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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. -
Identification of Driver Drowsiness Detection using a Regularized Extreme Learning Machine
In the field of accident avoidance systems, figuring out how to keep drivers from getting sleepy is a major challenge. The only way to prevent dozing off behind the wheel is to have a system in place that can accurately detect when a driver's attention has drifted and then alert and revive them. This paper presents a method for detection that makes use of image processing software to examine video camera stills of the driver's face. Driver inattention is measured by how much the eyes are open or closed. This paper introduces Regularized Extreme Learning Machine, a novel approach based on the structural risk reduction principle and weighted least squares, which is applied following preprocessing, binarization, and noise removal. Generalization performance was significantly improved in most cases using the proposed algorithm without requiring additional training time. This approach outperforms both the CNN and ELM models, with an accuracy of around 99% being achieved. 2023 IEEE. -
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. -
Enhanced Edge Computing Model by using Data Combs for Big Data in Metaverse
The Metaverse is a huge project undertaken by Facebook in order to bring the world closer together and help people live out their dreams. Even handicapped can travel across the world. People can visit any place and would be safe in the comfort of their homes. Meta (Previously Facebook) plans to execute this by using a combination of AR and VR (Augmented Reality and Virtual Reality). Facebook aims to bring this technology to the people soon. However, a big factor in this idea that needs to be accounted for is the amount of data generation that will take place. Many Computer Science professors and scientists believe that the amount of data Meta is going to generate in one day would almost be equal to the amount of data Instagram/Facebook would have generated in their entire lifetime. This will push the entire data generation by at least 30%, if not more. Using traditional methods such as cloud computing might seem to become a shortcoming in the near future. This is because the servers might not be able to handle such large amounts of data. The solution to this problem should be a system that is designed specifically for handling data that is extremely large. A system that is not only secure, resilient and robust but also must be able to handle multiple requests and connections at once and yet not slow down when the number of requests increases gradually over time. In this model, a solution called the DHA (Data Hive Architecture) is provided. These DHAs are made up of multiple subunits called Data Combs and those are further broken down into data cells. These are small units of memory which can process big data extremely fast. When information is requested from a client (Example: A Data Warehouse) that is stored in multiple edges across the world, then these Data Combs rearrange the data cells within them on the basis of the requested criteria. This article aims to explain this concept of data combs and its usage in the Metaverse. 2023 IEEE. -
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. -
Improving Speaker Gender Detection by Combining Pitch and SDC
Gender detection is helpful in various applications, such as speaker and emotion recognition, which helps with online learning, telecom caller identification, etc. This process is also used in speech analysis and initiating human-machine interaction. Gender detection is a complex process but an essential part of the digital world dealing with voice. The proposed approach is to detect gender from a speech by combining acoustic features like shifted delta cepstral (SDC) and pitch. The first step is preprocessing the speech sample to retrieve valid speech data. The second step is to calculate the pitch and SDC for each frame. The multifeature fusion method combines the speech features, and the XGBoost model is applied to detect gender. This approach results in accuracy rates of 99.44 and 99.37% with the help of RAVDESS and TIMIT datasets compared to the pre-defined methods. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Improvement of Speech Emotion Recognition by Deep Convolutional Neural Network and Speech Features
Speech emotion recognition (SER) is a dynamic area of research which includes features extraction, classification and adaptation of speech emotion dataset. There are many applications where human emotions play a vital role for giving smart solutions. Some of these applications are vehicle communications, classification of satisfied and unsatisfied customers in call centers, in-car board system based on information on drivers mental state, human-computer interaction system and others. In this contribution, an improved emotion recognition technique has been proposed with Deep Convolutional Neural Network (DCNN) by using both speech spectral and prosodic features to classify seven human emotionsanger, disgust, fear, happiness, neutral, sadness and surprise. The proposed idea is implemented on different datasets such as RAVDESS, SAVEE, TESS and CREMA-D with accuracy of 96.54%, 92.38%, 99.42% and 87.90%, respectively, and compared with other pre-defined machine learning and deep learning methods. To test the real-time accuracy of the model, it has been implemented on the combined datasets with accuracy of 90.27%. This research can be useful for development of smart applications in mobile devices, household robots and online learning management system. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Cancer Tumor Detection Using Genetic Mutated Data and Machine Learning Models
Early detection of a disease is a crucial task because of unavailability of proper medical facilities. Cancer is one of the critical diseases that needs early detection for survival. A cancer tumor is caused due to thousands of genetic mutations. Understanding the genetic mutations of cancer tumor is a tedious and time-consuming task. A list of genetic variations is analysed manually by a molecular pathologist. The clinical strips of indication are of nine classes, but the classification is still unknown. The objective of this implementation is to suggest a multiclass classifier which classifies the genetic mutations with respect to the clinical signs. The clinical evidences are text-evidences of gene mutations and analysed by Natural Language Processing (NLP). Various machine learning concepts like Naive Bayes, Logistic Regression, Linear Support Vector Machine, Random Forest Classifier applied on the collected dataset which contain the evidence based on genetic mutations and other clinical evidences that pathology or specialists used to classify the gene mutations. The performances of the models are analysed to get the best results. The machine learning models are implemented and analyzed with the help of gene, variance and text features. Based on the variants of gene mutation, the risk of the cancer can be detected and the medications can be prescribed accordingly. 2022 IEEE. -
Pathway toDetect Cancer Tumor byGenetic Mutation
Cancer detection is one of the challenging tasks due to the unavailability of proper medical facilities. The survival of cancer patients depends upon early detection and medication. The main cause of the disease is due to several genetic mutations which form cancer tumors. Identification of genetic mutation is a time-consuming task. This creates a lot of difficulties for the molecular pathologist. A molecular pathologist selects a list of gene variations to analyze manually. The clinical evidence strips belong to nine classes, but the classification principle is still unknown. This implementation proposes a multi-class classifier to classify genetic mutations based on clinical evidence. Natural language processing analyzes the clinical text of evidence of gene mutations. Machine learning algorithms like K-nearest neighbor, linear support vector machine, and stacking models are applied to the collected text dataset, which contains information about the genetic mutations and other clinical pieces of evidence that pathology uses to classify the gene mutations. In this implementation, nine genetic variations have been taken, considered a multi-class classification problem. Here, each data point is classified among the nine classes of gene mutation. The performance of the machine learning models is analyzed on the gene, variance, and text features. The gene, variance, and text features are analyzed individually with univariate analysis. Then K-nearest neighbor, linear support vector machine, and stacking model are applied to the combined features of a gene, variance, and text. In the experiment, support vector machine gives better results as compared to other models because this model provides fewer misclassification points. Based on the variants of gene mutation, the risk of cancer can be detected, and medications can be given. This chapter will motivate the readers, researchers, and scholars of this field for future investigations. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.