In this study, we have proposed the utilization of machine learning techniques to predict the I-V characteristics and transconductance curve of a multichannel junctionless FinFET design. Various machine learning models were employed to predict the current-voltage (I-V) and transconductance (g(m)) curve by training the models with data generated from Technology Computer Aided Design (TCAD) simulations for the 3D multichannel junctionless FinFET. Specifically, three different machine learning models were constructed using the random forest (RF), linear regression (LR), and decision tree regression (DTR) algorithms. These models aimed to uncover hidden relationships and establish correlations between different physical parameters. This approach offers valuable insights into extracting several short channel effects parameters, such as threshold voltage (V-th), on-state current (I-on), off-state current (I-off), and subthreshold swing, from the trained dataset. The results indicate that both the random forest and decision tree regression models achieved a similar level of accuracy in predicting the I-DS-V-GS and g(m) curves when compared to the TCAD simulations for the proposed device structure. The RF, LR, and DTR models were evaluated using metrics such as root mean squared error (RMSE) and error rate. The RF-ML and DTR-ML models exhibited 99% accuracy with an error rate below 1%, even when trained with only 20% of the dataset (i.e., 255 training samples). This work primarily aims to demonstrate the integration of machine learning techniques with device simulation tools to advance technology for new device development.

This research aims to classify the arsenic contamination in the groundwater along the banks of river Ganga of Varanasi, India. The groundwater is vital for various purposes, including agriculture and drinking. Groundwater contamination with high levels of arsenic pose a significant health risk. To tackle this problem, the authors build a model for classifying arsenic levels in groundwater samples that incorporates the extreme learning machine (ELM) algorithm and crowd search optimisation (CSO) technique. In the hybrid approach, they initialize the ELM components and randomly assign weights while employing CSO to guide the search for optimal solutions. By classifying new groundwater samples as having high or low arsenic concentrations, the developed model can be used to evaluate the new groundwater samples. The proposed hybrid approach offers a promising solution for monitoring and managing groundwater quality, ensuring a healthier environment for the city's population.

Arsenic contamination in groundwater due to natural or anthropogenic sources is responsible for carcinogenic and non-carcinogenic risks to humans and the ecosystem. The physicochemical properties of groundwater in the study area were determined in the laboratory using the samples collected across the Varanasi region of Uttar Pradesh, India. This paper analyses the physicochemical properties of water using machine learning, descriptive statistics, geostatistical and spatial analysis. Pearson correlation was used for feature selection and highly correlated features were selected for model creation. Hydrochemical facies of the study area were analyzed and the hyperparameters of machine learning models, i.e., multilayer perceptron, random forest (RF), naïve Bayes, and decision tree were optimized before training and testing the groundwater samples as high (1) or low (0) arsenic contamination levels based on the WHO 10 μg/L guideline value. The overall performance of the models was compared based on accuracy, sensitivity, and specificity value. Among all models, the RF algorithm outclasses other classifiers, as it has a high accuracy of 92.30%, a sensitivity of 100%, and a specificity of 75%. The accuracy result was compared to prior research, and the machine learning model may be used to continually monitor the amount of arsenic pollution in groundwater.

Groundwater is an essential resource; around 2.5 billion people depend on it for drinking and irrigation. Groundwater arsenic contamination is due to natural and anthropogenic sources. The World Health Organization (WHO) has proposed a guideline value for arsenic concentration in groundwater samples of 10 μ g/L. Continuous consumption of arsenic-contaminated water causes various carcinogenic and non-carcinogenic health risks. In this paper, we introduce a geospatial-based machine learning method for classifying arsenic concentration levels as high (1) or low (0) using physicochemical properties of water, soil type, land use land cover, digital elevation, subsoil sand, silt, clay, and organic content of the region. The groundwater samples were collected from multiple sites along the river Ganga’s banks of Varanasi district in Uttar Pradesh, India. The dataset was subjected to descriptive statistics and spatial analysis for all parameters. This study assesses the various contributing parameters responsible for the occurrence of arsenic in the study area based on the Pearson correlation feature selection method. The performance of machine learning models, i.e., Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Decision Tree, Random Forest, Naïve Bayes, and Deep Neural Network (DNN), were compared to validate the parameters responsible for the dissolution of arsenic in groundwater aquifers. Among all the models, the DNN algorithm outclasses other classifiers as it has a high accuracy of 92.30

Understanding the dynamics of temperature trends is vital for assessing the impacts of climate change on a regional scale. In this context, the present study focuses on Madhya Pradesh state in Central Indian region to explore the spatial-temporal distribution patterns of temperature changes from 1951 to 2021. Gridded temperature data obtained from the Indian Meteorological Department (IMD) in 1° × 1° across the state are utilised to analyse long-term trends and variations in temperature. The Mann-Kendall (MK) test and Sen’s slope (SS) estimator were used to detect the trends, and Pettitt’s test was utilised for change point detection. The analysis reveals significant warming trends in Madhya Pradesh during the study period during specific time frames. The temperature variables, such as the annual mean temperature ( T mean ), maximum temperature ( T max ), and minimum temperature ( T min ), consistently increase, with the most pronounced warming observed during winter. The trend analysis reveals that the rate of warming has increased in the past few years, particularly since the 1990s. However, Pettitt’s test points out significant changes in the temperature, with T mean rising from 25.46 °C in 1951–2004 to 25.78 °C in 2005–2021 (+0.33 °C), T max shifting from 45.77 °C in 1951–2010 to 46.24 °C in 2011–2021 (+0.47°C), and T min increasing from 2.65 °C in 1951–1999 to 3.19 °C in 2000–2021 (+0.46 °C). These results, along with spatial-temporal distribution maps, shed important light on the alterations and variations in monthly T mean , T max , and T min across the area, underlining the dynamic character of climate change and highlighting the demand for methods for adaptation and mitigation.

Climate change is a worldwide problem caused by various anthropogenic activities, leading to changes in hydroclimatic variables like temperature, rainfall, riverine flow, and extreme hydrometeorological events. In India, a significant change is noted in its natural resources and agriculture sectors. In this study, we analysed the long-term spatio-temporal change in rainfall patterns of Madhya Pradesh, Central India, using Indian Meteorological Department high-resolution gridded data from 439 grid points. The coefficient of variance analysis showed low variability in annual and monsoon rainfall but significant variability in pre-monsoon, post-monsoon, and winter seasons, indicating considerable seasonal variation. Pre-monsoon rainfall exhibited an increasing trend (0.018 mm annually), while annual, monsoon, post-monsoon, and winter rainfall showed decreasing trends. Change point analysis identified shifts in rainfall patterns in 1998 (monsoon, annual), 1955 ( pre-monsoon), 1987 (post-monsoon), and 1986 (winter). Spatio-temporal distribution maps depicted irregular rainfall, with some areas experiencing drastic declines in precipitation after 1998. The maximum average annual rainfall reduced from 1,769 to 1,401 mm after 1998 affecting water availability. The study's findings highlight a significant shift in Madhya Pradesh's seasonal rainfall distribution after 1998, urging researchers and policymakers to address water-intensive cropping practices and foster climate resilience for a sustainable future in the region.

Groundwater plays a significant role in sustaining life in terrestrial and marine ecosystems. Arsenic contamination in aquifers poses a serious threat to the ecosystem due to its carcinogenic effect. Arsenic contamination in aquifers of the Varanasi region was noticed after water sampled from random sites of the Varanasi region of Uttar Pradesh, India. Under the Capacity Building of Urban Development (CBUD) scheme, Varanasi was chosen by the Ministry of Housing and Urban Poverty Alleviation (MoHUPA) and the Ministry of Urban Development (MoUD). In this study, various machine learning classifiers have been developed to classify water samples collected from the Varanasi region as safe or unsafe for consumption. The water with less than 10 µg/L As concentration is termed safe per World Health Organisation (WHO). Firstly the water samples parameters were ranked then the samples were trained and tested. Various parameters obtained from confusion matrices such as accuracy, precision, and recall are used to analyze the performance of different machine learning classifiers like Simple Logistic, MLP Classifier, and Random Forest. Among these models, Simple Logistic outperforms other classifier models. The Simple Logistic algorithm was considered the best model among the different classifiers. It has the highest accuracy of 79.03

This paper presents a machine learning approach for classification of arsenic (As) levels as safe and unsafe in groundwater samples collected from the Indo-Gangetic region. As water is essential for sustaining life, heavy metals like arsenic pose a public health concern. In this study, various tree-based machine learning models namely Random Forest, Optimized Forest, CS Forest, SPAARC, and REP Tree algorithms have been applied to classify water samples. As per the guidelines of the World Health Organization (WHO), the arsenic concentration in water should not exceed 10 μg/L. The groundwater quality parameter was ranked using a classifier attribute evaluator for training and testing the models. Parameters obtained from the confusion matrix, such as accuracy, precision, recall, and FPR, were used to analyze the performance of models. Among all models, Optimized Forest outperforms other classifier as it has a high accuracy of 80.64%, a precision of 80.70%, recall of 97.87%, and a low FPR of 73.33%. The Optimized Forest model can be used to test new water samples for classification of arsenic in groundwater samples.

As per the latest report from Statista, there were 4.6 billion Internet users worldwide active in January 2021, which is more than 59.5% of the total population. In India, there are over 749 million Internet users, more than 50% of the entire country’s population. The users are almost equally distributed over rural and urban areas. With the growing technology and online platforms for entertainment, communication, shopping, and social media, Internet users will grow at an even higher pace in urban and rural areas. So the Internet is no more an optional facility. Instead, it is becoming an essential requirement for the day today. India has 22 official languages as “the 8th Schedule” of the Constitution till the year 2020. Now, the growing Internet users, especially in rural areas where Non-English native speakers are relatively high, require the accessibility of the Internet using not only English but in their native languages too.

This paper presents a machine learning approach for assessing groundwater arsenic contamination levels in Jharkhand, India. The water is essential for sustaining life, and the presence of heavy metals like arsenic poses a carcinogenic and non-carcinogenic risk. In this study, various machine learning models viz Decision tree, Random Forest, Multilayer Perceptron, and Naive Bayes algorithms were applied to classify the samples as safe or unsafe, considering a provisional guide value of 0.01 mg/l as the benchmark. For classification, different parameters viz DEM, subsoil clay content, subsoil silt content, subsoil sand content, subsoil organic content, type of soil, and LULC were considered. Pearson correlation exhibited a positive and a negative relation between considered parameters and arsenic occurrence. Parameters obtained were considered for the classification of arsenic, and various evaluation criteria, such as accuracy, sensitivity, and specificity, were used to analyze models' performance. Among the models, the Random Forest classifier outperforms other classifier models in terms of performance. Thus, the Random Forest model can be used to approximation people prone to arsenic contamination.

Sensor Networks consist of inexpensive nodes that are positioned over an area to collect the useful information and send the valuable data to the base station for further processing. In wireless sensor networks, nodes have limited power and shorter lifetime. So, it is crucial to aggregate the data in Energy Efficient manner and optimize the lifetime of the network. To solve the issue, Ant Colony Optimization, a Swarm Intelligence based Routing Technique is used. The proposed system is a Routing Approach based on ACO Algorithm along with the Clustering approach in which Cluster Head is elected based on maximum value of energy and degree to maximize Energy Efficiency of the network and to increase the lifetime of the network. Performance obtained from the simulation results shows that the proposed approach provides an Optimized solution in terms of Enhanced Network Lifetime and Efficient Energy Utilization.