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Election Forecasting with Machine Learning and Sentiment Analysis: Karnataka 2023
Data science is rapidly transforming the political sphere, enabling more informed and data- driven electoral processes. The ensemble machine model which is made up of Random Forest Classifier, Gradient Boosting Classifier, and Voting Classifier, introduced in this paper makes use of machine learning methods and sentiment analysis to correctly forecast the results of the Karnataka state elections in 2023. Election features such as winning party, runner- up party, district name, winning margin, and voting turnout are used to evaluate the effectiveness of different machine learning paradigms. Similarly, it also makes use of sentiment analysis through party tweet and public reactions for further breaking down reliance upon past elections data alone. This study demonstrates that using both past historical records and current public opinion yields precise predictions about how electable leaders are. This reduces reliance on a historical dataset. The experimented results shows that, how machine learning and sentiment analysis can predict election results and provide useful data for election decision making. We compared various machine learning models in this study, including logistic regression, Grid SearchCV, XGBoost, Gradient Boosting Classifier, and ensemble model. With an accuracy of 85%, we demonstrated that our ensemble model outperformed machine models such as XGBoost and Gradient Boosting Classifier. It also offers a novel method for predictive analysis. 2023 IEEE. -
Transforming healthcare engagement in the medtech industry through digital marketing
[No abstract available] -
TAMIL- NLP: Roles and Impact of Machine Learning and Deep Learning with Natural Language Processing for Tamil
Reading information in your mother tongue gives the feeling of enjoying juice of fruit. Researchers are working on regional languages to provide convenient and perfect automated tools to convert the content of knowledge from other languages. There exist many challenges based on the grammar of language. One of the classic regional languages, Tamil which is rich in Morphology, contains more processing challenges. The Natural Language Processing (NLP) technique along with Machine Learning (ML) and Deep Learning (DL) algorithms have been used to overcome those challenges. The accuracy of work is depending on the corpus provided to train the model. Among the reviewed papers using Support Vector Machine (SVM) of ML produced higher accuracy then other ML techniques. As DL techniques for NLP are booming one the researchers are working with different DL algorithms. Most of the NLP with Review Discussion in this paper will direct the researchers doing NLP in Tamil language to move further and to choose the right Machine Learning and Deep Learning algorithm to come out with accurate outcomes. 2023 IEEE. -
Toward precision agriculture in Cyber-Physical Agricultural System
Agriculture 4.0 or Agri 4.0 is a newly developed system that consists of various digital technologies adapted from Industry 4.0 based on smart automation. Agriculture 4.0 is a subset of Industry 4.0 aimed at sustainable precision agriculture (PA) and increasing agricultural efficiency using digital technologies and the Internet of Things. The cyber-physical system (CPS) is the seamless integration of digital and physical domains and when CPS is applied in agriculture, it is termed cyber-physical agricultural system (CPAS). The application of CPS in carrying out PA with sustainable management of resources is termed Agri 4.0. Research papers are reviewed to understand the bigger picture behind various details of digital technologies and CPS with a focus on agriculture 4.0 and to determine its applications, challenges, and developments in the field. It is apparent that most of the small and marginal farms in remote areas are not able to use this technology due to a lack of knowledge and resources. It is the need of the hour to support these farmers by making favorable policies and appropriating budgets such that it will lead to more profitable and sustained PA and in the process contribute to the social and economic upliftment of farmers of India. 2024 Elsevier Inc. All rights reserved. -
Research aligned analysis on web access behavioral pattern mining for user identification
Human activity understanding includes activity recognition and activity pattern discovery. Monitoring human activity and finding abnormality in their activities used by many field like medical applications, security systems etc. Basically it helps and support in decision making systems. Mining user activity from web logs can helps in finding hidden information about the user access pattern which reveals the web access behaviour of the users. Clustering and Classification techniques are used for web user identification. Clustering is the task of grouping similar patterns for web user identification. Classification is the process of classifying web patterns for user identification. In this paper we have implemented the existing works and discussed the results here to find the limitations. In existing methods, many data mining techniques were introduced for web user behaviour identification. But, the user identification accuracy was not improved and time consumption was not reduced. Our objective is to study the existing work and explore the possibility to improve the identification accuracy and reduce the time consumption using machine learning and deep learning techniques. BEIESP. -
Performance Analysis of User Behavior Pattern Mining Using Web Log Database for User Identification
User behavior analytics is a progressive research domain. Understanding the users behavior patterns and identifying their behavior patterns will provide solutions to many issues like identity theft and user authentication. So many research works are done in analyzing the frequent access patterns of the users by pre-processing access logs and applying various algorithms to understand the frequent access behavior of the user. From the literature, it founds that the frequent user access pattern identification needs improvement on prediction accuracy and the minimal false positives. To accomplish these, three different approaches were proposed to overcome the existing issues and intended to reduce false positives and improve the frequent pattern mining accuracy based on web access logs. Proposed methods were found to be good while compared with the existing works. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Text-Based Sentimental Analysis to Understand User Experience Using Machine Learning Approaches
Data Analysis is turning into a driving force in every industry. It is a process in which data is analyzed in multiple ways to come to certain conclusions for the given situation. Sentiment analysis can be said to be a sub-section of data analysis where analysis is carried out on the emotions and opinions of the text. Social media has a plethora of sentiment data in various forms such as tweets, updates on the status, and so forth. Sentiment analysis on the huge volume of data can help in identifying the opinions of the general mass.The primary goal is to find the opinion of customers on the services of the Bangalore airport and to enhance the nature of these services according to the feedback provided. In this paper, we aim to measure customer opinion on services provided by Bangalore Airport through sentiment. Data is collected by a python-based scraper. The tweets are processed to determine whether they are of positive or negative opinion. These opinions are then analyzed to determine the factors which cause the negative opinions and the airport staff are alerted about the same. Various algorithms were used as part of the experimental analysis. LSTM produces more accuracy compared with existing approaches. 2023 IEEE. -
Green accounting and its application: A study on reporting practices of environmental accounting in India
Green Accounting is an important device for understanding the role of business ventures in the economy towards environmental security and welfare. It is a well-known term for environment and natural resources accounting. Many companies all over the world have initiated the practices of making environmental disclosures in their annual reports. However, these practices are still largely voluntary in nature. The objective of this research paper is to study the environment-related disclosures of companies taken from Nifty 50 based on the summary of Global Reporting Standards. Content Analysis, both sector-wise and keyword-wise is used on the annual reports of 29 sample companies using MAXQDA software. A high count of the formulated keywords is observed in some relevant sectors of Energy, Cement and Metals. 2022 Inderscience Enterprises Ltd. -
Wound Healing, Cell Viability and Antimicrobial Potency of Mucus from Pangasianodon hypophthalmus
Acute and chronic wounds are the major cause of death according to World Health Organization (WHO), in which, antimicrobial resistance is considered to be a major plight. In this regard, our study is aimed at developing an antimicrobial agent using the mucus of Pangasianodon hypophthalmus against the clinically resistant microbial pathogens and to evaluate the cell cytotoxicity and cell viability followed by an in vitro wound healing analysis. The evaluation of antimicrobial activity was performed through well diffusion method and micro dilution method. The cell cytotoxicity and cell viability were assessed using MTT assay. The cell migration and in vitro wound healing was performed using scratch assay. The acidic extracts of mucus showed antimicrobial activity against the eight different selected bacterial strains while the organic extract showed against seven bacterial strains. L929 showed a cell viability of 102.96% at a concentration of 75 g/mL and did not show cell toxicity effect up to the concentration of 300 g/mL. In the in vitro wound healing analysis, the cell migration rate was 99.27% in the treated cells while, the untreated showed only 94.68%. The current research work clearly shows that the mucus of P. hypophthalmus possesses antimicrobial activity and wound healing potency. Furthermore, gene expression analysis and in vivo trials have to be performed for a thorough understanding of the actual cellular mechanism of wound healing. The Author(s) 2024. -
A Systematic Review of Fish-Based Biomaterial on Wound Healing and Anti-Inflammatory Processes
Objective: To conduct a systematic literature review to study the effects of fish-based biomaterials on wound healing in both in vivo and in vitro animal models. Approach: This review covers the study reported in different articles between 2016 and August 2022 concentrating mainly on the cytotoxicity evaluation of different fish-based biomaterials on inflammation, reepithelialization and wound healing. Significance: This review shows considerable amount of research work carried out with fish-based biomaterials and collagen for treating burn wounds. Surprisingly there are only a few commercial products developed so far in this particular regard for surgical purpose and therefore, there is a way out and need for developing medical support product from fish-based biomaterials to treat and cure wounds. Recent Advances: Three-dimensional skin bioprinting technique is a large-scale solution for severe burn wounds that requires collagen as a raw material for printing, wherein fish collagen can be used in place of bovine and porcine, as it is biocompatible, promotes cell proliferation, adhesion, and migration, and degrades enzymatically. In the recent times, there are a few fish-based surgical products that have been formulated by Kerecis in United States. Critical Issues: The different fish-based biomaterial products are all mere supplements taken in orally as food or supplements till date and there is no proper proven medications that has been formulated so far in the field of wound healing and inflammation based on fish biomaterials except the surgical products that can be finger counted. Future Directions: Fish-based biomaterials are known for the medicinal properties that are used throughout the world and further investigations should be carried out to understand the actual physiochemical properties of its derivatives for the discovery of novel products and drugs. Copyright 2024 by Mary Ann Liebert, Inc. -
Malicious node detection using heterogeneous cluster based secure routing protocol (HCBS) in wireless adhoc sensor networks
In wireless, every device can moves anywhere without any infrastructure also the information can be maintained constantly for routing the traffic. The open issues of wireless Adhoc network the attacks which are chosen the forwarding attack that is dropped by malicious node to corrupt the network performance then the information integrity exposure. Aim of the problem that existing methods in Adhoc network for malicious node detection which cannot assure the traceability of the node as well as the fairness of node detection. In this paper, the proposed heterogeneous cluster based secure routing scheme provides trust based secure network for detection of attacks such as wormhole and black hole caused by malicious nodes presence in wireless Adhoc network. The simulation result shows that the proposed model is detect the malicious nodes effectively in wireless Adhoc networks. The malicious node detection efficiency can be achieved 96% also energy consumption also 10% better than existing method. 2020, Springer-Verlag GmbH Germany, part of Springer Nature. -
Design and validation of the digital well-being scale
As the reliance on digital products and services continues to increase, there arises the need to measure and understand how the use of digital devices affects our well-being. In order to do so, the researchers attempted to create and validate an instrument. The items for the instrument were identified through an extensive review of literature, followed by a brainstorming session. The statements were then validated by a panel of experts, post which the instrument was administered, and the data was collected and analyzed for reliability and validity. The final instrument returned a Cronbachs alpha score of 0.921, indicating high reliability. The validity of the instrument was also established through a confirmatory factor analysis. 2023, University of Bologna. All rights reserved. -
Virtual Community Mentoring Models for Middle School Underachievers Psychosocial Development and Well-Being During COVID-19
Recent studies highlight the outcomes of COVID-19 on the psychosocial skills of early adolescents. It shows the unavailability of virtual community mentoring models for teenagers' individual and interpersonal growth in the virtual scenario. Hence, there emerges a need to explore and apply the available virtual communication resources by facilitators, families, and other community professionals for teenagers self-development. This article reports the application of virtual resources like WhatsApp, graphic design platforms (CANVA and Adobe), graphic interchange formats (GIPHY App), all-in-one visual content editing forums (InShot App), and memes (Meme Generator App) in engaging and supporting community mentoring capacities leading to psychosocial development and well-being for teenagers during COVID-19. Through this article, contemporary virtual models are explored and executed with community guidance to integrate the personal developmental skills of middle school underachievers. There is also a need to work with community interventions by using virtual mentoring skillsets for positive youth development. 2022, Commonwealth of Learning. All rights reserved. -
Development and validation of middle school under performers checklist in India through virtual platforms post-COVID-19
Education is a holistic development that must be nurtured through hybrid or virtual educational practices. The pandemic brought a sense of psychosocial distress among teenagers that urged the need to understand these psychosocial competencies. Often, our Indian education system is unable to assess the challenging psychosocial competencies of learners in varied learning platforms. Hence, there is a need in todays context to harness adolescents holistic learning to be more flexible, and interactive with innovative instructional methodologies, creative assessment strategies, and virtual resource tools. These psychosocial open learning resources need to be advocated by educators and counsellors for the well-being of teenagers. Thus, this quantitative study aimed to develop a checklist to identify the psychosocial concerns of underperformers in an open learning system. Hence, educators and counsellors must be equipped to recognise their psychosocial concerns to handhold them into becoming autonomous thinkers and contributors in their society. This would further establish the seed of sustainability. Thus, this study aimed to develop and validate a checklist as a psychometric measure to identify middle school underperformers social and personal abilities. The study group comprised 359 school educators and counsellors in Bangalore and Mashed, India (299 educators and 60 counsellors). The checklist was developed using Develop (2016) and Oldenburgs principles of scale development (2021). The Cranach coefficient of the checklist was.924 for 12 items. The statistical results indicated the validation of the checklist as a tool for identifying psychosocial challenges of eighth-grade underperformers as reliable. Exploratory Factor Analysis reduced these items into two distinctive factors. The findings suggest that the checklist can be used as an innovative educational toolkit to identify middle school underperformers personal and social abilities. Further experimentation of this study can be taken up with a larger intergenerational population. 2025 selection and editorial matter, Asma Parveen and Rajesh Verma; individual chapters, the contributors. -
Design, analysis and fabrication of EV with level-1 autonomous vehicle capability
The fact to this day remains true and the same for over a hundred years the Automobile industry and vehicles, in general, have become the pivoting point in our day to day lives. We might as well call it a necessary evil. Although it is very true that they have made our lives more convenient when we speak in terms of transportation; the pollution that conventional IC engine vehicles produce hasn't done much to create a cleaner environment especially with Global warming on the rise as we speak. The simplest remedy would be is to replace IC engine vehicles with Electric one, EV. A Problem common to both conventional IC engine vehicles and EV's alike is the accidents occurring due to collision caused by human error on-road. While safety measures have greatly been taken in order to reduce the damage done to the driver and passengers in the event of a collision it would be far better to avoid the collision altogether. Thus having at least, a Level-1 Autonomous Vehicles capability where the system alerts the driver in the event of a crash or collision and deploy full braking capability. Thanks to increasing urbanization and the advent of modern technology the need of the hour of the 21st century has given rise to high demands for employment in the motorized transport sectors. The authors were successfully able to design, analyze and fabricate an EV with Level-1 Autonomous Vehicles capability. The successful implementation of this project will help in reducing not only pollution and accidents occurring on-road due to vehicle collision but also pave paths in alimenting Level-1 Autonomous Vehicles capability in EV's inexpensively. 2020 Author(s). -
Performance Evaluation of Refractory Bodies Fabricated from Composite Oxide Powders Beneficiated from Black Al-dross
Aluminum Oxide (Corundum, ?-Al2O3) and Magnesium-Aluminum Oxides (Spinel, MgAl2O4) are highly desired refractory materials due to their ability to withstand high-temperature service conditions without corroding and cracking. They are present in composite form in black Aluminum Dross (Al-dross), a hazardous industrial waste. 1 Kg batch of this composite powder was beneficiated from Al-dross to 98+% purity after removing the hazardous Aluminum Nitride (AlN) by aqueous treatment of Al-dross in an environment-friendly manner. The treated slurry was oven dried, ball milled to fine powder, hydraulically pressed, and sintered at 1500 C/6 h into solid cylinders (50 mm diameter 20 mm height). The structural phase analysis of the sintered product (refractory blocks) revealed a highly crystalline XRD pattern with peaks pertaining to only ?-Al2O3 and MgAl2O4. The blocks with Rockwell Hardness values of 4850 HRC, were subjected to thermal shock cycling by following the guidelines of IS 1528 (heat quench between 1000 C and air at ambient) which successfully withstood > 100 shock cycles without failure. SEM was employed to study the fracture surface in an as-sintered state and after thermal shock cycling, to reveal a fine-grained microstructure with clear grain boundaries in the as-sintered state to a glassy matrix with fine cracks at the end of the thermal shock cycle test. The potential for utilization of Al-dross for refractory applications was thus established. 2023 -
Plasma sprayed magnesium aluminate and alumina composite coatings from waste aluminum dross
The absence of structured waste management practices for tons of black aluminum dross (Al-dross) when land-filled affects the ecosystem we live in. Researchers and technologists are now working towards three goals (a) minimization of Al-dross production (b) reducing its toxic effects on the environment and (c) treating the Al-dross to beneficiate useful materials from it in an environmentally friendly manner and to generate useful industrial products. The third aspect has been addressed in this study. Al-dross is an aluminum industry generated waste that mainly contains Al metal (oxidized during processing), Aluminum Nitride (AlN), ?-aluminum oxide (?-Al2O3) and magnesium aluminate (MgAl2O4). The oxides are highly suitable for refractory and thermally insulating material applications, but AlN is detrimental for two reasons - (a) thermal conductivity higher than the oxides and (b) carcinogenic gas evolution during processing. Hence AlN must be removed from Al-dross for further processing into refractories. In this work, AlN with minor quantities of halides were removed from Al-dross to extract the major useful refractory oxide constituents in an environmentally friendly manner. The process methodology involved sieving Al-dross to < 600 m particles, aqueous media treatment to remove the nitrides in the form of NH3 gas, oven drying and calcination at 10001150 C for 2 h (in an electrical muffle furnace in ambient air atmosphere) to obtain a mixture of the composite oxide powder of ? 99.0% purity. The calcined compound was mixed with suitable organic binders and sieved to obtain plasma sprayable powder and plasma spray-coated onto bond coated (commercial NiCrAlY) steel substrates. XRD and SEM with EDS facility were used to characterize the powders and coatings. A polished metallographic cross-section was prepared to study the microstructure and interface characteristics. The findings are presented. 2022 -
Sprouting in Seeds Aided by Nitrogen Sourced from Ammonia Fumes Leached from Aluminum Dross
Nitrogen and water are nutrients essential for the sprouting of seeds and healthy growth of plants. The seeds derive nitrogen from ammonia (NH3), found in ammonium hydroxide commonly added as manure to the soil. In a materials synthesis process, NH3 gas was released when Aluminum-dross (Al-dross), a hazardous industrial foundry waste was beneficiated to extract useful materials (metallic Al, oxides of Al and Mg, etc.) from the waste. Chemical tests, SEM with EDS and XRD were used to characterize sieved black Al-dross (starting raw material) before and after the beneficiation process. Al-dross also contained significant quantities of aluminum nitride (AlN). When treated with an aqueous media (plain or carbonated water), the AlN reacted to release NH3 gas fumes. This work explored the potential of using this gas to act as a source of nitrogen to accelerate the sprouting of seeds and plant germination. Vegetable and fruit seeds were sown in the soil that was directly infused with the NH3 released from Al-dross for two hours, followed by several (8 to 12) hours of self-diffusion time for homogeneous distribution of the gas in the soil. Five pairs of soils (untreated regular and NH3 fumes treated soils) were prepared under similar conditions. 5 different vegetable and fruit seedlings were planted in these pairs of soils. The germination patterns and growth of the sprouts with time were observed. The seeds that preferred an alkaline environment for germination (e.g., ridge gourd and watermelon seeds) sprouted early and in good health in the NH3 treated soil. Seeds preferring acidic soils did not germinate well in NH3 fume-infused soils. The experiments confirmed the viability of the novel concept, where the waste ammonia fumes released from Al-dross could be favorably generated and used in a controlled manner to promote sprouting of certain agricultural seedlings. 2023 Elsevier Ltd. All rights reserved. -
Hybrid Deep Learning Based GRU Model for Classifying the Lung Cancer from CT Scan Images
Lung cancer is a potentially fatal condition, posing significant challenges for early detection and treatment within the healthcare domain. Despite extensive efforts, the etiology and cure of cancer remain elusive. However, early detection offers hope for effective treatment. This study explores the application of image processing techniques, including noise reduction, feature extraction, and identification of cancerous regions within the lung, augmented by patient medical history data. Leveraging machine learning and image processing, this research presents a methodology for precise lung cancer categorization and prognosis. While computed tomography (CT) scans are a cornerstone of medical imaging, diagnosing cancer solely through CT scans remains challenging even for seasoned medical professionals. The emergence of computer-assisted diagnostics has revolutionized cancer detection and diagnosis. This study utilizes lung images from the Lung Image Database Consortium (LIDC-IDRI) and evaluates various image preprocessing filters such as median, Gaussian, Wiener, Otsu, and rough body area filters. Subsequently, feature extraction employs the Karhunen-Loeve (KL) methodology, followed by lung tumor classification using a hybrid model comprising a One-Dimensional Convolutional Neural Network (1D-CNN) and a Gated Recurrent Unit (GRU). Experimental findings demonstrate that the proposed model achieves a sensitivity of 99.14%, specificity of 90.00%, F -measure of 95.24%, and accuracy of 95%. 2024 IEEE. -
Sliced Bidirectional Gated Recurrent Unit with Sparrow Search Optimizer for Detecting the Attacks in IoT Environment
In an era characterized by pervasive interconnectivity facilitated by the widespread adoption of Internet of Things (IoT) devices across diverse domains, novel cybersecurity challenges have emerged, underscoring the imperative for robust intrusion detection systems. Conventional security frameworks, constrained by their closed-system architecture, struggle to adapt to the dynamic threat landscape marked by the continual emergence of unprecedented attacks. This paper presents a methodology aimed at mitigating the open set recognition (OSR) challenge within IoT-specific Network Intrusion Detection Schemes (NIDS). Leveraging image-based representations of data, our approach focuses on extracting geographical traffic patterns. We observe that the Recurrent Neural Network exhibits suboptimal classification accuracy and lacks parallelizability for attack analysis tasks. Our investigation concludes that the Sparrow Search Optimization Algorithm (SSOA) serves as a foundation for constructing an effective assault classification model. This research contributes significantly to the field of network security by emphasizing the importance and ramifications of meticulous hyperparameter tuning. It represents a critical stride toward developing IDSs capable of effectively navigating the evolving cyber threat landscape. In the experimental analysis of proposed model reached the accuracy and 0.963% respectively. 2024 IEEE.