Prashant Singh Rana

Thapar University · Department of Computer Science and Engineering

Machine learning approaches, such as artificial neural networks (ANN), effectively perform various tasks and provide new predictive models for complicated physiological systems. Examples of Robotics applications involving direct human engagement, such as controlling prosthetic arms, athletic training, and investigating muscle physiology. It is now...

Twitter is a micro-blogging website, which has amassed immense popularity over the past years. Many political parties are now using Twitter for publicity and running campaigns. These campaigns are run on various social media platforms to gain the attention of the voters. In this work, we analyze the effectiveness of such campaigns, by studying the...

This paper develops and compares a methodology for gearbox fault diagnosis based on calculus-enhanced energy operator (CEEO) and machine learning. This paper used three directional, i.e. X, Y, and Z, vibration signals of a bevel gearbox to classify the gearbox faults. Therefore, three directional, i.e. X, Y, and Z, vibration signals of a bevel gear...

Locating veins accurately is a common challenge in clinical settings, particularly with patients where veins are difficult to discern. The development of effective vein visualization technology is crucial for improving patient outcomes and minimizing procedural complications. This study aimed to design, develop, and evaluate a low-cost, high-resolu...

A 2D Map is a collection of pure lines connected to form unique features like roundabouts, cul-de-sacs, buildings, other figures etc. In practical maps created, lines using latitude and longitude. The idea in this paper identifies and validates the roundabouts utilizing geometry of the Map features by spline technique where the geometries are passe...

This article proposes a hybrid network model for video-based human facial expression recognition (FER) system consisting of an end-to-end 3D deep convolutional neural networks. The proposed network combines two commonly used deep 3-dimensional Convolutional Neural Networks (3D CNN) models, ResNet-50 and DenseNet-121, in an end-to-end manner with sl...

Detecting defects in fabrics is a difficult task as there are a lot of variations in the type of fabric and the defect itself. Many methods have been proposed to solve this problem, but their detection rate and accuracy were very low depending on the model tested. To eliminate the variations and to improve the performance, we implemented multilevel...

A Map is a collection of linear and polygonal geometry lines in 2D space. In an actual Map, these lines act as unique features such as roads, carto, points of interest .etc. This paper focuses on providing a solution to one of the basic, though practical, open problems; There is a need to identify kinks in forming a Road in 2D Maps, which is the li...

The transfer of artistic styles into the image has become prevalent in industry and academia. The neural style transfer (NST) is a method to transfer the style of an image to another image. The study and analysis of the NST methods are essential to obtaining realistic, stylized images efficiently. This study explored different methods to perform th...

Smart Grid (SG) has smart instruments which can communicate using Advanced Metering Infrastructure (AMI). This will require SG a large storage space for storing the time-series data. The veracity of data increases with respect to time and becomes a challenging task for data managers. For managing large data sets, preprocessing is a fundamental part...

As cancer cases are looming large worldwide, the applications of data science and machine learning in these fields possess a greater scope especially when there is the availability of data containing the drug synergy score of different combinations. Predictive analytics holds the ability to generate a more efficient and accurate drug synergy score,...

Machine learning (ML) and Deep learning (DL) models are popular in many areas, from business, medicine, industries, healthcare, transportation, smart cities, and many more. However, the conventional centralized training techniques may not apply to upcoming distributed applications, which require high accuracy and quick response time. It is mainly d...

Feature selection is commonly employed for identifying the top n features that significantly contribute to the desired prediction, for example, to find the top 50 or 100 genes responsible for lung or kidney cancer out of 50,000 genes. Thus, it is a huge time- and resource-consuming practice. In this work, we propose a divide-and-conquer technique w...

The combination of radiomics and artificial intelligence has emerged as a strong technique for building predictive models in radiology. This study aims to address the clinically important issue of whether a radiomic profile can predict the overall survival (OS) time of glioblastoma multiforme (GBM) patients having gross tumor resection (GTR) throug...

The purpose of this study was to determine electromyographically if there are significant differences in the movement associated with the knee muscle, gait, leg extension from a sitting position and flexion of the leg upwards for regular and abnormal sEMG data. Surface electromyography (sEMG) data were obtained from the lower limbs of 22 people dur...

Arm Venous Segmentation plays a crucial role in smart venipuncture. e di culties faced in locating veins for intravenous procedures can be diminished using computer vision for vein imaging. To facilitate this, a high-resolution dataset consisting of arm images was curated and has been presented in this study. Leveraging the ability of Near Infrared...

Identification of the activity induced by the macromolecular structures of the humans is of paramount importance for discovering a new drug in the pharmaceutical laboratories. In present study, a novel multilevel hybrid classification model is developed by combining the unsupervised and the supervised machine learning algorithms for the prediction...

To propose and implement an automated machine learning (ML) based methodology to predict the overall survival of glioblastoma multiforme (GBM) patients. In the proposed methodology, we used deep learning (DL) based 3D U‐shaped Convolutional Neural Network inspired encoder‐decoder architecture to segment the brain tumor. Further, feature extraction...

The single image super-resolution (SISR) is a challenging problem due to its ill-posed nature. The main aim of SISR methods is to generate a high-resolution image from a low-resolution image from a given high-resolution image. Recently, learning methods of SISR based gained popularity due to advanced convolution neural networks (CNN). These network...

Exponential growth in digital information outlets and the race to publish has made scientific misinformation more prevalent than ever. However, the task to fact-verify a given scientific claim is not straightforward even for researchers. Scientific claim verification requires in-depth knowledge and great labor from domain experts to substantiate su...

Smart Grids (SG) generate extensive data sets regarding the system variables, viz., and demand and supply. These extremely large data sets are known as big data. Hence, preprocessing of this vast data and integration become critical steps in the load forecasting process. The precise prediction of the load is the primary concern while balancing the...

Person Identification of individuals has dependably been a challenge particularly when it needs to manage the big data sets and the robustness against the components influencing authentication, for example, posture variety, subject to camera distance, light variation, low-quality images and so on. Thus deep learning ends up being an awesome solutio...

With the outbreak of the Coronavirus Disease in 2019, life seemed to be had come to a standstill. To combat the transmission of the virus, World Health Organization (WHO) announced wearing of face mask as an imperative way to limit the spread of the virus. However, manually ensuring whether people are wearing face masks or not in a public area is a...

The COVID-19 pandemic has affected all the countries in the world with its droplet spread mode. The colossal amount of cases has strained all the healthcare systems due to the serious nature of infections especially for people with comorbidities. A very high specificity Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) test is the principal...

This paper presents a novel significance driven inverse distance weighted (SDIDW) filter for the impulsive noise removal in the X-ray images. The proposed SDIDW filter restores the noisy pixel using minimum number of nearest noise-free pixels to achieve good estimation while exhibiting low computational complexity. In the proposed filter, higher pr...

Big data has been a topic of interest for many researchers and industries for the past few decades. Due to the exponential growth of technology today, a tremendous amount of data is generated every minute. This article provides a strategic review study on Big data in the healthcare sector. In particular, this article highlights various applications...

Objective Glioblastoma multiforme (GBM) is a grade IV brain tumour with very low life expectancy. Physicians and oncologists urgently require automated techniques in clinics for brain tumour segmentation (BTS) and survival prediction (SP) of GBM patients to perform precise surgery followed by chemotherapy treatment. Methods This study aims at exam...

Exponential growth in digital information outlets and the race to publish has made scientific misinformation more prevalent than ever. However, the task to fact-verify a given scientific claim is not straightforward even for researchers. Scientific claim verification requires in-depth knowledge and great labor from domain experts to substantiate su...

The escalating transmission intensity of COVID-19 pandemic is straining the healthcare systems worldwide. Due to the unavailability of effective pharmaceutical treatment and vaccines, monitoring social distancing is the only viable tool to strive against asymptomatic transmission. Pertaining to the need of monitoring the social distancing at popula...

In the electrical discharge machining (EDM) process, especially during the machining of hardened steels, changes in tool shape have been identified as one of the major problems. To understand the aforesaid dilemma, an initiative was undertaken through this experimental study. To assess the distortion in tool shape that occurs during the machining o...

This paper describes our participating system run to the argumentative text understanding shared task for AI Debater at NLPCC 2021 (http://www.fudan-disc.com/sharedtask/AIDebater21/tracks.html). The tasks are motivated towards developing an autonomous debating system. We make an initial attempt with Track-3, namely, argument pair extraction from pe...

Smart Grids (SG) have smart meters and advance metering infrasturutre (AMI) which generates huge data. This data can be used for predicting energy consumption using big data analytics. A very limited work has been carried out in the literature which shows the utilization of big data in energy consumption prediction. In this paper, the proposed meth...

Physical Classification of ripe fruits is an expensive affair in the agriculture industry and human error can lead to inaccurate results. This paper introduces the concept of an intelligent AI-based system using spectrophotometry and computer vision for automated fruit segregation based on their grade. When the fruit is fed into the proposed system...

Mental disorders have been identified as one among the leading causes of the global disease burden. Despite being one of the first nations in the world to identify mental health as an important indicator of personal well-being and having adequate plans and policies for ensuring the same, one in every seven Indians is affected by mental disorders of...

Approaches for detecting roundabouts in maps are heavily dependent on looking at the problem from a machine-learning powered computer vision perspective. In this paper, we propose a fresh approach, taking core map data into account, that supplements existing techniques in a phenomenal way thereby significantly reducing the machine learning effort i...

Generative Adversarial Network (GAN) has gained eminence in a very short period as it can learn deep data distributions with the help of a competitive process among two networks. GANs can synthesize images/videos from latent noise with a minimized adversarial cost function. The cost function plays a deciding factor in GAN training and thus, it is o...

Rice is a staple food crop around the world, and its demand is likely to rise significantly with growth in population. Increasing rice productivity and production largely depends on the availability of irrigation water. Thus, the efficient application of irrigation water such that the crop doesn’t experience moisture stress is of utmost importance....

Recent researchers widely used nanoparticle additives for improving thermal and rheological properties of machine lubricant. In present study the effect of Al2O3 and CeO2 nanoparticles on transmission oil (SAE30), hydraulic oil (HYDREX100) and gear oil (EP90) of heavy earth moving machinery is investigated. Nano-lubricant samples are prepared in 0....

The data flow is an important parameter used in the optimization problem of Wireless Sensor Networks. This paper presents an expert approach for improved data flow prediction based on data discretization and artificial intelligence. The proposed approach has been implemented on various machine learning methods (a total of 17 methods). This data flo...

Rice is one of the world’s most popular food crops. Since its production is dependent on intensive water use, water management is critical to ensure sustainability of water resource. However, very limited data is available on water use in rice irrigation. In the present study, traditional machine learning methods have been used to predict the irrig...

The big problem for neural network models which are trained to count instances is that whenever test range goes high training range generalization error increases i.e. they are not good generalizers outside training range. Consider the case of automating cell counting process where more dense images with higher cell counts are commonly encountered...

The in-silico toxicity prediction techniques are useful to reduce rodents testing (in-vivo). Authors have proposed a computational method (in silico) for the toxicity prediction of small drug molecules using their various physicochemical properties (molecular descriptors), which can bind to the antioxidant response elements (AREs). The software PaD...

The major intent of peptide vaccine designs, immunodiagnosis and antibody productions is to accurately identify linear B-cell epitopes. The determination of epitopes through experimental analysis is highly expensive. Therefore, it is desirable to develop a reliable model with significant improvement in prediction models. In this study, a hybrid mod...

Modern 5G-enabled Intelligent Transportation System (ITS) provides comfort and safety to the end users by using various models and techniques most of which are based on the machine learning-based techniques. However, a large number of issues such as congestion control, safety and security, traffic management exist in modern ITS for which AI-based t...

The wireless sensor network (WSN) is gaining paramount importance due to its application in real-time monitoring of vast geographical regions. The deployment paradigm shift is taking place from mobile computing to data science. Bridging the two technologies results in the development of dependable network in which security plays a pivotal role. Thi...

Mental disorders have been identified as one among the leading causes of the global disease burden. Despite being one of the first nations in the world to identify mental health as an important indicator of personal well-being and having adequate plans and policies for ensuring the same, one in every seven Indians is affected by mental disorders of...

OpenSim is a modeling and simulation-based open source software for the purpose of advanced rehabilitation research work. It has an extensive range of applications, which enables rehabilitation by discovering treatments for neurological disorders followed by therapies to cure movement abnormalities. In the field of orthopedics, OpenSim provides the...

This paper is a case study of utilizing machine learning for developing a decision-making system for auditors before initializing the audit fieldwork of public firms. Annual data of 777 firms from 14 different sectors are collected and a MCTOPE (Multi criteria ToPsis based Ensemble) framework is implemented to build an ensemble classifier. MCTOPE f...

Life threatening diseases like adult T-cell leukemia, neurodegenerative diseases, demyelinating diseases such as HTLV-1 based myelopathy/tropical spastic paraparesis (HAM/TSP), hypocalcaemia, and bone lesions are caused by group of human retrovirus known as Human T-cell Lymphotropic virus (HTLV). Out of the four different types of HTLVs, HTLV-1 is...

In humans, oxidative stress is involved in the development of diabetes, cancer, hypertension, Alzheimers' disease, and heart failure. One of the mechanisms in the cellular defence against oxidative stress is the activation of the Nrf2-antioxidant response element (ARE) signalling pathway. Computation of activity, efficacy, and potency score of ARE...

Ozone and particulate matter (PM), \(\hbox {PM}_{10}\) and \(\hbox {PM}_{2.5}\), were monitored along with meteorological parameters at a semi-urban location, Patiala, in the north-western Indo-Gangetic plain from December 2013 to November 2014. The annual mean concentration levels of \(\hbox {PM}_{10}, \hbox {PM}_{2.5}\) and ozone were recorded as...

Multiple sclerosis (MS) is a neurodegenerative disease characterized by lesions in the central nervous system (CNS). Inflammation and demyelination are the leading causes of neuronal death and brain lesions formation. The immune reactivity is believed to be essential in the neuronal damage in MS. Cytokines play important role in differentiation of...

The authors have proposed an efficient multilevel prediction model for better activity assessment to test whether certain chemical compounds can disrupt processes in the human body that may create negative health effects. Here, a computational method (in-silico) is proposed for the quality prediction of drugs in terms of their activity, activity sc...

Toxicity prediction of the pre-clinical trial drugs of AhR

Transactions through the web are now a progressive mechanism to access an ever increasing range of services over more and more diverse environments. The internet provides many opportunities for companies to provide personalized online services to their customers but the quality and novelty of some web services may adversely affect the appeal and us...