Intelligent Crime Anomaly Detection in Smart Cities Using Deep Learning

Chackravarthy, Sharmila; Schmitt, Steven; Yang, Li

doi:10.1109/cic.2018.00060

Cited by 50 publications

(17 citation statements)

References 3 publications

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“…The main issues identified in the healthcare systems is the late diagnosis, wrong treatment, and misinterpretation due to which it is required that the proper systems must be installed with the incorporation of advanced software, tools, and technologies [ 25 ]. For the utilisation of artificial intelligence effectively for the construction of Smart Cities, it is required that the available data be evaluated and stored so that it can be used for making effective algorithms of AI and deep learning for the betterment of healthcare systems and patients' health [ 48 ]. For the better future of the healthcare system, 6 key areas are required to give proper attention and detailed research including interoperability, data sharing, behavior change, scientific breakthrough, and empowered consumers [ 41 ].…”

Section: Discussionmentioning

confidence: 99%

New Opportunities, Challenges, and Applications of Edge-AI for Connected Healthcare in Internet of Medical Things for Smart Cities

Kamruzzaman

Alrashdi

Alqazzaz

2022

Journal of Healthcare Engineering

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Revolution in healthcare can be experienced with the advancement of smart sensorial things, Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Internet of Medical Things (IoMT), and edge analytics with the integration of cloud computing. Connected healthcare is receiving extraordinary contemplation from the industry, government, and the healthcare communities. In this study, several studies published in the last 6 years, from 2016 to 2021, have been selected. The selection process is represented through the Prisma flow chart. It has been identified that these increasing challenges of healthcare can be overcome by the implication of AI, ML, DL, Edge AI, IoMT, 6G, and cloud computing. Still, limited areas have implemented these latest advancements and also experienced improvements in the outcomes. These implications have shown successful results not only in resolving the issues from the perspective of the patient but also from the perspective of healthcare professionals. It has been recommended that the different models that have been proposed in several studies must be validated further and implemented in different domains, to validate the effectiveness of these models and to ensure that these models can be implemented in several regions effectively.

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Section: Discussionmentioning

confidence: 99%

New Opportunities, Challenges, and Applications of Edge-AI for Connected Healthcare in Internet of Medical Things for Smart Cities

Kamruzzaman

Alrashdi

Alqazzaz

2022

Journal of Healthcare Engineering

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“…Their proposed models also outperformed humans' success rate for identifying depression. The authors of [8] presented a method for detecting criminal activity in a video stream by recording the person's aberrant actions in subsequent video frames. They have employed a hybrid deep learning algorithm to analyze video stream data for surveillance in urban residential areas.…”

Section: Related Workmentioning

confidence: 99%

Pre-Trained Language Transformers are Universal Image Classifiers

Goel¹,

Sulaiman²,

Noorbakhsh³

et al. 2022

Preprint

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Facial images disclose many hidden personal traits such as age, gender, race, health, emotion, and psychology. Understanding these traits will help to classify the people in different attributes. In this paper, we have presented a novel method for classifying images using a pretrained transformer model. We apply the pretrained transformer for the binary classification of facial images in criminal and non-criminal classes. The pretrained transformer of GPT-2 is trained to generate text and then fine-tuned to classify facial images. During the finetuning process with images, most of the layers of GT-2 are frozen during backpropagation and the model is frozen pretrained transformer (FPT). The FPT acts as a universal image classifier, and this paper shows the application of FPT on facial images. We also use our FPT on encrypted images for classification. Our FPT shows high accuracy on both raw facial images and encrypted images. We hypothesize the meta-learning capacity FPT gained because of its large size and trained on a large size with theory and experiments. The GPT-2 trained to generate a single word token at a time, through the autoregressive process, forced to heavy-tail distribution. Then the FPT uses the heavy-tail property as its meta-learning capacity for classifying images. Our work shows one way to avoid bias during the machine classification of images. The FPT encodes worldly knowledge because of the pretraining of one text, which it uses during the classification. The statistical error of classification is reduced because of the added context gained from the text.

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“…Traditional machine learning methods do not perform well in these situations. Some methods which are used for proper monitoring of such sort of detection activities can be explored in [ 32 , 33 , 34 , 35 ].…”

Section: Applicationsmentioning

confidence: 99%

Advances and Trends in Real Time Visual Crowd Analysis

Khan

Albattah

Khan

et al. 2020

Sensors

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Real time crowd analysis represents an active area of research within the computer vision community in general and scene analysis in particular. Over the last 10 years, various methods for crowd management in real time scenario have received immense attention due to large scale applications in people counting, public events management, disaster management, safety monitoring an so on. Although many sophisticated algorithms have been developed to address the task; crowd management in real time conditions is still a challenging problem being completely solved, particularly in wild and unconstrained conditions. In the proposed paper, we present a detailed review of crowd analysis and management, focusing on state-of-the-art methods for both controlled and unconstrained conditions. The paper illustrates both the advantages and disadvantages of state-of-the-art methods. The methods presented comprise the seminal research works on crowd management, and monitoring and then culminating state-of-the-art methods of the newly introduced deep learning methods. Comparison of the previous methods is presented, with a detailed discussion of the direction for future research work. We believe this review article will contribute to various application domains and will also augment the knowledge of the crowd analysis within the research community.

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Intelligent Crime Anomaly Detection in Smart Cities Using Deep Learning

Cited by 50 publications

References 3 publications

New Opportunities, Challenges, and Applications of Edge-AI for Connected Healthcare in Internet of Medical Things for Smart Cities

New Opportunities, Challenges, and Applications of Edge-AI for Connected Healthcare in Internet of Medical Things for Smart Cities

Pre-Trained Language Transformers are Universal Image Classifiers

Advances and Trends in Real Time Visual Crowd Analysis

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