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A comparative analysis of opinions and sentiments on clean India campaign and sustainability goals of 2030
Human are blessed with natural intelligence. Artificial Intelligence can help human minds to make a best usage of machines to handle huge amount of data with accuracy and precision. AI has a widespread application in 21st century. Opinion mining is an application of artificial intelligence. The opinions expressed in social media can be extracted using python which can be used as an input for various machine learning algorithms to identify many patterns which can help policy makers to make effective policies. Clean India Campaign started in India with a set of goals to be achieved. Sustainability goals of 2030 given by United Nations puts light on many important aspects which need immediate attention in the next 9 years. Current pandemic Covid-19 has also triggered the necessity behind putting immediate attention for a better tomorrow. Without proper awareness programs, brainstorming knowledge cultivation, orienting minds towards the "what-why-where"aspects of sustainable growth in each sphere of life, aligning industrial development and digital era towards sustainable industrial development in digital era, sustainable economy, sustainable care of each natural resource; it is not easy to accomplish the sustainability goals of 2030 given by United Nations.This work emphasizes on the case study conducted as an initiative to motivate future policy makers to be aware of the different dimension of 2030 United Nations Agenda and the clean India campaign to take initiatives as a professional through the skills learned focusing on India. Realizing Individual social Responsibility can make a big difference in the planning and implementation of the goals and missions. Swachch Bharat Abhiyan (Clean India Campaign) started Swachch Bharat Mission-Urban (SBM-U) with a few objectives to make India Clean.This work has proposed two phases for analyzing opinions. This research have provided a methodology to apply AI to improve the opinion mining. The conventional opinion analysis is limited by reachability but the automated opinion analysis can be scaled up using artificial intelligence based applications. The uniqueness of the work lies in its focus on 'one-three verticals' in phase 1 of the methodology. Many prominent regions of India are considered as a part of the study. It helps us to provide a clearer picture across different regions of India. It also provide an avenue to list tasks to be done for each region and a set of ways which could be adopted by the future professionals and current stakeholders of higher education institute. Phase 2 focusses on more number of opinions collected from across the globe through digital platforms. 2021 Author(s). -
Performance Analysis of Logistic Regression, KNN, SVM, Nae Bayes Classifier for Healthcare Application During COVID-19
Heart disease is one of the main causes of mortality in India and the USA. According to statistics, a person dies out of a heart-related disease every 36s. COVID-19 has introduced several problems that have intensified the issue, resulting in increased deaths associated to heart disease and diabetes. The entire world is searching for new technology to address thesechallenges. Artificial intelligence [AI] and machine learning [ML] are considered as the technologies, which are capable of implementing a remarkable change in the lives of common people. Health care is the domain, which is expected to get the desirable benefit to implement a positive change in the lives of common people and the society at large. Previous pandemics have given enough evidence for the utilization of AI-ML algorithm as an effective tool to fight against and control the pandemic. The present epidemic, which is caused by Sars-Cov-2, has created several challenges that necessitate the rapid use of cutting-edge technology and healthcare domain expertise in order to save lives. AI-ML is used for various tasks during pandemic like tracing contacts, managing healthcare-related emergencies, automatic bed allocation, recommending nearby hospitals, recommending vaccine centers nearby, drug-related information sharing, recommending locations by utilizing their mobile location. Prediction techniques are used to save lives as early detections help to save lives. One of the problems that might make a person suffering from COVID-19 extremely sick is heart disease. In this research, four distinct machine learning algorithms are used to try to detect heart disease earlier. Many lives can be saved if heart disease can be predicted earlier. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Towards sustainable business: Review of sentiment analysis to promote business and well-being
Sustainability in business is expected considering the growth in the long run. Sustainable development goals are important for our sustainability on this planet. In case of a business, it is essential to ensure sustainable processes and sustainability of the existing customers. Sustainable customers can in turn contribute to improving the process by providing constructive suggestions to the business. This paper is an attempt to review sentiment analysis techniques to improve the customer experience of a business. 2024 Srinesh Thakur, Anvita Electronics, 16-11-762, Vijetha Golden Empire, Hyderabad. -
Exploring Sustainable and affordable Cancer Care using Artificial Intelligence
Now, in recent decades AI and ML have become a major part in developing and maintaining the healthcare system. Now, by using AI and ML in healthcare, it can provide a massive help for the healthcare workers.AI and ML help the healthcare workers for making better decisions, In some practical areas, it may take the place of human action for making decisions such as radiology, it can help to Gather medical knowledge or information from different journals, textbooks, or clinics which will help in reducing time for study and research. AI and ML help in predicting the early diagnosis of disease based on the patient's data and even help to prevent that dis-ease. Breast cancer is the most frequent category with an estimation of 2, 38,908 by 2025. Breast cancer is followed by lung cancer (1, 11,328); followed by mouth cancer (90,060). These statistics have triggered this research. Breast cancer is found in every one women among eight woman. Sustainable care shall help to fight with the disease. Sustainable care includes affordable cancer care and it's possible through early prediction of cancer. In this research we are using artificial intelligence based techniques for early prediction of cancer. Future direction of work will focus on usage of transfer learning and other models of AI-ML to help the society and mothers of nations to fight against the in-creasing spread of cancers. The Electrochemical Society -
In situ fabricated MOF-cellulose composite as an advanced ROS deactivator-convertor: Fluoroswitchable bi-phasic tweezers for free chlorine detoxification and size-exclusive catalytic insertion of aqueous H2O2
Combining the merits of structural diversity, and purposeful implantation of task-specific functionalities, metal-organic frameworks (MOFs) instigate targeted reactive oxygen species (ROS) scavenging and concurrent detoxification via self-calibrated emission modulation. Then again, grafting of catalytically active sites in MOFs can benefit developing a greener protocol to convert ROS generators to technologically important building blocks, wherein tailorable MOF-composite fabrication is highly sought for practical applications, yet unexplored. The chemo-robust and hydrogen-bonded framework encompassing free -NH2 moiety affixed pores serves as an ultra-fast and highly regenerable fluoro-probe for selective detection of toxic ROS producers hypochlorite ion (ClO-) and H2O2 with record-level nanomolar sensitivity. While the bio-relevant antioxidant l-ascorbic acid (AA) imparts notable quenching to the MOF, a significant 3.5 fold emission enhancement with bi-phasic colorimetric variation ensues when it selectively scavenges ClO- from uni-directional porous channels through an unprecedented molecular tweezer approach. Apart from a battery of experimental evidence, density functional theory (DFT) results validate "on-off-on"fluoroswitching from redistribution of MOF orbital energy levels, and show guest-mediated exclusive transition from "Tight state"to "Loose state". The coordination frustrated metal site engineered pore-wall benefits the dual-functionalized MOF in converting the potential ROS generator H2O2via selective alkene epoxidation under mild-conditions. Importantly, sterically encumbered substrates exhibit poor conversion and demonstrate first-ever pore-fitting-induced size selectivity for this benign oxidation. Judiciously planned control experiments in combination with DFT-optimized intermediates provide proof-of-concept to the ionic route of ROS conversion. Considering an effective way to broaden the advanced applications of this crystalline material, reconfigurable MOF@cotton fiber (CF) is fabricated via in situ growth, which scavenges free chlorine and concomitantly squeezes it upon exposure to AA with obvious colorimetric changes over multiple real-life platforms. Furthermore, multi-cyclic alkene epoxidation by MOF@CF paves the way to futuristic continuous flow reactors that truly serves this smart composite as a bimodal ROS deactivator-convertor and explicitly denotes it as an advanced promising analogue from contemporary state-of-the-art materials. The Royal Society of Chemistry. -
Machine Learning Technique to Detect Radiations in the Brain
The brain of humans and other organisms is affected in various ways through the electromagnetic field (EMF) radiations generated by mobile phones and cell phone towers. Morphological variations in the brain are caused by the neurological changes due to the revelation of EMF. Cellular level analysis is used to measure and detect the effect of mobile radiations, but its utilization seems very expensive, and it is a tedious process, where its analysis requires the preparation of cell suspension. In this regard, this research article proposes optimal broadcasting learning to detect changes in brain morphology due to the revelation of EMF. Here, Drosophila melanogaster acts as a specimen under the revelation of EMF. Automatic segmentation is performed for the brain to attain the microscopic images from the prejudicial geometrical characteristics that are removed to detect the effect of revelation of EMF. The geometrical characteristics of the brain image of that is microscopic segmented are analyzed. Analysis results reveal the occurrence of several prejudicial characteristics that can be processed by machine learning techniques. The important prejudicial characteristics are given to four varieties of classifiers such as nae Bayes, artificial neural network, support vector machine, and unsystematic forest for the classification of open or nonopen microscopic image of D. melanogaster brain. The results are attained through various experimental evaluations, and the said classifiers perform well by achieving 96.44% using the prejudicial characteristics chosen by the feature selection method. The proposed system is an optimal approach that automatically identifies the effect of revelation of EMF with minimal time complexity, where the machine learning techniques produce an effective framework for image processing. This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. -
Semantic Analysis and Topic Modelling of Web-Scrapped COVID-19 Tweet Corpora through Data Mining Methodologies
The evolution of the coronavirus (COVID-19) disease took a toll on the social, healthcare, economic, and psychological prosperity of human beings. In the past couple of months, many organizations, individuals, and governments have adopted Twitter to convey their sentiments on COVID-19, the lockdown, the pandemic, and hashtags. This paper aims to analyze the psychological reactions and discourse of Twitter users related to COVID-19. In this experiment, Latent Dirichlet Allocation (LDA) has been used for topic modeling. In addition, a Bidirectional Long Short-Term Memory (BiLSTM) model and various classification techniques such as random forest, support vector machine, logistic regression, naive Bayes, decision tree, logistic regression with stochastic gradient descent optimizer, and majority voting classifier have been adapted for analyzing the polarity of sentiment. The effectiveness of the aforesaid approaches along with LDA modeling has been tested, validated, and compared with several benchmark datasets and on a newly generated dataset for analysis. To achieve better results, a dual dataset approach has been incorporated to determine the frequency of positive and negative tweets and word clouds, which helps to identify the most effective model for analyzing the corpora. The experimental result shows that the BiLSTM approach outperforms the other approaches with an accuracy of 96.7%. 2022 by the authors. Licensee MDPI, Basel, Switzerland. -
Computational Modelling of Complex Systems for Democratizing Higher Education: A Tutorial on SAR Simulation
Engineering systems like Synthetic Aperture Radar (SAR) are complex systems and require multi-domain knowledge to understand. Teaching and learning SAR processing is intensive in terms of time and resources. It also requires software tools and computational power for preprocessing and image analysis. Extensive literature exists on computational models of SAR in MATLAB and other commercial platforms. Availability of computational models in open-source reproducible platforms like Python kernel in Jupyter notebooks running on Google Colaboratory democratizes such difficult topics and facilitates student learning. The model, discussed here, generates SAR data for a point scatterer using SAR geometry, antenna pattern, and range equation and processes the data in range and azimuth with an aim to generate SAR image. The model demonstrates the generation of synthetic aperture and the echo signal qualities as also how the pulse-to-pulse fluctuating range of a target requires resampling to align the energy with a regular grid. The model allows for changing parameters to alter for resolution, squint, geometry, radar elements such as antenna dimensions, and other factors. A successful learning outcome would be to understand where parameters need to be changed, to affect the model in a specific way. Factors affecting Range Doppler processing are demonstrated. Use of the discussed model nullifies use of commercial software and democratizes SAR topic in higher education. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
RayleighBard and BardMarangoni magnetoconvection in variable viscosity finitely conducting liquids
The thermorheological effect on magneto-Bard-convection is studied numerically in fluids with finite electrical conductivity. A nonlinear thermorheological equation is considered in the problem. The results are compared with the classical approach of constant viscosity, which depicts the fact that the effect of increasing the strength of the magnetic field is to delay the onset of convection. The magnetic field is shown to have a rheostatic influence on convective instabilities. The results obtained by the study have possible applications in the field of astrophysics, sunspots, and in space applications under microgravity. 2021 Wiley Periodicals LLC -
Sulfamic acid catalyzed grinding: A facile one-pot approach for the synthesis of polysubstituted pyrazoles under green conditions
A competent, rapid and simple grinding procedure for the synthesis of pharmacologically relevant polysubstituted pyrazoles catalyzed by sulfamic acid is reported via multicomponent reaction of substituted arylaldehydes, 4-nitrophenylacetonitrile, hydrazine hydrate, ethyl acetoacetate under solvent-free reaction conditions. In our reported protocol, four different reactants featuring diverse functional groups are assembled in one pot, enabling the synthesis of more diverse molecular structures in a facile manner. 2022 -
Mn2(CO)10 catalyzed visible-light-promoted synthesis of 1H-pyrazole-4-carboxamides; A sustainable multi-component statergy with antibacterial and cytotoxic evaluations
Multicomponent reactions play a pivotal role in synthesizing 1H-pyrazole-4-carboxamides, underscoring its significance in sustainable organic synthesis. These compounds, valued for their diverse biological activities, have garnered substantial attention in pharmaceutical research. A facile, rapid one-pot strategy to access an extensive array of 1H-pyrazole-4-carboxamide derivatives, utilizing substituted aldehydes, cyanoacetamide, and hydrazine hydrate as substrates and a readily accessible Mn2(CO)10 as photocatalyst in EL: H2O (1:1). Among the synthesized series, products 4b, 4 g, 4k showed remarkable antibacterial activity against E coli, P aeruginosa, S. aureus in agar medium and excellent cytotoxicity with Human colorectal carcinoma (HCT-116), Liver cancer cells (Hep-G2) and breast adenocarcinoma (MCF-7) cell lines. The current method is characterized by its affordability, non-toxicity, easy access to starting materials, and notably with minimal waste generation. Additionally, remarkable aspects include its mild operating conditions, environmentally friendly nature, and the ability to accommodate a wide range of both electron-donating and electron-withdrawing groups. 2024 The Author(s) -
Visible Light Mediated Organophotoredox-Catalyzed One-Pot Domino Synthesis of Novel 6,7 Disubstituted 1H-Pyrroles
The development of environmentally benign protocols to synthesize novel N-heterocycles is vital in the field of synthetic organic chemistry. We herein report a successful one-pot domino synthesis of novel 6,7 disubstituted 1H-pyrroles using substituted phenacyl bromide, barbituric acid/Meldrums acid, aromatic amines catalysed by 5mol% Fluorescein in presence of visible light. This procedure is a useful and adaptable method for the synthesis of pyrroles since it is compatible with a wide range of sensitive functional groups, does not require column chromatography purification. During the reaction, Fluorescein may catalyse the formation of enamine leading to amino alcohol which subsequently undergoes dehydration to give 6,7 disubstituted 1H-pyrroles. All the synthesized derivatives were obtained in 9095% yields and were characterized by 1H, 13C NMR and HRMS (ESI) analysis. Graphical Abstract: [Figure not available: see fulltext.] 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Sulphuric acid supported silica gel (H2SO4-SiO2) as an efficient catalyst for one-pot multicomponent synthesis of pyrano[2,3-c]pyrazol-amines under ultrasonication
In this study, the catalytic potential of a novel heterogeneous catalyst-sulphuric acid supported on silica gel (H2SO4-SiO2) has been assessed for the one-pot cyclo condensation reaction of aromatic aldehydes, 4-nitrophenylacetonitrile, ethyl acetoacetate and hydrazine/phenyl hydrazine to furnish poly functionalized pyrano[2,3-c]pyrazol-amine scaffolds under ultrasonication. Notably, within the framework of green chemistry, this divergent and step-economic approach has many benefits such as (i) use of water as solvent in the reaction, (ii) creation of up to five bonds in one sequence, (iii) avail of US irradiation as an efficient source of energy, (iv) application of nontoxic and reusable catalyst. Besides these, simple workup procedure, low catalyst loadings, shorter reaction time, high functional group compatibility, readily accessible starting materials and excellent yields without column chromatography render this protocol novel and greener towards the synthesis of poly functionalized pyrano[2,3-c]pyrazol-amines. 2020 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the Second International Symposium ''Functional Nanomaterials in Industrial Applications: Academy - Industry Meet''. -
Citric-Acid-Catalyzed Green and Sustainable Synthesis of Novel Functionalized Pyrano[2, 3-e]pyrimidin- and Pyrano[2, 3-d]pyrazol-amines in Water via One-Pot Multicomponent Approaches
An efficient entry into the preparation of elusive, novel pyrano[2, 3-e]pyrimidin-amines and pyrano[2, 3-d]pyrazol-amines has been accomplished using citric acid as a green catalyst in aqueous medium at 25 C. The strategy successively tolerates a variety of functional groups and interestingly, it is eco-compatible, environment-friendly, propitious and the products are obtained in excellent yields without chromatographic purification. The current methodology unfolds the benefits of citric acid as an effective, expeditious, economical, green catalyst and thus adheres to the principles of green chemistry. Ecstatically, the reaction was scaled to the gram level ascertaining the wide applicability of the protocol in academia and industry. The green metrics (E-factor: 0.0497, Mass intensity: 1.1022, PMI: 1.0497 and Emw: 0.0497) for the reaction was also envisaged and the pathway was found to acquaint excellent green chemistry metrics. 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim -
Comparative Analysis and Development of Recommendations for the Use of Machine Learning Methods to Identify Network Traffic Anomalies in the Development of a Subsystem for User Behavioral Analysis
This article discusses various machine learning methods in order to conduct a more effective analysis of user network traffic using a subsystem for analyzing user behavior and detecting network anomalies, since there is a need to evaluate big data. The methods and techniques used to detect network anomalies are analyzed. In analyzing the methods and technologies used to detect network anomalies, a classification of anomaly detection methods is proposed. To solve these problems, different algorithms can be used, differing in specificity and, as a result, efficiency. The classification of machine learning methods for detecting network anomalies is considered separately, since machine learning algorithms will be the most effective for the task. Various criteria for evaluating the effectiveness of machine learning models in solving the problem of network traffic profiling are considered. In accordance with the specifics of the tasks of user recognition and network anomaly detection, the most appropriate criteria for evaluating the effectiveness of machine learning models have been selected: AUC ROC the area under the error curve. Four stages of the subsystem for analyzing user behavior and detecting network anomalies are highlighted. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Bionanomaterials in Improving Food Quality and Safety
Current inventions in the area of nanotechnology opened several transformations in scientific and industrial sectors. One such rapidly developing technology gets a lot of application in the food industrys changing the culture of food cultivation to its several branches, like production, processing, packaging, preservation, detection of foodborne pathogens, transportation, shelf life and bioavailability of its valuable nutrients. Far smaller in size and in surface area is strongly related to its stability in terms of chemical and biological activities. Hence, food nanotechnology empowers advancement in several novel bio-nanomaterials with an extensive choice towards potential applications. Nanotechnology benefits the food industry in several ways: to extend and predictable for the growth due to recent and swiftly developing technology influences the characteristic of the food products, which should not get exposed to human and microbial activities. Therefore, implication of bio-nanomaterials in food-related industries pose a significant contribution for economy and also a key community concern. The involvement of nanotechnology throughout the life cycle of food processing, storage, transportation, safety, and potential benefits to mankind are also briefly reviewed in this chapter. Acceptance of nano-based ingredients by the public in various phases of the food business and their associated safety and regulatory measures pertaining to food items can be improved by many methods of nanotechnology. 2025 selection and editorial matter, Shakeel Ahmed; individual chapters, the contributors. -
Blending of Knowledge Management with Industry 4.0: A New Formula for Success!
The convergence of Industry 4.0 and knowledge management presents a transformative opportunity for organizations seeking enhanced efficiency and sustainable growth. In the context of organizational processes, the amalgamation of technological advancements and effective knowledge management practices can lead to a reduction in costs and an overall improvement in operational efficiency. Understanding the intricacies of knowledge management procedures is crucial, encompassing the production, transfer, acquisition, storage, and utilization of knowledge resources across the organizational spectrum. The advent of the fourth industrial revolution, commonly referred to as Industry 4.0, has significantly reshaped traditional knowledge management systems. Industry 4.0 introduces the interconnectivity of machines and their autonomous capacity to learn and share data. While both knowledge management and Industry 4.0 offer distinct benefits individually, a strategic approach that combines the strengths of both can unlock new opportunities for efficient business growth and success in the external environment. This article delves into the symbiotic relationship between Industry 4.0 and knowledge management, emphasizing their combined potential. Industry 4.0 generates vast volumes of data, and by leveraging knowledge management, organizations can derive valuable insights to inform decision-making processes. Historical data and best practices, accessible through knowledge management, contribute to process optimization. Integration with Industry 4.0 technologies, such as automation and the Internet of Things, further enhances process efficiency. The marriage of knowledge management and Industry 4.0 extends beyond process optimization to workforce development. Recognizing employees as the building blocks of an organization, this integration enables better management by upgrading knowledge and skills. Consequently, it enhances the overall productivity of the workforce, contributing to organizational success. In the dynamic landscape of globalization, technology, and competition, this chapter serves as a guide for organizations aiming to harness the collective power of knowledge management and Industry 4.0. By exploring their complementary benefits, it seeks to facilitate the informed utilization of these tools for the betterment and sustainability of businesses in the contemporary world. 2024 Scrivener Publishing LLC. -
Comparative analysis of rural consumers purchase behavior towards mobile phone in Karnataka
Indian urban market is getting saturated for many products. Thus, due to success of brands like Chik shampoo, Project Shakti, LG, Dabur, HLL (then2005), many marketers are now expanding their product offerings to rural markets as well. Also, since major part of India living in villages (around 70%) are now more improved due to increased literacy, TV penetration and improved affordability is a reason for marketers to expand. Of the research conducted on rural India, majority was either on understanding rural consumers on price, quality, brand, function and style or comparing rural consumers over urban consumers on buying behavior. This research focused on comparing rural consumers of two different districts on age, brand and opinion leaders role on influencing the rural preference towards mobile phone. The research focused on understanding the buying behavior of two villages, Keelara and Alekere of Mandya and two villages, Araleri and medahatti of Kolar with reference to mobile phone. 2019 SERSC. -
RIEMANN SOLITONS ON (?,?)-ALMOST COSYMPLECTIC MANIFOLDS
In this paper, we study almost cosymplectic manifolds with nullity distributions admitting Riemann solitons and gradient almost Riemann solitons. First, we consider Riemann soliton on (?,?)-almost cosymplectic manifold M with ? < 0 and we show that the soliton is expanding with (Formula Presented) and M is locally isometric to the Lie group G?. Finally, we prove the non-existence of gradient almost Riemann soliton on a (?,?)-almost cosymplectic manifold of dimension greater than 3 with ? < 0. 2023 Korean Mathematical Society -
IOT based no-parking notifier system
Traffic congestion due to vehicles parked in No-parking zones has become a serious problem in major cities of India. Due to traffic congestion environment, economy and overall quality of life is affected. Hence it is high time to effectively manage the traffic congestion problem. With increase in number of vehicles, discipline in road regulation or traffic system becomes mandatory. The existing traffic system is very accurate but not efficient enough to monitor all the vehicles on the road. With the advent of new technology this problem can be tackled by using Wi-Fi enabled micro-controllers, RFID and cloud systems to monitor every vehicle on the road all the time. This becomes easy for the government in regulating its traffic rules with high efficiency without affecting the smoothness of the traffic. 2018 IEEE.