Cancer is a deadly disease caused by various biochemical abnormalities and genetic diseases. Colon and lung cancer have developed as two major causes of disability and death in human beings. The histopathological detection of these malignancies is a vital element in determining the optimal solution. Timely and initial diagnosis of the sickness on either front diminishes the possibility of death. Deep learning (DL) and machine learning (ML) methods are used to hasten such cancer recognition, allowing the research community to examine more patients in a much shorter period and at a less cost. This study introduces a marine predator’s algorithm with deep learning as a lung and colon cancer classification (MPADL-LC3) technique. The presented MPADL-LC3 technique aims to properly discriminate different types of lung and colon cancer on histopathological images. To accomplish this, the MPADL-LC3 technique employs CLAHE-based contrast enhancement as a pre-processing step. In addition, the MPADL-LC3 technique applies MobileNet to derive feature vector generation. Meanwhile, the MPADL-LC3 technique employs MPA as a hyperparameter optimizer. Furthermore, deep belief networks (DBN) can be applied for lung and color classification. The simulation values of the MPADL-LC3 technique were examined on benchmark datasets. The comparison study highlighted the enhanced outcomes of the MPADL-LC3 system in terms of different measures.

The amount of multimedia content is growing exponentially and a major portion of multimedia content uses images and video. Researchers in the computer vision community are exploring the possible directions to enhance the system accuracy and reliability, and these are the main requirements for robot vision-based systems. Due to the change of facial expressions and the wearing of masks or sunglasses, many face recognition systems fail or the accuracy in recognizing the face decreases in these scenarios. In this work, we contribute a real time surveillance framework using Raspberry Pi and CNN (Convolutional Neural Network) for facial recognition. We have provided a labeled dataset to the system. First, the system is trained upon the labeled dataset to extract different features of the face and landmark face detection and then it compares the query image with the dataset on the basis of features and landmark face detection. Finally, it compares faces and votes between them and gives a result that is based on voting. The classification accuracy of the system based on the CNN model is compared with a mid-level feature extractor that is Histogram of Oriented Gradient (HOG) and the state-of-the-art face detection and recognition methods. Moreover, the accuracy in recognizing the faces in the cases of wearing a mask or sunglasses or in live videos is also evaluated. The highest accuracy achieved for the VMU, face recognition, and 14 celebrity datasets is 98%, 98.24%, 89.39%, and 95.71%, respectively. Experimental results on standard image benchmarks demonstrate the effectiveness of the proposed research in accurate face recognition compared to the state-of-the-art face detection and recognition methods.

The fast development of the Internet of Things (IoT) and widespread utilization in a large number of areas, such as vehicle IoT, industrial control, healthcare, and smart homes, has made IoT security increasingly prominent. Ransomware is a type of malware which encrypts the victim’s records and demands a ransom payment for restoring access. The effective detection of ransomware attacks highly depends on how its traits are discovered and how precisely its activities are understood. In this article, we propose an Optimal Graph Convolutional Neural Network based Ransomware Detection (OGCNN-RWD) technique for cybersecurity in an IoT environment. The OGCNN-RWD technique involves learning enthusiasm for teaching learning-based optimization (LETLBO) algorithms for the feature subset selection process. For ransomware classification, the GCNN model is used in this study, and its hyperparameters can be optimally chosen by the harmony search algorithm (HSA). For exhibiting the greater performance of the OGCNN-RWD approach, a series of simulations were made on the ransomware database. The simulation result portrays the betterment of the OGCNN-RWD system over other existing techniques with an accuracy of 99.64%.

Object detection is a computer vision based technique which is used to detect instances of semantic objects of a particular class in digital images and videos. Crowd density analysis is one of the commonly utilized applications of object detection. Since crowd density classification techniques face challenges like non-uniform density, occlusion, inter-scene, and intra-scene deviations, convolutional neural network (CNN) models are useful. This paper presents a Metaheuristics with Deep Transfer Learning Enabled Intelligent Crowd Density Detection and Classification (MDTL-ICDDC) model for video surveillance systems. The proposed MDTL-ICDDC technique mostly concentrates on the effective identification and classification of crowd density on video surveillance systems. In order to achieve this, the MDTL-ICDDC model primarily leverages a Salp Swarm Algorithm (SSA) with NASNetLarge model as a feature extraction in which the hyperparameter tuning process is performed by the SSA. Furthermore, a weighted extreme learning machine (WELM) method was utilized for crowd density and classification process. Finally, the krill swarm algorithm (KSA) is applied for an effective parameter optimization process and thereby improves the classification results. The experimental validation of the MDTL-ICDDC approach was carried out with a benchmark dataset, and the outcomes are examined under several aspects. The experimental values indicated that the MDTL-ICDDC system has accomplished enhanced performance over other models such as Gabor, BoW-SRP, Bow-LBP, GLCM-SVM, GoogleNet, and VGGNet.

Wearable devices such as smartwatches, wristbands, and GPS shoes are commonly employed for fitness and wellness as they enable people to observe their day-to-day health status. These gadgets encompass sensors to accumulate data related to user activities. Clinical act graph devices come under the class of wearables worn on the wrist to compute the sleep parameters by storing sleep movements. Sleep is very important for a healthy lifestyle. Inadequate sleep can obstruct physical, emotional, and mental health, and could result in several illnesses such as insulin resistance, high blood pressure, heart disease, stress, etc. Recently, deep learning (DL) models have been employed for predicting sleep quality depending upon the wearables data from the period of being awake. In this aspect, this study develops a new wearables-assisted smart health monitoring for sleep quality prediction using optimal deep learning (WSHMSQP-ODL) model. The presented WSHMSQP-ODL technique initially enables the wearables to gather sleep-activity-related data. Next, data pre-processing is performed to transform the data into a uniform format. For sleep quality prediction, the WSHMSQP-ODL model uses the deep belief network (DBN) model. To enhance the sleep quality prediction performance of the DBN model, the enhanced seagull optimization (ESGO) algorithm is used for hyperparameter tuning. The experimental results of the WSHMSQP-ODL method are examined under different measures. An extensive comparison study shows the significant performance of the WSHMSQP-ODL model over other models.

Weed control is a significant means to enhance crop production. Weeds are accountable for 45% of the agriculture sector’s crop losses, which primarily occur because of competition with crops. Accurate and rapid weed detection in agricultural fields was a difficult task because of the presence of a wide range of weed species at various densities and growth phases. Presently, several smart agriculture tasks, such as weed detection, plant disease detection, species identification, water and soil conservation, and crop yield prediction, can be realized by using technology. In this article, we propose a Modified Barnacles Mating Optimization with Deep Learning based weed detection (MBMODL-WD) technique. The MBMODL-WD technique aims to automatically identify the weeds in the agricultural field. Primarily, the presented MBMODL-WD technique uses the Gabor filtering (GF) technique for the noise removal process. For automated weed detection, the presented MBMODL-WD technique uses the DenseNet-121 model as feature extraction with the MBMO algorithm as hyperparameter optimization. The design of the MBMO algorithm involves the integration of self-population-based initialization with the standard BMO algorithm. At last, the Elman Neural Network (ENN) method was applied for the weed classification process. To demonstrate the enhanced performance of the MBMODL-WD approach, a series of simulation analyses were performed. A comprehensive set of simulations highlighted the enhanced performance of the presented MBMODL-WD methodology over other DL models with a maximum accuracy of 98.99%.