simonthomas K. Rohith K.Shantha Shalini Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology Chennai India Department of Artificial Intelligence and Data Science Aarupadai Veedu Institute of Technology Chennai India

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

The increasing incidence of vehicle-animal collisions poses significant risks to both human and wildlife safety. To address this challenge, the implementation of IoT (Internet of Things) sensor networks for wild-animal detection near roads offers a promising solution. This paper explores the design and deployment of an IoT-based system aimed at detecting the presence of wild animals near roadways, thereby enhancing road safety and wildlife conservation. The proposed system utilizes a network of infrared (IR) sensors, motion detectors, and cameras strategically placed along road sections prone to wildlife crossings. These sensors continuously monitor the area, and upon detecting an animal, they transmit real-time data to a central processing unit via wireless communication protocols. The system then processes this data to generate immediate alerts for drivers through connected road signs or mobile applications, warning them of potential hazards ahead. Additionally, the collected data is stored and analyzed to understand animal movement patterns, which aids in making informed decisions about further preventive measures and the placement of wildlife crossings. The incorporation of machine learning algorithms improves the system’s precision in distinguishing between different types of animals and minimizing false alarms. This IoT sensor network not only mitigates the risk of accidents but also contributes to the conservation of wildlife by preventing unnecessary fatalities. This project explores the use of Genetic algorithms (GA) to optimize a detection system that can identify wild animals near roadsides. The paper summarize with a discussion on the effectiveness of the system, potential challenges in implementation, and outlines future pathways for research and innovation in this domain.

关键词： Wireless communication machine learning algorithms Wildlife machine learning Road safety Real-time systems Internet of Things Monitoring Genetic algorithms Vehicles

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