Data Mining in Employee Healthcare Detection Using Intelligence Techniques for Industry Development

Background. Even in today’s environment, when there is a plethora of information accessible, it may be difficult to make appropriate choices for one’s well-being. Data mining, machine learning, and computational statistics are among the most popular arenas of training today, and they are all aimed at secondary empowered person in making good decisions that will maximize the outcome of whatever working area they are involved with. Because the degree of rise in the number of patient roles is directly related to the rate of people growth and lifestyle variations, the healthcare sector has a significant need for data processing services. When it comes to cancer, the prognosis is an expression that relates to the possibility of the patient surviving in general, but it may also be used to describe the severity of the sickness as it will present itself in the patient's future timeline. Methodology. The proposed technique consists of three stages: input data acquisition, preprocessing, and classification. Data acquisition consists of input raw data which is followed by preprocessing to eliminate the missed data and the classification is carried out using ensemble classifier to analyze the stages of cancer. This study explored the combined influence of the prominent labels in conjunction with one another utilizing the multilabel classifier approach, which is successful. Finally, an ensemble classifier model has been constructed and experimentally validated to increase the accuracy of the classifier model, which has been previously shown. The entire performance of the recommended and tested models demonstrates a steady development of 2% to 6% over the baseline presentation on the baseline performance. Results. Providing a good contribution to the general health welfare of noncommercial potential workers in the healthcare sector is an opportunity provided by this recommended job outcome. It is anticipated that alternative solutions to these constraints, as well as automation of the whole process flow of all five phases, will be the key focus of the work to be carried out shortly. Predicting health status of employee in industry or information trends is made easier by these data patterns. The proposed classifier achieves the accuracy rate of 93.265%.

Validation of a Method for Measuring the Position of Pick Holders on a Robotically Assisted Mining Machine’s Working Unit

For effective mining, it is essential to ensure that the picks are positioned correctly on the working unit of a mining machine. This is due to the fact that the design of roadheader cutting heads/drums using computer-aided tools is based on the operating conditions of the roadheader/shearer/milling machine. The geometry of the cutting head is optimized for selected criteria by simulating the mining process using a computer. The reclaimed cutting head bodies that are utilized in production are manufactured again in the overhaul process. Ensuring that the dimensions of the cutting head bodies match the rated dimensions is labor-intensive and involves high production costs. For dimensional deviations of the cutting head bodies, it is necessary to control the position of the pick holders relative to the cutting head side surface in real time during robotic-assisted assembly. This article discusses the possibility of utilizing a stereovision system for calculating the distance between the pick holder base and the roadheader cutting head side surface at the point where the pick holder is mounted. The proposed measurement method was tested on a robotic measurement station constructed for the purpose of the study. A mathematical measurement model and procedures that allow automatic positioning of the camera system to the photographed objects, as well as acquisition and analysis of the measurement images, were developed. The proposed method was validated by using it for measuring the position of the pick holders relative to the side surface of the working unit of a mining excavating machine, focusing on its application in robotic technology. The article also includes the results observed in laboratory tests performed on the developed measurement method with an aim of determining its suitability for the metrology task under consideration.

Information Science in the Analytics of Healthcare Data

Information science is an interdisciplinary field that deals with the effective collection, storage, retrieval, and use of information for better decision making through related technologies. Today, healthcare organizations are looking for more efficient and sophisticated means of collecting, managing, analyzing data, and delivering medical information to physicians, clinicians, and nurses. The role of information science in the healthcare domain is to improve the quality of patient care, reduce operational cost, and make the entire internal management process well organized for better decision making. Through the application of technology, data analytics and information science practitioners help drive data-informed healthcare decisions. Hence, this chapter covers the techniques that are useful for data analytics and information management in healthcare such as data mining, machine learning, cloud computing, and data visualization.

ADS Using NER Systems

Data accessible over the net is generally unstructured. Offers distributed by different sources like banks, digital wallets, merchants, etc., are one of the foremost gotten to advertising data in today’s world. This information gets gotten to by millions of people on a every day premise and is effortlessly deciphered by people, but since it is generally unstructured and differing, utilizing an algorithmic way to extricate significant data out of these offers is hard. Distinguishing the basic offer substances (for occasion, its amount, the item on which the offer is pertinent, the merchant giving the offer, etc.) from these offers plays a vital role in focusing on the proper clients to make strides deals.This work presents and assesses different existing Named Substance Recognizer (NER) models which can distinguish the desired substances from offer feeds. We moreover propose a novel NER demonstration constructed by two-level stacking of Conditional Arbitrary Field, Bidirectional LSTM and Spacy models at the primary level and an SVM classifier at the moment. The proposed cross breed demonstrate has been tried on offer feeds collected from different sources and has appeared better performance within the offered space when compared to the existing models. Index Terms—Named Substance Acknowledgment, Information Mining, Machine Learning, Stanford NER, Bidirectional LSTM, Spacy, Bolster Vector Machines.

Main Problems Using DEM Modeling to Evaluate the Loose Soil Collection by Conceptual Machine as a Background for Future Extraterrestrial Regolith Harvesting DEM Models

Nowadays, rapid product development is a key factor influencing a company’s success. In the Space 4.0. era, an integrated approach with the use of 3D printing and DEM modeling can be particularly effective in the development of technologies related to space mining. Unfortunately, both 3D printing and DEM modeling are not without flaws. This article shows the possibilities and problems resulting from the use of DEM simulation and 3D printing simultaneously in the rapid development of a hypothetical mining machine. For the subsequent development of the regolith harvesting model, loose soil harvesting simulations were performed and the underlying problems were defined and discussed. The results show that it is possible to use both technologies simultaneously to be able to effectively and accurately model the behavior of this type of machine in various gravitational conditions in the future.

Development and Subsea Testing of Polymetallic Nodule Crusher for Underwater Mining Machine

Polymetallic nodules found in the deep oceans are viewed as potential resources for meeting the world's demand of many metals in the near future. Polymetallic nodule mining systems require subsea crushing systems for reducing the size of nodules to facilitate energy-efficient and safe pumping through risers of optimum size. Polymetallic nodules are friable, and deep-sea crushing has to be done with care to minimize the formation of fines, while obtaining the required size reduction. The crusher could also encounter objects with greater hardness during operation like small rocks, splinters, long fish bones, and shark teeth. All components in the crusher should be capable of operating in the deep ocean environment, which is hyperbaric and sediment laden. The equipment should be compact with minimum weight. Reversal of direction and dumping arrangements in the event of stalling are other essential design requirements. An underwater crusher capable of crushing mined nodules from a maximum size of 100 mm to a crushed size of 30 mm was developed using principles of design synthesis. The crusher was tested in land and integrated into a remotely operated crawler-based underwater mining machine that could mine and pump nodules through a flexible riser. The system was tested using artificial nodules at 512-m water depth off the Malvan coast in the Arabian Sea. This paper describes developmental methodology, land-based performance tests, and sea trials conducted on the developed crusher.