Volume 13 | Issue 5 |

 Abstract

The performance of Continuous Punjabi language with different transcription variants is analyzed. The Performance of Automatic Speech Recognition system is much affected by the variations in terms of word error rate. The effect of training of acoustic model through canonical lexicon and lexicon with variants on the speech recognition system evaluated. The performance of the system with both training sets was investigated, and results were compared. Both monophone and triphone models of Speech Recognition were used to analyze the performance of the system on Kaldi toolkit. The language used for the Automatic Speech Recognition System is the Punjabi language. The Continuous Punjabi language is chosen for the recognition system.

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 Keywords

Automatic speech recognition system, Canonical lexicon, Continuous Punjabi language, Word error rate, Kaldi toolkit.

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 Abstract

Ayurveda system of medicine has emphasised on various modalities of treatments in Sandhigatavata. Upanaha sweda is one among the modality of Swedana used widely in the management of Sandhigatavata, which is found to be helpful clinically. According to the Authentic, Upanaha is sub type of Swedana. Upanaha (Application of poultice) is a mode of fomentation where in a combination of Churnas (powdered herbs) are made into paste using Sneha Dravya (unctuous materials-oil/ghee/muscle fat/marrow), kinva (fermented liquid), made hot and applied over the affected part followed by bandaging. The objective of this study is to find out the Concept of Kolakulaththadi Upanaha Sweda and its mode of action with clinical utility which have listed in Authentic. The data was collected from Authentic Ayurvedic texts, scientific journals, Research articles and through the electronic media. According to the findings, it is revealed that the action of the Upanaha depends on the materials used for the application, temperature of the paste, duration of retaining on the skin surface and thickness of the paste applied. It is explained that Upanaha Sweda reduced Vata dosha, Sheetha (coldness), Shoola (pain), Sthambha (stiffness), Gowravata (heaviness) in clinical. Hence the present study supports the opinion that the Concept of Kolakulaththadi Upanaha Sweda and its clinical utility improve the administration of different treatment modalities based upon the classical references with low cost and minimum side effects.

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 Keywords

Kolakulaththadi, Sandhigatavata, Swedana, Upanaha

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 Abstract

Before undertaking the construction of a project it is necessary to know its probable cost which is worked out by estimating. Building Estimation and Costing is a vital part of Civil Engineering. No project can begin without the total Building Estimation and Costing done by the Engineer. An estimate is the anticipated or probable cost of a work and is determined theoretically by mathematical calculations based on the drawing (plans, section.etc.), specifications and current rates. The entire Cost of construction and the infrastructure used for the purpose of construction is estimated and the final costing is done. An estimate is prepared by computing or calculating the quantities required and then calculating the cost at suitable rates, to get the expenditure likely to be incurred in the construction of a building or structure. The primary object of the estimate is to enable one to know beforehand, the cost of the work and hence its feasibility can be determined i.e., whether the project could be taken up within funds available or not. It also gives an idea of time required for the completion of the work. It is also required for inviting tenders and to arrange contract and to control the expenditure during the execution. The main objective of the project is to estimate the total cost required to construct a given residential building. Estimating involves preparing detailed estimate, calculating the rate of each unit of work and preparing abstract of estimate.

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 Keywords

Building, drawings, Construction, infrastructure..

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Urban transportation systems in rapidly growing cities face challenges such as traffic congestion, pollution, long travel times, and overburdened road networks. The introduction of metro rail systems has emerged as a transformative solution to these problems, offering a high-capacity, efficient, and environmentally friendly mode of public transportation. This study explores the impact of metro rail on urban transportation, focusing on improvements in connectivity, reduction in vehicular traffic, time savings, fuel conservation, and lower greenhouse gas emissions. Additionally, the metro rail contributes to socioeconomic development by enhancing access to employment, education, and other urban amenities. The paper also analyzes case studies from various Indian cities like Delhi, Hyderabad, and Bangalore, highlighting measurable benefits and challenges encountered in the implementation and operation of metro rail systems. Overall, the metro rail significantly reshapes urban mobility patterns and plays a crucial role in creating sustainable and inclusive cities.

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 Keywords

Urban transportation, road networks, vehicular traffic, gas emission.

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 Abstract

This project is focused on creating an smart crop recommendation system with machine learning algorithms and sensor data to provided better precision agriculture. We trained a Random Forest Classifier model with a dataset including most of the major features of how agriculture should be managed based on inputs of Nitrogen, Phosphorus, Potassium, temperature, humidity, soil pH, and rainfall values. After preprocessing our data with normalizing and label encoding we model reached an accuracy of 99% for predicting crop suitableity with respect to environmental conditions. To be used in real time with live input, we developed the trained model and deployed it using a REST API application we built in Flask. The REST API accommodates the ability to accept live input from IoT devices such as a NodeMCU. The API accepts live input in real time and output crop prediction. At the same time, it also collects the last sensor readings useful for preventive maintenance through its endpoints and stores the users prediction output. In addition to this, we also built a really nice and user friendly dashboard using Streamlit to plot the sensor values and crop recommendation system in real time. The dashboard is very modern in design, user-friendly for navigating in an intuitive manner, includes real time visualizations in a very nice display format, and talks about the environmental conditions data used to suggest a crop.

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 Keywords

Precision Agriculture, Crop Recommendation, Machine Learning, Real-time Prediction, IoT in Agriculture, Sustainable Farming, Data-Driven Agriculture, Sensor Data, Smart Farming

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The very volatile nature of Bitcoin, caused by a variety of economic, geopolitical, and technological considerations, is a constant challenge to making reliable short- and long-term price predictions. Such volatility has profound implications for traders, institutional investors, financial analysts, and algorithmic trading sites. This study investigates a deep learning-based method of Bitcoin price forecasting with a Long Short-Term Memory (LSTM) neural network, a dedicated type of Recurrent Neural Networks (RNNs) renowned for its ability to learn temporal dependencies and long-term trends in time-series data. Unlike more conventional statistical models like Autoregressive Integrated Moving Average (ARIMA), which are challenged by non-linear and abrupt market patterns, LSTM networks provide more excellent adaptability and strong resistance in learning from intricate sequential data. This work not only presents a high-performing prediction model but also an end-to-end deployment pipeline that closes the gap between research and practice. Furthermore, we present thorough mathematical explanations of the internal workings of LSTM through equations, and we address the deployment pipeline, system architecture, and practical usability of the application. Our study ends with suggested extensions including attention mechanisms, Transformer models, social media sentiment analysis, and multi-cryptocurrency forecasting functionality, opening the door to future development and wider use in financial prediction systems.

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 Keywords

Bitcoin, LSTM, Price Prediction, Time-Series Forecasting, Deep Learning, RMSE, Web Deployment

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 Abstract

Corporate workstation design has transitioned from rigid cubicles to flexible and user-centric environments that support collaboration, health, and adaptability. Traditional layouts prioritize efficiency but often compromised comfort, while open-plan offices improve communication yet raise concerns about noise and privacy (Saval, 2014; Kim & de Dear, 2013). Post-pandemic work culture has accelerated the adoption of ergonomic, technology-integrated, and wellness-focused workspaces (Sutherland & Jarrahi, 2021) This study explores the integration of anthropometric data and ergonomic principles in modern workstation design. Features such as sit-stand desks, adjustable chairs, and monitor arms address musculoskeletal risks and improve productivity. Flexible and modular systems further enable organizations to adapt the changing space and workforce needs. A comparative analysis of brands like IKEA, Godrej, Steelcase, Herman Miller, HNI, Urban Ladder, and Featherlite reveals global trends emphasizing modularity, sustainability, and localized adaptability. While international brands focus on innovation and environmental standards, Indian brands cater to SMEs with cost-effective, compact solutions (Business of Furniture in India 2023-2027). The findings highlight the growing need for human-centered, sustainable workspaces that align with both employee well-being and business agility in the evolving corporate landscape. Keywords: Corporate workstation, Ergonomics, Anthropometric, Furniture design, Comparative analysis.

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FURNITURE TREND

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Contemporary urban environments face critical challenges in parking infrastructure management due to exponential urbanization. This research presents an intelligent parking management framework integrating YOLOv8 computer vision algorithms with IoT technologies and AWS cloud infrastructure. The system employs real-time object detection to monitor parking occupancy, transmits data through AWS IoT Core using MQTT protocol, and provides instantaneous feedback through ESP8266-controlled displays. Canny edge detection enhances accuracy under varying environmental conditions. Experimental validation demonstrates 94.7% vehicle detection accuracy with 97ms response time, achieving 32% reduction in parking search time, 28% decrease in fuel consumption, and 24% improvement in traffic flow efficiency.

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 Keywords

Smart Parking, YOLOv8, Computer Vision, AWS IoT Core, Real-time Detection, MQTT Protocol

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 Abstract

Reinforced concrete demonstrates low shrinkage and cracking tendencies during the curing process. This feature enhances its dimensional stability, making it an attractive option for precast elements and components that require precise dimensions. Furthermore, its excellent fire resistance capabilities ensure that structures made with Reinforced concrete maintain their integrity and functionality even at elevated temperatures, which is crucial for fire safety in high-rise and industrial buildings. These properties collectively enhance the material's appeal, positioning it as a competitive alternative to traditional concrete in meeting contemporary construction demands.

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KEY WORDS: Coconut Fiber, traditional concrete, Cement, Compressive Strength.

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The swift advancement of technology has brought about a significant transformation in the healthcare sector, especially with the incorporation of mobile applications that optimize medical consultations, appointment scheduling, and other critical healthcare services. In order to improve patient outcomes, healthcare accessibility, and efficiency, this article describes the creation of a next-generation medical scheduling and information system utilizing Flutter and Firebase. Integrated into a single, user-friendly platform, the system enables functions like scheduling doctor's appointments, video consultations, tracking the location of live medical stores, online purchasing of medical supplies, and scheduling ambulances. The ability of the system to deliver individualized treatment is further improved by the incorporation of machine learning for personalized healthcare recommendations. The technical architecture, implementation difficulties, and possible advantages of the system in changing the healthcare environment are all examined in this study.

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 Keywords

Medical Scheduling Systems, Mobile Health Applications, Machine Learning in Healthcare, Healthcare Accessibility

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