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Study to assess attitudes towards statistics of business school students: An application of the SATS-36 in India
Students attitudes towards Statistics are pivotal to their learning process as positive attitudes lead to highly satisfactory course achievement and lead to positive outcomes outside class as well. In this paper we are exploring the perception of students of management apropos Statistics, familiarity with which is imperative in todays world of Analytics. The quantitative approach was used to compare attitudes of the students using the two versions of the SATS-36 instrument validated and copyrighted by Candace Schau. A Google form was used to collect responses and was sent to all the students who were enrolled in the Business Statistics course. 172 students responded for the pre-test study while 71 students responded for the post-test study. Data was analysed to see if gender, specialisation choices and previous math experiences accounted for differences in perceptions towards Statistics. It was found that students overall perception of statistics is positive and surprisingly they were more positive towards the beginning of the semester. These results are important as they can lead towards understanding of business students attitudes towards statistics and a way to refine the teaching learning process so that students are in a strong position to exploit the supply demand gap in the Analytics domain and deliver value to organisations. 2021 Eskisehir Osmangazi University. All rights reserved. -
Pattern Recognition: An Outline of Literature Review that Taps into Machine Learning to Achieve Sustainable Development Goals
The sustainable development goals (SDGs) as specified by the United Nations are a blueprint to make the Earth to be more sustainable by the year 2030. It envisions member nations fighting climate change, achieving gender equality, quality education for all, and access to quality healthcare among the 17 goals laid out. To achieve these goals by the year 2030, member nations have put special schemes in place for citizens while experimenting with newer ways in which a measurable difference can be made. Countries are tapping into ancient wisdom and harnessing newer technologies that use artificial intelligence and machine learning to make the world more liveable. These newer methods would also lower the cost of implementation and hence would be very useful to governments across the world. Of much interest are the applications of machine learning in getting useful information and deploying solutions gained from such information to achieve the goals set by the United Nations for an imperishable future. One such machine learning technique that can be employed is pattern recognition which has applications in various areas that will help in making the environment sustainable, making technology sustainable, and thus, making the Earth a better place to live in. This paper conducts a review of various literature from journals, news articles, and books and examines the way pattern recognition can help in developing sustainably. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Brain Tumor Classification: A Comparison Study CNN, VGG 16 and ResNet50 Model
Brain tumors pose a severe threat to global health and may be lethal. Early detection and classification of brain tumors are essential for successful therapy and better patient outcomes. The good news is that advances in deep learning techniques have shown tremendous promise in medical image analysis, particularly in the detection and classification of brain tumors. Convolutional Neural Networks (CNN), a class of deep learning models, are used to process and analyze visual input, notably images, and movies. They excel in computer vision tasks like object detection, image segmentation, and categorization. Popular and efficient image analysis methods include CNNs. VGG 16 and ResNet 50 are two examples of deep convolutional neural network architectures used for image categorization applications. A number of image identification problems have been successfully solved using the 16 layer VGG 16. ResNet50, a well known 50 layer architecture, employs residual connections to get over the vanishing gradient issue and permits the training of deeper networks. A proprietary CNN model, VGG 16, and ResNet50 were compared in studies to see how well they performed on a dataset. The VGG 16, ResNet50, and the tailored CNN model were the most precise models. As a consequence, VGG 16 accurately detects brain cancers in the dataset that was supplied. Overall, this study highlights the value of deep learning techniques for medical image processing and their potential to improve the accuracy and efficacy of brain tumor diagnosis and treatment. 2023 IEEE. -
Exploring the efficiency of green synthesized silver nanoparticles as photocatalysts for organic dye degradation: unveiling key insights
Silver nanoparticles (AgNPs) have received a lot of interest for their several applications, including their remarkable potential as photocatalysts for organic dye degradation. This research thoroughly investigates the efficacy of ecologically friendly, green-synthesized AgNPs in the treatment of synthetic dye-contaminated wastewater. The synthesis of AgNPs from various biological substrates is investigated, emphasizing their economic viability, significant conductivity, and considerable biocompatibility. The improper disposal of synthetic dyes in wastewater poses severe environmental and health risks due to their non-biodegradable nature and persistent chemical features. In response to this challenge, this review paper investigates the capability of AgNPs to serve as effective photocatalysts for degrading a range of organic dyes commonly found in industrial effluents. Specific dyes, including methyl orange, congo red, nitrophenol, methylene blue, and malachite green, are studied in the context of wastewater treatment, providing insights into the efficacy of AgNPs synthesized from diverse biological sources. The review sheds light on the photocatalytic degradation methods used by green-synthesized AgNPs, shedding light on the transition of these synthetic dyes into less hazardous compounds. It also delves into the toxicity aspect of the AgNPs and its possible remediation from the environment. The ecologically friendly synthesis procedures investigated in this work provide an alternative to traditional methods, highlighting the importance of sustainable technologies in solving modern environmental concerns. Furthermore, a comparative examination of various biological substrates for AgNPs synthesis is presented, evaluating their respective dye degradation efficiencies. This not only helps researchers understand the environmental impact of synthetic dyes, but it also directs them in choosing the best substrates for the production of AgNPs with enhanced photocatalytic activities. 2024 The Author(s). Published by IOP Publishing Ltd. -
Efficient cationic dye removal from water through Arachis hypogaea skin-derived carbon nanospheres: a rapid and sustainable approach
The present study investigates the potential of Arachis hypogaea skin-derived carbon nanospheres (CNSs) as an efficient adsorbent for the rapid removal of cationic dyes from aqueous solutions. The CNSs were synthesized through a facile, cost-effective, catalyst-free and environmentally friendly process, utilizing Arachis hypogaea skin waste as a precursor. This is the first reported study on the synthesis of mesoporous carbon nanospheres from Arachis hypogaea skin. The structural and morphological characteristics of the CNSs were confirmed by different nano-characterization techniques. The adsorption performance of the carbon nanospheres was evaluated through batch adsorption experiments using two cationic dyes-methylene blue (MB) and malachite green (MG). The effects of the initial dye concentration, contact time, adsorbent dosage, and pH were investigated to determine the optimal conditions for dye removal. The results revealed that the obtained CNSs exhibited remarkable adsorption capacity and rapid adsorption kinetics. Up to ?98% removal efficiency was noted for both dyes in as little as 2 min for a 5 mg L?1 dye concentration, and the CNSs maintained their structural morphology even after adsorption. The adsorption data were fitted to various kinetic and isotherm models to gain insights into the adsorption mechanism and behaviour. The pseudo-second-order kinetic model and Redlich-Peterson model best described the experimental data, indicating multi-layer adsorption and chemisorption as the predominant adsorption mechanism. The maximum adsorption capacity was determined to be 1128.46 mg g?1 for MB and 387.6 mg g?1 for MG, highlighting the high affinity of the carbon nanospheres towards cationic dyes. Moreover, CNS reusability and stability were examined through desorption and regeneration experiments, which revealed sustained efficiency over 7 cycles. CNSs were immobilised in a membrane matrix and examined for adsorption, which demonstrated acceptable efficiency values and opened the door for further improvement. 2024 RSC. -
An Inventory Model for Growing Items with Deterioration and Trade Credit
Growing items industry plays a vital role in the economy of most of the countries. Growing item industries consists of live stocks like sheep, fishes, pigs, chickens etc. In this paper, we developed a mathematical model for growing items by considering various operational constraints. The aim of the present model is to optimize the net profit by optimizing decision variables like time after growing period and shortages. Also, the delay in payment policy has been used to maximize the profit. A numerical example is provided in support of the solution procedure. Sensitivity analysis provides some important insights. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Profit function Optimization for Growing Items Industry
The economy of a country depends on many industries; growing item industries are one of them. Growing items also exhibit mortality in the growth period, which creates a complex environment for the procurement decision. A practical inventory model is required to overcome this situation, which provides the optimum solution. This work describes an economics ordering quantity model for growing items with constant demand and mortality. We also take into consideration that one of the real-life management practices for businesses is the allowance of a delay in payment. There is a solution procedure with a numerical example. We have discussed analytical results to verify the concavity of the profit function. Sensitivity analysis provides us with some very useful information. . 2023 IEEE. -
Optimal procurement and pricing policy for deteriorating items with price and time dependent seasonal demand and permissible delay in payment
In practice, items like food, nursery plants, medicines, etc. are seasonal and deteriorating in nature. For this type of products, permissible delay in payment is a common business policy, which is used to increase in the sell volume and to develop trust in buyer-seller relationship. In this paper, we developed an inventory model for time dependent deteriorating seasonal items with the permission of delay in payment. Shortages are permitted and partially back ordered. Our aim is to find optimal selling price and ordering quantity simultaneously. Concavity of profit function with respect to decision variables has been discussed analytically. A solution procedure followed by a numerical example and sensitivity analysis along with managerial insights are provided. Numerical analysis predicts that delay in payment profit policy is a better decision in order to maximise the profit or in order to get more profit. 2022 Inderscience Enterprises Ltd. -
Two inventory models for growing items under different payment policies with deterioration
Industries of growing items show an upward trend in the production as well as in consumption. Poultry and livestock are good examples of growing items which are both deteriorating and ameliorating in nature. In this study apart from these specific features of growing items, one of the real-world business policies, permission of delay in payment is also considered. Present paper proposed two inventory models, one with the permission of delay in payment and another without it. Concavity of the profit functions with respect to decision variables are discussed analytically for both the models. Solution procedure and numerical examples are provided in order to get the managerial insights. The numerical analysis growth in weight is approximated by Richard's growth function. The numerical analysis predicts that net profit and the initial purchase quantity both increases under the permissible delay payment policy compared to without it. Sensitivity analysis provides important managerial insights. Copyright 2022 Inderscience Enterprises Ltd. -
A Comparative Investigation on the use of Machine Learning Techniques for Currency Authentication
In the present banking sector, identifying the real and the fake note is a very challenging task because if we do it manually, it takes a long time to check which is real and which is fake. This research study article aims to authenticate the money between real and fake by using different machine algorithms facilitating learning, such as K-means Clustering, Random Forest Classification, Support Vector Machines, and logistics Regression. Specifically, we consider the banknote dataset. The data of money is extracted from various banknote images by using the wavelet transform tool, which is primarily used to remove elements from the images. However, we are mainly concerned with the different machine learning algorithms, so we take the two variables, where the first variable indicates image variance and the second indicates image skewness. We use these two variables to train our machine learning algorithms. So, majorly, by applying the different machine learning algorithms, which are supervised and unsupervised, we find the accuracy for the respective machine learning algorithms and then visualize and classify the real and fake notes separately. Finally, the prediction is based on integrity, which means the efficiency value is based on how much the mechanism system can uncover the fake notes. Then, after calculating the accuracy of currency authentication, there is a high possibility that the accuracy of the particular algorithm is the best algorithm, so the application of currency authentication will be very useful for the bank to easily find duplicate notes. 2022 IEEE. -
Deep Learning Character Recognition of Handwritten Devanagari Script: A Complete Survey
Recognition of handwritten characters is a concept in which the single characters are classified, it is a facility of an electronic device to scan and decipher the handwritten input from a variety of sources, including written texts, images, and other digital touch-screen devices. This concept is being used in distinctive sectors such as the processing of bank checks, form data entry, and parcel posting and nowadays it is becoming a very important issue in the pattern recognition domain and a very challenging task to resolve it. Since deep learning is a crucial strategy in solving detection and pattern recognition problems, several algorithms are available to classify the characters with better prediction rates on different datasets, and ultimately, whichever algorithm gives the optimized results will be considered the best solution for the character recognition problem. As a result, various solutions proposed by the existing researchers are discussed using deep learning algorithms in this survey article. 2023 IEEE. -
Consolidation of Cloud Computing in Smart and Sustainable Environment
Cloud computing has revolutionized IoT device data collection, administration, and analysis by offering a scalable and sustainable solution for managing vast amounts of data. The paper highlights cloud computing's benefits in data processing, device management, cost efficiency and scalability. However, challenges related to security, data ownership, and vendor lock-in require attention. A novel sustainable cloud-IoT model is presented by integrating smart computing with cloud infrastructure. It is observed that the model records promising performance. The mean response delay is 1.9 seconds and the 89.5% is the generated mean computational storage accuracy rate. In conclusion, the cloud computing empowered sustainable model can be used in organizations to gain insights from IoT data and make informed decisions, shaping future research in this rapidly evolving field. 2023 IEEE. -
Subsume Pediatric Headaches in Psychiatric Disorders? Critiques on Delphic Nosology, Diagnostic Conundrums, and Variability in the Interventions
Purpose of Review: Tension-type headache (TTH) continues to be the most prevalent type of headache across all age groups worldwide, and the global burden of migraine and TTH together account for 7% of all-cause years lived with disability (YLDs). TTH has been shown to have a prevalence of up to 80% in several studies and presents a wide range and high variability in clinical settings. The aim of this review is to identify gaps in diagnostics, nosology, and variability in the treatment of children and adolescents who present with headaches without an identifiable etiology. Recent Findings: Migraine and TTH have been debated to have more similarities than distinctions, increasing chances of misdiagnosis and leading to significant cases diagnosed as probable TTH or probable migraine. The lack of specificity and sensitivity for TTH classification often leads to the diagnosis being made by negating associated migraine symptoms. Although pathology is not well understood, some studies have suggested a neurological basis for TTH, in need of further validation. Some research indicates that nitric oxide signaling plays an integral part in the pain mechanisms related to TTH. Analgesics and non-steroidal anti-inflammatories are usually the first lines of treatment for children with recurring headaches, and additional treatment options include medication and behavioral therapies. Summary: With high prevalence and socioeconomic burden among children and adolescents, its essential to further study Tension-type headaches and secondary headaches without known cause and potential interventions. Treatment studies involving randomized controlled trials are also needed to test the efficacy of various treatments further. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Inventory model for deteriorating items with ramp type demand under permissible delay in payment
Permissible delay in payment is a common method of payment often used by the suppliers and it generally leads to higher sales and ultimately higher revenue. This method is significant in the case of deteriorating products. In this paper, an inventory model for the deteriorating items with price and time-dependent ramp type demand is presented with shortages allowed and partially backlogged. The solution procedure is illustrated by numerical examples. The concavity of the profit function with respect to the decision variable is discussed analytically. Numerical analysis shows that the profit per unit time increases with the delay payment facility. Copyright 2021 Inderscience Enterprises Ltd. -
Face Detection-Based Border Security System Using Haar-Cascade and LBPH Algorithm
Border security is a process which measures the border management ideas by a country or set of countries to wage against unspecific and unauthorized travel or trade across the country borders, to bound non-legal deport, various crimes combat, and foreclose dangerous criminals from entering in the country. A system will help in keeping a check on those personnels who forge with the legal document with an intension to cross the border. This article discusses about border security of whole Indian context, and there are various such systems which have been built since 2010 as wireless sensor network system named Panchendriya. Remote and instruction manual switch mode arm system using ultrasonic sensor for security of border. This article we have made use of Haar-Cascadian along with LBPH algorithm with their functioning. The result and discussion section we compare most recent face recognition techniques that have been used in the last ten years. The proposed prototype is discussed and shown through simulation model, it provide better result compare to existing model. The proposed Haar-Cascade and LBPH algorithm provide 10% better performance. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Promoting Net-Zero Economy for Sustainable Development: Practice-Based View
The present research investigates the utilization of various resources, including tangible assets, human expertise, and intangible assets, in a cohesive set of established procedures, which impact the development and implementation of net-zero practices. It also explores the effect on the environmental performance of SME enterprises operating in business markets. Additionally, the study explores whether digitalization plays a moderating role in this relationship. The samples of 291 were used in the study. Data were analyzed using partial least square structural equation modeling. For a sustainable net-zero economy (SNZE), it is essential for managers to acknowledge the importance of resource and capabilities management. While the management of tangible assets and human skills is vital, greater emphasis should be placed on intangible resources like organizational culture and learning. Furthermore, the capacity of small-sized enterprises (SMEs) to process and implement knowledge could prove to be instrumental in accomplishing net-zero targets. Consequently, managers should leverage Industry-4.0-based technological solutions to enhance resource and capabilities management effectively. This research pioneers an exploration into the influence of human capital and various assets (tangible and intangible), on the development and implementation of a SNZE in organizations, underpinned by empirical data. The study broadens the understanding of the practice-based view (PBV) framework in realizing SNZE, particularly within SME B2B enterprises. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Security and privacy issues in existing biometric systems and solutions
[No abstract available] -
Investigating the transport flexibility measures for freight transportation: a fuzzy best-worst method approach
Unpredicted disruptions force organisations to ensure flexibility for fulfilling customer demand. Enabling flexibility along the transportation system is the most suitable solution for unpredictable disruptions. Flexibility, being a potential element, requires more attention to gain competitive advantages. In this study, an effort has been made to investigate different transport flexibility measures (TFMs) related to freight transportation. Initially, an extensive literature survey is performed to identify different TFMs linked with the supply chain and logistics domain. Further, an integrated fuzzy best-worst method (FBWM) has been adopted to prioritise the identified TFMs and sensitivity analysis is performed to ensure robustness of the model. The findings of the study reflect mode, fleet, vehicle and speed flexibility as the significant flexibility measures for freight transportation. This study will help practitioners, managers and decision-makers associated with freight transportation to make better decisions to ensure flexibility in the freight transportation system. Copyright 2022 Inderscience Enterprises Ltd. -
Modelling of critical success factors for blockchain technology adoption readiness in the context of agri-food supply chain
The agri-food supply chain is continuously facing several challenges; the most severe are food quality and safety issues. These issues debilitate the performance of the supply chain and often harm the consumer's health. Therefore, there is an urgent need to address food quality and safety assurance in the supply chain. Blockchain technology (BCT) holds the potential to resolve these issues by enhancing security and transparency. The present study explores the critical success factors (CSFs) of BCT adoption readiness in the AFSC. Initially, CSFs are identified through a literature survey and finalised by experts' opinion. The finalised factors are prioritised using the fuzzy best-worst method, followed by sensitivity analysis. The results reflect that 'food quality control', 'provenance tracking and traceability', and 'partnership and trust' as the top three success factors. The study's findings will assist policymakers, managers, and practitioners in strategising the decision-making process while BCT dissemination. Copyright 2023 Inderscience Enterprises Ltd. -
Mirabijalones S-W, rotenoids from rhizomes of white Mirabilis jalapa Linn. and their cell proliferative studies
Five undescribed (2-6) rotenoid derivatives along with three known rotenoids (1, 7 and 8) were isolated from the rhizomes of white colored variety of Mirabilis jalapa Linn. The structures of these undescribed compounds were elucidated based on UV, IR, HR-MS (ESI), 1D and 2D NMR spectroscopic techniques. Selected compounds were evaluated for their cell viability and proliferation in two cancer cell lines namely, cervical (HeLa), breast (SKBR-3) and normal lung fibroblast (WI-38). Among them, the compounds Boeravinone C (1), Mirabijalone S (2), Mirabijalone T (3) and 4, 6, 11-trihydroxy-9-methoxy-10-methylchromeno [3, 4-b] chromen-12(6H)-one (8) showed moderate cytotoxicity against HeLa cells with IC50 values in the 8.40 ? 12.9 ?M range, and compound 8 exhibited cytotoxicity against SKBR-3 cells with IC50 value of 17.6 ?M. Molecular docking studies of isolated compounds were performed with three apoptosis proteins, 3H11, 2AR9 and 1X0X. These results revealed that the isolated compounds were found to interact with Caspase 8 and 9 along with the anti-apoptotic protein Survivin. Since these compounds exhibit cytotoxic effects against SKBR3 and HeLa cells, they are expected to show apoptosis and may be further utilized for wet lab apoptotic studies. 2021 Phytochemical Society of Europe