Skin cancer is one of the most rapidly spreading illnesses in the world and because of the limited resources available. Early detection of skin cancer is crucial accurate diagnosis of skin cancer identification for preventive approach in general. Detecting skin cancer at an early stage is challenging for dermatologists, as well in recent years, both supervised and unsupervised learning tasks have made extensive use of deep learning. One of these models, Convolutional Neural Networks (CNN), has surpassed all others in object detection and classification tests. The dataset is screened from MNIST: HAM10000 which consists of seven different types of skin lesions with the sample size of 10015 is used for the experimentation. The data pre-processing techniques like sampling, dull razor and segmentation using autoencoder and decoder is used. Transfer learning techniques like DenseNet169 and Resnet 50 were used to train the model to obtain the results.

One of the main problems with WSNs is that most sensor nodes in wireless sensor networks (WSNs) are motorized by energy-constrained, which significantly affects the system’s effectiveness, dependability, and lifespan. Numerous clustering strategies have been created to enhance the energy efficiency of WSNs in 5G and 6G transmission. To overcome these issues, we suggest a collaborative energy-efficient routing protocol (CEEPR) for sustainable communication in 5G/6G wireless sensor networks (WSNs). Initially, this study gathered and collected the data at the sink node. The network’s nodes are clustered using the reinforcement learning technique (R.L.). Cluster head selection is employed for better data transmission using residual energy (RE) based cluster head selection algorithm. A collaborative energy-efficient routing protocol (CEERP) is proposed. We use a multi-objective improved seagull algorithm (MOISA) as an optimization technique to enhance the system’s performance. Finally, the presentation of the system is analyzed. Compare with the existing methods, the primary metrics are throughput, energy consumption, network lifetime, packet transmission, routing overhead, and transmission speed. The proposed approach uses 50% less energy while improving network lifespan and energy efficiency compared to the current protocols.

Planning the path an autonomous robotic vehicle will take is an essential part of developing and utilizing such a system. The task is deciding on the best route and navigating technique to get the vehicle to its destination quickly and safely. The purpose of route planning is to select a route for the vehicle that will result in the greatest fuel savings. The planner aids the vehicle in accomplishing its goals in the least amount of time and using the least amount of fuel by determining the optimal route by considering variables including traffic, road conditions, and distance. The result is higher production and lower operating expenses. An autonomous robotic vehicle (ARVs) is a self-driving vehicle that uses advanced technologies to navigate through the environment without human intervention. These vehicles can be used for various applications, including transportation, logistics, surveillance, and exploration. Route planning (RP) is the process of determining the most efficient and safe route for a vehicle, pedestrian, or any other mode of transportation to reach a destination. Route management is the process of selecting a collision-free path through an environment, which in practice is frequently crowded. Therefore, offering a RP solution for robotic systems is essential. The particle swarm optimization (PSO) method incorporates inertia weights and imitates the cooperative behavior of the flock’s population as well as its predatory nature to address route modeling issues. The Dijkstra algorithm (DA) works by determining the shortest path among the closest vertices between the source and destination. To choose the best path, inertia weight is also taken into account. By analyzing algorithms, we presented the combination technique for RP. In order to give a reliable route planning method, we suggested the weight-controlled particle swarm-optimized Dijkstra algorithm (WCPSODA). MATLAB was used to run the simulation, and conventional tools were used to evaluate the results. The findings of the study show that the suggested systems are capable of performing well.

Face Recognition (FR) is the technology used to identify and verify individuals based on their facial features. In recent decades, FR plays a crucial role in various sectors including security, healthcare, banking, and criminal identification. For effective FR, numerous techniques are currently under development which range from appearance to hybrid approaches. Most of the existing methods offer diverse solutions to describe a face image either by focusing on specific facial features or by considering the entire face. This study explores a various range of such techniques and challenges related to FR. The existing solutions were analysed with respect to various perspectives of inputs, viz., illumination, pose variation, facial expressions, occlusions, and aging which led to the prominent implementation of FR systems. The primary contribution of this survey lies in the comprehensive review of state-of-the-art FR techniques and deriving the taxonomy of categorizing these methods into various classes which range from appearance to hybrid approaches. Moreover, the proposed detailed study highlights the significant features used by the most recent research developed in FR, also, provide a detailed classification of image and video-based FR methods, highlighting major advancements and core processing steps for handling huge volume of datasets. Moreover, the proposed study outlines the current trends in available datasets and emphasizing their enhancements. This survey also aims to provide a valuable resource for researchers and practitioners by offering insights into the latest developments and identifying open problems that require further investigation.

Background: The Internet of Medical Things, often known as IoMT, is a revolutionary method of connecting medical equipment and the software that operates on it to the computer networks that are used in healthcare 5.0. The rapid development of smart medical devices on IoMT platforms has led to the adoption of major technologies in the modernization of healthcare procedures, the administration of diseases, and the improvement in patient treatment standards. The IoMT offers a variety of cloud-based applications, including data exchange, data screening, patient surveillance, information collection and analysis, and hygienic hospital attention. Wireless sensor networks (WSNs) are responsible for both the gathering and delivery of data. Method: The safety of patients and their right to privacy are the top priorities in the healthcare sector. Anyone may see and modify the patient’s health information because the data from these smart gadgets are sent wirelessly through the airways. Hence, we developed a unique elliptic curve cryptography-based energy-efficient routing protocol (ECC-EERP) to provide a high level of security and energy efficient system for healthcare 5.0. Data can be encrypted using the key-based method ECC-EERP. It employs pairs of public and private keys to decrypt and encrypts web traffic and reducse the amount of energy needed by a WSN in aggregate. Result and Discussion: The efficiency of the suggested method was evaluated in comparison with that of a variety of existing methods. The suggested method was evaluated with the use of many parameters such as security, encryption throughput, energy efficiency, network lifetime, communication overload, computation time, and implementation cost. The results showed that the proposed technique provides enhanced security and energy efficiency.

Introduction/Background: Plant diseases and pernicious insects are a considerable threat in the agriculture sector. Leaf diseases impact agricultural production. Therefore, early detection and diagnosis of these diseases are essential. This issue can be addressed if a farmer can detect the diseases properly. Objective: The fundamental goal of this project is to create and test a model for precisely classifying leaf diseases in plants. Materials and Methods: This paper introduces a model designed to classify leaf diseases effectively. The research utilizes the publicly available PlantVillage dataset, which includes 38 different classes of leaf images, ranging from healthy to disease-infected leaves. Pretrained CNN (Convolutional Neural Network) models, including VGG16, ResNet50, InceptionV3, MobileNetV2, AlexNet, and EfficientNet, are employed for image classification. Results: The paper provides a performance comparison of these models. The results show that the EfficientNet model achieves an accuracy of 97.5% in classifying healthy and diseased leaf images, outperforming other models. Discussion: This research highlights the potential of utilizing advanced neural network architectures for accurate disease detection in the agricultural sector. Conclusion: This study demonstrates the efficacy of employing sophisticated CNN models, particularly EfficientNet, to properly identify leaf diseases. Such technological developments have the potential to improve disease detection in agriculture. These improvements help to improve food security by allowing for preventive actions to battle crop diseases.