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Synthesis, Photophysical, and Computational Studies of Mono-Azo-Bridged, Meso-Tris(2-Furyl/2-Thienyl) Substituted Porphyrin-Arene Hybrids
Porphyrins hybrids have been used as models to study various energy/electron transfer processes. The linkers connecting various subunits in such hybrids are vital in establishing good electronic communication between the subunits and the azo-bridge can be one of the efficient linkers to do so. Despite of these, the mono azo-bridged porphyrin-arene hybrids reported in the literature are only handful and the methods used to create them are not that efficient. In addition, the porphyrins used in this field so far contains only six-membered meso-substituents. By keeping these points in mind, we have developed a mild, one-pot, work-up-free, high-yielding method to synthesize mono-azo-bridged, porphyrin-arene hybrids which also features porphyrins containing three five-membered substituents like 2-furyl or 2-thienyl on their meso-positions. Along with the NMR and mass characterizations, the photophysical and computational studies of all the reported hybrids are presented. The hybrids containing meso-tris(2-furyl/thienyl) substituted porphyrins displayed red-shifted absorption and emission bands compared to their all-meso-aryl-containing counterparts. In general, all the hybrids displayed enhanced fluorescence quantum yields compared to their precursor porphyrins. Among the series, the meso-tris(2-furyl) substituted porphyrin-arene hybrids exhibited the more significant Stokes shift and small bandgap. The computational studies were in good agreement with the experimental findings. 2024 Wiley-VCH GmbH. -
Boosting enabled efficient machine learning technique for accurate prediction of crop yield towards precision agriculture
Due to the limited availability of natural resources, it is essential that agricultural productivity keep pace with population growth. Despite unfavorable weather circumstances, this project's major objective is to boost production. As a consequence of technological advancements in agriculture, precision farming as a way for enhancing crop yields is gaining appeal and becoming more prevalent. When it comes to predicting future data, machine learning employs a number of methods, including the creation of models and the acquisition of prediction rules based on past data. In this manuscript, we examine various techniques to machine learning, as well as an automated agricultural yield projection model based on selecting the most relevant features. For the purpose of selecting features, the Grey Level Co-occurrence Matrix method is utilised. For classification, we make use of the AdaBoost Decision Tree, Artificial Neural Network (ANN), and K-Nearest Neighbour (KNN) algorithms. The data set that was used in this study is simply a compilation of information about a variety of topics, including yield, pesticide use, rainfall, and average temperature. This data collection consists of 33 characteristics or qualities in total. The crops soya beans, maze, potato, rice, paddy, wheat, and sorghum are included in this data collection. This data collection was made possible through the collaboration of the Food and Agriculture Organisation (FAO) and the World Data Bank, both of which make their data available to the public. The AdaBoost decision tree has achieved the highest level of accuracy possible when used to anticipate agricultural yield. Both the accuracy rate and the recall rate are quite high at 99 percent. The Author(s) 2024. -
IoT Cloud Systems: A Survey
IoT has gained a massive prevalence in the last decade. Various businesses are leveraging IoT Applications for industrial and commercial use cases. IoT also presents use cases in research and academia. However, setting up IoT Systems is complex due to the distributed and multi-disciplinary nature of IoT Systems. As a direct consequence of this complexity, the entire service industry has emerged that assists users to deploy and manage IoT systems. This paper aims to survey some of the Cloud management systems that help simplify and shorten the deployment process of IoT Systems. 2023 IEEE. -
Aspect Based Multi Classification for Text Mining Using Neural Attention Model
Aspect-based text classification is crucial for multi-classification in e- commerce, including diverse sectors like food, online shopping, and restaurants. Traditional research often focuses on a few classes and domains, such as restaurants or electronics, and overlooks the need to categorize sentences based on domain- specific contexts. However, e-commerce involves numerous domains that require more sophisticated classification methods. E-commerce platforms generate vast amounts of textual data, including comments, product descriptions, and customer reviews, which contain valuable information about various aspects of products or services. Since customers often research product reviews from multiple sources before purchasing, these reviews become essential user-generated content for e-commerce businesses. To address this gap, the Aspect-Based Neural Attention Model (ABNAM) was developed. ABNAM enhances classification's accuracy and comprehensiveness by considering each domain's unique characteristics. This leads to better categorization and provides more relevant insights for businesses operating across various e- commerce sectors. Experimental real-world data results demonstrate that ABNAM identifies more meaningful and coherent features. It significantly outperforms other methods by achieving higher accuracy, better recall and precision, and more robust performance across different datasets. The current research introduces an efficient and innovative sentence classification model using ABNAM. Unlike traditional automated text classification models, which struggle to categorize data into sixteen classes, ABNAM excels by leveraging technologies such as TF-IDF, N-Gram, Convolutional Neural Networks (CNN), Linear Support Vector Machines (SVM), Random Forest, and Nae Bayes. Among these methods, ABNAM achieved the highest accuracy at 97%, successfully classifying sentences into one of the sixteen categories. The research positions ABNAM as a novel and highly effective classification model, particularly in achieving high-class categorizations. -
A Systematic Review on Features Extraction Techniques for Aspect Based Text Classification using Artificial Intelligence
Aspect Extraction is an important, challenging, and meaningful task in aspect-based text classification analysis. To apply variants of topic models on task, while reasonably successful, these methods usually do not produce highly coherent aspects. This review presents a novel neural/cognitive approach to discover coherent methods. They exploited the distribution of word co-occurrences through neural/cognitive word embeddings. Unlike topics that typically assume independently generated words, word embedding models encourage words that appear in similar factors close to each other in the embedding space. Also, use an attention mechanism to de-emphasize irrelevant words during training, improving aspects coherence. Methods results on datasets demonstrate that the approach discovers more meaningful and coherent aspects and substantially outperforms baseline. Aspect-based text analysis aims to determine people's attitudes towards different aspects in a review. The Electrochemical Society -
Hybrid Approach for Multi-Classification of News Documents Using Artificial Intelligence
In the context of news articles, text classification is essential for organizing and retrieving useful information from massive amounts of textual data. Effectively categorizing news titles has gotten more challenging due to the development of online news outlets and the ongoing production of news. A multi-text classification technique primarily targeted at news titles is shown. The suggested approach automates the classification of news titles into predetermined classes or subjects by combining deep learning approaches and natural language processing (NLP) algorithms. Data preprocessing, which includes text normalization, tokenization, and feature extraction, is the first step in the procedure. This prepares the raw news titles for deep learning models. 2024 IEEE. -
Sentence Classification Using Attention Model for E-Commerce Product Review
The importance of aspect extraction in text classification, particularly in the e-commerce sector. E-commerce platforms generate vast amounts of textual data, such as comments, product descriptions, and customer reviews, which contain valuable information about various aspects of products or services. Aspect extraction involves identifying and classifying individual traits or aspects mentioned in textual reviews to understand customer opinions, improve products, and enhance the customer experience. The role of product reviews in e-commerce is discussed, emphasizing their value in aiding customers' purchase decisions and guiding businesses in product stocking and marketing strategies. Reviews are essential for boosting sales potential, maintaining a good reputation, and promoting brand recognition. Customers extensively research product reviews from different sources before purchasing, making them vital user-generated content for e-commerce businesses. The current work provided an efficient and novel classification model for sentence classification using the ABNAM model. The automated text classification models available cannot categorize the data into sixteen distinct classes. The technologies applied for the mentioned work contain TF-IDF, N-gram, CNN, linear SVM, random forest, Nae bays, and ABNAM with significant results. The best-performing ML method for the successful classification of a given sentence into one of the sixteen categories is achieved with the proposed model named the based Neural Attention Model (ABNAM), which has the highest accuracy at 97%. The research acclaimed ABNAM as a novel classification model with the highest-class categorizations. 2024 Nagendra N and Chandra J. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. -
Deploying NLP Techniques for Earnings Call Transcripts for Financial Analysis: A Reverse Phenomenon Paradigm
This study analyses the influence of quarterly board room discussions conducted in the form of "Earnings Call Transcripts"and company's stock price changes in the subsequent periods. In this study, sentiments were extracted from the "textual quarterly transcripts"of three major software companies for the last ten years. The extracted sentiments were statistically analyzed for patterns and types. The study led to the development of a new response variable called the 'Inverse Effect'. The 'Inverse Effect' simply refers to the discordance between the sentiment in the boardroom discussions available in the document form and changes in the stock price movements. If the sentiment for the current quarter is positive and the changes in the stock price movements is also positive in the subsequent quarter, it is considered as "concordance"and if the changes in the stock price movements is opposite to the sentiments it will be called as "discordance"which is the inverse effect. The study basically looks at the areas where the Weak Market Hypothesis (WMH) is not valid.The findings emerged from the study suggest a possible causality between the sentiments in the transcripts and the stock price changes. It was also found that sentiment polarity, three-quarter average stock price and the previous quarter stock price are the key determinants of the 'Inverse Effect'. Based on the findings from the study, appropriate machine learning models were developed and evaluated to predict the 'Inverse Effect' on the performance of individual stocks of a few select companies. 2023 IEEE. -
Financial Lexicon based Sentiment Prediction for Earnings Call Transcripts for Market Intelligence
Sentiment based stock price direction detection has been an exciting study in the field of finance which is drawing a lot of attention from the investor community. Sentiments are used to detect the changes in the stock price movements for the subsequent periods. Investor community uses these sentiments derived from news, celebrity speech and events to plan trading and investment strategies. Several studies have been done in the past with sentiments, but use of Earnings Call Transcripts (ECT) has not been explored for market intelligence hitherto. Standard dictionary based lexicons like Vader, AFINN and NRC have not performed well in finance as they are domain agnostic. There is a need to develop a financial lexicon based on the ECT corpora, which may provide a better lift over the standard lexicons. This study has observed that Vader has performed poorly as opposed to the newly developed financial lexicon. Machine learning based generative lexicon engine using Bayesian approach, which is termed as FNB Lex was developed in this research study to overcome the limitations of standard domain agnostic lexicons. The lexicon development was performed on quarterly Earning Call Transcripts (ECT) of sixteen IT companies spanning over ten years. The study also investigates the detection of inverse effect in stock price movements based on the sentiments of the previous period. Machine Learning (ML) models like Naive Bayes, FNB Lex, SVM and biLSTM were developed and their discriminatory powers were assessed. NB Lex provided much better lift in detecting the inverse effect as opposed to other models. 2024 IEEE. -
Production of gymnemic acid from cell suspension cultures of gymnema sylvestre /
Protocols For In Vitro Cultures And Secondary Metabolite Analysis Of Aromatic And Medicinal Plants, Part of the Methods in Molecular Biology book series (MIMB,volume 1391), pp.229–239; 2nd ed. -
In vitro propagation and secondary metabolite production from Withania Somnifera (L.) dunal
Withania somnifera (L.) Dunal, commonly known as ashwagandha or Indian ginseng, is an important medicinal plant that belongs to the family Solanaceae. Ashwagandha has been used from time immemorial in different systems of medicine and extensively used in the Indian system of medicine, and there is discussion of this plant in different ayurvedic scripts like Charaka samhita, Ashtanga sangraha, etc. The plant is extensively used for anti-aging and general well-being, and also has anti-cancer potential. Ashwagandha is also known for its antioxidant, anti-inflammatory, and other therapeutic activities. In the recent days of Covid-19, the plant has been extensively used as an immunostimulant. The plant has great potential for its raw materials, especially for the extraction of bioactive molecules like withanolide-A, withaferin-A, withasomniferin, withanone, etc. The conventional mode of propagation could not meet the required commercial demand for either the pharmaceutical industries or the traditional practitioners. The conventional method of obtaining biomass is influenced by a large number of environmental factors, where biomass quality and quantity of bioactive molecules have shown variation. To overcome this, biotechnological approaches such as plant tissue culture techniques have been established for large-scale cultivation using micropropagation and also other techniques like a callus and cell suspension culture, shoot culture, adventitious root culture, and hairy root culture have been extensively used for in vitro production of bioactive molecules from ashwagandha. With the advent of metabolic engineering, biosynthetic pathway editing has made it possible to obtain higher yields of desired metabolites. The present chapter focuses on the in vitro propagation, biosynthesis of withanolides, and tissue culture strategies for obtaining high biomass and metabolites. The chapter also focuses on different elicitation strategies, metabolic engineering approaches, and the development of elite germplasms for improved metabolite content. The chapter also identifies research lacunas that need to be addressed for the sustainable production of important bioactive molecules from ashwagandha. 2024 Bentham Science Publishers. All rights reserved. -
In vitro production of bacosides from Bacopa monnieri
Bacopa monnieri (L.) Wettst. (Plantaginaceae) is an important Ayurvedic medicinal herb commonly known as brahmi, growing in the region of Indian subcontinent. Bacosides are the major chemical component having the major role in the biological and pharmacological field. Bacopa cultivation is time-consuming, requires labor team, and needs great efforts to maintain the quality of bacosides as growths are affected by environmental factors such as soil, water, temperature, climate, pests, and pathogens. To solve these problems, organ and cell cultures have been adopted for swift and efficient production of Bacopa biomass and bacosides. In the current chapter, various parameters, such as types of media, media composition, elicitors, salinity, drought, types of vessels used, and effect of heavy metals, were investigated against the in vitro production of bacosides from Bacopa monnieri. Springer Nature Singapore Pte Ltd. 2018. -
Production, Delivery, and Regulatory Aspects for Application of Plant-Based Anti-microbial Peptides: a Comprehensive Review
Antimicrobial peptides (AMPs) are small, positively charged biomolecules produced by various organisms such as animals, microbes, and plants. These AMPs play a significant role in defense mechanisms and protect from adverse conditions. The emerging problem of drug resistance in microbes poses a global health challenge in treating diseases. This plant-based antimicrobial peptide is a promising candidate for fighting against drug-resistant microbes. The PAMPs process specific key properties, proving their efficacy as antimicrobial agents against a broad spectrum of microbes such as Gram-positive, Gram-negative, and fungi. Extensive research on PAMPs has explored their potential as plant growth regulators and therapeutic agents. Their diverse mode of action on microbes encouraged their application in food industries. ThePAMPs are isolated and purified from various plant species organs such as roots, shoots, leaves, flowers, and seeds. These are bioactive molecules with significant stability, and low toxicity has encouraged their application as food additives. Furthermore, to meet the consumer demand, mass production of AMPs was possible with recombinant DNA technology. The advanced and nanotechnology-based delivery system has significantly improved the efficacy and bioavailability of PAMPs as food preservatives for improved shelf-life and prevent spoilage of food products. ThePAMPs are of green origin and can be used as natural bio preservatives that do not alter the sensory properties of food and are harmless to consumers. Plants being the rich resource of AMPs to support their quick identification, and retrieval for commercial applications there is a need to integrate the omics approach with databases. TheAMPs are small, positively charged biomolecules produced by various organisms such as animals, microbes, and plants. These AMPs play a significant role in defense mechanisms and protect from adverse conditions. The emerging problem of drug resistance in microbes poses a global health challenge in treating diseases. This plant-based antimicrobial peptide is a promising candidate for fighting against drug-resistant microbes. The PAMPs process specific key properties, proving their efficacy as antimicrobial agents against a broad spectrum of microbes such as Gram-positive, Gram-negative, and fungi. Extensive research on PAMPs has explored their potential as plant growth regulators and therapeutic agents. Their diverse mode of action on microbes encouraged their application in food industries. ThePAMPs are isolated and purified from various plant species organs such as roots, shoots, leaves, flowers, and seeds. These are bioactive molecules with significant stability, and low toxicity has encouraged their application as food additives. Furthermore, to meet the consumer demand, mass production of AMPs was possible with recombinant DNA technology. The advanced and nanotechnology-based delivery system has significantly improved the efficacy and bioavailability of PAMPs as food preservatives for improved shelf-life and prevent spoilage of food products. ThePAMPs are of green origin and can be used as natural bio preservatives that do not alter the sensory properties of food and are harmless to consumers. Plants being the rich resource of AMPs to support their quick identification, and retrieval for commercial applications there is a need to integrate the omics approach with databases. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
An Empirical and Statistical Analysis of Fetal Health Classification Using Different Machine Learning Algorithm
The health of both the mother and the baby is affected by how well the fetus is doing during pregnancy, making it a matter of utmost importance. To achieve the best results possible, it is essential to regularly monitor and intervene when needed. While there are many ways to observe the wellbeing of the fetus in the mother's womb, using artificial intelligence (AI) has the potential to enhance accuracy, efficiency, and speed when it comes to diagnosing any issues. This study focuses on developing a machine learning-driven system for accurate fetal health classification. The dataset comprises detailed information on the signs and symptoms of pregnant individuals, particularly those at risk or with emerging fetal health issues. Employing a set of ten machine learning models namely Nae Bayes, Logistic Regression, Decision Tree, Random Forest, KNN, SVM, Gradient Boosting, Linear Discriminant Analysis, Quadratic Discriminant Analysis Light Gradient Boosting Machine (LGBM) along with ensemble-based processes, the Light Gradient Boosting Machine (LGBM) has been identified as a standout performer, accomplishing an accuracy of 96.9%. Furthermore, our exploration demonstrates overall performance like character fashions, signaling promising prospects for sturdy and correct fetal fitness class systems. This study highlights the power of machine learning that could revolutionize prenatal care by identifying fetal health problems early. 2024 IEEE. -
Comparative electrochemical investigation for scheelite structured metals tungstate (MWO4 (M = Ni, Cu and Co)) nanocubes for high dense supercapacitors application
Scheelite structured metal tungstate MWO4 (M = Ni, Cu and Co) nanocubes were synthesized through the chemical reflux for supercapacitors application and ceyltrimethylammonium bromide (C-TAB) as surfactant. In X-ray diffraction (XRD) result are fit with relevant JCPDS cards, synthesized materials are closely matched with monoclinic and triclinic crystal phase corresponding to NiWO4, CoWO4 and CuWO4 with Scheelite type structure. To resist the growth of the particles and succeeding nanocubes morphology were achieving by using PEG-400 and C-TAB act as a surfactant. The prepared modified electrodes were examined electrochemical analysis after successive coating of working material in empty Ni foil. From the galvanostatic charge-discharge (GCD) comparative analysis, fast ions movements are interacts through the aqueous electrolyte medium with nanocubes NiWO4 electrode are achieving specific capacitance of 1185 Fg?1 at 0.5 Ag?1 and cyclic stability 93.084 % (retentivity) formerly compare to CuWO4 and CoWO4 electrodes. 2023 -
Bio-Inspired Energy Storage Electrode: Utilizing Co3O4 Hollow Spheres Derived from Sugarcane Bagasse Extract Synthesis Via Hydrothermal Route
Recent research has explored the utilization of sugarcane bagasse, a bio-industrial waste, to fabricate energy storage devices due to ecofriendly nature, low cost with industrial scale production. In this investigation, cobalt oxide hollow spheres (Co3O4 HSs) were synthesized from waste sugarcane bagasse extract with the carbon spheres (CSs) act as template. The main component of sucrose (C12H22O11) linked with cellulose fibers and other oxygenic functional groups were used to prepare CSs. Previously, a metal precursor (Co(NO3)2.6H2O) was mixed with sugarcane bagasse extract and subjected to a hydrothermal process, resulting in uniform-sized metal CSs. The uniform sized Co3O4 HSs were formed by calcined metal CSs. The calcination temperature plays a crucial role to eliminating implanted carbon material on inter surface area of the metal oxide, shaping the Co3O4 HSs. Structural, vibrational, morphology and elemental analyses were confirmed by X-ray diffraction (XRD), Fourier transformed infrared spectroscopy (FTIR), Scanning electron microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDX), respectively. Electrochemical tests show improved ion transport and low resistance, leading to high capacitance in asymmetric supercapacitor (ASC) devices. Subsequently, for asymmetric supercapacitor (ASC) devices, using with Co3O4 HSs has function of cathode and activated carbon (AC) as anode, the devices demonstrated impressive results of 33.1 Fg? 1 at 1 Ag? 1, 86.8% retention after 4,000 cycles, as well as the energy density and power density of 5.9W h kg? 1 at 1500W kg? 1. The Co3O4 HSs||AC device exhibits promising energy storage properties for future applications. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
ML-Based Prediction Model for Cardiovascular Disease
In this paper, the prediction of cardiovascular disease model based on the machine learning algorithm is implemented. In medical system applications, data mining and machine learning play an important role. Machine learning algorithms will predict heart disease or cardiovascular disease. Initially, online datasets are applied to preprocessing stage. Preprocessing stage will divide the data from baseline data. In the same way, CVD events are collected from data follow-ups. After that, data will be screened using the regression model. The regression model consists of logistic regression, support vector machine, nae Bayes, random forest, and K-nearest neighbors. Based on the techniques, the disease will be classified. Before classification, a testing procedure will be performed. At last from results, it can observe that accuracy, misclassification, and reliability will be increased in a very effective way. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Machine Learning Technology-Based Heart Disease Detection Models
At present, a multifaceted clinical disease known as heart failure disease can affect a greater number of people in the world. In the early stages, to evaluate and diagnose the disease of heart failure, cardiac centers and hospitals are heavily based on ECG. The ECG can be considered as a regular tool. Heart disease early detection is a critical concern in healthcare services (HCS). This paper presents the different machine learning technologies based on heart disease detection brief analysis. Firstly, Nae Bayes with a weighted approach is used for predicting heart disease. The second one, according to the features of frequency domain, time domain, and information theory, is automatic and analyze ischemic heart disease localization/detection. Two classifiers such as support vector machine (SVM) with XGBoost with the best performance are selected for the classification in this method. The third one is the heart failure automatic identification method by using an improved SVM based on the duality optimization scheme also analyzed. Finally, for a clinical decision support system (CDSS), an effective heart disease prediction model (HDPM) is used, which includes density-based spatial clustering of applications with noise (DBSCAN) for outlier detection and elimination, a hybrid synthetic minority over-sampling technique-edited nearest neighbor (SMOTE-ENN) for balancing the training data distribution, and XGBoost for heart disease prediction. Machine learning can be applied in the medical industry for disease diagnosis, detection, and prediction. The major purpose of this paper is to give clinicians a tool to help them diagnose heart problems early on. As a result, it will be easier to treat patients effectively and avoid serious repercussions. This study uses XGBoost to test alternative decision tree classification algorithms in the hopes of improving the accuracy of heart disease diagnosis. In terms of precision, accuracy, f1-measure, and recall as performance parameters above mentioned, four types of machine learning (ML) models are compared. Copyright 2022 Umarani Nagavelli et al. -
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Business Forecasting and Error Handling Using AI
Business forecasting is the technique of accurately predicting the future of a business and outcomes using historical data and present trends. To evaluate historical data and find patterns, trends, and other elements that might be used to forecast future events, a variety of analytical tools and techniques are used. Business forecasting is a crucial component of strategic planning because it enables businesses to foresee market changes, spot possible risks and opportunities that may arise in the future, and make wise resource allocation and investment decisions. Businesses that use effective business forecasting can plan and carry out their programs that help them stay competitive, expand their operations, and meet their objectives. According to Glueck [1], Forecasting is a formal process of predicting future events that will significantly affect the functioning of an enterprise.. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar.