Computer Science最新文献_第2页

Efficient Selection Methods in Evolutionary Algorithms 进化算法中的高效选择方法

Computer Science Pub Date : 2024-02-04 DOI: 10.7494/csci.2024.25.1.5330

J. T. Stańczak

{"title":"Efficient Selection Methods in Evolutionary Algorithms","authors":"J. T. Stańczak","doi":"10.7494/csci.2024.25.1.5330","DOIUrl":"https://doi.org/10.7494/csci.2024.25.1.5330","url":null,"abstract":"Evolutionary algorithms mimic some elements of the theory of evolution. The survival of individuals and the possibility of producing offspring play a huge role in the process of natural evolution. This process is called a natural selection. This mechanism is responsible for eliminating poor population members and gives the possibility of development for good ones. The evolutionary algorithm - an instance of evolution in the computer environment also requires a selection method, a computer version of natural selection. Widely used standard selection methods applied in evolutionary algorithms are usually derived from nature and prefer competition, randomness and some kind of ``fight'' among individuals. But computer environment is quite different from nature. Computer populations of individuals are usually small, they easily suffer from a premature convergence to local extremes. To avoid this drawback, computer selection methods must have different features than natural selection. In the computer selection methods randomness, fight and competition should be controlled or influenced to operate to the desired extent. Several new methods of individual selection are proposed in this work: several kinds of mixed selection, an interval selection and a taboo selection. Also advantages of passing them into the evolutionary algorithm are shown, using examples based on searching for the maximum α-clique problem and traditional TSP in comparison with traditionally considered as very efficient tournament selection, considered ineffective proportional (roulette) selection and similar classical methods.","PeriodicalId":503380,"journal":{"name":"Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139806345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generalizing Clustering Inferences with ML Augmentation of Ordinal Survey Data 用 ML 增强正序调查数据来推广聚类推断

Computer Science Pub Date : 2024-01-22 DOI: 10.7494/csci.2024.25.1.5685

Bhupendera Kumar, Rajeev Kumar

引用次数: 0

Machine Learning based Reconstruction for the MUonE Experiment 基于机器学习的重构 MUonE 实验

Computer Science Pub Date : 2024-01-21 DOI: 10.7494/csci.2024.25.1.5690

Milosz Zdybal, Macin Kucharczyk, Marcin Wolter

引用次数: 0

Survey on the Most Current Image Processing Methods in Huntington's Disease Diagnostics and Progression Assessment 亨廷顿舞蹈症诊断和进展评估中的最新图像处理方法调查

Computer Science Pub Date : 2024-01-19 DOI: 10.7494/csci.2023.24.4.5640

Aleksandra Kawala-Sterniuk, Dariusz Mikołajewski, Anna Bryniarska, Maria Myslicka, Damian Czarnecki, Anna Junkiert-Czarnecka, Adam Sudol, Emilia Mikołajewska, Mateusz Pawlowski, Anna Wlodarczyk, Piotr Walecki, Rafal Gasz, Witold Libionka, Bartosz Pańczyszak, Mariusz Pelc, J. Zygarlicki, Henryk Racheniuk, Katarzyna Bojkowska-Otrebska, Piotr Sterniuk, E. Gorzelanczyk, Raffaele Ferri

引用次数: 0

Process of Fingerprint Authentication using Cancelable Biohashed Template 使用可取消的生物隐藏模板进行指纹验证的过程

Computer Science Pub Date : 2024-01-19 DOI: 10.7494/csci.2023.24.4.4986

M. K R, Radhika K R

{"title":"Process of Fingerprint Authentication using Cancelable Biohashed Template","authors":"M. K R, Radhika K R","doi":"10.7494/csci.2023.24.4.4986","DOIUrl":"https://doi.org/10.7494/csci.2023.24.4.4986","url":null,"abstract":"Template protection using cancelable biometrics prevents data loss and hacking stored templates, by providing considerable privacy and security. Hashing and salting techniques are used to build resilient systems. Salted password method is employed to protect passwords against different types of attacks namely brute-force attack, dictionary attack, rainbow table attacks. Salting claims that random data can be added to input of hash function to ensure unique output. Hashing salts are speed bumps in an attacker’s road to breach user’s data. Research proposes a contemporary two factor authenticator called Biohashing. Biohashing procedure is implemented by recapitulated inner product over a pseudo random number generator key, as well as fingerprint features that are a network of minutiae. Cancelable template authentication used in fingerprint-based sales counter accelerates payment process. Fingerhash is code produced after applying biohashing on fingerprint. Fingerhash is a binary string procured by choosing individual bit of sign depending on a preset threshold. Experiment is carried using benchmark FVC 2002 DB1 dataset. Authentication accuracy is found to be nearly 97%. Results compared with state-of art approaches finds promising.","PeriodicalId":503380,"journal":{"name":"Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139613748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DEĞİŞİMLİ EN KÜÇÜK KARELER VE KOSİNÜS BENZERLİK TEKNİKLERİ KULLANILARAK YEMEK TAVSİYE SİSTEMİ OLUŞTURMA 利用交替最小二乘法和余弦相似性技术创建食品推荐系统

Computer Science Pub Date : 2024-01-08 DOI: 10.53070/bbd.1389078

Merve Cengi̇z, Tuğba Yildiz

{"title":"DEĞİŞİMLİ EN KÜÇÜK KARELER VE KOSİNÜS BENZERLİK TEKNİKLERİ KULLANILARAK YEMEK TAVSİYE SİSTEMİ OLUŞTURMA","authors":"Merve Cengi̇z, Tuğba Yildiz","doi":"10.53070/bbd.1389078","DOIUrl":"https://doi.org/10.53070/bbd.1389078","url":null,"abstract":"Bu çalışmada, Allrecipes.com web sitesindeki yemek tariflerine ve üyeler tarafından verilen oylara dayalı bir yemek tavsiye sistemi geliştirildi. Toplam 1840 yemek tarifi (Diyabetik - Glutensiz - Ketojenik - Düşük Sodyum - Düşük Kolesterol - Vejetaryen – Vegan) Allrecipes.com'dan web scraping yöntemi ile kazındı ve Python'da analiz edildi. Tavsiye Sistemi, Değişimli En Küçük Kareler (DEKK) yöntemi kullanılarak oluşturuldu. Diyet Yemek Tavsiye Sistemi, kosinüs benzerlik yöntemi kullanılarak gerçekleştirildi. DEKK yönteminin büyük veri ile uygulaması bulut üzerinde gerçekleştirildi. Modelin hata kareler ortalamasının karekökü 0.495 olarak bulundu. Modelin önerdiği yemekler kullanıcı bazlı incelendi ve sonuçların tutarlı olduğu belirlendi. En çok tavsiye edilen yemekler incelendiğinde, vejetaryen tariflerin ilk sırada yer aldığı; toplamda ise ketojenik tariflerin yüksek sayıda önerildiği görüldü. Sonuç olarak, yemek tarifleri aracılığıyla yiyecekler hakkında fikir sahibi olmak ve diyetlerine göre yiyecek seçmek isteyen kullanıcılara doğru öneriler üreten web tabanlı bir yemek öneri sistemi oluşturuldu.","PeriodicalId":503380,"journal":{"name":"Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140512205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stacked Denoising Autoencoder Based Parkinson’s Disease Classification using Improved Pigeon-Inspired Optimization Algorithm 基于堆叠去噪自动编码器的帕金森病分类，采用改进的鸽子启发优化算法

Computer Science Pub Date : 2024-01-07 DOI: 10.7494/csci.2023.24.4.4924

S. P., Srinivasa B. Rao

{"title":"Stacked Denoising Autoencoder Based Parkinson’s Disease Classification using Improved Pigeon-Inspired Optimization Algorithm","authors":"S. P., Srinivasa B. Rao","doi":"10.7494/csci.2023.24.4.4924","DOIUrl":"https://doi.org/10.7494/csci.2023.24.4.4924","url":null,"abstract":"One of the most common neurological conditions caused by gradual brain degeneration is Parkinson's disease (PD). Although this neurological condition has no known treatment, early detection and therapy can help patients improve their quality of life. An essential patient's health record is made of medical images used to control, manage, and treat diseases. However, in computer-based diagnostics, disease classification is a difficult task. To overcome this problem, this paper introduces a stacked denoising Autoencoder (SDA) for Parkinson's disease classification. The main aim of this paper is to derive an optimal feature selection design for an effective PD classification. Improved Pigeon-Inspired Optimization (IPIO) algorithm is introduced to enhance the performance of the classifier. Thus, the classification result improved by the optimal features and also increased the sensitivity, accuracy, and specificity in the medical image diagnosis. The proposed scheme is implemented in PYTHON and compared with traditional feature selection models and other classification approaches. The experimental outcomes show that the proposed method yields a superior classification of PD than the current state-of-the-art method","PeriodicalId":503380,"journal":{"name":"Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139448879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Square grid Path Planning for Mobile Anchor-Based Localization in Wireless Sensor Networks 无线传感器网络中基于移动锚点定位的方格网路径规划

Computer Science Pub Date : 2024-01-07 DOI: 10.7494/csci.2023.24.4.4608

Nawel Boukhari, Salim Bouamama

引用次数: 0

Mesh Compression Algorithm for Geometrical Coordinates in Computational Meshes 计算网格中几何坐标的网格压缩算法

Computer Science Pub Date : 2024-01-07 DOI: 10.7494/csci.2023.24.4.6036

Kazimierz Michalik, Łukasz Rauch

引用次数: 0

Hybrid implementation of the fastICA algorithm for high-density EEG using the capabilities of the Intel architecture and CUDA programming 利用英特尔架构和 CUDA 编程能力，为高密度脑电图混合实施 fastICA 算法

Computer Science Pub Date : 2023-12-23 DOI: 10.7494/csci.2023.24.4.5539

Anna Gajos-Balińska, Grzegorz M. Wójcik, Przemysław Stpiczyński

引用次数: 0