Free seo service in malaysia johor
Free seo service in malaysia johor
Free seo service in malaysia johor
Search engine optimization(SEO) – SEO Service In Malaysia Johor
Search engine optimization (SEO) is the process of improving the quality and quantity of website traffic to a website or a web page from search engines.[1] SEO targets unpaid traffic (known as “natural” or “organic” results) rather than direct traffic or paid traffic. Unpaid traffic may originate from different kinds of searches, including image search, video search, academic search,[2] news search, and industry-specific vertical search engines.
As an Internet marketing strategy, SEO considers how search engines work, the computer-programmed algorithms that dictate search engine behavior, what people search for, the actual search terms or keywords typed into search engines, and which search engines are preferred by their targeted audience. SEO is performed because a website will receive more visitors from a search engine when websites rank higher on the search engine results page (SERP). These visitors can then potentially be converted into customers.[3]
Relationship with Google
In 1998, two graduate students at Stanford University, Larry Page and Sergey Brin, developed “Backrub”, a search engine that relied on a mathematical algorithm to rate the prominence of web pages. The number calculated by the algorithm, PageRank, is a function of the quantity and strength of inbound links.[18] PageRank estimates the likelihood that a given page will be reached by a web user who randomly surfs the web, and follows links from one page to another. In effect, this means that some links are stronger than others, as a higher PageRank page is more likely to be reached by the random web surfer.
Page and Brin founded Google in 1998.[19] Google attracted a loyal following among the growing number of Internet users, who liked its simple design.[20] Off-page factors (such as PageRank and hyperlink analysis) were considered as well as on-page factors (such as keyword frequency, meta tags, headings, links and site structure) to enable Google to avoid the kind of manipulation seen in search engines that only considered on-page factors for their rankings. Although PageRank was more difficult to game, webmasters had already developed link building tools and schemes to influence the Inktomi search engine, and these methods proved similarly applicable to gaming PageRank. Many sites focused on exchanging, buying, and selling links, often on a massive scale. Some of these schemes, or link farms, involved the creation of thousands of sites for the sole purpose of link spamming.[21]
By 2004, search engines had incorporated a wide range of undisclosed factors in their ranking algorithms to reduce the impact of link manipulation. In June 2007, The New York Times’ Saul Hansell stated Google ranks sites using more than 200 different signals.[22] The leading search engines, Google, Bing, and Yahoo, do not disclose the algorithms they use to rank pages. Some SEO practitioners have studied different approaches to search engine optimization, and have shared their personal opinions.[23] Patents related to search engines can provide information to better understand search engines.[24] In 2005, Google began personalizing search results for each user. Depending on their history of previous searches, Google crafted results for logged in users.[25]
In 2007, Google announced a campaign against paid links that transfer PageRank.[26] On June 15, 2009, Google disclosed that they had taken measures to mitigate the effects of PageRank sculpting by use of the nofollow attribute on links. Matt Cutts, a well-known software engineer at Google, announced that Google Bot would no longer treat any nofollow links, in the same way, to prevent SEO service providers from using nofollow for PageRank sculpting.[27] As a result of this change the usage of nofollow led to evaporation of PageRank. In order to avoid the above, SEO engineers developed alternative techniques that replace nofollowed tags with obfuscated JavaScript and thus permit PageRank sculpting. Additionally several solutions have been suggested that include the usage of iframes, Flash and JavaScript.[28]
In December 2009, Google announced it would be using the web search history of all its users in order to populate search results.[29] On June 8, 2010 a new web indexing system called Google Caffeine was announced. Designed to allow users to find news results, forum posts and other content much sooner after publishing than before, Google Caffeine was a change to the way Google updated its index in order to make things show up quicker on Google than before. According to Carrie Grimes, the software engineer who announced Caffeine for Google, “Caffeine provides 50 percent fresher results for web searches than our last index…”[30] Google Instant, real-time-search, was introduced in late 2010 in an attempt to make search results more timely and relevant. Historically site administrators have spent months or even years optimizing a website to increase search rankings. With the growth in popularity of social media sites and blogs the leading engines made changes to their algorithms to allow fresh content to rank quickly within the search results.[31]
In February 2011, Google announced the Panda update, which penalizes websites containing content duplicated from other websites and sources. Historically websites have copied content from one another and benefited in search engine rankings by engaging in this practice. However, Google implemented a new system which punishes sites whose content is not unique.[32] The 2012 Google Penguin attempted to penalize websites that used manipulative techniques to improve their rankings on the search engine.[33] Although Google Penguin has been presented as an algorithm aimed at fighting web spam, it really focuses on spammy links[34] by gauging the quality of the sites the links are coming from. The 2013 Google Hummingbird update featured an algorithm change designed to improve Google’s natural language processing and semantic understanding of web pages. Hummingbird’s language processing system falls under the newly recognized term of “conversational search” where the system pays more attention to each word in the query in order to better match the pages to the meaning of the query rather than a few words.[35] With regards to the changes made to search engine optimization, for content publishers and writers, Hummingbird is intended to resolve issues by getting rid of irrelevant content and spam, allowing Google to produce high-quality content and rely on them to be ‘trusted’ authors.
In October 2019, Google announced they would start applying BERT models for English language search queries in the US. Bidirectional Encoder Representations from Transformers (BERT) was another attempt by Google to improve their natural language processing but this time in order to better understand the search queries of their users.[36] In terms of search engine optimization, BERT intended to connect users more easily to relevant content and increase the quality of traffic coming to websites that are ranking in the Search Engine Results Page.
aiFlows Technologies Sdn Bhd
NETWORKING | MARKETING
Free SEO Service in Malaysia! How SEO Helps Your Sales?
What Is SEO / Search Engine Optimization?
SEO stands for “search engine optimization.” In simple terms, it means the process of improving your site to increase its visibility for relevant searches. The better visibility your pages have in search results, the more likely you are to garner attention and attract prospective and existing customers to your business.
Why is SEO important for marketing?
SEO is a fundamental part of digital marketing because people conduct trillions of searches every year, often with commercial intent to find information about products and services. Search is often the primary source of digital traffic for brands and complements other marketing channels. Greater visibility and ranking higher in search results than your competition can have a material impact on your bottom line.
What should be done for your website SEO?
To have a better rank on Google Search, below are the basic requirements for your webpage.
1. On-page SEO
2. Off-page SEO
3. Link Building
4. Content Marketing
5. Implement SEO Tools like Google Analytics, Google Webmaster
6. Fix Internal & External Broken Links
7. Competitor Websote Analysis
How Can We Help For Your Website SEO?
Every Business Need A Marketing & Strategy Plan, Same Goes To Online World. Our Proven SEO Strategies Will Help You Dominate Your Competition & Drive More Profit Into Your Business.
Competitors SEO Check & Analysis
Keyword competition analysis is the process of evaluating how the top rankings fare when it comes to the most important SEO factors, including their use of specific keywords. The goal is to get a panoramic view of what you’re up against and where your opportunities are.
What exactly is SEO competitive analysis?
SEO competitive analysis involves researching the links, keywords, content, and more of your SEO competitors in order to reverse-engineer the most successful elements of these tactics into your own SEO strategy.
Instead of guessing which keywords to target, content to create, or links to build, you can instead see what’s already working for others, and build upon that success.
Consider a real-world example: Imagine you operate a grocery store — one of three competing stores in town. Your customers are happy, but you know they also visit other stores because they can’t buy everything in one place. So you go on a road trip to gather competitive intelligence. You visit the other stores to understand the popular items they offer. By offering these items yourself — or even superior ones — you help your customers make fewer trips, and in turn, you gain more business.
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Mobile Apps vs. Web Apps
Mobile apps are built for a specific platform, such as iOS for the Apple iPhone or Android for a Samsung device. They are downloaded and installed via an app store and have access to system resources, such as GPS and the camera function. Mobile apps live and run on the device itself. Snapchat, Instagram, Google Maps and Facebook Messenger are some examples of popular mobile apps.
Web apps
on the other hand, are accessed via the internet browser and will adapt to whichever device you’re viewing them on. They are not native to a particular system and don’t need to be downloaded or installed. Due to their responsive nature, they do indeed look and function a lot like mobile apps — and this is where the confusion arises.
While the designs are similar and follow the same fonts and color scheme, these are essentially two different products.
Web apps need an active internet connection in order to run, whereas mobile apps may work offline. Mobile apps have the advantage of being faster and more efficient, but they do require the user to regularly download updates. Web apps will update themselves.
Above all, mobile apps and web apps are designed and built very differently. To further differentiate between the two, it helps to understand how each is developed.
Azure Cognitive Computer Vision
Azure Cognitive Computer Vision
Azure Cognitive Computer Vision
What is Image Analysis?
Important
Transport Layer Security (TLS) 1.2 is now enforced for all HTTP requests to this service. For more information, see Azure Cognitive Services security.
The Computer Vision Image Analysis service can extract a wide variety of visual features from your images. For example, it can determine whether an image contains adult content, find specific brands or objects, or find human faces.
You can use Image Analysis through a client library SDK or by calling the REST API directly. Follow the quickstart to get started.
This documentation contains the following types of articles:
- The quickstarts are step-by-step instructions that let you make calls to the service and get results in a short period of time.
- The how-to guides contain instructions for using the service in more specific or customized ways.
- The conceptual articles provide in-depth explanations of the service’s functionality and features.
- The tutorials are longer guides that show you how to use this service as a component in broader business solutions.
Image Analysis features
You can analyze images to provide insights about their visual features and characteristics. All of the features in the list below are provided by the Analyze Image API. Follow a quickstart to get started.
Tag visual features
Identify and tag visual features in an image, from a set of thousands of recognizable objects, living things, scenery, and actions. When the tags are ambiguous or not common knowledge, the API response provides hints to clarify the context of the tag. Tagging isn’t limited to the main subject, such as a person in the foreground, but also includes the setting (indoor or outdoor), furniture, tools, plants, animals, accessories, gadgets, and so on. Tag visual features
Detect objects
Object detection is similar to tagging, but the API returns the bounding box coordinates for each tag applied. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this functionality to process further relationships between the objects in an image. It also lets you know when there are multiple instances of the same tag in an image. Detect objects
Detect brands
Identify commercial brands in images or videos from a database of thousands of global logos. You can use this feature, for example, to discover which brands are most popular on social media or most prevalent in media product placement. Detect brands
Categorize an image
Identify and categorize an entire image, using a category taxonomy with parent/child hereditary hierarchies. Categories can be used alone, or with our new tagging models.
Currently, English is the only supported language for tagging and categorizing images. Categorize an image
Describe an image
Generate a description of an entire image in human-readable language, using complete sentences. Computer Vision’s algorithms generate various descriptions based on the objects identified in the image. The descriptions are each evaluated and a confidence score generated. A list is then returned ordered from highest confidence score to lowest. Describe an image
Detect faces
Detect faces in an image and provide information about each detected face. Computer Vision returns the coordinates, rectangle, gender, and age for each detected face.
Computer Vision provides a subset of the Face service functionality. You can use the Face service for more detailed analysis, such as facial identification and pose detection. Detect faces
Detect image types
Detect characteristics about an image, such as whether an image is a line drawing or the likelihood of whether an image is clip art. Detect image types
Detect domain-specific content
Use domain models to detect and identify domain-specific content in an image, such as celebrities and landmarks. For example, if an image contains people, Computer Vision can use a domain model for celebrities to determine if the people detected in the image are known celebrities. Detect domain-specific content
Detect the color scheme
Analyze color usage within an image. Computer Vision can determine whether an image is black & white or color and, for color images, identify the dominant and accent colors. Detect the color scheme
Generate a thumbnail
Analyze the contents of an image to generate an appropriate thumbnail for that image. Computer Vision first generates a high-quality thumbnail and then analyzes the objects within the image to determine the area of interest. Computer Vision then crops the image to fit the requirements of the area of interest. The generated thumbnail can be presented using an aspect ratio that is different from the aspect ratio of the original image, depending on your needs. Generate a thumbnail
Get the area of interest
Analyze the contents of an image to return the coordinates of the area of interest. Instead of cropping the image and generating a thumbnail, Computer Vision returns the bounding box coordinates of the region, so the calling application can modify the original image as desired. Get the area of interest
Moderate content in images
You can use Computer Vision to detect adult content in an image and return confidence scores for different classifications. The threshold for flagging content can be set on a sliding scale to accommodate your preferences.
Image requirements
Image Analysis works on images that meet the following requirements:
- The image must be presented in JPEG, PNG, GIF, or BMP format
- The file size of the image must be less than 4 megabytes (MB)
- The dimensions of the image must be greater than 50 x 50 pixels
Data privacy and security
As with all of the Cognitive Services, developers using the Computer Vision service should be aware of Microsoft’s policies on customer data. See the Cognitive Services page on the Microsoft Trust Center to learn more.
Azure’s Computer Vision service gives you access to advanced algorithms that process images and return information based on the visual project.
Create a custom computer vision model in minutes
Customize and embed state-of-the-art computer vision image analysis for specific domains with Custom Vision, part of Azure Cognitive Services. Build frictionless customer experiences, optimize manufacturing processes, accelerate digital marketing campaigns, and more. No machine learning expertise is required.
What is Azure Cognitive Services – Computer Vision?
What is Azure Cognitive Services – Computer Vision?
Azure Cognitive Services makes AI project development to be simpler, and faster!
What Cognitive Services can do?
1. OCR
– Read text from image, pdf
2. Face Detection
– Detect how many human face in a photo
– gender and age analysis
3. Image descriptions
– If you upload a swimming photo, AI will tell you there is a male or female swimming
4. Spatial Analysis(link to CCTV or surveillance cameras)
– Detect how long the person appears in the cctv
– Detect the person wears mask or not
– Measure the distance between people
What is Computer Vision?
Computer Vision for digital asset management
Computer Vision can power many digital asset management (DAM) scenarios. DAM is the business process of organizing, storing, and retrieving rich media assets and managing digital rights and permissions. For example, a company may want to group and identify images based on visible logos, faces, objects, colors, and so on. Or, you might want to automatically generate captions for images and attach keywords so they’re searchable. For an all-in-one DAM solution using Cognitive Services, Azure Cognitive Search, and intelligent reporting, see the Knowledge Mining Solution Accelerator Guide on GitHub. For other DAM examples, see the Computer Vision Solution Templates repository.
Image requirements
Computer Vision can analyze images that meet the following requirements:
- • The image must be presented in JPEG, PNG, GIF, or BMP format
- • The file size of the image must be less than 4 megabytes (MB)
- • The dimensions of the image must be greater than 50 x 50 pixels
- • For the Read API, the dimensions of the image must be between 50 x 50 and 10000 x 10000 pixels.
Optical Character Recognition (OCR)
| Language | Language code | Read 3.2 | OCR API | Read 3.0/3.1 |
|---|---|---|---|---|
| Afrikaans | af |
✔ | ||
| Albanian | sq |
✔ | ||
| Arabic | ar |
✔ | ||
| Asturian | ast |
✔ | ||
| Basque | eu |
✔ | ||
| Bislama | bi |
✔ | ||
| Breton | br |
✔ | ||
| Catalan | ca |
✔ | ||
| Cebuano | ceb |
✔ | ||
| Chamorro | ch |
✔ | ||
| Chinese Simplified | zh-Hans |
✔ | ✔ | |
| Chinese Traditional | zh-Hant |
✔ | ✔ | |
| Cornish | kw |
✔ | ||
| Corsican | co |
✔ | ||
| Crimean Tatar Latin | crh |
✔ | ||
| Czech | cs |
✔ | ✔ | |
| Danish | da |
✔ | ✔ | |
| Dutch | nl |
✔ | ✔ | ✔ |
| English (incl. handwritten) | en |
✔ | ✔ (print only) | ✔ |
| Estonian | et |
✔ | ||
| Fijian | fj |
✔ | ||
| Filipino | fil |
✔ | ||
| Finnish | fi |
✔ | ✔ | |
| French | fr |
✔ | ✔ | ✔ |
| Friulian | fur |
✔ | ||
| Galician | gl |
✔ | ||
| German | de |
✔ | ✔ | ✔ |
| Gilbertese | gil |
✔ | ||
| Greek | el |
✔ | ||
| Greenlandic | kl |
✔ | ||
| Haitian Creole | ht |
✔ | ||
| Hani | hni |
✔ | ||
| Hmong Daw Latin | mww |
✔ | ||
| Hungarian | hu |
✔ | ✔ | |
| Indonesian | id |
✔ | ||
| Interlingua | ia |
✔ | ||
| Inuktitut Latin | iu |
✔ | ||
| Irish | ga |
✔ | ||
| Italian | it |
✔ | ✔ | ✔ |
| Japanese | ja |
✔ | ✔ | |
| Javanese | jv |
✔ | ||
| K'iche' | quc |
✔ | ||
| Kabuverdianu | kea |
✔ | ||
| Kachin Latin | kac |
✔ | ||
| Kara-Kalpak | kaa |
✔ | ||
| Kashubian | csb |
✔ | ||
| Khasi | kha |
✔ | ||
| Korean | ko |
✔ | ✔ | |
| Kurdish Latin | kur |
✔ | ||
| Luxembourgish | lb |
✔ | ||
| Malay Latin | ms |
✔ | ||
| Manx | gv |
✔ | ||
| Neapolitan | nap |
✔ | ||
| Norwegian | nb |
✔ | ||
| Norwegian | no |
✔ | ||
| Occitan | oc |
✔ | ||
| Polish | pl |
✔ | ✔ | |
| Portuguese | pt |
✔ | ✔ | ✔ |
| Romanian | ro |
✔ | ||
| Romansh | rm |
✔ | ||
| Russian | ru |
✔ | ||
| Scots | sco |
✔ | ||
| Scottish Gaelic | gd |
✔ | ||
| Serbian Cyrillic | sr-Cyrl |
✔ | ||
| Serbian Latin | sr-Latn |
✔ | ||
| Slovak | sk |
✔ | ||
| Slovenian | slv |
✔ | ||
| Spanish | es |
✔ | ✔ | ✔ |
| Swahili Latin | sw |
✔ | ||
| Swedish | sv |
✔ | ✔ | |
| Tatar Latin | tat |
✔ | ||
| Tetum | tet |
✔ | ||
| Turkish | tr |
✔ | ✔ | |
| Upper Sorbian | hsb |
✔ | ||
| Uzbek Latin | uz |
✔ | ||
| Volapük | vo |
✔ | ||
| Walser | wae |
✔ | ||
| Western Frisian | fy |
✔ | ||
| Yucatec Maya | yua |
✔ | ||
| Zhuang | za |
✔ | ||
| Zulu | zu |
✔ |
Mobile Apps vs. Web Apps
Mobile apps are built for a specific platform, such as iOS for the Apple iPhone or Android for a Samsung device. They are downloaded and installed via an app store and have access to system resources, such as GPS and the camera function. Mobile apps live and run on the device itself. Snapchat, Instagram, Google Maps and Facebook Messenger are some examples of popular mobile apps.
Web apps
on the other hand, are accessed via the internet browser and will adapt to whichever device you’re viewing them on. They are not native to a particular system and don’t need to be downloaded or installed. Due to their responsive nature, they do indeed look and function a lot like mobile apps — and this is where the confusion arises.
While the designs are similar and follow the same fonts and color scheme, these are essentially two different products.
Web apps need an active internet connection in order to run, whereas mobile apps may work offline. Mobile apps have the advantage of being faster and more efficient, but they do require the user to regularly download updates. Web apps will update themselves.
Above all, mobile apps and web apps are designed and built very differently. To further differentiate between the two, it helps to understand how each is developed.