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Femtocells are the feasible solutions to extend the network coverage of indoor users and to enhance the network capacity in long-term evolution advanced (LTE-A)-based 5G networks. However, the femtocell base station shares the same frequency spectrum of microcell base station in unplanned manner. Hence, interference mitigation is a crucial problem in densely deployed femtocell environment and it is more severe with the deployment of femtocells in LTE-A network. In this paper, a modified dirty paper coding is proposed for interference mitigation along with the optimization of feedback bits using natural inspired meta-heuristic firefly algorithm. The proposed meta-heuristic algorithm reduces the interference by periodically unicasting the channel state information. Since the bandwidth of feedback system is limited, it is optimized in such a way that it does not affect the performance of the system. As compared to the conventional zero-forcing pre-coding, the proposed modified dirty paper coding along with firefly algorithm scheme offers improved sum rate of 70% and 64% with increase in the number of feedback bits and number of users, respectively.

Digital image technology is penetrating into various fields of people's life, and it has been very mature and can effectively store and transmit data. Moreover, there are still various researches on image recognition, the core of this technology. The algorithm is mainly based on computer technology to obtain the target image for different scene categories, thus completely replacing the traditional classification form. Because of the limitations of traditional identification technology, there are some problems in the actual use. It does not depend on the prior knowledge requirements and can carry out complex feature space division. In this paper, an image recognition computer system is established by introducing an improved neural network algorithm. The algorithm is designed and tested, and the results show it has lower image recognition error rate. Through the computer image recognition technology design system test,it is found that the recognition accuracy is higher than the AlexNet model under four real environment conditions, which indicates that the improved and optimized model has better classification and recognition effect, the image recognition system designed in this paper can play an effective effect in practical application. By introducing the improved neural network algorithm into this field, this paper designs an image recognition algorithm with better effect.

In order to standardize the functions of the Internet of Things interactive system, such as cost constraints, probability, and speed, the basic principle is to expand the ability to express data flow or cost behavior, and to build logic to describe the nature of the Internet of Things. Interactive Internet of Things-cost probability time data flow logic PPTDL, in order to integrate the MODEST automatic verification tool, this paper studies the mechanism of converting the PPTCA reaction model into an action program with a random time automaton, which theoretically supports the tool use. This paper studies the theory and technology of enterprise logistics service management, introduces the B/S system structure, the database technology used, and the current research status at home and abroad. When analyzing program requirements, first use dynamic and static functional models to explain system business requirements and distinguish systems. The general design and detailed design of the system are carried out, and the functional structure of the system is established. The system performs user management module, company information management, customer service, transaction order and report management, user management module, user connection and registration, employee personal information management, order management, and other functions of the customer service module. In addition, users and developers conduct transactions by accessing the cloud computing service platform created by operators, and there are factors that promote each other between users and developers. Sending and completing service functions, the transaction control module performs order management, order comparison management and other functions, and the report management module performs the reporting function of customer information and user information reports.

The medical industry is constantly improving its own structure with the development of society. However, most of the current drug management systems cannot meet the needs of actual drug management. There are many problems such as incomplete system functions, confusion of drug management, unclear division of modules, loss and waste of human resources. At present, there is an urgent need for a new and perfect hospital drug information management system to meet the drug needs of the hospital. Drug management is an indispensable part of the hospital management system. In this context, this paper completes the design of the target drug rapid retrieval system, which is realized through multi-dimensional data analysis technology, and tests the multi-dimensional data analysis algorithm model used in the system. It can be seen that as the number of wrong words in drug names increases, the accuracy of fuzzy matching also decreases. Compared with the traditional algorithm, the improved multi-dimensional data analysis algorithm greatly improves the accuracy. The system can be roughly divided into three layers: application layer, data layer and business logic layer. The design and improvement of the system can effectively improve the drug processing efficiency of existing pharmacies through the design and simulation experiments, enable the pharmacy department to better cooperate with other departments to work, make the cooperation between different departments more effective, and solve the work efficiency problems of the hospital. By introducing multidimensional data analysis technology into the field of drug information retrieval, this paper designs an effective and fast retrieval method.

In recent years, people have witnessed the rapid growth of data, and big data has penetrated into every aspect of people's lives. If a big data processing system wants to extract the hidden value behind massive data, it is inseparable from the support of a large number of underlying infrastructure resources. However, the one-time expensive investment in the initial economy and the complexity of the later work of operation and maintenance hinder the use of some small and medium-sized enterprises. Based on this background, with the continuous development of computer technology, this paper constructs a large-scale data processing system that introduces genetic algorithms, making full use of the advantages of on-demand self-service and the elastic expansion of computer technology, shortening the time required for data processing and data analysis. life cycle, so that more and more enterprises and organizations can start using big data processing technology. For fragmented big data obtained from different data sources, this paper adopts load balancing technology to provide horizontal service cluster scalability, and designs a separate system module for routine testing. The experimental results show that the designed function of the system can be realized, and the actual error is always lower than the specified error limit. It is hoped that the research work in this paper can provide useful reference and help for the design of computer big data processing system. This paper designs a kind of effective big data processing system by studying genetic algorithm and computer technology.

When constructing the algorithm model of sports training action classification, the accuracy of action classification has an important impact on the algorithm model. How to improve the algorithm model to improve the accuracy of sports training action classification needs further research. Based on BP neural network algorithm, this paper carries out the modeling of BP neural network signal classification algorithm and the construction of BP neural network, and deduces the BP algorithm in detail. Firstly, this paper applies genetic algorithm to the initial parameter selection of BP algorithm to avoid the local optimization problem. When carrying out chromosome coding, binary coding is easy to cause the problem of too long coding string, which also needs to be restored and decoded. The algorithm runs too long and the learning accuracy is not high. Therefore, this paper uses real coding. Through simulation analysis, it can be seen that the classification accuracy of the improved algorithm model is significantly higher than that of the simple BP algorithm. In addition, this paper analyzes the requirements of the sports training management visualization system, introduces the system structure framework and network topology, describes in detail the functions of the user information management module, the training plan management module, the training test management module, the competition information management module and the scientific research information management module, and tests the visualization function of the system. Finally, this paper analyzes the problems existing in the current sports training management, and puts forward the development strategy of sports training management based on this, which lays a theoretical foundation for the scientific development of sports training.

As an important research method in the field of modern machine learning engineering technology, BP neural network has gradually developed into the most widely used and now the most widely used in the industry with its powerful non-linear feature function mapping computing capabilities, good data induction and feature recognition computing capabilities. This paper closely links the actual activities of project engineering technology management with modern machine learning network technology, and proposes a new type of engineering management activity model based on machine-integrated GABP neural network. This article briefly introduces the valuation theory based on basic knowledge points such as artificial intelligence technology, neural network theory, and genetic algorithm. It does a detailed analysis and research on the valuation models of commonly used neural networks in China, and points out the defects and defects of the neural network structure itself. Good generalization ability, in order to minimize the impact of subjective factors on the valuation results. Although there are many management software specializing in construction project engineering currently on the market, the service scope and demand range of these management software specializing in construction project engineering are relatively narrow. For example, construction project management software can only be used Manage a construction project. In the actual operation process, it is also found that unreasonable time arrangements often occur in various links such as personnel scheduling and material distribution of engineering projects, resulting in a waste of manpower and resources. In response to the urgent requirements of government departments for efficient management of a large number of engineering project resources, the paper develops a universal project engineering management system based on GABP neural network and artificial intelligence technology.

At present, wearable devices have some problems, such as poor adaptability to human motion behavior, and the recognition accuracy required for different wearers cannot be achieved. Based on the principle of deep learning algorithm, this paper realizes the development of intelligent rope skipping movement data monitoring system. Through the universal human body analysis model, the attention mechanism is introduced and embedded into the decoding network. The data set of rope skipping is classified by multiple labels, and the convolution of spatial graph is constructed, which is extended to the time series dynamics of moving human skeleton data. Aiming at the problem of complex information data in the process of moving human body recognition, we use pose estimation to calculate the key points of moving human body, extract the dynamic structure information of human skeleton sequence. Due to the problems of line of sight occlusion in the process of moving human target tracking, a target tracking algorithm based on multi domain convolution neural network is adopted to improve the feature extraction ability of the algorithm by segmenting the target to be tracked and identifying the area around the target. The data set of rope skipping is collected by wearable sensors, and the difference in the numerical range may be large, so the data is normalized. Finally, through the loss function, the fitting effect of neural network can be evaluated, and the gradient optimization model parameters can be calculated, and coping with different data changes. Through the final system performance test, it is verified that the accuracy rate of the system designed in this paper is above 90%, which can effectively monitor the data of skipping rope and be used in the actual operation of skipping rope.

Advances in artificial intelligence and data acquisition technology are growing, the research on deep learning algorithm has gone deep into various fields. At this stage, the demand supply matching model under the comprehensive passenger transport hub travel system is obtained by analyzing the travel mode data. This paper takes traffic time as the research direction,uses machine learning and complex network theory to conduct in-depth learning algorithm research, respectively discusses, explores and forecasts the traffic supply and demand mode data, and explores the traffic mode supply and demand model under the comprehensive passenger transport hub travel service system. It is shown in traffic data that using the prediction form of deep learning to predict traffic conditions and travel pressure can enable traffic managers to master traffic dynamics and lead the direction for future traffic development. Finally, from the perspective of MaaS system, the paper uses big data and information processing methods to explore the supply and demand matching model in terms of transportation modes. The research shows that MaaS system can make overall planning in terms of traffic resources, provide reasonable travel modes for traffic travelers and match corresponding travel services. It not only makes overall planning and guarantee for travel services, but also promotes the sustainable development of the transportation supply and demand system.

With the development of computer technology today, data storage technology is also gradually improving. Various industries can store massive amounts of data for analysis. The global climate change and the bad ecology led to frequent occurrence of natural disasters. Therefore, it has become a reality to build a system for the optimal distribution of emergency materials. The processing of big data can realize the prediction of future emergency materials storage and transportation. In this paper, the neural network model is used to calculate and the optimal emergency distribution route is analyzed according to the historical information and the data. Considering backpropagation, this paper further disposing a method to further improve the calculation of neural network algorithm. From the perspective of the structural parameters of the neural network algorithm, Using genetic algorithms to construct predictions, and combined with the actual purpose of material distribution after disasters occur. Considering the capacity constraints of distribution centers, time constraints, material needs of disaster relief points, and different means of transportation, a dual-objective path planning with multiple distribution centers and multiple disaster relief points with the shortest overall delivery time and lowest overall delivery cost is constructed. By establishing an emergency material distribution system, it can maximize the prompt and accurate delivery after a natural disaster occurs, and solves the urgent needs of the people.

In March 2018, the State Council issued a reform plan for institutions. Through the establishment of the Ministry of natural resources, the planning of the national space system was carried out, and the historical division phenomenon within the administrative institutions was solved by using the transformation of fragmentation. Subsequently, relevant functional departments began to design the land spatial planning system and coordinate the mechanism. From the policy guidance, it can be seen that land use spatial planning has risen to the central position in the national management system. From the current situation of planning control, planning control becomes more and more important in the management process. At the same time, the construction of ecological civilization requires us to examine the regulatory mechanism from a new perspective. Therefore, based on machine vision technology, this paper develops a land use spatial planning method system. The sub-pixel edge detection algorithm used in the system design can obtain the point coordinates on the edge of the gauge block with high accuracy. After testing, we can see that in terms of storage space, the octree map used in this paper can save more space than the point cloud map under the same resolution. Finally, this paper tests the system and finds that: in the land use spatial planning method system, the actual effect of the optimization algorithm in this paper is better than that before optimization, which proves that the land use spatial planning method system designed in this work is effective. In this paper, machine vision technology is introduced into the field of land use spatial planning, and a kind of effective system method is designed.

In order to achieve accurate fault diagnosis of rolling bearing under random noise, a new fault diagnosis method based on wavelet packet-variational mode decomposition (WP-VMD) and kernel extreme learning machine (KELM) optimized by particle swarm optimization (PSO) is proposed in this paper. Firstly, the time-frequency domain feature vectors of the original rolling bearing fault signals are effectively obtained by preprocessing of WMD and decomposition and reconstruction of VMD. Then, the extracted two-dimensional feature vector is input into the KELM neural network for fault identification, and combined with PSO, KELM parameters were optimized. The experimental results show that the proposed method can effectively diagnose the rolling bearing under random noise, with the features of fast speed, stable performance and high accuracy. By comparison, this paper obtains better accuracy and real-time performance with fewer features, which provides a simple and efficient solution for fault diagnosis of rolling bearings.

The outbreak of Coronavirus Disease 2019 (COVID-19) occurred at the end of 2019, and it has continued to be a source of misery for millions of people and companies well into 2020. There is a surge of concern among all persons, especially those who wish to resume in person activities, as the globe recovers from the epidemic and intends to return to a level of normalcy. Wearing a face mask greatly decreases the likelihood of viral transmission and gives a sense of security, according to studies. However, manually tracking the execution of this regulation is not possible. The key to this is technology. We present a Deep Learning-based system that can detect instances of improper use of face masks. A dual-stage Convolutional Neural Network (CNN)architecture is used in our system to recognie masked and unmasked faces. This will aid in the tracking of safety breaches, the promotion of face mask use, and the maintenance of a safe working environment. This paper will automate the tasks of mask detection in public places when incorporated with CCTV cameras and will alert the system manager when a person without mask or wearing incorrect mask tries to enter. This paper includes multi face detection model which has the potential to target and identify a group of people whether they are wearing masks or not. We tried to collect various facial pictures and tried to identify the face Region of Interest (ROI), and then we separated it. Applying facial milestones, to permit the restriction the eyes, nose, mouth, and so. face was then completed and we tried to detect the presence of mask. To prepare a custom face cover locator, breaking our venture into two unmistakable stages was required, each with its own separate sub-steps. 1. Preparing: Here, stacking our face veil discovery dataset from plate, preparing a model on this dataset, and afterward serializing the face cover locator to circle was the focus. 2. Sending: Once the face veil identifier is prepared, the accompanying advance of stacking the cover finder, performing face recognition, and afterward characterizing each face as with veil or without veil, can be executed.

This paper proposes a method to mitigate two major issues of Adversarial Transformation Networks (ATN) including the low diversity and the low quality of adversarial examples. In order to deal with the first issue, this research proposes a stacked convolutional autoencoder based on pattern to generalize ATN. This proposed autoencoder could support different patterns such as all-feature pattern , border feature pattern , and class model map pattern . In order to deal with the second issue, this paper presents an algorithm to improve the quality of adversarial examples in terms of L 0 -norm and L 2 -norm. This algorithm employs an adversarial feature ranking heuristics such as JSMA and COI to prioritize adversarial features. To demonstrate the advantages of the proposed method, comprehensive experiments have been conducted on the MNIST dataset and the CIFAR-10 dataset. For the first issue, the proposed autoencoder can generate diverse adversarial examples with the average success rate above 99%. For the second issue, the proposed algorithm could not only improve the quality of adversarial examples significantly but also maintain the average success rate. In terms of L 0 -norm, the proposed algorithm could decrease from hundreds of adversarial features to one adversarial feature. In terms of L 2 -norm, the proposed algorithm could reduce the average distance considerably. These results show that the proposed method is capable of generating high-quality and diverse adversarial examples in practice.

In recent years deep learning models improve the diagnosis performance of many diseases especially respiratory diseases. This paper will propose an evaluation for the performance of different deep learning models associated with the raw lung auscultation sounds in detecting respiratory pathologies to help in providing diagnostic of respiratory pathologies in digital recorded respiratory sounds. Also, we will find out the best deep learning model for this task. In this paper, three different deep learning models have been evaluated on non-augmented and augmented datasets, where two different datasets have been utilized to generate four different sub-datasets. The results show that all the proposed deep learning methods were successful and achieved high performance in classifying the raw lung sounds, the methods were applied on different datasets and used either augmentation or non-augmentation. Among all proposed deep learning models, the CNN-LSTM model was the best model in all datasets for both augmentation and non-augmentation cases. The accuracy of CNN-LSTM model using non-augmentation was 99.6%, 99.8%, 82.4%, and 99.4% for datasets 1, 2, 3, and 4, respectively, and using augmentation was 100%, 99.8%, 98.0%, and 99.5% for datasets 1, 2, 3, and 4, respectively. While the augmentation process successfully helps the deep learning models in enhancing their performance on the testing datasets with a notable value. Moreover, the hybrid model that combines both CNN and LSTM techniques performed better than models that are based only on one of these techniques, this mainly refers to the use of CNN for automatic deep features extraction from lung sound while LSTM is used for classification.

With the rapid development of the information age, various forms of digitalization have facilitated people's various lives, but digitalization has also brought crisis to people. The most obvious is that in the digital era, the original living environment of intangible cultural heritage of martial arts and its traditional development and protection mode have become less and less compatible with the digital era. Therefore, its development is currently facing difficulties, even endangered problems. Based on this phenomenon, this paper combines artificial intelligence technology and interactive system to establish an intelligent multimedia display interactive system for intangible cultural heritage of martial arts. In this system, the martial artist's moving image can be captured by the action image acquisition device, and the collected image sequence can be calculated and analyzed. The system host "remembers" the martial artist's action. The main functions of the system, such as random flow, click and zoom, multi-point movement and printing, are all in the charge of the picture control module; Video playback, full screen display and ring button control functions are mainly completed by the video control module. The experimental results show that the task time of using interactive system to locate visual objects with massive information in icon materials is significantly faster than that of using no interactive highlighting system. Therefore, we can preliminarily determine that the interactive system can improve the performance of target positioning in massive information. In this paper, the interactive system and artificial intelligence technology are applied to the intangible cultural heritage of martial arts, which has played a role in promoting its development.

Deterministic methods used to address the coverage problem in an uncertain deployment environment have not proven to be very successful. For this purpose, the original idea of this paper is to deal with the coverage problem in an uncertain environment with uncertain theories. We consider the imperfection in the deployment environment and in the characteristics of the sensor nodes. The selection of a minimum number of nodes for a minimum number of clusters to guarantee coverage in WSN is uncertain. As a consequence, this paper proposes a hybrid FuzzyPossibilistic model to schedule the Active/ Passive state of sensor nodes. This model helps to plan the scheduling of node states (Active / Passive) based on the possibilistic information fusion to make a possibilistic decision for the node activation at each period. We evaluated the proposed model with (a) a running example, (b) a statistical evaluation (calculation of the confidence interface), and (c) a comparison with maximum sensing coverage region problem (MSCR), Coverage Maximization with Sleep Scheduling (CMSS), Spider Canvas Strategy, Semi-Random Deployment Strategy (SRDP) and PEAS with location information protocols. The simulation results highlight the benefits of using the fuzzy and possibility theories for treating the area coverage problem.

Building a livable rural environment is the main process to achieve rural revitalization. An ideal rural environment contains many factors, such as health, safety, convenience and comfort. On the basis of livability, it can also be transformed into a livable ecological environment by promoting harmony between man and nature, thus responding to the policies and calls for sustainable development. At present, the research on ecological and livable environment mainly focuses on environmental improvement and ecological regulation, thus neglecting the research on space environment itself. Under this background, this paper completes the optimization of rural environment public space by introducing data simulation technology and combining high-precision real-time digital images, and then uses HLA to design the system to complete the simulation. The system can receive the data and configuration parameter information of each subsystem, and also can apply the system entity data and information of both sides of space confrontation to complete the target processing. The system itself is mainly divided into three parts: visual display, visual representation and data analysis modules. It can effectively complete the population calculation, building area calculation, living area calculation and proportion analysis of the target rural areas, so as to facilitate decision-makers to conduct reasonable regulation on the design of public space environment by studying data characteristics. This paper analyzes the transformation of rural public space environment by combining data simulation technology with high-precision digital real-time images.

Due to the complexity of network information, it presents an unbalanced feature. For the current book publishers, the publishing theme selection and planning methods of the newsroom can no longer meet the development speed of the Internet background. As the publishing house can not accurately analyze the needs of users, it is difficult to obtain the specific standards of the book publishing market. Therefore, the demand of consumers for books has decreased, and further generated practical problems such as inventory accumulation and revenue impairment. Based on the demand of extracting and analyzing target information for books, it can be realized by using deep learning methods. Therefore, this study establishes a book selection planning system. If the information of the book itself and the corresponding evaluation information are required, the system first uses Anaconda to crawl the website to obtain the book information data, and then uses the proposed KIEM algorithm to supplement the information of the data crawled by Anaconda. After completing this step, use the first layer of CRF model to separate the evaluation sentence from the opinion sentence, and split the separated target sentence to get the attribute and emotion words. Finally, ARIA algorithm is combined with the improved recommendation algorithm to improve the recommendation performance and achieve the accuracy and personalization of the system. It can be seen from the actual measurement that the MAE value calculated by the application of the system proposed in this paper is relatively small, which shows that the system has certain practicability in prediction accuracy. This paper designs an effective system application by introducing deep learning technology into the field of book topic selection planning.

Today, with the continuous progress of urbanization, public buildings have many environmental problems. Their high carbon emissions and energy consumption have caused considerable environmental pollution. Based on the analysis of the whole life cycle of public buildings, it can be seen from the results that due to its long time span, the service life will cause more pollution to the environment, high energy consumption and carbon emissions. In this environment, this paper completes the design and construction of carbon reduction measurement system for public buildings by combining intelligent big data technology and Apriori algorithm. The system mainly analyzes the whole life cycle of the building to calculate all energy consumption projects of the building, converts them into carbon footprint indicators, and uses the indicators to complete the quantitative assessment of environmental pollution level for public buildings in the whole life cycle, and obtains the carbon reduction assessment data of the building in the operating cycle in combination with the carbon emission factors of energy and electricity. The results of quantitative data analysis can be used for the design and arrangement of energy conservation and emission reduction policies, which can be realized by changing the lighting and ventilation, peripheral protection, shape coefficient and rainwater circulation of buildings. This paper conducts carbon reduction assessment for public buildings by integrating intelligent big data and Apriori algorithm.

Neural network recognition algorithm is a deep learning-based convolutional neural network, which has been applied to target detection, image recognition, sound recognition and other fields. Although the recognition technology of convolutional neural network has been gradually stabilized, it is mainly implemented in single machine serial mode. Therefore, there will be problems such as too long training time and insufficient memory capacity. According to the previous work, this paper has carried out related research and obtained some useful results. This paper focuses on the introduction of image recognition technology based on neural network algorithm. At the same time, this paper proposes a fast real-time image recognition algorithm, which has high recognition accuracy. In this paper, the existing algorithms mentioned in the paper are analyzed, the characteristics and shortcomings of all algorithms are summarized, and the appropriate combination of algorithms is selected for specific problems. On this basis, the acceleration method of artificial neural network is used to improve the algorithm of convolutional neural network image recognition technology. With the continuous development of education and the increase of the number of students year by year, the effect of classroom teaching becomes particularly important. Therefore, modern information technology is used to transfer the classroom teaching mode to the network and realize a new method of online remote online dance teaching. According to the actual situation of dance teaching, the advantages of multimedia dance teaching are analyzed. The software of multimedia dance teaching system is tested comprehensively, from the aspects of computer system memory occupancy rate and temperature, to test whether the activities of B/S mode multimedia dance teaching system meet the expected goals and requirements of each core module of multimedia dance teaching system.

With the rapid development of Internet technology, Internet finance has entered thousands of households, bringing a lot of convenience to people's lives. As a financial model based on Internet technology, compared with traditional bank loans, online loans have lower operating costs and faster returns, so they are developing rapidly. Providing users with better and faster services while standardizing operations has always been the development goal of various Internet finance companies. At present, many domestic Internet financial enterprises are facing many problems such as difficulty in risk control. Therefore, this paper makes full use of deep learning algorithms to build an Internet financial risk control system. After in-depth analysis and research on the deep learning algorithm, the Internet financial risk control system is divided into several modules. The project mainly includes: model management module, user behavior analysis module, alarm management module, monitoring module, product management module, etc., and then by analyzing the test results of each test scenario, it is concluded that the performance test of the system design meets the actual needs of users, and simulates the test. It is carried out in accordance with the constraints and regulations of the test plan, and the performance test meets the standard. The system not only ensures the safety of the company's funds, but also helps the company to form a smooth and effective financial risk control process. This paper designs a class of effective management systems by applying deep learning algorithms to the field of Internet financial risk control.

At present, with the rapid growth of electronic information and the continuous improvement of computing power, the Internet of Things (IoT) has become more and more widely used in practical development. In the field of Public Information (PI), the IoT public information platform has made good progress and promoted the scientific growth of public information and resource management. However, affected by its sharing and openness, its information and data security also faces huge challenges. Maintaining the platform’s information security and improving its reliability have become the primary task in the current public utilities construction. In order to solve this dilemma, based on the research of the platform characteristics, information security issues and the development status of the platform, combined with the open architecture, this paper conducted an in-depth study of the security optimization algorithm of the IoT public information platform. In order to verify the security optimization algorithm effect of the IoT public information platform based on the open architecture, this paper took an IoT public information platform as the test environment, and carried out simulation tests on its usability, execution efficiency and protection. The test results showed that in terms of protection, the average security protection degree of the security optimization algorithm based on open architecture under 80 security risk processing requests was about 86.70%. From the test results, the security optimization algorithm of IoT public information platform based on open architecture had stronger operability.

With the continuous development of the economy, people pay more attention to spiritual needs, so the cultural industry has developed by leaps and bounds. The development of Chinese culture and art is transforming towards internationalization and industrialization, and new art forms and styles are constantly emerging. In addition, China's online education is currently in a critical period of transformation. The form of online education is comprehensively deepened, first of all in the field of basic vocational education; The second is the training of students in basic education. At present, the art design teaching courses in Chinese schools have not been fully digitized, and they are faced with the problems of outdated teaching materials, slow updating speed and high purchase cost. Based on this, this study designs an online art teaching system by introducing computer image segmentation technology. Through analysis, it can be concluded that the core algorithm of the system is superior to FCM, FCM_S1 and FCM_S2, etc., and is excellent in division entropy, coefficient and accuracy. and can fully drive the system designed in this paper. The main functional modules of the system include learning guidance, course guidance and teaching resources. Through the simulation experiment, the online art design teaching system of the Internet can fully meet the needs of many students to access through the intranet at the same time, and the external network can meet 500 concurrent users without pressure. So the overall system has good performance. This paper introduces the computer image segmentation technology into the field of art design teaching and designs a kind of effective Internet online system.

In the context of the current era, if China wants to implement the strategic goal of rural revitalization, it must promote the integrated development of urban and rural areas. This paper takes Shaanxi Province as the main research goal, which is at the strategic intersection of the "the Belt and Road", has an important position, can affect the process of regional coordination and urban and rural integration throughout the country, and is a major province of characteristic agriculture and a national transportation hub. In this context, this paper uses BP neural network algorithm to simulate and analyze the coordinated development of urban and rural areas based on the actual development of science and technology finance in Shaanxi Province. After obtaining the economic status data of the province, this paper builds a regional characteristic platform that can provide targeted science and technology finance services, which can achieve diversified processing, complete multi entity fund supply and maintain the transparency of information between the supply and demand sides. Based on the field survey and literature review and analysis, it can be seen that the ten prefecture level cities included in the study area and province have different levels of coordinated development of urban and rural economy, and there is a Matthew effect characteristic of uneven strength, so the regional differences show a weakening feature, and they need to be adjusted gradually to make them develop in an orderly manner. Based on the research conclusion, this paper carries out the construction of urban-rural economic coordinated development and scientific and technological financial model support, and analyzes the financial support function and actual effect of the province in detail. In this paper, the BP neural network algorithm is applied to the integration of urban and rural development in Shaanxi Province to conduct big data simulation for science and technology finance.

The development direction of future architectural design should be in line with environmental protection, green and energy conservation, so as to meet people's sustainable development needs. The promotion of green building technology needs to involve multi-level and multi-disciplinary knowledge and social factors. Through a large number of green building projects, we can know that its ecological landscape not only has artistic value, but also can reflect obvious ecological value. An important concept in the concept of sustainable development is the green building landscape. Based on this background, this paper introduces artificial intelligence algorithm and designs a visual system around the concept of landscape space environment design. This system can be operated in two different forms, in offline application, image training is mainly carried out on the server side; In the online stage, the collected images are uploaded to the server using the Android platform, and the server side performs a series of operations such as image target detection, and then uses the Android mobile terminal to display the virtual reality fusion scene. The system test shows that the green building environment visualization system can not only make full use of the idle time of the cable drainage reader, but also will not affect the search operation in the next stage. The average delay is reduced by 16%, which proves the practicability of the software. In this paper, artificial intelligence algorithm is introduced into the field of green landscape space environment design, and an effective visualization system is constructed.

In this paper, an EOQ model has been considered where the demand rate is constant for both the stock period and stock out period (i.e. for the period of shortage) and during shortage i.e. stock out period, demand is partially fulfilled at a constant rate. The holding cost here is assumed as time-dependent.The effect of constant backlogging rate and variable backlogging rate are discussed depending on the previous model and present model.Inventory and related phenomena are of agreat importance in the study memory effect.To incorporate memory effect,the model can not be described by the standard ordinary differential equation,it should be developed by standard fractional order differential equation.Moreover,in order to show the relationship between fractional models and standard ordinary first order differential equations,two type of memory indices consider(i) Differential memory index,(ii) integral memory index,equationg two indices to 1,the model would be considered as memoryless model.Different type partial backlogging rate i.e. high partial backlogging rate and low partial backlogging rate has been considered and it is observed that at certain order of differential memory index i.e. at a level of memory effect,profit is equal for high partial backlogging rate and low partial backlogging rate.Atlast,some recommendation is given to develop another paper with the help of this paper.

With the support of enterprise intelligence system, an intelligent modeling method based on synthesizing various data sources and complex metabolic networks is adopted to provide technical support for the practical application of complex large-scale dynamic models. Academic research on international trade networks relies to a large extent on network models based on macro-national data and static analysis of international trade patterns, which are usually based on charts for trade flows and economic globalization. In this paper, we analyze the complex dynamic meta-network model of international trade based on improved neural network algorithm. Because BP neural networks run through the best neural network model that is constantly adjusted according to weighted values, the adjustment of neural network value is reduced, which may be an effective means to improve the efficiency of intrusion testing applications. The simulation results show that the model has good explanatory power. The internationalization of enterprise market as the main body of international business activities affects the establishment and development of a national or regional international trade network. Therefore, there is a need to study international trade networks using data at the micro level. This paper suggests an interdisciplinary approach to the study of international trade networks and a micro-level study of international trade networks. The model based on complex meta-network dynamic model elements, introduce the dynamic network representation, use temporary labels to define the network boundary characteristics.

Nowadays, physical dance is widely spread in the society as an emerging sport. Dance movement is favored by people because of its unique social function and fitness effect. For dance teaching, dance movement analysis can help optimize and improve the existing dance movements and the understanding and inheritance of traditional dance movements. With the rise of online teaching, intelligent identification and analysis of dance movements can promote the better development of sports dance teaching. However, the relevant research in this area is still very scarce. As the basis of this kind of research, there is an urgent need for dance motion recognition technology. Based on this background, this paper by introducing neural network algorithm for dance teaching sports image special recognition design, the algorithm can combine feature extraction technology to process video, extract the dance movements in the target data set, then for the extraction of cumulative feature extraction operation, in order to accumulate all the collected target features, so as to further complete the gradient histogram acquisition. Through the design experimental test, the cumulative feature image extraction results obtained through the algorithm are obviously better than the traditional image recognition results, so the design rationality and effectiveness of the algorithm are proved, and the sports dance teaching can be specially assisted. This paper designs an effective auxiliary image recognition algorithm by introducing the neural network algorithm into the field of sports dance teaching.

To expand the application scope of the WCA algorithm in the actual process, this research has optimized the problems in the algorithm in two aspects: The first aspect further improves the operating mechanism of the WCA algorithm and improves the performance of optimization problem. In the second aspect, other optimization strategy mechanisms of the WCA algorithm are introduced to enable the algorithm to optimize multi-objective and multi-dimensional problems. This paper studies a physical test system and sports intelligent based on hybrid wolf pack algorithm and IoT. The system collects and sends data from the data collection terminal of the Web server, receives the data through the wireless module, and sends it to the Web server through the network. The web server processes and stores the data information to generate a database, and users can view their own sports information by logging in to the web service program with the account password. In addition, the teacher and administrator accounts have the ability to view all users' exercise information. The system adds three sports items, pull-ups, squats, and standing long jumps. At the same time, the system uses a general motion recognition algorithm, which can effectively reuse and add new sports items. According to the actual needs of intelligent sports training, this paper combines somatosensory technology, bone tracking technology and motion recognition algorithm to realize a high-precision, low-latency intelligent sports training system.

Economic forecasting is affected by many factors. The analysis of economic data needs an intuitive and operable algorithm model. Therefore, based on this, this paper designs an urban and rural economic forecasting system based on neural network algorithm, and conducts a system test. First of all, in the data processing module, this paper adopts an efficient BP neural network algorithm based on error back propagation. This algorithm can effectively improve the accuracy of data recognition. At the same time, in order to solve the identification error caused by the complexity of urban and rural economic data during data input, the data processing layer of the system can preprocess the input data and normalize the multiple linear regression algorithm. Finally, in order to further prove the availability of the neural network algorithm used in this paper, by comparing the results of the original data and the predicted data, it is concluded that the data prediction results obtained by using the model algorithm in this paper have high accuracy and are basically consistent with the target value. Finally, through the analysis of the urban and rural economic development data, the degree of coordination of urban and rural economic development is analyzed, and the urban and rural development model is constructed. This paper also further analyzes the factors that restrict the coordinated development of urban and rural economy through indicators such as population structure, economic development, residents' life, social services and ecological construction, so as to achieve a more comprehensive urban and rural economic forecast, and provide a basis for improving the development of urban and rural economy in the regional economy.

Relying on network technology, the integration of personalized learning and Internet technology has become another trending industry. This paper explores a new strategy for education management, that is, a personalized information push system based on recommendation algorithms. The system can push personalized learning resources for teachers and students, and help them quickly locate interest points and learning directions by analyzing their usage history and tag attribute characteristics. The personalized information push algorithm achieves data fidelity by pre-cleaning or pre-processing the data. In addition, after the clustering algorithm is integrated into the system, its computing efficiency and mining depth are greatly improved than before. At the same time, based on collaborative filtering technology, this paper introduces information entropy and standard deviation to optimize the core algorithm, so as to distinguish the similarity between users, and further push recommendation accuracy and precision to a higher level. Finally, the existing problems in the current development of big data education management are analyzed, and future development strategies are proposed. To sum up, the personalized information recommendation system proposed in this study has a lower MAE value, so this has forward-looking significance for enhancing the depth of interactive learning and changing the inherent learning mode.

Tourism is the pillar industry of many cities, and it is also an important key point to promote urban development and maintain urban vitality. At present, the analysis of urban tourism activity in China can better assist the research of regional economic development and promote the orderly development of regional economy. Many scholars have carried out the analysis in this respect. As a new and growing field, artificial intelligence also plays an important role in urban tourism. With the continuous development of science and technology, and the human intelligence field of human research is also developing. New artificial intelligence products continue to emerge. The workload of most artificial intelligence may exceed the manual workload. In order to continuously update artificial intelligence, individuals effectively combine data mining and artificial intelligence, and combine many knowledge disseminated by the network with artificial intelligence technology to create an advanced knowledge network model. This paper uses the OPTICS-based clustering algorithm to analyze the clustering of photos on the Flickr website and obtain information about tourism activities in Chinese cities. With the help of visualization software to visualize the experimental data and verify the experimental results introduced in this article, city tourism activities can be recommended to the destination. At present, many scholars have studied the application of improved density clustering algorithm in the field of biology and image analysis, but there are still some gaps in the development of tourism. This paper can make some contributions to the related fields.

In the face of the impact of the New Coronary Pneumonia epidemic, Schools need to actively engage in online teaching in response to the Ministry of Education's call for "uninterrupted teaching". Ideological and political education is an important way to train socialist successors, and is the basis for establishing students' correct outlook on life, values and the world outlook. Therefore, in this paper, sparse neural network algorithm is introduced to complete the construction of ideological and political online education platform for colleges and universities. Through the design of simulation experiments, we can know that the sparse model can still maintain the stability and accuracy of the network under the condition of black box attacks, and even after a certain amount of tailoring, it can still exceed the accuracy of the original network. The experimental results show the superiority of this platform. In this paper, the platform system is roughly divided into three layers: user layer, data storage layer and functional logic layer. The evaluation is carried out from four dimensions: teaching resources, teaching activities, teacher-student interaction and teacher-student evaluation. According to the results, students have higher systematic evaluation than teachers. Among them, the recognition of teaching resources and activities is high, which proves that the platform in this paper is effective. The online teaching platform designed in this paper can make full use of the characteristics of network technology, realizes the reform and innovation development path of the ideological course in schools, and enhances the attraction of the political course and the enthusiasm of students to learn. This paper designs a kind of network education system by introducing sparse neural network into ideological online education.

Portfolio optimization has been studied extensively by researchers in computer science and finance, with new and novel work frequently published. Traditional methods, such as quadratic programming, are not computationally effective for solving complex portfolio models. For example, portfolio models with constraints that introduce nonlinearity and non-convexity (such as boundary constraints and cardinality constraints) are NP-Hard. As a result, researchers often use meta-heuristic approaches to approximate optimal solutions in an efficient manner. This paper conducts a comprehensive review of over 140 papers that have applied evolutionary and swarm intelligence algorithms to the portfolio optimization problem. These papers are categorized by the type of portfolio optimization problem considered, i.e., unconstrained or constrained, and are further categorized by single-objective and multi-objective approaches. Furthermore, the various portfolio models used, as well as the constraints, objectives, and properties in which they differ, are also discussed in a detailed analysis. Based on the findings of the reviewed work, guidance for future research in portfolio optimization is given. Possible areas for future work include dynamic portfolio optimization, predictive pricing, the further investigation of multi-objective approaches.

Healthcare social networks have a significant role in providing connected and personalized healthcare environment with real-time capabilities. However, building resilient, robust and technology-driven healthcare 5.0 has its own barriers. Especially with social media's high susceptibility to rumors and fake news, these networks can harm the society. Many researchers have been investigating the process of information diffusion, and it has been one of the most intriguing issues in network analysis. Modeling rumor propagation is one of the prominent researched topics in recent years. Traditional models assume that rumor propagation happens only in one direction, where only supporters are supposed to be active, whereas, in a real-life situation, both supporters and deniers of the information operate simultaneously. In this paper, we introduce a model for the recovery of nodes in a setting where rumor propagation and rumor control happen simultaneously. We propose the Susceptible-Infected-Recovered-Anti-spreader model based on the notion of spreading of epidemics and also its applications to modeling the propagation of rumors and control of rumor. Our model assumes people have multiple forms of reactions to rumor, either posting it, deleting it or announcing the rumor as fake. This paper also suggests how the model can act as a simulation method to compare two node centrality algorithms where spreaders chosen from one centrality algorithm try to spread the rumor, and the anti-spreaders chosen from other centrality try to dispel the rumor and vice versa. We simulate the proposed algorithm on different weighted and unweighted real-world network datasets and establish that the experimental results agrees with the proposed model.

In order to protect the color image copyright protection of the digital multimedia, it is necessary to design a color image watermarking algorithm. To achieve this purpose, an improved color image watermarking scheme based on QR decomposition for color image matrix is proposed in this paper. The proposed method gives a new algorithm to find elements of Q and R matrices instead of using Gram-Schmidt algorithm for QR factorization. First, R matrix is performed by solving a set of linear equations where diagonal elements of R are checked and modified if they are zero or negative. After that, Q matrix is computed based on R matrix. In addition, a novel formula is proposed to improve extracting time where the first element R(1,1) of R matrix is found instead of computing QR decomposition as the previous proposals. Experimental results show that the proposed method outperforms other methods considered in this paper in term of the quality of the watermarked images and the robustness of embedding method. Furthermore, the execution time is significantly improved and the watermark image is more robust under some tested attacks.

Human facial analysis (HFA) has recently become an attractive topic for computer vision research due to technological progress and mobile applications. HFA explores several issues as gender recognition (GR), facial expression, age, and race recognition for automatically understanding social life. This study explores HFA from the angle of recognizing a person's gender from their face. Several hard challenges are provoked, such as illumination, occlusion, facial emotions, quality, and angle of capture by cameras, making gender recognition more difficult for machines. The Archimedes optimization algorithm (AOA) was recently designed as a metaheuristic-based population optimization method, inspired by the Archimedes theory's physical notion. Compared to other swarm algorithms in the realm of optimization, this method promotes a good balance between exploration and exploitation. The convergence area is increased By incorporating extra data into the solution, such as volume and density. Because of the preceding benefits of AOA and the fact that it has not been used to choose the best area of the face, we propose utilizing a wrapper feature selection technique, which is a real motivation in the field of computer vision and machine learning. The paper's primary purpose is to automatically determine the optimal face area using AOA to recognize the gender of a human person categorized by two classes (Men and women). In this paper, the facial image is divided into several subregions (blocks), where each area provides a vector of characteristics using one method from handcrafted techniques as the local binary pattern (LBP), histogram-oriented gradient (HOG), or gray-level co-occurrence matrix (GLCM). Two experiments assess the proposed method (AOA): The first employs two benchmarking datasets: the Georgia Tech Face dataset (GT) and the Brazilian FEI dataset. The second experiment represents a more challenging large dataset that uses Gallagher's uncontrolled dataset. The experimental results show the good performance of AOA compared to other recent and competitive optimizers for all datasets. In terms of accuracy, the AOA-based LBP outperforms the state-of-the-art deep convolutional neural network (CNN) with 96.08% for the Gallagher's dataset.

The identification of apple leaf diseases is crucial to reduce yield reduction and timely take disease control measures. Employing machine learning-based methods, such as deep learning for accurate identification of multiple apple leaf diseases is challenging because of the limited availability of samples for supervised training and the serious class imbalance. Hence, this paper proposes an accurate deep learning-based pipeline to solve the problem of limited data sets on farms and reduce the partiality due to serious class imbalance. Firstly, this paper proposes the improved cycle-consistent adversarial networks (CycleGAN) to generate synthetic samples to improve the learning of data distribution and solve the problems of small data sets and class imbalance. On the basis of CycleGAN, two discriminators are introduced: one to judge whether the image is true or false, and one to judge whether the leaf image has disease. Specifically, healthy leaves are converted into disease-carrying leaves using two models, health-to-scab and health-to-rust to balance apple leaf disease datasets. Secondly, ResNet is trained as a baseline convolutional neural network classifier to classify apple leaf diseases. In experimental results, this paper carries out some experiments on evaluation of quality of the generated images by the improved CycleGAN and the performance of ResNet in terms of datasets and metrics. First of all, through qualitative observation of generated images and quantitative metrics, such as GAN-train and GAN-test, the generated diseases images from healthy image by the improved CycleGAN are superior to state of the art. Second, some experiments were carried out to verify the performance of the classification model, such as comparing five mainstream classification models and comparing them with traditional data augmentation methods. The results show that ResNet has the highest recognition accuracy on the test set, reaching 97.78%, and the classification accuracy is significantly improved by the generated synthetic samples (+14.7%). Finally, the experiment result of t-Distributed Stochastic Neighbor Embedding (t-SNE) visually confirmed that the images generated by improved CycleGAN have much better quality and are more convincing. Beyond that, a Visual Turing Test with three botanists showed that the generated images are indistinguishable from real apple leaf images.

Huge swirling storms known as hurricanes are tropical storms appearing in the North Atlantic Ocean and Northeast Pacific that result in winds of 120 km/hour and higher. The winds occurring during hurricanes are catastrophic resulting in immense damage to human life and property. Rapid assessment of damage caused by hurricanes is extremely important for the first responders. But this process is usually slow, expensive, labor intensive and prone to errors. The advancements in remote sensing and computer vision help in observing Earth at a different scale. In this paper, a Convolutional Neural Network model has been designed that assesses the damage caused to buildings of post hurricane satellite images. The images have been classified as Damaged and Undamaged. The model is composed of five convolutional layers, five pooling layers, one flattening layer, one dropout layer and two dense layers. Hurricane Harvey dataset consisting of 23000 images of size 128 X 128 pixels has been used in this paper. The proposed model performed best at learning rate of 0.00001 and 30 epochs with the Adam optimizer obtaining an accuracy of 0.95, precision of 0.97, recall of 0.96 and F1-score of 0.96. It also achieved the best accuracy and minimum loss.

In recent years, with the development of high-speed railway in China, the operating mileage and passenger transport capacity have increased rapidly. Due to the high density of trains in the daytime, we usually set up skylights at night within 0:00-6:00 on high-speed railway for comprehensive maintenance, which contradict with the operation demand of D-series overnight high-speed trains (overnight D-trains for short). In order to dynamically adjust the operation plan of overnight D-trains with skylights coordinately, it is necessary to predict the passenger demand of newly-added overnight D-trains. Therefore, the purpose of this paper is to predict transfer passenger demand by formulating a mixed logit model based on nonlinear random utility functions for different transport modes. Firstly according to Maximum Simulated Likelihood Method, the likelihood function of this mixed logit model is proposed to maximize the overall utility value of different passenger groups. And then we adopt Metropolis-Hastings Algorithm to iteratively solve the probabilities of discrete random variables in utility functions. After that, we estimate the unknown distributions of elements in parameter vectors and solve the optimal solution of this model by traditional algorithms, basic heuristic algorithms and improved heuristic algorithms including Imporved Fireworks-Simulated Annealing Algorithm which is proposed in this paper, respectively. Finally, a real-world instance with related data of Beijing-Shanghai corridor, is implemented to demonstrate the performance and effectiveness of the proposed approaches.

In the lung computer-aided detection (Lung CAD) system, the region of interest (ROI) of lung nodules has more false positives, making the imbalance between positive and negative (true positive and false positive) samples more likely to lead to misclassification of true positive nodules, a cost-sensitive multikernel learning support vector machine (CS-MKL-SVM) algorithm is proposed. Different penalty coefficients are assigned to positive and negative samples, so that the model can better learn the features of true positive nodules and improve the classification effect. To further improve the detection rate of pulmonary nodules and overall recognition accuracy, a score function named F-new based on the harmonic mean of accuracy (ACC) and sensitivity (SEN) is proposed as a fitness function for subsequent particle swarm optimization (PSO) parameter optimization, and a feasibility analysis of this function is performed. Compared with the fitness function that considers only accuracy or sensitivity, both the detection rate and the recognition accuracy of pulmonary nodules can be improved by this new algorithm. Compared with the grid search algorithm, using PSO for parameter search can reduce the model training time by nearly 20 times and achieve rapid parameter optimization. The maximum F-new obtained on the test set is 0.9357 for the proposed algorithm. When the maximum value of F-new is achieved, the corresponding recognition ACC is 91%, and SEN is 96.3%. Compared with the radial basis function in the single kernel, the F-new of the algorithm in this paper is 2.16% higher, ACC is 1.00% higher and SEN is equal. Compared with the polynomial kernel function in the single kernel, the F-new of the algorithm is 3.64% higher, ACC is 1.00% higher and SEN is 7.41% higher. The experimental results show that the F-new, ACC and SEN of the proposed algorithm is the best among them, and the results obtained by using multikernel function combined with F-new index are better than the single kernel function. Compared with the MKL-SVM algorithm of grid search, the ACC of the algorithm in this paper is reduced by 1%, and the results are equal to those of the MKL-SVM algorithm based on PSO only. Compared with the above two algorithms, SEN is increased by 3.71% and 7.41%, respectively. Therefore, it can be seen that the cost sensitive method can effectively reduce the missed detection of nodules, and the availability of the new algorithm can be further verified.

Economic growth in the information age is no longer a stage driven by unipolarity. It has entered a multi-polar driving stage characterized by integration, fusion, and integrated development on a larger scale between regions, and the trend of group competition with urban agglomerations as carriers has become increasingly obvious. This paper improves the neural network algorithm based on the needs of industrial economic integration in the digital age, and proposes an industry convergence analysis model based on the improved neural network algorithm. Moreover, this article combines industry models to analyze actual needs and constructs an industry convergence analysis model based on improved neural networks, and analyzes the integration of different industries. In addition, this article conducts experiments through multiple sets of data, and combines the neural network model of this article to conduct research. Through experimental research, we know that the model constructed in this paper can play an important role in the analysis of industry convergence.

The rapid and uncontrolled growth of the world's population technological developments, increase in the social welfare and the transformation of societies into consumer societies have changed the dimensions of environmental problems. Nowadays waste management has become an important issue for the solution of environmental problems. Hence, we discussed the municipal solid waste management. Municipal solid waste management problem is a complex and it has many different aspects as political, social, technological and economical criteria have to consider. The evaluation of these criteria numerically is complicated and vague. This paper deals with this complexity by proposed methodology. Also the contribution of the article to the literature is that the proposed methodology is applied for the first time in municipal solid waste management problems. In this paper two fuzzy decision making approaches are combined for sitting a waste to energy plant in the Kırıkkale in Turkey. Four alternative locations and nine criteria are defined from the expert opinions and the literature survey. A new hybrid methodology that has not been applied before for this decision problem is proposed. In proposed methodology, there are two main stages. Criteria weights determination is the first stage and ranking of the alternative locations is the second stage of the methodology. In first stage Interval type 2 Fuzzy Analytic Hierarchy process (AHP) method is performed and in the second stage hesitant fuzzy Technique for order preference by similarity to an ideal solution (TOPSIS) method is used for ranking the alternative locations. Also decision makers have different experience level and knowledge about the problem and different decision makers’ weights are considered for group decision making. Two fuzzy methods are combined for the solid waste energy production plant location selection problem. As a result of the study, the second alternative (Bahsılı-Bedesten) is determined as the most suitable area for waste to energy production plant. Besides, with scenario analysis the effect of criteria on ranking of the alternatives is analyzed.

As social media has become a ubiquitous part of daily life, researchers made a great progress in identifying the emotion in user-generated texts. However, it is a challenging task as people express their emotion in explicit and implicit ways. This paper focuses on the problem of identifying sentiments from implicit sentences which contain no emotional word or phrase. Most of the existing sentiment classification models cannot identify the sentiments accurately since they usually focus on extracting features from grammatical information without taking contextual information into account. In this paper, we argue that the contextual information is the key to identify sentiments in implicit sentences. Moreover, multiple features extracting from different aspects should be taken into account to improve sentiment identification. This paper proposes a multi-feature neural network model considering three aspects: contextual information, syntactic information and semantic information. To better get the semantic information of the sentence, we propose an attention mechanism based on contextual affective space. The experimental results on the SMP2019-ECISA dataset demonstrate that our model outperforms the previous systems and strong neural baselines.

This paper presents a new deep learning methodology to detect among up to 17 classes of cardiac arrhythmia based on beat-wise electrocardiography (ECG) signal analysis using iris spectrogram. Automatic analysis of each ECG heartbeat makes it possible to detect abnormalities. The main aim of this paper is to develop a fast deep learning and yet an efficient approach to classify cardiac arrhythmias. The approach is implemented using 744 ECG signals for 45 persons. The approach is based on analyzing a single ECG beat and calculating the iris spectrogram. Then the iris spectrogram is fed to a convolutional neural network. The proposed method is efficient, simple and fast, which makes it feasible for real-time classification. The results show that the proposed methodology has an overall recognition accuracy of 99.13% ± 0.25, 98.223% ± 0.85, and 97.494% ± 1.26 for 13, 15, and 17 arrhythmia classes, respectively. The training/testing is performed using tenfold cross-validation. When compared to existing studies, our method is promising, outperforms many others, and can be deployed on mobile devices.

Though there have been many contributions dealing with classical and Bayesian analysis of mixture data under right-censored samples, the Bayesian analysis of left-censored heterogeneous data is lacking in the literature. This paper attempts to bridge up the said gap in the literature by proposing Bayesian methods for estimation of left-censored heterogeneous data. The paper also explored two-component Weibull mixture distribution (2CMWD) as a suitable model for modeling left-censored mixture data. As the explicit derivations for the estimators of the parameters are not possible, MCMC methods and Lindley’s approximation (LA) have been considered for approximate estimation of the model parameters. The sensitivity of the proposed Bayes estimates with respect to change in sample size, mixing parameter, censoring rates, loss function, prior, true parametric value and approximate methods has been discussed. The reliability characteristics for the left-censored 2CMWD have been studied in detail. The proposed estimators performed efficiently to estimate the parameters and reliability characteristics from the left-censored 2CMWD. The proposed estimators were insensitive with respect to change in censoring rates, true parametric values, prior parameters and mixing weights. Three different real datasets have been used to discuss the applicability of the proposed estimators in different real-life studies.