Gesture recognition has found widespread applications in various fields, such as virtual reality, medical diagnosis, and robot interaction. The existing mainstream gesture-recognition methods are primarily divided into two categories: inertial-sensor-based and camera-vision-based methods. However, optical detection still has limitations such as reflection and occlusion. In this paper, we investigate static and dynamic gesture-recognition methods based on miniature inertial sensors. Hand-gesture data are obtained through a data glove and preprocessed using Butterworth low-pass filtering and normalization algorithms. Magnetometer correction is performed using ellipsoidal fitting methods. An auxiliary segmentation algorithm is employed to segment the gesture data, and a gesture dataset is constructed. For static gesture recognition, we focus on four machine learning algorithms, namely support vector machine (SVM), backpropagation neural network (BP), decision tree (DT), and random forest (RF). We evaluate the model prediction performance through cross-validation comparison. For dynamic gesture recognition, we investigate the recognition of 10 dynamic gestures using Hidden Markov Models (HMM) and Attention-Biased Mechanisms for Bidirectional Long- and Short-Term Memory Neural Network Models (Attention-BiLSTM). We analyze the differences in accuracy for complex dynamic gesture recognition with different feature datasets and compare them with the prediction results of the traditional long- and short-term memory neural network model (LSTM). Experimental results demonstrate that the random forest algorithm achieves the highest recognition accuracy and shortest recognition time for static gestures. Moreover, the addition of the attention mechanism significantly improves the recognition accuracy of the LSTM model for dynamic gestures, with a prediction accuracy of 98.3%, based on the original six-axis dataset.

New technological innovations are changing the future of healthcare system. Identification of factors that are responsible for causing depression may lead to new experiments and treatments. Because depression as a disease is becoming a leading community health concern worldwide. Using machine learning techniques this article presents a complete methodological framework to process and explore the heterogenous data and to better understand the association between factors related to quality of life and depression. Subsequently, the experimental study is mainly divided into two parts. In the first part, a data consolidation process is presented. The relationship of data is formed and to uniquely identify each relation in data the concept of the Secure Hash Algorithm is adopted. Hashing is used to locate and index the actual items in the data. The second part proposed a model using both unsupervised and supervised machine learning techniques. The consolidation approach helped in providing a base for formulation and validation of the research hypothesis. The Self organizing map provided 08 cluster solution and the classification problems were taken from the clustered data to further validate the performance of the posterior probability multi-class Support Vector Machine. The expectations of the importance sampling resulted in factors responsible for causing depression. The proposed model was adopted to improve the classification performance, and the result showed classification accuracy of 91.16%.

The future for WiMAX-based mobile wireless access is so bright. With the increasing popularity of WiMAX, it is gaining a great position in the next generation of wireless networks. Due to the evolution of new technologies wireless is not secured as like others networking technologies. On the other side the trend is towards converged network in which the same networking infrastructure is used for both data and voice transmission. A lot of security concerns are needed to secure a converged network. This paper aims to review the study of WiMAX and converged network and security considerations for both the technologies. WiMAX security in converged network is studied. The security threats that can occur in converged network to WiMAX and countermeasures that can be taken against these threats are reviewed.

IEEE 802.16, known as WiMax, is at the top of communication technology drive because it is gaining a great position in the next generation of wireless networks. Due to the evolution of new technologies wireless is not secured as like others networking technologies. A lot of security concerns are needed to secure a wireless network. By keeping in mind the importance of security, the WiMax working groups has designed several security mechanisms to provide protection against unauthorized access and malicious threats, but still facing a lot of challenging situations. An authentication and authorization model provides protection for a network or technology and protects its resources from unauthorized use. This paper examines the threats which are associated with MAC layer and physical layer of WiMax and also proposes some enhancements to the existing model for improving the performance of the encryption algorithm. The paper proposes some techniques in the existing model to enhance its functionality and capability. Simulation is done using Qualnet4.5 simulation tool and the results are given at the end.