With the rise of new technologies, such as the Internet of Things, raising the productivity of agricultural and farming activities is critical to improving yields and cost-effectiveness. IoT, in particular, can improve the efficiency of agriculture and farming processes by eliminating human intervention through automation. The fast rise of Internet of Things (IoT)-based tools has changed nearly all life sectors, including business, agriculture, surveillance, etc. These radical developments are upending traditional agricultural practices and presenting new options in the face of various obstacles. IoT aids in collecting data that is useful in the farming sector, such as changes in climatic conditions, soil fertility, amount of water required for crops, irrigation, insect and pest detection, bug location disruption of creatures to the sphere, and horticulture. IoT enables farmers to effectively use technology to monitor their forms remotely round the clock. Several sensors, including distributed WSNs (wireless sensor networks), are utilized for agricultural inspection and control, which is very important due to their exact output and utilization. In addition, cameras are utilized to keep an eye on the field from afar. The goal of this research is to evaluate smart agriculture using IoT approaches in depth. The paper demonstrates IoT applications, benefits, current obstacles, and potential solutions in smart agriculture. This smart agricultural system aims to find existing techniques that may be used to boost crop yield and save time, such as water, pesticides, irrigation, crop, and fertilizer management.

Medical images are helpful for the diagnosis, treatment, and evaluation of diseases. Precise medical image segmentation improves diagnosis and decision-making, aiding intelligent medical services for better disease management and recovery. Due to the unique nature of medical images, image segmentation algorithms based on deep learning face problems such as sample imbalance, edge blur, false positives, and false negatives. In view of these problems, researchers primarily improve the network structure but rarely improve from the unstructured aspect. The paper tackles these challenges, accentuating the limitations of deep convolutional neural network-based methods and proposing solutions to reduce annotation costs, particularly in complex images, and introduces the improvement strategies to solve the problems of sample imbalance, edge blur, false positives, and false negatives. Additionally, the article introduces the latest deep learning-based applications in medical image analysis, covering segmentation, image acquisition, enhancement, registration, and classification. Moreover, the article provides an overview of four cutting-edge deep learning models, namely convolutional neural network (CNN), deep belief network (DBN), stacked autoencoder (SAE), and recurrent neural network (RNN). The study selection involved searching benchmark academic databases, collecting relevant literature and appropriate indicator for analysis, emphasizing DL-based segmentation and classification approaches, and evaluating performance metrics. The research highlights clinicians’ and scholars’ obstacles in developing an efficient and accurate malignancy prognostic framework based on state-of-the-art deep-learning algorithms. Furthermore, future perspectives are explored to overcome challenges and advance the field of medical image analysis.

The Internet of Things (IoT) plays a crucial role in various sectors such as automobiles and the logistic tracking medical field because it consists of distributed nodes, servers, and software for effective communication. Although this IoT paradigm has suffered from intrusion threats and attacks that cause security and privacy issues, existing intrusion detection techniques fail to maintain reliability against the attacks. Therefore, the IoT intrusion threat has been analyzed using the sparse convolute network to contest the threats and attacks. The web is trained using sets of intrusion data, characteristics, and suspicious activities, which helps identify and track the attacks, mainly, Distributed Denial of Service (DDoS) attacks. Along with this, the network is optimized using evolutionary techniques that identify and detect the regular, error, and intrusion attempts under different conditions. The sparse network forms the complex hypotheses evaluated using neurons, and the obtained event stream outputs are propagated to further hidden layer processes. This process minimizes the intrusion involvement in IoT data transmission. Effective utilization of training patterns in the network successfully classifies the standard and threat patterns. Then, the effectiveness of the system is evaluated using experimental results and discussion. Network intrusion detection systems are superior to other types of traditional network defense in providing network security. The research applied an IGA-BP network to combat the growing challenge of Internet security in the big data era, using an autoencoder network model and an improved genetic algorithm to detect intrusions. MATLAB built it, which ensures a 98.98% detection rate and 99.29% accuracy with minimal processing complexity, and the performance ratio is 90.26%. A meta-heuristic optimizer was used in the future to increase the system’s ability to forecast attacks.

Diabetic Retinopathy (DR) is a prevalent acute stage of diabetes mellitus that causes vision-effecting abnormalities on the retina. This will cause blindness if not identified early. Because DR not an irreversible procedure, and only vision is preserved via care. Consequently, Early diagnosis and care with DR will significantly minimize the chance of vision loss. In modern ophthalmology, retinal image analysis has become a popular approach to disease diagnosis. The ophthalmologists and computerized systems extensively employ fundus angiography to detect DR-based clinical signs for early detection of DR. fundus photographs are commonly prone to low contrast, noise, and irregular illumination issues due to the complexity of imaging environments such as imaging variety of angles and light conditions. This research presents an Algorithm for improving the quality of images to strengthen the standard of color fundus images by reducing the noise and improving the contrast. The approach includes two main stages: cropping the images to remove insignificant content, then applying the shape crop and gaussian blurring for noise reduction and contrast improvement. The experimental results are evaluated using two standard datasets EyePACS and MESSIDOR. It’s clearly shown that the outcomes of feature extraction and classification of enhanced images is outperform the results without applying the enhancement approach. The improved algorithm is also tested in smart hospitals as an IoMT application.

In this era, there are plenty of wireless devices that are being used with the support of WI-FI (Wireless Fidelity) and they need to be maintained and authorized. Wireless Sensor Networks (WSN), a cornerstone of modern wireless technology, offer cost-efficient solutions for diverse monitoring tasks but are simultaneously exposed to a myriad of security threats, including unauthorized access, attacks, and suspicious activities. These vulnerabilities can significantly degrade the performance and reliability of WSNs, making the early detection and mitigation of such threats imperative. Intrusion Detection Systems (IDS) emerge as crucial tools in safeguarding WSNs against these challenges. Numerous studies focus on enhanced Intrusion Detection model accuracy and decrease in loss with higher Detection Rate and lower False Alarm Rate, because of this, eliminating the repetitive feature of the dataset is exhibited. This study introduces a sophisticated Network Intrusion Detection System (NIDS) designed to safeguard Wi-Fi-based WSNs from prevalent cyber threats, such as impersonation, flooding, and injection attacks. At the heart of our approach is a meticulous feature selection process that enhances the dataset’s utility by eliminating null values, substituting unknown entries with a placeholder (’NONE’), and refining the feature set to include only the most relevant indicators of potential security breaches. Initially, from a pool of 154 features, a subset of 76 is selected, which is further narrowed down to 13 pivotal features, ensuring a focused and efficient analysis. Employing standard scaler function for feature scaling and preprocessing, this research train proposed a Convolutional Neural Network (CNN) based approach aiming for optimal intrusion detection and prevention across multiclass classifications within WSN environments. The study aims to enhance detection accuracy, reduce loss values, and decrease false alarm rates, comparing it to CNN, Deep Neural Network (DNN) (5), DNN (3), and (Long Short-Term Memory) LSTM networks. The model’s performance is evaluated using various metrics, including precision, recall, support, F1 score, and macro-average. The culmination of our research efforts is evidenced by the exceptional performance of the CNN model, achieving an impressive accuracy rate of 97% and a loss metric of 0.14, all while maintaining a minimal False Alarm Rate. This study significantly advances IDS accuracy while simultaneously reducing false alarms, thus fortifying the security posture of WSNs in the face of evolving cyber threats.

In recent years, the realms of computer vision and deep learning have ushered in transformative changes across various domains. Among these, deep learning stands out for its remarkable capacity to handle vast datasets, revolutionizing numerous fields, including the biomedical sector. In particular, its prowess has been harnessed in the realm of brain tumor identification through MRI scans, yielding impressive results. This research project is dedicated to conducting an exhaustive exploration of existing endeavors in the domain of brain tumor identification and classification via MRI scans. This endeavor is poised to be of profound value to researchers looking to leverage their deep learning expertise in the realm of brain tumor detection and categorization. The initial phase involves an overview of prior studies that have employed deep learning for categorising and detecting brain tumors. Subsequently, a meticulous analysis of deep learning studies proposed in research publications spanning (2019 to 2022) is presented in tabular form. The conclusion section comprehensively assesses the merits and demerits inherent in deep neural networks. The insights gleaned from this study promise to equip future researchers with a holistic perspective on current research trends and a nuanced understanding of the effectiveness of diverse deep learning methodologies. It is our fervent belief that this research will significantly advance the understanding of brain tumors and their detection methodologies.