Performance Evaluation of Quadratic Probing and Random Probing Algorithms in modeling Hashing Technique

In hashing technique, a hash table and hash map represent a data structure for a group of objects to map between key and value pairs, as the hash table is affected by collision and overflow. The hash table collision and overflow can be handled by searching the hash table in some systematic fashion for a bucket that is not full. In open addressing, quadratic and random probing are well-known probe sequence algorithms for collision and overflow resolution. Key density, loading density, loading factor, collisions, overflows, keys clustering, space complexity, and time complexity are the main factors that highly affect the two algorithms during hash table systematic probing. Therefore, this project is conducted to compare the quadratic probing and random probing challenge performance in terms of the key density, loading density, loading factor, overflows, collisions, keys clustering, space complexity, time complexity using step count, the order of magnitude, the worst case, the average case, and the best case. Comparing both algorithms was performed by collecting data from an online survey about the English language proficiency of 104 students. The compression result shows that the random probing algorithm has achieved similar performance compared to quadratic probing in terms of key density, loading density, loading factor, space complexity, order of magnitude, worst case, and average and best case. While the quadratic probing algorithm has recorded less time complexity using the step count method compared to the random probing algorithm. On the other hand, the random probing algorithm has recorded fewer overflows, collisions, and key clustering compared to quadratic probing. However, the study has recommended the quadratic probing algorithm for better time complexity performance and the random probing algorithm for better performance resolving overflows, collisions, and key clustering

Sponge media drying using a swirling fluidized bed dryer

Surface preparation today has seen the introduction of sponge media as an alternative product against the traditionally used abrasive materials. Being soft and elastic, the sponge media reduces air borne emission significantly during surface preparation with capability to be re-used. However the environmental conditions limit the sponge media usage whereby wet surroundings prohibit the re-use of the sponge without being dried properly. This study proposes the swirling fluidized bed dryer as a novel drying technique for sponge media. Batch experiments were conducted to study the bed’s hydrodynamics followed by drying studies for three bed loadings of 0.5 kg, 0.75 kg and 1.0 kg at three drying temperatures of 80°C, 90°C and 100°C. It was found that, minimum fluidization velocities for the wet sponge particles were found to be 1.342, 1.361 and 1.382 m/s with minimum swirling velocities of 1.400, 1.469 and 1.526 m/s. Drying times were recorded between 6 to 16 minutes depending on bed loading and drying temperature. Smaller bed weights exhibits faster drying with constant-rate drying period while higher drying temperature and larger bed load resulted in falling-rate drying period. Thin layer modelling for the falling-rate region indicates that Verma et. al model provides the best fit for the present experimental data with coefficient of determination, R2 = 0.98773, root mean square error, RMSE = 0.05048, residuals = 0.3442 and reduced chi-square, χ2 = 0.00254. The effective diffusivity, Deff, for 0.5 kg bed load was found to be 3.454 x 10-9 m2/s and 1.751 x 10-9 m2/s for 0.75 kg bed load. In conclusion, SFBD was found to be a viable and efficient method in drying of sponge media for various industrial applications particularly surface preparation

Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks

With the rapid growth of informatics systems’ technology in this modern age, the Internet of Things (IoT) has become more valuable and vital to everyday life in many ways. IoT applications are now more popular than they used to be due to the availability of many gadgets that work as IoT enablers, including smartwatches, smartphones, security cameras, and smart sensors. However, the insecure nature of IoT devices has led to several difficulties, one of which is distributed denial-of-service (DDoS) attacks. IoT systems have several security limitations due to their disreputability characteristics, like dynamic communication between IoT devices. The dynamic communications resulted from the limited resources of these devices, such as their data storage and processing units. Recently, many attempts have been made to develop intelligent models to protect IoT networks against DDoS attacks. The main ongoing research issue is developing a model capable of protecting the network from DDoS attacks that is sensitive to various classes of DDoS and can recognize legitimate traffic to avoid false alarms. Subsequently, this study proposes combining three deep learning algorithms, namely recurrent neural network (RNN), long short-term memory (LSTM)-RNN, and convolutional neural network (CNN), to build a bidirectional CNN-BiLSTM DDoS detection model. The RNN, CNN, LSTM, and CNN-BiLSTM are implemented and tested to determine the most effective model against DDoS attacks that can accurately detect and distinguish DDoS from legitimate traffic. The intrusion detection evaluation dataset (CICIDS2017) is used to provide more realistic detection. The CICIDS2017 dataset includes benign and up-to-date examples of typical attacks, closely matching real-world data of Packet Capture. The four models are tested and assessed using Confusion Metrix against four commonly used criteria: accuracy, precision, recall, and F-measure. The performance of the models is quite effective as they obtain an accuracy rate of around 99.00%, except for the CNN model, which achieves an accuracy of 98.82%. The CNN-BiLSTM achieves the best accuracy of 99.76% and precision of 98.90%