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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Muke, Saurav Ahire, Samruddhi Kale, Geetanjali Navghare, Pranali Pune Institute of Computer Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350306613

Although a software developer is capable of creating excellent software without the necessity for competitive coding. Having their hands in it may increase the project's efficiency and scalability. For every developer, problem solving is a must-required skill that is gained through competitive coding. Many technical recruiters use competitive programming as a barometer to assess a candidate's potential for the post of software engineer, including many prestigious Silicon Valley companies like Google, Facebook,etc. Since there is no such platform that helps students showcase their coding status on every registered platform, the proposed system helps to have such. ICode is a unified platform to help students visualize their coding profile status on a single site at one go. There is no public API that can be used to retrieve the *** websites;hence, web scraping becomes relevant in this situation. It is one of the strategies of information *** of manually copying data from the browser, with web scraping, it becomes easy to automatically extract the data. without any effort and perform analysis on it. The paper mainly targets the visualization of the data scrapped from competitive coding platforms to create a digital resume and perform analysis based on the scraped data. This is a digital *** be a revolution in the modern-day recruitment process since it will contain realtime user's details and coding platform ratings. and their analysis, project information, and digital bridges to platforms like Github and LinkedIn, where recruiters can actually review the users's information in a better way as compared to the old paper resume. © 2024 IEEE.

关键词： API data data analysis retrieval visualisation web scraping websites

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Comparative Analysis of Machine Learning Algorithms for Cardiovascular Disease Risk Prediction 1

Comparative Analysis of Machine Learning Algorithms for Card...

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1st International Conference on Trends in engineering Systems and Technologies, ICTEST 2024

作者： Doiphode, Anushka Bora, Karan Navghare, P.R. Pune Institute of Computer Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350307795

Cardiovascular conditions (CVDs) remain a major global health concern, challenging early threat assessment and forestalment. In this study, we employ four distinct machine learning algorithms - K Nearest Neighbors (KNN), Random Forest, Decision Tree and Support Vector Machine - to prognosticate the threat of cardiovascular conditions. The dataset used in this exploration consists of 14 essential features and has been sourced from Kaggle, a prominent platform for data wisdom competitions. Our primary idea is to estimate the prophetic performance of these algorithms and determine which algorithm gives us maximum accuracy. Among the algorithms examined, the KNN classifier emerges as the top pantomime, achieving an accuracy of 84.48. This result showcases the efficacy of the KNN algorithm in handling the complexity of CVD threat vaticination. These findings provide critical guidance for healthcare professionals and experimenters in enforcing effective prophetic models for early cardiovascular complaint threat identification and intervention. © 2024 IEEE.

关键词： Decision trees

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An Efficient Machine Learning Classification Model for Rainfall Prediction in Bangladesh 2nd

An Efficient Machine Learning Classification Model for Rainf...

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2nd International Conference on Big Data, IoT and Machine Learning, BIM 2023

作者： Biplob, Md. Badiuzzaman Haque, Md. Mokammel Computer Science and Engineering Department Chittagong University of Engineering and Technology Chattogram Bangladesh Computer Science and Engineering Department Faculty of Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh

ISBN: (纸本)9789819989362

One of the most significant and difficult tasks in the modern world is rainfall forecast. Rainfall is a complicated and nonlinear phenomenon that requires sophisticated computer modeling and simulation to anticipate with any degree of accuracy. In many regions of the world, daily rainfall totals are dispersed based on various frequency distribution functions. Rainfall has become a significant factor in agricultural countries. We provide a machine learning-based framework for forecasting the total monthly precipitation. In this study, three Bangladeshi weather stations’ daily rainfall was forecasted with a 365-day lead time using backpropagation long short-term memory (LSTM), random forest, and neural network algorithms. The predicted results from the selected algorithms were compared with the observed data to determine prediction precision by mean absolute error (MAE) test results. We found that selected algorithms predicted daily rainfall with reasonable accuracy. Therefore, year-long, month-long, and day-long rainfall can be predicted using these models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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A Brief Survey on Video Stabilization Approaches 5

A Brief Survey on Video Stabilization Approaches

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5th IEEE International Conference on Recent Trends in computer Science and technology, ICRTCST 2024

作者： Kamble, Samarth Dixit, Mrunmay Nehete, Dinesh Joshi, Dhara Potdar, G.P. Madhuri Mane, P. Pune Institute of Computer Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350351378

Video stabilization is an important aspect of digital video processing, which addresses the challenge of reducing unwanted motion-induced artifacts in video recordings This research paper presents video provides a detailed overview of stability techniques from classical methods to recent developments. We classify these methods into several different categories, including feature-based methods, visual flow-based methods, and deep learning-based methods. Each category is examined in detail, covering the underlying principles, a story of outstanding strengths, weaknesses, and *** addition, we discuss the practical applications of video stabilization in various industries, highlighting its important role in film-making, action cameras, surveillance, medical imaging, and more. In addition to the systematic classification and analysis of existing methods, this study highlights recent developments and emerging trends in the field. A comparative study of different stability methods highlights their relative efficacy and computational requirements to help researchers and practitioners choose the most appropriate method for their particular application Furthermore, we address current challenges in deals with video stability and we present possible directions for future research, opening the way for continued innovation in this dynamic field. © 2024 IEEE.

关键词： Video recording

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An Analysis of Ethics-Based Foundation and Regulatory Issues for Genomic Data Privacy

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Journal of The Institution of Engineers (India): Series B 2024年第4期105卷 1097-1107页

作者： Balagurunathan, Yesodhai Sethuraman, Raja Rajeswari Department of Computer Applications Dr.Mahalingam College of Engineering and Technology Tamil Nadu Pollachi India

The ethical issues raised by the privacy of bioinformatics data storage are the core of our investigation. The storage, use, and sharing of genomic data raises additional ethical concerns because it is a storehouse of unique and priceless information about a person's genetic composition. In view of the significant effects on autonomy and privacy, it is imperative that the ethical issues be considered and that strong legal and normative frameworks be established. The intrinsic sensitivity of genetic data gives rise to serious privacy and autonomy concerns, highlighting the pressing need to address ethical challenges in the field of genetics. Concerns about data security, permission, and privacy are brought up by the extremely sensitive nature of this information. Healthcare professionals and academics working in the field of genetics have to maintain a careful balance between promoting scientific understanding and upholding human autonomy. While data sharing and collaborative efforts are crucial to the advancement of genetic research, they also rely on strict ethical standards and legal protections to safeguard individuals' rights and privacy. The challenges of getting meaningful consent from data contributors are also covered in this work, particularly in view of the various applications and possible consequences of their data. In particular, when working with extremely private material like genetic data, it highlights how crucial it is to defend one's right to privacy and autonomy. The difficulties in obtaining contributors' informed consent are examined, and the significance of honest and open communication regarding data usage and sharing is emphasized. The fact that current data protection regulations match and highlight any inadequacies in relation to the preservation of bioinformatics data is an additional factor. Establishing ethical norms and legislative frameworks is necessary to handle the challenges associated with data privacy in business. © The Institut

关键词： Digital storage

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NADR-Net: A Deep Learning Framework for Predicting Neurological Adverse Drug Reactions Using 17 Molecular Descriptors

NADR-Net: A Deep Learning Framework for Predicting Neurologi...

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2024 International Symposium on Computing and Intelligent Systems, SCI 2024

作者： Chaurasia, Anushka Kumar, Deepak Yogita Department of Computer Science and Engineering National Institute of Technology Meghalaya India Department of Computer and Engineering National Institute of Technology Kurukshetra India

Neurological adverse drug reactions (NADRs) pose a significant clinical challenge as they can have a profound impact on patient health and treatment outcomes. While diverse drug descriptors have been employed for neurological ADR prediction, the potential of using 17 molecular descriptors for this purpose has not been explored. To address this, a multilabel NADR-Net and MLSMOTE-based framework have been proposed for neurological adverse drug reaction prediction. The data for 17 MD and ADRs were extracted from PubChem and ADRECs databases and then mapped based on drug ID. The resulting dataset contained information on 2160 drugs, including their molecular properties, and 1030 ADRs. The methodology was then applied to this dataset, and it showed promising results in terms of hamming loss, precision, true positive rate, f1 score, and ROC-AUC. This study highlights the potential of using molecular descriptors for predicting neurological ADRs, which could improve patient outcomes and drug safety. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Deep neural networks

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Real-Time Prediction of Urban Traffic Problems Based on Artificial Intelligence-Enhanced Mobile Ad Hoc Networks(MANETS)

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computers, Materials & Continua 2024年第5期79卷 1903-1923页

作者： Ahmed Alhussen Arshiya S.Ansari Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Information Technology College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia

Traffic in today’s cities is a serious problem that increases travel times,negatively affects the environment,and drains financial *** study presents an Artificial Intelligence(AI)augmentedMobile Ad Hoc Networks(MANETs)based real-time prediction paradigm for urban traffic *** are wireless networks that are based on mobile devices and may *** distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion *** study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network(CSFPNN)technique to assess real-time data acquired from various sources within *** framework uses the proposed approach to learn from the data and create predictionmodels to detect possible traffic problems and their severity in real ***-time traffic prediction allows for proactive actions like resource allocation,dynamic route advice,and traffic signal optimization to reduce *** framework supports effective decision-making,decreases travel time,lowers fuel use,and enhances overall urban mobility by giving timely information to pedestrians,drivers,and urban *** simulations and real-world datasets are used to test the proposed framework’s prediction accuracy,responsiveness,and *** results show that the suggested framework successfully anticipates urban traffic issues in real-time,enables proactive traffic management,and aids in creating smarter,more sustainable cities.

关键词： Mobile AdHocNetworks(MANET) urban traffic prediction artificial intelligence(AI) traffic congestion chaotic spatial fuzzy polynomial neural network(CSFPNN)

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ANovel Light Weight CNN Framework Integrated with Marine Predator Optimization for the Assessment of Tear Film-Lipid Layer Patterns

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computer Modeling in engineering & Sciences 2023年第7期136卷 87-106页

作者： Bejoy Abraham Jesna Mohan Linu Shine Sivakumar Ramachandran Department of Computer Science and Engineering College of Engineering MuttatharaThiruvananthapuramKerala695008India Department of Computer Science and Engineering Mar Baselios College of Engineering and TechnologyThiruvananthapuramKerala695015India Department of Electronics and Communication Engineering College of Engineering TrivandrumKerala695016India

Tear film,the outermost layer of the eye,is a complex and dynamic structure responsible for tear *** tear film lipid layer is a vital component of the tear film that provides a smooth optical surface for the cornea and wetting the ocular *** eye syndrome(DES)is a symptomatic disease caused by reduced tear production,poor tear quality,or excessive *** diagnosis is a difficult task due to its multifactorial *** of several clinical tests available,the evaluation of the interference patterns of the tear film lipid layer forms a potential tool for DES *** instrument known as Tearscope Plus allows the rapid assessment of the lipid layer.A grading scale composed of five categories is used to classify lipid layer *** reported work proposes the design of an automatic system employing light weight convolutional neural networks(CNN)and nature inspired optimization techniques to assess the tear film lipid layer patterns by interpreting the images acquired with the Tearscope *** designed framework achieves promising results compared with the existing state-of-the-art techniques.

关键词： Dry-eye syndrome tearscope plus tear film deep neural networks

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A novel method for indian vehicle registration number plate detection and recognition using CNN

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8563-8585页

作者： Pandey, Vibha Choubey, Siddhartha Patra, Jyotiprakash Mall, Shachi Choubey, Abha Department of Computer Science Engineering Shri Shankaracharya Technical Campus Chhattisgarh Bhilai India Department of Computer Science Engineering Shri Shankaracharya Institute of Professional Management and Technology Chhattisgarh Raipur India School of Computer Science and Engineering Galgotias University Greater Noida India

Automated reading of license plate and its detection is a crucial component of the competent transportation system. Toll payment and parking management e-payment systems may benefit from this software’s features. License plate detection and identification algorithms abound, and each has its own set of strengths and weaknesses. computer vision has advanced rapidly in terms of new breakthroughs and techniques thanks to the emergence and proliferation of deep learning principles across several branches of AI. The practice of automating the monitoring process in traffic management, parking management, and police surveillance has become much more effective thanks to the development of Automatic License Plate Recognition (ALPR). Even though license plate recognition (LPR) is a technology that is extensively utilized and has been developed, there is still a significant amount of work to be done before it can achieve its full potential. In the last several years, there have been substantial advancements in both the scientific community’s methodology and its level of efficiency. In this era of deep learning, there have been numerous developments and techniques established for LPR, and the purpose of this research is to review and examine those developments and approaches. In light of this, the authors of this study suggest a four-stage technique to automated license plate detection and identification (ALPDR), which includes, image pre-processing, license plate extraction, character segmentation, and character recognition. And the first three phases are known as "extraction," "preprocessing," and "segmentation," and each of these processes has been shown to benefit from its own unique technique. In light of the fact that character recognition is an essential component of license plate identification and detection, the Convolution Neural Network (CNN), MobileNet, Inception V3, and ResNet 50 have all been put through their paces in this regard. © 2024 – IOS Press. All rights re

关键词： Image classification

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Suppression and synchronization of chaos in uncertain time-delay physical system

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Applied Mathematics(A Journal of Chinese Universities) 2024年第3期39卷 416-437页

作者： Israr Ahmad Muhammad Shafiq Department of Preparatory Studies Centre University of Technology and Applied SciencesNizwaOman Department of Electrical and Computer Engineering Sultan Qaboos UniversityOman

The mechanical horizontal platform(MHP)system exhibits a rich chaotic *** chaotic MHP system has applications in the earthquake and offshore *** article proposes a robust adaptive continuous control(RACC)*** investigates the control and synchronization of chaos in the uncertain MHP system with time-delay in the presence of unknown state-dependent and time-dependent *** closed-loop system contains most of the nonlinear terms that enhance the complexity of the dynamical system;it improves the efficiency of the *** proposed RACC approach(a)accomplishes faster convergence of the perturbed state variables(synchronization errors)to the desired steady-state,(b)eradicates the effect of unknown state-dependent and time-dependent disturbances,and(c)suppresses undesirable chattering in the feedback control *** paper describes a detailed closed-loop stability analysis based on the Lyapunov-Krasovskii functional theory and Lyapunov stability *** provides parameter adaptation laws that confirm the convergence of the uncertain parameters to some constant *** computer simulation results endorse the theoretical findings and provide a comparative performance.

关键词： chaotic horizontal platform system chaos suppression chaos synchronization robust adaptive control Lyapunov stability theory

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