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In our approach, a hybrid machine learning model is proposed which uses Enhanced Vector Space Model (EVSM) along with Hybrid Support Vector Machine (HSVM) classifier. Initially the social media-based information is retrieved using Enhanced Vector Space Model (EVSM). EVSMs are employed in order to characterize the text content by mapping them into high-dimensional vector spaces, capturing the relationships between words and their contextual meanings. Rigorous feature selection methods are employed to designate texts for review, and a multiclass semantic classification algorithm, specifically the HSVM classifier, is utilized for categorization. Decision tree algorithm is used along with SVM to refine the selection process. To enhance sentiment analysis accuracy, sentiment dictionaries are not only presented but also extended through the expansion of Stanford’s GloVE tool. To enhance precision, the proposed work introduces weight-enhancing methods for processing renowned text weights. Sentiments are classified into positive, negative, and neutral categories. Notably, the achieved results demonstrate improved accuracy, attributed to the incorporation of an emotional sentiment enhancement factor for determining weights and leveraging sentiment dictionaries for word availability. The accuracy is obtained to be 92.78% with 91.33% positive sentiment rate and 97.32% negative sentiment rate.

Abstract 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.

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. Abstract 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.

Wireless Sensor Networks (WSNs) are essential for monitoring operational environments. Efficient selection of cluster heads is crucial in minimizing energy consumption. This study introduces an innovative model for determining the positions of cluster heads in a WSN, using the residual energy of each sensor node as a selection metric. The sensor incorporates a neural network model that has been trained using different significant models to determine the best location for the cluster head. Our model is compared with the Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm to showcase its effectiveness, particularly in mobile node scenarios. We employ a feed-forward multilayer network, specifically Multilayer Perceptrons (MLPs), as the neural network technique. The proposed method comprises two stages: the training stage, where the neural network is trained with appropriate models, and the execution stage, where the trained network suggests the cluster head's location based on the sensor's conditions. The results demonstrate that the proposed method can accurately identify suitable locations for cluster heads in a sensor network and is capable of adapting to new models despite environmental changes and variations in input configurations.

Accurate time series forecasting has become increasingly important across various domains such as finance, energy, and medicine. This study introduces an innovative hybrid model that leverages the power of neural networks, precisely Many To Many LSTM (MTM LSTM) and Multilayer Perceptron (MLP), to improve time series forecasting accuracy. In this new combination, we trained network MTM LSTM to approximate the target at each step, and finally, we used network MLP to combine these approximations. To perform the evaluation, we made a forecast for the amount of solar energy radiation in the city of Mashhad, Iran. The experiment results concerning MSE and MAE showed that the proposed method with five lags outperforms the standard models.We hypothesize that MTM LSTM can effectively capture solar radiation's intricate temporal dependencies and nonlinearity. At the same time, MLP can enhance function approximation by modeling complex interactions, resulting in improved forecast accuracy. By employing the hybrid MTM LSTM and MLP model, we achieved improved accuracy in predicting solar energy radiation, which has significant implications for the renewable energy sector and its energy management and planning applications. This research advances time series forecasting techniques, highlighting the effectiveness of combining neural networks to address complex and dynamic patterns in time-dependent data.Overall, our findings underscore the potential and efficacy of the proposed hybrid model as a robust tool for accurate time series forecasting in various domains, supporting effective decision-making and planning processes.

Effective restoration strategies play a crucial role in mitigating the environmental impact of mining and colliery activities while promoting ecological resilience and rejuvenating ecosystem services. However, many organizations find it challenging to understand and balance their efforts in restoring degraded lands. For example, their restoration plans lack clarity and overlook relevant ecosystem services. This study reviews and focuses on the potential restoration of ecosystem services at TATA Steel's Noamundi Iron Ore Mine and West Bokaro Colliery to contribute to Sustainable Development Goals (SDGs), particularly SDG-15, for localization. The approach involved assessing the number of preventive measures being implemented to restore a particular ecosystem service. Moreover, the potential of each preventive measure is to restore that ecosystem service. The findings underscore the significance of preventive measures and comprehensive restoration plans in enhancing carbon sequestration, soil fertility, habitat creation, and genetic diversity conservation. Our results showed that the impact scores and ranks of various ecosystem services demonstrate the positive effects of restoration efforts, emphasizing the importance of reestablishing forests, restoring water bodies and wetlands, and allocating land for agriculture and public use. The research provides valuable insights for decision-makers in developing sustainable land management strategies, ensuring biodiversity conservation and local communities' well-being. By prioritizing ecosystem services in restoration initiatives, stakeholders can contribute to the sustainable management of natural resources and foster a harmonious coexistence between human activities and the environment.

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 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 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 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.

Over the last decade, there has been a notable and rapid advancement in artificial intelligence (AI), reflecting substantial progress in sophistication and problem-solving capabilities. This evolution has extended across various sectors, encompassing manufacturing, transportation, finance, education, and healthcare. Particularly noteworthy is AI's potential to drive progress in nuclear applications, science, and technology, raising ethical and legal considerations. This academic paper, titled "AI in Nuclear Medical Applications: Challenges and Opportunities," undertakes a thorough exploration of the intricate relationship between AI and nuclear technology. Moving beyond a simple acknowledgment of AI's current capabilities, the paper delves into the nuanced landscape of challenges and opportunities within the realm of nuclear medical applications. It meticulously examines the ethical and legal dimensions inherent in this symbiotic relationship, emphasizing responsible and accountable utilization of AI in the nuclear domain. The research focal point is the strategic deployment of AI capabilities in nuclear medicine, highlighting potential positive contributions to address contemporary challenges. From optimizing medical imaging methodologies to facilitating disease theranostics, the paper critically evaluates the transformative impact of AI on nuclear medical applications. By elucidating specific areas where AI has already demonstrated improvements, the research aims to provide a comprehensive understanding of the current landscape. Keywords include Artificial Intelligence, Radiomics, Radiotherapy, Medical Imaging, Radiopharmacy, and Disease Theranostics.

Due to the physicochemical properties of salt swelling and collapsibility, the potential geological hazards threaten the large-scale infrastructure and residential areas in the saline-soil region. Geodetic imaging and dynamic monitoring of the spatiotemporal evolution process of saline soil are vital to detect the potential geological hazards in a vast area. Therefore, this paper presents a coupled deformation model synthesizing environmental factors and trigonometric function model for estimating the time-series InSAR deformation and interpreting the dynamic evolutionary mechanism of the saline mudflat. To demonstrate the advantages of the proposed model, the sulphate saline mudflat is selected as the study area, which is traversed by the Tehran-Isfahan high-speed railway in Howz-e-Soltan Salt Lake, Qom, Iran. Moreover, 259 interconnecting interferograms were selected for the time-series InSAR estimation, composed of 87 Sentinel-1A images obtained from October 2014 through December 2018. Then, the time-series deformations, including the annual deformation velocity and the accumulated deformation, are extracted by the original estimation, polynomial, trigonometric and environmental models. The comparison results show that the environmental model's RMSEs (root-mean-square errors) are smaller than others. It should be noted that the linear deformation, periodic oscillation, and deformation derived from the environmental factors (i.e., precipitation and temperature) are extracted by the environmental model. Furthermore, the dynamic evolutionary mechanism of the saline mudflat is interpreted according to the physicochemical properties of sulphate/carbonate saline soil. The results derived from the environmental model suggested that the saline-soil deformation is directly related with the environmental changes: rapid collapsibility occurs when the precipitation increases from September to the next May, while uplift due to the salt swelling in the dry and rainless season from May to September every year. Our findings provide an opportunity to detect, prevent and control the potential geological hazards in saline mudflats.

BackgroundIn the modern world, mobile apps are essential for human advancement, and pandemic control is no exception. The use of mobile apps and technology for the detection and diagnosis of COVID-19 has been the subject of numerous investigations, although no thorough analysis of COVID-19 pandemic prevention has been conducted using mobile apps, creating a gap. ObjectiveWith the intention of helping software companies and clinical researchers, this study provides comprehensive information regarding the different fields in which mobile apps were used to diagnose COVID-19 during the pandemic. MethodsIn this systematic review, 535 studies were found after searching 5 major research databases (ScienceDirect, Scopus, PubMed, Web of Science, and IEEE). Of these, only 42 (7.9%) studies concerned with diagnosing and detecting COVID-19 were chosen after applying inclusion and exclusion criteria using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol. ResultsMobile apps were categorized into 6 areas based on the content of these 42 studies: contact tracing, data gathering, data visualization, artificial intelligence (AI)–based diagnosis, rule- and guideline-based diagnosis, and data transformation. Patients with COVID-19 were identified via mobile apps using a variety of clinical, geographic, demographic, radiological, serological, and laboratory data. Most studies concentrated on using AI methods to identify people who might have COVID-19. Additionally, symptoms, cough sounds, and radiological images were used more frequently compared to other data types. Deep learning techniques, such as convolutional neural networks, performed comparatively better in the processing of health care data than other types of AI techniques, which improved the diagnosis of COVID-19. ConclusionsMobile apps could soon play a significant role as a powerful tool for data collection, epidemic health data analysis, and the early identification of suspected cases. These technologies can work with the internet of things, cloud storage, 5th-generation technology, and cloud computing. Processing pipelines can be moved to mobile device processing cores using new deep learning methods, such as lightweight neural networks. In the event of future pandemics, mobile apps will play a critical role in rapid diagnosis using various image data and clinical symptoms. Consequently, the rapid diagnosis of these diseases can improve the management of their effects and obtain excellent results in treating patients.

Abstract 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.

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.

In today's world, everything is connected via the Internet. Smart cities are one application of the Internet of Things (IoT) that is aimed at making city management more efficient and effective. However, IoT devices within a smart city may collect sensitive information. Protecting sensitive information requires maintaining privacy. Existing smart city solutions have been shown not to offer effective privacy protection. We propose a novel continuous method called Differential Privacy-Preserving Smart City (DPSmartCity). When the IoT device produces sensitive data, it applies differential privacy techniques as a privacy-preserving method that uses Laplace distributions or exponential distributions. The controller receives the perturbed data and forwards it to the SDN. SDN controllers eventually send the data to the cloud for further analysis. Accordingly, if the data is not sensitive, it is directly uploaded to the cloud. In this way, DPSmartCity provides a dynamic environment from the point of view of privacy preservation. As a result, adversaries are unable to easily compromise the privacy of the devices. The solution incurs at most 10-18% overhead on IoT devices. Our solution can therefore be used for IoT devices that are capable of handling this overhead.

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.

Recently, wireless networks have been developed using cloud infrastructure and software-based networks [...]

Abstract 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.

Bone Marrow Cancer is a type of cancer that develops in the stem cells of the bone marrow that are responsible for blood formation. AML(Acute Myeloid Leukaemia) and MM(Multiple Myeloma) are both types of malignancy that can affect bone marrow. Conventionally, the process was carried out manually by a skilled professional in a considerable amount of time. Hence, in order to provide accurate treatment, a better diagnosis technique is required. In this regard, the suggested study has improved a unique technique, specifically classification and segmentation, allowing for the identification of more intricate disorders. The study's findings on the accuracy of cancer cell identification, as well as the decrease in the possibility of false alarm rates, continue to fall on the negative side of the outcome spectrum, according to the researchers. The hybrid approach has been proposed in this article that includes deep convolutional neural networks, hyperparameter sets utilising adaptive multi-objective CAT algorithms and other image processing techniques. To train the suggested model, it is critical to use previously processed cell images. The suggested model is then used to train an Optimized Convolutional Neural Network (OCNN), which is then used to determine the type of tumour identified in the bone area. The SN-AM datasets were thoroughly examined, and a number of presentation measures, such as accuracy (recall), F1-score, and specificity (specificity), were generated and analysed. When it came to predicting different types of cancer, researchers observed that they had an overall accuracy of 99.45 percent. In terms of recognizing the sorts of cancer cells, the proposed model surpassed all existing learning models.

Background: Few studies have evaluated patients’ motivations for seeking minimally invasive cosmetic procedures.Objective: This study aimed to assess the demographic characteristics and motivation of patients who seek minimally invasive cosmetic procedures in two academic referral dermatology centers.Methods: The study included adult patients seeking minimally invasive facial cosmetic procedures at two academic dermatology centers in Tehran, Iran, between January 2019 and June 2019.Results: The majority of patients were married, employed women with a mean age of 39.6 ± 10.74 years. The most common region of the face they expected to change was the eyes (56%). Patients who requested fillers desired their cheeks to improve significantly more than patients who requested toxin injections (20.5% vs. 3%; p = .005). Patients had mostly heard about the procedures from friends (40.4%). Approximately one third of patients had experienced a major life event during the preceding year (loss of a family member [50%], marriage [26.9%]). A minority of patients had ever visited a psychiatrist (13.9%), and 15.3% had a history of using psychiatric drugs. The procedure type requested by men was significantly different from that requested by women (88.2% vs. 60.8 % for Botox; 11.8% vs. 34.9% for fillers, and 0% vs. 4.3 % for fat injections; p = .044). The most common motivating factor for patients was gaining rejuvenation (39.2%). The most common preventive factors for patients to undergo the procedures earlier were financial issues (41.7%).Conclusion: Patients seeking minimally invasive cosmetic procedures were married, educated, employed women in their 30s desiring rejuvenation and commonly were not influenced by external factors, such as relationships or the media.

Abstract Although Necrotizing fasciitis can evolve from a trivial lesion, whenever it develops it requires a prompt surgical intervention and broad‐spectrum antibiotic therapy.

Abstract Keratosis lichenoides chronica (KLC) is a rare dermatosis which represents different clinical characteristics between adult‐ and pediatric‐onset cases. We described a childhood case of KLC with features typical for adult‐onset disease. Acitretin led to partial improvement of her skin, but not mucosal, lesions.

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.

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.

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.

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.

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.

In this paper, first, we state an operator LR on an ordered semihyperring R. We show that if φ:R⟶T is a monomorphism and K⊆R, then LT(φ(K))=φ(LR(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 hq∈A*(R)=A(R)\{0} such that hq≤q. Finally, we study the LR-graph of an ordered semihyperring and give some examples. Furthermore, we show that if φ:R⟶T is an isomorphism, G is the LR-graph of R and G′ is the LT-graph of T, then G≅G′.

Limitations of the pinhole method and astigmatic defocus detection have difficulties in determining the direction of defocus and a restricted linear correction range. Here, a distance-measuring approach based on the laser triangulation principle is proposed with a corresponding mathematical model to overcome these problems and to satisfy the requirements of an industrial thin-film transistor liquid-crystal display (TFT-LCD) inspection microscope system in active auto-focus applications. The approach combines infrared light at a wavelength of 808 nm with visible light. The light reflected from the surface of the object reaches a CCD camera and forms a spot via a set of optical devices. To control a PZT to achieve real-time auto-focusing, a Gaussian curve fitting method is used to process the spot information with the presented mathematical model giving the relationship between the detection value and the defocus value focus error signal (FES). The preliminary experiments with a 50× objective lens show that the measured values are consistent with the theoretical values, have good linearity and possess a focusing accuracy of 0.2 μm within ±30 μm of the focal plane. The analysis of the experimental results proves the feasibility of using this method in industrial applications. Compared with the traditional method of microscope focus correction, this method has the advantages of high accuracy, good linearity, and fast focus speed for the actual requirements of industrial inspection processes. This method has been applied to the circuit board component defect detection in our laboratory.

Stakeholder satisfaction is the main motive of the software industry. Consumer and producer satisfaction means achieving a software-quality product. There are so many techniques, methods, and models are present for the software-quality prediction. Computational intelligence is also playing a crucial role in the prediction of quality characteristics. This paper gives a new optimal mathematical model for the prediction of the degree of stakeholder satisfaction (Q). Optimal models validate the real data using the relationship impacts of various quality attributes. It uses the equations of constraints. The optimal model gives the maximum and minimum values for Q. Constraints constitutes of software quality characteristics. In the given case study example, the idle value of Q is 30 but using optimal model it gives the maximum optimal value of Q = 22.788020075 for xLAB IT consulting services on their project In-Reg Molecule Registration using the MATLAB. It means if there is any change in the value of any software quality characteristics, then it will decrease the value of Q. It proves that the given result is an optimal solution.

The main purpose of this paper is the allocation of orders to suppliers in an agile and flexible manner suitable to the automobile industry. In this paper, parts supplied by a single source were eliminated from the set of parts. Using mathematical modeling and through the interval-valued fuzzy-rough numbers best worst method (IVFRN-BWM), we try to achieve the results that can meet the proposed model's needs and provide the ideal results by introducing new modes. This paper addressed some new aspects of the subject and achieved robust results by considering five objective functions. These five functions are as follows: minimization of production line disruptions due to the performance of suppliers, minimization of the complaints of production line about supplied parts, minimization of defective parts received from suppliers (PPM), maximization of on-time delivery services, and minimization of overall costs of supplied parts. Reviewing the literature, the originality of this study are as follows: 1) identifying the structure of a supply chain (SC) in general and particularly in an automobile industry SC; 2) investigating the modeling techniques of the existing SC models for coordinating all the members of a product SC; 3) building a hybrid model of IVFRN-BWM and a robust goal programming agile and flexible supply chain in an uncertain situation; and 4) identifying the suitable scenarios/cases for testing the proposed models to validate the models. This paper can help decision makers and managers to opt for the best suppliers and also allocate the right numbers of parts to those supplier(s) based on a real situation of each firm.

Recently, IoT has greatly influenced our daily lives through various applications. One of the most promising application is smart city that leverages IoT devices to manage cities without any human intervention. The high possibility of sensing and publishing sensitive data in this smart environment leads to three significant issues: (1) privacy-preserving (2) heterogeneity, and (3) real-time services. We observe that current studies are in lack of addressing these challenges. In this paper, we propose a new privacy-preserving architecture for IoT devices in the smart city by leveraging ontology, a data model, at the edge of the network. At first, we propose an ontology that consists of privacy information of devices. Then, we mount a real-time privacy-preserving method on top of it that is achieved by providing a dynamic environment from the privacy-preserving point of view. Based on the simulation results using Protege and Visual Studio on a synthetic dataset, we find that our solution provides privacy at real-time while addressing heterogeneity issue so that many IoT devices can afford it. Thus, our proposed solution can be widely used for smart cities.

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This paper presents a system that can recognize handwritten words expressed using broken letters of the Persian alphabet. The proposed system can be used for most activities related to the gathering of public information. Statistical features of the separated/broken letters are employed in the system. Each letter is recognized using interconnected fuzzy neural network. The advantages of this method include high precision owing to the strength of the neural network algorithm and the possibility of extending dataset instance codes in a simple manner. At last, an evaluation for the proposed method is provided experimentally.

Systemic sclerosis (SSc) is a rare systemic autoimmune disease, characterized by progressive cutaneous and internal organ fibrosis. Orofacial manifestations of systemic sclerosis are extremely disabling and treatment options are limited. In this study, we aimed to assess the safety and efficacy of autologous fat grafting in the face of patients with systemic sclerosis. We enrolled 16 SSc patients suffering from facial sclerosis and limited mouth opening capacity. Autologous fat injection ranging from 15 to 40 ml was administered per patient, based on their face morphology. The patients were evaluated at baseline and 3 months after fat injection. Evaluations included mouth opening capacity, mouth handicap in systemic sclerosis (MHISS), Rodnan skin sclerosis score, skin biophysical properties using a sensitive biometrologic device with the assessment of cutaneous resonance running time (CRRT), volumizing and aesthetic effects based on pre- and posttreatment photographs, possible side effects, and global patient satisfaction. Clinical assessment showed autologous fat transfer significantly improved mouth opening capacity and the MHISS and Rodnan score of patients with facial scleroderma (p value