Volume 13 | Issue 12 |

 Abstract

The AI-Mock-Interview project aims to transform the traditional interview process by utilizing artificial intelligence to automate and enhance candidate evaluations. This AI-powered system assesses candidates across multiple dimensions, such as verbal responses, body language, eye contact, and emotional expressions. By incorporating cutting-edge technologies like machine learning, natural language processing (NLP), and facial recognition, it offers a more efficient and objective approach to candidate assessment. The project seeks to overcome the inherent biases and subjectivity often present in traditional interviews by adopting a data-driven, impartial evaluation process. The methodology centers around real-time video and audio analysis, leveraging emotion recognition and sentiment analysis through Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models.

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 Keywords

AI-Driven Interview Simulation Intelligent Mock Interview Platform Virtual Interview Assistant Automated Interview Engine Smart Interview Bot

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 Abstract

Phishing websites have emerged as one of the most prevalent cyber threats, deceiving users into revealing sensitive credentials and causing significant financial losses. Detecting these malicious websites automatically and accurately is crucial for online security. This paper presents a machine learning-based phishing website detection system that analyzes multiple data modalities including URL lexical features, HTML structure, and WHOIS information to identify phishing attempts. The system extracts key features such as domain age, URL length, special character patterns, SSL certificate validity, and embedded script behaviors, which are then processed using ensemble learning models such as Random Forest and XG Boost for classification. The proposed model achieves an overall accuracy of 96.4%, precision of 95.8%, and F1score of 96.1% on benchmark datasets, demonstrating superior performance compared to existing methods. The model is lightweight and optimized for real-time deployment, making it suitable for browser extensions and online verification systems. The key contributions include the integration of diverse feature sets, enhanced detection accuracy, and a scalable implementation for real-world phishing prevention.

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 Keywords

Phishing Website Detection, Machine Learning, Cybersecurity, Web Security, Feature Extraction, Classification Algorithms, Supervised Learning, URL Analysis, Malicious Website Detection, Data Mining.

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 Abstract

In this project we will develop an IoT Based Solar Power Monitoring System using ESP32 WiFi Module. The ESP32 connects to the WiFi Network and uploads the Solar Sensing parameters like Solar Panel Voltage, Temperature, and Light Intensity on Thingspeak Server.

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IoT based solar tracker, solar power

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 Abstract

The rapid expansion of Internet of Things (IoT) devices has been transformed into modern infrastructure around healthcare, industrial reformation, smart cities, and consumer applications. However, this rapid multiplication has never created security challenges, making IoT networks attractive targets for sophisticated cyberattacks [1]. Traditional signature-based intrusion detection systems (IDS) struggle to keep pace with the evolving nature of attacks, particularly botnet-based threats that exploit the inherent resource constraints of IoT devices. Deep learning has emerged as a promising solution for developing robust, adaptive intrusion detection systems capable of identifying both known and novel attack patterns in IoT environments [2]. The BoT-IoT dataset has become a critical standard for evaluating these advanced detection approaches, providing researchers with realistic network traffic data that captures the complexity of modern botnet attacks in IoT infrastructures.

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 Keywords

Internet of Things (IoT), Intrusion Detection System, Deep Learning, Cybersecurity, BoT-IoT Dataset, Botnet Attacks.

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Access to safe drinking water remains a critical public health challenge, particularly in developing regions with limited laboratory infrastructure. This paper presents Purifix H2O, an intelligent Internet of Things (IoT) system for real- time water quality monitoring that integrates low-cost sensors (pH, TDS, turbidity, and temperature) with an Arduino UNO microcontroller and cloud-enabled mobile/web interfaces. The system employs sensor calibration, multi-parameter processing, rule-based safety classification, and an optional machine learning module for AI-driven waterborne disease risk prediction. Experimental evaluations on diverse water sources including tap, borewell, packaged drinking, pond, and contaminated samples demonstrate that the system reliably discriminates water quality categories and predicts health risks based on sensor patterns. Results show that the proposed approach is cost-effective, feasible for large-scale deployment, and significantly improves accessibility to continuous water quality assessment compared to traditional laboratory-based methods.

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 Keywords

IoT, water quality monitoring, low-cost sensors, Arduino, machine learning, disease risk prediction, Android Application, real-time monitoring.

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The concept of gender in Indian families and society needs no introduction, as the age-old gender discrimination has been so deeply internalized that it doesn't come as a surprise if a woman becomes a victim of such discrimination. The paper attempts to study a woman's world bound by patriarchy, class, and gender exploitation, followed by the subsequent resistance in Indian society as portrayed in Arati Kadav's film Mrs. (2024). A Marxist feminist literary approach guides the analysis of the cinematic text. The film projects a quietly powerful and thought-provoking portrayal of a woman caught between her aspirations and the weight of deeply rooted patriarchal expectations. At the heart of the film is Richa, played with striking vulnerability by actress Sanya Malhotra--a middle-class housewife whose routine existence is upended by an unexpected event. What unfolds is not just a story of disruption, but of awakening and resistance. The findings conclude that many Indian married women, like the female lead in the film, are trapped in the endless cycle of domestic chores and constant patriarchal scrutiny. The identity that the woman protagonist had is lost in transition and remains a victim of patriarchy, class, and gender discrimination. It is through passion for a fulfilling work or economic independence that paves the way to the protagonist's ultimate freedom and empowerment.

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patriarchy, gender, Marxist feminism, Mrs., resistance

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ABSTRACT A 60-year-old post-hysterectomy woman presented with a one-year history of vaginal mass protrusion, difficulty walking, and incomplete micturition. Examination revealed vault prolapse with cystocele and enterocele. She was hemodynamically stable with mild pallor and pedal edema. Investigations showed hemoglobin 11 g%, platelet count 1.24 lakh/cumm, and normal renal, liver, thyroid, and viral profiles. Conservative management included lifestyle modification, pelvic floor exercises, multivitamins, antihypertensives, and topical estrogen. She was advised sexual abstinence for three months and follow-up for thyroid and fecal occult blood tests. Surgical correction such as sacrocolpopexy was planned if symptoms persisted. This case emphasizes early recognition, supportive care, and individualized management of post-hysterectomy vault prolapse with cystocele and enterocele.

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Keywords: Vault prolapse, cystocele, enterocele, hysterectomy complications, pelvic organ prolapsed

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 Abstract

Summarizes Jatharagni's role in Grahani-IBS pathogenesis, linking Mandagni-Ama to dysbiosis. Outlines review methodology, key findings (doshic-IBS mapping), and integrative therapeutic superiority. Aim To establish the mechanistic role of Jatharagni dysregulation in Grahani pathogenesis and its correlation with IBS pathophysiology. Objectives 1. Analyze classical Jatharagni physiology and Grahani classification from Ayurvedic texts. 2. Map tridoshic Grahani types to IBS subtypes. 3. Correlate Mandagni-Ama with modern dysbiosis and gut dysfunction. Introduction Defines Grahani Roga via classical Shloka, introduces Jatharagni physiology. Correlates with IBS prevalence/Rome IV, states research scope for Ayurvedic-modern synthesis. Methodology PRISMA-adapted systematic review of Ayurvedic texts/databases (2000-2025). Details inclusion criteria, qualitative dosha-IBS mapping, bias assessment via Jadad scale. Results Presents Jatharagni functions (Paka-Vibhajana), Grahani classification table (Vataja->IBS- C etc.). Shows Deepana-Pachana efficacy (70-80% relief) with Samprapti diagram.Discussion Elaborates Mandagni->Ama parallels (dysbiosis, leaky gut, motility chaos).Pathophysiological mechanism of Grahani and IBS and it's integrated interpretation Conclusion Affirms Jatharagni restoration as root-cause therapy outperforming IBS palliation. Recommends multicentric trials for global validation.

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 Keywords

Keywords: Jatharagni, Grahani Roga, Irritable Bowel Syndrome, Agni, Mandagni, Digestive Fire, Ayurveda, Ama, Dysbiosis

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Machine Learning and Deep learning-based software tools has facilitated the creation of credible face exchanges in videos and images that leave few traces of manipulation, in what they are known as "DeepFake"(DF) videos. Manipulations of digital videos have been demonstrated for several decades through the good use of visual effects, recent advances in deep learning have led to a drastic increase in the realism of fake content and the accessibility in which it can be created. These so-called AI-synthesized media (popularly referred to as DF).Creating the DF using the Artificially intelligent tools are simple task. But, when it comes to detection of these DF, it is major challenge. Because training the algorithm to spot the DF is not simple. We have taken a step forward in detecting the DF using Convolutional Neural Network and Recurrent neural Network. System uses a convolutional Neural network (CNN) to extract features at the frame level. These features are used to train a recurrent neural network (RNN) which learns to classify if a video has been subject to manipulation or not and able to detect the temporal inconsistencies between frames introduced by the DF creation tools. Expected result against a large set of fake videos collected from standard data set.

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 Keywords

: Deepfake Detection , Kaggle Dataset, convolutional Neural network (CNN), recurrent neural network (RNN), Machine Learning.

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 Abstract

Vikara Vighatakara Bhavas represent protective parlance in Ayurvedic pathogenesis that proactively inhibit disease manifestation. These elements modulate the interaction of triad of Nidana (Doshas, Dhatus, Malas etc, the etiological factors) Visheshas, preventing Samprapti (pathogenic processes). Vikara Vighatkara Bhavas act as barriers against Vyadhi (disease) by opposing the association of causative factors like Naidan, Doshas, Dhatus, Malas etc. Their Bhavas (presence) strengthens Vyadhikshamatva (immunity), while Abhavas (absence) facilitates pathogenesis, as described in Charaka Samhita's Prameha Nidana. These Bhavas include Ojas, balanced Doshas, Prashashta (strong) Dhatus, and Sattvic mental states that neutralize Nidana's impact. Proactively, these Bhavas enhance resilience through Dinacharya, Ritucharya, and Rasayana therapies, fortifying the body pre-exposure to pathogens. Bala (strength), Sattva (psyche), and Abhyantara Hetus (internal causative factors) play key roles in averting Dosha vitiation. This aligns with Ayurveda's Swasthavritta emphasis on prevention over cure. Protectively, they disrupt Samprapti by limiting Dosha-Dooshya Sammurchana, influencing Rogamarga and Krama. Factors like Prativishesha (counter-specifics) determine disease severity, onset, and prognosis. In modern terms, they parallel immunity modulators, explaining individual disease resistance variations. Understanding these Bhavas guides Chikitsa towards Samprapti-Vighatana and Nidana-Parivarjana. Enhancing them via Ahara-Vihara and Ojasya-Rasayanas offers holistic prophylaxis.

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Vikara Vighatkara Bhavas, Ayurvedic pathogenesis, Samprapti-Vighatana, Vyadhikshamatva, Nidana-Visheshas, protective factors, Ojas, diseases etc.

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