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Titanic Survivors Prediction Analysis Using Machine Learning Algorithms 1

Titanic Survivors Prediction Analysis Using Machine Learning...

引用

1st International Conference on Computational Intelligence for Security, Communication and Sustainable Development, CISCSD 2024

作者： Devamane, Shridhar B. Abhijith, B.H. Purushotham, Bhushan Ravindranath, R.C. Lakshmikantha, G.C. Udayaprasad, P.K. Global Academy of Technology Department of Computer Science and Engineering Bangalore India Global Academy of Technology Department of Artificial Intelligence and Machine Learning Bangalore India Global Academy of Technology Department of Ai and Ml Bangalore India

ISBN: (纸本)9798350365405

Machine learning algorithms are computational models that allow computers to acquire knowledge and improve performance on a task by automatically learning patterns and rules from input data provided by the user. In this work we are predicting the Titanic disaster survivors based on a particular dataset. Based on characteristics such as age, sex, ticket class, etc., we applied the Support Vector Machine, K-Nearest Neighbors, Decision Tree, and Random Forest algorithm to identify which passenger would survive. © 2024 IEEE.

关键词： Adversarial machine learning

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A Solution to finding Fruit Freshness Index using computer Vision 5

A Solution to finding Fruit Freshness Index using Computer V...

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5th International Conference on Emerging Smart Computing and Informatics, ESCI 2023

作者： Raja, S Ashwin Srivatsan, T.R. Vijay Maragatham, G. Srmist Ktr Computer Science with Ai and Ml Chennai India Srm Institute of Science and Technology Ktr Department of Computational Intelligence India

ISBN: (纸本)9781665475242

Fruits have now become a product to the market's eye, as fruits being the top recommended food to be consumed for better health, the prices drive higher and higher day by day. The main reason for the increase in the price of fruits is the manual labor behind it. It is the sorting and grading done by such humans which takes a prolonged period of time, inconsistent, onerous, subjective, variable, costly and influenced by other factors such as the environment around them which holds these fruits at an untouchable rate to the normal low paid consumers. To tackle this and hopefully reduce the rising price in fruits we have come up with a solution that a Fruit freshness System needs to be put in place. As computer vision has been used by many researchers to sort and grade fruits, we decided the best way to aid our solution would be by the implementation of computer Vision. The summary of this paper is that it provides an overview of the many methods which have been implemented to reduce and lower the manual labor in the grading and sorting of fruits in the market i.e., feature extraction, segmentation, preprocessing, classification which addresses the fruits and the quality of the fruits based on texture, color, shape, size, and it's defects. These steps lead to a much more accurate shelf-life date of the fruits which also tackles the problem of food wastage, this will decrease the wastage of fruits from turning spoiled much earlier than expected. © 2023 IEEE.

关键词： Fruits

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A New Approach for Sleeping Disorder Classification using Supervised Machine Learning Algorithms

A New Approach for Sleeping Disorder Classification using Su...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： P. Ravi Kishore G.Sai Kiran Reddy A.Shinjith P.Vikas Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

Sleep disorders are now a prevalent health condition worldwide and are identified as contributory causes of several chronic diseases like cardiovascular diseases, obesity, and reduced intellectual functions. Intemperate rates of sleep disorders such as insomnia and sleep apnea are shown to be related to abnormal sleeping hours, unsatisfactory lifestyles, and demographic variables. Here, we propose a machine learning method to predict sleep disorders with high accuracy on the Sleep Health and Lifestyle dataset, using key features like age, gender, BMI, physical activity, occupational type, sleep duration, and quality. We employ four supervised machine learning models: Logistic Regression, XGBoost, Decision Tree (DT), and Support Vector Machine (SVM). Data preprocessing techniques such as handling missing values, normalization, and label encoding are conducted to ensure data quality. Feature importance analysis is also performed to identify influential attributes such as BMI, blood pressure, and sleep quality in the prediction of sleep disorders. The measurements of precision, accuracy, recall, and F1-score are utilized to evaluate the models' performance. Hyperparameter tuning techniques are used for maximizing model performance. Our results indicate that these machine learning models are capable of classifying sleep disorders with high accuracy, with XGBoost and SVM demonstrating greater prediction accuracy. This study shows how machine learning technology can be utilized in medicine to analyze and treat sleep disorders early, helping medical professionals make better decisions and enhancing patient outcomes. The addition of genetic algorithm-based optimization methods further boosts classification accuracy by optimizing the parameters of the model. Comparative studies find the dominance of ensemble methods over traditional classifiers in solving high-dimensional healthcare data. Additionally, the system's ability to include variable lifestyle-related factors presen

关键词： Support vector machines Logistic regression Accuracy Social networking (online) Medical services Sleep apnea Vectors Decision trees Medical diagnostic imaging Tuning

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Re-Thinking of a Transformer on Real Time Text Information

Re-Thinking of a Transformer on Real Time Text Information

引用

Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： M. Mahesh Bellamkonda Akshara Bandela Shiva V. Pranav Sujith Reddy Department of Computer Science and Engineering (AI&ML) Vignan Institute of Technology and Science

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

The field of text prediction systems faces a dual challenge of improving accuracy, enhancing user experience, Learning from large and complex data. Existing Natural Language Processing (NLP) methods often fall short in delivering precise prediction. To address these challenges, we propose an innovative approach integrating Deep Learning techniques, notably a sequential model, with the Django Framework and user interface technologies. The proposed system aims to enhance text prediction accuracy by training information from huge corpus, thus generating domain-specific predictions. Central to our approach is the utilization of backpropagation in Deep Learning, enabling precise fine-tuning of the predictive model. By providing a web-based interface, users can seamlessly interact with the system, while backend Deep Learning operations ensure efficient and accurate results. The proposed system aims to leverage the capabilities of the sequential model in Deep Learning. By extracting knowledge from thetrained information from corpus, the sequential model predicts and generates the next n user-specified words. The proposed approach improves the text prediction accuracy and enhances user experience by ultimately advancing applications in various domains.

关键词： Deep learning Training Accuracy Predictive models User interfaces Transformers User experience Natural language processing Real-time systems Faces

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Advanced Detection of Machine Failure in Automated Industries using Long-Short Term Memory and LightGBM

Advanced Detection of Machine Failure in Automated Industrie...

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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： Muppidi Mahesh Ponnagani Sreeja Sunnapu Swetha Madala Ritwik Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

In the rapidly evolving industrial landscape, companies are using various technologies to optimize their operations which thereby reduce costs, and they focus on increasing their profits. Proactive maintenance has emerged as an important strategy that enable industries to foresee and prevent machine failures beforehand. This proactive approach not only extends the machine lifespan but also leads to cost savings. Machine failure detection is very essential in automated industries, medical fields, aerospace, transportation and many more industries where machinery is central for the production process. Traditional reactive maintenance methods only focus on repairing the machines after the occurrence of the failure which greatly affects the production. Machine learning, with it’s ability to analyze large datasets and identify the unusual patterns based on the historical data. This paper mainly proposes using a hybrid approach of the ml algorithms to increase the accuracy and efficiency of the detection of machine failures. The algorithms are Long-Short Term Memory (LSTM) networks and Light Gradient Boosting Machine (LightGBM). This approach is mainly used for early fault detection in machines. We combine the features of LSTM to find temporal patterns in sensor data with LightGBM’s capacity to refine predictions and analyze feature importance. This approach accurately finds the failure predictions, then classify failure severity that can be visualized using graphs and trigger actionable maintenance alerts. By this proposed approach we achieved a result of 98% accuracy on real sensor data when compared to traditional methods, outperforming them by 7%. This research fills the gap between the traditional reactive maintenance and the current predictive maintenance.

关键词： Industries Machine learning algorithms Costs Accuracy Transportation Production Predictive models Maintenance Long short term memory Predictive maintenance

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Food Nutritional Detection Personalized Dietary Recommendations for Health Monitoring using Image Processing with Machine Learning

Food Nutritional Detection Personalized Dietary Recommendati...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： M. Venkateswarlu M. Vinay M. Likhitha G. Shivaramu Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

A good nutrition monitoring that involves promoting healthy lifestyle and dietary challenges. In this paper, we propose an ai driven nutrition monitoring system utilizing deep learning, computer vision, and natural language processing to handle dish detection, freshness analysis and ingredient recognition. The proposed system takes into account the accuracy of food classification using a fine-tuned convolutional neural network, OpenCV based techniques to measure ingredient quality, and NLP to pull out detailed nutritional information. The system applies real time feedback and actionable insights to the limitations of manual logging and traditional dietary tracking. Built with a modular architecture, it is adaptable to different cuisines, and dietary needs, and can be used by individuals as well as healthcare professionals. One of the innovations of such an approach is to make nutrition management easier, to promote healthier eating habits, and to create a launchpad for future personalized health monitoring advances.

关键词： Deep learning computer vision Technological innovation Shape Medical services Manuals Natural language processing Real-time systems Pattern recognition Monitoring

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Image-based System for Blood Group Classification

Image-based System for Blood Group Classification

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International Conference on ent Computing and Control Systems (ICICCS)

作者： K. Ramakrishna Reddy G. Indrani N. Pavan Kumar K. Vamshi Krishna Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

It provides a classic method for blood image based system for blood group classification techniques. The suggested approach reduces reliance on traditional processes by automating the analysis of blood sample images through the use of sophisticated image processing algorithms. This strategy seeks to offer a dependable and expandable solution that is especially useful for application in distant or resource-constrained environments.

关键词： Image segmentation Histograms Control systems Classification algorithms Blood

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A Next-Generation Blockchain Framework for Secure and Tamper-Proof Online Examinations

A Next-Generation Blockchain Framework for Secure and Tamper...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Markala Anjali Sk. Ashraf Vali J. Divya B. Lokesh Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Online exams are a general aspect of education nowadays. However, they are still suffering with several security problems such as data breaches, result-changing cheating, and impersonation scams. Some of the modern systems utilize blockchain technology to improve security but come with problems such as being difficult to integrate, taking time to process, being costly, and lacking adequate privacy protection. This work introduces a blockchain-based structure that addresses these concerns. It utilizes smart contracts in a more improvised manner, employs zero-knowledge proofs to secure privacy. The system provides safe sharing of questions, decentralized storage of answers, and tamper-evident grading. It also has seamless integration with learning management systems (LMS). In comparison to traditional and blockchain-based examination systems, this method provides upgrades in security, efficiency, and speed. With the integration of decentralized technology and state-of-the-art security methods, this system provides internet-based exams that are secure, transparent, and economical.

关键词： Privacy Learning management systems Smart contracts Education Data breach Control systems Blockchains Security Protection Next generation networking

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Threat Eye: Behavior Analytics for Cloud Security using ml

Threat Eye: Behavior Analytics for Cloud Security using ML

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2025 International Conference on Intelligent Computing and Control Systems, ICICCS 2025

作者： Jeny, J.R.V. Shivaspandana, S. Pavan, K. Muthukumaran, N. Akash, K. Vignan Institute of Technology and Science Department of Computer Science and Engineering (AI & ML) 508284 India Sri Eshwar College of Engineering Centre for Computational Imaging and Machine Vision Department of ECE Coimbatore India

ISBN: (纸本)9798331512088

Cloud computing has developed for its storage and accessibility of data, providing flexibility, scalability, and affordability. But its extensive use has also resulted in cyberattacks and unauthorized access. The traditional intrusion detection mechanisms become ineffective for different attack patterns, insider attacks and zero-day exploits. This paper provides an efficient way to address this issue by providing a mechanism to identify the threat based on the behaviour of the user. Unlike traditional approaches this paper provides a system that monitors the user behaviour patterns considering the features such as login history, authentication attempts, session duration, file access activities, password change frequency and IP location modifications. By considering these features the attack can be detected, and the data access is restricted to prevent from unauthorized access. This paper provides handling of attack with a combination of machine learning algorithms including SVM, Decision tree and XBoost, these help in distinguishing between the attacker and the authorized user. This helps in identifying complex attack behaviours and detect insider threats. Additionally, RSA encryption is integrated to secure data transmissions, preventing unauthorized access. By combining behavioural analysis, attacks prediction, and encryption, this provides a robust cybersecurity framework. © 2025 IEEE.

关键词： Cloud Security Cyber Threat Detection Data Protection Data Security Gradient Boosting Classifier Logistic Regression Machine Learning (ml) Voting Classifier

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Navigating the Future: Deep Reinforcement Learning for Smart Autonomous Vehicles

Navigating the Future: Deep Reinforcement Learning for Smart...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： M. Venkateswarlu G. V. N. Shri Himaja V. Sainath Reddy M. Vineeth Department of Computer Science and Engineering(AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Intelligent transportation is gaining much attention about autonomous driving systems. This research will present a fully developed comprehensive framework to implement the navigation of autonomous vehicles using CARLA. Deep learning-based object detection by the use of YOLOv5 is used in the current project, which identifies both real-time vehicle traffic, traffic lights, and obstacles. In parallel, lane detection is achieved through image processing techniques, such as edge detection and Hough Line Transform. The proposed system integrates these capabilities with decision-making algorithms to facilitate responsive vehicle control in dynamic environments. In addition, the validation of autonomous behaviour from a holistic point of view uses third-party camera views. Multiple vehicle traffic simulations add realism to the environment. A pilot function simulates the actual condition, with a synchronized simulation approach for the correct timing in processing and analysing data. The outputs include saved images, processed frames, and videos demonstrating the efficacy of the system under varying scenarios. This research integrates modern computer vision techniques with simulated environments to create a robust testing platform for autonomous navigation. Future research can extend this framework to real-world applications or incorporate advanced reinforcement learning algorithms for enhanced decision-making capabilities.

关键词： YOLO computer vision Navigation Lane detection Heuristic algorithms Decision making Traffic control Vehicle dynamics Autonomous vehicles Videos

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