The interpretation of hand gestures is crucial for effective non-verbal communication, particularly in HumanRobot Interaction (HRI). Despite the importance, existing research has mainly concentrated on short-range ges...
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This research focuses on improving the identification of cyclone centers using deep learning and match recognition applied to radar images. Accurately pinpointing the cyclone's center is vital for predicting its i...
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The Internet of Things (IoT) combines billions of physical objects that can communicate with each device without minimal human interaction. IoT has grown to be one of the most popular technologies and an attractive fi...
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The Internet of Things (IoT) combines billions of physical objects that can communicate with each device without minimal human interaction. IoT has grown to be one of the most popular technologies and an attractive field of interest in the business world. The demand and usage of IoT are expanding rapidly. Several organizations are funding in this domain for their business use and giving it as a service for other organizations. The result of IoT development is the rise of different security difficulties to both organizations and buyers. Cyber Security gives excellent services to preserve internet privacy and business interventions such as disguising communication intrusions, denial of service interventions, blocked, and unauthorized real-time communication. Performing safety measures, such as authentication, encryption, network protection, access power, and application protection to IoT devices and their natural vulnerabilities are less effective. Therefore, security should improve to protect the IoT ecosystem efficiently. Machine Learning algorithms are proposed to secure the data from cyber security risks. Machine-learning algorithms that can apply in different ways to limit and identify the outbreaks and security gaps in networks. The main goal of this article ability to understand the efficiency of machine learning (ML) algorithms in opposing Network-related cyber security Assault, with a focus on Denial of Service (DoS) attacks. We also address the difficulties that require to be discussed to implement these Machine Learning (ML) security schemes in practical physical object (IoT) systems. In this research, our main aim is to provide security by multiple machine-learning (ML) algorithms that are mostly used to recognise the interrelated (IoT) network Assault immediately. Unique metadata, Bot-IoT, is accustomed to estimate different recognition algorithms. In this execution stage, several kinds of Machine-Learning (ML) algorithms were handled and mostly reached e
This paper presents a peer-to-peer (P2P) energy trading mechanism based on two-stage game theory. It classifies prosumers as buyers and sellers, according to the net energy during the trading period. The interaction b...
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A wireless sensor network (WSN) is a system of interconnected sensors that can gather environmental information. On the other hand, data redundancy is a common source of problems with WSNs. The literature presents a p...
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The utilization of multi-agent systems has been increasingly prevalent across various sectors, owing to their notable efficacy in execution. However, a multitude of hazards exist that possess the capability to undermi...
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In this paper, we propose a new entropy measure of Pythagorean fuzzy sets (PFSs). The proposed entropy measure of PFSs can conquer the shortcomings of the existing entropy measure of PFSs. We also propose the Pythagor...
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Open-world semi-supervised learning (Open-world SSL) for node classification, that classifies unlabeled nodes into seen classes or multiple novel classes, is a practical but under-explored problem in the graph communi...
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To alleviate the greenhouse effect and maintain the sustainable development, it is of great significance to find an efficient and low-cost catalyst to reduce carbon dioxide(CO_(2)) and generate formic acid(FA). In thi...
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To alleviate the greenhouse effect and maintain the sustainable development, it is of great significance to find an efficient and low-cost catalyst to reduce carbon dioxide(CO_(2)) and generate formic acid(FA). In this work, based on the first-principles calculation, the catalytic performance of a single transition metal(TM)(TM = Cr, Mn, Fe, Co, Ni, Cu, Zn,Ru, Rh, Pd, Ag, Cd, Ir, Pt, Au, or Hg) atom anchored on C_(9)N_(4) monolayer(TM@C_(9)N_(4)) for the hydrogenation of CO_(2) to FA is calculated. The results show that single TM atom doping in C_(9)N_(4) can form a stable TM@C_(9)N_(4) structure, and Cu@C_(9)N_(4) and Co@C_(9)N_(4) show better catalytic performance in the process of CO_(2) hydrogenation to FA(the corresponding maximum energy barriers are 0.41 eV and 0.43 e V, respectively). The partial density of states(PDOS), projected crystal orbital Hamilton population(p COHP), difference charge density analysis and Bader charge analysis demonstrate that the TM atom plays an important role in the reaction. The strong interaction between the 3d orbitals of the TM atom and the non-bonding orbitals(1πg) of CO_(2) allows the reaction to proceed under mild conditions. In general, our results show that Cu@C_(9)N_(4) and Co@C_(9)N_(4) are a promising single-atom catalyst and can be used as the non-precious metals electrocatalyst for CO_(2) hydrogenation to formic acid.
With technological advancement, the role of technology to make homes smart has increased. The prime aim is to achieve the occupant's comfort. Thus, the demand for temperature control has significantly increased. B...
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