The dataset of Leaf Color Chart (PaddyNet) is publicly unavailable. As far as the author's knowledge, this is the first dataset about paddy leaves based on LCC. This dataset has been generated by collecting images from a particular location such as Sajiali, Dogachia and Shyamnagar at Jashore, Bangladesh. This dataset contains 4 categories of Aman paddy leaves. The leaf images were captured by smart phones. There are 560 images of Aman paddy leaves. The data collection procedure was carried out according to the guidelines of Bangladesh Agricultural Research Institute (BARI). We meticulously categorized the entire dataset with regard to the LCC level and validated the data with the assistance of domain specialists. Hence, the images are analyzed and categorized with standards. The dataset is utilized for recognizing Leaf Color Chart level which will help of farmers recommending nitrogen fertilizer in their paddy fields.

The subject matter of this study is breast tumor prediction and feature importance score finding using machine learning algorithms. The goal of this study was to develop an accurate predictive model for identifying breast tumors and determining the importance of various features in the prediction process. The tasks undertaken included collecting and preprocessing the Wisconsin Breast Cancer original dataset (WBCD). Dividing the dataset into training and testing sets, training using machine learning algorithms such as Random Forest, Decision Tree (DT), Logistic Regression, Multi-Layer Perceptron, Gradient Boosting Classifier, Gradient Boosting Classifier (GBC), and K-Nearest Neighbors, evaluating the models using performance metrics, and calculating feature importance scores. The methods used involve data collection, preprocessing, model training, and evaluation. The outcomes showed that the Random Forest model is the most reliable predictor with 98.56 % accuracy. A total of 699 instances were found, and 461 instances were reached using data optimization methods. In addition, we ranked the top features from the dataset by feature importance scores to determine how they affect the classification models. Furthermore, it was subjected to a 10-fold cross-validation process for performance analysis and comparison. The conclusions drawn from this study highlight the effectiveness of machine learning algorithms in breast tumor prediction, achieving high accuracy and robust performance metrics. In addition, the analysis of feature importance scores provides valuable insights into the key indicators of breast cancer development. These findings contribute to the field of breast cancer diagnosis and prediction by enhancing early detection and personalized treatment strategies and improving patient outcomes.

Although the Bay of Bengal (BoB) is the world’s largest bay, possessing distinct physiochemical properties, it has garnered little research focus concerning its microbial diversity and ecological importance. Here, we present amplicon (16S and 18S) profiling and shotgun metagenomics data regarding microbial communities from BoB’s eastern coast, viz., Saint Martin and Cox’s Bazar, Bangladesh. From the 16S sequencing data, Proteobacteria appeared to be the dominant phylum in both locations, with Alteromonas , Methylophaga , Anaerospora , Marivita , and Vibrio dominating in Cox’s Bazar and Pseudoalteromonas , Nautella , Marinomonas , Vibrio , and Alteromonas dominating the Saint Martin site. From the 18S sequencing data, Ochrophyta, Chlorophyta, and Protalveolata appeared among the most abundant eukaryotic divisions in both locations, with significantly higher abundance of Choanoflagellida, Florideophycidae, and Dinoflagellata in Cox’s Bazar. Functional annotations revealed that the microbial communities in these samples harbor genes for biofilm formation, quorum sensing, xenobiotics degradation, antimicrobial resistance, and a variety of other processes. Together, these results provide the first molecular insight into the functional and phylogenetic diversity of microbes along the BoB coast of Bangladesh and lay the foundation for further in-depth assessment of microbial community dynamics and functional potential in the context of global change in this region.

The need for effective cattle identification technology is now more acutely felt than ever in maintaining biosecurity, food safety, and supply chain efficacy in livestock management. This paper presents a systematic review of recent research in cattle identification using machine learning and deep learning techniques. The present systematic review measures the effectiveness of traditional and modern cattle identification techniques using studies from major academic databases, where articles were subjected to full-text review. Among these techniques, classical Machine Learning Techniques such as K-Nearest Neighbors and Support Vector Machines have demonstrated good results in cattle identification; however, Deep Learning Techniques, such as Convolutional Neural Networks, Residual Networks, and You Only Look Once, are better in cognition, detection, and identification tasks. Feature extraction relies on common techniques like Local Binary Pattern (LBP), Speeded-Up Robust Features (SURF), and Scale-Invariant Feature Transform (SIFT), while key features commonly used in these studies include muzzle prints and coat patterns. The review highlights key hurdles involving cattle identification, such as the limited number of publicly accessible datasets, issues with data quality susceptible to environmental changes and animal mobility, and high demand for real-time processing ability. The paper aims to inform researchers, policymakers, and stakeholders about implementing scalable, humane, and effective cattle identification systems to achieve sustainable livestock management.

The sophistication of cyber attacks and privacy issues related to data sharing is improving and requires a decentralized approach. Conventional centralized approaches to IDS pose a threat to the privacy of data and data sovereignty. Contrarily, federated learning enables several clients to learn simultaneously without sharing their sensitive information, which is one of the most promising solutions to studying cyber threats in real time. This framework also adds value to IDS by using CTI, which is incorporated into the training process to make it more accurate in its detection while still maintaining privacy. Each client uses the local model, which is a random forest model that is trained on local datasets without sharing the raw data. Multiple aggregation methods, such as FedAvg, FedOPT, FedProx, and FedXGBoost, are then used to combine the local models into a global model. These techniques are judged with regard to accuracy and Cohen’s Kappa Score. The performance of various models in the NF-UNSW-NB15-v2 dataset experiments was tested. The local model took a value of 0.9941–0.9934 with Kappa scores of 0.8336–0.8088, showing strong performance in different configurations. The FedXGBoost aggregated global model was best in terms of its highest accuracy of 99.22 (Kappa score of 0.8417). More experiments were done on the DFedForest and DFedForest++ models. DFedForest++, incorporating diversity in local models alongside validation accuracy, achieved 99.76% accuracy, surpassing DFedForest (with 71% accuracy in local models). This framework operationalizes CTI through feature augmentation—appending three CTI-derived features (is_known_malicious_ip, is_suspicious_port, and ttp_match_score from MITRE ATT&CK v14 and AlienVault OTX) to each NetFlow record locally at each client before federated training begins. These results highlight the advantages of federated learning in providing collaborative, privacy-preserving solutions for cyber threat detection and emphasize the potential of CTI integration for improving the accuracy and robustness of IDS models across decentralized environments.