Data Mining in the Military {0}

By: Lyvon T.

We put a lot of trust in the United States military to protect us from harm. To do that, they must have access to the best technology, such as data mining. Our military has always been collecting and storing data but in recent decades, the amount of data as increased to an extent that data mining software is needed to aide them in their work. Today, they use it in tactical systems, surveillance, and medical databases just to name a few. The ability to collect, analyze, and determine new information from that data is extremely useful to the military. Having access to useful information makes them more efficient in what they do.
Due to the nature of their work, the software they use must be perfect for the task needed, therefore they do not use public data mining tools like most organizations that use data mining. Almost all the software and technology is created by the Defense Advanced Research Projects Agency, also known as DARPA. Each individual data mining task, such as a missile deflection system, has a custom software created to aid that specific task. A good handful of new data mining software are listed on the DARPA website along with a brief description of it and the person who developed it. The website even has a catalog of the software they can share to the public but that part of the website is insecure and currently inaccessible.

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Food database in food industry {0}

By Titan T.

Database management systems are becoming important for commercial and domestic users because these help in managing data efficiently, storing large size of information and carrying out multiple tasks at a time. Use of databases in food industry is the focus of this paper and it will help in finding out trends in food databases, applications of food databases, how these are making food business more competitive and successful.

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Data Mining in Pharmaceutical Research & Development {0}

By Ryan T.

The pharmaceutical industry has always relied heavily on data. That data consists of historical clinical trial results, cellular, genetic, microbial, molecular, proteomic, and metabolic data. With most, if not all, of this data being stored electronically and so much to sift through data mining has been highly advantageous in pharmaceutical research and development (Elvridge, 2016). Several proprietary and nonproprietary tools are available to researchers each with their own distinct differences. The pharmaceutical companies utilizing big data ranges from large companies to small firms since data mining effectively reduces the barrier of entry. This goes without saying, but big data mining does comes with risks and limitations when it comes to the pharmaceutical industry. Overall, the benefits far outweigh the risks though as developers and researchers continue improving their products.

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Data mining in Tourism {0}

By Billy S.

Nowadays, travel and tourism have grown into a large industry throughout the world. As technology has played a major role in our day to day life, it tends to affects the behavior of travelers since it was easy enough to find information on the spot. Lately, travelers have changed how they travel where they prefer to roam freely and use technology as a guide rather than strict their schedule to a plan (1). As a result, tourism industry requires quick and up to date information from many locations around the world. Therefore, data mining has become a necessity to obtain accurate data and information; from popular travel destination, places of interests and popular cultural attractions. However, globalization has changed the behavior of traveler where it made an impact on their cultural criteria, social criteria, personal criteria, and psychological criteria(1).

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Data Mining in Software Engineering {0}

By Jesus S.

The today’s day and age everyone thrives off of data. Data has become key for organizations to succeed. Whether it be in the marketing field, health care field, agriculture field, and the list goes on! Data can help you strike gold, if you know can find out what people want, what people dislike, what is trending than that can help you become successful. Data mining is the technique we use to collect as much information as possible and turn it into data which is useful to us. I will be describing how data mining is used in software engineering.

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Machine Learning in the Travel Industry {0}

By Binyong X.

The travel industry has changed in many ways due to the evolution of machine learning. It also made a great impact on how people decided on their trip plan such as when and where to buy tickets. Machine learning is the subfield of computer science that gives computers the ability to learn without being explicitly programmed (Samuel). There’re generally three types of machine learning: supervised, unsupervised and reinforcement. Supervised learning is where the algorithm generates a function that maps inputs to desired outputs. Unsupervised learning is which models a set of inputs where the labeled examples aren’t available. Reinforcement learning is where the algorithm learns a policy of how to act given an observation of the world (Ayodele). There’re some advantages of how machine learnings are being used to help the Travel Industry. Examples including Recommendation engines, dynamic pricing and fare forecasting, intelligent travel assistants, and customer support improvement (AltexSoft).

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Changing The Way We See Machine Learning In The Field Of Telecommunication {0}

By Edwin T.

In the age of information, the internet is becoming a vast source of information that is waiting to be delivered and collected. Without programming tools, the world is losing valuable information and opportunities. With machine learning, people and companies will be able to utilize some of that information and attempt to apply it to something useful that people and industries alike can benefit from. Putting machine learning in conjunction with telecommunication can bring about great changes that will revolutionize the way we see the internet.

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Machine Learning: Software Industry {0}

By Haykaz T.

Machine Learning has been around for a very long time. People have used algorithms to make better sense of the data they were dealing with. This has always been a hard subject though. You need to be an expert to properly code and develop a software that can learn and interpret your data. Over the last decade we have seen an increase in machine learning software that can take big data and automatically use complex algorithms to better understand it. Machine Learning is the next big thing and many experts think that it will dominate the software industry. We are going to look at two big companies and how their Machine Learning tools will help make such a complex subject an easy one that almost anyone could pick up.

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Machine Learning and Security {0}

By Justin S.

Is machine language going to be the future of cyber security or is it just a pipe dream? Many people in the technology community have their own opinions referring to this statement. According to Simon Crosby, CTO at Bromium, argues that “There is no silver bullet in security”. He states that in cyber security you are going up against people that for the most part were taught to learn security for good. Some people with devious minds however learn how to alter the minds of machine language for the bad. Crosby makes a good point because most black hat hackers can learn how to alter machine language so having machine language as an ally in the security field could be more of a con than a pro. Others believe that machine language can and will eventually be the future of security. According to Mike Paquette, VP of products at Prelert, says that “machine learning is cyber security’s answer to detecting advanced breaches, and it will shine in security IT environments as they grow increasingly complex”. Both have very valid arguments and it could be both a pipedream or potentially be the answer we have all been waiting for.

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Machine Learning in Retail {0}

By Esteban R.

Ever since the purchasing markets have been expanding, the demand for a better system that needs to be placed into play has been crucial. People tend to have different taste and preferences, so at the end of the day they will ultimately make the decision on what to spend their money. As new systems are applied to company’s ways of doing business in retail, the better off those companies will understand the needs of their customers. We must first understand the science behind these new systems how they will help companies in the end. These systems are using artificial intelligence to provide computer with the proper learning abilities to learn the proper algorithms. Nonetheless, these new systems known as machine learning. From here, these machine-learning systems will better understand the customer habits, purchase history, consumer demand, and market trends.

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