An intelligent approach for enhancing the Quality of Service in IoMT based on 5G

The concept and growth of superior individualized healthcare technologies are influenced in significant ways by the emerging areas of “Artificial Intelligence (AI) and the Internet of Things (IoT)”. Most people use wearable devices for mHealth, hence there are many potential applications for the “Internet of Medical Things (IoMT)”. Only 5G can provide the necessary support for smart medical devices to perform many different types of demanding computing activities. Today, heart disease was the major mortality on a global scale. For patients who need a greater accurate diagnosis and treatment, the advancement of medical innovation has created new obstacles. Although many studies have focused on diagnosing cardiac disease, the findings are often inaccurate and fail to fulfill patients' expectations of quality of service (QoS). So, this paper introduces a novel “feed-forward Bi-directional long-short term memory (FF-Bi-LSTM) algorithm to predict heart disease more accurately with enhanced QoS in IoMT based on 5G”. Linear discriminant analysis (LDA) and min-max normalization are employed, respectively, for preprocessing and feature extraction. The efficacy of the suggested approach is measured using several different metrics, including accuracy, precision, recall, and f1-score. The proposed method is also compared to certain existing techniques. These results show that the suggested strategy outperforms existing strategies in terms of improving QoS

Enhancing child safety with accurate fingerprint identification using deep learning technology

Utilizing deep learning algorithms to differentiate the fingerprints of children can greatly enhance their safety. This advanced technology enables precise identification of individual children, facilitating improved monitoring and tracking of their activities and movements. This can effectively prevent abductions and other forms of harm, while also providing a valuable resource for law enforcement and other organizations responsible for safeguarding children. Furthermore, the use of deep learning algorithms minimizes the potential for errors and enhances the overall accuracy of fingerprint recognition. Overall, implementing this technology has immense potential to significantly improve the safety of children in various settings. Our experiments have demonstrated that deep learning significantly enhances the accuracy of fingerprint recognition for children. The model accurately classified fingerprints with an overall accuracy rate of 93%, surpassing traditional fingerprint recognition techniques by a significant margin. Additionally, it correctly identified individual children's fingerprints with an accuracy rate of 89%, showcasing its ability to distinguish between different sets of fingerprints belonging to different children

Evaluation of wind-solar hybrid power generation system based on Monte Carlo method

The application of wind-photovoltaic complementary power generation systems is becoming more and more widespread, but its intermittent and fluctuating characteristics may have a certain impact on the system's reliability. To better evaluate the reliability of stand-alone power generation systems with wind and photovoltaic generators, a reliability assessment model for stand-alone power generation systems with wind and photovoltaic generators was developed based on the analysis of the impact of wind and photovoltaic generator outages and derating on reliability. A sequential Monte Carlo method was used to evaluate the impact of the wind turbine, photovoltaic (PV) turbine, wind/photovoltaic complementary system, the randomness of wind turbine/photovoltaic outage status and penetration rate on the reliability of Independent photovoltaic power generation system (IPPS) under the reliability test system (RBTS). The results show that this reliability assessment method can provide some reference for planning the actual IPP system with wind and complementary solar systems

A Deep learning approach for trust-untrust nodes classification problem in WBAN

The enormous growth in demand for WBAN services has resulted in a new set of security challenges. The capabilities of WBAN are developing to meet these needs. The complexity, heterogeneity, and instability of the mobile context make it difficult to complete these duties successfully. A more secure and flexible WBAN setting can be attained using a trust-untrust nodes classification, which is one method to satisfy the security needs of the WBAN. Considering this, we present a novel Deep Learning (DL) approach for classifying WBAN nodes using spatial attention based iterative DBN (SA-IDBN). Z-score normalization is used to remove repetitive entries from the input data. Then, Linear Discriminate Analysis (LDA) is employed to retrieve the features from the normalized data. In terms of accuracy, latency, recall, and f-measure, the suggested method's performance is examined and contrasted with some other current approaches. Regarding the classification of WBAN nodes, the results are more favorable for the suggested method than for the ones already in use

A novel nomadic people optimizer-based energy-efficient routing for WBAN

In response to user demand for wearable devices, several WBAN deployments now call for effective communication processes for remote data monitoring in real time. Using sensor networks, intelligent wearable devices have exchanged data that has benefited in the evaluation of possible security hazards. If smart wearables in sensor networks use an excessive amount of power during data transmission, both network lifetime and data transmission performance may suffer. Despite the network's effective data transmission, smart wearable patches include data that has been combined from several sources utilizing common aggregators. Data analysis requires careful network lifespan control throughout the aggregation phase. By using the Nomadic People Optimizer-based Energy-Efficient Routing (NPO-EER) approach, which effectively allows smart wearable patches by minimizing data aggregation time and eliminating routing loops, the network lifetime has been preserved in this research. The obtained findings showed that the NPO method had a great solution. Estimated Aggregation time, Energy consumption, Delay, and throughput have all been shown to be accurate indicators of the system's performance

Enhancing smart home energy efficiency through accurate load prediction using deep convolutional neural networks

The method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical energy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute parameters of electrical energy consumption. The method considers the timeseries homes of the information and offers parallelization of large-scale facts processing with magnificent operational efficiency, considering the timeseries aspects of the information and the problematic inherent correlations between variables. The exams have been done using the UCI public dataset, and the experimental findings validate the method's efficacy, which has clear, sensible implications for setting up intelligent strength grid dispatching

Examining the mediating role of strategic thinking on organizational performance: A quantitative analysis

Currently, private universities are making significant efforts to keep up with changing market demands and societal expectations, particularly in an environment that is rapidly evolving such as Baghdad, Iraq. In order to maintain high standards and achieve excellence, they must continuously seek out the most effective practices and strategies for success. Strategic management has been demonstrated to be an efficient tool for enhancing institutional performance, and the university's organizational culture, which is comprised of traditional institutional elements, is recognized as a significant factor in how organizations operate. Through the utilization of strategic thinking as a mediator, this research aims to evaluate and clarify how organizational culture impacts institutional excellence. A simple random sample was employed to gather information from individuals in managerial positions across multiple sections. Structural Equation Modeling (SEM) was employed to explain the variables under examination. The outcomes and analyses reveal a high level of relative proportions for the variables under investigation and indicate an indirect relationship between the independent and dependent variables. This research recommends several important suggestions and proposes additional research avenues for further investigatio

Exploring the potential of offline cryptography techniques for securing ECG signals in healthcare

In the research, a software for ECG signal based on Chaos encryption based on C#-programmed and Kit of Microsoft Visual Studio Development was implemented. A chaos logic map (ChLMp ) and its initial value are utilized to create Level-1 ECG signal based on Chaos encryption bit streams. A ChLMp, an initial value, a ChLMp bifurcation parameter, and two encryption level parameters are utilized to create level-2 ECG signal based on Chaos encryption bit streams. The level-3 ECG signal based on Chaos encryption software utilizes two parameters for the level of encryption, a permutation mechanism, an initial value, a bifurcation parameter of the level of encryption, and a ChLMp. We assess 16-channel ECG signals with great resolution utilizing encryption software. The level-3 ECG signal based on Chaos encryption program has the slowest and most reliable encryption speed. The encryption effect is superior, according to test findings, and when the right decoding parameter is utilized, the ECG signals may be completely recovered. The high resolution 16-channel ECG signals (HRMCECG) won't be recovered if an invalid input parameter occurred, such as a 0.00001% initial point error, which will result in chaotic encryption bit streams

Sustainable electronic document security: a comprehensive framework integrating encryption, digital signature and watermarking algorithms

Protecting electronic documents, especially those containing sensitive data, is a major challenge in an open web. The data security industry has long struggled to manage the security of e-books, data shows that information security issues can cause significant economic losses after three years Although cryptographic methods have been proposed as a solution in of these challenges Focusing on speed and efficiency The shortcomings of traditional encryption methods have been thoroughly examined Although a number of network management techniques assure retention privacy and integrity though, when it comes to encryption. Digital signature algorithms, although effective in detecting unauthorized changes and limiting the scope for copyright protection, do not ensure the confidentiality of shared electronic documents When addressing research gaps addressing this issue, the paper proposes a security framework for electronic documents that combines three important security mechanisms: encryption, digital signatures, and watermark algorithms Dhishu By matching their strengths, constraints are overcome. The combination of encryption and digital signatures is explored as a promising approach to protecting electronic documents, ensuring authenticity and confidentiality Importantly, the need to explore security mechanisms such as digital is highlighted emphasis on handwriting, encryption, and watermarking systems in depth

Unlocking Solar Potential: Advancements in Automated Solar Tracking Systems for Enhanced Energy Utilization

The use of solar tracking systems has become vital and has established itself as a vital element in the generation of solar energy by enhancing the collection efficiency. This paper seeks to understand the necessity of shifting from conventional energy sources and why issues like scarcity of fossil fuel, and pollution are some of the hurdles toward achieving sustainable energy. Solar power, in particular, is one of the lights at the end of this tunnel since it pioneers a shift towards the usage of clean energy in the world. The subject of interests of the study is on how tracking systems help in maximizing energy collection from solar systems by interchanging it with the movement of sun’s path. It discusses the method that was followed, which involves selecting component, designing circuit and developing software together with presenting empirical data that was obtained from a three-day, Twenty-four-hour experiment. Outcomes show that there is an improvement on voltage stability, the level of solar irradiation and temperature regulation when the system is applied as compared to static system and its applicability for the enhancement of the renewable energy harnessing methods by using the solar tracking technology. Finally, it outlines the future research directions to continue exploring the proposed methods and its wider impact on renewable energy generation