Abstract

Aim of the study: To examine the effect of controlled-cortical impact (CCI), a preclinical model of traumatic brain injury (TBI), on intestinal integrity using a binary classification model of machine learning (ML).Materials and methods: Adult, male C57BL/6 mice were subjected to CCI surgery using a stereotaxic impactor (Impact One™). The rotarod and hot-plate tests were performed to assess the neurological deficits.Results: Mice underwent CCI displayed a remarkable neurological deficit as noticed by decreased latency to fall and lesser paw withdrawal latency in rotarod and hot plate test, respectively. Animals were sacrificed 3 days post-injury (dpi). The colon sections were stained with hematoxylin and eosin (H&E) to integrate with machinery tool-based algorithms. Several stained colon images were captured to build a dataset for ML model to predict the impact of CCI vs sham procedure. The best results were obtained with VGG16 features with SVM RBF kernel and VGG16 features with stacked fully connected layers on top. We achieved a test accuracy of 84% and predicted the disrupted gut permeability and epithelium wall of colon in CCI group as compared to sham-operated mice.Conclusion: We suggest that ML may become an important tool in the development of preclinical TBI model and discovery of newer therapeutics. Keywords: Traumatic brain injury; artificial intelligence; controlled-cortical impact; gut sections; machine learning.

Abstract

Tuberculosis (TB) is a communicable pulmonary disorder and countries with low and middle-income share a higher TB burden as compared to others. The year 2020–2021 universally saw a brutal pandemic in the form of COVID-19, that crushed various lives, health infrastructures, programs, and economies worldwide at an unprecedented speed. The gravity of this estimation gets intensified in systems with limited technological advancements. To assist in the identification of tuberculosis, we propose the ensembling of efficient deep convolutional networks and machine learning algorithms that do not entail heavy computational resources. In this paper, the three of the most efficient deep convolutional networks and machine learning algorithms are employed for resource-effective (low computational and basic Imaging requirements) detection of Tuberculosis. The pivotal features extracted from the deep networks are ensembled and subsequently, the machine learning algorithms are used to identify the images based on the extracted features. The said model underwent k-fold cross-validation and achieved an accuracy of 87.90% and 99.10% with an AUC of 0.94 and 1 respectively in identifying TB infected images from Normal and COVID infected images. Also, the model’s error rate, F-score, and youden’s index values of 0.0093, 0.9901, and 0.9812 for TB versus COVID identification along with the model’s

Abstract

In this paper, we study the three-node Decode-and-Forward (D&F) relay network subject to random and burst packet erasures. The source wishes to transmit an infinite stream of packets to the destination via the relay. The three-node D&F relay network is constrained by a decoding delay of T packets, i.e., the packet transmitted by the source at time i must be decoded by the destination by time i + T . For the individual channels from source to relay and relay to destination, we assume a delay-constrained sliding-window (DCSW) based packet-erasure model that can be viewed as a tractable approximation to the commonly-accepted Gilbert-Elliot channel model. Under the model, any time-window of width w contains either up to a random erasures or else erasure burst of length at most b (≥ a). Thus the source-relay and relay-destination channels are modelled as (a 1 , b 1 , w 1 , T 1 ) and (a 2 , b 2 , w 2 , T 2 ) DCSW channels. We first derive an upper bound on the capacity of the three-node D&F relay network. We then show that the upper bound is tight for the parameter regime: max{b 1 , b 2 } | (T − b 1 − b 2 − max {a 1 , a 2 } + 1) by constructing streaming codes achieving the bound. The code construction requires field size linear in T , and has decoding complexity equivalent to that of decoding an MDS code.

Abstract

Currently, around half of the world's population lives in urban areas, which is only expected to increase in the future. The urban water resources took the brunt of this uncontrolled urbanization and expansion resulting in a swift decline in lakes and ponds in the cities. Bengaluru is one of the most prominent cities in India where the urban water body areas have fallen drastically over the years. This study attempts to assess the impact of urban growth in reducing the surface area of the water bodies in Bengaluru. The study used decadal Land Use Land Cover (LULC) maps between 1988 to 2020 generated from Landsat 4–5 and 8 datasets using the Maximum Likelihood (ML) technique to study and understand urban sprawl in Bengaluru and the changes occurring in water spread area. The results indicate that Bengaluru city has undergone a massive growth from 100 Sq. km to 600 Sq. km between 1988 to 2020. This excessive sprawl has blocked the ephemeral streams that replenish the water bodies, thus causing heavy damage not only to water bodies in the region but also to the vegetation present in the periphery of the expanding city.

Abstract

International Conference on Emerging Frontiers in Electrical and Electronic Technologies intends to provide scientists, researchers, academicians, and industry personnel with a forum to deliberate exchange ideas, and produce research collaborations for the technologies those have the potential to significantly impact the domains of Power system, Power Converters, Control systems, Machine Drives, Embedded systems, Measurement and Sensing technologies.

Abstract

We propose a pair of deep learning models, which employ unsupervised pretraining, attention mechanisms and contrastive learning for representation learning from dictionary definitions, and definition modeling from such representations. Our systems, the Transformers for Learning Dictionaries and Representations (TLDR), were submitted to the SemEval 2022 Task 1: Comparing Dictionaries and Word Embeddings (CODWOE), where they officially ranked first on the definition modeling subtask, and achieved competitive performance on the reverse dictionary subtask. In this paper we describe our methodology and analyse our system design hypotheses.

Abstract

)e temperament (Mizaj) of the body is an essential constituent for health conservancy and diagnosis of several diseases. Hence, general body temperament and uterine dystemperament (Su’-i-Mizaj) with abnormal vaginal discharge (Salayan al-Rahim) need evaluation. In addition, we also applied a computational intelligence technique for enhancing scientific validity to classify the warm-cold and wet-dry temperaments. )is trial included a total of 66 participants with a vaginal discharge of reproductive age. Data included demographic characteristics of the participants, symptoms associated with vaginal discharge, women’s general temperament, and symptoms of uterine dystemperament. Correlation between general body temperament and age, abnormal vaginal discharge, and its associated symptoms was also performed. Additionally, we used the Support Vector Machine-Radial Basis Function (SVM-RBF) model to classify the warm-cold and wet-dry temperaments. Warm general temperament was highly prevalent (77.27%), followed by moderate (19.69%) on the warm-cold temperament scale. In wet-dry temperament, moderate general body temperament (50%) was more prevalent. In warm-cold and wet-dry scores, 78.78% and 74.24% had warm and wet uterine dystemperament, respectively. )e age and symptoms were correlated with general temperament. A strong positive correlation was found between warm general temperament and warm dystemperament of the uterus (r � 0.40, p < 0.009). In addition, our SVM-RBF CV-5 classification model achieved the highest accuracy (99.2%). Our results showed that vaginal discharge is more common in warm general temperament and warm-wet dystemperament of the uterus. )e same has been proven by computational intelligence. Nevertheless, vaginal discharge can also happen in normal and other temperaments.

Abstract

The distributed nature of Internet of Things (IoT) and fog computing mandates that the security provided to the resources of networking and data on these applications is based on a distributed structure. The nodes in a fog computing environment should be working with equal capacities without any necessity of trust among them, in order to allow the layers and infrastructure that constitute the stack of a fog node to be owned and managed by different entities. This idea of security necessitates the need for distributed trust in fog computing that can be realized using the concept of blockchain technology. Blockchain is a distributed and

Abstract

Long documents like contracts, financial documents, etc., are often tedious to read through. Linearly consuming (via scrolling or navigation through default table of content) these documents is time-consuming and challenging. These documents are also authored to be consumed by varied entities (referred to as persona in the paper) interested in only certain parts of the document. In this work, we describe DYNAMICTOC, a dynamic table of contentbased navigator, to aid in the task of non-linear, persona-based document consumption. DYNAMICTOC highlights sections of interest in the document as per the aspects relevant to different personas. DYNAMICTOC is augmented with short questions to assist the users in understanding underlying content. This uses a novel deep-reinforcement learning technique to generate questions on these persona-clustered paragraphs. Human and automatic evaluations suggest the efficacy of both end-to-end pipeline and different components of DYNAMICTOC.

Abstract

The Hopfield network is an example of an artificial neural network used to implement associative memories. A binary digit represents the neuron’s state of a traditional Hopfield neural network. Inspired by the human brain’s ability to cope simultaneously with multiple sensorial inputs, this paper presents three multi-modal Hopfield-type neural networks that treat multi-dimensional data as a single entity. In the first model, called the vector-valued Hopfield neural network, the neuron’s state is a vector of binary digits. Synaptic weights are modeled as finite impulse response (FIR) filters in the second model, yielding the so-called convolutional associative memory. Finally, the synaptic weights are modeled by linear time-varying (LTV) filters in the third model. Besides their potential applications for multi-modal