Volume 11 | Issue 4 |

 Abstract

Indian Railways (IR), often regarded as the country's lifeline, connects every part of the second-most populated nation. The largest logistics carrier's logistical and financial performance has been at a low point for a few years. The progressive decline in IR's healthnot only grabs people's attention but also makes them curious about the underlying causes of this fragility. The worrying performance of IR during the past few years has attracted a tonne of literature attention, but there has been no satisfactory identification of the illnesses. In light of this, a study of declining performance to identify the underlying causes is necessary. For the aim of analysis, the study uses a variety of metrics,including OR, IR performance, etc. IR is plagued by subpar OR, and IR performance, and is on the edge of going bankrupt. Subventions like subsidies should no longer be used,and outdated pricing strategies should be abandoned in favor of a pricing plan based onhow much traffic can be supported to prevent collapse and paralysis. The goalof the analysis-based Python project IRCTC Live Dashboard is to create a dashboard that will provide information about the trains. This dashboard will assist in determining the availability of trains and their specifics from the source station to the destination station. This project is dynamic, takes in many different inputs, and produces results. It is a data analysis-based project where the dashboard is created and analyzed using various Python modules. This project has a huge amount of room for growth in a varietyof data science areas, including data analysis.

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 Keywords

Indian railways; performance of Indian railways; performance of IR; operating ratio (OR); Data Preprocessing, Data cleaning, Data Optimization, Visualization.

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Radiology imaging plays a crucial role in diagnosing and treating various medical conditions. However, in sterile environments such as operating rooms, accessing and analysing radiology images using traditional devices such as a mouse or keyboard poses a risk of infection transmission. To address this issue, we propose a gesture-based tool for sterile browsing of radiology images that enables healthcare workers to access and analyse radiology images using customizable hand gestures. The proposed system integrates with existing radiology software systems and provides enhanced security features to protect patient data and images. The system ensures mobility, enabling healthcare workers to access radiology images from anywhere in the hospital or clinic, leading to improved patient care and outcomes. Overall, the proposed system provides a safe, efficient, and user-friendly way for healthcare workers to access and analyse radiology images in sterile environments. This system captures the hand gestures through a HD camera and processes the radiology images in different ways like panning, zoom in, zoom out, swipe left and swipe right etc. in a touchless environment.

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 Keywords

OpenCV, Mediapipe, Numpy, CNN, HD Cam

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 Abstract

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Nowadays mobile smartphones are used everyday by almost everyone making it the basic needed commodity. Covid 19 pandemic caused increased usage of mobile smartphone in everyday life. Hand and wrist discomfort and pain is observed in many students due to overuse of mobile smartphones as seen in the recent studies. This study was undertaken to assess the hand and wrist discomfort in pharmacy students. Survey based study was conducted to assess the dimensions and features of smartphones. Usage of smartphones at a stretch as well as in everyday life along with hand dominance while using smartphones in specific domains like scrolling, taking selfies, educational purposes, etc. was analysed.. Purposive sampling was done and descriptive statistics is used to analyze the data. It was observed that 36.5% students were having hand and wrist discomfort hence the ergonomic advices like taking frequent breaks, stretching, mobility and strengthening of hand and wrist musculature was suggested to be inculcated.

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Nowadays mobile smartphones are used everyday by almost everyone making it the basic needed commodity. Covid 19 pandemic caused increased usage of mobile smartphone in everyday life. Hand and wrist discomfort and pain is observed in many students due to overuse of mobile smartphones as seen in the recent studies. This study was undertaken to assess the hand and wrist discomfort in pharmacy students. Survey based study was conducted to assess the dimensions and features of smartphones. Usage of s

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Abstract In order to compete in the ever growing competent market it is very important for a structural engineer to save time. As a sequel to this an attempt is made to analyse and design a Multi-storied building by using a software package staad pro. For analysing a multi storied building one has to consider all the possible loadings and see that the structure is safe against all possible loading conditions. There are several methods for analysis of different frames like kani's method, cantilever method, portal method, Matrix method. The present project deals with the analysis of a multi storeyed residential building using staad pro. The dead load &live loads are applied and the design for beams, columns, footing is obtained STAAD Pro with its new features surpassed its predecessors, and compotators with its data sharing capabilities with other major software like AutoCAD. We conclude that staad pro is a very powerful tool which can save much time and is very accurate in designs. Thus it is concluded that staad pro package is suitable for the design of a multi-storeyed building.

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Autocad , Staadpro , shear force, Bending moments

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Abstract: Warm-e-Lauztain (Tonsillitis) being very common clinical condition affecting mostly the school going children is the one of the important reason to visit a doctor frequently. Diagnostic features and findings on clinical examination are described very clearly in classical texts of Unani medicine, e.g. if the tonsillitis is caused due to abnormality of khilt-e-dam (humour sanguine), it is expressed by severe pain in the throat, fever, redness of eyes and face, sweetish taste in mouth etc.; if it is due to khilt-e-safra (humour bile), then it is characterized by severe pain in the throat, difficulty in the swelling, high grade fever, dryness, paleness of face; if there is imbalance in khilt-e-balgham, there will be soft whitish swollen tonsils associated with paresis and softness of the tongue; tonsillitis due to the abnormality in khilt-e-sauda exhibit hard swelling of tonsils encroached into the tongue and surrounding tissues. Chronic tonsillitis is a common disease found worldwide mostly in school going children. There are many single as well compound drugs for the management of tonsillitis which are being used for a longer duration without any known side effect. A randomized open comparative study was designed to validate the efficacy of two very commonly prescribed formulations in the patients of chronic tonsillitis viz. Laooq Sapistan Khyar Shambari and Sharbat Toot Siyah. Always first emphasis should be given for medical treatment, if it fails then opt surgical management. When the acute inflammation (warm-e-haar) has been subsided and the base of swelling is soft and thin, than it is suitable time for tonsillectomy. Therefore it may be concluded that scholars of Unani medicine were much aware about the signs & symptoms of chronic tonsillitis; they were able to diagnose the disease clinically and were expert enough to assess the condition, whether requiring medical management or surgical intervention. Certain Household remedies are Unani drugs and formulation used to control the Tonsillitis in unani system of medicine.

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Tonsillitis, Unani classical, Household Treatment, Tonsillectomy and history of surgery,

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Low back pain is a painful condition affecting the lower portion of spine and causes function disability in housemaid. The aim of this study is to understand the percentage of functional disability caused due to work related back pain in housemaid. The Hindi version of Quebec back pain disability scale is used to determine the level of functional disability due work related back pain in total of 100 housemaids for the research. The data was collected and statistical analysis was done. The results have concluded that there is functional disability due to work related back pain in housemaids due to the factors like wrong posture of working, pushing and pulling heavy load, repetitive bending forward and working, excessive twisting and trunk movements, lack of rest, lack of physical activity and low level of nutrition uptake.

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Functional disability, Low back pain, Housemaids, Quebec back pain disability scale.

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The current attendance mechanism is thought of as manual. Both teachers and students must put in a significant amount of time. Even with manual attendance, there is still a chance that there will be proxies in the class. The work's key contribution is the creation of an attendance control system that employs student faces as feed input. We have decided to use the Raspberry Pi and a webcam to make it accessible on all platforms. Connected to the Raspberry Pi module is a webcam. The web cam is used to capture student face photographs. The Qt creator IDE implements many face extraction methods utilizing the OpenCV platform. Face recognition technology is used to take attendance in this project, allowing us to identify each student's face

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 Keywords

Attendance management system, Face Recognition, Raspberry Pi.

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 Abstract

The level of pollution is increasing rapidly due to factors like industries, urbanization, increasing in population, vehicle use which can affect human health. IOT Based Air Pollution Monitoring System is used to monitor the Air Quality over a web server using Internet. It will trigger an alarm when the air quality goes down beyond a certain level, means when there are sufficient amount of harmful gases present in the air like CO2, smoke, alcohol, benzene, NH3 and NOx. It will show the air quality in PPM on the LCD and as well as on webpage so that air pollution can be monitored very easily. The system uses MQ135 and MQ6 sensor for monitoring Air Quality as it detects most harmful gases and can measure their amount accurately. The implementation of wireless sensor networks (WSNs) for monitoring the complex, dynamic, and harsh environment of underground coal mines (UCMs) is sought around the world to enhance safety. However, previously developed smart systems are limited to monitoring or, in a few cases, can report events. Therefore, this study introduces a reliable, efficient, and cost-effective internet of things (IoT) system for air quality monitoring with newly added features of assessment and pollutant prediction. Based on the sensed data, the proposed system assesses mine air quality in terms of the mine environment index (MEI). Principal component analysis (PCA) identified CH4, CO, SO2, and H2S as the most influencing gases significantly affecting mine air quality. According to the Mine Safety and Health Administration (MSHA), faulty equipment, negligence of labor towards explosions, structure failure, and gas accumulation are the most common causes of underground mine accidents. During the economic year of 2014, in the salt range coal mine in Punjab, Pakistan, more than 35% of accidents occurred due to the accumulation of toxic gases. Therefore, for the safety of workers and the mine itself, it is extremely important to monitor the mine environment continuously and accurately.

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 Keywords

Arduino, Underground coal mines, Internet of things, Mine environment index, Principal component analysis.

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 Abstract

Real estate is a significant investment, and it is essential to know the value of a property before investing hard-earned money. Machine learning techniques have been increasingly used to predict real estate prices in megacities like Mumbai, Chennai, Bangalore, and Pune. This paper focuses on the XGBoost regression algorithm, a powerful technique that can be used to predict housing prices with high accuracy. The XGBoost algorithm is an ensemble method that combines multiple decision trees to create a more robust and accurate model. It is particularly useful in handling non-linear relationships between input variables and real estate prices. The algorithm can efficiently process large datasets with multiple variables, making it an excellent tool for real estate prediction. This study highlights the importance of the XGBoost algorithm in predicting real estate prices accurately. It examines various input variables, including carpet area, number of bedrooms and baths, balcony, amenities, and area type, to build a reliable model. The XGBoost algorithm is evaluated based on various performance metrics, such as mean absolute error, mean squared error, and R-squared, to assess its accuracy and effectiveness. The results show that the XGBoost algorithm outperforms other machine learning techniques such as linear regression, decision trees, random forest, and neural networks, in predicting real estate prices. It can efficiently handle complex non-linear relationships and accurately predict the prices of properties in megacities like Mumbai, Chennai, Bangalore, and Pune. In conclusion, this study demonstrates the effectiveness of the XGBoost algorithm in predicting real estate prices. The algorithm can help investors make informed decisions by providing accurate predictions based on various input variables. The study emphasizes the importance of using advanced machine learning techniques like XGBoost for real estate investments, especially in megacities where property prices are highly volatile.

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 Keywords

: Real estate, investment, machine learning techniques, predicting, housing prices, XGBoost regression algorithm, ensemble method, decision trees, non-linear relationships, input variables, accuracy, performance metrics, mean absolute error, mean squared error, R-squared.

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