The research area of Vehicular Ad-Hoc Network (VANET) is rapidly expanding for different reasons. The main factors responsible for this are the unstable topology, fast vehicle movement, and security vulnerabilities. Identifying malicious attacks in vehicular ad-hoc networks is necessary to enhance the security and reliability of communication between all vehicles in the system. The intruders carry out numerous malicious attacks. The focus of this paper is to identify and mitigate attacks like Botnet, Sybil, DoS, Wormhole, PortScan, Blackhole, and BruteForce. The present work proposes an Improved LeeNET (I-LeeNet) architecture to identify and mitigate unidentified attack types. The suggested architecture intelligently blends Convolutional Neural Networks (CNN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to provide real-time solutions to unidentified attacks. The proposed approach includes a module KIDS for known attack detection and another module UIDS for learning and identifying previously unidentified attacks. The KIDS module uses ANFIS classification to identify destructive attacks. ANFIS and CNN use the UIDS module to identify unknown attacks on VANET further. The efficacy of the proposed I-LeeNet is tested on three different datasets, such as i-VANET, ToN-IoT, and CIC-IDS 2017. The results section discusses the testing in a regressive manner. In addition, the experimental results are compared to other advanced methods. The average accuracy achieved by the proposed method is 97.21% on i-VANET, 97.75% on ToN-IoT, and 96.66% on the CIC-IDS 2017 dataset. The analysis suggests the proposed method is promising and can be applied in real-time.

Diabetes, afflicting 537 million worldwide, is a prevalent and lethal non-communicable ailment. Its onset, influenced by factors like obesity and family history, manifests symptoms such as frequent urination. Long-term complications encompass heart, kidney, and nerve ailments. Early prediction mitigates risks. All these encompassing strategies are designed to improve prediction precision and facilitate proactive diabetes control. This research employed SMOTE methods to tackle imbalanced classes, utilizing various classification algorithms such as Random Forest, XGBoost, Multilayer Perceptron, Gradient Boost, and AdaBoost. Following extensive training and evaluation, the AdaBoost classifier delivered superior outcomes, achieving a 94.02% accuracy rate, an F1 score of 93.32%, and an AUC of 0.95. In the healthcare industry, accurately forecasting diabetes mellitus is crucial; however, privacy laws hinder the transfer of medical information from the Internet of Medical Things (IoMT), causing delays in diagnosis. This study introduces the Federated Learning with Weighted Conglomeration Optimization (FLWCO) model as a solution to these challenges. In Centralized Learning, AdaBoost with WCO achieves an accuracy of 95.32% when tested on a Kaggle dataset consisting of 96,146 instances. During the second stage, FLWCO achieves a superior 97.27% accuracy rate compared to other federated learning techniques. The method not only guarantees privacy conformity but also decreases communication expenses. FLWCO demonstrates superiority over existing federated learning algorithms in real-world heart illness prediction. Furthermore, the proposed model can be employed to estimate the likelihood of heart disease in individuals with diabetes. This highlights the potential of federated learning, especially FLWCO, in leveraging distributed data while preserving privacy, facilitating accurate diabetes mellitus diagnosis, and addressing challenges in sharing medical information securely and efficiently.

Road transportation is the most common and oldest mode of transportation. It provides quick connectivity to small businesses having customers nearby areas, transportation of perceivable products in nearby markets. Not only the nearby market, also for international market supply transferring goods from the supply unit to the railway station, ports, or airports. Also, from day to day life commute from home to the office for a service class person, from home to schools/colleges for students, commuting to hospitals, banks, malls are done by road transportation only. It has an essential contribution to economic growth. Although road transportation is an essential and most common mode of connectivity, it has a higher probability of road crashes than any other transportation mode. These results a lot of injuries, deaths in every year and every city. Government has lot of road safety protocols, rules, and regulations to prevent road accidents. Every year government is spending a lot for road safety. Not only government car/vehicle manufacture keep on redesigning the vehicle with multiple inbuilt safety equipment’s. This paper is proposing a device with a key feature of lane detection. The device is featured with monitoring of engine, tires and other peripherals. The device can be mounted in vehicle, and can make an ordinary vehicle to semi-smart vehicle. This paper explains a feature of the device that is lane detection. An identification of tracks and obstruction on streets is not an easy task to detect with proximity sensors and an IR sensor. So that a digital image processing based lane detection technique had been implemented in this paper. This technique is very efficient to detect the road path and obstructions on the street on different scope of ecological conditions under which these frameworks work: rain, shadow, daylight, day, night, haze and so forth.