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Performance Analysis of Machine Learning Algorithms to Predict Water Potability 1

Performance Analysis of Machine Learning Algorithms to Predi...

1st International Conference on Computational Intelligence for Information, Security and Communication Applications, CIISCA 2023

作者： Parnika, J. Devamane, Shridhar B Ramya, C.N. Swarna Laxmi, M.G. Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350339727

The survival of all living organisms, industry and agriculture is dependent on freshwater availability. A crucial step in the management of freshwater is the examination of the water's quality. According to the annual report of the World Health Organization, a lack of safe drinking water is resulting in the illness or death of many people, particularly children and pregnant women. Before the water is used for any purpose, including chemical spraying (for pesticides, etc.), or drinking water, it is essential to test its quality. Finding water that is safe to consume can be done to some extent by testing the water's quality. Therefore, water quality testing is essential. In this paper, we have carried out the implementation of 3 machine learning algorithms to check for potability of water after balancing, selecting important features using the Univariate Selection method and standardizing the data. Random Forest Classifier gave the maximum accuracy of 88.75%. The main objective of our work is to increase the accuracy and demonstrate the performance analysis of the considered algorithms' accuracy in this paper along with the existing works. © 2023 IEEE.

关键词： Decision trees

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Straightforward Guess and Determine Analysis Based on Genetic Algorithm

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Journal of Systems science & Complexity 2022年第5期35卷 1988-2003页

作者： CAO Chunping CEN Zhe FENG Xiutao WANG Zhangyi ZHU Yamin Department of Computer Science and Technology University of Shanghai for Science and TechnologyShang-hai 200093China Key Laboratory of Mathematics Mechanization Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Cyber Science and Engineering Wuhan UniversityWuhan 430072China School of Science Xi'an Technological UniversityXi'an 710021China

With the development of artificial intelligence,the genetic algorithm has been widely used in many *** cryptography,the authors find it is natural to code an individual and design its fitness in a genetic algorithm for a straightforward guess and determine analysis(SGDA,in short).Based on this observation,the authors propose an SGDA based on genetic *** it with the other three SGDAs based on exhaustive search,MILP method and CPP method respectively,the authors illustrate its effectiveness by three stream ciphers:Small scale SNOW 2.0,medium scale Enocoro-128v2 and large scale *** results show our method is significantly superior to them,especially for Trivium,the method can find a solution of 165 variables in less than one hour,while the other three methods are not applicable due to its enormous search space of size 2^(619.37).As far as we know,it is a best solution in an SGDA for Trivium so far.

关键词： Enocoro-128v2 genetic algorithm guess and determine analysis SNOW 2.0 trivium

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A Trading Strategy Portfolio Optimization Approach Based on Fuzzy Mean-Semivariance Model Using Memetic Algorithm 17th

A Trading Strategy Portfolio Optimization Approach Based on ...

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17th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2025

作者： Chen, Chun-Hao Wu, Ming-Chieh Ke, Ting-Ying Huang, Cheng-Yi Department of Computer Science and Information Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Electrical Engineering R.O.C. Naval Academy Kaohsiung Taiwan

ISBN: (纸本)9789819660070

Predicting stock trends through technical indicators is currently a highly popular research area. It primarily involves generating buy and sell signals through technical indicators to form a trading strategy portfolio (TSP), with the goal of increasing returns while mitigating risks. In the previous approach, an algorithm was designed to find a TSP based on the mean-semivariance model (MSV model) using the memetic algorithm (MA). However, in practice, numerous uncertainties may affect the profitability and hedging ability of trading strategies. To handle this problem, based on the fuzzy mean-semivariance model (FMSV model), an optimization algorithm is proposed to find a TSP using the memetic algorithm. In the global search, the genetic algorithm (GA) is employed. It first uses the selected technical indicators to generate candidate trading strategies. Then, the trading strategies are used to generate TSPs as chromosomes. Every trading strategy in a chromosome is represented by three parts: weight, buy and sell indicators, and parameters of indicators. The fuzzy return, risk, and trading frequency of a chromosome are calculated to evaluate fitness value of every chromosome. Next, in the local search, the simulated annealing (SA) is used to tune the parameters of the used technical indicators in chromosomes. Then, the genetic operators are applied to the population to form new offspring. The evolution process is repeated until the stop criteria are reached. Experimental results were made on real datasets and indicate that the proposed approach is capable of maintaining stable returns and mitigating risks compared to the existing methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Genetic algorithms

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Prediction and Comparative Analysis of the Stress Level of Humans by Using the Machine Learning Algorithms 4

Prediction and Comparative Analysis of the Stress Level of H...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Sivaraj, C. Angeljulie, J. Hemamalini, D. Neerugatti, Vikram Panchal, Narayan Mahesh, T.R. Madanapalle Institute of Technology & Science Andhra Pradesh Madanapalle India Karpagam Academy of Higher Education Department of Computer Science and Engineering Tamilnadu Coimbatore India Arjun College of Technology Department of Artificial Intelligence and Data Science Tamilnadu Coimbatore India Department of Computer Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350387490

Some of the techniques that have been used include Naive Bayes, K-Means Clustering, Support Vector Machine (SVM) and k-Nearest Neighbors (KNN). To provide useful information to researchers and practitioners, the study reflects the performance, strengths and weaknesses of each algorithm using several datasets. The ability of SVM to model complex decision boundaries is evaluated. It is widely applied in high-dimensional scenario. This paper focuses on the analysis of the advantages and disadvantages of using KNN for classification purposes, such as its simplicity of use, flexibility, and the fact that the classification accuracy and the sensitivity to the selection of parameters should not be overlooked. The performance of the unsupervised K-Means Clustering algorithm in partitioning data into clusters is assessed. The method of decision trees is considered, and the question of their capability to model non-linear dependencies is raised. Finally, the performance and relevancy of the probabilistic classifier Naive Bayes for text categorization is also evaluated. This research thus offers valuable guidance to practitioners regarding the strengths and weaknesses of the proposed algorithms for categorisation, based on a detailed comparative analysis. © 2024 IEEE.

关键词： k-nearest neighbors

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ACOVMD: Automatic COVID-19 misinformation detection in Twitter using self-trained semi-supervised hybrid deep learning model

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International Social science Journal 2024年第252期74卷 713-730页

作者： Birunda, S. Selva Devi, R. Kanniga Muthukannan, M. Babu, M. Mahesh Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamil Nadu Srivilliputhur India School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu Kelambakkam India Department of Civil Engineering KCG College of Technology Tamil Nadu Chennai India

During the COVID-19 pandemic, online social networks are extensively utilized, more than ever before by 8.4%, resulting in the propagation of false information related to COVID-19. Despite the existence of many fake news detection models;annotation inconsistency, memory consumption, accurate and self-trained efficient algorithms for detecting the emerging COVID-19 misinformation tweets are still challenging. Hence, the main aim of this work is to come up with a self-trained semi-supervised model that accurately and automatically detects the reliability of emerging COVID-19 tweets without delay. In this work, COVID-19 tweet dataset is created in English Language from the period January 2020 to January 2022 as a ground truth database. Then self-trained semi-supervised hybrid deep learning model is proposed to train both supervised and unsupervised components simultaneously using the created dataset. The proposed model is self-trained repeatedly and the model gets updated to predict the reliability of upcoming COVID-19 tweets that differ from training tweets. We performed experiments multiple times by limiting the percentage amount of labelled tweets shown to the model, namely 80%, 50%, 40%, 30%, 20% and 10% labelled tweets, respectively. Experimental results show that the proposed model achieves 80.92% accuracy and 98.15% accuracy in the 10% and 80% label-seen experiments, respectively. This shows a clear rising trend in the performance curve. Therefore, this technique will be useful for effectively classifying voluminous amounts of emerging tweets generated as part of the COVID-19 infodemic. The proposed model may efficiently use a huge amount of unlabelled tweets and enhance the model's generalization performance. © 2023 John Wiley & Sons Ltd.

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A Natural Light Simulation Mathematical Model for Mixed Reality Simulation Cockpit 13

A Natural Light Simulation Mathematical Model for Mixed Real...

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13th International Conference of Information and Communication technology, ICTech 2024

作者： Liu, Wenhai Li, Yang Feng, XinYuan Liang, GuiChong Li, Ning Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China School of Information Science and Engineering East China University of Science and Technology Shanghai China School of Computer Science and Technology Shenyang University of Chemical Technology Shenyang China

ISBN: (纸本)9798350376258

This paper introduces a mathematical model for simulating natural light, grounded in the simple sky model and advanced sun positioning techniques. By processing inputs like time, geographical coordinates, and weather, it accurately computes natural light scenarios, replicating sun positioning, angles, intensity, and realistic environmental illumination. ©2024 IEEE.

关键词： Azimuth component illumination nature light simulation simulation solar energy sun position Zenith

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Online Self-distillation and Self-modeling for 3D Brain Tumor Segmentation

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IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4290-4302页

作者： Pang, Yan Li, Yunhao Huang, Teng Liang, Jiaming Wang, Zhen Dong, Changyu Kuang, Dongyang Hu, Ying Chen, Hao Lei, Tim Wang, Qiong The Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China The School of Artificial Intelligence Guangzhou University China The Zhejiang Lab Hangzhou China Sun Yat-sen University China The Department of Computer Science and Engineering The Department of Chemical and Biological Engineering Hong Kong University of Science and Technology China The Department of Electrical Engineering University of Colorado Denver United States

In the specialized domain of brain tumor segmentation, supervised segmentation approaches are hindered by the limited availability of high-quality labeled data, a condition arising from data privacy concerns, significant costs, and ethical issues. In response to this challenge, this paper presents a training framework that adeptly integrates a plug-and-play component, MOD, into current supervised learning models, boosting their efficacy in scenarios with limited data. The MOD consists of an Online Tokenizer and a Dense Predictor, which employs self-distillation and self-modeling on masked patches, promoting swift convergence and efficient representation learning. During the inference phase, the plug-and-play MOD component is excluded, preserving the computational efficiency of the original model without incurring extra processing costs. We substantiated the value of our approach through experiments on leading 3D brain tumor segmentation baselines. Remarkably, models augmented with the MOD consistently showcased superior results, achieving elevated Dice coefficients and HD95 scores on two datasets: BraTS 2021 and MSD 2019 Task-01 Brain Tumor. Code: https://***/aigzhusmart/MOD © 2013 IEEE.

关键词： Self-supervised learning

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Global video object segmentation with spatial constraint module

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Computational Visual Media 2023年第2期9卷 385-400页

作者： Yadang Chen Duolin Wang Zhiguo Chen Zhi-Xin Yang Enhua Wu Engineering Research Center of Digital Forensics Ministry of EducationSchool of Computer and SoftwareNanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Internet of Things for Smart City Department of Electromechanical EngineeringUniversity of MacaoMacao 999078China State Key Laboratory of Computer Science Institute of SoftwareUniversity of Chinese Academy of SciencesBeijing 100190China Faculty of Science and Technology University of MacaoMacao 999078China

We present a lightweight and efficient semisupervised video object segmentation network based on the space-time memory *** some extent,our method solves the two difficulties encountered in traditional video object segmentation:one is that the single frame calculation time is too long,and the other is that the current frame’s segmentation should use more information from past *** algorithm uses a global context(GC)module to achieve highperformance,real-time *** GC module can effectively integrate multi-frame image information without increased memory and can process each frame in real ***,the prediction mask of the previous frame is helpful for the segmentation of the current frame,so we input it into a spatial constraint module(SCM),which constrains the areas of segments in the current *** SCM effectively alleviates mismatching of similar targets yet consumes few additional *** added a refinement module to the decoder to improve boundary *** model achieves state-of-the-art results on various datasets,scoring 80.1%on YouTube-VOS 2018 and a J&F score of 78.0%on DAVIS 2017,while taking 0.05 s per frame on the DAVIS 2016 validation dataset.

关键词： video object segmentation semantic segmentation global context(GC)module spatial constraint

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Design of Chest Visual Based Image Reclamation Method Using Dual Tree Complex Wavelet Transform and Edge Preservation Smoothing Algorithm

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SN computer science 2024年第4期5卷 352页

作者： Kumar, B. P. Pradeep Darshan, S. L. Shiva Naresh, E. Srinidhi, N.N. Shreyas, J. Department of ECE HKBKCE Karnataka Bangalore 560045 India Department of Computer Science and Engineering National Institute of Technology Warangal Warangal 506004 India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

The proposed article put forward a new scheme for image reclamation using second phase discrete symlet transform for medical images. The current medical image reclamation approaches have limitations in providing accurate reclamation fallouts with high visual insight and low computational complexity. To address these issues, this model presents a methodology for creating a medical image database using Image Reclamation using DT-CWT and EPS filter suited for the Resolution Enhancement of query chest image sample well utilized to get better retrieval rate where DWT algorithm is utilized for feature extraction of query input images. Flat and perpendicular prognoses of summation of pixels are analyzed to extract BC quantities, which are then used to compute the matching score of similarity for the images present in the database. The system selects the samples that are most pertinent to the given query sample image based on the matching score. The system’s untrained database is used to obtain the photographs with the highest BC value. The projected method aims to improve the enhancement of sampled image by DT-CWT EPS algorithm to leads to increase the accuracy and efficiency of medical image reclamation for various research applications. © The Author(s) 2024.

关键词： B quantities DWT Healthcare systems Medical image Visual BIR

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GS-PT: Exploiting 3D Gaussian Splatting for Comprehensive Point Cloud Understanding via Self-supervised Learning

GS-PT: Exploiting 3D Gaussian Splatting for Comprehensive Po...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Keyi Luo, Yeqi Yang, Weidong Xu, Jingyi Li, Zhijun Chen, Wen-Ming Fei, Ben School of Computer Science Fudan University China School of Mechanical Engineering Tongji University China Academy for Engineering and Technology Fudan University China

ISBN: (纸本)9798350368741

Self-supervised learning of point cloud aims to leverage unlabeled 3D data to learn meaningful representations without reliance on manual annotations. However, current approaches face challenges such as limited data diversity and inadequate augmentation for effective feature learning. To address these challenges, we propose GS-PT, which integrates 3D Gaussian Splatting (3DGS) into point cloud self-supervised learning for the first time. Our pipeline utilizes transformers as the backbone for self-supervised pre-training and introduces novel contrastive learning tasks through 3DGS. Specifically, the transformers aim to reconstruct the masked point cloud. 3DGS utilizes multi-view rendered images as input to generate enhanced point cloud distributions and novel view images, facilitating data augmentation and cross-modal contrastive learning. Additionally, we incorporate features from depth maps. By optimizing these tasks collectively, our method enriches the tri-modal self-supervised learning process, enabling the model to leverage the correlation across 3D point clouds and 2D images from various modalities. We freeze the encoder after pre-training and test the model's performance on multiple downstream tasks. Experimental results indicate that GS-PT outperforms the off-the-shelf self-supervised learning methods on various downstream tasks including 3D object classification, real-world classifications, and few-shot learning and segmentation. Project page: https://***/Luoyeqi1/***. © 2025 IEEE.

关键词： 3D Gaussian Splatting 3D understanding point clouds pre-training self-supervised learning

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