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An optimized back propagation neural network for automated evaluation of health condition using sensor data
Ships and other large equipment must meet strict standards for equipment integrity and operational dependability in order to perform missions. To meet this demand, one of the essential linkages is to guarantee the long-term safe and healthy functioning of their power transmission equipment. The Optimized Back Propagation Neural Network (OBPNN) technique used in this study introduces a unique method for monitoring sensor data and evaluating the health state, with the SVM being optimized using the fish swarm algorithm (FSA). A major problem that maintenance is facing nowadays is reliable fault prediction. One of the trickiest difficulties is arguably automatically modelling typical behaviour from condition monitoring data, particularly when there is little information about actual failures. A data-driven learning framework with the best bandwidth selection is suggested to address this challenge. It is based on nonparametric density estimation for outlier identification and OBPNN for normality modelling. The distance to the separating hyper plane's log-normalization is used to provide a health score that is also available. The algorithm's viability is shown by experimental findings while evaluating the progression of a major defect over time in a marine diesel engine. Improved prediction capabilities and low false positive rates on healthy data are realized. 2023 The Authors -
An optimized method for mulberry silkworm, Bombyx mori (Bombycidae:Lepidoptera) sex classification using TLBPSGA-RFEXGBoost
Silkworm seed production is vital for silk farming, requiring precise breeding techniques to optimize yields. In silkworm seed production, precise sex classification is crucial for optimizing breeding and boosting silk yields. A non-destructive approach for sex classification addresses these challenges, offering an efficient alternative that enhances both yield and environmental responsibility. Southern India is a hub for mulberry silk and cocoon farming, with the high-yielding double-hybrid varieties FC1 (foundation cross 1) and FC2 (foundation cross 2) being popular. Traditional methods of silkworm pupae sex classification involve manual sorting by experts, necessitating the cutting of cocoons a practice with a high risk of damaging the cocoon and affecting yield. To address this issue, this study introduces an accelerated histogram of oriented gradients (HOG) feature extraction technique that is enhanced by block-level dimensionality reduction. This non-destructive method allows for efficient and accurate silkworm pupae classification. The modified HOG features are then fused with weight features and processed through a machine learning classification model that incorporates recursive feature elimination (RFE). Performance evaluation shows that an RFE-hybridized XGBoost model attained the highest classification accuracy, achieving 97.2% for FC1 and 97.1% for FC2. The model further optimized with a novel teaching learning-based population selection genetic algorithm (TLBPSGA) achieved a remarkable accuracy of 98.5% for FC1 and 98.2% for FC2. These findings have far-reaching implications for improving both the ecological sustainability and economic efficiency of silkworm seed production. 2024. Published by The Company of Biologists Ltd. -
An optimized technique to foster omnichannel retail experience leveraging key technology dimensions in the context of an emerging digital market
Customers approach towards shopping has transformed, as a result of their reduced tolerance, increased technology usage and being well informed than ever before. As customers expect a seamless shopping experience regardless of where they are engaged within a retailers network, the line between physical and digital retailing is blurring. Retailers across the world are contemplating on transforming into Omnichannel hubs to deliver an elevated experience anytime anywhere. And, experts have often indicated that an Omnichannel strategy delivers a unified shopping experience than a mere channel experience. However, the true Omnichannel experience is still not evident in India with minimal action in this space, indicating a subverted outlook towards building necessary Omnichannel Capabilities. This paper examines the most essential and significant technology dimensions that are imperative towards fostering a seamless Omnichannel Retail Experience. The findings of this study serve as a basis for retailers in India to evaluate their strategies towards adopting these technology dimensions and respective capabilities, using an optimized approach. The study employed a quantitative research involving survey of executives from major retailers in India. The quantitative data was analyzed applying Structural Equation Modeling, to ascertain the technology dimensions that emerged and their significance in deriving Omnichannel Retail Experience. BEIESP. -
An ordered ideal intuitionistic fuzzy software quality model
Software is one of the major factors in the development of computer - based systems and products. Measurement of the software quality is thus the key factor that has to be taken into account while developing a software system. Many software quality models with numerous quality parameters are under use to measure the performance of a software system, on the basis of which the software is valued. This study intends to make available a fuzzy multiple criteria decision making (FMCDM) approach to measure software quality and to propose new similarity measures between ordered ideal intuitionistic fuzzy sets (OIIFSs). The proposed model is applied to five live software projects so as to quantify the software quality of each project under fuzzy environment. IAEME Publication. -
An organocatalytic C-C bond cleavage approach: A metal-free and peroxide-free facile method for the synthesis of amide derivatives
A facile organocatalytic approach has been devised towards the synthesis of amide derivatives using 1,3-dicarbonyls as easily available acyl-sources under peroxide-free reaction conditions. This transformation was accomplished by the cleavage of the C-C bond in the presence of TEMPO as an organocatalyst and excludes the use of transition-metals and harsh reaction conditions. A broad range of substrates with diverse functional groups were well tolerated and delivered the products in high yields. The Royal Society of Chemistry and the Centre National de la Recherche Scientifique. -
An outlook in blockchain technology- Architecture, applications and challenges
Blockchain is mechanism which stores and exchange data in a peer-peer network serving as an immutable ledger allowing transactions to take place in decentralized method which neglects the role of intermediaries. The technology reduces greater complexity by combining three key features; security, decentralization and transparency. This paper is an attempt explaining the concepts, structure, applications and challenges the technology has. The paper introduces blockchain taxonomy, reviews applications and discussed technical challenges and way of handling these challenges. Blockchain technology is springing up with promising applications in various fields and the authors have explored about three emerging field of blockchain say; Education, Government and Healthcare. Finally the paper concludes by stating other emerging fields of applications where further research can be explored. International Research Publication House. -
An overlap-based human gait cycle detection
Identification of a person by his/her style of walking is referred as gait recognition. Gait is one among the biometric used for human identification. In gait recognition, an inevitable step for accurate feature extraction is gait cycle detection. In this paper, a novel gait cycle detection algorithm based on the concept of overlap between legs during locomotion is proposed. To identify overlap, zero-crossing counts of silhouette frames as well as bottom halves of silhouette frames are considered. The efficiency of this algorithm is tested using normal walking sequence of subjects with 90 viewing angle from CASIA B as well as TUM-IITKGP human gait databases. The results obtained shows that gait cycle can be easily and efficiently detected with zero-crossing count of silhouette frames. Further zero-crossing counts taken from bottom halves of silhouette frames gives better performance. Copyright 2019 Inderscience Enterprises Ltd. -
An Overview of Nano-Catalysts in Biodiesel Production
Energy consumption and dependence on non-renewable resources is increasing over the years. The combustion of fossil fuels resulting in the emission of substantial amounts of CO2, NOX, SOX and some greenhouse gases. Biofuels are evolving as the primary alternatives to fossil fuels since they can be readily synthesised from discarded bioresources and yield lesser emission during the combustion process. However, the extraction of biofuels has thrown up new challenges that have widened the scope of the use of nano-particles in the synthesis of biofuels. From the literature, distinct findings concerning the use of nano-particles as a catalyst and process reactant during biodiesel production have been identified; this is majorly attributed to the fact that nano-catalysts enhance thermophysical properties, reaction speed and mass transport properties. Henceforth, the present paper aims to review, summarise and provide an insight into the research findings of effectively using nanocatalysts in biofuel production and consider the significance and its relevance for further researchers in the domain of biofuels. 2022, Books and Journals Private Ltd.. All rights reserved. -
An Understanding of Knowledge Management Perception and Implementation in Higher Education
Global Journal of Arts and Management, Vol. 2, No.3, pp 204-206, ISSN No. 2249-2658 -
Analgesic and Anti-Inflammatory Potential of Indole Derivatives
Some indole analogues show a good analgesic activity but on the other hand, it has some serious side effects like gastric ulcer. Therefore, there is still a need to develop derivatives of non-steroidal anti-inflammatory drugs (NSAIDs) with fewer side effects. For this purpose, some indole derivatives were prepared with objectives to develop new derivatives with maximum efficacy and minimum side effects. 1-(1H-indol-1-yl)-2-(sstituephenoxy)-ethan-1-one derivatives (M1M4) were analyzed further by thin-layer chromatorgarphy (TLC), melting point, IR, and 1H-NMR. The synthesized compounds then underwent oral toxicity studies that include hematological, biochemical, and histopathological findings. The compound was then evaluated for invivo anti-inflammatory and analgesic activities on carrageenan-induced rat paw edema and acetic acid-induced writhing methods. As a result of the biological activities, promising results were obtained in the compound M2 (2-(2-aminophenoxy)-1-(1H-indol-1-yl)ethanone) and it was subjected to further studies. It was found that compound M2 was practically nontoxic, and no clinical abnormalities were found in hematology and biochemistry, correlated with histopathological observation. It also showed significant anti-inflammatory and analgesic activities at its oral high dose (400 mg/kg). The study suggested that compound M2 was found to have significant anti-inflammatory and analgesic activities. The possible mechanism of M2 might suggest being act as a central anti-nociceptive agent and peripheral inhibitor of painful inflammation. The possible mechanism of action of the compounds whose biological activity was evaluated was explained by molecular docking study against COX-1 and COX-2, and the most active compound M2 formed ?9.3 and ?8.3 binding energies against COX-1 and COX-2. In addition, molecular dynamics (MD) simulation of both M2s complexes with COX-1 and COX-2 was performed to examine the stability and behavior of the molecular docking pose, and the MM-PBSA binding free energies were measured as ?153.820 11.782 and ?172.604 9.591, respectively. Based on computational ADME studies, compounds comply with the limiting guidelines. 2022 Taylor & Francis Group, LLC. -
Analysing grief on twitter: A study of digital expressions on Om Puri's death /
Funes Journal of Narratives And Social Sciences, Vol.2, pp. 136-152, ISSN No. 2532-6732. -
Analysing the Impact of Perceived Risk, Trust and Past Purchase Satisfaction on Repurchase Intentions in Case of Online Grocery Shopping in India
The Indian online grocery market has been propelling since last few years. The size of online grocery market in 2020 was estimated as $2.9 billion and it is further anticipated to reach at the compound annual growth rate (CAGR) of 37.1% during 2021 to 2028. Companies such as Amazon, Flipkart grocery, BigBasket, Grofers and Jiomart have been coming up with new attractions for consumers such as providing timely no contact delivery, accepting various digital modes of payment and offering several discounts which have fascinated consumers towards buying their regular grocery from various online platforms. Corona virus has also fuelled up the safety concerns of people; due to which a large section of the citizens are working from home and are dependent on the online platform for various purposes including grocery shopping. This has provided several growth opportunities to the online grocery market. This research investigates about the purchase behaviour of customers towards online grocery shopping. The study aims to understand the purchase behaviour of e-grocery shoppers of India and to examine the association between satisfactions with online purchase, trust on online grocers, perceived risk and online repurchase intention of grocery items. The study uses primary data collected from 555 online grocery buyers. The findings of the study indicate that online customer satisfaction is a significant factor that influences repurchase intentions of online grocery shopping. Perceived risk negatively influence trust as well as repurchase intentions. Trust is found to be a mediating factor between shopping satisfaction and repurchase intentions. The study also builds and tests an online customer behavioural model with actual purchasing behaviour and identifies the continued presence of perceived risk, shopping satisfaction and trust in grocery e-retailing. 2023 IMI. -
Analysing the impact of the taxation law amendment of 2019 on corporate taxation in India
The Taxation Law (Amendment) Act, 2019 in India has brought major changes in the taxation revenue as well as in legal provisions. The actual ground reality of the Amendment on a microeconomic level is unknown, but a correlation analysis on macroeconomic indicators show that there is a high positive correlation between the corporate tax revenue and the GDP growth. The author also interlinks the effects of tax cuts on the economy with privatization and how it can mitigate the risks of tax evasion. There is a generalized misconception with privatization that it leads to a significant loss in taxation revenue. The study shows that in fact, privatization helps to expand the earnings of the Government by widening the taxation structure and slab, which the author has found through statistics. It is high time to have strong regulatory measures to prevent tax evasion by encouraging more corporate entities to become a part of the tax base. Indian Institute of Finance. -
Analysing the market for digital payments in India using the predator-prey model
Technology has revolutionized the way transactions are carried out in economies across the world. India too has witnessed the introduction of numerous modes of electronic payment in the past couple of decades, including e-banking services, National Electronic Fund Transfer (NEFT), Real Time Gross Settlement (RTGS) and most recently the Unified Payments Interface (UPI). While other payment mechanisms have witnessed a gradual and consistent increase in the volume of transactions, UPI has witnessed an exponential increase in usage and is almost on par with pre-existing technologies in the volume of transactions. This study aims to employ a modified Lotka-Volterra (LV) equations (also known as the Predator-Prey Model) to study the competition among different payment mechanisms. The market share of each platform is estimated using the LV equations and combined with the estimates of the total market size obtained using the Auto-Regressive Integrated Moving Average (ARIMA) technique. The result of the model predicts that UPI will eventually overtake the conventional digital payment mechanism in terms of market share as well as volume. Thus, the model indicates a scenario where both payment mechanisms would coexist with UPI being the dominant (or more preferred) mode of payment. 2023 Balikesir University. All rights reserved. -
Analysis and dynamics of the Ivancevic option pricing model with a novel fractional calculus approach
The aim of the current study is to capture the complex behavior of the Ivancevic option pricing (IOP) model using the (Formula presented.) -homotopy analysis transform method ((Formula presented.) -HATM) with novel fractional operator. The generalization of the Black-Scholes model with the nonlinear Schringer equation plays a pivotal role in financial mathematics in studying the option-pricing wave function associated with two parameters. Based on adaptive market potential and volatility constant with distinct initial situations, we hired three distinct cases to exemplify the ability of (Formula presented.) -HATM. The considered method is elegant unification of the (Formula presented.) -homotopy analysis and Laplace transform algorithms. The derivative of fractional order is projected with the Atangana-Baleanu (AB) operator. The fixed-point theorem is used to present the existence and uniqueness of the attained result for the considered model, and we hire five distinct initial conditions. The hired scheme is highly methodical and exact to analyze the insights of the complex system with integer and fractional order exemplifying associated areas of science, which can be observed using plots and table. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Analysis and Forecasting of Area Under Cultivation of Rice in India: Univariate Time Series Approach
This study uses three distinct models to analyse a univariate time series of data: Holt's exponential smoothing model, the autoregressive integrated moving average (ARIMA) model, and the neural network autoregression (NNAR) model. The effectiveness of each model is assessed using in-sample forecasts and accuracy metrics, including mean absolute percentage error, mean absolute square error, and root mean square log error. The area under cultivation in India for the following 5years is predicted using the model whose fitted values are most like the observed values. This is determined by performing a residual analysis. The time series data used for the study was initially found to be non-stationary. It is then transformed into stationary data using differencing before the models can be used for analysis and prediction. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
Analysis and measurement of supply chain flexibility
Supply chain flexibility is a strategic and tactical necessity for sustenance and progress of business enterprises. Measurement of flexibility is therefore necessary for its monitoring, control and communication. The study proposes a framework and a methodology for flexibility performance measurement of supply chains. The framework identifies flexibility objectives and its contributing attributes at four levels of the supply chain and suggests taxonomy of flexibility performance measures. A methodology to prioritise the contribution of each performance attribute to achieve the desired flexibility objective using analytic hierarchy process (AHP) has also been proposed and demonstrated in this study. The research is based on detailed literature-based study and analysis of existing frameworks of flexibility performance measurement in supply chain and expert opinion. The proposed framework is suitable for measurement, monitoring and controlling flexibility in a supply chain in addition to prioritising contributing attributes of flexibility. The research does not test the model but suggests a platform for further development. Copyright 2015 Inderscience Enterprises Ltd. -
Analysis and optimization of uplink spectral efficiency in massive multiple-input and multiple-output
Fifth Generation (5G) specifications aims for data rate of 1 Gbps in high mobility and 10 Gbps in low mobility conditions, 15-30 bps/Hz of spectral efficiency with less than 1 milli second (ms) latency reduction. Massive multiple-input and multiple-output (Massive MIMO) is one of the promising technologies in 5G standard which offers a high spectral efficiency improvement. This work focus on the uplink scenario spectral efficiency in a Massive MIMO simulation network based on third generation partnership project (3GPP) and long term evolution (LTE) document of 5G. This work analyzes the spectral efficiency metric by simulating the 5G Massive MIMO network. Then, the research identified major constraint parameters; number of user antennas, K, number of base station antennas, M, transmission power, P, channel bandwidth, B, and coherence time, Tau_C and pilot time Tau_P which plays a significant role in varying this metric. The authors focus on improving the spectral efficiency by passing these constraint parameters through different meta-heurestic optimization algorithms, such as, convex optimization solver, White shark optimization (WSO) and Particle swarm optimization (PSO). The results show an overall, 1-10 percent of improvement of the parameter wnen compared with other research articles. The maximum value achieved is 49.84 bps/Hz, which is three times higher as per to the 3GPP and International Telecommunication Unioin (ITU) release document. 2022 Institute of Advanced Engineering and Science. All rights reserved. -
Analysis and prediction of Indian stock market: a machine-learning approach
Prediction of financial stock market is a challenging task because of its volatile and non- linear nature. The presence of different factors like psychological, sentimental state, rational or irrational behaviour of investors make the stock market more dynamic. With the inculcation of algorithms based on artificial intelligence, deep learning algorithms, the prediction of movement of financial stock market is revolutionized in the recent years. The purpose of using these algorithms is to help the investors for taking decisions related to the Stock Pricing. A model has been proposed to predict the direction of movement of Indian stock market in the near future. This model makes use of historical Indian stock data of companies in nifty 50 since they came existence along with some financial and social indicators like financial news and tweets related to stocks. After pre-processing and normalization various machine learning algorithms like LSTM, support vector machines, KNearest neighbour, random forest, gradient boosting regressor are applied on this time series data to produce better accuracy and to minimize the RMSE error. This model has the ability to reduce major losses to the investors who invest in stock market. The social indicators will give an insight for predicting the direction of stock market. The LSTM network will make use of historical closing prices, tweets and trading volume. 2023, The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden. -
Analysis and prediction of seed quality using machine learning
The mainstay of the economy has always been agriculture, and the majority of tasks are still carried out without the use of modern technology. Currently, the ability of human intelligence to forecast seed quality is used. Because it lacks a validation method, the existing seed prediction analysis is ineffective. Here, we have tried to create a prediction model that uses machine learning algorithms to forecast seed quality, leading to high crop yield and high-quality harvests. For precise seed categorization, this model was created using convolutional neural networks and trained using the seed dataset. Using data that can be used to forecast the future, this model is used to learn about whether the seeds are of premium quality, standard quality, or regular quality. While testing data are employed in the algorithms predictive analytics, training data and validation data are used for categorization reasons. Thus, by examining the training accuracy of the convolution neural network (CNN) model and the prediction accuracy of the algorithm, the projects primary goal is to develop the best method for the more accurate prediction of seed quality. 2023 Institute of Advanced Engineering and Science. All rights reserved.