Volume 13 | Issue 12 |

 Abstract

A study of the symptoms, diagnosis and treatment of suicidal tendencies among adolescents in tribal areas& 2024

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A study of the symptoms, diagnosis and treatment of suicidal tendencies among adolescents in tribal areas& 2024

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Camouflage Object Detection is a challenging problem in computer vision due to the high visual similarity between target objects and their surrounding environments. Conventional object detection methods often exhibit degraded performance in camouflage scenarios because of weak object boundaries, low contrast, and complex background textures. This paper presents a segmentation-based camouflaged object detection framework using the SiNet-V2 deep neural network architecture. The proposed approach performs pixel-level segmentation to accurately localize camouflaged regions in input images. The model was trained and evaluated on publicly available benchmark datasets.

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Camouflaged Object Detection, SiNet-V2, Deep Learning, Image Segmentation, Computer Vision.

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Assess the knowledge of diabetic foot among diabetic patients attending tertiary care hospital of Vidarbha region of Maharashtra

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Vision-based single-person human activity recognition (HAR) has advanced rapidly through deep learning. This review surveys 14 recent peer-reviewed studies (2017-2025) focusing on monocular video HAR. We searched IEEE Xplore, SpringerLink, MDPI, and other databases with keywords like "vision-based HAR," "single-person," "CNN," "LSTM," and "Transformer," screening for methods, datasets, and performance. We compare each study's model, dataset, and accuracy, and highlight common limitations. Major findings include the effectiveness of attention and fusion architectures for spatiotemporal feature learning[1][2]. Performance is now very high on benchmark datasets (e.g. >96% on UCF/KTH[3][4]), but generalization and real-time constraints remain challenging. We identify research gaps - notably, reliance on limited datasets and heavy computation - and suggest directions such as lightweight models and richer data. Our review underscores progress and open issues in vision-based single-person HAR.

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Human Activity Recognition (HAR), single-person, video, convolutional neural network, LSTM, attention, transformer.

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UNESCO has defined "Women's empowerment is the process by which women become aware of gender-based unequal power relationships and acquire a greater voice in which to speak out against the inequality found in the home, workplace, and community". The definition itself indicates that the women empowerment is process and it has change according to the situation. That process also include social, economic, political and cultural progress among women. The present research paper intend to find out those progress with a sociological point of view. Historical elements witnessed there was no space for women in any aspect. Treated as subordinate of men and denied in decision making. However, the present situation somehow better and progressive. Hence, the present research paper tries to find out some of the important aspects of women empowerment.

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Keywords: Empowerment, Inequality, Change, Subordinate, Process

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Abstract-- Cardiovascular diseases (CVDs) continue to be the primary cause of death globally, highlighting the critical need for efficient prevention and early detection. Traditional diagnostic approaches, though clinically valuable, often involve high costs, delayed diagnosis, and limited personalization. Leveraging extensive hospital records, this work proposes a robust machine learning-driven framework for automated feature selection, risk prediction, and clinical decision support in CVD management. A comprehensive prepossessing pipeline, including normalization, missing value handling, and feature selection, ensured reliable model inputs. Multiple machine learning algorithms classified patients into cardiovascular risk categories based on clinical and lifestyle parameters such as cholesterol, blood pressure, and fasting blood sugar, and ECG. To enhance predictive accuracy, a Super Learner ensemble algorithm was employed to combine multiple base models, achieving an F1-score of 92.3% with a 90.94% average accuracy, outperforming current methods. Metrics like accuracy and precision were employed in performance validation. recall, F1-score, and ROC-AUC, demonstrating robustness across datasets. The integrated recommendation engine provides personalized lifestyle and medical guidance, including dietary changes, exercise plans, and consultation needs, through a user-friendly graphical interface for instant predictions and recommendations. The novelty of this work lies in combining the Super Learner ensemble approach with a personalized recommendation system, thereby improving predictive accuracy and supporting preventive cardiovascular care to alleviate the overall healthcare burden.

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Keywords-- Cardiovascular Disease (CVD), Machine Learning (ML), Super Learner, Risk Prediction, Personalized Healthcare, Preventive Medicine, Recommendation System, GUI.

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Diabetic retinopathy (DR) is a microvascular complication caused by long-term diabetes and is a leading cause of vision loss. Due to lifestyle changes, retinal abnormalities may appear within a decade of diabetes onset or even earlier. Modern medicine offers mainly conservative, symptomatic treatments, and current DR therapies have notable limitations. In Ayurveda, DR correlates with Madhumehajanya Timir, and traditional Ayurvedic texts describe several formulations for diabetes and its ocular complications. This case study evaluates the actual effectiveness of these Ayurvedic treatments in managing DR. For many years, Ayurvedic pharmaceutical companies have prepared various kwath formulations, which Acharyas have traditionally used to treat numerous diseases. Diabetic retinopathy is increasingly common, and Ayurveda offers an effective line of treatment for managing its symptoms. Administering a kwath medicated with drugs possessing Pitta-Rakta Shamaka properties can significantly support the management of diabetic retinopathy.

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Diabetic retinopathy, Madhumehajanya Timir, Kwath, Pitta-Rakta Shamaka.

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Representational spaces that surfaced in Kerala by the 1930s significantly shaped public consciousness and bolstered communist ideology among the people. Work environments, tea shops, and reading rooms formed novel public spheres during the second quarter of the 20th century. These spaces fostered inter-caste interaction and expedited the process of socialization. The presence of subaltern people rendered these spaces crucial for communists leaders, predominantly upper-caste Hindus, to engage with. Transcending caste barriers, upper caste-Hindus dared to get mingled with lower strata of society. Dynamic political interactions at these spaces, nurturing numerous friendships, infused class consciousness among subaltern groups, thereby bringing them closer to communism.

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public sphere, communists, vayanasalas, work spaces, subalterns, class consciousness

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Obesity is a major public health issue shaped by many factors beyond simple calorie intake, with nutrient composition and overall dietary quality playing crucial roles in metabolic health and the regulation of weight gain. Obesity is better understood as a metabolic and nutritional disorder rather than simply a calorie issue. Dietary quality and nutrient composition strongly shape metabolic health, with processed, calorie-dense foods promoting dysfunction and whole, nutrient-rich foods supporting healthy weight management. Effective obesity management must go beyond calorie counting and focus on improving dietary quality. Prioritizing whole, nutrient-dense foods in public health strategies can support more sustainable and effective obesity prevention and management. A literature review was conducted to analyze studies on food quality, nutrient composition, and their relationship with metabolism and obesity, with the aim of critically evaluating how dietary quality and nutrient composition influence obesity and overall metabolic health.

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Keyword - Obesity, Caloric intake, Diet quality, processed foods, Physical activity

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This has raised a concern of West Bengal in regards to the quality of primary education in the region despite the tremendous growth of enrollment, development of infrastructure, and policy interventions. Recent evaluations have shown that a high percentage of students in lower grades are not well equipped with basics in literacy and numeracy activities which is a sign of systemic failure in the processes of teaching-learning. In this paper, the significant issues that have contributed towards the decline are critically looked at which include teacher shortages, poor training, infrastructural disconnects, socio-economic hindrances, and administrative inefficiencies. Using the current reports, observations in the classroom, and secondary information, the research illuminates the collective effect of these factors as they negatively affect learning outcomes and increase educational inequalities especially in rural and disadvantaged communities. The paper also gives a discussion of evidence-based solutions that can be used to restore the primary education system. These involve fortification of basic learning courses, timely hiring of teachers, professional development, school infrastructure and encouragement of parental and community involvement. They are focussed on the incorporation of technology, open governance, and inclusive practices in accordance with NEP 2020. It claims that learners should be subjected to holistic and multi- level reforms to gain meaningful and long-lasting reforms in the standard of learning. Finally, the paper recommends a team-based solution that entails the government agencies, the teaching fraternity, the communities, and the civil society to regain the trust and revive the credibility of primary education in West Bengal.

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West Bengal, learning outcomes, foundational literacy, Teacher shortages, NEP 2020, school infrastructure, learning governance, social-economic barriers to education, and quality education.

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Creative Commons Attribution 4.0 and The Open Definition