Volume 13 | Issue 1 |

 Abstract

Skin cancer, one of the most prevalent cancers worldwide, requires timely and accurate detection for better pa- tient outcomes. This project investigates the use of deep learning methods for automatically classifying skin cancer from dermoscopic pictures. Specifically, convolutional neural networks (CNNs) and hybrid models incorporating transfer learning are employed to enhance classification accuracy. To tackle issues including unequal class distribution, noise in images, and restricted data accessibility, preprocessing methods like data augmentation, denoising, and feature extraction are implemented. The project explores novel model architectures, including compact transformers and dual autoencoder models, aiming to improve the model's performance. Additionally, established benchmark datasets such as HAM10000 and ISIC-2019 are used for training and evaluation. The effective- ness of the developed model is assessed using key metrics, includ- ing accuracy, sensitivity, and specificity. The results highlight the potential of deep learning in accurately classifying skin lesions as malignant or benign, offering valuable support for clinicians in early skin cancer diagnosis, especially in resource-limited settings.By highlighting current trends, obstacles, and potential avenues for future research in the field, this work advances automated diagnostic systems for skin cancer

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Skin Cancer Classification, Deep Learning, Convolutional Neural Networks (CNNs), Compact Transformers, Radiomics, Patient Metadata, Transfer Learning, HAM10000 Dataset, ISIC-2019 Dataset, Class Imbalance

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Breast cancer, a significant health concern among women, requires prompt and accurate diagnosis to improve treatment outcomes and survival rates. Traditionally, specialized doctors perform the diagnosis; however, advancements in machine learning algorithms are enabling supportive diagnostic tools. In this study, we employ a hybrid approach combining Convolutional Neural Networks (CNNs), OpenCV, and Random Forest algorithms to classify breast cancer cases as malignant or benign. The dataset, sourced from the University of Wisconsin, includes 357 malignant and 212 benign tumors, with clinically relevant features extracted using feature engineering techniques. OpenCV is utilized for image preprocessing, ensuring standardized input quality for model analysis, particularly in image-based features. Following data normalization and preprocessing, the dataset is divided into training and testing sets. CNNs are applied to image data for in-depth feature extraction, identifying patterns indicative of malignancy.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Random Forest Algorithm, OpenCV, CNN algorithm, Machine learning, Breast Cancer Prediction.

  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

Machine Learning ,Job Prediction ,Employment Forecasting ,Classification Models, Data Preprocessing ,Feature Engineering, Random Forest, Neural Networks ,Logistic Regression, Model Evaluation

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The present study assessed the effectiveness of a planned teaching programme (PTP) on the knowledge of pre-university (PU) college students regarding mental hygiene in selected colleges in Bidar. The study revealed that examinations and grades were the primary stressors, followed by long working hours, criticism from supervisors, and fear of unemployment. A paired t-test analysis of pre-test and post-test knowledge scores showed a significant improvement, with a t value of 45.11 (p < 0.001) and a percentage mean difference of 49.76%. Key findings included a substantial increase in knowledge regarding the meaning, characteristics, and ways to restore mental hygiene, as well as awareness of risk factors and red-flag symptoms. The results highlight the need for interventional approaches, self-learning initiatives, and mass education to promote mental health awareness and minimize substance abuse among students.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Mental hygiene, planned teaching programme, knowledge, mental health, effectiveness

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

India is amongst few countries that does not consider marital rape as crime and continues to exempt husbands from being charges for rape. This article will examine the reason for exemption of marital rape from being considered crime in Bhartiya Nyaya Sanhita. The exemption stems from the belief in Indian culture that sexual consent is implied and irrefutable upon marriage. This depicts the patriarchal nature of the society which developed upon the notion that wife's body is a property of her husband thus undermining her liberty. The discussion on marital rape in India basically surrounded by intersection of marriage, consent and crime. Historically speaking the marriage in India is sacred concept with the deep-rooted beliefs that women should always fulfil the sexual needs of her partner. These notions clearly violate fundamental rights of women and stand in contradiction with constitutional values of gender equality, dignity and bodily integrity. The key issue relating marital rape is of consent. The exemption that consent is given automatically once marriage is solemnised which disregards the essential elements of consent which is consent should be free from any pressure and is dynamic and ongoing process which can be revoked at any time. The absence of legalisation of marital rape deprives women of right to bodily autonomy and personal security which are enshrined in constitution under Article 14 (equality before law), Article 19 (freedom of expression) and article 21 (right to life and personal liberty). This paper will also compare laws relating to marital rapes in other countries with Indian law.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Marital rape, Implied consent, criminalised, Constitution of India, Indian Penal Code, human dignity.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Rapid population growth and urbanization have limited accessible high-quality land, leading to construction on weak soils like black cotton soil. Despite being fertile, black cotton soil is unsuitable for foundations due to its expansive nature, which causes shrink-swell behavior with moisture changes. Cracks form during dry seasons, and even minimal rainfall can make these soils impassable. In India, 20% of the area is covered by black soil, posing challenges for highway engineers. Weak subgrade s in road construction can cause pavement failure. This study explores improving black cotton soil's geotechnical properties using geopolymer stabilization, an eco-friendly alternative to energy-intensive Portland cement. Fly ash (5-20%) is activated with sodium hydroxide, significantly enhancing soil strength and load-bearing capacity through geopolymerization.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Black cotton soil, Fly ash, Sodium hydroxide and Geo polymer

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This research focuses on the modeling of energy demand predictability, along with the integration of renewable sources of energy, such as wind and solar through hybrid approaches. This study seeks to develop a new hybrid predictive model for energy demand forecasting with precise and effective predictions through the integration of traditional energy forecasting techniques and renewable energy sources, including wind and solar. The model will address the challenges posed by the intermittent and erratic nature of renewable energy generation, assessing how hybrid strategies can help stabilize the grid while optimizing energy distribution. The study seeks to improve the accuracy and adaptability of energy demand forecasts using advanced artificial intelligence and machine learning techniques, enabling more reliable integration of renewable energy sources. Moreover, the study will examine the economic and environmental impacts of integrating energy storage systems with smart grid technology and hybrid energy models. This will be conducted to improve resource optimization while minimizing reliance on fossil fuels. The entire study will be based on an all-inclusive framework for the implementation of hybrids forecasting models using different approaches such as statistical, machine learning, and physical methodologies to improve the reliability, sustainability, and efficiency of energy systems. In summary, the study focuses on developing sustainable, resilient, and economically feasible energy systems, which are part of the shift toward renewable sources but ensure stability on the grid as well as environmentally friendly effects. A framework with data-based considerations to aid the transition to more sustainable and secure energy systems.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Forecasting Accuracy, Hybrid Renewable Energy System (HRES), Solar, Wind Power, Wind-Solar Hybrid System, Machine Learning (ML).

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study examines how well wet scavenging works to reduce particulate matter (PM) concentrations and how that affects the Air Quality Index (AQI) in different parts of Agra. One important natural method for purging the air of contaminants is wet scavenging, in which precipitation collects and removes atmospheric particles. In the urban and peri-urban regions of Agra, a city notorious for its severe air pollution problems, mostly brought on by industrial activity, construction dust, and vehicle emissions, the study attempts to measure the efficacy of this procedure. The research offers a thorough evaluation of the ways in which wet scavenging affects PM concentrations of various sizes, especially PM2.5 and PM10, by gathering and evaluating PM data before to, during, and following rainstorm events at several monitoring stations located around the city. High-precision sensors and weather stations are used in the study technique to continuously monitor air quality metrics and record rainfall events. PM levels and precipitation duration and severity are correlated using sophisticated statistical techniques and atmospheric models. Additionally, by contrasting data from heavily populated metropolitan centers, industrial zones, and less-trafficked residential regions, the spatial heterogeneity of wet scavenging efficiency is investigated. The study also examines the temporal component, emphasizing the rate and duration of post-rainfall improvements in air quality.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

AQI, wet scavenging, PM2.5

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Shanti Ko Badava Dene Me Siksha Ki Bhumika

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Shanti Ko Badava Dene Me Siksha Ki Bhumika

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Nanotechnology represents a groundbreaking field focused on manipulating materials at the nanoscale. Its integration with biology and medicine, termed nanobiotechnology, has transformed healthcare by enabling precise drug delivery, advanced diagnostics, and innovative therapeutic approaches. This review highlights the applications of nanotechnology in medicine, particularly in cancer therapy, cardiovascular diseases, and infectious diseases. It explores various drug delivery systems, such as liposomes, micelles, and dendrimers, which improve bioavailability, stability, and targeted drug efficacy. Furthermore, the challenges of scalability, regulatory issues, and potential toxicity are discussed. The future of nanotechnology lies in its potential for personalized medicine and vaccine development, positioning it as a cornerstone for advanced healthcare solutions.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Nanomedicine,Nanobiotechnology,Nanocarriers,Liposomes,Targeted Therapy,Biocompatibility

  License

Creative Commons Attribution 4.0 and The Open Definition