The diagnosis and surgical resection using Magnetic Resonance (MR) images in brain tumors is a challenging task to minimize the neurological defects after surgery owing to the non-linear nature of the size, shape, and textural variation. Radiologists, clinical experts, and brain surgeons examine brain MRI scans using the available methods, which are tedious, error-prone, time-consuming, and still exhibit positional accuracy up to 2–3 mm, which is very high in the case of brain cells. In this context, we propose an automated Ultra-Light Brain Tumor Detection (UL-BTD) system based on a novel Ultra-Light Deep Learning Architecture (UL-DLA) for deep features, integrated with highly distinctive textural features, extracted by Gray Level Co-occurrence Matrix (GLCM). It forms a Hybrid Feature Space (HFS), which is used for tumor detection using Support Vector Machine (SVM), culminating in high prediction accuracy and optimum false negatives with limited network size to fit within the average GPU resources of a modern PC system. The objective of this study is to categorize multi-class publicly available MRI brain tumor datasets with a minimum time thus real-time tumor detection can be carried out without compromising accuracy. Our proposed framework includes a sensitivity analysis of image size, One-versus-All and One-versus-One coding schemes with stringent efforts to assess the complexity and reliability performance of the proposed system with K-fold cross-validation as a part of the evaluation protocol. The best generalization achieved using SVM has an average detection rate of 99.23% (99.18%, 98.86%, and 99.67%), and F-measure of 0.99 (0.99, 0.98, and 0.99) for (glioma, meningioma, and pituitary tumors), respectively. Our results have been found to improve the state-of-the-art (97.30%) by 2%, indicating that the system exhibits capability for translation in modern hospitals during real-time surgical brain applications. The method needs 11.69 ms with an accuracy of 99.23% compared to 15 ms achieved by the state-of-the-art to earlier to detect tumors on a test image without any dedicated hardware providing a route for a desktop application in brain surgery.

Industrial Internet of Things (IIoT) formation of richer ecosystem of intelligent interconnected devices while enabling new levels of digital innovation has essentially transformed and revolutionized global manufacturing and industry 4.0. Conversely, the prevalent distributed nature of IIoT, Industrial 5G, underlying IoT sensing devices, IT/OT convergence, Edge Computing, and Time Sensitive Networking makes it an impressive and potential target for cyber-attackers. Multi-variant persistent and sophisticated bot attacks are considered catastrophic for connects IIoTs. Besides, botnet attack detection is extremely complex and decisive. Thus, efficient and timely detection of IIoT botnets is a dire need of the day. We propose a hybrid intelligent Deep Learning (DL)-enabled mechanism to secure IIoT infrastructure from lethal and sophisticated multi-variant botnet attacks. The proposed mechanism has been rigorously evaluated with latest available dataset, standard and extended performance evaluation metrics, and current DL benchmark algorithms. Besides,cross validation of our results are also performed to clearly show overall performance. The proposed mechanisms outperforms in identifying accurately multi-variant sophisticated bot attacks. Besides, our proposed technique also show promising results in terms of speed efficiency.

Kidney disease is a major public health concern that has only recently emerged. Toxins are removed from the body by the kidneys through urine. In the early stages of the condition, the patient has no problems, but recovery is difficult in the later stages. Doctors must be able to recognize this condition early in order to save the lives of their patients. To detect this illness early on, researchers have used a variety of methods. Prediction analysis based on machine learning has been shown to be more accurate than other methodologies. This research can help us to better understand global disparities in kidney disease, as well as what we can do to address them and coordinate our efforts to achieve global kidney health equity. This study provides an excellent feature-based prediction model for detecting kidney disease. Various machine learning algorithms, including k-nearest neighbors algorithm (KNN), artificial neural networks (ANN), support vector machines (SVM), naive bayes (NB), and others, as well as Re-cursive Feature Elimination (RFE) and Chi-Square test feature-selection techniques, were used to build and analyze various prediction models on a publicly available dataset of healthy and kidney disease patients. The studies found that a logistic regression-based prediction model with optimal features chosen using the Chi-Square technique had the highest accuracy of 98.75 percent. White Blood Cell Count (Wbcc), Blood Glucose Random (bgr), Blood Urea (Bu), Serum Creatinine (Sc), Packed Cell Volume (Pcv), Albumin (Al), Hemoglobin (Hemo), Age, Sugar (Su), Hypertension (Htn), Diabetes Mellitus (Dm), and Blood Pressure (Bp) are examples of these traits.

In recent years, object detection has gained significant interest and is considered a challenging problem in computer vision. Object detection is mainly employed for several applications, such as instance segmentation, object tracking, image captioning, healthcare, etc. Recent studies have reported that deep learning (DL) models can be employed for effective object detection compared to traditional methods. The rapid urbanization of smart cities necessitates the design of intelligent and automated waste management techniques for effective recycling of waste. In this view, this study develops a novel deep learning-based small object detection and classification model for garbage waste management (DLSODC-GWM) technique. The proposed DLSODC-GWM technique mainly focuses on detecting and classifying small garbage waste objects to assist intelligent waste management systems. The DLSODC-GWM technique follows two major processes, namely, object detection and classification. For object detection, an arithmetic optimization algorithm (AOA) with an improved RefineDet (IRD) model is applied, where the hyperparameters of the IRD model are optimally chosen by the AOA. Secondly, the functional link neural network (FLNN) technique was applied for the classification of waste objects into multiple classes. The design of IRD for waste classification and AOA-based hyperparameter tuning demonstrates the novelty of the work. The performance validation of the DLSODC-GWM technique is performed using benchmark datasets, and the experimental results show the promising performance of the DLSODC-GWM method on existing approaches with a maximum $acc{u}_y$ of 98.61%.

Currently, the amount of Internet of Things (IoT) applications is enhanced for processing, analyzing, and managing the created big data from the smart city. Certain other applications of smart cities were location-based services, transportation management, and urban design, amongst others. There are several challenges under these applications containing privacy, data security, mining, and visualization. The blockchain-assisted IoT application (BIoT) is offering new urban computing to secure smart cities. The blockchain is a secure and transparent data-sharing decentralized platform, so BIoT is suggested as the optimum solution to the aforementioned challenges. In this view, this study develops an Optimal Machine Learning-based Intrusion Detection System for Privacy Preserving BIoT with Smart Cities Environment, called OMLIDS-PBIoT technique. The presented OMLIDS-PBIoT technique exploits BC and ML techniques to accomplish security in the smart city environment. For attaining this, the presented OMLIDS-PBIoT technique employs data pre-processing in the initial stage to transform the data into a compatible format. Moreover, a golden eagle optimization (GEO)-based feature selection (FS) model is designed to derive useful feature subsets. In addition, a heap-based optimizer (HBO) with random vector functional link network (RVFL) model was utilized for intrusion classification. Additionally, blockchain technology is exploited for secure data transmission in the IoT-enabled smart city environment. The performance validation of the OMLIDS-PBIoT technique is carried out using benchmark datasets, and the outcomes are inspected under numerous factors. The experimental results demonstrate the superiority of the OMLIDS-PBIoT technique over recent approaches.