In this modern era, the use of vehicles is increasing tremendously due to the increase in the population. The escalating issue of traffic congestion has emerged as a critical challenge in the real world. We currently face a lack of viable solutions to efficiently manage and alleviate traffic-related issues. In the event of an accident, our ability to swiftly locate the incident and initiate requisite actions is hindered by existing constraints, contributing to delays in addressing the situation effectively. The delay in finding what the incident is and locating the problem results in the increased severity of the situation. In response to these challenges, we have devised a solution through deep learning models. Upon the occurrence of an incident, our system promptly dispatches immediate alerts to authorized personnel, and checks for the manipulation of the traffic signals, facilitating a faster and more efficient response. Therefore, action could be taken at the same time as the incident, which will reduce the impact of the situation.
Pests play a significant impact in crop destruction. Currently, crop yields are being reduced as a result of pest- infested crops, resulting in a reduction in output rate. The possibility of identifying the plant disease in an unfavorable environment is not pursued further. The key problem is to reduce pesticide use in the agricultural field while increasing the assembly rate's standard and quantity. This paper is laboring to explore the plant disease prediction at an untimely action. In spite of traditional convolution neural network (CNN)-based approaches, new method for detecting and recognizing insect pests have been created to produced adequate results. To optimize various parameters, CNN-based approaches necessitate a huge dataset. As a result, CNN-based two-stage recognition and identification process for insect pests is proposed. A region suggestion network for insect pest detection using YOLOv3 and a re-identification method are also presented in this paper. To train these models, knowledge augmentation method using image processing is proposed. YOLOv3 provides 92.11% accuracy when compared to CNN during pest detection.
Predictive maintenance is to foresee the failures and to take the preemptive actions. The recent advancements in cloud storage have provided an incredible opening to store the data coming from different sources such as factories, buildings, machines and sensors. The stored data are not only used to monitor the devices connected with the cloud but used to predict the failure of the devices in advance. The proposed system is to predict the failure of an induction motor and to schedule the preventive maintenance. Usually, induction motors are widely used in conveyer belts, elevators, compressors and pumps etc. The productivity of an induction motor can be increased by decreasing the fault occurrence. The various parameters which affect the performance are voltage, current, vibration, speed, power factor and frequency. Accelerometer is used for finding the vibration of the motor. Current sensor, voltage sensor and speed sensor are used to measure current, voltage and speed of the induction motor. The values from the sensors are digitized and stored in the cloud for further processing. Machine Learning (ML) based predictive model is developed based on the historical data from the sensors during normal and abnormal conditions of the induction motor. The induction motor's behavior is continuously monitored with respect to current operating characteristics. This can be monitored remotely in a dashboard using PC, Tablets or Mobile. The ML prediction algorithm generates the maintenance call automatically and thus eliminates the breakdown.
A developed urban city is always accustomed to lots of cars on its roads but is alternatively not equipped with the required parking space. The increased scalability of the number of vehicles due to increase in population makes allotment of parking space challenging. The other difficulties faced are congestion created due to increased vehicles, wastage of space and time, traffic issues, difficulty in cruising etc. Every time the person driving a vehicle enters the parking lot, they attempt to find an empty space for the vehicle to be parked which leads to loss of time and energy, especially during peak hours. Smart Parking Assistant provides an optimized solution to overcome the above problems. It also provides a sophisticated parking assistance in economical cars. It mainly deals to avoid crash and confusion during car parking in unorganized and unknown parking areas.
A cluster of wind turbines in the same site that generates power. Using turbines perform effectively with severe winds and optimal wind speed. For a wind farm, the wind direction and speed can be projected that wind turbines would operate efficiently. So, the wind generators' output will be having increased effectiveness. Big data and machine learning are defined as a large collection of datasets that are advanced to process. Wind speed forecasting is one of the most critical responsibilities in a wind farm. Machine learning approaches are frequently used to forecast time series non-linear wind behavior. This research provides a wind dataset prediction model that relies on the Extra Tree classifier in this context. The proposed model has the benefit of being simple, quick, and well-suited to the short term. The accuracy of the project is then compared with bagging classifier and Ada boost Classifier algorithms in their regression mode, and then the project aims to illustrate how wind direction may affect power generation and why it is vital to anticipate it. A real-time series data collection contains past values of characteristics like speed of wind, temperature, and atmospheric pressure, they are used to forecast the speed of the wind. The suggested model Extra Tree classifier will be evaluated using Mean Absolute, Mean Square Error values, and its performance will be compared to that of bagging classifier and Ada boost Classifier algorithm models.
Machine learning (ML) and deep learning (DL) are used in numerous fields, particularly to develop effective intrusion detection systems (IDS). Existing wireless network IDS, which rely on a single ML algorithm and have limitations. These include a high rate of false positives, difficulties in recognizing distinct attack patterns, and a high acquisition cost for annotated training datasets. However, hostile threats are always evolving, networks need a smart security solution. In comparison to other ML approaches, DL algorithms are more successful in intrusion detection. This paper presents a DL based ensemble model that combines Multi-verse through Chaotic Atom Search Optimization (MCA) for preprocessing, which eliminates unsolicited/recurrent information in the dataset. The process of optimized feature selection uses Principal Component Analysis (PCA), Chaotic Manta-ray Foraging Optimizations (CMFO), and a grounded grouping method to partition the optimized feature dataset into k-diverse clusters. The recommended model then stacks Support Vector Machine (SVM) as the ensemble model's meta-learner classifier, pre-training the hybrid DL prototypes using the optimized feature dataset cluster. The CNN-LSTM and CNN-GRU models, which integrate Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), are the hybrid DL prototype's key components. The suggested model's performance has been enhanced and compared to six ML techniques: NB, SVM, J48, RF, MLP, and kNN models, utilizing measures such as accuracy, precision, recall, and F-measure. The public can access the Aegean Wi-Fi Intrusion Dataset (AWID) which is used for evaluating the recommended model and is outperformed the contemporary models in the literature.
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