Abstract Cephalometric analysis has an important and essential role to treat the patients with craniofacial and dentofacial deformities. Cephalometric analysis is a relationship of human geometry which can be quantified and derived from the linear and angular measurements. To treat any patient, such analysis is required to be performed on the Head X‐ray image of the patient. The objective of the proposed work is to detect cephalometric landmarks automatically on CT (computational tomography) images. Twenty cephalometric landmarks were automatically localized on 100 CT scans using hybrid coarse‐to‐fine computational method. The mean error for landmark detection was computed as 2.88 mm and standard deviation of 1.85 mm. The highest detection rate for cephalometric landmarks was received as 100% for Nasion landmark under 4‐mm error and the highest detection rate was received as 99% for Nasion landmark under 3‐mm error. The less number of datasets were used for the training and higher number of datasets were used for the testing. Compared to the literature methods, our method used higher number of datasets to demonstrate the accuracy of the proposed method.

Abstract Intelligent and networked vehicles help build an efficient vehicular network's infrastructure. The widespread use of electronic software exposes these networks to cyber‐attacks. Intrusion detection systems (IDS) are useful for preventing vehicle network assaults. IDS have been customized using machine and deep learning networks for greater real‐time performance. Current learning‐based intrusion detection systems demand substantial processing capabilities to train and update intricate training models in vehicular devices, resulting in decreased efficiency and ability to defend against assaults. This study presents Blockchain‐based Multi‐Layer Federated Extreme Learning Machines (MLFEM) enabled IDS (BEF‐IDS) for safe data transfers. The proposed IDS leverages federated learning to generate Multi‐Layered Extreme Learning Machines, which are offloaded to dispersed vehicular edge devices such as Road‐Side Units (RSU) and connected vehicles. This federated strategy decreases resource use without sacrificing security. Blockchain technology records and shares training models, assuring network security. Using real‐time data sets, the suggested algorithm's performance under different attack scenarios were extensively tested. The suggested method obtained 98% accuracy and Recall, 97.9% Precision, and 97.9% F1 Score performance, which suggests it's incredibly secure and costs very little to transmit.

Traffic problems continue to deteriorate because of increasing population in urban areas that rely on many modes of transportation, the transportation infrastructure has achieved considerable strides in the last several decades. This has led to an increase in congestion control difficulties, which directly affect citizens through air pollution, fuel consumption, traffic law breaches, noise pollution, accidents, and loss of time. Traffic prediction is an essential aspect of an intelligent transportation system in smart cities because it helps reduce overall traffic congestion. This article aims to design and enforce a traffic prediction scheme that is efficient and accurate in forecasting traffic flow. Available traffic flow prediction methods are still unsuitable for real-world applications. This fact motivated us to work on a traffic flow forecasting issue using Vision Transformers (VTs). In this work, VTs were used in conjunction with Convolutional neural networks (CNN) to predict traffic congestion in urban spaces on a city-wide scale. In our proposed architecture, a traffic image is fed to a CNN, which generates feature maps. These feature maps are then fed to the VT, which employs the dual techniques of tokenization and projection. Tokenization is used to convert features into tokens containing Vision information, which are then sent to projection, where they are transformed into feature maps and ultimately delivered to LSTM. The experimental results demonstrate that the vision transformer prediction method based on Spatio-temporal characteristics is an excellent way of predicting traffic flow, particularly during anomalous traffic situations. The proposed technology surpasses traditional methods in terms of precision, accuracy and recall and aids in energy conservation. Through rerouting, the proposed work will benefit travellers and reduce fuel use.

In the process of drone development, most of the current state systems’ design is based on high-weight functionalities. Due to high-weight functionalities, it is observed that if the drone drops at a particular point, the entire design is fragmented. Also, well-defined functionalities of drones for a specific application can only be designed if radial functionalities are defined at proper angles. Therefore, this article addresses the issues present in the existing method using the CRA algorithm, where radial functions, represented in terms of input and hidden weighting functions, are explored utterly. Additionally, a novel analytical procedure that establishes the coverage area for the data transfer approach has been incorporated into the drones’ architecture. Additionally, employing motion signatures and a special identification system, the developed drone system can function along various paths. To evaluate the effectiveness of the suggested system, three scenarios are organized as a basic functionality model. With the right scattering ratio, the comparison inscriptions show that the proposed approach can achieve an 82% success rate.

Abstract The internet of vehicle (IoV) orchestration is an emerging technology in heterogeneous vehicles to contrivance diverse intelligent transportation applications. The roadside unit (RSU) plays a vital role during service provisioning. Vehicle‐to‐vehicle and vehicle‐to‐infrastructure communications have consistently accomplished the services in a vehicular network. However, persisting the increased vehicles' quality of experience and network vendors' utilities and which RSUs have to select for effective, reliable service are critical open research challenges to consolidate RSU services to enhance network service utility rate. In this article, we design a deep learning‐inspired RSU Service Consolidation Approach based on two‐models to enhance the service reliability by formulating the RSU coverage issue with the RSU Migration model and content delivery issue with Linear Programming‐based Multicast model. Adaptive Packet‐Error measurement system to optimize service reliability rate at the edge of cooperative vehicular network based on content correlation. The performance and efficiency are examined based on MATLAB. The simulation outcome shows RSC approach has low execution cost by 39%, service reliability rate by 71% than the state‐of‐art approaches.

Elderly people are the assets of the country and the government can ensure their peaceful and healthier life. Life expectancy of individuals has expanded with technological advancements and survey tells that the elderly population will become double in the year 2030. The noninfectious cognitive dysfunction is the most important risk factor among elderly people due to a decline in their physiological function. Alzheimer, Vascular Dementia, and Dementia are the key reasons for cognitive inabilities. These diseases require manual assistance, which is difficult to provide in this fast-growing world. Prevention and early detection are the wise solution for the above diseases. Diabetes and hypertension are considered as main risk factors allied with Alzheimer's disease. Our proposed work applies a two-stage classification technique to improve prediction accuracy. In the first stage, we train a Support vector machine and a Random Forest algorithm to analyze the influence of diabetes and high blood pressure on cognitive decline. In the second stage, the cognitive function of the person with the possibility of Dementia is assessed using the neuropsychological test called Cognitive Ability Test (CAT). Multinomial Logistic Regression algorithm is applied to CAT results to predict the possibility of cognitive decline in their postlife. We classified the risk factor using the operational definitions: “No Alzheimer’s,” “Uncertain Alzheimer’s,” and “Definite Alzheimer’s”. SVM of stage 1 classifier predicts with an accuracy of 0.86 and Random Forest with an accuracy of 0.71. Multinomial Logistic algorithm of stage 2 classifier accuracy is 0.89. The proposed work enables early prediction of a person at risk of Alzheimer's Disease using clinical data.

Internet of Things (IoT) is a phenomenon involving connecting things or objects with sensors. The IoT market is growing rapidly, and there are strong incentives for companies to follow the trend of IoT growth and development. However, the percentage of IoT measures that are considered successful seems low. The complexity of carrying out an IoT project lies in the need to adjust all the pieces of the puzzle: assets, sensors, communications, technology, coverage, and geographical locations with precision of the measures and regulations. All these requirements determine the economic viability of the business and its benefit. This study, therefore, examines how the project methodology can support the development of the concept and ensure the business value of IoT initiatives. The project methodology developed in this study is called PoC Design. A case study was evaluated, in which defects in street lighting were investigated and carried out. The evaluation of the methodology highlighted the importance of defining problems and solutions based on business value, calculating the potential of an IoT initiative, determining the continuation of the project, involving stakeholders at an early stage, and creating a PoC to validate the concept with stakeholders.

The vehicular adhoc network (VANET) is an emerging research topic in the intelligent transportation system that furnishes essential information to the vehicles in the network. Nearly 150 thousand people are affected by the road accidents that must be minimized, and improving safety is required in VANET. The prediction of traffic congestions plays a momentous role in minimizing accidents in roads and improving traffic management for people. However, the dynamic behavior of the vehicles in the network degrades the rendition of deep learning models in predicting the traffic congestion on roads. To overcome the congestion problem, this paper proposes a new hybrid boosted long short‐term memory ensemble (BLSTME) and convolutional neural network (CNN) model that ensemble the powerful features of CNN with BLSTME to negotiate the dynamic behavior of the vehicle and to predict the congestion in traffic effectively on roads. The CNN extracts the features from traffic images, and the proposed BLSTME trains and strengthens the weak classifiers for the prediction of congestion. The proposed model is developed using Tensor flow python libraries and are tested in real traffic scenario simulated using SUMO and OMNeT++. The extensive experimentations are carried out, and the model is measured with the performance metrics likely prediction accuracy, precision, and recall. Thus, the experimental result shows 98% of accuracy, 96% of precision, and 94% of recall. The results complies that the proposed model clobbers the other existing algorithms by furnishing 10% higher than deep learning models in terms of stability and performance.