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Consumer perception and factors influencing consumption of millets
Consumers purchase intention and preferences are influenced by price, quality, health-related benefits, and awareness about the product. This paper aims to know and understand the consumer perception of millets and to recognize the factors that influence their purchase. The primary data was collected through an online questionnaire covering fourteen districts of Kerala, India. Factor Analysis, Friedman test, T-test, and One-way ANOVA were used for testing the objectives and hypothesis. Factors identified were grouped as perceived value, essential nutrients, and a healthy lifestyle. Friedman test revealed that there wasa significant difference among the mean values of most nutritious cereals, and maize was the most preferred cereal over others in Kerala. Based on the findings, the study recommends certain strategies like food manufacturing companies could introduce variety of millet-based snacks. In addition to this, the concerned food and health department could also devise certain policies that would be aimed at promoting millet-based food. 2022, Kerala Agricultural University. All rights reserved. -
Exploration of carbon nano dots in hydro carbon soot and carbon black
Hydrocarbon soot, a prime component of particulate matter pollution, poses a great threat to the environment. In this study, we put forth a novel way of harnessing carbon nanodots from the soot particulates thereby converting an environmentally perilous component to an innocuous entity suitable for many applications such as biomedical tracers, gas detectors etc. Large scale production of pure carbon nanodots (PCN) was achieved via direct catalyst free thermal decomposition of kerosene and diesel. Nanostructure of carbon black and graphite is also investigated for comparative studies. In UV-Vis spectra, absorptions at 233, 232 and 229 nm are attributed to ?-?? transition of the C=C bonding. XRD of the samples shows a highly intense peak at ?24 and a slightly broadened peak around 42 due to (002) and (010) reflections of graphitic planes respectively. In IR spectra, peaks at 3431 and 1047 cm-1 were assigned to O-H and C-O stretching vibrations respectively. The band observed at 1619 cm-1 manifests the skeletal vibrations from graphitic domains and hence indicates the presence of crystalline graphitic carbon. The absorption bands at 2920 and 2850 cm-1 arise because of the existence of aliphatic groups in the soot sample. 2017, International Congress of Chemistry and Environment. All rights reserved. -
ENHANCING home security through visual CRYPTOGRAPHY
Home security systems in the recent times have gained greater importance due to increasing threat in the society. Biometrics deals with automated approaches of recognizing a user or verifying the user identity based on behavioral or physiological features. Visual cryptography is a scheme of secret sharing where a secret image is encrypted into shares which disclose no data independently about the original secret image. As the template of biometric are stored in centralized database due to the threats of security the template of biometric may be changed by attacker. If the template of biometric is changed then the authorized user will not be permitted to access the resource. To manage this problem the schemes of visual cryptography can be used to secure the face recognition. Visual cryptography offers huge ways for supporting such needs of security as well as additional authentication layer. To manage this problem the visual cryptography schemes can be used to secure digital biometric information privacy. In this approach the face or private image is dithered in two varied host images that is sheets and are stored in separate servers of data so as to assure that the original image can get extracted only by accessing both sheets together at a time and a single sheet will not be capable to show any data of private image. The main aim of the study is to propose an algorithm which is a combination of CVC and Siamese network. This research implements visual cryptography for face images in a biometric application. The Siamese network is essential to solve one shot learning by representation of learning feature that are compared to verification tasks. In this research face authentication helps in accomplishing robustness by locating face image from an n input image. This research explores the availability of using visual cryptography for securing the privacy to biometric data. The results of the proposed approach provide an accuracy of 93% which is found to be superior when compared with that of the approaches that are already in practice. 2020 -
Secure visual cryptography scheme with meaningful shares
Visual cryptography is an outstanding design, which is also known as visual secret sharing. It used to encode a secret portrait into various pointless share images. Normally, item bossed on transparencies and decrypts as loading one or two or the entire share images by means of the human visual system. Suppose, if we encompass great sets of secret shares then the pointless shares are complicated to handle. In this paper, a meaningful secret sharing algorithm and a modified Signcryption algorithm is used to enhance the security of the Visual Cryptography encryption schemes. The foremost intend of the anticipated format is to extend consequential shares and similarly make sure the isolation on conveying the secret data. The anticipated process is executed in the functioning platform of MATLAB and the presentation results are investigated. 2020, Engg Journals Publications. All rights reserved. -
Influence of Short Glass Fibre Reinforcement on Mechanical Properties of 3D Printed ABS-Based Polymer Composites
One of the most promising and widely used additive manufacturing technologies, fused deposition modelling (FDM), is based on material extrusion and is most commonly used for producing thermoplastic parts for functional applications with the objectives of low cost, minimal waste and ease of material conversion. Considering that pure thermoplastic materials have a significantly poor mechanical performance, it is necessary to enhance the mechanical properties of thermoplastic parts generated using FDM technology. One of the conceivable techniques is to incorporate reinforcing materials such as short glass fibre (SGF) into the thermoplastic matrix in order to produce a polymer composite that can be used in engineering applications, such as structural applications. The morphological and mechanical properties of SGF (short glass fibre) reinforced ABS- (Acrylonitrile Butadiene Styrene) based polymer composites created via the method of FDM (fused deposition modelling) were investigated in this work. Properties were evaluated at three different weight percentages (0, 15 and 30 wt%). The composite filaments were developed using the process of twin screw extrusion. The comparison was made between ABS + SGF (short glass fibre) composites and pure ABS of mechanical properties that include surface roughness, tensile strength and low-velocity impact. The tests were carried out to analyze the properties as per ASTM standards. It has been found that the impact strength and tensile strength show an improvement in glass fibre inclusion; moreover, alongside the direction of build, the surface roughness had been reduced. The studies also focused on studying the dispersion characters of SGF in ABS matrix and its impact on the properties. Strength and modulus of SGF reinforced ABS composite has been significantly improved along with reduction of ductility. A 57% increase in tensile strength has been noted for 30 wt% addition of SGF to ABS in comparison to pure ABS. It was also interesting to note the reduction in surface roughness with every incremental addition of SGF to ABS. A 40% reduction in surface roughness has been observed with a 30 wt% addition of SGF to ABS in comparison to pure ABS. 2022 by the authors.Licensee MDPI, Basel, Switzerland. -
Visible light responsive Gd, N co-doped mesoporous titania in the photo-oxidation of some novel 9-(N,N-Dimethylaminomethyl)anthracene systems
Oxidative semiconductor catalysis by light can be considered as an easy method for the conversion of harmful aromatics to less harmful products and at the same time move towards a sustainable chemistry. The present work reports the preparation of Gd, N co-doped TiO2 system by hydrothermal technique followed by calcination at 500C and checks its activity in photo-oxidation reactions. The prepared system was characterized by various physico-chemical techniques such as X-ray diffraction, Raman spectroscopy, IR spectroscopy, TGDTG, UV-DRS, SEM, TEM and XPS. Structural identity and mesoporous nature were identified from XRD and BET measurements respectively. On reaction, Tertiary amine appended anthracene and its phenyl substituted derivative in CH3CN yielded Anthraquinone as the major product. Substituted Anthracenemethanamine reacted slowly and a relatively stable intermediate could be isolated at shorter periods of time. The products were separated and purified by column chromatography and the resultant products were characterized thoroughly by 1H NMR, IR spectroscopy and GCMS analysis. 2014, Springer Science+Business Media New York. -
Cu/Pd bimetallic supported on mesoporous TiO2 for suzuki coupling reaction
Generally bimetallic catalysts are more superior to monometallic catalysts and provide a better platform for the development of novel catalysts with enhanced activity, selectivity, and stability. In the current work we have prepared Cu/Pd bimetallic supported on mesoporous TiO2 by hydrothermal method. The prepared system was characterized by various physico-chemical techniques such as XRD, TG-DTG, SEM, EDAX, BJH isotherm, and XPS. Thermal stability and complete electronic structure were identified from TG and XPS measurements respectively. The bimetallic system was found to be very active in Suzuki cross-coupling reaction using different substrates. The products were separated and purified by column chromatography and the resultant products were characterized thoroughly by 1H NMR, and FT-IR analysis. Copyright 2018 BCREC Group. All rights reserved. -
Eagle Strategy Arithmetic Optimisation Algorithm with Optimal Deep Convolutional Forest Based FinTech Application for Hyper-automation
Hyper automation is the group of approaches and software companies utilised to automate manual procedures. Financial Technology (FinTech) was processed as a distinctive classification that highly inspects the financial technology sector from a broader group of functions for enterprises with utilise of Information Technology (IT) application. Financial crisis prediction (FCP) is the most essential FinTech technique, defining institutions financial status. This study proposes an Eagle Strategy Arithmetic Optimisation Algorithm with Optimal Deep Convolutional Forest (ESAOA-ODCF) based FinTech Application for Hyperautomation. The ESAOA-ODCF technique has achieved exceptional performance with maximum accu y of 98.61%, and F score of 98.59%. Extensive experimental research revealed that the ESAOA-ODCF model beat more modern, cutting-edge approaches in terms of overall performance. 2023 Informa UK Limited, trading as Taylor & Francis Group. -
Sales Promotion Practices In Apparel Retail Sector And Challenges Ahead
International Journal of Research in Commerce & Management, Vol-5 (1), pp. 25-28. ISSN-0976-2183 -
Study on factors influencing purchase of branded formal apparel in Indian apparel industry /
International Journal of Business, Management & Social Sciences, Vol-3 (5(2), pp. 51-54. ISSN-2249-7463. -
A Study of In-store Factors Affecting Impulse Buying of Apparels amongst College Students and Young Professionals in Bangalore
Asian Journal of Research in Business Economics and Management, Vol. 7, Issue 3, pp. 1-15, ISSN No. 2249-7307. -
A Study on the Factors Influencing Customer Satisfaction in Multi-brand Apparel Retail
International Academic Research Journal of Business and Management Vol.1, Issue No. 7 ISSN No. 2227-1287 -
Improved Henon Chaotic Map-based Progressive Block-based visual cryptography strategy for securing sensitive data in a cloud EHR system
The core objective of secret sharing concentrates on developing a novel technique that prevents the destruction and leakage of original data during the distribution and encoding processes. Progressive Visual Cryptography (VC) is considered for the potential over the traditional VC schemes since the former does not require and does not suffer from the limitations of requiring a minimum number of participants during the process of encryption and sharing. The chaotic map-based Progressive VC is superior in facilitating predominant secrecy under sharing and encryption. In this paper, an Improved Henon Chaotic Map-based Progressive Block-based VC (IHCMPBVC) scheme is proposed to prevent the leakage and destruction of sensitive information during an exchange and encryption. This proposed IHCMPBVC technique uses the merits of Henon and Lorentz maps for effective encryption since it introduces the option of deriving non-linear behavior that results in sequence generation that covers the complete range with proper distribution in order to minimize the degree of leaks in sharing. The simulation results of the proposed IHCMPBVC technique investigated using entropy, PSNR, and Mean Square Error were improved at an average rate of 27%, 23%, and 31%, predominant to the baseline VC approaches considered in the comparison. 2022 The Authors -
Effect of Short Glass Fiber Addition on Flexural and Impact Behavior of 3D Printed Polymer Composites
Fused deposition modeling (FDM), one of the most widely used additive manufacturing (AM) processes, is used for fabrication of 3D models from computer-aided design data using various materials for a wide scope of applications. The principle of FDM or, in general, AM plays an important role in minimizing the ill effects of manufacturing on the environment. Among the various available reinforcements, short glass fiber (SGF), one of the strong reinforcement materials available, is used as a reinforcement in the acrylonitrile butadiene styrene (ABS) matrix. At the outset, very limited research has been carried out till date in the analysis of the impact and flexural strength of the SGF-reinforced ABS polymer composite developed by the FDM process. In this regard, the present research investigates the impact and flexural strength of SGF-ABS polymer composites by the addition of 15 and 30 wt % SGF to ABS. The tests were conducted as per ASTM standards. Increments in flexural and impact properties were observed with the addition of SGF to ABS. The increment of 42% in impact strength was noted for the addition of 15 wt % SGF and 54% increase with the addition of 30 wt % SGF. On similar lines, flexural properties also showed improved values of 44 and 59% for the addition of 15 and 30 wt % SGF to ABS. SGF addition greatly enhanced the properties of flexural and impact strength and has paved the path for the exploration of varied values of reinforcement into the matrix. 2023 The Authors. Published by American Chemical Society -
Epidemic Prediction using Machine Learning and Deep Learning Models on COVID-19 Data
A catastrophic epidemic of Severe Acute Respiratory Syndrome-Coronavirus, commonly recognised as COVID-19, introduced a worldwide vulnerability to human community. All nations around the world are making enormous effort to tackle the outbreak towards this deadly virus through various aspects such as technology, economy, relevant data, protective gear, lives-risk medications and all other instruments. The artificial intelligence-based researchers apply knowledge, experience and skill set on national level data to create computational and statistical models for investigating such a pandemic condition. In order to make a contribution to this worldwide human community, this paper recommends using machine-learning and deep-learning models to understand its daily accelerating actions together with predicting the future reachability of COVID-19 across nations by using the real-time information from the Johns Hopkins dashboard. In this work, a novel Exponential Smoothing Long-Short-Term Memory Networks Model (ESLSTM) learning model is proposed to predict the virus spread in the near future. The results are evaluated using RMSE and R-Squared values. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Enhanced Channel Division Method for Estimation of Discharge in Meandering Compound Channel
Accurate prediction of shear force distribution along the boundary in open channels is a key to the solution of numerous hydraulic problems. The problem becomes more complicated for meandering compound channels. A model is developed for predicting the percentage of shear force at the floodplain (%Sfp) of two-stage meandering channels using gene-expression programming (GEP) by considering five dimensionless parameters viz. the width ratio, relative depth, sinuosity, bed slope, and meander belt width ratio as the inputs in the model. Basing on the %Sfp, the apparent shear force along the division lines of separation in compound channels is selected for discharge calculation using the conventional channel division methods. An Enhanced Channel Division Method (ECDM) is introduced to calculate discharge by assuming interface line at main channel and floodplain junction. A modified variable-inclined (MVI) interface is suggested having zero apparent shear determined from flow contribution in the main channel and floodplain. The MVI interface is further used to calculate discharge in the meandering compound channels. Performance of the GEP model is tested against other analytical methods of calculating %Sfp. Error between the observed and calculated discharges using the MVI interface is found to be the minimum when compared to other interface methods. The enhance channel division method is successfully applied for validating the two available overbank discharge values for the river Baitarani at Anandapur (drainage area of 8570 sq. km), giving the minimum errors of 0.31% and 1.02% for flow depths of 7.5m and 8.63m, respectively. 2020, Springer Nature B.V. -
Assessment of Shear Stress Distribution in Meandering Compound Channels with Differential Roughness Through Various Artificial Intelligence Approach
Accurate prediction of shear stress distribution along the boundary in an open channel is the key to solving numerous critical engineering problems such as flood control, sediment transport, riverbank protection, and others. Similarly, the estimation of flow discharge in flood conditions is also challenging for engineers and scientists. The flow structure in compound channels becomes complicated due to the transfer of momentum between the deep main channel and the adjoining floodplains, which affects the distribution of shear force and flow rate across the width. Percentage sharing of shear force at floodplain (%Sfp) is dependent on the non-dimensional parameters like width ratio of the channel (?) , relative depth (?) , sinuosity (s) , longitudinal channel bed slope (So) , meander belt width ratio (?) , and differential roughness (?). In this paper, various artificial intelligence approaches such as multivariate adaptive regression spline (MARS), group method of data handling Neural Network (GMDH-NN), and gene-expression programming (GEP) are adopted to construct model equations for determining %Sfp for meandering compound channels with relative roughness. The influence of each parameter used in the model for predicting the %Sfp is also analyzed through sensitivity analysis. Statistical indices are employed to assess the performance of these models. Validation of the developed %Sfp model is performed for the experimental observations by conventional analytical models; to verify their effectiveness. Results indicate that the proposed GMDH-NN model predicted the %Sfp satisfactorily with the coefficient of determination (R2) of 0.98 and 0.97 and mean absolute percentage error (MAPE) of 0.05% and 0.04% for training and testing dataset, respectively as compared to GEP and MARS. The developed model is also validated with various sinuous channels having sinuosity 1.343, 1.91 and 2.06. 2021, The Author(s), under exclusive licence to Springer Nature B.V. -
Determination of Discharge Distribution in Meandering Compound Channels Using Machine Learning Techniques
Accurate flow rate prediction is essential to analyze flood control, sediment transport, riverbank protection, and so forth. The flow rate distribution becomes even more complicated in compound channels due to the momentum transfer between different subsections across the width of the channel. Conventional channel division methods estimate flow distribution at the main channel and floodplains by assuming a division line with zero apparent shear stress. The article attempts to develop a model to calculate the percentage of discharge in the main channel (%Qmc) using techniques such as Group Method of Data Handling - Neural Network (GMDH-NN) and gene-expression programming (GEP) by incorporating the effects of various geometric and hydraulic parameters. The paper proposes a modified channel division method with a variable-inclined interface, with zero apparent shear force distribution at the channel subsections according to the statistical indices employed to assess these models' performance in predicting %Qmc. This variable-inclined interface changes its slope according to the channel parameters. The model's effectiveness is verified by validating with experimental observations by conventional analytical methods. 2021 American Society of Civil Engineers. -
Whispered Speech Emotion Recognition with Gender Detection using BiLSTM and DCNN
Emotions are human mental states at a particular instance in time concerning ones circumstances, mood, and relationships with others. Identifying emotions from the whispered speech is complicated as the conversation might be confidential. The representation of the speech relies on the magnitude of its information. Whispered speech is intelligible, a low-intensity signal, and varies from normal speech. Emotion identification is quite tricky from whispered speech. Both prosodic and spectral speech features help to identify emotions. The emotion identification in a whispered speech happens using prosodic speech features such as zero-crossing rate (ZCR), pitch, and spectral features that include spectral centroid, chroma STFT, Mel scale spectrogram, Mel-frequency cepstral coefficient (MFCC), Shifted Delta Cepstrum (SDC), and Spectral Flux. There are two parts to the proposed implementation. Bidirectional Long Short-Term Memory (BiLSTM) helps to identify the gender from the speech sample in the first step with SDC and pitch. The Deep Convolutional Neural Network (DCNN) model helps to identify the emotions in the second step. This implementation is evaluated using the wTIMIT data corpus and gives 98.54% accuracy. Emotions have a dynamic effect on genders, so this implementation performs better than traditional approaches. This approach helps to design online learning management systems, different applications for mobile devices, checking cyber-criminal activities, emotion detection for older people, automatic speaker identification and authentication, forensics, and surveillance. (2023), (Iranian Academic Center for Education). All Rights Reserved. -
Sentiment Analysis on Banking Feedback and News Data using Synonyms and Antonyms
Sentiment analysis is crucial for deciphering customers enthusiasm, frustration, and the market mood within the banking sector. This importance arises from financial datas specialized and sensitive nature, enabling a deeper understanding of customer sentiments. In todays digital and social marketing landscape within the banking and financial sector, sentiment analysis is significant in shaping customer insights, product development, brand reputation management, risk management, customer service improvement, fraud detection, market research, compliance regulations, etc. This paper introduces a novel approach to sentiment analysis in the banking sector, emphasizing integrating diverse text features to enable dynamic analysis. This proposed approach aims to assess the sentiment score of distinct words used within a document and classify them as positive, negative, or neutral. After rephrasing sentences using synonyms and antonyms of unique words, the system calculates sentence similarity using a distance control mechanism. Then, the system updates the dataset with the positive, negative, and neutral labels. Ultimately, the ELECTRA model utilizes the self-trained sentiment-scored data dictionary, and the newly created dataset is processed using the SoftMax activation function in combination with a customized ADAM optimizer. The approachs effectiveness is confirmed through the analysis of post-bank customer feedback and the phrase bank dataset, yielding accuracy scores of 92.15% and 93.47%, respectively. This study stands out due to its unique approach, which centers on evaluating customer satisfaction and market sentiment by utilizing sentiment scores of words and assessing sentence similarities. 2023, Science and Information Organization. All rights reserved.