Food productivity, quantity and quality are at stake when plant diseases such as rice diseases undermine the food security. Rice leaf disease treatment necessitates accurate and timely diagnosing. This study describes a deep learning model for categorizing and forecasting rice plant diseases. Using the remora optimization algorithm (ROA) on a rice leaf dataset demonstrates its potential for plant disease classification. The ROA-DM method detects rice leaf diseases using the ROA algorithm, a deep maxout network (DMN), and a deep autoencoder (DAE). ROA is applied to the learning parameters of deep model in order to achieve better convergence and avoiding local minima, which usually happens with conventional gradient-based optimizers. Experiments show that the suggested framework is accurate and precise across illness categories. The confusion matrices display the training and validation accuracy, losses of this model. The performance of our optimal learning method with respect to other methods indicated its potential for identifying leaf diseases. The accuracy of the ROA-DM method is 98.5%.

Medical images are affected by various complications such as noise and deficient contrast. To increase the quality of an image, it is highly important to increase the contrast and eliminate noise. In the field of image processing, image enhancement is one of the essential methods for recovering the visual aspects of an image. However segmentation of the medical images such as brain MRI and lungs CT scans properly is difficult. In this article, a novel hybrid method is proposed for the enhancement and segmentation of lung images. The suggested article includes two steps. In the 1st step, lung images were enhanced. During enhancement, images were gone through many steps such as de-hazing, complementing, channel stretching, course illumination, and image fusion by principal component analysis (PCA). In the second step, the modified U-Net model was applied to segment the images. We evaluated the entropy of input and output images, mean square error (MSE), peak signal-to-noise ratio (PSNR), gradient magnitude similarity deviation (GMSD), and multi-scale contrast similarity deviation (MCSD) after the enhancement process. During segmentation we used both original and enhanced images and calculated the segmentation accuracy. We found that the Dice-coefficient was 0.9695 for the original images and 0.9797 for the enhanced images.

The investigation of emerging non-toxic perovskite materials has been undertaken to advance the fabrication of environmentally sustainable lead-free perovskite solar cells. This study introduces a machine learning methodology aimed at predicting innovative halide perovskite materials that hold promise for use in photovoltaic applications. The seven newly predicted materials are as follows: CsMnCl4, Rb3Mn2Cl9, Rb4MnCl6, Rb3MnCl5, RbMn2Cl7, RbMn4Cl9, and CsIn2Cl7. The predicted compounds are first screened using a machine learning approach, and their validity is subsequently verified through density functional theory calculations. CsMnCl4 is notable among them, displaying a bandgap of 1.37 eV, falling within the Shockley-Queisser limit, making it suitable for photovoltaic applications. Through the integration of machine learning and density functional theory, this study presents a methodology that is more effective and thorough for the discovery and design of materials.

Rapid and accurate diagnosis of the illness and related disorders is increasingly important to limiting the spread of the coronavirus disease, relaxing lockdown requirements, and reducing the burden on public health infrastructures. Recently, a variety of techniques and approaches have been proposed to classify the coronavirus using different clinical data and medical pictures. There are certain limitations and disadvantages to the coronavirus detection technology now available. Because of this, it is essential to develop and study new diagnostic tools that are more accurate while avoiding the shortcomings of existing tools. Using the SARS-CoV-2 CT scan dataset, this work separately evaluated non-linear SVM and Twin-SVM classifiers along with textural characteristics such as GLCM, GLRLM, and ILMFD. There are 1252 positive SARS-CoV-2 infection signs and 1230 negative ones among the 2482 CT scan images in this dataset. Eight different models were developed in this study to classify and predict the coronavirus. GLCM + NLSVM using RBF kernal, GLCM + TWSVM using linear kernal, GLRLM + NLSVM using RBF kernal, GLRLM + TWSVM using sigmoid, ILMFD + NLSVM using RBF kernal, ILMFD + TWSVM using polynomial kernal, Hybrid feature + NLSVM, and Hybrid feature + TWSVM were the models that outperformed when evaluated using the performance metrics used in this work. The Hybrid feature + NLSVM model with Linear Kernal yielded significantly better results than the other eight models tested for the dataset, including 100% accuracy, 100% recall, 100% precision, 100% F1-score, R-Squared = 1, and RMSE = 0. The speed and accuracy of the coronavirus diagnosis would therefore be greatly increased by the high accuracy of this kind of computer-aided screening approach, which would also promote the investigation of other related diseases using CT-scan pictures.

This work presents a machine learning approach to predict novel perovskite oxide materials in the Pr-Al-O and Pr-Sc-O compound families with the potential for photoluminescence applications. The predicted materials exhibit a large bandgap and high Debye temperature, and have remained unexplored thus far. The predicted compounds (Pr$_3$AlO$_6$, Pr$_4$Al$_2$O$_9$, Pr$_3$ScO$_6$ and Pr$_3$Sc$_5$O$_{12}$) are screened using machine learning approach, which are then confirmed by density functional theory calculations. The study includes the calculation of the bandgap and density of states to determine electronic properties, and the optical absorption and emission spectra to determine optical properties. Mechanical stability of the predicted compounds, as demonstrated by satisfying the Born-Huang criterion. By combining machine learning and density functional theory, this work offers a more efficient and comprehensive approach to materials discovery and design.

The timely detection and identification of crop diseases is a crucial aspect of the agricultural sector. It contributes significantly to the by and large productivity of the plant. One of the most crucial factors that we need to consider while determining a plant’s susceptibility to a particular disease is the visual characteristics of the affected plant. The increasing popularity of automation and availability of efficient techniques for disease identification has led to the development of novel methods and engraved impactful technologies in field of automated disease detection. The traditional methods have not been able to provide the researchers with the most accurate results. The proposed model in this work can identify the rice crop disease without relying on subjective data and have many advantages over traditional approaches as evident from the results derived. It has the potential to improve the efficiency of the process and aid in early detection. Machine learning method presents real-time automated decision support systems and can help improve crop or plant growth productivity and quality. This work aims to introduce a new and enhanced method as Neuro-GA, which is a combination of both the artificial neural network (ANN) and the genetic algorithm (GA). It has been claimed that it is more powerful and accurate than the traditional methods. The pioneer and nascent stages of this analysis includes preprocessing of the data was carried out. The features were then extracted using Gray-level co-occurrence matrix (GLCM) and subsequently the finally extracted features were cascaded to the Neuro-GA classifier. The digital image processing (DIP) techniques used in this study for rendering visual images along with Neuro-GA classifier resulted in skyrocket accuracy level of 90

It is now essential to identify COVID-19 quickly and accurately in order to stop the disease from spreading quickly, loosen lockdown regulations, and lessen the strain on public health infrastructures. Recently, a number of strategies and methods have been put forth to use various clinical data and medical images to identify the SARS-CoV-2 virus (COVID-19). The COVID-19 detection tools that are currently on the market have a number of drawbacks and restrictions. As a result, it becomes necessary to create and research new diagnostic instruments with increased diagnostic accuracy while avoiding the drawbacks of current instruments. In this work, non-linear SVM and Twin-SVM (TWSVM) classifier along with texture features i.e. GLCM, GLRLM and ILMFD individually, was applied on SARS-CoV-2 CT scan dataset. This database comprises 2482 CT scan images total, out of which 1252 images are positive for SARS-CoV-2 infection (COVID-19) and 1230 images are for negative COVID-19. In this work, six models are developed for classification and prediction of COVID-19 in which we obatined better performance in terms of accuracy, recall, precision and F1-score for GLCM + NLSVM model using RBF kernal, GLCM + TWSVM using linear kernal, GLRLM + NLSVM using RBF kernal, GLRLM + TWSVM using sigmoid, ILMFD + NLSVM using RBF kernal, ILMFD + TWSVM using polynomial kernal. GLCM + NLSVM model using RBF kernal produced comparatively better accuracy 94.33%, recall 96.40%, precision 92.64, F1-score 94.46% and lowest miss-classification 5.67%. Therefore, the high accuracy of this kind of computer-aided diagnostic tool would contribute to a notable increase in COVID-19 diagnosis speed and accuracy.

This study aimed to evaluate the influence of skill development training programs on the promotion of backyard poultry farming practices in the arid regions of Rajasthan. Data were collected from 120 respondents through a meticulously designed interview schedule and questionnaire. The findings revealed a significant positive impact on various aspects of poultry farming. Notably, 99% of respondents demonstrated an uptake in the timely treatment of sick birds, while 98% adopted practices such as feed formulation at the household level, culling and selection, use of antibiotics, rearing of quality birds, and consulting veterinary doctors. Additionally, 97% of respondents incorporated feed supplements into their practices, and a similar percentage (97%) ensured appropriate feed provision based on the age of the birds. These results underscore the effectiveness of skill development training programs in enhancing the adoption of best practices in backyard poultry farming within the challenging arid environment of Rajasthan. The analysis of the collected data further revealed a consistent adoption index across various aspects of backyard poultry farming practices. Specifically, a noteworthy adoption rate of 92% was observed for the segregation of diseased birds, record-keeping, and de-beaking. However, a slightly lower adoption rate of 88% was noted for feed supplement usage. The data indicated that the maximum gain in adoption was observed in the practice of consulting veterinary doctors, with an impressive rate of 85%. Following closely were the practices of segregating diseased birds (84%), feed formulation at the household level, and rearing of quality birds (82%). Additionally, high adoption rates were observed for record-keeping (81%) and feed supplement use (79%). Post-mortem examinations conducted by veterinary doctors on deceased birds also showed a substantial adoption rate of 77%. It is noteworthy that while the majority of practices demonstrated significant gains, culling exhibited a comparatively lower adoption rate of 53%. This suggests that there may be factors influencing the reluctance or challenges associated with the implementation of culling practices among the respondents. Overall, the findings underscore the positive impact of the training programs on the farmers' knowledge enhancement and their familiarity with advanced poultry farming technologies. The consistent adoption across multiple practices highlights the effectiveness of the training in empowering farmers to incorporate improved techniques into their backyard poultry farming endeavors.

This work proposes a novel approach to object recognition, particularly for human faces, based on the principle of human cognition. The suggested approach can handle a dataset or problem with a large number of classes for classification more effectively. The model for the facial recognition-based object detection system was constructed using a combination of decision tree clustering based multi-level Backpropagation neural network classifier-TFMLBPNN-DTC and hybrid texture feature (ILMFD+GLCM) and applied on NS and ORL databases. This model produced the classification accuracy (±standard deviation) of 95.37 ±0.951877% and 90.83 ± 1.374369% for single input and 96.58 ±0.5604582% and 91.50 ± 2.850439% for group-based decision for NS and ORL database respectively. The better classification results encourage its application to other object recognition and classification issues. This work's basic idea also makes it easier to improve classification management for a wide range of classes.

Now a days, secure data communication over computer network system is a major issue in which impact of feature reduction plays a vital role to secure network by early detection of intrusion. It not only keeps a deep impact on the performance of existing Intrusion Detection System (IDS) algorithms but also affects the computational complexity. Although lots of techniques have been offered for feature reduction by researchers and they have their own perks and quirks, but still they are several flows. To manipulate the same dataset for different classifiers and to select different number of features for the detection of attacks are not only having too much computational cost but also time consuming. The experiments have been carried out using "Python" programming language based library "Scikit-Learn" software on "Kddcup99" dataset from UCI machine learning repository as a test bed. In this article a classification and regression trees (CART) based feature selection algorithm has been proposed which offers optimum set of features. Further optimum set of features has been offered by our proposed work passed over various classifiers for training and testing to establish network intrusion detection system (NIDS). We have compared the performance accuracy of various existing machine learning (ML) based classification algorithms and obtained higher performance accuracy with lower computational cost. The proposed algorithm having optimum time complexity and accuracy in designing of IDS.

Rapid and accurate identification of COVID-19 and also other associated diseases is now crucial to limiting the disease's transmission, relaxing lockdown laws, and reducing the burden on public health infrastructures. Recently, several approaches and techniques have been proposed to identify the SARS-CoV-2 virus (COVID-19) using different clinical data and medical pictures. There are some limitations and shortcomings with the COVID-19 detection technologies that are currently available on the market. Because of this, it becomes essential to develop and study new diagnostic tools that have higher diagnostic accuracy while avoiding the shortcomings of existing tools. This study used the SARS-CoV-2 CT scan dataset to test non-linear SVM and Twin-SVM (TWSVM) classifiers in addition to textural characteristics such as GLCM, GLRLM, and ILMFD separately. There are a total of 2482 CT scan images in this database; 1252 of the scans show positive signs of SARS-CoV-2 infection (COVID-19), and 1230 show negative signs. Eight different models were developed in this work for the purpose of classifying and predicting COVID-19. We found that the GLCM + NLSVM model using RBF kernal, GLCM + TWSVM using linear kernal, GLRLM + NLSVM using RBF kernal, GLRLM + TWSVM using sigmoid, ILMFD + NLSVM using RBF kernal, ILMFD + TWSVM using polynomial kernal, Hybrid feature + NLSVM, and Hybrid feature + TWSVM all performed better in terms of evaluation done by performance metrics used in this work. For the given dataset, the Hybrid feature + NLSVM model with Linear Kernal yielded significantly better results out of eight models tested, including 100% accuracy, 100% recall, 100% precision, 100% F1-score, R-Squared = 1, and RMSE = 0. As a result, the high accuracy of this type of computer-aided screening method would significantly boost the speed and accuracy of COVID-19 diagnosis also encourage the study of other associated diseases with CT-scan images.

In the digital age, images are widely used to document events, provide evidence, and communicate information. Ensuring the authenticity of digital images is crucial to maintaining trust and integrity in various domains, including journalism, forensics, legal proceedings, and historical documentation. In this research, a PeCA-DCNN model for digital image detection is proposed, employing a hybrid approach. Initially, the data from image forgery databases is collected and preprocessed to eliminate noise and artefacts. Subsequently, the Viola-Jones algorithm detects frontal faces, and feature extraction is performed using pre-trained models VGG-16 and Resnet-101. To reduce the computational overhead, feature extraction is performed and generates a feature vector. The PeCA algorithm, combined with an adaptive self-boosted DCNN, is used to classify fake and genuine images. The PeCA algorithm enhances model performance by adjusting classifier parameters' weights and biases. When evaluating the PeCA-DCNN, significant improvements in accuracy, sensitivity, and specificity are obtained with enhancement rates of 1.48, 3.06, and 0.05 in an 80% training scenario, and 3.92, 3.24, and 2.22 in k-fold cross-validation. These results demonstrate the effectiveness of the proposed approach compared to existing techniques.

Agriculture, a pivotal sector in the Indian economy, plays a crucial role in national development.A significant challenge within this domain is the detection of crop diseases, with brown spot, leaf blast, and bacterial blight being prevalent afflictions in rice crops.This study presents an innovative approach, integrating Gray-level Co-occurrence Matrix (GLCM) and Intensity-Level Based Multi-Fractal Dimension (ILMFD) for feature extraction in disease identification.The efficacy of this integrated technique was evaluated through a comparison with various classifiers.Specifically, the Artificial Neural Network (ANN), Support Vector Machine (SVM), and Neuro-Genetic Algorithm (Neuro-GA) were employed to ascertain their precision in disease detection.It was observed that the combination of GLCM and ILMFD with the Neuro-GA classifier achieved an accuracy exceeding 90%.Remarkably, when paired with the SVM classifier, this integrated approach yielded a precise accuracy of 96.7% in detecting brown spot disease in rice.These findings not only validate the effectiveness of the GLCM and ILMFD methods in feature extraction but also highlight the superior performance of the SVM classifier in crop disease detection.This research contributes significantly to the field, offering a robust solution for accurate disease diagnosis in rice crops, thereby aiding in the sustainable management of agricultural practices.

This work showed the capability of handling large number of classes for classification with human cognition inspired methods. A cognition based techniques for both feature extraction, (self-similarity feature, Intensity Level Multi Fractal Dimension (ILMFD)) as well as classification purpose (decision tree clustering based multi-level Artificial Neural Network classifier-MLANN-DTC) were employed to implement facial recognition based object detection system. A DTC based approach reduces the search space time and also provides opportunity for very less amount of classes (a smaller part of the large number of classes) to be handled by the respective classifier for classification. It also mimics fast recognition capability of humans. In this work, two different databases were used for experiment, first one is our own collected facial images from rotation based video clips (117 persons and 40 facial images per person) named as NS database, and other is standard ORL database (40 persons and 10 facial images per person). In pre-processing step, the facial images were segmented to obtain facial part using context window based texture of pixels (CWTP) back-propagation neural network (BPNN) based model and then a scale and rotation independent ILMFD feature was computed from each segmented image. Further, a combination of K-means and hierarchal clustering was used to build super classes. All classes’ data were distributed among these 6 super classes (heuristically chosen) for own NS database and 3 for ORL database as per their similarity based on ILMFD features. Multi-level ANNs models were employed for all super classes and further their classification results were fed into decision clustering based model to obtain fine-tuned results, which showed significant improvement in terms of classification efficiency. This approach believes in center tendency of largest cluster to refer the actual class decision from multiple decisions obtain corresponding to multiple input data of the same class. In this work, the MLANN-DTC based proposed model has produced 89.542 ± 1.167

This study investigates the impact of Ga substitution for Al in V2MnAl inverse Heusler alloys, focusing on lattice parameter variations. Despite similar valence electrons, Ga's distinct size and surface energy prompt questions about its role in alloy characteristics. Experimental synthesis of V2Mn1Al1-xGax alloys (x = 0 to 0.60) reveals crystal parameter changes. Theoretical exploration using density functional theory (DFT) probes how Ga doping at the Al site influences the density of states, altering the half-metallic properties of V2MnAl. Additionally, machine learning, with a dataset of 391 entries, predicts lattice constants based on atomic properties, enhancing our ability to maneuver material characteristics. This multifaceted approach aims to deepen our understanding of Ga's impact on Heusler alloys, bridging experimental and theoretical realms for comprehensive insights into materials design and behavior.

A straightforward, metal-free multicomponent tandem cyclization/aromatization approach for the synthesis of naphthothiazoles and benzothiazoles is described. This method employs alpha-tetralones and cyclohexenones as the starting materials, styrene derivatives as a one-carbon synthon, an ammonium salt as the N source, elemental sulfur as the S source and an oxidant. This approach offers a broad substrate scope, including alpha-tetralones and cyclohexenones adorned with different functional groups, resulting in moderate to high yields of the desired products.

Optimization plays an important role in solving complex computational problems. Meta-Heuristic approaches work as an optimization technique. In any search space, these approaches play an excellent role in local as well as global search. Nature-inspired approaches, especially population-based ones, play a role in solving the problem. In the past decade, many nature-inspired population-based methods have been explored by researchers to facilitate computational intelligence. These methods are based on insects, birds, animals, sea creatures, etc. This research focuses on the use of Meta-Heuristic methods for the feature selection. A better optimization approach must be introduced to reduce the computational load, depending on the problem size and complexity. The correct feature set must be chosen for the diagnostic system to operate effectively. Here, population-based Meta-Heuristic optimization strategies have been used to pick the features. By choosing the best feature set, the Butterfly Optimization Algorithm (BOA) with the Enhanced Lion Optimization Algorithm (ELOA) approach would reduce classifier overhead. The results clearly demonstrate that the combined strategy has higher performance outcomes when compared to other optimization strategies.