bluuebunny/crossref_metadata_2025 huggingface dataset:
Impact of Quality Management Systems on Corporate Efficiency in Public Health Providers
Authors: Saini Dr. Rajvir , Anand Dr. Utkarsh
Published: September 2024

The quality management system (QMS) develops an ethnicity of constant development and legal obedience, which advanced minor errors, process efficacy and develop manufactured goods quality, these benefits are increase corporate effectiveness. Inferior expanses, improved client approval, and residential corporate efficiency (CE) are inferences of QMS. The study investigates the impact of QMSs on the CE of Indian public health providers. Research dataset was gathered from a survey of 380 Indian public healthcare providers, involving respondents (178) from various categories. Data evaluates Statistical Package for Social Sciences (SPSS) software utilizing factor analysis, one-way analyses of variance (ANOVA), and correlation analysis. This investigation exposed the QMSs of these Indian public health care providers necessitate a separate sector devoted to excellence and fundamental quality opinion. The results reveal that subjective and objective performance (SOP) is the most significant factor influencing performance in healthcare, with the highest P-value of 0.004. This indicates a strong impact on organizational performance. Moreover, it was explored how these QMS elevated the company’s perceived performance levels while having a minor optimistic outcome on financial presentation. QMSs in Indian public health providers recover CE and foster a quality and responsibility ethnicity by inserting an importance on identical processes, continuous enlargement and patient care.
Health Care Surveillance Using Machine Learning and Data Analytics
Authors: Nayak Ashu , Raghatate Kapesh Subhash
Published: September 2024

Any country's healthy population are a true asset. Both developed and developing nations are spending enormous sums of money to strengthen their "healthcare systems" and the required "health infrastructure." Globally, only few countries have a proactive approach to healthcare. The pandemic that has been going on recently has taught countries hard lessons about how important it is to have strong healthcare systems. The formulation of effective health policies and initiatives depends heavily on studies on Public Health Surveillance (PHS) and the conclusions that follow. This task has become clearer with the introduction of modern computing techniques and technology. Humanity has always been saved by technology when it is applied correctly. In this context, the most promising machine learning techniques are applied in this work. An empirical investigation of RBFN classifiers is presented in this paper. A variety of performance criteria, including recall, f-score, accuracy, precision, and False Positive Rate (FPR), are used to evaluate the efficacy of the recommended procedures. The RBFN approach has the highest accuracy and the least amount of time complexity in identifying health diseases.
Management Challenges in Healthcare Service of Hospitals in India
Authors: John Byju , Rai Nitin Ranjan
Published: September 2024

The true values of human life are found in medicine and health care. Human life expectancy has grown as a result of their development, giving them a true dictatorship. Not only does the opening of hospitals or health centres impact the development of healthcare facilities, but their administration and management have a greater impact. Proper management of hospitals and health care centres would allow for the expansion of medical facilities with the least amount of investment. Any act or performance that is primarily intangible and does not lead to the ownership of anything is considered a service. By this view, the hospital offers us perishable, intangible medical and health care services. The service sector is highly diverse, and hospitals are no exception. They could be employed by the government, a private charity, or a commercial enterprise. The following adjectives best describe the qualities of the goods they sell: Kindness Paying attention and showing compassion. Any country's lifeline is its medical and health care facilities, which fall under the broad category of people-based services.
Novel AI-driven Malaria Prediction for Optimizing Public Health Management
Authors: Vij Priya , Prashant Patil Manisha
Published: September 2024

Malaria is an unmoving real common well-being apprehension in Asian nations like the Republic of India, where the state characterizes approximately 55% of the session events cutting-edge the infirmaries. They are usually distinguished by the absence of appropriate therapeutic care provision and the often late and error-prone diagnosis of the condition. In particular, commonly used devices such as the Rapid Diagnosis Test (RDT) are not completely dependable. They are primarily notable for their failure to provide adequate medical treatment and their tendency to diagnose the disease late and incorrectly. For example, widely used devices like the RDT are not consistent. To improve public health management actions, this work offers a unique augmented tree with penguin search optimization (AT+PSO) methodology for malaria forecasting. The suggested method combines the PSO algorithm with the augmented tree model, also known as random forest (RF). In the preprocessing stage, raw data samples are subjected to data normalization. Then, we applied the PSO to improve the characteristics of the RF model after successfully predicting malaria with the RF. The Python program is used to implement the suggested technique and analyze performance using a range of measures, including accuracy (0.988), sensitivity (0.987), specificity (0.991), F1-score (0.988), and MCC (0.975). In summary, our suggested approach produced the best results in terms of accuracy as opposed to other current strategies for predicting malaria to improve governance of public health.
Public Health Monitoring System In COVID-19 Conditions Using Machine Learning-Based Sentimental Analysis
Authors: Haval Dr. Abhijeet Madhukar , Afzal Md
Published: September 2024

Twitter is a significant forum for individuals to discuss and disseminate health-related data.  The system offers substantial data for immediate monitoring of contagious diseases (such as COVID-19), relieving disease-prevention organizations from the laborious tasks associated with Personal Health Measures (PHM). PHM identification is a crucial technique for staying informed about the status of an epidemic. It aims to determine an individual's health by analyzing web text data. This research investigates the process of identifying PHM related to COVID-19 using data from Twitter. The research has constructed a COVID-19 PHM dataset with tweets labeled with four distinct categories of health disorders connected to COVID-19: self-mention (SM), other-mention (OM), awareness, and non-healthcare (NHC). The research achieved favorable outcomes in the PHM identification task. The categorizing results enable prompt health tracking and oversight for digital epidemiology. The study assesses the impact of the attention strategy and training methodology on the predictive capabilities.
Satisfaction Level of Medical Tourism Services Based on Classified Factors - A Discriminatory Approach
Authors: Girimurugan B. , A S Poornima , C Natarajan , Padma Prabha C  , Adhinarayanan Poomagal , N Senthil Prabhu
Published: September 2024

Medical tourism has the potential to promote high-class learning, well equipped human resources, flattering visa policies, nations’ charisma, and sound flourished infrastructure facilities available in the nation. Countries have recognized this opportunity as a means in support of overall communal and economic upliftment. More governments are valuing these advantages and developing plans and policies for the sector as a result, WTTC states that by means of the appropriate policies, guidance and regulation mechanisms, medical tourism can achieve sustainable growth among the developed and developing nations. With a sample of 600 respondents, the data related to satisfaction in the medical tourism services is directly observed at the hospitals   from the International Patients in the six states of Tamil Nadu, Kerala, Karnataka, Telungana, Andhrapradesh, and Pondicherry. Data collected are compiled for the analytical purpose and the hypothesis is framed with regard to the opinion of the patients regarding various facilities and services offered and their satisfaction level towards the medical tourism services. In this present study, the Canonical Discriminate statistics was successfully employed for identifying the satisfaction over various reasons for preferring the medical tourism services in India. The outcome of the study is the classification of different groups under the satisfaction derived from the medical tourism services of four continuous variables are assigned and they are general reasons, treatment reason, cost reason, and sources reason with all 600 respondents. Findings reveal that Treatment Factor is the strong reason contributing towards the construction of low satisfaction group, followed by general reasons. The study highlights that cost is the least reason for low level satisfaction and reveals that cost of medical tourism services are nominal   in India.
Securing Data Transmission from Adversaries in Cybersecurity WSN Using Efficient Key Management Techniques
Authors: Yadav Kamlesh Kumar , Kirit Dhablia Dharmesh
Published: September 2024

Concern over cyber security is becoming more and more prevalent in today's international politics. National security is now evaluated not only in terms of military might or economic might, but also in terms of technological innovation. The fact that today's society is heavily dependent on computer communication and control systems, which opens up infinite opportunities, shows how important technology is in the national security area. By effectively changing the keys, the suggested work aimed to provide WSN with complicated security solutions that would ensure data integrity, security, and genuine transmission for many additional network operations through structured cryptography approaches. The study's protective measures can be applied to a variety of WSN configurations, including heterogeneous WSNs with and without mobility aid and static homogeneous WSNs. Because WSN is more limited to hardware resources, not all applications can be protected using the same method.
Study On AI-Assisted Health Detection Mechanism Based On ECG Data For Public Health
Authors: Yadav Kamlesh Kumar , Kirit Dhablia Dharmesh
Published: September 2024

Health care is being revolutionized by Artificial Intelligence (AI) technologies that provide more precise diagnosis, customized therapies, streamlined administrative procedures and better patient care. A health detection mechanism assesses electrocardiogram (ECG) data to detect cardiac anomalies, offering precise and immediate atrial fibrillation diagnosis. We propose artificial intelligence (AI) assisted public health monitoring using ECG data. This study introduces the dynamic Ant Colony Optimized Intelligent Gaussian Naïve Bayes (DACO-IGNB) method to identify the atrial fibrillation detection mechanism. The Atrial Fibrillation (AF) ECG data were collected and Baseline Wander was to facilitate pre-processing. The Principal Component Analysis (PCA) method was used to extract the feature. Compared to other traditional methods, it demonstrated superior performance across accuracy (90.9%), F1-Score (0.87), recall (0.89) and precision (0.88) metrics. These findings underscore its efficiency in DACO-IGNB assisting health detection and diagnostics, significantly improving the accuracy and reliability of public health monitoring systems.
The Effectiveness of Birthing Ball Gymnastics on Primigravida Maternity Gestational Age
Authors: Handayani Nanik , Anggasari Yasi , D.A Rata Ariesta , Mardiyanti Ika
Published: September 2024

One of the riskiest pregnancy conditions is post-term pregnancy, which is defined as a gestational age greater than the estimated day of delivery (HPL). This is because difficulties may arise for both the mother and the fetus, particularly when birthing is imminent. Birthing ball gymnastics is one complementary therapy that can help labor commence at full gestational age (term). The aim of the research was to evaluate the impact of birthing ball gymnastics on the gestational age of moms who were primigravidae. The study's quasi-experimental design, which included a treatment group and a control group, was randomized post-test only. The Midwife Independent Practice Place (TPMB) Sidoarjo served as the research site for five months, starting in February 2023 and ending in June 2023. Purposive sampling procedures were used in the study to collect samples from participants who satisfied the inclusion criteria. This resulted in the collection of 30 samples, 15 of which were treatment groups and 15 of which were control groups. Birthing ball exercises and birthing ball gymnastics are provided twice a week for 20 minutes to the treatment group starting at 32 weeks gestation. The control group, on the other hand, had no birthing ball gymnastics instruction and was assessed right away. Utilizing the chi square statistical test to analyze the data, the study's results (p value (0.000) < 0.05) indicated that birthing ball gymnastics was effective in reducing primigravida maternity mothers' gestational age at the Midwife Independent Practice Place (TPMB) in Sidoarjo. In order to prevent the delivery process with post-term gestational age, it is envisaged that the installation of birthing ball gymnastics can be carried out in healthcare facilities such as hospitals, clinics, or Midwife Independent Practice Places (TPMD) for primigravida pregnant women.
The Impact of Wearable Devices on Public Health Outcomes in The Treatment of Chronic Diseases Through Continuous Physiological Monitoring
Authors: Kumar Sanjay , Sharma Priti
Published: September 2024

Chronic illnesses contribute to elevated levels of disability and death. The active involvement of patients in the treatment of chronic diseases is a crucial element of healthcare systems that are focused on chronic diseases. Wearable devices provide real-time health information focused on the patient, enabling them to make informed decisions about self-management. Although wearables are believed to offer advantages in enhancing the self-management of chronic illnesses, their impact on healthcare outcomes still needs to be well comprehended. This study sought to investigate the effect of wearables on healthcare results in adults with chronic conditions by conducting a comprehensive analysis of existing evidence for physiological monitoring. A narrative systematic literature review was performed by searching six databases for randomized and observational research published from January 2018 to July 2023. These studies focused on utilizing a wearable intervention in a group of individuals with chronic diseases to evaluate its effect on a predetermined end measure. The outcomes were defined as any impact on patient or practitioner experience, cost-effectiveness, or healthcare outcomes resulting from the wearable intervention. The findings from the research included in the analysis were gathered according to 6 main themes, which were used as the foundation for a qualitative summary.  This research adhered to the requirements outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) declaration.
The Intersection of Health Policy and Technology: Applying Quantum Computing for Optimizing Healthcare System Simulations and Predictions
Authors: Jaleel Uruj , Lalmawipuii R
Published: September 2024

The convergence of fitness coverage and generation is a vital frontier in present-day healthcare, giving probabilities for innovation and optimization that have no means to visible earlier. The cause of this paper is to research the capacity for quantum computing to enhance computer simulations and forecasts of healthcare systems. To revolutionise customized medication processes, provided a progressive approach that everybody termed Efficient Personalised Medicine through Quantum Machine Learning Analysis (EPM-QMLA). This approach combines the abilities of quantum computing with device learning. The significance of this work is living inside the fact that it has the potential to substantially beautify the results of healthcare by way of making it possible to expand remedy plans which are greater correct and individualized. Nevertheless, there are limitations, inclusive of the fact that quantum computing programs in healthcare are still in their infant degrees, which necessitates the introduction of stable infrastructure and algorithmic advancements. The modern capacity of EPM-QMLA is verified through simulated analyses in a lot of healthcare situations. This demonstrates the capability of EPM-QMLA to optimise useful resource allocation, predict infection progression, and personalize remedies to particular patient profiles. Through this examine, the promise of quantum computing as a sturdy device for advancing healthcare coverage and exercise is highlighted. This will pave the manner for a healthcare device this is greater efficient and targeted on the patient.
The KIA Book's digitalization as a means to educate breastfeeding and postpartum moms on their children's health
Authors: Amalia Rizki , Indriani Diah , Laili Uliyatul , Windarti Yunik , Nisa Fauziyatun
Published: September 2024

The study aims to develop skilled and empowered human resources, particularly through interventions like strengthening governance and increased availability of services for the health of mothers and children. One major focus is on the postpartum period and breastfeeding, where efforts include providing support through digital tools for new mothers. The study seeks to identify a mentoring model for postpartum and breastfeeding mothers using a digital version of the KIA book. A non-equivalent control group was used in a pretest-posttest setting as part of a quasi-experimental design. The study involved 86 postpartum and breastfeeding mothers, with 43 receiving digital accompaniment through the KIA book as an intervention, while the other 43 served as a control group. Additionally, a qualitative approach was used, involving in-depth interviews with 12 postpartum and breastfeeding mothers, 6 primary healthcare midwives, and health service staff. The study found that digital accompaniment via the KIA book significantly influenced early risk detection, knowledge, and actions among the mothers. The digital assistance provided to postpartum and breastfeeding mothers positively impacted maternal and child health, enabling independent detection of health risks, increasing knowledge, and encouraging proactive behavior, along with family support. The research suggests the effectiveness of a mentoring model for these mothers through the digitization of the KIA book.
The Psychometric Device of the Scale of Cultural Intelligence in the Context of Social Media for Indonesian Adolescents
Authors: Ramli M. , Saputra Nur Mega Aris , Saputra Wahyu Nanda Eka , Fitriyah Fifi Khoirul
Published: September 2024

Through social media, any individual can interact with individuals from numerous cultural foundations. One element that becomes significant in maintaining amicable correspondence on social media is cultural intelligence. As a psychometric device, the objective of this examination was to make and approve the Scale of Cultural Intelligence in the Context of Social Media (SCIcSM). Very little exploration has been finished to foster a device for assessing cultural intelligence in the setting of social media. A trial of develop legitimacy was administered to 1,000 understudies in Malang, Indonesia. The 20-thing SCIcSM is delegated great, careful, and proper with the model according to the information investigation using the Rasch model. The SCIcSM was viewed as a pertinent and reliable instrument for assessing understudies' social media-based cultural intelligence. The results and commitments to the advancement of direction and counseling administrations are shrouded in this article.
The Relationship between the Degrees of Clinical Manifestations and IgG Antibodies When a Confirmed COVID-19 Recovered Patient at the M. Yunus Hospital and The Harapan and Doa Hospital of Bengkulu City in 2020
Authors: Nugraheni Enny , Rizqoh Debie , Pursel Nurh Syadawani , Kurniati Annelin , Widyawati  , Suryani Utari Hartati
Published: September 2024

Background: COVID-19, or coronavirus disease, was declared a pandemic by the WHO in 2020. Coronavirus 2 (SARS-CoV-2) is the infectious agent responsible for this disease. The antibodies that the human body makes in reaction to a viral infection include IgM and IgG. The body will display different clinical indications according on how effectively it generates antibodies. Investigate the relationship between COVID-19 patients' clinical manifestations and their levels of IgG Anti-SARS-Cov-2 antibodies in Bengkulu City in 2020. The severity of clinical signs and symptoms during the sickness influences the level of antibody response generated. Methods: Descriptive research methodologies are utilized in this study, which is a retrospective. The sample strategy employed was consecutive sampling with non-probability sampling. The level of clinical manifestation of COVID-19 patients is established by the Indonesian Ministry of Health using its 2021 categorization. When looking for a connection between the two variables, the Pearson correlation test was used. Results: The research methods used in this study are descriptive and are structured like a retrospective. A non-probability sampling strategy based on the progressive sampling method was used to collect the samples. The Indonesian Ministry of Health will categorize COVID-19 patients in 2021 based on their clinical manifestation level. To look at the link between the two variables, we used the Pearson correlation test. Conclusion: Levels of IgG anti-SARS-Cov-2 antibodies and clinical symptoms in COVID-19 patients who have recovered in Bengkulu City are linked.
An Investigation on Inception V3 Network Models for Disease Severity Identification
Authors: Nayak Ashu , Raghatate Kapesh Subhash
Published: September 2024

Due to the high incubation period of COVID-19 and the use of restricted data, it is difficult to anticipate the number of casualties and the rate at which the virus would spread in the COVID-19 scenario. Monitoring those who are impacted and those who have interacted with them is perhaps the most difficult procedure. Since COVID-19 does require 14 days to incubate, it is doubtful that patients have been detected earlier. Patients have the ability to influence everyone they interact with while they are in the designated incubation period. Typically, the various machine learning algorithms are given the COVID-19 deep properties in order to classify the illness severity levels. Consequently, this may help in the early diagnosis of COVID-19. The created a deep feature extraction and classification model that successfully classified the COVID-19 severity levels using a variety of deep and machine learning algorithms. It has the ability to classify COVID-19 severity levels for early, accurate diagnosis. Ultimately, the results showed that the Inception V3 classifier outperformed all other models in both feature extraction and classificational, successfully extracting deep features from CT images and classifying the severity levels of COVID-19 infection. The testing results also showed that the classification of deep characteristics achieves promising use in COVID-19 severity prediction and diagnosis.
Generalized Denoising Coder with Multi-modal Approach for Handling Incomplete Information in Public Health Big Data Analytics
Authors: Yadav Kamlesh Kumar , Kirit Dhablia Dharmesh
Published: September 2024

A data-driven health model incorporates many missing and incomplete values, and the effectiveness of a health model that relies on characteristics not captured by the consumer is low. A Deep Learning (DL) health paradigm aims to improve the precision of learning weights. A DL-based health model is trained by optimizing the weights to maximize accuracy. If the learning process is excluded, the accuracy of applying a DL-based health model to the user's circumstance may be reduced, and it becomes the responsibility of people to manage Public Health Big Data Records (PHBDR). To handle incomplete information, this study suggested a generalized denoising coder with a multi-modal approach in public health big data records (GDC-MM-PHBDR). The suggested technique uses a Generalized Denoising Coder (GDC), which estimates the output values following the relevant input values using Neural Networks (NN). The National Health and Nutrition Examination Study (NHNES) provided data for this investigation. This approach allows for the estimation of incomplete information in the PHBDR. Multimodality, which the PHBDRs provide, enables compiling data for a single item from many sources. The layout of the GDC is multi-modal. The GDC-MM-PHBDR approach continuously demonstrates excellent performance, attaining maximum accuracy across all noise levels. It begins at 98.31% accuracy with a noise factor of 0.05 and significantly decreases to 94.5% with a noise factor of 0.25.
Genetic Algorithm-based Optimization Model for Emergency Shelters Location Allocation in Public Health Emergency Situations
Authors: Vij Dr. Priya , Prashant Patil Manisha
Published: September 2024

Deploying emergency resources after a disaster is crucial for reducing the event’s impact. A multi-objective (MO) resource allocation (RA) method is developed to achieve efficient distribution of relief items and the best choice of transportation routes, taking into account the variable and persistent nature of rescue operations.  The MO Cellular Genetical Approach (MOC-GA) addresses the concept by incorporating auxiliary populations and neighborhood architecture into the cellular automaton.  The comparative experiment conclusively demonstrates that MOC-GA performs favorably compared to Particle Swarm Optimisation (PSO), Ant Colony Optimisation (ACO), and Dragon Fly Optimisation (DFO) in several metrics such as the Pareto Front (PF), hypervolume, mean objective function value, and PF ratios.  The findings demonstrate that MOC-GA effectively addresses the MO dynamical emergency RA approach, offering decision-makers a more comprehensive range of superior and diversified candidate rescue strategies than alternative methods.  This research analyzes the existing rescue methods and proposes a theoretical rescue strategy to aid decision-makers in making scientifically informed decisions.
Multi-Step Forecasting Method For Influenza Pandemic Using Long Short-Term Memory Model To Improve Public Health Conditions
Authors: Deshmukh Dr. Rajesh Keshavrao , Shrivastav Mohit
Published: September 2024

The flu spread is a major global public health problem that gets attention around the world because it can cause serious illness, cost a lot of money, and kill a lot of people every year. To avoid influenza-like disease (ILD) and run healthcare systems well, predicting when an influenza outbreak will happen is essential. Researchers have used Machine Learning (ML) techniques but have yet to find the best ways to see complicated, non-linear trends in sequential data about flu outbreaks.  A Long Short-Term Memory (LSTM) and a Genetic Algorithm (GA) are used together in this study to show a new way to predict multi-step influenza breakouts. Every week, the study gathers information on ILD, which covers flu and other illnesses with signs similar to the flu. When measuring performance during busy times, the suggested model does much better than other advanced Machine Learning (ML) methods and Fully Connected Neural Networks (F-CNN).
Predicting Severity Of Osteoarthritis Using Recurrent Convolutional Neural Networks (Rcnn) And Medical Imaging Data
Authors: Ashalatha B. , T Thangarasan , Geetha M. , T Saranya , Mittal Manisha , K VenkataRamana
Published: September 2024

Cartilage degeneration causing functional incapacity and discomfort defines common joint condition Osteoarthritis (OA). Correct classification of OA severity from knee X-ray images determines diagnosis and treatment course. Conventional methods of OA severity classification rely on hand inspection or basic machine learning techniques, which could not effectively capture the complex trends in imaging data. Using a dataset of 9,786 knee X-ray images, this work uses Recurrent Convolutional Neural Networks (RCNN) to project OA severity. Combining recurrent layers in the RCNN architecture helps to capture temporal correlations in spatial information, hence enhancing classification performance. Defining the dataset are five KL grades: 0 (healthy) to 4 (severe). This beats by around 5%, 7%, and 3%, respectively standard CNN, Deep Neural Network (DNN), and Deep Convolutional Neural Network (DCNN), with a precision of 87.2%, recall of 88.9%, accuracy of 89.5%, and F1-score of 88.0%.
Public Health Program Planning And Evaluation: A Management Approach
Authors: Diwakar Dr.
Published: September 2024

Since the health care sector is essentially a human services organisation, any hospital setup must have administration with a high degree of personal touch. Maximum efficiency in hospitals can only be attained when all hospital employees collaborate diligently and participate in management. All health professionals must be appropriately motivated in order to harness their intellectual and practical resources for the organization's advantage and to ensure that everyone benefits from the organization's work. Although money is the greatest motivator, it is not the only one. Serious elements like job satisfaction, accomplishment orientation, recognition, acceptance, etc. are needed to motivate people who have already reached their basic psychological needs. These are only possible if management fosters a friendly environment of respect for one another and acknowledges each employee for the high calibre of their work.
Spatial Variation of The Impact of Salmonella Bacteria on The Soil of The Plateau (Najaf – Karbala) Using Modern Technologies
Authors: Al-Salami Maryam A. Rahim , Menkhi Saadia Akol
Published: September 2024

The research concluded through field studies and laboratory work that there  is spatial and temporal variation of soil contamination with Salmonella bacteria in the soil of the plateau (Najaf – Karbala) within the center of Najaf district and Al-Haydariyah sub-district by taking (36)sites and(144)models and by four seasons ( summer, autumn, winter and spring) and then  diagnosed and analyzed  microbiologically and  know their types and numbers and the effects caused by these pathological species on humans, animals and plants, as two types of Salmonella  bacteria were discovered in the soil of the study area and these two types were (Salmonella enteritidis,  Salmonella typhi) was also known to have spatial variation for all the previous two species, as it was found that there are sites that record the highest presence of Salmonella bacteria in the soil of the study area, on the contrary, there are sites that did not record any presence of this bacteria , and the temporal variation between the seasons was also known, as autumn and spring recorded the highest presence and then winter ranked third and finally recorded the summer in terms of the total number of Salmonella bacteria in the soil of the study area, and finally the research concluded that the study area poses an environmental threat to the population in particular and the population of Najaf Governorate In general, what these types of bacteria leave on the environment .
An Overview of the Public Health Risk Management Model: A Food Safety-Related Study
Authors: Maurya Dr. Shyam , Sandilya Ranjit
Published: September 2024

Food Safety (FS) in the Food Market (FM) is an essential aspect of Public Health (PH) since it impacts people of all ages, races, genders, and income levels. Regional and global FM continues to influence the FS and PH. The globalization of Health Risk (HR) is increasing because food supply networks span several nations the research aimed to identify prevalent PH issues associated with FS in the FM. A theoretical analysis employs the population, people's interests, and environment. The study evaluates the FS using the Mixed Methods Appraisal Tool (MMAT). After analyzing 200 full-text papers, seven prevalent PH issues associated with FS  in the FM were identified. This study evaluated the literature to identify the FM's predominant PH hazards related to FS. The findings suggest that local and worldwide FM influences PH. Countries must guarantee the security and reliability of the exported food products and meet their national standards.
AI-enabled Landslide Recognition for Effective Public Health Risk Management
Authors: Sharma Nikita , Ikhar Sharyu Bhushan
Published: September 2024

The frequency of hazardous landslides has increased worldwide as a result of increased heavy rainfall events and increased human construction activities. The assessment of landslide vulnerability is an essential and effective method for preventing landslides. To resolve these issues, this paper develops a novel Rat Swarm integrated Random Forest (RSRF) method to manage the risk evolved through the landslide. The LISS-III satellite dataset of remote sensing (RS) images was gathered for the study. The preprocessing is performed by employing the z-score normalization to standardize the data images. The rat swarm (RS) optimization enhances the feature selection in the landslide by efficiently forecasting relevant assessment and the random forest (RF) employed to improve the classification accuracy in the landslide recognition process. Various existing methods are utilized for the comparison performance with the proposed RSRF techniques. The outcome shows that the proposed RSRF method improved more significantly than all other existing techniques in terms of area under the curve (AUC (93.3%)), F1-score (85.5%), log-loss (40.4%), and recall (97%).
Analysis of Food Nutrients Impact for Improving Public Health Using Multi Features Deep Recurrent Neural Network for Health Care Management
Authors: Deshmukh Dr. Rajesh Keshavrao , Fatma Eram
Published: September 2024

The food nutrients in daily life is incredible source to make good food for good life cycle. In recent analysis, the new food styles and ingredients affects the public health especially children to cause various disease and impacts which leads dangerous to healthier life. By analyzing food nutrients and presence healthy ingredients in daily life is important.  World health organization pays number of research to healthcare management and suggestions to improve the public health by recommending different protocols.  The risk is identifying presence of nutrients scaling is tedious due to prevailing techniques machine learning techniques does not provide good results to make better good food recommendation. The problem is more data labels and features are need to analyze which increase the false rate to reduce accuracy. To address the issue, to propose a deep LSTM gated recurrent neural network (LSTM-GRNN) based on Support vector feature selection (SVFS) to identify the presence of food nutrients to recommend the good food to improve the public health. Initially the food product and nutrients scaling logs are collected and to make preprocessing based on C-score normalization form feature labels. Then nutrients scaling impact rate is analyzed for each food ingredients presence by extracting the margins from features list. The important feature is selected by using SVSR to reduce the feature margins based on support vector. Then the selected features are trained into LSTM unit with recurrent neural network to identify the presence of nutrients rate in each food and to categorize the class by presence. The higher scaling rate of class is considered as good food to recommend for healthy facts. The proposed system attains higher detection accuracy in precision, recall rate and f1 measure, also the lower false negative rate increase the performance of accuracy compared to the existing system.
Analysis of Public Health Care Safeguard Measures in India
Authors: Upadhyay Itishri , Patel Namrata
Published: September 2024

Public health is a branch of science that focuses on preventing disease and injuries in large populations by doing research, educating the public, and creating policies that promote health in all communities. Each person may define public health in a different way. In order to address global health issues, address community-wide health issues, and influence global health policies at both local and global levels, public health is a field of study that encompasses a variety of academic fields, including basic science, commerce, the arts, education, technology, engineering, computer science, pharmacy, and bioscience. Excluding the effects of non-intellectual property barriers like distribution, infrastructure, quality, and medical facilities, the goal of this study is to examine the public health barriers to intellectual property that affect medical access. This study aims to investigate how patent law protects drug accessibility while taking drug prices into consideration.
Boosting Data Communication: Studying Health Information Security Using Digital and IoT Technologies for Public Health
Authors: Haval Dr. Abhijeet Madhukar , Afzal Md
Published: September 2024

The integration of Internet of Things (IoT) technologies in healthcare has revolutionized data management, yet challenges in data security and efficient communication persist. Existing solutions often struggle with balancing security and performance, particularly under varying conditions. To address these limitations, we propose a novel approach using Homomorphic Fuzzy Identity-Based Encryption (HFIE) to enhance both data security and communication efficiency in healthcare IoT systems. Our implementation includes a robust architecture that securely collects and transmits health data from sensor nodes to cloud storage, employing a three-tier user access system. HFIE combines homomorphic encryption for secure data processing and FIBE for flexible, error-tolerant access control. This combination addresses the limitations of traditional methods by providing enhanced privacy and performance. Evaluation metrics, including decryption time, energy consumption, and execution time, demonstrate the effectiveness of our approach. HFIE's ability to ensure secure and efficient data management in public health makes it a significant advancement in healthcare IoT technology.
Effective influence of Community Pharmacists in Public Health Using Blockchain Technology to Secure Patient Data and Enhance Healthcare Delivery
Authors: Soy Aakansha , Balkrishna Sutar Manisha
Published: September 2024

The advent of healthcare delivery drones, a novel unmanned aerial vehicle (UAV), has opened the door to a more comprehensive approach to developing smart health services. Internet of Things (IoT)-based healthcare delivery via UAVs has not been fully realized because of a lack of augmentation-aware concepts. Despite community pharmacists' strong clinical expertise and ease of patient access, they cannot provide comprehensive clinical services due to a lack of access to patient data. Blockchain could be a solution if it's concerned about the reliability and security of drone deliveries over untrusted open channels. Hence, this study proposes the Internet of Things and Blockchain Technology-assisted Drone Healthcare Delivery System (IoT-BT-DHDS) for enhancing access to healthcare information in the community pharmacy environment. Ensuring the security of drone operations is crucial to protect users from any breaches that might result in physical and financial loss. This research suggests an oT-BT-DHDS that authenticates and registers the involved entities, such as objects (healthcare supplies), warehouses (healthcare centres), and drones, to address these security concerns and make the delivery progression visible. This research achieves its goals by analyzing several parameters that impact the time and number of transactions required for authentication on the Ethereum platform, which is used to build smart contracts and blockchain. The suggested strategy is shown to be effective by results acquired from a simulated environment compared to alternative models based on an access control ratio of 98.9%, data management ratio of 97.4% and resource utilization ratio of 96.4% to other existing models.
Effects of Web-Based Survey Tool and Public Health Services for Low-Cost Detection of Vulnerable Medication Errors Using Telepharmacy
Authors: Sahu Prerana , Patel Ritesh
Published: September 2024

Telepharmacy, in which prescription requests are reviewed and approved online by a pharmacist in a different geography, is an effective way to lower the number of mistakes made when giving medicines. However, not enough studies have been done on the benefits of error reduction and the possible cost savings that come with these teleservices. This paper discusses a study examining what happened when a web-based survey tool and telepharmacy solutions were used to find Vulnerable Medication (VM) errors and low-cost Public Health (PH) services. It is very important to get each patient's drug records when they come into the Emergency Room (ER) so that any mistakes in their current medication record can be found. The trained pharmacists were interviewed after using a safe online tool to keep track of drug information. Details about each patient were gathered, along with the amount and type of VM and any PH errors found during the data-gathering procedure. From May 2022 to November 2022, 190 patient files were successfully filled up utilizing the survey instrument throughout the experimental period. Among the 1090 drugs documented, 41.38% were classified as drugs of high risk. 42.33% of possible prescription mistakes were categorized as VM errors. This online survey tool has enhanced the caliber and effectiveness of identifying possible errors during the gathering of medication records by pharmacists. This data may be readily accessed and contribute to debates about the reconciliation of drugs at the management level. It can also positively affect patient care outcomes by facilitating the development of virtual procedures that may reduce drug-related incidents.
Establish an Effective Framework for Accurate Prediction of Employee Mental Health for Public Health
Authors: Dewangan Omprakash , Prakash Vasani Vaibhav
Published: September 2024

Employee mental health is a primary feature of workplace efficiency and overall public health. With rising mental health problems impaired by variables contains stress, burnout, and isolation, there is an urgent need for effective prediction models that can identify at-risk employees. This study proposes a robust predictive model utilizing dynamic lion optimized VGG16 (DLO-VGG16), for employee mental health prediction represents a significant advancement in public healthcare. The use of these sophisticated prediction models can improve medical results for groups at risk and is essential in the battle against heart disease. Employee’s mental health data collected from Kaggle website. The data preprocessing phase employed Z-score normalization to standardize input values, thereby addressing data inconsistencies and errors. The Sequential Forward Floating Search Algorithm (SFFS) was utilized for extracting the employee’s mental health. This enhanced heuristic search method begins with an empty feature set, incrementally adding features until the optimal set is identified. The proposed DLO-VGG16 framework to balance accuracy, ROC curve, and f1-score ensuring reliable detection and forecasting of employee mental health issues, thereby contributing to the broader public health landscape.
Exploring the Impact of Organizational Initiatives on Work Environment Safety and Health for Public Health
Authors: Nayak Ashu , Raghatate Kapesh Subhash
Published: September 2024

Workplace safety and health efforts in public health are organizational endeavours that include practices, policies, and interventions intended to reduce hazards, enhance well-being, and guarantee adherence to health and safety laws.We provide the findings from a phone conversation and in-person interviews with 286 manufacturing companies.We carried out a cross-sectional study of the attitudes, behaviours, and policies related to health and safety at work. All company types were subjected to analyses using data from the Lost Time Injury frequency rate (LTIFR). Paternalistic efforts, the documentation of occupational safety and public health (OSPH) measures, more worker participation in making decisions, and enhancing managerial perspectives about the importance of OSPH were all linked to very low LTIFR. The results show the advantages of a proactive strategy for developing a culture of wellness and security in public health contexts and emphasize the crucial role that organizational commitment plays in establishing safer and healthier workplaces.In conclusion, safer workplaces are characterized by worker and management engagement in OSPH activities. Additionally, there is a correlation between reduced injury rates and management's overall concern for OSPH, as demonstrated by both attitudes and practical actions.
Global Health Management: Addressing Health Disparities And Inequalities
Authors: Diwakar Dr. , Pandey Shinki Katyayani
Published: September 2024

It is the state's responsibility to provide access to public health care management for the poor and vulnerable, as the cost of curative care is disproportionately higher among lower income groups, the majority of whom are Dalits. India's population health has significantly improved during the past 70 years. The disparity across social groupings is still rather large, though. Low health status is clearly associated with poverty, being a woman, living in a rural area, belonging to a tribal ethnic group, being a scheduled caste (SC), and belonging to particular minority groups. In order to guarantee health fairness, it is necessary to review the environment and policy implementation regime. The purpose of this article is to provide context for relevant healthcare concerns and challenges by examining a few key health, poverty, illness-related costs, and coping techniques across social groupings indicators. The conceptual foundation for the health effects of public health care management's globalisation is described in this study. An examination of the main social determinants influencing population health is known as the social determinant of health.
Healthcare Access and Financing: A Legal and Public Health Perspective
Authors: Kaur Harleen , Das Abhinav
Published: September 2024

The welfare provisions of the current Constitution centre on healthcare. The Indian Constitution's Article 42 declares that the state has an obligation to take all reasonable steps to promote public health. Additionally, Article 47 of the Indian Constitution, another article containing Directive Principles of State Policy, mandates that the state carry out its primary duties to improve public health by raising the standard of living, improving nutrition, and improving public health. In particular, the state must work to outlaw the use of drugs and alcohol, unless prescribed, as these substances are harmful to health.  It follows that welfare regulations clearly place an obligation on the state to increase its assistance in promoting the health of those individuals who are unable to pay for the expensive medical care needed to treat their illnesses. In light of the urgency and importance of the issue, the World Health Organisation designated Universal Health Coverage (UHC) as its top goal. Ensuring that everyone has access to necessary health services of adequate quality and is shielded from the financial burden of medical expenses is the goal of universal health coverage. In underdeveloped nations, financial security against medical expenses is just as important as access to healthcare.