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EPCA—Enhanced Principal Component Analysis for Medical data Dimensionality Reduction

SN computer science 2023年第3期4卷 243页

作者： Vinutha, M.R. Chandrika, J. Krishnan, Balachandran Kokatnoor, Sujatha Arun Department of Information Science and Engineering Malnad College of Engineering Hassan 573202 India Department of Computer Science and Engineering School of Engineering and Technology CHRIST (Deemed to be University) Bangalore 560074 India

Innovations in technology from the last one decade have led to the generation of colossal amounts of medical data with comparably low cost. Medical data should be collected with utmost care. Sometimes, the data have high features but not all the features play an important role in drawing the relations to the mining task. For the training of machine learning algorithms, all the attributes in the data set are not relevant. Some of the characteristics may be negligible and some characteristics may not influence the outcome of the forecast. The pressure on machine learning algorithms can be minimized by ignoring or taking out the irrelevant attributes. Reducing the attributes must be done at the risk of information loss. In this research work, an Enhanced Principal Component Analysis (EPCA) is proposed, which reduces the dimensions of the medical dataset and takes paramount care of not losing important information, thereby achieving good and enhanced outcomes. The prominent dimensionality reduction techniques such as Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Partial Least Squares (PLS), Random Forest, Logistic Regression, Decision Tree and the proposed EPCA are investigated on the following Machine Learning (ML) algorithms: Support Vector Machine (SVM), Artificial Neural Networks (ANN), Naïve Bayes (NB) and Ensemble ANN (EANN) using statistical metrics such as F1 score, precision, accuracy and recall. To optimize the distribution of the data in the low-dimensional representation, EPCA directly mapped the data to a space with fewer dimensions. This is a result of feature correlation, which made it easier to recognize patterns. Additionally, because the dataset under consideration was multicollinear, EPCA aided in speeding computation by lowering the data's dimensionality and thereby enhanced the classification model's accuracy. Due to these reasons, the experimental results showed that the proposed EPCA dimensionality reduction technique per

关键词： Accuracy Artificial neural networks Liver cirrhosis Machine learning Medical data Principal component analysis Singular value decomposition Support vector machine

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A Comprehensive Study of Web Phishing Detection Using Machine and Deep Learning Architecture 14

A Comprehensive Study of Web Phishing Detection Using Machin...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Prabhu, N. Senthil Kumar, T. Jeyakumar, G. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350335095

Phishing is the easiest way to compromise a user or an entity and first choice of malicious users;as it is hard to circumvent and exploit technology & security controls to gain unauthorised access or to steal information. Industry and institutions spending time, effort, money and invest knowledge on combating against phishing attack in two broad aspects;one is cyber security awareness program and the other mean is building machine and deep learning architectures to detect phishing attempts. This systematic study presents the trend analyses of the common attribute present in the dataset and significance of different machine and deep learning architectures and its hyperparameters in detecting web phishing attempts. © 2023 IEEE.

关键词： Cybersecurity

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A Constructivism-based Approach to Treemap Literacy in the Classroom

A Constructivism-based Approach to Treemap Literacy in the C...

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44th Annual Conference on European Association for computer Graphics, EUROGRAPHICS 2023

作者： Firat, Elif E. Lang, Colm Srinivas, Bhumika Peng, Ilena Laramee, Robert S. Joshi, Alark Department of Computer Engineering Cukurova University Turkey Visualization and Graphics Lab Department of Computer Science University of San Francisco CA United States School of Computer Science University of Nottingham United Kingdom

ISBN: (纸本)9783038682103

Treemaps are a popular representation to show hierarchical as well as part-to-whole relationships in data. While most students are aware of node-link representations/network diagrams based on their K-12 education, treemaps are often a novel representation to them. We present our experience of developing a software using principles from constructivism to help students understand treemaps using linked, side-by-side views of a node-link diagram and a treemap of the same data. Based on the qualitative survey conducted at the end of the intervention, students found the linked views to be beneficial for understanding hierarchical representation of data using treemaps. © 2023 The Authors.

关键词： Students

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IoT-Enabled Optimized Irrigation System using Soil Moisture

IoT-Enabled Optimized Irrigation System using Soil Moisture

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Nambiar, Anushka Bhuvaneswari, R. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350315455

Agriculture serves as a crucial foundation for developing any growing nation, being the primary source of sustenance and employment. However, in the face of a rapidly changing world, the agricultural sector must adapt to continue fulfilling its role in supporting the country. With climate change altering rainfall patterns, the traditional knowledge that has been passed down through generations for predicting rainfall is no longer adequate for ensuring efficient farming practices. This can result in situations where crops experience either inadequate hydration or excessive irrigation. Under-hydration can be detrimental to crop survival, while over-irrigation poses issues with soil aeration and salinity. The consequences of over-irrigation include increased weed growth and the development of diseases that may shorten plants' lifespans and diminish overall tree health and vitality. To address these challenges, it becomes essential to adopt innovative techniques that conserve water during the irrigation process. In the proposed system, both soil moisture levels using a soil moisture sensor and the probability of precipitation over the field - fetched from the weather API - are continuously monitored. By combining data from these factors, an optimized decision is made to ensure the plants receive water only when necessary. This approach aims to enhance watering efficiency while promoting optimal plant growth and sustainability. © 2023 IEEE.

关键词： IoT Precipitation prediction Raspberry Pi Smart Irrigation system

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Weaponizing Real-world Applications as C2 (Command and Control)

Weaponizing Real-world Applications as C2 (Command and Contr...

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2023 IEEE International Conference on Innovative data Communication Technologies and Application, ICIDCA 2023

作者： Sidhardhan, Abhishek Keerthana, S. Kannimoola, Jinesh M. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Applications Amritapuri India

ISBN: (纸本)9798350397208

The cybersecurity domain faces growing threats from Advanced Persistent Threat (APT), a targeted attack against high-profile organizations that can remain undetected for extended periods despite improvements in detection mechanisms. Despite developing sophisticated detection methods, large-scale attacks continue to occur worldwide using traditional techniques such as C2C (Command & Control) communication with various frameworks and protocols. To combat these attacks, researchers must understand new trends in intrusion techniques. This study utilizes real-world applications, including social media and collaborative platforms, as C2 channels to communicate stealthily and undetectably with compromised hosts in the post-exploitation phase of APT attacks. Through a case study, the authors demonstrate how this method can evade state-of-the-art intrusion detection methods, clearly understanding how APT can weaponize Twitter as a C2 channel, enabling defenders to detect and respond to such attacks. © 2023 IEEE.

关键词： Cybersecurity

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Employing Blockchain and Machine Learning for Monitoring the Accumulation and Dispensation of Covid-19 Vaccine 2nd

Employing Blockchain and Machine Learning for Monitoring the...

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2nd International Conference on Signal and data Processing, ICSDP 2022

作者： Singh, Suraj Mahadevan, Anbazhagan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9789819914098

Fair vaccine distribution is a humanitarian priority. Despite this scientific breakthrough, inoculation of the global population itself is an enormous challenge. Even though the vaccination process for SARS-CoV-2 (COVID-19) started globally just after a year of outbreak, it still continues to present significant challenges. One such challenge was gaining and maintaining public trust about the viability of the antibody and the effectiveness of inoculation itself. However, vaccines’ adequacy is dictated by the accessibility and receptiveness of the dissemination framework, which is divided among the collaborators. Most disseminated records, such as the blockchain, provide an open, public, and permanent framework for a variety of uses. In this work, we propose a framework that uses the Blockchain mechanism for vaccines’ safety, security, transparency, and traceability. The Blockchain technology employed in this proposed endeavor ensures and manages vaccine registration, storage, and delivery. Furthermore, Machine Learning (ML) based prediction is also used for predicting the future trends of the COVID-19 vaccination. A feedback system is also provided to keep track of the number of vials in case a patient has any negative effects. In many aspects, such as good efficiency and scalability, the proposed work shows promising results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Meta-Learned Word Embeddings for Few-Shot Sentiment Classification 7th

Meta-Learned Word Embeddings for Few-Shot Sentiment Classifi...

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7th International Conference on Smart Trends in Computing and Communications, SmartCom 2023

作者： Pandey, Sumit Sharma, Srishti Department of Computer Science School of Engineering and Technology The Northcap University Haryana Gurugram122017 India

ISBN: (纸本)9789819907687

For each new domain, researchers and technologists developing sentiment classification algorithms must gather and curate data. In particular for domains where the features change over time, the work required to annotate these corpora is prohibitive. The idea of training classifiers on a limited set of domains before using them on further domains is explored in this study. One issue is that classifiers frequently perform poorly when the distributions of the training and testing sets of data are different. Another issue is that it is unclear how to choose domains that are suitable for training and testing other domains based on similarities. To train classifiers that generalize and still perform well on test sets that come from various distributions, the authors approach this challenge by making use of numerous, smaller training datasets. The authors suggest using current developments in meta-learning to learn stronger cross-domain embeddings that can generalize for the few-shot learning context to achieve this. This work investigates sentiment classification using few-shot learning. There are a variety of existing approaches to this problem, but recent advances in meta-learning have proven to be quite effective in the few-shot context. For few-shot learning, the authors suggest using MAML (Model Agnostic Meta-Learning) to quickly train better cross-domain embeddings and networks. Our final model performs at a cutting-edge level. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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Unlocking Complex Patterns Through the Power of Deep Learning for Advanced data Analysis and Prediction

Unlocking Complex Patterns Through the Power of Deep Learnin...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Sasikumar, S. Kumar, M. Guru Vimal Paramesh, J. Sriram, K.P. Anbalagan, E. Saveetha Engineering College Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai600062 India St. Joseph's Institute of Technology Information Technology OMR Chennai600119 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331542344

Supervised deep learning has brought changes to data analysis and prediction function by leveraging its capacity to make many number of relationships with heterogeneous forms of data. This paper compares deep learning techniques like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks in three tasks: There are three types of tasks, including image classification, time-series prediction, and text classification. In the picture simulation of the CIFAR-10 set for training, the accuracy of the ResNet model was 95% while the accuracy of VGG16 w 93%. When applied to the M4 competition dataset in time-series prediction, the LSTM model was superior to other benchmarks with MAE of 0.40 and RMSE of 0.60, with the RNN model giving MAE of 0.50 and RMSE of 0.65. It is 0.92 of accuracy, 0.91 of F1 score, and 0.921 for precision using the IMDB reviews dataset in text classification of movies using the CNN-LSTM hybrid model while the Bi-LSTM only had an accuracy of 0.89 and an F1 score of 0.889. Even though models, such as ResNet and Bi-LSTM, took slightly more time to train-Approximately 5.0 hours-it was because the epoch models were significantly superior to the baseline model. The present work focuses on the benefits of deep learning architectures in providing interpretable patterns in numerous applications. © 2024 IEEE.

关键词： Convolutional neural networks

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Automatic essay scoring for discussion forum in online learning based on semantic and keyword similarities

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Asian Association of Open Universities Journal 2023年第3期18卷 262-278页

作者： Dhini, Bachriah Fatwa Girsang, Abba Suganda Sufandi, Unggul Utan Kurniawati, Heny Department of Multimedia Teaching Material Production Center Universitas Terbuka Tangerang Selatan Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Sains and Technology Universitas Terbuka Tangerang Selatan Indonesia

Purpose: The authors constructed an automatic essay scoring (AES) model in a discussion forum where the result was compared with scores given by human evaluators. This research proposes essay scoring, which is conducted through two parameters, semantic and keyword similarities, using a SentenceTransformers pre-trained model that can construct the highest vector embedding. Combining these models is used to optimize the model with increasing accuracy. Design/methodology/approach: The development of the model in the study is divided into seven stages: (1) data collection, (2) pre-processing data, (3) selected pre-trained SentenceTransformers model, (4) semantic similarity (sentence pair), (5) keyword similarity, (6) calculate final score and (7) evaluating model. Findings: The multilingual paraphrase-multilingual-MiniLM-L12-v2 and distilbert-base-multilingual-cased-v1 models got the highest scores from comparisons of 11 pre-trained multilingual models of SentenceTransformers with Indonesian data (Dhini and Girsang, 2023). Both multilingual models were adopted in this study. A combination of two parameters is obtained by comparing the response of the keyword extraction responses with the rubric keywords. Based on the experimental results, proposing a combination can increase the evaluation results by 0.2. Originality/value: This study uses discussion forum data from the general biology course in online learning at the open university for the 2020.2 and 2021.2 semesters. Forum discussion ratings are still manual. In this survey, the authors created a model that automatically calculates the value of discussion forums, which are essays based on the lecturer's answers moreover rubrics. © 2023, Bachriah Fatwa Dhini, Abba Suganda Girsang, Unggul Utan Sufandi and Heny Kurniawati.

关键词： Discussion forum Essay scoring Keyword similarity Semantic similarity

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BERT-Driven Deep Learning Approach for Depression Detection in Social Media Posts 1

BERT-Driven Deep Learning Approach for Depression Detection ...

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1st International Conference on Optimization Techniques for Learning, ICOTL 2023

作者： Karna, Prashant Keshari, Sonu Kumar Mandal, Ashis Kumar Chakraborty, Basabi Hajee Mohammad Danesh Science and Technology University Department of Computer Science and Engineering Dinajpur Bangladesh University of Saskatchewan Department of Computer Science Saskatoon Canada Iwate Prefectural University Research and Regional Cooperation Division Takizawa Japan Madanapalle Institute of Technology and Science School of Computing Madanapalle India

ISBN: (纸本)9798350328042

Depression constitutes a worldwide health emergency, impacting countless individuals daily. However, timely diagnosis and treatment for depression are infrequent. Recent research has revealed affirmative links between the use of social media and the presence of depression. The use of learning techniques has become increasingly popular in detecting signs of depression in social media posts. This study introduces a method that utilizes transformer based models like BERT and RoBERTa. During the methodology phase we carefully preprocess the data followed by word embedding procedures. We then employ a combination of deep learning architectures such as LSTM, CNN with BiLSTM, RNN, BERT with Dense and RoBERTa with Dense to thoroughly evaluate and validate our models performance. Interestingly our results consistently show that the BERT with Dense and RoBERTa with Dense approaches outperform methods achieving an accuracy rate of 98%. This refined approach allows for accurate identification of signals in social media posts offering promising potential to make significant contributions, to the field of mental health advancement. © 2023 IEEE.

关键词： Social networking (online)

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