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66 articles for “Community Detection”
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Awareness and Knowledge of Postpartum Psychosis Among Antenatal Women: A Call for Educational Intervention
Abstract: Postpartum psychosis (PP) is a rare yet critical mental health disorder that poses serious risks to the well-being of new mothers. This condition manifests through a range of acute symptoms, including manic episodes, depressive states, confusion, hallucinations, and delusions. Symptoms typically present abruptly within the first two weeks following childbirth, making it a medical emergency that necessitates urgent care. Left untreated, PP can lead to severe consequences, including self-harm or …
Published in Research and Reviews : A Journal of Life Sciences · Vol. 14, Issue 3, 2024 · pp. 125–134 Read article
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AI in Mental Health: New Developments and Prospects
Abstract: Artificial intelligence (AI) has revolutionised numerous industries, including the mental health care sector.In order to clarify present trends, ethical issues, and future prospects in this ever-evolving subject, this paper examines the integration of AI into mental healthcare. Recent research, AI application examples, and ethical issues influencing the area were all included in this study. Research and development trends and regulatory frameworks were also examined.With applications including the early detection of …
Published in Research and Reviews: A Journal of Health Professions · Vol. 15, Issue 3, 2025 Read article
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Towards smart cancer care - AI enhanced monitoring and intervention
Abstract: According to estimates from the World Health Organization (WHO) for 2022, cancer is one of the leading causes of mortality, accounting for roughly 16% of all deaths globally. The goal of the cancer community is to improve the lives of those who are impacted by cancer and to cut the cancer death rate in half during the next several years. If cancer is identified early and treated, its impact on …
Published in Journal of Microwave Engineering and Technologies · Vol. 12, Issue 1, 2025 · pp. 38–44 Read article
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Diabetic Risk Prediction Using Machine Learning
Abstract: The global prevalence of Type 2 diabetes has risen dramatically in recent years, posing a serious public health risk. Long-term risk prediction is an important technique for evaluating who is most likely to develop type 2 diabetes. Early detection and response can lead to better management and prevention of diabetes complications. Developing a user-friendly Windows program for long-term Type 2 diabetes risk prediction could revolutionize preventive healthcare due to technological …
Published in International Journal of Satellite Remote Sensing · Vol. 2, Issue 1, 2024 · pp. 11–17 Read article
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Gradient Boosted Regression Tree Approach to Predicting Toxic Interactions on X and YouTube
Abstract: In the digital age, social media platforms play a vital role in facilitating user engagement, encompassing both positive interactions and avenues for negative, often harmful behaviors. Recognizing and addressing toxic exchanges is paramount to nurturing healthy online communities and preserving users’ well-being. This study introduces a novel method for identifying toxic interactions by utilizing Gradient Boosting Regression Trees (GBRT) algorithm, a machine learning approach renowned for its exceptional accuracy and …
Published in Trends in Opto-electro & Optical Communication · Vol. 15, Issue 3, 2025 · pp. 7–14 Read article
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Role of a Nurse in Addressing Mobile Addictions
Abstract: Nurses are essential in recognizing, evaluating, and addressing mobile addiction by using their skills in thorough screening and assessing patients' histories. Through keen observation and structured assessments, nurses can detect signs of mobile addiction, such as social withdrawal, disrupted sleep patterns, and neglect of personal responsibilities. It is crucial to establish this initial identification promptly to begin timely interventions and provide necessary support to those affected. Patient education stands at …
Published in Journal of Nursing Science & Practice · Vol. 14, Issue 3, 2024 · pp. 25–29 Read article