Cetrimonium bromide and potassium thiocyanate assisted post-vapor treatment approach to enhance power conversion efficiency and stability of FAPbI3 perovskite solar cells.

Formamidinium lead iodide (FAPbI3) is the most promising perovskite material for producing efficient perovskite solar cells (PSCs). Here, we develop a facile method to obtain an α-phase FAPbI3 layer with passivated grain boundaries and weakened non-radiative recombination. For this aim, during the FAPbI3 fabrication process, cetrimonium bromide + 5% potassium thiocyanate (CTABr + 5% KSCN) vapor post-treatment is introduced to remove non-perovskite phases in the FAPbI3 layer. Incorporation of CTA+ along with SCN- ions induces FAPbI3 crystallization and stitch grain boundaries, resulting in PSCs with lower defect losses. The vapor-assisted deposition increases the carriers' lifetime in the FAPbI3 and facilitates charge transport at the interfacial perovskite/hole transport layer via a band alignment phenomenon. The treated α-FAPbI3 layers bring an excellent PCE of 22.34%, higher than the 19.48% PCE recorded for control PSCs. Besides, the well-oriented FAPbI3 and its higher hydrophobic behavior originating from CTABr materials lead to improved stability in the treated PSCs.

IFND: a benchmark dataset for fake news detection.

Spotting fake news is a critical problem nowadays. Social media are responsible for propagating fake news. Fake news propagated over digital platforms generates confusion as well as induce biased perspectives in people. Detection of misinformation over the digital platform is essential to mitigate its adverse impact. Many approaches have been implemented in recent years. Despite the productive work, fake news identification poses many challenges due to the lack of a comprehensive publicly available benchmark dataset. There is no large-scale dataset that consists of Indian news only. So, this paper presents IFND (Indian fake news dataset) dataset. The dataset consists of both text and images. The majority of the content in the dataset is about events from the year 2013 to the year 2021. Dataset content is scrapped using the Parsehub tool. To increase the size of the fake news in the dataset, an intelligent augmentation algorithm is used. An intelligent augmentation algorithm generates meaningful fake news statements. The latent Dirichlet allocation (LDA) technique is employed for topic modelling to assign the categories to news statements. Various machine learning and deep-learning classifiers are implemented on text and image modality to observe the proposed IFND dataset's performance. A multi-modal approach is also proposed, which considers both textual and visual features for fake news detection. The proposed IFND dataset achieved satisfactory results. This study affirms that the accessibility of such a huge dataset can actuate research in this laborious exploration issue and lead to better prediction models.

An ensemble deep learning classifier for sentiment analysis on code-mix Hindi-English data.

Code-mixing on social media is a trend in many countries where people speak multiple languages, such as India, where Hindi and English are major communication languages. Sentiment analysis is beneficial in understanding users' opinions and thoughts on social, economic, and political issues. It eliminates the manual monitoring of each and every review, which is a cumbersome task. However, performing sentiment analysis on code-mix data is challenging, as it involves various out of vocabulary terms and numerous issues, making it a new field in natural language processing. This work includes dealing with such text and ensembling a classifier to detect sentiment polarity. Our classifier ensembles a multilingual variant of RoBERTa and a sentence-level embedding from Universal Sentence Encoder to identify the sentiments of these code-mixed tweets with higher accuracy. This ensemble optimises the classifier's performance by using the strength of both for transfer learning. Experiments were conducted on real-life benchmark datasets and revealed their sentiment. The performance of the proposed classifier framework is compared with other baselines and deep learning models on five datasets to show the superiority of our results. Results showed improved and increased performance in the proposed classifier's accuracy, precision, and recall. The accuracy achieved by our classifier on code-mix datasets is 66% on Joshi et al. 2016, 60% on SAIL 2017, and 67% on SemEval 2020 Task-9 dataset, which is on average around 3% as compared to contemporary baselines.

Prediction of influencing atmospheric conditions for explosion Avoidance in fireworks manufacturing Industry-A network approach.

This research study uses Artificial Neural Networks (ANNs) to predict occupational accidents in Sivakasi firework industries. Atmospheric temperature, pressure and humidity are the causes of explosion during chemical mixing, drying, and pellet making. The Proposed ANN model predicts the accidents and the session of accidents (FN/AN) based on atmospheric conditions. This prediction takes values from historical accident data due to the atmospheric conditions of Sivakasi (2009-2021). In the development of ANN model, the Feed-Forward Back Propagation (FFBP) with the Levenberg-Marquardt function has been employed with hidden layers of 5 and 10 to train the network. The performance accuracy of both the hidden layers is evaluated and compared with other models like Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (K-NN). The accuracy of the proposed model for accident classification is 82.7% and 67.8% for hidden layers 5 and 10, respectively. Also, the model predicts the session of accident with the accuracy of 72% and 54%, specificity of 77.7% and 60.1%, sensitivity of 69% and 52.92% for hidden layers 5 and 10, respectively. It is found that hidden layer 5 gives higher accuracy than hidden layer 10. The proposed ANN model gives the highest accuracy when compared to other models. This study is helpful in the firework industry management, and workers improve safety precautions and avoid explosions due to atmospheric conditions.

Predicting image credibility in fake news over social media using multi-modal approach.

Social media are the main contributors to spreading fake images. Fake images are manipulated images altered through software or by other means to change the information they convey. Fake images propagated over microblogging platforms generate misrepresentation and stimulate polarization in the people. Detection of fake images shared over social platforms is extremely critical to mitigating its spread. Fake images are often associated with textual data. Hence, a multi-modal framework is employed utilizing visual and textual feature learning. However, few multi-modal frameworks are already proposed; they are further dependent on additional tasks to learn the correlation between modalities. In this paper, an efficient multi-modal approach is proposed, which detects fake images of microblogging platforms. No further additional subcomponents are required. The proposed framework utilizes explicit convolution neural network model EfficientNetB0 for images and sentence transformer for text analysis. The feature embedding from visual and text is passed through dense layers and later fused to predict fake images. To validate the effectiveness, the proposed model is tested upon a publicly available microblogging dataset, MediaEval (Twitter) and Weibo, where the accuracy prediction of 85.3% and 81.2% is observed, respectively. The model is also verified against the newly created latest Twitter dataset containing images based on India's significant events in 2020. The experimental results illustrate that the proposed model performs better than other state-of-art multi-modal frameworks.

Classification of COVID-19 by using supervised optimized machine learning technique.

In recent two years, covid-19 diseases is the most harmful diseases in entire world. This disease increase the high mortality rate in several developed countries. Earlier identification of covid-19 symptoms can avoid the over illness or death. However, there are several researchers are introduced different methodology to identification of diseases symptoms. But, identification and classification of covid-19 diseases is the difficult task for every researchers and doctors. In this modern world, machine learning techniques is useful for several medical applications. This study is more focused in applying machine learning classifier model as SVM for classification of diseases. By improve the classification accuracy of the classifier by using hyper parameter optimization technique as modified cuckoo search algorithm. High dimensional data have unrelated, misleading features, which maximize the search space size subsequent in struggle to process data further thus not contributing to the learning practise, So we used a hybrid feature selection technique as mRMR (Minimum Redundancy Maximum Relevance) algorithm. The experiment is conducted by using UCI machine learning repository dataset. The classifier is conducted to classify the two set of classes such as COVID-19, and normal cases. The proposed model performance is analysed by using different parametric metrics, which are explained in result section.

Small and medium-sized enterprises' contribution in digital technology.

Researchers have mentioned the importance of digitization in improving efficiency and productivity in Small and Medium Enterprises (SME). Fortunately, there is no proof that Digitization can be used to deal with the outcome of severe incidents like COVID-19. The research paper suggested that the increased rate of SMEs has increased significantly. This was entirely due to the advent of Digital Technology (DT). In this way, both product and the process become more automated in digitalization, resulting in increased quality and demand. Considering the high scope for higher development, India's SME sector still has much space for new digital technologies to be integrated. This paper addresses the main scenario of SMEs in India and their benefit in GDP. Also, the research includes a brief analysis of CRM applications and digital payment options in SMEs.

Unique identification code for medical fundus images using blood vessel pattern for tele-ophthalmology applications.

BACKGROUND AND OBJECTIVE: Identification of fundus images during transmission and storage in database for tele-ophthalmology applications is an important issue in modern era. The proposed work presents a novel accurate method for generation of unique identification code for identification of fundus images for tele-ophthalmology applications and storage in databases. Unlike existing methods of steganography and watermarking, this method does not tamper the medical image as nothing is embedded in this approach and there is no loss of medical information. METHODS: Strategic combination of unique blood vessel pattern and patient ID is considered for generation of unique identification code for the digital fundus images. Segmented blood vessel pattern near the optic disc is strategically combined with patient ID for generation of a unique identification code for the image. RESULTS: The proposed method of medical image identification is tested on the publically available DRIVE and MESSIDOR database of fundus image and results are encouraging. CONCLUSIONS: Experimental results indicate the uniqueness of identification code and lossless recovery of patient identity from unique identification code for integrity verification of fundus images.