Volume 13 | Issue 11 |

 Abstract

The Virtual Assistant Chatbot is a web-based application developed using HTML, CSS, and JavaScript to provide users with quick, interactive, and automated responses. The main objective of this project is to simulate human-like conversation and assist users in accessing information efficiently without manual navigation. The chatbot uses predefined intents, keyword matching, and dynamic messaging to understand user queries and deliver relevant answers in real time. The interface is designed to be user-friendly, responsive, and similar to modern mobile chat applications. This project demonstrates how front-end technologies can be combined to create an intelligent, lightweight, and accessible assistant capable of handling basic inquiries, guiding users, and improving user experience on websites. The system can be easily integrated with college, business, or personal websites to automate information delivery and reduce the workload on support teams. Future enhancements may include API integration, voice input, and machine learning for advanced responses.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

The project uses HTML, CSS, and JavaScript to create an interactive virtual assistant interface. Voice recognition and speech synthesis technologies are integrated to allow the assistant to listen and respond to users. A responsive user interface is designed using CSS to ensure smooth interaction across devices.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The present research applies UTAUT2 model with perceived risk and perceived trust to study the online electronic consumer behavior. Facilitating condition have significantly influence buying intention and hedonic motivation positively significantly influence by the buying intent of the online shopper under study.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

perceived risk, perceived trust, buying intention

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Contribution of women has always been vital in the growth of economies especially developing nations. And with India aiming to be an "Atm Nirbhar Bharat", it seems quite imperative now to understand that how do women in business identify opportunities to create successful businesses. Entrepreneurship itself is a key to the development of an economy and this pace of development may be fastened by encouraging more number of women to enter businesses. By understanding the factors behind setting up businesses, more women may be motivated and guided by females who have established themselves. The objective of this paper is to understand that how these women entrepreneurs take cues from their environment, how they identify a business opportunity and turn them into a successful business venture. The study aims to find out which factors play a role when it comes to them choosing a specific business. Also once the opportunity is identified, how is it converted into a business idea and what do they do to steer their businesses ahead. This paper examines how women entrepreneurs in Lucknow identify viable business opportunities, the local ecosystem that aids opportunity discovery, sectoral strengths, and barriers that shape opportunity selection. Drawing on recent empirical studies, government program data, and local sectoral analyses, it proposes a practical framework for opportunity identification tailored to women in Lucknow, along with policy and practice recommendations to increase venture success. Key findings indicate that (1) traditional sectors (Chikankari/handicrafts, textiles, food processing, and e-commerce-enabled micro-enterprises) continue to lead opportunity formation; (2) digital adoption and government schemes have materially increased women-led ventures; and (3) incubation, SHG networks, and market linkages substantially improve opportunity realization (Kumar & Singh, 2023).

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Key Words: Women, Entrepreneurship, Opportunity identification, Successful business, Lucknow.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Impulse buying has emerged as a critical area of interest within consumer behavior research, particularly with the rapid expansion of organized retail in developing economies such as India. Organized retail formats--supermarkets, hypermarkets, departmental stores, and large-format specialty outlets--have transformed the shopping environment and significantly influenced unplanned purchases. This paper presents a comprehensive literature review on impulse buying behavior in organized retail settings, synthesizing key insights from studies published over the past decade. The review examines psychological factors such as emotions, mood states, hedonic motives, and personality traits; external retail stimuli including store layout, lighting, music, and promotional cues; and situational influences such as time pressure, shopping companions, and payment methods. The findings reveal that impulse buying is a multidimensional construct shaped by both internal consumer characteristics and external environmental triggers. Despite extensive global research, significant gaps remain in the Indian organized retail context, particularly regarding district-level consumer patterns, socio-cultural influences, and the role of emerging behavioral variables. This review lays the foundation for future empirical studies and offers meaningful implications for retailers aiming to design effective in-store strategies that ethically influence consumer purchase decisions.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Impulse Buying; Consumer Behavior; Organized Retail; Retail Atmospherics; Purchase Triggers; Shopping Psychology

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The districts of Mirzapur and Sonbhadra are part of Uttar Pradesh's Vindhya area, which is distinguished by a discordant range of old hills and mountains that naturally divide India. There are many different tribal communities living in this region, which has tropical dry deciduous forests. Pleurotus djamour is also known as the Pink Oyester Mushroom due to the pink color of its fruiting bodies. Among all the Pleurotus mushroom species, this one is distinct. Their high-quality protein, high-fiber, low-fat, vitamin, and critical minerals make them incredibly nutritious. Using the Polypropylene bag method, the yield performance of Pleurotus djamour was evaluated on four readily available substrates: sugarcane bagasse, banana stem, wheat straw, and Eichhornia. During the 57-day crop period in August and September, wheat straw produced the highest yield (725.7 g/kg dry substrate). In October-November (the 65-day crop), the banana stem yielded the lowest amount (200.5 g/kg); but, because of the longer culture time, a higher yield was achieved. The primary objective of the study is to cultivate P. djamour using a cost-effective approach and to assess its yield performance on various substrates.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Pleurotus djamour, Vindhya region, substrate, yield performance.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

With the rapid development of Artificial Intelligence, there is a rise in the use of AI-enabled technologies in the healthcare domain. The field of Orthopedics, which deals with bone-related disorders like fractures, improper development of bone, high load stress, and osteoporosis, is very much in need of a mechanism for swift and precise detection of these complications at an early stage. Traditional systems rely on medical experts who employ manual interpretation of images obtained by radiography. Manual analysis might be slow and prone to mistakes. To overcome this, the paper proposes an automated system for abnormality detection. The system uses Convolutional Neural Network (CNNs) to classify the bones and identify bone disorders like osteoporosis with an accuracy of 91.8% using the DenseNet121 model and 86.9% with the MobileNetV2 model, which proved to be more competitive and faster. The proposed system provides an interpretative visualization and appropriate reasoning of the obtained results using Grad-CAM (Gradient Class Activation Maps). A graphical interface facilitates interaction, annotation, and report generation

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Orthopedics, Osteoporosis, CNN, Bone Classification, Abnormality Detection, X-ray Analysis, Feature Extraction, Image Analysis Techniques, Healthcare Support, Graphical Interface, Explainable AI, Fracture Detection, Grad-CAM.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Safety of an individual is one of the most sensitive aspects of life. Non-locals and tourists often lack local crime-awareness and cannot take necessary precautions. This work presents Suraksha, an integrated system for crime awareness, prediction, and visualization that leverages historical crime data, predictive analytics, and interactive dashboards. The system fetches crime records, processes and normalizes them, predicts next-year crime trends, and presents structured outputs which the frontend consumes to display interactive visualizations and precaution messages.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Suraksha, Crime Awareness , Crime Prevention ,Data Normalization, Ridge Regression, Data Visualization, Indian Crimes Dataset ,Predictive Analytics

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

: Malware continues to rapidly evolve with sophisticated techniques aimed at evading traditional detection mechanisms, posing significant challenges to cybersecurity. This combined project presents the design and implementation of a customized, Docker container-based sandbox environment to perform secure and scalable malware behavior analysis. The mini project focuses on creating a lightweight, isolated Python sandbox using Docker, restricting CPU, memory, and network access to safely execute untrusted scripts. This sandbox is optimized for academic and testing purposes, enabling secure volume mounting for output retrieval without compromising host security. Building upon this, the major project advances the sandbox to accommodate comprehensive malware detection by integrating behavioral monitoring tools, including system call tracing, network traffic capture, and file system integrity checks. Leveraging Docker's rapid deployment and resource management capacities, the system orchestrates automated container lifecycles, ensuring malware analysis is conducted in isolated, ephemeral environments. This approach balances performance with security, allowing detailed behavior capture while preventing host infection. Experimental results demonstrate the sandbox's effectiveness in simulating various malware actions, including file creation, network operations, and privilege escalation attempts, under strict resource constraints. The project significantly reduces overhead compared to traditional virtual machine sandboxes, offering extensibility for future enhancements like machine learning-based detection and cross-platform support. Through this research, Docker containerization is validated as a practical foundation for dynamic malware analysis, capable of supporting academic research, malware investigation labs, and automated threat intelligence workflows with improved speed, safety, and scalability.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Docker, Sandbox, Cybersecurity, Malware Analysis, Containerization, Python, Isolated Environment.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Fake Profile Detection, Multimodal Fusion, Machine Learning, Social Media Security, Deep Learning, Meta-Classifier

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In the context of Indian democracy, Pervasive Right-wing digital Propaganda is used by political actors as a strategic tool to accomplish their political and ideological agendas, undermining the freedom of speech and belief, both promised and upheld as fundamental rights in our constitution. The primary objective of this paper is to understand the digital discourse of the right-wing in the context of Twitter. And, build an ANN and CNN-BiLSTM RNN to detect and predict the right-wing nature of the tweet. The model will be trained on a real-time 51000 dataset of various propaganda and non-propaganda Twitter handles. The ANN model yields an accuracy of 97% after hyperparameter tuning, and CNN-BiLSTM RNN yields an 87% AUC score after hyperparameter tuning. Analysing the empirical data drawn from the exploratory data analysis and insights drawn from the models, the paper will further highlight the trends of right-wing account usage and their digital propaganda, calling for a new approach to using metadata to understand right-wing tendencies.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Indian Digital right-wing propaganda, real-time dataset, ANN, CNN-BiLSTM, metadata analysis

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Creative Commons Attribution 4.0 and The Open Definition