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Are visual face processing mechanisms the same in the left and right cerebral hemispheres? The possibility of such ‘duplicated processing’ seems puzzling in terms of neural resource usage, and we currently lack a precise characterization of the lateral differences in face processing. To address this need, we have undertaken a three-pronged approach. Using functional magnetic resonance imaging, we assessed cortical sensitivity to facial semblance, the modulatory effects of context and temporal response dynamics. Results on all three fronts revealed systematic hemispheric differences. We found that: (i) activation patterns in the left fusiform gyrus correlate with image-level face-semblance, while those in the right correlate with categorical face/non-face judgements. (ii) Context exerts significant excitatory/inhibitory influence in the left, but has limited effect on the right. (iii) Face-selectivity persists in the right even after activity on the left has returned to baseline. These results provide important clues regarding the functional architecture of face processing, suggesting that the left hemisphere is involved in processing ‘low-level’ face semblance, and perhaps is a precursor to categorical ‘deep’ analyses on the right.

Detection and recognition of military vehicle from a given image or a video frame with the help of unmanned aircraft system is the major issue which we are concerned about. Vehicle identification and classification from a resource constraint device embedded on an aerial vehicle integrated with an intelligent object detection algorithm, is a big support for defence agency. The vehicle can be controlled both manually and autonomously. However, there is no military objects/vehicles dataset openly available with different varieties of military classes. Hence, we propose our dataset having 6772 images with classes namely, Military Trucks, Military Tanks, Military Aircrafts, Military Helicopters, Civilian Car and Civilian aircraft. Quantize SSD Mobilenet v2 and Tiny Yolo v3 deep learning models are trained on our dataset and compared its performance over resources constraint edge devices. The observations and results from the research show that Tiny Yolo v3 performs well over the other model and is highly efficient and can even run with edge based devices due to it’s light weight. There is a detailed generalised mathematical calculation provided which calculates the number of flight paths and the total number of frames required to cover a given area for surveillance using available hardware specification. This work will be suitable for classifying the military and civilian vehicle in the real time scenario using edge device over UAVs.

Electric power load forecasting is an essential task in the power system restructured environment for successful trading of power in energy exchange and economic operation. In this paper, various regression models have been used to predict the active power load. Model optimization with dimensionality reduction has been done by observing correlation among original input features. Load data has been collected from a 33/11 kV substation near Kakathiya University in Warangal. The regression models with available load data have been trained and tested using Microsoft Azure services. Based on the results analysis it has been observed that the proposed regression models predict the demand on substation with better accuracy.

In India, nearly 12 million visually impaired people had difficulty in identifying the currency notes. There is a need to develop an application that can recognize the currency note and provide a vocal message. In this paper, a novel lightweight Convolutional Neural Network (CNN) model is developed for efficient web and mobile applications to recognize the Indian currency notes. A new dataset for Indian currency notes has been created to train, validate, and test the CNN model. This CNN based web and mobile applications will provide a text and audio output based on the recognized currency note. The proposed model is developed using TensorFlow and improved by selection of optimal hyperparameter value, and compared with existing well known CNN architectures using transfer learning. Based on the results it has been observed that proposed model perform well over six widely used existing architectures in terms of training and testing accuracy.

Software-defined networking will be a critical component of the networking domain as it transitions from a standard networking design to an automation network. To meet the needs of the current scenario, this architecture redesign becomes mandatory. Besides, machine learning (ML) and deep learning (DL) techniques provide a significant solution in network attack detection, traffic classification, etc. The DDoS attack is still wreaking havoc. Previous work for DDoS attack detection in SDN has not yielded significant results, so the author has used the most recent deep learning technique to detect the attacks. In this paper, we aim to classify the network traffic into normal and malicious classes based on features in the available dataset by using various deep learning techniques. TCP, UDP, and ICMP traffic are considered normal; however, malicious traffic includes TCP Syn Attack, UDP Flood, and ICMP Flood, all of which are DDoS attack traffic. The major contribution of this paper is the identification of novel features for DDoS attack detection. Novel features are logged into the CSV file to create the dataset, and machine learning algorithms are trained on the created SDN dataset. Various work which has already been done for DDoS attack detection either used a non-SDN dataset or the research data is not made public. A novel hybrid machine learning model is utilized to perform the classification. The dataset used by the ML/DL algorithms is a collection of public datasets on DDoS attacks as well as an experimental DDoS dataset generated by us and publicly available on the Mendeley Data repository. A Python application performs the classification of traffic into one of the classes. From the various classifiers used, the accuracy score of 99.75% is achieved with Stacked Auto-Encoder Multi-layer Perceptron (SAE-MLP). To measure the effectiveness of the SDN-DDoS dataset, the other publicly available datasets are also evaluated against the same deep learning algorithms, and traffic classification accuracy is found to be significantly higher with the SDN-DDoS dataset. The attack detection time of 216.39 s also serve as experimental evidence.

Cardiac resynchronization therapy (CRT) is often deferred in dialysis-dependent patients with heart failure (HF) because of a perceived lack of benefit and potentially higher risks, although the outcomes associated with CRT in dialysis have not been reported. We therefore studied our center's experience with CRT in dialysis-dependent patients. We constructed a descriptive assessment of these patients (n = 15) and performed a case-control analysis matching for age, gender, bundle branch morphology, diabetes mellitus, cardiomyopathy origin, and β-blocker and angiotensin-converting enzyme inhibitor or angiotensin II receptor blocker use. Baseline and 6-month echocardiograms were assessed for evidence of reverse remodeling. No periprocedural or long-term complications were observed among dialysis patients. Heterogenous improvement in ejection fraction (+3.1 ± 9.2%) was noted and 2 patients derived absolute improvements of 8% and 22%, respectively. Dialysis patients demonstrated the following 3-year event rates: HF hospitalization, 31%; all-cause hospitalization, 100%; mortality, 73%; and HF hospitalization or death, 82%. In the case-control analysis, controls demonstrated superior reverse remodeling (+9.2 ± 9.5% increase in ejection fraction), decreased mortality (73% vs 44%, p = 0.038), and all-cause hospitalizations (76% vs 100%, p = 0.047), with no difference in HF hospitalizations (p = 0.39), compared with dialysis patients. In conclusion, at our center, the dialysis-dependent patients with HF who underwent CRT implantation did so safely and no serious complications were observed. Certain dialysis patients demonstrated compelling improvement after device implantation. Compared with matched controls, dialysis patients were at increased risk for adverse events and worsened echocardiographic outcomes.

Biometrics is the study of unique characteristics present in the human body such as fingerprint, palm-print, retina, iris, footprint, etc. While other traits have been explored widely, only a few people have been considered the foot-palm region, despite having unique properties. Prior work has explored the foot shape features using length, width, major axis, minor axis, centroid, etc. but they are not reliable for personal verification due to similarity in the physical composition of two persons. It increases the demand for more unique features based on the footprint. Footprint texture features coming from creases of foot palm are unique and permanent like palmprint texture features. Hence the main objective of the paper is to investigate various kinds of texture feature techniques. These techniques will be further used in correct extraction of footprint features. After extraction of footprint features a detailed experimental analysis is performed to discover the uniqueness in foot texture. It is further utilized to test its viability as a human recognition trait. We describe a detailed feature extraction and classification technique applied to a collected footprint data-set. For feature extraction, we use three techniques: Gray Level Co-occurrence Matrix (GLCM), Histogram Oriented Gradient (HOG), and Local Binary Patterns (LBP). Feature classification is performed using four techniques: Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Ensemble Subspace Discriminant (ESD). GLCM provides less accuracy, while HOG generates a big feature vector which takes more execution time. LBP provides a trade-off between the accuracy and the execution time. Detailed quantitative experiments show: GLCM with LDA provides an accuracy of 88.5%, HOG with Fine-KNN achieves 86.5% accuracy and LBP with LDA achieves the accuracy of 97.9%.

The majority of US adult cancer patients today are diagnosed and treated outside the context of any clinical trial (that is, in the real world). Although these patients are not part of a research study, their clinical data are still recorded. Indeed, data captured in electronic health records form an ever-growing, rich digital repository of longitudinal patient experiences, treatments, and outcomes. Likewise, genomic data from tumor molecular profiling are increasingly guiding oncology care. Linking real-world clinical and genomic data, as well as information from other co-occurring data sets, could create study populations that provide generalizable evidence for precision medicine interventions. However, the infrastructure required to link, ensure quality, and rapidly learn from such composite data is complex. We outline the challenges and describe a novel approach to building a real-world clinico-genomic database of patients with cancer. This work represents a case study in how data collected during routine patient care can inform precision medicine efforts for the population at large. We suggest that health policies can promote innovation by defining appropriate uses of real-world evidence, establishing data standards, and incentivizing data sharing.

Wireless energy transfer (WET) technology has been proven to mitigate the energy shortage challenge faced by the Internet of Things (IoT), which encompasses sensor networks. Exploiting a Mobile Charger (MC) to energize critical sensors provides a new dimension to maintain continual network operations. Still, existing solutions are not robust as they suffer from high charging delays at the sensor end due to inefficient scheduling. Moreover, charging efficiency is degraded in those schemes due to fixed charging thresholds and ignoring scheduling feasibility conditions. Thus, intelligent scheduling for an MC is needed based on decision-making through multiple network performance-affecting attributes, but blending multiple attributes together for wise scheduling decision-making remains challenging, which is overlooked in previous research. Fortunately, Multi-Criteria Decision Making (MCDM) is best-fit herein for considering numerous attributes and picking the most suitable sensor node to charge next. To this end, we have proposed solving the scheduling problem by combining two MCDM techniques, i.e., Combinative Distance Based Assessment (CODAS) and the Best Worst Method (BWM). The attributes used for the decision are the distance to MC, energy consumption rate, the remaining energy of nodes, and neighborhood criticality. The relative weights of all considered network attributes are calculated by BWM, which is followed by CODAS to select the most appropriate node to be charged next. To make the scheme more realistic and practical in time-critical applications, the dynamic threshold of nodes is calculated along with formulation scheduling feasibility conditions. Simulation results demonstrate the efficiency of the proposed scheme over the competing approaches on various performance parameters.

Tumor mutational burden (TMB) is a potential biomarker associated with response to immune checkpoint inhibitor therapies. The prognostic value associated with TMB in the absence of immunotherapy is uncertain. To assess the prevalence of high TMB (TMB-H) and its association with overall survival (OS) among patients not treated with immunotherapy with the same 10 tumor types from the KEYNOTE-158 study. This retrospective cohort study evaluated the prognostic value of TMB-H, assessed by Foundation Medicine (FMI) and defined as at least 10 mutations/megabase (mut/Mb) in the absence of immunotherapy. Data were sourced from the deidentified Flatiron Health-FMI clinicogenomic database collected up to July 31, 2018. Eligible patients were aged 18 years or older with any of the following solid cancer types: anal, biliary, endometrial, cervical, vulvar, small cell lung, thyroid, salivary gland, mesothelioma, or neuroendocrine tumor. Patients with microsatellite instability-high tumors were excluded from primary analysis. For OS analysis, patients were excluded if immunotherapy started on the FMI report date or earlier or if patients died before January 1, 2012, and patients were censored if immunotherapy was started later than the FMI report date. Data were analyzed from November 2018 to February 2019. Overall survival was analyzed using the Kaplan-Meier method and Cox proportional hazards model, adjusting for age, sex, cancer types, practice type, and albumin level. Of 2589 eligible patients, 1671 (64.5%) were women, and the mean (SD) age was 63.7 (11.7) years. Median (interquartile range) TMB was 2.6 (1.7-6.1) mut/Mb, and 332 patients (12.8%) had TMB-H (≥10 mut/Mb). Prevalence of TMB-H was highest among patients with small cell lung cancer (40.0%; 95% CI, 34.7%-45.6%) and neuroendocrine tumor (29.3%; 95% CI, 22.8%-36.6%) and lowest was among patients with mesothelioma (1.2%; 95% CI, 0.3%-4.4%) and thyroid cancer (2.7%; 95% CI, 1.2%-5.7%). Adjusted hazard ratio for OS of patients not treated with immunotherapy with TMB-H vs those without TMB-H was 0.94 (95% CI, 0.77-1.13). Comparable results were observed when including patients with high microsatellite instability tumors and calculating OS from first observed antineoplastic treatment date. These findings suggest that prevalence of TMB-H varies widely depending on tumor type and TMB-H does not appear to be a factor associated with OS among patients across these cancer types treated in the absence of immunotherapy.