Manipal University Jaipur

Multidrug resistance of the pathogenic microorganisms to the antimicrobial drugs has become a major impediment toward successful diagnosis and management of infectious diseases. Recent advancements in nanotechnology-based medicines have opened new horizons for combating multidrug resistance in microorganisms. In particular, the use of silver nanoparticles (AgNPs) as a potent antibacterial agent has received much attention. The most critical physico-chemical parameters that affect the antimicrobial potential of AgNPs include size, shape, surface charge, concentration and colloidal state. AgNPs exhibits their antimicrobial potential through multifaceted mechanisms. AgNPs adhesion to microbial cells, penetration inside the cells, ROS and free radical generation, and modulation of microbial signal transduction pathways have been recognized as the most prominent modes of antimicrobial action. On the other side, AgNPs exposure to human cells induces cytotoxicity, genotoxicity, and inflammatory response in human cells in a cell-type dependent manner. This has raised concerns regarding use of AgNPs in therapeutics and drug delivery. We have summarized the emerging endeavors that address current challenges in relation to safe use of AgNPs in therapeutics and drug delivery platforms. Based on research done so far, we believe that AgNPs can be engineered so as to increase their efficacy, stability, specificity, biosafety and biocompatibility. In this regard, three perspectives research directions have been suggested that include (1) synthesizing AgNPs with controlled physico-chemical properties, (2) examining microbial development of resistance toward AgNPs, and (3) ascertaining the susceptibility of cytoxicity, genotoxicity, and inflammatory response to human cells upon AgNPs exposure.

Bitcoin is a popular cryptocurrency that records all transactions in a distributed append-only public ledger called blockchain. The security of Bitcoin heavily relies on the incentive-compatible proof-of-work (PoW) based distributed consensus protocol, which is run by the network nodes called miners. In exchange for the incentive, the miners are expected to maintain the blockchain honestly. Since its launch in 2009, Bitcoin economy has grown at an enormous rate, and it is now worth about 150 billions of dollars. This exponential growth in the market value of bitcoins motivate adversaries to exploit weaknesses for profit, and researchers to discover new vulnerabilities in the system, propose countermeasures, and predict upcoming trends. In this paper, we present a systematic survey that covers the security and privacy aspects of Bitcoin. We start by giving an overview of the Bitcoin system and its major components along with their functionality and interactions within the system. We review the existing vulnerabilities in Bitcoin and its major underlying technologies such as blockchain and PoW-based consensus protocol. These vulnerabilities lead to the execution of various security threats to the standard functionality of Bitcoin. We then investigate the feasibility and robustness of the state-of-the-art security solutions. Additionally, we discuss the current anonymity considerations in Bitcoin and the privacy-related threats to Bitcoin users along with the analysis of the existing privacy-preserving solutions. Finally, we summarize the critical open challenges, and we suggest directions for future research towards provisioning stringent security and privacy solutions for Bitcoin.

Deep learning models are widely used in the automatic analysis of radiological images. These techniques can train the weights of networks on large datasets as well as fine tuning the weights of pre-trained networks on small datasets. Due to the small COVID-19 dataset available, the pre-trained neural networks can be used for diagnosis of coronavirus. However, these techniques applied on chest CT image is very limited till now. Hence, the main aim of this paper to use the pre-trained deep learning architectures as an automated tool to detection and diagnosis of COVID-19 in chest CT. A DenseNet201 based deep transfer learning (DTL) is proposed to classify the patients as COVID infected or not i.e. COVID-19 (+) or COVID (-). The proposed model is utilized to extract features by using its own learned weights on the ImageNet dataset along with a convolutional neural structure. Extensive experiments are performed to evaluate the performance of the propose DTL model on COVID-19 chest CT scan images. Comparative analyses reveal that the proposed DTL based COVID-19 classification model outperforms the competitive approaches.Communicated by Ramaswamy H. Sarma.

In the modern era, deep learning techniques have emerged as powerful tools in image recognition. Convolutional Neural Networks, one of the deep learning tools, have attained an impressive outcome in this area. Applications such as identifying objects, faces, bones, handwritten digits, and traffic signs signify the importance of Convolutional Neural Networks in the real world. The effectiveness of Convolutional Neural Networks in image recognition motivates the researchers to extend its applications in the field of agriculture for recognition of plant species, yield management, weed detection, soil, and water management, fruit counting, diseases, and pest detection, evaluating the nutrient status of plants, and much more. The availability of voluminous research works in applying deep learning models in agriculture leads to difficulty in selecting a suitable model according to the type of dataset and experimental environment. In this manuscript, the authors present a survey of the existing literature in applying deep Convolutional Neural Networks to predict plant diseases from leaf images. This manuscript presents an exemplary comparison of the pre-processing techniques, Convolutional Neural Network models, frameworks, and optimization techniques applied to detect and classify plant diseases using leaf images as a data set. This manuscript also presents a survey of the datasets and performance metrics used to evaluate the efficacy of models. The manuscript highlights the advantages and disadvantages of different techniques and models proposed in the existing literature. This survey will ease the task of researchers working in the field of applying deep learning techniques for the identification and classification of plant leaf diseases.

Blockchain is a revolutionary technology that enables users to communicate in a trust-less manner. It revolutionizes the modes of business between organizations without the need for a trusted third party. It is a distributed ledger technology based on a decentralized peer-to-peer (P2P) network. It enables users to store data globally on thousands of computers in an immutable format and empowers users to deploy small pieces of programs known as smart contracts. The blockchain-based smart contract enables auto enforcement of the agreed terms between two untrusted parties. There are several security vulnerabilities in Ethereum blockchain-based smart contracts, due to which sometimes it does not behave as intended. Because a smart contract can hold millions of dollars as cryptocurrency, so these security vulnerabilities can lead to disastrous losses. In this paper, a systematic review of the security vulnerabilities in the Ethereum blockchain is presented. The main objective is to discuss Ethereum smart contract security vulnerabilities, detection tools, real life attacks and preventive mechanisms. Comparisons are drawn among the Ethereum smart contract analysis tools by considering various features. From the extensive depth review, various issues associated with the Ethereum blockchain-based smart contract are highlighted. Finally, various future directions are also discussed in the field of the Ethereum blockchain-based smart contract that can help the researchers to set the directions for future research in this domain.

Chronic Kidney Disease is one of the most critical illness nowadays and proper diagnosis is required as soon as possible. Machine learning technique has become reliable for medical treatment. With the help of a machine learning classifier algorithms, the doctor can detect the disease on time. For this perspective, Chronic Kidney Disease prediction has been discussed in this article. Chronic Kidney Disease dataset has been taken from the UCI repository. Seven classifier algorithms have been applied in this research such as artificial neural network, C5.0, Chi-square Automatic interaction detector, logistic regression, linear support vector machine with penalty L1 & with penalty L2 and random tree. The important feature selection technique was also applied to the dataset. For each classifier, the results have been computed based on (i) full features, (ii) correlation-based feature selection, (iii) Wrapper method feature selection, (iv) Least absolute shrinkage and selection operator regression, (v) synthetic minority over-sampling technique with least absolute shrinkage and selection operator regression selected features, (vi) synthetic minority over-sampling technique with full features. From the results, it is marked that LSVM with penalty L2 is giving the highest accuracy of 98.86% in synthetic minority over-sampling technique with full features. Along with accuracy, precision, recall, F-measure, area under the curve and GINI coefficient have been computed and compared results of various algorithms have been shown in the graph. Least absolute shrinkage and selection operator regression selected features with synthetic minority over-sampling technique gave the best after synthetic minority over-sampling technique with full features. In the synthetic minority over-sampling technique with least absolute shrinkage and selection operator selected features, again linear support vector machine gave the highest accuracy of 98.46%. Along with machine learning models one deep neural network has been applied on the same dataset and it has been noted that deep neural network achieved the highest accuracy of 99.6%.

This review article compiles the characteristics of resin based dental composites and an effort is made to point out their future perspectives. Recent research studies along with few earlier articles were studied to compile the synthesis schemes of commonly used monomers, their characteristics in terms of their physical, mechanical and polymerization process with selectivity towards the input parameters of polymerization process. This review covers surface modification processes of various filler particles using silanes, wear behaviour, antimicrobial behaviour along with its testing procedures to develop the fundamental knowledge of various characteristics of resin based composites. In the end of this review, possible areas of further interests are pointed out on the basis of literature review on resin based dental materials.

The occurrences of noisy data in data set can significantly impact prediction of any meaningful information. Many empirical studies have shown that noise in data set dramatically led to decreased classification accuracy and poor prediction results. Therefore, the problem of identifying and handling noise in prediction application has drawn considerable attention over past many years. In our study, we performed a systematic literature review of noise identification and handling studies published in various conferences and journals between January 1993 to July 2018. We have identified 79 primary studies are of noise identification and noise handling techniques. After investigating these studies, we found that among the noise identification schemes, the accuracy of identification of noisy instances by using ensemble-based techniques are better than other techniques. But regarding efficiency, usually single based techniques method is better; it is more suitable for noisy data sets. Among noise handling techniques, polishing techniques generally improve classification accuracy than filtering and robust techniques, but it introduced some errors in the data sets.

Miniaturized, specific, rapid response and economical biosensors are finding applications in biotechnology, environmental studies, agriculture, food inspection and safety, disease diagnosis and medical utilities. Of the many categories of biosensors, optical biosensors have brought about an extra edge in sensing applications due to their selective, rapid and extremely sensitive measurements. Biosensors are analytical tools used to detect specific analytes such as cholesterol, urea, etc. having biomolecules such as nucleic acids, proteins, carbohydrates as key element for detecting these analytes along with a transducer and a data analysis and visualization tool. In case of optical biosensors the analyte is detected using light with either label based or label free techniques. In this paper some of the marked advances in the last decade in the field of optical biosensors have been reviewed with an emphasis on their fabrication approaches and growing application areas. Along with some of the carefully selected article on new developments in optical biosensors through the last decade, a brief historical review of optical biosensors since the breakthrough in optical biosensors in 1970s has also been presented. Another focus of the current review is the classification of biosensors, typical structures along with emerging developments in optical biosensing that are likely to impact the current decade. Major application areas and emerging applications through the last decade have been outlined to present a clear picture on the versatility of optical biosensors. Finally, the review also considers the challenges and future of emerging optical biosensing technologies in the current decade.

1 Department of Electrical and Computer Engineering, Wayne State University, Detroit, MI 48202, USA 2 Intelligent Systems Research Laboratory, College of Engineering, University of Saskatchewan, Saskatoon, SK, Canada S7N 5A9 3 Electrified Armor Lab, RDECOM TARDEC, 6501 E. 11 Mile Road, RDTA-RS, MS 263, Warren, MI 48397-5000, USA 4Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, P.O. Box 2728, Beijing 100190, China 5Manipal University Jaipur, Jaipur 303007, India 6Maverick Technologies America Inc., Suite 808, 1220 North Market Street, Wilmington, DE 19801, USA 7Department of Electrical Engineering and Computer Sciences, University of California Berkeley, CA 94720-1776, USA

AbstractPredicting the probability of CORONA virus outbreak has been studied in recent days, but the published literature seldom contains multiple model comparisons or predictive analysis of uncertainty. Time series parameters are the core factors influencing infectious diseases such as severe acute respiratory syndrome (SARS) and influenza. As a global pandemic is imminent, the prediction of real-time transmission of COVID-19 is crucial. The objective of this paper is to produce a real-time forecasts using the SVM model. The purpose of this study is to investigate the Corona Virus Disease 2019 (COVID-19) prediction of confirmed, deceased and recovered cases. This prediction will help to plan resources, determine government policy, and provide survivors with immunity passports, and use the same plasma for care. In this analysis, data including attributes such as location wise confirmed, deceased, recovered COVID-19, longitude and latitude were collected from January 22, 2020 to April 25, 2020 worldwide. Support Vector Machine was used to explore the impact on identification, deceased, and recovery.Subject Classification: 97R40Keywords: PandemicSupport vector machineCOVID-19Machine learning

Optimization is a buzzword, whenever researchers think of engineering problems. This paper presents a new metaheuristic named dingo optimizer (DOX) which is motivated by the behavior of dingo (Canis familiaris dingo). The overall concept is to develop this method involving the collaborative and social behavior of dingoes. The developed algorithm is based on the hunting behavior of dingoes that includes exploration, encircling, and exploitation. All the above prey hunting steps are modeled mathematically and are implemented in the simulator to test the performance of the proposed algorithm. Comparative analyses are drawn among the proposed approach and grey wolf optimizer (GWO) and particle swarm optimizer (PSO). Some of the well-known test functions are used for the comparative study of this work. The results reveal that the dingo optimizer performed significantly better than other nature-inspired algorithms.

In this paper, Supervised Maximum Likelihood Classification (MLC) has been used for analysis of remotely sensed image. The Landsat ETM+ image has used for classification. MLC is based on Bayes' classification and in this classificationa pixelis assigned to a class according to its probability of belonging to a particular class. Mean vector and covariance metrics are the key component of MLC that can be retrieved from training data. Classification results have shown that MLC is the robust technique and there is very less chances of misclassification. The classification accuracy has been achieved overall accuracy of 93.75%, producer accuracy 94%, user accuracy 96.09% and overall kappa accuracy 90.52%.

Agriculture is a significant contributor to India's economic growth. The rising population of country and constantly changing climatic conditions have an impact on crop production and food security. A variety of factors influence crop selection, including market price, production rate, soil type, rainfall, temperature, government policies, etc. Many changes are required in the agricultural sector in order to enhance the Indian economy. In this research work authors have implemented various machine learning techniques to estimate the crop yield in Rajasthan state of India on five identified crops. The results indicate that among all the applied algorithms; Random Forest, SVM, Gradient Descent, long short-term memory, and Lasso regression techniques; the random forest performed better than others with 0.963 R2, 0.035 RMSE, and 0.0251 MAE. The results were validated using R2, root mean squared error, and the mean absolute error to cross-validation techniques. This paper intends to put the crop selection method into practice to help farmers solve crop yield problems.

The COVID-19 pandemic has adversely affected almost all aspects of human life, various sectors of business, and regions of the world. The flow of human activities halted for several months, and are now being carefully redefined to align with guidelines and recommendations to avoid the spread of the novel coronavirus. In contrast to other pandemics the world has witnessed in the past, the technological advancements of the current era are a boon that can play a key role in safeguarding humanity. In this work, we begin by highlighting general challenges that have arisen during the COVID-19 pandemic. Next, to gauge the applicability of blockchain as a key enabling technology, we identify potential use cases to meet current needs. Furthermore, for each use case, we present a high-level view of how blockchain can be leveraged and discuss the expected performance. Finally, we highlight the challenges that must be addressed to harness the full potential of blockchain technology and discuss plausible solutions.

Decrease in crop yield and degradation in product quality due to plant diseases such as rust and blast in pearl millet is the cause of concern for farmers and the agriculture industry. The stipulation of expert advice for disease identification is also a challenge for the farmers. The traditional techniques adopted for plant disease detection require more human intervention, are unhandy for farmers, and have a high cost of deployment, operation, and maintenance. Therefore, there is a requirement for automating plant disease detection and classification. Deep learning and IoT-based solutions are proposed in the literature for plant disease detection and classification. However, there is a huge scope to develop low-cost systems by integrating these techniques for data collection, feature visualization, and disease detection. This research aims to develop the ‘Automatic and Intelligent Data Collector and Classifier’ framework by integrating IoT and deep learning. The framework automatically collects the imagery and parametric data from the pearl millet farmland at ICAR, Mysore, India. It automatically sends the collected data to the cloud server and the Raspberry Pi. The ‘Custom-Net’ model designed as a part of this research is deployed on the cloud server. It collaborates with the Raspberry Pi to precisely predict the blast and rust diseases in pearl millet. Moreover, the Grad-CAM is employed to visualize the features extracted by the ‘Custom-Net’. Furthermore, the impact of transfer learning on the ‘Custom-Net’ and state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19 is shown in this manuscript. Based on the experimental results, and features visualization by Grad-CAM, it is observed that the ‘Custom-Net’ extracts the relevant features and the transfer learning improves the extraction of relevant features. Additionally, the ‘Custom-Net’ model reports a classification accuracy of 98.78% that is equivalent to state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19. Although the classification of ‘Custom-Net’ is comparable to state-of-the-art models, it is effective in reducing the training time by 86.67%. It makes the model more suitable for automating disease detection. This proves that the proposed model is effective in providing a low-cost and handy tool for farmers to improve crop yield and product quality.

Colorectal cancer (CRC) is the third most frequently detected type of cancer, and the second most common cause of cancer‑related mortality globally. The American Cancer Society predicted that approximately 147,950 individuals would be diagnosed with CRC, out of which 53,200 individuals would succumb to the disease in the USA alone in 2020. CRC‑related mortality ranks third among both males and females in the USA. CRC arises from 3 major pathways: i) The adenoma‑carcinoma sequence; ii) serrated pathway; and iii) the inflammatory pathway. The majority of cases of CRC are sporadic and result from risk factors, such as a sedentary lifestyle, obesity, processed diets, alcohol consumption and smoking. CRC is also a common preventable cancer. With widespread CRC screening, the incidence and mortality from CRC have decreased in developed countries. However, over the past few decades, CRC cases and mortality have been on the rise in young adults (age, <50 years). In addition, CRC cases are increasing in developing countries with a low gross domestic product (GDP) due to lifestyle changes. CRC is an etiologically heterogeneous disease classified by tumor location and alterations in global gene expression. Accumulating genetic and epigenetic perturbations and aberrations over time in tumor suppressor genes, oncogenes and DNA mismatch repair genes could be a precursor to the onset of colorectal cancer. CRC can be divided as sporadic, familial, and inherited depending on the origin of the mutation. Germline mutations in APC and MLH1 have been proven to play an etiological role, resulting in the predisposition of individuals to CRC. Genetic alterations cause the dysregulation of signaling pathways leading to drug resistance, the inhibition of apoptosis and the induction of proliferation, invasion and migration, resulting in CRC development and metastasis. Timely detection and effective precision therapies based on the present knowledge of CRC is essential for successful treatment and patient survival. The present review presents the CRC incidence, risk factors, dysregulated signaling pathways and targeted therapies.

Zinc oxide (ZnO) nanoparticles have attracted significant interest due to their vast applications. However, green synthesis of ZnO nanoparticles is a challenge, and therefore, here we report zinc tolerant bacteria assisted microbial synthesis of ZnO nanoparticles. Furthermore, the effective antimicrobial potential and photocatalytic dye degradation capabilities of these nanoparticles are also evaluated. The ZnO nanoparticles are characterized using a range of physicochemical characterization techniques confirming nanoscale size, uniformspherical shape, and high negative surface charge. The ZnO nanoparticles showed considerable antibacterial and antifungal activities. Further, the photocatalytic degradation of methyl orange under UV lamp was studied and results reveal that ZnO nanoparticles have good photocatalytic potential as well.

Software defined networking (SDN) is an emerging network paradigm which emphasizes the separation of the control plane from the data plane. This decoupling provides several advantages such as flexibility, programmability, and centralized control. However, SDN also introduces new vulnerabilities due to the required communication between data plane and control plane. Examples of threats that leverage such vulnerabilities are the control plane saturation and switch buffer overflow attacks. These attacks can be launched by flooding the TCP SYN packets from data plane (i.e., switches) to the control plane. This paper presents SAFETY, a novel solution for the early detection and mitigation of TCP SYN flooding. SAFETY harnesses the programming and wide visibility approach of SDN with entropy method to determine the randomness of the flow data. The entropy information includes destination IP and few attributes of TCP flags. To show the feasibility and effectiveness of SAFETY, we implement it as an extension module in Floodlight controller and evaluate it under different conditional scenarios. We run a thorough evaluation of our implementation through extensive emulation via Mininet. The experimental results show that when compared to the state-of-the-art, SAFETY brings a significant improvement (13%) regarding processing delay experienced by a legitimate node. Other parameters such as CPU utilization at the controller and attack detection time are also examined and shows improvement in various scenarios.

BACKGROUND: Eco-friendly synthesis of nanoparticles is viewed as an alternative to the chemical method and initiated the use of microorganisms for synthesis. The present study has been designed to utilize plant pathogenic fungi Sclerotinia sclerotiorum MTCC 8785 strain for synthesis and optimization of silver nanoparticles (AgNPs) production as well as evaluation of antibacterial properties. The AgNPs were synthesized by reduction of aqueous silver nitrate (AgNO3) solution after incubation of 3-5 days at room temperature. The AgNPs were further characterized using UV-visible spectroscopy, Fourier transform infrared spectroscopy (FTIR) and transmission electron microscopy (TEM). Reaction parameters including media, fungal biomass, AgNO3 concentration, pH and temperature were further optimized for rapid AgNPs production. The antibacterial efficacy of AgNPs was evaluated against Escherichia coli ATCC 25922 and Staphylococcus aureus ATCC 25923 by disc diffusion and growth kinetics assay at the concentration determined by the minimum inhibitory concentration (MIC). RESULTS: AgNPs synthesis was initially marked by the change in colour from pale white to brown and was confirmed by UV-Vis spectroscopy. Optimization studies showed that potato dextrose broth (PDB) media, 10 g of biomass, addition of 2 mM AgNO3, pH 11 and 80 °C temperature resulted in enhanced AgNPs synthesis through extracellular route. TEM data revealed spherical shape AgNPs with size in the range of 10 nm. Presence of proteins capped to AgNPs was confirmed by FTIR. AgNPs showed antibacterial activity against E. coli and S. aureus at 100 ppm concentration, corresponding MIC value. CONCLUSION: S. sclerotiorum MTCC 8785 mediated AgNPs was synthesized rapidly under optimized conditions, which showed antibacterial activity.