投稿時間:2023-01-30 16:30:06 RSSフィード2023-01-30 16:00 分まとめ(37件)

カテゴリー等	サイト名等	記事タイトル・トレンドワード等	リンクURL	頻出ワード・要約等/検索ボリューム	登録日
IT	InfoQ	Optimized Reads and Optimized Writes Improve Amazon RDS Performances for MySQL Compatible Engines	https://www.infoq.com/news/2023/01/aws-rds-optimized-reads-writes/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global	Optimized Reads and Optimized Writes Improve Amazon RDS Performances for MySQL Compatible EnginesAWS recently introduced RDS Optimized Reads and RDS Optimized Writes which are designed to enhance the performance of MySQL and MariaDB workloads running on RDS These new functionalities can improve query performances and provide higher write throughput but are available on a limited subset of instances and have multiple prerequisites By Renato Losio	2023-01-30 06:28:00
ROBOT	ロボスタ	ボストン・ダイナミクスとデクスタリティの考える次世代ロボット　「第７回ロボデックス」講演レポート	https://robotstart.info/2023/01/30/moriyama_mikata-no167.html	ボストン・ダイナミクスとデクスタリティの考える次世代ロボット「第回ロボデックス」講演レポートシェアツイートはてブ「第回ロボデックスロボット開発・活用展」が、年月日から日の日程で東京ビッグサイトで行われた。	2023-01-30 06:45:22
IT	ITmedia 総合記事一覧	[ITmedia PC USER] Google、文章だけで音楽を自動生成してくれるAI「MusicLM」を発表	https://www.itmedia.co.jp/pcuser/articles/2301/30/news134.html	google	2023-01-30 15:31:00
IT	ITmedia 総合記事一覧	[ITmedia News] LINE BLOGサービス終了　6月末で	https://www.itmedia.co.jp/news/articles/2301/30/news132.html	itmedianewslineblog	2023-01-30 15:27:00
IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] 「公共交通機関でベビーカーを使って、嫌な思いをした」6割、具体的には？	https://www.itmedia.co.jp/business/articles/2301/30/news122.html	itmedia	2023-01-30 15:08:00
IT	情報システムリーダーのためのIT情報専門サイト IT Leaders	特権ID管理「SecureCube Access Check」に新機能、許可していない画面操作をAIで検出 | IT Leaders	https://it.impress.co.jp/articles/-/24376	特権ID管理「SecureCubeAccessCheck」に新機能、許可していない画面操作をAIで検出ITLeadersNRIセキュアテクノロジーズは年月日、特権IDアクセス制御ゲートウェイ「SecureCubeAccessCheck」に新オプション「AI動画ログ監査支援ツール」を追加した。	2023-01-30 15:20:00
AWS	AWS Japan Blog	新しい AWS コースでバッチ分析ソリューションを構築する方法を学ぶ	https://aws.amazon.com/jp/blogs/news/learn-to-build-batch-analytics-solutions-with-new-aws-classroom-course/	kumark	2023-01-30 06:31:27
AWS	AWS Japan Blog	新しい AWS コースでデータウェアハウスソリューションを構築する方法を学ぶ	https://aws.amazon.com/jp/blogs/news/learn-to-build-a-data-warehousing-solution-with-new-aws-course/	kumar	2023-01-30 06:31:25
Ruby	Rubyタグが付けられた新着投稿 - Qiita	macOS に rvm をインストールする	https://qiita.com/toshiok/items/ae7f499167b392441a14	macosven	2023-01-30 15:18:30
Ruby	Railsタグが付けられた新着投稿 - Qiita	eメールの正規表現を使うバリデーション	https://qiita.com/masatom86650860/items/c79714347858c3d03eef	tltapplicationrecordvali	2023-01-30 15:50:10
Ruby	Railsタグが付けられた新着投稿 - Qiita	macOS に rvm をインストールする	https://qiita.com/toshiok/items/ae7f499167b392441a14	macosven	2023-01-30 15:18:30
Ruby	Railsタグが付けられた新着投稿 - Qiita	rails + tailwind でherokuにデプロイするとエラーになる	https://qiita.com/tashua314/items/97c9addc26bb20bf8889	railstailwind	2023-01-30 15:15:07
技術ブログ	Developers.IO	I solved and error showing Failed to convert ‘Body’ to string S3.InvalidContent arn:aws:states:::aws-sdk:s3:getObject in step function	https://dev.classmethod.jp/articles/error-convert-body-to-string-s3-invalidcontent-s3getobject-in-step-function/	I solved and error showing Failed to convert Body to string S InvalidContent arn aws states aws sdk s getObject in step functionProblem I wrote a step function state machine that uses the AWS SDK to retrieve a file from S I input the bu	2023-01-30 06:47:12
技術ブログ	Developers.IO	[小ネタ]node-fetch で TypeError: body used already for エラーが発生した場合の対応	https://dev.classmethod.jp/articles/resolve-body-used-already-for-error/	delivery	2023-01-30 06:45:31
技術ブログ	Developers.IO	opswitch v2をリリースしました	https://dev.classmethod.jp/articles/2023-01-30-opswitch-v2-is-generally-available/	opswitch	2023-01-30 06:37:38
技術ブログ	Developers.IO	RAMで共有しているプレフィックスリストが、他AWSアカウントで利用されているかどうかを調べる	https://dev.classmethod.jp/articles/check-if-other-assounts-refer-to-my-sharing-prefix-list/	awsorganizations	2023-01-30 06:16:34
技術ブログ	Developers.IO	【Security Hub修復手順】[S3.8] S3 ブロックパブリックアクセス設定は、バケットレベルで有効にする必要があります	https://dev.classmethod.jp/articles/securityhub-fsbp-remediation-s3-8/	awssecurityhub	2023-01-30 06:08:14
海外TECH	DEV Community	Uncovering the Best Email Spam Classifier: A Comparative Study of Machine Learning Algorithms	https://dev.to/anurag629/uncovering-the-best-email-spam-classifier-a-comparative-study-of-machine-learning-algorithms-118l	Uncovering the Best Email Spam Classifier A Comparative Study of Machine Learning Algorithms Agenda Aim Preprocess the data Clean the data and remove any irrelevant information As our data is already in numerical form so we don t need to convert it Train the models Train several supervised classification models such as Logistic Regression KNN SVM Naive Bayes Decision Trees Random Forest and Gradient Boosting using the preprocessed data Evaluate the models Evaluate the performance of the models using metrics such as accuracy precision recall and F score Choose the best model Based on the evaluation choose the best model that provides the highest accuracy and has the best overall performance The overall aim of this project is to train a machine learning model on the given email data to predict whether an email is spam or not spam and to choose the best model for this classification task About the datasetThe dataset is from kagge LinkThe emails csv file contains rows each row for each email There are columns The first column indicates Email name The name has been set with numbers and not recipients name to protect privacy The last column has the labels for prediction for spam for not spam The remaining columns are the most common words in all the emails after excluding the non alphabetical characters words For each row the count of each word column in that email row is stored in the respective cells Thus information regarding all emails are stored in a compact dataframe rather than as separate text files Import librariesimport pandas as pdimport numpy as npfrom sklearn model selection import train test splitfrom sklearn linear model import LogisticRegressionfrom sklearn neighbors import KNeighborsClassifierfrom sklearn svm import SVCfrom sklearn naive bayes import GaussianNBfrom sklearn tree import DecisionTreeClassifierfrom sklearn ensemble import RandomForestClassifier GradientBoostingClassifierfrom sklearn metrics import accuracy score precision score recall score f scoreimport matplotlib pyplot as plt Load and Preprocess the dataBelow code will load the data from the csv file into a pandas dataframe remove the first column which is the email number replace all non numeric characters with NaN values fill the missing values with convert the data into integer type and store it as dataframe named df Load the data from csv file into a pandas dataframedf pd read csv kaggle input email spam classification dataset csv emails csv Remove the first column Email name as it is not relevant for the predictiondf df drop columns Email No Replace non numeric characters with NaN valuesdf df replace r d value float nan regex True Fill missing values with df fillna inplace True Convert the data into integer typedf df astype int df the to ect and for of a you hou in connevey jay valued lay infrastructure military allowing ff dry Prediction rows × columns Train the models Split the data into features X and labels y X df iloc valuesy df iloc values Split the data into training and testing setsX train X test y train y test train test split X y test size random state Define a dictionary to store the results of each modelresults Train Logistic Regression modelmodel LR LogisticRegression model LR fit X train y train Predict the target values for test sety pred LR model LR predict X test Evaluate the Logistic Regression modelaccuracy LR accuracy score y test y pred LR precision LR precision score y test y pred LR recall LR recall score y test y pred LR f LR f score y test y pred LR Store the results of Logistic Regression model in the dictionaryresults Logistic Regression accuracy accuracy LR precision precision LR recall recall LR f score f LR Train KNN modelmodel KNN KNeighborsClassifier model KNN fit X train y train Predict the target values for test sety pred KNN model KNN predict X test Evaluate the KNN modelaccuracy KNN accuracy score y test y pred KNN precision KNN precision score y test y pred KNN recall KNN recall score y test y pred KNN f KNN f score y test y pred KNN Store the results of KNN model in the dictionaryresults KNN accuracy accuracy KNN precision precision KNN recall recall KNN f score f KNN Train SVM modelmodel SVM SVC model SVM fit X train y train Predict the target values for test sety pred SVM model SVM predict X test Evaluate the SVM modelaccuracy SVM accuracy score y test y pred SVM precision SVM precision score y test y pred SVM recall SVM recall score y test y pred SVM f SVM f score y test y pred SVM Store the results of SVM model in the dictionaryresults SVM accuracy accuracy SVM precision precision SVM recall recall SVM f score f SVM Train Naive Bayes modelmodel NB GaussianNB model NB fit X train y train Predict the target values for test sety pred NB model NB predict X test Evaluate the Naive Bayes modelaccuracy NB accuracy score y test y pred NB precision NB precision score y test y pred NB recall NB recall score y test y pred NB f NB f score y test y pred NB Store the results of Naive Bayes model in the dictionaryresults Naive Bayes accuracy accuracy NB precision precision NB recall recall NB f score f NB Train Decision Tree modelmodel DT DecisionTreeClassifier model DT fit X train y train Predict the target values for test sety pred DT model DT predict X test Evaluate the Decision Tree modelaccuracy DT accuracy score y test y pred DT precision DT precision score y test y pred DT recall DT recall score y test y pred DT f DT f score y test y pred DT Store the results of Decision Tree model in the dictionaryresults Decision Tree accuracy accuracy DT precision precision DT recall recall DT f score f DT Train Random Forest modelmodel RF RandomForestClassifier model RF fit X train y train Predict the target values for test sety pred RF model RF predict X test Evaluate the Random Forest modelaccuracy RF accuracy score y test y pred RF precision RF precision score y test y pred RF recall RF recall score y test y pred RF f RF f score y test y pred RF Store the results of Random Forest model in the dictionaryresults Random Forest accuracy accuracy RF precision precision RF recall recall RF f score f RF Train Logistic Regression modelmodel LR LogisticRegression model LR fit X train y train Predict the target values for test sety pred LR model LR predict X test Evaluate the Logistic Regression modelaccuracy LR accuracy score y test y pred LR precision LR precision score y test y pred LR recall LR recall score y test y pred LR f LR f score y test y pred LR Store the results of Logistic Regression model in the dictionaryresults Logistic Regression accuracy accuracy LR precision precision LR recall recall LR f score f LR Visualization of performance of all the models Print the final results of all modelsprint results Output Logistic Regression accuracy precision recall f score KNN accuracy precision recall f score SVM accuracy precision recall f score Naive Bayes accuracy precision recall f score Decision Tree accuracy precision recall f score Random Forest accuracy precision recall f score Plot the accuracy of all modelsplt figure figsize plt bar results keys result accuracy for result in results values plt title Accuracy of Different Models plt xlabel Models plt ylabel Accuracy plt ylim plt show Plot the precision of all modelsplt figure figsize plt bar results keys result precision for result in results values plt title Precision of Different Models plt xlabel Models plt ylabel Precision plt ylim plt show Plot the recall of all modelsplt figure figsize plt bar results keys result recall for result in results values plt title Recall of Different Models plt xlabel Models plt ylabel Recall plt ylim plt show Plot the F score of all modelsplt figure figsize plt bar results keys result f score for result in results values plt title F Score of Different Models plt xlabel Models plt ylabel F Score plt ylim plt show extract metrics valuesaccuracies results model accuracy for model in results precisions results model precision for model in results recalls results model recall for model in results f scores results model f score for model in results plot bar chartbar width index np arange len results plt bar index accuracies bar width label Accuracy plt bar index bar width precisions bar width label Precision plt bar index bar width recalls bar width label Recall plt bar index bar width f scores bar width label F Score plt xticks index bar width list results keys plt legend plt show Based on the model performance result the Logistic Regression model seems to be the best among all the models with highest accuracy precision recall and f score This suggests that the Logistic Regression model has the highest ability to correctly identify emails as spam or not spam with minimal false positives and false negatives It is worth noting that the performance of a model depends on the specific use case and the problem at hand For example if the cost of misclassifying an email as spam when it is not is higher then a model with a high recall even if its precision is lower might be more desirable Any question then comment please I will be happy to answer them If you want detailed knowledge of evaluation metrices then check out this article Link GitHub link Complete Data Science Bootcamp Main Post Complete Data Science Bootcamp Hope you liked it Sharing love and knowledge	2023-01-30 06:47:59
海外TECH	DEV Community	ZMOTION | 2023 New Beginning	https://dev.to/zmotion/zmotion-2023-new-beginning-4k7b	ZMOTION New BeginningDear friends nice to meet you in this new year In China CNY New Year means the new beginning Now welcome to our nice Let s work hard from today we will make progress cherish the present and look forward to the future we will be better definitely ZMotion continues to provide motion control products and automation solutions for you ZMotion will strive more in motion control area “do the best to use motion control As the former news before CNY holiday mentioned ZMotion technology provides mainly motion control products motion controller motion control card and expansion module There are also machine vision products vision motion controller hardware and ZDevelop software especially the software is free for you it is a good choice to program and compile There are standalone motion controller EtherCAT motion controller Rtex motion controller EtherCAT amp Rtex motion controller ZMCXX series ZMCXX series ZMCXX series ZMCXX series and Linux motion controller There are ECI IO series ECI series PCI motion control card and XPCI XPCIE series There also are vision motion controllers that are with machine vision motion control VPLC series VPLCE VPLCE and VPLC There are relative axis expansion when there is no enough resources IO expansion modules and AIO expansion modules are needed EtherCAT Expansion and ZCAN Expansion Except motion control parts there are HMI developed by ZMotion ZHD series One important and useful thing for you ZDvevlop can help you program easily and conveniently It is developed by ZMotion itself which support English environment For many years ZMotion provides many solutions for customers Nowadays our products are widely used in C electronics semiconductors dispensing laser processing printing and packaging special CNC robotic entertainment medical devices etc ZMotion always puts quality in the first place regards customer requirements as first priority bases on creating value pursues improvement of products performance What we do is to supply smart manufacturing with more valuable motion control products solutions and services Therefore new year new beginning and new life Hope meet you in let s be friends Have a nice day For more information please pay close attention to Support and Download and there are other platforms about ZMOTION Youtube amp LinkedIn amp Twitter amp Tiktok amp Facebook including technical information development environment routine code product showing company development etc Hope to meet you talk with you and be friends with you Welcome This article is edited by ZMOTION here share with you let s learn together ZMOTION DO THE BEST TO USE MOTION CONTROL Note Copyright belongs to ZMotion Technology if there is reproduction please indicate article source Thank you ZMOTION Technology has attracted experienced talents from famous companies or institutions such as Huawei ZET Huazhong University of Science and Technology etc ZMOTION insists self innovating and collaborating with comprehensive universities to research basic knowledge of motion control Due to its concentration and hard work in motion control technology ZMOTION already become one of the fastest growing industrial motion control companies in China and is also the rare company who has managed core technologies of motion control and real time industrial control software completely ZMotion Technology provides motion control card motion controller vision motion controller expansion module and HMI more keywords for ZMOTION EtherCAT motion control card EtherCAT motion controller motion control system vision controller motion control PLC robot controller vision positioning	2023-01-30 06:13:21
海外TECH	CodeProject Latest Articles	async void – How to Tame the Asynchronous Nightmare	https://www.codeproject.com/Articles/5353314/async-void-How-to-Tame-the-Asynchronous-Nightmare	async void How to Tame the Asynchronous NightmareYou re an intermediate dotnet programmer and you mostly know your way around using Tasks You sprinkle async and await through your code and everything is working just as expected	2023-01-30 06:02:00
金融	JPX マーケットニュース	[東証]グロース市場からプライム市場への変更：（株）アクシージア	https://www.jpx.co.jp/listing/stocks/transfers/index.html	東証	2023-01-30 16:00:00
金融	JPX マーケットニュース	[東証]新規上場日の呼値の単位：iFreeETF S&P500レバレッジ（コード2237） 他1銘柄	https://www.jpx.co.jp/news/1030/20230130-01.html	ifreeetfsampp	2023-01-30 16:00:00
金融	JPX マーケットニュース	[東証]市場区分の見直しに関するフォローアップ会議の論点整理及び論点整理を踏まえた東証の対応について	https://www.jpx.co.jp/news/1020/20230130-01.html	論点	2023-01-30 15:30:00
金融	JPX マーケットニュース	[東証]上場廃止等の決定：サムスンＫＯＤＥＸ２００証券上場指数投資信託[株式]　他１銘柄	https://www.jpx.co.jp/news/1021/20230130-01.html	kodex	2023-01-30 15:30:00
海外ニュース	Japan Times latest articles	Nissan and Renault near deal on restructured alliance	https://www.japantimes.co.jp/news/2023/01/30/business/corporate-business/renault-nissan-stake-lower/	nissan	2023-01-30 15:20:53
海外ニュース	Japan Times latest articles	NATO chief urges South Korea to step up military support for Ukraine	https://www.japantimes.co.jp/news/2023/01/30/world/nato-chief-seoul-weapons/	NATO chief urges South Korea to step up military support for UkraineNATO Secretary General Jens Stoltenberg is in Seoul the first stop on a trip that will include Japan and is aimed at strengthening ties with U S	2023-01-30 15:03:06
ニュース	BBC News - Home	Chris Mason: What does Zahawi sacking mean for Sunak?	https://www.bbc.co.uk/news/uk-politics-64449027?at_medium=RSS&at_campaign=KARANGA	editor	2023-01-30 06:35:18
ニュース	BBC News - Home	FA Cup: Watch the best moments from Wrexham's thrilling draw against Sheffield United	https://www.bbc.co.uk/sport/av/football/64440920?at_medium=RSS&at_campaign=KARANGA	FA Cup Watch the best moments from Wrexham x s thrilling draw against Sheffield UnitedWrexham co owner Ryan Reynolds experiences all the emotions of a Hollywood drama during a thrilling FA Cup fourth round tie against Sheffield United	2023-01-30 06:04:26
ニュース	BBC News - Home	Novak Djokovic: Why can't the next generation stop the Australian Open champion?	https://www.bbc.co.uk/sport/tennis/64443257?at_medium=RSS&at_campaign=KARANGA	Novak Djokovic Why can x t the next generation stop the Australian Open champion After year old Novak Djokovic beats another young pretender to win a record equalling nd Grand Slam title BBC Sport analyses how he is staying ahead of the rest	2023-01-30 06:46:09
ビジネス	ダイヤモンド・オンライン - 新着記事	省エネで見やすい新型ディスプレー、すぐそこに - WSJ発	https://diamond.jp/articles/-/316942	省エネ	2023-01-30 15:23:00
IT	週刊アスキー	愛犬用「節分恵方巻」が登場！　ペットOK焼肉店「うしすけ」にて2月3日まで期間限定販売	https://weekly.ascii.jp/elem/000/004/122/4122584/	数量限定	2023-01-30 15:50:00
IT	週刊アスキー	「駅すぱあと」アプリの経路検索から新幹線・特急列車のきっぷ購入が可能に	https://weekly.ascii.jp/elem/000/004/122/4122588/	予約サイト	2023-01-30 15:50:00
IT	週刊アスキー	ついにメインストーリー13章が解禁！『DQウォーク』新情報動画まとめ	https://weekly.ascii.jp/elem/000/004/122/4122606/	位置情報ゲーム	2023-01-30 15:50:00
IT	週刊アスキー	プロのデモ・パフォーマンスを見られる！　新宿住友ビル三角広場「GIBBON SLACKLINES WINTER FES」2月4日開催	https://weekly.ascii.jp/elem/000/004/122/4122582/	gibbonslacklineswinterfes	2023-01-30 15:40:00
IT	週刊アスキー	高級いちごを厳選！　コロンバン、京王百貨店新宿店でフルーツワッフルの専門店「WafflePalette -ワッフルパレット-」をオープン	https://weekly.ascii.jp/elem/000/004/122/4122572/	wafflepalette	2023-01-30 15:30:00
IT	週刊アスキー	転出届オンライン提出可能に　マイナンバーカード所有者のみ	https://weekly.ascii.jp/elem/000/004/122/4122601/	引っ越し	2023-01-30 15:30:00
海外TECH	reddit	TIL the government posts the home address of everyone who recieves Japanese nationality in their regular gazette. Is this not dangerous?	https://www.reddit.com/r/japanlife/comments/10oug20/til_the_government_posts_the_home_address_of/		2023-01-30 06:03:34