As the world keeps advancing, the need for automated interconnected devices has started to gain significance; to cater to the condition, a new concept Internet of Things (IoT) has been introduced that revolves around smart devicesʼ conception. These smart devices using IoT can communicate with each other through a network to attain particular objectives, i.e., automation and intelligent decision making. IoT has enabled the users to divide their household burden with machines as these complex machines look after the environment variables and control their behavior accordingly. As evident, these machines use sensors to collect vital information, which is then the complexity analyzed at a computational node that then smartly controls these devicesʼ operational behaviors. Deep learning-based guessing attack protection algorithms have been enhancing IoT security; however, it still has a critical challenge for the complex industries’ IoT networks. One of the crucial aspects of such systems is the need to have a significant training time for processing a large dataset from the networkʼs previous flow of data. Traditional deep learning approaches include decision trees, logistic regression, and support vector machines. However, it is essential to note that this convenience comes with a price that involves security vulnerabilities as IoT networks are prone to be interfered with by hackers who can access the sensor/communication data and later utilize it for malicious purposes. This paper presents the experimental study of cryptographic algorithms to classify the types of encryption algorithms into the asymmetric and asymmetric encryption algorithm. It presents a deep analysis of AES, DES, 3DES, RSA, and Blowfish based on timing complexity, size, encryption, and decryption performances. It has been assessed in terms of the guessing attack in real-time deep learning complex IoT applications. The assessment has been done using the simulation approach and it has been tested the speed of encryption and decryption of the selected encryption algorithms. For each encryption and decryption, the tests executed the same encryption using the same plaintext for five separate times, and the average time is compared. The key size used for each encryption algorithm is the maximum bytes the cipher can allow. To the comparison, the average time required to compute the algorithm by the three devices is used. For the experimental test, a set of plaintexts is used in the simulation—password-sized text and paragraph-sized text—that achieves target fair results compared to the existing algorithms in real-time deep learning networks for IoT applications.
Every year, a large amount of population reconciles gun-related violence all over the world. In this work, we develop a computer-based fully automated system to identify basic armaments, particularly handguns and rifles. Recent work in the field of deep learning and transfer learning has demonstrated significant progress in the areas of object detection and recognition. We have implemented YOLO V3 “You Only Look Once” object detection model by training it on our customized dataset. The training results confirm that YOLO V3 outperforms YOLO V2 and traditional convolutional neural network (CNN). Additionally, intensive GPUs or high computation resources were not required in our approach as we used transfer learning for training our model. Applying this model in our surveillance system, we can attempt to save human life and accomplish reduction in the rate of manslaughter or mass killing. Additionally, our proposed system can also be implemented in high-end surveillance and security robots to detect a weapon or unsafe assets to avoid any kind of assault or risk to human life.
Purpose The purpose of this paper is to present the first economic valuation of four environmental attributes of the Yanachaga–Chemillén National Park (PNYCH – Parque Nacional Yanachaga-Chemillén) in Peru. Design/methodology/approach This study included households in three cities adjacent to the PNYCH and assessed the willingness to pay (WTP) for preservation efforts of these natural services to avoid the predicted loss in forest area by 2030 (currently 143,425 hectares per year). Findings The results showed that the average WTP was US$0.695 (2.3197 soles) per household annually. Added to all households in Peru (9 million), this is equivalent to approximately 6.255 million dollars annually. Practical implications The economic valuation of these attributes is complementary to the contingent valuation and can have a significant impact, as this data influences decision-making and public policies focused on conserving forests and biodiversity. Social implications Upon using the choice experiment (CE) model, the attributes that have the most significant impact on inhabitants’ well-being were economic benefits. The flora and fauna coverage attributes were beneficial for the inhabitants of the place because they valued the proposed recovery and conservation program in a positive and differential way. Originality/value From the collection of valuable economic data, the novelty lies in using the CE method, which has not yet been applied in valuations of natural ecosystem services in Peru.
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