Blockchain-Powered Healthcare Systems: Enhancing Scalability and Security with Hybrid Deep Learning.

The rapid advancements in technology have paved the way for innovative solutions in the healthcare domain, aiming to improve scalability and security while enhancing patient care. This abstract introduces a cutting-edge approach, leveraging blockchain technology and hybrid deep learning techniques to revolutionize healthcare systems. Blockchain technology provides a decentralized and transparent framework, enabling secure data storage, sharing, and access control. By integrating blockchain into healthcare systems, data integrity, privacy, and interoperability can be ensured while eliminating the reliance on centralized authorities. In conjunction with blockchain, hybrid deep learning techniques offer powerful capabilities for data analysis and decision making in healthcare. Combining the strengths of deep learning algorithms with traditional machine learning approaches, hybrid deep learning enables accurate and efficient processing of complex healthcare data, including medical records, images, and sensor data. This research proposes a permissions-based blockchain framework for scalable and secure healthcare systems, integrating hybrid deep learning models. The framework ensures that only authorized entities can access and modify sensitive health information, preserving patient privacy while facilitating seamless data sharing and collaboration among healthcare providers. Additionally, the hybrid deep learning models enable real-time analysis of large-scale healthcare data, facilitating timely diagnosis, treatment recommendations, and disease prediction. The integration of blockchain and hybrid deep learning presents numerous benefits, including enhanced scalability, improved security, interoperability, and informed decision making in healthcare systems. However, challenges such as computational complexity, regulatory compliance, and ethical considerations need to be addressed for successful implementation. By harnessing the potential of blockchain and hybrid deep learning, healthcare systems can overcome traditional limitations, promoting efficient and secure data management, personalized patient care, and advancements in medical research. The proposed framework lays the foundation for a future healthcare ecosystem that prioritizes scalability, security, and improved patient outcomes.

A Compact Circular Rectenna for RF-Energy Harvesting at ISM Band.

With low-power gadgets proliferating, the development of a small, effective rectenna is crucial for wirelessly energizing devices. A simple circular patch with a partial ground plane for RF-energy harvesting at ISM (2.45 GHz) band is proposed in this work. The simulated antenna resonates at 2.45 GHz with an input impedance of 50 Ω and a gain of 2.38 dBi. An L-section matching a circuit with a voltage doubler is proposed to provide excellent RF-to-DC transformation efficiency at low power input. The proposed rectenna is fabricated and the results show that the return loss and realized gain have good characteristics at the ISM band with 52% of RF-to-DC transformation efficiency, with an input of 0 dBm power. The projected rectenna is apt for power-up low sensor nodes in wireless sensor applications.

Automated Classification of Brain Tumors from Magnetic Resonance Imaging Using Deep Learning.

Brain tumor classification is crucial for medical evaluation in computer-assisted diagnostics (CAD). However, manual diagnosis of brain tumors from magnetic resonance imaging (MRI) can be time-consuming and complex, leading to inaccurate detection and classification. This is mainly because brain tumor identification is a complex procedure that relies on different modules. The advancements in Deep Learning (DL) have assisted in the automated process of medical images and diagnostics for various medical conditions, which benefits the health sector. Convolutional Neural Network (CNN) is one of the most prominent DL methods for visual learning and image classification tasks. This study presents a novel CNN algorithm to classify the brain tumor types of glioma, meningioma, and pituitary. The algorithm was tested on benchmarked data and compared with the existing pre-trained VGG16, VGG19, ResNet50, MobileNetV2, and InceptionV3 algorithms reported in the literature. The experimental results have indicated a high classification accuracy of 98.04%, precision, recall, and f1-score success rate of 98%, respectively. The classification results proved that the most common kinds of brain tumors could be categorized with a high level of accuracy. The presented algorithm has good generalization capability and execution speed that can be helpful in the field of medicine to assist doctors in making prompt and accurate decisions associated with brain tumor diagnosis.

An efficient antenna system with improved radiation for multi-standard/multi-mode 5G cellular communications.

This paper introduces a multi-input multiple-output (MIMO) antenna array system that provides improved radiation diversity for multi-standard/multi-mode 5G communications. The introduced MIMO design contains four pairs of miniaturized self-complementary antennas (SCAs) fed by pairs of independently coupled structures which are symmetrically located at the edge corners of the smartphone mainboard with an overall size of 75 × 150 (mm2). Hence, in total, the design incorporates four pairs of horizontally and vertically polarized resonators. The elements have compact profiles and resonate at 3.6 GHz, the main candidate bands of the sub-6 GHz 5G spectrum. In addition, despite the absence of decoupling structures, adjacent elements demonstrate high isolation. To the best of the authors' knowledge, it is the first type of smartphone antenna design using dual-polarized self-complementary antennas that could possess anti-interference and diversity properties. In addition to exhibiting desirable radiation coverage, the presented smartphone antenna also supports dual polarizations on different sides of the printed circuit board (PCB). It also exhibits good isolation, high-gain patterns, improved radiation coverage, low ECC/TARC, and sufficient channel capacity. The introduced antenna design was manufactured on a standard smartphone board and its main characteristics were experimentally measured. Simulations and measurement results are generally in good agreement with each other. Moreover, the presented antenna system delivers low SAR with adequate efficiency when it comes to the appearance of the user. Hence, the design could be adapted to 5G hand-portable devices. As an additional feature, a new ultra-compact phased array millimeter-wave antenna with super-wide bandwidth and end-fire radiation is being introduced for integration into the MIMO antenna systems. As a result, the proposed antenna system design with improved radiation and multi-standard operation is a good candidate for future multi-mode 5G cellular applications.

Joint Power Control and Phase Shift Design for Future PD-NOMA IRS-Assisted Drone Communications under Imperfect SIC Decoding.

Intelligent reflecting surfaces (IRS) and power-domain non-orthogonal multiple access (PD-NOMA) have recently gained significant attention for enhancing the performance of next-generation wireless communications networks. More specifically, IRS can smartly reconfigure the incident signal of the source towards the destination node, extending the wireless coverage and improving the channel capacity without consuming additional energy. On the other side, PD-NOMA can enhance the number of devices in the network without using extra spectrum resources. This paper proposes a new optimization framework for IRS-enhanced NOMA communications where multiple drones transmit data to the ground Internet of Things (IoT) devices under successive interference cancellation errors. In particular, the power budget of each drone, PD-NOMA power allocation of IoT devices, and the phase shift matrix of IRS are simultaneously optimized to enhance the total spectral efficiency of the system. Given the system model and optimization setup, the formulated problem is coupled with three variables, making it very complex and non-convex. Thus, this work first transforms and decouples the problem into subproblems and then obtains the efficient solution in two steps. In the first step, the closed-form solutions for the power budget and PD-NOMA power allocation subproblem at each drone are obtained through Karush-Kuhn-Tucker (KKT) conditions. In the second step, the subproblem of efficient phase shift design for each IRS is solved using successive convex approximation and DC programming. Numerical results demonstrate the performance of the proposed optimization scheme in comparison to the benchmark schemes.

New Method to Implement and Analysis of Medical System in Real Time.

The use of information technology and technological medical devices has contributed significantly to the transformation of healthcare. Despite that, many problems have arisen in diagnosing or predicting diseases, either as a result of human errors or lack of accuracy of measurements. Therefore, this paper aims to provide an integrated health monitoring system to measure vital parameters and diagnose or predict disease. Through this work, the percentage of various gases in the blood through breathing is determined, vital parameters are measured and their effect on feelings is analyzed. A supervised learning model is configured to predict and diagnose based on biometric measurements. All results were compared with the results of the Omron device as a reference device. The results proved that the proposed design overcame many problems as it contributed to expanding the database of vital parameters and providing analysis on the effect of emotions on vital indicators. The accuracy of the measurements also reached 98.8% and the accuracy of diagnosing COVID-19 was 64%. The work also presents a user interface model for clinicians as well as for smartphones using the Internet of things.

Ultrasound triggered organic mechanoluminescence materials.

Ultrasound induced organic mechanoluminescence materials have become one of the focal topics in wireless light sources since they exhibit high spatiotemporal resolution, biocompatibility and excellent tissue penetration depth. These properties promote great potential in ultrahigh sensitive bioimaging with no background noise and noninvasive nanodevices. Recent advances in chemistry, nanotechnology and biomedical research are revolutionizing ultrasound induced organic mechanoluminescence. Herein, we try to summarize some recent researches in ultrasound induced mechanoluminescence that use various materials design strategies based on the molecular conformational changes and cycloreversion reaction. Practical applications, like noninvasive bioimaging and noninvasive optogenetics, are also presented and prospected.

Effect of Hesperidin against Induced Colon Cancer in Rats: Impact of Smad4 and Activin A Signaling Pathway.

SCOPE: To evaluate the chemopreventive efficacy of hesperidin (Hsd) in 1,2-dimethylhydrazine (DMH)-induced colorectal cancer (CRC) and demonstrate its role in mothers against decapentaplegic homolog 4(Smad4) and activin A signaling pathways. METHODS AND RESULTS: A CRC rat model was established by DMH exposure, and the animals were randomly divided into five groups: Control group, Hsd, DMH, DMH + Hsd, and DMH followed by Hsd. The resected colon was subjected to macroscopic, microscopic, molecular, histopathological, and immunohistochemical examination. Activin A, Smad4, malondialdehyde (MDA), nitric oxide (NO), reduced glutathione (GSH), and superoxide dismutase (SOD) levels in tissues were also measured. The DMH group exhibited a significant increase in the gene and protein expression of activin A as well as MDA and NO levels in tissues. There was a significant reduction in the gene and protein expression of Smad4 as well as GSH and SOD levels in tissues. Administration of Hsd significantly upregulated Smad4 and activin A gene expressions in both the DMH + Hsd and DMH followed by Hsd groups. Moreover, Hsd improved the antioxidant status of the former two groups. CONCLUSION: This study demonstrated the chemopreventive effect of Hsd against CRC by modulating Smad4 and activin A signaling in vivo. Further studies are needed to demonstrate its clinical value and explore its possible role in advanced malignancy.

Slotted E-Shaped Meta-Material Decoupling Slab for Densely Packed MIMO Antenna Arrays.

In contemporary wireless communication systems, the multiple-input and multiple-output systems are extensively utilized due to their enhanced spectral efficiency and diversity. Densely packed antenna arrays play an important role in such systems to enhance their spatial diversity, array gain, and beam scanning capabilities. In this article, a slotted meta-material decoupling slab (S-MTM-DS) with dual reflexes slotted E-shapes and an inductive stub is proposed. Its function was validated when located between two microstrip patch antenna elements to reduce the inter-element spacing, the mutual coupling, the return losses, and manufacturing costs due to size reduction. A prototype is simply fabricated in a volume of 67.41 × 33.49 × 1.6 mm3 and frequency-span measured from 8.4:11 GHz. At 9.4 GHz frequency, the spaces between the transmitting elements are decreased to 0.57 of the free space wavelength. When the proposed isolation S-MTM-DS is applied, the average isolation among them is measured to be -36 dB, the operational bandwidth is enhanced to be 1.512 GHz, the fractional bandwidth improved to be 16.04%, and the return losses are decreased to be -26.5 dB at 9.4 GHz center frequency. Consequently, the proposed design has the potential to be implemented simply in wireless contemporary communication schemes.

An Intelligent Combined Visual Navigation Brain Model/GPS/MEMS-INS/ADSFCF Method to Develop Vehicle Independent Guidance Solutions.

This paper presents an integrated navigation system that can function more efficiently than an inertial navigation system (INS), the results of which are not precise enough because of drifts caused by accelerometers. The paper's proposed approach depends primarily on integrating micro-electrical-mechanical system (MEMS)-INS smartphone integrated sensors, the Global Positioning System (GPS), and the visual navigation brain model (VNBM) to enhance navigation in bad weather conditions. The recommended integrated navigation model, using an adaptive DFS combined filter, has been well studied and tested under severe climate conditions on reference trajectories. This integrated technique can easily detect and disable less accurate reference sources (GPS or VNBM) and activate a more accurate one. According to the results, the proposed integrated data fusion algorithm offers a reliable solution for errors in the previous strategies. Furthermore, compared to the pure MEMS-INS method, the proposed system reduces navigational errors by approximately 93.76 percent, whereas the conventional centralized Kalman filter technique reduces such errors by 82.23 percent.

Non-Linear Hopped Chaos Parameters-Based Image Encryption Algorithm Using Histogram Equalization.

Multimedia wireless communications have rapidly developed over the years. Accordingly, an increasing demand for more secured media transmission is required to protect multimedia contents. Image encryption schemes have been proposed over the years, but the most secure and reliable schemes are those based on chaotic maps, due to the intrinsic features in such kinds of multimedia contents regarding the pixels' high correlation and data handling capabilities. The novel proposed encryption algorithm introduced in this article is based on a 3D hopping chaotic map instead of fixed chaotic logistic maps. The non-linearity behavior of the proposed algorithm, in terms of both position permutation and value transformation, results in a more secured encryption algorithm due to its non-convergence, non-periodicity, and sensitivity to the applied initial conditions. Several statistical and analytical tests such as entropy, correlation, key sensitivity, key space, peak signal-to-noise ratio, noise attacks, number of pixels changing rate (NPCR), unified average change intensity randomness (UACI), and others tests were applied to measure the strength of the proposed encryption scheme. The obtained results prove that the proposed scheme is very robust against different cryptography attacks compared to similar encryption schemes.

A Novel Color Image Encryption Algorithm Based on Hyperchaotic Maps and Mitochondrial DNA Sequences.

Multimedia encryption innovation is one of the primary ways of securely and privately guaranteeing the security of media transmission. There are many advantages when utilizing the attributes of chaos, for example, arbitrariness, consistency, ergodicity, and initial condition affectability, for any covert multimedia transmission. Additionally, many more benefits can be introduced with the exceptional space compliance, unique information, and processing capability of real mitochondrial deoxyribonucleic acid (mtDNA). In this article, color image encryption employs a confusion process based on a hybrid chaotic map, first to split each channel of color images into n-clusters; then to create global shuffling over the whole image; and finally, to apply intrapixel shuffling in each cluster, which results in very disordered pixels in the encrypted image. Then, it utilizes the rationale of human mitochondrial genome mtDNA to diffuse the previously confused pixel values. Hypothetical examination and trial results demonstrate that the anticipated scheme exhibits outstanding encryption, as well as successfully opposes chosen/known plain text, statistical, and differential attacks.

A Bijective Image Encryption System Based on Hybrid Chaotic Map Diffusion and DNA Confusion.

Modern multimedia communications technology requirements have raised security standards, which allows for enormous development in security standards. This article presents an innovative symmetric cryptosystem that depends on the hybrid chaotic Lorenz diffusion stage and DNA confusion stage. It involves two identical encryption and decryption algorithms, which simplifies the implementation of transmitting and receiving schemes of images securely as a bijective system. Both schemes utilize two distinctive non-consecutive chaotic diffusion stages and one DNA scrambling stage in between. The generation of the coded secret bit stream employs a hybrid chaotic system, which is employed to encrypt or decrypt the transmitted image and is utilized in the diffusion process to dissipate the redundancy in the original transmitted image statistics. The transmitted image is divided into eight scrambled matrices according to the position of the pixel in every splitting matrix. Each binary matrix is converted using a different conversion rule in the Watson-Crick rules. The DNA confusion stage is applied to increase the complexity of the correlation between the transmitted image and the utilized key. These stages allow the proposed image encryption scheme to be more robust against chosen/known plaintext attacks, differential attacks, cipher image attacks, and information entropy. The system was revealed to be more sensitive against minimal change in the generated secret key. The analysis proves that the system has superior statistical properties, bulkier key space, better plain text sensitivity, and improved key sensitivity compared with former schemes.