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A Survey on Decentralization and Virtualization of Medical Trials: An approach through Ensemble learning models and Convolutional Neural Networks

A Survey on Decentralization and Virtualization of Medical T...

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computer science and Emerging Technologies (CSET), International Conference on

作者： Diksha Chakravarty Amy Mariam Thomas V. Vivek Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning) Jain(Deemed-to-be University) Bangalore India

- In sectors such as medicine where accuracy and time are the most prioritized factors the use of technology and its tools have led to huge advancements and contributed to saving millions of lives. Technology has been bifurcated into various categories to suit multiple requirements of the medical sector. The use of machine learning along with well-designed models and sister technologies such as computer Vision has opened up a whole new dimension in medical sciences. machine learning deals with the building of algorithms that can predict using fed patterns which further specializes in Deep learning. Deep learning's success in a variety of pattern recognition applications has sparked excitement and raised expectations that deep learning, or artificial intelligence (AI), could revolutionize the health care that we know today. Right from diagnosis to treatment it can revolutionize the way we construct and operate the medical sector. Over the last decade, Convolutional Neural Networks have achieved breakthroughs in a range of pattern recognition domains. CNN's are generally employed to solve complex image-driven pattern recognition tasks. They have proven to be a great tool in image recognition and classification and are tuned according to the needs of the user. Ensemble learning, as a research area, strives to bring data fusion, data modeling, and data mining together in a single framework. Ensemble learning combines the informative knowledge gained from the previous outcomes to produce knowledge, discovery, and improved predictive performance using adaptive voting methods. In this study, the methodologies of numerous models of image recognition and classification, as well as ensemble learning from multiple authors, are evaluated. In addition, this study shall discuss any feasible enhancements that could be implemented to the existing models as well as how the ideologies of the models could be used for efficient diagnosis in the field of medicine.

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Vehicle Type Detection and Classification Using Improved Relief Feature Selection Algorithm and Bidirectional Gated Recurrent Unit 2

Vehicle Type Detection and Classification Using Improved Rel...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Monica, K.M. Al-Farouni, Mohammed Madhura, G.K. Ghamya, K. Geethalakshmi, M. Vellore Institute Of Technology School Of Computer Science And Engineering Chennai India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of Artificial Intelligence And Machine Learning Bengaluru India Tirupati India Kongunadu College Of Engineering And Technology Department Of Electronics And Communication Engineering Trichy India

ISBN: (纸本)9798350381665

Nowadays, Traffic density estimation can be utilized to control traffic light signals for managing the traffic effectively. Vehicle Type Classification (VTC) is used in this traffic density. Because of the factors like minimal interclass variance and maximum intraclass variation, the VTC is challenging which is handled by proposed Improved Relief Feature Selection Algorithm and Bi-directional Gated Recurrent Unit (IRFSA-Bi-GRU) method. Bi-directional Gated Recurrent Unit (Bi-GRU) model is proposed for enhancing Vehicle Type Classification (VTC) within Intelligent Transport Systems. Multiview In Out Traffic Complexity Database (MIOTCD) is used for data collection then a camera response model is used for enhancing visual data quality. Kalman filtering and Gaussian mixture model techniques are utilized for vehicle recognition and tracking. Local Binary Pattern (LBP), Steerable Pyramid Transform (SPT), and Dual Tree Complex Wavelet Transform (DTCWT) are used for feature extraction and Improved Relief Feature Selection Algorithm (IRFSA) is used for feature selection. Finally, the Bi-directional Gated Recurrent Unit (Bi-GRU) model is used for classifying 11 vehicle categories on MIO-TCD. The obtained results exposed that the IRFSA-BiGRU method outperformed the existing methods by achieving an accuracy of 99.02%, 98.34% F-score, 98.78% MCC,99.93% specificity, 99.32% NPV, 98.75% precision, 98.14% recall on the MIO-TCD database. © 2024 IEEE.

关键词： Classification (of information)

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Deep learning-Based Dynamic Scheduling for Semiconductor Manufacturing with High Uncertainty of Automated Material Handling System Capability 2

Deep Learning-Based Dynamic Scheduling for Semiconductor Man...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Dineshkumar, R. Al-Attabi, Kassem Kumar, Komuravelly Sudheer Madhura, G.K. Sharma, Jaya Saveetha Institute Of Medical And Technical Sciences Saveetha University Saveetha School Of Engineering Department Of Ece Chennai India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Sr University School Of Computer Science & Artificial Intelligence Telangana Warangal India Nitte Meenakshi Institute Of Technology Department Of Artificial Intelligence And Machine Learning Bengaluru India Gitam University Department Of Cse Bengaluru India

ISBN: (纸本)9798350381665

To improve the dynamic responsiveness and production efficiency of manufacturing workshops to personalized orders, a multi-agent manufacturing system. However, the previous methods cannot work well in a changeable workshop environment when encountering a large number of stochastic disturbances of orders and resources. Develop a robust scheduling approach for dynamic workshop environments, effectively managing stochastic disturbances in orders and resources to optimize production efficiency and minimize disruptions. Dynamic scheduling approach for semiconductor manufacturing using Proximal Policy Optimization (PPO) and Autoencoder (AE) for feature extraction, defining state space to capture task and production attributes. Action space design employs dispatching rules for operation selection, while the reward function adapts production to demand, trained in action and state phases. Autoencoders compress high-dimensional input data into a lower-dimensional latent space while preserving important features. The obtained results demonstrate that the proposed method achieves better performance Delivery Data Rate (DDR) of 41.20 %, Mean Cycle Time (MCT) of 51.66%, Mean of 12.85%, and Execution time of 5.07s. These results ensure superior dynamic scheduling performance compared to other existing methods, such as Dynamic Scheduling Using Flexible Job Shop Scheduling problem (FJSP), and Multiple-DynamicScheduling-Rule (MDSR). © 2024 IEEE.

关键词： Production efficiency

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TIER BALANCING: TOWARDS DYNAMIC FAIRNESS OVER UNDERLYING CAUSAL FACTORS 11

TIER BALANCING: TOWARDS DYNAMIC FAIRNESS OVER UNDERLYING CAU...

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11th International Conference on learning Representations, ICLR 2023

作者： Tang, Zeyu Chen, Yatong Liu, Yang Zhang, Kun Department of Philosophy Carnegie Mellon University United States Computer Science and Engineering Department University of California Santa Cruz United States Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates

The pursuit of long-term fairness involves the interplay between decision-making and the underlying data generating process. In this paper, through causal modeling with a directed acyclic graph (DAG) on the decision-distribution interplay, we investigate the possibility of achieving long-term fairness from a dynamic perspective. We propose Tier Balancing, a technically more challenging but more natural notion to achieve in the context of long-term, dynamic fairness analysis. Different from previous fairness notions that are defined purely on observed variables, our notion goes one step further, capturing behind-the-scenes situation changes on the unobserved latent causal factors that directly carry out the influence from the current decision to the future data distribution. Under the specified dynamics, we prove that in general one cannot achieve the long-term fairness goal only through one-step interventions. Furthermore, in the effort of approaching long-term fairness, we consider the mission of "getting closer to" the long-term fairness goal and present possibility and impossibility results accordingly. © 2023 11th International Conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Decision making

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Detection of AI Empathy Using Deep learning

Detection of AI Empathy Using Deep Learning

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computer science and Emerging Technologies (CSET), International Conference on

作者： Namburi Venkat Vinay Charugulla Santhosh Kumar Jayanth Asipalli Metturu Mouli G. Sindhu Madhuri Department of Computer Science And Engineering (Artificial Intelligence And Machine Learning) Jain Deemed to be University Banglore India

Deep learning enables the learning of detailed representations of emotions and facial expressions that can be challenging to recognize using conventional machine learning methods. Deep learning algorithms have demonstrated higher accuracy rates in tasks requiring emotion recognition than traditional machine learning methods. Deep learning models can be customized to each person, enabling more precise emotion recognition based on each person's individual facial expressions. Emotion detection aims to identify and measure human emotions using various physiological, behavioural, and computational techniques. Deep learning algorithms and computer vision techniques are used to assess facial expressions, vocal cues, and other physiological information to ascertain a person's emotional state. Emotion detection has a wide range of possible uses, from mental health diagnosis and treatment to enhancing the effectiveness of virtual assistants and strengthening user interactions with computers. Technology developments and the growing accessibility of massive datasets have improved emotion recognition, making possible new prospects for research and growth in the field. This abstract provides a comprehensive overview of the current state of the art in emotion detection and its applications in various domains.

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Deep learning for Sentiment Analysis with Adaptive Neural Networks for Emotion Classification 5

Deep Learning for Sentiment Analysis with Adaptive Neural Ne...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Jayalakshmi, V. Thilakavathy, P. Manikandan, G. Deepa, R. Surendran, R. SRM Institute of Science and Technology Department of Computer Science and Applications Tamilnadu Chennai India Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India St. Joseph's College of Engineering Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM institute of Science and Technology Department of Computing Technologies Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

In understanding human emotions, sentiment analysis has been invaluable in social media monitoring, consumer comment analysis, and psychological evaluations. Customer service, human-computer interface, and public mood comprehension depend on appropriate emotion categorisation. Despite its potential, sentiment analysis struggles with sarcasm, multimodal data integration, and emotion processing. Implementing real-time applications requires improving deep learning model computational complexity. Multimodal Optimised Emotion Analysis using Deep learning (MOEA-DL) uses CNNs for image processing, RNNs for text., and LSTM networks for sequential audio data. This improves emotion recognition by combining modality strengths. Marketing sentiment, mental health monitoring, social media sentiment, and consumer feedback use MOEA-DL. Multimodal solutions are better than text-based ones for communicating emotion. Customer contacts, film reviews, and social media platforms are used to evaluate the proposed strategy. According to simulations, MOEA-DL categorises emotions more accurately and robustly than previous sentiment analysis methods. Its adaptive neural network lets the system learn and perform well in many cultural and language environments. © 2024 IEEE.

关键词： Emotion Recognition

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Improved Contextual Understanding and Emotion Detection in Large-Scale Text Data with Hybrid Deep learning Models 5

Improved Contextual Understanding and Emotion Detection in L...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Thilakavathy, P. Manikandan, G. Deepa, R. Jayalakshmi, V. Surendran, R. Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India St. Joseph's College of Engineering Anna University Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM institute of science and technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Ramapuram Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

With the growth of large-scale text datasets from reviews, social media, and other online sources, sentiment analysis is essential for understanding public opinion. Traditional models struggle to understand complicated linguistic sentiments and interdependencies. This research could improve sentiment classification in customer feedback analysis, market sentiment predictions, and social media monitoring. Addressed issues include ambiguous language, sentence context flipping, and the need for robust models that can manage massive datasets. Contextual Detection Text Analysis Using Deep learning (CDTA-DL) improves contextual knowledge and emotion identification in this research. RNNs and CNNs are used in the CDTA-DL approach to extract spatial and sequential information from text. CDTA-DL helps large-scale sentiment analysis detect sarcasm, implicit emotions, and many expressions. In a comprehensive simulation investigation employing open-access sentiment analysis datasets, CDTA-DL surpassed standard deep learning models in accuracy, recall, and F1 scores. The recommended strategy may increase emotion recognition and contextual understanding, making it ideal for large-scale sentiment analysis in various domains. © 2024 IEEE.

关键词： Contrastive learning

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Enhanced Camouflage Detection Using Advanced Image Processing Techniques for Real-Time Object Recognition and Pattern Differentiation 5

Enhanced Camouflage Detection Using Advanced Image Processin...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Manikandan, G. Deepa, R. Jayalakshmi, V. Thilakavathy, P. Surendran, R. St. Joseph's College of Engineering Anna University Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Chennai India Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

The identification of objects that blend in with their surroundings has long been a concern in fields including defence, wildlife monitoring, and surveillance due to the difficulty of detecting such objects. This finding is significant because it could greatly enhance our capacity to detect camouflaged objects, transforming the field of real-time object detection. Existing image processing methods often fail miserably when adequately capturing this aspect. This dissertation's primary focus is identifying devices with complicated patterns, legacy interference, and inadequate contrast;nonetheless, in disguised scenarios, all of these features cause identification to be unsuccessful. This research often suggests a new approach called the Linear Edge Detecting Scheme for Camouflaged Object Detection (LEDS-COD) in these difficult cases. The LEDS-COD system can enhance item visibility in low-comparison settings by highlighting critical edges and descriptions. Pattern differentiation methodologies and modern facility-side recognition techniques are combined to accomplish this goal. The approach successfully differentiated items because it zeroed in on texture and comparative details that would otherwise go unnoticed by humans or advanced algorithms. LEDS-COD is useful in many areas, including the Navy, autonomous surveillance systems, and animal protection, where the real-time identification of hidden devices is essential. Results from a simulation study using various datasets, including concealed objects, show that LEDS-COD surpasses conventional detection methods in recognition accuracy, processing speed, and perseverance. The recommended technique exceeded the requirements for obtaining improved object detection estimates for challenging conditions like those with complex backdrops and changing illumination. According to this study, LEDS-COD can be devastating in improving real-time camouflage detection. © 2024 IEEE.

关键词： Accuracy Image processing Surveillance Image edge detection Wildlife Lighting Object detection Real-time systems Object recognition Protection

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Heart Disease Prediction Using Logistic Regression

Heart Disease Prediction Using Logistic Regression

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International Conference on computer, Electrical & Communication engineering (ICCECE)

作者： Dipanjan Saha Soham Guha Kankana Kundu Suchana Das Sajili Chatterjee Pritusna Banik Sohinee Mondal Computer Science & Technology Narula Institute of Technology Kolkata India Computer Science Engineering (Artificial Intelligence & Machine Learning) Institute of Engineering & Management Kolkata India Computer Science & Engineering Narula Institute of Technology Kolkata India

ISBN: (数字)9798331513894

ISBN: (纸本)9798331513900

Cardiovascular diseases (CVDs) are one of the leading causes of death worldwide, and early identification is crucial to improve the patient’s prognosis. Traditional diagnostic methods for heart diseases are highly dependent on clinical expertise and can be time-consuming, making machine learning an attractive alternative for improving accuracy to diagnose and support faster decision making. Previously, research has explored various machine learning algorithms, such as decision trees, support vector machines, and neural networks, to improve prediction accuracy. However, many of these models face challenges related to their interpretability and generalization to unexpected data. This paper presents a logistic regression model to predict CVDs based on a dataset of 303 samples, incorporating key demographic, clinical, and diagnostic features. The model achieved an accuracy of $85.12 \%$ in the training data and 81. $97 \%$ in the testing data, demonstrating its effectiveness for binary classification tasks. The study emphasizes the computational efficiency and interpretability of logistic regression, making it appropriate for real-time applications in clinical settings. In conclusion, the results indicate that logistic regression can serve as a reliable tool for early detection of heart disease, and future work can explore dataset expansion, advanced models, and explainable AI techniques to further improve prediction accuracy and model transparency.

关键词： Heart Logistic regression Accuracy Computational modeling Training data Predictive models Data models Computational efficiency Reliability Testing

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Avoidance of Ship Collision Using Deep Reinforcement learning with Bi-Directional Long Short-Term Memory in Continuous Action Spaces

Avoidance of Ship Collision Using Deep Reinforcement Learnin...

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Data science and Network Security (ICDSNS), International Conference on

作者： Revatthy Krishnamurthy Myasar Mundher Adnan Sunil Kumar V T. Aditya Sai Srinivas Srinivas Aluvala Artificial Intelligence & Machine Learning Department Rajalakshmi Engineering College Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic university Najaf Iraq Department of Artificial intelligence and Machine learning Nitte Meenakshi Institute of Technology Bengaluru India Artificial Intelligence and Machine Learning Jayaprakash Naryan College of Engineering Mahabubnagar India School of Computer Science and Engineering SR University Warangal India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Recent days, with the globalization of the world economy the ships count are increasing for marine transportation and the Waterways are growing increasingly overcrowded than previous. This situation may leads to problem of collision of ships which may cause losing of life and damage for property and to nature. Many automatically collision models of ships are implemented but many focused only on the ship-ship encounter situation only. By using a grid sensor which is virtual sensor, agents of Deep Reinforcement learning (DRL) classify approach of a multiple ships. This framework introduces an automatically collision detection algorithm for ships using DRL in continuous action spaces. DRL is used for avoidance of collision with a maximum distance of safe passing between ships. A unique method is developed named inside Obstacle Zone by Target (OZT) used to change learning capability that expands the OZT. Using Bi-directional Long Short-Term Memory (BI-LSTM) cell, network is redesigned and continuous action spaces training is carried out to train a model with longer safe distance of ships. In collision detection model that the bow cross range is effective for COLREGs compliant collision avoidance that is proposed in this model. The propose model also validates a scenario that included more ships and have passed that Imazu problem. The proposed BI-LSTM model achieved 80.43 % of accuracy, 95.67% of precision, 85.89% of recall and 93.54% of f1 score values.

关键词： Accuracy Biological system modeling Bidirectional control Power system stability Deep reinforcement learning Stability analysis Vectors Marine vehicles Collision avoidance Long short term memory

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