Netaji Subhas University of Technology

As our dependence on intelligent machines continues to grow, so does the demand for more transparent and interpretable models. In addition, the ability to explain the model generally is now the gold standard for building trust and deployment of artificial intelligence systems in critical domains. Explainable artificial intelligence (XAI) aims to provide a suite of machine learning techniques that enable human users to understand, appropriately trust, and produce more explainable models. Selecting an appropriate approach for building an XAI-enabled application requires a clear understanding of the core ideas within XAI and the associated programming frameworks. We survey state-of-the-art programming techniques for XAI and present the different phases of XAI in a typical machine learning development process. We classify the various XAI approaches and, using this taxonomy, discuss the key differences among the existing XAI techniques. Furthermore, concrete examples are used to describe these techniques that are mapped to programming frameworks and software toolkits. It is the intention that this survey will help stakeholders in selecting the appropriate approaches, programming frameworks, and software toolkits by comparing them through the lens of the presented taxonomy.

These molecules or classes of natural substances are characterized by two phenyl rings at least and one or more hydroxyl substituents. This description comprehends a large number of heterogeneous compounds with reference to their complexity. Therefore, polyphenols can be simply classified into flavonoids and non-flavonoids, or be subdivided in many sub-classes depending on the number of phenol units within their molecular structure, substituent groups, and/or the linkage type between phenol units. Polyphenols are widely distributed in plant tissues where they mainly exist in form of glycosides or aglycones. The structural diversity of flavonoid molecules arises from variations in hydroxylation pattern and oxidation state resulting in a wide range of compounds: flavanols, anthocyanidins, anthocyanins, isoflavones, flavones, flavonols, flavanones, and flavanonols.

The COVID-19 pandemic forced universities around the world to shut down their campuses indefinitely and move their educational activities onto online platforms. The universities were not prepared for such a transition and their online teaching-learning process evolved gradually. We conducted a survey in which we asked undergraduate students in an Indian university about their opinion on different aspects of online education during the ongoing pandemic. We received responses from 358 students. The students felt that they learn better in physical classrooms (65.9%) and by attending MOOCs (39.9%) than through online education. The students, however, felt that the professors have improved their online teaching skills since the beginning of the pandemic (68.1%) and online education is useful right now (77.9%). The students appreciated the software and online study materials being used to support online education. However, the students felt that online education is stressful and affecting their health and social life. This pandemic has led to a widespread adoption of online education and the lessons we learn now will be helpful in the future.

The paper summarizes the different characteristics of Web data, the basic components of Web mining and its different types, and the current state of the art. The reason for considering Web mining, a separate field from data mining, is explained. The limitations of some of the existing Web mining methods and tools are enunciated, and the significance of soft computing (comprising fuzzy logic (FL), artificial neural networks (ANNs), genetic algorithms (GAs), and rough sets (RSs) are highlighted. A survey of the existing literature on "soft Web mining" is provided along with the commercially available systems. The prospective areas of Web mining where the application of soft computing needs immediate attention are outlined with justification. Scope for future research in developing "soft Web mining" systems is explained. An extensive bibliography is also provided.

The purpose of this study is to develop a hierarchy-based model for supply chain resilience (SCRES), explaining the dynamics between various enablers and validating the model empirically. Literature review and a survey identified the enablers. Interpretive structural modelling (ISM) is used to analyse the levels of relationships among enablers. Based on their driving power and dependence, these enablers are also classified into different categories. Structural equation modelling is used to validate the hierarchical SCRES model and test the path analytical model. The study provides empirical justification for a framework that identifies 13 key enablers of resilient supply chain practices and describes the relationship among them using ISM. It also classifies them using Matrix of Cross Impact Multiplications Applied to Classification analysis on the basis of their driver power and dependence. The key finding is that using the proposed model, organisations can enhance their resilience potential by modifying their strategic assets. The model was tested using rigorous statistical tests including convergent validity, discriminant validity and reliability. The holistic view offered by the proposed model depicts the relationship among enablers to achieve SCRES.

With enhanced global scrutiny in the backdrop of climate change, we attempt to identify the importance of the ESG framework during Covid-19 pandemic to produce guidelines for future sustainability practices. A comprehensive review of literature on ESG regulatory frameworks for sample developed and developing country was performed leading to undertaking of a cross-country comparative ESG analysis. It was revealed that a country's social and governance disclosure were driven by either voluntary or by mandatory codes that could not be a standalone factor for uplifting the country's overall ESG level. Other governance measures like sustainability reporting and integrated reporting practices need to be considered in order to uplift the ESG practice. Country-level environmental commitment was vital for both developed and emerging markets for solving information asymmetry issues and establishment of resilient business operations and reporting practices, leading to an emerging sustainable practice which needs to be adopted. Our findings offer valuable insights for regulators, institutional investors and policymakers in terms of considering ESG practices adopted by developed countries and bridging the gap from unsustainability to sustainability in countries with least developed emerging ESG countries. The study encourages the regulators to devise disclosure policies as per the Triple ‘C’ framework namely policies that are convenient, credible and comparable with the flexibility to encompass black swan events like Covid-19. The purpose of such disclosures should be to resolve the information asymmetry problem which primarily exists when regulations are non-mandatory.

In the present study, an attempt has been made to develop and characterize natural fiber-based composites (jute/epoxy, hemp/epoxy, flax/epoxy) and their hybrid composites (jute/hemp/epoxy, hemp/flax/epoxy, and jute/hemp/flax/epoxy) using hand-lay-up technique. Mechanical characterization (tensile, flexural, impact, and hardness test) of the developed composites was performed. The interface between fiber and matrix was examined using scan electron microscopy (SEM). Among (jute/epoxy, hemp/epoxy, flax/epoxy), flax/epoxy composite has shown higher hardness (98 Shore-D) and tensile strength (46.2 MPa) whereas better flexural and impact strength have been shown by hemp/epoxy (85.59 MPa) and jute/epoxy (7.68 kJ/m2) composites respectively. Results showed that hybrid composites observed better mechanical properties. Jute/hemp/flax/epoxy hybrid composite showed the highest tensile strength, modulus and impact strength of 58.59 MPa, 1.88 GPa, and 10.19, kJ/m2, respectively. Jute/hemp/epoxy hybrid composite achieved the maximum flexural strength of 86.6 MPa.

Abstract The usage of the gas sensor has been increasing very rapidly in the industry and in daily life for various potential applications. In the recent years, metal oxide semiconductors (MOS) become the primary choice for designing highly sensitive, stable, and low‐cost real‐life applications‐based gas sensors due to their inherent physical and chemical properties. Researchers have proposed numerous sensing mechanisms to explain the functionality of MOS‐based gas sensors. In this review, we have comprehensively covered different sensing mechanisms used for MOS. We have also discussed different parameters affecting the sensitivity and selectivity of the gas sensors. Moreover, the different techniques used to enhance the gas sensing response of MOS‐based sensors are also extensively covered. And finally, we give our prospective on recent opportunities and challenges on the future applications of MOS‐based gas sensors.

Topic modelling is the new revolution in text mining. It is a statistical technique for revealing the underlying semantic structure in large collection of documents. After analysing approximately 300 research articles on topic modeling, a comprehensive survey on topic modelling has been presented in

The ability of algae to accumulate metals and reduce metal ions make them a superior contender for the biosynthesis of nanoparticles and hence they are called bio-nano factories as both the live and dead dried biomass are used for the synthesis of metallic nanoparticles. Microalgae, forming a substantial part of the planet’s biodiversity, are usually single-celled colony-forming or filamentous photosynthetic microorganisms, including several legal divisions like Chlorophyta, Charophyta, and Bacillariophyta. Whole cells of Plectonema boryanum (filamentous cyanobacteria) proved efficient in promoting the production of Au, Ag, and Pt nanoparticles. The cyanobacterial strains of Anabaena flos-aquae and Calothrix pulvinate were used to implement the biosynthesis of Au, Ag, and Pt nanoparticles. Once synthesized within the cells, the nanoparticles were released into the culture media where they formed stable colloids easing their recovery. Lyngbya majuscule and Chlorella vulgaris have been reported to be used as a cost-effective method for Ag nanoparticle synthesis. Dried edible algae ( Spirulina platensis ) was reported to be used for the extracellular synthesis of Au, Ag, and Au/Ag bimetallic nanoparticles. Synthesis of extracellular metal bio-nanoparticles using Sargassum wightii and Kappaphycus alvarezi has also been reported. Bioreduction of Au (III)-Au (0) using the biomass of brown alga, Fucus vesiculosus , and biosynthesis of Au nanoparticles using red algal ( Chondrus crispus ) and green algal ( Spyrogira insignis ) biomass have also been reported. Algae are relatively convenient to handle, less toxic, and less harmful to the environment; synthesis can be carried out at ambient temperature and pressure and in simple aqueous media at a normal pH value. Therefore, the study of algae-mediated biosynthesis of metallic nanoparticles can be taken toward a new branch, termed phyco-nanotechnology.

Industry 5.0 is the next stage in industrial evolution, collaborating between human ingenuity and intelligent technologies to provide manufacturing solutions. Integrating modern technology like Artificial Intelligence (AI), robotics, and the Internet of Things (IoT) into manufacturing and production processes characterizes Industry 5.0. On the other hand, edge computing provides real-time data processing and analysis at the networks edge, closer to the data source and a vital component of Industry 5.0. Edge computing enables Industry 5.0 to access and communicate information about their industrial sectors using more accessible, standard hardware and software resources. However, no recent survey papers have examined the importance of edge computing in Industry 5.0. This study aims to fill that gap by presenting a survey on the importance of edge computing in Industry 5.0 and discussing a variety of technologies that could be used to implement and support this new industrial paradigm. First, we outline an overview and fundamentals of edge computing in Industry 5.0 architecture. Then objectives of Industry 5.0 are summarized to address various research challenges, including privacy, human-robot co-working, sustainability, and robust networks. Afterwards, this paper provides an extensive overview of emerging technologies for Industry 5.0, such as collaborative robots, AI, Digital Twins, and many more. In addition, this survey highlights various open research challenges and potential solutions that should be addressed further to achieve Industry 5.0.

Motor Imagery Brain Computer Interface (MI-BCI) provides a non-muscular channel for communication to those who are suffering from neuronal disorders. The designing of an accurate and reliable MI-BCI system requires the extraction of informative and discriminative features. Common Spatial Pattern (CSP) has been potent and is widely used in BCI for extracting features in motor imagery tasks. The classifiers translate these features into device commands. Many classification algorithms have been devised, among those Support Vector Machine (SVM) andLinear Discriminate Analysis (LDA) have been widely used. In recent studies, the researchers are using deep neural networks for the classification of motor imagery tasks. This paper provides a comprehensive review of dominant feature extraction methods and classification algorithms in brain-computer interface for motor imagery tasks. Authors discuss existing challenges in the domain of motor imagery brain-computer interface and suggest possible research directions.

Sensor and smart phone technologies present opportunities for data explosion, streaming and collecting from heterogeneous devices every second. Analyzing these large datasets can unlock multiple behaviors previously unknown, and help optimize approaches to city wide applications or societal use cases. However, collecting and handling of these massive datasets presents challenges in how to perform optimized online data analysis `on-the-fly', as current approaches are often limited by capability, expense and resources. This presents a need for developing new methods for data management particularly using public clouds to minimize cost, network resources and on-demand availability. This paper presents an implementation of the lambda architecture design pattern to construct a data-handling backend on Amazon EC2, providing high throughput, dense and intense data demand delivered as services, minimizing the cost of the network maintenance. This paper combines ideas from database management, cost models, query management and cloud computing to present a general architecture that could be applied in any given scenario where affordable online data processing of Big Datasets is needed. The results are presented with a case study of processing router sensor data on the current ESnet network data as a working example of the approach. The results showcase a reduction in cost and argue benefits for performing online analysis and anomaly detection for sensor data.

Actinobacteria are wide spread in nature and represent the largest taxonomic group within the domain Bacteria. They are abundant in soil and have been extensively explored for their therapeutic applications. This versatile group of bacteria has adapted to diverse ecological habitats, which has drawn considerable attention of the scientific community in recent times as it has opened up new possibilities for novel metabolites that may help in solving some of the most challenging problems of the day, for example, novel drugs for drug-resistant human pathogens, affordable means to maintain ecological balance in various habitats, and alternative practices for sustainable agriculture. Traditionally, free dwelling soil actinobacteria have been the subject of intensive research. Of late, symbiotic actinobacteria residing as endophytes within the plant tissues have generated immense interest as potential source of novel compounds, which may find applications in medicine, agriculture, and environment. In the light of these possibilities, this review focuses on the diversity of endophytic actinobacteria isolated from the plants of extreme habitats and specific ecological niches. Furthermore, an attempt has been made to assign chemical class to the compounds obtained from endophytic actinobacteria. Potential therapeutic applications of these compounds and the utility of endophytic actinobacteria in agriculture and environment are discussed.

The aim of this study is to propose a model based on machine learning (ML) and Internet of Things (IoT) to diagnose patients with COVID-19 in smart hospitals. In this sense, it was emphasized that by the representation for the role of ML models and IoT relevant technologies in smart hospital environment. The accuracy rate of diagnosis (classification) based on laboratory findings can be improved via light ML models. Three ML models, namely, naive Bayes (NB), Random Forest (RF), and support vector machine (SVM), were trained and tested on the basis of laboratory datasets. Three main methodological scenarios of COVID-19 diagnoses, such as diagnoses based on original and normalized datasets and those based on feature selection, were presented. Compared with benchmark studies, our proposed SVM model obtained the most substantial diagnosis performance (up to 95%). The proposed model based on ML and IoT can be served as a clinical decision support system. Furthermore, the outcomes could reduce the workload for doctors, tackle the issue of patient overcrowding, and reduce mortality rate during the COVID-19 pandemic.

Friction stir additive techniques (FSATs) constitute innovative approaches aimed at utilizing layer by layer additive manufacturing (AM) principle with solid state friction stir welding (FSW) technique. These constitute a special class of friction based additive techniques (FATs) and can be easily thought of as major breakthrough of metal additive manufacturing (MAM) domain. Ability of MAM techniques to be used for fabricating intricate parts has led them to be considered as a lucrative option for aviation, automotive, and marine sectors. However, fusion based MAM techniques have manifold limitations mainly owing to solidification related as well as poor shear strength issues. FATs overcome drawbacks of fusion based MAM methods chiefly because of their solid-state nature. FATs fabricate defect free components possessing superior structural and mechanical characteristics. Basic principles of additive manufacturing and FSW necessary for highlighting need and concept of FATs are introduced first. All FATs with their basic principles and specific merits are then presented. Special emphasis is given to two most effective FATs based upon FSW, i.e., FSATs which are friction stir additive manufacturing and additive friction stir (AFS). Aim of this work is to present a critical review of timeline and recent developments in the field of FSATs. In addition, challenges and future trends of these innovative techniques are highlighted followed by detailed discussion.

Pneumonia is an infection that causes inflammation of lungs and can be deadly if not detected on time. The commonly used method to detect Pneumonia is using chest X-ray which requires careful examination of chest X-ray images by an expert. The method of detecting pneumonia using chest X-ray images by an expert is time-consuming and less accurate. In this paper, we propose different deep convolution neural network (CNN) architectures to extract features from images of chest X-ray and classify the images to detect if a person has pneumonia. To evaluate the effect of dataset size on the performance of CNN, we train the proposed CNN's using both the original as well as augmented dataset and the results are reported.

Abstract As specified by World Health Organization, the occurrence of skin cancer has been growing over the past decades. At present, 2 to 3 million nonmelanoma skin cancers and 132 000 melanoma skin cancers arise worldwide annually. The detection and classification of skin cancer in early stage of development allow patients to have proper diagnosis and treatment. The goal of this article is to present a novel deep learning internet of health and things (IoHT) driven framework for skin lesion classification in skin images using the concept of transfer learning. In proposed framework, automatic features are extracted from images using different pretrained architectures like VGG19, Inception V3, ResNet50, and SqueezeNet, which are fed into fully connected layer of convolutional neural network for classification of skin benign and malignant cells using dense and max pooling operation. In addition, the proposed system is fully integrated with an IoHT framework and can be used remotely to assist medical specialists in the diagnosis and treatment of skin cancer. It has been observed that performance metric evaluation of proposed framework outperformed other pretrained architectures in term of precision, recall, and accuracy in detection and classification of skin cancer from skin lesion images.

Friction stir processing (FSP) is a rapidly emerging newer solid-state technique for composite fabrication. It involves surface modification which in turn enables successful adaptation of surface properties through plastic deformations in solid state. During initial years of FSP inception, it was primarily employed in development of metal matrix composites of light metal alloys like aluminum. However, recently, it has gained an alluring role in fabrication of composites of various nonferrous and ferrous metal alloys as well as of polymers. In addition to composite fabrication, FSP has evolved as a revolutionary technique in developing functionally graded systems/surfaces (FGS) of metal matrix. This article covers all aspects of FSP in which reinforcement particles are embedded in the base matrix to develop composites and FGS. It presents a critical review on domains of recent developments, effects of different types of reinforcement particles and properties enhancement of composites, and FGS fabrication. In addition to this, various issues, challenges, and future work that demand attention are systematically addressed.

Supply chain management (SCM) is a core corporate activity responsible for moving commodities and services from one point to another through various stakeholders. The traditional SCM is based on a centralized approach managed at the central headquarter, and all other sub-offices get instructions from the main office. Some major issues with present SCM systems are security, transactional transparency, traceability, stakeholder involvement, product counterfeiting, additional delays, fraud, and instabilities. Blockchain (BC) emerges as a technology that can manage the data and build trust efficiently and transparently. It can also aid in transaction authorization and verification in the supply chain or payments without a third party. To address the present SCM issues, BC technology is a feasible solution. Motivated by the aforementioned considerations, in this paper, we present a survey on the adoption of BC in SCM. This paper undertakes a comprehensive analysis of the literature on BC characteristics, implementations, and business consequences in various SCM. This Blockchain-centered study, in particular, discloses the research state and delineates future research directions by studying and analyzing 97 up-to-date publications highlighting BC’s supply chain uses. Transparency and traceability, information sharing, product anti-counterfeiting, and building trust are the major aspects propelling BC’s implementation in SCM. Further, we analyzed various applications of SCM in which BC can be used as a probable technology to secure all transactions. Then, we have highlighted open issues and research challenges for adopting BC technology in SCM that open the doors for beginners eager to start work in this amazing area.