Volume 12 | Issue 5

 Abstract

This case study aims to highlight the integration among st all first year subjects done to the O.P.D level cases observed by the first B.H.M.S students as prescribed by National commission for homoeopathy through the newly introduced Competency based dynamic curriculum (C.B.D.C) in 2023. This kind of integration would be an exhaustive exercise and needs a thorough study of the case prior to the actual evaluation, however, it fosters a comprehensive understanding of each case by making the results less subjective.

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Acute case based learning----Integrated learning----Homoeopathic prescriptions

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This study investigates the structural integrity of a tricycle frame using finite element analysis (FEA) in ANSYS. The research focuses on identifying stress distribution, deformation, and potential failure points under various loading conditions. The tricycle frame model, designed with specific material properties, is subjected to realistic boundary conditions and loads to simulate operational stresses. Validation of the simulation model is performed through comparison with theoretical calculations and existing experimental data. The results reveal critical stress regions and deformation patterns, providing insights into design vulnerabilities and areas needing reinforcement. This analysis contributes to optimizing tricycle frame design for improved durability and performance. The findings align with existing literature, offering both confirmations and novel perspectives on tricycle frame structural behavior. Recommendations for design enhancements and suggestions for future research are also presented, aiming to advance the field of vehicle frame engineering.

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Tricycle frame, Finite element analysis (FEA), Structural integrity, ANSYS simulation, Stress distribution

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Heart disease remains a leading cause of mortality worldwide, necessitating the development of accurate predictive models for early diagnosis. This paper presents a heart disease prediction system based on the Naive Bayes algorithm. Utilizing a dataset from the UCI Machine Learning Repository, we preprocess the data, implement the Naive Bayes classifier, and evaluate its performance. The results indicate that the Naive Bayes algorithm provides a reliable prediction with an accuracy of XX%, demonstrating its potential in clinical applications.

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Heart Disease, Naive Bayes, Machine Learning, Predictive Model, UCI Dataset

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Blockchain technology can benefit in many ways in the supply chain as it does in many other application areas. Employing blockchain in supply chain processes provides transparent, decentralized, secure, faster and low cost transactions. By eliminating unnecessary third parties and covering more daily life processes in digital systems minimizes paperwork. Blockchain establishes trust among trading partners. Making more detailed data available in blockchain, improves supply chain monitoring ability and safety. Thus, increasing focus on providing integration and cooperation with technologies such as Artificial Intelligence, Big Data Analytic, Cloud Computing and IoT will help to realize advanced supply chain systems.

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Blockchain, Supply chain, Smart contracts, traceability, efficiency, transparency

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The rapid development of e-commerce, e-banking, and social organizations has exposed a large number of phishing attacks to cyber security systems. To support the viability of anti-phishing efforts, we present an advanced scientific show based on machine learning. In this image, six specific calculations are tuned: estimated recurrence, K- nearest neighbours, trusted Bayes, random forest, support vector machine, and extreme gradient boosting (XGBoost). Our study used a large dataset consisting of 58,000 genuine and 30,647 phishing websites, each with 112 characteristics. Improvements in highlight selection, including dataset tuning and removal of immutable highlights, have generally improved the execution process. Extensive testing of eight unique scenarios showed that all calculations reliably achieved over 93% accuracy, with XGBoost achieving the highest score. Accuracy is 99.2%, precision is 99.1%, recall is 99.4%, and specificity is 99.1%. Additionally, the Foremost Component Examination (PCA) technique was used to significantly reduce the XGBoost calculation execution time from 1500ms to 869ms. These findings validate the suitability of our strategy for both offline and real-time dispatch, highlighting its reasonable relevance in modern cyber security operations.

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Phishing Detection, Machine Learning Algorithm, Feature selection, Dataset, Model Evaluation Random Forest, SupportVector Machine, Machine Extreme Gradient, Feature Engineering Performance Metrics.

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Background: A stroke is often described as a Cerebral Vascular Accident. Many people don't know what a stroke is or why it happened. It is important to know about stroke when caring a stroke patient. Patient and family education is a fundamental component of rehabilitation, ample opportunity for learning about the stroke causes and prevention. The gain in knowledge will reduce the caregiver burden and will contribute to improve the quality of life of both the patient and caregiver. Aim: The aim of this study was to assess the knowledge among the caregivers of stroke patient regarding home care management, to assess the practices among the caregivers of stroke patient regarding home care management, to correlate the knowledge and practices among the caregivers of stroke patient regarding home care management, to associate the knowledge and practices among the caregivers of stroke patient regarding home care management with selected demographic variables. Methodology: Quantitative Descriptive approach with Descriptive research design was adopted for the study. Non probability convenient sampling technique was used to draw 50 caregivers of stroke patients from GRNC (Dispur) hospital. Data collection was done by using structured demographic performa, structured knowledge questionnaire and practice checklist. Results: Out of 50 samples, 10 samples (20%) had inadequate knowledge, 23 samples (46%) had moderately adequate knowledge and 17 sample (34%) had adequate knowledge. The overall mean knowledge score was found to be 18.50 and standard deviation 3.30. Out of 50 samples, 28 sample (56%) had inadequate practices and 22 samples (44%) had moderately adequate practices. The overall mean score of practice was 17.44 standard deviation 1.89. The study also revealed that there was positive correlation between knowledge and practice of caregivers is 0.42. There was no association between knowledge and practice of caregivers of stoke patients with selected demographic variables. . Conclusion: The study concluded that most of the caregivers of stoke patients had moderately adequate knowledge and moderately adequate practices regarding home care management of stroke patient. Therefore an information booklet on home care management of stroke patient was prepared and distributed by the investigator to help the caregivers to know more about the care of stroke patient.

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Knowledge, practice, caregivers and stroke patient

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In this research paper, we will explore how web engineering techniques can be used to improve the performance and user experience of Indian traveling websites. We will also look at the key engineering challenges that these platforms face, such as scalability and reliability, as well as security and UI design. We will combine empirical analysis with user studies and best practices to present strategies for improving the website's functionality and usability. We will also explore how to integrate advanced technologies like machine learning to provide personalized recommendations, and data analytics to understand user behavior.

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Tourism Industry | Web Engineering |Economic Impact | Data Analytics |Tourism in India | GDP | Culture Heritage | Employment in Tourism | FTA | Websites

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Rucaparib, RP-HPLC, Validation, Accuracy, Precision, Robustness, ICH Guidelines.

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Aim-The present study aims at assess the effect of teaching cum demonstration on knowledge regarding selected aspects of Essential Newborn Care among postnatal mothers. The present study is based on General System Theory (Ludwig von Bertalanffy). Introduction- The Essential Newborn Care Course (ENCC) aims to ensure health workers have the skills and knowledge to provide appropriate care at the most vulnerable period in a baby's life. Health workers are taught to use WHO's Pregnancy, Childbirth, Postpartum and Newborn Care. The investigator during her clinical posting observed that postnatal mothers were not having sufficient knowledge regarding newborn care, so that's why their baby becomes sick and unhealthy. Globally 4 million newborns die every year before they reach the age of one month and approximately 3.4 million newborns die within the first week of life. Objective-The main objective of the present study is to assess the pre-existing knowledge regarding selected aspects of Essential Newborn Care among postnatal mothers. Methodology-The research approach is quantitative approach and the design selected for the study is pre-experimental design, in which one group pre-test - post-test design. Sampling technique is convenience sampling. Samples consists of 30 postnatal mothers who was admitted in selected hospital and those postnatal mothers who satisfies the inclusion criteria are considered as samples. Content validity is done with the help of experts. Split-Half method is used to check the reliability. Result- Evaluate the effect of teaching cum demonstration on selected aspects of Essential Newborn Care among postnatal mothers. And associate the knowledge with selected demographic variable among postnatal mothers. The mean 22.433 of post-test score was more than the mean 13.200 of pre-test score of postnatal mothers. There is total enhancement occur 9.233. The comparison of Pre-test and post-test knowledge on selected aspects of Essential Newborn Care among postnatal mother had significant difference with t value (13.052) at p< 0.05 Level of significance. Conclusion- From this study researcher found that the postnatal mothers have gained knowledge on selected aspects of Essential Newborn Care among postnatal mothers.

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Keyword: Teaching cum demonstration, Knowledge, Postnatal mothers.

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Smart home security and remote monitoring have become vital and indispensable in recent times, and with the advent of new concepts like Internet of Things and development of advanced authentication and security technologies, the need for smarter security systems has only been growing. The design and development of an intelligent web-based door lock control system using face recognition technology, for authentication, remote monitoring of visitors and remote control of smart door lock have been reported in this paper. This system uses Haar-like features for face detection and Local Binary Pattern Histogram (LBPH) for face recognition. The system also includes a web-based remote monitoring, an authentication module, and a bare-bones embedded IoT server, which transmits the live pictures of the visitors via email along with an SMS notification, and the owner can then remotely control the lock by responding to the email with predefined security codes to unlock the door. This system finds wide applications in smart homes where the physical presence of the owner at all times is not possible, and where a remote authentication and control is desired. The system has been implemented and tested using the Arduino Uno Board, Python along with OpenCV are used to program the various face recognition and control modules.

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Arduino Uno, Face Module Camera, Motion Sensor, Wire.

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This study aims investigate the word "emotional intelligence" refers to the capacity, aptitude, skill, or self-perceived knowledge to identify, evaluate, and manage one's own and other people's emotions. Emotional Intelligence helps the employees to increase their emotional selfawareness, emotional expression, creativity, increase tolerance, increase trust and integrity, improve relations within and across the organization and thereby increase the performance of each employee and the organization as a whole. "Emotional intelligence is one of the few key characteristics that gives rise to strategic leaders in organizations". Emotional intelligence plays a significant role in the organization and becomes an important criterion of evaluation for judgment of an effective employee, increases productivity and trust within and across the organization. In today's dynamic and interconnected workplace, the significance of Emotional Intelligence (EI) has garnered increasing attention, particularly in the context of Information Technology (IT) employees.

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Emotional Intelligence, IT Employees, Strategies, Stress, Workplace, Self Knowledge.

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Weed control is essential in agriculture since weeds reduce yields, increase production cost, impede harvesting, and degrade product quality. As a result, it is indeed critical to recognize weeds early in their vegetation cycle to evade negative impacts to crop growth. Earlier traditional methods used machine learning to determine crops along with weed species, but they had issues with weed detection efficiency at early growth stages. The current work proposes the implementation of a deep learning method that provides accurate results for precise weed recognition. Two different deep convolution neural networks have been used for our classification framework, namely Efficient Net B2 and Efficient Net B4. The plant seedlings dataset is utilized to investigate the proposed work. The evaluation metrics average accuracy, precision, recall, and F1-score were used. The findings demonstrate that the proposed approach is capable of differentiating between 12 species of a plant seedling dataset which contains 3 crops and 9 weeds. We intend to detect diseases in the identified plant species in our future research.

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Deep learning, efficient net, Machine learning, Plant seedling classification, Weed recognition, Deep convolutional neural networks

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 Abstract

Mithila ki sanshkrutik lok chitrakala

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Mithila ki sanshkrutik lok chitrakala

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In the competitive job market, selecting the right candidate for a given position is crucial for organizational success.Traditional human-based hiring processes can be time-consuming, subjective and prone to biases. Machine learning (ML) has emerged as a promising tool to streamline and enhance the recruitment process, particularly in the realm of personality prediction. This paper explores the application of ML for personality prediction in job applicant screening, aiming to automate initial candidate assessment and reduce the burden on HR personnel. By discussing various feature extraction techniques and ML algorithms for personality prediction, emphasizing the importance of data quality and ethical considerations. The potential benefits of ML-based personality prediction in job screening include improved efficiency, reduced biases, and enhanced decision-making. However, challenges remain in ensuring fairness, transparency, and accountability in the implementation of these algorithms. Further research is warranted to refine ML models, address ethical concerns, and establish clear guidelines for responsible use in the hiring process.

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Personality Prediction

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Evaluation as a dynamic system produces information that affordable conclusions are derived via stakeholders for decision making that expectedly effect on college students' studying consequences. The facts mining method while extracting useful, legitimate patterns from better schooling database environment contribute to proactively ensuring students maximize their academic output. This paper develops a technique by the derivation of overall performance prediction signs to deploying a simple student performance assessment and tracking machine inside a teaching and gaining knowledge of environment via specifically specializing in overall performance tracking of college students' continuous assessment (assessments) and exam ratings as a way to predict their very last achievement reputation upon commencement. based totally on various records mining techniques (DMT) and the utility of device mastering methods, regulations are derived that permit the type of college students of their predicted lessons. The deployment of the prototyped answer, integrates measuring, 'recycling' and reporting procedures inside the new gadget to optimize prediction accuracy. from the traditional measurement and evaluation strategies to the usage of DMT which employs diverse intrusive facts penetration and investigation strategies to isolate vital implicit or hidden statistics. because of the reality that numerous new technologies have contributed and generated massive explicit information, causing implicit understanding to be unobserved and stacked away inside large amounts of records. the primary attribute of records mining is that it subsumes knowledge Discovery (KD) which in step with [2] is a nontrivial process of figuring out valid, novel, probably useful and in the end comprehensible styles in statistics procedures, thereby contributing to predicting trends of results by using profiling overall performance attributes that helps effective selections making. This paper deploys idea and exercise of records mining because it relates to student overall performance and monitoring partner diploma application in a community university. Technological tendencies and new programming strategies have stepped forward expertise and use of artificial Intelligence (AI). This paper profiled students from actual and partly behavioral factors. The factual profile content material consisting of gender, date of birth, race and others like distinctive check effects from the college each semester obtained from the pupil information. performance profiling is dependent upon motivation, attitudes, peer influence, curriculum and by the ongoing real-time tracking of pupil's overall performance the use of a easy rapid response device and as [5] cited predicts successfully which student may want some attention or reinforcements in the course of their education.. The model evolved facilitates reap a measurable scholar development monitoring manner that offers consequences speedy and meets a larger instructional aim benefiting stakeholders in the educational gadget and the wider network.

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DM, DMT, KD, Decision rules, Assessment, performance monitoring, Stakeholders

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Abstract Teaching as a profession has changed the very attitude and perception of the role of future educators in our society. Teacher trainees irrespective of whether they are studying in an undergraduate or postgraduate degree in teaching, require various skills to be able to become facilitators in the future. The present study has been conducted with an aim of finding correlation between demographic variables of age, marital status, type of institute and working status with the aspect of well-being. A total of 134 trainee teachers, from both types of institutes (government and private),were administered the Ryff scale on psychological well-being. Findings were calculated using ANOVA and t-test within the groups and between the groups on different demographics. One significant finding was t-test 0.040 indicating high well-being among trainees of a graduate teacher training programme as compared to trainees in an undergraduate teacher training programme, indicating that exposure and experiences in life contribute towards wellbeing. Findings on ANOVA revealed significant mean differences in two dimensions of wellbeing which are Autonomy, Personal Growth and overall well-being scores among trainees irrespective of their training programmes. However findings on the sample distribution indicate no significant correlation between wellbeing and age. Wellbeing needs to be looked at from psychological demographics like family life, social support and self-awareness that can bring about the much required change in the future educators.

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Wellbeing, trainee teachers, female, training institutes.

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The accumulation of excess body fat causes obesity, a complicated multifactorial disease with detrimental effects on health. The rate of obesity is still rising, leading to an unparalleled crisis that doesn't appear to be abating anytime soon. Elevated body mass index (BMI) is associated with a significant reduction in life expectancy and quality of life due to its role as a risk factor for noncommunicable diseases such diabetes, cardiovascular diseases, and musculoskeletal problems. Long-term energy imbalance between calories taken and calories burned is the primary cause of obesity. Here, we examine the molecular underpinnings of obesity in an effort to offer practical therapeutic approaches for achieving a healthy body weight through both nature and nurture.

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Obesity,genetics,modifiable,genetics,causes,pathophysiology

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The period of the COVID-19 pandemic impacted education, especially on early childhood education aged 5-6 years. This study aims to determine the influence of pandemic situation on developing language aspects of children aged 5-6 years. Since spring 2020, parents have been working from home and children have been home from daycare and school. Parents are experiencing stress in an attempt to satisfy the demands of work, family, and COVID-19 concerns. Due to the fact that children have been home from daycare and school, parents have the sole responsibility of caring for and teaching their children until schools are able to fully and effectively meet the needs of educating students in an adapted format. This impacted different developmental areas of young children like social, cognitive language etc. Research provides a wealth of information regarding influence of COVID-19 pandemic on language development. Charnery et al. (2021) reported that the language development of children can be negatively affected and there may be language developmental delays due to the pandemic period. With this view present review study mainly focuses on the studies which revealed influence of COVID-19 pandemic on language development among preschool children.

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COVID-19, Pre School, language development, Pandemic

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Worldwide agricultural production and food security are being threatened by insects. Conventional approaches to controlling insect pests frequently include the use of chemical pesticides, which can have negative consequences on both the environment and human health. This research study outlines a development project that aims to create an agricultural self-powered intelligent insect collector-killer that is outfitted with multiple wavelength light technology in response to these difficulties. The system provides an efficient way to develop the self-powered mechanism utilising clever algorithms and renewable energy sources, the system targets specific insect pests with the least amount of damage to the ecosystem and beneficial organisms. These techniques, along with contemporary technologies, have enormous potential for quickly detecting and monitoring dangerous pests and placing them under quarantine. The paper discusses the primary attributes, purposes, and potential consequences of this cutting-edge farming instrument.

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Agriculture, Environmental Concerns, Natural resources, Insect-Pest population, Self -Powered Insect Trap, Automated trap, Multiple Wavelength light.

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Text-to-Image and Text-to-Video AI generation models represent groundbreaking technologies that leverage deep learning and natural language processing (NLP) to produce images and videos from textual descriptions. This paper delves into the most advanced methods in the realms of Text-to-Image and Text-to-Video AI generation. It offers a comprehensive survey of the current literature and analyzes various approaches applied in multiple studies. This includes data preprocessing techniques, types of neural networks, and the evaluation metrics utilized in this field. Moreover, the paper explores the challenges and limitations associated with Text-to-Image and Text-to-Video AI generation and outlines potential directions for future research. These models hold significant promise for applications across diverse domains such as video production, content creation, and digital marketing.

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artificial intelligence, deep learning, natural language processing (NLP), large language model, AI text-to-image generation, AI text-to-video generation, DALL-E, CogView, Imagen, NUWA, Phenaki, GODIVA.

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