Volume 13 | Issue 6 |

 Abstract

India's transition to electric mobility is propelled by rising fuel prices, supportive government policies, and growing environmental awareness. A study conducted in Coimbatore city examined consumer behavior towards electric two - wheelers, focusing on factors such as Brand perception, Affordability, Eco - friendliness, Technological advancements, Awareness levels, and the availability of charging infrastructure. The results showed that while consumers are becoming more aware of the benefits of electric vehicles, challenges like inadequate charging facilities, high upfront costs, and concerns about batteries continue to hinder widespread adoption. The study provides valuable insights for manufactures, policymakers, and marketers who aim to promote sustainable transportation in urban areas.

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 Keywords

Government policies, Charging infrastructure, Sustainable transportation.

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 Abstract

Introduction: Early ambulation is a powerful catalyst for enhanced postoperative recovery, particularly for post-Caesarean mothers. According to National Family Health Survey 2019-2020, there has been an increase in the number of Caesarean section deliveries in a majority of states. This proactive approach significantly reduces pain, shortens hospital stays, and lowers the risk of complications. Aim: To determine the effectiveness of early ambulation among post caesarean mothers in experimental group. Methodology: A quasi-experimental study was conducted in the Maternity Ward of SLBSGMC&H, Nerchowk, Mandi (H.P), using a post-test-only design. 40 post-natal mothers (20 in the experimental group and 20 in the control group) who underwent LSCS were selected through purposive sampling technique. Data were collected using a socio-demographic data sheet (9 items), a postoperative recovery assessment scale (9items), and a subjective checklist (15 items) to evaluate the effectiveness of early ambulation. Ethical approval was obtained from the ethical committee and the Medical Superintendent of SLBSGMC&H. Data were analyzed using descriptive and inferential statistics. Results: Majority of the participants in both groups were aged 19-24 years (50% experimental, 45% control) and had education levels above graduation (60% in both groups). The majority were homemakers (70% experimental, 60% control). The experimental group had a mean score was 36.25 with SD 2.807, while the control group had a mean score was 12.25 with SD = 2.633 and the p value is <0.001 at p<0.05. Conclusion: Findings of the study revealed that the early ambulation group experienced significantly improved bowel and bladder function, shorter hospital stays, and fewer postoperative complications compared to the control group. An informational booklet was developed to promote the benefits and best practices of early ambulation for mothers undergoing LSCS, highlighting its role in improving postoperative maternal health outcomes.

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 Keywords

Early Ambulation, post-operative mothers, LSCS, post-operative recovery.

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 Abstract

With the rise of deepfake content on social media, distinguishing between genuine and AI generated tweets has become a major challenge. This project proposes an advanced detection framework that utilizes Fast-Text embeddings and deep learning models to identify manipulated text. Fast-Text is chosen for its ability to capture semantic meaning, sub word information, and contextual nuances in social media text, including slang and misspellings. The system integrates LSTM, GRU, and Transformer models to enhance classification accuracy, following a structured workflow of data collection, text preprocessing, Fast Text-based feature extraction, and model training. To ensure reliable detection, the system is evaluated using key metrics like accuracy, precision, recall, and F1 score, demonstrating superior performance over traditional methods. By effectively handling out-of-vocabulary (OOV) words and noisy tweet data, the proposed framework provides a scalable and robust solution for detecting machine-generated tweets. Future enhancements may include real-time detection, integration with BERT, RoBERT, or GPT

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 Keywords

DEEPFAKE DETECTION ON SOCIAL MEDIA: LEVERAGING DEEP LEARNING AND FASTTEXT EMBEDDINGS FOR IDENTIFYING MACHINE-GENERATED TWEETS

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Conflicting narratives are frequently presented by various media, such as social media, news sources, and the entertainments sector, which leads to misunderstandings and divisions in the general public's perception. While some perceive feminism as an extremist philosophy. Others see it as an essential movement for equality. These disparate portrayals have a big influence on how young people understand and interact with feminist ideas. This study aims to evaluate how young people view and engage with feminist narratives, as well as examine how the media shapes misunderstandings about feminism. Through a survey-based approach, this research determines how much different media platforms influence youth attitudes toward feminism, whether social media promotes awareness and engagement or contributes to misinformation, how news coverage and entertainment media reinforce or challenge feminist stereotypes, and whether these portrayals discourage active participation in feminist discussions or influence shifts in perception over time. The study's objectives are to analyze the role of media in shaping misconceptions about feminism and to evaluate how the youth perceive and interact with feminist narratives. In order to determine whether the youth perceives feminism differently from older generations, this study also looks at how various generations perceive it. The youth now mostly encounters feminist discourse through internet channels, whereas earlier generations may have been exposed to feminism through historical movements and direct participation. This change in exposure may lead to changing viewpoints, either making the youngsters more progressive or encouraging skepticism because of media misrepresentations. In order to contribute to the larger conversation on media responsibility in accurately portraying feminist values, this study attempts to comprehend how the media shapes the image of feminism and how it affects youth engagement. The results will promote critical thinking in the consumption of media narratives regarding gender equality and feminism.

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 Keywords

Media representation, feminism, misconceptions, youth perception, 21st century, social media influence, gender equality, media influence, feminist engagement, generational differences.

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 Abstract

Medicinal plants, which continue to be an essential source of bioactive compounds for modern therapeutic applications, have been a major component of traditional medical systems around the world. In order to verify traditional claims, identify new therapeutic possibilities, and ensure the effectiveness, safety, and quality of herbal remedies, the pharmacological evaluation of these botanicals is crucial. Given the growing interest in plant-based medications worldwide, there is an urgent need for scientifically sound methods that incorporate in vitro, in vivo, and ex vivo processes to evaluate the pharmacological activity of herbal extracts and their constituents. Specifically, this research focusses on the systematic approaches used in the pharmacological evaluation of medicinal plants in the context of Herbal Drug Technology (HDT). Hepatoprotective, anti-inflammatory, antibacterial, antioxidant, antidiabetic, and anticancer properties are among the biological activities that are evaluated using a variety of models. The review also emphasises the importance of standardisation, quality control, and regulatory norms to ensure the safety and repeatability of plant-based formulations. Recent advances in experimental pharmacology, bioassay-guided fractionation, and molecular docking have greatly enhanced the scientific validation of ethnopharmacological claims. The paper's conclusion highlights the challenges and future directions in combining traditional herbal knowledge with modern pharmacological research to produce safe and effective phytomedicines.

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 Keywords

Medicinal plants, Herbal drugs, Pharmacology, Herbal medicine, Plant extracts, Natural medicine, Traditional medicine, Drug evaluation, Herbal treatment, Plant-based drugs, Phytochemicals, Herbal research, Bioactive compounds, Herbal drug testing, Herbal formulations

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This study presents a deep learning approach for automated lung cancer classification using a custom-labeled dataset of CT scan images. The system leverages a Convolutional Neural Network (CNN) to detect and classify lung conditions into six categories: adenocarcinoma, benign cases, large cell carcinoma, malignant cases, normal, and squamous cell carcinoma. The model was trained, validated, and tested on this custom dataset, achieving high classification accuracy and robust performance across all classes. This implementation paves the way for scalable, early-stage detection tools in medical imaging.

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CNN, Lung Cancer, CT Scan, Deep Learning, Image Classification, Medical Imaging

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In this work, we introduce an advanced intelligent system designed to use deep learning techniques to identify and distinguish between human tissue status and vitamin defi- ciencies effectively. Our approach begins with the use of image aggregation techniques to isolate and better focus lesion areas in images. The large amount of data available on the image databases is used to fine-tune the pre-trained networks to obtain color, texture, and structure features related to the pathologies. This approach is effective and provides an oppor- tunity to detect vitamin deficiencies quickly and unequivocally even to expose patients to invasive procedures. The system aims to conduct an image analysis of physical characteris- tics associated with deficiency diseases, which include pale skin, smooth tongue, and dry eyes. Our primary objectives are to thoroughly evaluate the effectiveness of the proposed segmentation procedure, determine which aspects are most critical for exact classification, and compare the effectiveness of our grouping results with those achieved using alternative methods. This method aims to enhance the precision and reliability of identifying and distinguishing between various tissue types and deficiencies, thereby advancing diagnostic capabilities in medical imaging significantly.

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 Keywords

Vitamin Deficiency, Deep Learning, CNN, ResNet, Medical Imaging.

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 Abstract

Artificial Intelligence (AI) refers back to the application of smart algorithms and device gaining knowledge of techniques to decorate the detection, prevention, and response to cyber threats. AI empowers cybersecurity systems to investigate sizable amounts of information, identify styles, and make knowledgeable choices at speeds and scales beyond human abilities. The function of AI in bolstering safety features is multifaceted. It revolutionizes danger detection, automates responses, and strengthens vulnerability management. AI-powered structures can hit upon threats in real time, allowing rapid response and mitigation. by way of analyzing behaviors, detecting phishing, and adapting to new threats, AI allows proactive protection and safeguards touchy information. Additionally, AI can automate ordinary cybersecurity duties including log evaluation and vulnerability scanning, freeing up human analysts to consciousness on more complex and strategic activities. AI constantly learns from new statistics, adapting and evolving to enhance its capacity to pick out and counter rising threats.

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Keywords: Artificial Intelligence, Cyber Security, Detection, Prevention

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 Abstract

The Attendance System performs image and class training that enables OpenCV data extraction functionality. The central objective behind this project involves building Face Recognition technology for attendance management to transform existing manual procedures into new automated systems. The system operates within the classroom space to train student with information that consists of name along with roll number and class details and sections and images. The extraction of images occurred through OpenCV software. When the corresponding class period began students would approach the machine for a photo capture session against the registered database photos. The development of a facial recognition-based attendance solution through Raspberry Pi forms the main objective of this project. The integration of face recognition algorithms in this system removes the requirement for manual user contact and achieves better precision levels together with enhanced dependability. The system first recognizes faces through its capture function and then creates records about attendance while determining presence as well as absence based on time spent in front of the system. The application features Face Detection and Face Recognition functionalities conducted through the Haar Cascade classifier using Open CV algorithms executed on Raspberry Pi hardware.

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 Keywords

Face Detection, Face Recognition, Haar Cascade classifier, Open CV, Raspberry Pi.

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This research investigates the influence of Titanium Oxide nanoparticles as an additive into the magnetorheological fluid experimentally. The optimization parameters were considered based on their significance in achieving the magnetorheological effect such as density, sedimentation rate, shear stress, viscosity so as to enhance the stability of magnetorheological fluid and to achieve better damping in case of magnetorheological damper. Different volume percentage of Titanium Oxide nanoparticles viz;0.2%,0.4%,0.6%. have been blended with the magnetorheological fluid and the developed composites have been ranked using most preferred MCDM technique, TOPSIS. The experimental results revealed that 0.2 weight %TiO2 (Titanium Oxide) indicates highest relative closeness and ranking.

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TiO2, nanoparticle, TOPSIS, magnetorheological fluid, MR Damper

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