Buildout of Methodology for Meticulous Diagnosis of K-Complex in EEG for Aiding the Detection of Alzheimer’s by Artificial Intelligence

Self Cite

A crime is a deliberate act that can cause physical or psychological harm, as well as property damage or loss, and can lead to punishment by a state or other authority according to the severity of the crime. The number and forms of criminal activities are increasing at an alarming rate, forcing agencies to develop efficient methods to take preventive measures. In the current scenario of rapidly increasing crime, traditional crime-solving techniques are unable to deliver results, being slow paced and less efficient. Thus, if we can come up with ways to predict crime, in detail, before it occurs, or come up with a “machine” that can assist police officers, it would lift the burden of police and help in preventing crimes. To achieve this, we suggest including machine learning (ML) and computer vision algorithms and techniques. In this paper, we describe the results of certain cases where such approaches were used, and which motivated us to pursue further research in this field. The main reason for the change in crime detection and prevention lies in the before and after statistical observations of the authorities using such techniques. The sole purpose of this study is to determine how a combination of ML and computer vision can be used by law agencies or authorities to detect, prevent, and solve crimes at a much more accurate and faster rate. In summary, ML and computer vision techniques can bring about an evolution in law agencies.

Section: Introductionmentioning

confidence: 99%

Crime forecasting: a machine learning and computer vision approach to crime prediction and prevention

Shah¹,

Shah

2021

Self Cite

“…Augmented reality (AR) and virtual reality (VR) have the capability to alter the way entertainment, shopping, health activities, recreation, etc. are perceived [2]. Although VR and AR are often assumed to be the same, they are considerably different.…”

Section: Introductionmentioning

confidence: 99%

Systematic review and meta-analysis of augmented reality in medicine, retail, and games

Parekh

Patel

et al. 2020

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This paper presents a detailed review of the applications of augmented reality (AR) in three important fields where AR use is currently increasing. The objective of this study is to highlight how AR improves and enhances the user experience in entertainment, medicine, and retail. The authors briefly introduce the topic of AR and discuss its differences from virtual reality. They also explain the software and hardware technologies required for implementing an AR system and the different types of displays required for enhancing the user experience. The growth of AR in markets is also briefly discussed. In the three sections of the paper, the applications of AR are discussed. The use of AR in multiplayer gaming, computer games, broadcasting, and multimedia videos, as an aspect of entertainment and gaming is highlighted. AR in medicine involves the use of AR in medical healing, medical training, medical teaching, surgery, and post-medical treatment. AR in retail was discussed in terms of its uses in advertisement, marketing, fashion retail, and online shopping. The authors concluded the paper by detailing the future use of AR and its advantages and disadvantages in the current scenario.

“…One of the spheres where technology has shown great development is in the field of machine learning (ML) [ 4 – 7 ]. ML is not just one technique or technology; however, it is a field of procedure science that includes various technologies to create systems that may learn from the information in their setting and then make predictions and perform actions once confronted with a replacement scenario [ 8 – 10 ]. ML is powerfully grounded in trendy arithmetic, drawing on experience in performing analysis, applied mathematics, set theory, chaos and dynamic systems, and calculus of variations among different areas [ 8 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Denouements of machine learning and multimodal diagnostic classification of Alzheimer’s disease

Naik

Mehta

Shah

2020

Self Cite

Alzheimer’s disease (AD) is the most common type of dementia. The exact cause and treatment of the disease are still unknown. Different neuroimaging modalities, such as magnetic resonance imaging (MRI), positron emission tomography, and single-photon emission computed tomography, have played a significant role in the study of AD. However, the effective diagnosis of AD, as well as mild cognitive impairment (MCI), has recently drawn large attention. Various technological advancements, such as robots, global positioning system technology, sensors, and machine learning (ML) algorithms, have helped improve the diagnostic process of AD. This study aimed to determine the influence of implementing different ML classifiers in MRI and analyze the use of support vector machines with various multimodal scans for classifying patients with AD/MCI and healthy controls. Conclusions have been drawn in terms of employing different classifier techniques and presenting the optimal multimodal paradigm for the classification of AD.