Volume 13 | Issue 8 |

 Abstract

Diabetes mellitus (DM) represents a significant global health challenge, impacting 537 million adults across the globe. Among its various complications, diabetic retinopathy (DR) stands out as the primary cause of preventable blindness. The traditional manual screening for DR is costly, invasive, and reliant on skilled ophthalmologists, rendering it impractical for widespread screening in areas with limited resources. This research introduces a deep learning-driven automated framework for DR detection utilizing retinal fundus images. We assessed DenseNet121, EfficientNet-B4, and an Ensemble model that integrates both architectures using the EyePACS dataset. The preprocessing steps included green-channel extraction, Contrast Limited Adaptive Histogram Equalization (CLAHE), and comprehensive data augmentation to enhance lesion visibility and address class im- balance. The Ensemble model achieved an accuracy of and a Cohen's Kappa of surpassing earlier techniques, including the Symmetric CNN (SCNN) method. This study highlights the promise of AI-enhanced, non-invasive retinal screening for the early detection of DR and the facilitation of telemedicine-based healthcare services.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Diabetes Mellitus, Diabetic Retinopathy, Retinal Fundus Images, Deep Learning, DenseNet, EfficientNet, Ensemble Learning

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper presents the synthesis of samples such as, BiFeO3, BiFe0.95Pb0.05O3, BiFe0.85Pb0.15O3, BiFe0.75Pb0.25O3 and BiFe0.65Pb0.35O3 multiferroic ceramics via solution combustion method (SCM). These BiFeO3, BiFe0.95Pb0.05O3, BiFe0.85Pb0.15O3, BiFe0.75Pb0.25O3 and BiFe0.65Pb0.35O3 ceramic materials were formulated by using metal nitrates and urea as an initial starting precursors. The temperature dependence of dielectric constant shows a dielectric anomalies at various temperatures in BiFe0.95Pb0.05O3, BiFe0.85Pb0.15O3, BiFe0.75Pb0.25O3 and BiFe0.65Pb0.35O3 ceramics at 1 kHz, 3 kHz and 5 kHz frequencies. Each of these powdered samples were ground in an acetone medium, then calcined and sintered at elevated temperatures before being pelletized.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Multiferroics, Pure BiFeO3, Pb doped BiFeO3, SCM, Dielectric, applications.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

With the exponential growth of video surveillance systems in public and private spaces, automatic video anomaly detection has become a critical research area. Manual monitoring of surveillance footage is labor-intensive and prone to human error. Deep learning, particularly neural networks, has revolutionized video anomaly detection through robust spatiotemporal feature learning. This survey presents a comprehensive analysis of recent advancements in neural network-based methods for anomaly detection in video surveillance. We discuss foundational approaches, evaluate cutting-edge techniques such as autoencoders, GANs, transformers, and skeletal-based models, and highlight key datasets and performance metrics. Comparative analyses demonstrate the trade-offs across architectures and detection paradigms. The paper concludes with future research directions in privacy-preserving models, multimodal fusion, and explainable AI for real-world deployments.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Video anomaly detection, deep learning, surveillance systems, neural networks, spatiotemporal modeling, convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, autoencoders, generative adversial networks (GANs), transfer learning, skeleton-based detection.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This project introduces a non-invasive method to predict an individual's blood group using fingerprint images. A deep learning model was trained on a dataset containing fingerprint samples labeled with different blood groups. We used Convolutional Neural Networks (CNN) along with techniques like ReLU activation and max pooling to extract and learn important image features. A user-friendly HTML interface allows users to log in and upload their fingerprint image. The trained model then analyzes the image and predicts the blood group. This system provides a quick, contactless alternative to traditional blood testing. It demonstrates the use of biometric data and AI in healthcare, aiming to improve accessibility and speed in emergency medical situations. Overall, it reflects the potential of deep learning in modern medical applications.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Blood group prediction, Fingerprint recognition, Convolutional Neural Network (CNN), Deep learning, Non-invasive diagnostics.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Occupational Safety Package, Petroleum Factories, Refinery Staff, Workplace Hazards, Knowledge Assessment, Pre-test and Post-test, Safety Education, Industrial Safety, Bina Refinery, Hazard Awareness.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

One of the biggest problems existing in the healthcare sector is the lack of medical professionals, who are the foundation of the healthcare system. The significant turnover rate of the healthcare professionals has dangerous effects. Therefore, the goal of this study was to fill a gap in the literature and determine how various aspects of workplace spirituality relate to the intention of healthcare professionals working in private hospitals in Ernakulum district of Kerala State, to leave their positions. In this cross-sectional quantitative study, information from 160 healthcare professionals was gathered by convenience sampling. A questionnaire was created using two scales, workplace spirituality and turnover intention, to gather the data. Cronbach's alpha was used to analyze the internal reliability of the items and linear regression was used to analyze the data. Out of four dimensions of workplace spirituality, compassion and values alignment were discovered to have a substantial impact on intended turnover. It is essential for the hospital sector to improve workplace spirituality by focusing on its two aspects, compassion and alignment of organizational principles with personal values of healthcare professionals, to lower the turnover rate.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Workplace Spirituality, Turnover Rate, Compassion, Values Alignment

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The risk-return tradeoff is a core principle in portfolio management, helping investors balance potential gains against the uncertainty of outcomes. This empirical study investigates this tradeoff across four major sectors-IT, Banking, Automobile, and FMCG-using a sample of 20 stocks and 1-year current-period data. The topic was chosen in light of increasing market volatility and investor need for strategic, data-driven portfolio decisions. A descriptive research methodology was employed using secondary data from stock exchanges and financial reports. Analysis was carried out using financial tools such as Mean, Standard Deviation, Correlation Coefficient, CAPM, APT, and Portfolio Risk & Return, along with statistical tools including Descriptive Statistics, T-tests, and Correlation Analysis. The T-test yielded a p-value of 0.024, indicating a statistically significant difference between diversified and traditional portfolios in terms of performance and risk and a strong correlation (r = 0.71) between risk perception and investment behavior (H04). These findings affirm that effective portfolio management depends not only on financial metrics but also on investor psychology and sectoral positioning. The study provides valuable insights for investors aiming to make informed, balanced decisions in today's dynamic markets.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Risk-Return Trade-off, Portfolio Management, Investment Behaviour, Sectoral Analysis, Investor Perception, Descriptive Statistics, Empirical Study, Diversified Portfolios, stock market volatility

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study looks at how 5-star hotels in Maharashtra, India, can develop good carbon neutral plans. The hospitality industry is becoming a bigger part of carbon emissions, so it's important to find ways to reduce that. As the world works to stop climate change, hotels need to use methods that are good for the environment without hurting their business. The research tries to create real steps for becoming carbon neutral by looking at rules, standards, and what other hotels are doing. Information was gathered through research, guidelines from IGBC, and examples from ITC Grand Central and Orchid Hotel in Mumbai, focusing on how they save energy both actively and passively. The results show that using renewable energy, efficient technology, sustainable water and waste systems, and educating staff and guests can greatly cut down on carbon emissions. The outcome of study analyzing carbon neutrality based on comparing different approaches, which helps the environment and sets a good example for hotels in India.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Carbon neutrality, 5-star hotels, Maharashtra, Hospitality industry, Carbon emissions, Climate change, Environmental sustainability, IGBC guidelines, ITC Grand Central, Orchid Hotel Mumbai. Active and passive energy-saving measures, Renewable energy, Efficient technology, Water and waste management, Guest and staff education, Comparative analysis, Sustainable practices

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study investigates the impact of social media on personal and group identity, self- perception, and behavior within Himachal Pradesh, a region known for its rich cultural heritage. Utilizing a mixed-methods approach, the research involved a survey of 117 respondents and qualitative interviews to explore how demographic factors such as age and education influence social medias role in shaping identity. The findings reveal that younger individuals are more likely to perceive their social media profiles as accurate representations of their identities, whileeducation plays a crucial role in moderating social media's influence on beliefs and values. Thestudy also highlights the significant correlations between social comparison, self-perception, andthe pressure to maintain online personas. These results contribute to the understanding of socialmedias complex influence on identity, offering insights for policymakers and educators to address the challenges posed by digital media in culturally diverse regions.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Social Media, Identity Formation, Self-Perception and Cultural Influence

  License

Creative Commons Attribution 4.0 and The Open Definition