Railway system maintenance, crucial for ensuring safety and efficiency, faces challenges in effectively managing its vital components, including tracks, railroad ties, and fasteners. While various methodologies target these components, the limited availability of diverse railway datasets presents a significant hurdle. Addressing this, we introduce SeMA-UNet, a pioneering deep learning model designed to optimize performance in data-constrained scenarios. Seamlessly integrating semi-supervised learning with multimodal strategies, SeMA-UNet excels in preprocessing railway images, conducting comprehensive feature extraction to generate rich multimodal data. This process is further augmented by advanced techniques, notably the Monte Carlo simulation. Empirical results underscore SeMA-UNet’s robustness, with metrics such as an IoU of 0.9464, an AUC of 0.9796, and an mAP of 0.9468. Beyond its primary function of accurately identifying maintenance-critical regions, the model’s capabilities extend to advanced anomaly detection, heralding a new era in enhancing the reliability and safety of railway systems.

Exploration of mobile robot without prior data about environments is a fundamental problem during the SLAM processes. In this work, we propose improved version of previous Rmap algorithm by modifying its Exploration submodule. Despite the previous Rmap's performance which significantly reduces the overhead of the grid map, its exploration module costs a lot because of its rectangle following algorithm. To prevent that, we propose a new Rmap+ algorithm for autonomous path planning of mobile robot to explore an unknown environment. The algorithm bases on paired frontiers. To navigate and extend an exploration area of mobile robot, the Rmap+ utilizes the inner and outer frontiers. In each exploration round, the mobile robot using the sensor range determines the frontiers. Then robot periodically changes the range of sensor and generates inner pairs of frontiers. After calculating the length of each frontiers' and its corresponding pairs, the Rmap+ selects the goal point to navigate the robot. The experimental results represent efficiency and applicability on exploration time and distance, i.e., to complete the whole exploration, the path distance decreased from 15% to 69%, as well as the robot decreased the time consumption from 12% to 86% than previous algorithms.

Observing advancements in safety systems of the railway, it can be seen that the past researches have done a remarkable contribution in finding and creating techniques to remove noise and vibration caused by wheel and rail friction on the railroad tracks. As high-speed trains grow in popularity, the safety monitoring system is becoming an integral part of the railway network to decrease the effect of the vibration and noise, stemming from train pass-by, on our welfare and livelihood, making it a significant study topic. With the current modern technologies such as AI and Internet of Things (IoT), we have now, those advances can be further developed close to perfection as AI, IoT technologies have a huge capability to deliver security, safety, and working efficacy in railway overpasses, underpasses and whole railroad arrangements. The present study proposes a railroad security and safety controlling solution powered by the Internet of Things (IoT) to provide a synchronized evaluation and impost of the status of operation efficiency along with safety and security in railway overpasses, underpasses and the well-being of residents living in areas surrounding the railroad infrastructure.

Bitcoin is one of the main phenomena in recent times together with other cryptocurrencies due to the redefinition of the money term and its price fluctuations. Moreover, scientists are increasingly recognizing Twitter's predictive power for a wide range of events, and particularly for financial markets. This article examines to what degree Bitcoin returns can be estimated using public opinion on Twitter. Using a sentiment analyzer on Bitcoin-related tweets and financial data, the Twitter sentiment was found to have predictive power for Bitcoin's results. Once again, our findings confirm the presence of a correlation between them. We observed 62.48% accuracy when making predictions based on bitcoin-related tweet sentiment and historical bitcoin price.

The net profit of investors can rapidly increase if they correctly decide to take one of these three actions: buying, selling, or holding the stocks. The right action is related to massive stock market measurements. Therefore, defining the right action requires specific knowledge from investors. The economy scientists, following their research, have suggested several strategies and indicating factors that serve to find the best option for trading in a stock market. However, several investors' capital decreased when they tried to trade the basis of the recommendation of these strategies. That means the stock market needs more satisfactory research, which can give more guarantee of success for investors. To address this challenge, we tried to apply one of the machine learning algorithms, which is called deep reinforcement learning (DRL) on the stock market. As a result, we developed an application that observes historical price movements and takes action on real-time prices. We tested our proposal algorithm with three-Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH)-crypto coins' historical data. The experiment on Bitcoin via DRL application shows that the investor got 14.4% net profits within one month. Similarly, tests on Litecoin and Ethereum also finished with 74% and 41% profit, respectively.

A glance on the developments of the railway safety system reveals that the consistent research has made a remarkable progresses finding and devising techniques to alleviate noise and vibration resulting from wheel and rail interaction alongside the railway track infrastructure. Internet of Things (IOT) and AI has an immense potential to provide safety and operation efficiency in railroad bridges, tunnels and overall railway infrastructures. In this research paper, we propose a railway safety monitoring application based on the Internet of Things (IOT) to deliver a real-time assessment of the state of safety and efficiency in railroad bridges, tunnels and welfare of residence areas surrounding the railroad infrastructure.

Collisions between vehicle and animal still do not lose its significance in terms of traffic safety. In spite of, a lot measures to reduce the conflict have been established around the world it still hazardous for both of wildlife and people. In that case for remaining traffic safety we need advanced technology used control system. LDM (Local Dynamic Map) is a data store standardized by European Telecommunications Standards Institute, utilized for displaying location and status of road users on a dynamic map. Machine learning SVM (Support Vector Machine) model, which is used for both regression and classification problems, the objective of algorithm finding hyperplane to classify the data points in N-dimensional space. The main purpose of this research is to decrease the collisions between wildlife-vehicles by performing machine learning SVM predictions in LDM based database environment.

The monitoring utilization and workloads of computer hardware components, such as CPU, RAM, bus, and storage, are an ideal way to evaluate the effectiveness of these components. In this paper, we surveyed the basic concepts, characteristics, and parameters of computer systems that determine system performance, and the types of models that provide adequate modeling of these systems. We investigated and developed the applied aspects of the theory of fuzzy sets’ principles and the Matlab environment tools for monitoring and evaluating the state of computing systems. The idea of the paper is to identify the state of the computer infrastructure by using the models of Mamdani and Sugeno FIS (fuzzy inference system) to evaluate the impact of RAM and storage on CPU performance. With this approach, we observed the behavior of computer infrastructure. The results are useful for understanding performance issues with regard to specific bottlenecks and determining the correlation of performance counters. Moreover, the model presents linguistic results. Hereafter, performance counter correlations will support the development of algorithms that can detect whether the performance of a given computer will be affected by a reasonable priority. The performance assertions derived from these approaches allow resource management policies to prevent performance degradation, and as a result, the infrastructure will be able to serve safely as expected. These methods can be applied across the entire spectrum of computer systems, from personal computers to large mainframes and supercomputers, including both centralized and distributed systems. We look forward to their continued use, as well as their improvement when it is necessary to evaluate future systems.

Interest in self-driving vehicle research has been rapidly increasing, and related research has been continuously conducted. In such a fast-paced self-driving vehicle research area, the development of advanced technology for better convenience safety, and efficiency in road and transportation systems is expected. Here, we investigate research in self-driving vehicles and analyze the main technologies of driverless car software, including: technical aspects of autonomous vehicles, traffic infrastructure and its communications, research techniques with vision recognition, deep leaning algorithms, localization methods, existing problems, and future development directions. First, we introduce intelligent self-driving car and road infrastructure algorithms such as machine learning, image processing methods, and localizations. Second, we examine the intelligent technologies used in self-driving car projects, autonomous vehicles equipped with multiple sensors, and interactions with transport infrastructure. Finally, we highlight the future direction and challenges of self-driving vehicle transportation systems.

Global Positioning Systems (GPS) are successfully used in many fields such as navigation, meteorology, military tasks, mapping, virtual fencing, and more. Smart collars are currently the most convenient device for determining animal location in virtual fencing systems, however; these systems are still suffering from environmental effects and propagation in direct visibility. These types of side effects may reduce the work of GPS receivers. The current article defines a method for improving animal location accuracy using a virtual fence smart collar worn around the animal’s neck by the aid of maximum probability of movement from one point to another. The proposed approach first checks the current position of the animal, and after receiving a GPS signal from satellites it calculates the distance between the two GPS signals. Secondly, the method checks the animal’s behavior for the receiving period of the two points. Finally, the approach calculates a probability of maximum animal movement for the two-point receiving period. If the animal can pass the distance in the time frame of the two signals, then the second signal is taken as the correct position; otherwise, the point is taken which the animal could pass. Real-time animal behavior is classified using Support Vector Machines (SVM). The proposed method was verified within seven days of experiments. Consequently, the proposed approach experiments were sufficiently successful. The recreated locations from our approach appeared very close to the real point. The mean average of passed distance by the marked line decreased to 16.2, 5, 0 m for running, walking, and resting conditions, respectively. On the other hand, the unfiltered geolocations of the GPS receiver, give results significantly further from the animal’s actual position such as 148.8, 182.7, 136.2 m for running, walking, and resting conditions.

Nowadays, vehicles have become a very significant role in transport system which a significant issue in various cities around the world, especially in developing countries resulting in turn traffic congestion, massive delays, increased fuel wastage. There are many alternative systems, such as parking places, building bridges. However at the present time, Intelligent Transport System is becoming more debatable topic among the experts. Creating cooperative safety driving systems can solve the issues which are mentioned above. ITS using V2I (vehicle-to-infrastructure) and V2V (vehicle-to-vehicle) communications. Local Dynamic Map (LDM) is essential technology to increase availability of cooperative ITS with dynamic, static and temporary information in geographical context. In this work, to provide to future cooperative traffic services and applications with traffic collection of data data fusion middleware and data processing belongs on public infrastructure and sensor nodes. There are various components assist to supporting Middleware features which are reusable and reconfigurable data processing components, interfaces to the system and dynamic component, and modular component based architecture.

u0027FinTechu0027, a fusion of financial service and IT technique is recently becoming a new profit model in the financial industry through the creation of new business models. In order to use the FinTech services on mobile, customers must enter their card information for each service and this implies that chance of customer information leakage will increase as each single customeru0027s card details and personal information will be saved in servers of various service providers. To minimize this problem, this research propose a user card registration method, which can minimize card information leakage during user card registration process of user.

Nowadays, vehicles have become a very significant role in transport which remain a major problem in most cities around the world, especially in developing regions resulting in massive delays, turn traffic congestion, increased fuel wastage and monetary losses. There are many alternative systems, but one possible solution to this problem is parking system has become the sparking factors of the mentioned problems above. In this paper, we present various techniques as far as the smart parking system is mainly debated which are already implemented. When we are deepening into this parking issue, many contributors contributed a lot in monitoring smart parking system (SPS) and management of SPS with an asset of various gadgets and technologies including Bluetooth, Cameras, Image Processing, wireless sensors, ZigBeee accompanied by software explanations based on mobile application. Following this analysis will enhance researcher's thought on SPS which will result in a real solution of the technique.

Recently, with the development of the systems that support tracking of various objects and the component technology of Internet of Things (IoT), the use of tracking system is increasing in social infrastructures and various industrial sectors. With the application of the IT technology that can manage the movement of objects in real time in industrial sites, the sectors where the technology of tracking system can be applied are expanding. However, to satisfy the various demands of industrial sites, the existing tracking system has many technological problems. For example, due to the technological problems in solving spatial errors and errors related to time, there are limitations in introducing object-based tracking system in the medical industry or industrial sites with high risks, and these errors, when involved in service sectors, can seriously hurt customers’ reliability. Also, even in sectors where error range is not important, there are many problems such as high power consumption, high utilization of data, and the trouble of object tracking in some locations. Analyzing problems such as errors of tracking, power consumption, data use, and situations where object-tracking is not supported, this study tried to reduce error range and design and develop an intelligent smart tracking system that minimizes data use with low-power base.

In dangerous industry fields such as construction, shipbuilding, the mining industry, and other field, many employees have lost their life due to risky environment. As result, the costing of social and industry have been increased. To solve this problem, wireless body area network technology can implement by converging ubiquitous sensor network and wireless data communications. The wireless body area network technology has been issued recently and studied by standard institutes (e.g. IEEE TG 802.15.6), universities, and research labs. This paper studies management issues in wireless body area network for industry safety. We design human object as wireless body area network on the employee’s body and propose management method for industry safety.

Due to the recent technical development of an object tracking system and Internet of Everything (IOE), our social environment and almost every industry fields have adopted tracking system and applied IT technology where we can monitor conditions in real time. But many problems still need to solved with the current tracking system to satisfy various requirements for a variety of industry fields. For instance, object tracking has limitation to be adopted in military field since error range is a critical issue and also not only in medical but fields that are related directly to life or in service areas where they need to deal with tons of customers, the error range is a critical issue linked to customer reliability. This research presents smart middleware system which can effectively manage tracked objects to reduce errors through improved algorithm and analyzed result.