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111 articles for “randomization”
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The Effectiveness of Honey Application for Oral Mucositis in Cancer Patients: A Review of Randomized Controlled Trials
Abstract: Background: Oral mucositis (OM) is a prevalent and distressing adverse outcome of cancer therapies, including chemotherapy and radiation therapy. It is an inflammatory ailment that impacts the mucous membrane inside the mouth, tongue, gums, and throat. OM can cause pain, discomfort, difficulty swallowing, and in severe cases, it can lead to infection and delayed cancer treatment. Although there are various treatments available, such as painkillers, anti-inflammatory drugs, and topical anesthetics, …
Published in International Journal of Oncological Nursing and Practices · Vol. 1, Issue 1, 2023 · pp. 32–39 Read article
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Impact of Pre-Operative Nebulized Lidocaine–Dexmedetomidine Combination Versus Monotherapy on Incidence, Severity, and Patient Comfort Scores of Post-Operative Sore Throat: A Randomized Controlled Clinical Trial
Abstract: Post-operative sore throat (POST) is a common issue after endotracheal intubation and can reduce patient comfort and hinder recovery. This randomized, double-blind clinical study investigated how effective pre-operative nebulized lidocaine, dexmedetomidine, and their combination are in minimizing POST. Eligible adult patients classified as ASA I–II and scheduled for elective surgery under general anesthesia were randomly assigned to three groups. Nebulization was administered 15–20 minutes before induction using standardized dosing and …
Published in International Journal of Biomedical Innovations and Engineering · Vol. 4, Issue 1, 2026 · pp. 29–35 Read article
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Examining the Impact of a Nurse Navigator Program on Anxiety, Psychological Well-being, and Quality of Life in Breast Cancer Patients at a Tertiary Care Hospital in Haryana: A Randomized Controlled Trial
Abstract: Background of study: Breast cancer stands as the leading cancer type affecting women and remains a significant contributor to female mortality. It is a complex ailment influenced by multiple factors. Globally, in 2020, approximately 2.3 million women received diagnoses of breast cancer, resulting in 685,000 fatalities. This condition can profoundly impact various aspects of a person's existence, encompassing physical, psychological, emotional, social, and familial spheres. A notable observation is the …
Published in International Journal of Oncological Nursing and Practices · Vol. 2, Issue 1, 2024 · pp. 31–39 Read article
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DDoS Detection Using Cascade Correlation for Improving Network Resources in Cloud Environment
Abstract: Intrusion detection is critical for protecting network security from emerging cyber threats. This study describes a unique intrusion detection system (IDS) based on the Random Forest algorithm. Random Forests are used as an effective classifier to identify patterns linked with malevolent behaviour. This technique uses Random Forests to improve the accuracy and efficiency of intrusion detection systems. The suggested methodology's value is shown by its performance on the benchmark KDD …
Published in International Journal of Wireless Security and Networks · Vol. 3, Issue 2, 2025 · pp. 17–22 Read article
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Mathematical Modeling of Epidemics Using Stochastic Differential Equations: A Review
Abstract: The accurate modeling of infectious disease dynamics is crucial for predicting outbreaks and informing public health interventions. While deterministic models such as the SIR (Susceptible-Infected-Recovered) framework have traditionally been used to understand disease transmission, they often fail to account for the randomness inherent in real-world scenarios. Disease spread is influenced by numerous uncertain factors, including individual behavioral changes, environmental fluctuations, and imperfect data reporting. These uncertainties can significantly impact model …
Published in Recent Trends in Mathematics · Vol. 1, Issue 2, 2024 · pp. 1–6 Read article
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Optimizing Heart Disease Prediction: Comparative Analysis of Machine Learning Algorithm for Early Detection
Abstract: The expanding realm of data analysis holds considerable importance in healthcare, particularly in the medical sector where forecasting heart disease is considered a complex endeavor. Early prediction of serious health conditions can be the determining factor between survival and fatality, with heart disease being one such critical health issue. Over the past decade, the main reason for death has been heart disease. Heart disorders come in many different forms, and …
Published in International Journal of Computer Science Languages · Vol. 2, Issue 1, 2024 · pp. 1–10 Read article
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Effectiveness of Video-assisted Teaching on Anxiety Related to Labor Process and Preparation for Labor Among the Primigravida Mothers Attending Antenatal OPD at GMCH-32 Chandigarh
Abstract: Aims and background: A randomized control trial to assess the effectiveness of video-assisted teaching on anxiety related to labor process and preparation for labor among primigravida mothers attending antenatal OPD of GMCH-32, Chandigarh, was carried out in full compliance with ethical standards laid down by the Research and Ethical Committee of GMCH-32, Chandigarh. Method: A systematic random sampling technique was used to select 80 subjects, 40 each in control and …
Published in International Journal of Midwifery Nursing And Practices · Vol. 2, Issue 1, 2024 · pp. 9–13 Read article
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Comparative Analysis of Heart Disease Prediction System
Abstract: In the present world, where heart illnesses are on the rise, it is crucial to forecast these diseases. Performing the task on heart disease is a bit difficult and it must be finished precisely and successfully. Heart disease identification relies heavily on Machine Learning (ML) and data mining approaches. The primary focus of the review paper is that patients are easily prone to cardiac diseases depending on medical traits. Using …
Published in International Journal of Advance in Molecular Engineering · Vol. 1, Issue 1, 2023 · pp. 1–6 Read article
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Assessing the Robustness of Machine Learning Models for Wireless Intrusion Detection Under Adversarial Traffic Perturbations
Abstract: As the Internet of Things (IoT) devices and wireless communication networks continue to grow rapidly, protecting systems from cyber threats has become increasingly important. Machine learning–based intrusion detection systems (IDS) have shown strong potential in detecting abnormal and malicious network activities, yet their effectiveness and resilience when facing adversarial attacks are still not sufficiently explored. This research evaluates Machine Learning (ML) models–XGBoost, random forest, and multi-layer perceptron (MLP)—in detecting attacks …
Published in International Journal of Wireless Security and Networks · Vol. 4, Issue 1, 2026 · pp. 29–34 Read article
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Predictive Analytics and Adaptive Learning: A Machine Learning Framework for Reducing Learning Gaps
Abstract: Most contemporary digital learning environments encounter persistent challenges when it comes to accurately identifying students who are at-risk of academic underperformance. These challenges often arise due to limited visibility in learners’ engagement levels and gaps in conceptual understanding, particularly during the early stages of a course. To address this issue, the present study proposes an early prediction framework that leverages comprehensive student-related data through the application of machine learning techniques. …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 4, Issue 1, 2026 · pp. 16–21 Read article
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Diabetes Risk Prediction from Survey Data Using Machine Learning Algorithms
Abstract: Diabetes mellitus represents one of the most significant global health challenges, affecting millions worldwide and leading to severe complications if left undiagnosed or poorly managed. Early detection and risk assessment are crucial for preventing the progression of this chronic condition. This research presents a comprehensive machine learning approach for predicting diabetes risk using survey-based health parameters. The study implements and compares four prominent classification algorithms: Logistic Regression, K-Nearest Neighbors (KNN), …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 2, 2026 Read article
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ML Analysis of Factors Affecting Vaccination in Rural Children: A Machine Learning Approach
Abstract: Vaccination remains one of the most effective public health interventions for preventing childhood diseases, yet rural regions in India continue to experience uneven immunization coverage due to multiple socioeconomic and geographic barriers. This research applies machine learning techniques to identify and analyze the major determinants influencing childhood vaccination uptake in rural communities. The study utilizes survey-based demographic, socioeconomic, and healthcare-related parameters to build predictive models that classify children as vaccinated …
Published in International Journal of Vaccines · Vol. 3, Issue 2, 2026 Read article
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Predicting and Prohibiting the Risk of Heart Failure Using Machine Learning
Abstract: It is challenging to estimate the likelihood of complex chronic disease while treating conditions like heart failure. The application of machine learning, an area of artificial intelligence, in cardiovascular care is growing quickly. In essence, it defines how computers classify and understand data, or choose a task with or without human intervention. The theoretical underpinnings of machine learning are models that accept input data (such as images or text) and …
Published in International Journal of Computer Science Languages · Vol. 1, Issue 1, 2023 · pp. 15–20 Read article
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Machine Learning Based Early Cataract Detection: A Predictive Modeling Approach
Abstract: Cataracts, characterized by dense cloudy areas in the eye’s lens, afflict more than 50% of elderly individuals, leading to impaired vision and potential blindness. Detecting cataracts at an early stage is crucial to facilitate simpler treatments, as neglecting the condition may necessitate complex eye surgery. To address this issue, we are creating a predictive system that identifies cataract disease by analyzing user-provided eye features. To achieve this, we leverage OpenCV, …
Published in International Journal of Computer Science Languages · Vol. 1, Issue 2, 2023 · pp. 1–8 Read article
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Effectiveness of Structured Teaching on Immunization Knowledge and Attitude Among Mothers of Children Under-5 years of Age at Bal Mahila Chikitsalaya, Lucknow
Abstract: This section offers a thorough overview of the study's background, explaining the significance of vaccination, the variables affecting vaccination coverage, the role mothers play in the decision-making process regarding vaccinations, and the necessity of focused interventions to improve immunization uptake among children under five. As a fundamental component of preventive healthcare, immunization shields both individuals and communities from a variety of infectious diseases. In order to comprehensively assess changes in …
Published in International Journal of Children · Vol. 1, Issue 1, 2024 · pp. 5–22 Read article
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A Study and Prediction of Psychological Disorders Through Machine Learning
Abstract: Physical illness is very much visible but not psychological illness therefore, it requires more attention and care. Psychological disorders also known as psychiatric disorders refer to a wide range of conditions affecting a person’s thought process, leading to significant changes in the behavior of an individual. The most prevalent psychological disorders include depression, anxiety disorders, and post-traumatic stress disorder (PTSD). Symptoms of psychological disorders vary greatly but include common symptoms …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 2, Issue 2, 2024 · pp. 32–38 Read article
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Primality Testing: A Comprehensive Analysis of Methods and Time Complexity
Abstract: This paper examines various primality testing algorithms and analyzes their time complexity. The algorithms we examine include the trial division, which is straightforward but becomes inefficient with large numbers; Fermat’s little theorem which is a probabilistic method included in Monte Carlo type of randomized algorithm; the Solovay–Strassen, based on properties from number theory, particularly those related to Euler’s criterion and Jacobi symbols; and the Miller–Rabin Probabilistic Test, which balances efficiency …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 2, Issue 2, 2024 · pp. 25–31 Read article
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The Study of Preference on Skill-Based Training Among Household Women in Gudalur Town
Abstract: This study explores the need for skill-based training among women in households in Gudalur town, identifying preferred training types, choice-influencing factors, and participation barriers. Skill development is a critical component of women's empowerment, promoting financial autonomy and social mobility. Surveys were conducted with 50 women, who were chosen using a stratified random sampling method. The survey comprised demographics, most preferred training courses, perceived benefits, and participation barriers. Descriptive statistics was …
Published in International Journal of Community Health Nursing And Practices · Vol. 3, Issue 2, 2025 Read article
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Enhancing Smart Grid Resilience Through AI-Based Fault Classification
Abstract: Traditional power grids can be developed into smart grids, and they are comprised of the latest information and communication technologies (ICTs), which are based on establishing the relationship between the conventional electricity systems along with the usage of smart meters and distributed generation. This dynamic improves energy efficiency and the integration of renewables. Well, the dynamic and reversible power injection from Distributed Energy Resources (DERs) creates substantial operational problems. These …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 4, Issue 1, 2026 · pp. 10–15 Read article
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Methods Based on Machine Learning for Large-scale Classification of Crop Leaf Diseases
Abstract: Worldwide productivity of crops is seriously threatened by crop leaf diseases, which can result in large crop losses and negative economic effects. Effective disease management and crop protection depend on the early and precise detection and classification of these illnesses. Machine learning approaches have gained popularity recently due to their ability to automate procedures related to illness diagnosis and classification. An overview of the several machine learning–based methods used for …
Published in International Journal of Computer Science Languages · Vol. 2, Issue 1, 2024 · pp. 11–23 Read article