Putra Sumari

Mustafa M. Abd Zaid, Ahmed Abed Mohammed, Putra Sumari

Satellite imagery has dramatically revolutionized the field of geography by giving academics, scientists, and policymakers unprecedented global access to spatial data. Manual methods typically require significant time and effort to detect the generic land structure in satellite images. This study can produce a set of applications such as urban planning and development, environmental monitoring, disaster management, etc. Therefore, the research presents a methodology to minimize human labor, reducing the expenses and duration needed to identify the land structure. This article developed a deep learning-based approach to automate the process of classifying geographical land structures. We used a satellite image dataset acquired from MLRSNet. The study compared the performance of three architectures, namely CNN, ResNet-50, and Inception-v3. We used three optimizers with any model: Adam, SGD, and RMSProp. We conduct the training process for a fixed number of epochs, specifically 100 epochs, with a batch size of 64. The ResNet-50 achieved an accuracy of 76.5% with the ADAM optimizer, the Inception-v3 with RMSProp achieved an accuracy of 93.8%, and the proposed approach, CNN with RMSProp optimizer, achieved the highest level of performance and an accuracy of 94.8%. Moreover, a thorough examination of the CNN model demonstrated its exceptional accuracy, recall, and F1 scores for all categories, confirming its resilience and dependability in precisely detecting various terrain formations. The results highlight the potential of deep learning models in scene understanding, as well as their significance in efficiently identifying and categorizing land structures from satellite imagery.

Mustafa Majeed Abd Zaid, Ahmed Abed Mohammed, Putra Sumari

This study investigates the classification of aerial images depicting transmission towers, forests, farmland, and mountains. To complete the classification job, features are extracted from input photos using a Convolutional Neural Network (CNN) architecture. Then, the images are classified using Softmax. To test the model, we ran it for ten epochs using a batch size of 90, the Adam optimizer, and a learning rate of 0.001. Both training and assessment are conducted using a dataset that blends self-collected pictures from Google satellite imagery with the MLRNet dataset. The comprehensive dataset comprises 10,400 images. Our study shows that transfer learning models and MobileNetV2 in particular, work well for landscape categorization. These models are good options for practical use because they strike a good mix between precision and efficiency; our approach achieves results with an overall accuracy of 87% on the built CNN model. Furthermore, we reach even higher accuracies by utilizing the pretrained VGG16 and MobileNetV2 models as a starting point for transfer learning. Specifically, VGG16 achieves an accuracy of 90% and a test loss of 0.298, while MobileNetV2 outperforms both models with an accuracy of 96% and a test loss of 0.119; the results demonstrate the effectiveness of employing transfer learning with MobileNetV2 for classifying transmission towers, forests, farmland, and mountains.

Hadeel Saleh Haj Aliwi, Putra Sumari

At the last few years, multimedia communication has been developed and improved rapidly in order to enable users to communicate between each other over the internet. Generally, multimedia communication consists of audio and video communication. However, this research concentrates on audio conferencing only. The audio translation between protocols is a very critical issue, because it solves the communication problems between any two protocols. So, it enables people around the world to talk with each other even they use different protocols. In this research, a real time audio translation module between two protocols has been done. These two protocols are: InterAsterisk eXchange Protocol (IAX) and Real-Time Switching Control Protocol (RSW), which they are widely used to provide two ways audio transfer feature. The solution here is to provide inter-working between the two protocols which they have different media transports, audio codecs, header formats and different transport protocols for the audio transmission. This translation will help bridging the gap between the two protocols by providing inter-working capability between the two audio streams of IAX and RSW. Some related works have been done to provide translation between IAX and RSW control signalling messages. But, this research paper concentrates on the translation that depends on the media transfer. The proposed translation module was tested and evaluated in different scenarios in order to examine its performance. The obtained results showed that the Real-Time Audio Translation Module produces lower rates of packet delay and jitter than the acceptance values for each of the mentioned performance metrics.

Jaafer Al-Sabateen, Saleh Ali Alomari, Putra Sumari

Despite the fact that Solid State Disk (SSD) data storage media had offered a revolutionary property storages community, but the unavailability of a comprehensive allocation strategy in SSDs storage media, leads to consuming the available space, random writing processes, time-consuming reading processes, and system resources consumption. In order to overcome these challenges, an efficient allocation algorithm is a desirable option. In this paper, we had executed an intensive investigation on the SSD-based allocation algorithms that had been proposed by the knowledge community. An explanatory comparison had been made between these algorithms. We reviewed these algorithms in order to building advanced knowledge armature that would help in inventing new allocation algorithms for this type of storage media.