Volume 13 | Issue 1 |

 Abstract

Psychosis refers to a collection of symptoms that affect the mind, where there has been some loss of contact with reality. During an episode of psychosis, a person's thoughts and perceptions are disrupted and they may have difficulty recognizing what is real and what is not. This case highlights the importance of psychosis, especially in cases where mood disorders and false paranormal believes have complicate the clinical picture. It also stresses on the need for early intervention in patients with untreated psychiatric disorders to prevent the onset of severe psychotic symptoms.

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 Keywords

Psychosis, Homoeopathy, Psychotic disorder, Psychiatric, Paranoid, behaviour

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 Abstract

The number of cars on the road is increasing rapidly, causing more traffic problems. The "Brainy Parking System" project offers a brainy, autonomous parking solution with AI and IoT technology. It aims to handle problems in metro cities as well as rural areas. One major issue with current systems is the difficulty in tracking parking fees, which can change based on the time and parking space. This new system allows people to easily locate available parking spots. It uses the same entrance and exit gate, counting vehicles in reverse when they leave. The project combines RFID technology with IoT to enhance efficiency. The smart parking system not only reduces fuel consumption but also lowers urban pollution, contributing positively to the country's economy.

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 Keywords

Internet of Things, Brainy Parking System, RFID, Sensors

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 Abstract

Disease prediction systems based on symptoms are a revolutionary way to deal with the important problem of early identification and treatment, especially in places with limited resources. Accurate and timely detection of diseases is a key part of better healthcare results, lowering the number of preventable illnesses, and boosting public health around the world.

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 Keywords

Disease Prediction, Symptoms, Dataset, Neural Network, Normalization, Early Stopping, Epoch.

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 Abstract

The platform helps detect early signs of autism with AI assistance and tailors programs to fit every core area of development that has been identified, which may include communication skills, daily living skills and adaptive learning through interactive modules. The integrated chatbot gives conversational support, engagement, answering questions and assisting both users and caregivers in getting the most out of what is available. This AI also helps in detecting autism by analyzing language patterns and styles of interaction for early ASD markers. With data privacy and ethical use at its forefront, this platform gives a complete, supportive ecosystem to all people with autism, aiding them in the development of necessary skills and increasing their independence. The potential for the platform is to address the diverse needs of the autism community with a model of continuous, adaptive, and compassionate support that fits with personal growth as well as social integration for people with autism.

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 Keywords

HSV COLOR SPACE, COMPUTER VISION, GESTURE RECOGNITION, HUMAN-COMPUTER INTERACTION (HCI)

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 Abstract

Abstract - Digitalization in all aspects has become profound and inevitable in our private and work lives. Technology creates opportunities for new business models and lifestyles. In the same capacity technology also has become an eminent part of CSR initiatives. Recently, the term "Corporate Digital Responsibility" has come into existence which summarizes the Business world responsibilities towards digitalization-related impacts, risks, challenges, and opportunities. Out of Many societal issues which are being delt as part of CSR initiatives such as education, sanitation, ecological initiatives, sex education and gender sensitization has emerged as an inevitable social issue which needs to be delt while keeping pace with this fast digital world. The paper at hand reviews the role of CDR using a multi-step approach towards gender sensitization in Indian digital society. This paper also flashes light on key gender issues, challenges and key roles of corporations for joining hands in bringing gender sensitization in our society in digital era. Key words: Corporate digital responsibility, Gender Sensitization, Sex Education, Digital

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 Keywords

Corporate Digital responsibility, Digital Gender sensitisation, Se

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 Abstract

Political protests have been a defining feature of India's democratic journey, influencing significant policy shifts and legislative reforms. This paper explores how collective dissent, mass mobilization, and grassroots activism have shaped policy changes in India. It examines key instances, such as the Chipko Movement, the Anti-Corruption Movement, and recent farmer protests, highlighting the strategies, outcomes, and challenges faced by protest movements. Furthermore, the study delves into the interplay between protest dynamics, public opinion, and governmental responses, providing insights into how protests shape the policy-making process. The analysis underscores the critical role of citizen engagement in a vibrant democracy like India. ________________________________________

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 Keywords

public protest, politics, policies

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 Abstract

The integration of drone technology with the Internet of Things (IoT) has given rise to the Drone of Things (DoT), a paradigm that revolutionizes data collection, monitoring, and delivery across diverse domains such as agriculture, logistics, environmental monitoring, and emergency response. This paper examines the potential of edge computing and fog computing to enhance data transfer and processing in DoT systems. By enabling localized data processing on drones and intermediary fog nodes, these technologies address critical challenges such as latency, bandwidth limitations, and data privacy concerns. Edge computing allows drones to process and analyze data in real time, reducing dependency on central servers, while fog computing provides a distributed infrastructure for aggregating and pre-processing data from multiple drones before forwarding essential information to central systems. This paper also explores the benefits of these approaches, including improved efficiency, scalability, and reliability, alongside strategies to address challenges such as interoperability, resource management, and cybersecurity risks. By leveraging edge and fog computing, the DoT paradigm can achieve robust, secure, and scalable operations, paving the way for its widespread adoption in future technological landscapes.

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 Keywords

Drone, edge computing, fog computing, adoption,implementation

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 Abstract

Underwater Wireless Sensor Networks (UWSNs) are vital for monitoring and managing disaster-prone environments, including flood zones and wildfire-affected areas. However, the extreme conditions in such environments--water corrosion, sediment accumulation, heat damage, and mechanical wear--pose significant challenges to the reliability and operational lifespan of UWSN devices. This work introduces a predictive maintenance framework designed to anticipate hardware failures in UWSN nodes by leveraging machine learning algorithms. The proposed approach is tailored to unique disaster-zone conditions and is optimized for low-power devices, ensuring compatibility with the constrained resources of UWSN deployments. By analyzing usage patterns and environmental stressors in real time, the system provides early failure predictions and actionable maintenance alerts to field operators. The novelty lies in its environment-specific modeling, lightweight implementation for edge devices, and integration of predictive analytics with disaster response strategies. This framework enhances network reliability, minimizes downtime, and ensures sustained operation during critical disaster management activities.

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 Keywords

Predictive ,UWSNs, Machine Learning

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 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

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 Keywords

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

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 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.

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 Keywords

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

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