Phishing is the easiest method for gathering sensitive information from unwary people. Phishers seek to get private data including passwords, login information, and bank account details. Cyber security experts are actively seeking for trustworthy and effective ways to identify phishing websites. In order to distinguish between legal and phishing URLs, we used machine learning (ML) technology. In this research work using ML technology extraction and analysis of both types of URLs was performed. Extreme Gradient Boosting (XGBoost), Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) were used to identify phishing websites. The goal was to identify phishing URLs and determine the most effective ML technique by comparing the accuracy rates of each algorithm. In this, proposed methodology two datasets were used. The accuracy of models was calculated on Phishtank and UCI dataset using kfold, feature selection and hyperparameter tuning method. Performance measures precision, recall and F1-score and Receiver Operating Characteristics (ROC) curve were calculated. RF provided an accuracy of 98.80% and 97.87% on the Phishtank dataset and UCI respectively. Highest precision, recall, F1-score value was 99% each and AUC-ROC value was 99.89% with Phishtank dataset. Validation with other researchers showed better results with proposed methodology. Therefore this methodology can be of help to identify phishing websites.

Protecting plants from diseases involves recognizing the symptoms and identifying practical, safe, and reasonable treatment methods. Holistic approaches based on particular times or seasons can reduce plant resistance and minimize tedious work. Technological advancements have led to the development of microscopic examinations and computational methods using machine learning techniques to detect diseases automatically and quickly using leaf images. This study builds the prediction model using EfficientNet and YOLO neural network architectures from computer vision. The development of a model that assists farmers in identifying cotton disease so that they use pesticides that may treat it further utilizes this concept. In the physical world, the input is accepted from many different sources, so observing the model’s output is necessary. This work concentrates on model response to the inputs from physical devices, and analysis shows that the monitoring varies the results. A novel convolutional neural network (CNN) based on the EfficientNet architectures and variations of YOLO architectures is used to classify and identify the objects in cotton leaf. The EfficientNetB4 yielded 100% accuracy for healthy leaf and powdery mild leaf classes, and YOLO v4 version with 96%, 98.3%, 99.2%, and 0.70 for precision, recall, mAP@0.5, mAP120.5:095 respectively. These results indicate that consequences vary in real-time per environmental parameters such as light effect and devices, and analysis shows that monitoring affects the results.

In a brand-new era, with chaotic scenario that exists within the world, people are undermined with diverse psychological assaults. There have been numerous sensible approaches on the way to understand and lessen those attacks. Bioscrypt developments have verified to be one of the beneficial approaches for intercepting these troubles. Identifying recognition through human iris organ is said as one of the well-known biometric strategies because of its reliability and higher accurate return in comparison to different developments. Reviewing beyond literatures, terrible imaging condition, low flexibility of version, and small length iris image dataset are the constraints desiring solutions. Among these kinds of developments, the iris popularity structures are suitable gear for the human identification. Iris popularity has been an energetic studies location for the duration of previous couple of decades, due to its extensive packages in the areas, from airports to native land protection border protection. In the past, various functions and methods for iris recognition have been presented. Despite of the very fact that there are many approaches published in this field, there are still liberal amount of problems in this methodology like tedious and computational intricacy. We suggest an all-encompassing deep learning architecture for iris recognition supported by a genetic algorithm and a Wavelet Transformation, which may jointly learn the feature representation and perform recognition to realize high efficiency. With just a few training photos from each class, we train our model on a well-known iris recognition dataset and demonstrate improvements over prior methods. We think that this architecture can be frequently employed for various biometric recognition jobs, assisting in the development of a more scalable and precise system. The exploratory aftereffects of the proposed technique uncover that the strategy is effective inside the iris acknowledgment.

Wildfires are among the world's most pressing issues, and they are getting more prevalent as global warming and other environmental conditions deteriorate. These wildfires might be caused by humans or by natural causes. Wildfires are one of the factors contributing to the extinction of rare flora and wildlife that serve to maintain our planet's ecological balance. In this paper, a comparative analysis of various machine learning classifier models for predicting forest fires was undertaken using two separate datasets. The suggested system's processing is dependent on a few characteristics such as temperature, humidity, oxygen, and wind. Several machine learning classification techniques, including logistic regression, support vector classifier, decision tree, k neighbors and random forest, were used in this study. For further optimization of the model, K-fold cross validation method and hyperparameter tuning were implemented. The system reveals Support Vector Machine as the best strategy for the forest fire dataset, with 96.88% accuracy. Random Forest method was found to be the best for the Cortez and Morais dataset, with 90.24% accuracy.

Existing crop recommender related to either binary classification or multiclass classification. This paper presents conglomerate crop recommendations which consist of a number of different and distinct crops that are grouped together. In this work we focus on transferring knowledge from single label output prediction to multiple label predicted output for a given input data instances. We proposed ESCT algorithm i.e. Ensemble Supervised Clustering Techniques in our research work. ESCT provides a combined approach of conventional clustering and enhanced supervised clustering methodology to optimize the conglomerate recommendation. We are focusing on K-mean clustering for conventional approach and ICCC i.e. Inter cluster correlation coefficient to achieve enhancement in supervised clustering. In conventional K-mean clustering there is a big challenge on how to optimize the k-value of clustering which directly affects the convergence of the clusters. To resolve this problem, we mainly apply function approximation on K-Value which provides us with better clustering and fast convergence. Existing methods for inter-clustering do not adequately address one of the key challenges i.e. exploiting correlations between labels and that is achieved by ICCC algorithm. This model provides learning and prediction of unknown observation by using Back propagation MLL algorithm which provides improved performance.