Write Essay about Artificial Intelligence - Essay Prowess

Write Essay about Artificial Intelligence

$5.99

Kindly ADD to CART and Purchase an Editable word file at $5.99 Only

Artificial Intelligence

Novatchkov, Hristo, and Arnold Baca. “Artificial intelligence in sports on the example of weight training.” Journal of sports science & medicine 12.1 (2013): 27.

A research by Novatchkov and Baca explore the utilization of artificial intelligence in sports particularly in weight training. The study concentrated on the application of pattern recognition techniques for the assessment of completed aerobics on training machines. The weight machines were fitted with cable force sensors, which assisted in data collection (Novatchkov and Baca 1). In so doing, it facilitates the determination of important force and displacement determinants in the process of training (Novatchkov and Baca 1). Practically, the execution of such practices and techniques is vital for the examination of the value of the execution. Importantly, the use of AI techniques in training exercises would greatly assist coaches and athletes because they help to measure automatically performances especially on weight training equipment hence offering them with instant advice (Novatchkov and Baca 3).

The study participants included 15 sportsmen who were inexperienced engaging in 3-5 sets of physical exercise using a leg-press machine. The machine had AI techniques where professional trainers were employed in classification and assessment processes by examining the video documented performances (Novatchkov and Baca 9). Based on the data collected, the article noted that AI is a favourable industry for analysing sports-related activities.

Zang, Yaping, et al. “Advances of flexible pressure sensors toward artificial intelligence and health care applications.” Materials Horizons 2.2 (2015): 140-156.

The article by Zang et al. attempted to determine the application of artificial intelligence in health care monitoring. In fact, it reviewed the use of pressure sensors in the healthcare sector especially on assessment of intraocular pressure and blood pressure. The research noted that pressure sensors had the ability to produce signals under a particular pressure and operate in signal transduction (Zang et al. 140). Importantly, the feature facilitates the successful utilization of pressure sensors in AI. In this regard, active sensing tools have been invented, which are important applications for electronic skin.

Pressure sensors utilize AI system, which enable mobile bio-monitoring in health care and medical diagnostics. They are made up of organic materials that offer flexibility in pursuit of devices, which are foldable and lightweight (Zang et al. 147). The e-skin systems are extremely sensitive to touch hence they are able to monitor the health of an individual.

Ali, Yasir Hassan, R. Abd Rahman, and Raja Ishak Raja Hamzah. “Acoustic emission signal analysis and artificial intelligence techniques in machine condition monitoring and fault diagnosis: a review.” Journal Technology 69.2 (2014).

A research by Ali, Rahman, and Hamzah studied the use of artificial intelligence and acoustic emission (AE) technique in fault diagnosis and monitoring machine condition. The AI process is largely useful owing to its sensitivity in identifying small cracks. For this reason, the AI techniques and AE process could be utilized for regular maintenance (Ali, Rahman, and Hamzah 1). The application of artificial intelligence in maintenance of machines has been beneficial because they enhance machine availability, productivity, and reduction of maintenance costs especially in industrial machinery. The use of tools such as acoustic emission (AE) technology offers an exceptional opportunity integrate intelligent detection analysis. Moreover, acoustic emission technique is one form of AI processes that can be used in engineering. It refers to production of transitory elastic waves generated from a contained source within the substance (Ali, Rahman, and Hamzah 5)

The review also established that the technique is quite popular because it delivers accurate data on real energy source within the machinery. More importantly, the AI networks acts and reasons like human brain hence solving engineering problems. Some of the most popular AI techniques include Genetic Algorithms (GA) and Artificial Neural Networks (Ali, Rahman, and Hamzah 10). For instance, the Artificial Neural Network (ANN) functions through data-processing strategy. Moreover, they are quite effective in identifying faults particularly in process of machining hence they are utilized in industrial automation (Ali, Rahman, and Hamzah 11).

Baig, Mirza Mansoor, and Hamid Gholamhosseini. “Smart health monitoring systems: an overview of design and modeling.” Journal of medical systems 37.2 (2013): 9898.

The article by Baig and Gholamhosseini reviewed the use of artificial intelligence in monitoring of health. The study noted that AI is transforming the health care system because it enhances the management of patients. Due to rise in the population of older patients suffering from myriad of health problems, the need of health monitoring system is evident (Baig and Gholamhosseini 2). Other patients experience health difficulties associated with dosage inaccuracies, incorrect medication and contraindications (Baig and Gholamhosseini 7). They are a major milestone in the healthcare delivery because they empower the patient to enjoy professional healthcare in their convenient environment. Moreover, wearable medical monitoring tools are biosensors, which are usually worn by persons.

A smart vest is fundamentally a wearable physiological detecting mechanism, integrated in a vest. It has the ability to gather bio-signals in a modest and non-invasive manner. The smart vest measures parameters such as galvanic skin response, body temperature, blood pressure, photo-plethysmograph (PPG), pulse rate, and ECG (Baig and Gholamhosseini 6). The artificial intelligence has also been developed with an aim of helping the elderly stay self-reliantly. In fact, AI technologies have fabricated to help them monitor their weight, blood pressure and pulse rates.

Fergus, Paul, et al. “Automatic epileptic seizure detection using scalp EEG and advanced artificial intelligence techniques.” BioMed research international 2015 (2015).

The study by Fergus et al. analysed the use of innovative artificial intelligence systems in automatic identification of epileptic seizures. Epilepsies are neurological syndromes and disorders with symptoms such as involuntary, recurrent seizure processes. Detection of this disorder is challenging at the initial stages, which subsequently contribute, to delayed treatment (Fergus et al. 1). For instance, it is difficult to detect the origin of the seizure in either right or left hemisphere especially in the event of occipital and temporal lobe epilepsies. The study used 392 records, which helped to compute the EEG data. The study concluded that the utilization of automated detection of epilepsies using artificial intelligence is a solution in determination of seizure activity. The research noted an improvement of 10 per cent in seizure detection after utilization of automated approach having a specificity and sensitivity of 94 per cent and 93 per cent respectively (Fergus et al. 6).

Schulz, Peter J., and Kent Nakamoto. “Patient behavior and the benefits of artificial intelligence: the perils of “dangerous” literacy and illusory patient empowerment.” Patient education and counseling 92.2 (2013): 223-228.

A review by Schulz and Nakamoto assessed the importance of patient empowerment through literacy and counseling on the artificial intelligence. The research pointed out that artificial intelligence can deliver essential support to the health of the patient (Schulz and Nakamoto 223). Nevertheless, barriers emerge as patients take an active part in decision making of their health. The study discovered through enhancing the health literacy needs substantial guidance. Therefore, patients should be guided towards suitable tools also offered critical background knowledge facilitating them to utilize tools (Schulz and Nakamoto 225). In addition, they should be empowered to take advantage of artificial intelligence innovations that are available in the sector.

Salih, Abdelhamid, and A. Abraham. “A review of ambient intelligence assisted healthcare monitoring.” International Journal of Computer Information Systems and Industrial Management (IJCISIM) 5 (2013): 741-750.

A study by Salih and Abraham focused on the use of artificial intelligence through ambient intelligence in promoting monitoring in chronic diseases among the elderly and disabled. It sought to assess whether AI could be applied effectively to reduce the cost of care (Salih and Abraham 741). The study examined the wearable sensors techniques, which are used in monitoring treatments, home rehabilitation, and wellness and health of elderly patients with chronic illness. The parameters were used for the design of intelligent systems adapted from traditional machine learning idea, permitting an automatic evaluation of the exercise techniques. Additionally, they are important because they help individuals to acquire personal feedback after the training exercise. The findings of the study indicated that AI techniques are of great use (Salih and Abraham 743).

Manna, Claudio, et al. “Artificial intelligence techniques for embryo and oocyte classification.” Reproductive biomedicine online 26.1 (2013): 42-49.

A study by Manna et al. analyzed the importance of Ai in reproductive health such as identification and classification of feasible oocytes and embryo especially in in-vitro fertilization (IVF) (Manna et al. 42). Traditionally, specialists use visual examination, which is subjective to bias. In this regard, health-monitoring networks can assist in reduction of cost of care, waiting lists, consultation time, and hospitalization. Advancement of artificial intelligence has enabled development of health monitoring networks, which can be used in mobile devices such as personal computers, personal digital assistants (PDAs), and smartphones. The study noted that AI is essential in prediction and selection of high quality oocytes and embryo. In this regard, they enhance the validity of assisted reproduction technology. The AI technique was used among 104 women, 269 embryos and oocytes (Manna et al. 46). The study concluded that this technique is viable because it strengthens the classification ability.

Girela, Jose L., et al. “Semen parameters can be predicted from environmental factors and lifestyle using artificial intelligence methods.” Biology of reproduction 88.4 (2013): 99-1.

The study by Girela et al. scrutinized the application of AI in prediction of semen’s quality due to health status, life habits, and environmental factors. The study included 123 males who volunteered semen. The artificial neural network was utilized to analyse how these determinants affected the sperm quality and concentration (Girela et al. 2). Additionally, due to advancement in technology, developed of low-cost, flexible and compatible pressure sensors have ensued. Similarly, pressure sensory devices with hundreds of focused receptors on pressure sensing have been developed. The study concluded that artificial intelligence provides beneficial techniques, which can easily detect seminal disorders (Girela et al. 2).

Cismondi, Federico, et al. “Reducing unnecessary lab testing in the ICU with artificial intelligence.” International journal of medical informatics 82.5 (2013): 345-358.

A study by Cismondi et al. determined the usefulness of artificial intelligence in lowering avoidable lab tests. The study noted that some lab tests do not lead to information gain hence fail to enhance clinical management of patient. In fact, recurrent blood draws can be a source of complications among patients (Cismondi et al. 345). In this respect, AI can be used in prediction of preventable lab tests in ICU among 746 patients suffering from gastrointestinal bleeding. The study noted that the use of predictive AI was imperative in intensive care because of its high accuracy and sensitivity (Cismondi et al. 346).

Work cited

Ali, Yasir Hassan, R. Abd Rahman, and Raja Ishak Raja Hamzah. “Acoustic emission signal analysis and artificial intelligence techniques in machine condition monitoring and fault diagnosis: a review.” Journal Technology 69.2 (2014).

Baig, Mirza Mansoor, and Hamid Gholamhosseini. “Smart health monitoring systems: an overview of design and modeling.” Journal of medical systems 37.2 (2013): 9898.

Cismondi, Federico, et al. “Reducing unnecessary lab testing in the ICU with artificial intelligence.” International journal of medical informatics 82.5 (2013): 345-358.

Fergus, Paul, et al. “Automatic epileptic seizure detection using scalp EEG and advanced artificial intelligence techniques.” BioMed research international 2015 (2015).

Girela, Jose L., et al. “Semen parameters can be predicted from environmental factors and lifestyle using artificial intelligence methods.” Biology of reproduction 88.4 (2013): 99-1.

Manna, Claudio, et al. “Artificial intelligence techniques for embryo and oocyte classification.” Reproductive biomedicine online 26.1 (2013): 42-49.

Novatchkov, Hristo, and Arnold Baca. “Artificial intelligence in sports on the example of weight training.” Journal of sports science & medicine 12.1 (2013): 27.

Salih, Abdelhamid, and A. Abraham. “A review of ambient intelligence assisted healthcare monitoring.” International Journal of Computer Information Systems and Industrial Management (IJCISIM) 5 (2013): 741-750.

Schulz, Peter J., and Kent Nakamoto. “Patient behavior and the benefits of artificial intelligence: the perils of “dangerous” literacy and illusory patient empowerment.” Patient education and counseling 92.2 (2013): 223-228.

Zang, Yaping, et al. “Advances of flexible pressure sensors toward artificial intelligence and health care applications.” Materials Horizons 2.2 (2015): 140-156.

error: Content is protected !!