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Heat transfer innovation is essential in modern society because thermal management systems need effective heating and cooling processes. It is also an essential component in the vehicle industry and other types of transportation, in addition to automobile industry, aviation technology, the computer industry, and the manufacturing industry. By the inspiration of importance of magnetohydrodynamic hybrid nanofluid over a stretching cylinder with the influence of Williamson fluid and porous medium is examined in this current study. To convert the PDEs into ODEs, suitable self-similarity transformation is used. After applying transformations, for graphical purpose we have used the bvp5c technique. The impact of active parameters affecting the fluid's capacity to transfer significance is demonstrated in graphs and tables. In the result section we noticed on the velocity outlines decreased for increasing M parameter. The Cf and Nu increased for larger values of the M and curvature parameters. Additional properties of M and Rd parameter inputs result in improved temperature profiles. [ABSTRACT FROM AUTHOR]

The term "smart highway" refers to the way advanced technologies and smart systems are built into roads to make them safer, more efficient and more environment-friendly. Smart highways have sensors, cameras and other monitoring systems that collect and send information about traffic flow, weather and road hazards in real time. This information is subsequently processed and utilized to optimize traffic flow, enhance road safety and minimize congestion. Moreover, smart roads contain technologies such as charging stations for electric vehicles and intelligent lighting systems, which further improve their sustainability and efficiency. This paper provides a concise summary of the intelligent highway idea and illustrates the possible benefits of this unique approach to transportation infrastructure. [ABSTRACT FROM AUTHOR]

The largest disruptive component of traffic management system is road transport. Intelligent Transportation Systems (ITS) necessitate exact traffic forecast. However, transport departments continue to struggle to choose an ITS prediction method. A user must be able to appropriately utilize estimation model information. This paper covers contemporary estimation approaches, explains their fundamental concepts and evaluates different traffic estimation approaches for the benefit of decision makers. Each method is classified as Machine Learning (ML), Artificial Intelligence (AI), Deep Learning (DL) or hybrid algorithms. Many surveys are model or datadriven. This study is the first to examine traffic prediction using a variety of algorithmic and methodological approaches to key traffic parameters. Predictive evaluation of dependent variables is done and each algorithm's traffic characteristics are examined in chronological order. The application and effectiveness of each strategy are summarized, and the issues found by analysis are addressed by the review. Hybrid Computational Intelligence (CI)-ML and DL traffic prediction systems are found to be better than previous approaches. Open questions and future possibilities for DL and hybrid traffic prediction systems are also discussed. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is advancing rapidly, and this has improved the efficiency of many businesses and industries, including the transportation industry. AI in transportation might minimize travel, lower emissions and make roadways safer. Some examples of AI technologies used in the transportation sector include: Artificial Neural Networks (ANN), Genetic Algorithms (GA), Simulated Annealing (SA), Artificial Immune System (AIS), Ant Colony Optimizer (ACO) and Bee Colony Optimizer (BCO). The paper explores how AI is being used to address some of the most pressing issues in transportation today, including traffic control and safety. The limitations of AI transport are also discussed. [ABSTRACT FROM AUTHOR]

Computerized image processing techniques are extremely useful in agriculture. The technology can help detect plant diseases and improve cultivation quality. The study examines the advantages and disadvantages of previous research on the subject. To find the most effective image processing methods for diagnosing plant diseases, cutting-edge techniques are examined. To find plant pathogens, many computerized image processing methods are used. This review compares the results and many different approaches to develop algorithms such as Support Vector Machines (SVM) and Deep Learning Neural Networks (DLNN), which are important in the detection and classification of leaf diseases. [ABSTRACT FROM AUTHOR]

The paper studies the approaches to detecting, evaluating and categorizing plant diseases from digital images in the visible spectrum using appropriate processing techniques. Despite the fact that disease symptoms might appear anywhere on the plant, only approaches that looked at obvious symptoms in leaves and stems were examined. This was designed for various reasons: to keep the report short and because methods dealing with roots, seeds, and fruits have some unique characteristics that would necessitate a separate survey. The concepts chosen are organized into three categories based on their goal: detection, severity quantification and categorization. Each classification is further categorized based on the algorithm's primary technical solution. The paper also examines and contrasts the benefits and drawbacks of different prospective strategies. Image acquisition, image preprocessing, feature extraction and neural network-based categorization are a few of the techniques included. Researchers working on both vegetable pathology and pattern recognition can benefit from this study, which provides a detailed and accessible summary of this vital field of research. [ABSTRACT FROM AUTHOR]

In the current health care scenario, security is the major concern in IoT-WSN with more devices or nodes. Attack or anomaly detection in the IoT infrastructure is increasing distress in the field of medical IoT. With the enormous usage of IoT infrastructure in every province, threats and attacks in these infrastructures are also mounting commensurately. This paper intends to develop a security mechanism to detect and prevent the black hole and selective forwarding attack from medical IoT-WSN. The proposed secure strategy is developed in five stages: First is selecting the cluster heads, second is generating k-routing paths, third is security against black hole attack, fourth is security against the selective forwarding attack, and the last is optimal shortest route path selection. Initially, a topology is developed for finding the cluster heads and discovering the best route. In the next phase, the black hole attacks are detected and prevented by the bait process. For detecting the selective forwarding attacks, the packet validation is done by checking the transmitted packet and the received packet. For promoting the packet security, Elliptic Curve Cryptography (ECC)-based hashing function is deployed. As the main contribution of this paper, optimal shortest route path is determined by the proposed hybrid algorithm with the integration of Deer Hunting Optimization Algorithm (DHOA), and DragonFly Algorithm (DA) termed Dragonfly-based DHOA (D-DHOA) by concerting the parameters like trust, distance, delay or latency and packet loss ratio in the objective model. Hence, the entire phases will be very active in detecting and preventing the two fundamental attacks like a black hole and selective forwarding from IoT-WSN in the health care sector. [ABSTRACT FROM AUTHOR]