Body posture dynamics have garnered significant attention in recent years due to their critical role in understanding the emotional states conveyed through human movements during social interactions. Emotions are typically expressed through facial expressions, voice, gait, posture, and overall body dynamics. Among these, body posture provides subtle yet essential cues about emotional states. However, predicting an individual’s gait and posture dynamics poses challenges, given the complexity of human body movement, which involves numerous degrees of freedom compared to facial expressions. Moreover, unlike static facial expressions, body dynamics are inherently fluid and continuously evolving. This paper presents an effective method for recognizing 17 micro-emotions by analyzing kinematic features from the GEMEP dataset using video-based motion capture. We specifically focus on upper body posture dynamics (skeleton points and angle), capturing movement patterns and their dynamic range over time. Our approach addresses the complexity of recognizing emotions from posture and gait by focusing on key elements of kinematic gesture analysis. The experimental results demonstrate the effectiveness of the proposed model, achieving a high accuracy rate of 91.48% for angle metric + DNN and 93.89% for distance + DNN on the GEMEP dataset using a deep neural network (DNN). These findings highlight the potential for our model to advance posture-based emotion recognition, particularly in applications where human body dynamics distance and angle are key indicators of emotional states.

The purpose of this review is to investigate the application of artificial intelligence (AI), machine learning (ML), and the internet of things (IoT) within the context of 5G and 6G. The abstract provides an overview of this investigation. It sheds light on the growing demand for these technologies as well as the ways in which they could encourage innovation across a variety of business sectors. The most recent work in these disciplines will be examined, and insights into the potential and difficulties of implementing it in 5G and 6G networks will be provided by the study. The focus of the review is discussed briefly in the abstract; however, additional information on specific research and development fields would be valuable. It is also important to explore the potential repercussions that these technologies may have in the context of 5G and 6G, including the implications that they may have on businesses and on society.

In this paper, we extend some unique fixed point theorem results on a complete symmetric G-metric space by using asymptotically regular mappings with a new approach.Moreover, the new structure of extended G-contractive mapping on suitable spaces is used to create images with reduced size in this paper.A digital image is a representation of two-dimensional pixels arrays.A mild but typical system of extended G-contractive mapping on the Euclidean plane, known as the digital plane, is implemented for image processing to produce images with compressed dimensions that take up less storage space and can be transmitted efficiently.The size of the initial picture matrix has

AbstractThis research introduces an innovative algorithm for the encryption and decryption of greyscale digital imaging and communications in medicine images utilizing Laplace transforms. The proposed method presents a ground breaking approach to image encryption, effectively concealing visual information and ensuring a robust, secure, and reliable encryption process. By leveraging the inherent strengths of Laplace transform, the algorithm guarantees the complete retrieval of the original image without any loss, provided the correct decryption key is used. To thoroughly evaluate the performance of the algorithm, multiple tests were conducted, including extensive statistical analyses and assessments of encryption quality. Key performance metrics were carefully measured, including correlation coefficients and entropy values, which ranged from 7.89 to 7.99. Additionally, the algorithm's effectiveness was demonstrated through peak signal‐to‐noise ratio values, which spanned from 7.597 to 9.915, indicating the degree of similarity between the original and encrypted images. Furthermore, the number of pixels change rate values, ranging from 99.519241 to 99.609375, highlighted the algorithm's ability to produce significantly different encrypted images from the original. The unified average changing intensity values, falling between 35.72345678 and 35.78233456, further underscored the algorithm's proficiency in altering pixel intensities uniformly. Overall, this research offers a significant advancement in the field of image encryption, combining theoretical robustness with practical efficiency.

The rapid advancement of innovation has sparked significant improvements in the field of music creation, with chord progression being one of the notable areas affected. Many musicians face challenges in playing instruments like the piano by relying solely on auditory perception. Therefore, the need for a chord generator arises to assist them in this endeavor. Mastering music composition demands a substantial investment of time, particularly for novice musicians. To alleviate the time-consuming nature of this task, we explore the practicality of automated chord progression generation by analyzing audio files and converting them into strings. First, the music or audio file will be converted into strings using the frequency of that record. Secondly, these strings will be employed to identify the individual musical notes corresponding to those specific strings. Ultimately, the best chord for the music will be generated, making the process more efficient for musicians.

Sleep disorders are common in a significant part of the entire population with diseases of the central nervous system, continuous monitoring of respiration during sleep has an important role in early diagnosis and treatment. It introduces the most comfortable way to monitor sleep apnea disorder using a wearable smartwatch and monitor it using the application based on IoT. A possibly lethal condition known as sleep apnea disorder causes frequent airflow stalls or stops when a person is asleep. PSG (Polysomnography) is a challenging procedure that requires the patient to undergo relatively intrusive test methods performed in a clinic, restricting the patient's movement and leading to a change in sleep pattern.

In the present time world, digital images are crucial for various applications, that includes the medical industry, aircraft and satellite imaging, underwater imaging and so on. For this huge quantities of digital images are produced and used by these applications. For a variety of reasons, these images also need to be transmitted and stored. Therefore, a technique known as compression is applied to resolve this storage issue while transmitting these images. In this article, by extending some unique fixed point theorem results for comparison function on a complete symmetric G-metric space are used and it is a new approach. Moreover, this paper focuses on a compression method using the new structure of extended G-contraction mapping as it assists in compressing the size of the image. Thus, grayscale images are compressed using extended G-contraction mapping. And thus, grayscale images can be represented as matrices in this structure (pixel values). Also, similar images of reduced size can be obtained using an appropriate matrix G-metric and extended G-contraction mapping. The size of the matrix can be substantially reduced without losing any quality by controlling the order of sub matrices. These images are easy to store and transmit, with little variation between the original and contracted image.

Aerial image-based target object detection has several glitches such as low accuracy in multi-scale target detection locations, slow detection, missed targets, and misprediction of targets. To solve this problem, this paper proposes an improved You Only Look Once (YOLO) algorithm from the viewpoint of model efficiency using target box dimension clustering, classification of the pre-trained network, multi-scale detection training, and changing the screening rules of the candidate box. This modified approach has the potential to be better adapted to the positioning task. The aerial image of the unmanned aerial vehicle (UAV) can be positioned to the target area in real-time, and the projection relation can convert the latitude and longitude of the UAV. The results proved to be more effective; notably, the average accuracy of the detection network in the aerial image of the target area detection tasks increased to 79.5%. The aerial images containing the target area are considered to experiment with the flight simulation to verify its network positioning accuracy rate and were found to be greater than 84%. This proposed model can be effectively used for real-time target detection for multi-scale targets with reduced misprediction rate due to its superior accuracy.

In recent years, human action recognition is modeled as a spatial-temporal video volume. Such aspects have recently expanded greatly due to their explosively evolving real-world uses, such as visual surveillance, autonomous driving, and entertainment. Specifically, the spatio-temporal interest points (STIPs) approach has been widely and efficiently used in action representation for recognition. In this work, a novel approach based on the STIPs is proposed for action descriptors i.e., Two Dimensional-Difference Intensity Distance Group Pattern (2D-DIDGP) and Three Dimensional-Difference Intensity Distance Group Pattern (3D-DIDGP) for representing and recognizing the human actions in video sequences. Initially, this approach captures the local motion in a video that is invariant to size and shape changes. This approach extends further to build unique and discriminative feature description methods to enhance the action recognition rate. The transformation methods, such as DCT (Discrete cosine transform), DWT (Discrete wavelet transforms), and hybrid DWT+DCT, are utilized. The proposed approach is validated on the UT-Interaction dataset that has been extensively studied by past researchers. Then, the classification methods, such as Support Vector Machines (SVM) and Random Forest (RF) classifiers, are exploited. From the observed results, it is perceived that the proposed descriptors especially the DIDGP based descriptor yield promising results on action recognition. Notably, the 3D-DIDGP outperforms the state-of-the-art algorithm predominantly.

A caution for motorist, a new technique is developed to prevent accidents. The accidents are mostly occurs due to three reasons such as: drunk and drive, no medical assistant as early as possible and speaking over the mobile phones. As taken this into account a smart helmet is designed to prevent accidents. The drunk and drive is prevented by using Alcohol sensor(MQ2).The fastest medical assistant can be given by using Piezoelectric plate made of Berlinite material and PIC Microcontroller 16F887. The major accidents occur only when motorist speak over the mobile phone. To avoid this mobile phone is connected to the PIC microcontroller via Bluetooth and Bluetooth and PIC Microcontroller is connected using UART. Initially the call will be cut two times and if it is important to caller, the caller will be directed for some information and it is indicated to the motorist using LED-BIGGY and a buzzer.

The concept of new Hilbert sequence space was introduced by Harun Polat[8]. The initial works on double sequences are found in Bromwich[15]. In this paper, we study some new Hilbert double sequence space defined by Orlicz function and also study some topological properties of the resulting sequence spaces were examined.

In this paper, we study certain new difference sequence spaces by using Hilbert sequence space defined by Orlicz function. We characterize some topological properties and inclusion relations involving these sequence spaces.

An attempt is made to analyse the effects of skin friction on unsteady flow past a moving vertical plate in a rotating fluid with variable temperature and mass diffusion. An analytical solution is obtained by considering a complex velocity with the axial and transverse components. The effects of skin friction for the parameters like Schmidt number, radiation parameter, thermal Grashof number Prandtl number, rotation parameter and mass Grashof number on the plate are observed and analysed.

Due to the impact of network technology, all information are transmitted in digital mode and the security of information is ever more important. In order to ensure the secret messages are not been stolen when transmitting, it will be a good countermeasure to encrypt the secret message before transmitting. The level of security of information is based on the number of participants. Security is definite when a single person is involved. In the scenario, if many people are involved, security may be ensured if secrets are kept in. This lead the direction for researchers to develop new cryptographic scheme in the past two decades. This paper proposes techniques for transmission of secret messages between two parties and also sharing of messages between multiple parties. These methods uses well known public key cryptography algorithm RSA and Hilbert matrix for authentication and encryption. The proposed method overcome the issues addressed by the existing scheme and ensures secure transmission of text messages with less computational Complexity and no additional code book. The proposed (N, N) Secret sharing Scheme also reduces the overhead of generation of keys for each pair of parties.

Remote satellite imaging provides vital information for observing number of applications such as, land region detection and urban area classification. This paper proposed a novel approach for Classification and extraction of texture features from high resolution satellite images dataset. Preprocessing is done for satellite sensing image using Hilbert matrix filter and Modified Hilbert matrix filter. Then the texture features are extracted from the Hilbert image and Modified Hilbert image using Gray Level Co-occurrence Matrix (GLCM). Finally, obtained features are classified using Decision tree and Random Forest and the accuracy, precision, recall and F-measure is analyzed for performance evaluation