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A Document Clustering Approach Using Shared Nearest Neighbour Affinity, TF-IDF and Angular Similarity
Quantum of data is increasing in an exponential order. Clustering is a major task in many text mining applications. Organizing text documents automatically, extracting topics from documents, retrieval of information and information filtering are considered as the applications of clustering. This task reveals identical patterns from a collection of documents. Understanding of the documents, representation of them and categorization of documents require various techniques. Text clustering process requires both natural language processing and machine learning techniques. An unsupervised spatial pattern identification approach is proposed for text data. A new algorithm for finding coherent patterns from a huge collection of text data is proposed, which is based on the shared nearest neighbour. The implementation followed by validation confirms that the proposed algorithm can cluster the text data for the identification of coherent patterns. The results are visualized using a graph. The results show the methodology works well for different text datasets. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Towards Sustainable Living through Sentiment Analysis during Covid19
Artificial intelligence is the process of the machine to perform with the simulation of human intelligence. Computing within the field of emotions paves the recognitions to sentiment analysis. Sentiment analysis is the method of capturing the emotions behind a text whether or not it's positive, negative or neutral. Sentiment Analysis (SA) or Opinion Mining (OA) is the process to provide computational treatment to unstructured data to categorize and identify the sentiments or emotions expressed in a piece of text. It combines Natural Language Processing Techniques and Machine Learning Techniques. This technology is additionally referred to as opinion mining or feeling computing. Sentiment Analysis uses the ideas of machine learning alongside an AI based process called NLP to extract and analyse the data, emotions, information from the text. This work explores the impact of social media during covid 19 and possible link between sustainable living and health care with the usage of sentiments. This paper address the sustainable development goal 3 (good health and wellbeing) of SDG 2030 and a possible way towards sustainable living through sentiment analysis. The Electrochemical Society -
Sustainability & Comparative Impact Analysis of Coral reef bleaching in Indian context
An estimated value of 500 million of the population are directly benefited through coral reefs related jobs, food and defence of coastal areas. Coral reefs help to reduce wave energy by 97%. They help to protect the coastal areas from storms, floods and wave energy by 97%. Natural disasters such as Tsunami and erosion of coastal areas are protected by reefs. In this process, they help to protect the lives of many staying in the coastal areas including animals, properties, and other natural resources. There are reasons for reef deterioration like change of climate, high pollution, destructive fishing, bleaching of coral reefs is a big concern now worldwide. Severe coral bleaching is also reported in India. A significant rise in the surface temperature of Sea has become a critical reason for coral bleaching. This work attempts to Study the link between sustainability, SDG goals of 2030 given by United Nations and coral bleaching. In this work study period is focussed from 1985 to 2021 in the Indian coral reef bleaching areas. The Electrochemical Society -
eHED2SDG: A Framework Towards Sustainable Professionalism & Attaining SDG through Online Holistic Education in Indian Higher Education
To enable sustainable development of society it is essential to train the leaders and professionals of tomorrow. Developing a sustainable society and holistically developed future for budding professional is a significant objective of higher education Institutions. Every professional course learner is expected to utilize his skills, knowledge and time to contribute towards the development of society. Fostering sustainability in various domains of development is a requirement for Sustainable Development Goals (SDG). This research is inspired by multiple mental health related problems among professionals, inability to cope up with stress, quick dissatisfaction and frustrations, suicide, poor happiness quotient measured through multiple psychological tests and many other negative mental status which have paved the path for more serious approaches towards holistic development of young professions. This research addresses the SDG goal 4, Quality Education directly. Indirectly it can work as a catalyst to ignite the interest and create awareness about all the sustainable development goals. The Electrochemical Society -
Sentiment and Emotion Analysis of Significant Diseases in India and Russia
Healthcare organizations need this information to understand and treat the patient's concerns. The motivation for this kind of analysis is how patients provide this information while wrapping it in their thoughts and emotions. It is less practicable to manually study all the free and abundant health-related knowledge accessible online to arrive at decisions that might contribute to an immediate and beneficial decision. Sentiment analysis methods perform this function through automated procedures with minimal human intervention. In this paper, an investigation is conducted to compare the region-wise, language-wise, and sentiment analysis of the tweets collected from Russia and India. The results obtained through research have shown some significant characteristics of the language models used for language detection. The inferenc and analysis obtained from the observations are included in this paper. 2023 IEEE. -
Intelligent Approaches of Clinical and Nonclinical Type-1 Diabetes Data Clustering and Analysis
Every year in India, there are nearly 15,600 fresh cases being reported among these age groups. In 2011, in the United States, 18,000 children under 15 were newly reported for T1DM. Over 13years, the Karnataka state government has a list of records showing that out of 100,000, 37% of boys and 40% of girls are affected by T1DM Disease. This paper investigates two methodologies to identify significant details about Type-1 diabetes. The first methodology is applicable to clinical data. The second methodology is demonstrated for the NDA T1D dataset. The dataset is utilized further to apply machine learning techniques to group similar patient traits. Exploratory data analysis on the dataset has revealed significant information answering a few research questions. This analysis can be useful for India, China, and other countries with high populations. In this paper, a unique methodology based on Artificial Intelligence Technique is proposed for both clinical and non-clinical data. The Autoimmune Disease, Diabetes Type 1-T1D, is focused. Non Clinical data based on 2021 reports are collected to identify patterns. Substantial unique issues are addressed in this work which were never reported before. The knowledge generated can be helpful for creating new clinical datasets, methodology and new insights related to Type-1 diabetes. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
An empirical analysis of similarity measures for unstructured data
With fast growth in size of digital text documents over internet and digital repositories, the pools of digital document is piling up day by day. Due to this digital revolution and growth, an efficient and effective technique is required to handle such an enormous amount of data. It is extremely important to understand the documents properly to mine them. To find coherence among documents text similarity measurement pays a humongous role. The goal of similarity computation is to identify cohesion among text documents and to make the text ready for the required applications such as document organization, plagiarism detection, query matching etc. This task is one of the most fundamental task in the area of information retrieval, information extraction, document organization, plagiarism detection and text mining problems. But effectiveness of document clustering is highly dependent on this task. In this paper four similarity measures are implemented and their descriptive statistics is compared. The results are found to be satisfactory. Graphs are drawn for visualization of results. 2019 COMPUSOFT, An international journal of advanced computer technology. -
Discovering patterns using feature selection techniques and correlation
Term Frequency and inverse document frequency is reported to have a significant contribution for various text categorization, document clustering and many other text mining related tasks. A collection of the applications and the enhancements of the Term Frequency and Inverse Document Frequency based document representation technique is examined in this work. The document representation algorithm is essential in the field of text - script mining. In this algorithm, unstructured data is converted into a vector space model where each related document is considered as a point in the vector space. Related documents come in proximity to the other related documents while the documents that are very far away from being coherent remain different from each other. In this paper, four feature selection techniques are implemented to discover the patterns from a repository of unstructured data by using correlation similarity measure. Analysis and comparison with other existing technique is also included. The validation of the patterns formed is performed by using silhouette values. Experiments are conducted to compare performance. Results indicate that TDMp1 performance is poor compared to others. Springer Nature Switzerland AG 2020. -
Evaluation of ML-Based Sentiment Analysis Techniques with Stochastic Gradient Descent and Logistic Regression
In recent times, along with the expansion of technology, the Internet also has flourished exponentially. World is more connected today not only through the technology, but also through sharing sentiments to express views, either be constructive or destructive in front of the world through social media. Twitter, Facebook, Instagram, etc., are being used as social media to reach the world. The study of understanding peoples emotions, intentions, attitudes from unstructured data is opinion mining/sentiment analysis. This is an application of NLP or text mining. In this paper, an attempt is made to realize sentiment analysis's multiple dimensions using approaches such as ML and NLP-based technqies like word frequency and TF-IDF. Using ML approach, experiments were conducted, and the performance of the predictions was visualized. Three different datasets are used. A comparison of logistic regression (LR) and stochastic gradient descent (SGD) algorithms are compared using two different document representation. An extensive comparison is carried out using three different types of dataset. Amazon instant video datasets, bank dataset and movie reviews datasets are being used for the same. Analysis of performance is accomplished by using different graphs. The results indicate that logistic regression performs better than stochastic gradient descent for movie review dataset by using word frequency and TF-IDF-based approach. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Comparative Analysis of Sentiment Analysis Using RNN-LSTM and Logistic Regression
Social media analytics makes a big difference in the success or failure of an organization. The data gathered from social media can be used to get a hit type product by analyzing the data and getting important information about the need of the people. This can be done by implementing sentiment analysis on the available data and then accessing the feelings of the customers about the product or service and knowing if it is actually being liked by them or not. Tracking data of the customers helps the organization in many ways. This study was done to get familiarized with the concept of data analytics and how social media plays an important role in it. Furthermore, Web scraping of Twitter and YouTube data was done following which a standard dataset was selected to do the other analytics. The field of sentiment analysis was used to get the emotions of the people. Logistic regression and RNN-LSTM models were used to perform the same, and then, the results were compared. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
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. -
System and method for transmission of data /
Patent Number: 201841003452, Applicant: Bhargavi Goswami.
System and method for transmission of data are provided. The method includes computing a minimum congestion window size and a maximum congestion window size to initiate the transmission of data. The method also includes determining a congestion window size for an instantaneous transmission of data. The method further includes setting a rate for a determined congestion window size based on a computed minimum congestion window size and a computed maximum congestion window size, adjusting the congestion window size to the maximum congestion window size, when the congestion window size exceeds the maximum congestion window size, adjusting the congestion window size when the congestion window size is less than a threshold congestion window size, transmitting the data based on an adjusted congestion window size, updating the rate of the congestion window size based on a round trip time. -
System and method to secure data using substitution box /
Patent Number: 201841035014, Applicant: Bhargavi Goswami.
System and method to secure data using substitution box are provided. The method include generating a round key of a pre-defined size of a byte, computing a sum for each value of the round key, applying a mod function on a computed sum with a value equal to the pre-defined size of the byte associated with the round key for generating an index value, computing a dynamic substitution box based on the compound sum and the index value, wherein computing the dynamic substitution box based on the computed sum and the index value comprises computing the dynamic substitution box is equal to an exclusive OR (XOR) operation of inverse of a static substitution box and a byte value of the round key. -
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. -
Spectroscopic Studies of Galactic Field Be Stars
Be stars provide excellent opportunity to study circumstellar disks. But the disc formation mechanism of classical Be (CBe) stars- the Be phenomenon- is still poorly understood. This can be understood by studying CBe stars in various locations like clusters and fields. Spectra of Be stars show interesting emission lines of different elements like hydrogen, helium, iron, oxygen calcium, etc. These emission lines are valuable indicators in providing information about the circumstellar disks of Be stars. In the past several decades various aspects of Be stars have been studied. But literature review clearly indicates the need of further studies to frame a consolidated picture about Be phenomenon in CBe stars. It is found that especially, the region ?????? 7500 - 8800 ??? is a less studied, and thus poorly understood area in Be star research. But this area shows some interesting features like emission lines calcium, iron, oxygen and Paschen series. So, here we have studied a sample of 118 field CBe stars taken from the catalogue of Jaschek & Egret (1982) and whose medium resolution spectra were obtained in ?????? 3800 ?? 9000 ??? region during December, 2007 to January, 2009 with the 2.1-m Himalayan Chandra Telescope (HCT), located at Hanle, Ladakh, India and operated by the Indian Institute of Astrophysics (IIA), Bangalore. In this thesis, we present three works which investigate the disc properties of our 118 program Be stars by studying their spectral line features, focussing primarily on the less explored ?????? 7500 - 8800 ??? region. Firstly, we have analyzed the less studied Fe II 7712 ??? emission line for our stars to understand the possible Fe II line excitation mechanism in CBe stars. Our work predicts that Ly???fluorescence may be the possible Fe II line excitation mechanism in CBe stars. Secondly, we have studied the Ca II triplet emission lines for our stars and have developed a new technique for deblending Ca II components from their counterpart Paschen lines, thus providing a more efficient way to analyze Ca II lines. Analyzing Ca II lines through this technique, we suggest that the gas producing these lines is optically thick. This leads us to predict that Ca II lines may be an indicator of binarity in Be stars. Lastly, we have estimated the Balmer decrement values, D34 and D54 for 81 of our sample stars to shed light on opacity effects in Be star disks. Our work confirms the disc transient nature of Be stars through epoch-wise D34 and D54 variation study and also suggests that Be star disks are optically thick. -
