Further when compared to other state-of-the-art architectures, the proposed model showed excellent segmentation effects with a high DC and F1 score of 0.8031 and 0.82 respectively.Cervical intraepithelial neoplasia (CIN) and cervical cancer are major health problems faced by women worldwide. The conventional Papanicolaou (Pap) smear analysis is an effective method to diagnose cervical pre-malignant and malignant conditions by analyzing swab images. Various computer vision techniques can be explored to identify potential precancerous and cancerous lesions by analyzing the Pap smear image. The majority of existing work cover binary classification approaches using various classifiers and Convolution Neural Networks. However, they suffer from inherent challenges for minute feature extraction and precise classification. We propose a novel methodology to carry out the multiclass classification of cervical cells from Whole Slide Images (WSI) with optimum feature extraction. The actualization of Conv Net with Transfer Learning technique substantiates meaningful Metamorphic Diagnosis of neoplastic and pre-neoplastic lesions. As the Progressive Resizing technique (an advanced method for trainingDue to the massive progression of the Web, people post their reviews for any product, movies and places they visit on social media. The reviews available on social media are helpful to customers as well as the product owners to evaluate their products based on different reviews. Analyzing structured data is easy as compared to unstructured data. The reviews are available in an unstructured format. Aspect-Based Sentiment Analysis mines the aspects of a product from the reviews and further determines sentiment for each aspect. In this work, two methods for aspect extraction are proposed. The datasets used for this work are SemEval restaurant review dataset, Yelp and Kaggle datasets. In the first method a multivariate filter-based approach for feature selection is proposed. This method support to select significant features and reduces redundancy among selected features. It shows improvement in F1-score compared to a method that uses only relevant features selected using Term Frequency weight. In another method, selective dependency relations are used to extract features.  https://www.selleckchem.com/products/Temsirolimus.html  This is done using Stanford NLP parser. The results gained using features extracted by selective dependency rules are better as compared to features extracted by using all dependency rules. In the hybrid approach, both lemma features and selective dependency relation based features are extracted. Using the hybrid feature set, 94.78% accuracy and 85.24% F1-score is achieved in the aspect category prediction task.Due to advancements in malware competencies, cyber-attacks have been broadly observed in the digital world. Cyber-attacks can hit an organization hard by causing several damages such as data breach, financial loss, and reputation loss. Some of the most prominent examples of ransomware attacks in history are WannaCry and Petya, which impacted companies' finances throughout the globe. Both WannaCry and Petya caused operational processes inoperable by targeting critical infrastructure. It is quite impossible for anti-virus applications using traditional signature-based methods to detect this type of malware because they have different characteristics on each contaminated computer. The most important feature of this type of malware is that they change their contents using their mutation engines to create another hash representation of the executable file as they propagate from one computer to another. To overcome this method that attackers use to camouflage malware, we have created three-channel image files of ma, which is quite satisfactory.Artificial neural networks (ANN) perform well in real-world classification problems. In this paper, a robust classification model using ANN was constructed to enhance the accuracy of breast cancer classification. The Taguchi method was used to determine the suitable number of neurons in a single hidden layer of the ANN. The selection of a suitable number of neurons helps to solve the overfitting problem by affecting the classification performance of an ANN. With this, a robust classification model was then built for breast cancer classification. Based on the Taguchi method results, the suitable number of neurons selected for the hidden layer in this study is 15, which was used for the training of the proposed ANN model. The developed model was benchmarked upon the Wisconsin Diagnostic Breast Cancer Dataset, popularly known as the UCI dataset. Finally, the proposed model was compared with seven other existing classification models, and it was confirmed that the model in this study had the best accuracy at breast cancer classification, at 98.8%. This confirmed that the proposed model significantly improved performance.Air, water, and soil are essential for terrestrial life, but pollution, overexploitation, and climate change jeopardize the availability of these primary resources. Thus, assuring human health and food production requires efficient strategies and technologies for environmental protection. Knowing key parameters such as soil moisture, air, and water quality is essential for smart farming and urban development. The MeteoMex project aims to build simple hardware kits and their integration into current Internet-of-Things (IoT) platforms. This paper shows the use of low-end Wemos D1 mini boards to connect environmental sensors to the open-source platform ThingsBoard. Two printed circuit boards (PCB) were designed for mounting components. Analog, digital and I2C sensors are supported. The Wemos ESP8266 microchip provides WiFi capability and can be programed with the Arduino IDE. Application examples for the MeteoMex aeria and terra kits demonstrate their functionality for air quality, soil, and climate monitoring. Further, a prototype for monitoring wastewater treatment is shown, which exemplifies the capabilities of the Wemos board for signal processing. The data are stored in a PostgreSQL database, which enables data mining. The MeteoMex IoT system is highly scalable and of low cost, which makes it suitable for deployment in agriculture 4.0, industries, and public areas. Circuit drawings, PCB layouts, and code examples are free to download from https//github.com/robert-winkler/MeteoMex.