A Hybrid Classification and Identification of Pneumonia Using African Buffalo Optimization and CNN from Chest X-Ray Images

An illness known as pneumonia causes inflammation in the lungs.Since there is so much information available fromvarious X-ray images,diagnosing pneumonia has typically proven challenging.To improve image quality and speed up the diagnosis of pneumonia,numerous approaches have been devised.To date,several methods have been employed to identify pneumonia.The Convolutional Neural Network(CNN)has achieved outstanding success in identifying and diagnosing diseases in the fields of medicine and radiology.However,these methods are complex,inefficient,and imprecise to analyze a big number of datasets.In this paper,a new hybrid method for the automatic classification and identification of Pneumonia from chest X-ray images is proposed.The proposed method(ABOCNN)utilized theAfrican BuffaloOptimization(ABO)algorithmto enhanceCNNperformance and accuracy.The Weinmed filter is employed for pre-processing to eliminate unwanted noises from chest X-ray images,followed by feature extraction using the Grey Level Co-Occurrence Matrix(GLCM)approach.Relevant features are then selected from the dataset using the ABO algorithm,and ultimately,high-performance deep learning using the CNN approach is introduced for the classification and identification of Pneumonia.Experimental results on various datasets showed that,when contrasted to other approaches,the ABO-CNN outperforms them all for the classification tasks.The proposed method exhibits superior values like 96.95%,88%,86%,and 86%for accuracy,precision,recall,and F1-score,respectively.

Context-aware GPS integrity monitoring for intelligent transport systems

The integrity of positioning systems has become an increasingly important requirement for location-based intelligent transport systems （ITS）, for example electronic toll collection （ETC）, public transport operations and traffic control services. In ITS, satellite navigation systems, such as global positioning system （GPS）, are used to provide real-time vehicle positioning information including details of longitude, latitude, direction and speed. Map matching algorithms are used to integrate the positioning information into the digital road map. However, the navigation systems used in ITS cannot provide the high quality positioning information required by most services, due to the various types of errors made in the map matching process and experienced by GPS sensors such as signal outage, and errors due to atmospheric effects and receiver measurement errors, all of which are difficult to measure. An error in the positioning information or map matching process might lead to the inaccurate determination of a vehicle location. This could have legal or economic consequences for ITS applications such as traffic law enforcement systems （e.g., speed fining）. Such applications require integrity when measuring the vehicle position and speed information and in the map matching process when locating the vehicle on the correct road segment to avoid errors when charging drivers. Consequently, the integrity algorithm for the navigation system should include a guarantee that the systems do not produce misleading or faulty information as this may lead to significant errors in the ITS services provided. In this paper, a high integrity GPS monitoring algorithm based on the concept of context-awareness that can be applied with real time ITS services to integrate changes in the integrity status of the navigation system was developed. Results suggest that the new integrity algorithm can support various types of location-based ITS services （e.g., route guidance）.