This paper discusses the contribution of AI and ML in the information systems of a smart city, particularly being concerned with enhancing the precision of weather forecasts and the effectiveness of emergency responses. Satellite imagery forms part of the complex data sets incorporated in the study, along with historical data regarding emergencies. The datasets were analyzed with the help of AI and ML models, such as LSTM and CNNs. Results show that the AI-based systems performed better than the traditional method, as it accounted for 92% of accuracy in short-range weather forecasting, while the legacy model only accounted for 78%. During extreme events such as heat waves and wildfires, AI models produced more timely and accurate warnings, cutting the response time for emergencies by as much as 25%. The paper also touches on broader implications of AI in relation to its potential and contribution toward the betterment of enhanced urban resilience, sustainability, and public safety. Although there are considerable merits with AI, the issues of data privacy and ethical concerns along with computational overheads pose challenges and drawbacks. Such a research effort is hoped to conclude in hybrid AI models and advanced IoT sensor networks as possibleF futures toward optimizing the smarts for smart city applications.

A significant data protection technique for a number of problems, the most prominent being identity authentication and copyright protection, is digital image watermarking. The rapid digital transformation of the world has given rise to a variety of vision modification techniques, which have significant consequences for picture data security. As a result, maintaining the validity and integrity of digital images is crucial, which is why researchers are focusing on creating effective watermarking techniques. This study proposes an optimized robust watermarking technique based on lifting wavelet transform (LWT) with singular value decomposition (SVD) using the particle swarm optimization (PSO) algorithm for achieving multiple scaling factors (MSF). To increase security and durability, cover images are exposed to numerous attacks. The evaluation criteria, which encompass normalized cross-correlation (NCC) and peak signal-to-noise ratio (PSNR), were used to compare our outcomes with those of leading watermarking methods. The comparison reveals that our proposed strategy surpasses existing methods in terms of both robustness and imperceptibility. The results suggest that this technique is suitable for tamper detection in various domains, including cryptography, medical imaging and multimedia transmission.

Emotions are very crucial for humans as they determine our ways of thinking, our actions, and even how we interrelate with other persons. Recognition of emotions plays a critical role in areas such as interaction between humans and computers, mental disorder detection, and social robotics. Nevertheless, the current emotion recognition systems have issues like noise interference, inadequate feature extraction, and integration of data for the multimodal context that embraces audio, video, and text. To address these issues, this research proposes an "Enhanced Trimodal Emotion Recognition Using Multibranch Fusion Attention with Epistemic Neural Networks and Fire Hawk Optimization." The proposed method begins with modality-specific preprocessing: Natural Language Processing (NLP) for text to address linguistic variations, Relaxed instance Frequency-wise Normalization (RFN) for the audio to minimize distortion of noise’s importance and iterative self-Guided Image Filter (isGIF) for the videos to enhance the image quality and minimize the artifacts. This preprocessing facilitates and optimizes data for feature extracting; an Inception Transformer for capturing the textual contexts; Differentiable Adaptive Short-Time Fourier transform (DA-STFT) to extract the audio's spectral and temporal features; and class attention mechanisms to emphasize important features in the videos. Following that, these features are combined through a Multi-Branch Fusion Attention Network to harmonize all the multifarious modalities into one. The last sanity check occurs through an Epistemic Neural Network (ENN), which tackles issues of uncertainty involved in the last classification, and the Fire Hawk algorithm is used to enhance the emotion recognition capabilities of the framework. Finally the proposed approach attains 99.5% accuracy with low computational time. Thus, the proposed method addresses important shortcomings of the systems developed previously and can be regarded as a contribution to the development of the multimodal emotion recognition field.

Deep learning has revolutionized healthcare applications, particularly in the diagnosis, treatment, and management of infectious diseases. The main objectives of this investigation are to propose several methods for assessing high-resolution X-ray images with the purpose of identifying the occurrence or not of symptoms associated with pneumonia. The objective of this exam was to identify fixes for these existing problems. Our offering entails a deep learning (DL) technique for detecting chest anomalies using the X-ray modality using the EfficientNet B0 model. In order to make accurate diagnoses of pneumonia, both the EfficientNet B0 and the upgraded CNN model undergo extensive data-driven training. The CNN model that underwent upgrades was determined to be the most effective in this analysis because to its high level of accuracy. The results of our research conclusion are that DL models are capable of monitoring pneumonia's development, increasing diagnostic precision overall and giving patients new optimism for immediate relief.

Wireless Sensor Networks (WSN) has top-notch capacity for numerous domains of utility due to the potential to sense and apprehend unattended environments. IoT is playing an increasingly more essential position in clever healthcare, smart transportation and elegant grid using WSN. Therefore, challenges like privacy and data security associated with IoT wireless sensor networks because of sensor node being captured easily, consume less computing energy and low storage. Designing a secure authentication scheme will enable secure communication in WSN. Here, we proposed a improved three-factor authentication scheme for WSN utilizing biometric, password and smart card withstand against security attacks. Subsequently, by comparing the security, functional and performance parameters of our proposed scheme to the other schemes, our scheme provides better and efficient one. Therefore, our scheme is fairly workout in real time IoT applications involved in WSN scenario.

Even though Convolutional Neural Networks (CNNs) have greatly improved face-related algorithms, it is still difficult to keep both accuracy and efficiency in real-world applications. The most cutting-edge approaches use deeper networks to improve performance, but the increased computing complexity and number of parameters make them impractical for usage in mobile applications. To tackle these issues, this article presents a model for object detection that combines Deeplabv3+ with Swin transformer, which incorporates GLTB and Swin-Conv-Dspp (SCD). To start with, in order to lessen the impact of the hole phenomena and the loss of fine-grained data, we employ the SCD component, which is capable of efficiently extracting feature information from objects at various sizes. Secondly, in order to properly address the issue of challenging object recognition due to occlusion, the study builds a GLTB with a spatial pyramid pooling shuffle module. This module allows for the extraction of important detail information from the few noticeable pixels of the blocked objects. Crocodile search algorithm (CSA) enhances classification accuracy by properly selecting the model's fine-tuning. On a benchmark dataset known as WFLW, the study experimentally validates the suggested model. Compared to other light models, the experimental findings show that it delivers higher performance with significantly fewer parameters and reduced computing complexity.

This article focuses on the progress of underwater robots and the importance of software architectures in building robust and autonomous systems. The researchers underscore the challenges linked to implementation and stress the need for comprehensive validation of both reliability and efficacy. Their argument is on the extensive implementation of a globally applicable architectural framework that complies with established standards and guarantees interoperability within the field of robotics. The research also covers advancements in underwater soft robotics, which include the development of models, materials, sensors, control systems, power storage, and actuation techniques. This article explores the challenges and potential applications of underwater soft robotics, highlighting the need of collaboration across many fields and advancements in mechanical design and control methods. In the last section of the paper, the control approach and algorithms used to underwater exploration robots are reviewed. Particular attention is given to the application of Proportional Integral Derivative (PID) control and the incorporation of Backpropagation Neural Network (BPNN) for PID parameter determination.

This article aims to provide a concise overview of diverse methodologies employed at different stages of a pattern recognition system, highlighting contemporary research challenges and applications in this dynamic field. The integration of machine learning techniques has played a pivotal role in converging pattern recognition frameworks in academic literature. The process relies heavily on supervised or unsupervised categorization methods to achieve its objectives, with a notable focus on statistical approaches. More recently, there is a growing emphasis on incorporating neural network methodologies and insights from statistical learning theory. Designing an effective recognition system necessitates careful consideration of various factors, including pattern representation, pattern class definition, feature extraction, sensing environment, feature selection, classifier learning and design, cluster analysis, test and training sample selection, and performance assessment.

The ever-increasing amount of network traffic generated by various devices and applications has made it crucial to have efficient methods for analyzing and managing network traffic. Traditional approaches, such as statistical modeling, have yet to be proven enough due to network traffic's complex nature and dynamic characteristics. Recent research has shown the effectiveness of complex network analysis techniques for understanding network traffic patterns. This paper proposes multilayer seasonal autoregressive integrated moving average models for analyzing and predicting network traffic. This approach considers the seasonal patterns and interdependencies between different layers of network traffic, allowing for a more accurate and comprehensive representation of the data. The Multilayer Seasonal Autoregressive Integrated Moving Average (MSARIMA) model consists of multiple layers, each representing a different aspect of network traffic, such as time of day, day of week, or type of traffic. Each layer is modeled separately using SARIMA, a popular time series forecasting technique. The models for different layers are combined to capture the overall behavior of network traffic. The proposed approach has several benefits over traditional statistical approaches. It can capture network traffic's complex and dynamic nature, including short-term and long-term seasonal patterns. It also allows for the detection of anomalies and the prediction of future traffic patterns with high accuracy.

The usefulness of ensemble-based total time series analysis in Wi-Fi sensor networks is examined in this paper. A device to uses an ensemble approach combines multiple strategies to enhance overall predictive performance. This research assesses various tactics using unique metrics, such as robustness and accuracy. It contrasts the effectiveness of traditional time series methods with ensemble-based total fashions. An experimental approach focusing mostly on exceptional Wi-Fi sensor network scenarios is employed to evaluate the overall effectiveness of the suggested methods. Additionally, this study looks into how changes to community features like energy delivery, conversation range, and node density affect how effective the suggested methods are. The study's findings maintain the capacity to create effective Wi-Fi sensor networks with improved predicted overall performance. The usefulness of ensemble-based time collecting and analysis techniques for wireless sensor networks is investigated in this research. This study primarily looks upon function extraction and seasonality discounting of time series records in WSNs. In this analysis, seasonality is discounted using an ensemble median filter, and feature extraction is accomplished by primary component assessment. To assess the performance of the suggested ensemble technique on every simulated and real-world international WSN fact, multiple experiments are carried out. The findings suggest that the ensemble approach can improve the exceptional time-gathering records within WSNs and reduce seasonality. Furthermore, when compared to single-sensor strategies, the ensemble technique further improves the accuracy of the function extraction system. This work demonstrates the applicability of the ensemble approach for the investigation of time collection data in WSNs