Volume 20
Published on October 2023Volume title: Proceedings of the 5th International Conference on Computing and Data Science
The wireless system did the huge cost-cutting in monitoring the structure so, now it can be used permanently as an integral part of the system as a smart infrastructure that will give them real-time information the structure. The wireless devices transmit the collected data about cracks, displacement, and excess vibration in slab-tracks. The train which will collect the data and train will be used as a data mule in this paper which will upload the information to a remote-control centre. The data which will be collected stored in the database and to know the status of the track a query will be fired from an application. In this paper, many design for communication systems are proposed which are efficient, with fine accuracy, and most importantly it is a low-cost system.
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The Unmanned Aerial Vehicle (UAV) environmental monitoring system in an area is multipurpose and easy to use. With the use of targeted mobile networks and drone flight control, the relevant staff can effectively monitor regional temperature and humidity. This study draws on the author's previous expertise to outline the key points of a programme demonstration and system design, as well as to examine the software flow architecture of regional environmental monitoring. As a result of this work, users will be able to easily collect weather as well as other data by using a drone that can be controlled from an Android phone and flown to some faraway, inaccessible site. The mobile node MCU may receive commands from the WiFi module and relay them to the drone. The drone will be flown when the instructions have been sent via the IoT based NodeMCU module. From there, it will gather data via the network and communicate that information to the cloud database over WiFi. This setup has the potential to be implemented into a model for a weather prediction that can accurately foretell winds and temperatures in the immediate vicinity of the surface to an accuracy of 100 meters.
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NLPstands for Natural Language Processing, it is a kind of artificial intelligence. That demonstrate with scrutinize, understanding, and accuse natural human languages. In such a manner that analog procedure would get in touch and human language excluding computer-propel language like programming language such as c, c++, python, javascript. NLP field contains the intellect computer programming into an understanding language which is understandable by humans. So, powerful algorithms can even interpret one language into another language scrupulously. Natural language processing occasionally also known as “computational linguistics”, it uses semantics and syntax. It assists computers to acknowledge how humans talk or write and also it will know how to deduce meaning of words that they are saying. A language is designate as a synchronize of rules and symbols. These Symbol are amalgamated and used for disseminate the data as well as make contact to other resources. Symbols are potentiate by the rules and regulations.
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Tamil Nadu reportedly is the alternate loftiest rates of pupil self-murder in India. According to report from the National Crime Records Bureau (NCRB) for 2020, about 46 people failed each day by self-murder in the state; further than two of these victims each day were scholars. The government of India’s domestic seminaries for meritorious pastoral children have reportedly witnessed 49 self-murders in just five times and half of the children who killed themselves on the premises were Dalits and Adivasis, Educationalists point at the pressure mounted on scholars for marks and competitive examinations as one of the main reasons. Utmost seminaries have councillors but going to the council has a smirch about it still. Domestic seminaries where these cases are passing more need to be covered more. Children dying by self-murder due to academic pressure are unheard of in other countries. Utmost of the A huge number of enquiries revealed depression and anxiety to be implicit risk factors for self-murder. Heart health is good among students between the ages of 15 and 25. A few physiological parameters are altered by anxiety and depression. Hence, current pulsation is the symptom for psychiatric diseases. Clinical trials have demonstrated that mental reasons, most frequently anxiety, depression, and somatoform illnesses, are present in cases with pulsation. Psychiatric problems like anxiety and somatization are linked to palpitation. A study found that individuals with high levels of both depression and anxiety are 54.77 times more likely to commit suicide, which is a significant increase above individuals with either high levels of anxiety or depression (2.46). (26.32).. Naturally students studying +2 and preparing for high marks are in under depression and it's dragged for two times. When the examinations are nearing and the results are nearing, the combined depression and anxiety factor increases the threat for committing self-murders for84.5. Hence, a system with literacy and decision making medium is needed to constantly monitor the students to take prompt conduct. A wearable IoT device transmits with scholars Id, the pulsation position constantly to the fellow knot. Logistic Retrogression machine literacy algorithm is used for decision making grounded on the probability of circumstance. The authorities are informed in their widgets and prompt conduct can be taken. This system will be more applicable to the boarding seminaries, where the children are far down from their parents and the number of self-murders are high particularly among +2 grade. This system can be used for colourful communities, where the working culture gives depression and anxiety.
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The purpose of licence plate recognition is to analyze pictures or videos of moving vehicles to read the plate and identify the vehicle's owner. Traffic data management and smart transportation systems rely heavily on licence plate reading technology. Initial picture capture, image preprocessing, licence plate analysis, character segmentation, and recognition are the building blocks of licence plate recognition. The present analysis centres on the examination of the above key steps. In this paper, we introduce the latest research progress in the implementation of licence plate recognition utilizing deep learning techniques, including the classic framework of licence plate location and character recognition, representative methods, and their advantages and disadvantages. We also perform a quantitative comparison of existing representative methods. Finally, we summarize the challenges in the research domain of licence plate recognition and discuss the future development direction from the aspects of neural network interpretability, more general small sample learning methods, and incremental learning.
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With the increasing demand for intelligent systems capable of comprehending visual information, the discipline of image object detection has experienced rapid expansion. Despite the fact that numerous methods have been proposed, the existing literature lacks exhaustive analyses and summaries of these methods. This paper seeks to address this deficiency by providing a thorough overview and analysis of image object detection techniques. This paper analyzes and discusses traditional methods and deep learning-based methods, with a focus on analyzing the current state and shortcomings of traditional methods. Further discussion is given to deep network-based object detection methods, mainly through a comparative analysis of two-stage and one-stage methods. The basic performance of the You Look Only Once (YOLO) series methods is highlighted. The contribution of large-scale datasets and evaluation metrics to the advancement of the state of the art is also examined. This comprehensive analysis is a useful reference for researchers who aim to contribute to the continual progress of image object detection.
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This paper focuses on designing a robot that can efficiently relieve pressure and heal children with autism. autism, a common and unavoidable disease, happens in 1 in 100 children worldwide, and is not easy to recover, especially for children. While many organizations have explored the effectiveness of social robots, only a few robots are available that encompass helpful functions. In this paper, a few new functions or changes, such as portable design and more attractive and complex interaction design, can significantly improve the possibility of healing. The research incorporated the detailed design of the robot and meta-analysis as the data analysis method. Our data come from previous research from different areas of the world. The robot reveals that current robots are still incapable of comprehensive interactions and what future designs are expected to be included.
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In traditional machine learning, supervised and semi-supervised learning is designed to be used in a closed-world setting where the training data is fixed and does not change over time. Unfortunately, these methods still require a large number of labels for the categories to be categorized, which is expensive and impractical. A new category discovery algorithm is designed so that it can discover new categories while classifying and recognizing labeled images. In this case, the machine can automatically identify new categories without manual marking of image feature categories, which can greatly reduce the cost of image classification. Kai Han et al. named this problem a new category discovery problem and proposed that deep clustering can be used to solve it well. This paper focuses on the comparison of two commonly used robust baselines in the new category discovery and proposes that adding a post-processing model can better improve the accuracy of the model result. This paper applied the relaxed contrast learning method to the Ranking Statistics, and the accuracy of CIFAR-100 is improved by 6%.
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Customer churn has long been a concern for companies because it not only reduces the company's profit in the short term, but is also extremely detrimental to the company's growth in the long term. This paper focuses on the analysis of customer churn in banks by using two machine learning methods, namely logistic regression and decision tree, to predict the churn rate of customers and analyze the decision tree results based on the premise that decision trees are more accurate in prediction and do not have a large prediction bias for a certain group as logistic regression does. The results show that age, estimated salary and the number of products are important factors when predicting and customer groups with some specific characteristics will show a higher departure rate. To address this situation, this paper recommends that bankers continuously optimize their business systems and focus on user groups with high churn rates.
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Unmanned Aerial Vehicle (UAV) are currently gaining popularity. This paper proposes a method to apply Linear Active Disturbance Rejection Control (LADRC) to Quadrotor Unmanned Aerial Vehicle (QUAV) controller to optimize the traditional PID controller. Firstly, the application and shortcomings of traditional PID control in UAV are introduced, and the LADRC method is proposed. Then linear simplification and Parameter Setting of ADRC are carried out. In the Simulink environment, according to the mathematical model of the QUAV, a QUAV dynamics simulation platform is established. Finally, according to different control channels, different control algorithms are designed, and tracking models are introduced in various attitudes to simulate and verify the control effect of LADRC. The results show that the LADRC controller is effective, the LADRC can be effectively combined with the traditional PID control. The application in the QUAV can realize more precise QUAV speed tracking control and the stable flight of the QUAV. Compared with the traditional PID controller, under the experimental conditions of this paper, the LADRC controller has more precise control accuracy and more efficient control efficiency. Finally, this paper summarizes the design of LADRC and makes a brief outlook on the development of UAVs.
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