Volume 12 | Issue 4 |

 Abstract

As per the modern principles of white analytical chemistry (WAC), it is imperative to refrain from utilizing organic solvents that are carcinogenic and teratogenic in order to safeguard both the environment and analysts. A innovative idea for evaluating analytical techniques based on their economical effectiveness, greenness, and validation efficiency is white analytical chemistry. As a result, the reverse-phase high-pressure liquid chromatography (RP-HPLC) method for the simultaneous analysis was developed and validated using a WAC. In the field of chemistry, environmental protection as well as individual health and safety are being given more weight these days. As a result, there are more published studies explaining how to follow green guidelines, heed environmental agencies' recommendations, and handle chemicals more cleanly. Even the concept of "analytical quality-by-design" has been put up to highlight the importance of analytical chemistry in this industry's quality control system. But in theory, every sector of the chemical industry has a quality system, with a significant emphasis on chemical analysis and the corresponding quality control system. With time, more green chemistry ideas will find their way into the manufacturing of chemicals, such as minimizing waste output, using less materials and energy, and incorporating analytical measurements into large-scale quality-control operations. Green chemistry concepts will be included into QbD for a specific industry as a result of their integration with analytical chemistry. In this present research review article, a general introduction to green chemistry is explained using analytical quality by design approach.

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 Keywords

White analytical chemistry(WAC), Green chemistry ,Reverse phase liquid chromatography(RP-HPLC), Analytical quality by design(aQBD)

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study investigates investor perceptions of money-back policies provided by particular life insurance providers. With a growing focus on financial security and various investing opinions, the study provides insight into the preferences, attitudes, and decision-making factors influencing investors for these money-back insurance products. The research explores demographic trends, policy features, returns, and level of satisfaction among investors. This study's factors include premium, quality service, bonus, transparency, trust, and risk. In this study, purposive sampling is used to collect 350 respondents and analysed using percentage analysis, Chi-square analysis, Correlation, Regression analysis, and ANOVA. Results indicated that the money-back policies influence the investor attitude. The percentage of male and female is 57.4% and 42.6%, in the occupation status 59.7% of the respondents are in the category of private employees. Recommendations include money-back policies improved customer service, and transparency in policy and premium structure to enhance customer satisfaction.

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 Keywords

Life Insurance, Money-Back policy, Bonus, Premium, Level of satisfaction.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Solar energy, such as photovoltaic or solar thermal energy, powers thermoelectric cooling (TEC) solar refrigerators. The Peltier effect is the basis for the thermoelectric module refrigerator's operation. The use of TEC modules in an industry has grown significantly in recent years. They have been using solar refrigeration, which is well known for being an environmentally benign and energy-efficient substitute for the traditional vapor compression technique. The design of TEC solar refrigeration with thermoelectric heating and cooling is presented in this study. The purpose of this work is to develop a different, environmentally friendly refrigeration cycle that can achieve the same temperature as a typical conventional refrigerator. The realm of refrigeration is expanded by the design and production of such a refrigerator. The solar refrigeration system that is being suggested

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 Keywords

Keyword: Thermo-Electric Module, Peltier Effect, Solar Energy, Refrigeration, Oven system.

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 Abstract

There have been tremendous advancements in technology that have led to an increase in the needs of people. This, in turn, has led to more loan approval requests in the banking sector. Some attributes are considered to check loan status while selecting an applicant for loan approval. Banks are facing a serious challenge when it comes to evaluating loan applications and mitigating the risks associated with borrowers potentially defaulting on their loans. This process is particularly laborious for banks, as they must thoroughly verify each individual's loan eligibility. This paper proposes utilizing machine learning models with Ensemble Learning Techniques to determine the viability of granting individual loan requests. By using this approach, it is possible to enhance the accuracy with which suitable candidates are selected from an existing list. Therefore, this process can be used to address the aforementioned concerns surrounding loan approval processes. The model is helpful to both bank staff and applicants as it drastically reduces the time taken to sanction the loan

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 Keywords

Loan Status, Loan Lending, Decision Tree, Random Forest, Machine Learning

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 Abstract

KISHORO ME MANSHIK TNAV KI STHITI KA ADHYAYAN

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KISHORO ME MANSHIK TNAV KI STHITI KA ADHYAYAN

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Skin diseases afflict millions worldwide, requiring expertise and advanced instruments for accurate diagnosis due to limited visual resolution in skin images. Laser and Photonics-based medical technology aids quick and precise diagnosis, yet equipment is scarce and costly. Deep learning techniques facilitate early detection of skin diseases, minimizing reliance on human labor. Feature extraction is pivotal in skin disease classification, with deep learning algorithms automating this process. The adoption of CNN algorithms has notably reduced manual labor in feature extraction and data reconstruction. Achieving a 70% accuracy rate in skin disease classification is feasible through CNN algorithms.

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 Keywords

Skin diseases Convolutional Neural Network (CNN) Medical image analysis, Feature extraction, Image processing, Skin lesion detection, Deep learning, Image classification, Image classification

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 Abstract

Abstract: In the realm of programming languages, Python and Java stand as two giants, each with strengths and weaknesses. This research paper presents a comparative analysis to guide beginners in their choice between Python and Java. The study delves into various aspects including syntax, ease of learning, community support, performance, versatility, and ecosystem. Through an extensive review of existing literature and practical experiments, this paper examines the suitability of Python and Java for beginners, considering factors such as readability, simplicity, and availability of learning resources. Additionally, the performance disparities between the two languages are investigated, shedding light on scenarios where one language might be more advantageous over the other. Furthermore, this paper explores the ecosystems surrounding Python and Java, encompassing libraries, frameworks, and tools available for development. The comparative analysis includes discussions on the applicability of each language in different domains such as web development, data science, and software engineering. Ultimately, this research aims to provide beginners with valuable insights to make an informed decision when choosing between Python and Java as their first programming language. By highlighting the strengths and weaknesses of each language, this paper serves as a comprehensive guide for newcomers embarking on their journey into the world of programming.

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 Keywords

Comparative analysis, Python, Java, beginners, programming languages, syntax, learning curve, performance, ecosystem, versatility, decision-making.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

With more than 1.3 billion people living in India, the country's health care system still has a ways to go before it can adequately serve this sizable population with dependable and reasonably priced medical treatment. Telemedicine is one of the growing technologies that might facilitate access to medical services for the rural population. Since medical images take up a lot of storage space and transmission time is a bottleneck for real-time telemedicine requirements, developing algorithms and implementing them on VLSIs is crucial for processing images and videos quickly. In this work, fast and robust algorithms and their VLSI architectures have been designed for medical image compression supporting both 2D and 3D medical images.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

DWT, IDWT, SPHIT, MATLAB, FPGA, Telemedicine, FFT Analysis.

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

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: Natural dye, Avocado Dye, Coconut Husk Dye, Bamboo fabric.

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Heartbeat level prediction is crucial for assessing cardiovascular health and diagnosing related conditions. In this study, we conduct a comparative analysis of machine learning algorithms including Support Vector Machine (SVM), Random Forest, Linear Regression, and k-Nearest Neighbors (KNN) for predicting heartbeat levels. Each algorithm is trained and evaluated using a comprehensive dataset comprising features such as chest pain type, resting blood pressure, serum cholesterol level, fasting blood sugar, resting electrocardiographic results, and maximum heart rate achieved. Standard performance metrics including accuracy, precision, recall, F1 score, and confusion matrix are employed to assess the predictive capabilities of the models. Through this comparative analysis, we aim to identify the most effective algorithm for accurate heartbeat level prediction, considering the trade-offs between computational complexity, interpretability, and predictive performance. The findings of this study contribute to the advancement of machine learning applications in cardiovascular health monitoring and diagnosis. This meticulous approach culminates in the section of the optimal machine learning algorithm for heart beat level prediction, offering insights into cardiovascular health with precision and efficacy.The finding aim to identify the most effective algorithm for heart rate prediction, providing insights for practical health care applications.

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 Keywords

Heartbeat level prediction, Machine learning algorithms, Support Vector Machine (SVM), Random Forest, Linear Regression, k-Nearest Neighbors (KNN), Comparative analysis, Performance metrics, Accuracy, Precision, Recall, F1 score, Confusion matrix.

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Creative Commons Attribution 4.0 and The Open Definition