The performance quality and searching speed of Block Matching (BM) algorithm are affected by shapes and sizes of the search patterns used in the algorithm. In this paper, Kite Cross Hexagonal Search (KCHS) is proposed. This algorithm uses different search patterns (kite, cross, and hexagonal) to search for the best Motion Vector (MV). In first step, KCHS uses cross search pattern. In second step, it uses one of kite search patterns (up, down, left, or right depending on the first step). In subsequent steps, it uses large/small Hexagonal Search (HS) patterns. This new algorithm is compared with several known fast block matching algorithms. Comparisons are based on search points and Peak Signal to Noise Ratio (PSNR). According to resul

An encryption system needs unpredictability and randomness property to maintain information security during transmission and storage. Although chaotic maps have this property, they have limitations such as low Lyapunov exponents, low sensitivity and limited chaotic regions. The paper presents a new improved skewed tent map to address these problems. The improved skew tent map (ISTM) increases the sensitivity to initial conditions and control parameters. It has uniform distribution of output sequences. The programs for ISTM chaotic behavior were implemented in MATLAB R2023b. The novel ISTM produces a binary sequence, with high degree of complexity and good randomness properties. The performance of the ISTM generator shows effective s

Abstract Candida albicans is a commensal fungal pathogen that grows in yeast and hyphal forms in the human gut. C. albicans causes mucosal and cutaneous diseases that can result in significant mortality following systematic infections and it also exhibits drug resistance. Zebrafish have been an excellent model to investigate C. albicans infections because of their transparency and the availability of many transgenic lines. However, there is a limitation in using zebrafish as a model because the fish embryos cannot survive at 37°C therefore it is not suitable for studying Candida infections at physiological relevant human body temperature. In this thesis, the normal embryonic development of Arabian killifish (A. dispar) is investigated, rev

The precise classification of DNA sequences is pivotal in genomics, holding significant implications for personalized medicine. The stakes are particularly high when classifying key genetic markers such as BRAC, related to breast cancer susceptibility; BRAF, associated with various malignancies; and KRAS, a recognized oncogene. Conventional machine learning techniques often necessitate intricate feature engineering and may not capture the full spectrum of sequence dependencies. To ameliorate these limitations, this study employs an adapted UNet architecture, originally designed for biomedical image segmentation, to classify DNA sequences.The attention mechanism was also tested LONG WITH u-Net architecture to precisely classify DNA sequences

Nowadays, there is a trend to promote the use of natural materials in dentistry. One of these compounds that has lately grown significantly in relevance in clinical research is Pelargonium Graveolens .

Intrusion detection systems (IDS) are useful tools that help security administrators in the developing task to secure the network and alert in any possible harmful event. IDS can be classified either as misuse or anomaly, depending on the detection methodology. Where Misuse IDS can recognize the known attack based on their signatures, the main disadvantage of these systems is that they cannot detect new attacks. At the same time, the anomaly IDS depends on normal behaviour, where the main advantage of this system is its ability to discover new attacks. On the other hand, the main drawback of anomaly IDS is high false alarm rate results. Therefore, a hybrid IDS is a combination of misuse and anomaly and acts as a solution to overcome the dis

     The searching process using a binary codebook of combined Block Truncation Coding (BTC) method and Vector Quantization (VQ), i.e. a full codebook search for each input image vector to find the best matched code word in the codebook, requires a long time.   Therefore, in this paper, after designing a small binary codebook, we adopted a new method by rotating each binary code word in this codebook into 900 to 2700 step 900 directions. Then, we systematized each code word depending on its angle  to involve four types of binary code books (i.e. Pour when , Flat when  , Vertical when, or Zigzag). The proposed scheme was used for decreasing the time of the coding procedure, with very small distortion per block, by designing s

Transformers are a specific category of neural network design. Transformers often depend on extensive pre-training on a large scale and exhibit a notable degree of computational complexity. The disadvantage of using this method is a significant increase in computational complexity, which necessitates a significant commitment of time and computing resources in order to successfully work with these models. Transformer networks possess the desirable benefit of extracting distant characteristics effectively via their self-attention mechanism. In this paper, the Global Self-Attention Transformer module is applied to tackle these issues. The model is based on a segmentation problem called Brain-GS that works as a mechanism and encompasses

YY Lazim, NAB Azizan, 2nd International Conference on Innovation and Entrepreneurship, 2014