Volume 12 | Issue 5 |

 Abstract

The industrial sector has seen significant change in recent years, moving towards efficient and cutting-edge technologies that have decreased waste production, improved product quality overall, and sped up the time it takes to make goods. Within these industrial areas, where technology has advanced to such an extent that project completion times are a fraction of what they were just a few years ago, is the wood cutting industry. It is not possible to say that this is true for devices that are between two and three decades old. These devices employ antiquated technology and procedures, which results in much reduced accuracy, imprecision, and energy consumption, needless energy, and it is quite difficult to get replacement components for the antiquated systems in the event of a malfunction. The owner of the machine is compelled by these issues to discard these outdated devices. It brings up another issue with getting rid of these computers. Modernising these devices can readily solve the solutions to these issues. The latest systems available on the market offer significantly more processing power and greater connectivity with contemporary devices, enhancing machine precision and facilitating improved operator communication to save energy and time usage. The aspects of the machines will be significantly enhanced by updating them with the aforementioned contemporary technology.

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Wood working, CNC, Multi-headed, high speed spindle, up-gradation, energy conservation.

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The semiotics of love and passion in the ancient Arabic culture; experience of Majnun Laila (Qais bin AL-Malouh) as a model.

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The semiotics of love and passion in the ancient Arabic culture; experience of Majnun Laila (Qais bin AL-Malouh) as a model.

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The psychology of adopted children is a multifaceted field that delves into various aspects of their development, adjustment, and well-being within adoptive families. Research in this area encompasses studies from developmental psychology, clinical psychology, and social psychology, shedding light on the unique experiences and challenges faced by adopted children and their families. This exploration seeks to understand how factors such as attachment, identity formation, and pre-adoption experiences shape the psychological landscape of adopted children. By examining these dynamics, researchers aim to provide insights that can inform supportive interventions and promote positive outcomes for adopted individuals.

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Adoption, Mental Health, Psychological Development, Adopted Child, Behavioural Issues

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Research on drug development is crucial for scientists and pharmaceutical companies. However, there are obstacles and difficulties that affect medication design and development, such as low efficacy, off-target delivery, time consumption, and excessive cost. Big and complex data from clinical trials, proteomics, microarray, and genomes all represent a challenge to the drug discovery process. Drug development and discovery heavily rely on artificial intelligence and machine learning technologies. Stated differently, the field has been modernized by artificial neural networks and deep learning algorithms. Peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning,polypharmacology, and physiochemical activity and other drug discovery processes have all made use of machine learning and deep learning algorithms. Additionally, by accelerating research and development procedures, AI can help lower development costs. Pharmacokinetics and toxicity of potential drugs can be predicted using machine learning algorithms, which also help with experimental design. This capacity lessens the need for extensive and expensive animal research by enabling the prioritization and optimization of lead compounds. Artificial intelligence (AI) algorithms that evaluate real-world patient data can support personalized medicine strategies, improving patient adherence and treatment outcomes. This thorough analysis examines the many uses of AI in medication development, dosage form designs for drug delivery, process optimization, testing.In conclusion, artificial intelligence (AI) in the pharmaceutical sector provides previously unattainable improvements in drug development, clinical trial design, manufacturing, and regulatory affairs. AI has the potential to revolutionize pharmaceutical research and development by speeding up procedures, improving decision-making, and increasing overall efficiency.

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Artifical intelligence ,Clinical Research , Pharmaceutical Industry

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The project titled "A Study on exploring the impact of positive work place culture on employee morale and performance." aims to investigate and analyze the extent of awareness among employees regarding leadership and management practices, growth and development opportunities, recognition and reward system, worklife balance initiative in TI Cycles Of India. This research project will employ both qualitative and quantitative research methods using statistical analysis to assess the employees knowledge regarding leadership and management practices, growth and development opportunities, recognition and reward system, worklife balance initiative . The findings of this study will not only provide valuable insights for TI Cycles Of India to enhance their policies to solve the issues faced regarding on leadership and management practices, growth and development opportunities, recognition and reward system, worklife balance initiative . Ultimately, this research endeavours to promote a more informed and equitable working environment for employees while fostering better compliance practices among organizations.

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Employee morale, workplace culture

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A Go-kart is a small four wheeled vehicle. Go-kart, by definition, has no suspension and no differential. They are usually raced on scaled down tracks, but are sometimes driven as entertainment or as a hobby by non-professionals. Our goal is to design and fabricate an eco-friendly go-kart that offers exceptional performance, driver comfort, and safety. The primary focus is on creating a lightweight kart that delivers impressive performance. Adherence to the rulebook of Edgeline Championship is mandatory and significantly influences our objectives. The go-kart, which is propelled by a rear- wheel internal combustion engine without suspension or differential, has been designed in accordance with standard principles. All critical factors and design parameters have been taken into account, resulting in a thoroughly failure-analysed and ergonomically optimized go-kart. Comprehensive calculations have been conducted for each component, followed by 3D modelling and simulation using professional software tools. The analysis was carried out with different iterations and finally the optimum design of each part of the Go kart was done so as to obtain the optimum result.

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DESIGN, ANALYSIS AND FABRICATION OF GO-KART VEHICLE

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Advances in technology have resulted in a rise in automation across multiple industries in recent times. One such sector is the retail sector, where automation is essential for increasing productivity and simplifying processes. The creation and deployment of a centralized system for automated bill scanning and product information extraction is suggested in this research study. By automatically extracting pertinent information from scanned bills using machine learning and computer vision techniques, the system seeks to completely transform the way bills are processed in retail settings. The suggested system's architecture, including its hardware and software components, is covered in the paper. It also describes the approaches taken in bill scanning, product recognition, text extraction, and picture preprocessing. The system's accuracy and efficacy are illustrated by the presentation of the experimental findings and implementation specifics. The study concludes with a discussion of the possible uses and advantages of implementing such a system in retail environments, including increased customer satisfaction, quicker processing times, and improved accuracy.

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Automated Bill Scanning, Product Information Extraction, Machine Learning, Computer Vision, Retail Industry.

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This research study examines the critical link between financial literacy and personal financial management in the aftermath of India's COVID-19 outbreak. The study's goal is to look at how people's financial literacy skills have affected their capacity to navigate and manage their money in the face of economic challenges caused by this pandemic. The research investigates different factors such as savings behaviour, investment decisions, debt management, and overall financial well-being in the context of post-COVID-19 India using a complete review of current research as well as actual data. For this research project, we employed a primary data collecting technique to acquire real-time information for an investigation of financial literacy towards personal financial management during COVID 19 in India. We adopted the SPSS data analysis technique to check the reliability, which allowed us to locate the genuine data. According to the findings of this survey, the majority managing their own money by budgeting and savings, with some investing in stocks during the pandemic. Everyone has adequate financial literacy for personal financial management, regardless of their educational level or experience. The study concluded that, despite the challenges encountered due to the COVID-19 epidemic, individuals in India have displayed excellent financial literacy, as seen by their assertive approaches to budgeting, savings, and investing, regardless of educational background or experience. They all are aware of the government assistance and programmes offered throughout the epidemic to support the individual life cycle.

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Financial literacy, Personal financial management, COVID-19 outbreak, Savings behavior, Investment decisions, Debt management.

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In alignment with the United Nations' Sustainable Development Goals (SDGs), this research explores a novel approach to promoting eco-consumer behavior through the linking of Reverse Vending Machines (RVMs) with online shopping platforms. The proposed method encourages people to minimize waste and conserve resources by giving them discounts on purchases when they recycle at RVMs. Basically, customers would get a code after buying something which they would use when recycling at specific RVM locations. They would then take a photo of their RVM receipt, with the code. Upload it for verification on the online shopping platform. This paper investigates the role of integrating RVMs in driving sustainable practices and shaping the overall sustainability of e-commerce. It also raises an issue with excessive discounts but promotes customer involvement in environmental responsibility. Multinational corporations may substitute discounts for rewarding behavior to influence customers to be proactive in engaging in environmentally friendly activities. The findings imply that the fusion of RVMs with shopping platforms might help attain more ecological awareness and boost loyalty and engagement levels among consumers. engagement, Environmental responsibility

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Reverse Vending Machines (RVMs), Eco-consumer behavior, Recycling incentives, Consumer

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-- The design and implementation of various wellknown data mining techniques in a variety of real- world applications (e.g., industry, healthcare, and bioscience) has led to their use in machine learning environments to extract meaningful information from provided data in healthcare communities, biological disciplines, and other fields. Early illness prediction, patient treatment, and community services all benefit from precise medical database analysis. Machine learning techniques have been effectively used in a variety of applications, including disease prediction. The goal of developing a classifier system utilising machine learning algorithms is to greatly assist physicians in predicting and diagnosing diseases at an early stage, which will greatly aid in solving health-related difficulties. For our study, a sample of 4920 patient records diagnosed with 41 disorders was chosen. We chose 95 out of 132 independent variables (symptoms) that are strongly associated to illnesses and improved them. The disease prediction system built utilising Machine learning techniques such as Decision Tree classifier, Random forest classifier, and Nave Bayes classifier is demonstrated in this research paper. This paper "Disease Prediction Using Django and Machine Learning" gives a comparison of the outcomes of the aforementioned algorithms.

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--Machine learning, data mining, decision tree classifier, random forest classifier, naive bayes classifier

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