Brain tumors (BTs) are spreading very rapidly across the world. Every year, thousands of people die due to deadly brain tumors. Therefore, accurate detection and classification are essential in the treatment of brain tumors. Numerous research techniques have been introduced for BT detection as well as classification based on traditional machine learning (ML) and deep learning (DL). The traditional ML classifiers require hand-crafted features, which is very time-consuming. On the contrary, DL is very robust in feature extraction and has recently been widely used for classification and detection purposes. Therefore, in this work, we propose a hybrid deep learning model called DeepTumorNet for three types of brain tumors (BTs)—glioma, meningioma, and pituitary tumor classification—by adopting a basic convolutional neural network (CNN) architecture. The GoogLeNet architecture of the CNN model was used as a base. While developing the hybrid DeepTumorNet approach, the last 5 layers of GoogLeNet were removed, and 15 new layers were added instead of these 5 layers. Furthermore, we also utilized a leaky ReLU activation function in the feature map to increase the expressiveness of the model. The proposed model was tested on a publicly available research dataset for evaluation purposes, and it obtained 99.67% accuracy, 99.6% precision, 100% recall, and a 99.66% F1-score. The proposed methodology obtained the highest accuracy compared with the state-of-the-art classification results obtained with Alex net, Resnet50, darknet53, Shufflenet, GoogLeNet, SqueezeNet, ResNet101, Exception Net, and MobileNetv2. The proposed model showed its superiority over the existing models for BT classification from the MRI images.

Currently, remote sensing images (RSIs) are often exploited in the explanation of urban and rural areas, change recognition, and other domains. As the majority of RSI is high-resolution and contains wide and varied data, proper interpretation of RSIs is most important. Land use and land cover (LULC) classification utilizing deep learning (DL) is a common and efficient manner in remote sensing and geospatial study. It is very important in land planning, environmental monitoring, mapping, and land management. But, one of the recent approaches is problems like vulnerability to noise interference, low classification accuracy, and worse generalization ability. DL approaches, mostly Convolutional Neural Networks (CNNs) revealed impressive performance in image recognition tasks, making them appropriate for LULC classification in RSIs. Therefore, this study introduces a novel Land Use and Land Cover Classification employing the River Formation Dynamics Algorithm with Deep Learning (LULCC-RFDADL) technique on RSIs. The main objective of the LULCC-RFDADL methodology is to recognize the diverse types of LC on RSIs. In the presented LULCC-RFDADL technique, the dense EfficientNet approach is applied for feature extraction. Furthermore, the hyperparameter tuning of the Dense EfficientNet method was implemented using the RFDA technique. For the classification process, the LULCC-RFDADL technique uses the Multi-Scale Convolutional Autoencoder (MSCAE) model. At last, the seeker optimization algorithm (SOA) has been exploited for the parameter choice of the MSCAE system. The achieved outcomes of the LULCC-RFDADL algorithm were examined on benchmark databases. The simulation values show the better result of the LULCC-RFDADL methods with other approaches in terms of different metrics.

The integration of Internet of Things (IoT) technology with deep learning (DL) algorithms has revolutionized plant disease detection and crop management and paved the way for sustainable agricultural practices. Real-time information on soil moisture, plant health, and environmental conditions can be collected by deploying a network of connected devices and sensors in agricultural fields. DL algorithms, specifically convolutional neural networks (CNN), analyze this massive dataset, facilitating timely and accurate recognition of plant diseases. This early detection allows farmers to implement targeted interventions, like adjustment to irrigation or precision application of pesticides, maximizing crop yield, and minimizing resource wastage. Therefore, this article develops an automated Plant Disease Detection and Crop Management using a spotted hyena optimizer with deep learning (APDDCM-SHODL) technique for Sustainable Agriculture. The APDDCM-SHODL approach aims to detect the existence of plant diseases and improve crop productivity in the IoT infrastructure. To achieve this, the APDDCM-SHODL method primarily employs the Vector Median Filter (VMF) technique. In addition, the Densely Connected Networks (DenseNet201) model is deployed for feature extraction. In addition, the SHO technique is exploited for optimum hyperparameter tuning of the DenseNet201 model. Furthermore, the classification algorithm is implemented by using the recurrent spiking neural network (RSNN) model. A brief set of experiments has been made to determine the experimental validation of the APDDCM-SHODL model. The comprehensive results inferred that the APDDCM-SHODL method reaches remarkable performance over other existing methods with the highest accuracy of 98.60%.

Artificial Intelligence (AI) techniques have changed the general perceptions about medical diagnostics, especially after the introduction and development of Convolutional Neural Networks (CNN) and advanced Deep Learning (DL) and Machine Learning (ML) approaches. In general, dermatologists visually inspect the images and assess the morphological variables such as borders, colors, and shapes to diagnose the disease. In this background, AI techniques make use of algorithms and computer systems to mimic the cognitive functions of the human brain and assist clinicians and researchers. In recent years, AI has been applied extensively in the domain of dermatology, especially for the detection and classification of skin cancer and other general skin diseases. In this research article, the authors propose an Optimal Multi-Attention Fusion Convolutional Neural Network-based Skin Cancer Diagnosis (MAFCNN-SCD) technique for the detection of skin cancer in dermoscopic images. The primary aim of the proposed MAFCNN-SCD technique is to classify skin cancer on dermoscopic images. In the presented MAFCNN-SCD technique, the data pre-processing is performed at the initial stage. Next, the MAFNet method is applied as a feature extractor with Henry Gas Solubility Optimization (HGSO) algorithm as a hyperparameter optimizer. Finally, the Deep Belief Network (DBN) method is exploited for the detection and classification of skin cancer. A sequence of simulations was conducted to establish the superior performance of the proposed MAFCNN-SCD approach. The comprehensive comparative analysis outcomes confirmed the supreme performance of the proposed MAFCNN-SCD technique over other methodologies.

Lung and colon cancers are deadly diseases that can develop concurrently in organs and undesirably affect human life in some special cases. The detection of these cancers from histopathological images poses a complex challenge in medical diagnostics. Advanced image processing techniques, including deep learning algorithms, offer a solution by analyzing intricate patterns and structures in histopathological slides. The integration of artificial intelligence in histopathological analysis not only improves the proficiency of cancer detection but also holds the potential to increase prognostic assessments, eventually contributing to effective treatment strategies for patients with lung and colon cancers. This manuscript presents an Improved Water Strider Algorithm with Convolutional Autoencoder for Lung and Colon Cancer Detection (IWSACAE-LCCD) on HIs. The major aim of the IWSACAE-LCCD technique aims to detect lung and colon cancer. For noise removal process, median filtering (MF) approach can be used. Besides, deep convolutional neural network based MobileNetv2 model can be applied as a feature extractor with IWSA based hyperparameter optimizer. Finally, convolutional autoencoder (CAE) model can be applied to detect the presence of lung and colon cancer. To enhance the detection results of the IWSACAE-LCCD technique, a series of simulations were performed. The obtained results highlighted that the IWSACAE-LCCD technique outperforms other approaches in terms of different measures.

The Internet of Things (IoT) based Wireless Sensor Networks (WSNs) contain interconnected autonomous sensor nodes (SN), which wirelessly communicate with each other and the wider internet structure. Intrusion detection to secure IoT-based WSNs is critical for identifying and responding to great security attacks and threats that can cooperate with the integrity, availability, and privacy of the network and its data. Machine learning (ML) algorithms are deployed for detecting difficult patterns and subtle anomalies in IoT data. Artificial intelligence (AI) driven methods are learned and adapted from novel data for improving detection accuracy over time. In this article, we introduce a Red Kite Optimization Algorithm with an Average Ensemble Model for Intrusion Detection (RKOA-AEID) technique for Secure IoT-based WSN. The purpose of the RKOA-AEID methodology is to accomplish security solutions for IoT-assisted WSNs. To accomplish this, the RKOA-AEID technique performs pre-processing to scale the input data using min-max normalization. In addition, the RKOA-AEID technique performs an RKOA-based feature selection approach to elect an optimum set of features. For intrusion detection, an average ensemble learning model is used. Finally, the Lévy-fight chaotic whale optimization Algorithm (LCWOA) can be executed for the optimum hyperparameter chosen for the ensemble models. The performance evaluation of the RKOA-AEID algorithm can be tested on the benchmark WSN-DS dataset. The extensive experimental outcomes stated the higher outcome of the RKOA-AEID algorithm with other approaches with an improved accuracy of 98.94%.