Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (MobileNet) was trained to identify key features of various satellite images that contained fire or without fire. Then, the trained system is used to classify new satellite imagery and sort them into fire or no fire classes. A cloud-based development studio from Edge Impulse Inc. is used to create a NN model based on the transferred learning algorithm. The effects of four hyperparameters are assessed: input image resolution, depth multiplier, number of neurons in the dense layer, and dropout rate. The computational cost is evaluated based on the simulation of deploying the neural network model on an Arduino Nano 33 BLE device, including Flash usage, peak random access memory (RAM) usage, and network inference time. Results supported that the dropout rate only affects network prediction performance; however, the number of neurons in the dense layer had limited effects on performance and computational cost. Additionally, hyperparameters such as image size and network depth significantly impact the network model performance and the computational cost. According to the developed benchmark network analysis, the network model MobileNetV2, with 160 × 160 pixels image size and 50% depth reduction, shows a good classification accuracy and is about 70% computationally lighter than a full-depth network. Therefore, the proposed methodology can effectively design an ML application that instantly and efficiently analyses imagery from a spacecraft/weather balloon for the detection of wildfires without the need of an earth control centre.
This study was conducted on Lake Hamrin situated in Diyala governorate, focal Iraq, between latitudes 44º 53ʹ 26.16 '- 45º 07 ʹ 28.03ʺ and 34º 04ʹ 24.75ʺ ــ 34º 19ʹ 12.74ʺ . As in this study, the surface area of Hamrin Lake was calculated from satellite images during the period from October 2019 to September 2020, with an average satellite image for each month, furthermore,by utilizing the Normalized Differences Water Index (NDWI), the largest surface area was 264,617 km2 for October and the lowest surface area 140.202 km2 for September. The surface temperature of the lake water was also calculated from satellite images of the Landsat 8 satellite, based on ban
... Show MoreSymmetric cryptography forms the backbone of secure data communication and storage by relying on the strength and randomness of cryptographic keys. This increases complexity, enhances cryptographic systems' overall robustness, and is immune to various attacks. The present work proposes a hybrid model based on the Latin square matrix (LSM) and subtractive random number generator (SRNG) algorithms for producing random keys. The hybrid model enhances the security of the cipher key against different attacks and increases the degree of diffusion. Different key lengths can also be generated based on the algorithm without compromising security. It comprises two phases. The first phase generates a seed value that depends on producing a rand
... Show MoreGlobal technological advancements drive daily energy consumption, generating additional carbon-induced climate challenges. Modifying process parameters, optimizing design, and employing high-performance working fluids are among the techniques offered by researchers for improving the thermal efficiency of heating and cooling systems. This study investigates the heat transfer enhancement of hybrid “Al2O3-Cu/water” nanofluids flowing in a two-dimensional channel with semicircle ribs. The novelty of this research is in employing semicircle ribs combined with hybrid nanofluids in turbulent flow regimes. A computer modeling approach using a finite volume approach with k-ω shear stress transport turbulence model was used in these simu
... Show MoreEpithelial ovarian cancer is the leading cause of cancer deaths in women. To date, an effective screening tool for ovarian cancer has not been identified Several clinical and biological factors including serum cancer antigen 125 (CA- 125) have been assessed for prognostic and predictive relevance CA-125 is an epithelial marker derived from coelomic epithelium. It is elevated in 90% of advanced ovarian cancers and in 50% of early ovarian cancers while 20% of ovarian cancers have low or no expression of CA- 125 CA-125 concentrations were measured by Mini Vidas test (VIDAS CA125 II / BIOMERIEUX / France). The median CA-125 levels were significantly higher in the sera of ovarian cancer patients than in those with benign tumors an
... Show MoreResearch aims to shed light on the concept of corporate failures , display and analysis the most distinctive models used to predicting corporate failure; with suggesting a model to reveal the probabilities of corporate failures which including internal and external financial and non-financial indicators, A tested is made for the research objectivity and its indicators weight and by a number of academics professionals experts, in addition to financial analysts and have concluded a set of conclusions , the most distinctive of them that failure is not considered a sudden phenomena for the company and its stakeholders , it is an Event passes through numerous stages; each have their symptoms that lead eve
... Show MoreThe prevalence of using the applications for the internet of things (IoT) in many human life fields such as economy, social life, and healthcare made IoT devices targets for many cyber-attacks. Besides, the resource limitation of IoT devices such as tiny battery power, small storage capacity, and low calculation speed made its security a big challenge for the researchers. Therefore, in this study, a new technique is proposed called intrusion detection system based on spike neural network and decision tree (IDS-SNNDT). In this method, the DT is used to select the optimal samples that will be hired as input to the SNN, while SNN utilized the non-leaky integrate neurons fire (NLIF) model in order to reduce latency and minimize devices
... Show MoreDetecting protein complexes in protein-protein interaction (PPI) networks is a challenging problem in computational biology. To uncover a PPI network into a complex structure, different meta-heuristic algorithms have been proposed in the literature. Unfortunately, many of such methods, including evolutionary algorithms (EAs), are based solely on the topological information of the network rather than on biological information. Despite the effectiveness of EAs over heuristic methods, more inherent biological properties of proteins are rarely investigated and exploited in these approaches. In this paper, we proposed an EA with a new mutation operator for complex detection problems. The proposed mutation operator is formulate
... Show MoreA biconical antenna has been developed for ultra-wideband sensing. A wide impedance bandwidth of around 115% at bandwidth 3.73-14 GHz is achieved which shows that the proposed antenna exhibits a fairly sensitive sensor for microwave medical imaging applications. The sensor and instrumentation is used together with an improved version of delay and sum image reconstruction algorithm on both fatty and glandular breast phantoms. The relatively new imaging set-up provides robust reconstruction of complex permittivity profiles especially in glandular phantoms, producing results that are well matched to the geometries and composition of the tissues. Respectively, the signal-to-clutter and the signal-to-mean ratios of the improved method are consis
... Show MoreThe prevalence of using the applications for the internet of things (IoT) in many human life fields such as economy, social life, and healthcare made IoT devices targets for many cyber-attacks. Besides, the resource limitation of IoT devices such as tiny battery power, small storage capacity, and low calculation speed made its security a big challenge for the researchers. Therefore, in this study, a new technique is proposed called intrusion detection system based on spike neural network and decision tree (IDS-SNNDT). In this method, the DT is used to select the optimal samples that will be hired as input to the SNN, while SNN utilized the non-leaky integrate neurons fire (NLIF) model in order to reduce latency and minimize devices
... Show More