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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Priyadharshini, K. Vinitha, S. Sivaprakash, S. Kayalvili, S. Thilakavathy, P. Vasavi, M. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Artificial Intelligence and Machine Learning Jerusalem College of Engineering Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Artificial Intelligence Kongu Engineering College Erode India Vels Institute of Science Technology and Advanced Studies Chennai India ET Department CVR College of Engineering Hyderabad India

ISBN: (纸本)9798350383867

RA is an inflammatory, autoimmune disease with an unpredictable course, during which patients experience exacerbations (increases in symptoms) and its associated complications, such as deterioration of the joint and decreased quality of life. Tipping the exact point when remission is reached or RA flares is most essential for devising optimal treatment and enhancing patient's well-being. In this study, we put forward the usage of various types of ensemble learning algorithms aimed at improved the predictive power of RA flares. We use diverse data sources, including clinical data, medical imagery, genetic profiles and patient-reported data, and ensemble methods like Random Forest, Gradient Boosting, AdaBoost and Stacking processors to help us identify the patterns automatically. Thus ensembles of models are shown to be leading in performance, reaching superior accuracy, accuracy precision, recall rate, F1-score and area under the receiver operating characteristic curve (AUC-ROC). Feature importance analysis highlights the significant impact of clinical features which include the levels of CRP, ESR, age of the patient, duration of the disease on the likelihood of RA flares among the individual variants. The sensitivity analysis shows how well the ensemble model behaves under the generation of different model parameters and input data. The presented study emphasizes the immense facilitative ability of the ensemble methods of learning as predicting RA flares and this can also provide great information about the disease process and personalized medical decision-making. The areas for future research could be improved ensembles models, identification of novel feature selection approaches and validation of model performance in a variety of clinical settings. This will give the researchers an opportunity to implement the models in practice and make clinics more effective. © 2024 IEEE.

关键词： Sensitivity analysis

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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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An Efficient Proposal for Deep learning-Based Diabetes Prediction 2

An Efficient Proposal for Deep Learning-Based Diabetes Predi...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Baswaraj, D. Raju, Ch V V Narasimha Reddy, Pundru Chandra Shaker Kiran, Ajmeera Shaik, Mohammad Khaja Kumar, D Anil Computer Science and Engineering Vasavi College of Engineering Telangana Hyderabad India Marri Laxman Reddy Institute of Technology and Management Department Information Technology Telangana Hyderabad India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal India Mlr Institute of Technology Computer Science and Engineering Dundigal Telangana Hyderabad India St. Ann's College of Engineering and Technology Artificial Intelligence and Machine Learning Andhra Pradesh Chirala India Pace Institute of Technology and Sciences Electronics and Communication Engineering Andhra Pradesh Ongole India

ISBN: (纸本)9798350372892

As a result of its exponential growth in recent years, diabetes now poses a serious danger on a worldwide scale. As of right now, diabetes is one of the foremost causes of death worldwide. Failure to treat diabetes increases the risk of death from cardiovascular disease, cerebrovascular accident, brain stroke, and a host of other complications. In order to minimize further complications, it is crucial to discover diabetes early so that action can be taken to stop the condition from progressing. Predicting diabetes in its early stages may be possible with the use of cutting-edge machine learning techniques that uncover previously unseen patterns. Using the PIMA dataset and a variety of machine learning algorithms, this study lays forth an approach for diabetes prediction. All four of these functional classifiers - Deep learning (DL), artificial Neural Network (ANN), and Naive Bayes (NB) - achieve accuracy rates between 90% and 98%. With a 98.07% accuracy rate on the PIMA dataset, DL outperforms the other three methods when it comes to diabetes onset. In light of this, the suggested system offers healthcare officials a useful tool for prognosis. Incorporating omics data for illness onset prediction considerably improves the accuracy of the DL technique. © 2024 IEEE.

关键词： Adversarial machine learning

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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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Revamping Primary Programming Education with Augmented Reality Teaching via EnvisionAR

Revamping Primary Programming Education with Augmented Reali...

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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： S H Annie Silviya R Anitha P Gayathri N Hiranmaie R Harini D Hareene Computer Science and Engineering Rajalakshmi Institute of Technology Chennai India Artificial Intelligence and Machine Learning Rajalakshmi Engineering college Chennai India

This study introduces a transformative approach to primary programming education, leveraging Augmented Reality (AR) teaching methodologies through the innovative EnvisionAR platform. In response to the dynamic technological landscape, this research addresses the need for engaging, immersive learning experiences for young learners. The EnvisionAR platform, a pioneering fusion of AR technology and tailored curriculum, caters to the cognitive development stages of primary school students. By seamlessly integrating virtual elements into the physical environment, students engage in interactive, hands-on learning that cultivates creativity, problem solving skills, and a profound understanding of programming concepts. This research signifies a broader potential for AR integration in diverse educational disciplines.

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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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Diabetes Prediction Using machine learning: A Detailed Insight 1

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1st International Conference on Computational sciences and Sustainable Technologies, ICCSST 2023

作者： Thakur, Gour Sundar Mitra Dutta, Subhayu Das, Bratajit Department of Artificial Intelligence and Machine Learning Dr. B. C. Roy Engineering College West Bengal Durgapur India Department of Computer Science and Engineering Dr. B. C. Roy Engineering College West Bengal Durgapur India

ISBN: (数字)9783031509933

ISBN: (纸本)9783031509926

Diabetes often referred to as Diabetes mellitus is a general, continuing and deadly syndrome occurring all over the world. It is characterized by hyperglycemia which occurs due to abnormal insulin secretion which results in an irregular rise of glucose level in the blood. It is affecting numerous people all over the world. Diabetes remained untreated over a long period of time, may include complications like premature heart disease and stroke, blindness, limb amputations and kidney failure, making early detection of diabetes mellitus important. Now a days in healthcare, machine learning is used to draw insights from large medical data sets to improve the quality of patient care, improve patient outcomes, enhance operational efficiency and accelerate medical research. In this paper, we have applied different ML algorithms like Logistic Regression, Gaussian Naive Bayes, K-nearest neighbors, Support Vector machine, Decision Tree, Random Forest, Gradient Boost, AdaBoost and Multi Layered Perceptron using artificial Neural Network on reduced PIMA Indian Diabetes dataset and provided a detailed performance comparison of the algorithms. From this article readers are expected to gain a detailed insight of different symptoms of diabetes along with their applicability in different ML algorithms for diabetes onset prediction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Forecasting

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A recurrent neural network architecture for android mobile data analysis for detecting malware infected data

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Soft Computing 2024年第21期28卷 12917-12928页

作者： Murugan, Prabhu Manimaran, A. Sundar, Ramesh Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman Garg, Shruti Department of ECE Saveetha School of Engineering Simats Chennai602105 India Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Simats Chennai602105 India Department of Netwoking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India Department of Data Science and Business System School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India Department of Applied Machine Learning Saveetha School of Engineering Saveetha Institute of Medical and Technological Sciences Tamilnadu Chennai India Birla Institute of Technology Mesra Ranchi India

One of the latest modern communication devices is a mobile device seriously affected by multiple malware. Malware is a virus software installed automatically by hackers on various computing devices. Malware corrupts the system software, kills *.exe files, and tries to access user-sensitive data from the device. Around the world, 80% of people use smartphones, and 70% use Android phones. All Android phones are installed with anti-virus software but not used properly. Thus, it creates a pathway for malware to enter Android mobile devices and deploy vulnerabilities. Several earlier methods have focused on using the malware detection method by analyzing static data, which cannot identify the dynamic behavior of the malware. Thus, this paper implemented one of the deep learning algorithms, RNN, for identifying and detecting malware activities in Android mobile devices statically and dynamically. An IoT network architecture is proposed for experimenting with the RNN algorithms verifying the performance. The results of the RNN method show that it is better than other models in malware detection in mobile devices. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Android malware

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Knowledge Enhanced Pre-training for Cross-lingual Dense Retrieval 30

Knowledge Enhanced Pre-training for Cross-lingual Dense Retr...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Zhang, Hang Gong, Yeyun Liu, Dayiheng Zhang, Shunyu He, Xingwei Lv, Jiancheng Guo, Jian College of Computer Science Sichuan University China Engineering Research Center of Machine Learning and Industry Intelligence China Beihang University China The University of Hong Kong Hong Kong IDEA Research China

ISBN: (纸本)9782493814104

In recent years, multilingual pre-trained language models (mPLMs) have achieved significant progress in cross-lingual dense retrieval. However, most mPLMs neglect the importance of knowledge. Knowledge always conveys similar semantic concepts in a language-agnostic manner, while query-passage pairs in cross-lingual retrieval also share common factual information. Motivated by this observation, we introduce KEPT, a novel mPLM that effectively leverages knowledge to learn language-agnostic semantic representations. To achieve this, we construct a multilingual knowledge base using hyperlinks and cross-language page alignment data annotated by Wiki. From this knowledge base, we mine intra- and cross-language pairs by extracting symmetrically linked segments and multilingual entity descriptions. Subsequently, we adopt contrastive learning with the mined pairs to pre-train KEPT. We evaluate KEPT on three widely-used benchmarks, considering both zero-shot cross-lingual transfer and supervised multilingual fine-tuning scenarios. Extensive experimental results demonstrate that KEPT achieves strong multilingual and cross-lingual retrieval performance with significant improvements over existing mPLMs. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Semantics

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GraphMorph: Tubular Structure Extraction by Morphing Predicted Graphs 38

GraphMorph: Tubular Structure Extraction by Morphing Predict...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhang, Zhao Zhao, Ziwei Wang, Dong Wang, Liwei Center for Data Science Peking University China Yizhun Medical AI Co. Ltd. China State Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University China Center for Machine Learning Research Peking University China Guangdong Guangzhou China

Accurately restoring topology is both challenging and crucial in tubular structure extraction tasks, such as blood vessel segmentation and road network extraction. Diverging from traditional approaches based on pixel-level classification, our proposed method, named GraphMorph, focuses on branch-level features of tubular structures to achieve more topologically accurate predictions. GraphMorph comprises two main components: a Graph Decoder and a Morph Module. Utilizing multi-scale features extracted from an image patch by the segmentation network, the Graph Decoder facilitates the learning of branch-level features and generates a graph that accurately represents the tubular structure in this patch. The Morph Module processes two primary inputs: the graph and the centerline probability map, provided by the Graph Decoder and the segmentation network, respectively. Employing a novel SkeletonDijkstra algorithm, the Morph Module produces a centerline mask that aligns with the predicted graph. Furthermore, we observe that employing centerline masks predicted by GraphMorph significantly reduces false positives in the segmentation task, which is achieved by a simple yet effective post-processing strategy. The efficacy of our method in the centerline extraction and segmentation tasks has been substantiated through experimental evaluations across various datasets. Source code will be released soon. © 2024 Neural information processing systems foundation. All rights reserved.

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