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Visual observation and dermoscopic analysis are the most common methods of diagnosing skin cancer. In advanced stages, melanomas spread faster and are less responsive to treatment. Because different lesions in the skin appear similar to one another and sometimes errors in identification occur, the accuracy of diagnosis will decrease significantly when the amount of received images is large. However, the proposed methods for estimating skin lesions and their separation from melanoma have uncertainties and are not generalizable. This paper proposes an optimal decision tree (DT)-based approach, including fuzzy-ID3-pValue and Bayes learning algorithms, which overcomes these challenges. When classifying images, DTs employ a multi-stage procedure to partition the feature space, which enhances their ease of use, precision, and speed. Inference engines are used in fuzzy logic to derive logical deductions about knowledge, which facilitates learning DT and Bayesian learning. Taking advantage of the DT dependency structure, we present a novel fuzzy DT for extracting precise and collaborative fuzzy rules. Furthermore, to emphasize the cohesive nature of the laws, a weighted method is employed. Besides, the inference engine system is constructed through deductive and inductive inference engines. The proposed method is verified using the ISIC-2019 dataset as well as the PH2 images, both of which contain dermoscopic images of multiple lesions. The proposed method provides efficient results with 96% and 88% accuracy for ISIC-2019 and PH2 data, respectively. [ABSTRACT FROM AUTHOR]

Background: Innovative treatments for non‐melanoma skin cancers (NMSCs) are required to enhance patient outcomes. Aims: This review examines the effectiveness and safety of receptor tyrosine kinase inhibitors (RTKIs). Methods: A comprehensive review was conducted on the treatment potential of several RTKIs, namely cetuximab, erlotinib, gefitinib, panitumumab, and lapatinib. Results: The findings indicate that these targeted therapies hold great promise for the treatment of NMSCs. However, it is crucial to consider relapse rates and possible adverse effects. Further research is needed to improve treatment strategies, identify patient groups that would benefit the most, and assess the long‐term efficacy and safety, despite the favorable results reported in previous studies. Furthermore, it is crucial to investigate the potential benefits of integrating RTKIs with immunotherapy and other treatment modalities to enhance the overall efficacy of therapy for individuals with NMSC. Conclusions: Targeted therapies for NMSCs may be possible with the use of RTKIs. The majority of studies focused on utilizing epidermal growth factor receptor inhibitors as the primary class of RTKIs for the treatment of NMSC. Other RTKIs were only employed in experimental investigations. Research indicates that RTKIs could potentially serve as a suitable alternative for elderly patients who are unable to undergo chemotherapy and radiotherapy. [ABSTRACT FROM AUTHOR]

An alarm or an alarm system is a device that warns or detects any violation of privacy in different places by making noise, sound, or warning the person in a variety of ways, including via email, SMS, phone call, or sounding. Alarms that are currently available in the market have different shapes and types. Generally, they have a common structure and their main purpose is similar to each other. There is another type of burglar alarm in the market, which is used today in certain places to protect property and assets, and in certain organizations such as banks and car alarms. This article reviews and presents a different alarm, whose main purpose is to provide possible security in public and private places, especially military places. [ABSTRACT FROM AUTHOR]

Software-Defined Networking (SDN) represents a significant paradigm shift in network architecture, separating network logic from the underlying forwarding devices to enhance flexibility and centralize deployment. Concurrently, the Internet of Things (IoT) connects numerous devices to the Internet, enabling autonomous interactions with minimal human intervention. However, implementing and managing an SDN-IoT system is inherently complex, particularly for those with limited resources, as the dynamic and distributed nature of IoT infrastructures creates security and privacy challenges during SDN integration. The findings of this study underscore the primary security and privacy challenges across application, control, and data planes. A comprehensive review evaluates the root causes of these challenges and the defense techniques employed in prior works to establish sufficient secrecy and privacy protection. Recent investigations have explored cutting-edge methods, such as leveraging blockchain for transaction recording to enhance security and privacy, along with applying machine learning and deep learning approaches to identify and mitigate the impacts of Denial of Service (DoS) and Distributed DoS (DDoS) attacks. Moreover, the analysis indicates that encryption and hashing techniques are prevalent in the data plane, whereas access control and certificate authorization are prominently considered in the control plane, and authentication is commonly employed within the application plane. Additionally, this paper outlines future directions, offering insights into potential strategies and technological advancements aimed at fostering a more secure and privacy-conscious SDN-based IoT ecosystem. [ABSTRACT FROM AUTHOR]

Working with pushbroom imagery in photogrammetry and remote sensing presents a fundamental challenge in object-to-image space transformation. For this transformation, accurate estimation of Exterior Orientation Parameters (EOPs) for each scanline is required. To tackle this challenge, Best Scanline Search or Determination (BSS/BSD) methods have been developed. However, the current BSS/BSD methods are not efficient for real-time applications due to their complex procedures and interpolations. This paper introduces a new non-iterative BSD method specifically designed for line-type pushbroom images. The method involves simulating a pair of sets of points, Simulated Control Points (SCOPs), and Simulated Check Points (SCPs), to train and test a Multilayer Perceptron (MLP) model. The model establishes a strong relationship between object and image spaces, enabling a direct transformation and determination of best scanlines. This proposed method does not rely on the Collinearity Equation (CE) or iterative search. After training, the MLP model is applied to the SCPs for accuracy assessment. The proposed method is tested on ten images with diverse landscapes captured by eight sensors, exploiting five million SCPs per image for statistical assessments. The Root Mean Square Error (RMSE) values range between 0.001 and 0.015 pixels across ten images, demonstrating the capability of achieving the desired sub-pixel accuracy within a few seconds. The proposed method is compared with conventional and state-of-the-art BSS/BSD methods, indicating its higher applicability regarding accuracy and computational efficiency. These results position the proposed BSD method as a practical solution for transforming object-to-image space, especially for real-time applications. [ABSTRACT FROM AUTHOR]

This study explores the effect of incorporating steel fibres and fly ash into self‐compacting concrete (SCC) in various combinations on its mechanical properties. The study begins with thoroughly investigating the fresh properties to ensure compliance with SCC standards when adding steel fibres. Subsequently, mixes that meet SCC standards are evaluated for their mechanical properties. The study aims to enhance the mechanical properties of SCC while maintaining its flowability, filling ability, and passing ability. The research findings provide valuable insights into the optimal combination of steel fibres and fly ash for improving the mechanical properties of SCC. The addition of hooked end type steel fibres increases the compressive strength. When compared to crimped type the mechanical properties increased to 14.4%; however, the strength decreases beyond 1% of steel fibres. Satisfactory results are obtained for fresh properties test. [ABSTRACT FROM AUTHOR]

Key Clinical Message: Rituximab‐induced serum sickness (RISS) is a rare complication of Rituximab (RTX) in immunobullous disorders. Clinicians should be aware of the occurrence of serum sickness symptoms during RTX administration, and prompt initiation of corticosteroid therapy is crucial in these patients. Additionally, RISS may occur with subsequent RTX doses and patients should be counseled accordingly. Rituximab (RTX) is a chimeric monoclonal anti‐CD20 antibody which has gained approval for the treatment of various autoimmune and lymphoproliferative disorders. While RTX‐induced minor reactions, including immediate infusion‐related reactions, are common, serum sickness is rare. Limited data exist regarding rituximab‐induced serum sickness (RISS) in pemphigus vulgaris (PV) and mucous membrane pemphigoid (MMP). We report two cases of RISS following RTX administration in PV and MMP patients. Both patients presented with typical symptoms of serum sickness after RTX infusion, necessitating drug cessation and corticosteroid therapy for resolution. RISS represents a rare complication of RTX therapy. Clinicians should maintain awareness of serum sickness presentations during and post‐RTX administration. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks (WSNs) are faced with the challenge of energy conservation, which makes efficient routing protocols crucial for prolonging network lifetime. In addition, delay time from sensors to the base station is critical in applications such as military, medical, and security monitoring systems. Chain‐based protocols like PEGASIS, CCBRP, and CCM have been developed to address routing in WSNs, with the aim of reducing energy consumption and delay. However, node failures are inevitable due to energy reduction and node mobility. Therefore, fault tolerance techniques must be integrated into routing protocols. A new fault tolerance method has been proposed to prevent early chain failures in WSNs by formulating network availability and reliability using Markov chain analysis. The results indicate that chain‐based routing protocols with one spare node are more reliable than those with two spare nodes. [ABSTRACT FROM AUTHOR]

Background: Psoriasis as a common cutaneous inflammatory disease affect many aspects of patients' life. Disease registries render it possible to collect valuable data regarding a disease prevalence and burden as well as long‐term observations concerning possible therapeutic regimens. Methods: This registry was designed for the ongoing systematic data collection on patients with psoriasis at two referral dermatology centers in Iran. The pilot phase of the registry was used to identify possible obstacles in the application and execution of systematic registration. Results: A total of 281 patients were registered with the mean age of 42.02 years. The disease duration was 12.06 ± 10.90 years with the variety of clinical presentations. There was no significant difference between males and females in the age of disease onset (p = 0.53). Notably, 167 patients had children. Among them, 13 had children with psoriasis. The gender of the affected parent did not affect the possibility of psoriasis transmission to the child, and no significant difference was seen between the two sexes (P = 0.569). Regarding treatment, 99.4% of patients (n = 280) had used topical agents, 52.3% (n = 147) biologics, and 60.9% (n = 171) nonbiologic medications. Conclusion: Clinical trials report the efficacy and safety data regarding limited study populations in a restricted time window, and the results may differ from the general population. This highlights the importance of registry‐based studies for collecting and analyzing longitudinal information. In terms of long‐term disease complications such as malignancies, cardiovascular events, and serious adverse events, registry‐based studies will help clinicians better recognize and manage each disease. [ABSTRACT FROM AUTHOR]

Autonomous Vehicles (AVs) and driverless cars which are equipped with communication capabilities, advanced sensing, and Intelligent Control Systems (ICS), aim to modernize the transportation system. It increases user satisfaction by enhancing personal safety, reducing infrastructure costs, decreasing environmental interruption, and saving time for passengers. On the other hand, in emergency cases when AVs require maintenance, their generated sensitive information (e.g., AV location, low brake fluid amount of an AV) should be shared with Road Side Units (RSUs) and other vehicles to address their problems and provide quality services. Despite its appealing benefits, sensitive data sharing carries security and privacy issues that trigger serious risks like unintentional physical accidents. If the privacy of the AV is breached and its sensitive data is unintentionally disclosed during data transmission, adversaries can misuse them and cause artificial accidents. Current studies in this area lack efficiency and cost-effectiveness. To fill this gap and reduce the number of potential accidents, this article proposes a new Context-Aware Privacy-Preserving method for Autonomous Driving (CAPPAD). In particular, the Software-Defined Networking (SDN) paradigm is employed to bring flexibility to AVs' privacy management while its SDN controller runs a novel algorithm for privacy preservation. Depending on whether the data generated is sensitive or not and whether there is an emergency, the AV applies Differential Privacy (DP) or Data Aggregation (DA) as its privacy-preserving method. Finally, extensive simulations are performed through MININET-WIFI to show the performance of CAPPAD in terms of privacy-preserving degree, computational cost overhead, computational complexity overhead, and latency. We also compare it with other relevant well-known studies to show its superior effectiveness. [ABSTRACT FROM AUTHOR]

The method of indeterminate monitoring of household appliances involves analyzing voltage and current signals detected at the power supply. Each electrical appliance exhibits a distinct consumption pattern (e.g., vacuum cleaners, ovens, etc.), including their switch-on and switch-off timings. The monitoring system determines these consumption patterns of electric appliances. This study introduces a wireless sensor network designed for subtle monitoring of household appliance consumption. This study addresses the challenges and issues concerning wireless sensor networks, alongside the notable advantages and capabilities in the areas mentioned above, such as network structure, performance methods, monitoring types, and energy consumption factor analysis. Through empirical investigation and scientific discourse, the study's findings indicate that a higher number of nodes and windows in a building correlates with a reduced rate of energy transfer. Conversely, employing fewer nodes and windows increases the speed at which energy is transferred. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue on privacy-preserving techniques in cloud/fog and Internet of Things (IoT) networks. The issue explores various methods and strategies for protecting privacy in these networks, including cryptographic algorithms, federated learning models, and password-less authentication. The authors also discuss the challenges of securing IoT devices with limited computational power and the importance of safeguarding data in large networks. The special issue includes 10 selected papers out of a total of 21 submissions, covering topics such as true random number generation, shared key generation, watermarking, dependability assessment, authentication schemes, federated learning, batch processing, stylometry, and searchable encryption. The authors express their gratitude to the contributors for their valuable research in this field. [Extracted from the article]

Tinea capitis is a common dermatophyte infection of the scalp in children. It is an uncommon infection in adults and usually affects postmenopausal women and immunocompromised patients. We report an immunocompetent elderly female with inflammatory tinea capitis caused by Trichophyton violaceum and review the literature for the past 5 years to describe the disease, its epidemiologic characteristics, dermatophyte species involved and treatment options used. The total number of cases was 11, including 8 women and 3 men, with an average age of 48.36. The most commonly isolated dermatophyte was Trichophyton tonsurans, and most cases were treated successfully with oral terbinafine with no side effects. In our case, the diagnosis was established by direct examination, culture and histological examination. Remedy with itraconazole and prednisolone was very successful. Early diagnosis of tinea capitis in adults is necessary to provide early treatment and minimize sequelae of the disease. [ABSTRACT FROM AUTHOR]

Background: Acne vulgaris is a common skin disease that is more common in young population and it can be associated with some sequels after resolving the lesions. Post‐inflammatory erythema is one of these complications that can be disturbing for patients and does not have any definite treatment. This study was aimed to evaluate the efficacy and safety of tranexamic acid (TA) as mesotherapy in treatment of post‐acne erythema (PAE) treatment. Method: This clinical trial study was performed in the dermatology clinic on 17 patients with persistent PAE (3 months after acne recovery). Two sessions of treatment were performed by a physician with 2‐week intervals; TA was injected as mesotherapy into the right side of each patient's face as the case group, while the opposite side was used as the control group. A Visioface device was used to compare before and after treatment photographs of each side of the face in color mode with quantitative measures such as lesions count, area, and area percent. Results: Finally, 15 patients completed treatment sessions. There were statistically significant differences in right side lesions before and after treatment with p‐values of 0.047, 0.002, and 0.035 for count, area, and area percent, respectively. There was no significant difference before and after treatment in terms of count, area. and area‐percent on the left side. Conclusion: According to the results of this study, TA injection as mesotherapy for resolving PAE can be effective. However, due to small sample size, further studies are needed. [ABSTRACT FROM AUTHOR]

Centralized training of deep learning models poses privacy risks that hinder their deployment. Federated learning (FL) has emerged as a solution to address these risks, allowing multiple clients to train deep learning models collaborativelywithout sharing rawdata. However, FL is vulnerable to the impact of heterogeneous distributed data, which weakens convergence stability and suboptimal performance of the trained model on local data. This is due to the discarding of the old local model at each round of training, which results in the loss of personalized information in the model critical for maintaining model accuracy and ensuring robustness. In this paper, we propose FedTC, a personalized federated learning method with two classifiers that can retain personalized information in the local model and improve the model's performance on local data. FedTC divides the model into two parts, namely, the extractor and the classifier, where the classifier is the last layer of the model, and the extractor consists of other layers. The classifier in the local model is always retained to ensure that the personalized information is not lost. After receiving the global model, the local extractor is overwritten by the globalmodel's extractor, and the classifier of the globalmodel serves as an additional classifier of the localmodel to guide local training. The FedTCintroduces a two-classifier training strategy to coordinate the two classifiers for local model updates. Experimental results on Cifar10 and Cifar100 datasets demonstrate that FedTC performs better on heterogeneous data than current studies, such as FedAvg, FedPer, and local training, achieving a maximum improvement of 27.95% in model classification test accuracy compared to FedAvg. [ABSTRACT FROM AUTHOR]

In this paper, a new chaotic image encryption and authentication model based on Elliptic Curves is proposed. The Elliptic Curve Diffie-Hellman (ECDH) is used to generate a reliable session key prior to encryption. The scheme uses the Henon map for the initial shuffling of the pixels and the Hopfield chaotic neural network to compute random chaotic values. The chaotic matrix is XORed with henon shuffled image. The model encrypts the scrambled image using improved ElGamal encoding to obtain the cipher image. The model employs a low computational cost digital signature to check for the authenticity of the received encrypted images before decryption. The proposed model makes use of a large key space to resist brute force attacks, and produces randomized cipher images with high average Shannon's entropies, 7.9994 for grayscale and 7.9993 for color images, and lower adjacent pixel correlation to resist statistical attacks and ensure a good quality encryption. The model can withstand the chosen-plaintext and known-plaintext attacks, and attains average Number of Pixel Change Rate (NPCR) and Unified Average Change Intensity (UACI) values of (99.63%, 33.345%) for grayscale and (99.625%, 33.34%) for color images signifying its effectiveness against differential attacks. The ability to recover decipherable images after masking up to 75% of cipher image indicates the robustness of the proposed scheme against the occlusion attacks. [ABSTRACT FROM AUTHOR]

Deep Learning (DL) is a subfield of machine learning that significantly impacts extracting new knowledge. By using DL, the extraction of advanced data representations and knowledge can be made possible. Highly effective DL techniques help to find more hidden knowledge. Deep learning has a promising future due to its great performance and accuracy. We need to understand the fundamentals and the state‐of‐the‐art of DL to leverage it effectively. A survey on DL ways, advantages, drawbacks, architectures, and methods to have a straightforward and clear understanding of it from different views is explained in the paper. Moreover, the existing related methods are compared with each other, and the application of DL is described in some applications, such as medical image analysis, handwriting recognition, and so on. [ABSTRACT FROM AUTHOR]

Owing to increased energy demand, renewable energy sources have recently reached greater heights in the power industry. In this regard, integrating renewable energy with the power grid system has become essential in meeting consumer's energy demands. The novelty in this article is the design of an advanced Aalborg inverter with a reduced number of switches which results in enhanced system performance by reducing the switching losses. Integrating renewable energy sources with the power grid network has gained massive importance in supplying electric power to consumers. Integrating the power grid with renewable energy has become possible with the implementation of grid tie inverters. The only drawback in implementing renewable energy sources with the power grid is that the output of the resources varies from time to time in nature. Voltage Source Inverter‐based grid system requires two or three‐stage inverters for interfacing power units to transfer DC energy into the power grid network. To reduce the switching frequency across the system, many new excellent inverters have been designed to ensure the healthy operation of the system operating at a higher frequency. This article has been coined with the investigation of a novel buck and boosts converter‐based inverter operation named Aalborg inverter. The inverter operates at a higher frequency and minimum voltage drop across the inductor, where only one power stage has been implied to enhance the system's reliability and efficiency. This novel Aalborg inverter is applied and investigated for photovoltaic application. [ABSTRACT FROM AUTHOR]

In the era of digital marketing, advertisements have become an indispensable part. One of the central challenges is advertising attribution which explains the amount of contribution every publisher has with the conversions. However, through observation, we have found that current advertising platforms attribution, advertisers attribution, or third‐party platforms attribution all have the problems of trust, data leakage, and data forgery. To fill the gap, our work's main contribution is combining blockchain with advertising attribution to propose an architecture for improving the privacy‐preserving degree and amount. In the proposed architecture, publishers, and advertisers can store real‐time data on a blockchain. The attribution results are credible because blockchain is decentralized, tamper‐proof, and traceable. We combine privacy set intersection and zero‐knowledge proof technology to increase the privacy of flowing data. In addition, we describe a preliminary prototype in which publishers, advertisers, and advertising platforms can get the corresponding attribution details. To show its effectiveness, we analyze it from different perspectives, including communication cost, attribution accuracy, and time cost. The results show that our communication cost has significantly reduced compared to the recent studies. [ABSTRACT FROM AUTHOR]

Key Clinical Message: Systemic lupus erythematosus (SLE) is a systemic autoimmune disease that affects several organs including the skin. Clinical cutaneous symptoms of SLE come in a broad range, consisting of both non‐specific and specific cutaneous lesions. Except for cases of amicrobial pustulosis of the folds, generalized pustular psoriasis, acneiform eruptions, pustular vasculitis, Wells' syndrome, subcorneal pustular dermatosis, and neutrophilic dermatosis, there are no reports of pustular lesions linked to SLE. The unusual cutaneous features of our patient were annular plaques with pustules and crusts on the margins. [ABSTRACT FROM AUTHOR]

Network‐on‐Chip (NoC) is a key component in chip multiprocessors (CMPs) as it supports communication between many cores. NoC is a network‐based communication subsystem on an integrated circuit, most typically between modules in a system on a chip (SoC). Designing a reliable NoC against failures that can prevent failure using some measures or preventing error or system failure while failure happens and proper performance became a significant concern. For a reliable design against failures, first, the system should be analysed to discover the critical points. Hence, in this research, it is tried first to investigate the scale of fault tolerance effect on the mechanism in the router on the network by injecting simulated errors, and then these errors are prevented. As the major novelty, the authors implemented a router on a synchronised network and calculated the network buffering fault tolerance by injecting error in the buffer. Specifically, a new method for improving fault tolerance is proposed, which uses the existing resources efficiently. So, it does not impose any overhead on hardware and improves the error tolerance scale. The authors also evaluate it from different perspectives to show its superior performance. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has been considered in various fields in the last decade. With the increasing number of IoT devices in the community, secure, accessible, and reliable infrastructure for processing and storing computed data has become necessary. Since traditional security protocols are unsuitable for IoT devices, IoT implementation is fraught with privacy and security challenges. Thus, blockchain technology has become an effective solution to the problems of IoT security. Blockchain is an empirical data distribution and storage model involving point-to-point transmission, consensus mechanism, asymmetric encryption, smart contract, and other computer technologies. Security and privacy are becoming increasingly important in using the IoT. Therefore, this study provides a comprehensive framework for classifying security criteria based on blockchain technology. Another goal of the present study is to identify causal relationship factors for the security issue using the Fuzzy Decision-Making Trial-and-Evaluation Laboratory (FDEMATEL) approach. In order to deal with uncertainty in human judgment, fuzzy logic is considered an effective tool. The present study's results show the proposed approach's efficiency. Authentication (CR6), intrusion detection (CR4), and availability (CR5) were also introduced as the most effective and essential criteria, respectively. [ABSTRACT FROM AUTHOR]

Today, Internet of things (IoT), Cloud and Fog networks have spread out around the world. The more these networks grow, the more their energy consumption comes to attention. Many efforts have been made during recent years to decrease this energy consumption, mainly focused on utilizing low‐power devices. Green algorithms are recently proposed to reduce energy consumption by modifying the structure of many algorithms employed in the network and its protocols. This paper proposes a new green reliability algorithm for Transmission Control Protocol/Internet Protocol (TCP/IP protocol) in Fog computing. The proposed algorithm does not require extensive TCP/IP protocol changes or relevant hardware. It is based on transferring less number of packets in the network by using the advantage of differences between TCP and User Datagram Protocol (UDP). TCP and User Datagram Protocol (UDP) are different in nature as the number of total packets in UDP is half that of TCP. As a result, the number of complete packets in UDP is half that of TCP. The proposed method is built around the loss of some packets in applications, such as voice and online video, does not severely degrade the end results. Therefore, the UDP protocol can substitute TCP in such situations. The criterion to switch between the two is the minimum acceptable Quality of Service (QoS) of the overall network. In other words, the UDP protocol will be used as long as QoS requirements are met. The switching process between UDP and TCP is dynamic, optimized by estimating network noise in the period. Additionally, we evaluated the proposed method based on several QoS functions, including delay, throughput, and energy usage. [ABSTRACT FROM AUTHOR]

Data is generated over time by each device in the Internet of Things (IoT) ecosphere. Recent years have seen a resurgence in interest in the IoT due to its positive impact on society. However, due to the automatic management of IoT devices, the possibility of disclosing sensitive information without user consent is high. A situation in which information should not be unintentionally disclosed to outside parties we do not trust, i.e., privacy-preservation. Additionally, IoT devices should share their data with others to perform data aggregation and provide high-level services. There is a trade-off between the amount of data utility and the amount of disclosure of data. This trade-off has been causing a big challenge in this field. To improve the efficiency of this trade-off rather than current studies, in this study, we propose a Privacy-Preserving Data Aggregation architecture, PPDMIT, that leverages Homomorphic Paillier Encryption (HPE), K-means, a One-way hash chain, and the Chinese Remainder Theorem (CRT). We have found that the proposed privacy-preserving architecture achieves more efficient data aggregation than current studies and improves privacy preservation by utilizing extensive simulations. Moreover, we found that our proposed architecture is highly applicable to IoT environments while preventing unauthorized data disclosure. Specifically, our solution depicted an 8.096% improvement over LPDA and 6.508% over PPIOT. [ABSTRACT FROM AUTHOR]

Artificial intelligence techniques are excessively used in computing for training, forecasting and evaluation purposes. Among these techniques, artificial neural network (ANN) is widely used for developing prediction models. ANNs use various Meta-heuristic algorithms including approximation methods for training the neural networks. ANN plays a significant role in this area and can be helpful in determining the neural network input coefficient. The main goal of presented study is to train the neural network using meta-heuristic approaches and to enhance the perceptron neural network precision. In this article, we used an integrated algorithm to determine the neural network input coefficients. Later, the proposed algorithm was compared with other algorithms such as ant colony and invasive weed optimization for performance evaluation. The results reveal that the proposed algorithm results in more convergence with neural network coefficient as compared to existing algorithms. However the proposed method resulted in reduction of prediction error in the neural network. [ABSTRACT FROM AUTHOR]

Evidence suggests that bullous pemphigoid (BP) is associated with multiple neurological disorders. We aimed to compare brain magnetic resonance (MRI) findings between BP patients and a control group. This case–control study included patients with BP referred to two dermatology clinics during a two‐year period. A group of individuals attending the same clinics for cosmetic procedures were selected as controls. First, participants' general information including age, gender, education, weight and underlying disease was recorded. For BP patients, the drugs and the BP Disease Area Index (BPDAI) were recorded as well. Then, all participants underwent brain MRI without contrast. The Fazekas scale, the general cerebral atrophy (GCA) score, and the Medial Temporal lobe Atrophy (MTA) score were used to assess MRI images. Overall, 24 BP patients and 24 controls were evaluated in this study. Both groups were comparable regarding age, gender and education. However, diabetes and hypertension were more frequent in the control group. The mean BPDAI total score was 51.39 ± 68.92 in BP patients and most of them used rituximab (41.7%). None of the participants had MS or Alzheimer MRI patterns. There was no difference between groups in terms of GCA and MTA scores. Furthermore, the frequency of partially empty sella did not differ significantly between BP patients and controls (p = 0.461); nevertheless, grade‐3 Fazekas was significantly higher in BP patients compared to controls (25% vs. 0%, p = 0.019). Of note, one BP patient had an epidermoid cyst and another had moderate enlargement of three ventricles. Also, new infarcts were observed in two and old infarcts in four BP patients. Although the majority of abnormal brain MRI findings were more frequent in BP patients compared to controls, only grade‐3 Fazekas was significantly higher and acute infarcts were exclusively observed in BP patients. [ABSTRACT FROM AUTHOR]

With the recent development and advancement of business growth models, information exchange has emerged as a critical component of computer communication technology. To serve dynamic business models with the latest data, most system architecture tends to retrieve data from back-end systems in real-time. Numerous leading organizations have started to think of or are implementing two different platforms: read cluster and write cluster. In this article, we propose a model that adopts a data streaming system and a data caching mechanism for faster access to real-time and non-real-time data. This system only focuses on providing the read data elements and reducing most of the load in the existing cluster(s). Our mathematical and simulation performance analysis shows a clear distinction and comparison between the current and proposed systems architecture. [ABSTRACT FROM AUTHOR]

Nowadays, the unmanned aerial vehicle (UAV) has a wide application in transportation. For instance, by leveraging it, we are able to perform accurate and real-time vehicle speed detection in an IoT-based smart city. Although numerous vehicle speed estimation methods exist, most of them lack real-time detection in different situations and scenarios. To fill the gap, this paper introduces a novel low-altitude vehicle speed detector system using UAVs for remote sensing applications of smart cities, forging to increase traffic safety and security. To this aim, (1) we have found the best possible Raspberry PI's field of view (FOV) camera in indoor and outdoor scenarios by changing its height and degree. Then, (2) Mobile Net-SSD deep learning model parameters have been embedded in the PI4B processor of a physical car at different speeds. Finally, we implemented it in a real environment at the JXUST university intersection by changing the height (0.7 to 3 m) and the camera angle on the UAV. Specifically, this paper proposed an intelligent speed control system without the presence of real police that has been implemented on the edge node with the configuration of a PI4B and an Intel Neural Computing 2, along with the PI camera, which is armed with a Mobile Net-SSD deep learning model for the smart detection of vehicles and their speeds. The main purpose of this article is to propose the use of drones as a tool to detect the speeds of vehicles, especially in areas where it is not easy to access or install a fixed camera, in the context of future smart city traffic management and control. The experimental results have proven the superior performance of the proposed low-altitude UAV system rather than current studies for detecting and estimating the vehicles' speeds in highly dynamic situations and different speeds. As the results showed, our solution is highly effective on crowded roads, such as junctions near schools, hospitals, and with unsteady vehicles from the speed level point of view. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a self‐configuring, intelligent system in which autonomous things connect to the Internet and communicate with each other. As 'things' are autonomous, it may raise privacy concerns. In this study, the authors describe the background of IoT systems and privacy and security measures, including (a) approaches to preserving privacy in IoT‐based systems, (b) existing privacy solutions, and (c) recommending privacy models for different layers of IoT applications. Based on the results of our study, it is clear that new methods such as Blockchain, Machine Learning, Data Minimisation, and Data Encryption can greatly impact privacy issues to ensure security and privacy. Moreover, it makes sense that users can protect their personal information easier if there is fewer data to collect, store, and share by smart devices. Thus, this study proposes a machine learning‐based data minimisation method that, in these networks, can be very beneficial for privacy‐preserving. [ABSTRACT FROM AUTHOR]

A 33-year-old female presented with lethargy due to multidrug toxicity. At physical examination, both gluteal regions showed brown patchy scars. The atrophic scars surrounding necrotic lesions were round and brown in appearance, and gluteal mass had gradually been lost. The patient disclosed using intramuscular ketamine injections for 3.5 years along with smoking hashish, alcohol use, intranasal use of methamphetamine (sniffing), and oral use of methadone. Since recreational drug use can affect multiple organs, dermatologists should be familiar with the dermatologic features of intravenous or intramuscular injecting drug use. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT), an application of the Internet of Things (IoT) in the medical domain, allows data to be transmitted across communication networks. In particular, IoMT can help improve the quality of life of citizens and older people by monitoring and managing the body's vital signs, including blood pressure, temperature, heart rate, and others. Since IoMT has become the main platform for information exchange and making high-level decisions, it is necessary to guarantee its reliability and security. The growth of IoMT in recent decades has attracted the interest of many experts. This study provides an in-depth analysis of IoT and IoMT by focusing on security concerns from different points of view, making this comprehensive survey unique compared to other existing studies. A total of 187 articles from 2010 to 2022 are collected and categorized according to the type of applications, year of publications, variety of applications, and other novel perspectives. We compare the current studies based on the above criteria and provide a comprehensive analysis to pave the way for researchers working in this area. In addition, we highlight the trends and future work. We have found that blockchain, as a key technology, has solved many problems of security, authentication, and maintenance of IoT systems due to the decentralized nature of the blockchain. In the current study, this technology is examined from the application fields' points of view, especially in the health sector, due to its additional importance compared to other fields. [ABSTRACT FROM AUTHOR]

Machine learning as a service (MLaaS) plays an essential role in the current ecosystem. Enterprises do not need to train models by themselves separately. Instead, they can use well-trained models provided by MLaaS to support business activities. However, such an ecosystem could be threatened by model extraction attacks—an attacker steals the functionality of a trained model provided by MLaaS and builds a substitute model locally. In this paper, we proposed a model extraction method with low query costs and high accuracy. In particular, we use pre-trained models and task-relevant data to decrease the size of query data. We use instance selection to reduce query samples. In addition, we divided query data into two categories, namely low-confidence data and high-confidence data, to reduce the budget and improve accuracy. We then conducted attacks on two models provided by Microsoft Azure as our experiments. The results show that our scheme achieves high accuracy at low cost, with the substitution models achieving 96.10% and 95.24% substitution while querying only 7.32% and 5.30% of their training data on the two models, respectively. This new attack approach creates additional security challenges for models deployed on cloud platforms. It raises the need for novel mitigation strategies to secure the models. In future work, generative adversarial networks and model inversion attacks can be used to generate more diverse data to be applied to the attacks. [ABSTRACT FROM AUTHOR]

In this study, we propose the concept of left extension of a hyperideal by generalizing the concept of k-hyperideals in ordered semihyperrings with symmetrical hyper-operation ⊕. By using the notion of extension of a k-hyperideal, we prove some results in ordered semihyperrings. The results of this paper can be viewed as a generalization for k-ideals of semirings. [ABSTRACT FROM AUTHOR]

The concept of convex ordered hyperrings associated with a strongly regular relation was investigated in this study. In this paper, we first studied hyperatom elements of ordered hyperrings and then investigated characterizations of quotient ordered rings. Is there a strongly regular relation θ on a convex ordered hyperring R for which R / θ is a convex ordered ring? This leads to an ordered ring obtained from an ordered hyperring. [ABSTRACT FROM AUTHOR]

Automated guided vehicles (AGVs) are popular subsets of robots that come in various shapes and sizes. The group's use in the industry ranges from applications for carrying pallets, carts, and utensils to helping the elderly or transporting medicine to hospitals. Even recently, they have been used in libraries to carry books on shelves. The main part of an AGV includes its body, motor, driver, processor, and sensors, which are more or less the same in all types of AGVs, and addons vary depending on the application and the work environment. The part that affects AGV performance is the control strategy, to which researchers have shown different approaches. Using various techniques and simulations to obtain a model is the key and can help to improve and evaluate the performance of the strategy of the robot. In this study, based on the actual design of the AGV system, all data and components are described, and the simulation is performed in MATLAB software. Then, for controlling the platform based on the PID controller tuning, four methods of Ziegler Nichols, empirical, Particle Swarm Optimization (PSO), and Beetle Antennae Searching (BAS) (optimizer) are discussed, and the results are compared in the four paths including the circle, ellipse, Spiral and 8-shaped paths by observing and testing the tuned PID parameters. Finally, a series of subsequent experiences were carried out in CoppeliaSim (VREP) as a famous robot simulator to overcome the environmental constraints for the same paths that were used in Matlab based on the extracted PID values. Based on the results, the empirical methods, PSO, and BAS errors are very close together. But in general, the BAS algorithm is the fastest, and the PSO had better performance. In general, the maximum error is linked to the path of 8 shapes and the minimum is related to circle shape one. Finally, the analysis of results in different paths in both simulators shows the same results. Therefore, concerning the limited test on the real platform and using the PID coefficients obtained from the simulation shows the model's ability for the researchers in robotic research. [ABSTRACT FROM AUTHOR]

In this paper, first, we state an operator L R on an ordered semihyperring R. We show that if φ : R ⟶ T is a monomorphism and K ⊆ R , then L T (φ (K)) = φ (L R (K)) . Afterward, hyperatom elements in ordered semihyperrings are defined and some results in this respect are investigated. Denote by A (R) the set of all hyperatoms of R. We prove that if R is a finite ordered semihyperring and | R | ≥ 2 , then for any q ∈ R \ { 0 } , there exists h q ∈ A * (R) = A (R) \ { 0 } such that h q ≤ q . Finally, we study the L R -graph of an ordered semihyperring and give some examples. Furthermore, we show that if φ : R ⟶ T is an isomorphism, G is the L R -graph of R and G ′ is the L T -graph of T, then G ≅ G ′ . [ABSTRACT FROM AUTHOR]

This study deals with identifying the accident blackspots and the influencing factors causing accidents using factored analysis in a medium-sized city (Tirunelveli) in India. From the literature review, the geospatial technique to identify the blackspots and the factors causing accidents was used for analysis. The most influencing factors driving the accidents were identified and ranked based on the repetitive occurrence of accidents in the blackspot area. The spearman ranking system showed the correlation among the factors causing accidents. The factor analysis technique was utilized to identify the key factors driving the repetitive accidents and group them. This study will help transportation planners understand the factors causing accidents and take appropriate measures to reduce the casualties in the road construction planning stage and existing conditions. [ABSTRACT FROM AUTHOR]

Melasma is a chronic and acquired pigmentary condition that primarily affects women and undermines patient satisfaction and confidence. Melasma mostly affects females, accounting for 90% of all cases. It affects people of all races, particularly those with skin types IV and V who live in areas with lots of UV radiation. According to the studies, Melasma lesions are seen throughout the face in centrofacial, malar, and mandibular patterns. Melasma lesions on the forehead, cheeks, nose, upper lip, and/or chin are the most prevalent centrofacial pattern. Melasma lesions can also be detected along the periorbital area, especially in Asian people. Melasma is notably resistant to treatment, with many patients experiencing only temporary relief and relapses. Combining therapies that target numerous pathologic components, including photodamage, inflammation, aberrant vascularity, and abnormal pigmentation, generally results in the most dramatic therapeutic improvements. Treatments for dark circles include topical depigmenting medicines like hydroquinone, kojic acid, azelaic acid, and topical retinoic acid, and physical treatments such as chemical peels, surgical adjustments, and laser therapy. The objective of therapy should be to figure out what is causing the hyperpigmentation and what is contributing to it. This article provides an overview of melasma therapies and the efficacy of methimazole and cysteamine for melasma therapy. [ABSTRACT FROM AUTHOR]

Stress is the response or a change in our bodies to environmental factors like challenges or demands that are physical and emotional. The main cause of stress is illnesses and it is gaining more interest, a hot topic for many researchers. Stress can be brought about by a wide range of normal life occasions that are hard to avoid. Stress generally refers to two things: first, the psychological perception of pressure and the body's response to it. On the other hand, it involves multiple systems, from metabolism to muscles to memory. Many methods and tools are being developed to reduce stress in humans. Stress can be a short-term issue or a long-term problem, depending on what changes in your life. The emphasis of this article is to reduce the effects of stress by developing a stress-releasing game and verifying its results through the Profile of Mood States (POMS) and POMS-2 survey. Games are associated with stress levels; hence, parameters like sounds, visuals, and colors associated with reducing stress are used to develop a game for the stress reduction in the players. The survey research aims to determine that the purpose-built game will affect the player's stress level using a reliable psychological survey paper. The survey collected a variety of information from its participants over six months. Different aspects of a person's psychology and reactions are recorded in this scenario by calculating the mean, standard deviation, degree of freedom, zero-error, and probability-value%. The POMS and POMS-2 results are obtained from the custom-built game, and these are found to be effective in reducing stress. [ABSTRACT FROM AUTHOR]

Vague incidence graph (VIG), belonging to the FG family has good capabilities when facing with problems that cannot be expressed by FGs. When an element membership is not clear, neutrality is a good option that can be well-supported by a VIG. The previous definitions limitations in connectivity concept have led us to offer new definitions in VIGs. Hence, in this paper, the VIG and its matrix form are proposed. Vague incidence subgraph (VISG) is defined with several properties. Incidence pairs, paths, and connectivities between pairs in VIGs are introduced. Likewise, different types of strong and cut pair in VIGs are examined with their properties. Universities are one of the most important centers for human education and play an important role in the development of the country. But the point to be very careful is that the employees of a university must do their job in the best possible way. Therefore, we have tried to identify the most effective person in a university according to its performance by presenting an application. [ABSTRACT FROM AUTHOR]

With new telecommunications engineering applications, the cognitive radio (CR) network‐based internet of things (IoT) resolves the bandwidth problem and spectrum problem. However, the CR‐IoT routing method sometimes presents issues in terms of road finding, spectrum resource diversity and mobility. This study presents an upgradable cross‐layer routing protocol based on CR‐IoT to improve routing efficiency and optimize data transmission in a reconfigurable network. In this context, the system is developing a distributed controller which is designed with multiple activities, including load balancing, neighbourhood sensing and machine‐learning path construction. The proposed approach is based on network traffic and load and various other network metrics including energy efficiency, network capacity and interference, on an average of 2 bps/Hz/W. The trials are carried out with conventional models, demonstrating the residual energy and resource scalability and robustness of the reconfigurable CR‐IoT. [ABSTRACT FROM AUTHOR]

We report two cases of lichen planus following COVID‐19 vaccination in two middle‐aged women, where the first patient presented with lichenplanopilaris (LPP) relapse and development of lichen planus 14 days after the second dose of AstraZeneca vaccine, and the other patient who had a previous scattered lesion of LP which extended and increased in severity after the first and second dose of Sinopharm. The suggested cause could be due to immune dysregulation and up regulation of T cell lymphocytes which was triggered after COVID‐19 vaccination. What supports our hypothesis that LP had occurred due to COVID‐19 vaccination, is that one of the patients responded successfully to Metronidazole. This means that the infection process after vaccination could be the cause in aggravating LP. To add, one of the suggested mechanisms for the appearance of LP or reactivation of a dormant LPP can be cytotoxic CD8 T‐lymphocytes which increase the secretion of IFN‐γ and IL‐5 cytokines and may also result in basal keratinocytes' apoptosis leading to cutaneous manifestations. This was supported by the efficacy of Tofacitinib that was used in the other patient who presented with reactivation of LPP in addition to LP. Tofacitinib decrease the number of T cell infiltration and adjust IFN expression. [ABSTRACT FROM AUTHOR]

Quantum-enhanced machine learning plays a vital role in healthcare because of its robust application concerning current research scenarios, the growth of novel medical trials, patient information and record management, procurement of chronic disease detection, and many more. Due to this reason, the healthcare industry is applying quantum computing to sustain patient-oriented attention to healthcare patrons. The present work summarized the recent research progress in quantum-enhanced machine learning and its significance in heart failure detection on a dataset of 14 attributes. In this paper, the number of qubits in terms of the features of heart failure data is normalized by using min-max, PCA, and standard scalar, and further, has been optimized using the pipelining technique. The current work verifies that quantum-enhanced machine learning algorithms such as quantum random forest (QRF), quantum K nearest neighbour (QKNN), quantum decision tree (QDT), and quantum Gaussian Naïve Bayes (QGNB) are better than traditional machine learning algorithms in heart failure detection. The best accuracy rate is (0.89), which the quantum random forest classifier attained. In addition to this, the quantum random forest classifier also incurred the best results in F 1 score, recall and, precision by (0.88), (0.93), and (0.89), respectively. The computation time taken by traditional and quantum-enhanced machine learning algorithms has also been compared where the quantum random forest has the least execution time by 150 microseconds. Hence, the work provides a way to quantify the differences between standard and quantum-enhanced machine learning algorithms to select the optimal method for detecting heart failure. [ABSTRACT FROM AUTHOR]

Quantum-enhanced machine learning plays a vital role in healthcare because of its robust application concerning current research scenarios, the growth of novel medical trials, patient information and record management, procurement of chronic disease detection, and many more. Due to this reason, the healthcare industry is applying quantum computing to sustain patient-oriented attention to healthcare patrons. The present work summarized the recent research progress in quantum-enhanced machine learning and its significance in heart failure detection on a dataset of 14 attributes. In this paper, the number of qubits in terms of the features of heart failure data is normalized by using min-max, PCA, and standard scalar, and further, has been optimized using the pipelining technique. The current work verifies that quantum-enhanced machine learning algorithms such as quantum random forest (QRF), quantum K nearest neighbour (QKNN), quantum decision tree (QDT), and quantum Gaussian Naïve Bayes (QGNB) are better than traditional machine learning algorithms in heart failure detection. The best accuracy rate is (0.89), which the quantum random forest classifier attained. In addition to this, the quantum random forest classifier also incurred the best results in F 1 score, recall and, precision by (0.88), (0.93), and (0.89), respectively. The computation time taken by traditional and quantum-enhanced machine learning algorithms has also been compared where the quantum random forest has the least execution time by 150 microseconds. Hence, the work provides a way to quantify the differences between standard and quantum-enhanced machine learning algorithms to select the optimal method for detecting heart failure. [ABSTRACT FROM AUTHOR]

Bleomycin is an antineoplastic agent, which is used off label for various dermatologic conditions. There are numerous reports on the use of intralesional bleomycin (ILB) for the treatment of common warts. However, reports on the efficacy of bleomycin in the treatment of anogenital warts (AGWs) are still limited. The aim is to compare the efficacy/tolerability and recurrence rates of AGW treatment with ILB versus cryotherapy. In this prospective study, 50 patients with AGWs were assigned either to receive triple freeze-thaw cycle of cryotherapy or to receive 1.5 mg/mL ILB for a maximum of four sessions with 3-week intervals. Clinical efficacy was determined by the percentage of the patients with complete clearance. The patients with complete clearance were visited by passing 3 months from the last treatment session to evaluate any recurrence. Of 44 patients completing the study, 16 of 21 (76.19%) patients in the ILB group and 15 of 23 (65.22%) patients in the cryotherapy group showed complete resolution (p value = .425). Moreover, recurrence occurred after 3 months in 18.75% and 46.66% of the ILB and the cryotherapy groups' patients, respectively (p value = .096). The most common local adverse events in both treatment groups were pain, dyspigmentation, and ulceration/erosion, while the delayed ulceration and secondary infection were only observed in the bleomycin group. Intralesional bleomycin is as effective as cryotherapy in the treatment of AGWs, but it is more invasive and associated with post-treatment pain, the delayed ulceration, and cutaneous infection. Intralesional bleomycin is not accompanied with the major risk of necrosis or fibrosis, so the use of ILB in the anogenital area is likely to be safe.This clinical trial was registered in Iranian Registry of Clinical Trials site with code: IRCT20190519043631N1. [ABSTRACT FROM AUTHOR]

The problems of Internet stability, heterogeneity, fairness of bandwidth sharing among streams, efficiency of use and congestion control have been solved in this article. This paper proposes an improved scheme of TCP proxy acknowledgement based on Automatic Repeat Request (ARQ), which improves throughput, reduces delay and saves uplink bandwidth of wireless link, and is more suitable for future asymmetric networks. The substantial improvement is observed during the experimentation as processing efficiency of protocol. The observed results revealed that overall processing time for each packet is approximately equals to one fourth of the transfer control protocol and the reduction of 59% is also observed in the utility of resources. The protocol also incorporates various simple techniques for the recovery of loss to improve the throughput in noisy wireless conditions. The results show that the adoption of the average diversity combining technology is helpful to improve the throughput and effective factor performance, and can reduce the requirement of radio link protocol (RLP) maximum retransmission times. As nearly 90% of uplink acknowledgement frames are filtered, the uplink bandwidth utilization rate is significantly improved. Decomposing large data frames into small data frames is also helpful to improve system performance. [ABSTRACT FROM AUTHOR]