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Cerebral Stroke Risk Assessment: A Comparative Study of Machine Learning Techniques

Cerebral Stroke Risk Assessment: A Comparative Study of Mach...

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International Conference on Communication and Electronics Systems (ICCES)

作者： Akshara Adith Sajeev Anjali T Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

This study uses a data-driven machine learning (ML) model that takes into account important health markers like age, blood pressure, history of heart disease, and lifestyle factors like physical activity and smoking in order to predict the risk of stroke. Through thorough model comparison and optimization, a variety of algorithms for machine learning are used, such as Random Forest classifiers, K-Nearest Neighbors, and Support Vector Machines, to improve predicted accuracy and efficiency. It has been proven that machine learning plays a key role in identifying minor risk patterns that conventional diagnostic techniques often neglect, empowering medical professionals to make quick, evidence-based judgments. According to the results, this non-invasive, data-driven method greatly increases the accuracy of stroke risk assessments, facilitates the customization of treatment plans, and eventually improves patient outcomes while reducing the strain on healthcare systems. The Random Forest classifier shows the best prediction ability among the models that were assessed. This solution highlights the potential of machine learning to improve stroke risk assessment and management while blending in smoothly with clinical practice as a useful tool for identifying stroke risk, improving patient care and quality of life.

关键词： Support vector machines Adaptation models Accuracy Machine learning algorithms Machine learning Medical services Predictive models Nearest neighbor methods Random forests Medical diagnostic imaging

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A Novel Image Encryption Technique Leveraging Irrational Expansions and New 3D Chaotic Map

A Novel Image Encryption Technique Leveraging Irrational Exp...

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International Conference on Circuits, Communication, Control and computing

作者： Priyanshu Aggarwal K Ashwini Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798331528539

ISBN: (纸本)9798331528546

To solve the significant issues of data security in the digital age, this work investigates the combination of chaotic map-based approaches with irrational expansions for picture encryption. Chaotic maps offer a strong foundation for encrypting picture data, guaranteeing high degrees of security and protection against unwanted access. They are distinguished by their sensitivity to beginning circumstances and pseudorandom behaviour. In addition to exploring the theories and techniques of chaotic map encryption, a brand-new chaotic map has also been put out. To increase the security and effectiveness of digital image processing systems, the study highlights the importance and promise of chaotic maps through a thorough examination of existing methods and new developments. The findings underscore the relevance of continuous research and development to address the growing demands for secure and efficient image data management in various applications.

关键词： Data privacy Visualization Sensitivity Three-dimensional displays Chaotic communication Social networking (online) Smart cities Streaming media Encryption Resilience

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Optimizing Lung Cancer Classification Prediction based on Random Forest and SMOTE Techniques

Optimizing Lung Cancer Classification Prediction based on Ra...

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Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： R. Bhuvaneswari Roshan S Department of Computer Science Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369090

Lung cancer is the most familiar and deadliest varieties of most cancers global, representing a considerable public health situation. It is a main purpose for cancer-associated deaths among the human race globally, with a remarkable range of new cases identified each 12 months. Despite advances in analysis and treatment, the analysis for lung most cancers sufferers stay poor, highlighting the pressing need for persevered research into etiology, prevention, early detection, and remedy. This examine employs superior statistics analytics and supervised gadget gaining knowledge of techniques, focusing on a comparative evaluation of algorithms such as Random Forest, K-Nearest Neighbor (KNN), Decision Tree, Naive Bayes, and Support Vector Machine (SVM), to classify and predict lung cancer severity. Utilizing dataset from Kaggle optimized using SMOTE technique, the examine demonstrates the Random Forest set of rules’s superior overall performance, reaching an accuracy of 98%. The facts, meticulously organized and pleasant-tuned, supports the ability of gadget mastering in enhancing early detection and remedy outcomes. This painting underscores the trans-formative potential of era in healthcare, in particular in addressing complex sicknesses like lung cancer. The research findings have vast implications for records-driven healthcare techniques and customized remedy tactics, paving the manner for modern solutions to fight lung cancer.

关键词： Support vector machines Accuracy Lung cancer Lung Nearest neighbor methods Prediction algorithms Bayes methods Classification algorithms Decision trees Random forests

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Prediction of Shot Selection of a Batsman in the death over of a T20 Cricket Match using Machine Learning

Prediction of Shot Selection of a Batsman in the death over ...

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International Conference for Convergence of Technology (I2CT)

作者： Deepak Kumar Sahoo Deheem U Deyar Deepa Gupta Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Cricket has seen remarkable changes since the beginning of the 21 st century. T20 cricket has gained immense popularity, captivating a global fan base with its fast-paced action and continuous excitement. This study delves into the intriguing dynamics of the final over in T20 cricket, aiming to predict a batsman's shot selection during this crucial phasethat can drastically influence the match's outcome through the implementation of machine learning algorithms. Leveraging a comprehensive dataset obtained from sources like ESPN Cricinfo and ***, which includes ball-by-ball commentary, delivery outcomes, ball types, shot types, required run rates, and more. The proposed model used Ensemble techniques to show noteworthy improvement in the performance metrics. SHAP, an explainable AI tool, was employed to study the impact of different features on the prediction. This research seeks to provide valuable insights for cricket teams, both established and emerging, to enhance their game analysis and strategic planning during the last over, as strategic planning and execution are paramount, particularly in the second innings of a match.

关键词： Measurement Technological innovation Navigation Stacking Games Strategic planning Feature extraction

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Automated Math Equation Recognition and Problem Solving using Texify and LLMs

Automated Math Equation Recognition and Problem Solving usin...

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International Conference on computing and Networking Technology (ICCNT)

作者： M. Kalyan P. Rama Krishna Raju N Ravi Kiran L. Kullayi Swamy Reddy Rimjhim Padam Singh Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The “Automated Math Equation Recognition and Problem Solving with computer Vision” research work is to develop a framework that utilizes computer vision methods to consequently recognize mathematical equations and then utilizes symbolic computational engines to solve them automatically. The proposed system utilizes various computer vision techniques like texify, pix2tex, Nougat etc. to recognize text and equations from the pictures. The proposed framework then utilizes recent large language models to solve the recognized mathematical equations. Overall, the research plans to work on the efficient productivity and quick availability of solving mathematical problems by consolidating computer vision and mathematical methods to make them user friendly.

关键词： Productivity Integrated optics computer vision Large language models Optical computing Optical fiber networks Mathematical models Problem-solving Character recognition Engines

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A Comparative Study of Machine Learning Algorithms and Explainable AI for Fitness Club Attendance Classification

A Comparative Study of Machine Learning Algorithms and Expla...

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IEEE Recent Advances in Intelligent Computational Systems (RAICS)

作者： Divyasri S Deepa Gupta Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bangalore India

ISBN: (数字)9798350381689

ISBN: (纸本)9798350381696

The growing interest in health and fitness has resulted in an increase in the number of people joining fitness clubs. However, maintaining member engagement remains a challenge for fitness club operators. The goal of this study is to create a binary classification model that predicts whether a member is likely to attend a fitness club or not. In this paper, we propose a framework to explain how good the fitness club attendance classification performance will be under various Machine learning models by understanding its metrics. In this study by employing several classification models to determine Fitness Club attendance Classification using machine learning algorithms such as Random Forest, Gradient Boosting, Light GBM, etc. The Random Forest classifier performs with a higher accuracy of 0.87, and Explainable AI has been used to reveal the most significant feature.

关键词： Measurement Graphics Accuracy Machine learning algorithms Explainable AI Receivers Forestry Predictive models Random forests Testing

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Comparative Analysis of Classifiers for Cyber Attack Prediction on LUflow Dataset with Explainability

Comparative Analysis of Classifiers for Cyber Attack Predict...

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International Conference for Convergence of Technology (I2CT)

作者： Geethika Gunti Kuruva Divya Sree Deepa Gupta Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bangalore India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

As the cyber threats areevolving, intrusions exploit vulnerabilities in software or systems and can occur 24/7, targeting individuals and *** Intrusion detection system (IDS) is designed to help in the detection of cyberattacks as a part of cybersecurity. Researchers are developing machine learning(ML) based IDS to detect the attacks in a network, and this has proven to be very effective. In this work, we propose a framework using various machine learning classifiers to classify different types of attack classes include benign, malicious and outlier on different sets of LUFlow intrusion detection data. Random Forest and Decision tree have outperformed with an accuracy of 91% on combined year data whileconsidering the individual years data, logistic regression performed less well than all other algorithms including ensemble *** study includes the examination of precision, recall, and F1 score metrics for each of the employed classification and ensemble methods. Additionally, the most accurate model has been subjected to model interpretability procedures, which have shown the notable characteristics that have a major impact on the categorization of classes.

关键词： Measurement Logistic regression Atmospheric modeling Intrusion detection Forestry Prediction algorithms Software

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Real-time Tweets Analysis using Machine Learning and Bigdata

Real-time Tweets Analysis using Machine Learning and Bigdata

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North Karnataka Subsection Flagship International Conference (NKCon), IEEE

作者： P Nandieswar Reddy Sai Aswath S Rithvika Alapati Radha D Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350364569

ISBN: (纸本)9798350364576

Twitter's exponential increase in data has transformed it into a rich source for machine learning research, unveiling patterns of opinions and behaviors. This research introduces an automated pipeline built on Kafka, adept at handling large volumes of real-time Twitter data. Utilizing the Twitter API, tweets bearing the hashtag #justice are retrieved and efficiently processed via Kafka. PySpark facilitates data analysis by enabling aggregation, Jaccard similarity computation, and k-means clustering. Preprocessing with Python libraries and TF-IDF enhances analysis accuracy. Jaccard similarity aids in comparing tweets across socio-economic categories. Aggregated data is stored in MongoDB for further examination. Additionally, Streamlit offers visualization tools for effortless exploration of Twitter discourse trends. Thus, this project exemplifies the potential of big data and its impact on investigating social justice discourse in the digital age.

关键词： Social networking (online) Blogs Pipelines Data visualization Machine learning Market research Information age Real-time systems Libraries Socioeconomics

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Bone Fracture Detection Using YoloV9

Bone Fracture Detection Using YoloV9

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Intelligent & Innovative Practices in engineering & Management (IIPEM), International Conference on

作者： Nivethitha V Suthir Sriram G Kesava Praadeep S Prarthana Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350390049

ISBN: (纸本)9798350390056

This paper presents the design of a Bone Fracture Detection System at the forefront of YOLOv9-T concerning the detection and localization of the actual site of bone fracture in X-ray images. The system would help quickly and reliably in giving a fracture diagnosis, and that is a critical side of health care sometimes it has the power to actually determine the result of some treatment. This included elbow, finger, forearm, humerus, shoulder, and wrist fractures. Efficiency and accuracy of object detection by the model were improved with the use of the YOLOv9-T on object detection tasks. With system bounding box annotation, the testing accuracy reached up to 96.50%. This study, therefore, highlights the needs for developing more advanced models of machine learning into the workflow of medical imaging probably that could be leading to lesser numbers of diagnostic errors and faster time lines in treatments in the clinic. Results The system proposed by this study is likely to be helpful in a medical practice where the efficiency as well as the accuracy about the diagnosis may be augmented.

关键词： Wrist Training Accuracy Neural networks Shoulder Object detection Bones Robustness X-ray imaging Testing

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DeepPave: Detection of Potholes Using Deep Learning Techniques

DeepPave: Detection of Potholes Using Deep Learning Techniqu...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Tummala Varshith Thanush S Koneri Uppalapati Dhanush Sriramoju Rahul Jyotsna C Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

In a developing country like India, the major difficulty lies in identifying and addressing problems. Improving road quality is crucial for economic growth, as well-maintained roads contribute significantly. The proposed model DeepPave, a combination of Visual Geometry Group 16 (VGG 16), Convolutional Neural Network (CNN), and Multilayer Perceptron (MLP), along with regularization techniques such as dropout and L2 regularization and optimization techniques, has been developed for the detection of potholes. The aim is to enhance road infrastructure and contribute positively to the nation's economy. The proposed pothole detection system holds great potential for enhancing road safety, optimizing maintenance operations, and reducing infrastructure maintenance expenses. By promptly identifying potholes, transportation authorities can efficiently allocate resources, improve road quality, and ensure safer driving conditions for motorists.

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