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In recent times, deep learning has emerged as a great resource to help research in medical sciences. A lot of work has been done with the help of computer science to expose and predict different diseases in human beings. This research uses the Deep Learning algorithm Convolutional Neural Network (CNN) to detect a Lung Nodule, which can be cancerous, from different CT Scan images given to the model. For this work, an Ensemble approach has been developed to address the issue of Lung Nodule Detection. Instead of using only one Deep Learning model, we combined the performance of two or more CNNs so they could perform and predict the outcome with more accuracy. The LUNA 16 Grand challenge dataset has been utilized, which is available online on their website. The dataset consists of a CT scan with annotations that better understand the data and information about each CT scan. Deep Learning works the same way our brain neurons work; therefore, deep learning is based on Artificial Neural Networks. An extensive CT scan dataset is collected to train the deep learning model. CNNs are prepared using the data set to classify cancerous and non-cancerous images. A set of training, validation, and testing datasets is developed, which is used by our Deep Ensemble 2D CNN. Deep Ensemble 2D CNN consists of three different CNNs with different layers, kernels, and pooling techniques. Our Deep Ensemble 2D CNN gave us a great result with 95% combined accuracy, which is higher than the baseline method., (© 2023. The Author(s).)

Chronic wounds affect the lives of millions of individuals globally, and due to substantial medical costs, treating chronic injuries is very challenging for the healthcare system. The classification of regular wound type is essential in wound care management and diagnosis since it can assist clinicians in deciding on the appropriate treatment method. Hence, an effective wound diagnostic tool would enable clinicians to classify the different types of chronic wounds in less time. The majority of the existing chronic wound classification methods are mainly focused on the binary classification of the wound types. A few approaches exist that classify chronic wounds into multiple classes, but these achieved lower performances for pressure and diabetic wound classification. Furthermore, cross-corpus evaluation is absent in chronic wound type classification, in order to better evaluate the efficacy of existing methods on real-time wound images. To address the limitations of the current studies, we propose a novel Swish-ELU EfficientNet-B4 (SEEN-B4) deep learning framework that can effectively identify and classify chronic wounds into multiple classes. Moreover, we also extend the existing Medetec and Advancing the Zenith of Healthcare (AZH) datasets to deal with the class imbalance problem of these datasets. Our proposed model is evaluated on publicly available AZH and Medetec datasets and their extended versions. Our experimental results indicate that the proposed SEEN-B4 model has attained an accuracy of 87.32%, 88.17%, 88%, and 89.34% on the AZH, Extended AZH, Medetec, and Extended Medetec datasets, respectively. We also show the effectiveness of our method against the existing state-of-the-art (SOTA) methods. Furthermore, we evaluated the proposed model for the cross-corpora scenario to demonstrate the model generalization aptitude, and interpret the model's result through explainable AI techniques. The experimental results show the proposed model's effectiveness for classifying chronic wound types. [ABSTRACT FROM AUTHOR]

The increasing prevalence rate of chronic wounds across the globe has negatively impacted the patients' lives and placed a burden on the healthcare system. With the advancement in artificial intelligence techniques, the wound assessment process is also moving towards automation, which will facilitate clinicians in monitoring the wound and prescribing suitable medication. Wound area measurement is important in assessing wounds and their healing progress; however, wound segmentation aids in measuring wound area. Because of the hazy wound boundaries, varied colorations, irregular shapes, and diverse wound attributes, accurate wound segmentation is challenging in medical imaging. Therefore, this paper presents SwishRes-U-Net (SRU-Net), a deep-learning framework for accurately segmenting chronic wounds, particularly diabetic foot ulcers. SRU-Net is based on dual UNets having encoders and decoders. To deal with the issue of limited data samples, pre-trained SwishResNet is utilized in the encoder1; however, the encoder2 is trained from scratch on the wound dataset. We also introduce spatial-channel squeeze-&-excitation (SC-S&E) blocks embedded in decoders and encoder2 modules. SC-S&E block extracts spatial and channel-wise relevant features that help accurately segment wound area. The effectiveness of the proposed model is evaluated utilizing two wound segmentation datasets: the AZH wound segmentation dataset and the foot ulcer segmentation (FUSeg) challenge dataset. SRU-Net has achieved 92.57% and 91.81% dice scores on AZH and FUSeg datasets. Moreover, cross-dataset validation is also conducted to assess the model's generalization potential. Quantitative and qualitative analysis of the obtained results reveal the efficacy of the proposed SRU-Net for the segmentation of chronic wound images. [ABSTRACT FROM AUTHOR]

In this article, a novel drive mode, "intelligent vehicle drive mode" (IVDM), was proposed, which augments the vehicle engine performance in real-time. This drive mode predicts the driver behavior vector (DBV), which optimizes the vehicle engine performance, and the metric of optimal vehicle engine performance was defined using the elements of engine operating point (EOP) and heating ventilation and air conditioning system (HVAC). Deep learning (DL) models were developed by mapping the vehicle level vectors (VLV) with EOP and HVAC parameters, and the trained functions were utilized to predict the future states of DBV reflecting augmented vehicle engine performance. The iterative analysis was performed by empirically estimating the future states of VLVin the allowable range of DBV and was fed into the DL model to predict the performance vectors. The defined vehicle engine performance metric was applied to the predicted vectors, and thus optimal DBV is the instantaneous output of the IVDM. The analytical and validation techniques were developed using field data obtained from General Motors Inc., Warren, Michigan. Finally, the proposed concept was quantified by analyzing the instantaneous engine efficiency (IEE) and smoothness measure of the instantaneous engine map (IEM). [ABSTRACT FROM AUTHOR]

Vehicular technology has integrated many features in the system, which enhances the safety and comfort of the user. Among these features, heating, ventilation, and air conditioning (HVAC) is the only feature that maintains the set cabin air temperature (CAT). The user's command drives the set CAT, and the thermostat provides feedback to the HVAC to maintain the set CAT. The CAT is increased by extracting the heat from the engine surface produced by the fuel combustion, whereas the CAT is reduced by the known processes of the air conditioning system (ACS). Therefore, the CAT driven by the user's command may not be optimal, and estimating the optimal CAT is still unsolved. In this work, we propose a new process where the user can input a range for CAT instead of a single value. Optimal HVAC criteria were defined, and the CAT was estimated by performing iterative analysis in the user-selected range satisfying the criteria. The HVAC criteria were defined based on two measurable parameters: air conditioning refrigerant fluid pressure (ACRFP) and engine surface temperature (EST) empirically defined as the vector CATOP. In this article, a NARX DL model was used by mapping the vehicle-level vectors (VLV) to predict the CATOP in real-time using field data obtained from a 2020 Cadillac CT5 test vehicle. Utilising the DL model, CATOP for future time steps was predicted by varying the CAT in the definite range and applying HVAC criteria. Thus, an optimal set CAT was estimated, corresponding to the optimal CATOP defined by the HVAC criteria. We performed the validation of the DL model for multiple datasets using traditional statistical techniques, namely, signal-to-noise ratio (SNR) values, first-order derivatives (FOD), and root-mean-square error (RMSE). [ABSTRACT FROM AUTHOR]

This article presents a novel methodology to predict the optimal adaptive cruise control set speed profile (ACCSSP) by optimizing the engine operating conditions (EOC) considering vehicle level vectors (VLV) (body parameter, environment, driver behaviour) as the affecting parameters. This paper investigates engine operating conditions (EOC) criteria to develop a predictive model of ACCSSP in real-time. We developed a deep learning (DL) model using the NARX method to predict engine operating point (EOP) mapping the VLV. We used real-world field data obtained from Cadillac test vehicles driven by activating the ACC feature for developing the DL model. We used a realistic set of assumptions to estimate the VLV for the future time steps for the range of allowable speed values and applied them at the input of the developed DL model to generate multiple sets of EOP's. We imposed the defined EOC criteria on these EOPs, and the top three modes of speeds satisfying all the requirements are derived at each second. Thus, three eligible speed values are estimated for each second, and an additional criterion is defined to generate a unique ACCSSP for future time steps. A performance comparison between predicted and constant ACCSSP's indicates that the predictive model outperforms constant ACCSSP. [ABSTRACT FROM AUTHOR]

Diabetic patients are at the risk of developing different eye diseases i.e., diabetic retinopathy (DR), diabetic macular edema (DME) and glaucoma. DR is an eye disease that harms the retina and DME is developed by the accumulation of fluid in the macula, while glaucoma damages the optic disk and causes vision loss in advanced stages. However, due to slow progression, the disease shows few signs in early stages, hence making disease detection a difficult task. Therefore, a fully automated system is required to support the detection and screening process at early stages. In this paper, an automated disease localization and segmentation approach based on Fast Region-based Convolutional Neural Network (FRCNN) algorithm with fuzzy k-means (FKM) clustering is presented. The FRCNN is an object detection approach that requires the bounding-box annotations to work; however, datasets do not provide them, therefore, we have generated these annotations through ground-truths. Afterward, FRCNN is trained over the annotated images for localization that are then segmented-out through FKM clustering. The segmented regions are then compared against the ground-truths through intersection-over-union operations. For performance evaluation, we used the Diaretdb1, MESSIDOR, ORIGA, DR-HAGIS, and HRF datasets. A rigorous comparison against the latest methods confirms the efficacy of the approach in terms of both disease detection and segmentation. [ABSTRACT FROM AUTHOR]

The growing number of voice-controlled devices (VCDs), i.e. Google Home, Amazon Alexa, etc., has resulted in automation of home appliances, smart gadgets, and next generation vehicles, etc. However, VCDs and voice-activated services i.e. chatbots are vulnerable to audio replay attacks. Our vulnerability analysis of VCDs shows that these replays could be exploited in multi-hop scenarios to maliciously access the devices/nodes attached to the Internet of Things. To protect these VCDs and voice-activated services, there is an urgent need to develop reliable and computationally efficient solutions to detect the replay attacks. This paper models replay attacks as a nonlinear process that introduces higher-order harmonic distortions. To detect these harmonic distortions, we propose the acoustic ternary patterns-gammatone cepstral coefficient (ATP-GTCC) features that are capable of capturing distortions due to replay attacks. Error correcting output codes model is used to train a multi-class SVM classifier using the proposed ATP-GTCC feature space and tested for voice replay attack detection. Performance of the proposed framework is evaluated on ASVspoof 2019 dataset, and our own created voice spoofing detection corpus (VSDC) consisting of bona-fide, first-order replay (replayed once), and second-order replay (replayed twice) audio recordings. Experimental results signify that the proposed audio replay detection framework reliably detects both first and second-order replay attacks and can be used in resource constrained devices. [ABSTRACT FROM AUTHOR]