Progression in Computer networks and emerging of new technologies in this field helps to find out new protocols and frameworks that provides new computer network-based services. E-government services, a modernized version of conventional government, are created through the steady evolution of technology in addition to the growing need of societies for numerous services. Government services are deeply related to citizens’ daily lives; therefore, it is important to evolve with technological developments—it is necessary to move from the traditional methods of managing government work to cutting-edge technical approaches that improve the effectiveness of government systems for providing services to citizens. Blockchain technology is among the modern technologies highly suitable for developing digital governance services, and its technological ability to sustain information stability is vital in digital governance systems since it improves integrity and transparency measures while preventing corruption. In this study, computer networking protocols are built to form a peer-to-peer network framework for managing official documents. using blockchain technology was built to illustrate how any element of government work may be developed using it. The suggested framework comprises the addition of a new official document, and the verification of an existing document. The system was created in socket programming using Java and tested the response times for many simultaneous requests. The system was tested using transactions per second (throughput) measurement. The result showed that the proposed system processed 200 document verification transactions within 50 seconds. In addition, the test of the proposed system presented the time required for document retrieval—about three seconds to answer 100 document retrieval transactions. Furthermore, the results of throughput were compared to the results of the same measurement of some popular applications such as bitcoin. And the result of the proposed system was within the average value of output throughput of the other compared applications.

Audio command recognition methods are essential to be recognized for performing user instructions, especially for people with disabilities. Previous studies couldn’t examine and classify the performance optimization of up to twelve audio commands categories. This work develops a microphone-based audio commands classifier using a convolutional neural network (CNN) with performance optimization to categorize twelve classes including background noise and unknown words. The methodology mainly includes preparing the input audio commands for training, extracting features, and visualizing auditory spectrograms. Then a CNN-based classifier is developed and the trained architecture is evaluated. The work considers minimizing latency by optimizing the processing phase by compiling MATLAB code into C code if the processing phase reaches a peak algorithmically. In addition, the method conducts decreasing the frame size and increases the sample rate that is also contributed to minimizing latency and maximizing the performance of processing audio input data. A modest bit of dropout to the input to the final fully connected layer is added to lessen the likelihood that the network will memorize particular elements of the training data. We explored expanding the network depth by including convolutional identical elements, ReLu, and batch normalization layers to improve the network's accuracy. The training progress demonstrated how fast the accuracy of the network is increasing to reach about 98.1 %, which interprets the ability of the network to over-fit the data of training. This work is essential to serve speech and speaker recognition such as smart homes and smart wheelchairs, especially for people with disabilities

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