Knowledge and Attitude Toward Otitis Externa: A Saudi Arabian Perspective.

Objectives: This study intended to assess the level of awareness and attitude toward otitis externa (OE) and specific limitations that counter the attempts to uplift the understanding and notion related to OE among the general population of Saudi Arabia. Methods: A cross-sectional quantitative study using a questionnaire was done via Google Forms between May 2023 and July 2023. The scoring method was used to determine the participant's awareness or attitude; participants who scored >50% were considered aware or to have a good attitude. Results: Approximately 52.2% had a good attitude toward the OE, and majority were willing to visit healthcare professionals (81%) to provide care and receive proper education during office visits (80.1%). Of all the participants, 69%, 33.4%, and 30.8% suggested that a lack of awareness, cost, and health insurance, respectively, might prevent patients from seeking a healthcare professional. Only 10.9% of participants demonstrated good awareness (score >13) of outer ear inflammation. Conclusion: The findings indicate a poor level of awareness regarding OE, and a positive attitude toward seeking healthcare, with the majority recommending professional visits and relying on healthcare professionals for information.

Assessment of the Saudi Population's Knowledge and Awareness Toward a Sedentary Lifestyle Risk Factors in the Post-COVID-19 Era.

Introduction: Sedentary behaviors have been on the rise, potentially exacerbated by lockdowns, remote work, and lifestyle shifts during the pandemic, emphasizing the need to assess public awareness regarding associated health risks. This study investigated the knowledge and awareness levels of the Saudi population regarding sedentary lifestyle risk factors in the post-COVID-19 era. Methodology: This cross-sectional study included 400 participants and was conducted from May 2023 to July 2023 using a questionnaire distributed randomly through social media in multiple regions of Saudi Arabia. Results: The majority of the participants were females (57.1%), aged 15-25 years (54.3%), had a higher educational degree (65.4%) and were single (62%). Most (62%) of the participants reported declined/physical activity levels in the post-COVID-19 era. Our study found that 65% of the participants had increased usage of online shopping and delivery applications after the pandemic, and a significant portion (66%) had less than 150-300 min of moderate aerobic activity weekly. Barriers, such as bad weather and insufficient time for exercise, had significant impacts. Conclusion: This study provides valuable insights into the Saudi population's demographic characteristics, knowledge, and behaviors related to physical activity and health.

Volume of Enlarged White Matter of Thalamus among Individuals with Depression on Magnetic Resonance Image: A Study of BrainSuite Segmentation.

Background: The thalamus, located in the diencephalon, regulates emotions and memories. If there is a problem in this area of the brain, it can cause an amnestic syndrome characterized by difficulties in remembering and recognizing things. The objective of this study was to identify changes in the volume of the thalamus while contrasting them among individuals with depression. Materials and Methods: The study involved measuring the volumes of the white matter of the thalamus in 79 patients with depression (42 males and 37 females) between 20 and 40 years (24 ± 5.51). This was compared to a control group of 53 individuals (24 ± 4.91) consisting of 29 males and 24 females, who were comparable in terms of sex and age. The measurements were taken employing BrainSuite version 18a. 021 Win 64bit software on a Philips 1.5 Tesla Magnetom Avanto Vision System magnetic resonance imaging (MRI). The Magnetization Prepared Rapid Acquisition (MPRA) was utilized to acquire three-dimensional images with T1 weighting. Results: The volume of white matter in the respective right and left thalamus was 5.09 cm3 and 4.58 cm3 (±standard deviation (SD) = 6.43 and 4.74) among individuals with depression. In the control group, the volume of white matter in the right and left thalamus was 3.66 cm3 and 4.16 cm3 (±SD = 3.99 and 5.06), respectively. The P-value is more than 0.05. The average volume of white matter in the right and left thalamus of females with depression and controls was 6.47 cm3 and 6.77 cm3 (with SD of 4.17 and 4.3), and 3.25 cm3 and 3.13 cm3 (with SD of 6.55 and 6.77), respectively. Conclusions: Our data suggest that individuals with depression exhibit an augmentation in the white matter of the thalamus, particularly in female patients where there is an upsurge in white matter volume. Depression appears to be linked to a decrease in volume on the left side of the brain.

8-port MIMO antenna at 27 GHz for n261 band and exploring for body centric communication.

This paper presents a compact 5G wideband antenna designed for body-centric networks (BCN. The single element antenna design includes a simple T-shaped radiator patch with ring shaped ground plane and transformer impedance feedline. First, the antenna was simulated in free-space, and its resonant frequency is found to be 27 GHz, falling within 5G's n261 band. The proposed single radiator antenna has a size of 23.375 mm3, and it offers a wide impedance bandwidth of 2.0 GHz (26-28 GHz). Parametric studies demonstrated that by increasing the length of slots in patch, the antenna frequency can be reduced further. Single radiator antenna is used as 8-element MIMO structure. Parallel adjacent antenna in X-direction has minimal coupling effect, whereas antenna placed in Y-direction has high coupling effect. Thus, coupling is reduced by etching a wall of slots in ground plane. It alters the surface current interference in Y-direction and limits the coupling effect. The antenna is investigated to use in body area network applications. To evaluate its on-body performance, an equivalent body model is virtually developed. The on-body performance is assessed by placing the antenna in close proximity to body model. Stable and robust performance is achieved for the on-body operation. At the resonant point, the antenna exhibits a reflection coefficient of -30 dB (free space) and -40 dB (on-body), high isolation of above 20 dB between adjacent radiators and above 30 dB for other radiators. Antenna has stable performance for different body tissues and on the non-planar structures. Bidirectional radiation pattern with gain of 2.53 dB and broadside type orientations with gain of 4.64 dB are achieved for free space and on body operations respectively. low specific absorption rate makes antenna safe for health care devices. Further, diversity performance is measured in terms of envelope correlation coefficient (ECC), and diversity gain (DG). Maximum Value of ECC is 0.005 and minimum value DG is 9.97 at 27 GHz which confirms the excellence of antenna for MIMO applications.

A modified particle swarm optimization rat search algorithm and its engineering application.

Solar energy generation requires photovoltaic (PV) systems to be optimised, regulated, and simulated with efficiency. The performance of PV systems is greatly impacted by the fluctuation and occasionally restricted accessibility of model parameters, which makes it difficult to identify these characteristics over time. To extract the features of solar modules and build highly accurate models for PV system modelling, control, and optimisation, current-voltage data collecting is essential. To overcome these difficulties, the modified particle swarm optimization rat search algorithm is presented in this manuscript. The modified rat search algorithm is incorporated to increase the PSO algorithm's accuracy and efficiency, which leads to better outcomes. The RSA mechanism increases both the population's diversity and the quality of exploration. For triple diode model of both monocrystalline and polycrystalline, PSORSA has showed exceptional performance in comparison to other algorithm i.e. RMSE for monocrystalline is 3.21E-11 and for polycrystalline is 1.86E-11. Similar performance can be observed from the PSORSA for four diode model i.e. RMSE for monocrystalline is 4.14E-09 and for polycrystalline is 4.72E-09. The findings show that PSORSA outperforms the most advanced techniques in terms of output, accuracy, and dependability. As a result, PSORSA proves to be a trustworthy instrument for assessing solar cell and PV module data.

Design and performance analysis of an L-shaped radiator and defected ground antenna for enhancing wireless connectivity in brain implants.

Brain implantable wireless microsystems has potential to treat neurological diseases and maintain the quality of life. Highly efficient miniaturized antenna is the fundamental part of BID (brain implantable device) for reliable signaling of data through dissipative intracranial material. In this paper, a patch antenna with L-shaped defected ground is demonstrated. L-shaped radiator contributed to achieve the resonance at 2.45 GHz industrial scientific and medical (ISM) band. Antenna size is reduced to 10 × 10 × 0.25 mm3. The proposed L-shaped ground plane geometry is contributing in improving the radiation performance. |S11| value shifts from 15 dB to 30 dB after modifying the ground plane. Proposed structure attained the gain of -14 dBi when located between the Dura and CSF layers at the depth of 12 mm in human brain model. Full wave simulated antenna prototype is fabricated and measured for performance verification. Impedance bandwidth of 270 MHz and broadside radiation pattern (for transferring maximum electromagnetic energy away from tissue) are maintained by the proposed antenna. Brain tissue safety is ensured by specific absorption rate which is 0.709 W/kg and in compliance with the safety limits of 1.6 W/kg for 1-g averaged tissue. Proposed antenna structure is the promising candidate for medical implant technology.

Investigation of a Circularly Polarized Metasurface Antenna for Hybrid Wireless Applications.

The increasing prevalence of the Internet of Things (IoT) as the primary networking infrastructure in a future society, driven by a strong focus on sustainability and data, is noteworthy. A significant concern associated with the widespread use of Internet of Things (IoT) devices is the insufficient availability of viable strategies for effectively sustaining their power supply and ensuring their uninterrupted functionality. The ability of RF energy-harvesting systems to externally replenish batteries serves as a primary driver for the development of these technologies. To effectively mitigate concerns related to wireless technology, it is imperative to adhere strictly to the mandated limitations on electromagnetic field emissions. A TA broadband polarization-reconfigurable Y-shaped monopole antenna that is improved with a SADEA-tuned smart metasurface is one technique that has been proposed in order to accomplish this goal. A Y-shaped printed monopole antenna is first taken into consideration. To comprehend the process of polarization reconfigurability transitioning from linear to circular polarization (CP), a BAR 50-02 V RF PIN Diode is employed to shorten one of the parasitic conducting strips to the ground plane. A SADEA-driven metasurface, which utilizes the artificial intelligence-driven surrogate model-assisted differential evolution for antenna synthesis, is devised and positioned beneath the radiator to optimize performance trade-offs while increasing the antenna's gain and bandwidth. The ultimate prototype achieves the following: an impedance bandwidth of 2.58 GHz (3.27-5.85 GHz, 48.45%); an axial bandwidth of 1.25 GHz (4.19-5.44 GHz, 25.96%); a peak gain exceeding 8.45 dBic; and when a highly efficient rectifier is integrated, the maximum RF-DC conversion efficiency of 73.82% and DC output of 5.44 V are obtained. Based on the results mentioned earlier, it is considered appropriate to supply power to intelligent sensors and reduce reliance on batteries via RF energy-harvesting mechanisms implemented in hybrid wireless applications.

Emerging Technologies for 6G Communication Networks: Machine Learning Approaches.

The fifth generation achieved tremendous success, which brings high hopes for the next generation, as evidenced by the sixth generation (6G) key performance indicators, which include ultra-reliable low latency communication (URLLC), extremely high data rate, high energy and spectral efficiency, ultra-dense connectivity, integrated sensing and communication, and secure communication. Emerging technologies such as intelligent reflecting surface (IRS), unmanned aerial vehicles (UAVs), non-orthogonal multiple access (NOMA), and others have the ability to provide communications for massive users, high overhead, and computational complexity. This will address concerns over the outrageous 6G requirements. However, optimizing system functionality with these new technologies was found to be hard for conventional mathematical solutions. Therefore, using the ML algorithm and its derivatives could be the right solution. The present study aims to offer a thorough and organized overview of the various machine learning (ML), deep learning (DL), and reinforcement learning (RL) algorithms concerning the emerging 6G technologies. This study is motivated by the fact that there is a lack of research on the significance of these algorithms in this specific context. This study examines the potential of ML algorithms and their derivatives in optimizing emerging technologies to align with the visions and requirements of the 6G network. It is crucial in ushering in a new era of communication marked by substantial advancements and requires grand improvement. This study highlights potential challenges for wireless communications in 6G networks and suggests insights into possible ML algorithms and their derivatives as possible solutions. Finally, the survey concludes that integrating Ml algorithms and emerging technologies will play a vital role in developing 6G networks.

A Miniaturized Tri-Band Implantable Antenna for ISM/WMTS/Lower UWB/Wi-Fi Frequencies.

This study aims to design a compact antenna structure suitable for implantable devices, with a broad frequency range covering various bands such as the Industrial Scientific and Medical band (868-868.6 MHz, 902-928 MHz, 5.725-5.875 GHz), the Wireless Medical Telemetry Service (WMTS) band, a subset of the unlicensed 3.5-4.5 GHz ultra-wideband (UWB) that is free of interference, and various Wi-Fi spectra (3.6 GHz, 4.9 GHz, 5 GHz, 5.9 GHz, 6 GHz). The antenna supports both low and high frequencies for efficient data transfer and is compatible with various communication technologies. The antenna features an asynchronous-meandered radiator, a parasitic patch, and an open-ended square ring-shaped ground plane. The antenna is deployed deep inside the muscle layer of a rectangular phantom below the skin and fat layer at a depth of 7 mm for numerical simulation. Furthermore, the antenna is deployed in a cylindrical phantom and bent to check the suitability for different organs. A prototype of the antenna is created, and its reflection coefficient and radiation patterns are measured in fresh pork tissue. The proposed antenna is considered a suitable candidate for implantable technology compared to other designs reported in the literature. It can be observed that the proposed antenna in this study has the smallest volume (75 mm3) and widest bandwidth (181.8% for 0.86 GHz, 9.58% for 1.43 GHz, and 285.7% for the UWB subset and Wi-Fi). It also has the highest gain (-26 dBi for ISM, -14 dBi for WMTS, and -14.2 dBi for UWB subset and Wi-Fi) compared to other antennas in the literature. In addition, the SAR values for the proposed antenna are well below the safety limits prescribed by IEEE Std C95.1-1999, with SAR values of 0.409 W/Kg for 0.8 GHz, 0.534 W/Kg for 1.43 GHz, 0.529 W/Kg for 3.5 GHz, and 0.665 W/Kg for 5.5 GHz when the applied input power is 10 mW. Overall, the proposed antenna in this study demonstrates superior performance compared to existing tri-band implantable antennas in terms of size, bandwidth, gain, and SAR values.

Performance Analysis of an Aperture-Coupled THz Antenna for Diagnosing Breast Cancer.

The most important technique for exposing early-stage breast cancer is terahertz imaging. It aids in lowering the number of breast cancer-related fatalities and enhancing the quality of life. An essential component of developing the THz imaging system for high-quality photos is choosing the right sensor. In this article, a wideband antenna for microwave imaging of breast tissue with an operating frequency of 30 GHz (107 GHz to 137 GHz) is constructed and analyzed. An aperture-coupled antenna with an optimized ground aperture is proposed and analyzed, which made it possible to obtain better and consistent impedance matching in the wideband spectrum. The variation of backscattered signal energy in body tissue is assessed with healthy breast tissue and in the presence of malignant cells. A significant difference in energy scattering is observed for both situations. The suggested antenna's linear and stable time domain characteristics make it an appropriate component for THz imaging technology.

Sustainable development of fuel cell using enhanced weighted mean of vectors algorithm.

Using the mathematical model of a Direct Methanol Fuel Cell (DMFC) stack, a new optimum approach is presented for estimating the seven unknown parameters i.e., ( e o , α , R , j e i d , C 1 , ß ,req) optimally. Specifically, a method is proposed for minimization of the Sum of Squared Errors (SSE) associated with the estimated polarization profile, based on the experimental data from simulations. The Enhanced Weighted mean of vectors (EINFO) algorithm is a novel metaheuristic method that is proposed to achieve this goal. An analysis of the results of this method is then compared to various metaheuristic algorithms such as the Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Dragonfly Algorithm (DA), Atom Search Optimization (ASO), and Weighted mean of vectors (INFO) well known in literature. As a final step to confirm the proposed approach's effectiveness, the sensitivity analysis is carried out using temperature changes, along with comparison against different approaches described in the literature to demonstrate its superiority. After comparison of parameter estimation and different operating temperature a non-parametric test is also performed and compared with the rest of the metaheuristic algorithms used in the manuscript. From these tests it is concluded that the proposed algorithm is superior to the rest of the compared algorithms.

Unmanned aerial vehicles (UAVs): practical aspects, applications, open challenges, security issues, and future trends.

Recently, unmanned aerial vehicles (UAVs) or drones have emerged as a ubiquitous and integral part of our society. They appear in great diversity in a multiplicity of applications for economic, commercial, leisure, military and academic purposes. The drone industry has seen a sharp uptake in the last decade as a model to manufacture and deliver convergence, offering synergy by incorporating multiple technologies. It is due to technological trends and rapid advancements in control, miniaturization, and computerization, which culminate in secure, lightweight, robust, more-accessible and cost-efficient UAVs. UAVs support implicit particularities including access to disaster-stricken zones, swift mobility, airborne missions and payload features. Despite these appealing benefits, UAVs face limitations in operability due to several critical concerns in terms of flight autonomy, path planning, battery endurance, flight time and limited payload carrying capability, as intuitively it is not recommended to load heavy objects such as batteries. As a result, the primary goal of this research is to provide insights into the potentials of UAVs, as well as their characteristics and functionality issues. This study provides a comprehensive review of UAVs, types, swarms, classifications, charging methods and regulations. Moreover, application scenarios, potential challenges and security issues are also examined. Finally, future research directions are identified to further hone the research work. We believe these insights will serve as guidelines and motivations for relevant researchers.

Role of Drone Technology Helping in Alleviating the COVID-19 Pandemic.

The COVID-19 pandemic, caused by a new coronavirus, has affected economic and social standards as governments and healthcare regulatory agencies throughout the world expressed worry and explored harsh preventative measures to counteract the disease's spread and intensity. Several academics and experts are primarily concerned with halting the continuous spread of the unique virus. Social separation, the closing of borders, the avoidance of big gatherings, contactless transit, and quarantine are important methods. Multiple nations employ autonomous, digital, wireless, and other promising technologies to tackle this coronary pneumonia. This research examines a number of potential technologies, including unmanned aerial vehicles (UAVs), artificial intelligence (AI), blockchain, deep learning (DL), the Internet of Things (IoT), edge computing, and virtual reality (VR), in an effort to mitigate the danger of COVID-19. Due to their ability to transport food and medical supplies to a specific location, UAVs are currently being utilized as an innovative method to combat this illness. This research intends to examine the possibilities of UAVs in the context of the COVID-19 pandemic from several angles. UAVs offer intriguing options for delivering medical supplies, spraying disinfectants, broadcasting communications, conducting surveillance, inspecting, and screening patients for infection. This article examines the use of drones in healthcare as well as the advantages and disadvantages of strict adoption. Finally, challenges, opportunities, and future work are discussed to assist in adopting drone technology to tackle COVID-19-like diseases.

Incidental detection of paranasal sinuses abnormalities on CT imaging of the head in Saudi adult population.

The paranasal sinuses are hollowed, air-filled cavities surrounding the nasal cavity. Many pathological processes affect the sinuses, but inflammatory conditions are the commonest, even in asymptomatic patients who undergo head imaging for other indications showing one or more abnormalities of the sinuses. Our research aims to determine the prevalence of incidental paranasal sinuses abnormalities seen among patients who undergo head CT scanning. In addition, it provides baseline information for further investigations required. The study was designed to evaluate all patients who underwent head CT scanning for any reason unrelated to paranasal sinuses abnormalities. 1849 cases were selected and retrospectively analyzed from the elective and emergency CT in the last nine months, from August 2020 to April 2021. In order to meet the inclusion criteria, indications for imaging must not be sinus-related. The study was conducted on 1849 cases who had undergone head CT scans for pathology, 1204 (65%) were male and 645 (35%) were female. Abnormalities of the sinuses were found in about 617 (33%) of all patients, with a higher rate in males (22.23%) than females (11.14%). In addition, these abnormalities were found in younger patients at a higher rate than in middle and old ages 19.74%, 7.19%, and 6.44%, respectively. Our findings revealed that the prevalence of paranasal sinuses abnormalities in asymptomatic Saudi patients was high (33%). Most of the affected sinuses were the maxillary. The male patients were more affected than females in all findings.

Recent Progress of Triboelectric Nanogenerators for Biomedical Sensors: From Design to Application.

Triboelectric nanogenerators (TENG) have gained prominence in recent years, and their structural design is crucial for improvement of energy harvesting performance and sensing. Wearable biosensors can receive information about human health without the need for external charging, with energy instead provided by collection and storage modules that can be integrated into the biosensors. However, the failure to design suitable components for sensing remains a significant challenge associated with biomedical sensors. Therefore, design of TENG structures based on the human body is a considerable challenge, as biomedical sensors, such as implantable and wearable self-powered sensors, have recently advanced. Following a brief introduction of the fundamentals of triboelectric nanogenerators, we describe implantable and wearable self-powered sensors powered by triboelectric nanogenerators. Moreover, we examine the constraints limiting the practical uses of self-powered devices.

Intelligent Reflecting Surfaces Assisted UAV Communications for Massive Networks: Current Trends, Challenges, and Research Directions.

An intelligent reflecting surface (IRS) can intelligently configure wavefronts such as amplitude, frequency, phase, and even polarization through passive reflections and without requiring any radio frequency (RF) chains. It is predicted to be a revolutionizing technology with the capability to alter wireless communication to enhance both spectrum and energy efficiencies with low expenditure and low energy consumption. Similarly, unmanned aerial vehicle (UAV) communication has attained a significant interest by research fraternity due to high mobility, flexible deployment, and easy integration with other technologies. However, UAV communication can face obstructions and eavesdropping in real-time scenarios. Recently, it is envisaged that IRS and UAV can combine together to achieve unparalleled opportunities in difficult environments. Both technologies can achieve enhanced performance by proactively altering the wireless propagation through maneuver control and smart signal reflections in three-dimensional space. This study briefly discusses IRS-assisted UAV communications. We survey the existing literature on this emerging research topic for both ground and airborne scenarios. We highlight several emerging technologies and application scenarios for future wireless networks. This study goes one step further to elaborate research opportunities to design and optimize wireless systems with low energy footprint and at low cost. Finally, we shed some light on open challenges and future research directions for IRS-assisted UAV communication.

Investigation of a Miniaturized Four-Element Antenna Integrated with Dipole Elements and Meta-Couplers for 5G Applications.

A miniaturized four-element antenna of 20 mm × 20 mm with edge-to-edge distance of 4.9 mm between the array antennas operating from 4.6−8.6 GHz is investigated in this article. The antenna consists of 4 × integrated dipole driven elements, and complementary split ring resonator (CSRR) metacells are loaded on the both sides of each dipole arms. The loaded meta-couplers magnetically couple to dipole drivers, and the induced resonance effect improves the 10-dB impedance bandwidth (IBW) to 60.6%. To improvise the isolation between antenna elements, metallic vias are implemented that trap electromagnetic (EM)-surface waves to condense into the ground. So, the meta-couplers induce electromagnetic (EM)-propagation as surface wave trapments for radiation and decouple near-field condensed currents, acting as couplers/decouplers. The maximum isolation achieved is >−22.5 dB without any external decoupling network. The diversity parameters indicate good attributes in isotropic, indoor, and outdoor channel environments with an envelope correlation coefficient (ECC) < 0.165 and realized gain of 5.5 dBi with average radiation efficiency of 80−90% in the desired operating bands. An equivalent circuit model using lumped components is designed for the proposed four-element antenna. For validation, a prototype antenna is fabricated and measured to be implemented in 5G applications, which shows good correlation with the full-wave simulated results.

A Novel Method for Improved Network Traffic Prediction Using Enhanced Deep Reinforcement Learning Algorithm.

Network data traffic is increasing with expanded networks for various applications, with text, image, audio, and video for inevitable needs. Network traffic pattern identification and analysis of traffic of data content are essential for different needs and different scenarios. Many approaches have been followed, both before and after the introduction of machine and deep learning algorithms as intelligence computation. The network traffic analysis is the process of incarcerating traffic of a network and observing it deeply to predict what the manifestation in traffic of the network is. To enhance the quality of service (QoS) of a network, it is important to estimate the network traffic and analyze its accuracy and precision, as well as the false positive and negative rates, with suitable algorithms. This proposed work is coining a new method using an enhanced deep reinforcement learning (EDRL) algorithm to improve network traffic analysis and prediction. The importance of this proposed work is to contribute towards intelligence-based network traffic prediction and solve network management issues. An experiment was carried out to check the accuracy and precision, as well as the false positive and negative parameters with EDRL. Also, convolutional neural network (CNN) machines and deep learning algorithms have been used to predict the different types of network traffic, which are labeled text-based, video-based, and unencrypted and encrypted data traffic. The EDRL algorithm has outperformed with mean Accuracy (97.20%), mean Precision (97.343%), mean false positive (2.657%) and mean false negative (2.527%) than the CNN algorithm.

A Novel Forward-Propagation Workflow Assessment Method for Malicious Packet Detection.

In recent times, there has been a huge upsurge in malicious attacks despite sophisticated technologies in digital network data transmission. This research proposes an innovative method that utilizes the forward-propagation workflow of the convolutional neural network (CNN) algorithm to detect malicious information effectively. The performance comparison of this approach was accomplished using accuracy, precision, false-positive and false-negative rates with k-nearest neighbor (KNN) and support vector machine (SVM) algorithms. To detect malicious packets in the original dataset, an experiment was carried out using CNN's forward-propagation workflow method (N = 11) as well as the KNN and the SVM machine learning algorithms with a significant value of 0.005. The accuracy, precision, false-positive and false-negative rates were evaluated to detect malicious packets present in normal data packets. The mean performance measures of the proposed forward-propagation method of the CNN algorithm were evaluated using the Statistical Package for the Social Sciences (SPSS) tool. The results showed that the mean accuracy (98.84%) and mean precision (99.08%) of the proposed forward propagation of the CNN algorithm appeared to be higher than the mean accuracy (95.55%) and mean precision (95.97%) of the KNN algorithm, as well as the mean accuracy (94.43%) and mean precision (94.58%) of the SVM algorithm. Moreover, the false-positive rate (1.93%) and false-negative rate (3.49%) of the proposed method appeared to be significantly higher than the KNN algorithm's false-positive (4.04%) and false-negative (6.24%) as well as the SVM algorithm's false-positive (5.03%) and false-negative rate (7.21%). Hence, it can be concluded that the forward-propagation method of the CNN algorithm is better than the KNN and SVM algorithms at detecting malicious information.

Blockchain Based Authentication and Cluster Head Selection Using DDR-LEACH in Internet of Sensor Things.

This paper proposes a blockchain-based node authentication model for the Internet of sensor things (IoST). The nodes in the network are authenticated based on their credentials to make the network free from malicious nodes. In IoST, sensor nodes gather the information from the environment and send it to the cluster heads (CHs) for additional processing. CHs aggregate the sensed information. Therefore, their energy rapidly depletes due to extra workload. To solve this issue, we proposed distance, degree, and residual energy-based low-energy adaptive clustering hierarchy (DDR-LEACH) protocol. DDR-LEACH is used to replace CHs with the ordinary nodes based on maximum residual energy, degree, and minimum distance from BS. Furthermore, storing a huge amount of data in the blockchain is very costly. To tackle this issue, an external data storage, named as interplanetary file system (IPFS), is used. Furthermore, for ensuring data security in IPFS, AES 128-bit is used, which performs better than the existing encryption schemes. Moreover, a huge computational cost is required using a proof of work consensus mechanism to validate transactions. To solve this issue, proof of authority (PoA) consensus mechanism is used in the proposed model. The simulation results are carried out, which show the efficiency and effectiveness of the proposed system model. The DDR-LEACH is compared with LEACH and the simulation results show that DDR-LEACH outperforms LEACH in terms of energy consumption, throughput, and improvement in network lifetime with CH selection mechanism. Moreover, transaction cost is computed, which is reduced by PoA during data storage on IPFS and service provisioning. Furthermore, the time is calculated in the comparison of AES 128-bit scheme with existing scheme. The formal security analysis is performed to check the effectiveness of smart contract against attacks. Additionally, two different attacks, MITM and Sybil, are induced in our system to show our system model's resilience against cyber attacks.