The domain of Artificial Intelligence (AI) is made important strides recently, leading to developments in several domains comprising biomedical diagnostics and research. The procedure of AI-based systems in biomedical analytics takes opened up novel avenues for the progress of disease analysis, drug discovery, and treatment. Cancer is the second major reason of death worldwide; around one in every six people pass away suffering from it. Among several kinds of cancers, the colon and lung variations are the most frequent and deadliest ones. Initial detection of conditions on both fronts significantly reduces the probability of mortality. Deep learning (DL) and Machine learning (ML) systems are exploited to speed up such cancer detection, permitting researchers to analyze a huge count of patients in a lesser time count and at a minimal cost. This study develops a new Biomedical Image Analysis for Colon and Lung Cancer Detection using Tuna Swarm Algorithm with Deep Learning (BICLCD-TSADL) model. The presented BICLCD-TSADL technique examines the biomedical images for the identification and classification of colon and lung cancer. To accomplish this, the BICLCD-TSADL technique applies Gabor filtering (GF) to preprocess the input images. In addition, the BICLCD-TSADL technique employs a GhostNet feature extractor to create a collection of feature vectors. Moreover, AFAO was executed to adjust the hyperparameters of the GhostNet technique. Furthermore, the TSA with echo state network (ESN) classifier is utilized for detecting lung and colon cancer. To demonstrate the more incredible outcome of the BICLCD-TSADL system, an extensive experimental outcome is carried out. The comprehensive comparative analysis highlighted the greater efficiency of the BICLCD-TSADL technique with other approaches with maximum accuracy of 99.33%.

A more widespread Internet of Things (IoT) device is performing a surge in cyber-attacks, with Distributed Denial of Service (DDoS) attacks posing a major risk to the reliability and availability of IoT services. DDoS attacks overwhelm target methods by flooding them with a huge volume of malicious traffic from several sources. Mitigating and identifying these attacks in IoT platforms are vital to maintaining the seamless function of IoT services and the maintenance of secret information. Feature selection (FS) is a key stage in the machine learning (ML) pipeline as it supports decreasing the data size, enhancing model outcomes, and speeding up training and inference. As part of IoT with DDoS attack detection, FS proposes to recognize a subset of IoT-related features that is optimum to represent the traffic features and distinguish between malicious and benign activities. This study designs a new DDoS attack detection using a snake optimizer with ensemble learning (DDAD-SOEL) technique on the IoT platform. The purpose of the DDAD-SOEL approach lies in the effectual and automated identification of DDoS attacks. To attain this, the DDAD-SOEL technique utilizes the SO algorithm for feature subset selection. Besides, an ensemble of three DL approaches namely long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), and deep belief network (DBN) approach. Finally, the Adadelta optimizer can be applied for the parameter tuning of the DL algorithms. The simulation value of the DDAD-SOEL methodology was tested on the benchmark database and the outcome indicates the improvements of the DDAD-SOEL methodology over other recent models in terms of distinct measures.

Internet of Things (IoT)-assisted skin cancer recognition integrates several connected devices and sensors for supporting the primary analysis and monitoring of skin conditions. A preliminary analysis of skin cancer images is extremely difficult because of factors such as distinct sizes and shapes of lesions, differences in color illumination, and light reflections on the skin surface. In recent times, IoT-based skin cancer recognition utilizing deep learning (DL) has been used for enhancing the early analysis and monitoring of skin cancer. This article presents an optimal deep learning-based skin cancer detection and classification (ODL-SCDC) methodology in the IoT environment. The goal of the ODL-SCDC technique is to exploit metaheuristic-based hyperparameter selection approaches with a DL model for skin cancer classification. The ODL-SCDC methodology involves an arithmetic optimization algorithm (AOA) with the EfficientNet model for feature extraction. For skin cancer detection, a stacked denoising autoencoder (SDAE) classification model has been used. Lastly, the dragonfly algorithm (DFA) is utilized for the optimal hyperparameter selection of the SDAE algorithm. The simulation validation of the ODL-SCDC methodology has been tested on a benchmark ISIC skin lesion database. The extensive outcomes reported a better solution of the ODL-SCDC methodology compared with other models, with a maximum sensitivity of 97.74%, specificity of 99.71%, and accuracy of 99.55%. The proposed model can assist medical professionals, specifically dermatologists and potentially other healthcare practitioners, in the skin cancer diagnosis process.

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.

Nowadays, the malware on the Android platform is found to be increasing. With the prevalent use of code obfuscation technology, the precision of antivirus software and classical detection techniques is low. Classical detection techniques of signature matching and manual analysis have exposed issues like low accuracy and slow detection speed. Several authors have overcome the issue of Android malware detection utilizing machine learning (ML) techniques and had more research outcomes. With the growth of deep learning (DL), many researchers started to use DL methods for detecting Android malware. This article introduces a Gauss-Mapping Black Widow Optimization with Deep Learning Enabled Android Malware Classification (GBWODL-AMC) model. The major intention of the GBWODL-AMC technique lies in the automated classification of Android malware. To accomplish this, the GBWODL-AMC technique involves the design of GBWO based feature selection approach to enhance the classification performance. For Android malware classification purposes, the GBWODL-AMC technique employs a deep extreme learning machine (DELM) model and its parameter are optimally selected by the ant lion optimization (ALO) algorithm. The simulation analysis of the GBWODL-AMC technique is tested on CICAndMal2017 dataset. Extensive experimental results signify the better performance of the GBWODL-AMC technique over other malware detectors with maximum accuracy of 98.95%.

In recent times, mostly in the Middle East region, Red Palm Weevils (RPW) are one of the most dangerous pests of palm trees worldwide. The RPW causes significant harm to several palm species. The existing detection method includes the symptoms detection of RPW through sound or visual assessment and chemical recognition of volatile signatures created by diseased palm trees. However, an effective recognition of RPW disease at earlier stages is assumed that a very complex problem for cultivating date palms. This is another reason why the use of state-of-the-art technologies is supported in the avoidance of the spread of the RPW on palm trees. Several researchers are working on determining the correct process for the localization, classification, and detection of RPW pests. Therefore, this paper presents an intelligent Red Palm Weevil Detection using the Bird Swarm Algorithm with Deep Learning (IRPWD-BSADL) model. The major aim of the IRPWD-BSADL technique focuses on the identification and classification of RPW using CV and DL models. Primarily, the bilateral filtering (BF) approach can be utilized to remove the noise that exists in the images. In the presented IRPWD-BSADL technique, an improved ShuffleNet model can be applied for feature extraction purposes. To enhance the recognition results, the IRPWD-BSADL technique makes use of BSA for the hyperparameter tuning process. For RPW detection and classification, an extreme gradient boosting (XGBoost) classifier can be used. The simulation analysis of the IRPWD-BSADL method can be tested on the RPW dataset. An extensive comparison study stated the improved performance of the IRPWD-BSADL algorithm on the RPW detection method.