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Epilepsy is a common neurological disorder that results in seizures in patients of all ages. The frequency of seizure episodes can be controlled by prescribing anti-seizure drugs. Drug-resistant epilepsy is a condition where the seizures are uncontrolled by strong medications. Such patients are at a high risk of getting seizures frequently and prone to injuries due to sudden falls. Many countries prohibit epileptic patients from driving as sudden seizure attacks can cause loss of lives and property. In the past decades immense work has been carried out in the to monitor the seizure activity in patients and alert caregivers to extend help in emergencies. The study proposes a smart health care Internet of things framework to provide immediate help to the epileptic patient during an episode while travelling in a self-driving car. In the proposed framework, the seizure alert from a wearable device of the patient is transmitted to the control application via cloud. The control application also receives data from the vision, ultrasonic, and radar sensors. The critical information of seizure alert and the sensor data commands the car to force stop. The seizure google map location of the car is sent to the patient’s caregiver as well as the registered hospital. Many applications are being developed to provide luxury and comfort to fully automated car drivers. Far from providing luxury to the driver Emergency Rapid Response to Epileptic Seizure aims to propose a solution that could save the life of an epileptic patient who is drug-resistant or prone to frequent attacks despite severe medications. The experimental results on synchonization of clouds show that the minimum time is 30 sec 30 ms and maximum time is 31 sec 63 ms. The experimental results prove that its recommended to alert the patient's caregiver directly from control application rather than alerting via cloud.

Cancer is presently one of the prominent causes of death in the world. Early cancer detection, which can improve the prognosis and survival of cancer patients, is challenging for radiologists. Low-dose computed tomography, a commonly used imaging test for screening lung cancer, has a risk of exposure of patients to ionizing radiations. Increased radiation exposure can cause lung cancer development. However, reduced radiation dose results in noisy LDCT images. Efficient preprocessing techniques with computer-aided diagnosis tools can remove noise from LDCT images. Such tools can increase the survival of lung cancer patients by an accurate delineation of the lung nodules. This study aims to develop a framework for preprocessing LDCT images. The authors propose a noise removal technique of discrete wavelet transforms with adaptive thresholding by computing the threshold with a genetic algorithm. The performance of the proposed technique is evaluated by comparing with mean, median, and Gaussian noise filters.

Cancer is presently one of the prominent causes of death in the world. Early cancer detection, which can improve the prognosis and survival of cancer patients, is challenging for radiologists. Low-dose computed tomography, a commonly used imaging test for screening lung cancer, has a risk of exposure of patients to ionizing radiations. Increased radiation exposure can cause lung cancer development. However, reduced radiation dose results in noisy LDCT images. Efficient preprocessing techniques with computer-aided diagnosis tools can remove noise from LDCT images. Such tools can increase the survival of lung cancer patients by an accurate delineation of the lung nodules. This study aims to develop a framework for preprocessing LDCT images. The authors propose a noise removal technique of discrete wavelet transforms with adaptive thresholding by computing the threshold with a genetic algorithm. The performance of the proposed technique is evaluated by comparing with mean, median, and Gaussian noise filters.