Volume 12 | Issue 5

 Abstract

Python is an interpretable and scriptable programming language. That is object oriented, the primary data wrangling, cloud computing and machine learning. The philosophy, architecture, and application of the data wrangling process--which is utilized in business intelligence and data warehousing--are presented in this work. The art of transforming or preparing data is known as "data wrangling." It is a technique designed for fundamental data management, where data must be appropriately formed, processed, and made available for the most convenient usage by possible users in the future. To support extensive ad-hoc queries, a lot of historical data is either aggregated or kept in data warehouses as facts or dimensions. Data wrangling makes it possible to process business queries quickly and provide analysts and end users with the appropriate answers. The wrangler suggests predicted transcription scripts and uses interactive language. This facilitates the user's understanding of the elimination of manual iterative processes. The best examples in this case are decision support systems. Big data principles have a significant impact on the methods used to prepare data for mining insights, from self-service analytics and visualization tools to the data source layer. Python is an interpretable and scriptable programming language that can be used for both learning and practical applications. Guido van Rossum created the potent high-level language python. It is an interpretable programming language that is object-oriented. The primary Python programming software tools for data wrangling, cloud computing, and machine learning approaches will be introduced in this presentation. In summary, this paper will begin with an introduction to Python programming and data wrangling.

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AWS, Cloud Computing, Data Wrangling, Storage

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Freshwater mussels, like Lamellidens marginalis, indeed play a crucial role in ecology and are often regarded as ecological bioindicators due to their sensitivity to environmental changes. The morphometric analysis conducted on these mussels from the Gomti River and Sai River in Lucknow, Uttar Pradesh, provides valuable insights into their variation in size, length, height, and weight. By examining these parameters, researchers can glean important information about the health of the aquatic ecosystem and the potential impact of environmental factors on these mussel populations. For instance, changes in the size, weight, or distribution of Lamellidens marginalis could indicate shifts in habitat quality, water quality, or other ecological factors. Additionally, studying morphometric variations among populations from different rivers can help identify specific environmental stressors or differences in habitat conditions between these locations. Such research contributes to our understanding of freshwater mussel ecology and informs conservation efforts aimed at preserving these important organisms and the ecosystems they inhabit. By monitoring and studying freshwater mussel populations, scientists can assess the overall health of freshwater ecosystems and implement targeted conservation strategies to protect these vital habitats.

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 Keywords

Fresh water mussel, Lamellidens marginalis, Gomti River, Sai River, Morphometric, Uttar Pradesh, Conservation.

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 Abstract

Research hotspots in academic and financial circles include understanding the pattern of financial activity and forecasting their growth and changes. Predicting financial data's development trends is a very challenging task because financial data contain complex, incomplete and fuzzy information. The movement of stock prices can be predicted and analysed using a variety of techniques. Use Artificial Intelligence algorithms for forecasting. Aim of this method is to improve the quality of output of stock market predicted by using stock value. In this paper is used to predict the futuristic prices of stocks and use wide range of algorithms like long short-term memory (LSTM), recurrent neural networks (RNN), Autoregressive Integrated Moving Average (ARIMA). This bibliometric study focusses on the study based primarily on the Scopus database.

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Artificial Intelligence; LSTM; RNN; ARIMA

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Virtual tourism, enabled by digital technologies, has become a major breakthrough that provides immersive travel experiences without the need for actual tourism. This research paper explores the phenomenon of virtual tourism within the context of Sultanate of Oman, investigating its implementation, economic advantages and future prospects. By leveraging virtual platforms, Oman aims to enhance its tourism landscape, attract visitors and contribute to economic diversification. In this paper, the phenomenon of virtual tourism within the context of Oman is investigated. It examines the economic benefits of virtual tourism for Oman, including revenue generation, employment opportunities and sectoral growth. It investigates the country's potential to establish itself as a leading destination in the global tourism market through virtual tourism initiatives. Government policies, private sector investments and collaborative partnerships play crucial roles in driving the adoption and success of virtual tourism in Oman. Sultanate of Oman's rich cultural heritage and natural landscapes make it an ideal candidate for promotion of virtual tourism. By showcasing its attractions through virtual platforms, Oman can reach a wider tourists and compete effectively. Virtual tourism also serves as a valuable marketing tool, inspiring travelers to visit Oman in Person. Despite challenges such as technological infrastructure and cultural preservations, Oman's virtual tourism sector shows promising growth potential. Technological advancements, coupled with increasing internet accessibility, are expected to drive the popularity of virtual tourism in the country. To sum up, Virtual tourism offers new opportunities for Oman to diversify its tourism revenue streams, promote cultural heritage and stimulate economic growth. By embracing virtual tourism alongside traditional methods, Sultanate of Oman can position itself as a leading destination for immersive travel experiences, meeting the changing needs of tourists from throughout the world.

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Economy, Oman, Virtual tourism.

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With the ever increasing demand of automobiles in our daily life, rubber waste is also increasing enormously through tyres resulting in disposal of rubber waste in land. Instead of disposing rubber waste into landfills, it can be reused in flexible pavements as crumb rubber as it has elastic properties. So the crumb rubber can be used in road construction works by partially replacing bitumen. Addition of crumb rubber in flexible pavements enhances the properties of the road surfaces as well as it reduces the rubber waste from the lands. In the experimental work, bitumen was partially replaced by crumb rubber with varying percentages i.e.8%, 10% & 12%, for performing Marshall Stability Test on the prepared samples. Tests were also performed to check the physical properties of the aggregates and bitumen. The optimum percentage of bitumen and crumb rubber for stability value and flow value was found out to be 12.2 KN and 2.7mm respectively.

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Crumb rubber, Flexible pavements, Bitumen, Marshall Stability.

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The Real-Time Cloud-Based Bus Pass System Project serves as a convenient solution for commuters who are encountering difficulties with the current manual bus pass system. Passengers are afforded the convenience of seamless travel through the utilization of a mobile ticket QR code, serving as a safeguard in case of ticket loss during inspections. The QR code can be checked by the Bus and the system administrator to verify its authenticity. The unique identification system ensures that each passenger is assigned a distinct number, effectively eliminating the possibility of duplication. In addition, the system prolongs the validity duration and issues timely notifications to commuters via SMS or email prior to the expiration of their pass. The renewal or registration process offers flexibility with multiple payment options, such as credit cards or debit cards. Initially, passengers are required to register on the application by providing personal information, which includes a photo and proof of address, through an online platform. The system undertakes a thorough assessment of the provided information and subsequently either approves or declines the bus pass application based on its findings. Customers have the option to purchase bus tickets online, round the clock, seven days a week, thereby resolving the problem of lost or stolen bus tickets. Users have the ability to recharge using either credit cards or debit cards. The website may experience overload as a result of a large volume of users accessing it simultaneously. Hence, the system is constructed using cloud infrastructure to enhance its performance. Moreover, the online system allows customers to verify the availability of bus tickets prior to making a purchase. Additionally, customers are relieved from the necessity of paying cash for bus tickets as they have the option to use their Credit Card for payment

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Bus pass approval system using cloud

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This study explores the influence of emotional intelligence on impulse buying and post-purchase regret among young working adults. Emotional intelligence, the capacity to recognize, understand, and manage one's own emotions and those of others, serves as the independent variable in this study. The dependent variables are impulse buying, the tendency to make spontaneous purchases, and post-purchase regret, the sense of remorse after a purchase. Utilizing a quantitative research design, the study surveyed 120 professionals aged 18-30. The findings indicate significant correlations between emotional intelligence and impulse buying, suggesting that individuals with higher emotional intelligence are less likely to engage in impulse purchases. Similarly, a significant relationship was found between emotional intelligence and post-purchase regret, with higher emotional intelligence correlating with lower levels of regret following an impulsive purchase. Additionally, the study revealed a significant correlation between impulse buying and post-purchase regret, confirming that impulsive purchases often lead to subsequent feelings of remorse. Interestingly, the analysis showed no significant gender differences in emotional intelligence, impulse buying, or post-purchase regret, highlighting that these psychological phenomena affect young working adults regardless of gender. This research contributes to the understanding of emotional intelligence's role in consumer behaviour and offers insights into mitigating negative purchasing experiences. The implications of these findings are valuable for developing strategies to foster emotional intelligence, potentially reducing the frequency of impulse buying and the associated regret among young professionals.

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Emotional Intelligence (EI), Impulse Buying, Post Purchase Regret, Buying Behaviour, Schutte Self Report Emotional Intelligence Test (SSEIT), Impulse Buying Scale, Purchase Regret Scale, Consumer Behaviour, Consumer Psychology

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Abstract The much-needed educational transformation in India, known as STEM education, has the power to revolutionize the development of a child's future. By equipping students with STEM skills that will be in high demand in the coming years, STEM learning lets them become the leaders of tomorrow. Students will have the opportunity to realize their full potential and acquire a new, engaging approach to learning concepts through participating in STEM learning-based activities.It makes students future-ready by teaching them skills that will be in demand in the upcoming years.Hence, this study aims at exploring the enormous potentials that can be achieved through the implementation of STEM Education at the secondary school level. This study was conducted using the survey method with 50 secondary school teachers as the study samples. The data were collected using opinionnnaire and analyzed using quantitative descriptive analysis. The study results revealed that STEM education implemented at secondary school level helped in the development of the higher cognitive abilities of the learners, such as problem solving, critical thinking, creative thinking etc. On the whole it could be understood that STEM education has enhanced the performance rate of the learners and brought about drastic change in the learning outcomes of the learners which ultimately result in the all-round development of them.

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STEM Education, Secondary School Level

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Transformer is one of the important components of the power system, capable of transmitting and distributing the electricity generated by renewable energy sources. Dissolved Gas Analysis (DGA) is one of the effective techniques to diagnose early faults in oil-immersed transformers. It correlates the concentration and ratio of dissolved gases with transformer faults. Researchers have proposed many methods for fault diagnosis, such as double ratio method, Rogers method, Duval triangle method, etc., but all of them have some problems. Based on the strong data mining capability and good robustness of AI techniques, many researchers introduced AI techniques to mine the features of DGA data. According to the characteristics and scale of DGA data, researchers select appropriate AI techniques or make appropriate improvements to AI techniques to improve diagnostic performance.

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fault diagnosis, power transformer, artificial intelligence, data mining, dissolved gas analysis

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School children often face challenges in consuming pure goat milk on a daily basis due to its unpleasant taste and odor, along with its short shelf life. To address these issues, converting goat milk into tablets offers a practical solution. This transformation not only enhances the product's shelf life but also facilitates easier consumption, particularly for distribution in remote areas. The primary focus of this project was formulating goat milk tablet by using direct compression method and also enhance hardness and disintegration of tablet. Direct compression techniques were instrumental in improving the poorly flowing properties of goat milk powder. This method effectively enhanced the flow ability and compressibility of the tablets, resulting in uniform weight, low friability, and optimal hardness. Thus, the development of goat milk tablets represents a significant advancement in making this nutritious product more accessible and appealing to school children.

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Goat milk, direct compression, tablet dosage, oral forms, nutraceutical, goat milk tablets.

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Human hair significantly influences one's personality, and various cosmetic products are used to maintain it. Herbal remedies often outperform synthetic formulations due to their effectiveness and minimal side effects. This study aimed to evaluate the benefits of polyherbal products in addressing common hair issues such as baldness, alopecia, hair loss, grey hair, dryness, and dandruff. Many cosmetics are utilized daily for their diverse applications. People use these products to enhance their appearance and maintain a youthful, appealing look. Activating hair roots is essential for promoting hair growth and reducing hair loss. Herbal treatments remain popular among the general public due to their lower risk of side effects and high safety profile. The objective of this study was to develop a general-use herbal hair serum using various natural ingredients. The formulated oil was tested for viscosity, pH, acidity, and saponification value, among other properties, and all parameters were found to be within acceptable standards.

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Polyherbal, Alopecia, Cosmetics, Formulation, Viscosity, Dandruff.

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The experiment was conducted to determine the effect of Banana peel liquid organic fertilizer on the growth of mustard plants. The mineral fertilizers applied to the soil, provide the plant nutrients but ultimately results in environmental pollution. Liquid organic fertilizers increase the soil fertility and enhances the plant growth and yield. There is no toxicity by using excess amount of Liquid fertilizer. Mustard plants showed more growth in soil supplemented with banana peel fermented water. This was observed by measuring growth parameters.

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Liquid organic fertilizers, Banana peel, Fermentation.

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Herbal cosmetics are the preparations used to enhance the human appearance. The aim of the present research was to formulate the herbal Cream for the purpose of Moistening, Nourishing & Treatment of various diseases of the skin such as scabies, eczema, ringworm and other bacterial infection. Different herbal ingredients like camphor, coconut oil, sulphur, bees wax and borax are taken to prepare this antibacterial cream. Evaluation of the final cream done by using different evaluation methods, such as Colour, odour, appearance and different test like Stability Test, spread ability test, wash ability test, PH. The results shown that cream gave satisfied results.

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herbal Cream, scabies, eczema, bacterial infection, camphor.

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A Cone-in-cone Morse taper connection between abutments and crowns has been proposed to retain implant supported definitive fixed dental prostheses (FDPs). The "Conometric Concept," a prosthetic technique, was applied to preserve zirconia and lithium disilicate (LS2) restorations. In order to keep an implant-supported restoration without the use of screws or cement, the conometric idea involves creating a cone-in-cone relationship between an abutment and the corresponding coping. Prefabricated parts are used to fit the restoration into the abutment. Conical coupling abutments function without cement by utilizing friction between the abutment and the titanium coping to hold a prosthesis in place. A spring-fixed partial denture remover can be used to remove the restoration with ease, as it lacks any access holes. The emergence profile of the restoration can be placed sub-gingivally without the risk of cement remaining at the abutment coping interface. The main objective of this study is to critically review articles that have used conometric concept and to evaluate its clinical effectiveness.

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IMPLANT-SUPPORTED DENTAL PROSTHESIS; FIXED PARTIAL DENTURE; CONOMETRIC CONNECTION; DENTAL IMPLANT ABUTMENT DESIGN, CLINICAL OUTCOME

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Helminth parasites are found in freshwater environments across the globe, including rivers, lakes, ponds and wetlands. The distribution of helminths is influenced by factors such as climate, water quality, host availability, and the presence of intermediate hosts or vectors. Climate plays a significant role in determining the distribution and prevalence of helminth infections. Prevalence of helminth infection in fishes shows strong, very strong and very weak correlation with temperature and dissolved oxygen according to seasons.

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Helminth, Dindori, Prevalence, Narmada river

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Unmanned Aerial Vehicle (UAV) is commonly known as Drone. It is extensively being used these years. Nowadays drones are used in various Military applications, Commercial Cargo Transport, and 3-D Mapping etc. For supporting the weight of the plane, and shock absorption functions, landing gear design is highly needed. Unmanned aerial vehicles (UAV) are the logical successors to modern aircraft and advancements in automated technology. The current generation of UAVs is focused on wartime capabilities and reconnaissance, leaving an existing market untapped by UAV technology: the commercial field. There are thousands of applications for UAV technology in the civilian market, from quick response applications and media outlets to communication technicians and horticulturalists. Drones represent a compelling subject of inquiry, prompting an exploration into their intricate mechanics, constituent elements, and their expansive array of applications that profoundly shape their future prospects. Integrating principles from electronics, mechanical engineering, and particularly aviation, drones epitomize a convergence of diverse disciplines. These aerial vehicles manifest in various forms, categorized by distinct configurations such as bicopters, tricopters, quadcopters, hexcopters, octocopters, and more.

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Unmanned Aerial Vehicle(UAV), Artificial intelligence(AI), Random Access Memory(RAM), Integrated Development Environment (IDE), Visual Studio

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Businesses operating on small to medium scale face a significant challenge known as the long-tail phenomenon. Each business vendor may have low individual demand, however, collectively it represents a substantial market share. As online markets continue to expand and diversify, navigating this long tail becomes increasingly complex and presents a growing obstacle for businesses aiming to reach their target audience effectively. This escalating problem underscores the pressing need for innovative solutions to facilitate the growth and success of small enterprises in the competitive landscape of online marketplaces. In navigating the multifaceted landscape of product catalogues, the study delves into the exploration of Machine Learning and Deep Learning based techniques that could help e-commerce collect data and use it to optimize its business operations, and increase profits and revenue. This study talks about Opinion Mining, Sales Prediction, Dynamic Pricing, Product Recommendation and Market Segmentation - aiming to develop a framework so versatile as it fits with any type of products catalogue, serving multiple businesses through a unified tool. The techniques used to achieve the aim are inclusive of but not limited to RoBERTa, K-Means Clustering, Regression, and Time-Series Analysis. This issue has been addressed by various familiar entities, and numerous solutions have been discussed. This study throws focus on the basic methodologies with minimal computational, development, and maintenance costs, further developments have been discussed, which leverage the publicly available AGIs - Artificial General Intelligence featuring GPT.

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Deep Learning, Machine Learning, E-Commerce, Artificial Intelligence, Sentiment Analysis, Opinion Mining, Product Recommendation, Dynamic Pricing, Sales Prediction, Market Segmentation, Customer Segmentation, Prescriptive AI, Business Intelligence, Neural Networks, RoBERTa, Support Vector Machines, Transformers, K-Means Clustering, Time-Series Forecasting, Time-Series Analysis, Linear Regression.

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Curcuma longa, commonly known as turmeric, has garnered significant attention in recent years due to its diverse pharmacological properties and potential health benefits. This review provides a comprehensive overview of the medicinal properties, bioactive compounds, pharmacokinetics, and therapeutic applications of Curcuma longa. Its principal bioactive component, curcumin, has been extensively studied for its anti-inflammatory, antioxidant, antimicrobial, and anticancer properties. Additionally, Curcuma longa demonstrates promising effects in the management of various chronic diseases such as cardiovascular disorders, neurodegenerative diseases, and metabolic syndromes. Despite its remarkable therapeutic potential, challenges such as poor bioavailability and limited clinical evidence remain. Strategies to enhance the bioavailability of curcumin, including the use of adjuvants and novel delivery systems, are discussed. Furthermore, the safety profile and potential adverse effects associated with Curcuma longa consumption are addressed. Overall, this review underscores the importance of Curcuma longa as a valuable natural remedy and highlights avenues for future research and clinical applications.

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Curcuma longa, Ayurveda, Anti-inflammatory, Antioxidant, Cancer Prevention, Immunomodulatory effect, Neuroprotection.

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The marketed medicine for cancer has various side effects, and the minimization of the side effects using a natural product is rising. In this study, Alangium salviifolium was for its constituents. The alkaloids, namely, Deoxytubulosine and Beta-carboline harmaline, present in plants, have anticancer properties. To understand the anticancer activity of the alkaloids, CADD, a computational-based designing method, was used. The anticancer properties of the compounds using the NPACT database, and the binding affinity study were carried out with six different receptors using the Auto Dock software, with results visualized in Discovery Studio. These results were evaluated with the standard marketed drug Paclitaxel. For the extra benefits, the compounds were modified with Zinc Oxide. After modification, the molecular docking study using the PyRx software results were visualized in Discovery Studio. The pharmacokinetics study using the pkCSM database needs in-vitro validation of Beta-carboline harmaline.

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Cancer, Colorectal, Bioinformatics, treatment

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Innovative platform Ensure revolutionizes insurance valuation by offering predictive analytics for a range of commodities. To reliably predict future valuations, our website uses cutting-edge machine learning algorithms, such as time series analysis and regression. Our platform takes in a wide variety of data, processes it using our unique algorithms, and produces accurate forecasts of future values. Users can price their goods more properly, evaluate their options well, and eventually increase profitability because of this predictive capability. To maintain the highest level of accuracy, our machine learning models are regularly updated with fresh data and trained on old data. Regression models allow us to quantify the relationship whereas time series analysis helps us analyze trends and patterns over time. It's a comprehensive solution for insurance companies looking to stay ahead in the competitive market. Join us in redefining insurance valuation and embarking on a journey toward data- driven decision-making.

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Predictive Analytics, Machine Learning, Regression, Time- Series Analysis, Valuation and Pricing.

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Creative Commons Attribution 4.0 and The Open Definition