Volume 13 | Issue 4

 Abstract

This paper presents an automated waste segregation system designed to classify and sort waste into four categories: metals, glass, plastic, and wet. The system uses a Wifi module, along with a camera module to capture images and sensors to detects the metals and moisture. These images undergo processing to extract important features that help in classification. The classification process is performed using the algorithm YOLO (You only look once) algorithm. which compares these features with a predefined dataset to determine the type of waste materials. Once categorized, the system uses a stepper motor-driven automated sorting mechanism that directs each item into its designated bin, reducing manual labor and ensuring proper waste disposal. By improving classification accuracy and efficiency, this system contributes to a more effective recycling process, making it a promising solution for automated waste management in various applications.

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 Keywords

Waste Segregation, YOLO algorithms, Wifi module, Image Processing, Automated Sorting, Recycling, Sustainable Waste Management.

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Underwater mine detection,Rock classification

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This paper presents an Automated Multi-Size Radiator Cleaning System designed to adapt to various radiator dimensions. The system utilizes a servo-driven ball screw mechanism for precise movement, ensuring thorough cleaning without manual intervention. Advanced sensor-based adaptability allows dynamic path adjustments, optimizing efficiency for different radiator configurations. By integrating automation, this system enhances radiator performance, extends equipment lifespan, and reduces operational costs. Industrial cleaning automation improves operational efficiency, safety, and cost-effectiveness by reducing manual labor, minimizing downtime, and ensuring consistent, high-quality results. Radiators, essential for effective engine cooling, accumulate dust, oil, and debris, leading to performance degradation, overheating, and increased maintenance costs. Conventional cleaning techniques are inefficient, labor-intensive, and inconsistent, making automation a necessary solution.

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radiator cleaning, automation, Servo-Driven Mechanism

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Cancer is a leading cause of mortality worldwide accounting for almost 13% of death in the world. Cancer is often perceived as a disease that strikes for no apparent reason. While scientists do not yet know all the reasons. Many of the causes of cancer have already been identified beside intrinsic factors such as hereditary diet and hormones etc. Scientific studies point that there are many intrinsic and extrinsic factors that contribute to cancer development. Anti-oxidants are present in our food components which provides preventive measures against Cancer and many other disease conditions. Antioxidants counteract the effects of free radicals and reduces the damage caused by oxidative stress. The excessive free radical formation and oxidative stress are the most common factors responsible for cancer like disease conditions. The only thing is to know about this type of food intake and a healthy life style. Now let us know about the antioxidants, free radicals, and oxidative damages.

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Antioxidants, Free radicals, Oxidative damages, Cancer

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Natural dyes made from plants are becoming more and more well-liked because they are sustainable and kind to the environment. In order to dye fabrics using a natural mordant made from banana peels, this study looks into the process of extracting dye from Alkanna tinctoria, also known as alkanet root, an old source of red and purple dyes. The goal of the study is to assess the dyed fabric's colour fastness by putting it through a variety of tests on a hobo bag, such as rubbing, washing, and exposure to sunshine. The banana peel mordant was chosen for its environmentally beneficial qualities, providing a substitute for synthetic mordants, while the alkanet bark was picked for its potential for rich colour. Tannins found in banana peel improve the dye's durability and absorption on textiles, resulting in a vivid yet long-lasting hue. After soaking the alkanet bark in water, the extracted dye was applied to fabric that had already been treated with a banana peel mordant. Several tests of fastness were performed to evaluate the performance of the coloured fabric is sunlight fastness (exposing the cloth to prolonged UV radiation), washing fastness (using various detergents and wash cycles), and rubbing fastness (both dry and wet). The fabric mordanted with banana peel and dyed with alkanet bark demonstrated acceptable to good colour fastness in every test, according to the results. There may be a need for more dyeing process optimization since the dye displayed a moderate resilience to rubbing and sun exposure, with a small fading seen after prolonged washing cycles. This work offers important insights into the feasibility of employing alkanet bark for dyeing using banana peel as a mordant, highlighting the possibilities of integrating natural plant-based dyes and mordants for sustainable textile applications. The encouraging outcomes imply that these natural dyeing methods might be expanded for usage in environmentally aware fashion, particularly in artisanal and handcrafted goods like hobo bags. To achieve industry standards for colour fastness, more research into enhancing the dyed fabrics' durability and vibrancy is advised.

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Keywords: Alkanet bark, Natural dye, Sustainable dye, Rich colour, tie and dye, Handcrafts.

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Face morphing attacks pose a significant threat to identity verification systems by seamlessly blending multiple identities into a single facial image. In this paper, we propose a deep learning-based approach for detecting morphed images using a Convolutional Neural Network (CNN) designed to identify GAN-generated artifacts and forensic inconsistencies. Our system dynamically loads or generates a detection model, ensuring adaptability to new morphing techniques. Additionally, we implement image forensics techniques, including noise analysis, edge consistency, JPEG compression artifacts, and hue variation detection, to enhance classification accuracy. A user-friendly graphical interface, developed using Tkinter, provides real-time feedback on the authenticity of an image by displaying real and morphed scores along with detailed forensic analysis. Experimental results demonstrate the effectiveness of our approach in distinguishing real and morphed images, making it a robust solution for face morphing detection in security applications.

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Face Morphing Detection, GAN-Based Forgery, Convolutional Neural Network (CNN), Image Forensics, Identity Verification, Deep Learning, Morphing Attack Detection, Noise Analysis, Edge Consistency, JPEG Compression Artifacts, Hue Variation, Tkinter GUI, AI-Based Security.

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Polycystic Ovary Syndrome (PCOS) is a complex endocrine, metabolic, and genetic disorder affecting women of reproductive age. Conventional treatments often target specific symptoms but come with risks and adverse effects. This review highlights the efficacy of a tea bag formulation containing Ashoka, Shatavari, Lodhra, Guduchi, and Haritki in reducing PCOS symptoms. These herbs improve ovarian morphology, lower hyperandrogenism, and exhibit hypoglycemic, anti-obesity, and hormone-regulating qualities. The tea bag formulation offers a safe, effective, and convenient management strategy for PCOS, with no documented adverse effects. This review aims to compile existing research on the use of herbal formulations, particularly the tea bag formulation, as a supplemental management strategy for PCOS.

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PCOS, herbal formulation, tea bag, Ashoka, Shatavari, Lodhra, Guduchi, Haritki.

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Microfinance is often viewed as a catalyst for poverty reduction and inclusive growth, yet its true impact at the grassroots level remains contested. This study explores the socio-economic outcomes of microfinance participation in two active districts of West Bengal--North 24 Parganas and Hooghly. Focusing on 100 beneficiaries, primary data was collected through structured questionnaires and analyzed using SPSS, employing descriptive statistics, Chi-Square tests, and logistic regression. The study examines income generation and employment, women empowerment, savings and asset creation, access to basic amenities, and challenges faced by users. Findings indicate that long-term MFI involvement positively influences income levels, employment opportunities, financial independence among women, and access to essential services. However, issues such as limited financial literacy, uneven impact, and persistent debt remain challenges. The study recommends targeted interventions, enhanced borrower education, and inclusive strategies to optimize the developmental impact of microfinance institutions.

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MFI, Socio-economic Impact, Poverty alleviation.

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Autobiography becomes a way of testifying to oppression and empowering the subject through their cultural inscription and recognition. The idea that autobiography can become the text of the oppressed, articulating through one person's experience, and experience which may be representative of a particular marginalized group, is an important one. Autobiography helps us to analyze the experiences and attitudes of a person. It is a sociological documentation of subjective experiences. It is the product of the surrounding in which the writer comes up and grown up. Indian women autobiographies express their social, political, economic and psychological struggle and their experiences throughout their life journey. In this research paper M.C. Mary Kom, Rtd. Lft. Gunjan Saxena, Indira Gandhi, Kamala Das, Sania Nehwal, Kiran Bedi, Kamala Markandaya ,Indira Goswani all this extraordinary women's autobiographical writing analyzed in detailed. This all autobiographical writing represents the fight of the self to create order out of random experiences and often mix arguments of history and culture.

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Identity, women Empowerment, Gender Study, Culture, Psychoanalytical analysis.

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This paper presents an automated queue management and prediction system designed to mitigate prolonged wait times in hospital settings. Leveraging machine learning, data science, and IoT technologies, the system utilizes RFID tag-sensors to capture real-time patient service process data. We compare various simulation and prediction techniques, including Random Forest, Monte Carlo simulation, Queue Theory, and statistical methods, to accurately predict queue wait times. The system integrates a Raspberry Pi for data processing, NodeMCU for wireless transmission, a MySQL database for data storage, and a Java Swing GUI for real-time monitoring. By automating data collection and providing accurate wait time predictions, this system aims to enhance patient experience, improve hospital efficiency, and optimize resource allocation.

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RFID, Queue Theory, Raspberry Pi, NodeMCU, MySQL, IoT

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 Abstract

?????- ???? ???? ??????? ??? ????? ?? ???? ??? ?? ?????? ?? ?????? ???????? ?? ??? ??? ????? ??????? ??? ??????? ??? ??? ?? ???????? ?? ?? ?? ?????????? ??????? ?? ?? ?? ???? ???? ?? ??????? ???? ??? ????????? ?????? ?? ?? ????? ???? ??? ???? ????????? ?????? ????? ??? ???? ?? ??????? ??????? ??? ??? ???? ???????? ?????? ?????? ?????????? ?????? ?? ?? ??????? ?? ???? ?? ???? ??? ?????????? ?????? ?? ??? ???? ??????? ???? ???????? ??? ????????? ??? ????????? ???????? ?? ????? ??? ??? ???? ???????? ??? ??? ????? ?? ??? ?? ??? ??? ??? ?????? ??? ???????? ??? ??????? ???? ?????? ?? ?????? ??????, ?????? ?? ????? ?????? ?????? ????? ??? ????? ???? ???? ???? ????? ?????? ????? ????? ??? ????? ?? ??????-?????????????????? ????????? ???? ??? ??? ?? ???? ??? ??????????? ?? ??? ???? ??? ?? ?? ???? ??? ????? ???? ???? ?? ??? ???? ???????? ??????? ??? ??? ??????? ???? ??? ???????? - ??? ???? ?? ????? ???????? ???? ?????? ?? ?????? ??????, ?????? ??? ????? ?????? ?? ?????????? ????? ???? ??? ????? ?? ????? ????????? ?? ???-??? ??????????? ??? ??????? ??? ??????? ??????? ?? ???????? ???? ??? ??????? - ??? ?? ?????????? ?? ???????? ???? ??? ????????? ??? ??????? ?? ???????? ?????? ????? ???????? ??? ??? ?????? ???? ??? ??? ????????? ??? ??????? ?? ??????? ?????? ?? ?????? ?? ???????? ????? ?????? ?? ????????? ?????? ?? ???? ?? ????? ?? ???? ????????? ????? ???????? ?? ????? ????????? ???? ??? ??? ?????? ??? ???????? - ??????? ??? ????? ??? ??????? ??????, ?????? ?? ????? ?????? ?????? ???????? ????? ????? ??? ?? ????? ?? ??? ?? ??????? ??????? ?????? ???? ???? ???? ?? ??? ?? ???? ?? ???? ??????? ??? ??????? ?????? ?????? ??? ?? ??????? ????, ???? ?? ????? ???? ?? ????? ?? ???? ???? ???? ??? ???????? - ?????????, ???????, ?????????? ??? ????????? ?? ???? ???? ?????? ?? ????????? ????? ??? ????? ?? ???? ???? ?? ???? ??? ???? ?? ???????? ??? ???

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???? ??? ?????, ??????-??????????????????, ????

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Automatic video summarization is an essential solution to the rapidly growing volume of digital video content, enabling efficient information retrieval and content management. This project introduces a deep learning-based approach that combines multiple advanced architectures to generate context-aware and user-specific video summaries. TransNetV2 is used for precise shot segmentation, while a Convolutional Neural Network (CNN) extracts abstract visual features. DBSCAN clustering filters redundant frames, and CLIP enhances content understanding by aligning visual and textual information. The system processes both visual and audio data and generates concise summaries tailored to user preferences. Implemented as a Flask web application, it allows users to upload videos or provide YouTube URLs for summarization. Evaluation results demonstrate improved performance in keyframe selection, with higher precision, recall, and mean Average Precision (MAP), while significantly reducing video length without losing essential content. The system holds potential for enhancing content recommendation, discovery, and digital media organization across domains like education, journalism, and research. Future work aims to support real-time summarization and multilingual capabilities.

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Deep learning, Video summarization, CNN, TransNetV2, CLIP, DBSCAN, Flask Web Application, Audio-visual processing.

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With rapid urbanization and the exponential growth of vehicular traffic, traditional traffic management systems struggle with increasing congestion, inefficiency, and environmental impact. Intelligent Traffic Signaling (ITS) systems offer a solution by utilizing real-time data analysis, machine learning algorithms, and Internet of Things (IoT) devices to optimize traffic flow and reduce delays. These systems monitor key parameters like vehicle density, speed, and pedestrian activity using sensors, cameras, and connected vehicles, dynamically adjusting signal timings to improve traffic distribution. Advanced algorithms, such as reinforcement learning, enable ITS to continuously adapt based on historical traffic patterns, seasonal variations, and special events. By reducing unnecessary idling and stop-and-go driving, ITS also lowers fuel consumption and emissions. As cities evolve toward smart infrastructure, ITS plays a key role in reducing traffic accidents, enhancing road safety, and improving urban mobility.

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Dynamic Signal timings, Intelligent Traffic Signalling(ITS), Reduce Delays, Fuel Consumption, Rapid Urbanization, Optimize traffic flow, Advanced algorithms.

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This research paper explores the development, functionality, and impact of RealAR, an advanced real estate platform integrating 3D model visualization and augmented reality (AR) to enhance property showcasing and buyer engagement. The study adopts a user-centered approach, combining quantitative analysis of user interactions with qualitative evaluation of the platform's AR capabilities, interactive UI, and real-time communication features. Through systematic evaluation, the research assesses the effectiveness of 3D model-based property exploration, real-time chat functionalities, and advanced filtering mechanisms in improving user decision-making and engagement. The findings underscore the importance of integrating WebAR, dynamic property listings, and real-time interaction in creating an immersive and informed propertybuying experience. Additionally, the paper examines the implications of interactive 360-degree views, AI-powered recommendations, and seamless navigation on user satisfaction and adoption rates.

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Trading, Market, Educative

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India's foreign policy has entered a challenging phase of strategic recalibration as global power dynamics evolve. The country must strike a difficult balance between its long-standing defence and energy relations with Russia and its growing technological, economic, and security ties with the US. This paper critically investigates how India has manoeuvred its diplomatic stance to assure military readiness, economic growth, and regional stability in the context of the Ukraine conflict, rising US-China tensions, and global trade realignments. The study examines India's strategic autonomy, defence modernisation, and involvement in multilateral forums using a qualitative analysis of secondary sources, revealing that India's decisions are primarily motivated by pragmatic national interests.

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India-US Relations, India-Russia Relations, Strategic Autonomy, Defense Diplomacy, Ukraine War. U.S.-China Rivalry, Indo- Pacific, Geopolitics

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Miniature painting in India originated from the Buddhist manuscript Illustrations in the Pala period of Nepal and the Eastern side of India in the 8th to 11th century. Miniature art thrived from the early 16th century until the mid-19th century. The notion of portraiture, or the accurate representation of humans, originated in Western art. Its application in Mughal painting started in the late 16th century. However, the Mughal Empire in India added a level of elegance, refinement, and grace to this style of painting. With the fall of Mughal patronage, a significant number of artists relocated to the Punjab Hills. The Sikh School, which prospered under the guidance of Maharaja Ranjit Singh, had many different sites, and most of the paintings featured the life of Guru Nanak Dev and other Sikh Gurus.

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Sikh Miniature, Portraits, Spiritual, Simplicity, Janamsakhi.

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We have employed first-principles density functional theory (DFT) calculations to systematically explore the influence of partial substitution of Fe with Mg in FeAl?O? on its structural properties. This strategic substitution of Mg emerges as a crucial factor in enhancing the multifunctional characteristics of the material, particularly for advanced thermocatalytic applications. Despite its promising potential, the effect of Mg incorporation on FeAl?O? has remained unexplored from a first-principles perspective, especially in the context of solar thermochemical fuel generation and photovoltaic technologies. Our electronic density of states (DOS) analysis reveals a significant band gap upon Mg doping, when applied GGA there is no band gap and when we apply GGA+U there is band gap with 2.0 eV. Which in turn strongly modulates the electronic structure of the material making it a more efficient candidate for energy harvesting and conversion applications.

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Density Functional Theory, Electronic property, GGA+U

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: Plastic waste scattered in public spaces such as streets, parks, and industrial areas poses a major environmental concern. Traditional waste collection methods are labor-intensive, time-consuming, and often ineffective in covering large areas. To address this issue, an intelligent robotic system is proposed for the detection and collection of plastic bottle waste. The system utilizes a Raspberry Pi to control the robot and process input data. For object detection, a deep learning model, YOLOv8, based on Convolutional Neural Networks , is employed to identify and classify plastic bottles in real-time camera footage. The model is trained on a curated dataset of plastic waste images to ensure reliable detection. Once a plastic bottle is identified, the robot uses a mechanical arm to pick it up and place it into a collection bin. This system enhances the efficiency and safety of waste collection, particularly in hard-to-reach or hazardous areas. By reducing human involvement and automating the process, the proposed solution contributes to a cleaner, smarter, and more sustainable environment.

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 Keywords

Robotic Waste Collection, Waste Detection, Image Processing, Raspberry Pi Camera, OpenCV, Real-time Waste Monitoring, Deep Learning, Smart Waste Management, Autonomous Robot, Efficiency and Cleanliness, Vision-Based System.

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This study works towards the relationship of using AI (artificial intelligence), namely AI-PR(AI-based personalized recommendations), on consumer green product awareness (GPA), aimed at the online shopping is used in this study. Although AI-driven technologies are changing the way products are interacted with by consumers, their impact on green buying behavior is unknown. As it was recognized that human cognitive and emotional factors may affect AI's ability to promote sustainable choices, this study included emotional intelligence (EI) as a moderating variable. Based on this, the research aims to dig deeper into the role of AI upon these compounds (e.g. AI, AI-PR and EI) on environmental consciousness. The descriptive research method is used to study under which conditions data was collected using self self-administered survey to 403 respondents. The descriptive approach renders various dependence relationships among AI-based recommendations, emotional intelligence and consumers' knowledge on green products, available for study. The study achieves this by conducting a class-based statistical summary and comparative analysis of the behavioral tendencies that make AI effective in dictating how consumers can make sustainable choices. These findings suggest that AI-based and AI usage make an important contribution to increasing the green product awareness of consumers. However, emotional intelligence moderates such a relationship. Secondly, individuals with higher emotional intelligence have a stronger positive correlation between perceived AI usage and GPA, namely, emotionally intelligent consumers are more willing to accept the selling of their sustainable product through AI messaging. Conversely, emotional intelligence negatively moderates the relationship between AI-PR and GPA, meaning that highly emotionally intelligent individuals may be less influenced by AI-based personalized recommendations, whereas those with lower EI show a stronger response to AI-driven suggestions. Furthermore, this study contributes towards growing discourse on the intersection of AI, consumer behavior, and sustainability, highlighting the need for a balanced approach that integrates technology with human cognitive factors to drive positive environmental change.

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Keywords: AI-personalized recommendations, Emotional Intelligence, Green Product awareness, Green behavioural intentions, Mediated moderation.

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Bone fractures are a significant medical concern requiring accurate and timely diagnosis to ensure effective treatment. This paper presents a deep learning-based system for bone fracture detection and classification using Convolutional Neural Networks (CNNs). The system integrates a user-friendly Graphical User Interface (GUI) to facilitate the input of bone fracture images. The process begins with the selection of a bone fracture image, followed by image resizing for uniformity. A pre-processed dataset of bone fracture images is utilized for training the CNN model to detect and classify fractures into three categories: Mild, Moderate, and Severe. The deep learning model leverages advanced CNN architectures for feature extraction and classification, achieving high accuracy in predicting fracture severity. The GUI enables users to input images, run the detection process, and view classified outputs seamlessly. This automated system demonstrates the potential to assist healthcare professionals in diagnosing fractures quickly and accurately, reducing dependency on manual assessments and enhancing clinical decision-making. The achieved accuracy underscores the effectiveness of the proposed approach, making it a valuable tool in medical imaging applications and orthopaedic care.

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 Keywords

Bone Fracture images Dataset, Deep Learning algorithm, Convolutional Neural Network, GUI and Accuracy.

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