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An Extensive Time Series Analysis of Covid-19 Data Sets on the Indian States
Pandemic influenza coronavirus is causing a great loss to mankind. It is creating a chaos on the global economy. Fight against this unseen enemy is affecting all the sectors of the global economy. Mankind is quivering with fear and scared to do something. This study gives a detailed presentation of the current position of virus escalation in India. Sentiment analytics from Twitter data is evaluated on sentiment, emotions and fear opinions are analyzed in the study. The analysis is on red, orange and green zones in several states of India and also gave a comprehensive interpretation on various phases of lockdown. Confirmed, active, recovered and deceased cases in all states are modeled to predict the increase of number of cases. Textual, geographical and graphical analytics are extensively described in the research study. Time series analysis is broadly elaborated as a case study till July 22, 2020, forecasting the impact of virus on Maharashtra, Kerala, Gujarat, Delhi and Tamil Nadu. This study will favor the administrative system to control the disease spread across the nation. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Loan Default Prediction Using Machine Learning Techniques and Deep Learning ANN Model
Loan default prediction is a critical task in the financial sector, aimed at assessing the creditworthiness of borrowers and minimizing potential losses for lending institutions. Online loans continue to reach the public spotlight as Internet technology develops, and this trend is expected to continue in the foreseeable future. In this paper, the authors proposed loan default loan prediction system based on ML and DL models. This work makes use of the information on loan defaults provided by Lending Club. The dataset is preprocessed by applying various data preprocessing techniques and preprocessed dataset is generated. Later, we proposed four ML algorithms decision tree, random forest, logistic regression, K-NN and Feed forward neural network. The experimental results shown that proposed feed forward neural network achieved good accuracy for loan default prediction with an accuracy of 99%. 2023 IEEE. -
Airline Twitter Sentiment Classification using Deep Learning Fusion
Since the advent of the Internet, the way people express their ideas and beliefs has undergone significant transformation. Blogs, online forums, product review websites and social media are increasingly the primary means of distributing information about new products. Twitter, in particular, is giving people a platform to air their views and opinions about a variety of events and products. In order to continually enhance the quantity and quality of their products and services, entrepreneurs constantly need input from their customers. Businesses are always looking for ways to increase the quality of their products and services. As a result, it's tough to understand the consumer's sentiments because of the large volume of data. In this research work, a Kaggle dataset of airline tweets for sentiment analysis was used. The dataset contains 11,540 reviews. We proposed an ensemble CNN, LSTM architecture for sentiment analysis. For comparison of the proposed system, LSTM alone also tested for similar dataset. LSTM was given an accuracy of 91% and the proposed ensemble framework with LSTM and CNN was given an accuracy of 93%. The experiments showed that the proposed model achieved better accuracy when compared to conventional techniques. 2022 IEEE. -
Stock Price Prediction using Deep Learning and FLASK
The forecasting of stock prices is one of the most explored issues, and it attracts the attention of both academics and business professionals. It is quite difficult to make predictions about the stock market, and it takes extensive research into the patterns of data. With the expansion of the internet and indeed the growth of social media, online media and opinions frequently mirror investor sentiment. The volatility and non-linear structure of the financial stock markets makes accurate forecasting difficult. One of the sophisticated analysis techniques that is being used by academics in a variety of fields is the neural network. In this paper, we proposed deep learning techniques for google stock price prediction. A dataset from Kaggle was collected and applied deep learning techniques RNN, LSTM variants. We achieved better results with Bidirectional LSTM. We also created a web app for stock prediction using Christ University python FLASK. 2022 IEEE. -
Homomorphic DNA Security in IoT Edge Data
The Internet of Things (IoT) based intelligent medical system possesses sensitive and private patient data. Most data relates to the patient's medical records and highly sensitive information. For this reason, safety and confidentiality of information are crucial. The preservation of patient privacy when sharing medical data is the primary concern of this study. Due to their excellent performance, biological notations based on deoxyribonucleic acid (DNA) are becoming increasingly admired for guaranteeing encryption and image protection. This paper proposes lightweight homomorphic with DNA-based medical image encryption (HDNA_MIE) for heterogeneous IoT in edge computing. The proposed approach contains two steps: In the first step, the secure DNA keys are generated using lightweight operations such as shifting and Josephus ring-based permutation (JRP). In the second step, the lightweight homomorphic cryptographic algorithm with DNA sequence-based encryption algorithm is suggested for secure encryption. The suggested strategy is evaluated using computational time and statistical analysis with several measures to determine its efficacy. The experimental findings of the proposed strategy exhibited a high level of security and a noticeable enhancement in the Number of Pixels Change Rate (NPCR), Unified Average Changing Intensity (UACI) and encryption processing time. The experiment outcomes demonstrate that our technique may be applied to highly confidential image encryption. 2024, Iquz Galaxy Publisher. All rights reserved. -
On Leech labelings of graphs and some related concepts
Let G=(V,E) be a graph and let f:E?{1,2,3,} be an edge labeling of G. The path weight of a path P in G is the sum of the labels of the edges of P and is denoted by w(P). The path number of G, tp(G) is the total number of paths in a graph G. If the set of all path weights S in G with respect to the labeling f is {1,2,3,,tp(G)}, then f is called a Leech labeling of G. A graph which admits a Leech labeling is called a Leech graph. Leech index is a parameter which evaluates how close a graph is towards being Leech. In this paper, the path number of the wheel graph Wn is obtained. We also determine a bound for the Leech index of Wn and a subclass of unicyclic graphs. A python program that gives all possible Leech labelings of a cycle Cn for n?3, if it exists, is also provided. 2023 Elsevier B.V. -
2D Metal-based Electrocatalysts: Properties and Applications
Metallic nanostructures with thickness ranging from a single atom up to 100 nanometers fall under the category of 2D metals. The modified electronic band structure due to quantum confinement effects leads to intriguing electrical and electronic properties. Moreover, the properties can be further altered by variations in their shape, thickness, and lateral size. The exceptionally high surface area to volume ratio of 2D metals and stretchability are beneficial in electrocatalysis. The exposed atoms on the outer surface of 2D metals with low coordination numbers, possess unique properties, forming numerous active sites on the surface. As a result, 2D metals demonstrate a high ability towards the activation of small molecules, including O2, H2, CO2, HCOOH, CH3OH, C2H5OH, etc. This exceptional oxidation reactivity enables 2D metals to be excellent electrocatalysts for hydrogen/oxygen evolution reaction (HER/OER), oxygen reduction reaction (ORR), and oxidation of small molecules (formic acid, methanol, and ethanol) for fuel-cell applications. As the localized surface plasmon resonance (SPRs) is sensitive to the size/shape of plasmonic 2D metals, the optical absorption enabled by SPRs offers additional advantages for photo-electrocatalytic processes. The stability of highly active catalytic 2D metals presents a challenge due to the propensity of metal surfaces with high reactivity to undergo oxidation. Recent developments in the synthesis, properties, and applications of 2D metal nanostructures for electrocatalytic processes are discussed. The challenges and opportunities in the electrocatalytic application of 2D metal nanostructures have been summarized. 2025 Ram K. Gupta. -
Elusive Justice to Dalits in the 'Land of Social Justice'
The recent inhuman incident of mixing human faeces in the overhead tank supplying water to Dalit colony in Vengaivayal village in Pudukkottai district of Tamil Nadu refl ects the perpetuating violence against the Dalits. Locating this brutal violence within the larger framework of violence against Dalits in Tamil Nadu, the lackadaisical attitude of Dravidian parties when dealing with the issues related to Dalits is brought to the fore.. 2023 Economic and Political Weekly. All rights reserved. -
Sustainable biodegradation of textile dye reactive blue 222 by the novel strain Enterobacter CU2004, isolated from the industrial waste: A design of experiment based optimization study and characterisation of metabolites
Reactive Blue 222 (RB222) is widely used in textile industries and hence a common recalcitrant pollutant in the industrial effluent. Bioremediation of this dye is of significance as its one of the complex dyes with high molecular weight. In the present study, we isolated a novel bacterial strain Enterobacter CU2004 from the industrial waste and characterize using16S rRNA gene sequencing. Its potential to dye degradation was evaluated in a simple minimal salt media with the parameters namely dye concentration (1001000 ppm), pH (49), temperature (1555C), Carbon source (Lactose, Sucrose, Glucose, Starch, and Fructose), and Nitrogen source (Casein, Yeast extract, Peptone, Tryptone, Ammonium sulphate, and Urea) in a 24 h culture. Finally, data obtained were extended to design of experiment based optimization for the degradation efficacy of Enterobacter CU2004 and to validated design space was established. The novelty is in optimizing the design space parameters for highest percentage of degradation ?90% by the bacterial isolate Enterobacter CU2004 were finalized as 3037C temperature, 133249 ppm dye concentration, Lactose as Carbon source, Yeast extract as Nitrogen source, and the pH as 8. Microbial dye degradation was confirmed by FTIR, HPLC and GCMS studies. Further studies revealed the dye intermediates and the potential of Enterobacter CU2004 toward the degradation of complex, high molecular weight industrial dye RB222. 2024 Vasantha Veerappa Lakshmaiah, et al. -
Bio-Decolorization and Degradation of Reactive Blue 222 by a Novel Isolate Kucoria marina CU2005
In this study, a novel bacterial strain, Kucoria marina CU2005, was isolated and identified using 16S rRNA gene sequencing from an industrial wastewater sludge sample capable of degrading Reactive Blue 222 (RB222) dye. Batch mode bio stimulation studies were performed with minimal salt media to optimize key physiological parameters for effective decolorization of RB222. When cultured at 35 C and pH 7 under static conditions, this bacterium decolorized 82 percent of the dye after 24 hours. Decolorization was monitored using UV-vis spectrophotometry. Isolates ability to decolorize the complex dye was attributed to its degradation potential rather than a passive surface adsorption. FTIR, HPLC, GC-MS studies were used to confirm microbial dye metabolism. The results indicated breakdown of dye upon decolorization as some peaks were shifted and generation of aromatic amine for monosubstituted benzene ring as intermediates of dye degradation in decolorized solutions. This study has shown the potential of Kucoria marina CU2005 to decolorize RB222 dye at a better pace and efficiency than previously reported bacterial strains. Thus, we propose that our isolated strain can be utilized as a potential dye decolorizer in environmental biotechnology as effluent treatment for decolorization of RB 222. 2023, Association of Biotechnology and Pharmacy. All rights reserved. -
In Vitro Production of Tocopherols
Tocopherols are an essential dietary nutrient for mammals and photosynthetic products produced by green plants. Tocopherols commonly referred to as vitamin E exist in four forms (?-, ?-, ?-, and ?-tocopherol). Synthetic ?-tocopherol is a mixture of eight racemic forms and is less effective than natural tocopherol, thus the demand for plant-derived tocopherols is high. Tocopherols are lipophilic antioxidant and extensively used as therapeutic agents such as anti-inflammatory, anti-infection, anticancer, immune-stimulant, and nephro-protectant. They are also used as food additives and nutraceuticals. Plant cell and tissue culture is one of the promising techniques for mass production of tocopherols to meet the commercial demand. Optimizing physical and chemical factors for in vitro culture system has resulted in better accumulation of the product. Moreover, using bioreactors, precursor feeding, elicitation, biotransformation, and metabolic engineering approaches have resulted in enhanced yield of tocopherols from in vitro cultures. The present chapter deals with various important aspects of tocopherol in vitro production such as biosynthesis of tocopherol with special emphasis on key enzymes involved in the pathway whose modulation in expression can increase the yield of the product. Topics discussed include production of tocopherol from callus, cell and organ culture, metabolic engineering for mass production, different methods employed for extraction and quantification of tocopherols, and their biological activities and commercial applications. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
In Vitro Production of Bioactive Compounds from Plant Cell Culture
Secondary metabolites (SMs) are bioactive compounds widely used in various industries as pharmaceutical agents and food additives and serve as precursor substances for the synthesis of commercially important products. These natural bioactive metabolites are quickly replacing chemicals as efficient coloring, flavoring, texturizing, and preservative agents. Productions of these SMs are hampered due to physiological and technological parameters. Although SMs do not have any significant role in the growth and development of the organisms where they are found, they have commercial importance. Humankind has harnessed its application in every walk of their life. In the medical field, SMs are used as antibiotics, antifungal, antiviral, metabolic inhibitors, anticancer agents, and many more. The biological and pharmacological benefits of medicinal plants are attributed to SM produced by subsidiary pathways that are highly specific to target molecules. Most pharmaceuticals are either directly or indirectly derived from plant sources. Production of SMs from field plants suffers from various limitations like seasonal production, choosing specific plant organs for specific metabolites, low yield, cost of purification, and seasonal variations. Biotechnological approaches such as plant cell, tissue, and organ cultures are the best alternative methods for commercial production. The current chapter focuses on establishment of plant cell culture system for the production of SMs, strategies to improve biomass yield and metabolite content, and biosynthetic pathways. The chapter also emphasizes elicitation strategies, application of CRISPR Cas9 in metabolite synthesis, large-scale production, and commercial aspects of SMs. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
Engineering applications of artificial intelligence
Artificial intelligence (AI) has evolved rapidly over the past few decades, permeating various aspects of our lives and transforming industries. This chapter explores the emerging applications of AI across diverse fields, including healthcare, finance, transportation, education, and entertainment. In healthcare, AI is revolutionizing diagnostics, drug discovery, personalized medicine, and patient care. In finance, AI-powered algorithms are enhancing trading strategies, risk assessment, fraud detection, and customer service. The transportation sector is witnessing advancements in autonomous vehicles, traffic management, and logistics optimization through AI technologies. AI is also reshaping education with adaptive learning platforms, personalized tutoring, and educational analytics. Moreover, in the entertainment industry, AI is driving content creation, recommendation systems, and virtual experiences. Despite the remarkable progress, challenges such as ethical concerns, bias mitigation, data privacy, and regulatory frameworks need to be addressed for the responsible deployment of AI. 2024, IGI Global. All rights reserved. -
Extraction of Web News from Web Pages Using a Ternary Tree Approach
The spread of information available in the World Wide Web, it appears that the pursuit of quality data is effortless and simple but it has been a significant matter of concern. Various extractors, wrappers systems with advanced techniques have been studied that retrieves the desired data from a collection of web pages. In this paper we propose a method for extracting the news content from multiple news web sites considering the occurrence of similar pattern in their representation such as date, place and the content of the news that overcomes the cost and space constraint observed in previous studies which work on single web document at a time. The method is an unsupervised web extraction technique which builds a pattern representing the structure of the pages using the extraction rules learned from the web pages by creating a ternary tree which expands when a series of common tags are found in the web pages. The pattern can then be used to extract news from other new web pages. The analysis and the results on real time web sites validate the effectiveness of our approach. 2015 IEEE. -
A Pre-trained YOLO-v5 model and an Image Subtraction Approach for Printed Circuit Board Defect Detection
Almost every electronic product used regularly contains printed circuit boards, which in addition to being used for business purposes are also used for security applications. Manual visual inspection of anomalies and faults in circuit boards during manufacture and usage is extremely challenging. Due to a shortage of training data and the uncertainty of new abnormalities, identifying undiscovered flaws continues to be complicated. The YOLO-v5 technique on a customized PCB dataset is used in the study to incorporate computer vision to detect six potential PCB defects. The algorithm is designed to be feasible, deliver precise findings, and operate at a considerable pace to be effective. A technique of image subtraction is also implemented to detect flaws in printed circuit boards. The structural similarity index, a perception-based method, gauges how similar non-defective and defective PCB images are to one another. 2023 IEEE. -
Hunter Prey Optimization for Optimal Allocation of Photovoltaic Units in Radial Distribution System for Real Power Loss and Voltage Stability Optimization
Renewable Energy (RE) based Distribution Generation (DG), is a widely accepted eco-friendly alternative to conventional energy production. On the basic note, a DG is used to provide a part of or all of a customers real power demand and/or as a standby supply, and of all various existing types of DG technologies, Photovoltaic (PV) type distribution generation is considered for the study. The location of distributed generation is defined as the installation and operation of electric power generation modules connected directly to the distribution network or the network on the customer side of the meter, hence signifying the optimal location and size of the DGs used. This paper proposes a new algorithm of Hunter-Prey Optimization (HPO) to determine the optimal allocation of PV integration in the radial distribution systems (RDS). HPO is a new population-based algorithm inspired by the hunting behavior of a carnivore. The optimal sizing and siting of the PVs are determined by the proposed algorithm of HPO and are tested in MATLABR2021b on standard IEEE-33 and 69 test bus systems. On the basic of comparative study with literature, HPO is performed efficiently for solving multi-variable complex optimization problem. Also, the performance of RDSs is significantly improved with optimal PV allocations. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
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Relationship between Emotional Intelligence and Academic Achievement among College Students
Indian Journal of Applied Psychology Vol. 50, pp. 78-81, ISSN No. 0019-5073 -
Executives Perception about Project Management Practices in BEML Bangalore
International Journal of Research in Commerce, IT & Management Vol.2, No. 7, pp 69-74, ISSN No. 2231-5756 -
Synthetic Biology Tools for Genome and Transcriptome Engineering of Solventogenic Clostridium
Strains of Clostridium genus are used for production of various value-added products including fuels and chemicals. Development of any commercially viable production process requires a combination of both strain and fermentation process development strategies. The strain development in Clostridium sp. could be achieved by random mutagenesis, and targeted gene alteration methods. However, strain improvement in Clostridium sp. by targeted gene alteration method was challenging due to the lack of efficient tools for genome and transcriptome engineering in this organism. Recently, various synthetic biology tools have been developed to facilitate the strain engineering of solventogenic Clostridium. In this review, we consolidated the recent advancements in toolbox development for genome and transcriptome engineering in solventogenic Clostridium. Here we reviewed the genome-engineering tools employing mobile group II intron, pyrE alleles exchange, and CRISPR/Cas9 with their application for strain development of Clostridium sp. Next, transcriptome engineering tools such as untranslated region (UTR) engineering and synthetic sRNA techniques were also discussed in context of Clostridium strain engineering. Application of any of these discussed techniques will facilitate the metabolic engineering of clostridia for development of improved strains with respect to requisite functional attributes. This might lead to the development of an economically viable butanol production process with improved titer, yield and productivity. Copyright 2020 Kwon, Paari, Malaviya and Jang.