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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Rajendran, Adithya Abhishek, S. Sha, Akhbar Anjali, T. Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350340600

Lung cancer, a complex and formidable disease, frequently necessitates surgical intervention as a pivotal aspect of its treatment. Accurate prediction of post-operative life expectancy holds paramount significance in the realm of clinical decisionmaking and patient counselling This study undertook a rigorous comparison of various machine learning algorithms, encompassing Support Vector Classifier (SVC), Logistic Regression, XGBoost, Random Forest, K-Nearest Neighbors (K-NN), and CatBoost. Using an expansive dataset containing clinical and demographic variables collected from lung cancer patients, the research team rigorously assessed the predictive capabilities of these models. The outcome of the research team analysis unveiled exceptional mean accuracy scores across the models, underscoring their utility in this context. Specifically, SVC, Random Forest, and CatBoost consistently exhibited remarkable mean accuracy rates, each reaching 0.864. Logistic Regression also demonstrated robust performance, achieving a commendable mean accuracy of 0.862, while XGBoost and K-NN delivered competitive mean accuracies of 0.846 and 0.84, respectively. These findings underscore the substantial potential of machine learning in furnishing precise predictions regarding post-operative life expectancy. By doing so, these models empower healthcare professionals to make well-informed treatment decisions, paving the path for the development of personalized treatment strategies and the enhancement of patient care in the management of lung cancer. © 2023 IEEE.

关键词： Forecasting

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Adversarial Modality Alignment Network for Cross-Modal Molecule Retrieval

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 278-289页

作者： Zhao, Wenyu Zhou, Dong Cao, Buqing Zhang, Kai Chen, Jinjun Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan425100 China Swinburne University of Technology Department of Computing Technologies MelbourneVIC3122 Australia Guangdong University of Foreign Studies School of Information Science and Technology Guangzhou510420 China

The cross-modal molecule retrieval (Text2Mol) task aims to bridge the semantic gap between molecules and natural language descriptions. A solution to this nontrivial problem relies on a graph convolutional network (GCN) and cross-modal attention with contrastive learning for reasonable results. However, there exist the following issues. First, the cross-modal attention mechanism is only in favor of text representations and cannot provide helpful information for molecule representations. Second, the GCN-based molecule encoder ignores edge features and the importance of various substructures of a molecule. Finally, the retrieval learning loss function is rather simplistic. This article further investigates the Text2Mol problem and proposes a novel adversarial modality alignment network (AMAN) based method to sufficiently learn both description and molecule information. Our method utilizes a SciBERT as a text encoder and a graph transformer network as a molecule encoder to generate multimodal representations. Then, an adversarial network is used to align these modalities interactively. Meanwhile, a triplet loss function is leveraged to perform retrieval learning and further enhance the modality alignment. Experiments on the ChEBI-20 dataset show the effectiveness of our AMAN method compared with baselines. © 2020 IEEE.

关键词： Semantics

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Energy-Efficient Green Information Centric Networking for Future Wireless Communications

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第23期18卷 149-158页

作者： Girirajan, S. Vinoth Kumar, S. Karthick, M. Saju Raj, T. Ponnaian, Geetha Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Nandha College of Technology Tamil Nadu Erode India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

Energy-efficient green information-centric networking (EEGICN) is proposed in this paper for advancing future wireless communication networks by addressing the challenge of energy consumption. This model can adapt the power consumption of network nodes to optimized values according to the associated link utilization. The model aimed to reduce energy consumption and increase network performance and stability. The EEGICN model incorporates efficient routing mechanisms, content caching strategies, and energy-intelligent communication protocols to improve resource utilization across the network infrastructure. EEGICN minimizes large data transmission, reduces power consumption, and reduces network congestion. Popular resources are cached in key network locations, and proximity data is exchanged to achieve this. The model also includes dynamic power management algorithms that adapt to changing traffic demand and network conditions to provide consistent performance across a variety of operational scenarios. In comparison to current wireless network systems that employ various forms of cache, the evaluation findings demonstrated that EEGICN can increase network efficiency by dramatically lowering the number of hops and energy consumption. Future networks may find this application to be a quick and easy way to transmit content. © 2024 by the authors of this article.

关键词： energy efficiency (EE) future wireless communications green information-centric networking information-centric networking (ICN) wireless network

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Semantic Analysis Using Deep Learning for Predicting Stock Trends 2

Semantic Analysis Using Deep Learning for Predicting Stock T...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Galphade, Manisha Nikam, V.B. Yedurkar, Dhanalekshmi Singh, Prabhishek Stephan, Thompson Department of Computer Science & Engineering School of Computing MIT Art Design & Technology University Pune India Department of Computer Engineering & IT Veermata Jijabai Technological Institute Mumbai India Department of Computer Science & Engineering School of Computing MIT Art Design & Technology University Pune India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science & Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun India

Company Investors and financial professionals mostly rely on quarterly reports to help them decide the ways to invest in stocks and assess the company's current performance. Quarterly company reports offer an abstracted perspective of the company's overall past performance, as well as its present situation and the market value of its market share. Financial text streams in quarterly report are unstructured naturally, but they represent cooperative expressions that are of important in any financial decision for stake holder. It will be both daunting and necessary to procedure intelligence of unstructured textual data. In this study, we address important queries related with the explosion of interest in a method to extract useful information from unstructured data and the way to work out if such insight provides any hints regarding the trends of financial markets. There is a lack of availability in the labeled dataset for financial sentiment analysis applications. The pre-trained language model employs very little labeled parameters that is used for a variety of domain specific corpora including financial sentiment analysis. In this paper, FinBERT, a model built on the BERT framework, to address linguistics challenges in the financial domain. The proposed work uses twelve transformer layers and twelve attention layers with several million parameters. The design of encoder and decoder comprises of several attention layers along with RNN. This arrangement aids to recognize instances processing the strongest relation between the words within a particular sentence. The experimentation results shows that the presented method surpass the state-of-the-art methods for financial datasets. The results are also compared with other existing models using the same financial dataset. It is observed that the FinBERT attains an accuracy of 84.77% on quarterly reports despite using a lesser training set. © 2024 Elsevier B.V.. All rights reserved.

关键词： Sentiment analysis

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Autograding of Programming Skills 8

Autograding of Programming Skills

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8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Narmada, Nakka Pati, Peeta Basa Amrita School of Computing Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350334012

To evaluate a learner's knowledge of programming language skills, assessments are given. Grading of these is usually done manually which not only is tedious but prone to error due to repetition and fatigue. In this work, we employ pre-trained language models to perform automated grading of "C"programming language. Embeddings from different transformers on pre-assessed codes are used as feature vectors to train a wide range of regressors for the scoring task. Root-mean-square error (RMSE) is the metric utilized to compare the scores of these regressors. It's observed that embeddings from T5-model with CatBoost regressor gives the least error around 15%. © 2023 IEEE.

关键词： Embeddings

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Predicting the Coronary Heart Disease Risk Over the Next Decade through Ensemble Methodologies 3

Predicting the Coronary Heart Disease Risk Over the Next Dec...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Arun, Aashna Singh, Rimjhim Padam Kanchan, Sneha Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Universiti Tunku Abdul Rahman Department of Internet Engineering and Computer Science Malaysia

ISBN: (纸本)9798350372120

Ascertaining diagnosis for Coronary Heart Disease (CHD) is a difficult procedure & must be completed precisely within a stipulated period. Numerous researchers have taken a gander at coronary illness risk factors & recommended specific Machine Learning (ML) & Artificial Intelligence (AI) models. This paper attempts to demonstrate the development of various predictive models for the risk of CHD over the next decade using the Framingham dataset with relevant features. The data undergoes thorough preprocessing, including handling missing values, outliers, & duplicates based on which a predictive model was built & evaluated. The work encapsulates essential steps, emphasizing the approach to predicting CHD & providing insights for potential healthcare applications. Attempting to improvise the predictive accuracy the work proposes implementing several ensemble approaches, combining the strengths of multiple models which showcased exceptional performance. Through the ensemble techniques, the individual models were integrated to mitigate weaknesses & enhance overall predictive performance that contributes to a more advanced and accurate prediction of CHD. The various strategic ensemble approaches enhance the reliability of the predictive model, offering a more effective tool for healthcare practitioners in assessing the likelihood of CHD in subjects. © 2024 IEEE.

关键词： Diagnosis

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Automation of Short Answer Grading Techniques: Comparative Study using Deep Learning Techniques 5

Automation of Short Answer Grading Techniques: Comparative S...

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5th IEEE International Conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Divya, Arunima Haridas, Vivek Narayanan, Jayasree Amrita School of Computing Deportment of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9781665493604

Automatic short answer grading (ASAG) techniques have been shown to cut down on the time and work needed to grade exams, and it is a method that is becoming more and more common, especially with the rise in popularity of online courses. This study compares the results of 7 pre-trained embedding models using just one feature to automatically grade brief responses: the similarity between the model answer's and the student answer's embeddings. Regression models are developed and evaluated to predict a short answer's score based on the similarities between all pairs of answers in the Mohler dataset. The predictions are evaluated by comparing the Root Mean Squared Error (RMSE) and Pearson correlation scores of each model. © 2023 IEEE.

关键词： Grading

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Machine Learning Approaches for Myers-Briggs Personality Prediction 7

Machine Learning Approaches for Myers-Briggs Personality Pre...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Santhosh, Sethulakshmi Meenakshi, M. Avani, F. Abhishek, S. Anjali, T. Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350340600

In order to predict Myers-Briggs personality types from text input, this research article compares the abilities of Stochastic Gradient Descent (SGD), Naive Bayes, k-Nearest Neighbours (KNN), and Logistic Regression models. The Myers-Briggs Type Indicator(MBTI) captures distinctive patterns of behaviour, cognition, and preferences of human personality. It divides people into one of sixteen personality types. This study assesses the accuracy, support, recall, precision, and F1-score for the chosen models using a dataset made up of text inputs and matching Myers-Briggs types. The project attempts to determine the most successful model among the evaluated ways for precisely predicting Myers-Briggs personality types through extensive experimentation and review. The findings of this study have applications in building accurate and reliable models for personality prediction utilizing natural language processing methods. © 2023 IEEE.

关键词： Stochastic systems

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CRAYM: Neural Field Optimization via Camera RAY Matching 38

CRAYM: Neural Field Optimization via Camera RAY Matching

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Lin, Liqiang Wu, Wenpeng Fu, Chi-Wing Zhang, Hao Huang, Hui College of Computer Science and Software Engineering Shenzhen University China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong School of Computing Science Simon Fraser University Canada

We introduce camera ray matching (CRAYM) into the joint optimization of camera poses and neural fields from multi-view images. The optimized field, referred to as a feature volume, can be "probed" by the camera rays for novel view synthesis (NVS) and 3D geometry reconstruction. One key reason for matching camera rays, instead of pixels as in prior works, is that the camera rays can be parameterized by the feature volume to carry both geometric and photometric information. Multi-view consistencies involving the camera rays and scene rendering can be naturally integrated into the joint optimization and network training, to impose physically meaningful constraints to improve the final quality of both the geometric reconstruction and photorealistic rendering. We formulate our per-ray optimization and matched ray coherence by focusing on camera rays passing through keypoints in the input images to elevate both the efficiency and accuracy of scene correspondences. Accumulated ray features along the feature volume provide a means to discount the coherence constraint amid erroneous ray matching. We demonstrate the effectiveness of CRAYM for both NVS and geometry reconstruction, over dense- or sparse-view settings, with qualitative and quantitative comparisons to state-of-the-art alternatives. © 2024 Neural information processing systems foundation. All rights reserved.

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Recursive Descent Parser for Abstract Syntax Tree Visualization of Mathematical Expressions 7

Recursive Descent Parser for Abstract Syntax Tree Visualizat...

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7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Pecheti, Shiva Teja Basavadeepthi, M.H. Kodurupaka, Nithin Belwal, Meena Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350343144

This study presents a novel method for finding and evaluating mathematical expressions using Abstract Syntax Trees (AST). The approach is particularly adaptable for both academic and professional applications since it accurately parses complex equations and accommodates a large number of mathematical operations. The AST model ensures a hierarchical expression structure, enabling accurate and efficient evaluation. Parsed expressions are improved by incorporating interactive graph vi-sualisation, which simplifies mathematical computations and engineering tasks for understanding and application. The project's complete approach to detecting mathematical expressions stands out since it takes into account a wide range of functions and variables and ultimately enhances the system's accuracy and adaptability. The visual representation of AST also helps with debugging and checking the accuracy of parsed expressions, which adds to its significance in the field of mathematical expression recognition. © 2023 IEEE.

关键词： Syntactics

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