Internet of Things (IoT) is an instantly exacerbated communication technology that is manifesting miraculous effectuation to revolutionize conventional means of network communication. The applications of IoT are compendiously encompassing our prevalent lifestyle and the integration of IoT with other technologies makes this application spectrum even more latitudinous. However, this admissibility also introduces IoT with a pervasive array of imperative security hazards that demands noteworthy solutions to be swamped. In this scientific study, we proposed Deep Learning (DL) driven Software Defined Networking (SDN) enabled Intrusion Detection System (IDS) to combat emerging cyber threats in IoT. Our proposed model (DNNLSTM) is capable to encounter a tremendous class of common as well as less frequently occurring cyber threats in IoT communications. The proposed model is trained on CICIDS 2018 dataset, and its performance is evaluated on several decisive parameters i.e Accuracy, Precision, Recall, and F1-Score. Furthermore, the designed framework is analytically compared with relevant classifiers, i.e., DNNGRU, and BLSTM for appropriate validation. An exhaustive performance comparison is also conducted between the proposed system and a few preeminent solutions from the literature. The proposed design has circumvented the existing literature with unprecedented performance repercussions such as 99.55% accuracy, 99.36% precision, 99.44% recall, and 99.42% F1-score.

The Internet of Things (IoT) has established itself as a multibillion-dollar business in recent years. Despite its obvious advantages, the widespread nature of IoT renders it insecure and a potential target for cyber-attacks. Furthermore, these devices broad connectivity and dynamic heterogeneous nature can open up a new surface of attack for refined malware attacks. There is a critical need to protect the IoT environment from such attacks and malware. Therefore this research aims to propose an intelligent, SDN-enabled hybrid framework leveraging Cuda Long Short Term Memory Gated Recurrent Unit (cuLSTMGRU) for efficient threat detection in IoT environments. To properly assess the proposed system, a state-of-the-art IoT-based dataset and standard evaluation metrics were used. The proposed model achieved 99.23 % detection accuracy with a low false-positive rate. For further verification, we compare the proposed model results with two of our constructed models (i.e., cuBLSTM and cuGRUDNN) and current benchmark algorithms. The proposed model outclassed the other models regarding speed efficiency, detection accuracy, precision, and other standard evaluation metrics. Finally, the proposed work employed 10-fold cross-validation to ensure that the results were completely unbiased.

Brain tumors (BTs) are deadly diseases that can strike people of every age, all over the world. Every year, thousands of people die of brain tumors. Brain-related diagnoses require caution, and even the smallest error in diagnosis can have negative repercussions. Medical errors in brain tumor diagnosis are common and frequently result in higher patient mortality rates. Magnetic resonance imaging (MRI) is widely used for tumor evaluation and detection. However, MRI generates large amounts of data, making manual segmentation difficult and laborious work, limiting the use of accurate measurements in clinical practice. As a result, automated and dependable segmentation methods are required. Automatic segmentation and early detection of brain tumors are difficult tasks in computer vision due to their high spatial and structural variability. Therefore, early diagnosis or detection and treatment are critical. Various traditional Machine learning (ML) techniques have been used to detect various types of brain tumors. The main issue with these models is that the features were manually extracted. To address the aforementioned insightful issues, this paper presents a hybrid deep transfer learning (GN-AlexNet) model of BT tri-classification (pituitary, meningioma, and glioma). The proposed model combines GoogleNet architecture with the AlexNet model by removing the five layers of GoogleNet and adding ten layers of the AlexNet model, which extracts features and classifies them automatically. On the same CE-MRI dataset, the proposed model was compared to transfer learning techniques (VGG-16, AlexNet, SqeezNet, ResNet, and MobileNet-V2) and ML/DL. The proposed model outperformed the current methods in terms of accuracy and sensitivity (accuracy of 99.51% and sensitivity of 98.90%).

In this paper, we investigate unmanned aerial vehicle (UAV)-aided multiuser reconfigurable intelligent surface (RIS) communication for next generation communication networks. We aim to jointly optimize the active beamforming, passive beamforming, and UAV trajectory jointly to minimize power consumption in presence of UAV jitters and imperfect hardware constraints. We decouple the formulated nonconvex problem into three subproblems. For active beamforming subproblem, we linearize and approximate the constraints using S-procedure and general sign-definitiveness technique. Then, we again apply S-procedure and convex-concave technique to handle the passive beamforming. For UAV trajectory subproblem, we apply first-Taylor expansion to transform the problem into a tractable form. On the highlights of the proposed solution, we design a hybrid semi-unfolding deep neural network (HSUDNN) to mitigate the constraints during the channel state information gain for RIS and UAV links in real-time. Using our proposed active beamforming solution and the optimality conditions, we design the unfolding-based sub neural network. Moreover, we design inception-like multi-kernel convolutional long short-term memory (IL-MK-CLSTM) sub networks to handle the UAV trajectory and passive beamforming. IL-MK-CLSTM provides spatiotemporal connection which helps in overcoming vanishing gradient problem and provides multi-step prediction. The proposed HSUDNN achieves 99.24% accuracy which demonstrates its superior performance in comparison to the existing state-of-the-art techniques in literature.

Breast cancer, a leading cause of female mortality worldwide, poses a significant health challenge. Recent advancements in deep learning techniques have revolutionized breast cancer pathology by enabling accurate image classification. Various imaging methods, such as mammography, CT, MRI, ultrasound, and biopsies, aid in breast cancer detection. Computer-assisted pathological image classification is of paramount importance for breast cancer diagnosis. This study introduces a novel approach to breast cancer histopathological image classification. It leverages modified pre-trained CNN models and attention mechanisms to enhance model interpretability and robustness, emphasizing localized features and enabling accurate discrimination of complex cases. Our method involves transfer learning with deep CNN models—Xception, VGG16, ResNet50, MobileNet, and DenseNet121—augmented with the convolutional block attention module (CBAM). The pre-trained models are finetuned, and the two CBAM models are incorporated at the end of the pre-trained models. The models are compared to state-of-the-art breast cancer diagnosis approaches and tested for accuracy, precision, recall, and F1 score. The confusion matrices are used to evaluate and visualize the results of the compared models. They help in assessing the models’ performance. The test accuracy rates for the attention mechanism (AM) using the Xception model on the “BreakHis” breast cancer dataset are encouraging at 99.2% and 99.5%. The test accuracy for DenseNet121 with AMs is 99.6%. The proposed approaches also performed better than previous approaches examined in the related studies.

In this article, we study the secure communication in unmanned aerial vehicle (UAV) aided and reconfigurable intelligent surface (RIS) empowered multiuser networks. In the proposed configuration, we consider UAV-mounted RIS to reflect the signal between the base station and the legitimate users in the presence of eavesdroppers. The problem is formulated as secrecy rate maximization with channel uncertainty constraints, which is nonconvex. Therefore, we decouple it into beamforming, phase shift, and UAV trajectory subproblems which are jointly optimized. For the beamforming and phase shift subproblems, we apply S-procedure and general sign-definiteness to transform them into tractable forms. And for the UAV trajectory, we unroll the constraints and apply first-order Taylor transform and the difference of two convex functions (DC) to obtain a relaxed convex problem that can be numerically solved. The proposed solution show effectiveness and improved the secrecy rate of the network.