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Curfews and lockdowns around the world in the Covid-19 era have increased the usage of the internet drastically and accordingly the amount of data shared on social media. In addition to using social media for sharing useful information, some miscreants are using the power of social media to spread hate speech and offensive content. Filtering the offensive language content manually is a laborious task due to the huge volume of data. Further, rapid developments in hardware and software technology have provided opportunities for users to post their comments not only in English but also in their native language scripts. However, based on the ease of Roman script usage, social media users specifically in multilingual countries like India, prefer to comment in code-mixed and multi-script texts. The typical systems that are employed to process and analyze monolingual texts are usually not appropriate for these kinds of texts. Further, as these texts do not adhere to the rules and regulations of any language to frame the words and sentences, the complexity of analyzing such texts increases. The novelty of the present study is to address the Offensive Language Identification (OLI) task in code-mixed and multi-script texts, this paper proposes to use relevant syllable and character n-grams features to train Machine Learning (ML) classifiers. The performance of the proposed models is evaluated on three Dravidian language pairs, namely: Malayalam-English, Tamil- English, and Kannada-English. The performances of ML classifiers prove the effectiveness of syllable and character n-grams features for code-mixed and multi-script texts analysis. [ABSTRACT FROM AUTHOR]

Tyre condition monitoring system (TCMS) is an emerging electronic safety system present in most of the new generation vehicles. It is intended to warn the driver when wheels are unbalanced or inflation pressure in a tyre goes below a certain recommended value. In India only limited vehicles having this critical safety system installed because it is highly expensive. The existing TCMS monitor only the tyre pressure using direct or indirect methods. In recent days the indirect TCMS using vibration signals and machine learning techniques are gaining momentum. This paper reports the effect of wheel balancing on a proposed affordable indirect TCMS. The present study goes one step forward to include the wheel balancing in the TCMS, thus increasing the scope of TCMS. The study was carried out using vibration signal data acquired from a low cost accelerometer placed on the wheel hub. The features required were taken out from the acquired data with the help of statistical feature extraction techniques. Features selection and feature classification were processed with J48 decision tree algorithm. The effect of wheel balancing in classification accuracy is clearly explained. [ABSTRACT FROM AUTHOR]

Predictive medicine for a holistic and proactive approach to health management is steadily replacing the reactive healthcare model as the dominant paradigm in the twenty-first century. The Ayurvedic medical system, which incorporates all parts of predictive medicine, divides people into seven constitution types, or Prakriti, to help practitioners determine their initial homeostatic conditions. This article uses data on the phenotypic characteristics of 217 healthy people who fall into three extreme Prakriti types to conduct a study for predicting Prakriti classes. Those who fit the Prakriti type are drawn from two genetically different northern and western India cohorts. In order to dichotomize inter-individual variability in various individuals, eight machine learning (ML) classifiers are used. The prediction skills of the ML algorithms are evaluated here using ten pairs of predefined training and testing datasets for each cohort. Lastly, a performance comparison of various ML algorithms is carried out using six crucial performance criteria. The study aims to investigate and appraise using artificial intelligence (AI) to evaluate Prakriti in Ayurveda. The use of AI in Prakriti assessment may have several advantages, including enhancing the consistency and accuracy of assessments and minimizing reliance on subjective judgements. This study aims to further our knowledge of how technology can be applied to enhance the practice of Ayurveda and possibly improve patient outcomes. [ABSTRACT FROM AUTHOR]

In India, around 7 million people depend on fishing for their livelihoods. They are assisted with a reliable and fast brief forecast for the areas of fish aggregations. Habitat mapping is critical in supporting strategic choices on fish usage and protection. In conjunction with techniques for machine learning, remote control has made comprehensive fish mapping on relevant scales possible. In machine learning, supervised algorithms are utilized to make forecasts from datasets, when data is accessible without relating output factors. In this research work, Ocean Surface Temperature (OST) and Satellite derived Chlorophyl material are the basic inputs to generating the information of Potential Fishing Zone (PFZ). The 16 features and additional financial derivative features are used for accurate future prediction of PFZ. The unwanted and missing data are removed using effective pre-processing techniques. Among the various methods available for forecasting nonlinear phenomena, the Neural Network is the best and the efficient method to get a forecast. Therefore, the Function Fitting Neural Network (FFNN) technique is mainly used to predicting the Integrated Potential Fishing Zone (IPFZ). The practical analyses are performed by analysing the 80% -20%, 60% -40% and future data in terms of various parameters. From the results, it is proved that the suggested FFNN achieved 90% of accuracy, where the existing neural network achieved 86% of accuracy by implementing with financial derivative features for the 80% -20% of available dataset. [ABSTRACT FROM AUTHOR]

Limestone mining is a significant economic activity in India, accounting for around 10% of the GDP however, it has certain negative environmental consequences. The objective of this study is to determine the spatial distribution area of captive limestone mines using remote sensing datasets, spectral index, and machine learning algorithms and compare their area estimation with industrial field survey reports for the financial year 2019. The study area includes a limestone resource area of 2226.16 ha with an excavation area of 487.10 ha in 2019. In the present research, we used a high-resolution Sentinel-2A satellite dataset to map and compute the active mining area by implementing the Normalised Vegetation Index (NDVI), Iterative Self-Organizing Data Analysis Technique (ISODATA), K-Nearest Neighbours (KNN), and Random Forest (RF) algorithms in the QGIS 3.18 software tool. The RF classifier estimated a limestone mine area of 379.57 ha with user accuracy (UA) of 97.25% and producer accuracy (PA) of 99.18% with a kappa coefficient value of 0.957. The mine area was estimated at 417.47 ha with a UA of 98.99% and PA of 99.10% and kappa value of 0.947 of the KNN method, The NDVI method estimated 469.92 ha with a UA of 93.63% and PA of 92.04% and kappa value 0.685. This research confirmed that the RF classifier well performed in classification with overall accuracy (OA) of 95.79% to KNN (OA of 94.78%), NDVI (OA of 79.84%) classifiers, and ISODATA poor in classification with OA of 64.16%. This research assists limestone mine owners and environmental engineers in making environmentally sustainable decisions, eco-friendly mine design, and monitoring. [ABSTRACT FROM AUTHOR]

False information is becoming more frequent in distributing disinformation by distorting people's awareness and decision-making by altering their views or knowledge. The propagation of disinformation has been aided by the proliferation of social media and online forums. Allowing it to readily blend in with true information. Parody news and rumors are the most common types of misleading and unverified information, and they should be caught as soon as possible to avoid their disastrous consequences. As a result, in recent years, there has been a surge in interest in effective detection approaches. For this study, a customized dataset was built that included both real and parody tweets from Pakistan and India. This study proposes a two-step strategy for detecting parody tweets. In the first stage of the approach the unstructured data is converted into structured data set. In the second step, multiple supervised artificial intelligence algorithms were employed. An experimental assessment of the different classification methods inside a customized dataset was undertaken in this study, and these classification models were compared using evaluation metrics. Our results showed accuracy of 92%. [ABSTRACT FROM AUTHOR]

Monitoring indoor air quality stays needed for human health because people use more than 95% of air in their indoor rooms. An Intelligent Internal Air Quality Monitoring (IIAQM) system built on the Internet of Things (IoT) devices has been developed and tested in Quantanics Techserv Private Limited, Tamilnadu, India. To monitor the levels of CO2, PM2.5 (Particle Matters 2.5), and moisture measurement, the IIAQM model has been used to monitor the present level of air quality. The gateway collects IIAQM sensor data in a few seconds and transfers data to cloud server. Approved users can incorporate the cloud systems through mobile applications or web servers. Installation of sensor networks, instrument transformers, and IoT-powered microcontrollers will provide air quality monitoring for buildings. The proposed window controller configuration is designed with the help of a Recurrent Neural Network (RNN) to predict the air quality level in advance. If the air quality level is above the normal level, the window controller automatically will open with the help of sensor activity control system. After the AQI (Air Quality Index) becomes normal, hence the window controller is closed automatically. The air quality index, CO2, and humidity data are visualized on the Grafana dashboard. [ABSTRACT FROM AUTHOR]

Plant species identification is essential for healthy survival as well as the preservation and protection of biodiversity. Manual identification is time-consuming, hence to address this issue deep learning algorithms for automated plant species identification have been developed. A Novel Architecture comprising of EfficientB4Net, Convolutional Block Attention Module (CBAM) and Residual Block Decoder is proposed to act as Autoencoder for identification and retrieval of twenty distinct groups of medicinal plants, widely available in southern India. The EfficientB4 encoder compresses and encodes the input features along with channel and spatial features to the Residual Block Decoder for efficient learning. Residual Block Decoders work to reconstruct the data from the encoded form to be as close to the original input as possible, by eliminating noise. The information-rich encoded features and the global features from the CBAM are transferred to the fully connected layer and stored in the database for retrieval of the plants. When a query image is received, the encoded feature of the query image and the database images are compared using similarity measurement, and the related images are retrieved. From the retrieved images, the query image is identified and the experimental results clearly show that the proposed method has achieved 95% accuracy when compared with other methods. [ABSTRACT FROM AUTHOR]