Sohaib Ahmed

Majid Ali, Hina Shakir, Asia Samreen et al.

Accurate diagnosis of Parkinson disease, especially in its early stages, can be a challenging task. The application of machine learning techniques helps improve the diagnostic accuracy of Parkinson disease detection but only few studies have presented work towards the prediction of disease progression. In this research work, Long Short Term Memory LSTM was trained using the diagnostic features on Parkinson patients speech signals, to predict the disease progression while a Multilayer Perceptron MLP was trained on the same diagnostic features to detect the disease. Diagnostic features selected using two well-known feature selection methods named Relief-F and Sequential Forward Selection and applied on LSTM and MLP have shown to accurately predict the disease progression as stage 2 and 3 and its existence respectively.

Zain Mumtaz, Zeeshan Ilyas, Ahmed Sohaib et al.

Our proposed system has seven main contributions, i.e., Smart street lights, Smart home, Bio-metric door and home security system, Intelligent traffic lights management and road security system, Private and smart parking, Intelligent accident management system and Smart information display/ notice board system. Our prototypes / products employ Arduino UNO board, Node MCU, Ultrasonic sensor, Fingerprint module, Servo motors, GSM, GPS, LEDs, Flame Sensor, Bluetooth and Wi-Fi module etc. We are very confident that our proposed systems are efficient, reliable, and cost-effective and can be easily tested and implemented on a large scale under real conditions.