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This paper presents a smart water pipeline monitoring system to control the water leakages occurring in it. In day by day life, usage of water is increasing with proportional to increase in wastage of water. So, to overcome from this, a smart monitoring system with the help of Internet of Things (IoT) is designed and proposed. In this modern era, usages and advantages of IoT are immeasurable. There are a lot of sensors are available in the market to measure the water flow [2]. In this system, to monitor the flow of water, the water flow sensor is used in the pipeline and also to measure the contamination of water a turbidity sensor has been used. Flow sensor works on the principle of a hall effect [5]. Nodemcu microcontroller, is one of the most common microcontrollers used for IoT purposes has been used in this system [8]. Main purpose of this microcontroller used is because of its interrupt pins. The values measured by the water flow sensor and turbidity sensor are uploaded to the cloud server. For storing the data in the cloud, the ThingSpeak cloud server has been used for this system, because ThingSpeak cloud server is open and free to use. With the values measured by the water flow sensor the data is displayed in the ThingSpeak cloud webserver. So, monitoring of the water flow in the pipeline will be done very easily.

Air quality has recently been a huge concern as it directly affects people’s lives. An air quality level assessment and prediction system is essential to keep track of air quality. Therefore, developing an efficient air quality assessment and prediction system has become one of the most important concerns. In the present work air quality level of Surat city, India is assessed and predicted for the period from 2020 to 2023 using the Autoregressive integrated moving average (ARIMA) model. Experimental results show that the ARIMA model outperforms the other models. According to the findings, the maximum quantity of SO2 and NO2 present in the air in 2020 is 37 mm and 18 mm, respectively, with a maximum of 27 mm and 31 mm in 2021. Thus, we can observe that even though SO2 has reduced a bit, the amount of NO2 has increased, thus degrading the quality of air.

This research paper presents a comprehensive assessment of the built-up area in Mysuru City over the decade spanning from 2010 to 2020, employing advanced geospatial techniques. The study aims to analyze the spatiotemporal patterns of urban expansion, land-use dynamics, and associated factors influencing the city’s built environment. Remote sensing imagery, Geographic Information System (GIS) tools, and machine learning algorithms are leveraged to process and interpret satellite data for accurate land-cover classification. The methodology involves the acquisition and preprocessing of multi-temporal satellite imagery to delineate and map the built-up areas at different time intervals. Land-use change detection techniques are employed to identify and quantify alterations in urban morphology over the specified period. Additionally, socio-economic and environmental variables are integrated into the analysis to discern the drivers of urban growth. The outcomes of this research contribute valuable insights into urbanization dynamics and land-use planning strategies, facilitating informed decision-making for sustainable urban development.

This project will help the non-native people of Karnataka to easily understand the kannada boards and travel easily. The main task of this work is to recognize the kannada traffic text boards and translate that to English language. Histogram equalization is used to find the gap between the characters. K-means clustering is used to divide the characters into different clusters then the segmented characters are passed to the pretrained model to recognize what the characters means. The model used for recognizing the traffic text is convolutional neural networks. The methodologies used here is the image augmentation, converting RGB image to grey scale and normalizing the image to reduce the noise. The validation accuracy obtained while training the model with coloured images, normalized image, grey scale image and normalized grey scale image is respectively 98.88%, 98.85%, 98.8% and 99.39% and while testing this model with kannada language, the testing accuracy obtained respectively with coloured images, normalized image, grey scale and normalized grey scale image is 95.91%, 96.58%, 95.42% and 96.98 % . In this work, word spotting method is employed for kannada language recognition. The performance of this system is faster since machine learning algorithms are used here.

Land Use and Land Cover (LULC) classification is critical for monitoring and managing natural resources and urban development. This study focuses on LULC classification for change detection analysis of remotely sensed data using a machine learning-based Random Forest classifier. The research aims to provide a detailed analysis of LULC changes between 2010 and 2020. The Random Forest classifier is chosen for its robustness and high accuracy in handling complex datasets. The classifier achieved a classification accuracy of 86.56% for the 2010 data and 88.42% for the 2020 data, demonstrating an improvement in classification performance over the decade. The results indicate significant LULC changes, highlighting areas of urban expansion, deforestation, and agricultural transformation. These findings highlight the importance of continuous monitoring and provide valuable insights for policymakers and environmental managers. The study demonstrates the effectiveness of using advanced machine-learning techniques for accurate LULC classification and change detection in remotely sensed data.

In this present work the banana/polyester was fabricated using hand layup method followed by compression moulding technique. The moulded composite was studied with special references to the Taguchi’s method for optimizing the cutting parameters of AWJM for minimizing the kerf width and kerf taper. An L9 (34) orthogonal array was selected to conduct the experiment with the identified significant parameters such as Feed rate, Pressure, Standoff distance and Abrasive Method. The significance of cutting parameters was analysed with the help of ANOVA. The optimum process parameters were identified with the help of signal to noise ratio (SN). The decreasing value of hardness was observed after the machining of composite using shore-D hardness tester. The influence of abrasive particle was found to be predominant affecting the performance of the output quality measures.

Objectives: Statins by their anti-inflammatory and endothelial stabilizing effect can be beneficial in patients with chronic obstructive pulmonary disease (COPD) and pulmonary hypertension (PH). The present study was done to evaluate the effect of rosuvastatin on pulmonary functions and quality of life (QOL) in patients with concomitant COPD and PH. Materials and Methods: It was a prospective, randomized, double-blind, placebo-controlled, study conducted in patients with COPD and PH. A total of sixty patients were assigned to receive either rosuvastatin 10 mg or placebo once a day in addition to their conventional treatment for 12 weeks. Routine blood investigations, pulmonary functions, echocardiogram, exercise capacity, and QOL using a questionnaire were assessed at the baseline and after 12 weeks. Results: In patients of rosuvastatin group, there was a statistically significant increase in peak expiratory flow rate (PEFR) (P = 0.04) but no significant change in other pulmonary functions: Forced vital capacity (FVC), forced expiratory volume at 1 s (FVC, FEV 1 , FEV 1 /FVC), and echocardiogram parameters. There was a significant increase in 6-min walk test (6-min walk distance) (P = 0.03) at the end of 12 weeks. On comparing with placebo, rosuvastatin showed a significant reduction (P = 0.045) in COPD exacerbations while adverse effects did not differ. Conclusion: Statins have a favorable effect on patients with COPD and PH regarding the improvement in PEFR, COPD exacerbations, and exercise capacity. Such effects can be beneficial in these patients and more so in patients with concomitant coronary artery disease or hyperlipidemia where long-term benefits of statins have been established.

Modern integrated circuit design manufacturing involves outsourcing intellectual property to third-party vendors to cut down on overall cost. Since there is a partial surrender of control, these third-party vendors may introduce malicious circuit commonly known as Hardware Trojan into the system in such a way that it goes undetected by the end-users’ default security measures. Therefore, to mitigate the threat of functionality change caused by the Trojan, a technique is proposed based on the testability measures in gate level netlists using Machine Learning. The proposed technique detects the presence of Trojan from the gate-level description of nodes using controllability and observability values. Various Machine Learning models are implemented to classify the nodes as Trojan infected and non-infected. The efficiency of linear discriminant analysis obtains an accuracy of 92.85 %, precision of 99.9 %, recall of 80%, and F1 score of 88.8% with a latency of around 0.9 ms.

Soil salinization is one of the most frequent environmental concerns that contribute to the degradation of agricultural land, particularly in arid and semi-arid regions. The correct methods must be developed by farm owners and decision-makers in order to reduce soil erosion and increase crop output. For this, accurate spatial forecasting and soil salinity modeling in agricultural areas are needed. The accurate consideration of environmental elements under the scale effects, which have received less attention in prior research, is essential for digital soil mapping. The goal of this research is to create a special technique for predicting soil salinity. Preprocessing is done on the sentinel image input first. The next step is to determine the spectral channels, salinity index, and vegetation index. The development of transformation-based features also takes advantage of enhanced PCA. The suggested hybrid classifier uses \"Deep Belief Network (DBN) and Bidirectional Long Short Term Memory (Bi-LSTM)\" to predict salinity while accounting for these variables. The final forecast result is determined by the increased score level fusion. To improve the precision and accuracy of the prediction, Self Upgraded BSO (SU-BSO) calibrates the weights of the Bi-LSTM and DBN. The MSE values of the suggested technique are lower than those of other conventional methods like CNN, DBN, SVM, BI-LSTM, MLP-FFA, and MLSR metrics, achieving lower values of 0.13, 0.07, 0.03, 0.05, 0.09, and 0.094%, respectively. Finally, numerous measurements are employed to demonstrate the value of the selected approach.

Land Use and Land Cover (LULC) classification is critical for monitoring and managing natural resources and urban development. This study focuses on LULC classification for change detection analysis of remotely sensed data using a machine learning-based Random Forest classifier. The research aims to provide a detailed analysis of LULC changes between 2010 and 2020. The Random Forest classifier is chosen for its robustness and high accuracy in handling complex datasets. The classifier achieved a classification accuracy of 86.56% for the 2010 data and 88.42% for the 2020 data, demonstrating an improvement in classification performance over the decade. The results indicate significant LULC changes, highlighting areas of urban expansion, deforestation, and agricultural transformation. These findings highlight the importance of continuous monitoring and provide valuable insights for policymakers and environmental managers. The study demonstrates the effectiveness of using advanced machine-learning techniques for accurate LULC classification and change detection in remotely sensed data.