National Institute of Technology Raipur

Multilevel inverters have created a new wave of interest in industry and research. While the classical topologies have proved to be a viable alternative in a wide range of high-power medium-voltage applications, there has been an active interest in the evolution of newer topologies. Reduction in overall part count as compared to the classical topologies has been an important objective in the recently introduced topologies. In this paper, some of the recently proposed multilevel inverter topologies with reduced power switch count are reviewed and analyzed. The paper will serve as an introduction and an update to these topologies, both in terms of the qualitative and quantitative parameters. Also, it takes into account the challenges which arise when an attempt is made to reduce the device count. Based on a detailed comparison of these topologies as presented in this paper, appropriate multilevel solution can be arrived at for a given application.

Diabetes is considered as one of the deadliest and chronic diseases which causes an increase in blood sugar. Many complications occur if diabetes remains untreated and unidentified. The tedious identifying process results in visiting of a patient to a diagnostic center and consulting doctor. But the rise in machine learning approaches solves this critical problem. The motive of this study is to design a model which can prognosticate the likelihood of diabetes in patients with maximum accuracy. Therefore three machine learning classification algorithms namely Decision Tree, SVM and Naive Bayes are used in this experiment to detect diabetes at an early stage. Experiments are performed on Pima Indians Diabetes Database (PIDD) which is sourced from UCI machine learning repository. The performances of all the three algorithms are evaluated on various measures like Precision, Accuracy, F-Measure, and Recall. Accuracy is measured over correctly and incorrectly classified instances. Results obtained show Naive Bayes outperforms with the highest accuracy of 76.30% comparatively other algorithms. These results are verified using Receiver Operating Characteristic (ROC) curves in a proper and systematic manner.

Heavy metal contamination has been recognized as a major public health risk, particularly in developing countries and their toxicological manifestations are well known. Conventional remediation strategies are either expensive or they generate toxic by-products, which adversely affect the environment. Therefore, necessity for an environmentally safe strategy motivates interest towards biological techniques. One of such most profoundly driven approach in recent times is biosorption through microbial biomass and their products. Extracellular polymeric substances are such complex blend of high molecular weight microbial (prokaryotic and eukaryotic) biopolymers. They are mainly composed of proteins, polysaccharides, uronic acids, humic substances, lipids etc. One of its essential constituent is the exopolysaccharide (EPS) released out of self defense against harsh conditions of starvation, pH and temperature, hence it displays exemplary physiological, rheological and physio-chemical properties. Its net anionic makeup allows the biopolymer to effectively sequester positively charged heavy metal ions. The polysaccharide has been expounded deeply in this article with reference to its biosynthesis and emphasizes heavy metal sorption abilities of polymer in terms of mechanism of action and remediation. It reports current investigation and strategic advancements in dealing bacterial cells and their EPS in diverse forms - mixed culture EPS, single cell EPS, live, dead or immobilized EPS. A significant scrutiny is also involved highlighting the existing challenges that still lie in the path of commercialization. The article enlightens the potential of EPS to bring about bio-detoxification of heavy metal contaminated terrestrial and aquatic systems in highly sustainable, economic and eco-friendly manner.

The successful application of data mining in highly visible fields like e-business, marketing and retail has led to its application in other industries and sectors. Among these sectors just discovering is healthcare. The healthcare environment is still "information rich" but "knowledge poor". There is a wealth of data available within the healthcare systems. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. This research paper intends to provide a survey of current techniques of knowledge discovery in databases using data mining techniques that are in use in today"s medical research particularly in Heart Disease Prediction. Number of experiment has been conducted to compare the performance of predictive data mining technique on the same dataset and the outcome reveals that Decision Tree outperforms and some time Bayesian classification is having similar accuracy as of decision tree but other predictive methods like KNN, Neural Networks, Classification based on clustering are not performing well. The second conclusion is that the accuracy of the Decision Tree and Bayesian Classification further improves after applying genetic algorithm to reduce the actual data size to get the optimal subset of attribute sufficient for heart disease prediction.

The present study focuses on determining the relationship of estimated land surface temperature (LST) with normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) for Florence and Naples cities in Italy using Landsat 8 data. The study also classifies different land use/land cover LU–LC) types using NDVI and NDBI threshold values, iterative self-organizing data analysis technique and maximum likelihood classifier, and analyses the relationship built by LST with the built-up area and bare land. Urban thermal field variance index was applied to determine the thermal and ecological comfort level of the city. Several urban heat islands (UHIs) were extracted as the most heated zones within the city boundaries due to increasing anthropogenic activities. The difference between the mean LST of UHI and non-UHI is 3.15°C and 3.31°C, respectively, for Florence and Naples. LST build a strong correlation with NDVI (negative) and NDBI (positive) for both the cities as a whole, especially for the non-UHIs. But, the strength of correlation becomes much weaker within the UHIs. Moreover, most of the UHIs (85.21% in Naples and 76.62% in Florence) are developed within the built-up area or bare land and are demarcated as an ecologically stressed zone.

Eight isolates of cellulose-degrading bacteria (CDB) were isolated from four different invertebrates (termite, snail, caterpillar, and bookworm) by enriching the basal culture medium with filter paper as substrate for cellulose degradation. To indicate the cellulase activity of the organisms, diameter of clear zone around the colony and hydrolytic value on cellulose Congo Red agar media were measured. CDB 8 and CDB 10 exhibited the maximum zone of clearance around the colony with diameter of 45 and 50 mm and with the hydrolytic value of 9 and 9.8, respectively. The enzyme assays for two enzymes, filter paper cellulase (FPC), and cellulase (endoglucanase), were examined by methods recommended by the International Union of Pure and Applied Chemistry (IUPAC). The extracellular cellulase activities ranged from 0.012 to 0.196 IU/mL for FPC and 0.162 to 0.400 IU/mL for endoglucanase assay. All the cultures were also further tested for their capacity to degrade filter paper by gravimetric method. The maximum filter paper degradation percentage was estimated to be 65.7 for CDB 8. Selected bacterial isolates CDB 2, 7, 8, and 10 were co-cultured with Saccharomyces cerevisiae for simultaneous saccharification and fermentation. Ethanol production was positively tested after five days of incubation with acidified potassium dichromate.

Synthesis of copper oxide nanoparticles with tunable size and desirable properties is a foremost thrust area of the biomedical research domain. Though these features primarily rely on the synthetic approaches involved, with advancements in this area, it has been documented that the synthesis parameters and surface modifiers have a direct impact on the morphology and eventually on the biomedical properties. “Sensing” remains a major application of nanomaterials owing to their small size and unusual physicochemical properties, but in the past few years, a paradigm shift has occurred toward “theranostic” combination of the sensing and therapeutic features on a single platform. Copper oxide nanoparticles have been efficiently used for sensing and targeting in both in-vivo and in-vitro environments, although few key challenges are yet to be resolved before implementing at a commercial level. This review article attempts to summarize the recent advancements in the various synthetic approaches toward copper oxide nanoparticles and their biomedical applications. It highlights various synthetic methodologies including electrochemical, chemical, and biogenic methods, the role of surface modifiers in growth mechanisms, and their impact on biomedical applications. Finally, the current status, key challenges, and future perspective of copper oxide nanoparticles will be discussed that inevitably have an impact on their current and future scenarios.

Anomalous traffic detection on internet is a major issue of security as per the growth of smart devices and this technology. Several attacks are affecting the systems and deteriorate its computing performance. Intrusion detection system is one of the techniques, which helps to determine the system security, by alarming when intrusion is detected. In this paper performance of NSL-KDD dataset is evaluated using ANN. The result obtained for both binary class as well as five class classification (type of attack). Results are analyzed based on various performance measures and better accuracy was found. The detection rate obtained is 81.2% and 79.9% for intrusion detection and attack type classification task respectively for NSL-KDD dataset. The performance of the proposed scheme has been compared with existing scheme and higher detection rate is achieved in both binary class as well as five class classification problems.

With the evolution of the Internet of Things (IoT), smart cities have become the mainstream of urbanization. IoT networks allow distributed smart devices to collect and process data within smart city infrastructure using an open channel, the Internet. Thus, challenges such as centralization, security, privacy (e.g., performing data poisoning and inference attacks), transparency, scalability, and verifiability limits faster adaptations of smart cities. Motivated by the aforementioned discussions, we present a Privacy-Preserving and Secure Framework (PPSF) for IoT-driven smart cities. The proposed PPSF is based on two key mechanisms: a two-level privacy scheme and an intrusion detection scheme. First, in a two-level privacy scheme, a blockchain module is designed to securely transmit the IoT data and Principal Component Analysis (PCA) technique is applied to transform raw IoT information into a new shape. In the intrusion detection scheme, a Gradient Boosting Anomaly Detector (GBAD) is applied for training and evaluating the proposed two-level privacy scheme based on two IoT network datasets, namely ToN-IoT and BoT-IoT. We also suggest a blockchain-InterPlanetary File System (IPFS) integrated Fog-Cloud architecture to deploy the proposed PPSF framework. Experimental results demonstrate the superiority of the PPSF framework over some recent approaches in blockchain and non-blockchain systems.

Historically, pickling is one of the oldest preservation methods of several foodstuffs such as vegetables, fruits, fish, and meat. Pickling imparts unique and desirable changes in flavor, texture and color that take place over time in fermented pickles. Microorganisms (mainly lactic acid bacteria, Micrococcaceae, Bacilli, yeasts, and filamentous fungi) play a pivotal role in the pickling of foodstuffs while affecting the quality and safety of the final product. This review focuses on the common traditional fermented pickles and their nutritional, therapeutic, and economic potentials. Furthermore, the technological progress in screening microbial communities associated with the traditional pickles is summarized. Finally, this paper will tackle with the role of pickles in filing the gap in food security, the safety aspect of traditional pickles and biofortication as an interesting technique to improve the quality of traditional pickles.

In this paper, Recurrent Neural Networks (RNN) have been applied for classifying the normal and abnormal beats in an ECG. The primary aim of this paper was to enable automatic separation of regular and irregular beats. The MIT-BIH Arrhythmia database is being used to classify the beat classification performance. The methodology used is carried out using huge volume of standard data i.e. ECG time-series data as inputs to Long Short Term Memory Network. We divided the dataset as training and testing sub-data. The effectiveness, accuracy and capabilities of our methodology ECG arrhythmia detection is demonstrated and quantitative comparisons with different RNN models have also been carried out.

Abstract Availability of limited resources presents the need for sustainable development strategies to achieve sustainable performance. However, in the era of digitalization and globalization many researchers explored the role of digital technologies in improving sustainable performance. However, the literature on the role of collaboration and coordination in a digitally enabled supply chain (SC) to achieve sustainability is still lacking. This study aims to investigate the effect of supply chain collaboration and coordination (SCC), sustainable development strategy (SDS), digital transformation (DIT), and collaborative advantages (COA) on sustainable supply chain firm performance (SSCFP). The conceptual model is based on the relational view (RV), transaction cost economics (TCE), technology, organization and environment (TOE), and resource‐based view (RBV) theories. This study utilizes structural equation modeling (SEM) to analyze data collected from 361 respondents of the automotive industry in India. The findings show that SCC positively affects SDS and DIT. SDS positively affects DIT, COA, and DIT positively affects SSCFP. DIT fully mediates the relationship between SCC and COA. The study suggests that managers can apply SCC, SDS, and DIT in series to achieve sustainable performance. However, the COA can only be enhanced in the digitalized SC. The study provides empirical evidence to policymakers and practitioners for the synergy between SCC, SDS, DIT, and COA to achieve sustainable performance in the SC's manufacturing firm.

The integration of the Internet of Things (IoT) with traditional healthcare systems has improved quality of healthcare services. However, the wearable devices and sensors used in Healthcare System (HS) continuously monitor and transmit data to the nearby devices or servers using an unsecured open channel. This connectivity between IoT devices and servers improves operational efficiency, but it also gives a lot of room for attackers to launch various cyber-attacks that can put patients under critical surveillance in jeopardy. In this article, a Blockchain-orchestrated Deep learning approach for Secure Data Transmission in IoT-enabled healthcare system hereafter referred to as “BDSDT” is designed. Specifically, first a novel scalable blockchain architecture is proposed to ensure data integrity and secure data transmission by leveraging Zero Knowledge Proof (ZKP) mechanism. Then, BDSDT integrates with the off-chain storage InterPlanetary File System (IPFS) to address difficulties with data storage costs and with an Ethereum smart contract to address data security issues. The authenticated data is further used to design a deep learning architecture to detect intrusion in HS network. The latter combines Deep Sparse AutoEncoder (DSAE) with Bidirectional Long Short-Term Memory (BiLSTM) to design an effective intrusion detection system. Experiments on two public data sources (CICIDS-2017 and ToN-IoT) reveal that the proposed BDSDT outperformed state-of-the-arts in both non-blockchain and blockchain settings and have obtained accuracy close to 99% using both datasets.

Diabetes Mellitus, characterized by persistent hyperglycaemia, is a heterogeneous group of disorders of multiple aetiologies. It affects the human body at multiple organ levels thus making it difficult to follow a particular line of the treatment protocol and requires a multimodal approach. The increasing medical burden on patients with diabetes-related complications results in an enormous economic burden, which could severely impair global economic growth in the near future. This shows that today's healthcare system has conventionally been poorly equipped towards confronting the mounting impact of diabetes on a global scale and demands an urgent need for newer and better options. The overall challenge of this field of diabetes treatment is to identify the individualized factors that can lead to improved glycaemic control. Plants are traditionally used worldwide as remedies for diabetes healing. They synthesize a diverse array of biologically active compounds having antidiabetic properties. This review is an endeavour to document the present armamentarium of antidiabetic herbal drug discovery and developments, highlighting mechanism-based antidiabetic properties of over 300 different phytoconstituents of various chemical categories from about 100 different plants modulating different metabolic pathways such as glycolysis, Krebs cycle, gluconeogenesis, glycogen synthesis and degradation, cholesterol synthesis, carbohydrate metabolism as well as peroxisome proliferator activated receptor activation, dipeptidyl peptidase inhibition and free radical scavenging action. The aim is to provide a rich reservoir of pharmacologically established antidiabetic phytoconstituents with specific references to the novel, cost-effective interventions, which might be of relevance to other low-income and middle-income countries of the world.

Industrialization played an important role for scio-economy of the country. Generally, a lot of water is used and lot of wastewater generated from industries due their processes and washing purpose. A large number of chemicals are used for the production of potable water and in the treatment of wastewater effluents. In potable water treatment chemicals such as inorganic salts and polymeric organic coagulants are used for primary coagulation, as coagulant aids and for sludge dewatering; lime and soda ash allowed for pH correction and water stabilization; caustic soda is used for pH adjustment, powdered activated carbon (PAC) can remove taste and odour compounds and micro pollutants such as atrazine, bentonite aid's coagulation, and ammonium hydroxide is used in chloramination. The main object of review is focus on research work done as well as the basic concept behind treatment and application by the researcher on different industry's waste-water treatment. ©JASEMKeywords: Coagulants, COD, Dissolved solid, Flocculants, Settling

Perovskite solar cells (PSC) have been identified as a game-changer in the world of photovoltaics. This is owing to their rapid development in performance efficiency, increasing from 3.5% to 25.8% in a decade. Further advantages of PSCs include low fabrication costs and high tunability compared to conventional silicon-based solar cells. This paper reviews existing literature to discuss the structural and fundamental features of PSCs that have resulted in significant performance gains. Key electronic and optical properties include high electron mobility (800 cm2/Vs), long diffusion wavelength (>1 μm), and high absorption coefficient (105 cm−1). Synthesis methods of PSCs are considered, with solution-based manufacturing being the most cost-effective and common industrial method. Furthermore, this review identifies the issues impeding PSCs from large-scale commercialisation and the actions needed to resolve them. The main issue is stability as PSCs are particularly vulnerable to moisture, caused by the inherently weak bonds in the perovskite structure. Scalability of manufacturing is also a big issue as the spin-coating technique used for most laboratory-scale tests is not appropriate for large-scale production. This highlights the need for a transition to manufacturing techniques that are compatible with roll-to-roll processing to achieve high throughput. Finally, this review discusses future innovations, with the development of more environmentally friendly lead-free PSCs and high-efficiency multi-junction cells. Overall, this review provides a critical evaluation of the advances, opportunities and challenges of PSCs.

The main issues with drug safety in the counterfeit medicine supply chain, are to do with how the drugs are initially manufactured. The traceability of right and active pharmaceutical ingredients during actual manufacture is a difficult process, so detecting drugs that do not contain the intended active ingredients can ultimately lead to end-consumer patient harm or even death. Blockchain's advanced features make it capable of providing a basis for complete traceability of drugs, from manufacturer to end consumer, and the ability to identify counterfeit-drug. This paper aims to address the issue of drug safety using Blockchain and encrypted QR(quick response) code security.

The industrial healthcaresystem has enabled the possibility of realizing advanced real-time monitoring of patients and enriched the quality of medical services through data sharing among intelligent wearable devices and sensors. However, this connectivity brings the intrinsic vulnerabilities related to security and privacy due to the need of continuous communication and monitoring over public network (insecure channel). Motivated from the aforementioned discussions, we integrate permissioned blockchain and smart contract with deep learning (DL) techniques to design a novel secure and efficient data sharing framework named PBDL. Specifically, PBDL first has a blockchain scheme to register, verify (using zero-knowledge proof), and validate the communicating entities using the smart contract-based consensus mechanism. Second, the authenticated data are used to propose a novel DL scheme that combines stacked sparse variational autoencoder (SSVAE) with self-attention-based bidirectional long short term memory (SA-BiLSTM). In this scheme, SSVAE encodes or transforms the healthcare data into new format, and SA-BiLSTM identifies and improves the attack detection process. The security analysis and experimental results using IoT-Botnet and ToN-IoT datasets confirm the superiority of the PBDL framework over existing state-of-the-art techniques.

Fly ash and slag leachate pollution can be of great environmental concern due to generation of these wastes in huge quantities from their respective industrial units, mainly coal-based thermal power plants and iron and steel plants. For simulation of natural leaching in laboratory, various leaching methods are available, but selection of a method that can exactly simulate the real-life scenario for accurate estimation of various pollutants is challenging; particularly, the heavy metals present and impact due to reuse or disposal of these wastes. For choosing the most suitable leaching method according to specific situation, one must primarily consider the chemical and physical properties of wastes, the composition of the source, age of waste disposal, and the climatic conditions of the disposal area. Since these factors may not be specified, a variety of leaching methods with relevant equipment have been proposed by researchers; that are based on their required information to particular conditions in absence of a prescribed protocol and non standardization of equipment. The present review is an attempt to investigate the suitable leaching method for coal fly ash and slag.

The esterification reaction is reversible and has low yield. In order to increase the yield of reaction, it is required to simultaneously remove the product of reaction. For this membranes are the viable approach. Pervaporation membranes have success in removal of components in dilute forms. Membrane performance is represented in terms of flux, sorption coefficient, separation factor, and permeance. These factors are related to the thickness of membrane, temperature, and feed concentration. Higher flux is observed at lower membrane thickness and higher feed concentration of water and lower selectivity is observed at higher temperatures due to increased free volume, lower viscosity, and higher feed side pressure. Different factors affect the pervaporation aided esterification reactor setup such as effect of initial molar ratios of the reactants, effect of catalyst concentration, effect of membrane area, and effect of temperature. Large membrane size could provide higher surface for the transfer of acid, though the challenges of membrane rupture do surround the studies. In the present review work, we tried to collaborate the works in totality of the pervaporation design starting from the membrane behavior to the process behavior. Different prospective fields are also explored which need investigation.