Artificial Intelligence and Internet of Things Enabled Disease Diagnosis Model for Smart Healthcare Systems

Mansour, Romany F.; Amraoui, Adnen El; Nouaouri, Issam; García‐Díaz, Vicente; Gupta, Deepak; Kumar, Sachin

doi:10.1109/access.2021.3066365

Cited by 139 publications

(66 citation statements)

References 19 publications

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“…Recent developments in the field of data science and artificial intelligence have opened a lot of opportunities in developing new methods of diagnosis and detection of diseases by deploying sophisticated algorithms to these problems. Several attempts have been made in diseases affecting humans, including many other species of plants and animals [19,[27][28][29][30].…”

Section: Discussionmentioning

confidence: 99%

MasPA: A Machine Learning Application to Predict Risk of Mastitis in Cattle from AMS Sensor Data

Ghafoor

Sitkowska

2021

AgriEngineering

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Mastitis is a common disease that prevails in cattle owing mainly to environmental pathogens; they are also the most expensive disease for cattle in dairy farms. Several prevention and treatment methods are available, although most of these options are quite expensive, especially for small farms. In this study, we utilized a dataset of 6600 cattle along with several of their sensory parameters (collected via inexpensive sensors) and their prevalence to mastitis. Supervised machine learning approaches were deployed to determine the most effective parameters that could be utilized to predict the risk of mastitis in cattle. To achieve this goal, 26 classification models were built, among which the best performing model (the highest accuracy in the shortest time) was selected. Hyper parameter tuning and K-fold cross validation were applied to further boost the top model’s performance, while at the same time avoiding bias and overfitting of the model. The model was then utilized to build a GUI application that could be used online as a web application. The application can predict the risk of mastitis in cattle from the inhale and exhale limits of their udder and their temperature with an accuracy of 98.1% and sensitivity and specificity of 99.4% and 98.8%, respectively. The full potential of this application can be utilized via the standalone version, which can be easily integrated into an automatic milking system to detect the risk of mastitis in real time.

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Section: Discussionmentioning

confidence: 99%

MasPA: A Machine Learning Application to Predict Risk of Mastitis in Cattle from AMS Sensor Data

Ghafoor

Sitkowska

2021

AgriEngineering

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“…The hyperparameters of the MN are tuned using QOFFO algorithm. In general, Firefly algorithm (FA) is defined as a meta-heuristic model to solve the optimization issues [15][16][17][18][19][20][21][22]. The development of FA is applied in 3 ideas:…”

Section: Parameter Tuning Using Qoffomentioning

confidence: 99%

Target Detection Algorithm in Crime Recognition Using Artificial Intelligence

Shin¹,

Kim²,

Lee³

2022

Computers, Materials &Amp; Continua

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Presently, suspect prediction of crime scenes can be considered as a classification task, which predicts the suspects based on the time, space, and type of crime. Performing digital forensic investigation in a big data environment poses several challenges to the investigational officer. Besides, the facial sketches are widely employed by the law enforcement agencies for assisting the suspect identification of suspects involved in crime scenes. The sketches utilized in the forensic investigations are either drawn by forensic artists or generated through the computer program (composite sketches) based on the verbal explanation given by the eyewitness or victim. Since this suspect identification process is slow and difficult, it is required to design a technique for a quick and automated facial sketch generation. Machine Learning (ML) and deep learning (DL) models find it useful to automatically support the decision of forensics experts. The challenge is the incorporation of the domain expert knowledge with DL models for developing efficient techniques to make better decisions. In this view, this study develops a new artificial intelligence (AI) based DL model with face sketch synthesis (FSS) for suspect identification (DLFSS-SI) in a big data environment. The proposed method performs preprocessing at the primary stage to improvise the image quality. In addition, the proposed model uses a DL based MobileNet (MN) model for feature extractor, and the hyper parameters of the MobileNet are tuned by quasi oppositional firefly optimization (QOFFO) algorithm. The proposed model automatically draws the sketches of the input facial images. Moreover, a qualitative similarity assessment takes place with the sketch drawn by a professional artist by the eyewitness. If there is a higher resemblance between the two sketches, the suspect will be determined. To validate the effective performance of the DLFSS-SI method, a detailed qualitative and quantitative examination takes place. The experimental outcome stated that the DLFSS-SI model has outperformed the compared methods in terms of mean square error (MSE), peak signal to noise ratio (PSNR), average actuary, and average computation time.

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“…Integrated with data processing, feature extraction and pattern recognition, superior algorithms can address the issues associated with wearable sensors to a certain extent and give better performance of wearable devices in practical healthcare applications in the face of confounding factors. For example, Mansour and coworkers [211] designed a combined algorithm for diabetes and heart disease diagnosis based on long short-term memory networks combined with crow search and isolation forest algorithms. To improve the diagnostic accuracy, this combined algorithm covered different steps, including data acquisition, preprocessing, classification and parameter tuning.…”

Section: Ai-assisted Data Processing and Healthcare Decision-makingmentioning

confidence: 99%

Smart Materials Enabled with Artificial Intelligence for Healthcare Wearables

Zheng

Tang

Omar

et al. 2021

Adv Funct Materials

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Contemporary medicine suffers from many shortcomings in terms of successful disease diagnosis and treatment, both of which rely on detection capacity and timing. The lack of effective, reliable, and affordable detection and real-time monitoring limits the affordability of timely diagnosis and treatment. A new frontier that overcomes these challenges relies on smart health monitoring systems that combine wearable sensors and an analytical modulus. This review presents the latest advances in smart materials for the development of multifunctional wearable sensors while providing a bird's eye-view of their characteristics, functions, and applications. The review also presents the state-of-the-art on wearables fitted with artificial intelligence (AI) and support systems for clinical decision in early detection and accurate diagnosis of disorders. The ongoing challenges and future prospects for providing personal healthcare with AI-assisted support systems relating to clinical decisions are presented and discussed.

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Artificial Intelligence and Internet of Things Enabled Disease Diagnosis Model for Smart Healthcare Systems

Cited by 139 publications

References 19 publications

MasPA: A Machine Learning Application to Predict Risk of Mastitis in Cattle from AMS Sensor Data

MasPA: A Machine Learning Application to Predict Risk of Mastitis in Cattle from AMS Sensor Data

Target Detection Algorithm in Crime Recognition Using Artificial Intelligence

Smart Materials Enabled with Artificial Intelligence for Healthcare Wearables

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