🕷️ Crawler Inspector

URL Lookup

Direct Parameter Lookup

Raw Queries and Responses

1. Shard Calculation

Query:

Response:

Calculated Shard: 31 (from laksa107)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa031.int.ahrefs:8124/' \
  -H 'Content-Type: text/plain' \
  -H 'X-ClickHouse-Database: crawler3' \
  -H 'Authorization: Basic YXBpOg==' \
  -d 'SELECT getAhrefsURLFromUnparsed(src_unparsed) AS found_url, ifNull(toUnixTimestamp(download_stamp), 0) AS crawl_time, ifNull(toUnixTimestamp(props_url_first_seen), 0) AS first_indexed_time, download_http_code AS http_code, src_unparsed AS src_unparsed, src_root_hash AS src_root_hash, history_drop_reason AS history_drop_reason, meta_title AS meta_title, meta_descriptions AS meta_descriptions, meta_canonical AS meta_canonical, ml_categories_json AS ml_categories_json, ml_types_json AS ml_types_json, ml_intent_types_json AS ml_intent_types_json, meta_language AS meta_language, attrs_author AS attrs_author, ifNull(toUnixTimestamp(attrs_publish_time), 0) AS attrs_publish_time, ifNull(toUnixTimestamp(attrs_original_publish_time), 0) AS attrs_original_publish_time, ifNull(attrs_is_republished, 0) AS attrs_is_republished, ifNull(attrs_nr_words, 0) AS attrs_nr_words, ifNull(attrs_boilerpipe_nr_words, 0) AS attrs_boilerpipe_nr_words, ifNull(body_ext_links_number, 0) AS body_ext_links_number, ifNull(body_int_links_number, 0) AS body_int_links_number, ifNull(meta_nofollow, 0) AS meta_nofollow, ifNull(meta_noarchive, 0) AS meta_noarchive, ifNull(props_was_rendered, 0) AS props_was_rendered, ifNull(src_redirect, \'\') AS src_redirect, ifNull(download_time_msec, 0) AS download_time_msec, ifNull(download_ttfb_msec, 0) AS download_ttfb_msec, ifNull(download_size, 0) AS download_size FROM crawler3.page_info_local FINAL PREWHERE int_partition_id = 55 AND (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://www.mdpi.com/2073-431X/12/8/151\')), getAhrefsUnparsedNoserviceFromURL(\'https://www.mdpi.com/2073-431X/12/8/151\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/www.mdpi.com\/2073-431X\/12\/8\/151","crawl_time":1776334589,"first_indexed_time":1690832156,"http_code":200,"src_unparsed":"com,mdpi!www,\/2073-431X\/12\/8\/151 s443","src_root_hash":"11974975279136771031","history_drop_reason":null,"meta_title":"Convolutional Neural Networks: A Survey","meta_descriptions":["Artificial intelligence (AI) has become a cornerstone of modern technology, revolutionizing industries from healthcare to finance. Convolutional neural networks (CNNs) are a subset of AI that have emerged as a powerful tool for various tasks including image recognition, speech recognition, natural language processing (NLP), and even in the field of genomics, where they have been utilized to classify DNA sequences. This paper provides a comprehensive overview of CNNs and their applications in image recognition tasks. It first introduces the fundamentals of CNNs, including the layers of CNNs, convolution operation (Conv_Op), Feat_Maps, activation functions (Activ_Func), and training methods. It then discusses several popular CNN architectures such as LeNet, AlexNet, VGG, ResNet, and InceptionNet, and compares their performance. It also examines when to use CNNs, their advantages and limitations, and provides recommendations for developers and data scientists, including preprocessing the data, choosing appropriate hyperparameters (Hyper_Param), and evaluating model performance. It further explores the existing platforms and libraries for CNNs such as TensorFlow, Keras, PyTorch, Caffe, and MXNet, and compares their features and functionalities. Moreover, it estimates the cost of using CNNs and discusses potential cost-saving strategies. Finally, it reviews recent developments in CNNs, including attention mechanisms, capsule networks, transfer learning, adversarial training, quantization and compression, and enhancing the reliability and efficiency of CNNs through formal methods. The paper is concluded by summarizing the key takeaways and discussing the future directions of CNN research and development."],"meta_canonical":null,"ml_categories_json":"{\"\/Computers_and_Electronics\":845,\"\/Computers_and_Electronics\/Software\":812,\"\/Science\":442,\"\/Science\/Computer_Science\":438,\"\/Science\/Computer_Science\/Machine_Learning_and_Artificial_Intelligence\":437,\"\/Computers_and_Electronics\/Software\/Intelligent_Personal_Assistants\":389}","ml_types_json":"{\"\/Article\":934,\"\/Article\/Study_or_Research_Findings\":800}","ml_intent_types_json":"{\"Informational\":997}","meta_language":"en","attrs_author":null,"attrs_publish_time":0,"attrs_original_publish_time":1690832156,"attrs_is_republished":0,"attrs_nr_words":"30604","attrs_boilerpipe_nr_words":"25737","body_ext_links_number":500,"body_int_links_number":116,"meta_nofollow":0,"meta_noarchive":0,"props_was_rendered":1,"src_redirect":"","download_time_msec":315,"download_ttfb_msec":198,"download_size":127343}

3. Robots.txt Check

Query:

Response:

4. Spam/Ban Check

Query:

Response:

5. Seen Status Check

ℹ️ Skipped - page is already crawled

📍

LOCATION

Host 31 · Partition 55

laksa031

11974975279136771031

📄

INDEXABLE

✅

CRAWLED

1 month ago

🤖

ROBOTS ALLOWED

Page Info Filters

Filter	Status	Condition	Details
HTTP status	PASS	`download_http_code = 200`	HTTP 200
Age cutoff	PASS	`download_stamp > now() - 6 MONTH`	1.6 months ago
History drop	PASS	`isNull(history_drop_reason)`	No drop reason
Spam/ban	PASS	`fh_dont_index != 1 AND ml_spam_score = 0`	ml_spam_score=0
Canonical	PASS	`meta_canonical IS NULL OR = '' OR = src_unparsed`	Not set

Page Details

Property

Value

URL

https://www.mdpi.com/2073-431X/12/8/151

Last Crawled

2026-04-16 10:16:29 (1 month ago)

First Indexed

2023-07-31 19:35:56 (2 years ago)

HTTP Status Code

200

Content

Meta Title

Convolutional Neural Networks: A Survey

Meta Description

Artificial intelligence (AI) has become a cornerstone of modern technology, revolutionizing industries from healthcare to finance. Convolutional neural networks (CNNs) are a subset of AI that have emerged as a powerful tool for various tasks including image recognition, speech recognition, natural language processing (NLP), and even in the field of genomics, where they have been utilized to classify DNA sequences. This paper provides a comprehensive overview of CNNs and their applications in image recognition tasks. It first introduces the fundamentals of CNNs, including the layers of CNNs, convolution operation (Conv_Op), Feat_Maps, activation functions (Activ_Func), and training methods. It then discusses several popular CNN architectures such as LeNet, AlexNet, VGG, ResNet, and InceptionNet, and compares their performance. It also examines when to use CNNs, their advantages and limitations, and provides recommendations for developers and data scientists, including preprocessing the data, choosing appropriate hyperparameters (Hyper_Param), and evaluating model performance. It further explores the existing platforms and libraries for CNNs such as TensorFlow, Keras, PyTorch, Caffe, and MXNet, and compares their features and functionalities. Moreover, it estimates the cost of using CNNs and discusses potential cost-saving strategies. Finally, it reviews recent developments in CNNs, including attention mechanisms, capsule networks, transfer learning, adversarial training, quantization and compression, and enhancing the reliability and efficiency of CNNs through formal methods. The paper is concluded by summarizing the key takeaways and discussing the future directions of CNN research and development.

Meta Canonical

null

Boilerpipe Text

heavy column, fetched on demand

Markdown

heavy column, fetched on demand

Readable Markdown

heavy column, fetched on demand

ML Classification

ML Categories

/Computers_and_Electronics		84.5%
/Computers_and_Electronics/Software		81.2%
/Science		44.2%
/Science/Computer_Science		43.8%
/Science/Computer_Science/Machine_Learning_and_Artificial_Intelligence		43.7%
/Computers_and_Electronics/Software/Intelligent_Personal_Assistants		38.9%

Raw JSON

{
    "/Computers_and_Electronics": 845,
    "/Computers_and_Electronics/Software": 812,
    "/Science": 442,
    "/Science/Computer_Science": 438,
    "/Science/Computer_Science/Machine_Learning_and_Artificial_Intelligence": 437,
    "/Computers_and_Electronics/Software/Intelligent_Personal_Assistants": 389
}

ML Page Types

/Article		93.4%
/Article/Study_or_Research_Findings		80.0%

Raw JSON

{
    "/Article": 934,
    "/Article/Study_or_Research_Findings": 800
}

ML Intent Types

Informational

99.7%

Raw JSON

{
    "Informational": 997
}

Content Metadata

Language

Author

null

Publish Time

not set

Original Publish Time

2023-07-31 19:35:56 (2 years ago)

Republished

Word Count (Total)

30,604

Word Count (Content)

25,737

Links

External Links

500

Internal Links

116

Technical SEO

Meta Nofollow

Meta Noarchive

JS Rendered

Yes

Redirect Target

null

Performance

Download Time (ms)

315

TTFB (ms)

198

Download Size (bytes)

127,343

Location

Host ID

31 (laksa031)

Partition ID

Root Hash

11974975279136771031

Unparsed URL

com,mdpi!www,/2073-431X/12/8/151 s443