The study included the collection of 75 bronchial wash samples from patients suspected to have lung cancer. These samples were subjected to a diagnostic cytological study to detect the dominant type of lung cancer. It was noticed that 33 patients proved to have a lung cancer out of 75 (44%) of these, 19 cases (57.6%)were diagnosed having Squamus cell carcinoma,7cases (21.21%) showed Adenocarcinoma ,6 cases (18.18%) were having small cell carcinoma while only one case (3.03%)was large cell carcinoma .Nearly 70% of cases were correlated with smokers .Bacteria were isolated from 53 patients in which 33 isolates were associated with the cancer cases while 20 of them from non infected patients. By using different morphological ,biochemical test

Metaheuristic is one of the most well-known fields of research used to find optimum solutions for non-deterministic polynomial hard (NP-hard) problems, for which it is difficult to find an optimal solution in a polynomial time. This paper introduces the metaheuristic-based algorithms and their classifications and non-deterministic polynomial hard problems. It also compares the performance of two metaheuristic-based algorithms (Elephant Herding Optimization algorithm and Tabu Search) to solve the Traveling Salesman Problem (TSP), which is one of the most known non-deterministic polynomial hard problems and widely used in the performance evaluations for different metaheuristics-based optimization algorithms. The experimental results of Ele

Text based-image clustering (TBIC) is an insufficient approach for clustering related web images. It is a challenging task to abstract the visual features of images with the support of textual information in a database. In content-based image clustering (CBIC), image data are clustered on the foundation of specific features like texture, colors, boundaries, shapes. In this paper, an effective CBIC) technique is presented, which uses texture and statistical features of the images. The statistical features or moments of colors (mean, skewness, standard deviation, kurtosis, and variance) are extracted from the images. These features are collected in a one dimension array, and then genetic algorithm (GA) is applied for image clustering.

The recent emergence of sophisticated Large Language Models (LLMs) such as GPT-4, Bard, and Bing has revolutionized the domain of scientific inquiry, particularly in the realm of large pre-trained vision-language models. This pivotal transformation is driving new frontiers in various fields, including image processing and digital media verification. In the heart of this evolution, our research focuses on the rapidly growing area of image authenticity verification, a field gaining immense relevance in the digital era. The study is specifically geared towards addressing the emerging challenge of distinguishing between authentic images and deep fakes – a task that has become critically important in a world increasingly reliant on digital med

With the rapid development of computers and network technologies, the security of information in the internet becomes compromise and many threats may affect the integrity of such information. Many researches are focused theirs works on providing solution to this threat. Machine learning and data mining are widely used in anomaly-detection schemes to decide whether or not a malicious activity is taking place on a network. In this paper a hierarchical classification for anomaly based intrusion detection system is proposed. Two levels of features selection and classification are used. In the first level, the global feature vector for detection the basic attacks (DoS, U2R, R2L and Probe) is selected. In the second level, four local feature vect

In the present paper, three reliable iterative methods are given and implemented to solve the 1D, 2D and 3D Fisher’s equation. Daftardar-Jafari method (DJM), Temimi-Ansari method (TAM) and Banach contraction method (BCM) are applied to get the exact and numerical solutions for Fisher's equations. The reliable iterative methods are characterized by many advantages, such as being free of derivatives, overcoming the difficulty arising when calculating the Adomian polynomial boundaries to deal with nonlinear terms in the Adomian decomposition method (ADM), does not request to calculate Lagrange multiplier as in the Variational iteration method (VIM) and there is no need to create a homotopy like in the Homotopy perturbation method (H

A content-based image retrieval (CBIR) is a technique used to retrieve images from an image database. However, the CBIR process suffers from less accuracy to retrieve images from an extensive image database and ensure the privacy of images. This paper aims to address the issues of accuracy utilizing deep learning techniques as the CNN method. Also, it provides the necessary privacy for images using fully homomorphic encryption methods by Cheon, Kim, Kim, and Song (CKKS). To achieve these aims, a system has been proposed, namely RCNN_CKKS, that includes two parts. The first part (offline processing) extracts automated high-level features based on a flatting layer in a convolutional neural network (CNN) and then stores these features in a

Background: diagnostic radiology field workers are at elevated risk level for systemic and oral diseases like periodontal diseases. This study was aimed to estimate the periodontal condition and salivary flow rate among diagnostic radiology workers. Material and method: The sample for this study consisted of a study group radiographers (forty subjects) working for 5 years at least and control group consisted of nurses and laboratory workers away from radiation (forty subjects) in Baghdad hospitals. All the 80 subjects aged 30-40 year-old and looking healthy without systemic diseases. Plaque, gingival, periodontal pocket depth and clinical attachment loss indices were used for recording the periodontal conditions. Under standardized condi

Beta-lactamase was purified from local isolate Klebsiella pneumonia by several steps included precipitation with ammonium sulphate at 20-40% saturation, DEAE- ion exchange chromatography and gel filtration on Sephacryl S-200 column. The obtained purification fold and recovery were 32.66; 47.04% respectively. The characterization of the purified beta-lactamase showed that the molecular weight was about 4000 daltons as determined by gel filtration.Purified enzyme had an optimal pH of 7 for activity and an optimal stability between pH 6.5-7.5, results shows that the optimal temperature appear to be 35 ? C .During storage the enzyme retained 72% at -20 ? C and retained 25% of the activity at the same period at 4 ? C.

Although the Wiener filtering is the optimal tradeoff of inverse filtering and noise smoothing, in the case when the blurring filter is singular, the Wiener filtering actually amplify the noise. This suggests that a denoising step is needed to remove the amplified noise .Wavelet-based denoising scheme provides a natural technique for this purpose .

                In this paper  a new image restoration scheme is proposed, the scheme contains two separate steps : Fourier-domain inverse filtering  and wavelet-domain image denoising. The first stage is Wiener filtering of the input image , the filtered image is inputted to adaptive threshold wavelet