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.

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 Keywords

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

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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.

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 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.

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 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.

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 Keywords

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

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 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.

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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.

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 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

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Key word: Brand advocates. Storytelling, Relationships

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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.

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 Keywords

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

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 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

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Construction Management, Groundwater,

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 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).

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Biometric Authentication,finger veins

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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.

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 Keywords

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

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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.

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 Keywords

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

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 Abstract

This paper analyzes how Kantha is a heritage of Bengal and how it is predominant mainly in the Bengali houses. It is an ongoing practice followed from centuries and still unknown to many. Like other a few Bengal-made stitches, Kantha with its unique and easy style of stitching has leveled up Bengal's identity. It has become one of the luxurious sarees of Bengal because of its running stitch embroideries and the intricate and heavy details made by the crafters. There are different types of Kanthas as given in this paper which shows the non-repetitive designs made on fabrics. Kantha is not only just a piece of embroidered cloth but it is also a symbolization of lifestyle, household of many houses. It depicted the lives of farmers, laborers and the pastimes of the makers or women crafters. Thus it becomes significant to the Eastern parts of India and even today the new generations are learning the process from their mothers to their grandmothers.

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 Keywords

Kantha, Stitches, Household, Embroidery, Women, Patchworks

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 Abstract

Persons with disabilities are increasingly being offered a variety of therapies as alternatives, and one of the popular therapies in exploring the effect on their learning and social skill is the use of Music Therapy. Music therapy uses music as a medium for individuals to express their feeling and address their emotions in therapeutic process. In the current study, the authors explore the use of music therapy among the individuals suffering from mental health and disability. Music has a proven to have improvement in overall quality life in people with disabilities. 17 participants were observed on different domains including psycho-motor domain, social skills domain, communication skill domain, emotional domain and cognitive domain over different periods of time. The investigators scored participants in the above domains. The research findings indicate the need for further investigation as although there is a positive effect of music therapy on the beneficiaries, there were no significant differences found in the use of music therapy across domains.

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 Keywords

Psycho-motor Skills, Social Skills, Communication Skills, Emotional Domain, Cognitive Domain

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 Abstract

The mobile application is designed to enhance residential building management by streamlining communication between residents and Treasurer/Committee members. It allows users to submit and track maintenance requests, view payment statuses in real-time, and securely upload payment proofs. The application supports cloud-based data storage and synchronization, providing a platform for detailed maintenance analysis, expense tracking, and asset management. The system is designed to be user-friendly, secure, reliable, and maintainable, addressing common challenges in residential building management such as inefficient communication and manual data handling. In addition to the core features, the mobile application offers robust role-based access control, ensuring that sensitive actions, such as verifying payment proofs or adding new members, are restricted to authorized personnel like committee members. This enhances security by preventing unauthorized changes. The application also incorporates a notification system that sends real-time alerts to residents about important updates, such as payment requests or community notices, fostering better engagement.

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 Keywords

Society Management, Two factor authentication, Cloud Storage

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Infrastructure development is the backbone of a country's economic growth. But infrastructure projects nowadays mostly suffer from overruns in respect of time and cost. They have become a global obstacle hampering the pace of infrastructure development. Presently, infrastructure projects globally are suffering from cost and time overruns irrespective of private or government-owned projects. In India, the Central Sector Projects are infrastructure projects planned, implemented and monitored by the Union Government which plays a pivotal role in India's infrastructure development. However, due to economic malaises like cost and time overruns, a massive financial burden has been put on the government even exaggerating some sectors. Mostly, these projects commence with their date of commissioning but either face delays or cost overrun or with both. This paper is an attempt to analyse the trends of cost and time overruns in the Central Sector Projects for a specified period (2015-2021). The present study has adopted a secondary method of data collection and hence referred to government reports. Some sectors witnessed a maximum number of projects with cost and time overruns. An analysis is done through Kendall's Rank Correlation Coefficient (T) between cost and time overruns of varied sectors. The paper concludes by providing some implications for future research and policy formulation for the government and project participants.

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 Keywords

Project management, Project monitoring, Central Sector Projects, Cost overrun, Time overrun

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This research traces the evolution of insolvency laws in India, culminating in the enactment of the Insolvency and Bankruptcy Code (IBC), 2016. It examines the fragmented and ineffective pre-IBC legal framework and highlights the significant role played by the Bankruptcy Law Reforms Committee (BLRC) in shaping a unified insolvency regime. The study further explores the institutional architecture and key features of the IBC, such as time-bound resolution, creditor-driven processes, and the establishment of specialized adjudicating authorities. By analyzing these aspects, the research underscores how the IBC has transformed India's insolvency landscape, promoting efficiency, accountability, and financial stability.

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 Keywords

Insolvency and Bankruptcy Code, IBC 2016, Insolvency Laws in India, Bankruptcy Law Reforms Committee, Pre-IBC Framework, Corporate Insolvency Resolution Process, Insolvency Professionals, NCLT, Financial Distress, Institutional Framework, Creditor Rights, Debt Resolution, Legal Reforms in India, Insolvency Ecosystem, Economic Recovery.

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 Abstract

Pregnancy is a unique and complex physiological state, marked by significant changes in the physical, emotional, and social dimensions of a woman's life. The first trimester, in particular, is a critical period, characterized by rapid embryonic development, hormonal fluctuations, and adaptations to the pregnancy state. Common discomforts such as morning sickness, fatigue, mood swings, and breast tenderness are ubiquitous during the first trimester, affecting up to 80% of pregnant women. These discomforts can significantly impact a woman's quality of life, daily functioning, and overall wellbeing.Conventional treatments provide symptomatic relief, but individualized homeopathic medicine offers a holistic approach by addressing the underlying susceptibility of each patient.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Homoeopathy, pregnancy, first trimester, common discomforts, morning sickness, fatigue, mood swings.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

PharmaNext is a pioneering project aimed at revolutionizing the medicine delivery process through intelligent automation. By integrating Artificial Intelligence (AI) and Robotic Process Automation(RPA), PharmaNext seeks to streamline healthcare logistics, enhance the accuracy of medication dispensation, and improve patient experience. The system leverages AI algorithms to predict and manage Inventory needs, ensuring timely delivery of medications while minimizing errors. RPA components automate repetitive tasks such as Order processing and tracking, allowing healthcare providers to focus on patient care. This project emphasized the importance of a hassle-free medicine delivery system, ultimately leading to greater efficiency in pharmaceutical services and better health outcomes for patients. By leveraging AI and RPA to optimize healthcare data and medicines supply which help to reduce waiting time of consumers and also avoid congestion over peak outpatient hours.

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 Keywords

API Integration, Robotics in Automation, Real-time Data processing, Machine learning , QR Code scan, Medicine dispenser

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Advertising is a powerful tool in the confectionery industry, where consumer choices are often driven by brand perception, emotional appeal, and marketing influence rather than necessity. This study explores the impact of advertising on confectionery products, analyzing how various promotional strategies shape consumer behavior, brand loyalty, and market competition. The research examines traditional and modern advertising methods, including television commercials, digital and social media marketing, celebrity and influencer endorsements, and in-store promotional activities. A key focus of the study is understanding the psychological and emotional triggers employed in confectionery advertising, such as nostalgia, happiness, indulgence, and sensory appeal, which play a crucial role in consumer decision-making. Additionally, the study evaluates the effectiveness of different advertising campaigns through case studies of successful confectionery brands and consumer surveys. It further investigates the role of demographic factors, including age, income, and cultural influences, in determining the impact of advertising strategies. The findings reveal that effective advertising not only increases brand visibility and product sales but also fosters long-term customer engagement and preference. However, the study also highlights challenges such as market saturation, changing consumer preferences, and regulatory restrictions on advertising sugary products, which can influence marketing strategies. The research provides valuable insights for confectionery businesses and marketers on optimizing advertising efforts to maintain competitive advantage and enhance consumer engagement.

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 Keywords

Advertising, Confectionery industry, Consumer behavior, Brand loyalty, Marketing strategies, Emotional appeal, Psychological triggers, Traditional advertising, Digital marketing, Social media marketing, Celebrity endorsements, Influencer marketing, In-store promotions, Nostalgia, Indulgence, Sensory appeal, Demographic factors, Cultural influences, Market competition, Consumer engagement, Brand perception, Product sales, Regulatory restrictions, Marketing effectiveness, Case studies

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Creative Commons Attribution 4.0 and The Open Definition

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The textile industry is experiencing a significant shift in operations with the integration of Artificial Intelligence (AI) in production, quality and supply chain processes. AI-driven automation improves operation efficiency, decreases errors, and helps organizations to become more efficient, but it also can impact employee performance, job roles, and skill. This research studies the impact of AI on employees in the textile industry in relation to changes in productivity, job satisfaction, and workforce adjustment. An exhaustive literature review was carried out to assess previous research on AI in textiles. This research identifies primary challenges such as job loss, skill misalignment, and employee apprehension to AI integration, but has also found AI changes to improve productivity, work-life balance, and collaboration. The findings of this research underscore organizational needs for targeted reskilling initiatives, employee engagement programs, and human-AI integration models to maintain a sustainable workforce in textiles. This research delivers valuable contribution to textile industry employers, human resource professionals, and public policy developers to help develop AI integration models which balance operational efficiency and employee satisfaction. The research concludes that AI integration in textiles can be optimized by putting in place proactive workforce planning initiatives and employee training, alongside developing a positive approach to technological adoption in the workplace.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, Employee Performance, Textile Industry, Automation, Job Roles, Workforce Adaptation, Skill Development, AI Integration.

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 Abstract

Statement of Problem: - A descriptive study to assess the knowledge regarding anthropometric measurement of 0-6 years of age among Nursing Students of Desh Bhagat University School of Nursing Mandi Gobindgarh District Fatehgarh Sahib Punjab. Material and Method: - Quantitative research approach and non-experimental descriptive research design was used to accomplish the stated objectives data was collected with the help of self-structured knowledge questionnaire. Pilot study was done on [1/10 th sample] which constituted of students of Desh Bhagat University School of Nursing to check reliability and feasibility of the study. Subjects were chosen by the purposive sampling technique and sample size was 60. The data collected was analysed by using descriptive and inferential statistics. RESULTS The percentage distribution of Nursing students as per their knowledge regarding anthropometric measurement of 0-6 years of age. 0.0% students have Adequate knowledge,18.3% students have Moderate knowledge and a considerable portion of Nursing Students i.e 81.7% have Inadequate knowledge. The Chi-square value shows that there is significance association between the score level and demographic variables (Types of Houses). The calculated chi-square values were more than the table value at the 0.05 level of significance. There is no significance association between the level of scores and other demographic variables (Age, Gender, Religion, Education of Student, Type of Family, Total Family Income, Occupation of Father, Occupation of Mother) The calculated chi-square values were less than the table value at the 0.05 level of significance Conclusion: - The data collected was analysed by using descriptive and inferential statistics. The pie and bar diagrams were used to depict the findings. The maximum students [81.7%] were having inadequate knowledge regarding anthropometric measurement of 0-6 years of age among Nursing students of Desh Bhagat University School of Nursing, followed by [18.3%] moderate and [0.0%] were having adequate knowledge regarding anthropometric measurement of 0-6 years of age among Nursing students of Desh Bhagat University School of Nursing respectively. Pamphlets were prepared and distributed to the students to bring more knowledge on anthropometric measurements.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

anthropometric measurement, students and school.

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Creative Commons Attribution 4.0 and The Open Definition