In order to improve security as well as operational efficiency, this empirical paper investigates the integration of Internet of things (IoT) technology into the banking industry. Because the banking sector is an attr...
In order to improve security as well as operational efficiency, this empirical paper investigates the integration of Internet of things (IoT) technology into the banking industry. Because the banking sector is an attractive target for cyberattacks, strong security measures are required. IoT technology provides data-driven security improvements, and automated responses, alongside real-time surveillance through its network of connected devices and sensors. this covers the following: property monitoring, innovations in blockchain technology, biometric identification, intelligent monitoring infrastructure, along fraud detection. the technical analysis explores the benefits and drawbacks of IoT-based security for banks. data security, connectivity, adaptability, upkeep, and financial considerations are among the challenges. On the contrary, benefits like improved cybersecurity, cost-effectiveness, client trust, flexibility, and data analysis are provided by IoT-based security. the paper looks ahead, discussing the manner in which AI, blockchain technology, biometrics, cloud-based security, as well as quantum computing, will all be integrated into IoT-based security in the years to come. the financial sector is expected to implement a significant increase in IoT-based security measures.
Decision tree classification is one of the most practical and effective methods which is used in inductive learning. Many different approaches, which are usually used for decision making and prediction, have been inve...
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ISBN:
(纸本)9781424429141
Decision tree classification is one of the most practical and effective methods which is used in inductive learning. Many different approaches, which are usually used for decision making and prediction, have been invented to construct decision tree classifiers. these approaches try to optimize parameters such as accuracy, speed of classification, size of constructed trees, learning speed, and the amount of used memory. there is a trade off between these parameters. that is to say that optimization of one may cause obstruction in the other, hence all existing approaches try to establish equilibrium. In this study, considering the effect of the whole data set on class assigning of any data, we propose a new approach to construct not perfectly accurate, but less complex trees in a short time, using small amount of memory. To achieve this purpose, a multi-step process has been used. We trace the training data set twice in any step, from the beginning to the end and vice versa, to extract the class pattern for attribute selection. Using the selected attribute, we make new branches in the tree. After making branches, the selected attribute and some records of training data set are deleted at the end of any step. this process continues alternatively in several steps for remaining data and attributes until the tree is completely constructed. In order to have an optimized tree the parameters which we use in this algorithm are optimized using genetic algorithms. In order to compare this new approach with previous ones we used some known data sets which have been used in different researches. this approach has been compared with others based on the classification accuracy and also the decision tree size. Experimental results show that it is efficient to use this approach particularly in cases of massive data sets, memory restrictions or short learning time.
Each and every aspect of daily life has undergone a change thanks to the Internet of things technology that renders everything intelligent. the wide range of IoT applications has captivated numerous researchers, and I...
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ISBN:
(数字)9798350384369
ISBN:
(纸本)9798350384376
Each and every aspect of daily life has undergone a change thanks to the Internet of things technology that renders everything intelligent. the wide range of IoT applications has captivated numerous researchers, and IoT enabled dairy farming is being leveraged to undertake innovative studies integrating IoT and Artificial Intelligence (AI) technology. More prospects for intelligent dairy farming are being created by the IoT and data-driven methods. Since the global population is increasing, there is a constant appetite for milk. Developed countries use a greater amount of dairy products than developing nations do. Enhanced technological approaches are required to elevate milk production and meet the escalating demand for dairy products. It is anticipated that a farmer would be able to significantly increase milk output and conquer several traditional farming obstacles withthe help of IoT and various AI solutions. this study discusses various difficulties that dairy farmers deal with on every day of their lives. the primary objective of this paper is to undertake a comprehensive review on various Io'T, machine learning(ML) and deep learning(DL) technologies used in the field of smart dairy farming. It also looks into the difficulties and issues that traditional methods encounter as well as their results. Furthermore, the development of an IoT -enabled automated system for dairy farming may benefit further from this evaluation of research.
the evaluation of sperm morphology is an essential factor in the diagnosis of male-related infertility. Manual sperm morphology assessments are labor intensive and can vary significantly between observers. Although au...
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ISBN:
(数字)9798331532543
ISBN:
(纸本)9798331532550
the evaluation of sperm morphology is an essential factor in the diagnosis of male-related infertility. Manual sperm morphology assessments are labor intensive and can vary significantly between observers. Although automated sperm morphology analysis approaches have shown significant success in the last two decades, there is still a need for deep learning based solutions due to the challenges faced in new data sets such as the dramatic increase seen in number of sperm samples and the diversity of the labeled abnormality types. As a solution to these challenges, this research performs a comparative analysis of the conventional capsule network (CapsNet) and its modified version (FixCaps), using a recently introduced imbalanced dataset referred to as Hi-LabSpermMorpho which contains a total of 18,456 images labeled into 18 different classes. the classes include variants of abnormalities of the head, neck, and tail of the sperm, as well as the normal class. the evaluation focused on assessing the accuracy, computational efficiency, and the effects of different optimization strategies for both models. the results have shown that FixCaps achieved higher performance than CapsNet, with its improvements such as using large-kernel convolutions and the CBAM attention mechanism, which allow the model to capture more complex features and spatial relationships of the images in the dataset. FixCaps resulted in 52.67% classification accuracy while CapsNet performed only 37.89%.
the technology known as Automatic License Plate Recognition (ALPR) originated from the application of computer vision, a subfield of artificial intelligence systems. the purpose of developing this ALPR system was to v...
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ISBN:
(数字)9798350377057
ISBN:
(纸本)9798350377064
the technology known as Automatic License Plate Recognition (ALPR) originated from the application of computer vision, a subfield of artificial intelligence systems. the purpose of developing this ALPR system was to verify the tax status and comprehensive vehicle information in the West Java region of Indonesia. the objective of this study was to instill in each motorist a sense of traffic order and to assist Indonesia in the implementation of an intelligent Traffic System (ITS). In order to operate efficiently, the system incorporated in this design is constructed using a variety of Python programming language modules. YOLOv8 (You Only Look Once version 8), EasyOCR, OpenCV, and Selenium are the primary Python modules utilized; additional modules include RegEx (Regular Expression), Time, Numpy, and BytesIO. In this application, number plates are searched for and detected using the dilation method so that EasyOCR can extract every character with minimal error. Additionally, machine learning serves as the foundation for each of the aforementioned procedures and processes. the fundamental computation of machine learning from bespoke data sets is performed in Google Colab. the outcome of this investigation is a system capable of furnishing information pertaining to motor vehicle systems to an individual who has failed to remit vehicle tax. Aside from that, the developed system can detect counterfeit license plates, making it a valuable tool for educating drivers about the importance of remaining vigilant.
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