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Implementing strategic responses in the COVID-19 market crisis: a study of small and medium enterprises (SMEs) in India
Purpose: The COVID-19 pandemic presents unprecedented challenges for small and medium enterprises (SMEs) in emerging economies. This paper aims to examine how India's SMEs implement their strategic responses in this crisis. Design/methodology/approach: The study uses dynamic capability theory to explore the strategic responses of SMEs. Strategy implementation theory helps to explain how they implement innovative practices for outcomes. A research model defines the COVID-19 challenges, strategic responses and performance outcomes. The study reports the findings of an initial pilot study of 75 firms and follow-up case study results in the context of COVID-19. Findings: Firms choose their approaches according to their perceived market risks. Case studies illustrate that firms display diverse attitudes depending on their strategic direction, leadership vision and organizational culture. They achieve different outcomes by implementing specific styles of risk management practices (e.g. risk-averting, risk-taking and risk-thriving). Research limitations/implications: Although the study context is Indian SMEs, the findings suggest meaningful lessons for other emerging economies in similar crisis events. The propositions may be extended to future research in broad contexts. Practical implications: Even in the extraordinary COVID-19 market crisis, SMEs with limited resources display their strategic potential by recognizing their unique capabilities, translating them into effective actions and achieving desirable outcomes. Social implications: In the COVID-19 pandemic, top leaders' mental attitude, strategic perspective and routine practices are contagious. Positive leadership motivates both internal and external stakeholders with an enormous level of collaboration. Originality/value: This rare study of Indian SMEs provides a theoretical framework for designing a pilot survey and conducting a case study of multiple firms. Based on these findings, testable propositions are articulated for future research in diverse organizational and national contexts. 2021, Emerald Publishing Limited. -
Implication of emotional labor, cognitive flexibility, and relational energy among cabin crew: A review
The primary aim of the civil aviation industry is to provide a secured and comfortable service to their customers and clients. This review concentrates on the cabin crew members, who are the frontline employees of the aviation industry and are salaried to smile. The objective of this review article is to analyze the variables of emotional labor, cognitive flexibility, and relational energy using the biopsychosocial model and identify organizational implications among cabin crew. Online databases such as EBSCOhost, JSTOR, Springerlink, and PubMed were used to gather articles for the review. The authors analyzed 17 articles from 2001 to 2016 and presented a comprehensive review. The review presented an integrative approach and suggested a hypothetical model that can prove to be a signitficant contribution to the avaition industry in particular and to research findings of aviation psychology. 2018 Indian Journal of Occupational and Environmental Medicine | Published by Wolters Kluwer-Medknow. -
Importance of Artificial Intelligence (AI) and Robotic Process Automation (RPA) in the Banking Industry: A Study from an Indian Perspective
The papers primary focus is intelligence creation by AI and fast implementation by RPA. It starts with the applicability of the Turing test propounded by Alan Turing, the father of Artificial Intelligence (AI). At the backdrop lies various events, namely the recent motivation addition of various bank account holders. These factors fuelled the demand for AI and RPA implementation in the banking industry. It pitched how AI and RPA work in real-time scenarios such as financial fraud and money laundering. It discusses how AI builds the knowledge graph and recommends products and services for each customer. This knowledge is implemented and delivered using RPA. The AI application gained prominence in every banking business segment, such as equity, personal, investment and loan. The application of RPA is present in all business segments, although the percentage is increasing yearly. The AI and RPA can help banks to convert the challenges to opportunities. There have been various challenges, and the application of AI and RPA combinations is the key to solving the inefficiencies. Advanced analytical techniques on open-source data have been used in this paper. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Importance Of Artificial Intelligence in Improving Human Resource Management For Companies To Find Suitable Candidature
The efficient use of pertinent human resources both inside and outside the company via management structures guided by economic and humanistic principles is known as human resource management. It is a catch-all word for a set of actions that guarantee the accomplishment of group objectives and the optimization of member growth. Employers need the correct recruitment tools to fill available positions since traditional recruiting approaches are not up to par in the global talent battle. First, as the digital tool redesigns business, we look at how talent acquisition has evolved from digital 1.0 to 3.0 (AI-enabled). Artificial intelligence technology has made recruiting more efficient and made recruiters' daily tasks easier. Additionally, the analysis in the paper shows that artificial intelligence (AI) is crucial to every step of the hiring process, including promotion, application, screening, evaluation, and coordination. This study demonstrates how organizations are realizing the value of talent management in gaining a competitive edge as the need for higher-level talent grows. Even though some HR managers are using AI for talent acquisition, our research shows that there is still an opportunity for development. 2024 IEEE. -
Importance of brain-based learning in effective teaching process
Neuroscience plays an important role to inform about brain functioning and corresponding behaviors. While scientists have continuously studied about brain in its entirety, several other fields in science, arts, and humanities are drawing applications from these neuroscientific studies. Similarly, in terms of education too, there are several applications. Once such is brain-based learning. This is to learn and teach with an understanding about brain's capability and functioning to increase one's learning potential. In order to do so, effective teaching is required. This particular chapter thus focuses on different teaching strategies which adopt brain-based learning and are evidence-based. The chapter initially informs about neuroscience and cognitive science interaction and its impact on effective teaching-learning process. After which evidence-based teaching strategies in classrooms, their importance via evidence-based studies, and techniques to incorporate them in classroom settings are explained. Finally, the chapter concludes with challenges and benefits. The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. All rights reserved. -
Importance of Genetic Model in Huntingtons Disease
Huntingtons disease (HD) is the first defect-mapped autosomal-dominant, progressive neurodegenerative disorder with a distinct phenotype found in 1983, which contributed to the concept of human genome project. Thus, the search for genetic defects pioneered various mapping and gene study technological prototypes that culminated in identification of distinct disorders. Huntingtons disease has many symptoms, including chorea and dystonia, incoordination, cognitive decline and dementia, and behavioral difficulties. This disease can manifest any time over the age of 30 years, with the first sign and symptom being behavioral changes, which might include lack of emotions, periods of aggression, excitement, and anger. HD duration varies, ranging from 10 to 25 years or more depending on the individual. It is caused by a single gene defect on chromosome number 4, wherein a person requires only a single copy of the defected gene to show the symptoms; that is, a person with parent with the HD gene has a 50% chance of suffering. The disease becomes prominent in a human being as a result of mutations in a gene called Huntington that is located on the p arm (short arm) of chromosome 4 (4p 16.3). This chapter discusses the genetic model of Huntingtons disease and its importance. An increase in the normal number of repeat CAG (cytosine, adenine, and guanine) segments, (i.e. > 35 CAG) is seen in the Huntington (HTT) mutation that causes the disease. The severity of the disease depends on the sized expansion (i.e. increasing CAG repeats will accelerate the age of onset of the disease). Continuing studies of genetic modifiers-genes whose natural polymorphic variation contribute to the alteration and development of the D gene-offers to open new gateways for early diagnosis by unlocking the biochemical changes that occur years before diagnosis, thereby providing validated target protein and pathways for rational therapeutic interventions. This is also added as a section in this chapter. 2025 selection and editorial matter, Sachchida Nand Rai, Sandeep Singh, Santosh Kumar Singh. -
Importance of positive emotions in software developers performance: a narrative review
Software development depends heavily on human efforts and collaborations, which are simultaneously influenced by human emotions, and it is considered among the most difficult tasks. Software developers are susceptible to various health concerns such as burnout, anxiety and depression due to the work nature and demands of the software industry, and this may have detrimental effects on their performance. However, evidence portray significant beneficial effects of positive emotions on well-being, growth, and success. Accordingly, this paper investigates the relevance of positive emotions among software developers and strives to understand how positive emotions benefit developers performance. It surveys literature that examine the importance of affect among developers and the benefits of positive emotions on human well-being and employs a narrative style to present these aspects. This review discusses the potential mechanisms that underlie the association between frequent experiences of positive emotions and improved performance and suggests that positive emotions benefit developers performance. This emotional awareness has the potential to enhance the developer efficacy, thus contributing to the discipline of ergonomics. Findings also have positive implications for organisational counsellors as they help in developing and maintaining a positive organisational climate. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Importance of Sustainable Marketing Initiatives for Supporting the Sustainable Development Goals
Businesses that engage in sustainable marketing can benefit both the world and their bottom line. Earlier, companies could satisfy many customers by simply providing low pricing and high-quality goods. However, peoples concern for the environment and other social concerns have grown, and so has their desire to support groups that share their beliefs. Because they often generate strong market returns and demonstrate durability during economic downturns, many investors want to support businesses that use sustainable business methods. Also, these businesses are more likely to comply with social and environmental laws. Several companies use sustainable marketing to succeed in todays ethical and ecologically sensitive marketplace. Organizations must finance sustainability programs in order to practice sustainable marketing. But, it can also improve employee engagement, promote regulatory compliance, raise revenues, and build brand loyalty. 2023 by IGI Global. -
Imposter detection with canvas and WebGL using Machine learning.
Authentication offers a way to confirm the legitimacy of a user attempting to access any protected information that is hosted on the web as organizations are moving their applications online. It has long been believed that IP addresses and Cookies are the most reliable digital fingerprints used to authenticate and track people online. But after a while, things got out of hand when modern web technologies allowed interested organizations to use new ways to identify and track users. There are many new reliable digital fingerprints that can be used such as canvas and WebGL. The canvas and WebGL render the image which is dependent on the software and hardware of the system. In our work with the generated hash value value from canvas and WebGL we create a model using KNN to identify the imposters. The model has proved to be accurate in authentication of user with an accuracy of 89%. 2023 IEEE. -
Imprint of Fertiliser Policies on Farming Practices Evidence from the Top Five Consuming States
The policies related to the use of nitrogen fertilisers since independence are reviewed using primary and secondary databases to derive the present status of nitrogen, phosphorus, and potassium fertiliser use among farmers. Recommendations for increasing nitrogen use efficiency in agriculture for sustainability are provided. 2023 Economic and Political Weekly. All rights reserved. -
Improved Acceptance model: Unblocking Potential of Blockchain in Banking Space
Over the past ten years, blockchain has emerged as the new buzzword in the banking sector.The new technology is being adopted globally in many industries, including the business sector,because of its unique uses and features. However, no adoption model is available to help with this process.This research paper examines the new technology known as blockchain, which powers cryptocurrencies like Bitcoin and others. It looks at what blockchain technology is, how it works especially in the banking sector, and how it can change and upend the financial services sector. It outlines the features of the technology and discusses why these can have a significant effect on the financial industry as a whole in areas like identity services, payments, and settlements in addition to spawning new products based on things like 'smart contracts'. The adoption variables found in the literature study were used to gather, test, and evaluate the official papers that are currently available from regulatory organizations, practitioners, and research bodies. This study was able to classify adoption factors into three categories - supporting, impeding, and circumstantial - identify a new adoption factor, and determine the relative relevance of the factors. Consequently, an institutional adoption paradigm for blockchain technology in the banking sector is put out. In light of this, it is advised to conduct additional research on using the suggested model at banks using the new technology in order to assess its suitability. 2024 IEEE. -
Improved Bald Eagle Search for Optimal Allocation of D-STATCOM in Modern Electrical Distribution Networks with Emerging Loads
Currently, modern electrical distribution networks (EDNs) are experiencing high demand with emerging electric vehicle loads and are being planned for specific load requirements such as agricultural loads. In this connection, characterization and optimization of their performance become essential in planning studies. In this paper, optimal reactive power compensation using a distribution-static synchronous compensator (D-STATCOM) is proposed with the aim of loss reduction, voltage profile improvement and voltage stability enhancement different types of loads including agricultural and electric vehicle loads. A recent efficient meta-heuristic approach, improved bald eagle search (IBES), is implemented for solving the proposed optimization problem considering different operational and planning constraints. The simulation results are performed on IEEE 33-bus for different types of load modelling. The computational efficiency of IBES is compared with basic BES and other literature works. From the results, IBES has shown superior computational characteristics than all compared works. On the other hand, the optimal location and size of D-STATCOM caused significant loss reduction, voltage profile improvement and voltage stability enhancement for kinds of loads as experiencing in the modern EDNs 2022,International Journal of Intelligent Engineering and Systems.All Rights Reserved -
Improved Collaborative Filtering using Evolutionary Algorithm based Feature Extraction
International Journal of Computer Applications Vol.64,No.20, pp.20-26 ISSN No. 0975-8887 -
Improved Computer Vision-based Framework for Electronic Toll Collection
The world is moving towards artificial intelligence and automation because time is the most crucial asset in today's scenario. This paper proposes an automatic vehicle fingerprinting system that avoids long waiting times in toll plazas with the help of computer vision. The number plate recognition and vehicle re-identification focus on this research. Day/night IR cameras are used to get the images of the vehicle and its number plate. The VeRi776 datum, which contains real-world vehicle images, is used to facilitate the research of vehicle re-identification. The proposed framework employs Siamese model architecture to identify the attributes such as color, model, and type of vehicle. The Car License Plate Detection datum is used to evaluate the efficiency of the proposed license plate recognition system. An ensemble of image localization techniques using CNNs and application of the OCR model on the localized snapshot is used to recognize the vehicle's license plate. A combination of license plate recognition and vehicle re-identification techniques is used in the proposed framework to improve the efficiency of identifying vehicles in toll plazas 2022 IEEE. -
Improved Crypto Algorithm for High-Speed Internet of Things (IoT) Applications
Modern technologies focus on integrated systems based on the Internet of Things (IoT). IoT based devices are unified with various levels of high-speed internet communication, computation process, secure authentication and privacy policies. One of the significant demands of present IoT is focused on its secure high-speed communication. However, traditional authentication and secure communication find it very difficult to manage the current need for IoT applications. Therefore, the need for such a reliable high-speed IoT scheme must be addressed. This proposed title introduces an enhanced version of the Rijndael Cryptographic Algorithm (Advanced Encryption Standard AES) to obtain fast-speed IoT-based application transmission. Pipeline-based AES technique promises for the high-speed crypto process, and this secure algorithm targeted to fast-speed Field Programmable Gate Array (FPGA) hardware. Thus, high-speed AES crypto algorithms, along with FPGA hardware, will improve the efficiency of future IoT design. Our proposed method also shows the tradeoff between High-Speed communications along with various FPGA platforms. 2020, Springer Nature Switzerland AG. -
Improved dhoa-fuzzy based load scheduling in iot cloud environment
Internet of things (IoT) has been significantly raised owing to the development of broadband access network, machine learning (ML), big data analytics (BDA), cloud computing (CC), and so on. The development of IoT technologies has resulted in a massive quantity of data due to the existence of several people linking through distinct physical components, indicating the status of the CC environment. In the IoT, load scheduling is realistic technique in distinct data center to guarantee the network suitability by falling the computer hardware and software catastrophe and with right utilize of resource. The ideal load balancer improves many factors of Quality of Service (QoS) like resource performance, scalability, response time, error tolerance, and efficiency. The scholar is assumed as load scheduling a vital problem in IoT environment. There are many techniques accessible to load scheduling in IoT environments.With this motivation, this paper presents an improved deer hunting optimization algorithm with Type II fuzzy logic (IDHOA-T2F) model for load scheduling in IoT environment. The goal of the IDHOA-T2F is to diminish the energy utilization of integrated circuit of IoT node and enhance the load scheduling in IoT environments. The IDHOA technique is derived by integrating the concepts of Nelder Mead (NM) with the DHOA. The proposed model also synthesized the T2L based on fuzzy logic (FL) systems to counterbalance the load distribution. The proposed model finds useful to improve the efficiency of IoT system. For validating the enhanced load scheduling performance of the IDHOA-T2F technique, a series of simulations take place to highlight the improved performance. The experimental outcomes demonstrate the capable outcome of the IDHOA-T2F technique over the recent techniques. 2022 Tech Science Press. All rights reserved. -
Improved diabetes disease prediction IWFO model using machine learning algorithms
Diabetic disease is the mostly affected and massive disease on a global level. Diagnosing the diabetic earlier will help the medicalist to give the improved and latest clinical treatment. The healthcare specialist unit uses many machine learning techniques, methodologies and tools for decision making in diabetic field. The machine learning techniques are utilized for the prediction of the diabetic diseases in the initial level. To eliminate such issues, optimized detection techniques are proposed. First of all, the training samples are increased using the sliding window protocol. Further, class imbalanced training data classes are balanced and resolved using the adaptive and gradient booster technique. Further, the diabetic feature selection process is improved by the Intensity Weighted Firefly Optimization firefly techniques (IWFO), in which irrelevant features are reduced based on the correlation between the features that deducts the unwanted features involved in the diabetic disease process. Then the feature transformation problem is faced by the PCA technique, which manages the several types of features. Finally, the improved and optimal hybrid random forest is applied into the normal and diabetes classes respectively. The proposed system predicts the diabetic disease efficiently and maximizes its precision of the prediction system. The present paper is compared with different classifiers to determine the efficiency of the work. Overall, the initiated system improved the present studies accuracy level. 2024 Author(s). -
Improved dragonfly optimizer for intrusion detection using deep clustering CNN-PSO classifier
With the rapid growth of internet based services and the data generated on these services are attracted by the attackers to intrude the networking services and information. Based on the characteristics of these intruders, many researchers attempted to aim to detect the intrusion with the help of automating process. Since, the large volume of data is generated and transferred through network, the security and performance are remained an issue. IDS (Intrusion Detection System) was developed to detect and prevent the intruders and secure the network systems. The performance and loss are still an issue because of the features space grows while detecting the intruders. In this paper, deep clustering based CNN have been used to detect the intruders with the help of Meta heuristic algorithms for feature selection and preprocessing. The proposed system includes three phases such as preprocessing, feature selection and classification. In the first phase, KDD dataset is preprocessed by using Binning normalization and Eigen-PCA based discretization method. In second phase, feature selection is performed by using Information Gain based Dragonfly Optimizer (IGDFO). Finally, Deep clustering based Convolutional Neural Network (CCNN) classifier optimized with Particle Swarm Optimization (PSO) identifies intrusion attacks efficiently. The clustering loss and network loss can be reduced with the optimization algorithm. We evaluate the proposed IDS model with the NSL-KDD dataset in terms of evaluation metrics. The experimental results show that proposed system achieves better performance compared with the existing system in terms of accuracy, precision, recall, f-measure and false detection rate. 2022 Tech Science Press. All rights reserved. -
Improved Feature Selection Method for the Identification of Soil Images Using Oscillating Spider Monkey Optimization
Precision agriculture is the process that uses information and communication technology for farming and cultivation to improve overall productivity, efficient utilization of resources. Soil prediction is one of the primary phases in precision agriculture, resulting in good quality crops. In general, farmers perform the soil prediction manually. However, the efficiency of soil prediction may be enhanced by using current digital technologies. One effective way to automate soil prediction is image processing techniques in which soil images may be analyzed to determine the soil. This paper presents an efficient image analysis technique to predict the soil. For the same, a robust feature selection technique has been incorporated in the image analysis of soil images. The developed feature selection technique uses a new oscillating spider monkey optimization algorithm (OSMO) for the selection of features that are relevant and non-redundant. The new oscillating spider monkey optimization algorithm increases precision and convergence behavior by using an oscillating perturbation rate. A set of standard benchmark functions was deployed to visualize the performance of the new optimization technique (OSMO), and results were compared based on mean and standard deviation. Furthermore, the soil prediction approach is validated on a soil dataset, having seven categories. The proposed feature selection method selects the 41% relevant features, which provide the highest accuracy of 82.25% with 2.85% increase. 2013 IEEE. -
Improved File Security System Using Multiple Image Steganography
Steganography is the process of hiding a secret message within an ordinary message extracting it at its destination. Image steganography is one of the most common and secure forms of steganography available today. Traditional steganography techniques use a single cover image to embed the secret data which has few security shortcomings. Therefore, batch steganography has been adopted which stores data on multiple images. In this paper, a novel approach is proposed for slicing the secret data and storing it on multiple cover images. In addition, retrieval of this secret data from the cover images on the destination side has also been discussed. The data slicing ensures secure transmission of the vital data making it merely impossible for the intruder to decrypt the data without the encrypting details. 2019 IEEE.