Forecasting rainfall is crucial to the well-being of individuals and is significant everywhere in the world. It contributes to reducing the disastrous effects of floods on agriculture, human life, and socioeconomic systems. This study discusses the challenges of effectively forecasting rainfall and floods and the necessity of combining data with flood channel mathematical modelling to forecast floodwater levels and velocities. This research focuses on leveraging historical meteorological data to find trends using machine learning and deep learning approaches to estimate rainfall. The Bangladesh Meteorological Department provided the data for the study, which also uses eight machine learning algorithms. The performance of the machine learning models is examined using evaluation measures like the R² score, root mean squared error and validation loss. According to this research’s findings, polynomial regression, random forest regression, and long short-term memory (LSTM) had the highest performance levels. Random forest and polynomial regression have an R² value of 0.76, while LSTM has a loss value of 0.09, respectively.

The amount of data captured is expanding day by day which leads to the need for a monitoring system that helps in decision making. Current technologies such as cloud, machine learning (ML) and Internet of Things (IoT) provide a better solution for monitoring automation systems efficiently. In this paper, a prediction model that monitors real-time data of sensor nodes in a clinical environment using a machine learning algorithm is proposed. An IoT-based smart hospital environment has been developed that controls and monitors appliances over the Internet using different sensors such as current sensors, a temperature and humidity sensor, air quality sensor, ultrasonic sensor and flame sensor. The IoT-generated sensor data have three important characteristics, namely, real-time, structured and enormous amount. The main purpose of this research is to predict early faults in an IoT environment in order to ensure the integrity, accuracy, reliability and fidelity of IoT-enabled devices. The proposed fault prediction model was evaluated via decision tree, K-nearest neighbor, Gaussian naive Bayes and random forest techniques, but random forest showed the best accuracy over others on the provided dataset. The results proved that the ML techniques applied over IoT-based sensors are well efficient to monitor this hospital automation process, and random forest was considered the best with the highest accuracy of 94.25%. The proposed model could be helpful for the user to make a decision regarding the recommended solution and control unanticipated losses generated due to faults during the automation process.

Monitoring the Earth’s surface and objects is important for many applications, such as managing natural resources, crop yield predictions, and natural hazard analysis. Remote sensing is one of the most efficient and cost-effective solutions for analyzing land-use and land-cover (LULC) changes over the Earth’s surface through advanced computer algorithms, such as classification and change detection. In the past literature, various developments were made to change detection algorithms to detect LULC multitemporal changes using optical or microwave imagery. The optical-based hyperspectral highlights the critical information, but sometimes it is difficult to analyze the dataset due to the presence of atmospheric distortion, radiometric errors, and misregistration. In this work, an artificial neural network-based post-classification comparison (ANPC) as change detection has been utilized to detect the muti-temporal LULC changes over a part of Uttar Pradesh, India, using the Hyperion EO-1 dataset. The experimental outcomes confirmed the effectiveness of ANPC (92.6%) as compared to the existing models, such as a spectral angle mapper (SAM) based post-classification comparison (SAMPC) (89.7%) and k-nearest neighbor (KNN) based post-classification comparison (KNNPC) (91.2%). The study will be beneficial in extracting critical information about the Earth’s surface, analysis of crop diseases, crop diversity, agriculture, weather forecasting, and forest monitoring.

Wireless Body Area Network (WBAN) is among the most promising technologies for enhancing life quality. WBANs enable constant monitoring of physiological processes by implanting or wearing small, low-power, intelligent sensor nodes in or on the human body. These sensor nodes may be placed either invasively or non-invasively. Patient data must be disseminated reliably and promptly for WBAN’s healthcare apps to function. For this reason, medical teams may use real-time apps for sharing vital information like blood pressure, an ECG, and an EEG. Critical data packets are delay-sensitive and must arrive at sink nodes within the time constraints that satisfy QoS for WBAN. However, networks’ unpredictable and dynamic nature (node mobility, link partitioning) makes reliable data transfer a challenging task. Additionally, postural mobility and ultra-short wireless range cause rapid topology changes, resulting in network partitioning. The network partitioning causes failure of data delivery to the sink or coordinator and causes a delay as well. In the case of normal data, it is not a big issue, but it is not tolerable for emergency data because it may be life-threatening. Consequently, compromising the link reliability and stability results in higher delays, increased packet re-transmissions, and decreased throughput performance. Therefore, we propose an Enhanced Probabilistic Route Stability (EPRS) scheme to address these issues. The proposed EPRS scheme introduces a cost function called Link Assessment Cost (LAC) that makes coherent decisions regarding route reliability in determining whether an active route is a good candidate for routing and satisfying QoS requirements. The LAC is based on two critical factors about link status, i.e., Route Stability Factor (RSF) and Expected Probability of link E(p) . Based on these factors, a score is assigned to a link that determines the status (likelihood) of a link, either connected or disconnected . In this way, the multi-facet EPRS selects the most stable and reliable routes despite the disconnection in the networks, thereby improving the route stability and throughput, minimizing the end-to-end delay, route discovery calls, and re-transmissions as depicted by simulation results.

Software vulnerabilities pose a significant threat to system security, necessitating effective automatic detection methods. Current techniques face challenges such as dependency issues, language bias, and coarse detection granularity. This study presents a novel deep learning-based vulnerability detection system for Java code. Leveraging hybrid feature extraction through graph and sequence-based techniques enhances semantic and syntactic understanding. The system utilizes control flow graphs (CFG), abstract syntax trees (AST), program dependencies (PD), and greedy longest-match first vectorization for graph representation. A hybrid neural network (GCN-RFEMLP) and the pre-trained CodeBERT model extract features, feeding them into a quantum convolutional neural network with self-attentive pooling. The system addresses issues like long-term information dependency and coarse detection granularity, employing intermediate code representation and inter-procedural slice code. To mitigate language bias, a benchmark software assurance reference dataset is employed. Evaluations demonstrate the system's superiority, achieving 99.2% accuracy in detecting vulnerabilities, outperforming benchmark methods. The proposed approach comprehensively addresses vulnerabilities, including improper input validation, missing authorizations, buffer overflow, cross-site scripting, and SQL injection attacks listed by common weakness enumeration (CWE).

Due to a continuous change in people’s lifestyle and dietary habits, gastrointestinal diseases are on the increase, with dietary changes being a major contributor to a variety of bowel problems. Around two million people around the world die due to gastrointestinal (GI) diseases. Endoscopy is a medical imaging technology helpful in diagnosing gastrointestinal diseases like polyps and esophagitis. Its manual diagnosis is time-consuming; hence, computer-aided techniques are now widely used for accurate and fast GI disease diagnosis. In this paper, the Kvasir dataset of 4000 endoscopic images, comprising 500 images of each of the eight gastrointestinal tract disease classes have been classified using seven grid search fine-tuned transfer learning models. The fine-tuned transfer learning models employed in this paper are ResNet101, InceptionV3, InceptionResNetV2, Xception, DenseNet121, MobileNetV2, and ResNet50. The grid search algorithm has been used to determine the architectural and fine-tuning hyperparameters. The fine-tuned ResNet101 model performed the best, with a learning rate 0.001 and a batch size of 32 for the SGD optimizer at 40 epochs. These hyperparameters were optimized through grid search along with new set of layers added to the model. The newly added layers include one flatten layer, two dropout layers and five dense layers optimized using grid search. The grid search fine-tuned ResNet101 model obtained an accuracy of 0.90, a precision of 0.92, a recall of 0.92, and an f1-score of 0.91. Further, the grid search fine-tuned ResNet101 model was integrated with an attention mechanism to enhance performance by focusing on essential image features, notably in medical imaging where some regions may contain vital diagnostic information. The proposed grid search fine-tuned and attention mechanism integrated ResNet101 model achieved an accuracy of 0.935, precision of 0.93, recall of 0.94 and an f1-score of 0.93.