Machine Learning in the Construction Industry {0}

By Stephanie C.

Technology has changed drastically in the past twenty years and continues to grow today. Many industries are using up-to-date technology in order to get ahead in the game. In particular, the construction industry has been using a method of data analysis, known as machine learning, to increase productivity and safety, reduce costs, and much more. “Machine learning is a subset of artificial intelligence” that allows computers to learn from previous data without intervention from humans, and it does this by creating algorithms. These algorithms are grouped into two main categories: Supervised learning and unsupervised learning (“Machine Learning,” n.d.). Supervised machine-learning algorithms require the data to be labeled in order for decisions to be made, while unsupervised machine-learning only require the raw data and nothing more. The construction industry can greatly benefit from machine learning, but it also faces challenges along the way.

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Machine Learning: Business Products and Services {0}

By Alex C.

Business products and services is something that companies purchase for their own productions or operations. This includes many things such as component parts, raw materials, and any service that assists in the operation of a firm. It is hard to distinguish between a consumer and business product, but they are set apart by the end user. If a product is to be used for personal use, then it is a consumer product while a product used for a company or to be made into a product is a business product. Machine learning is artificial intelligence where computers can learn without being explicitly programmed. Most machine learning methods include supervised learning, where algorithms use labeled examples, and unsupervised learning, where data has no historical labels. When machine learning is applied to business products and services, it deals with the behind the scenes to help a business operate.

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Applications of Machine Learning in Advertising and Marketing {0}

By Germaine A.

For years now, machine learning has been an integral part of the advertising and marketing industry. With so much data pouring in from users, effective leveraging that data requires the help of machine learning. It is no wonder that advertising agencies are using machine learning to more effectively cater to potential customers through personalized advertising and returning customers by offering more effective customer service and support (Biewald 2016).
In the domain of web-based advertising, machine learning has made its mark known. Since ads are sold on a per-click basis it is useful to know how effective they are before committing time and resources to their deployment. In the past, advertising firms relied on simple human intuition. However, to gain a competitive advantage they need more: enter ad click-through rate prediction. Google’s AdSense is a juggernaut in this arena. Utilizing machine learning for predicting ad click-through rates, Google AdSense can maximize advertising effectiveness by honing in on a user’s particular wants and needs to provide a tailored experience using data pulled from search history, page dwell time, website content, and click behavior (McMahan 2013). A number of advertising applications such as sponsored search advertising, contextual advertising, display advertising, and real-time bidding auctions have benefitted from the increased efficiency from ad click-through prediction.
Moving from cyberspace to human space, machine learning-augmented advertising continues to flourish. Data intelligence firms are seeking ways to tie in a customers’ digital footprint with their footprints in the real world. That is, matching a user’s browsing data on an online store to their buying habits at a physical store location. Firms like Qualia are using an opt-in location system to collect information by pulling from GPS and wi-fi signals and integrating them into machine learning algorithms so that advertisers can offer specialized services and recommend commodities (Qorbani 2017).
In the customer relations theatre of marketing, machine learning is also making large strides. Gone are the days of having the user fill out forms with varying degrees of accuracy, instead machine learning can produce help tickets by gleaning relevant information from the substance of the user’s complaint directly (Biewald 2016). The result is efficient ticket tagging and routing, saving time and money for any firm’s customer service department.
The benefits of machine learning aren’t limited to fully automating processes: it can also augment a marketers’ skills and bolster their toolset. Salespeople face the problem of personalizing messages to every potential customer while also reaching out to a large number of people. The email analytics platform offered by Nova provides a solution. Integrating directly into the salesperson’s gmail inbox, Nova’s software tracks the rates of potential customers’ clicks, reads, replies, and bounces to the salesperson’s correspondence and correlating it with public data (Matney 2016).
As with any new technology, machine learning is not without its limitations and concerns especially in the field of advertising and marketing. Even if information is publicly available it does not necessarily mean that accessing it and correlating it with other sources is not a breach of privacy. The main advantage of machine learning is in “filling the gaps” and it is precisely this feature that makes the potential breach of privacy so severe, especially if location services are involved. It allows, potentially, for a corporation to track a person’s movements and location at any time. Even assuming that corporation is entirely benign, a data breach can put many customers at risk. For customer service, machine learning cannot replace a true one-to-one client interaction no matter how much it can streamline.
Machine learning-augmented advertising and marketing is a powerful tool to reach and service customers. As this technology grows and matures, it will no doubt form a greater part of the advertising industry. 

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Database and Data Mining for Consumer Products and Services {0}

By Jonathan C.

A successful business needs to have quality products and services. Today, to achieve both at the same time with efficiency, business must have a way to achieve the data collecting, storing, analyzing and sharing the information. The introduction of computer enabled business a new way to deal with increase data instead of pen and papers. Some company have their database as early as 1970s. Per Jianfeng Wang, Wal-Mart’s database of programs and control systems were activated in 1972 for store and management levels use. At the early stage, the database is mostly used for product distribution and communication between the suppliers and retailer. Moreover, per Constance L. Hays from The New York Times, database and mining for consumer did not start until early 2000. Now many major business services such as retail, banking, insurance, health, and many more are using database and mining to benefit their relationship with their customers. The world of marketing has change from product-orientation to customer-orientation.

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Data Mining Within E-Commerce {0}

By Gary C.

In e-commerce, data mining is critically essential in order to compete with the rapidly growing competition amongst retailers. E-commerce is the exchange of data within the online world in order to garner business transactions. There are patterns and trends within shoppers that are analyzed and broken down in order to determine strategies to identify a multitude of situations, such as from what customers may like based on a previously purchased product all the way to why customers tend to avoid a certain product. The amount of raw data that is transmitted through data mining is astounding and requires a tremendous amount of research in order to determine the most of every possible likely scenario.

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Machine Learning Applied to Agriculture {0}

By Johnny C.

Technology is always changing and growing. In the modern day, advancements in technology come at exponential rates. One of the fastest ways to get ahead in an industry is by investing in cutting-edge technology. Agriculture is a huge part of the economy and is very important in sustaining countries. A more recent development in agriculture industry, is machine learning. Machine learning is a type of artificial intelligence that provides computers with the ability to learn without being explicitly programmed (Rouse, 2016). An example of this is, is an advancement made by biologist David Hughes and epidemiologist Marcel Salathe. “They fed a computer more than 50,000 images, and by learning on its own, the program can correctly identify 99.35 percent of the new images they throw at it.” This is a huge advantage for farmers, allowing them to understand what is affecting the crops, and finding faster ways to cure them. Machine learning can also help discover the best times and locations for crops, maximizing the yield. Although there are limits and challenges to machine learning, it is capable of pushing the boundaries in agriculture.

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Data Mining and Database use in the Construction industry {0}

By Mackenzie B.

With the advancement of technology and our ability to pool and collect information digitally, we’re able to minimize the collection of previously written copies of work and compile them into simple small archives of information. Where previously files, folders, and endless drawers of signed documents were once stored; Databases allows us to maximize the efficiency of the construction industry ranging from a collection of completed land permits, to legal documents and leases, to client information and a collection of parts for labour. Having engineers being able to pre-test their designed structures and building managers and clients being able to understand how strongly designed their structures are, are some of the most important steps in creating a well built building.

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Real World Product Database {0}

By Aerold B.

A product or a service is the bread and butter of all businesses. Every business needs to keep track of every item that they sell. Whether it’s the current stock of a product or the product’s location. In the past, businesses do not have a computer to keep track of their products. Instead, they use paper to communicate and keep track of every product that they sell. These papers are installed in cabinets so that it can be accessed in the future. The introduction of personal computing revolutionized how businesses store their data. Database replaced cabinets. According to AJ Graham, there are several types of databases that have been around since the 1960s. It was not until the 1970s when the most commonly used type of database was created. This most commonly used type of database is the Relational databases. Database made it easier for businesses to keep track of their product. Database also made it possible for businesses to enter their product data in the cloud and can be accessed by authorized employees in the company. Database are also used for business services such as banking, insurance, and transportation services.

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Real World Product Database {0}

By Aerold B.

A product or a service is the bread and butter of all businesses. Every business needs to keep track of every item that they sell. Whether it’s the current stock of a product or the product’s location. In the past, businesses do not have a computer to keep track of their products. Instead, they use paper to communicate and keep track of every product that they sell. These papers are installed in cabinets so that it can be accessed in the future. The introduction of personal computing revolutionized how businesses store their data. Database replaced cabinets. According to AJ Graham, there are several types of databases that have been around since the 1960s. It was not until the 1970s when the most commonly used type of database was created. This most commonly used type of database is the Relational databases. Database made it easier for businesses to keep track of their product. Database also made it possible for businesses to enter their product data in the cloud and can be accessed by authorized employees in the company. Database are also used for business services such as banking, insurance, and transportation services.

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Data Mining in Agriculture {0}

By: Paola A.

Every year, technology changes and new developments help many economic sectors discover new ways to improve, forecast a change, etc. For example, one economic sector that is benefitting from using new developments in technology is agriculture. In agriculture, a way to discover this type of changes is through data mining. But what exactly is data mining and how is agriculture benefitting from this. “Data mining involves the process of finding large quantity of previously unknown data, and then their use in important business decision making” (Milovic & Radojevic, 2015). For instance, in developing countries such as India, using data mining for “price prediction helps the farmers and also Government to make effective decision[s]” (Hemageetha & Nasira, 2012). Furthermore, data mining is something that could also help consumers by preparing them in case of any change in prices. Finally, this method of collecting data is something that can definitely improve and benefit the way farmers, government, and consumers make better decisions in the future with different applications, but it also has challenges and limitations.
Using data mining in agriculture benefits farmers, government, etc. in many ways. One of the ways that using data mining is beneficial in agriculture is “possibility to study hidden patterns in datasets in agricultural domain. These patterns can be used for diagnosing crop condition, prognosing market development, monitoring customer solvency” (Milovic & Radojevic, 2015). In other words, it can help farmers tell around the time that their crop would flourish, make predictions of when their product is more likely to sell, and what product customers are buying more. Also, another benefit is that “Agricultural institutions use data mining technique and applications for different areas, for instance agronomists use patterns measuring growth indicators of plants, crop quality indicators, success of taken agro technical measures and managers of agricultural organizations pay attention on user satisfaction and economically optimal decisions” (Milovic & Radojevic, 2015).

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