Deep Conviction Systems for Biomedical Applications Using Intuiting Procedures With Cross Point Approach

A model study of teaching method reform of computer laboratory course integrating internet of things technology

The Internet of Things (IoT) is gradually changing the way teaching and learning take place in on-campus programs. In particular, face capture services improve student concentration to create an efficient classroom atmosphere by using face recognition algorithms that support end devices. However, reducing response latency and executing face analysis services effectively in real-time is still challenging. For this reason, this paper proposed a pedagogical model of face recognition for IoT devices based on edge computing (TFREC). Specifically, this research first proposed an IoT service-based face capture algorithm to optimize the accuracy of face recognition. In addition, the service deployment method based on edge computing is proposed in this paper to obtain the best deployment strategy and reduce the latency of the algorithm. Finally, the comparative experimental results demonstrate that TFREC has 98.3% accuracy in face recognition and 72 milliseconds in terms of service response time. This research is significant for advancing the optimization of teaching methods in school-based courses, meanwhile, providing beneficial insights for the application of face recognition and edge computing in the field of education.

A Compact High-Isolation Four-Element MIMO Antenna with Asymptote-Shaped Structure

The demand for high-speed wireless communication systems has led to the development of ultrawide-band (UWB) antennas with a compact size and high performance. In this paper, we propose a novel four-port multiple-input multiple-output (MIMO) antenna with an asymptote-shaped structure that overcomes the limitations of existing designs for UWB applications. The antenna elements are placed orthogonally to each other for polarization diversity, and each element features a stepped rectangular patch with a tapered microstrip feedline. The unique structure of the antenna significantly reduces its dimensions to 42 × 42 mm² (0.43λ×0.43λ@ 3.09GHz), making it highly desirable for use in small wireless devices. To further enhance the antenna's performance, we use two parasitic tapes on the ground plane at the back as decoupling structures between adjacent elements. The tapes are designed in a windmill shape and a rotating extended cross shape, respectively, to further improve the isolation. We fabricated and measured the proposed antenna design on a single-layer substrate (FR4) with a dielectric constant of 4.4 and a thickness of 1 mm. The measured results show that the impedance bandwidth of the antenna is 3.09-12 GHz, with an isolation of -16.4 dB, an envelope correlation coefficient (ECC) of 0.02, a diversity gain (DG) of 9.991 dB, an average total effective reflection coefficient (TARC) of -20 dB, an overall group delay value less than 1.4 ns, and a peak gain of 5.1 dBi. Although there may be some antennas that have better performance in one or two specific aspects, our proposed antenna has an excellent trade-off among all the antenna characteristics including bandwidth, size, and isolation. The proposed antenna also exhibits good quasi-omnidirectional radiation properties, making it well-suited for a range of emerging UWB-MIMO communication systems, particularly in small wireless devices. In summary, the compact size and ultrawide-band capabilities of the proposed MIMO antenna design, coupled with its improved performance compared to other recent UWB-MIMO designs, make it a promising candidate for 5G and next-generation wireless communication systems.

Deep Learning and Medical Image Processing Techniques for Diabetic Retinopathy: A Survey of Applications, Challenges, and Future Trends

Diabetic retinopathy (DR) is a common eye retinal disease that is widely spread all over the world. It leads to the complete loss of vision based on the level of severity. It damages both retinal blood vessels and the eye's microscopic interior layers. To avoid such issues, early detection of DR is essential in association with routine screening methods to discover mild causes in manual initiation. But these diagnostic procedures are extremely difficult and expensive. The unique contributions of the study include the following: first, providing detailed background of the DR disease and the traditional detection techniques. Second, the various imaging techniques and deep learning applications in DR are presented. Third, the different use cases and real-life scenarios are explored relevant to DR detection wherein deep learning techniques have been implemented. The study finally highlights the potential research opportunities for researchers to explore and deliver effective performance results in diabetic retinopathy detection.

Exploration of Despair Eccentricities Based on Scale Metrics with Feature Sampling Using a Deep Learning Algorithm

The majority of people in the modern biosphere struggle with depression as a result of the coronavirus pandemic's impact, which has adversely impacted mental health without warning. Even though the majority of individuals are still protected, it is crucial to check for post-corona virus symptoms if someone is feeling a little lethargic. In order to identify the post-coronavirus symptoms and attacks that are present in the human body, the recommended approach is included. When a harmful virus spreads inside a human body, the post-diagnosis symptoms are considerably more dangerous, and if they are not recognised at an early stage, the risks will be increased. Additionally, if the post-symptoms are severe and go untreated, it might harm one's mental health. In order to prevent someone from succumbing to depression, the technology of audio prediction is employed to recognise all the symptoms and potentially dangerous signs. Different choral characters are used to combine machine-learning algorithms to determine each person's mental state. Design considerations are made for a separate device that detects audio attribute outputs in order to evaluate the effectiveness of the suggested technique; compared to the previous method, the performance metric is substantially better by roughly 67%.

Mathematical Framework for Wearable Devices in the Internet of Things Using Deep Learning

To avoid dire situations, the medical sector must develop various methods for quickly and accurately identifying infections in remote regions. The primary goal of the proposed work is to create a wearable device that uses the Internet of Things (IoT) to carry out several monitoring tasks. To decrease the amount of communication loss as well as the amount of time required to wait before detection and improve detection quality, the designed wearable device is also operated with a multi-objective framework. Additionally, a design method for wearable IoT devices is established, utilizing distinct mathematical approaches to solve these objectives. As a result, the monitored parametric values are saved in a different IoT application platform. Since the proposed study focuses on a multi-objective framework, state design and deep learning (DL) optimization techniques are combined, reducing the complexity of detection in wearable technology. Wearable devices with IoT processes have even been included in current methods. However, a solution cannot be duplicated using mathematical approaches and optimization strategies. Therefore, developed wearable gadgets can be applied to real-time medical applications for fast remote monitoring of an individual. Additionally, the proposed technique is tested in real-time, and an IoT simulation tool is utilized to track the compared experimental results under five different situations. In all of the case studies that were examined, the planned method performs better than the current state-of-the-art methods.

LBSS: A Lightweight Blockchain-Based Security Scheme for IoT-Enabled Healthcare Environment

Recently, global healthcare has made great progress with the use of Internet of Things technology. However, for there to be excellent patient care, there must be a high degree of safety for the IoT health system. There has been a massive increase in hacking systems and the theft of sensitive and highly confidential information from large health centers and hospitals. That is why establishing a highly secure and reliable healthcare system has become a top priority. In this paper, a security scheme for the IoT-enabled healthcare environment, LBSS, is proposed. This security scheme comprises three security mechanisms. The first mechanism is based on the blockchain technology and is used for transaction integrity. The second mechanism is used to store the healthcare system data in a secure manner through the distribution of its data records among multiple servers. The third mechanism is used to access the healthcare data after applying a proposed authorization test. To minimize the security overhead, the healthcare data is prioritized in regard to its importance. Therefore, each security mechanism has specific steps for each level of data importance. Finally, the NS3 package is used to construct a simulation environment for IoT-enabled healthcare systems to measure the proposed security scheme performance. The simulation results proved that the proposed healthcare security scheme outperformed the traditional models in regard to the performance metrics.

A Radical Safety Measure for Identifying Environmental Changes Using Machine Learning Algorithms

Due to air pollution, pollutants that harm humans and other species, as well as the environment and natural resources, can be detected in the atmosphere. In real-world applications, the following impurities that are caused due to smog, nicotine, bacteria, yeast, biogas, and carbon dioxide occur uninterruptedly and give rise to unavoidable pollutants. Weather, transportation, and the combustion of fossil fuels are all factors that contribute to air pollution. Uncontrolled fire in parts of grasslands and unmanaged construction projects are two factors that contribute to air pollution. The challenge of assessing contaminated air is critical. Machine learning algorithms are used to forecast the surroundings if any pollution level exceeds the corresponding limit. As a result, in the proposed method air pollution levels are predicted using a machine learning technique where a computer-aided procedure is employed in the process of developing technological aspects to estimate harmful element levels with 99.99% accuracy. Some of the models used to enhance forecasts are Mean Square Error (MSE), Coefficient of Determination Error (CDE), and R Square Error (RSE).