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Enhancing the Efficiency of Computational Genetic Epidemiology using Advanced Method 13

Enhancing the Efficiency of Computational Genetic Epidemiolo...

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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Patil, Shashikant Sharma, Sunil Prakash, Febin Atlas SkillTech University Department of UGDX Maharashtra Mumbai India Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350305463

The incorporation of cutting-edge computational methodologies and machine learning strategies and genetic epidemiology has made tremendous strides in recent years. To speed up and refine genetic epidemiology studies, we present a new method called 'Enhancing the efficiency of computational genetic epidemiology using Advanced Methods' (ECEGEM), which utilizes modern computational methods and innovative technologies. The three primary methods used by ECEGEM are Principal Component Analysis (PCA), Logistic Regression, and the determination of the Polygenic Risk Score (PRS). Using PCA, we can ensure that the underlying substructure of the population is not confounding genetic connections. Through its ability to predict the association between genetic variations and illness outcomes, Logistic Regression sheds light on the hereditary nature of a variety of conditions. Individuals may get more accurate risk estimates using PRS computation, which considers their unique genetic makeup. Logistic Regression, Random Forest, Support Vector Machines, Naive Bayes, kNearest Neighbors, and Decision Trees were some of the six classic approaches we used to evaluate ECEGEM against. It was shown that ECEGEM was much more accurate and precise than the conventional approaches. This highlights the promise of sophisticated computational methods for improving the accuracy of illness risk assessment and association research. Because of its ability to take into consideration population stratification and provide more accurate modeling and risk assessment, ECEGEM has been shown to be an effective tool in genetic epidemiology. Personalized medicine and the understanding of the genetic basis of complicated illnesses will benefit greatly from the use of cutting-edge technologies and machine learning as the field develops. © 2024 IEEE.

关键词： Principal component analysis

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Sentiment Analysis of Makeup Alley Website Cosmetic Reviews Using SVM and TF-IDF 9

Sentiment Analysis of Makeup Alley Website Cosmetic Reviews ...

引用

9th International Conference on Information Management and technology, ICIMTech 2024

作者： Putri, Christine Salim, Claudya Veronica Dwiyanti, W.K. Achmad, Said Moniaga, Jurike V School of Computer Science Bina Nusantara University Mobile Application & Technology Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

This paper focuses on performing sentiment analysis of cosmetic reviews using a machine learning algorithm. The study aims to classify reviews into positive, neutral, and negative opinions using aspect-based sentiment analysis with TF-IDF and SVM algorithms. The research questions include understanding user opinions on cosmetic products and identifying the most common positive and negative sentiments in reviews. Data cleaning, tokenization, stopwords removal, normalization, and lemmatization techniques are employed to achieve these goals. The study uses the Makeup Alley website as a data source and employs web scraping to obtain the data. The Natural Language Toolkit (NLTK) is used to simplify the removal of stopwords. The research seeks to document the step-by-step process of developing a sentiment analysis application tailored to cosmetic product reviews. © 2024 IEEE.

关键词： Support vector machines

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Implementation of Gamification to Help Teenagers Prevent Diseases 9

Implementation of Gamification to Help Teenagers Prevent Dis...

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9th International Conference on Information Management and technology, ICIMTech 2024

作者： Haryanto, Michael Djaja, Fabian Putera Chrisandy, Michael Achmad, Said Moniaga, Jurike V Bina Nusantara University Mobile Application & Technology Program Computer Science Department School of Computer Science Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

Adolescence is an important part of growing up to become an adult;keeping adolescent physique healthy is very important because poor dietary choices and a sedentary lifestyle will lead to diseases that they shouldn't have experienced yet. That is why teenagers need to be engaged to do more health- promoting behaviors, one way is by implementing gamification. This paper seeks to study which kind of gamification elements can be implemented from past research and whether the results were effective or not to prove whether gamification is a valid and effective strategy. At the same time, we are creating a gamified prototype for the mobile platform. The methodology starts with a systematic review of PubMed, MDPI, and Google Scholar to get a general idea of which gamification elements are the most implemented. Then a prototype will be made with the most used gamified elements from the systematic review. And then participants ranging from 10 - 19 years old will test the prototype and will give their opinions via a survey, From this, it is proven by our own gamified prototype survey results that using gamification as a way to engage teenagers is a fun and engaging way to do more health-promoting behaviors with a mostly positive feedbacks with the average rating of 8.33 out of 10 given from the participants. © 2024 IEEE.

关键词： Gamification

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Trend analysis in competitive programming using topic modeling 9th

Trend analysis in competitive programming using topic modeli...

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9th International Conference on computer Science and Computational Intelligence, ICCSCI 2024

作者： Ramahalim, Albert Yulius Karuniawan, Christoffer Edbert Purwanto, Eko Setyo Ario, Muhamad Keenan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Competitive programming (CP) is a mind sports activity where people solve problems using command-line computer programs to provide correct output for the given test cases. Competitors need to practice problem-solving and mathematics as well as study algorithms and data structures to perform well in CP. This study aims to provide an original way to perform a trend analysis in CP, distinguishing topics frequently used in CP contests. To fulfill our goal, we create topic models based on previous topic modeling works to do natural language processing tasks using Latent Dirichlet Allocation (LDA) and Biterm Topic Model (BTM). For our dataset, we constructed a corpus from Codeforces blog posts, a popular website for competitive programmers, by extracting its content and user comments. Our results indicate that BTM is powerful enough to do trend analysis in CP. The trend analysis recognized that dynamic programming and complexity analysis have been the most prominent topics for the last ten years. Data structures and string algorithms are runners-up that may have potential trends in the future. This study opens up further research on other methods to perform trend analysis using better topic models and corpora. © 2024 The Authors.

关键词： Modeling languages

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Reimagining Architecture and Protocols for the Edge Computing Revolution in Web3

Reimagining Architecture and Protocols for the Edge Computin...

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2024 International Conference on Advances in Computing Research on Science Engineering and technology, ACROSET 2024

作者： Loonkar, Shweta Ahmed, Zahid Kavitha, R. Atlas SkillTech University Department of Isme Maharashtra Mumbai India Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350388800

In the context of Web3.0, the rapid pace of technological innovation has resulted in the emergence of novel computing paradigms. This is a natural result of the exponential growth of technology. Within the ecosystem of Web 3.0, the major emphasis of this research is on rethinking architecture and protocols in order to be ready for the revolution in edge computing. To improve the efficacy, security, and scalability of data processing and transmission, DECABI provides a dynamic edge computing architecture with integrated blockchain characteristics. The study accelerates data transfers without compromising their integrity by using dynamic resource allocation, encrypted data transmission, and a consensus protocol for edge computing (CPEC). The suggested method has much better performance than the alternatives. This exemplifies how the method has the ability to completely alter the way edge computing is carried out in the Web3.0 era. Countless simulations and exhaustive studies confirmed the existence of this option. This study lays the groundwork for a more stable and secure Web3.0 ecosystem, and it also makes a substantial contribution to the current conversation about the future of computer architectures and protocols. © 2024 IEEE.

关键词： Data integrity

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Deep Learning Techniques For Improving NearField Synthetic Aperture Radar Imaging 13

Deep Learning Techniques For Improving NearField Synthetic A...

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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Deepak, Shashikant Kharbas, Vikash Kumar Murugan, R. Atlas SkillTech University Department of UGDX Maharashtra Mumbai India Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350305463

Improvements in near-field Synthetic Aperture Radar (SAR) imaging are among the most notable developments in SAR imaging overall. To improve the quality and consistency of automated near-field SAR imaging, this study presents a unique strategy based on deep learning techniques. DeepSARNet, the suggested technique, is meant to be more effective than conventional approaches in terms of picture quality, interpretability, and speed. In this research, we introduce the strategy, evaluate its efficacy in comparison to six made-up classic approaches, and offer our findings. Automatic focus correction using a convolutional neural network (CNN) is the foundation of DeepSARNet, successfully minimizing focus defects inherent to near-field SAR imagery. The second part, a convolutional neural network (CNN)-based Near-Field Scatterer Removal (NFSR) technique, is designed to eliminate near-field scatterer artifacts for clearer, easier-tounderstand final pictures. Finally, the Object Detection and Classification (ODC) method integrates region proposal networks with CNNs to provide reliable results in object recognition. When compared to conventional techniques, DeepSARNet produces pictures with superior clarity and interpretability in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). Moreover, DeepSARNet needs less calibration work, shortens processing time, and uses less resources than competing solutions, making it a prime candidate for use as a fully automated, efficient SAR imaging solution. The authors propose the DeepSARNet technique, which leverages deep learning capabilities, to address these ongoing issues with near-field SAR imaging. It improves picture quality, lessens artifacts, and increases automation, making it a useful asset in fields including disaster relief, ecological surveying, and military preparation. Research into improving and expanding these approaches in the realm of SAR imaging is warranted, since the results highlight the

关键词： Remote sensing

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Enhancing User Interface and Experience in Skin Type Classification and Skincare Product Recommendation System for Young Adults 9

Enhancing User Interface and Experience in Skin Type Classif...

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9th International Conference on Information Management and technology, ICIMTech 2024

作者： Himawan, Felicia Soerjadi, Vania Paramitha Handoyo, Yonathan Achmad, Said Moniaga, Jurike V School of Computer Science Bina Nusantara University Mobile Application & Technology Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

The escalating interest of skincare among Indonesian young adults reflects the rising trend in the skincare industry. However, this heightened interest also brings several challenges, like the lack of knowledge and confusion when selecting the right skincare products for our skin type. This underscores the need for an effective and efficient skin type classification and product recommendation system. This paper addresses this challenge by focusing on enhancing a skincare application's user interface and overall experience. This research employed User-Centered Design (UCD), implementing Design Sprint, and Goal-Directed Design (GDD) methodologies to achieve a high value of user satisfaction. The Sprint process is used to create the design prototype efficiently, and the Goal-Directed method is performed to create specific, measurable goals. Our evaluation shows that an iterative design process based on user feedback results in higher user satisfaction. The analysis indicated a significant increase of approximately 25% in both effectiveness and efficiency among users when using the application. Initially, the first iteration reveals a low value of approximately 59% in overall effectiveness and efficiency. However, a substantial boost to around 83.5% was seen after a redesign. This improvement proved that User-Centered Design methodologies highly influence user perception and satisfaction. © 2024 IEEE.

关键词： design sprint goal-directed design skincare UI/UX user-centered design

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Hybrid Electroencephalography-Induced Robot Navigation for Rehabilitative Platform 20

Hybrid Electroencephalography-Induced Robot Navigation for R...

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20th IEEE India Council International Conference, INDICON 2023

作者： Ghosh, Ahona Ghosh, Lidia Dey, Sharmistha Sarkar, Indranil Saha, Sriparna Maulana Abul Kalam Azad University of Technology Department of Computer Science & Engineering West Bengal India Rcc Institute of Information Technology Department of Computer Application Kolkata India University Institute of Computing Chandigarh University Punjab Gharuan India Guru Nanak Institute of Technology Department of Computer Application India

ISBN: (纸本)9798350305593

Decoding upper limb movement from electroencephalography (EEG) signals is an emerging field of research that has the potential to enhance the quality of life for individuals with motor impairments. This paper provides an informative overview of EEG-based upper limb movement decoding and addresses the limitations found in existing studies by proposing a novel rehabilitative robotics framework. The framework utilizes three types of EEG signals: motor imagery, error-related potential, and P300 signals, to navigate an OpenManipulatorX robotic arm. The novelty of this research lies in the utilization of a phase-sensitive Common Spatial Pattern approach for feature extraction, as well as the successful recognition of commands for robot control using Linear Discriminant Analysis. The proposed method achieves an impressive average success rate of 95% for target reaching, making it suitable for the design of prosthetics in real-time rehabilitative platforms. © 2023 IEEE.

关键词： Electroencephalography Linear Discriminant Analysis OpenManipulatorX Phase-sensitive Common Spatial Pattern Rehabilitation

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AI-Powered Mobile application for Image-Based Food Ingredient Detection and Recipe Generation 9

AI-Powered Mobile Application for Image-Based Food Ingredien...

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9th International Conference on Information Management and technology, ICIMTech 2024

作者： Rosaline, Thessalonica Maria Luisse Suciawan, Calvin Anacia Ng, William Achmad, Said Moniaga, Jurike V School of Computer Science Bina Nusantara University Mobile Application & Technology Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

This research explores the critical impact of dietary habits on health, addressing the lack of awareness regarding nutritional content that leads to issues such as malnutrition. The proposed mobile application utilizes the YOLOv8 algorithm with a deep convolutional neural network (CNN) architecture, deployed on the Roboflow server, a local MySQL database server, and ChatGPT. The architectural framework of this application is integrated through the NodeJs server via an API system embedded within the Android application. Despite this approach, the study highlights the need for further refinement in the accuracy of the ingredients' object detection model. The refined YOLOv8 model, trained on an extensive dataset of 24,583 images across 13 food ingredient classes, outperforms the initial YOLOv8 model trained on 9,867 images. Improvements are notable, particularly in addressing misclassifications of ingredients such as beef, pork, onion, and egg in the initial model. However, the YOLOv8 model can still be improved by introducing more ingredient variations and expanding the training dataset. This application can detect multiple food ingredients from an image and evaluate their nutritional value, specifically in calorie count. It also provides tailored recipes to help users meet their daily nutritional requirements, offering a practical solution for users to achieve their dietary goals. This research connects computer vision and nutrition, providing insights for developing practical solutions in this domain. © 2024 IEEE.

关键词： Convolutional neural networks

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An AI-integrated Enhanced Anatomical Brain Tumor Segmentation in Substantia Nigra for Early Disease Identification using Deep Resnet50-CNN

An AI-integrated Enhanced Anatomical Brain Tumor Segmentatio...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Ganeshkumar, P.V. Prasanna, S. Vels Institute of Science Technology and Advanced Studies Chennai India Vels Institute of Science Technology and Advanced Studies Department of Computer Application Chennai India

ISBN: (纸本)9798350375442

The early identification of brain tumours is crucial for improving patient prognosis and treatment planning. Recent advancements in neuroimaging techniques have paved the way for the enhanced detection and characterization of these lesions through CT or MRI image scans. During medical image analysis, the feature analysis is important factor to know the object relations, entity resolution information and disease relational properties, from the prior analysis, machine learning models are mostly covered to segment the disease region by retaining non scaling factors. Due to lower precision class, poor segmentation leads low accuracy to identifying the disease regions to produce higher false rates. In this research explores a novel approach to brain tumour identification utilizing Neuromelanin-Sensitive Magnetic Resonance Imaging (NMS-MRI). We propose a novel preprocessing methodology leveraging the Weigner filter to enhance image normalization and overall quality, facilitating more accurate subsequent analyses. Following preprocessing, we employ Cascading Slice Window Volumetry Segmentation (CSWVM) to achieve refined tumor delineation in three-dimensional MRI scans. The segmentation results are further refined through Cross Modality Particle Swarm Optimization (CMPSO)-based feature selection, which effectively identifies the most relevant features across various imaging modalities to optimize the classification process. To classify the detected tumors, further to integrate a ResNet 50-Convolutional Neural Network (ResNet50-CNN), leveraging its deep learning capabilities for robust pattern recognition in neuroimaging data. The proposed system proves higher performance in precision, recall, f1 score by attaining feature selection, and deep learning algorithms, ultimately contributing to enhanced accuracy and effectiveness in brain tumor identification. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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