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Neurodegeneration and impaired neuronal transmission in the brain are at the root of Alzheimer’s disease (AD) and dementia. As of yet, no successful treatments for dementia or Alzheimer’s disease have indeed been found. Therefore, preventative measures such as early diagnosis are essential. This research aimed to evaluate the accuracy of the Open Access Series of Imaging Studies (OASIS) database for the purpose of identifying biomarkers of dementia using effective machine learning methods. In most parts of the world, AD is responsible for dementia. When the challenge level is high, it is nearly impossible to get anything done without assistance. This is increasing due to population growth and the diagnostic period. Two current approaches are the medical history and testing. The main challenge for dementia research is the imbalance of datasets and their impact on accuracy. A proposed system based on reinforcement learning and neural networks could generate and segment imbalanced classes. Making a precise diagnosis and taking into account dementia in all four stages will result in high-resolution sickness probability maps. It employs deep reinforcement learning to generate accurate and understandable representations of a person’s dementia sickness risk. To avoid an imbalance, classes should be evenly represented in the samples. There is a significant class imbalance in the MRI image. The Deep Reinforcement System improved trial accuracy by 6%, precision by 9%, recall by 13%, and F-score by 9–10%. The diagnosis efficiency has improved as well.

The present paper deals with the analytical investigation of representative planar masonry-infilled reinforced concrete (MIRC) frames for seismic fragility, performance and demand. The study includes the effect of various patterns of layout for infills panels along the height of reinforced concrete frames. The analytical investigation has been done using non-linear dynamic time-history analysis under collection of forty SAC near-field ground motions using rational hysteretic models for structural components; the results are presented in terms of parameters such as peak inter-storey drift, residual drift and damage index. The outcomes of study are used to develop seismic fragility curves in probabilistic terms for the generic medium-rise MIRC frames. The developed fragility curves can be useful tools in predicting the life and economic losses in the future seismic event. In the current study, efforts are also made to develop curves demonstrating seismic performance and seismic demand for representative MIRC frames.

A brain tumor is a tumor in the brain that has grown out of control, which is a dangerous condition for the human body. For later prognosis and treatment planning, the accurate segmentation and categorization of cancers are crucial. Radiologists must use an automated approach to identify brain tumors, since it is an error-prone and time-consuming operation. This work proposes conditional deep learning for brain tumor segmentation, residual network-based classification, and overall survival prediction using structural multimodal magnetic resonance images (MRI). First, we propose conditional random field and convolution network-based segmentation, which identifies non-overlapped patches. These patches need minimal time to identify the tumor. If they overlap, the errors increase. The second part of this paper proposes residual network-based feature mapping with XG-Boost-based learning. In the second part, the main emphasis is on feature mapping in nonlinear space with residual features, since residual features reduce the chances of loss information, and nonlinear space mapping provides efficient tumor information. Features mapping learned by XG-Boost improves the structural-based learning and increases the accuracy class-wise. The experiment uses two datasets: one for two classes (cancer and non-cancer) and the other for three classes (meningioma, glioma, pituitary). The performance on both improves significantly compared to another existing approach. The main objective of this research work is to improve segmentation and its impact on classification performance parameters. It improves by conditional random field and residual network. As a result, two-class accuracy improves by 3.4% and three-class accuracy improves by 2.3%. It is enhanced with a small convolution network. So, we conclude in fewer resources, and better segmentation improves the results of brain tumor classification.

Due to the recent advances in the Internet and communication technologies, network systems and data have evolved rapidly. The emergence of new attacks jeopardizes network security and make it really challenging to detect intrusions. Multiple network attacks by an intruder are unavoidable. Our research targets the critical issue of class imbalance in intrusion detection, a reflection of the real-world scenario where legitimate network activities significantly out number malicious ones. This imbalance can adversely affect the learning process of predictive models, often resulting in high false-negative rates, a major concern in Intrusion Detection Systems (IDS). By focusing on datasets with this imbalance, we aim to develop and refine advanced algorithms and techniques, such as anomaly detection, cost-sensitive learning, and oversampling methods, to effectively handle such disparities. The primary goal is to create models that are highly sensitive to intrusions while minimizing false alarms, an essential aspect of effective IDS. This approach is not only practical for real-world applications but also enhances the theoretical understanding of managing class imbalance in machine learning. Our research, by addressing these significant challenges, is positioned to make substantial contributions to cybersecurity, providing valuable insights and applicable solutions in the fight against digital threats and ensuring robustness and relevance in IDS development. An intrusion detection system (IDS) checks network traffic for security, availability, and being non-shared. Despite the efforts of many researchers, contemporary IDSs still need to further improve detection accuracy, reduce false alarms, and detect new intrusions. The mean convolutional layer (MCL), feature-weighted attention (FWA) learning, a bidirectional long short-term memory (BILSTM) network, and the random forest algorithm are all parts of our unique hybrid model called MCL-FWA-BILSTM. The CNN-MCL layer for feature extraction receives data after preprocessing. After convolution, pooling, and flattening phases, feature vectors are obtained. The BI-LSTM and self-attention feature weights are used in the suggested method to mitigate the effects of class imbalance. The attention layer and the BI-LSTM features are concatenated to create mapped features before feeding them to the random forest algorithm for classification. Our methodology and model performance were validated using NSL-KDD and UNSW-NB-15, two widely available IDS datasets. The suggested model’s accuracies on binary and multi-class classification tasks using the NSL-KDD dataset are 99.67% and 99.88%, respectively. The model’s binary and multi-class classification accuracies on the UNSW-NB15 dataset are 99.56% and 99.45%, respectively. Further, we compared the suggested approach with other previous machine learning and deep learning models and found it to outperform them in detection rate, FPR, and F-score. For both binary and multiclass classifications, the proposed method reduces false positives while increasing the number of true positives. The model proficiently identifies diverse network intrusions on computer networks and accomplishes its intended purpose. The suggested model will be helpful in a variety of network security research fields and applications.

Accurate detection and classification of cellular and non-cellular components in urine microscopy images are essential for early diagnosis of renal and systemic health conditions. This study presents an optimized object detection framework based on the Red Fox Optimization (RFO)-enabled Roboflow-DEtection TRansformer (RF-DETR) model, designed to automate urine sediment analysis with high precision and low latency. The RF-DETR model leverages a transformer-based architecture with deformable attention and a DINOv2 (self-distillation with no labels) pre-trained visual backbone to capture multi-scale features effectively. RFO, a nature-inspired metaheuristic, is employed to fine-tune critical hyperparameters such as learning rate, decoder layers, and dropout, enhancing the model’s convergence and generalization capabilities. Experiments were conducted on the RF100-VL urine microscopy dataset, where the proposed model achieved a precision of 0.78, recall of 0.66, mAP@0.5 of 0.737, and mAP@0.5:0.95 of 0.44 after 100 training epochs. Compared to baseline models, the optimized RF-DETR demonstrated improved performance in detecting small and medium objects like leukocytes and erythrocytes—crucial components for urinary tract infection and kidney disease detection. The model’s NMS-free design and multi-resolution training enable real-time inference on both GPU and edge devices. Additionally, visualization tools such as confusion matrices, F1-curves, and prediction overlays validate the robustness and interpretability of the system. The results confirm the suitability of the RFO-optimized RF-DETR framework for clinical deployment, offering a powerful tool for automated, scalable, and accurate urine analysis. Future work will focus on lightweight model variants, enhanced small-object detection, and domain adaptation using self-supervised and vision-language learning techniques.

The severity of vehicle damage is very important in determining road safety, insurance claim and automated vehicle inspection. Although the recent methods of computer vision were promising in the localization of damaged areas, it is difficult to discriminate the level of damage precisely because of subtle visual variations, class disparity, and irregular damage images. In a bid to solve these problems, this paper suggests a severity-sensitive YOLOv8-based instance segmentation system, which incorporates curriculum learning, Harris Hawks Optimization (HHO) to hyperparameter optimization, and confidence thresholding at the per-class level. The proposed learning strategy in the curriculum allows the training to be progressive, whereby initial training occurs with understanding of types of coarse damage, and then further training is given in fine-grained severity discrimination. HHO is used to automatically tune important hyperparameters with severity-sensitive fitness objectives to enhance the robustness of segmentation without changing the architecture. Also, class-specific confidence thresholds are proposed to adjust the precision and recalling to the levels of severity. Experimental analysis of a curated vehicle damage severity dataset shows that convergence is stable and the performance is competitive, with Box mAP50 of 0.271 and Mask mAP50 of 0.135, showing the best performance at an early training phase. The proposed lightweight YOLOv8s-Seg model has shown to be better than a larger YOLOv8m-Seg baseline with practical thresholds of IoU, which proves its applicability in practice. The confusion matrix and class-wise analysis indicate the presence of a strong detection of severe damages, whereas surface defects that emerge in subtle cases are noted as a challenge. In general, the proposed framework offers an effective, understandable, and implementable algorithm of automated damage severity in vehicles.

Deforestation continues to pose a major threat to global ecosystems, biodiversity, and climate resilience, demanding intelligent and timely monitoring solutions. This study introduces a novel framework that integrates YOLOv8 (You Only Look Once) object detection with LangChain-based Agentic AI for real-time deforestation anomaly detection. The proposed system leverages YOLOv8’s rapid and accurate visual recognition of deforestation indicators—such as tree stumps, logging machinery, and unauthorized human presence—while enhancing contextual reasoning and decision-making through LangChain agents. Extensive experiments using annotated satellite and drone imagery demonstrate steady improvements in training performance, with box_loss, cls_loss, and distribution focal loss reduced by more than 50%. Despite modest mean Average Precision (mAP50 ≈ 0.07), the integration of LangChain agents enabled dynamic threshold adjustment, reinforcement-learning-based feedback, and GIS-driven reporting, thereby reducing false positives and increasing recall (up to 24%) compared to baseline YOLO models. The framework not only provides actionable, geolocated alerts but also supports adaptive learning for evolving deforestation patterns. By combining the speed of deep learning with the autonomy of agentic AI, this work highlights a scalable, interpretable, and real-time approach for environmental monitoring. The findings establish a foundation for future research in multi-modal data fusion, edge deployment on drones and satellites, and sustainable forest management.

In health care, an accurate diagnosis with the help of a data-driven forecasting framework takes the risk factors associated with heart disease. However, building such an effective model using deep learning (DL) methods requires high-quality data, i.e., data free of outliers or anomalies. The current paper proposes a new approach to diagnosing and controlling heart diseases by utilizing a multi-tiered data acquisition model, data pre-processing, feature extraction, and DL. The framework encompasses four types of datasets. The first phase of the proposed methodology consists of data acquisition, while the second phase includes advanced data preprocessing for each data type. In phase three, multi-feature extraction methods are used to extract the features from the dataset. In phase four, a combined feature selection technique of ReliefF and Pearson correlation is used to select the best features. Phase five of the study is the formulation of the CILAD-Net DL model that integrates CNN, Inception Net, LSTM, and Angle DetectNet to accurately detect heart disease. The sixth phase implements Deep Reinforcement Learning (DRL) for nutrition recommendations based on the detected disease, thus improving the treatment individualization. The developed model’s experimental outcomes are validated with other prevailing models in terms of accuracy, recall, hamming loss, and so on. Finally, the outcomes of the proposed model attain the higher accuracy of 0. 998 for the CILAD-Net model, which is significantly better than DenseNet-201 with 0. 988, ANN with 0. 987, KNN with 0. 977, and CL-Net with 0. 984.

Skin disease classification is a choir cognate for early diagnosis and therapy. The novelty of this study lies in integrating the Grasshopper Optimisation Algorithm (GOA) with a DETR (DEtection TRansformer) model which is developed for the classification of skin disease. Hyperparameter tuning using GOA optimizes the critical parameters of the proposed model to improve classification accuracy. After extensive testing on a large dataset of skin disease photos, the optimised DETR model returned an accuracy of at least 99.26%. The superiority of the DETR improved using GOA compared to standard ones indicates its potential to be used for automatically diagnosing skin diseases. Findings demonstrate that the proposed method contributes to enhancing diagnostic accuracy and creates a basis for improving transformer-based medical image analysis.

Noise removal means eliminating noise from a noisy image, thereby improving the quality of original image. Elimination of noise from the input signal remains a major issue for investigators. With the increasing number of digital images captured day-to-day, the requirement for more accurate perceptibly appealing image is enhancing. However, images taken by contemporary cameras are automatically deteriorated by noise, resulting in degraded visual quality. In general, retrieval of essential data from noisy images in the procedure of denoising to acquire the best quality of images is a big issue today. Therefore, work must be done to eliminate noise in the image without falling image characteristics, like edges, corners, and other sharp frameworks. Hence, this research paper overviews numerous techniques for image denoising and image quality enhancement. This overview investigates 50 Research papers related to noise removal and image quality enhancement, and developed technique-wise reviews, namely spatial domain-based techniques, optimization-based approaches, transform domain-based approaches deep learning (DL)-based techniques and machine learning (ML)-based techniques. An investigation participate in a survey based upon classifying experiment approaches, datasets, year of publication, toolset employed, effectual metrics for image denoising and image quality improvement. Finally, the challenges of overviewed techniques are illustrated to concentrate investigators for developing various efficient techniques for image denoising and image quality improvement.