Cervical cancer (CC), the most common cancer among women, is most commonly diagnosed through Pap smears, a crucial screening process that includes collecting cervical cells for examination. Artificial intelligence (AI)-powered computer-aided diagnoses (CAD) system becomes a promising tool for improving CC diagnosis. Deep learning (DL), a branch of AI, holds particular potential in CAD systems for early detection and accurate diagnosis. DL algorithm is trained to identify abnormalities and patterns in Pap smear images, such as dysplasia, cellular changes, and other markers of CC. So, this study presents a Computer Aided Cervical Cancer Diagnosis utilizing the Gazelle Optimizer Algorithm with Deep Learning (CACCD-GOADL) model on Pap smear images. The foremost objective of the CACCD-GOADL approach is to examine the image detection of CC. To accomplish this, the CACCD-GOADL methodology uses an improved MobileNetv3 model for extracting complex patterns in Pap smear images. In addition, the CACCD-GOADL technique designs a new GOA for the hyperparameter tuning of the improved MobileNetv3 system. For the classification and identification of cancer, the CACCD-GOADL technique uses a stacked extreme learning machine (SELM) methodology. The simulation validation of the CACCD-GOADL approach is verified on a benchmark dataset of Herlev. Experimental results highlighted that the CACCD-GOADL algorithm reaches superior outcomes over other methods.

Epileptic seizures are a chronic and persistent neurological illness that mainly affects the human brain. Electroencephalogram (EEG) is considered an effective tool among neurologists to detect various brain disorders, including epilepsy, owing to its advantages, such as its low cost, simplicity, and availability. In order to reduce the severity of epileptic seizures, it is necessary to design effective techniques to identify the disease at an earlier stage. Since the traditional way of diagnosing epileptic seizures is laborious and time-consuming, automated tools using machine learning (ML) and deep learning (DL) models may be useful. This paper presents an intelligent deep canonical sparse autoencoder-based epileptic seizure detection and classification (DCSAE-ESDC) model using EEG signals. The proposed DCSAE-ESDC technique involves two major processes, namely, feature selection and classification. The DCSAE-ESDC technique designs a novel coyote optimization algorithm (COA)-based feature selection technique for the optimal selection of feature subsets. Moreover, the DCSAE-based classifier is derived for the detection and classification of different kinds of epileptic seizures. Finally, the parameter tuning of the DSCAE model takes place via the krill herd algorithm (KHA). The design of the COA-based feature selection and KHA-based parameter tuning shows the novelty of the work. For examining the enhanced classification performance of the DCSAE-ESDC technique, a detailed experimental analysis was conducted using a benchmark epileptic seizure dataset. The comparative results analysis portrayed the better performance of the DCSAE-ESDC technique over existing techniques, with maximum accuracy of 98.67% and 98.73% under binary and multi-classification, respectively.

Increasing waste generation has become a key challenge around the world due to the dramatic expansion in industrialization and urbanization. This study focuses on providing effective solutions for real-time monitoring garbage collection systems via the Internet of things (IoT). It is limited to controlling the bad odor of blowout gases and the spreading of overspills by using an IoT-based solution. The inadequate and poor dumping of waste produces radiation and toxic gases in the environment, creating an adversarial effect on global warming, human health, and the greenhouse system. The IoT and deep learning (DL) confer active solutions for real-time data monitoring and classification, correspondingly. Therefore, this paper presents an artificial ecosystem-based optimization with an improved deep learning model for IoT-assisted sustainable waste management, called the AEOIDL-SWM technique. The presented AEOIDL-SWM technique exploits IoT-based camera sensors for collecting information and a microcontroller for processing the data. For waste classification, the presented AEOIDL-SWM technique applies an improved residual network (ResNet) model-based feature extractor with an AEO-based hyperparameter optimizer. Finally, the sparse autoencoder (SAE) algorithm is exploited for waste classification. To depict the enhancements of the AEOIDL-SWM system, a widespread simulation investigation is performed. The comparative analysis shows the enhanced outcomes of the AEOIDL-SWM technique over other DL models.

The seamless operation of interconnected smart devices in wireless sensor networks (WSN) and the Internet of Things (IoT) needs continuously accessible end-to-end routes. However, the sensor node (SN) relies on a limited power source and tends to cause disconnection in multi-hop routes because of a power shortage in the WSN, eventually leading to the inefficiency of the total IoT network. Furthermore, the density of available SNs affects the existence of feasible routes and the level of path multiplicity in the WSN. Thus, an effective routing model is predictable to extend the lifetime of WSN by adaptively choosing the better route for the data transfers between interconnected IoT devices. This study develops a Hybrid Muddy Soil Fish Optimization-based Energy Aware Routing Scheme (HMSFO-EARS) for IoT-assisted WSN. The presented HMSFO-EARS technique majorly focuses on the identification of optimal routes for data transmission in the IoT-assisted WSN. To accomplish this, the presented HMSFO-EARS technique involves the integration of the MSFO algorithm with the Adaptive $\beta$-Hill Climbing (ABHC) concept. Moreover, the presented HMSFO-EARS technique derives a fitness function for maximizing the lifespan and minimizing energy consumption. To demonstrate the enhanced performance of the HMSFO-EARS technique, a series of experiments was performed. The simulation results indicate the better performance of the HMSFO-EARS algorithm over other recent approaches with reduced energy consumption, less delay, high throughput, and extended network lifetime.

Rapid advancements in the internet and communication domains have led to a massive rise in the network size and the equivalent data. Consequently, several new attacks have been created and pose several challenging issues for network security. In addition, the intrusions can launch several attacks and can be handled by the use of intrusion detection system (IDS). Though several IDS models are available in the literature, there is still a need to improve the detection rate and decrease the false alarm rate. The recent developments of machine learning (ML) and deep learning (DL)-based IDS systems are being deployed as possible solutions for effective intrusion detection. In this work, we propose an arithmetic optimization-enabled density-based clustering with deep learning (AOEDBC-DL) model for intelligent intrusion detection. The presented AOEDBC-DL technique follows a data clustering process to handle the massive quantity of network data traffic. To accomplish this, the AOEDBC-DL technique applied a density-based clustering technique and the initial set of clusters are initialized using the arithmetic optimization algorithm (AOA). In order to recognize and classify intrusions, a bidirectional long short term memory (BiLSTM) mechanism was exploited in this study. Eventually, the AOA was applied as a hyperparameter tuning procedure of the BiLSTM model. The experimental result analysis of the AOEDBC-DL algorithm was tested using benchmark IDS datasets. Extensive comparison studies highlighted the enhancements of the AOEDBC-DL technique over other existing approaches.

Breast cancer is the second most dominant kind of cancer among women. Breast Ultrasound images (BUI) are commonly employed for the detection and classification of abnormalities that exist in the breast. The ultrasound images are necessary to develop artificial intelligence (AI) enabled diagnostic support technologies. For improving the detection performance, Computer Aided Diagnosis (CAD) models are useful for breast cancer detection and classification. The current advancement of the deep learning (DL) model enables the detection and classification of breast cancer with the use of biomedical images. With this motivation, this article presents an Aquila Optimizer with Bayesian Neural Network for Breast Cancer Detection (AOBNN-BDNN) model on BUI. The presented AOBNN-BDNN model follows a series of processes to detect and classify breast cancer on BUI. To accomplish this, the AOBNN-BDNN model initially employs Wiener filtering (WF) related noise removal and U-Net segmentation as a pre-processing step. Besides, the SqueezeNet model derives a collection of feature vectors from the pre-processed image. Next, the BNN algorithm will be utilized to allocate appropriate class labels to the input images. Finally, the AO technique was exploited to fine-tune the parameters related to the BNN method so that the classification performance is improved. To validate the enhanced performance of the AOBNN-BDNN method, a wide experimental study is executed on benchmark datasets. A wide-ranging experimental analysis specified the enhancements of the AOBNN-BDNN method in recent techniques.