Histopathological images are commonly used imaging modalities for breast cancer. As manual analysis of histopathological images is difficult, automated tools utilizing artificial intelligence (AI) and deep learning (DL) methods should be modelled. The recent advancements in DL approaches will be helpful in establishing maximal image classification performance in numerous application zones. This study develops an arithmetic optimization algorithm with deep-learning-based histopathological breast cancer classification (AOADL-HBCC) technique for healthcare decision making. The AOADL-HBCC technique employs noise removal based on median filtering (MF) and a contrast enhancement process. In addition, the presented AOADL-HBCC technique applies an AOA with a SqueezeNet model to derive feature vectors. Finally, a deep belief network (DBN) classifier with an Adamax hyperparameter optimizer is applied for the breast cancer classification process. In order to exhibit the enhanced breast cancer classification results of the AOADL-HBCC methodology, this comparative study states that the AOADL-HBCC technique displays better performance than other recent methodologies, with a maximum accuracy of 96.77%.

Intelligent Cyber-Physical Transportation Systems (ICTS) rely deeply on data exchange between moving vehicles and fixed infrastructure. While exchanging data between vehicles in the network, security is a main constraint. ICTS encompasses many facilities and claims, such as managing road traffic, traveler data, public transportation systems, and autonomous vehicles. It is anticipated that ICTS will make significant contributions to future smart cities and urban design in road and traffic security, transportation and transport optimization, energy efficiency, and environmental pollution control. Flexibility, variety of quality-of-service needs, and massive volumes of data generated all present challenges when working with ITS. Previous works on cyber-physical attack detection lag in ensuring security at various levels. To monitor In-Vehicle Networks (IVN), Vehicle-to-Vehicle (V2V) Communications, and Vehicle-to-Infrastructure (V2I) Networks for malicious behavior, we introduce a deep learning-based Intrusion Detection System (IDS) for ICTS. In this research, we look into how Deep Learning (DL), a relatively new but rapidly growing subject, could help improve ICTS. This research investigates whether deep learning can enhance the security of autonomous vehicles and other forms of smart transportation. We improve the smart transport network using the deep learning method, simulate it, and statistically study its data transmission economy, accuracy projections of the future, and path-switching strategy. To identify malicious activity in the core networks of autonomous vehicles (AV), we have developed an ensemble Long-Short Term Memory (LSTM) technique based on Deep Learning architecture. The car hacking dataset (used to monitor communications within a vehicle) and the UNSWNB15 dataset (used to monitor communications with the outside world) are used to test and compare the performance of the proposed IDS. The experimental findings showed that our suggested system outperformed other intrusion detection methods by achieving 100% accuracy in detecting all sorts of assaults on the auto-hacking dataset and 99% on the UNSW-NB15 dataset.

Cloud computing (CC) refers to an Internet-based computing technology in which shared resources, such as storage, software, information, and platform, are offered to users on demand. CC is a technology through which virtualized and dynamically scalable resources are presented to users on the Internet. Security is highly significant in this on-demand CC. Therefore, this paper presents improved metaheuristics with a fuzzy logic-based intrusion detection system for the cloud security (IMFL-IDSCS) technique. The IMFL-IDSCS technique can identify intrusions in the distributed CC platform and secure it from probable threats. An individual sample of IDS is deployed for every client, and it utilizes an individual controller for data management. In addition, the IMFL-IDSCS technique uses an enhanced chimp optimization algorithm-based feature selection (ECOA-FS) method for choosing optimal features, followed by an adaptive neuro-fuzzy inference system (ANFIS) model enforced to recognize intrusions. Finally, the hybrid jaya shark smell optimization (JSSO) algorithm is used to optimize the membership functions (MFs). A widespread simulation analysis is performed to examine the enhanced outcomes of the IMFL-IDSCS technique. The extensive comparison study reported the enhanced outcomes of the IMFL-IDSCS model with maximum detection efficiency with accuracy of 99.31%, precision of 92.03%, recall of 78.25%, and F-score of 81.80%.

Image captioning is a deep learning technique that intends to create and generate textual descriptions or captions for images. It integrates computer vision and natural language processing (NLP) to comprehend the visual content of an image and generate human-like descriptions. Deep learning (DL) based image captioning models can be trained on large-scale datasets, allowing them to generalize various types of images and generate captions that apply to a wide range of visual scenarios. By combining computer vision and natural language processing, DL-enabled image captioning models can understand both visual and textual information, which enables them to generate captions that not only describe the visual content but also incorporate contextual and semantic information. This study develops an Automated Image Captioning using Sparrow Search Algorithm with Improved Deep Learning (AIC-SSAIDL) technique. The major intention of the AIC-SSAIDL technique lies in the automated generation of textual captions for the input images. To accomplish this, the AIC-SSAIDL technique utilizes the MobileNetv2 model to generate feature descriptors of the input images and its hyperparameter tuning process takes place using SSA. For the image captioning process, the AIC-SSAIDL technique utilizes an attention mechanism with long short-term memory (AM-LSTM) network. Finally, the hyperparameter selection of the AM-LSTM model is performed by the fruit fly optimization (FFO) algorithm. A wide range of experiments has been conducted on benchmark data to depict the better performance of the AIC-SSAIDL method. The comprehensive result analysis highlighted the enhanced captioning results of the AIC-SSAIDL method with maximum CIDEr of 46.12, 61.89, and 137.45 on Flickr8k, Flickr30k, and MSCOCO datasets, respectively.

5G networks offer high-speed, low-latency communication for various applications. As 5G networks introduce new capabilities and support a wide range of services, they also become more vulnerable to different kinds of cyberattacks, particularly Distributed Denial of Service (DDoS) attacks. Effective DDoS attack classification in 5G networks is a critical aspect of ensuring the security, availability, and performance of these advanced communication infrastructures. In recent days, machine learning (ML) and deep learning (DL) models can be employed for an accurate DDoS attack detection process. In this aspect, this study designs a Modified Equilibrium Optimization Algorithm with Deep Learning based DDoS Attack Classification (MEOADL-ADC) method in 5G networks. The goal of the MEOADL-ADC technique is the automated classification of DDoS attacks in the 5G network. The MEOADL-ADC technique follows a three-stage process such as feature selection, classification, and hyperparameter tuning. Primarily, the MEOADL-ADC technique employs MEOA based feature selection approach. Next, the MEOADL-ADC technique utilizes the long short-term memory (LSTM) model for the classification of DDoS attacks. Finally, the tunicate swarm algorithm (TSA) is exploited to adjust the hyperparameter of the LSTM model. The design of MEOA-based feature selection and TSA-based hyperparameter tuning shows the novelty of the work. The experimental outcome of the MEOADL-ADC method is tested on a benchmark dataset, and the outcomes indicate the betterment of the MEOADL-ADC algorithm over the current methods with maximum accuracy of 97.60%.

Oral cancer is considered one of the most common cancer types in several counties. Earlier-stage identification is essential for better prognosis, treatment, and survival. To enhance precision medicine, Internet of Medical Things (IoMT) and deep learning (DL) models can be developed for automated oral cancer classification to improve detection rate and decrease cancer-specific mortality. This article focuses on the design of an optimal Inception-Deep Convolution Neural Network for Oral Potentially Malignant Disorder Detection (OIDCNN-OPMDD) technique in the IoMT environment. The presented OIDCNN-OPMDD technique mainly concentrates on identifying and classifying oral cancer by using an IoMT device-based data collection process. In this study, the feature extraction and classification process are performed using the IDCNN model, which integrates the Inception module with DCNN. To enhance the classification performance of the IDCNN model, the moth flame optimization (MFO) technique can be employed. The experimental results of the OIDCNN-OPMDD technique are investigated, and the results are inspected under specific measures. The experimental outcome pointed out the enhanced performance of the OIDCNN-OPMDD model over other DL models.