Paper deals with the predictive apriori algorithm for a disease related to hemoglobin blood test data. The dataset has been collected from public dataset. The dataset was pre-processed to remove missing and unwanted d...
详细信息
The fundamental security aspect of the classical crypto-system depends on integer factorization and discrete logarithm problems. The quantum factorization problem is a crucial problem in quantum computing as it has si...
详细信息
Supervised learning algorithms are effective in most application domains, however they have limitations. A single learning model may miss out on some local regions of the feature space, impacting overall performance. ...
详细信息
Supervised learning algorithms are effective in most application domains, however they have limitations. A single learning model may miss out on some local regions of the feature space, impacting overall performance. Ensemble learning techniques can be helpful here as they bring together a diverse set of learners, ensuring that even if one misses a region of the feature space other members in the ensemble may be able to learn the pattern. It aims to combine the output of the individual learners in such a way that it can yield best possible final prediction. The purpose of the study is to empirically investigate the performance of different ensemble methods in conjunction with hyper-parameter tuning of the predictive models. We perform an experimental investigation with ensemble learning methods namely Bagging, Boosting, Bagging-Boosting and Stacking using different benchmark datasets. The investigation is based on a data-centric supervised ensemble framework comprising of five engines each with its own functionality. Feature engineering.and parameter tuning engines are the highlights of the framework, where relevant-independent features are selected and appropriate set of hyper-parameter values for the participating predictive models are experimentally found out. Extensive detailed experiments are conducted on 25 datasets. For all security datasets we indicated the optimal list of features. For Bagging, Support Vector Machine (SVM) emerged as winner for all the dataset. On the other hand, Gradient Boosting (GB) is the winner in case of Boosting, while both Adaboost and Extreme Gradient Boosting (XGB) performs well in Bagging-Boosting methods. The implications of the empirical study is that the quality of the learning process of any predictive model depends on a number of factors such as choice of hyper-parameters, values of the hyper-parameters, quality of the data and also the pre-processing techniques employed. Also, predictive modeling when combined with hyper-pa
The aim of this research is to design a facial emotion recognition system based on Raspberry Pi and Convolutional Neural Network (CNN) for analyzing customers' facial expressions in academic customer service. The ...
详细信息
Due to densely populated urban environment leads to huge traffic in peak hours, Intelligent traffic light management system becomes paramount for emergency vehicle transportation on leveraging the sensor technologies....
详细信息
ISBN:
(纸本)9798331505745
Due to densely populated urban environment leads to huge traffic in peak hours, Intelligent traffic light management system becomes paramount for emergency vehicle transportation on leveraging the sensor technologies. However sensor data acquired from densely populated urban environment helps to process the traffic congestion based traffic density. Many researches has been carried out to enable intelligent transportation system using internet of things, artificial intelligence and communication technologies but still it requires sustainable solutions for intelligent transportation., traffic congestion management, traffic light controlling with respect to the detection of emergency vehicles like ambulance as it saves the life of the human being. In this paper, AI driven Intelligent of Things enabled sustainable solutions for intelligent traffic light management system for emergency vehicles in the large scale urban traffic. Initially sensor or camera deployed in the smart cities monitors the roads and its surroundings environments. Those acquired information is transmitted to the base station containing IoT servers. In IoT Server., video data is transformed into image frames and processed using YoloV9 based AI model. YoloV9 Model uses multiple component like backbone., neck and head for processing the image frame to recognize and tack the objects in each frame. Especially Backbone model employs convolution neural network for multi scale feature extraction and feature map generation on inclusion of the Generalized Efficient aggregation Network while neck component uses the path aggregation network for future fusion process and head component uses anchor box bounding box prediction method to detect and recognize the object of interest. On detect of the object of interest, distance and speed of the object is computed using gradient flow. Further model incorporates prediction approaches to detected emergency vehicle to estimate its speed and distance from traffic signal
The advanced integrated circuits have been widely used in various situations including the Internet of Things,wireless communication,*** its manufacturing process exists unreliability,so cryptographic chips must be ri...
详细信息
The advanced integrated circuits have been widely used in various situations including the Internet of Things,wireless communication,*** its manufacturing process exists unreliability,so cryptographic chips must be rigorously *** to scan testing provides high test coverage,it is applied to the testing of cryptographic integrated ***,while providing good controllability and observability,it also provides attackers with a backdoor to steal *** the text,a novel protection scheme is put forward to resist scan-based attacks,in which we first use the responses generated by a strong physical unclonable function circuit to solidify fuseantifuse structures in a non-linear shift register(NLSR),then determine the scan input code according to the configuration of the fuse-antifuse structures and the styles of connection between the NLSR cells and the scan *** the key is right,the chip can be tested normally;otherwise,the data in the scan chain cannot be propagated normally,it is also impossible for illegal users to derive the desired scan *** proposed technique not only enhances the security of cryptographic chips,but also incurs acceptable overhead.
Birds are a huge hazard to agriculture all around the world,causing harm to profitable field *** use a variety of techniques to keep them away,including visual,auditory,tactile,and olfactory deterrents. This study pre...
详细信息
Birds are a huge hazard to agriculture all around the world,causing harm to profitable field *** use a variety of techniques to keep them away,including visual,auditory,tactile,and olfactory deterrents. This study presents a comprehensive overview of current bird repellant approaches used in agricultural contexts,as well as potential new ways. The bird repellent techniques include Internet of Things technology,Deep Learning,Convolutional Neural Network,Unmanned Aerial Vehicles,Wireless Sensor Networks and Laser biotechnology. This study’s goal is to find and review about previous approach towards repellent of birds in the crop fields using various technologies.
This study investigates the effectiveness of two fairness-based distribution approaches—type I and type II—in enhancing the resilience of supply chain networks (SCNs) during disruptions. The research contributes to ...
详细信息
Nowadays online users are prone to lot of security related issues in protecting their data. In order to achieve this privacy preservation in cloud plays a major role. For this purpose various technologies related to c...
详细信息
With the continuous rise in computer tech, programmers and hacking occurrences are expanding and require additional security requests. Malware has been an extraordinary torment for computer clients around the world. B...
详细信息
暂无评论