Volume 13 | Issue 4 |

 Abstract

With the aid of Long Short-Term Memory (LSTM) networks, this project seeks to explore and introduce a deep learning approach to sentiment analysis of film reviews. The proposed model utilizes an LSTM structure with embedding and dropout layers following a text preprocessing pipeline involving tokenization, sequencing, and padding. The IMDB dataset, comprising out of 50,000 movie reviews, is employed to train the model. The model classifies reviews as either negative or positive with a 0.8999% accuracy. Binary cross-entropy loss and Adam optimizer are employed to train the LSTM model, and hyperparameters are tuned for improved performance. Experimental results indicate the performance of the proposed method in addressing sequential dependencies in text data and captures intricate emotion patterns. The study demonstrates how deep learning methods can be applied to sentiment analysis, offering.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Sentiment Analysis, Text preprocessing, LSTM, Deep Learning, CNN, NLP.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This project focuses on the design, simulation, and intelligent control of a 6-axis robotic arm developed for automated pick-and-place operations integrated with object detection and artificial intelligence (AI) capabilities. The mechanical structure of the arm was precisely modelled using CATIA, allowing for detailed configuration of joints, links and degrees of freedom required for complex spatial movements. The robotic system was then simulated in MATLAB and Simulink, where kinematic models were constructed using Denavit-Hartenberg parameters for accurate forward and inverse kinematics. AI was integrated through the use of MATLAB's Machine Learning and Image Processing toolboxes, enabling the robotic arm to detect and classify objects using vision-based techniques. Object detection was enhanced using AI-driven image recognition algorithms, which allowed the system to identify object shape, colour and type with improved accuracy and adaptability. A PID control algorithm and trajectory planning ensured smooth, responsive motion for pick-and-place tasks. Additionally, the implementation of AI allowed the robot to learn from its environment and improve task efficiency over time, demonstrating adaptive decision-making capabilities. Simulation results confirmed the robotic arm's ability to autonomously detect, grasp and place objects in varying conditions with high reliability. This project illustrates the synergy of mechanical design, control systems and artificial intelligence in creating smart robotic solutions suited for modern industrial automation, warehousing, and smart manufacturing environments.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Collaborative robots (cobots), Machine learning (ML), Computer vision, Artificial intelligence (AI), Industrial automation, Robotic process automation (RPA), Predictive maintenance, Object recognition, Motion planningSensor, integrationAssembly, Welding, , Material handling, Quality inspection, Pick and place.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Efficient water distribution is crucial for sustainable water management, particularly in urban and agricultural settings. Artificial Intelligence (AI) is transforming water distribution systems by optimizing resource allocation, minimizing wastage, and improving efficiency. AI-powered models leverage machine learning (ML), predictive analytics, and real-time data processing to monitor water demand, detect leaks, and adjust supply dynamically. This paper explores the integration of AI in water distribution networks, focusing on smart sensors, IoT devices, and AI-driven algorithms for predictive maintenance and demand forecasting. AI enables automated control of pumping stations, pipeline monitoring, and pressure regulation, ensuring optimal water supply while reducing operational costs. Additionally, AI enhances water quality management by detecting contamination risks and facilitating prompt corrective actions. By implementing AI-based water distribution systems, municipalities and industries can achieve sustainable water management, reduce losses, and improve service reliability. The research highlights case studies and future prospects of AI-driven solutions in water conservation and smart city planning.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Water Distribution, Artificial Intelligence, Machine Learning, Smart Water Management, IoT, Predictive Analytics

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Artificial Intelligence (AI) is revolutionizing Heating, Ventilation, and Air Conditioning (HVAC) systems, particularly in the context of smart buildings. By leveraging machine learning algorithms, predictive analytics, and IoT integration, AI-powered HVAC systems enhance occupant comfort while reducing energy consumption. This paper explores the intersection of AI and HVAC technologies, focusing on their role in optimizing energy efficiency and comfort in smart buildings. Key benefits, challenges, and future directions are examined, supported by a review of the latest literature and empirical data analysis.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

AI-enhanced HVAC leverages machine learning, IoT sensors, and predictive analytics to optimize energy efficiency, comfort, and maintenance in smart buildings. Key technologies include digital twins, real-time monitoring, and adaptive control for sustainability and cost savings.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

ABSTRACT Storytelling is a powerful tool that allows brands to connect with audiences on a deeper, more emotional level. In today's competitive market, where consumers are bombarded with advertisements and content, a compelling narrative helps a brand stand out. Rather than simply promoting products or services, successful brands craft stories that evoke emotions, inspire trust, and build lasting relationships with their audience. Whether it's a tale of perseverance, a mission-driven initiative, or a relatable human experience, storytelling transforms a brand into something memorable and meaningful. By using authentic voices, real-life scenarios, and engaging narratives, companies create connections that go beyond transactions--turning customers into loyal brand advocates. Storytelling has become the heart of modern marketing, allowing brands to forge deep emotional connections with their audiences. A well-crafted narrative goes beyond selling products--it builds a brand's identity, evokes emotions, and fosters loyalty. Brands like Nike inspire resilience with stories of athletes overcoming adversity, while air bnb promotes a sense of belonging through authentic guest experiences. By using relatable characters, conflict, and resolution, brands create narratives that resonate. Whether through social media, video campaigns, or user-generated content, compelling storytelling transforms a brand into an experience that customers remember and cherish Key word: Brand advocates. Storytelling, Relationships

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Key word: Brand advocates. Storytelling, Relationships

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Voice assistants like Alexa, Siri, and Google Assistant have become an important part of modern consumer life, changing how people search for products, gather information, and make purchase decisions. This study examines the impact of voice assistants on consumer purchase intentions, focusing on how convenience, trust, ease of use, and personalized experiences influence buying behaviour. Through surveys and data analysis, the research highlights the growing role of voice technology in shaping customer preferences and loyalty. The findings suggest that as voice assistants continue to evolve, businesses must adapt their marketing strategies to meet the new expectations of voice-driven consumers, ultimately enhancing engagement and driving sales.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Voice Assistants, Voice search, Purchase Intentions, Alexa, Siri, Google Assistant, Voice Commerce.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The construction sector in India is rapidly expanding, contributing significantly to urban development--but also emerging as a major consumer of groundwater, a resource already under severe stress. This literature-based review explores the complex relationship between construction activities and groundwater depletion across India. While agriculture has long been considered the primary driver of groundwater extraction, recent studies indicate that construction practices--such as concrete curing, material mixing, and site preparation--consume substantial amounts of water, often sourced directly from local aquifers through unregulated borewells. Evidence from national standards (IS: 456-2000) and empirical studies suggests that water demand during construction can reach up to 27 kiloliters per square meter of built-up area. Urbanization and land use changes have further exacerbated recharge loss, with impervious surfaces reducing infiltration by nearly 30%. This review consolidates findings from government reports, journal articles, and policy analyses to underscore the urgent need for water-efficient construction technologies, regulatory frameworks, and sustainable site management. Recommendations include rainwater harvesting, groundwater impact assessments, water audits, and integration of green building norms. The study concludes that recognizing and addressing the construction sector's groundwater footprint is essential for ensuring long-term water security and sustainable urban development in India

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Construction Management, Groundwater,

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The biometric authentication is improved over the recent last years to enhance the security. Face Recognition and AI based authentication are regular models to validate the human presence. The attackers can hack the above mentioned biometrics so that there is a need to protect the users data with other biometrics like Finger Veins . In this work, we are proposing that Finger Veins as they are presented under the skin so there is less chance to hack and also economically viable. The feature extraction and registration process is carried out by the deep learning techniques like Convolution neural networks (CNN).

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Biometric Authentication,finger veins

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Efficient and fair water distribution is essential for sustainable farming, especially in areas with hilly terrain and unpredictable rainfall. This study focuses on the planning and practical use of a Pipe Distribution Network (PDN) developed under the Tarandale Minor Irrigation Project in Tarandale village, located in Kankavali Taluka of Sindhudurg district, Maharashtra. The system taps into a nearby stream (nalla) with a total storage of 10.084 million cubic meters. It compares the performance of the PDN with that of traditional open canal methods by looking at technical feasibility, costs, water efficiency, and community benefits. Results clearly show that the PDN reduces water loss, ensures better water access for all farmers, and supports a wider variety of crops--especially when paired with modern micro-irrigation techniques.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Pipe Distribution Network (PDN), WaterGEMs, Water Supply, Sustainable Practices, etc.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Sentiment analysis is the process of analyzing online conversations like tweets, blog posts, or comments to understand people's opinions about specific topics or services. It categorizes these opinions as positive, negative, or neutral, helping businesses assess customer sentiment regarding their products.Aspect-Based Sentiment Analysis (ABSA) takes this a step further by identifying specific features or aspects of a product mentioned in customer reviews. It also determines whether the sentiment about each aspect is positive, negative, or neutral. This detailed feedback helps businesses focus on improving specific product features based on customer opinions, making their products more appealing and successful.The proposed method for ABSA uses BERT, a powerful pre-trained language model, to better understand the context in reviews. By fine-tuning BERT on data specific to a particular domain and using techniques that address multiple tasks at once (like extracting aspects and classifying sentiment), this approach improves the accuracy of ABSA. It is especially effective at handling complex language and understanding relationships between different aspects in reviews.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Aspect-Based Sentiment Analysis ,Sentiment Analysis , Marathi Language Processing , Natural Language Processing (NLP) , BERT

  License

Creative Commons Attribution 4.0 and The Open Definition