Background and motivation: Lung computed tomography (CT) techniques are high-resolution and are well adopted in the intensive care unit (ICU) for COVID-19 disease control classification. Most artificial intelligence (AI) systems do not undergo generalization and are typically overfitted. Such trained AI systems are not practical for clinical settings and therefore do not give accurate results when executed on unseen data sets. We hypothesize that ensemble deep learning (EDL) is superior to deep transfer learning (TL) in both non-augmented and augmented frameworks. Methodology: The system consists of a cascade of quality control, ResNet–UNet-based hybrid deep learning for lung segmentation, and seven models using TL-based classification followed by five types of EDL’s. To prove our hypothesis, five different kinds of data combinations (DC) were designed using a combination of two multicenter cohorts—Croatia (80 COVID) and Italy (72 COVID and 30 controls)—leading to 12,000 CT slices. As part of generalization, the system was tested on unseen data and statistically tested for reliability/stability. Results: Using the K5 (80:20) cross-validation protocol on the balanced and augmented dataset, the five DC datasets improved TL mean accuracy by 3.32%, 6.56%, 12.96%, 47.1%, and 2.78%, respectively. The five EDL systems showed improvements in accuracy of 2.12%, 5.78%, 6.72%, 32.05%, and 2.40%, thus validating our hypothesis. All statistical tests proved positive for reliability and stability. Conclusion: EDL showed superior performance to TL systems for both (a) unbalanced and unaugmented and (b) balanced and augmented datasets for both (i) seen and (ii) unseen paradigms, validating both our hypotheses.

The paper disccuses about the sentimenal analysis process which is a classification problem. To study the emotions of people about alternate energy sources, we carried out sentiment analysis on Twitter data. Sentiment analysis is used to analyze opinions of the user for decision making with the help of natural language processing techniques. In this paper, we carried out sentiment analysis and classification task of tweets belonging to #RenewableEnergy. We applied five different machine learning algorithms for the classification of tweets into three categories. We have carried classification without feature selection technique and with feature selection techniques. We have used CfsSubsetEvaluation and Information Gain feature selection methods to reduce the number of features from the dataset. The result obtained through the techniques followed in this paper, shows that the accuracy of sentiment classification is better with feature selection methods. The best accuracy (92.96%) is achieved with Support Vector Machine(Using PUK Kernel) and CfsSubsetEval feature selection method.

Use of conventional energy sources causes enormous amount of pollution leading to the problem of global warming. Most of the countries in Asia are facing the problem of pollution leading to lots of health problems. To curb these problems, renewable energy sources play a very important role. Sentiment analysis is used to analyze opinions of the user for decision making with the help of natural language processing techniques. Opinions are the sentiment or emotions about a subject that is expressed by a user. To study about the emotions of people about alternate energy sources we have carried out comparative sentiment analysis on various renewable energy sources using Twitter data. In our paper we have considered five sources namely: Solar energy, Bioenergy, Wind power, Hydro power and Geothermal energy. Data has been collected from Twitter which is approximately 20,000 tweets for each energy source amounting approximately to 100,000 data which have been considered for analysis. Eight sentiments are calculated for each renewable energy source. It has been found that the people’s opinion about renewable energy sources—mainly solar and wind energy—fetches most tweets and people are more positive towards renewable energy sources for better environment.

Applying the ad hoc network on vehicles has formed a new kind of network called VANET. The higher speed of the nodes (vehicles) and frequent change in network topology make this type of network different from the traditional MANET. VANET is implemented for safety of the vehicles and to make road traffic better. Therefore, before implementing the VANET, the security of the network must be considered. We have carried out analysis of attacks and their affects that can be mounted on VANET. Hence the evaluation of attacks in VANET should be performed before implementing it. Here, in this research paper we have carried out the evaluation of the Black Hole attack (BHA) in City, Highway, and combination of both scenarios based on throughput, packet drop and routing overhead. A comparison of attack in different scenarios is also drawn which gives the clear picture of the effects of the attack in scenarios. Graphs have been drawn using MATLAB

Every year, countless people lose their lives in serious car accidents, and drowsy driving is a major cause. However, because the earliest indications of exhaustion can be identified before a dangerous scenario develops, the detection and signaling of driver drowsiness are ongoing research topics. This study presents a real-time detection system designed to detect driver’s sleep and deliver timely warnings. The mechanism employs an eye monitoring system that uses eye feature points to detect whether a driver’s eye is tired and delivers an alert if the driver is drowsy. The proposed work recognizes the driver’s face by collecting facial landmarks and gives warning to the driver to avoid crashes by performing adaptive thresholding and drowsiness diagnoses based on Eye Characteristic Ratio. The empirical results show that using random forest classifier, decision tree classifier, KNN classifier, and multinomial classifier, the proposed model achieved 90.48%, 89.44%, 88.63%, and 84.32% accuracy, respectively.

Chronic kidney disease (CKD) is a progressive medical condition with significant public health impact, where early detection is critical for effective intervention. This work presents a clearly defined and structured data-driven framework designed to improve CKD prediction through robust preprocessing and optimized ensemble learning. This study proposes a novel hybrid framework that combines Random Forest (RF)-based imputation for missing value handling, categorical feature encoding, and synthetic minority oversampling technique (SMOTE) for addressing class imbalance, integrated with a Grey wolf optimizer (GWO)-based weighted ensemble of top-performing classifiers (Decision Tree, Logistic Regression, and Gaussian Naïve Bayes). The ensemble weights are optimized using the Grey Wolf optimizer (GWO) to enhance predictive accuracy. We evaluate the proposed framework on the UCI CKD dataset, demonstrating that it outperforms individual classifiers and conventional ensemble methods, achieving an accuracy of 98.75%, precision of 98.8%, recall of 98.6%, and F1-score of 98.7%. Additionally, explainable AI (XAI) techniques including SHAP and LIME are employed to analyze feature contributions, providing interpretable insights and confirming the clinical relevance of the predictions. Overall, the proposed framework offers a transparent, reliable, and computationally efficient clinical decision support model that bridges the gap between data-driven AI and nephrology practice.