As the recent development of the prosthetic control systems, it is necessary to develop the sub-components such as myoelectric control system. This sub-system is used to acquire electromyogram (EMG) signals from a person's muscles and convert it to movements to control prosthetic hands or fingers. Recently, researchers started focusing on providing feature extraction methods for both time domain and frequency domain for predicting either hand or finger movements. This paper proposes a new mixed feature set for both time and frequency domain for the classification of surface EMG (sEMG) into ten classes for controlling prosthetic finger movements. The features are based on enhanced statistical features extracted directly from a non-overlapped window with a predefined length which was selected carefully from the sEMG directly. Ten classes of individual and combined finger movements are to be recognized by using two sEMG channels from two electrodes are fixed on the human forearm to employ effective knowledge discovery and pattern recognition algorithms to enhance the recognition and classification accuracy. and the other features are statistical features extracted from Instantaneous frequency of the signal to utilize a suitable classifier helps detecting and recognizing the pattern from sEMG signals of different classes of the fingers movements either combined or single movement to enhance the classifier performances and to find the whole class of all extracted window ,a majority voting technique was applied.The method used the random forest as a classifier to build the classifier model which achieved an accuracy of 93.75% and sensitivity of 93.73% and specificity of 99.31%

This paper focuses on building a non-invasive, low-cost sensor that can be fitted over tree trunks growing in a semiarid land environment. It also proposes a new definition that characterizes tree trunks’ water retention capabilities mathematically. The designed sensor measures the variations in capacitance across its probes. It uses amplification and filter stages to smooth the readings, requires little power, and is operational over a 100 kHz frequency. The sensor sends data via a Long Range (LoRa) transceiver through a gateway to a processing unit. Field experiments showed that the system provides accurate readings of the moisture content. As the sensors are non-invasive, they can be fitted to branches and trunks of various sizes without altering the structure of the wood tissue. Results show that the moisture content in tree trunks increases exponentially with respect to the measured capacitance and reflects the distinct differences between different tree types. Data of known healthy trees and unhealthy trees and defective sensor readings have been collected and analysed statistically to show how anomalies in sensor reading baseds on eigenvectors and eigenvalues of the fitted curve coefficient matrix can be detected.

One of the most extensively used technologies for improving the security of IoT devices is blockchain technology. It is a new technology that can be utilized to boost the security. It is a decentralized peer-to-peer network with no central authority. Multiple nodes on the network mine or verify the data recorded on the Blockchain. It is a distributed ledger that may be used to keep track of transactions between several parties. No one can tamper with the data on the blockchain since it is unchangeable. Because the blocks are connected by hashes, the transaction data is safe. It is managed by a system that is based on the consensus of network users rather than a central authority. The immutability and tamper-proof nature of blockchain security is based on asymmetric cryptography and hashing. Furthermore, Blockchain has an immutable and tamper-proof smart contract, which is a logic that enforces the Blockchain’s laws. There is a conflict between the privacy protection needs of cyber-security threat intelligent (CTI) sharing and the necessity to establish a comprehensive attack chain during blockchain transactions. This paper presents a blockchain-based data sharing paradigm that protects the privacy of CTI sharing parties while also preventing unlawful sharing and ensuring the benefit of legitimate sharing parties. It builds a full attack chain using encrypted threat intelligence and exploits the blockchain’s backtracking capacity to finish the decryption of the threat source in the attack chain. Smart contracts are also used to send automatic early warning replies to possible attack targets. Simulation tests are used to verify the feasibility and efficacy of the suggested model.

Valvular heart disease (VHD) is a significant global health issue, contributing to increased morbidity and mortality rates, particularly in aging populations. Current diagnostic methods, such as echocardiography and manual auscultation, face limitations in accessibility and accuracy, particularly in resource-constrained environments. This study introduces ValveHealthNet, a lightweight deep learning model designed to classify various VHDs using heart sound recordings. Leveraging a dataset of over 10,000 heart sounds, minimal preprocessing was applied by converting the audio signals into power spectra before feeding them into a convolutional neural network (CNN) combined with a bidirectional long short-term memory (BiLSTM) network. This model achieved impressive results, with an accuracy of 98% in training and testing and 98.4% through 10-fold cross-validation. This highly efficient model can be used in embedded systems, providing a cost-effective, AI-driven solution for early detection of VHD in settings where advanced diagnostic tools may be unavailable.