Volume 13 | Issue 1 |

 Abstract

This paper introduces TO-DO ASSIST (Todify), a desktop application designed for task management that integrates a user's to-do list with artificial intelligence. Todify offers a unified platform for organizing personal tasks, utilizing AI for task delegation, and facilitating collaborative efforts to enhance task efficiency. Leveraging to-do list and instant messaging metaphors, the application allows users to initiate, manage, and modify complex tasks executed by agents. The interaction between the user and AI is streamlined through simple operations on to-do items and direct-manipulation chat. The goal is to establish seamless interactions that result in workload reduction, decreased cognitive load, and improved task performance. Unlike other task management applications, Todify not only assists with task organization but also provides creative suggestions for task completion, fostering user creativity. Importantly, the AI suggestions aim to enhance creativity without promoting laziness.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

API, Open AI, TODO

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Nirgundi Taila, a classical Ayurvedic preparation mentioned in the Yogaratnakara, is known for its efficacy in healing Dusta Vrana (non-healing ulcers). To enhance patient compliance with its topical application, the formulation was modified into a Malahara. This study aimed to evaluate the primary analytical properties and antimicrobial effects of the modified Nirgundi Malahara, ensuring its safety and efficacy in promoting ulcer healing. Analytical evaluations confirmed compliance with standard parameters, with pH at 3.88, low total ash content (0.03), absence of acid-insoluble ash, and minimal loss on drying (0.03). In vitro antimicrobial assays revealed that the Malahara exhibited significant activity against Escherichia coli (NCIM 2065) with inhibition zones of 15 mm and 13 mm at 80% and 50% concentrations, respectively, and against Staphylococcus aureus (NCIM 2127) with an inhibition zone of 11 mm at 80% concentration. These findings suggest that the modified Malahara may retain the therapeutic potential of the original Taila and offer a more effective and convenient formulation for managing Dusta Vrana .

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Malahara, Nirgundi Malahara, Analytical study, antimicrobial study

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study investigates the scepticism of feminist movements in the Digital Age of social media where people, brands, influencers etc. are advocating for feminism, inclusivity and body positivity while endorsing products for their respective businesses that contradict the same ideology. This research explores the paradox in upholding the feminist ideology while justifying the marketing language of monetisation through set beauty standards for the sake of consumerism through thematic analysis of secondary data.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Social Media, Feminism, Capitalism, Paradox, Hypocrisy, Female Body, Monetisation.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Although significant progress has been made in the pursuit of gender equality, women around the world still face persistent challenges that hinder their full participation in society. These challenges, ranging from economic inequality and cultural constraints to systemic violence, are present across different regions and cultures, albeit in varying forms. Women continue to experience a gender pay gap, underrepresentation in leadership positions, and a disproportionate share of unpaid domestic and caregiving work. Moreover, cultural practices such as child marriage and gender-based violence further exacerbate their struggles. Intersectionality adds another layer of complexity, as women from marginalized groups face compounded discrimination. Despite these barriers, efforts through global movements, policy changes, and educational initiatives offer pathways for progress. This article explores the ongoing predicaments faced by women today, emphasizing the need for comprehensive approaches that address systemic, cultural, and institutional inequalities, and calls for sustained efforts to empower women for a more inclusive and equitable future.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Gender equality, economic inequality, gender pay gap, leadership representation, unpaid care work, gender-based violence, cultural constraints, child marriage, intersectionality, systemic discrimination, women empowerment, global movements, policy reforms, education, social inequality, women's rights, gender stereotypes, societal barriers, empowerment initiatives, gender parity, cultural practices.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Drug discovery is undergoing a transformative shift, driven by the integration of innovative approaches that bridge the gap between scientific research, advanced technologies, and therapeutic development. Modern drug discovery faces significant challenges, including the complexity of disease mechanisms, drug resistance, and the need for more personalized therapies. This abstract explores the latest innovations shaping the field, focusing on cutting-edge strategies such as artificial intelligence (AI), machine learning (ML), and high-throughput screening, which are revolutionizing target identification, compound screening, and drug optimization. Additionally, the application of advanced technologies like CRISPR gene editing, organ-on-a-chip models, and systems biology is providing deeper insights into disease biology, enabling more precise drug development. The rise of biologics, including monoclonal antibodies and gene therapies, is also playing a pivotal role in targeting previously "undruggable" diseases. Furthermore, the increasing emphasis on precision medicine is guiding the development of more tailored therapeutic solutions based on genetic, environmental, and lifestyle factors. However, challenges related to drug safety, efficacy, and regulatory hurdles remain. As the pharmaceutical industry continues to embrace multidisciplinary collaboration, these innovations promise to significantly accelerate drug discovery timelines and lead to more effective and personalized treatments.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Drug discovery, innovative approaches, artificial intelligence, machine learning, high-throughput screening, CRISPR, organ-on-a-chip, biologics.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Biopesticides are eco-friendly alternatives to chemical pesticides, but their potential toxicological impacts on non-target aquatic organisms remain a concern. This study aimed to evaluate the toxic effects of Newtech biopesticides on the liver of Danio aequipinnatus (Ham Buch), a freshwater fish species widely distributed in the region of Navapur taluka of Nandurbar District. Specimens were exposed to varying concentrations of the biopesticide over 21 days, followed by histological examination of the liver tissues. The results showed significant dose-dependent alterations, including vacuolation, hepatocyte necrosis, and congestion of blood sinusoids. These findings suggest that although Newtech biopesticides are designed to minimize environmental damage, they may pose risks to aquatic organisms at higher concentrations as well as prolonged exposure. This study highlights the need for further research to assess the ecological safety and potential sublethal effects of biopesticides on aquatic ecosystems.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Toxicology, effect New tech, Liver Danio aequipinnatus.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The authors present a new deep learning architecture designed specifically for Deepfake image detection using CNNs. Their methodology is based on the concept of training a CNN model with a carefully compiled dataset consisting of genuine and fake images, which the authors have extracted very carefully from the Kaggle competition and datasets website. Following the training phase, the model undergoes a critical transfer learning phase, leveraging the powerful representations encoded within the Xception architecture, pre-trained on the vast and diverse ImageNet dataset. The crux of this approach lies in the model's ability to discern intricate patterns and nuanced features that distinguish authentic imagery from its synthetic counterparts. Through the systematic study of underlying structures and pixellevel subtleties, the CNN architecture learns to deconstruct subtle disparities present in Deepfake images and hence, strengthen its predictability. Preliminary results from the experimentation phase show the validity of the proposed CNN-based approach in the task of detecting and reporting the counterfeit image with an appreciable accuracy level. In all fairness, strides have been taken so far. However, this is still something that the entire research community strives to further polish and improve performance metrics of the model. As the iterations continue and the improvements are made, the goal remains to reach unprecedented levels of precision and robustness in Deepfake detection, making our defenses much stronger against falsified multimedia proliferation in the digital space.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Deepfake, Facial Forgery, Fake Media Synthetic media, AI-generated material, pattern recognition image classification, computer vision, data augmentation, overfitting, underfitting, model training, and evaluation.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Counterfeit detection in art and antiques is a persistent issue that undermines the value of cultural heritage and economic markets globally. Traditional methods of art authentication, while effective, rely heavily on expert evaluations, which can be time-consuming, costly, and prone to human error. This research aims to develop an innovative solution using Artificial Intelligence (AI) to automate and enhance the process of counterfeit detection. The proposed system integrates advanced machine learning techniques, such as Convolution Neural Networks (CNNs) for image-based analysis, which can detect subtle inconsistencies in brushstrokes, patterns, and textures, which are typically difficult for the human eye to discern. In addition to visual analysis, hyper spectral imaging (HSI) will be utilized to examine the materials and hidden layers of artworks, identifying anachronistic pigments, modern binders, or mismatched materials, further improving the accuracy of detection. Provenance validation, an integral aspect of art authentication, will be achieved using blockchain technology to ensure secure, immutable records of an artwork's history. By merging these AI approaches, the system offers a comprehensive solution that is both scalable and cost-effective for art galleries, auction houses, and collectors. This research has the potential to significantly reduce the occurrence of fraud in the art market, improve transparency, and preserve the integrity of cultural artifacts for future generations.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Counterfeit Detection, Art Authentication, Machine Learning, Convolution Neural Networks, Hyper spectral Imaging, Forgery Detection

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In an increasingly digital world, the threat of phishing attacks through malicious URLs poses a significant risk to users and organizations alike. This project aims to develop an AI-powered detection system that utilizes machine learning algorithms to evaluate and assess the maliciousness of URLs. The system features a robust REST API and a user?friendly web application, allowing users to input URLs and receive AI-generated probabilities regarding their safety in real time. Additionally, a feedback mechanism is integrated into the application, enabling users to confirm or correct assessed probabilities, thus enhancing the accuracy and reliability of the URL analysis. This continuous feedback loop not only improves the machine-learning model's performance over time but also fosters user engagement and cybersecurity awareness. The backend architecture is designed to be scalable, accommodating increased user load and facilitating integration with existing cybersecurity solutions. Ultimately, this project contributes to a safer online environment by empowering users with the tools and knowledge needed to recognize and avoid phishing threats.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

AI-Enhanced Phishing Defense, Malicious URL Detection,

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The project focuses on the development of an advanced system for the real-time detection of bacterial blight disease in pomegranate leaves using machine learning techniques. Utilizing Convolutional Neural Networks (CNNs) for image analysis, the system processes high-resolution images captured by mobile devices to identify specific visual indicators of bacterial blight. The integration of this technology enables farmers to receive immediate feedback regarding the health of their crops, facilitating timely intervention and management practices. The ultimate goal is to enhance agricultural productivity, reduce crop losses, and promote sustainable farming practices through early detection and accurate diagnosis optionally intelligent contact center environment.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Bacterial Blight, Disease Detection, Machine Learning, CNN, Agricultural Technology

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Creative Commons Attribution 4.0 and The Open Definition